AE&M 61-6 by Sociedade Brasileira de Endocrinologia e Metabologia

ISSN 2359-3997

OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM Vol. 61 – No. 06 – December 2017

Archives of Endocrinology

and Metabolism

OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM

Archives of

Endocrinology

and Metabolism OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM

OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM Vol. 61 – No. 06 – December 2017

editorials

511 Muscle, inflammation and metabolic health: is irisin the missing link? Rogério Friedman

512 Poor glycemic control can lead to an early appearance of atherosclerosis in patients with type 1 diabetes – Can this be avoided by effective educational programs?

Archives of Endocrinology Melanie Rodacki

OFFICIAL JOURNAL OF THE BRAZILIAN Omur Tabak, Gonul Simsek, Fusun Erdenen, Volkan Sozer, Tuna Hasoglu, Remise Gelisgen, Esma Altunoglu, Cuneyt Muderrisoglu, Abdulhalim Senyigit, Hafize Uzun SOCIETY OF 524 Obese with higher FNDC5/Irisin levels have a better metabolic profile, lower lipopolysaccharide levels and type 2 diabetes risk Ivan Luiz Padilha Bonfante, Mara Patricia Traina Chacon-Mikahil, Diego Trevisan Brunelli Arthur Fernandes Gáspari, Renata Garbellini Duft, ENDOCRINOLOGY Alexandre Gabarra Oliveira, Tiago Gomes Araujo, Mario Jose Abdalla Saad, Cláudia Regina Cavaglieri 534 Effects of a structured education program on glycemic control in type 1 diabetes AND METABOLISM Ana Paula F. Pacheco, Simone van de Sande-Lee, Rita de Cássia B. Sandoval, Sônia Batista, Jefferson L. B. Marques

original articles

515 The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome

and Metabolism

542 Impaired flow-mediated dilation response and carotid intima-media thickness in patients with type 1 diabetes mellitus with a mean disease duration of 4.1 years Lúcia Helena Bonalume Tacito, Antonio Carlos Pires, Juan Carlos Yugar-Toledo

550 Search for DQ2.5 and DQ8 alleles using a lower cost technique in patients with type 1 diabetes and celiac disease in a population of southern Brazil

Marília D. Bastos, Thayne W. Kowalski, Márcia Puñales, Balduíno Tschiedel, Luiza M. Mariath, Ana Luiza G. Pires, Lavínia S. Faccini, Themis R. Silveira

556 11β-hydroxysteroid dehydrogenase type-II activity is affected by grapefruit juice and intense muscular work Christopher Kargl, Mohammad Arshad, Fahad Salman, Regina C. Schurman, Pedro Del Corral

Archives of

562 Is fibroblast growth factor 23 a new cardiovascular risk marker in gestational diabetes?

Muhammed Kizilgul, Seyfullah Kan, Selvihan Beysel, Mahmut Apaydin, Ozgur Ozcelik, Mustafa Caliskan, Mustafa Ozbek, Seyda Ozdemir, Erman Cakal

567 Circulating omentin-1 might be associated with metabolic health status in different phenotypes of body size Shahab Alizadeh, Khadijeh Mirzaei,Chonur Mohammadi, Seyed Ali Keshavarz, Zhila Maghbooli

575 Thyroid disorders in obese patients. Does insulin resistance make a difference?

Endocrinology

Nicoleta Ra6ca6ta6ianu, Nicoleta Leach, Cosmina Ioana Bondor, Smaranda Mârza, Daniela Moga, Ana Valea, Cristina Ghervan

584 Metastatic lymph node characteristics as predictors of recurrence/persistence in the neck and distant metastases in differentiated thyroid cancer Mayara Peres Barbosa, Denise Momesso, Daniel Alves Bulzico, Terence Farias, Fernando Dias, Roberto Araújo Lima, Rossana Corbo, Mario Vaisman, Fernanda Vaisman

590 Thyroglobulin levels before radioactive iodine therapy and dynamic risk stratification after 1 year in patients with differentiated thyroid cancer

and Metabolism

Leonardo Bandeira, Rosália do Prado Padovani, Ana Luiza Ticly, Adriano Namo Cury, Nilza Maria Scalissi, Marília Martins Silveira Marone, Carolina Ferraz

600 Serum selenium and selenoprotein-P levels in autoimmune thyroid diseases patients in a select center: a transversal study Marco Aurélio Ferreira Federige, João Hamilton Romaldini, Ana Beatriz Pinotti Pedro Miklos, Marcia Kiyomi Koike, Kioko Takei, Evandro de Souza Portes

608 Laparoscopic sleeve gastrectomy in severely obese adolescents: effects on metabolic profile

OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM

Ruth Rocha Franco, Marina Ybarra, Louise Cominato, Larissa Mattar, Leandra Steinmetz, Durval Damiani, Manoel Carlos Prieto Velhote

review

614 Relationships between adiponectin levels, the metabolic syndrome, and type 2 diabetes: a literature review Anize Delfino von Frankenberg, André F. Reis, Fernando Gerchman

brief reports

623 Effect of biliopancreatic diversion on sleep quality and daytime sleepiness in patients with obesity and type 2 diabetes

Mayra Mello, Ana Carolina J. Vasques, José C. Pareja, Maria da S. de Oliveira, Fernanda S. Novaes, Élinton A. Chaim, Bruno Geloneze

628 High prevalence of insulin resistance among Brazilian chronic hepatitis C patients

Livia Melo Villar, Gabriela Cardoso Caldas, Leticia de Paula Scalioni, Juliana Custódio Miguel, Elisangela Ferreira da Silva, Vanessa Alves Marques, Cristiane Alves Villela-Nogueira, Lia Laura Lewis-Ximenez, Elisabeth Lampe

case reports

633 Growth hormone deficiency with advanced bone age: phenotypic interaction between GHRH receptor and CYP21A2 mutations diagnosed by sanger and whole exome sequencing

Fernanda A. Correa, Marcela M. França, Qing Fang, Qianyi Ma, Tania A. Bachega, Andresa Rodrigues, Bilge A. Ozel, Jun Z. Li, Berenice B. Mendonca, Alexander A. L. Jorge, Luciani R. Carvalho, Sally A. Camper, Ivo J. P. Arnhold

637 More than kin, less than kind: one family and the many faces of diabetes in youth

Luciana F. Franco, Renata Peixoto-Barbosa, Renata P. Dotto, José Gilberto H. Vieira, Magnus R. Dias-da-Silva, Luiz Carlos F. Reis, Fernando M. A. Giuffrida, Andre F. Reis

643 Coexistence of diffuse large B-cell lymphoma and papillary thyroid carcinoma in a patient affected by Hashimoto’s thyroiditis

Maria Trovato, Giuseppe Giuffrida, Antonino Seminara, Simone Fogliani, Vittorio Cavallari, Rosaria Maddalena Ruggeri, Alfredo Campennì

AND METABOLISM OFFICIAL JOURNAL OF THE BRAZILIAN SOCIETY OF ENDOCRINOLOGY AND METABOLISM

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Informations: Title: Barcos a vela Year: 1957 Author: Arcangelo Ianelli Technique: OilOFFICIAL on canvas JOURNAL OF THE BRAZILIAN Measures: 92 SOCIETY x 73 cm OF ENDOCRINOLOGY AND METABOLISM Collection: Brazilian Art Museum (MAB), FAAP, SP

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Circulation of this issue: 3,500 copies Subscription: R$450.00/year – Single issue: R$55.00 Indexed in Biological Abstracts, Index Medicus, Latindex, Lilacs, MedLine, SciELO, Scopus, ISI-Web of Science BRAZILIAN ARCHIVES OF ENDOCRINOLOGY AND METABOLISM Brazilian Society of Endocrinology and Metabolism – São Paulo, SP: Brazilian Society of Endocrinology and Metabolism, volume 5, 1955Six issues/year Continued from: Brazilian Archives of Endocrinology (v. 1-4), 1951-1955 ISSN 2359-3997 (printed issues) ISSN 2359-4292 (online issues) 1. Endocrinology – journals 2. Metabolism – journals I. Brazilian Society of Endocrinology and Metabolism II. Brazilian Medical Association CDU 612.43 Endocrinology CDU 612.015.3 Metabolism

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Archives of endocrinology and metabolism Official journal of SBEM – Brazilian Society of Endocrinology and Metabolism (Department of the Brazilian Medical Association), SBD – Brazilian Diabetes Society, ABESO – Brazilian Association for the Study of Obesity and Metabolic Syndrome

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editorial

Muscle, inflammation and metabolic health: is irisin the missing link? Rogério Friedman1

n the first years of the current decade, irisin gained significant attention from the scientific community (1). A myokine, it was found to be exercise-mediated and to regulate the metabolic rate in myocytes and adipocytes, thus acting as an exerciseinduced insulin sensitizer. At first, it offered a promise of a potential target of new interventions for obesity, metabolic syndrome and type 2 diabetes. As 2017 marches to the end, no real breakthroughs have happened. Nevertheless, as the interest on the effects of exercise on metabolic health gather more and more attention, research on irisin is still going strong, helping to elucidate potential hormonal pathways that will eventually lead to applicable interventions. In this issue of Archives of Endocrinology and Metabolism, two papers look into irisin and its potential associations with markers of metabolic status. Tabak and cols. (2) demonstrate that irisin is associated with waist circumference, waist-hip ratio, plasma LDL cholesterol levels, and inflammation markers. In the same line of reasoning, Bonfante and cols. (3) show that higher irisin levels are associated with better metabolic profile and lower risk for Type 2 diabetes mellitus in obese men. These studies stress the role of irisin as an indicator of metabolic health. A more protagonistic role for irisin in the metabolic/inflammatory cascade, in both health and disease has yet to be shown. This will be the moment when irisin will eventually become a potential therapeutic target, if ever. So far, the most important factor driving irisin and its known associations seems to be exercise. And the health impact of exercise is so noticeable and complex that irisin alone will unlikely explain the entire cohort of benefits arising from physical activity. We look forward to more investigational results on irisin, other myokynes, and the metabolic effects of exercise in the promotion of health.

1 Departamento de Medicina Interna, Universidade Federal do Rio Grande do Sul (UFRGS), Serviço de Endocrinologia, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brasil

Correspondence to: Rogério Friedman rogeriofriedman@gmail.com Received on Nov/29/2017 Accepted on Nov/29/2017 DOI: 10.1590/2359-3997000000303

Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Boström P, Wu J, Jedrychowski MP, Korde A, Ye L, Lo JC, et al. A PGC1-α-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature. 2012;481(7382):463-8. 2. Tabak O, Simsek G, Erdenen F, Sozer V, Hasoglu T, Gelisgen R. The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome. Arch Endocrinol Metab. 2017;61(6):515-23.

Arch Endocrinol Metab. 2017;61/6

3. Bonfante ILP, Chacon-Mikahi MPT, Brunelli DT, et al. Obese with higher FNDC5/Irisin levels have a better metabolic profile, lower lipopolysaccharide levels and type 2 diabetes risk. Arch Endocrinol Metab. 2017;61(6):524-33.

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editorial

Poor glycemic control can lead to an early appearance of atherosclerosis in patients with type 1 diabetes – Can this be avoided by effective educational programs? Melanie Rodacki1 1 Faculdade de Medicina da Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil

Correspondence to: Melanie Rodacki melanierodacki@gmail.com Received on Nov/30/2017 Accepted on Nov/30/2017

DOI: 10.1590/2359-3997000000304

512

his issue of “Archives of Endocrinology and Metabolism” features two papers about type 1 diabetes mellitus (T1DM). Pacheco and cols. report the results of a structured education program for adults with T1DM from a single center in the south of Brazil. The education strategy consisted of workshops, individualized care and 24-hour distant support (1). The design of the program was inspired by the educational support provided in the Dose Adjustment for Normal Eating Study (DAFNE), performed in the UK (2). Fortunately, the authors report successful results, leading to a significant improvement in HbA1c (approximately 20% in one year with an additional reduction of 11% in the next 8 months) (1). Achieving the HbA1c goals in individuals with T1DM is still a major challenge worldwide. Although adults tend to have a slightly better glycemic control than younger patients, the proportion of individuals with 25 years of age or more who present HbA1c below 7.5% still varies between 20.5% and 53.6% in different countries, with a mean HbA1c between 8% and 8.5% in most populations that provide enough resources for diabetes care (3). A mean HbA1c of 9.2% has been previously reported for the Brazilian population, where the resources for diabetes care are limited and efficient national programs for diabetes care are still lacking (4). Pacheco and cols. (1) reported a mean HbA1c of approximately 10.7% in males and 9.6% in females before the initiation of the educational program in one center in south Brazil. The authors achieved a mean HbA1c of approximately 8.5% for both men and women in the end of the study, which is still above the recommended goals for T1DM but at the same range obtained for countries that offer more resources for diabetes treatment and glucose monitoring than Brazil. A control group that did not participate in the educational program did not show any differences in the glycemic control in a similar time period, which reinforces the impact of the intervention in this study group. These results indicate that education can be an effective tool to obtain significant improvements in diabetes care in patients with T1DM, including poor countries with limited health-care resources. Although the success of educational intervention programs had been reported in previous studies, including the DAFNE study itself (2,5), this is not a rule. A meta-analysis failed to demonstrate strong benefits of educational programs in the improvement of glycemic control in patients with T1DM from different populations (6). As educational programs can vary significantly, the lack of success in achieving better glycemic control with such intervention might indicate the choice of an inadequate program or an erroneous selection of the intervention group. Therefore, successful models of educational programs for diabetes care, such as the one reported by Pacheco Arch Endocrinol Metab. 2017;61/6

and DAFNE, should be replicated and further studied, in order to be included as part of the routine health care policies for patients with diabetes. This can represent a simple and cheap intervention to improve glycemic control in patients with T1DM worldwide, especially in poor countries. Improving glycemic control in people with T1DM is particularly important as it is known that this can reduce the risk of diabetic chronic complications (7). The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Trial has shown that intensive insulin treatment aiming to achieve HbA1c < 7% has beneficial effects in the risk reduction not only of microvascular complications (8), but also of macrovascular complications (9). After the completion of the DCCT, which compared intensive therapy (IT) with a conventional therapy (CON) in the risk of chronic complications, patients were invited to take part of the EDIC Trial. This was an observational study where IT was offered to all participants that completed DCCT, independently of the previous group, due to the significant impact obtained with IT in the risk reduction of diabetic complications. Although the mean HbA1c significantly differed between the IT and CON groups during the DCCT, both groups had similar mean HbA1c levels during the EDIC trial. Nevertheless, the benefits of the intensive control observed during the DCCT not only persisted but also became more prominent during the EDIC, indicating the existence of a “metabolic memory” and showing the importance of maintaining a good glycemic control since the beginning of the disease (10). Also in this issue of “Archives of Endocrinology and Metabolism”, Tacito and cols. have shown that patients with T1DM had impaired endothelial function, when compared to healthy controls (11). The authors also compared the carotid artery intimamedia thickness (CIMT) between patients with T1DM and a control group using an automated system. Both endothelial dysfunction and CIMT are early markers of atherosclerosis, which are implicated in the pathogenesis of macrovascular complications. Even though both groups described in the study performed by Tacito and cols. had CIMT values within the normal range for age, patients with T1DM had higher CIMT than controls. Curiously, the study group was young, had a short disease duration (of approximately 4 years), normal body mass index (BMI), but had a high mean HbA1c Arch Endocrinol Metab. 2017;61/6

(of 9.95%) (11). It is possible that the poor glycemic control contributed to the precocious appearance of early atherosclerosis markers, even in this group of young lean patients with a short disease duration. This is consistent with the concept of “metabolic memory” and stresses the importance of starting an intensive treatment and achieving a good glycemic control since the beginning of the disease. Although endothelial function is closely related to T1DM duration, the early development of endothelial dysfunction in patients with T1DM, short disease duration and poor glycemic control had already been previously reported (12,13). On the other hand, alterations in CIMT have been reported mostly in patients with longer disease duration (14-16). In the EDIC, after 1 year of follow-up, the carotid intimamedia thickness in patients with T1DM with a mean disease duration of approximately 14 years was similar to that in an age- and sex-matched nondiabetic population, both in CON and IT groups (17). Another possible explanation for the findings seen in the study performed by Tacito and cols. (11) is the difference in TSH levels between the groups (patients vs controls). Several studies have reported a link between CIMT and subclinical hypothyroidism (18-20), but Takamura and cols. have also reported an association between TSH and CIMT in euthyroid individuals (21). Although TSH levels were within the normal range in both groups, the levels significantly differed between them. Indeed, it has been suggested that a narrower thyrotropin reference range should be used in the general population (22) as the current reference ranges have been defined in populations previously considered healthy but included individuals with various degrees of thyroid dysfunction. Moreover, we have previously found that TSH levels > 2.5 mU/l, even in the normal range, are associated with a higher risk of retinopathy and renal dysfunction in patients with T1DM than TSH levels between 0.4 and 2.5 mU/l (23). Possibly, the same association could be observed for macrovascular complications. This would be an alternative explanation for the finding obtained by Tacito and cols. (11) and worth further investigation, since thyroid diseases are more frequent in patients with T1DM than in the general population. To conclude, the two interesting papers featured in this issue of “Archives of Endocrinology and Metabolism” bring us very important messages. Firstly, patients with T1DM and poor glycemic control might develop early signs of atherosclerosis even in the first 513

Atherosclerosis and educational programs in patients with type 1 diabetes

few years of the disease, at a young age and with normal BMI. Secondly, efficient educational programs can lead to a significant reduction of HbA1c levels in patients with T1DM, which can lead to a significant risk reduction of micro and macrovascular diabetic complications. Therefore, effective educational programs should be implemented soon after the diagnosis of T1DM, in order to avoid poor glycemic, metabolic memory and their possible adverse effects. Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Pacheco APF, Sande-Lee SV, Sandoval RCB, Batista S, Marques JLB. Effects of a structured education program on glycemic control in type 1 diabetes. Arch Endocrinol Metab. 2017;61(6):534-41. 2. Kruger J, Brennan A, Thokala P, Basarir H, Jacques R, Elliott J, et al. The cost-effectiveness of the Dose Adjustment for Normal Eating (DAFNE) structured education programme: an update using the Sheffield Type 1 Diabetes Policy Model. Diabet Med. 2013;30(10):1236-44. 3. McKnight JA, Wild SH, Lamb MJ, Cooper MN, Jones TW, Davis EA, et al. Glycaemic control of Type 1 diabetes in clinical practice early in the 21st century: an international comparison. Diabet Med. 2015;32(8):1036-50. 4. Gomes MB, Cobas RA, Matheus AS, Tannus LR, Negrato CA, Rodacki M, et al. Regional differences in clinical care among patients with type 1 diabetes in Brazil: Brazilian Type 1 Diabetes Study Group. Diabetol Metab Syndr. 2012;4(1):44. 5. Cooke D, Bond R, Lawton J, Rankin D, Heller S, Clark M, et al.; U.K. NIHR DAFNE Study Group. Structured type 1 diabetes education delivered within routine care: impact on glycemic control and diabetes-specific quality of life. Diabetes Care. 2013;36(2):270-2. 6. Pillay J, Armstrong MJ, Butalia S, Donovan LE, Sigal RJ, Chordiya P, et al. Behavioral Programs for Type 1 Diabetes Mellitus: A Systematic Review and Meta-analysis. Ann Intern Med. 2015;163(11):836-47. 7. Fullerton B, Jeitler K, Seitz M, Horvath K, Berghold A, Siebenhofer A. Intensive glucose control versus conventional glucose control for type 1 diabetes mellitus. Cochrane Database Syst Rev. 2014;(2):CD009122. 8. Diabetes Control and Complications Trial Research Group, Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977-86.

11. Tacito LHB, Pires AC, Yugar-Toledo JC. Impaired flow-mediated dilation response and carotid intima-media thickness in patients with type 1 diabetes mellitus with a mean disease duration of 4.1 years. Arch Endocrinol Metab. 2017;61(6):542-9. 12. Mahmud FH, Earing MG, Lee RA, Lteif AN, Driscoll DJ, Lerman A. Altered endothelial function in asymptomatic male adolescents with type 1 diabetes. Congenit Heart Dis. 2006;1(3):98-103. 13. Bertoluci MC, Cé GV, da Silva AM, Wainstein MV, Boff W, Puñales M. Endothelial dysfunction as a predictor of cardiovascular disease in type 1 diabetes. World J Diabetes. 2015;6(5):679-92. 14. Kupfer R, Larrúbia MR, Bussade I, Pereira JRD, Lima GAB, Epifanio MA, et al. Predictors of subclinical atherosclerosis evaluated by carotid intima-media thickness in asymptomatic young women with type 1 diabetes mellitus. Arch Endocrinol Metab. 2017;61(2):115-21. 15. Shah AS, Dabelea D, Fino NF, Dolan LM, Wadwa RP, D’Agostino R Jr, et al. Predictors of Increased Carotid Intima-Media Thickness in Youth With Type 1 Diabetes: The SEARCH CVD Study. Diabetes Care. 2016;39(3):418-25. 16. Sun YP, Cai YY, Li HM, Deng SM, Leng RX, Pan HF. Increased carotid intima-media thickness (CIMT) levels in patients with type 1 diabetes mellitus (T1DM): A meta-analysis. J Diabetes Complications. 2015;29(5):724-30. 17. Nathan DM, Lachin J, Cleary P, Orchard T, Brillon DJ, Backlund JY, et al.; Diabetes Control and Complications Trial; Epidemiology of Diabetes Interventions and Complications Research Group. Intensive diabetes therapy and carotid intima-media thickness in type 1 diabetes mellitus. N Engl J Med. 2003;348(23):2294-303. 18. Peixoto de Miranda ÉJ, Bittencourt MS, Pereira AC, Goulart AC, Santos IS, Lotufo PA, et al. Subclinical hypothyroidism is associated with higher carotid intima-media thickness in cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Nutr Metab Cardiovasc Dis. 2016;26(10):915-21. 19. Unal E, Akın A,Yıldırım R, Demir V,Yildiz İ, HaspolatYK. Association of Subclinical Hypothyroidism with Dyslipidemia and Increased Carotid Intima-Media Thickness in Children. J Clin Res Pediatr Endocrinol. 2017;9(2):144-9. 20. Gao N, Zhang W, Zhang YZ, Yang Q, Chen SH. Carotid intimamedia thickness in patients with subclinical hypothyroidism: a meta-analysis. Atherosclerosis. 2013;227(1):18-25. 21. Takamura N, Akilzhanova A, Hayashida N, Kadota K, Yamasaki H, Usa T, et al. Thyroid function is associated with carotid intima-media thickness in euthyroid subjects. Atherosclerosis. 2009;204(2):e77-81. 22. Baloch Z, Carayon P, Conte-Devoix B, Demers LM, FeldtRasmussen U, Henry JF, et al. Laboratory medicine practice guidelines. Laboratory support for the diagnosis and monitoring of thyroid disease. Thyroid. 2003;13:3-126. 23. Rodacki M, Zajdenverg L, Dantas JR, de Oliveira JE, Luiz RR, Cobas RA, et al. Should thyroid-stimulating hormone goals be reviewed in patients with type 1 diabetes mellitus? Results from the Brazilian Type 1 Diabetes Study Group. Diabet Med. 2014;31(12):1665-72.

9. Nathan DM, Cleary PA, Backlund JY, Genuth SM, Lachin JM, Orchard TJ, et al.; Diabetes Control and Complications Trial/ Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643-53.

10. Zhang L, Chen B, Tang L. Metabolic memory: mechanisms and implications for diabetic retinopathy. Diabetes Res Clin Pract. 2012;96(3):286-93.

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original article

The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome Omur Tabak1, Gonul Simsek2, Fusun Erdenen3, Volkan Sozer4, Tuna Hasoglu5, Remise Gelisgen6, Esma Altunoglu4, Cuneyt Muderrisoglu4, Abdulhalim Senyigit7, Hafize Uzun6

ABSTRACT Objective: We wanted to investigate whether there is a relationship between circulating irisin, retinol binding protein-4 (RBP-4), adiponectin and proinflammatory mediators implicated in the development of insulin resistance (IR) in metabolic syndrome (MetS). Subjects and methods: In 180 individuals, including controls and patients with MetS, we measured fasting plasma insulin, high sensitivity C-reactive protein (hsCRP), pentraxin-3 (PTX-3), interleukin-33 (IL-33), irisin, RBP-4, and adiponectin using ELISA kits. Results: While fasting plasma hsCRP, PTX-3, IL-33, irisin, RBP-4 concentrations were higher, adiponectin levels were lower in patients with MetS than in controls. A correlation analysis revealed that plasma irisin levels were positively associated with MetS components such as waist circumference and waist-hip ratio, low density lipoprotein (LDL) and markers of systemic inflammation such as PTX-3, hsCRP, uric acid, and RBP-4. Adiponectin levels were negatively associated with waist circumference, waist-hip ratio, PTX-3 and LDL. Conclusions: Although the precise mechanisms are still unclear, irisin, RBP-4, adiponectin and PTX-3 are hallmarks of the MetS, which is related to low-grade inflammation. It is conceivable that irisin and adiponectin might contribute to the development of MetS and may also represent novel MetS components. Future clinical studies are needed to confirm and extend these data. Arch Endocrinol Metab. 2017;61(6):515-23 Keywords Metabolic syndrome; high sensitivity C-reactive protein; pentraxin-3; interleukin-33; irisin; retinol binding protein-4; adiponectin

1 Internal Medicine Clinic, lstanbul Kanuni Sultan Suleyman Education and Research Hospital, Istanbul, Turkey 2 Department of Physiology, Istanbul University, Cerrahpasa Medical Faculty, Istanbul, Turkey 3 Istanbul Education and Research Hospital, Internal Medical Clinic, Istanbul, Turkey 4 Department of Biochemistry, Yildiz Technical University, Istanbul, Turkey 5 Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey 6 Department of Biochemistry, Istanbul University, Cerrahpasa Medical Faculty, Istanbul, Turkey 7 Medicine Hospital, Internal Medical Clinic, Istanbul, Turkey

Correspondence to: Hafize Uzun Department of Medical Biochemistry, Cerrahpasa Faculty of Medicine, Istanbul University 34303 – Cerrahpasa – Istanbul, Turkey huzun59@hotmail.com Received on Dec/20/16 Accepted on May/9/2017

INTRODUCTION

he metabolic syndrome (MetS), which has an important role in cardiovascular morbidity and mortality, is generally characterized by obesity, insulin resistance (IR), hypertension, cardiovascular disease (CVD), non-alcoholic fatty liver disease and/ or a proinflammatory state due to the accumulation of adipose tissue. Visceral obesity, which is central to MetS, leads to altered adipokines, IR, endothelial dysfunction and atherogenesis. When adipose tissue inflammation and dysfunction are established, adipokine secretion is significantly skewed toward a diabetogenic, proinflammatory and atherogenic pattern (1,2).

Arch Endocrinol Metab. 2017;61/6

Irisin is a novel exercise-mediated myokine that regulates energy metabolism by increasing metabolic rate and mitochondrial content in both myocytes and adipocytes and plays an important role in metabolic diseases. Irisin acts as an exercise-induced insulin sensitizing hormone. The main effects of irisin are the browning of white adipose tissue and increased energy expenditure. Irisin expression and/or circulating levels have been associated with anthropometric and biochemical parameters, other hormones and adipokines, and obesity, IR, type 2 diabetes and MetS (3,4). Vitamin A is an essential nutrient that is mainly stored in the liver. Retinol binding protein (RBP) is a 515

DOI: 10.1590/2359-3997000000289

Circulating inflammatory mediators in the metabolic syndrome

plasma transporter that carries retinol from the liver to the periphery. Very little RBP originates from adipose tissue. RBP-4 is a novel adipokine and a negative acute phase reactant (5). The high concentration of RBP-4 plays a role in the progression of IR through immunity and inflammatory mechanisms in adipose and vascular tissues. RBP-4 seems to be a cardiometabolic marker in chronic inflammatory diseases including obesity, type 2 diabetes, MetS and CVD. These effects result from the direct activation of antigen presenting cells by RBP-4 (6). Cytokines released by adipose tissue are involved in initiating and promoting a proinflammatory status, which contributes to IR. One of these cytokines, adiponectin, is suggested to sensitize the body to insulin and has a cardioprotective effect (2). Adiponectin is produced by adipocytes and has insulin sensitizing, anti-inflammatory, antioxidative and antiapoptopic properties. Adiponectin enhances insulin secretion by stimulating both the expression of the insulin gene and the exocytosis of insulin granules. Adiponectin also acts in the brain to increase energy expenditure and may thereby promote weight loss. Adiponectin is independently and negatively related to MetS, IR, type 2 diabetes, body weight, blood pressure and serum lipids (7-10). Pentraxins are divided into the short and long pentraxin families. CRP is the prototype of the short pentraxin subfamily and pentraxin-3 (PTX-3) belongs to the long pentraxin family. PTX-3 is a multimeric acute phase inflammatory glycoprotein in the same family as CRP, a well-known cardiovascular biomarker (11). Its secretion is stimulated in endothelial cells, macrophages, myeloid cells, and dendritic cells by cytokines and endotoxins. After release, PTX-3 binds with C1q to initiate complement activation and facilitate pathogen recognition by macrophages. PTX-3 levels are increased in CVD. Although PTX-3 is an acute inflammatory protein in the same family as CRP, its levels may more directly reflect the inflammatory status of the vasculature (12,13). Interleukin-33 (also known as IL-1) is a newly identified cytokine. Its receptor, soluble ST-2, has been shown to be protective in certain conditions such as obesity and atherosclerosis. IL-33 exerts protective metabolic effects by decreasing IR, leading to the accumulation of protective Th2 cells and cytokines and consequently reducing adipogenesis (14,15). We aimed to investigate whether there is a relationship between circulating irisin, RBP-4, PTX-3, IL-33 and adiponectin along with the anthropomorphic and biochemical variables implicated in the development of IR in MetS. 516

SUBJECTS AND METHODS Subjects A total of 180 patients and control subjects between the ages of 30-65 were included in the study. MetS was diagnosed according to the Adult Treatment Panel III (ATPIII) criteria by the same physician (16). The components of MetS were waist circumference (WC) > 102 cm for males and > 88 cm for females, hypertension (HT) (systolic blood pressure (SBP > 130 mmHg, diastolic blood pressure (DBP) > 85 mmHg), treatment with antihypertensive drugs, high density lipoproteincholesterol (HDL-C) < 40 mg/dL in males and < 50 mg/dL in females, hypertriglyceridemia (TG) > 150 mg/dL, fasting blood glucose (FBG) > 100 mg/dL or the presence of type 2 diabetes mellitus (DM). MetS was defined as the presence of at least three components. Pregnant women, subjects with endocrine disorders, chronic cardiovascular, renal, hepatic, rheumatic diseases, smokers and subjects who were taking drugs which could affect our results were excluded. The control group consisted of 50 healthy people who were hospital staff. This group did not have diabetes, dyslipidemia or glucose intolerance as confirmed by an oral glucose tolerance test (OGTT). They neither had hepatic nor renal disease and were not taking drugs to affect carbohydrate metabolism. Pregnant women, patients with acute vascular or infectious illness or malignancy were also excluded. All subjects were of Turkish descent. The weight and height of each person were measured, and body mass index (BMI) was calculated according to the following formula: weight/ height (m2). Waist circumference was measured with a flexible tape measurer at the level of the navel. All participants were informed about the survey and freely signed and dated the consent form. The protocol was approved by the Ethics Committee of Istanbul Education and Research Hospital and was conducted in accordance with the Declaration of Helsinki (No: 2015/628).

Laboratory analysis

Sample collection and preparation Drugs were administered at least 24 h prior to blood collection. Clinical parameters, including routine biochemical parameters, were measured using standard protocols. Blood samples were collected in EDTAArch Endocrinol Metab. 2017;61/6

Circulating inflammatory mediators in the metabolic syndrome

Measurement of plasma pentraxin-3 (PTX3) concentrations Plasma PTX-3 levels were measured by a commercially available competitive enzyme-linked immunoassay kit (Hycult Biotech, Netherlands, Catno; HK347). The coefficients of intra- and inter-assay variation were 4.4% (n = 15) and 5.5% (n = 15), respectively.

Measurement of serum interleukin-33 (IL-33) concentrations Serum Il-33 levels were measured by a commercially available competitive enzyme-linked immunoassay kit (eBiosience, Austria, BMS2048). The coefficients of intra- and inter-assay variation were 5.0% (n = 15) and 6.5% (n = 15), respectively.

Measurement of serum irisin concentrations Serum irisin levels were measured by a commercially available competitive enzyme-linked immunoassay kit (EASTBIOPHARM, Hangzhou Eastbiopharm Co. Ltd. China). The coefficients of intra- and interassay variation were 6.7% (n = 15) and 7.5% (n = 15), respectively.

Measurement of serum retinol binding protein-4 (RBP-4) Serum RBP-4 levels were measured by a commercially available competitive enzyme-linked immunoassay kit (EASTBIOPHARM, Hangzhou Eastbiopharm Co. Ltd. China). The coefficients of intra- and interassay variation were 6.4% (n = 15) and 7.2% (n = 15), respectively.

Measurement of serum adiponectin concentrations Serum adiponectin levels were measured by a commercially available sandwich enzyme-linked immunoassay kit Arch Endocrinol Metab. 2017;61/6

(DRG International, Inc., Marburg, Germany). The coefficients of intra- and inter-assay variation were 6.3% (n = 15) and 7.3% (n = 15), respectively. Glucose, TC, TG, HDL-C and LDL-C concentrations were determined by enzymatic methods (Abbott Diagnostics, Abbott Park, IL, USA). The intra-assay and inter-assay coefﬁcients of variation were 2.6% and 3.0% for the assayed glucose, respectively, 2.6% and 3.0% for the total cholesterol assayed, respectively, 2.7% and 3.5% for the assayed TG, respectively, 3.5% and 4.1% for the assayed HDL-C, respectively, and 3.9% and 4.2% for the assayed LDL-C, respectively. Insulin concentrations were measured by an electrochemiluminescence immunoassay (ECLIA) method on a Roche-Hitachi E170. IR was calculated using the homeostasis model assessment formula (HOMA-IR, fasting insulin (mU/L) * glucose (mmol/dL/22.5). Analysis of CRP was performed by nephelometric means (IMAG-Bechman Coulter, Krefeld, Germany).

Statistical analysis Statistical analyses were performed using SPSS 20.0 for Windows (SPSS, Inc., Chicago, Illinois). The results are expressed as the mean ± standard deviation. An independent samples t-test was used to compare the mean values between the groups. Spearman’s rho test was used to determine the correlations with MetS risk factors. Pearson’s correlation was used for numerical data. In addition, a linear regression analysis was conducted to determine the parameters that significantly affect the MetS. Chi square test was performed to observe the quantitative similarity between genders. To assess the diagnostic accuracy, we performed receiver operating characteristic (ROC) curve analysis. ROC analysis was performed using MedCalc Statistical Software version 14.8.1 ((MedCalcSoftware bvba, Ostend, Belgium). The area under the ROC curve (AUC) was then estimated. p < 0.05 values were considered to be statistically significant.

RESULTS The demographic and biochemical values of MetS patients and controls are shown in Table 1. Age and sex matched groups revealed significant differences with regard to their demographic and biochemical values, as expected (Table 1). The comparison of irisin, retinol binding protein-4, adiponectin and inflammatory 517

containing tubes and anticoagulant-free tubes after an overnight fast. After immediate centrifugation (3000 g) for 10 min at 4 °C, plasma and serum were separated in Eppendorf tubes and frozen immediately at -80 °C until analysis. The homeostasis model assessment (HOMA) was used to detect the degree of insulin resistance (IR) by measuring the levels of basal (fasting) glucose and insulin. HOMA-IR was calculated using the following formula: HOMA-IR = (fasting glucose [mg/dL] x fasting insulin [μU/mL]) / 405.

Circulating inflammatory mediators in the metabolic syndrome

mediators of the groups according to HbA1c levels are shown in Table 2. Circulating irisin, RBP-4, adiponectin and inflammatory mediators of are shown in Table 3.

Correlation analysis between biochemical variables of the whole group and the MetS group are shown in the Tables 4 and 5.

Table 1. Demographic and biochemical values of metabolic syndrome patients and controls Age Sex (F/M)

Control group (n = 50)

Metabolic syndrome (n = 130)

48.72 ± 5.98

50.29 ± 5.67

26/24

68/62

Waist circumference (cm)

82.62 ± 9.13

102.87 ± 8.21

< 0.001 < 0.001

Hip circumference (cm)

96.82 ± 8.76

113.60 ± 9.19

BMI (kg/m2)

23.86 ± 2.40

33.54 ± 4.35

< 0.001

Systolic Blood Pressure (mmHg)

114.80 ± 8.06

137.08 ± 11.71

< 0.001

Diastolic Blood Pressure (mmHg)

78.40 ± 8.57

85.23 ± 8.06

< 0.001

Total protein (g/dL)

7.32 ± 0.40

7.46 ± 0.36

Albumin (g/dL)

4.54 ± 0.29

4.42 ± 0.30

Uric acid (mg/dL)

3.94 ± 1.00

5.35 ± 1.57

< 0.001 < 0.001

162.50 ± 24.88

201.86 ± 46.22

HDL-C (mg/dL)

Total cholesterol (mg/dL)

49.68 ± 9.52

46.18 ± 11

< 0.05

LDL-C (mg/dL)

97.84 ± 24.35

110.98 ± 37.49

< 0.01

Triglyceride (mg/dL)

94.00 ± 30.33

210.82 ± 112.08

< 0.001

Fibrinogen (mg/dL)

260.54 ± 54.07

330.68 ± 69.26

< 0.001 < 0.001

Glucose (mg/dL)

91.38 ± 9.29

177.15 ± 67.89

C-peptide (ng/mL)

1.73 ± 0.61

2.22 ± 1.14

< 0.01

HbA1C (%)

5.51 ± 0.44

8.10 ± 1.73

< 0.001

Insulin (µU/mL)

9.51 ± 5.03

27.45 ± 30.79

< 0.001

HOMA-IR

2.16 ± 1.20

12.32 ± 16.82

< 0.001

BMI: body mass index; LDL: low density lipoprotein; HDL: high density lipoprotein; NS: non-significant.

Table 2. Comparison of irisin, retinol binding protein-4, adiponectin and inflammatory mediators of the groups according to HbA1c levels Group A (HbA1c < 6.5%) n = 68

Group B (HbA1c: 6.5-8%) n = 56

Group C (HbA1c > 8%) n = 56

P (A-B)

P (A-C)

hsCRP (mg/dL)

0.97 ± 1.37

2.97 ± 1.63

3.19 ± 1.72

0.000

PTX-3 (pg/mL)

256.89 ± 120.27

326.53 ± 107.10

317.83 ± 112.30

0.001

0.005

IL-33 (pg/mL)

5.18 ± 1.14

5.23 ± 0.82

5.37 ± 0.86

0.793

0.325

Irisin (ng/mL)

47.95 ± 19.05

62.61 ± 18.07

62.56 ± 17.23

0.000

RBP- 4 (mg/mL)

40.35 ± 12.28

54.49 ± 10.71

57.64 ± 15.51

0.000

6.13 ± 2.14

3.61 ± 1.27

3.46 ± 1.30

0.000

Adiponectin (mg/mL)

hsCRP: high sensitivity C-reactive protein; PTX-3: pentraxin-3; IL-33: interleukin-33; RBP- 4: retinol binding protein – 4. There was no difference between Group B and C with regard to these biomarkers.

Table 3. Circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators of MetS and controls Control group n = 50

Metabolic syndrome n = 130

hsCRP (mg/dL)

0.35 ± 0.24

3.04 ± 1.67

< 0.001

PTX-3 (pg/mL)

235.29 ± 111.08

321.46 ± 110.63

< 0.001

IL-33 (pg/mL)

5.02 ± 1.12

5.35 ± 0.88

< 0.05

Irisin (ng/mL)

4.03 ± 1.29

6.35 ± 1.75

< 0.001

RBP-4 (ng/mL)

34.55 ± 6.63

56.13 ± 12.83

< 0.001

Adiponectin (µg/mL)

7.03 ± 1.71

3.56 ± 1.23

< 0.001

hsCRP: high sensitivity C-reactive protein; PTX-3: pentraxin-3; IL-33: interleukin-33; RBP- 4: retinol binding protein – 4.

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Table 4. Correlation analysis of groups Irisin

Adiponectin

SBP

RBP-4

PTX-3

-0.55

< 0.001

0.467

0.0001

DBP BMI

0.36

< 0.001

0.34

< 0.001

0.479

0.0001

0.229

< 0.002

0.49

< 0.001

0.54

< 0.001

0.474

< 0.0001

0.229

< 0.002

WHR

0.34

< 0.001

0.23

0.02

hsCRP

0.30

< 0.001

HbA1c

0.28

< 0.001

0.221

0.003

0.49

< 0.001

0.487

< 0.0001

Glucose

0.47

< 0.001

0.51

< 0.0001

Insulin

0.24

< 0.001

HOMA-IR

0.26

< 0.001

0.258

< 0.0001

Cholesterol

0.40

< 0.001

0.656

< 0.0001

0.508

< 0.0001

0.29

< 0.001

0.596

< 0.0001

0.555

< 0.0001

0.559

< 0.0001

0.416

< 0.0001

0.01

0.88

Uric acid

0.19

0.01

Fibrinogen PTX-3

0.16

RBP-4

0.25

0.001

Irisin

0.36

< 0.001

0.349

< 0.0001

0.34

< 0.001

0.512

< 0.0001

0.36

< 0.001

0.253

< 0.001

Table 5. Correlation analysis in MetS patients r

Uric acid-Cholesterol

0.541

0.0001

Uric acid-LDL-C

0.606

0.0001

Uric acid-hsCRP

0.454

0.0001

Uric acid-PTX-3

0.414

0.0001

Uric acid-PBB-4

0.485

0.0001

Uric acid-Adiponectin

-0.246

0.0001

LDL-hsCRP

0.652

0.0001

LDL-PTX-3

0.638

0.0001

LDL-RBP

0.694

0.0001

LDL-Adiponectin

-0.323

0.0001

hsCRP-PTX-3

0.472

0.0001

hsCRP-RBP-4

0.521

0.0001

hsCRP-Adiponectin

-0.219

0.012

PTX-3-RBP4

0.487

0.0001

PTX-3-Adiponectin

-0.373

0.0001

RBP-4-Adiponectin

-0.215

0.014

Irisin-WC

0.246

0.005

Irisin-WHR

0.468

0.0001

Adiponectin-SBP

-0.208

0.017

Regression analysis for irisin revealed that only WC had a significant effect on MetS (p = 0.02). WHR, BMI, HOMA-IR, uric acid, LDL-C, hsCRP, PTX-3, IL-33, RBP-4, and adiponectin levels did not correlate with irisin. Adiponectin levels did not correlate with these parameters in MetS. Correlation analyses of the new biomarkers are shown in Table 6. Arch Endocrinol Metab. 2017;61/6

A comparison of the ROC curves with sensitivity, speciﬁcity, AUC, cut-off and asymptotic signiﬁcance of systolic BP, DBP, WC, glucose, TG, HDL-C, hsCRP, irisin, RBP-4, PTX-3, adiponectin and IL-33 levels in the whole group are shown in Table 7 and in ROC curves in Figures 1 and 2.

DISCUSSION Adipose tissue is an important source of adipokines that have proinflammatory effects and may be the link between obesity, CVD, diabetes and MetS (1,2). In our study, there was a statistically significant difference between the MetS group and the control group with respect to WC, WHR, BMI, blood pressure, lipid parameters, uric acid, fibrinogen, glucose, insulin, C-peptide and HbA1c values. Some of these variables are components of MetS, so this is not surprising. More importantly, there were significant differences with regard to irisin, RBP-4, IL-33, PTX-3 and hsCRP levels in favor of the MetS group; the opposite finding was observed for adiponectin. We found that circulating irisin levels were positively associated with MetS components including BMI, WC and WHR, while in some studies, there were conflicting results regarding the relation between irisin levels with metabolic parameters. We also observed that women had lower levels of plasma irisin levels compared to men. In the control group, although women had 519

LDL-C

Circulating inflammatory mediators in the metabolic syndrome

Table 6. Correlation analysis of the new parameters with each other Irisin MetS

RBP-4 PTX-3 Adiponectin

PTX-3

RBP-4

Adiponectin

hsCRP

0.487

-0.215

0.521

0.0001

0.014

< 0.0001

-0.373

0.472

< 0.0001

-0.219

p Whole

Irisin

0.012

r p

RBP-4

r p

PTX-3 Adiponectin IL-33

0.253

-0.363

0.299

0.001

0.0001

0.512

-0.567

0.702

0.0001

0.157

-0.339

0.502

0.035

0.0001

-0.363

-0.339

-0.566

0.0001

0.153

0.041

Table 7. Sensitivity, specificity, cut-off, AUC and asymptotic significance of parameters Sensitivity (%)

Specificity (%)

Cut-off

AUC

95% CI

Systolic BP

90.00

> 120

0.938

0.016

0.892-0.968

< 0.0001

BMI

96.15

94.00

> 27.18

0.980

0.010

0.947-0.995

< 0.0001

HbA1c

87.69

98.00

> 6.2

0.958

0.013

0.918-0.982

< 0.0001

93.08

80.00

> 2.68

0.927

0.019

0.878-0.960

< 0.0001

85.38

100.00

> 0.93

0.975

0.009

0.940-0.992

< 0.0001

RBP-4

81.54

98.00

> 44.56

0.945

0.015

0.901-0.973

< 0.0001

100

60 Sensitivity

Sensitivity

HOMA-IR hsCRP

GLUC WC SBP TRG DBP HDL

Asymptotic sig.

hsCRP ADIPON RBP4 IRISIN PTX3 LL33

0 0

40 60 100-Specificity

Figure 1. ROC curves of MetS parameters. 520

100

40 60 100-Specificity

100

Figure 2. ROC curves of the new biomarkers. Arch Endocrinol Metab. 2017;61/6

higher levels, this was not statistically significant. We do not know the mechanism underlying the difference between the two genders. We suggest that this may be due to increased muscle mass, hormonal effects, the duration of obesity or drugs. hsCRP, IL-33, PTX-3, RBP-4 and fibrinogen levels did not change between males and females in the MetS group. Among other variables, only HDL-C levels were different between the two genders, as expected. Rizk and cols. (17) found that irisin levels were increased and positively correlated with BMI, serum triglycerides, HOMA-IR and liver enzymes in patients with MetS. Some authors showed a positive association between irisin levels and plasma glucose and TG levels in type 2 diabetic patients (18,19). Contradictory to these studies, Assyov and cols. (20) found a negative relationship with fasting glucose levels, although he observed a positive association of irisin with BMI. Wang and Li (21) reported decreased irisin levels in patients with type 2 DM compared with nondiabetics and suggested that irisin was not associated with beta cell function (21). We did not observe a positive relationship between irisin with plasma glucose, insulin and cholesterol levels. In a cohort of sedentary women, Duran and cols. (22) observed a negative correlation between irisin and BMI, postprandial glucose, LDL-C and TG levels, but a positive relationship with HDL-C. We suggest that irisin might be an early marker of MetS which only affected WC and WHR, but did not influence blood pressure and biochemical parameters. IL-6 is secreted from adipose tissue in obesity and circulating levels of CRP are increased through adipocyte-derived IL-6 (23). We showed that hsCRP levels were positively correlated with PTX-3 and RBP-4, which are molecules involved in inflammatory conditions; hsCRP was negatively correlated with adiponectin. Inflammation in the vasculature might be an important pathogenic link between CVD and MetS. In our study, we found significantly higher levels of PTX-3 in MetS patients than in control subjects. There was a positive association between PTX-3 and irisin, RBP-4 levels and MetS components; a negative relationship was found between PTX-3 and adiponectin levels. A normal PTX-3 concentration was found to be approximately 2 ng/mL; men had significantly lower values than women (12,13). There was no difference between genders regarding PTX-3 levels in our study. We found an association of PTX-3 with risk factors such as obesity, uric acid, LDL-C, hsCRP, RBP-4 and Arch Endocrinol Metab. 2017;61/6

adiponectin. Inoue and cols. (12) showed that the PTX-3 level was increased in the oldest age group and was also inversely correlated with triglyceride levels and BMI. It has also been found to be independent of other established coronary risk factors (13). Kardas and cols. (24) reported that PTX-3 levels were higher in obese children and adolescents with MetS and CVD, and that these levels were positively correlated with TG and negatively correlated with HDL-C levels (24,25). It was suggested that PTX3 levels may increase in order to confer protection against cardiac tissue damage. PTX3 binds to activated platelets and reduces inflammation in the cardiovascular bed (12), and might be a novel marker for subclinical atherosclerosis (25). These findings suggested that both PTX-3 and RBP-4 may be used to predict inflammatory status in MetS instead of hsCR, which is a well- known acute phase inflammatory marker. In accordance with some studies, we found that RBP-4 levels were higher in obese subjects than controls. We observed that RBP-4 was positively correlated with uric acid, LDL-C, hsCRP, and PTX-3, and negatively related to adiponectin. The concentration of RBP-4 was found to be elevated in obesity and type 2 DM, MetS and CVD (5). Many authors found that RBP-4 levels were associated with MetS parameters and HOMA-IR, the duration of diabetes and carotid atherosclerosis as determined by CIMT (26-29). There are conflicting results about the association of RBP-4 and CRP levels, probably due to methodological differences (30,31). Jialal and cols. suggested that serum irisin and RBP-4 levels would be independent predictors of CVD in diabetes (30). We think that RBP-4 is significantly associated with nearly all components of MetS and inflammation. It should be kept in mind that circulating RBP concentrations depend on vitamin A status; therefore, the serum retinol concentration may be a confounder (5). We did not measure vitamin A levels. As expected, we found lower levels of adiponectin in MetS patients than controls. Adiponectin levels were negatively associated with SBP, WC, WHR, PTX-3, RBP4, hsCRP, uric acid and LDL-C. Bidulescu and cols. (32) observed higher levels of adiponectin in African American women compared to men. Chiara and cols. (33) showed that subjects with low adiponectin levels had a higher prevalence of obesity, MetS, DM and CVD. In our research, we found IL-33 levels were associated with HbA1c and insulin in the control group, but there no association of this marker with 521

Circulating inflammatory mediators in the metabolic syndrome

other variables. Interestingly, there was no association between IL-33 and adiponectin. IL-33 might be either proinflammatory or anti-inflammatory depending on the disease and the model. However, IL-33 was shown to have various protective effects in CVD, obesity and diabetes. Reduced levels may increase the risk of developing insulin resistance (14,15). A strength of our study is that we evaluated multiple biomarkers involved in MetS and searched for the relationship between each of them with anthropomorphic and biochemical variables. However, our study has some limitations. First, our sample size is relatively small. Second, dietary habits, physical activity and the exercise level of the subjects were not documented. Third, we did not investigate cardiovascular comorbidities and drugs which could affect our results. Last, we did not measure vitamin A levels which might affect the RBP-4 level. Due to the cross-sectional design of our study, we cannot make any suggestions about the association between the laboratory and clinical parameters of the subjects. Prospective trials are needed to observe this relationship. We think that obesity induced cytokines initiate and promote a proinflammatory status leading to clinical consequences such as IR, DM, HT and atherosclerosis. Discrepancies in our results from previous studies may be due to different study populations or differences in their diet and exercise. MetS is associated with impaired glucose homeostasis and low-grade chronic inflammation, as well as myokines and adipokines that interact through complex networks. Although the precise mechanisms are still unclear, elevated PTX-3, RBP-4, IL-33, irisin and decreased adiponectin levels increase the risk of obesity-related metabolic disorders. The inflammatory condition associated with overweight plays an important role in the components of the MetS and largely contributes to the related pathological outcomes. Our findings suggested that irisin might be an early marker of MetS that emerges before anthropomorphic, biochemical and clinical parameters. We also suggest that both PTX-3 and RBP-4 may be used to predict the inflammatory status in MetS instead of hsCR, which is a well-known acute phase inflammatory marker. The contradictory results between this study and others may be linked to the different stages of the MetS. Subjects should be evaluated prospectively with anthropomorphic, biochemical and clinical aspects at regular intervals. Future clinical studies are needed to confirm and extend these data. 522

Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Ritchie SA, Connell JM. The link between abdominal obesity, metabolic syndrome and cardiovascular disease. Nutr Metab Cardiovasc Dis. 2007;17:319-26. 2. Freitas Lima LC, Braga VA, do Socorro de França Silva M, Cruz JC, Sousa Santos SH, de Oliveira Monteiro MM, Balarini CM. Adipokines, diabetes and atherosclerosis: an inflammatory association. Front Physiol. 2015;6:304. 3. Panagiotou G, Mu L, Na B, Mukamal KJ, Mantzoros CS. Circulating irisin, omentin-1, and lipoprotein subparticles in adults at higher cardiovascular risk. Metabolism. 2014;63:1265-71. 4. Vaughan RA, Gannon NP, Mermier CM, Conn CA. Irisin, a unique non-inflammatory myokine in stimulating skeletal muscle metabolism. J Physiol Biochem. 2015;71:679-89. 5. Zabetian-Targhi F, Mahmoudi MJ, Rezaei N, Mahmoudi M. Retinol Binding Protein 4 in Relation to Diet, Inflammation, Immunity, and Cardiovascular Diseases. Adv Nutr. 2015;6:748-62. 6. Moraes-Vieira PM, Yore MM, Dwyer PM, Syed I, Aryal P, Kahn BB. RBP4 activates antigen-presenting cells, leading to adipose tissue inflammation and systemic insulin resistance. Cell Metab. 2014;19:512-26. 7. Fasshauer M, Blüher M. Adipokines in health and disease. Trends Pharmacol Sci. 2015;36:461-70. 8. Kim SH, Chung JH, Song SW, Jung WS, Lee YA, Kim HN. Relationship between deep subcutaneous abdominal adipose tissue and metabolic syndrome: a case control study. Diabetol Metab Syndr. 2016;8:10. 9. Esfahani M, Movahedian A, Baranchi M, Goodarzi MT. Adiponectin: an adipokine with protective features against metabolic syndrome. Iran J Basic Med Sci. 2015;18:430-42. 10. Turer AT, Scherer PE. Adiponectin: mechanistic insights and clinical implications. Diabetologia. 2012;55:2319-26. 11. Daigo K, Mantovani A, Bottazzi B. The yin-yang of long pentraxin PTX3 in inflammation and immunity. Immunol Lett. 2014;161:38-43. 12. Inoue K, Kodama T, Daida H. Pentraxin 3: a novel biomarker for inflammatory cardiovascular disease. Int J Vasc Med. 2012;2012:657025. 13. Yamasaki K, Kurimura M, Kasai T, Sagara M, Kodama T, Inoue K. Determination of physiological plasma pentraxin 3 (PTX3) levels in healthy populations. Clin Chem Lab Med. 2009; 47:471-7. 14. Hasan A, Al-Ghimlas F, Warsame S, Al-Hubail A, Ahmad R, Bennakhi A, Al-Arouj M. IL-33 is negatively associated with the BMI and confers a protective lipid/metabolic profile in nondiabetic but not diabetic subjects. BMC Immunol. 2014;15:19. 15. Miller AM. Role of IL-33 in inflammation and disease. J Inflamm (Lond). 2011;8:22. 16. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al.; American Heart Association; National Heart, Lung, and Blood Institute. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735-52. 17. Rizk FH, Elshweikh SA, Abd El-Naby AY. Irisin levels in relation to metabolic and liver,ctions in Egyptian patients with metabolic syndrome. Can J Physiol Pharmacol. 2015:1-4. 18. García-Fontana B, Reyes-García R, Morales-Santana S, ÁvilaRubio V, Muñoz-Garach A, Rozas-Moreno P, et al. Relationship between myostatin and irisin in type 2 diabetes mellitus: a compensatory mechanism to an unfavourable metabolic state? Endocrine. 2016;52:54-62. Arch Endocrinol Metab. 2017;61/6

Circulating inflammatory mediators in the metabolic syndrome

19. Park KH, Zaichenko L, Brinkoetter M, Thakkar B, Sahin-Efe A, Joung KE, et al. Circulating irisin in relation to insulin resistance and the metabolic syndrome. J Clin Endocrinol Metab. 2013;98:4899-907. 20. Assyov Y, Gateva A, Tsakova A, Kamenov Z. Irisin in the Glucose Continuum. Exp Clin Endocrinol Diabetes. 2016;124:22-7. 21. Wang W, Li N. Correlation of retinol binding protein 4 with metabolic indexes of glucose and lipid, bile cholesterol saturation index. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2015;40:657-65. 22. Duran ID, Gülçelik NE, Ünal M, Topçuog#lu C, Sezer S, Tuna MM, et al. Irisin levels in the progression of diabetes in sedentary women. Clin Biochem. 2015;48:1268-72. 23. Trayhurn P, Wood IS. Adipokines: inflammation and the pleiotropic role of white adipose tissue. Br J Nutr. 2004;92:347-55. 24. Kardas F, Akın L, Kurtoglu S, Kendirci M, Kardas Z. Plasma Pentraxin 3 as a biomarker of metabolic syndrome. Indian J Pediatr. 2015;82:35-8. 25. Zanetti M, Bosutti A, Ferreira C, Vinci P, Biolo G, Fonda M, et al. Circulating pentraxin 3 levels are higher in metabolic syndrome with subclinical atherosclerosis: evidence for association with atherogenic lipid profile. Clin Exp Med. 2009;9:243-8.

28. Ingelsson E, Sundström J, Melhus H, Michaëlsson K, Berne C, Vasan RS, et al. Circulating retinol-binding protein 4, cardiovascular risk factors and prevalent cardiovascular disease in elderly. Atherosclerosis. 2009;206:239-44. 29. Feng S, Zhu Y, Yan C, Wang Y, Zhang Z. Retinol binding protein 4 correlates with and is an early predictor of carotid atherosclerosis in type 2 diabetes mellitus patients. J Biomed Res. 2015 Jul 3;29. doi: 10.7555/JBR.29.20140087. [Epub ahead of print]. 30. Jialal I, Adams-Huet B, Duong F, Smith G. Relationship between retinol-binding protein-4/adiponectin and leptin/adiponectin ratios with insulin resistance and inflammation. Metab Syndr Relat Disord. 2014;12:227-30. 31. Zhang M, Chen P, Chen S, Sun Q, Zeng QC, Chen JY, et al. The association of new inflammatory markers with type 2 diabetes mellitus and macrovascular complications: a preliminary study. Eur Rev Med Pharmacol Sci. 2014;18:1567-72. 32. Bidulescu A, Liu J, Hickson DA, Hairston KG, Fox ER, Arnett DK, et al. Gender differences in the association of visceral and subcutaneous adiposity with adiponectin in African Americans: the Jackson Heart Study. BMC Cardiovasc Disord. 2013;13:9. 33. Chiara TD, Argano C, Scaglione A, Corrao S, Pinto A, Scaglione R. Circulating adiponectin: a cardiometabolic marker associated with global cardiovascular risk. Acta Cardiol. 2015;70:33-40.

26. Wang L, Song J, Wang C, Lin P, Liang K, Sun Y, et al. Circulating levels of betatrophin and irisin are not associated with pancreatic β-cell function in previously diagnosed type 2 diabetes mellitus patients. J Diabetes Res. 2016;2016:2616539.

27. Park SE, Lee NS, Park JW, Rhee EJ, Lee WY, Oh KW, et al. Association of urinary RBP4 with insulin resistance, inflammation, and microalbuminuria. Eur J Endocrinol. 2014;171:443-9.

Arch Endocrinol Metab. 2017;61/6

523

original article

Obese with higher FNDC5/Irisin levels have a better metabolic profile, lower lipopolysaccharide levels and type 2 diabetes risk Ivan Luiz Padilha Bonfante1, Mara Patricia Traina Chacon-Mikahil1, Diego Trevisan Brunelli1, Arthur Fernandes Gáspari1, Renata Garbellini Duft1, Alexandre Gabarra Oliveira2, Tiago Gomes Araujo3, Mario Jose Abdalla Saad3, Cláudia Regina Cavaglieri1

Laboratório de Fisiologia do Exercício, Faculdade de Educação Física, Universidade Estadual de Campinas (Unicamp), Campinas, SP, Brasil 2 Instituto de Biociências, Universidade Estadual Paulista “Júlio de Mesquita Filho” (Unesp), Rio Claro, SP, Brasil 3 Departamento de Medicina Interna, Escola de Ciências Médicas, Universidade Estadual de Campinas (Unicamp), Campinas, SP, Brasil 1

Correspondence to: Ivan Luiz Padilha Bonfante Laboratório de Fisiologia do Exercício, Faculdade de Educação Física, Caixa Postal 6134 Universidade Estadual de Campinas Av. Érico Veríssimo, 701 13083-851 – Campinas, SP, Brasil ivanlpb@hotmail.com Received on June/7/2016 Accepted on June/6/2017

ABSTRACT Objective: Thus, the aim of this study was to compare if higher or smaller fibronectin type 3 domain-containing protein 5 (FNDC5)/irisin levels are associated with inflammatory and metabolic markers, caloric/macronutrient intake, physical fitness and type 2 diabetes mellitus (T2DM) risk in obese middle-aged men, and also to correlate all variables analyzed with FNDC5/irisin. Subjects and methods: On the basis of a cluster study, middle-aged obese men (IMC: 31.01 ± 1.64 kg/m²) were divided into groups of higher and smaller levels of FNDC5/irisin. The levels of leptin, resistin, adiponectin, tumor necrosis factor alpha (TNFα), interleukin 6 and 10 (IL6, IL10), lipopolysaccharide (LPS), glucose, insulin, glycated hemoglobin, insulin resistance and sensibility, lipid profile, risk of T2DM development, body composition, rest energy expenditure, caloric/macronutrient intake and physical fitness were measured. Results: The higher FNDC5/ irisin group presented improved insulin sensibility (homeostasis model assessment – sensibility (HOMA-S) (p = 0.01) and QUICKI index (p < 0.01)), insulin (p = 0.02) and triglyceride levels (p = 0.01), lower insulin resistance (homeostasis model assessment – insulin resistance (HOMA-IR) (p = 0.01), triglycerides/glucose (TYG index) (p = 0.02), neck circumference (p = 0.02), risk of T2DM development (p = 0.02), tendency to decrease serum resistin (p = 0.08) and significant lower LPS levels (p = 0.02). Inverse correlations between FNDC5/irisin and body weight (r -0.46, p = 0.04), neck circumference (r -0.51, p = 0.02), free fat mass (r -0.49, p = 0.02), triglycerides (r -0.43, p = 0.05) and risk of developing T2DM (r -0.61, p = 0.04) were observed. Conclusions: These results suggest that higher FNDC5/irisin levels in obese middle-aged men are related to a better metabolic profile and lower risk of T2DM development and serum LPS, a potential inducer of insulin resistance. Arch Endocrinol Metab. 2017;61(6):524-33 Keywords Irisin; metabolism; obese; type 2 diabetes; lipopolysaccharide

DOI: 10.1590/2359-3997000000305:

INTRODUCTION

risin is a peptide secreted mainly by adipose tissue and muscles after the stimulation of peroxisome proliferator activates receptor gamma coactivator 1 alpha (PGC1alpha) and subsequent secretion and cleavage of fibronectin type 3 domain-containing protein 5 (FNDC5) by stimulus such as physical exercise and exposure to cold (1). In adipose tissue, especially in inguinal fat cells, irisin increases the expression of mitochondrial uncoupling protein 1 (UCP1) and, consequently, the energy expenditure and consumption of lipid reserves, which could contribute to treatment and prevention of metabolic diseases (1). 524

Indeed, such irisin effect in inguinal seems to increase a special kind of adipocyte cell named brite or beige that show characteristics of white (basal) and brown adipocytes (after irisin stimulus) (2). Several factors are related interfering in FNDC5/irisin levels, as body composition, cold exposure, physical exercise, physical fitness and leptin levels (3-5). The optimal levels of circulating FNDC5/irisin in humans are not established, however, some evidence indicates that individuals with type 2 diabetes mellitus (T2DM) (6,7) and metabolic syndrome and insulin resistance (IR) (8) have lower levels of this peptide, indicating that a larger amount of FNDC5/irisin Arch Endocrinol Metab. 2017;61/6

Metabolic profile, LPS and FNDC5/irisin

SUBJECTS AND METHODS Subjects Inactive middle-aged (48,54 ± 5,91 years) male individuals, with body mass index (IMC: 31.01 ± 1.64 kg/m²), were recruited for the study through the local media. Before the men’s inclusion in the study, a complete medical examination was carried out, and individuals were excluded if they had an acute illness, severe hypertension, diabetes mellitus, myocardial infarction or orthopedic limitations. They should not be using any medication (as anti-diabetic, beta blocker, exogenous insulin, anti-inflammatory, thyroid hormone) that would interfere with the results. Participants could Arch Endocrinol Metab. 2017;61/6

not be involved in regular exercise programs during the previous 12 months according to the Baecke Habitual Physical Activity Questionnaire and had to be insufficiently active according to the International Physical Activity Questionnaire (IPAQ) (15). The individuals who met the criteria, and were approved at the initial medical evaluation, were assigned to the higher irisin group (HIG) and the smaller irisin group (SIG) after the cluster analysis was performed, as described in the Statistical analyses section. Twentytwo individuals matched the inclusion criteria, but two of them were excluded for not having all the required qualifications. Therefore, twenty individuals completed the study, and were divided into HIG (n = 11) and SIG (n = 9). The study protocol were explained before written consent was obtained. The study was in compliance with the Declaration of Helsinki and the procedures were previously approved by the Research Ethics Committee of the University of Campinas (appraisal number n° 1278/2011) and all subjects gave written informed consent before taking part.

Experimental design This is a cross-sectional study using a cluster approach. We evaluated cardiorespiratory fitness, muscle strength, resting metabolic rate (RMR), anthropometric parameters, body composition, feeding behavior, measurements of FNDC5/irisin, inflammatory markers and total cholesterol (TC), HDL-cholesterol (HDL), LDL-cholesterol (LDL), triglycerides (TG), insulin, glucose, glycated hemoglobin (HbA1c), homeostatic model assessment 2 beta, insulin sensibility and resistance (HOMA2B, S, IR), triglycerides and glucose index (TYG index), Quick index, systolic and diastolic blood pressure and diabetes mellitus type 2 index risk (T2DM index risk).

Anthropometry and body fat Body weight was measured with a calibrated manual scale (Filizola, São Paulo, Brazil) with a precision of 0.1 kg. Height was measured with a wall-mounted stadiometer, with a precision of 0.1 cm. BMI was calculated from the weight and height values. Neck (NC) and waist circumference (WC) were measured by the commonly established anatomical landmarks. Body density was estimated by the skinfold procedure with a skin-fold caliper (Lange, Beta Technology, Santa Cruz, CA, USA) at the chest, abdomen, thigh, triceps, 525

could be a protective factor against these diseases. Paradoxically, some studies have shown that the presence of these diseases positively correlates with high levels of FNDC/irisin (9-11). Thus, such topic is still controversial and need to be further carefully investigated. It is well known that there is an important imbalance in the secretion of cytokines in obese individuals, which increases the risk of developing metabolic disorders and diseases (12). Cytokines levels associated with clinical metabolic markers are strong predictors of the risk of developing IR and T2DM (12). Besides cytokines, the toll-like receptor 4 (TLR4) stimulus by its main ligand lipopolysaccharide (LPS) is also strongly associated with IR because TLR4 activation increases tumor necrosis factor alpha (TNFα) expression, which in turn impairs insulin signaling pathway in several tissues such as muscle and adipose tissue (13,14). Although FNDC5/irisin levels and inflammatory markers have been related to glucose metabolism, the potential association between these markers is not yet established. Moreover, there are doubts whether higher or lower FNDC5/irisin levels are related to metabolic homeostasis, mainly in individuals with metabolic risk due to excess body fat. Based on these aspects, the aim of this study was to compare if higher or smaller FNDC5/ irisin levels are associated with inflammatory and metabolic markers, along with caloric/macronutrient intake, physical fitness and T2DM risk in obese middleaged men with the absence of overt disease, through a cluster study, and also to correlate all variables analyzed with FNDC5/irisin.

Metabolic profile, LPS and FNDC5/irisin

subscapular, suprailiac, and mid-axillary points. The body fat percentage was obtained from body density with the Siri equation (16). The subcutaneous and visceral abdominal fat thickness was measured by the abdominal ultrasound method (17). All assessments were performed by the same professional.

Blood sampling Blood samples (~20 mL) were obtained from the antecubital vein in the morning (07.00 to 09.00), after 12h overnight fasting. All samples were divided into aliquots, processed immediately after collection and frozen at -80°C until later analysis. Serum samples were used for lipid profile and plasma (using EDTA antigulant) samples were used for irisin, glucose, insulin and HbA1c analysis.

Lipid profile, glucose and insulin Concentrations of TC, TG, HDL-C, and glucose were analyzed with an automatic analyzer (Technicon RA 1000 Chemistry Analyzer) and a commercially available kit (Laborlab, São Paulo, Brazil). The LDL-C was calculated according to the Friedewald equation (18). The insulin was determined by chemiluminescence with commercial kits (Elecsys insulin kit, Roche Diagnostics GmbH, Indianapolis, IN, USA) and an automatic biochemical analyzer (ARCHITECT i2000 SR, Abbot Diagnostics, IL, USA). The HbA1c was verified by high pressure liquid chromatography high performance (HPLC).

FNDC5/irisin and adipokine measurements FNDC5/irisin values were determined, duplicated by enzyme-linked immunosorbent assay (ELISA), according to the specifications of the manufacturer (Quantikine High-Sensitivity Kit (Lot n° L13112659), United States Biological, Swampscott, MA, USA). This is a polyclonal antibody kit, with capacity to identify FNDC5 complete protein and cleaved FNDC5 (Irisin). The kit also has the not cross-react with human peptides that have molecular patterns similar to FNDC5/Irisin as: fibronectin type III domain containing 4 (FNDC4), adiponectin, nicotinamide phosphoribosyltransferase (Nampt), retinol-binding protein 4 (RBP4), clusterin, leptin, vaspin, glutathione peroxidase 3 (GPX3), resistin, angiotensin-converting enzyme 2 (ACE2), lipocalin-2, angiopoietin-like protein3 (ANGPTL3), angiopoietin-like protein3 (ANGPTL6), delta and 526

notch-like epidermal growth factor-related receptor (DNER), delta homolog 1 (DLK1), calreticulin and interleukin 33. The values are presented in micrograms per milliliter (ug/mL). The sample sensitivity was 1 nanogram per milliliter (ng/ml). The test range was 0.001-5 ug/ml. The values of all subjects, regardless of the group, are within range. The intra-assay and inter-assay kit coefficient and sensitivity were as follows: 4.86%, 8.02% and 1 ng/mL. All samples were measured in the same plates and collect in the same period. Serum concentrations of resistin, leptin and adiponectin, TNF-α, interlukin 6 and interleukin 10 (IL6; IL10) were also determined by ELISA, following the specifications of the manufacturer (Quantikine High-Sensitivity Kit, R&D Systems, Minneapolis, MN, USA). The intra- and interassay coefficients and the sensitivity were as follows: TNF-α; 3.8%, 6.0% and 0.010 ng/mL; 7.4%, 6.5%, and 0.039 pg/mL for IL-6; 4.6%,7.8%, and 0.09 pg/mL for IL-10; 3.0%, 3.5% and 7.8 pg/mL for leptin; 2.8%, 5.9% and 0.246 ng/ml for adiponectin; 3.8%, 7.8% and 0.026 ng/mL for resistin. To analyze LPS levels, plasma samples were diluted to 20% with endotoxin-free water and then heated to 70°C for 10 min to inactivate plasma proteins. Then serum LPS was quantified with a commercially available Limulus Amebocyte assay from Cambrex (Walkersville, MD, USA) according to the manufacturer’s protocol. The samples were duplicated and the background subtracted.

Blood pressure Systolic and diastolic blood pressure assessments were performed after approximately 10 minutes of rest with a mercury sphygmomanometer and a stethoscope. The measurements were taken in the supine position by the same professional. All measurements were duplicated and the average of the two assessments was used.

Formulas/Indexes Beta-cell function, insulin sensibility and resistance were calculated respectively by the HOMA calculator using fasting concentrations of glucose and insulin equation (19). Insulin resistance was also verified by the TYG index using the equation (fasting triglycerides (mg/dL) x fasting glucose (mg/dL)/2) (20). Insulin sensibility was evaluated using the QUICKI index by Arch Endocrinol Metab. 2017;61/6

Metabolic profile, LPS and FNDC5/irisin

the following equation: Quicki = 1 ÷ (Log insulin + Log glicose) (21). The risk of T2DM development was calculated with the algorithm for prediction of this disease in middleaged adults in the Framingham Offspring study (22).

satisfactory consumer information could be compiled accurately. DRs were analyzed by Dietpro version 5i software. The estimation of nutrient intake was made with based in TACO/Unicamp table and USDA. The food intake assessment was derived from the average data of three recalls (25).

Maximal-strength assessments

Cardiorespiratory fitness test The individuals performed a maximum-effort protocol on a Quinton TM55 treadmill (Bothell, WA, USA), where gas exchange data was collected continuously by means of an automated breath-by-breath metabolic cart (CPX; Medical Graphics, St Paul, MN, USA) (23).

Rest metabolic rate (RMR) The RMR was determined from oxygen consumption (O2) and carbon dioxide production (CO2) by indirect calorimetry of open circuit by the gas analysis system (CPX Ultima, MedGraphics, USA) and calculated in daily values (kcal/day) by the Weir equation (24). The test was performed under laboratory conditions and after a fasting period of 12 hours. Volunteers used the gas analyzer connected to a facial mask, remaining silent in the supine position, avoiding movement and sleeping for 30 min (the initial 10 min were discarded), so that breath after breath could be obtained. The gas analyzer was calibrated before each test.

Statistical analysis Initially, using cluster analysis was performed (K-means cluster) with Statistica 6 software (StatSoft, USA) using the FNDC5/irisin level of each subject. This is an exploratory multi-variance analysis technique that allows classifying a set of data into homogeneous groups through similarities or dissimilarities between them. By “k means cluster” is possible to include the number of groups according to convenience. In function on the number of subjects we established two groups (higher and smaller irisin). After the establishment of the number of groups, the system assigned a centroid to each group. Subsequently each data (numerical object of the data set) has its Euclidean distance calculated with these centroids by means of a distance measure. The criterion for a data/numerical object to be allocated in a given group is its shortest distance from the centroid. After the cluster group’s definition, data distribution was tested by the Shapiro–Wilk test. A Student’s t-test was applied to analyze differences between clusters for parametric distributed variables and the Mann-Whitney U-test was used to compare the non-parametric distributed variables. We performed also correlations, using the Pearson’s correlation between FNDC5/irisin for the parametric distributed variables and the Spearman’s rank correlation to FNDC5/irisin for non-parametric distributed variables. Parametric distributed variables were expressed as mean ± standard deviation while non-parametric variables were expressed as median (Interquartile range). The p-value significance level was P ≤ 0.05 for all analyses.

Evaluation of caloric and macronutrient intake

RESULTS

Volunteers received diet records (DR) from trained nutritionists, who explained individually how to complete these records. Food scales were distributed and individuals were requested to list all food ingested during three-day food records to register intake on three different and non-consecutive days (two weekdays and one weekend day). After completing the DR, the team of nutritionists met the volunteers so that if the data were not

Table 1 presents anthropometric, body composition, caloric/macronutrient intake, physical fitness and RMR values of Higher Irisin Group (HIG) and Smaller Irisin Group (SIG). The hallmark results were that HIG had smaller weight (p = 0.04), neck circumference (p = 0.02) and lipid intake (p = 0.05). Table 2 shows the glycemic homeostasis markers, lipid profile and systolic/diastolic blood pressure values of both groups. HIG compared to SIG presented

Arch Endocrinol Metab. 2017;61/6

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Maximal strength was measured by a one maximum repetition test (1RM) on bench press, leg press and arm curl exercises, performed on NakaGym equipment (São Paulo, Brazil). The 1RM tests were conducted as in Libardi and cols. (23). Before the beginning of the study, individuals performed two familiarization trials interleaved with 48h periods, to reduce the learning effects, as well as to establish the reproducibility of the tests in the exercise.

Metabolic profile, LPS and FNDC5/irisin

smaller insulin (p = 0.02), triglyceride levels (p = 0.01), and insulin resistance by the HOMA-IR (p = 0.01), along with TYG index (p = 0.02). HIG still had better

insulin sensibility according to HOMA-S (p = 0.01) and QUICKI index (p < 0.01). There was also a tendency of HIG to exhibit smaller VLDL levels (p = 0.07).

Table 1. Anthropometric markers, body fat, caloric/macronutrient intake, physical fitness and rest metabolic rate of groups higher and smaller irisin Variable

Higher Irisin Group (HIG) n = 11

Smaller Irisin Group (SIG) n = 9

Age (years)

49.09 (45.33-52.84)

48 (43.20-52.80)

Weight (kg)

90.16 ± 6.66

96.68 ± 7.02*

Height (m)

1.72 ± 0.06

1.75 ± 0.05

BMI (kg/m )

30.63 ± 1.68

31.47 ± 1.57

Body fat (%)

35.47 ± 5.43

34.05 ± 4.59

Waist circumference (cm)

101.1 ± 5.2

102.7 ± 4.7

Neck circumference (cm)

40.88 ± 1.56

42.87 ± 1.99*

US SF (mm)

26.1 ± 8.7

24.76 ± 6.10

US VF (mm)

65.46 ± 14.33

68.32 ± 19.43

257 ± 56

276 ± 90

76.10 ± 22

100.59 ± 32*

99.5 ± 6

107.8 ± 27.5

Caloric Intake (kcal)

2154 ± 471

2515 ± 695

1 RM leg press (kg)

293 ± 84.88

302 ± 56.4

Carbohydrates (g/day) Lipids (g/day) Proteins (g/day)

1RM bench press (kg) 1RM arm curl (kg) VO2max. (mL/kg-1/min-1) RMR (kcal)

70.5 ± 15.79

70.14 ± 15.81

29.18 (26.16-32.19)

30.57 (26.62-34.51)

26.84 ± 3.64

25.95 ± 4.04

1331 ± 200.82

1397 ± 235.52

BMI: body mass index; US SF: ultrasound subcutaneous fat; US VF: ultrasound visceral fat; RM: maximum repetition; VO2max.: maximum volume of oxygen; RMR: Rest metabolic rate. *: Significant difference HIG and SIG (p < 0.05). Parametric variables were expressed as the mean ± standard deviation; non-parametric variables were expressed as the median (Interquartile range).

Table 2. Biochemical, metabolic indexes and hemodynamic markers of groups higher and smaller Irisin Higher Irisin Group (HIG) n = 11

Smaller Irisin Group (SIG) n = 9

Fasting insulin (uU/mL)

9.14 ± 2.09

14.26 ± 5.9*

Fasting glucose (mmol/l)

5.18 ± 0.25

5.24 ± 0.72

HbA1c (%)

5.34 (5.15-5.53)

5.35 (4.79-5.91)

HOMA2-B

107.03 (90.93-123.13)

143.03 (87.66-198.39)

HOMA2-S

83.48 ± 23.7

58.16 ± 19.24*

HOMA2-IR

1.23 ± 0.27

1.81 ± 0.71*

QUICKI Index

0.34 ± 0.02

0.31 ± 0.01*

Variable

TYG Index Triglycerides (mmol/l)

Total Cholesterol (mmol/l)

4.69 ± 0.19

4.87 ± 0.21*

1.43 9 (1.09-1.81)

2.27 (1.60-2.94)*

4.64 ± 0.90

5.22 ± 1.15

HDL (mmol/l)

1.05 ± (0.94-1.59)

1.09 (0.87-1.31)

VLDL (mmol/l)

0.72 ± 0.33

1.04 ± 0.40†

2.86 ± 0.76

3.02 ± 1.04

Systolic pressure (mm/Hg)

LDL (mmol/l)

125.82 ± 16.26

117.14 ± 12.44

Diastolic pressure (mm/Hg)

87.09 ± 11.95

80.28 ± 5.6

HbA1c: glycated hemoglobin; HOMA-B: homeostatic model assessment – beta; HOMA-S: homeostatic model assessment – sensibility; HOMA-IR: homeostatic model assessment – insulin resistance; TYG Index: triglycerides/glucose index; HDL: high density lipoprotein; VLDL: very low density lipoprotein; LDL: low density lipoprotein. *: significant difference between HIG and SIG (p < 0.05). †: trend difference between HIG and SIG (p between 0.051 and 0.090). Parametric variables were expressed as the mean ± standard deviation; non-parametric variables were expressed as the median (interquartile range).

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Arch Endocrinol Metab. 2017;61/6

Metabolic profile, LPS and FNDC5/irisin

The FNDC5/irisin, LPS, cytokines and adipokine levels are presented in Figure 1 A-H. Besides higher FNDC5/irisin levels in HIG (HIG 4.46 ± 0.10 x SIG 4.07 ± 0.14, p < 0.01), this group exhibited smaller LPS levels (HIG 0.50 ± 0.05 x SIG 0.56 ± 0.06, p = 0.02) and tendency to lower levels of resistin (HIG 14.88 ± 8.21 x SIG 23.65 ± 12.93, p = 0.08). A

B 0.7

FNDC5/irisin (ug/mL) p < 0.001*

Proving the better metabolic status in HIG, this group still showed a smaller risk of developing T2DM (HIG 4.55 ± 3.01 x SIG 10.88 ± 9.82, p = 0.02) (Figure 2). About the correlations, there are significant inverse correlations between FNDC5/irisin and body weight (r -0.46, p = 0.04), neck circumference (r -0.51, p = 0.02), free fat mass (r -0.49, p = 0.02), triglycerides (r -0.43, p = 0.05) and risk of developing T2DM (r -0.61, p = 0.04).

0.6

0.5

0.4 0.3

0.2

1 0

LPS (EU/mL) p < 0.02*

0.1 0.0

HIG

SIG

HIG TNF alpha (ng/mL) p = 0.46

SIG IL 6 (ng/mL) p = 0.20

D 2.5 2.0 1.5

1.0 1

0.5

SIG

HIG

0.0

IL 10 (pg/mL) p = 0.31

Leptin (pg/mL) p = 0.85

0.4

SIG

HIG

0.3

30 0.2

0.1

0.0

SIG

HIG G

SIG

HIG Adiponectin (ng/mL) p = 0.49

H 6

Resistin (ng/mL) p = 0.08†

4 3

10 HIG

SIG

0 HIG

SIG

Figure 1. FNDC5/irisin levels and inflammatory markers of groups higher and smaller irisin. HIG and SIG levels of A: FNDC5/irisin. B: LPS (lipopolysaccharide). C: TNF alpha (tumor necrosis factor alpha). D: IL6 (interleukin 6). E: IL10 (interleukin 10). F: leptin. G: Resistin. H: adiponectin. *: significant difference between HIG and SIG (p < 0.05). †: Trend difference between HIG and SIG (p between 0.051 and 0.09). Arch Endocrinol Metab. 2017;61/6

529

Metabolic profile, LPS and FNDC5/irisin

T2DM index risk (%) p = 0.02*

20 15 10 * 5 0

HIG

SIG

Figure 2. Diabetes mellitus type 2 index risk of groups higher and smaller Irisin. T2DM: diabetes mellitus type 2. * Significant difference between HIG and SIG (p < 0.05).

DISCUSSION Herein, using a cluster study, our results show that obese middle-aged men with higher irisin levels have a better metabolic profile along with lower risk of T2DM development and LPS levels. Furthermore some anthropometrics/body composition variables and risk of T2DM are inversely related to FNDC5/irisin levels. Interestingly, the better clinical parameters observed were associated with high irisin levels, differently of other studies that associated lower FNDC5/irisin levels to healthy state (9-11). Our results also go against works that put in doubt the FNDC5/irisin beneficial effects (26). Thus, the present study corroborate clinically with early studies in animals and in vitro (1,2) that associated FNDC5/irisin stimulus with a metabolic improvement in humans. The FNDC5/irisin levels have been reason of several questionings and doubts. Initially higher serum levels were associated with a possible FNDC5/irisin resistance, as occur with insulin (11), however our results showed that this fact did not occur because the HIG showed smaller insulin resistance. Some authors have suggested a possible â&#x20AC;&#x153;irisinemiaâ&#x20AC;? as a compensatory effect of organism to try maintain the metabolic homeostasis by irisin secretion (27). However, the better hypothesis must be the negative influence of hyperglycemia and serum lipids on FNDC5/irisin levels, since free fatty acid and glucose in vitro stimulation decrease expression/secretion of these peptides (28). Our metabolic results associated a cluster of FNDC5/irisin levels bring similar clinical evidence of this in-vitro study, and consolidate the studies that observed lower levels of FNDC5/irisin correlated with dysfunctions and metabolic diseases (6,8,29,30). 530

Studies that analyzed the relation of inflammatory markers and FNDC5/irisin levels are scarce in the literature and the present work show for the first time evidences about this context. Given the metabolic profile results, it would be expected that HIG exhibited better levels of global inflammatory markers, but this is partially evidenced through results observed for LPS and the trend towards reduction of circulating resistin (p = 0.08). LPS is an endotoxin related to insulin resistance, once it binds TLR4 and triggers intracellular inflammatory responses (14). The exact relation of FNDC5/irisin and LPS levels is something that needs to be better understood, however, some hypotheses may be raised, such as the LPS interfering negatively on FNDC5/ irisin secretion as observed in other study when glucose and free fat acid stimulus in vitro down-regulation FNDC5/irisin secretion. On the other hand, FNDC5/ irisin has been associated with endothelial integrity (31), and vascular disease and injury can influence LPS gut permeability and systemic inflammation (32). Interestingly, a recent study showed that LPS is a negative regulator of adipose tissue browning process (33). However, this work not analyzed the relation between LPS and FNDC5/irisin (33). Our present results seem to indicate that this negative LPS influence in browning process could also involve a decrease of FNDC5/irisin, one of the main stimulator of these process. Reinforce this idea the higher fat intake in SIG because the increase of fat, specially the saturated type, is related to LPS absorption, thus the LPS increase could be an inhibitor of FNDC5/ irisin, as observed by excess of glucose and free fat acids (28), which could explain the LPS as a negative regulator of browning (33). Previous study found a positive correlation between FNDC5/irisin levels and carbohydrate intake (34), indicated, together with our result that FNDC5/irisin and macronutrients might have an important interaction that certainly needs to be deeply addressed in future researches, as well as better analyze the relationship between FNDC5/irisin and LPS. The tendency of lower resistin level is another positive HIG result, because the increase in this adipokine is related to the insulin resistance and worse inflammatory condition (12). Also, it is not possible to explain the exact relation between FNDC5/irisin and resistin levels. New studies could be to focus also on these markers relations. Arch Endocrinol Metab. 2017;61/6

Even for adipokine results, although leptin could be a negative regulator of FNDC5/irisin (3), our results demonstrate that, at least in serum obese men, an interrelationship between these biomarkers does not seem to occur. Similar to leptin, TNFα, adiponectin, IL 6 and IL 10 serum are not related to higher or smaller FNDC5/irisin levels. However, molecular relationships need to be further investigated. All of the actions assigned to FNDC5/irisin seems to stimulate the increase of lipid substrate consumption by mitochondria (1). Based in the FNDC5/irisin effects on energy expenditure increase observed in animals and in vitro studies (1,2), and also in the better metabolic profile verified in HIG, we could expect a difference in RMR between HIG and SIG. However, our results did not show any significant difference in the RMR between two clusters groups. Indeed, such result is in line with previous studies (35,36), which also not observed the relation between FNDC5/irisin levels and rest energy expenditure in humans. The methodology for evaluating the rest energy expenditure in the current study has been validated previously (24), but it is a mix of direct and indirect tests, and perhaps the use of only direct methods or tests to evaluate the percentage of energy substrates used during rest would results in significant difference between groups, because several factors can interfere in that variable. The significant lowest levels of triglycerides and downtrend in VLDL in HIG observed in the present study corroborate with other study (30), which observed that higher levels of lipids in blood and liver have correlation with lower FNDC5/irisin levels. About correlations, were observed inverse associations between FNDC5/irisin and body weight, free fat mass, neck circumference, triglycerides and risk of developing T2DM risk results, strengthening the others find of present work. The body weight, free fat mass and triglycerides have been related to FNDC/ irisin (5,30,37), indicating that body composition, especially body fat, and serum lipids could has influence about levels of this peptides, however, it is important to mention that these correlations presented a fragile “r” values. Although several metabolic glycemic control variables has been associated to FNDC5/ irisin levels, to our knowledge, it is the first time that neck circumference (a practice factor related to insulin resistance) (38) and a developing risk T2DM global index are inversely related to FNDC5/irisin levels, indicating, once again, that in humans, the levels of this Arch Endocrinol Metab. 2017;61/6

peptides is fully combined with insulin resistance and glucose tolerance, as observed in animals and in vitro studies (1,39). Based on insulin sensibility and resistance observed between groups it would be expected that correlation with others markers of glucose homeostasis were observed besides neck circumference, however, it is important to cite that there are trend statistic between FNDC5/irisin and HOMA-S (p = 0.07), Quicki index (p = 0.08) and TYG index (p = 0.07), what indicate that results go in this direction, perhaps in a higher sample of subjects these results could be significant. The HIG showed higher body weight, however this result should not be an intervening factor in the results, because both cluster groups have similar BMI, fat mass, and waist circumference, parameters that can interfere more significantly in FNDC5/irisin levels than body weight analyzed in isolation (5). Moreover, the difference between the body weight of the groups may be related to the average height difference among them (HIG 1.72 m x SIG 1.75 m) and BMI similarity between groups prove this idea, being this analyze more applicable because the weight must be related to the height. FNDC5/irisin levels difference average between HIG and SIG is approximately 10%. Two points can support this difference as relevant. First, the difference between fasting glucose levels of diabetic subject (126 mg/dL) and the normoglycemia (99 mg/dL) is around 20%. Second, a previous study has demonstrated a difference about 30% in irisin levels, when comparing type 2 diabetic and health subjects (7). Together, these data strongly support the idea that in non-diabetic people, i.e., insulin resistant people, such 10% difference in irisin levels is really significant. Physical exercise and temperature was reported having influence on FNDC5/irisin levels (39). However, these factors should not have interfered in present results, once both groups underwent the experiment during similar periods; the region where the research was conducted did not present large thermal fluctuations or seasons with extreme temperatures; all subjects were not engage in regular exercise programs during the previous 12 months and were classified as insufficiently active according to the Baecke Habitual Physical Activity Questionnaire and International Physical Activity Questionnaire (IPAQ) (15). The present work is a cross-sectional study, with a relatively limited sample size of obese individuals, and did not investigate the relationship of cause and effect for the results found. However, it is important 531

Metabolic profile, LPS and FNDC5/irisin

to emphasize that these group analyzed is extremely homogeneous and were selected by strict inclusion criteria. Moreover, the present study analyzed more than 40 clinical variables, to our knowledge, are scarce in literature human’s studies with FNDC5/irisn and these significant numbers of variables investigated. Even with a small sample, the evidence here presented confirms the FNDC5/irisin positive relation with metabolic homeostasis in humans, particularly in obese men. Based in the relation of smaller FNDC5/irisin with worse metabolic state, these peptides may, in the future, be a marker for the presence of metabolic diseases and also a therapeutic target. However, the ideal FNDC5/irisin values are not known, so further work should focus on this aim. In conclusion, higher FNDC5/irisin levels in grade 1 obese men are related to a better metabolic profile, less risk of developing T2DM, decrease of serum LPS. Epidemiological studies with a largest number of subjects must be performed to confirm the present results and to establish cut-off points for optimal FNDC5/irisin levels and also for this peptide to be used as a metabolic risk marker. To evaluate a possible and exact relationship between FNDC5írisin and LPS, other works should also be designed. Lastly, how physical exercise are one of the main stimulators of FNDC5/ irisin secretion, studies comparing the exercise effects on these peptides level in groups of high and small irisin levels also must be considered. Acknowledgements: the authors would like to acknowledge the Foundation of São Paulo Research (FAPESP) by supporting the study under Grant [number 11/09446-6]. We would like to thank the Dioze Guadagnini for lab support. Finally, we wish to thank our enthusiastic participants. Disclosure: no potential conflict of interest relevant to this article was reported.

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Metabolic profile, LPS and FNDC5/irisin

23. Libardi CA, Souza GV, Gaspari AF, Dos Santos CF, Leite ST, Dias R, et al. Effects of concurrent training on interleukin-6, tumour necrosis factor-alpha and C-reactive protein in middle-aged men. J Sports Sci. 2011;29:1573-81. 24. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1-9. 25. Medicine in food and nutrition board. Dietary references intakes. Washington, D.C.: Institute of Medicine of the National Academies, 2005. 26. Raschke S, Elsen M, Gassenhuber H, Sommerfeld M, Schwahn U, Brockmann B, et al. Evidence against a beneficial effect of irisin in humans. PLoS One. 2013;8:e73680. 27. Sanchis-Gomar F, Perez-Quilis C. Irisinemia: a novel concept to coin in clinical medicine? Ann Nutr Metab. 2013;63:60-1. 28. Kurdiova T, Balaz M, Vician M, Maderova D, Vlcek M, Valkovic L, et al. Effects of obesity, diabetes and exercise on Fndc5 gene expression and irisin release in human skeletal muscle and adipose tissue: in vivo and in vitro studies. J Physiol. 2014;592:1091-107.

32. Drewe J, Beglinger C, Fricker G. Effect of ischemia on intestinal permeability of lipopolysaccharides. Eur J Clin Invest. 2001;31:138-44. 33. Gavalda-Navarro A, Moreno-Navarrete JM, Quesada-Lopez T, Cairo M, Giralt M, Fernandez-Real JM, et al. Lipopolysaccharidebinding protein is a negative regulator of adipose tissue browning in mice and humans. Diabetologia. 2016;59(10):2208-18. 34. Lopez-Legarrea P, de la Iglesia R, Crujeiras AB, Pardo M, Casanueva FF, Zulet MA, et al. Higher baseline irisin concentrations are associated with greater reductions in glycemia and insulinemia after weight loss in obese subjects. Nutr Diabetes. 2014;4:e110. 35. Pardo M, Crujeiras AB, Amil M, Aguera Z, Jimenez-Murcia S, Banos R, et al. Association of irisin with fat mass, resting energy expenditure, and daily activity in conditions of extreme body mass index. Int J Endocrinol. 2014;2014:857270. 36. Swick AG, Orena S, Oâ&#x20AC;&#x2122;Connor A. Irisin levels correlate with energy expenditure in a subgroup of humans with energy expenditure greater than predicted by fat free mass. Metabolism. 2013;62:1070-3. 37. Nygaard H, Slettalokken G, Vegge G, Hollan I, Whist JE, Strand T, et al. Irisin in blood increases transiently after single sessions of intense endurance exercise and heavy strength training. PLoS One. 2015;10:e0121367.

30. Zhang HJ, Zhang XF, Ma ZM, Pan LL, Chen Z, Han HW, et al. Irisin is inversely associated with intrahepatic triglyceride contents in obese adults. J Hepatol. 2013;59:557-62.

38. Laakso M, Matilainen V, Keinanen-Kiukaanniemi S. Association of neck circumference with insulin resistance-related factors. Int J Obes Relat Metab Disord. 2002;26:873-5.

31. Hou N, Han F, Sun X. The relationship between circulating irisin levels and endothelial function in lean and obese subjects. Clin Endocrinol (Oxf). 2015;83(3):339-43.

39. Lee P, Linderman JD, Smith S, Brychta RJ, Wang J, Idelson C, et al. Irisin and FGF21 are cold-induced endocrine activators of brown fat function in humans. Cell Metab. 2014;19:302-9.

29. Kuloglu T, Aydin S, Eren MN, Yilmaz M, Sahin I, Kalayci M, et al. Irisin: a potentially candidate marker for myocardial infarction. Peptides. 2014;55:85-91.

Arch Endocrinol Metab. 2017;61/6

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original article

Effects of a structured education program on glycemic control in type 1 diabetes Ana Paula F. Pacheco1, Simone van de Sande-Lee2, Rita de Cássia B. Sandoval2, Sônia Batista2, Jefferson L. B. Marques1,3

ABSTRACT Programa de Pós-Graduação em Ciências Médicas, Centro de Ciências da Saúde, Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC, Brasil 2 Hospital Universitário, UFSC, Florianópolis, SC, Brasil 3 Instituto de Engenharia Biomédica, Departamento de Engenharia Elétrica e Eletrônica, UFSC, Campus Universitário – Trindade, Florianópolis, SC, Brasil 1

Correspondence to: Jefferson L. B. Marques Instituto de Engenharia e Biomédica, Departamento de Engenharia Elétrica e Eletrônica, Universidade Federal de Santa Catarina Florianópolis, SC, Brasil jefferson.marques@ufsc.br

Objective: Diabetes mellitus is associated with significant morbidity and mortality, and education is known to play a key role in managing this disease. This study addresses the effects of a structured education program (SEP) on self-care in subjects with type 1 diabetes mellitus (T1DM). The aim was to evaluate the effect of a SEP on glycemic control, knowledge, and skills associated with diabetes care in subjects with T1DM. Subjects and methods: A total of 47 adults with T1DM were followed up for 20 months (32 participated in the SEP and 15 served as a control group). The SEP consisted of workshops, individualized care, 24-hour distant support, and a questionnaire assessing knowledge of diabetes care. Glycosylated hemoglobin (HbA1c) levels were measured before and after the SEP implementation. Results: Compared with pre-SEP levels, the mean HbA1c levels decreased by approximately 20% (21 mmol/mol) at 1 year, with a further 11% reduction (10 mmol/mol) observed 8 months later (p < 0.001). Knowledge about diabetes care increased by 37% between the pre-SEP and post-SEP questionnaires (p < 0.005). Conclusion: Relevant improvements occurred after SEP activities. The sustained decrease in HbA1c levels and the overall increase in knowledge and confidence regarding diabetes care reinforce the importance, necessity, and positive outcomes of a SEP intervention in T1DM. Arch Endocrinol Metab. 2017;61(6):534-41 Keywords Diabetes mellitus, type 1; education; self-care; quality of life

Received on Jul/25/2016 Accepted on Feb/13/2017 DOI: 10.1590/2359-3997000000278

INTRODUCTION

iabetes mellitus (DM), one of the diseases with the highest mortality rates, is considered a worldwide epidemic (1). According to the International Diabetes Federation (IDF) 2015 Update, DM affects 15.8% of the population aged 20-79 years in Brazil, and has been associated with approximately 130,700 deaths. DM is also the leading cause of hospitalization for complications such as cardiovascular disease, dialysis for chronic renal failure, and lower limb amputations (2). The number of cases of DM worldwide reached 415 million in 2015, of which 5-10% were type 1 DM (T1DM). Although T1DM affects only a minority of the patients with DM, it is responsible for a great share of the serious complications of the disease. T1DM is usually diagnosed in youths (3) and requires continuous measures such as diet, medication, and lifestyle changes (2). Less than 1 in 5 individuals with DM achieves

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the recommended glycemic targets, which increases the risk of development of chronic complications (4). With an increasing public health burden arising from chronic diseases such as DM, emphasis is placed on the patient to take responsibility for managing his own disease. Patient education can lead to improved knowledge and self-management skills (5), reducing the risks of complications (6). However, qualitative studies based on the patients’ own understanding and views regarding T1DM have shown concern about these patients’ current information needs (7,8), including administration of medication, management of hypoglycemia, glucose testing, diet, following of sick-day guidelines, foot care, and understanding of HbA1c results (9-12). In Germany, an approach of structured education in health has been delivered to thousands of individuals with T1DM and successfully introduced in an outpatient setting (11,13). Other countries should also provide health services and Arch Endocrinol Metab. 2017;61/6

Structured Education in T1DM

SUBJECTS AND METHODS Study design and ethics This was a prospective, cohort study approved by the Ethics Committee on Human Research at Federal University of Santa Catarina.

Subjects We included 47 consecutive subjects with T1DM, of both genders, and attending the Endocrinology Arch Endocrinol Metab. 2017;61/6

Clinic at the University Hospital in Florianopolis, who met the following inclusion criteria: clinical diagnosis of T1DM for at least 5 years, minimum age of 18 years, glycosylated hemoglobin (HbA1c) > 7% (53 mmol/mol), and literacy. The exclusion criteria comprised refusal to participate in the study, disorder or disability preventing attendance at the activities, and residence outside the Florianopolis metropolitan area. All subjects were invited to participate in the SEP; 32 of them accepted and comprised the SEP group, while the remaining 15, who were unable or unwilling to participate in the SEP, constituted the control group.

Pre-SEP and post-SEP evaluations All subjects underwent clinical examination and blood collection for measurement of HbA1c levels at three time points: baseline (pre-SEP), after the intervention (post-SEP; approximately 1 year after the beginning of the study); and at the end of the study period at 20 months (follow-up). Levels of HbA1c were determined at the University Hospital laboratory using the ion exchange chromatography method. The questionnaire used by the DAFNE program was applied to evaluate the previous knowledge of the participants about DM care. The questionnaire was translated into Portuguese, adapted to the Brazilian population by the investigators, and applied to 30 subjects (25 in the SEP group and 5 in the control group) at two time points, pre-SEP and post-SEP. The questionnaire comprises 22 questions scoring between 0-10 and totaling 220 maximum points, and is divided into four categories: 1) food/carbohydrates and physical activity, 2) insulin regimen, 3) management of blood glucose, and 4) complications.

Structured education program The program consisted of group meetings (workshops) during the first 6 months, which were planned and carried out in a multidisciplinary scenario according to the topics to be discussed. A total of 32 individuals participated in this stage, which equated to approximately 10 individuals per session, with each individual attending three group meetings. Corresponding to DAFNE guidelines, they also attended individual consultations with the nurse and the nutritionist, which occurred once a week or according to the patients’ needs during 535

DM education tailored to the patients’ individual, social, and cultural needs (14-16). An exemplary education approach offered to T1DM patients in the United Kingdom is the DAFNE (Dose Adjustment For Normal Eating) program (14). The program includes courses with topics on physical activity, nutrition, management of hypoglycemia, alcohol consumption, sickness, potential complications, and pregnancy (15). While it has been recognized that the skills of DM self-management are best provided by structured education programs (SEPs) delivered by professionals with appropriate training (14), more needs to be known about patient self-care behavior and the perceived barriers which influence an individual’s decision to effectively managing his DM. Additionally, offering DM education in groups may also be very effective (16). If we are to improve the effectiveness of educational interventions, we ought to record the patients’ characteristics and psychosocial variables and explore with details the difficulties and barriers they face to implement and sustain DM self-management (17,18). Reports have shown that patients entering DAFNE with an HbA1c > 8.5% (69 mmol/mol) experienced a 0.8% (9 mmol/mol) decrease in HbA1c levels at 1 year, with a subsequent health economic analysis concluding that the intervention was highly cost-effective and would pay for itself within 3 years (17). Based on the considerations above, the objectives of this study were to 1) evaluate the effect of a SEP on HbA1c levels in individuals with T1DM; 2) assess the acquisition of knowledge by the participants, identifying the most challenging areas; and 3) elaborate an approach to structured education in health based on DAFNE, along with a self-care incentive for individuals with T1DM, providing these individuals with means to improve their knowledge about the disease and selfcare management skills.

Structured Education in T1DM

the SEP period, with psychological referral being offered when necessary. The communication with these individuals occurred via landline and mobile phones, e-mail, social networking, and intelligent web system. The 15 individuals who comprised the control group attended routinely the outpatient clinic, but did not participate in any of the SEP activities. For the SEP group, we discussed each individual’s daily activities, carbohydrate count, information regarding healthy eating, and blood glucose levels. We also discussed the participants’ insulin management, insulin application with syringe/pen, selection of the best insulin injection site and the reasons for that, how to react when hypoglycemia occurs, information regarding hyperglycemia and ketoacidosis, how to proceed at parties/events, ingestion of alcohol, consumption of different types of food, changes in daily routine, how to perform treatment in case of pregnancy, how to act when travelling and/or living in a different environment than the usual one, and finally, the most appropriate actions when practicing light, moderate, and strenuous exercise. A differential methodology was implemented under an “immediate” clinical support, in which the subjects had a direct contact with a full-time nurse for clarification of urgent matters, so they would not need to wait for the next appointment or meeting to resolve their concerns. The means of contact used for this purpose depended on the need or urgency. In this case, the contact was reciprocal: the participants contacted us when necessary, and we contacted them to monitor and assess the achievement of the goals proposed in the consultation.

Semistructured interview At the end of the study period (20 months), the subjects in both the SEP and control groups underwent a qualitative semistructured interview with the aim of obtaining their feedback about the methodology applied. This interview included 10 questions with four possible answers: (1) a lot, (2) moderate, (3) little, and (4) no/nothing/none. These questions evaluated the participant’s self-confidence, assessment of the clarifications received during the program or by the staff of the clinic, satisfaction in self-care, satisfaction with quality of life, diet flexibility, assessment of the questions posed by the support staff at the outpatient clinic, motivation, acceptance of the condition of 536

having DM, and interest in continuing the treatment approach they had just received (SEP).

Statistical analysis The results of the clinical data are expressed as mean absolute values (± standard deviation [SD]) as well as percentages, when appropriate. A p value < 0.05 was considered statistically significant.

Questionnaire Statistical differences between the parameters obtained in the pre-SEP and post-SEP time points were determined using Student’s paired t test, and differences between parameters in the SEP group and control group were assessed with Student’s independent samples t test.

Glycosylated hemoglobin and body mass index Statistical differences in HbA1c levels between the pre-SEP, post-SEP, and follow-up time points were determined by repeated measures analysis of variance (ANOVA), while those related to body mass index (BMI) values between the SEP and control groups were determined with Student’s independent samples t test.

Semistructured interview The data related to the semistructure interview were described and analyzed in an Excel spreadsheet, and the results were generated in graphical form.

RESULTS A total of 47 subjects with T1DM were selected for this study, including 25 (53%) females and 22 (47%) males. All participants had a diagnosis of T1DM for at least 5 years and a maximum of 31 years, with a mean of 12.9 ± 6.8 years. In terms of age, they ranged between 18 and 44 years, with a mean of 26 ± 6.7 years among males and 28 ± 8.7 years among females. Overall, 32 (68%) patients participated in the SEP and 15 (32%) comprised the control group. The main reasons for patients declining to attend the program were residence far from the hospital or incompatibility with work schedule. Among the participants in the SEP group, 12 (37.5%) were male, and 20 (62.5%) were female, and among those in the control group, 10 (67%) were male, and 5 (33%) were female. The baseline characteristics of the groups are depicted in Table 1. Arch Endocrinol Metab. 2017;61/6

Structured Education in T1DM

Table 1. Baseline characteristics of the subjects Control group M n Age (yrs) Diabetes duration (min-max) (yrs)

SEP group Total

Total

26 ± 6.9

32 ± 10.4

28 ± 8.4

26 ± 6.8

27 ± 8.2

26.5 ± 7.6

5-23

6-31

5-31

5-23

5-26

Weight (kg)

70 ± 9.5

61 ± 6.6

66 ± 9.3

77 ± 12.2

70 ± 12.2

73 ± 11.8

Height (m)

1.73 ± 0.04

1.56 ± 0.07

1.66 ± 0.1

1.75 ± 0.07

1.65 ± 0.05

1.69 ± 0.07

BMI (kg/m²)

23.4 ± 2.6

25.3 ± 4.1

24.1 ± 3.2

25.3 ± 3.0

26.1 ± 3.9

25.8 ± 3.5

Fasting glucose (mg/dL)

211 ± 79

177 ± 67

198 ± 75

190 ± 47

165 ± 62

175 ± 57

HbA1c (%)

10.7 ± 1.7

9.6 ± 1.7

10.4 ± 1.8

10.4 ± 1.9

10.2 ± 2.0

10.3 ± 1.9

Data are shown as mean ± standard deviation (SD), except for diabetes duration, which is shown as the minimum and maximum number of years since diagnosis. M: male, F: female, BMI: body mass index.

At baseline (pre-SEP), the percentage of correct answers were as follows: insulin regimen, 47 ± 3.1%; eating and carbohydrate counting/physical activity, 35 ± 3.2%; management of glucose levels, 65 ± 1.8%; complications, 40 ± 3.7%; and total hits, 48 ± 1.9%. At post-SEP, the corresponding results were: insulin regimen, 87 ± 2.5%; eating and carbohydrate counting/ physical activity, 83.5 ± 2.6%; blood glucose levels management, 91 ± 1.9%; complications 80 ± 2.2%; and total hits, 86 ± 1.4%. There was a significant difference (p < 0.05) between pre-SEP and post-SEP results among the subjects who participated in the SEP program (of note, the mean scores for “total hits” increased by 37% from 49.5 ± 1.0% to 86 ± 0.6%). In contrast, the difference between the two time points was not significant in the control group (from 41.5 ± 0.9% to 48 ± 0.5%, respectively) (Figure 1).

1.0

0.8

0.6

0.4

0.2

0.0

Glycosylated hemoglobin and body mass index At pre-SEP, the mean HbA1c levels were 10.7 ± 1.7% (93 ± 19 mmol/mol) in males and 9.6 ± 1.7% (81 ± Arch Endocrinol Metab. 2017;61/6

Control group

SEP group Pre-SEP

Post-SEP

Figure 1. Comparison of the total hits on the questionnaire about diabetes care knowledge, in the pre-SEP and post-SEP evaluations. 537

Questionnaire

19 mmol/mol) in females in the control group, and 10.4 ± 1.9% (90 ± 21 mmol/mol) in males and 10.2 ± 2.0% (88 ± 22 mmol/mol) in females in the SEP group. The corresponding levels at post-SEP were 10.5 ± 1.4% (91 ± 15 mmol/mol) in males and 10.2 ± 1.5% (88 ± 16 mmol/mol) in females in the control group, and 8.5 ± 1.4% (69 ± 15 mmol/mol) in males and 8.4 ± 1.4% (68 ± 15 mmol/mol) in females in the SEP group, and at the end of the study (follow-up), they were 11.0 ± 1.7% (97 ± 19 mmol/mol) in males and 11.1 ± 1.8% (98 ± 20 mmol/mol) in females in the control group compared with 7.5 ± 1.0% (58 ± 11 mmol/mol) in

Total hits (x 100)

One patient in the SEP group died from complications of systemic lupus erythematosus and was excluded from the analysis. Otherwise, no hospitalizations or serious complications were reported during the study period. All DM supplies were provided by the publicly funded Brazilian health care system (SUS). However, due to a temporary problem with the supply of glucose test strips during the study, the strips were provided to the patients by the investigators during this time.

Structured Education in T1DM

males and 7.5 ± 1.2% (58 ± 13 mmol/mol) in females in the SEP group. Repeated measures ANOVA using Greenhouse-Geisser correction showed a significant effect of time on HbA1c levels recorded at different stages of the program (pre-SEP, post-SEP, and followup; p < 0.001). In addition, there was a significant interaction (p < 0.001) between groups (control and SEP groups) and time. Similarly, pairwise comparisons between the control and SEP groups showed a significant difference (p < 0.001). These results are shown in Figure 2. The mean baseline and final BMI values were 24.1 ± 3.2 kg/m2 and 23.8 ± 3.3 kg/m2, respectively, in the control group and 25.8 ± 3.5 kg/m2 and 25.6 ± 3.6 kg/m2, respectively, in the SEP group (all nonsignificant, p > 0.05).

Qualitative semistructured interview Overall, there was growing improvement in the health status of the participants in the SEP group, which led to improved general physical and mental statuses and reflected on their quality of life. In order to obtain feedback on the program, we sought the opinion of both participants and non-participants (SEP and control groups, respectively) using semistructured interviews on some important points. 15

p < 0.001

13 12

HbA1c (%)

11 10 9 8 7

p < 0.001

6 5

Control group Pre-SEP

SEP group Post-SEP

Follow-up

Figure 2. Glycosylated hemoglobin (HbA1c) values of 47 subjects (32 in the SEP group and 15 in the control group) at three time points (pre-SEP, post-SEP, and follow-up). 538

Among the individuals who participated in the SEP, 91% reported having developed a lot of confidence in the health team, 87.5% received substantial clarification about T1DM, 100% found the meetings in the T1DM group to be very useful, and 59% were very satisfied with their self-care relative to the T1DM (compared with 22% who were moderately satisfied and 19% who were somewhat satisfied). Furthermore, 81% of the participants were very satisfied with their quality of life, 94% were very satisfied with the flexibility of their diet, 100% were very satisfied with the questions posed by the support team, 91% were very motivated to continue with their treatments, and 19% were very accepting of their condition of having T1DM (versus 66% who were moderately accepting and 16% who just accepted this condition). Lastly, 100% said they would like to continue participating in the SEP if a group of T1DM patients was formed. These results are shown in Figure 3A. Among the individuals in the control group, 73% reported having a lot of confidence in the health team (while 27% had moderate confidence); 47% received some clarifications about T1DM (but 26% answered no/none, and 13% answered a lot). In this group, 0% said that they thought the T1DM group was very useful (while 20% felt it was moderately useful and 53% and 27% found little or did not find anything useful, respectively). Furthermore, 33% reported having no satisfaction in their self-care in relation to the T1DM (versus 20% who had low satisfaction, 40% moderate, and 7% a lot). Regarding satisfaction in quality of life, 0% answered having a lot (while 40% had moderate, and 27% believed they had little). In terms of diet flexibility, 0% reported having a lot of flexibility (versus 33% who reported poor flexibility and 53% reported having no flexibility). None of the patients (0%) in the control group reported having a lot of support for questions (versus 46% who reported having moderate support). Concerning motivation for treatment, 7% reported having a lot (while 33% said they had little). Moreover, 0% said they were entirely accepting of their condition of having T1DM (while 40% moderately agreed, 46% somewhat agreed, and 13% did not agree). Lastly, 7% said they did not wish to participate if a group with T1DM was formed. These results are shown in Figure 3B.

DISCUSSION The results of this study show that individuals with T1DM have a deficit of information/knowledge about Arch Endocrinol Metab. 2017;61/6

Structured Education in T1DM

this condition, which coupled with the continued lack of professional support for self-care, prevents them from achieving effective self-management. Therefore, we observed significant improvements in knowledge and glycemic control after SEP activities. In the original DAFNE trial, the mean HbA1c levels decreased from 9.4% (79 mmol/mol) to 8.4% A

(68 mmol/mol) 6 months after the training and increased to 8.9% (74 mmol/mol) at 12 months but remained significantly improved compared with baseline (17). A follow-up study of the participants in the original trial showed that after 4 years they still maintained a significant HbA1c improvement of 0.36% (4 mmol/mol) from baseline levels (13). Even

Confidence in the team of professionals Willingness to continue treatment

Acceptance of DM condition

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

SEP group

Clarifications on DM

How useful is the group approach

Satisfaction in self care

Motivation to adhere to treatment

Support for questions and doubts

Satisfaction with quality of life Diet flexibility

A lot

Medium

Little

No/none

Confidence in the team of professionals

Willingness to continue treatment

80% 70% 60%

Control group

Clarifications on DM

50% 40% 30% 20% 10% 0%

Acceptance of DM condition

How useful is the group approach

Support for questions and doubts

Satisfaction with quality of life Diet flexibility

A lot

Medium

Little

No/none

Figure 3. Patientsâ&#x20AC;&#x2122; feedback about the program: results of a semistructured interview with subjects from the SEP group (A) and the control group (B). Arch Endocrinol Metab. 2017;61/6

539

Satisfaction in self care

Motivation to adhere to treatment

Structured Education in T1DM

7 years after training, a persistent and clinically relevant reduction of 0.3% (3 mmol/mol) in HbA1c level remained (19). More recent studies using DAFNE-based interventions have shown that this method is associated with modest improvements in glycemic control. The benefits of the method in routine clinical practice were assessed 1 year after the intervention, compared with baseline assessment, in 639 participants in the United Kingdom. The mean HbA1c levels decreased from 8.51% to 8.24%, while the participants experienced a greater sense of well-being and less psychological distress (20). Similarly, an evaluation of the outcomes of 145 participants undergoing DAFNE training at Australian DM centers showed improved quality of life and an HbA1c decrease from 8.2% to 7.8% at 1 year (21). The differences in the magnitude of HbA1c decrease observed in the above-mentioned studies when compared with the initial DAFNE trial were most likely related to different baseline values in both studies, as a greater decrease is expected with poorer baseline glycemic control. Moreover, the insulin regimen may have varied according to the time when each study was conducted. In the present study, the mean HbA1c levels decreased by 1.9% (21 mmol/mol) at 1 year and further by 0.9% (10 mmol/mol) over the following 8 months, showing a significant 2.8% decrease (31 mmol/mol). Additionally, the participants in the present study presented lower HbA1c values after the intervention than those in the first DAFNE trial cohort (17). One reason for this large variation is clearly our participants’ exceedingly high baseline HbA1c values. In addition, both studies employed different approaches: while the original DAFNE course involved a 5-day outpatient program for a group of 6-8 people, we offered a DAFNE-based SEP with group meetings, individual consultations, and remote access on demand. We believe that this model of assistance accounted at least partially for the differences observed regarding HbA1c improvement, and we recommend that such approaches should be considered in future education programs planning. Although the impact of a single DAFNE course on glycemic control in the long term has been demonstrated, further interventions are required to help patients achieve the recommended HbA1c targets. The main limitation of our study was the difference in HbA1c levels observed between the SEP and control groups, which may be partially due to a selection bias, 540

as subjects in the present study were not randomized but, rather, selected through convenience sampling. Patients who choose to attend an education program are more likely to adhere to the proposed treatment and, therefore, show more improvements. Nevertheless, the highly significant intragroup differences between time points indicate that the SEP was an effective intervention in terms of glucose control. An additional limitation was the lack of data about insulin doses at the beginning and end of the study. However, all individuals were followed up by the same medical team who performed the dose adjustments according to the patients’ glycemic control during medical consultations, regardless of them participating or not in the SEP. Therefore, we believe that the program was the main factor that led to the HbA1c improvement. Finally, a previously validated translation of the questionnaire was not available; therefore, comparisons with international studies would not be reliable. The transcultural adaptation and validation of the DAFNE questionnaire into Portuguese would strengthen the results of studies on DM education programs in our country and should be performed as a next step. In the interview feedback, the analysis showed that the SEP participants improved their knowledge about T1DM and self-confidence to carry out their treatment, demonstrated increasing satisfaction with diet flexibility, improved their quality of life, and were motivated to continue controlling and taking care of their health. The only topic that failed to show a positive response greater than 50% was the full acceptance of their T1DM (19%), which was moderately accepted by 65% of the respondents. This may be considered a natural behavior since even individuals who comply with their treatments are not necessarily comfortable with the idea of having a chronic disease. Despite the success and influence of the SEP initiative, much work still needs to be done. A Brazilian survey including 6,671 adults with DM showed that 90% of the patients with T1DM were poorly controlled, and participation in a DM health education program was one of the characteristics significantly associated with improved glycemic control (22). Education in DM should no longer be regarded as an extra option in the treatment of the disease: it should be considered as essential as medication and, therefore, resourced, researched, evaluated, and quality assured to a similar standard. This study demonstrated that when gaps in the knowledge of self-care and treatment are identified Arch Endocrinol Metab. 2017;61/6

Structured Education in T1DM

in individuals with T1DM, these individuals can receive better information about their disease and its possible consequences, therefore empowering them to manage this chronic health condition. The SEP based on DAFNE showed significant results achieving the original objectives. The participants reached a higher degree of information and knowledge about DM, improved their skills in daily self-care (as demonstrated by the results of the questionnaires and interviews), and reported in the consultations and workshops more confidence to perform the correct treatment. Furthermore, the decrease in HbA1c levels among the subjects who participated in the SEP was significant when different time points (pre-SEP and post-SEP) were compared, and a further decline was observed at the end of the study. This fact confirms the importance of a SEP to individuals with T1DM, a chronic disease that requires close attention to complex constant care beyond a daily routine. These preliminary results support the application of this methodology in the treatment of individuals with T1DM in the Brazilian context: simple approaches for effective management.

Acknowledgments: APFP was supported by a scholarship offered by CAPES Foundation (Brazil-DF).

16. National Institute for Clinical Excellence, and Great Britain. Guidance on the use of patient-education models for diabetes. National Institute for Clinical Excellence, 2003. Available from: <https://www.nice.org.uk/guidance/ta60>. Access on: Dec. 14, 2015.

Disclosure: no potential conflict of interest relevant to this article was reported.

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Buckloh LM, Lochrie AS, Antal H, Milkes A, Canas JA, Hutchinson S, Wysocki T. Diabetes complications in youth. Diabetes Care. 2008;31(8):1516-20.

8. Olsen Roper S, Call A, Leishman J, Ratcliffe GC, Mandleco BL, Dyches TT, Marshall ES. Type 1 diabetes: children and adolescents’ knowledge and questions. J Adv Nurs. 2009;65(8):1705-14. 9. Clement S. Diabetes self-management education. Diabetes Care. 1995;18(8):1204-14. 10. Heisler M, Piette JD, Spencer M, Kieffer E, Vijan S. The relationship between knowledge of recent HbA1c values and diabetes care understanding and self-management. Diabetes Care. 2005;28(4):816-22. 11. Moran A, Hessett C, Pooley SRN, Boulton M. An assessment of patients’ knowledge of diabetes, its management and complications. Practical Diabetes Int. 1989;6(6):265-7. 12. Speight J, Bradley C. The ADKnowl: identifying knowledge deficits in diabetes care. Diabet Med. 2001;18(8):626-33. 13. Speight J, Amiel SA, Bradley C, Heller S, Oliver L, Roberts S, et al. Long-term biomedical and psychosocial outcomes following DAFNE (Dose Adjustment For Normal Eating) structured education to promote intensive insulin therapy in adults with sub-optimally controlled Type 1 diabetes. Diabetes Res Clin Pract. 2010;89(1):22-9. 14. Kruger J, Brennan A, Thokala P, Basarir H, Jacques R, Elliott J, et al. The cost-effectiveness of the Dose Adjustment for Normal Eating (DAFNE) structured education programme: an update using the Sheffield Type 1 Diabetes Policy Model. Diabet Med. 2013;30(10):1236-44. 15. McIntyre HD. DAFNE (Dose Adjustment for Normal Eating): structured education in insulin replacement therapy for type 1 diabetes. Med J Aust. 2006;184(7):317-8.

17. DAFNE Study Group. Training in flexible, intensive insulin management to enable dietary freedom in people with type 1 diabetes: dose adjustment for normal eating (DAFNE) randomised controlled trial. BMJ. 2002;325(7367):746.

World Health Organization. The world health report 2002: reducing risks, promoting healthy life. World Health Organization, 2002.

18. Rankin D, Heller S, Lawton J. Understanding information and education gaps among people with type 1 diabetes: a qualitative investigation. Patient Educ Couns. 2011;83(1):87-91.

2. IDF. IDF Diabetes Atlas Seventh Edition. (2015). Available from: <http://www.idf.org/idf-diabetes-atlas-seventh-edition>. Access on: Feb. 16, 2016.

19. Gunn D, Mansell P. Glycaemic control and weight 7 years after Dose Adjustment For Normal Eating (DAFNE) structured education in Type 1 diabetes. Diabet Med. 2012;29(6):807-12.

3. Wu YL, Ding YP, Gao J, Tanaka Y, Zhang W. Risk factors and primary prevention trials for type 1 diabetes. Int J Biol Sci. 2013;9(7):666-79.

20. Hopkins D, Lawrence I, Mansell P,Thompson G, Amiel S, Campbell M, et al. Improved biomedical and psychological outcomes 1 year after structured education in flexible insulin therapy for people with type 1 diabetes: the U.K. DAFNE experience. Diabetes Care. 2012;35(8):1638-42.

4. National Audit Office. The management of adult diabetes services in the NHS. (2012). Available from: <https://www.nao.org.uk/ report/the-management-of-adult-diabetes-services-in-the-nhs/>. Access on: Feb. 16, 2016. 5. Beeney LJ, Bakry AA, Dunn SM. Patient psychological and information needs when the diagnosis is diabetes. Patient Educ Couns. 1996;29(1):109-16. 6. Rickheim PL, Weaver TW, Flader JL, Kendall DM. Assessment of group versus individual diabetes education. Diabetes Care. 2002;25(2):269-74.

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21. McIntyre HD, Knight BA, Harvey DM, Noud MN, Hagger VL, Gilshenan KS. Dose adjustment for normal eating (DAFNE)-an audit of outcomes in Australia. Med J Aust. 2010;192(11):637-40. 22. Mendes AB, Fittipaldi JA, Neves RC, Chacra AR, Moreira ED Jr. Prevalence and correlates of inadequate glycaemic control: results from a nationwide survey in 6,671 adults with diabetes in Brazil. Acta Diabetol. 2010;47(2):137-45. Copyright© AE&M all rights reserved.

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original article

Impaired flow-mediated dilation response and carotid intima-media thickness in patients with type 1 diabetes mellitus with a mean disease duration of 4.1 years Lúcia Helena Bonalume Tacito1, Antonio Carlos Pires1, Juan Carlos Yugar-Toledo2

ABSTRACT Disciplina de Endocrinologia, Departamento de Medicina, Faculdade de Medicina de São José do Rio Preto (Famerp), São Paulo, SP, Brasil 2 Endocor – Instituto de Cardiologia e Endocrinologia de São José do Rio Preto, São Paulo, SP, Brasil 1

Correspondence to: Juan Carlos Yugar-Toledo Av. Francisco Chagas Oliveira, 244 15091-330 – São José do Rio Preto, SP, Brasil yugarjuan@uol.com.br Received on Apr/19//2016 Accepted on Mar/26/2017

Objective: This study aimed at assessing the endothelial function in patients with Type 1 diabetes (T1DM) using flow-mediated dilation (FMD) response and carotid artery intima-media thickness (CIMT). Materials and methods: This study enrolled 32 T1DM patients (mean disease duration 4.1 years) and 28 age-matched controls (CTL Group). Endothelial function and CIMT were assessed with high-resolution ultrasound using standardized offline measurements. Results: FMD was significantly lower in patients in the T1DM Group (8.9 ± 3.2%) compared with those in the CTL Group (13.3 ± 4.3%; P-value < 0.0001). Similarly, CIMT differed significantly between T1DM patients (0.525 ± 0.03 mm) and controls (0.508 ± 0.03 mm; P-value = 0.041). Even though, the values are within the normal range for age. Conclusions: Patients with T1DM have impaired endothelial function characterized by reduced FMD when compared to controls. However, vascular remodeling as seen by increases in CIMT was not found in this study. Arch Endocrinol Metab. 2017;61(6):542-9 Keywords Nitric oxide; carotid intima-media thickness; diabetes mellitus, type 1; cardiovascular diseases; diabetes complications

DOI: 10.1590/2359-3997000000281:

INTRODUCTION

ardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with diabetes. Macro- and micro-vascular complications are involved in the pathophysiology of CVD and the increased risk of developing atherosclerosis in this population (1,2). Type 1 diabetics have a six- to ten-times higher risk of premature cardiovascular death (before the age of 60 years) than nondiabetic individuals (3). Studies have also shown that the mortality rate due to cerebrovascular disease is higher in all age groups of type 1 diabetics (3). However, the traditional risk factors for coronary heart disease do not entirely explain this higher risk. A probable association between type 1 diabetes mellitus (Type 1 DM) and CVD has been attributed to chronic uncontrolled hyperglycemia, inflammation, endothelial dysfunction (ED), and subclinical manifestations of vascular disease (4,5). ED and increases in the common carotid intimamedia thickness (CIMT) are early markers of 542

atherosclerosis in individuals with risk factors for CVD (6). These markers have played a central role in the pathophysiology of macro-vascular complications in Type 1 DM. Incipient atherosclerosis and ED can be investigated noninvasively in different vascular beds using mechanical or pharmacological stimulation (7). Flow-mediated dilation (FMD) can be induced by reactive hyperemia after compression and decompression of the brachial artery. The increased flow resulting from this maneuver causes an increase in the “shear stress” on the vascular wall, which is detected by endothelial mechanical sensors. In normal arteries, this leads to the production and release of vasodilatory substances, such as nitric oxide (NO), by the endothelium. Thus, the increase of NO bioavailability promotes NO-mediated artery dilation, that is, endothelium-dependent dilation (8). A decrease in endothelium-dependent dilation is interpreted as a functional change of endothelial cells, mediated via the nitric oxide – cyclic guanosine monophosphate (NO – cGMP) pathway. In contrast, Arch Endocrinol Metab. 2017;61/6

Impaired flow-mediated dilation & CIMT in T1DM

Arch Endocrinol Metab. 2017;61/6

measurements of the CIMT in patients with Type 1 DM and correlate the findings with metabolic parameters.

MATERIALS AND METHODS This case-control study enrolled 60 individuals. Thirtytwo were diabetics (Type 1 DM – 20 female and 12 male) treated in the Diabetes Outpatient Clinic of the Medicine School in São José do Rio Preto (Famerp) with a mean time after diagnosis of 4.1 years, and 28 were apparently healthy volunteer controls (CTL Group – 20 female and 8 male). Individuals with primary and secondary forms of hypertension, impaired renal function, or dyslipidemia, smokers and those with any other major disease were excluded. This research project was approved by the Research Ethics Committee of the Medicine School in São José do Rio Preto (Famerp). All patients were followed clinically by experts and received treatment for their disorders according to routine clinical standards and norms. The nature of the study was carefully explained to patients and all individuals, after agreeing to participate in the study, signed informed consent forms. All patients and volunteers who accepted to participate filled out a standard questionnaire. The study was conducted in compliance with the principles of the Declaration of Helsinki.

Flow-mediated dilation High-resolution ultrasound was used to evaluate the endothelium-dependent function of a medium-caliber artery (brachial artery) after applying the compression/ decompression test (occlusion for five minutes). This method has been validated and is standardized according to the International Brachial Artery Reactivity Task Force Guidelines for the Ultrasound Assessment of Endothelial-dependent Vasodilation of the Brachial Artery (9). A Philips HDI ultrasound equipment with a high-resolution 5-12 MHz vascular linear transducer was used connected to a microcomputer to study the vascular function dynamically. All scanned images were stored on a compact disc for future analysis by two independent observers. The variability between the arterial diameter measurements should be less than 2%, and intra-observer differences less than 1%, as was seen in this study. The brachial artery diameter was measured using offline image analysis software (M’ATh – Metris 543

NO donors, such as nitroglycerin, acting directly on the vascular smooth muscle, promote endotheliumindependent dilation. One important tool in clinical research is highresolution ultrasound, which allows the determination of percentage variations of the diameter of the brachial artery. The results of this test are a measure of the NO-mediated (endothelium-dependent) dilation. This technique involves reactive hyperemia and shear stress to stimulate FMD (9). The endothelial vasodilator mechanism is mediated by vasoactive substances, mainly by NO (a physiological antagonist of endogenous vasoconstricting substances such as catecholamines, angiotensin II and endothelin-1), but also by prostacyclin, bradykinin, endothelium-derived hyperpolarizing factor (EDHF), monooxygenase metabolites, and others (10-12). Endothelial cells release NO not only into the layer of smooth muscle cells, but also into the lumen of the blood vessel. This vasodilator is formed from the terminal nitrogen of the guanidine group of the L-arginine amino acid by the action of the endothelial NO synthase enzyme (eNOS) which is constitutive (NO synthase III); this is dependent on the intracellular concentration of calcium ions and calmodulin and requires reduced levels of nicotinamideadenine-dinucleotide phosphate (NADPH) and tetrahydrobiopterin (4HB) for optimal activity (13). NO also inhibits platelet and leukocyte adhesion to the endothelium, has an inhibitory action on platelet aggregation (synergistically with prostacyclin), inhibits the proliferation of smooth muscle cells and modulates the production of adhesion molecules, and endothelin-1 that are involved in the pathophysiology of atherosclerosis (14). The measurement of the CIMT in the anterior and posterior walls using high-resolution ultrasound is another commonly used research tool employed to detect vascular remodeling at an early stage; this is believed to be the first structural change in atherosclerosis. Since the initial works by Pignoli and cols. in 1986 (15), several population and case-control studies (16,17) have demonstrated the excellent safety, reliability, reproducibility and applicability of this technique in studies on primary prevention and cardiovascular risk stratification. Today, this method is recognized as a marker of risk for myocardial infarction, stroke, and peripheral artery disease (18). This study aims to assess endothelial function using FMD and

Impaired flow-mediated dilation & CIMT in T1DM

France). Measurements were taken between the arterial lumen-wall interface of the front and posterior walls at the end of diastole. The mean diameter was calculated in four cardiac cycles identified using the R wave of the electrocardiogram (ECG). Percentage changes in the brachial artery diameter were calculated compared to the first baseline diameter (100%) according to the formula: FMD = [(diameter after decompression – baseline diameter) / baseline diameter] x 100.

Common carotid intima-media thickness measurements Measurements of the CIMT of the anterior and posterior walls were achieved using high-resolution ultrasound. This method is established and standardized by the report of the 34th Bethesda Conference Task Force #3 Noninvasive Measurement of Atherosclerosis (19). The brightness of the examination room was controlled, and the room temperature was set at 24°C. In order to perform the examination, the patient was positioned in the supine position with the head flexed slightly toward the side opposite to that being examined. An image of the vessel was positioned on the screen so that the cephalic portion was on the left. Care was taken not to excessively compress the tissue with the transducer so that the venous structures anterior to the carotid artery were not collapsed. The lumen-intima interface and the adventitia-media interface were clearly defined with the proper use of gain control, correction of angle and slope of the sampling box, and its amplification. A Philips HDI ultrasound equipment with a high-resolution 5-12 MHz vascular linear transducer connected to a microcomputer was used to measure the CIMT automatically. The image acquisition protocol for the distal segment of the common carotid artery was standardized to a minimum of 100 measurement points or a longitudinal length of at least 1.0 cm of the artery excluding the carotid bulb (20). The CIMT was measured in the distal segment during four cardiac cycles identified by the R wave of the ECG using image analysis software (M’ATh-Metris – France) which allows measurement of the CIMT from stored images. Analysis is based on the gray-scale density and a specific tissue recognition algorithm, which allows automatic measurement without depending on the observer. The variability between the CIMT measurements should be less than 2%, as was seen in this study. 544

Statistical analysis All calculations were performed using SPSS for Windows, version 17.0 (SPSS Inc., Chicago, IL, USA) and the Graph Pad Prism 5 Statistical package (CA) was used for all analyses. The results of the continuous variables with normal distribution are presented as means and standard deviations; comparative analysis employed the unpaired t-test. Variables with nonGaussian distributions are presented as medians with the Mann-Whitney test being used for comparative analysis. Multivariate logistic regression was performed to determine predictors of ED and increases in the CIMT. P-value < 0.05 was considered significant.

RESULTS The main demographic, clinical and anthropometrical characteristics of the study groups are presented in Table 1. The mean age in the control group was higher than in patients with DM1. However, no statistically significant difference was found between the median ages of both groups. There were no statistical differences between the Type 1 DM and CTL Groups in regards to body mass index (BMI), total cholesterol, triglycerides, and high-density lipoprotein (HDL) cholesterol levels. The low-density Table 1. Demographic, clinical and anthropometrical characteristics of the Type 1 diabetes mellitus (Type 1 DM) and Control Groups Type 1 DM

Controls

p-value

17.25 ± 4.43

20.11 ± 5.62

0.03*

20/12

20/8

Body mass index

21.71 ± 3.14

21. 34 ± 2.32

Duration of Type 1 DM

4.14 ± 1.97

170.1 ± 77.99

79.82 ± 11.89

< 0.0001†

9.95 ± 2.97

5.47 ± 0.43

< 0.0001†

Total cholesterol

160.7 ± 32.13

145.7 ± 19.91

0.057

Triglycerides

68.12 ± 37.24

67.75 ± 19.91

0.96

HDL cholesterol

57.19 ± 16.75

65.81 ± 13.44

0.056

LDL cholesterol

92.15 ± 28.19

65.75 ± 17.67

0.0005#

VLDL cholesterol

13.81 ± 7.38

13.75 ± 4.27

0.97

Microalbuminuria

48.40 ± 9.3

13.90 ± 1.8

< 0.0001†

TSH

3.48 ± 1.63

2.65 ± 1.11

0.035‡

FT4

1.17 ± 0.18

1.11 ± 0.21

0.27

Parameters Age Gender F/M

Glycemia HbA1c

* P-value < 0.03 (absolute difference between Type 1 diabetes mellitus and Control groups). †

P-value < 0.0001 (absolute differences between Type 1 diabetes mellitus and control groups).

P-value = 0.0005 (absolute difference between Type 1 diabetes mellitus and control groups).

‡

P-value = 0.035 (absolute difference between Type 1 diabetes mellitus and control groups).

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Impaired flow-mediated dilation & CIMT in T1DM

Flow-mediated brachial artery dilation (%)

P < 0.0001

Type 1 DM

CTL

Figure 1. Flow-mediated dilation (FMD) of the brachial artery in Type 1 diabetes mellitus (Type 1 DM) patients and Control (CTL) groups. The Type 1 DM group had a significant reduction in FMD (P-value < 0.0001).

0.650

P = 0.041

0.600 Carotid intima-media thickness CIMT (mm)

lipoprotein cholesterol (LDL) and TSH plasma levels were increased in Type 1 DM when compared with the CTL Group (P-value = 0.0005 and P-value = 0.035, respectively). However, despite the difference in mean values of LDL cholesterol, no correlation was found between the LDL cholesterol and FMD in the control and DM1 groups. Microalbuminuria levels were increased in Type 1 DM when compared with the CTL Group (P-value < 0.0001). Nonetheless, even with the difference in mean values of albuminuria, no correlation was found between the microalbuminuria and FMD in the control and DM1 groups. However, as expected, there was a correlation between elevated levels of glycemia and microalbuminuria in patients with DM1 (R = 0.48; P-value = 0.038). The absolute variations in the diameter of the brachial artery and the relative difference between Type 1 DM and CTL groups during the assessment of endotheliumdependent vascular function and CIMT using highresolution ultrasound are presented in Table 2. The results of the assessment of endotheliumdependent vascular function using high-resolution ultrasound and mechanical stimulation (compressiondecompression) of the brachial artery (FMD) are presented in Figure 1. The values found for the FMD in the Type 1 DM and CTL Groups were 8.94 ± 3.21% and 13.27 ± 4.22%, respectively (P-value < 0.0001). The results of the measurement of the CIMT are presented in Figure 2. The mean CIMT in the Type 1 DM and CTL Groups were 0.525 ± 0.03 mm and 0.508

0.550 0.500 0.450 0.400 Type 1 DM

CTL

Figure 2. Common carotid artery intima-media thickness (CIMT) in Type 1 diabetes mellitus (Type 1 DM) patients and Control (CTL) groups. The Type 1 DM group had a significantly greater CIMT (P-value = 0.041). Even though, the values are within the normal range for age.

Table 2. The absolute and relative differences in the diameter of the brachial artery and the carotid intima-media thickness comparing Type 1 diabetes mellitus (Type 1 DM) and Control Groups Controls

Mean

95% CI

Mean

95% CI

Brachial artery diameter (mm) – BL

3.23

0.36

2.70 – 3.93

3.13

0,37

2.58 – 3.97

Brachial artery diameter (mm) – FMD

3.52

0.42

2.91 – 4.29

3.54

0.406

2.96 – 4.30

Absolute variation in the diameter of the brachial artery [FMD-BL] (mm)

0.29*

0.11

0.15 – 0.52

0.41

0.13

0.20 – 0.62

Relative difference (%)

8.94#

3.21

4.62 – 14.65

13.27

4.22

7.10 – 20.90

Carotid IMT (mm)

0.525‡

0.03

0.467 – 0.599

0.508

0.03

0.456 – 0.566

SD: standard deviation; CI: confidence interval; mm: millimeters; BL: baseline; FMD: flow-mediated-dilation; Relative differences %: percentage from the formula ([FMD-BL/BL]*100); IMT: intimamedia thickness. * P-value = 0.003 (absolute difference in the diameter of the brachial artery between Type 1 diabetes mellitus and Control groups). #

P-value < 0.0001 (relative difference in the diameter of the brachial artery between Type 1 diabetes mellitus and Control groups).

‡

P-value: 0.41 (absolute difference in the common carotid artery intima-media thickness – IMT – between Type 1 diabetes mellitus and Control groups).

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Type 1 DM

Impaired flow-mediated dilation & CIMT in T1DM

± 0.03 mm, respectively with statistical significance (P-value = 0.041). Even though, the values are within the normal range for age.

DISCUSSION The main results of this study show: 1) a reduction in the FMD in Type 1 DM patients in relation to controls individuals and 2) the absence of the vascular remodeling as seen by the increased CIMT for age. This study of a population of young Brazilian individuals with Type 1 DM demonstrates that impaired vascular function (reduced FMD of the brachial artery) is a manifestation of vascular involvement early in the evolution of Type 1 DM preceding vascular remodeling characterized by increased CIMT, an early marker of subclinical atherosclerosis. The duration of diabetes in this population was less than five years (mean disease duration 4.1 years). Wiltshire and cols. (21), who evaluated 36 Type 1 DM patients with a mean age of 16 years by comparing them to 20 healthy control individuals found reduced FMD only in patients with Type 1 DM. Similarly, on evaluating young adolescents with Type 1 DM, Singh and cols. (22) demonstrated that changes in the endothelial function occur within the first decade after the onset of Type 1 DM. However, morphological alterations of the CIMT appear later due to, according to the authors, prolonged and chronic exposure to hyperglycemia and the metabolic changes related to this clinical condition. Hurks and cols. (23) investigated the endothelial function of Type 1 DM patients and controls, matched by age and gender (aged 16-36 years), and without subclinical atherosclerosis or risk factors for CVD. Duration of diabetes was 9.2 ± 5.3 years. The authors demonstrated that even diabetics with moderate metabolic control [glycated hemoglobin (HbA1c): 7.6 ± 1.0] have changes in FMD, and they concluded that even without preclinical atherosclerosis, the endothelial function is already affected as can be confirmed by assessing the endothelial function using high-resolution ultrasound. The pathophysiology of ED and of macro- and micro-vascular changes in Type 1 DM involve multiple factors such as age at onset, time of evolution of the disease, and the presence of risk factors for heart disease such as smoking, hypertension and dyslipidemia. Thus, good metabolic control, which is difficult to achieve, seems to be insufficient to prevent the development of micro- and macro-vascular complications (24). 546

Hyperglycemia leads to increased oxidative stress and the formation of non-enzymatic glycation end products. These, in turn, increase the inactivation of NO and promote the oxidative modification of lipoproteins (25). Moreover, the metabolism of lipoproteins is altered in Type 1 DM leading to hypertriglyceridemia associated with decreased concentrations of HDL cholesterol and increases in small and dense particles of LDL cholesterol with normal or slightly increased total cholesterol. This constitutes the so-called atherogenic profile; this condition, associated with ED, appears to increase the susceptibility of young people with Type 1 DM to the harmful effects of LDL cholesterol and the occurrence of early-onset atherosclerosis (26). On the other hand, through the elevation of intracellular calcium, hyperglycemia stimulates the synthesis of NO which, in the presence of peroxide anions, is quickly converted to peroxynitrite, a potent oxidizing molecule, thus contributing to the perpetuation of oxidative stress (27). Some studies, such as the one by Sibal and cols. (28), showed impairment of endothelial function and an increase in CIMT in young people with Type 1 DM without macro-vascular disease or microalbuminuria. However, their study included 62% of individuals with retinopathy, 24% of smokers and metabolic control outside the goals recommended by guidelines (mean HbA1c = 8.5%). Previously, Larsen and cols. (29) reported that an increased CIMT in Type 1 DM is significantly associated with elevated levels of HbA1c (r2 = 0.77; P-value < 0.0001 adjusted for age) in women with Type 1 DM while no correlation was observed in men. The atherogenicity in Type 1 DM has been widely recognized. However, there is controversy over the timing of the early markers of atherosclerosis, such as vascular remodeling as seen by an increased CIMT, in young people; this change is not present in the first years of the development of this metabolic disorder. However, the results of the Epidemiology of Diabetes Interventions and Complications (EDIC) Study (30) show that intensive insulin therapy delays increases in CIMT more than conventional therapy. Nevertheless, many questions remain unanswered such as the minimum time of exposure required for a clinical event to happen, the specific determinants in Type 1 DM for vascular damage, and whether the atherosclerotic process is already active in the prepubertal period. Arch Endocrinol Metab. 2017;61/6

Studies on the CIMT in children and adolescents have conflicting results; some studies show an increase in the CIMT in diabetic patients, while others do not (31-33). The seemingly contradictory results may depend on the different ultrasound techniques used as well as on the different populations studied. However, the use of automated programs that measure the CIMT reduces the variability related to human error and allows comparisons between studies. Our results on the CIMT measured using an automated system comparing Type 1 DM and controls are similar to other publications (22,33). However, recent publications such as the study by Bradley and cols. (34) that included a sample of patients with longer disease duration (on average 6.2 years) demonstrated a higher blood pressure, impaired endothelial function and increased arterial stiffness, altered myocardial velocities and strain. Similar results were observed by Abd Al Dayem and cols. (35) who included patients with longer duration of type 1 DM (9.4 ± 2.9 years). These authors demonstrated that the mean CIMT was significantly higher, whereas the FMD and FMD – nitrate mediated dilatation (NMD) ratio was significantly lower in diabetics; CIMT had a significant negative correlation with the FMD and FMD – NMD ratio. CIMT had a significant positive correlation with left ventricular end diastolic dimension, inter-ventricular septum thickness, peak mitral flow velocity during early diastole/peak mitral flow velocity during late diastole, left ventricular mass, and left ventricular mass index. In addition, CIMT had a significant correlation with waist circumference, waist/height ratio, albumin/creatinine ratio, total cholesterol, and triglyceride. Obviously, it is expected that with this period of exposure to type 1 DM there will be increased cardiovascular and renal damage; we consider that the findings are relevant for this population. Moreover, the work of Murat Ciftel and cols. (36), who studied 40 DM1 with disease duration < 5 years and 42 controls from Turkey, demonstrated, that the aortic strain (8.40 ± 2.98 vs. 20.12 ± 5.04; p-value < 0.001), aortic distensibility (7.36 ± 2.92 vs. 16.59 ± 4.25; p-value < 0.001) and FMD% (7.70 ± 2.83 vs. 11.33 ± 2.85; p-value < 0.001) were decreased, and CIMT (0.52 ± 0.09 mm vs. 0.47 ± 0.08 mm; p-value < 0.05) was increased in the diabetic group. Additionally, left ventricular lateral segment, right ventricular freewall isovolumic relaxation time (IVRT) and myocardial performance index were found increased. Correlation Arch Endocrinol Metab. 2017;61/6

analyses demonstrated a negative correlation between FMD and IVRT and MPI. These results corroborate our findings. Ce and cols. (37) observed that ED in Type 1 DM is an early phenomenon that is relatively common in adolescents with recent onset of diabetes regardless of age, smoking, hypertension or hyperlipidemia. The reduction of flow-mediated vasodilation is particularly influenced by glycemic control and duration of disease. The authors suggest that medium-term and non-shortterm glycemic control has a great influence on ED in the early years of Type 1 DM. Thus, we believe that, HbA1c at the time of the analysis of endothelial function was similar between those with and without ED (8.2 ± 0.9 vs. 8.0 ± 1.4%, respectively; P-value = 0.66), whereas the mean second-year HbA1c was significantly higher in individuals with ED compared to those without ED (9.6 ± 2.4 vs. 8.1 ± 1.3%, respectively; P-value = 0.048). Moreover, FMD was inversely correlated with mean second-year HbA1c (r = −0.287; P-value = 0.031) but not with mean first-year HbA1c (r = −0.126; P-value = 0.37). In patients with less than 5 years of Type 1 DM, ED was a common finding (35.7%), but it was more prevalent in patients with longer duration of Type 1 DM (60%; P-value < 0.01). The mechanism by which chronic hyperglycemia is associated with ED is complex and not fully understood. Oxidative stress, activation of the polyol pathway, activation of the protein kinase C system and the presence of advanced glycation end products are all potential mechanisms involved. The concept of metabolic memory was recently proposed by Ceriello and cols. (38), who pointed out that the mechanisms that propagate this phenomenon seem to be related to the non-enzymatic glycation process and the excess of reactive species of oxygen and nitrogen, originating at the glycated mitochondrial protein level and acting synergistically to maintain glucose-independent stress signaling. However, a few points need to be stressed. This study of a Brazilian population of young individuals with Type 1 DM demonstrates that impaired functional vascular (reduced FMD of the brachial artery) is an early functional manifestation in the evolution of Type 1 DM and precedes vascular remodeling characterized by increased CIMT. These individuals had duration of diabetes of less than five years (mean disease duration 4.1 years). Even so, the necessity of new markers 547

Impaired flow-mediated dilation & CIMT in T1DM

to monitor the integrity of cardiovascular health is undeniable because of the clinical importance of this pathology. A better understanding of this disease may help to create appropriate therapeutic strategies that limit the development of micro- and macro-vascular lesions and cardiovascular events. Moreover, early detection of ED with prompt intervention may improve the treatment of patients with Type 1 DM at high cardiovascular risk. In fact, ED, characterized by an imbalance between vasodilator substances (particularly NO) and vasoconstrictor substances is an early process involved in the pathophysiology of several CVDs and is observed in humans with vascular risk factors. Furthermore, the presence of ED is predictive of future cardiovascular events in patients with vascular disease including preclinical Type 1 DM (39). The significant difference found in our study in the CIMT between Type 1 DM and control subjects, even though the values were within the normal range for age, may represent an early stage of the atherosclerotic process in this group of patients. Identifying this condition may allow adaptations to treatment to improve glycemic control and combat cardiovascular risk factors thereby preventing future cardiovascular events (40). In conclusion, endothelial dysfunction, characteÂ rized by reduced FMD, is an early marker of vascular involvement that appears within the first few years after the onset of Type 1 DM. However, increases in the CIMT, a preclinical marker of atherosclerosis that initiates the pathologic vascular remodeling process, is not present as early in the evolution of T1DM. Acknowledgments: this study received no funding. We would like to thank David Hewitt for his assistance in preparing the English version of the manuscript and our colleagues from the Endocrinology Section of the Department of Medicine Famerp. Disclosure: no potential conflict of interest relevant to this article was reported.

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3. Laing SP, Swerdlow AJ, Slater SD, Burden AC, Morris A, Waugh NR, et al. Mortality from heart disease in a cohort of 23,000 patients with insulin-treated diabetes. Diabetologia. 2003;46(6):760-5.

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34. Bradley TJ, Slorach C, Mahmud FH, Dunger DB, Deanfield J, Deda L, et al. Early changes in cardiovascular structure and function in adolescents with type 1 diabetes. Cardiovasc Diabetol. 2016;15:31. 35. Abd El Dayem SM, El Magd El Bohy A, Battah AA. Carotid intimal medial thickness and its relation to endothelial dysfunction and echocardiographic changes in adolescents with type 1 diabetes. J Pediatr Endocrinol Metab. 2015;28(9-10):1029-37. 36. Ciftel M, Ertug H, Parlak M, Akcurin G, Kardelen F. Investigation of endothelial dysfunction and arterial stiffness in children with type 1 diabetes mellitus and the association with diastolic dysfunction. Diab Vasc Dis Res. 2014;11(1):19-25. 37. Ce GV, Rohde LE, da Silva AM, Punales MK, de Castro AC, Bertoluci MC. Endothelial dysfunction is related to poor glycemic control in adolescents with type 1 diabetes under 5 years of disease: evidence of metabolic memory. J Clin Endocrinol Metab. 2011;96(5):1493-9. 38. Ceriello A, Ihnat MA, Thorpe JE. Clinical review 2: The “metabolic memory”: is more than just tight glucose control necessary to prevent diabetic complications? J Clin Endocrinol Metab. 2009;94(2):410-5.

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original article

1 Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil. Universidade de Santa Cruz do Sul (Unisc), Santa Cruz do Sul, RS, Brasil 2 Departamento de Genética, UFRGS, Porto Alegre, RS, Brasil 3 Instituto da Criança com Diabetes, Hospital da Criança Conceição, Porto Alegre, RS, Brasil 4 Programa de Pós-Graduação em Saúde da Criança e do Adolescente, UFRGS, Porto Alegre, RS, Brasil 5 Programa de Pós-Graduação em Saúde da Criança e do Adolescente, UFRGS, Porto Alegre, RS, Brasil. Departamento de Genética, UFRGS, Porto Alegre, RS, Brasil. Instituto Nacional de Genética Médica Populacional, Porto Alegre, RS, Brasil 6 Programa de Pós-Graduação em Saúde da Criança e do Adolescente, UFRGS, Porto Alegre, RS, Brasil. Hospital da Criança Santo Antônio, Porto Alegre, RS, Brasil

Study linked at the Pós-Graduação em Saúde da Criança e do Adolescente, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil Correspondence to: Marília D. Bastos Rua Fernando Abott, 391, sala 204 96810-072 – Santa Cruz do Sul, RS, Brasil mdbastos@unisc.br Received on Nov/02/2016 Accepted on Mar/26/2017

Search for DQ2.5 and DQ8 alleles using a lower cost technique in patients with type 1 diabetes and celiac disease in a population of southern Brazil Marília D. Bastos1, Thayne W. Kowalski2, Márcia Puñales3, Balduíno Tschiedel3, Luiza M. Mariath2, Ana Luiza G. Pires4, Lavínia S. Faccini5, Themis R. Silveira6

ABSTRACT Objective: To evaluate the frequency of DQ2.5 and DQ8 alleles using the Tag-single-nucleotide polymorphism (Tag-SNP) technique in individuals with type 1 diabetes mellitus (T1DM) and celiac disease (CD) in southern Brazil. Materials and methods: In a prospective design, we performed the search for DQA1*0501 and DQB1*0201 alleles for DQ2.5 and DQB1*0302 for DQ8 through RealTime Polymerase Chain Reaction (RT-PCR) technique, using TaqMan Genotyping Assays (Applied Biosystems, USA). The diagnosis of CD was established by duodenal biopsy and genotypic determination performed by StepOne Software v2.3. Allelic and genotypic frequencies were compared between groups using Chi-square and Fisher’s exact tests and the multiple comparisons using Finner’s adjustment. Results: Three hundred and sixty two patients with a median age of 14 years were divided into 3 groups: T1DM without CD (264); T1DM with CD (32) and CD without T1DM (66). In 97% of individuals with T1DM and CD and 76% of individuals with CD without T1DM, respectively, the alleles DQ2.5 and/or DQ8 were identified (p < 0.001). DQ2.5 was more common in individuals with CD (p = 0.004) and DQ8 was more common in individuals with type 1 diabetes (p = 0.008). Conclusions: The evaluation of the alleles for DQ2.5 and DQ8 by Tag-SNP technique showed a high negative predictive value among those with T1DM, similar to that described by the conventional technique. The high frequency of DQ8 alleles in individuals with T1DM did not allow differentiating those at higher risk of developing T1DM. Arch Endocrinol Metab. 2017;61(6):550-5 Keywords Celiac disease; type 1 diabetes mellitus; HLA; genetic polymorphism

DOI: 10.1590/2359-3997000000282

INTRODUCTION

eliac disease (CD) is a chronic and permanent enteropathy caused by intolerance to gluten proteins of wheat, rye, barley in genetically-predisposed subjects (1). The immunological base of CD results from an imbalance of the innate and adaptive immune systems. In these conditions, gliadin, the main toxic component of gluten, crosses the intestinal epithelium activating the adaptive immune system and determining an increase in intestinal permeability. Peptides contained in gluten go through the lamina propria, where they can be de-starched by the tissue transglutaminase (TTG) enzyme. Such peptides are presented by HLA class II molecules (DQ2 and DQ8), which promote the activation of tissue inflammation effector cells seen in CD: CD4 helper T lymphocytes (2). 550

The combination of alleles will determine a higher or lower risk of developing the diseases. The alleles DQB1*0201 and DQA1*0501 form haplotype DQ2.5; the alleles DQB1*0302 and DQA1*0301 form haplotype DQ8. There are also combinations DQA1*0201 and DQB1*0202 the form haplotype DQ 2.2 and alleles DQA1*0505 and DQB1*0301 that form haplotype DQ7. In the case of CD, the higherrisk combinations are DQ2.5 and DQ8 or DQ2.5 in homozygous form (3). There is wide variation in the prevalence of CD in different countries. In Europe and the United States, the prevalence varies between 1 and 3% in the general population (4). The prevalence of CD confirmed by biopsy, in studies performed in Brazil to date, show a variation of 0.15 to 1.94% (5). Arch Endocrinol Metab. 2017;61/6

DQ2.5 and DQ8 with a lower cost technique

Arch Endocrinol Metab. 2017;61/6

risk factor (12,13). Among the identified risk alleles, we highlight HLA-DQA1*05 and DQB1*02 on chromosomes 6p21, which can estimate a high negative predictive value for CD, but a low positive predictive value, since 35 to 40% of the population usually have one or both alleles (14,15). CD screening in patients with T1DM is recommended, aiming at reducing both the morbidity of T1DM and the consequences of untreated CD, even if it is asymptomatic (16,17). Consequently, with the genetic investigation of these groups, we hope to rationalize the performance of high-cost tests that would not help in the identification of the groups with the concomitant diseases. The aim of this study was to evaluate the frequency of alleles for DQ2.5 and DQ8 using the Tag SNP technique in individuals with T1DM and CD, in a population of southern Brazil.

MATERIALS AND METHODS Study design and population A prospective study was carried out from August 2012 to October 2014, involving individuals diagnosed with type 1 diabetes treated at Instituto da Criança com Diabetes (ICD) – Hospital da Criança Conceição, located in Porto Alegre, the state capital of Rio Grande do Sul (RS) – Brazil and individuals diagnosed with CD, confirmed by duodenal biopsy, residents in the city of Porto Alegre, RS – Brazil, participating in Associação dos Celíacos do Brasil – Rio Grande do Sul (ACELBRA-RS). Blood and/or saliva samples were collected from individuals with T1DM, when undergoing periodic assessment for diabetes control and after providing authorization to participate in the study. Among these individuals, we identified those who had TTG-IgA < 9.0 U/mL or TTG-IgA > 16 U/mL and duodenal biopsy classified as ≥ 2, according to Marsh criteria (18) modified by Oberhuber (19). The following individuals were excluded from the study: those with TTG-IgA values between 9.0 U/mL and 16 U/mL; TTG-IgA values > 16 U/mL that did not undergo duodenal biopsy or those in whom Marsh classification was < 2. Authorization was requested from individuals with a diagnosis of CD to participate in the study during an event sponsored by ACELBRA-RS. A saliva sample was collected and an interview was carried out to identify those who had the diagnosis confirmed by biopsy. 551

Individuals with type 1 diabetes have a higher prevalence of CD. A recent systematic review described a prevalence rate between 1.6 and 16.4% of CD among individuals with T1DM and recommended screening after the age of two years in this population (6). In Brazil, CD screening in patients with T1DM based on the serology is recommended, as the prevalence is considered similar to that of European countries and the United States (7,8). HLA genes play an important role in autoimmune diseases such as T1DM and CD and its identification in individuals with such diseases is very important to understand susceptibility aspects, as well as different clinical presentations. The genotyping is carried out by methods based on Polymerase Chain Reaction (PCR) using Sequence-Specific Probes (SSO) with sequence specific primers (SSP) or Sequence-Based Typing (SBT). These traditional genotyping techniques involve many reactions, which makes them complex and expensive. Monsuur and cols. (9) validated a technique using Tag single-nucleotide polymorphisms (Tag SNP), allowing the performance of tests with high sensitivity and specificity, at a lower cost. Brandao and cols. (10) evaluated patients with T1DM in northeastern Brazil and compared the costs of a conventional technique (SSP) with the technique using Tag SNP and observed an average cost of US$ 90-100 per person for the conventional technique, and US$ 5 per subject analyzed with the Tag SNP technique. Considering these results, the authors recommend that this genotyping method be used instead of the conventional technique, thereby reducing the overall costs of genetic identification for T1DM in areas with limited financial resources. The HLA system is very polymorphic and displays variability between different geographical areas and ethnic groups. Brazil is a country with a high degree of miscegenation and great racial variation between regions. A recent study identified African ancestry in 50% of the northeastern population and 70% of European ancestry in the south and southeast regions of the country (11). There are no studies on the search for HLA DQ2 and DQ8 genes among individuals with T1DM and CD in the state of Rio Grande do Sul, Brazil. The assessment of the frequency of HLA-DQ types in individuals with T1DM and/or CD is interesting because both diseases have an autoimmune etiology, where the presence of HLA (Human Leukocyte Antigen) class 2 molecules represents the main genetic

DQ2.5 and DQ8 with a lower cost technique

Individuals who did not undergo duodenal biopsy and those whose diagnosis was doubtful were excluded from the study.

Laboratory methods

DNA extraction A peripheral blood sample (10 mL of whole blood with anticoagulant) was collected for subsequent DNA extraction using the salting out method (20). When unable to collect blood, a saliva sample was collected using the Oragene kit (DNA Genotek®) and submitted to DNA extraction according to the manufacturer’s instructions.

Haplotypic Determination of HLA-DQ2.5 and -DQ8 The prediction of haplotypes HLA-DQ2.5 and HLADQ8 was carried using the Tag Single nucleotide polymorphism (Tag-SNP) technique (9,10). The Real-Time Polymerase Chain Reaction technique (RT-PCR) was performed through an assay by TaqMan® Genotyping Assays (Applied Biosystems, USA) according to the manufacturer’s instructions. The assays used were previously deposited in Custom TaqMan Genotyping Assay (Applied Biosystems) and included: C_58662585_10 (rs2187668 C>T of HLADQA1) and C_29817179_10 (rs7454168 C>T of HLA-DQB1). The analysis of results and the genotypic determination of polymorphisms was performed using StepOne Software v2.3 (Applied Biosystems). The DQ8 and DQ2.5 haplotypes were predicted from the identified genotypes, as described by Monsuur and cols. (9) and Brandao and cols. (10).

Statistical analyses Statistical analyses were performed using the Statistical Package for Social Sciences (SPSS), version 22.0 (IBM Corp. – Armonk, NY, USA) and R, version 3.2.2 (R core team, 2015). The chi-square test or Fisher’s exact test were used to compare the frequencies of haplotypes HLA-DQ2.5 and DQ8 between the groups. In situations involving multiple comparisons, Finner’s adjustment was used for significant values (P), with significance being set at p < 0.05.

Ethical considerations The study was developed according to the rules of the National Health Congress based on Resolution 552

466/12 and was approved through Brazil Platform (CAE 01260412.7.0000.5347) by the Research Ethics Committee of the following institutions: Grupo Hospitalar Conceição, where the data collection from individuals with T1DM was carried out and Universidade Federal do Rio Grande do Sul (UFRGS), a committee associated to the institution where the research originated (Postgraduate Program in Child and Adolescent Health – UFRGS). Upon protocol completion, patients or their caregivers were asked to provide authorization for study participation by reading and signing the free and informed consent form.

RESULTS A total of 362 patients were evaluated, divided into 3 groups: individuals with T1DM and negative antibodies for CD (group 1 = 264 individuals), individuals with T1DM and with CD diagnosis (group 2 = 32 individuals) and people with CD without a diagnosis of T1DM (group 3 = 66 individuals). Table 1 shows the distribution of the combined haplotypes of DQ2.5 and DQ8 in the 3 groups, in which there is the presence of at least one DQ2.5 or DQ8 allele in 97% of individuals in the group with T1DM and CD (group 2) and in 76% in individuals with CD without a diagnosis of T1DM (group 3) (p < 0.001). It was also observed that the combination DQ2.5/DQx was more frequent in group 3, whereas the combination DQ2.5/DQ8 was more frequent in group 2 and the absence of alleles DQ2.5 and/or DQ8 occurred more frequently in group 3. Table 1. Distribution of combined DQ2.5 and DQ8 haplotypes HLA Haplotypes

Group 1 T1DM without CD n (%)

Group 2 T1DM with CD n (%)

Group 3 CD without T1DM n (%)

DQ2.5/DQ2.5

23 (8.7)

7 (21.9)

9 (13.6)

DQ2.5/DQXa

50 (18.9)1

9 (28.1)

28 (42.4)1

DQ2.5/DQ8

71 (26.9)2

10 (31.3)3

5 (7.6)2,3

DQ8/DQ8

16 (6.1)

0 (0.0)

1 (1.5)

DQ8/DQXa

58 (22.0)

5 (15.5)

7 (10.6)

DQXa/DQXa

46 (17.4)4

1 (3.1)4,5

16 (24.2)5

Total

264 (100)

32 (100)

66 (100)

n: number of analyzed individuals. a DQX: non-DQ2.5 or DQ8 haplotypes; p < 0.001. 1, 2, 3, 4 and 5 Finner’s adjustment for multiple comparisons with p < 0.05.

The presence of DQ2.5 alleles occurred in 170 (57.4%) individuals with T1DM (Groups 1 and 2), being more frequent in the group diagnosed with CD Arch Endocrinol Metab. 2017;61/6

DQ2.5 and DQ8 with a lower cost technique

Table 2. Presence of DQ2.5 and DQ8 alleles among individuals with T1DM HLA

Group 1 T1DM without CD n (%)

Group 2 T1DM with CD n (%)

DQXa

120 (45.5)

6 (18.8)

DQ2.5

144 (54.5)

26 (81.2)

DQX

119 (45.1)

17 (53.1%)

DQ8

145 (54.9)

15 (46.9%)

0.004 0.388

n: number of analyzed individuals. a DQX: non-DQ2.5 or DQ8 haplotypes.

When comparing individuals with T1DM and a CD diagnosis (group 2) and individuals with CD without a diagnosis of T1DM (group 3) no significant difference was observed between groups regarding DQ2 alleles. However, individuals in group 2 had a significantly higher frequency of DQ8 allele (Table 3). Table 3. Presence of DQ2.5 and DQ8 alleles among individuals with T1DM and CD and among those with CD and without T1DM Group 2 T1DM with CD n (%)

Group 3 CD without T1DM n (%)

DQXa

6 (18.8)

24 (36.4)

DQ2.5

26 (81.2)

42 (63.6)

DQX

17 (53.1)

53 (80.3%)

DQ8

15 (46.9)

13 (19.7%)

HLA

0.102 0.008

n: number of analyzed individuals. a DQX: non-DQ2.5 or DQ8 haplotypes.

Table 4 shows the comparison between individuals with T1DM without CD (Group 1) and those with a diagnosis of CD, regardless of the presence of T1DM (groups 2 and 3). It was observed that the presence of the DQ2.5 allele is more frequent in patients with CD, while the DQ8 allele is more frequent in the group that has T1DM. Table 4. Presence of DQ2.5 and DQ8 alleles among individuals with T1DM without CD and among those with CD irrespective of the presence of T1DM HLA

Group 1 T1DM without CD n (%)

Groups 2 and 3 CD n (%)

DQXa

120 (45.5)

30 (30.6)

DQ2.5

144 (54.5)

68 (69.4)

DQXa

119 (45.1)

70 (71.4)

DQ8

145 (54.9)

28 (28.6)

n: number of analyzed individuals. a DQX: non-DQ2.5 or DQ8 haplotypes. Arch Endocrinol Metab. 2017;61/6

0.011 < 0.001

DISCUSSION The performance of genotyping using the Tag SNP technique has been validated in individuals with T1DM and CD, being considered effective and less costly, allowing the performance of population screening studies (9,21,22). Our study aimed to perform the genotyping of HLA DQ2.5 and DQ8 using the Tag SNP technique in individuals with T1DM and individuals with CD in a population of southern Brazil, which confirmed the high negative predictive value of the test in the group with T1DM. Megiorni and Pizzuti (23), in a review on the practical implications of identifying HLA risk alleles in individuals with CD, reaffirmed the importance of the negative tests as a more significant value. A study carried out in Italy in 1005 patients with CD, used the Tag SNP technique to genotype haplotypes DQ2.5, DQ8, DQ2.2 and DQ7, comparing it with the traditional technique by PCR-SSP and obtained high sensitivity and specificity, recommending it to be used in population screenings and suggesting studies in other population groups (24). A more recent study carried out in Brazil with DNA extracted from 329 umbilical cord blood samples, compared the two techniques, using the same haplotypes of the present study and concluded that the results obtained by realtime PCR are highly reliable, with no discordant results when compared to the PCR-SSP technique (25). Megiorni and cols. (26) established a risk gradient for CD based on HLA DQ and also defined the combination of haplotypes DQ2 and DQ8 as higher risk. Gutierrez-Achury and cols. (27) performed an extensive genetic study in patients from the United States, England, and the Netherlands with concomitant T1DM and CD, with T1DM without CD and with CD without T1DM and concluded that genotype DQ2.5/DQ8 shows an increased risk of concomitant disease. We found that 27% of individuals with T1DM (Group 1) and 31% of individuals with T1DM and CD (Group 2) had this combination of haplotypes, whereas in the group of individuals with CD without T1DM (Group 3), the concomitant haplotypes occurred in 7.6%, with this group showing a higher frequency of individuals with only DQ2.5 alleles. Individuals with T1DM, at any age, have a higher risk of CD, but because both diseases are associated with HLA DQ genotypes, the search for HLA-DQ2 and DQ8 is not always useful for identifying predisposed 553

(group 2). The presence of DQ8 alleles occurred in 160 (54.1%) individuals with T1DM, and there was no difference between the groups with or without CD (Table 2).

DQ2.5 and DQ8 with a lower cost technique

groups (28). The difference in frequency of DQ8 alleles disclosed in Table 4, confirms this difficulty in finding risk alleles for CD in the three groups. We found that the alleles for DQ2.5 were more prevalent among individuals with CD; however, the DQ8 alleles are more frequent in individuals with T1DM, which does not allow differentiating between the groups with and without CD. The search for alternative techniques is based on the fact that the high negative predictive value of the test will help to reduce costs with the periodic investigation of patients who do not have the alleles, but also to reassure the patients or their relatives about the prospect of having the concomitant diseases. In Brazil, in 2009, the Federal Government (Official Gazette) published a statement recommending that T1DM patients should undergo CD screening through TTG-IgA at the start of T1DM and every year regardless of the clinical manifestations (29). When considering the costbenefits of the genotype assessment of individuals with T1DM, we observed that the average cost of serological screening in our country is US$ 6 per person and should be performed annually, while the genotype assessment for each allele with the described technique costs US$ 5 per patient and will be performed on only one occasion. Although we observed a high negative predictive value among individuals with T1DM and CD, it was observed that 24% did not show the haplotypes for DQ2.5 and DQ8 in the group with CD without T1DM. Karell and cols. (30) evaluated populations of different European countries and screened for the haplotypes DQA1*05 and DQB1*02 for DQ2 and DQA1*03 DQB1*0302 for DQ8 and found a heterogeneous distribution, with a higher prevalence of negative DQ2 and DQ8 in Italy when compared to France, Finland and England. Koskinen and cols. (31) evaluated risk haplotypes for CD in 3 countries and identified in the group of Italian patients the presence of alleles for DQ2.2 (DQB1*0202 and DQA1*0201) and DQ7 (DQB1*0301) by 27% and 18% of the population, respectively. Kotze and cols. (32), in a study carried out in southern Brazil, found a frequency of 8.9% of individuals with CD with DQ2 and/or DQ8 negative alleles, and warned for the high degree of miscegenation in the country. Considering this study was carried out in a region of Brazil with high prevalence of Italian immigrants and taking into account the high rate of European ancestry seen in the south and southeast regions of the country (11), the possibility of a higher incidence of other DQ 554

risk alleles, different from DQ2.5, should be considered. The search for DQA1*0501 and DQB1*0201 alleles allowed the identification of DQ2.5 individuals, but did not identify DQ2.2 and DQ7 individuals. In conclusion, the search for DQ2.5 and DQ8 alleles using the Tag-SNP technique allowed us to obtain a high negative predictive value for the diagnosis of CD in a population with T1DM, similar to what is described in the literature using the conventional technique. There was a high frequency of DQ8 allele in individuals with T1DM when compared to individuals with CD without T1DM. However, the presence of this allele in individuals with T1DM does not indicate an increased risk of CD in the assessed population. Considering the high degree of miscegenation of the Brazilian population, we recommend the inclusion of the search for DQ2.2 and DQ7 alleles in the southern and southeastern regions of Brazil, to increase the sensitivity and specificity of CD risk investigation. Acknowledgments: to the patients and their family members for participating in the study. To the Postgraduate Program in Child and Adolescent Health – Universidade Federal do Rio Grande do Sul (UFRGS). To Universidade de Santa Cruz do Sul (UNISC). To the physicians and employees of Instituto da Criança com Diabetes (ICD). To Associação dos Celíacos do Brasil, RS (ACELBRA-RS). To Endocrimeta Laboratório de Análises Clínicas. Funding sources: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Postgraduate Program in Child and Adolescent Health – Universidade Federal do Rio Grande do Sul (PPG-SCA/UFRGS) and Universidade de Santa Cruz do Sul. Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Schuppan D, Junker Y, Barisani D. Celiac disease: from pathogenesis to novel therapies. Gastroenterology. 2009;137(6):1912-33. 2. Rubio-Tapia A, Murray JA. Celiac disease. Curr Opin Gastroenterol. 2010;26(2):116-22. 3. Liu E, Lee HS, Aronsson CA, Hagopian WA, Koletzko S, Rewers MJ, et al Risk of pediatric celiac disease according to HLA haplotype and country. N Engl J Med. 2014;371(1):42-9. 4. Gutierrez-Achury J, Coutinho de Almeida R, Wijmenga C. Shared genetics in coeliac disease and other immune-mediated diseases. J Intern Med. 2011;269(6):591-603. 5. Diniz-Santos D, Machado A, Silva L. Doença Celíaca. In: Carvalho E de, Silva LR, Ferreira CT, editors. Gastroenterologia e Nutrição em Pediatria. São Paulo: Manole; 2012. p. 359-403. 6. Pham-Short A, Donaghue KC, Ambler G, Phelan H, Twigg S, Craig ME. Screening for Celiac Disease in Type 1 Diabetes: A Systematic Review. Pediatrics. 2015;136(1):e170-6. 7. Araújo J, da Silva GA, de Melo FM. Serum prevalence of celiac disease in children and adolescents with type 1 diabetes mellitus. J Pediatr (Rio J). 2006;82(3):210-4. Arch Endocrinol Metab. 2017;61/6

DQ2.5 and DQ8 with a lower cost technique

8. Diniz-Santos DR. Doença celíaca em crianças e adolescentes com diabetes mellitus tipo1 Salvador, Bahia. Tese (Doutorado). Programa de Pós-graduação em Medicina e Saúde. Universidade Federal da Bahia; 2010. 9. Monsuur AJ, de Bakker PIW, Zhernakova A, Pinto D, Verduijn W, Romanos J, et al. Effective detection of human leukocyte antigen risk alleles in celiac disease using tag single nucleotide polymorphisms. PLoS One. 2008;3(5):e2270. 10. Brandao LC, Vatta S, Guimaraes R, Segat L, Araujo J, De Lima Filho JL, et al. Rapid genetic screening for major human leukocyte antigen risk haplotypes in patients with type 1 diabetes from Northeastern Brazil. Hum Immunol. 2010;71(3):277-80. 11. Kehdy FSG, Gouveia MH, Machado M, Magalhães WCS, Horimoto AR, Horta BL, et al.; Brazilian EPIGEN Project Consortium. Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations. Proc Natl Acad Sci U S A. 2015;112(28):8696-701. 12. Tsouka A, Mahmud FH, Marcon MA. Celiac disease alone and associated with type 1 diabetes mellitus. J Pediatr Gastroenterol Nutr. 2015;61(3):297-302. 13. Troncone R, Discepolo V. Celiac disease and autoimmunity. J Pediatr Gastroenterol Nutr. 2014;59 Suppl 1:S9-S11.

21. Lavant EH, Agardh DJ, Nilsson A, Carlson JA. A new PCR-SSP method for HLA DR-DQ risk assessment for celiac disease. Clin Chim Acta. 2011;412(9-10):782-4. 22. de Bakker PIW, McVean G, Sabeti PC, Miretti MM, Green T, Marchini J, et al. A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat Genet. 2006;38(10):1166-72. 23. Megiorni F, Pizzuti A. HLA-DQA1 and HLA-DQB1 in Celiac disease predisposition: practical implications of the HLA molecular typing. J Biomed Sci. 2012;19(1):88. 24. Vatta S, Fabris A, Segat L, Not T, Crovella S. Tag-single nucleotide polymorphism-based human leukocyte antigen genotyping in celiac disease patients from northeastern Italy. Hum Immunol. 2011;72(6):499-502. 25. Selleski N, Almeida LM, Almeida FC, Gandolfi L, Pratesi R, Nóbrega YK. Simplifying celiac disease predisposing HLA-DQ alleles determination by the real time PCR method. Arq Gastroenterol. 2015;52(2):143-6. 26. Megiorni F, Mora B, Bonamico M, Barbato M, Nenna R, Maiella G, et al. HLA-DQ and risk gradient for celiac disease. Hum Immunol. 2009;70(1):55-9.

14. Romanos J, Rosen A, Kumar V, Trynka G, Franke L, Szperl A, et al. Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants. Gut. 2014;63(3):415-22.

27. Gutierrez-Achury J, Romanos J, Bakker SF, Kumar V, de Haas EC, Trynka G, et al. Contrasting the Genetic Background of Type 1 Diabetes and Celiac Disease Autoimmunity. Diabetes Care. 2015;38 Suppl 2:S37-44.

15. Wijmenga C, Gutierrez-Achury J. Celiac disease genetics: past, present and future challenges. J Pediatr Gastroenterol Nutr. 2014;59 Suppl 1:S4-7.

28. Korponay-Szabó IR, Troncone R, Discepolo V. Adaptive diagnosis of coeliac disease. Best Pract Res Clin Gastroenterol. 2015;29(3):381-98.

16. Sud S, Marcon M, Assor E, Palmert M, Daneman D, Mahmud F. Celiac disease and pediatric type 1 diabetes: diagnostic and treatment dilemmas. Int J Pediatr Endocrinol. 2010;2010:161285.

29. Diário Oficial da União. Poder Executivo. Brasília, Portaria Oficial da União; Poder Executivo, Brasília, DF, 18 de Setembro. Protocolo Clínico e Diretrizes Terapêuticas da Doença Celíaca. 2009. p. Seção I: 79-81.

17. Weiss B, Pinhas-Hamiel O. Celiac disease and diabetes: when to test and treat. J Pediatr Gastroenterol Nutr. 2017 Feb;64(2):175-9. 18. Marsh MN. Gluten, major histocompatibility complex, and the small intestine. A molecular and immunobiologic approach to the spectrum of gluten sensitivity (‘celiac sprue’). Gastroenterology. 1992;102(1):330-54. 19. Oberhuber G, Granditsch G, Vogelsang H. The histopatology of CD: time for standardized report scheme for pathologists. Eur J Gastroenterol Hepatol. 1999;11(10):1185-94.

31. Koskinen L, Romanos J, Kaukinen K, Mustalahti K, KorponaySzabo I, Barisani D, et al. Cost-effective HLA typing with tagging SNPs predicts celiac disease risk haplotypes in the Finnish, Hungarian, and Italian populations. Immunogenetics. Immunogenetics. 2009;61(4):247-56. 32. Kotze LM da S, Nisihara R, Utiyama SR da R, Kotze LR. Letters to the Editor. Rev Esp Enfermedades Dig. 2014;106(8):561-2.

20. Lahiri DK, Nurnberger JI. A rapid non-enzymatic method for the preparation of HMW DNA from blood for RFLP studies. Nucleic Acids Res. 1991;19(19):5444.

30. Karell K, Louka AS, Moodie SJ, Ascher H, Clot F, Greco L, et al. Hla types in celiac disease patients not carrying the DQA1*05DQB1*02 (DQ2) heterodimer: results from the european genetics cluster on celiac disease. Hum Immunol. 2003;64(4):469-77.

Arch Endocrinol Metab. 2017;61/6

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original article

11β-hydroxysteroid dehydrogenase type-II activity is affected by grapefruit juice and intense muscular work Christopher Kargl1, Mohammad Arshad1, Fahad Salman1, Regina C. Schurman1, Pedro Del Corral1

ABSTRACT Department of Biological Sciences, Benedictine University, Lisle, IL

Correspondence to: Pedro Del Corral Department of Biological Sciences, Benedictine University 5700 College Rd Lisle, IL 60532 pdelcorral@ben.edu Received on Nov/17/2016 Accepted on Jun/12/2017 DOI: 10.1590/2359-3997000000296

Objective: The enzymatic activity of 11β-hydroxysteroid dehydrogenase-2 (11β-HSD2) is key to protecting mineral corticoid receptors from cortisol and has been implicated in blood pressure regulation. Grapefruit juice (GFJ) and acidity are thought to inhibit this enzyme in vitro. This study examines the effect of GFJ and intense exercise on 11β-HSD2 enzyme activity in vivo. Subjects and methods: Eighteen subjects ingested GFJ or apple juice (CON) on separate days prior to reporting to the laboratory in a randomized order. Saliva (Sal) samples were obtained at baseline, 15 and 45 minutes post-treadmill stress test; Sal cortisone (E) and cortisol (F) levels were determined, and the Sal cortisone:cortisol (E:F) ratio was used as an index of 11β-HSD2 enzyme activity at rest and after intense muscular work. Results: GFJ treatment decreased baseline 11β-HSD2 enzyme activity (44%) and Sal-E (28%) compared to CON (both, p < 0.05). Sal-E (r = 0.61, p < 0.05) and Sal-F (r = 0.66, p < 0.05) were correlated with diastolic blood pressure (DBP) in GFJ-treated individuals. Treadmill stress significantly increased Sal-E and Sal-F but did not alter 11β-HSD2 enzyme activity regardless of treatment. When treatments were examined separately, CON 11β-HSD2 enzyme activity decreased by 36% (p < 0.05) from baseline to 15 post-treadmill exercise. Conclusion: Our findings suggest that GFJ and intense muscular work decrease 11β-HSD-2 activity independently, and no additive effect was noted. The association between DBP and the levels of Sal-F and Sal-E during the GFJ trial should be interpreted cautiously and warrants further investigation. Arch Endocrinol Metab. 2017;61(6):556-61 Keywords Cortisol; cortisone; exertion; stress; citrus flavonoids

INTRODUCTION

he enzyme 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) is expressed in mineralocorticoid target tissues such as the kidney, colon, salivary glands, and placenta. This enzyme oxidizes cortisol (F) into inactive cortisone (E), thereby rendering the mineralocorticoid receptor-hormone complex inactive (1). The enzymatic activity of 11β-HSD2 can be estimated using the ratios of these hormones in urine (2-5) and saliva (Sal) fluids (4,6,7). Subtle deficiencies in 11β-HSD2 activity have been reported in subsets of hypertensives and normotensives (8,9), whereas more severe hypertension and hypokalemia are observed in cases of substantial or complete loss of 11β-HSD2 activity (1,9). Preliminary in vitro (10) and in vivo (5,10) studies suggest that grapefruit juice (GFJ) transiently decreases 11β-HSD2 enzyme activity, and this has been associated with high levels of bioflavonoids, such as naringin and

556

its aglycone, naringenin. In vivo pilot studies have been limited by small sample sizes of 1-6 research subjects and have used large (1-2 L/day) amounts of GFJ (5,10). A study using a larger sample size and a moderate (0.7 L/ day) GFJ intake along with a more convenient matrix, such as Sal sampling, to assess 11β-HSD2 activity is warranted. It has been reported that acidosis decreases 11β-HSD2 activity in the human placenta and in rodent inner medullary-collecting duct cells (11,12). Intense muscular work is associated with transient perturbations in the acid-base balance, in which the pH can drop below 7.0, and is related to lactic acidemia (≥ 15 mmol/L) (13). It is conceivable that intense muscular work may decrease 11β-HSD2 enzyme activity. Taken together, little is known about the regulation of 11β-HSD2 enzyme activity in vivo in humans. Given the importance of this enzyme on blood pressure regulation in adult (8,9) and pediatric (3) populations, Arch Endocrinol Metab. 2017;61/6

Flavonoids and stress affect 11β-HSD-2

further studies are warranted. This study had the following aims: 1) to examine the effect of moderate GFJ ingestion compared to a control treatment of apple juice (CON) on sal 11β-HSD2 enzyme activity in a group of healthy volunteers under resting conditions; and 2) to examine the effect of intense muscular work, which is known to induce lactic acidemia in response to 11β-HSD2 enzyme activity in tests using GFJ vs CON treatments. Based on a preliminary analysis of the literature, we hypothesized that GFJ intake will be decreased more by sal 11β-HSD2 activity than by the CON treatment and that intense muscular work will inhibit 11β-HSD2 activity to a greater extent after GFJ intake than in the CON.

SUBJECTS AND METHODS Subjects Eighteen research volunteers were recruited to participate in this study. The subject characteristics are presented in Table 1. Informed consent was obtained and approved by the Institutional Review Board

of Benedictine University in accordance with the international code of ethics (Declaration of Helsinki). Criteria for participation included ages between 18 and 55, BMI between 18 and 30, non-smokers, no hypertension/diabetes, and no use of oral or topical glucocorticoids for the last 3 months. Exclusion criteria included metabolic, endocrine, renal, cardiopulmonary, or orthopedic diseases that prevent intense exercise. Subjects taking medication or dietary supplements known to alter F metabolism or taking drugs that could be altered by GFJ were disqualified from study participation.

Study design This study used an open label, randomized, crossover assignment (ClinicalTrials.gov identifier: NCT02187328). Each subject reported to the laboratory on three separate days. The first visit was an orientation visit in which the subjects were walked through the protocol step-by-step. There were two experimental visits (Figure 1) that were preceded by a dietary intervention. In one of the experimental

Table 1. Subjects physical and cardiovascular characteristics Number of subjects

18 (15 males & 3 females)

Age (years)

31.1 ± 2.7

BMI

24.5 ± 0.9

% body fat

18.5 ± 1.8

Treatments Grapefruit Juice (GFJ)

Apple (CON)

Systolic blood pressure (mmHg)

124 ± 2

Diastolic blood pressure (mmHg)

79 ± 2

78 ± 1

Mean arterial pressure (mmHg)

94 ± 2

Heart rate (beats/min)

63 ± 3

65 ± 3

Laboratory visit flow chart Orientation Visit: Randomized crossover assignment order, 2 experimental visits: 1 under CON juice, and 1 under GFJ, on different days. Subjects left the laboratory with their juice bottles for experimenta-visit-1.The two experimental visits follow similar sequence (see below) Laboratory visit data collection

0700-0900h Juice intake

1500 or 1600h Juice intake

Arrival at 1800 or 1900h (3h after 2nd juice intake) Baseline BP

Baseline

post-Ex-15 post-Ex-45 Salivary sampling

Figure 1. Experimental design flow-chart for visits 1 and 2. Grapefruit juice (GFJ). Each GFJ bottle contained 375 mL of 100% Pink Pure Grapefruit Juice, 1 ingested after breakfast, and 1 ingested mid-afternoon, 3h before their scheduled visit. For CON, each bottle contained 300 mL of 100% apple juice from concentrate, see Study Design, under Material & Methods for additional information. Arch Endocrinol Metab. 2017;61/6

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Treadmill Stress-Test

Flavonoids and stress affect 11β-HSD-2

visits, subjects ingested GFJ (375 mL of Pink Pure Grapefruit Juice; Lakewood Farms, Miami, FL) after breakfast (0700 h – 0900 h), at mid-afternoon, and 3h before their scheduled visit. The GFJ was made from organic grapefruits that were fresh-pressed (not from concentrate) and unsweetened. In the other experimental visit (5 to 20 days apart), subjects performed the CON intervention (300 mL of 100% Apple juice from concentrate; Wal-Mart Stores, Inc., Bentonville, AR) under the same experimental conditions to equalize the carbohydrate content between the trials but keep differences in naringin content. Subjects reported to the laboratory at 1800 h or 1900 h (~5-5.5 h after lunch). Upon arrival, the subjects rested quietly for 5 minutes prior to obtaining supine blood pressure (cuff size 17-42 cm) and heart rate measurements using a validated oscillometric automated device (Omron BP791IT; OMRON Healthcare, INC. Lake Forest, IL 60045). Two treadmill stress tests were conducted to determine the maximal oxygen uptake (VO2 max). The subject breathed through a non-rebreathing valve while wearing a facemask so that expired gases were analyzed continuously by previously calibrated O2 and CO2 analyzers (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects jogged/ran at 7.2-12.0 km.h-1 with a 0% grade (depending on fitness level) for 3 min, and the treadmill speed was subsequently increased by 1.6 km.h-1 for the next two 3 min stages. Thereafter, treadmill speed was held constant and the grade was increased by 3% every 3 minutes until volitional exhaustion was achieved, after which treadmill speed and grade were reduced to 4.8 km.h-1 and 0% for 1 minute before the test was ended and a 10 µL blood sample was collected to measure blood lactate levels (Lactate Plus analyzer; Nova Biomedical, Waltham, MA).

Both assays used 50 µL samples, and the assays were performed in duplicate per the manufacturer’s instructions. The manufacturer reported that F antibody cross-reactivity for E was 1.2% and the sensitivity of the assay was 0.0477 nmol/L. For the E antibody, cross reactivity for F and corticosterone was < 0.1%, and the sensitivity of the assay was 0.0293 nmol/L. The intra-assay coefficients of variation were 5.6% (F) and 5.9% (E). The enzymatic activity of 11β-HSD2 was calculated as the ratio of E/F (2-6). Tests for equality of variances were run, and ANOVA and paired t-tests were used when appropriate to examine differences in E, F, and 11β-HSD2 enzyme activity. Paired t-tests were conducted to test the basic vital statistics between trials, and Pearson correlations were used to examine relationships between saliva hormones (and 11β-HSD2 enzyme activity) and blood pressure parameters. Statistical significance was set at α = 0.05. The results are presented as means ± standard error (SE).

RESULTS Blood pressure and heart rate There were no significant differences in resting blood pressure and heart rate between the GFJ and CON treatment groups (Table 1).

Stress test Subject VO2max (48.1 ± 2.1; 48.9 ± 2.1 ml.kg.min-1), maximal heart rate (185 ± 3; 187 ± 3 beats.min-1), and post-exercise lactate levels (10.8 ± 0.8, 11.5 ± 0.4 mmol/L) were similar between the CON and GFJ trials, respectively.

Saliva glucocorticoids

Sample collection and analysis For each experimental visit, samples were collected at baseline and 15 min (Post-15) and 45 min (Post-45) post-treadmill stress test. Subjects drank 100-200 mL of water at least 10 min prior to collection of the baseline sample, and immediately after, the Post-15 sample was collected. Sal samples were collected using a Salivette device (Sarstedt, Newton, NC) as described by the manufacturer, and then centrifuged and stored at -20 °C until analyzed. Sal-F and Sal-E were analyzed using an enzyme immunoassay and chemiluminescence with commercially available kits (Arbor Assays, Ann Arbor, MI). 558

Sal-E increased over time (p < 0.05) from baseline in GFJ and CON (Figure 2A). Similarly, the Sal-F concentration increased over time from baseline (p < 0.05) in the GFJ and CON trials (Figure 2B). For both hormones, within each trial, the results at the Post-15 and Post-45 time points differed from their corresponding baselines. However, no differences were found between the GFJ and CON trials (p > 0.05). When the isolated effect of treatment on the baseline hormone concentration was examined, a significant difference was noted for Sal-E (p < 0.05; one-tail, paired t-test). Figure 3 shows the E:F ratio at baseline and Arch Endocrinol Metab. 2017;61/6

Flavonoids and stress affect 11β-HSD-2

Saliva-E (nmol/L)

GFJ

* 20

10 ¥ 0

8 7

Saliva-F (nmol/L)

6 5

4 *

3 2 1 0

Baseline

Post-15

Post-45

Figure 2. (A) Saliva-E; (B) Saliva-F at baseline, 15 min post and 45 min post treadmill stress test. Close diamonds GFJ; Open squares CON. * Denotes significant difference (p < 0.05) compare to baseline; ¥ denotes significant difference (p < 0.05) between GFJ and CON at baseline.

Saliva-E:F ratio

12 10

8 4

2 0

r = 0.77 p < 0.05

0.5

B 15 10 5 0 -5 0 -10 -15 -20 -25 -30 -35 -40

1.5

2.5 3 3.5 Saliva-F (nmol/L)

4.5

Saliva-E:F CON (arbitrary units) 10

r = - 0.93 p < 0.05

Figure 4. (A) Correlation between Saliva-F and the ∆-DBP between trials, r = 0.77, p < 0.05; (B) Correlation between baseline Saliva-E:F ratio during CON and ∆-Sal-E:F ratio between CON and GFJ (r = - 0.93, p < 0.05).

Correlations

Δ-Diastolic blood pressure (mmHg)

CON

A 30 25 20 15 10 5 0 -5 0 -10 -15 -20

Δ-Saliva-E:F (arbitrary units)

A 40

Baseline

Post-15

Post-45

Figure 3. Saliva E:F ratio at baseline and at 15 min post- and 45 min post-treadmill stress test. Closed diamonds = GFJ; Open squares = CON. ¥ denotes significant differences (p < 0.05) between GFJ and CON at baseline; * denotes significant differences (p < 0.05) compared to baseline for the CON treatment.

Sal-E (r = 0.613, p = 0.007) and Sal-F (r = 0.658, p = 0.003) were correlated with diastolic blood pressure (DBP) in GFJ but not in CON. When CON and GFJ samples were pooled (n = 36), Sal-E (r = 0.337, p = 0.045) and Sal-F (r = 0.333, p = 0.047) were correlated with DBP. Figure 4A shows that there was a significant correlation between Sal-F during the GFJ trial and the ∆-DBP between trials. Figure 4B shows a significant inverse correlation between the E:F ratio at baseline during CON and the ∆- for the E:F ratios (Sal E:F ratio CON - Sal E:F ratio GFJ).

during the post-treadmill stress test. Overall, ANOVA indicated that there was no statistically significant difference between GFJ and CON. However, when we examined the independent effect of treatment on the baseline E:F ratio, a statistically significant difference was found (p < 0.05), suggesting that GFJ inhibited baseline 11β-HSD2 enzyme activity. Similarly, when we examined the effect of intense muscular work during the CON trial, we found that the E:F ratio decreased from baseline to Post-15 (p < 0.05). Arch Endocrinol Metab. 2017;61/6

Our baseline CON values for the E:F ratio were similar to those previously reported by others (6). The short term GFJ treatment in the present study lowered enzyme activity by 44% (Figure 3), which compares favorably to the ~40% inhibition estimated from the urinary-E/F ratio obtained in a pilot study (10) and to the ~60% obtained in a patient presenting with edema and hypokalemia associated with the habitual oral intake of 1 liter/day of GFJ (5). Taken together, our findings suggest that Sal effluents are a cost-effective and convenient matrix (compared to plasma/urine) for 559

DISCUSSION

Flavonoids and stress affect 11β-HSD-2

probing alterations in 11β-HSD2 activity induced by GFJ ingestion. In our study, the decrease in enzyme activity induced by GFJ at baseline was mainly driven by a significant decrease (~30%) in Sal-E concentration. This is in line with a decrease in F oxidation to E, a reaction that is mediated by 11β-HSD2 (1,9,10) and likely inhibited by one or more flavonoids found in GFJ (naringenin, quercetin, hesperetin, and apigenin), all of which have been shown to inhibit 11β-HSD2 activity in vitro (10). Urine, Sal-F, and Sal-E are determined by plasma-free F & E fractions and 11β-HSD2 activity. The plasma-free F & E fractions are in turn determined by CBG. Given the short time (10 h) between the morning GFJ ingestion and Sal sample collection and the half-life of CBG (1-3 days), it is unlikely that the effects of GFJ on Sal-E and Sal-F are linked to changes in CBG concentration. Finally, GFJ is well known for its inhibitory effects on intestinal CYP3A4 enzyme activity, which leads to numerous drug interactions (14), and this enzyme is also involved in the metabolism of E and F to 6β-hydroxy-E and 6β-hydroxy-F, respectively. It has also been shown that GFJ decreases the urinary ratio of 6β-hydroxy-F to F in urine, possibly because CYP3A4 is inhibited by GFJ (15). CYP3A4 enzyme protein and mRNA expression has been reported in ductal and seromucous/serous acinar cells in parotid, submandibular, and labial salivary glands (16). Future studies examining E, F, 6β-hydroxy-E, and 6β-hydroxy-F levels and their respective ratios in salivary effluents are warranted. We hypothesized that intense muscular work would inhibit 11β-HSD2 activity and that this inhibition would be enhanced by GFJ intake. Our findings indicate that intense muscular work under CON inhibited 11β-HSD2 enzyme activity to the same extent as seen at baseline in the GFJ treatment group (Figure 2). On the other hand, intense muscular work under GFJ treatment did not inhibit enzyme activity beyond its corresponding baseline level. The decrease in the E:F ratio in the CON treatment could be due to decreases in physiological pH (11,12), and lactate has been reported to inhibit 11β-HSD2 activity in cultured intestinal cells (17). During intense muscular work, there is a transient perturbation in the acid-base balance that is related to lactic academia in which pH can drop below 7.0 (13), and this is associated with increased glycolytic flux and results in lactic acidemia with reciprocal and stoichiometric changes in bicarbonate. At exhaustion, our subjects reached a blood lactate 560

level of ~11 mmol/L, a level that is probably not high enough to attenuate 11β-HSD2 enzyme activity. Alternatively, it is also plausible that 11β-HSD2 activity may be inhibited by substrate saturation at high or rapid increases in F (4-fold in the present study), as has been suggested for renal 11β-HSD2 activity in patients with ectopic Cushing’s disease (4,5) or as a result of a combination of both inhibitory mechanisms. There is evidence suggesting a link between deficiencies in 11β-HSD2 activity and increased blood pressure in normotensive and hypertensive individuals (8,9), particularly in patients diagnosed with “apparent mineralcorticoid excess” (1,9). In our study, GFJ was associated with the inhibition of 11β-HSD2 enzyme activity but not with increased systolic or diastolic blood pressure compared to CON. Upon closer examination of the data, we found a moderate correlation between Sal-F (r = 0.658, p = 0.003) and diastolic blood pressure (DBP) and a slightly stronger correlation between Sal-F and ∆-DBP between the treatment groups (Figure 4A). The association between Sal-F and DBP agrees with the findings of other investigations (18). The significant inverse correlation (Figure 4B) between baseline E:F CON and ∆-E:F (baseline E:F CON – baseline E:F GFJ) suggests that individuals with the highest E:F ratio during the CON treatment were more sensitive to the inhibitory effects of GFJ. The present study was designed to examine the acute effects of GFJ and intense muscular exercise on the E:F ratios, not-the chronic effects of GFJ. Future studies should examine the effect of chronic GFJ ingestion on E:F ratios and blood pressure. While there is a case report linking high GFJ intake, the inhibition of 11β-HSD2 enzyme activity, and increases in high blood pressure (5), a randomized clinical trial suggested that chronic GFJ resulted in a borderline decrease in systolic blood pressure in overweight and obese men and women. That trial (19) did not report 11β-HSD2 enzyme activity surrogates, making it difficult to interpret their findings. Collectively, in the short-term, GFJ intake may decrease 11β-HSD2 enzyme activity and increase blood pressure in susceptible individuals, while sustained GFJ may have vasodilating effects that could negate the adverse effects of decreased 11β-HSD2 enzyme activity on blood pressure. Future studies should examine the long-term effects of regular grapefruit intake on 11β-HSD2 enzyme activity and blood pressure regulation in normotensive and hypertensive populations. Arch Endocrinol Metab. 2017;61/6

Flavonoids and stress affect 11β-HSD-2

In conclusion, our findings suggest that: 1) shortterm GFJ treatment inhibits 11β-HSD2 enzyme activity; 2) intense muscular work inhibits 11β-HSD2 enzyme activity under CON conditions to a similar extent as GFJ alone, and no additive/synergistic effect by intense muscular work and GFJ were noted; and 3) the associations between Sal-F and Sal-E levels and DBP during the GFJ trial should be interpreted cautiously and warrant further investigation. Acknowledgements: the authors would like to thank Dr. Lawrence Kamin for assistance in statistical analysis.

7. Vieira JG, Nakamura OH, Carvalho VM. Determination of cortisol and cortisone in human saliva by a liquid chromatographytandem mass spectrometry method. Arq Bras Endocrinol Metabol. 2014;58(8):844-50. 8. Campino C, Martinez-Aguayo A, Baudrand R, Carvajal CA, Aglony M, Garcia H, et al. Age-related changes in 11β-hydroxysteroid dehydrogenase type 2 activity in normotensive subjects. Am J Hypertens. 2013;26(4):481-7. 9. Ferrari P. The role of 11β-hydroxysteroid dehydrogenase type 2 in human hypertension. Biochim Biophys Acta. 2010;1802(12): 1178-87. 10. LeeYS, Lorenzo BJ, KoufisT, Reidenberg MM. 1996 Grapefruit juice and its flavonoids inhibit 11 beta-hydroxysteroid dehydrogenase. Clin Pharmacol Ther. 1996;59(1):62-71.

Funding: provided by Benedictine University Summer Research Program.

11. Brown RW, Chapman KE, Edwards CR, Seckl JR. Human placental 11 beta-hydroxysteroid dehydrogenase: evidence for and partial purification of a distinct NAD-dependent isoform. Endocrinology. 1993;132(6):2614-21.

Disclosure: no potential conflict of interest relevant to this article was reported.

12. Nolan PJ, Knepper MA, Packer RK. Inhibition of IMCD 11 betahydroxysteroid dehydrogenase type 2 by low pH and acute acid loading. J Am Soc Nephrol. 1997;8(4):530-4.

REFERENCES 1. Quinkler M, Stewart PM. Hypertension and the cortisol-cortisone shuttle. J Clin Endocrinol Metab. 2003;88(6):2384-92. 2. Best R, Walker BR. Additional value of measurement of urinary cortisone and unconjugated cortisol metabolites in assessing the activity of 11 beta-hydroxysteroid dehydrogenase in vivo. Clin Endocrinol (Oxf). 1997;47(2):231-6. 3. Krupp D, Shi L, Maser-Gluth C, Pietzarka M, Remer T. 11β-Hydroxysteroid dehydrogenase type 2 and dietary acid load are independently associated with blood pressure in healthy children and adolescents. Am J Clin Nutr. 2013;97(3):612-20. 4. Morineau G, Boudi A, Barka A, Gourmelen M, Degeilh F, et al. Radioimmunoassay of cortisone in serum, urine, and saliva to assess the status of the cortisol-cortisone shuttle. Clin Chem. 1997;43(8 Pt1):1397-407. 5. Palermo M, Armanini D, Delitala G. Grapefruit juice inhibits 11beta-hydroxysteroid dehydrogenase in vivo, in man. Clin Endocrinol (Oxf). 2003;59(1):143-44.

14. Bailey DG, Malcolm J, Arnold O, Spence JD. Grapefruit juice-drug interactions. Br J Clin Pharmacol. 1998;46(2):101-10. 15. Xiao YJ, Hu M, Tomlinson B. Effects of grapefruit juice on cortisol metabolism in healthy male Chinese subjects. Food Chem Toxicol. 2014;74:85-90. 16. Kragelund C, Hansen C, Torpet LA, Nauntofte B, Brøsen K, Pedersen AM, et al. Expression of two drug-metabolizing cytochrome P450-enzymes in human salivary glands. Oral Dis. 2008;14(6):533-40. 17. Andrieu T, Fustier P, Alikhani-Koupaei R, Ignatova ID, Guettinger A, Frey FJ, et al. Insulin, CCAAT/enhancer-binding proteins and lactate regulate the human 11β-hydroxysteroid dehydrogenase type 2 gene expression in colon cancer cell lines. PLoS ONE. 2014;9:e105354. 18. Rosmond R, Dallman MF, Björntorp P. Stress-related cortisol secretion in men: relationships with abdominal obesity and endocrine, metabolic and hemodynamic abnormalities. J Clin Endocrinol Metab. 1998;83(6):1853-9. 19. Dow CA, Going SB, Chow HH, Patil BS, Thomson CA. The effects of daily consumption of grapefruit on body weight, lipids, and blood pressure in healthy, overweight adults. Metabolism. 2012;61(7):1026-35.

6. Lee S, Kwon S, Shin HJ, Lim HS, Singh RJ, Lee KR, et al. Simultaneous quantitative analysis of salivary cortisol and cortisone in Korean adults using LC-MS/MS. BMB Rep. 2010;43(7):506-11.

13. Nielsen HB. Arterial desaturation during exercise in man: implication for O2 uptake and work capacity. Scand J Med Sci Sports. 2003;13(6):339-58.

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original article

Is fibroblast growth factor 23 a new cardiovascular risk marker in gestational diabetes? Muhammed Kizilgul1,2, Seyfullah Kan1, Selvihan Beysel1, Mahmut Apaydin1, Ozgur Ozcelik1, Mustafa Caliskan1, Mustafa Ozbek1, Seyda Ozdemir3, Erman Cakal1

Department of Endocrinology and Metabolism, Diskapi Teaching and Research Hospital, Ankara, Turkey 2 Schulze Diabetes Institute, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA 3 Department of Biochemistry, Diskapi Teaching and Research Hospital, Ankara, Turkey 1

Correspondence to: Muhammed Kizilgul Schulze Diabetes Institute MMC 195, 420 Delaware Street S.E. 55455 – Minneapolis, MN mkizilgu@umn.edu Received on June/28/16 Accepted on Apr/6/2017 DOI: 10.1590/2359-3997000000287

ABSTRACT Objective: This study was designed to compare the serum levels of fibroblast growth factor 23 (FGF23) among patients with gestational diabetes mellitus (GDM) and healthy pregnant women, and to evaluate the association between hormonal and metabolic parameters. Subjects and methods: A total of 82 pregnant women were consecutively enrolled in the study. Of these, 46 were diagnosed as having GDM; the remaining 36 healthy pregnant women served as controls in a cross-sectional study design. The womens’ ages ranged from 22 to 38 years and gestational ages, from 24 to 28 weeks. Serum samples were analyzed for FGF23 levels using an enzyme-linked immunosorbent assay. Results: Serum FGF23 levels were increased in patients with GDM compared with controls (median, 65.3 for patients with GDM vs. 36.6 ng/mL for healthy controls; p = 0.019). Mean fasting glucose (105.6 ± 7.4 vs. 70.2 ± 7.2 mg/dL, p < 0.001), HbA1c (5.6 ± 0.5 vs. 4.9 ± 0.5%, p < 0.001), insulin (median, 11.1 vs. 8.7 µIU/mL, p = 0.006) and HOMA-IR (3.0 (1.8) vs 1.4 (0.6), p < 0.001) levels were significantly higher in patients with GDM than in controls. Serum FGF23 level was positively correlated with body mass index (r2 = 0.346, p < 0.05), FPG (r2 = 0.264, p < 0.05), insulin (r2 = 0.388, p < 0.05), HOMA-IR (r2 = 0.384, p < 0.05). Conclusion: Serum FGF23 levels were higher in women with GDM compared with controls. The present findings suggest that FGF23 could be a useful marker of cardiovascular disease in GDM. Arch Endocrinol Metab. 2017;61(6):562-6 Keywords Gestational diabetes mellitus; fibroblast growth factor 23; cardiovascular risk

INTRODUCTION

estational diabetes mellitus (GDM) is characterized by glucose intolerance with onset or first recognition during pregnancy. GDM is one of the most commonly encountered complications of pregnancy, affecting 1.1-14.3% of pregnant women (1). GDM poses an increased risk of adverse maternal and fetal outcomes (2). The disease has significant health implications for both mother and child, including the development of type 2 diabetes mellitus (T2DM), obesity, and even cardiovascular disease later in life (3-5). Fibroblast growth factors (FGF) play a role in various biologic activities such as angiogenesis, mitogenesis, cell differentiation, cell migration, and the repair of injured tissue (6). Fibroblast growth factor 23 (FGF23) is a 32 kDa (251 amino acids) polypeptide with an Nterminal and C-terminal region that is released by osteocytes and osteoblasts in response to elevated serum phosphorus levels (7). FGF23 is a hormone involved in 562

phosphorus homeostasis, vitamin D metabolism, and bone mineralization. FGF23 is included in the group of hormones called FGFs, along with FGF19 and FGF21. The heparin-binding region of FGF23 differs from the topologic point of view, unlike many other FGFs that attach to heparin sulfate in the extracellular matrix exerting endocrine influences. Accordingly, FGF23 binds less vigorously to the extracellular matrix, hence it is more likely to enter the systemic circulation, which allows FGF23 to present paracrine and autocrine effects (8,9). Higher FGF23 levels, even in individuals without renal insufficiency, correspond to an increased risk of cardiovascular mortality in the normal population (10), and cardiovascular risk factors including vascular dysfunction, atherosclerosis, and left ventricular hypertrophy (11-13). GDM is associated with an increased risk of T2DM and cardiovascular disease. This study was designed to compare serum FGF23 levels of GDM women with those of non-GDM women, and to evaluate the association between hormonal and metabolic parameters. Arch Endocrinol Metab. 2017;61/6

Fibroblast growth factor 23 levels in gestational diabetes mellitus

Study population A total of 82 pregnant women who were followed up by the Endocrinology and Metabolism clinic of Ankara Diskapi Teaching and Research Hospital were consecutively enrolled in the study. Of these, 46 women were diagnosed as having GDM; the remaining 36 healthy pregnant women served as controls in a crosssectional study design. Ethics committee approval was obtained and written informed consent was given by the participants before the performance of any study procedures. The womens’ ages ranged from 22 to 38 years and gestational ages, from 24 to 28 weeks. Gestational age was estimated according to the date of the last menstrual period and simultaneous clinical evaluation (14). International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria were used for the diagnosis of GDM. A 2-hour, 75-gr oral glucose tolerance test (OGTT) was performed on all pregnant women, at 24 to 28 weeks of gestation. Glucose levels after fasting, and 1 and 2 h after glucose administration < 92 mg/dL, < 180 mg/dL, and < 153 mg/dL, respectively, were considered normal; if the glucose level was higher than the standard at any point, the patient was diagnosed as having GDM (15). Pregnant women with a thyroid disorder, infectious disease, hypertension, pre-eclampsia, hepatic or renal dysfunction, cardiac disease, metabolic bone disease, and fetal anomalies were excluded. The patients with GDM received several treatments (diet or diet plus insulin therapy) for maintaining blood glucose control. All blood samples were taken before starting treatment.

Clinical, biochemical, and hormone measurements Weight, height, systolic and diastolic blood pressure (BP) were measured. Body mass index (BMI) was calculated as weight (kg)/height (m)2. A venous blood sample was collected after an overnight fast of at least 8 hours. Samples were centrifuged within 30 to 45 minutes of collection and stored at -80°C. Insulin resistance was calculated using homeostasis model assessment (HOMA-IR) (16). Plasma glucose was determined using the glucose oxidase method (Siemens ADVIA 2400 Chemistry System, Siemens Medical Solutions Diagnostics Tarrytown, NY, USA). The level of total cholesterol was Arch Endocrinol Metab. 2017;61/6

determined using an enzymatic method (Siemens, ADVIA 2400 Chemistry System, Siemens Medical Solutions Diagnostics Tarrytown, NY, USA). Serum triglyceride was determined using the Trinder method without a blank serum (Siemens ADVIA 2400 Chemistry System, Tarrytown, NY, USA). Low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured using the elimination/catalase method (Siemens ADVIA 2400 Chemistry System, Tarrytown, NY, USA). High-sensitivity C-reactive protein (HsCRP) was determined using the latex-enhanced immunoturbidimetric method (Siemens ADVIA 2400 Chemistry System, Tarrytown, NY, USA). Thyroid-stimulating hormone (TSH) and insulin were measured using chemiluminescence immunoassays (Advia Centaur XP, Siemens Healthcare Diagnostics, Tarrytown, NY, USA).

Measurement of FGF23 Serum samples were analyzed for FGF23 levels using an enzyme-linked immunosorbent assay (ELISA) (Aviscera Bioscience, Santa Clara, USA). According to manufacturer’s indications, the calculated overall intraassay coefficient of variation (CV) was between 6.0 and 8.0% and the inter-assay CV was between 8.0 and 12.0%. The minimum detectable level of FGF23 was typical at ~15 pg/mL.

Statistical analyses Statistical analysis was performed using SPSS 18.0 (SPSS, Inc) software. Variables are presented as mean ± standard deviation (SD). Normality was tested using the Kolmogorov-Smirnov and Shapiro-Wilk W test. Student’s t-test was used for normally distributed continuous variables. The Mann-Whitney U test was used for continuous variables that were not normally distributed. Correlations were analyzed using Pearson and Spearman’s correlation. Statistical significance was defined as a p < 0.05.

RESULTS The mean age (30.2 ± 4.8 vs. 29 ± 4.0 years, p = 0.278) was similar between the groups. Women with GDM had a significant higher body weight (78.2 ± 11.4 vs. 67.8 ± 12.2 kg, p = 0.001) and BMI (30.8 ± 4.6 vs. 26.9 ± 5.4 kg/m2, p = 0.002) as compared with the 563

SUBJECTS AND METHODS

Fibroblast growth factor 23 levels in gestational diabetes mellitus

controls. The mean fasting glucose (105.6 ± 7.4 vs. 70.2 ± 7.2 mg/dL, p < 0.001), glycated hemoglobin (HbA1c) (5.6 ± 0.5 vs. 4.9 ± 0.5%, p < 0.001), insulin (11.1 (6.8) vs. 8.7 (2.6) µIU/mL, p = 0.006), and HOMA-IR (3.0 (1.8) vs. 1.4 (0.6), p < 0.001) levels were significantly higher in women with GDM than in controls. The mean serum FGF23 level (65.3 (213.5) vs. 36.6 (50.3) ng/mL, p = 0.019) was significantly higher in women with GDM as compared with controls (Figure 1). Clinical and biochemical characteristics of the women with GDM and controls are shown in Table 1. There were no significant differences between women with GDM and controls in terms of gestational weeks, 70 60

FGF23 levels

50 40 30

height, phosphate and 25-OH Vitamin D levels (p > 0.05). The serum fasting glucose level was positively correlated with age (r = 0.365, p = 0.001), BMI (r = 0.295, p = 0,007), HbA1c (r = 0.564, p = 0.001), insulin (r = 0.327, p = 0.007), and HOMA-IR (r = 0.234, p = 0.058). Serum FGF23 level was positively correlated with BMI (r2 = 0.346, p < 0.05), FPG (r2 = 0.264, p < 0.05), insulin (r2 = 0.388, p < 0.05), and HOMA-IR (r2 = 0.384, p < 0.05) (Table 2). Table 2. The correlation between FGF-23 levels and clinical, biochemical and hormonal parameters in PCOS group GDM group

Control group

Age

-0.003

0.073

Gestational age

-0.016

0.305

Height

0.206

0.012

Weight

0.012

0.037

BMI

0.346*

0.033

FPG

0.264*

-0.119

HbA1c

0.106

0.265

Insulin

0.388*

-0.210

HOMA-IR

0.384*

-0.284

* p < 0.05.

10 0

GDM

Control

Figure 1. Plasma levels of FGF23 in the study groups.

Table 1. The clinical and biochemical characteristics of the women with gestational diabetics and controls Parameters

Control group (n = 40)

Age

30.9

5.6

28.0

4.6

0.023

Gestational age (week)

25.9

1.6

26.4

1.5

0.175

Height

159.5

5.5

159.1

4.6

0.778

Weight

78.2

11.4

67.8

12.2

< 0.001

BMI

30.8

4.6

26.9

5.4

0.002

FPG

105.6

7.4

70.2

7.2

< 0.001

5.6

0.5

4.9

0.3

< 0.001

GDM group (n = 40)

Insulin Phosphate FGF23

11.1 (6.8)

8.7 (2.6)

0.006

3.1 ± 0.5

3.2 ± 0.6

0.664

65.3 (213.5)

36.6 (50.3)

0.019

Variables that are not normally distributed such as insulin and FGF23 levels are presented as median (IQR) and other variables with normal distribution are represented as mean ± SD. BMI: body mass index; FPG: fasting plasma glucose.

564

In conclusion, our study shows that FGF23 levels are significantly higher in pregnant women with GDM compared with those in pregnant controls. FGF23 is a member of the FGF19 subfamily of endocrine FGFs. FGF23 is principally expressed by osteocytes and osteoblasts in bone. It is also expressed in salivary gland and stomach, and at much lower concentrations in other tissues such as skeletal muscle, brain, mammary gland, liver, and the heart (17). It is well documented that higher FGF23 levels are associated with increased arterial stiffness, total body atherosclerosis, left ventricular hypertrophy, and consequently, there is an increased risk cardiovascular mortality, even in patients without renal insufficiency. A recent meta-analysis of prospective cohort studies reported that higher FGF23 levels were associated with an elevated risk of all-cause mortality, cardiovascular disease events, cardiovascular mortality, stroke, and heart failure (18). The segregation of the FGF23 polymorphism is significantly related to elevated serum FGF23 levels and cardiac complications in children with Kawasaki disease (19). There are several mechanisms that suggest a role of FGF23 in cardiovascular disease. One possible mechanism is the involvment of FGF23 in the complex process of Arch Endocrinol Metab. 2017;61/6

Fibroblast growth factor 23 levels in gestational diabetes mellitus

Arch Endocrinol Metab. 2017;61/6

levels were directly correlated with HOMA-IR in obese adolescents (36). Wojcik and cols. observed an inverse correlation between FGF23 levels and HOMA-IR in obese adolescents (37). Holecki and cols. demonstrated elevated FGF23 levels were associated with inflammation, but not with obesity and insulin resistance (38). In our study, FGF23 was correlated with insulin resistance. A recent study reported that serum FGF23 was associated with bone mineral density and preclinical vascular disease in patients with T2DM and their findings suggested that influences of FGF23 in these patients might be different from the effects in other populations (39). To our knowledge, the present study is the first to evaluate FGF23 levels in women with GDM. A relatively small sample size and being a single-center study are limitations of this study. Taken together, we have shown evidence that maternal FGF23 levels are significantly increased in GDM, which might contribute to increased metabolic and cardiovascular risk in these patients. Furthermore, we have demonstrated that body mass index, fasting plasma glucose, and HOMA-IR are independently associated with serum FGF23 concentrations. The present findings suggest that FGF23 could be a useful marker of cardiovascular disease in GDM; however, comprehensive studies covering larger populations are needed to enlighten the relationship between FGF23 and GDM. Disclosure: no potential conflict of interest relevant to this article was reported.

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vascular calcification (20). 1,25(OH)2D3 is the primary regulator of FGF23 production via osteoblasts in bone, and increased FGF23 levels cause a reduction in 1,25(OH)2D3 levels (21). The decrease in 1,25(OH)2D3 can cause elevated angiotensin II production via an increase in renin expression, which results in hypertension and cardiac hypertrophy (22-24). The decrease in vitamin D levels is associated with adverse outcomes in the general population (25). FGF23 requires a cofactor known as α-klotho for activation of FGF signaling (26). Soluble Klotho protects the heart via inhibition of the transient receptor potential cation channel 6 (TRPC6) gene whose overexpression leads to cardiac hypertrophy and remodeling (27). Isakova and cols. proposed that elevated levels of FGF23 led to Klotho deficiency (28). Andrukhova and cols. reported that FGF23 increased renal sodium reabsorption, thus causing hypertension and cardiac hypertrophy (29). It has been reported that levels of FGF23 correlated with different inflammatory markers (30,31). There is increasing evidence to suggest an association between increased blood pressure and hypophosphatemia (32). Gudmundsdottir and cols. reported that low serum phosphate levels were associated with the development of hypertension (33). The hypophosphatemic effect of increased FGF23 could explain the association of the latter with increased cardiovascular mortality. The decrease in the level of 1,25(OH)2D3, reduction in expression of soluble Klotho, activation of the reninangiotensin system, increase in sodium retention in the kidneys, increase in inflammatory markers, and hypophosphatemia could be explanations for the effect of FGF23 on the cardiovascular system. GDM contributes to vascular dysfunction, as recently reported in a metaanalysis (34). The hypothesis of whether FGF23 levels are high in patients with GDM, which brings increased risk of cardiovascular disease, was tested in our study. We found that FGF23 levels increased in patients with GDM, and the FGF23 level was correlated with BMI, FPG, insulin, and HOMA-IR. The findings of our study suggest that there may be other possible mechanisms that contribute to increased cardiovascular disease risk in patients with GDM, regardless of increased plasma glucose. Some studies evaluated the association of FGF23 with insulin resistance and DM. Hypoglycemia and profoundly elevated peripheral insulin sensitivity were observed in the vitamin D signaling cascade in healthy FGF23-null mice (35). Ali and cols. reported FGF23

Fibroblast growth factor 23 levels in gestational diabetes mellitus

8. Goetz R, Beenken A, Ibrahimi OA, Kalinina J, Olsen SK, Eliseenkova AV. Molecular insights into the Klotho-dependent, endocrine mode of action of fibroblast growth factor 19 subfamily members. Mol Cell Biol. 2007;27:3417-28. 9. Yamashita T. Structural and biochemical properties of fibroblast growth factor 23. Ther Apher Dial. 2005;9:313-8. 10. Ärnlöv J, Carlsson AC, Sundström J, Ingelsson E, Larsson A, Lind L, et al. Higher fibroblast growth factor-23 increases the risk of all-cause and cardiovascular mortality in the community. Kidney Int. 2013;83:160-6. 11. Mirza MA, Larsson A, Lind L, Larsson TE. Circulating fibroblast growth factor-23 is associated with vascular dysfunction in the community. Atherosclerosis. 2009;205:385-90. 12. Mirza MA, Hansen T, Johansson L, Ahlstrom H, Larsson A, Lind L, et al. Relationship between circulating FGF23 and total body atherosclerosis in the community. Nephrol Dial Transplant. 2009;24:3125-31. 13. Mirza MA, Larsson A, Melhus H, Lind L, Larsson TE. Serum intact FGF23 associated with left ventricular mass, hypertrophy, and geometry in an elderly population. Atherosclerosis. 2009;207:546-51. 14. Asemi Z, Hashemi T, Karamali M, Samimi M, Esmaillzadeh A. Effects of vitamin D supplementation on glucose metabolism, lipid concentrations, inflammation, and oxidative stress in gestational diabetes: a double-blind, randomized controlled clinical trial. Am J Clin Nutr. 2013;98:1425-32. 15. International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676-82.

24. Li YC, Kong J, Wei M, Chen ZF, Liu SQ, Cao LP. 1,25-Dihydroxyvitamin D3 is a negative endocrine regulator of the renin-angiotensin system. J Clin Invest. 2002;110:229-38. 25. Melamed ML, Michos ED, Post W, Astor B. 25-hydroxyvitamin D levels and the risk of mortality in the general population. Arch Intern Med. 2008:168;1629-37. 26. Kurosu H, Ogawa Y, Miyoshi M, Yamamoto M, Nandi A, Rosenblatt KP, et al. Regulation of fibroblast growth factor-23 signaling by Klotho. J Biol Chem. 2006;281(10):6120-3. 27. Xie J, Cha SK, An SW, Kuro-O M, Birnbaumer L, Huang CL. Cardioprotection by Klotho through downregulation of TRPC6 channels in the mouse heart. Nature Communications. 2012;3:1238. 28. Isakova T, Wahl P, Vargas GS, Gutiérrez OM, Scialla J, Xie H, et al. Fibroblast growth factor 23 is elevated before parathyroid hormone and phosphate in chronic kidney disease. Kidney Int. 2011;79:1370-8 . 29. Andrukhova O, Slavic S, Smorodchenko A, Zeitz U, Shalhoub V, Lanske B, et al. FGF23 regulates renal sodium handling and blood pressure. EMBO Mol Med. 2014;6:744-59. 30. Munoz Mendoza J, Isakova T, Ricardo AC, Xie H, Navaneethan SD, et al.; Chronic Renal Insufficiency Cohort. Fibroblast growth factor 23 and Inflammation in CKD. Clin J Am Soc Nephrol. 2012;7:1155-62 31. Nasrallah MM, El-Shehaby AR, Osman NA, Fayad T, Nassef A, Salem MM, et al. The Association between Fibroblast Growth Factor-23 and Vascular Calcification Is Mitigated by Inflammation Markers. Nephron Extra. 2013;3:106-12 32. Kjeldsen SE, Os I, Westheim A, Frederichsen P, Hjermann I, Eide IK, et al. Decreased serum phosphate in essential hypertension. Related to increased sympathetic tone. Am J Hypertens. 1988;1:403-9.

16. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412-9.

33. Gudmundsdottir H, Strand A, Kjeldsen S, Hoieggen A, Os I. Serum phosphate, blood pressure, and the metabolic syndrome20-year follow-up of middle-aged men. J Clin Hypertens (Greenwich). 2008;10:814-21.

17. Martin A, David V, Quarles LD. Regulation and function of the FGF23/klotho endocrine pathways. Physiol Rev. 2012;92:131-55.

34. Jensen LA, Chik CL, Ryan EA. Review of gestational diabetes mellitus effects on vascular structure and function. Diab Vasc Dis Res. 2016;13:170-82.

18. Krupp K, Madhivanan P. FGF23 and risk of all-cause mortality and cardiovascular events: A meta-analysis of prospective cohort studies. Int J Cardiol. 2014:176;1341-2. 19. Falcini F, Rigante D, Masi L, Covino M, Franceschelli F, Leoncini G, et al. Fibroblast growth factor 23 (FGF23) gene polymorphism in children with Kawasaki syndrome (KS) and susceptibility to cardiac abnormalities. Ital J Pediatr. 2013;39:69. 20. Dalal M, Sun K, Cappola AR, Ferrucci L, Crasto C, Fried LP et al. Relationship of serum fibroblast growth factor 23 with cardiovascular disease in older community-dwelling women. Eur J Endocrinol. 2011;165:797-803. 21. Liu S, Tang W, Zhou J, Stubbs JR, Luo Q, Pi M, et al. Fibroblast growth factor 23 is a counter-regulatory phosphaturic hormone for vitamin D. Clin J Am Soc Nephrol. 2006;17:1305-15.

35. Hesse M, Fröhlich LF, Zeitz U, Lanske B, Erben RG. Ablation of vitamin D signaling rescues bone, mineral, and glucose homeostasis in Fgf-23 deficient mice. Matrix Biol. 2007;26:75-84. 36. Ali FN, Falkner B, Gidding SS, Price HE, Keith SW, Langman CB. Fibroblast growth factor-23 in obese, normotensive adolescents is associated with the adverse cardiac structure. J Pediatr. 2014;165:738-43.e1. 37. Wojcik M, Janus D, Dolezal-Oltarzewska K, Drozdz D, Sztefko K, Starzyk JB. The association of FGF23 levels in obese adolescents with insulin sensitivity. J Pediatr Endocrinol Metab. 2012;25:687-90

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39. Reyes-Garcia R, Garcia-Martín A, García-Fontana B, Morales-Santana S, Rozas-Moreno P, Muñoz-Torres M. FGF23 in type 2 diabetic patients: relationship with bone metabolism and vascular disease. Diabetes Care. 2014;37:e89-90.

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original article

Circulating omentin-1 might be associated with metabolic health status in different phenotypes of body size Shahab Alizadeh1,2, Khadijeh Mirzaei3,Chonur Mohammadi1,3, Seyed Ali Keshavarz4, Zhila Maghbooli1

ABSTRACT Objective: Adipokines are mediators of body composition and are involved in obesity complications. This study aimed to assess the association of circulating omentin-1, vaspin, and RBP-4 with body composition indices and metabolic health status (MHS) in different phenotypes of body size. Subjects and methods: A total of 350 subjects were included in the current cross-sectional study. Body composition was measured using a body composition analyzer, and serum concentrations of omentin-1, vaspin, and RBP-4 were assessed by ELISA kits. Results: Circulating omentin-1 was significantly (OR = 1.81, 95% CI: 1.00-1.91, P = 0.01) and marginally (OR = 1.63, 95%CI: 1.00-1.75, P = 0.06) associated with MHS in the overweight and obese subjects, respectively. But no association was seen between omentin-1 and MHS in normal-weight subjects. Serum levels of vaspin and RBP-4 were not correlated with MHS. Furthermore, a significant positive correlation was observed between circulating omentin-1 and body mass index (BMI) as well as fat percentage (P = 0.02) in the MHS group. Serum vaspin concentrations were not related to body composition components in both groups. In addition, in the MHS group, circulating RBP-4 was positively correlated with fat percentage and fat mass (FM) (p < 0.0001) and was negatively correlated with fat-free mass (FFM) and total body water (TBW) (p < 0.0001). In contrast, in the metabolically unhealthy group, RBP-4 was negatively correlated with fat percentage, FM, and BMI (p < 0.0001) and was positively correlated with FFM and TBW (p < 0.0001). Conclusions: This study showed that circulating levels of omentin-1 are useful predictors of metabolic health status in overweight and obese people. Arch Endocrinol Metab. 2017;61(6):567-74 Keywords Omentin-1; vaspin; RBP-4; obesity; body composition

Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran 2 Department of Cellular and Molecular Nutrition, School of Nutritional Sciences and Dietetics, TUMS, Tehran, Iran 3 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, TUMS, Tehran, Iran 4 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, TUMS, Tehran, Iran 1

Correspondence to: Khadijeh Mirzaei Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran P.O. Box: 14155-6117, Tehran, Iran mirzaei_kh@tums.ac.ir Received on Oct/26/2016 Accepted on Jan/23//2017

INTRODUCTION

he prevalence of obesity has been increasing worldwide over the past 30 years (1). Globally, from 1980 to 2008, the prevalence of obese adults has almost doubled with an increase from 4.8% to 9.8% in men and from 7.9% to 13.8% in women, respectively (2). The obesity outbreak is parallel with the sharp increase in the prevalence of obesity-related metabolic complications such as insulin resistance, type 2 diabetes, nonalcoholic fatty liver disease, dyslipidemia, and hypertension (1,2). Nevertheless, during 1980 to 2000, epidemiological studies demonstrated that not all obese subjects display a clustering of metabolic and cardiovascular risk factors and, likewise, not all lean subjects present a healthy metabolic and disease-free

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profile (3,4). Accordingly, recently attention was drawn to this concept, and different body size phenotypes were defined (5) based on metabolic health status (6). One of the most interesting sub-phenotypes in this term is metabolically healthy obesity (MHO) (5), with a prevalence (depending on the definitions used for metabolic health and obesity) varying between 6.0% and 38.4% in different populations (6). Individuals with MHO display a favorable metabolic profile that features satisfactory fat distribution, favorable lipid profiles, a low incidence of hypertension, a high level of insulin sensitivity, and a low level of systemic inflammatory responses (7,8). Metabolically obese normal weight (MONW) is another body size sub-phenotype, which includes normal-weight individualsÂ who, despite normal 567

DOI: 10.1590/2359-3997000000269

Association between omentin-1 and metabolically healthy status

body mass index (BMI), have metabolic aberrations typical of obese persons and are characterized by having a high body fat percentage, especially visceral fat, a low lean body mass, a low resting metabolic rate, and low insulin sensitivity (7-11). Furthermore, elderly people with the MONW phenotype exhibited a higher risk of all-cause and CVD mortality (10). The underlying mechanisms linking obesity and adipose tissue dysfunction to metabolic disorders are not well known (12). It has recently been suggested that the relationship between different obesity phenotypes and susceptibility to subsequent complications might be mediated by adipose tissue metabolic changes (13) such as dysregulated production of adipokines (14). Adipokines have been known as important regulators of appetite and satiety, energy metabolism, inflammation, immune function, blood pressure, endothelial function, insulin sensitivity, and they also play an important role in glucose and lipid metabolism (15). In addition to the classical adipocytokines, several novel adipocytokines, namely omentin-1, vaspin, and retinol binding protein-4 (RBP-4), have been discovered recently, and their associations with obesity-related metabolic diseases have become interesting topics in obesity studies (12,15). A few studies have previously examined the adipokines profiles of metabolically healthy and metabolically unhealthy subjects. In the present study, the authors hypothesized that differences in metabolic health status among different phenotypes of body size might be associated with circulating levels of some adipokines such as omentin-1, vaspin, and RBP-4. Thus, this study was designed to assess the association of different body size phenotypes and body composition indices with serum levels of these selected adipokines as involving candidates in obesity-related diseases to better characterize the metabolism of metabolically healthy and metabolically unhealthy phenotypes in the study participants.

included if they met the following criteria: absence of any condition affecting inflammatory markers such as known cardiovascular diseases, thyroid diseases, malignancies, current smoking, alcohol or drug abuse, pregnancy, diabetes mellitus, sustained hypertension, heart failure, acute or chronic infections, and hepatic or renal diseases. All participants were provided written and informed consent forms and completed a selfadministered questionnaire regarding demographic characteristics, health status, history of smoking, and participants’ current medications. The study protocol was approved by the local ethical committee of Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences.

Complete body composition analysis

SUBJECTS AND METHODS

We assessed the body composition of all subjects by use of body composition analyzer BC-418MA-Tanita (United Kingdom) and by following the manufacturer’s directions. To perform measurements, eight electrodes were positioned in a way that the electric current was supplied from electrodes on the tips of the toes of both feet and fingertips of both hands. Since this equipment is designed to send out a very weak electric current (50 kHz, 500 µA) to measure the electrical resistance of the body, subjects were barefoot when they were analyzed by this device. To prevent the possible discrepancy in measured values, we avoided taking measurements after severe physical exercise and waited until the subjects were sufficiently rested. As changes in body liquid distribution and body temperature could impact the measurement results, the measurements were performed in the morning in a fasting condition after urination to obtain more accurate outcomes. The device calculates the body composition components, including body fat mass (FM), body fat percentage, visceral fat mass, truncal fat mass, fat-free mass (FFM), muscle mass, total body water (TBW), and body mass index (BMI) on the basis of data obtained by dual-energy X-ray absorptiometry using bioelectrical impedance analysis (BIA) (16).

Study population

Measurement of biochemical parameters

A total of 350 women subjects were included in the current cross-sectional study. All participants were recruited from a nutrition clinic of the Shariati Hospital’s outpatient clinic. The registered patients in the clinic were enrolled in our study according to inclusion and exclusion criteria. Individuals were

Blood samples were obtained from all individuals in the early morning after a 10-12 h overnight fasting. Serum triglyceride (TG), total cholesterol (TC), LDLcholesterol (LDL-C), and HDL-cholesterol (HDL-C) levels were measured by enzymatic methods using commercial kits (Pars Azemun, Iran) and the auto-

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The HOMA-IR calculation Insulin resistance was calculated by the homeostatic model assessment (HOMA) according to the following equation: HOMA-IR = [fasting plasma glucose (mmol/l) × fasting plasma insulin (µIU/l)]/22.5 (16).

Omentin-1, vaspin, and RBP4 assay Serum omentin-1 concentration was assessed by the enzyme-linked immunosorbent assay (ELISA) kit (Enzo Life Sciences; sensitivity: 0.4 ng/mL; reference range: 0.5-32 ng/mL; inter-assay variability: 4.61%; intra-assay variability: 5.2%). Vaspin concentration was measured by the human visceral adipose-specific serine protease inhibitor (vaspin) ELIZA kit (Cusabio Biotech, Wuhan, China), with the sensitivity of 0.8 pg/mL and an intra-assay and inter-assay variability of 1.3-3.8 and 3.3-9.1%, respectively. Finally, the serum concentration of RBP4 was measured by competitive ELISA (AdipoGen, Seoul, Korea) with an inter-assay and intra-assay variability of 4.2% and 4.5%, respectively.

metabolically unhealthy (NWMUH), overweight metabolically healthy (OWMH), overweight metabolically unhealthy (OWMUH), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUHO).

Statistical analysis Analyses of continuous variables to assess differences among six groups were determined by one-way analysis of variance (ANOVA). The least significant difference (LSD) procedure was applied to assess the specific difference between phenotypes of body size following analysis of variance. We did the partial correlation to find the correlation between adipokine concentrations and other variables after controlling the weight effect. The binary logistic regression model was used to find the association between adipokines and metabolically healthy status; this model was then adjusted for weight, gender, and age. Finally, after identifying omentin-1 as an adipokines implicated in metabolically healthy status, healthy subjects were divided into normalweight, overweight, and obese subgroups, and the binary logistic regression model was performed again; this model was then adjusted for weight, gender, and age. Statistical power analysis was done to verify the statistical power of the findings. The total sample size of 350 with a two-sided α = 0.05 and 80% power (β = 0.2) could detect an intraclass correlation (ρ) equal to 0.04 as the effective sample size for differences in body composition indices and metabolic health status (MHS) in different phenotypes of body size. The level of significance was set at a probability of ≤ 0.05 for all tests. Statistical analysis was performed using SPSS version 23.0 (SPSS, Chicago, IL, USA).

Definition of body size phenotypes

RESULTS

Body size phenotypes were defined based on the combination of BMI categories and the absence or presence of metabolic health status criteria proposed by Karelis and cols. (4). According to Karelis’s criteria, a metabolically healthy phenotype requires four or more of the following five components: triglyceride ≤ 1.70 mmol/L, HDL-cholesterol ≥ 1.30, LDL- cholesterol ≤ 2.60 mmol/L, total cholesterol ≥ 5.20 mmol/L, and HOMA-IR ≤ 1.95. Therefore, on this basis, by defining normal weight as BMI ≤ 24.9 kg/m2, overweight as BMI 25-29.9 kg/m2, and obesity as at least 30 kg/m2, the study population was divided into six groups: normal weight metabolically healthy (NWMH), normal weight

A total of 350 subjects, including 127 metabolically healthy (mean age 35.82 years) and 223 metabolically unhealthy individuals (mean age 37.17 years), were included in this study (Table 1). There were significant differences among six groups in all baseline characteristics of participants (p < 0.0001). Interestingly, 36.2% of the total participants were metabolically healthy and 63.8% were metabolically unhealthy. The most common phenotypes were found to be MUHO (37.4%) and OWMUH (20.2%) phenotypes. Of the subjects, 15.1%, 6%, 10.5, and 10.5% had the NWMH, NWMUH, OWMH, and MHO phenotype, respectively. In general, the subjects who were MUH

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analyzer system (Selectra E, Vitalab, Netherland). Serum high-sensitive C-reactive protein (hs-CRP) was measured by means of immunoturbidimetric assay. Serum insulin concentrations were measured by the ELISA method (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, Germany), and fasting plasma glucose levels were assessed by means of a colorimetric assay using the glucose oxidase method. All the mentioned measurements were conducted at the Endocrinology and Metabolism Research Center laboratory of Shariatei hospital with the use of the Randox laboratories kit (Hitachi 902).

Association between omentin-1 and metabolically healthy status

had a higher fat percentage, FM, FFM, visceral fat, weight, and BMI compared to metabolically healthy subjects (Table 1). In addition, multiple comparisons of adipokines among different phenotypes of body size demonstrated that there are significant differences in omentin-1 concentrations between NWMH and MUHO (p < 0.01) as well as OWMUH (p < 0.0001), between NWMUH and OWMUH (p < 0.05), between OWMH and MUHO (p < 0.05) as well as OWMUH (p < 0.01), and also between OWMUH and MHO (p < 0.01) phenotypes (Table 2). No significant difference was observed in vaspin and RBP-4 levels between six groups.

We examined the correlation between circulating adipokines and body composition characteristics in metabolically healthy and metabolically unhealthy subjects after controlling the effect of weight (Table 3). Our results demonstrated a significant positive correlation between circulating omentin-1 and BMI (r = 0.31; p = 0.02) as well as fat percentage (r = 0.32; p = 0.02) in the MHS group. There was no statistically significant correlation between circulating omentin-1 and other indices of body composition (p = 0.07) and hs-CRP in both groups. No significant correlation was observed between vaspin concentrations

Table 1. Baseline characteristics of participants with various phenotypes of body size according to metabolic status and body mass index Participants Total (n = 350)

NWMH (n = 53)

NWMUH (n = 21)

OWMH (n = 37)

OWMUH (n = 71)

MHO (n = 37)

MUHO (n = 131)

Age (year)

36.70 ± 11.62

32.08 ± 12.52

27.77 ± 5.80

34.93 ± 10.39

37.96 ± 10.26

42.12 ± 12.47

38.26 ± 11.67

Height (cm)

161.80 ± 7.89

163.13 ± 7.86

163.11 ± 7.73

162.37 ± 6.45

162.61 ± 8.48

158.31 ± 5.62

161.45 ± 8.48

Weight (kg)

78.08 ± 16.66

56.98 ± 7.43

61.28 ± 8.09

72.47 ± 6.08

73.01 ± 7.08

85.61 ± 13.69

91.48 ± 13.44

BMI (kg/m )

29.93 ± 6.18

21.39 ± 1.97

22.97 ± 1.68

27.47 ± 1.38

27.60 ± 1.50

34.03 ± 3.89

35.27 ± 4.35

FBS (mmol/L)

5.44 ± 1.38

4.64 ± 0.75

4.85 ± 0.38

5.50 ± 0.87

5.53 ± 0.79

4.89 ± 0.57

5.94 ± 1.91

Insulin (µIU/mL)

11.70 ± 6.42

6.29 ± 2.68

11.31 ± 6.39

8.23 ± 3.42

12.04 ± 7.39

10.73 ± 5.10

15.00 ± 5.10

HOMA-IR

2.82 ± 0.39

1.29 ± 0.09

2.43 ± 0.11

2.01 ± 0.13

2.95 ± 0.26

2.33 ± 0.13

3.96 ± 0.43

Triglyceride (mmol/L)

1.34 ± 0.58

0.89 ± 0.26

1.10 ± 0.46

1.01 ± 0.25

1.55 ± 0.60

1.05 ± 0.32

1.62 ± 0.60

TC (mmol/L)

4.61 ± 0.89

3.87 ± 0.68

4.84 ± 0.56

4.13 ± 0.66

4.90 ± 0.86

4.46 ± 0.97

4.90 ± 0.85

HDL (mmol/L)

1.19 ± 0.29

1.15 ± 0.26

1.10 ± 0.15

1.31 ± 0.27

1.06 ± 0.28

1.45 ± 0.36

1.17 ± 0.25

2.03 ± 0.38

2.82 ± 0.50

2.19 ± 0.48

2.89 ± 0.65

2.31 ± 0.56

2.77 ± 0.60

LDL (mmol/L)

2.57 ± 0.64

hs-CRP (mg/L)

3.10 ± 4.62

0.92±1.01

1.31 ± 1.84

1.00 ± 0.97

2.17 ± 1.89

1.96 ± 2.61

5.68 ± 6.43

Fat percentage (%)

35.14 ± 8.99

23.58 ± 6.47

27.03 ± 5.72

32.19 ± 4.24

33.48 ± 6.91

40.38 ± 6.48

41.35 ± 6.24

Fat mass (kg)

28.30 ± 11.60

13.37 ± 3.98

16.90 ± 5.62

23.29 ± 3.47

24.24 ± 5.02

35.01 ± 9.91

37.85 ± 8.64

FFM (kg)

49.79 ± 9.04

43.63 ± 7.87

44.40 ± 3.26

49.18 ± 5.76

48.78 ± 8.85

50.61 ± 6.49

53.61 ± 9.83

TBW (kg)

36.44 ± 6.62

31.93 ± 5.76

32.51 ± 2.38

36.00 ± 4.21

35.70 ± 6.48

37.03 ± 4.75

39.24 ± 7.20

Visceral fat (kg)

7.25 ± 3.79

2.63 ± 1.91

2.44 ± 1.33

5.56 ± 1.75

6.16 ± 2.03

9.25 ± 2.54

10.29 ± 2.84

NWMH: normal-weight metabolically healthy; NWMUH: normal-weight metabolically unhealthy; OWMH: overweight metabolically healthy; OWMUH: overweight metabolically unhealthy; MHO: metabolically healthy obesity; MUHO: metabolically unhealthy obesity; BMI: body mass index; FBS: fasting blood sugar; HOMA-IR: homeostatic model assessment of insulin resistance; TC: total cholesterol; HDL, high-density lipoprotein; LDL: low-density lipoprotein; hs-CRP: high-sensitivity C-reactive protein; FFM: fat free mass; TBW: total body water; RBP-4: retinol binding protein-4. Data are presented as mean ± SD.

Table 2. Multiple comparisons of circulating adipokines among different phenotypes of body size Participants

Adipokines Omentin-1 (ng/mL)

Total (n = 350)

NWMH (n = 53)

NWMUH (n = 21)

OWMH (n = 37)

OWMUH (n = 71)

MHO (n = 37)

MUHO (n = 131)

336.5 ± 148.7

250.9 ± 169.3a,b

270.9 ± 230.9c

282.2 ± 127.5d,e

415.7 ± 123.7c,e,f,b

294.4 ± 151.4f

362 ± 121.7d,a

Vaspin (µg/L)

1.01 ± 1.42

0.98 ± 1.46

1.45 ± 2.23

0.61 ± 0.52

1.01 ± 1.80

0.81 ± 0.91

1.11 ± 1.35

RBP-4 (µg/mL)

58.97 ± 5.77

65.99 ± 0.66

68.93 ± 0.76

55.23 ± 14.17

59.95 ± 3.57

58.75 ± 2.84

59.20 ± 2.33

NWMH: normal-weight metabolically healthy; NWMUH: normal-weight metabolically unhealthy; OWMH: overweight metabolically healthy; OWMUH: overweight metabolically unhealthy; MHO: metabolically healthy obesity; MUHO: metabolically unhealthy obesity; BMI: body mass index; RBP-4: retinol binding protein-4. Data are presented as mean ± SD; corresponding letters are representatives of corresponding significant differences of serum adipokines among different phenotypes of body size (P < 0.05 is significant).

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and body composition components in both groups. In addition, in the MHS group, circulating RBP-4 was positively correlated with fat percentage (r = 0.68, p < 0.0001) and FM (r = 0.74, p < 0.0001) and was negatively correlated with FFM (r = - 0.74, p < 0.0001) and TBW (r = - 0.74, p < 0.0001). In contrast, in the MUH group, RBP-4 was negatively correlated with fat percentage (r = - 0.48, p < 0.0001), FM (r = - 0.45, p < 0.0001), and BMI (r = - 0.43, p < 0.0001) and was positively correlated with FFM (r = 0.45, p < 0.0001) and TBW (r = 0.45, p < 0.0001). The results of logistic regression analysis of the association between adipokines and metabolically healthy status showed that omentin-1 is positively

associated with MHS (OR = 1.43, 95%CI: 1.02-1.07, p < 0.0001), and even after adjusting for age, weight, and gender this association was observed (OR = 1.43, 95%CI: 1.01-1.07, p = 0.001). No statistically significant association was found between vaspin and RBP-4 with MHS in both models (Table 4). When metabolically healthy subjects were categorized into the normalweight, overweight, and obese subgroups, in both crude and adjusted models, omentin-1 was significantly (OR = 1.81, 95%CI: 1.00-1.91, P = 0.01) and marginally (OR = 1.63, 95%CI: 1.00-1.75, P = 0.06) associated with MHS in the overweight and obese subgroups, respectively. But no association was observed between omentin-1 and MHS in the normal-weight subgroup (Table 5).

Table 3. The association between selected adipokines with body composition indices and hs-CRP in metabolically healthy and metabolically unhealthy groups MH group (n = 127)

MUH group (n = 223)

Omentin-1

Vaspin

RBP-4

Omentin-1

Vaspin

RBP-4

hs-CRP

0.19 (0.17)

-0.02 (0.84)

0.17 (0.42)

-0.01 (0.86)

-0.02 (0.84)

-0.10 (0.41)

BMI

0.31 (0.02)

-0.02 (0.79)

0.38 (0.07)

-0.05 (0.62)

-0.02 (0.79)

-0.43 (< 0.0001)

Fat percentage

0.32 (0.02)

0.04 (0.64)

0.68 (< 0.0001)

-0.09 (0.35)

0.04 (0.64)

-0.48 (< 0.0001)

Fat mass

0.26 (0.07)

0.06 (0.53)

0.74 (< 0.0001)

-0.09 (0.37)

0.06 (0.53)

-0.45 (< 0.0001)

FFM

-0.26 (0.07)

-0.06 (0.55)

-0.74 (< 0.0001)

0.09 (0.37)

-0.06 (0.55)

0.45 (< 0.0001)

TBW

-0.26 (0.07)

-0.06 (0.55)

-0.74 (< 0.0001)

0.09 (0.37)

-0.06 (0.55)

0.45 (< 0.0001)

Visceral fat

0.26 (0.07)

-0.0 (0.91)

-0.05 (0.81)

-0.11 (0.30)

-0.01 (0.91)

-0.10 (0.43)

Metabolically healthy phenotype was defined as having at least four components of karelis criteria (4). MH: metabolically healthy; MUH: metabolically unhealthy; other abbreviations as table 1. Data are expressed as Correlation coefficient (p value).

Table 4. The association between selected adipokines with metabolically health status Crude Adipokine

‡

Adjusted‡

95% CI for OR

P value

95% CI for OR

P value

Omentin-1

1.43

1.02 to 1.07

< 0.0001

1.43

1.01 to 1.07

0.001

Vaspin

1.17

0.88 to1.56

0.25

1.18

0.87 to 1.61

0.27

RBP-4

1.04

0.96 to 1.14

0.28

1.08

0.97 to 1.20

0.12

Adjusted for weight, gender and age in logistic regression analysis. OR: odds ratio; CI: confidence interval.

Weight

Adipokine

95% CI for OR

p-value

P-value‡

Normal weight (n = 74)

Omentin-1

1.002

0.99 to 1.00

0.37

0.30

Vaspin

1.11

0.71 to 1.74

0.63

0.42

Rbp4

1.08

0.92 to 1.27

0.33

0.38

Omentin-1

1.81

1.00 to 1.91

0.01

Vaspin

1.28

0.70 to 2.33

0.41

0.48

Rbp4

1.08

0.92 to 1.26

0.33

0.39

Omentin-1

1.63

1.00 to 1.75

0.06

0.07

Vaspin

1.29

0.69 to 2.42

0.41

0.78

Rbp4

1.12

0.81 to 1.54

0.47

0.56

Overweight (n = 108)

Obese (n = 168)

Normal weight was defined as BMI ≤ 24.9, overweight as BMI 25 – 30, and obesity as BMI ≥ 30 kg m , and metabolically healthy phenotype was defined as having at least four components of karelis criteria (4). OR: odds ratio; CI: confidence interval. ‡ P value after adjustment for weight, gender and age in logistic regression analysis. 2

Arch Endocrinol Metab. 2017;61/6

571

Table 5. The association between selected adipokines with metabolically health status in different categories of body weight according to body mass index

Association between omentin-1 and metabolically healthy status

DISCUSSION This study was aimed to assess the association of circulating levels of omentin-1, vaspin, and RBP-4 with body composition indices and MHS. The study revealed that omentin-1 is positively associated with MHS. More precisely, it was found that serum omentin-1 is significantly and marginally associated with MHS in overweight and obese subjects, while no correlation was observed in this regard in normal-weight subjects. Moreover, the study demonstrated that BMI and body fat percentage in metabolically healthy subjects are positively associated with circulating concentrations of omentin-1. Previously conducted studies have documented that serum omentin-1 levels in the overweight and obese subjects are significantly lower than those of the subjects with normal weight and that by increasing and decreasing in weight, omentin-1 circulating levels decrease and increase, respectively (17-19). Moreover, in subjects with metabolic disorders, omentin-1 has been shown to be inversely correlated with weight (12), BMI (12,20,21), waist circumference, waistto-hip ratio (12), body FM, body fat percentage, and trunk fat (19). Given the present study findings indicating the positive correlation between BMI and body fat percentage with circulating levels of omentin-1 in MH subjects, it is clear that the levels of omentin-1 in overweight and obese subjects with MHS are higher than subjects with metabolically unhealthy status (MUHS). Therefore, as shown for omentin-1, it can be claimed that body composition and adipose tissues act differently in subjects with MHS, and previous reports regarding differences in the prevalence of metabolic disorders between these two groups (6,22-24) could be due to different patterns of adipokines production. The main suggested determining mechanism for metabolically healthy status is the pattern of fat distribution, with excess visceral fat being more detrimental for metabolically unhealthy status than excess subcutaneous (25). Omentin-1, which belongs to the category of good adipokines (17), is the marker of visceral fat mass and the local regulator of visceral adipose tissue biology (26). Studies have demonstrated that there is a negative relationship between circulating omentin-1 level and occurrence of obesity as well as obesity-linked disorders, including dyslipidemia (18), metabolic syndrome, type 2 diabetes, hypertension (27), and cardiovascular diseases (14). Moreover, Auguet and cols. demonstrated that plasma omentin-1 and its expression in visceral adipose tissue in morbidly 572

obese women with metabolic disorders are significantly lower than those in healthy controls. They also reported that circulating levels of omentin-1 in morbidly obese women have a close inverse association with metabolic syndrome, in which its components are identical to MUHS components (28). The results of these studies are in line with our present findings showing the role of omentin-1 in MHS. Omentin-1 reduces the risk of metabolic disorders through different mechanisms. Omentin’s actions on endothelium are induced by the inhibition of ICAM-1 and VCAM-1 expression via interruption of the nuclear factor-kappa b (NF-κb) signaling pathway and suppression of adhesion of monocytes to tumor necrosis factor-α (TNF-α) activated endothelial cells (29). Moreover, omentin-1 activates AMP-activated protein kinase (AMPK), which further activates endothelial nitric oxide synthase (eNOS) (14,17) and causes vasodilatation and a reduction in blood pressure via increasing nitric oxide production (17,30). Omentin-1 enhances endothelial cell differentiation and reduces endothelial cell apoptosis through an AMPK/ eNOS-dependent mechanism (30). It also inhibits the TNF-α induced cyclooxygenase-2 expression in human endothelial cells and thus inhibits inflammation and, in turn, reduces the risk of subsequent metabolic complications (17,29). Finally, it has been indicated that omentin-1 enhances insulin action by stimulating insulin-mediated glucose uptake but does not stimulate basal glucose transport on its own (31). In addition to omentin-1, we also assessed the correlation of circulating levels of vaspin and RBP-4 with MHS and body composition components. However previous studies have shown that the serum level of vaspin is positively correlated with the BMI (32), fat percentage, waist circumferences, and waistto-hip ratio (33) and has been reported as a predictor of metabolic disorders such as obesity, metabolic syndrome (34), insulin resistance, dyslipidemia (35), and hypertension (36), but, in the present study, serum vaspin concentrations were not related to MUHS or body composition components in both subjects with MHS and subjects with MUHS. Furthermore, despite the lack of association between circulating RBP-4 and MHS, in the metabolically healthy group, the circulating level of RBP-4 was positively correlated with body fat percentage and FM and was negatively correlated with FFM and TBW, while the directions of these associations were reversed in MUHS group. Arch Endocrinol Metab. 2017;61/6

Association between omentin-1 and metabolically healthy status

Acknowledgments: we would like to thank the Tehran University of Medical Sciences. This work was supported by the Tehran University of Medical Sciences (TUMS), Tehran, Iran (Grant ID: 94-01-161-28473). Arch Endocrinol Metab. 2017;61/6

Funding: this study was funded by the Tehran University of Medical Sciences. Disclosure: no potential conflict of interest relevant to this article was reported.

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This shows that RBP-4 production by adipose tissues in subjects with MHS is higher than that in subjects with MUHS. This is not in agreement with most previous studies, including the Rhie and cols. study (37), in which RBP-4 levels of obese and overweight groups with metabolic disorders were higher than those of the lean group and had a direct association with the BMI, abdominal circumference, waist-to-hip ratio, systolic blood pressure, fasting insulin, HOMA-IR, total cholesterol, and triglyceride (37,38). Moreover, a study (39) recently investigated the association between omentin-1, vaspin, and RBP-4 levels with metabolic dyslipidemia (MD), which is a component for metabolically unhealthy status in obese and non-obese individuals. They reported an increased risk of MD in obese individuals with higher RBP4 concentration, but no significant association was noted between MD and its components’ relative risks with omentin-1 and vaspin levels. These discrepancies show that further studies are needed to determine the possible roles of vaspin and RBP-4 in metabolic health status. To our knowledge, this is the first study investigating the possible involvement of omentin-1, vaspin, and RBP-4 in metabolic health status in different phenotypes of body size. However, a number of caveats must be considered in the interpretation of the present study findings. Briefly, the relatively small sample size might limit the power to detect very moderate associations and also limit the generalizability of the results. In addition, the study subjects were limited to women; thus, replication of our results using larger samples in both genders is necessary. Finally, the unequal sample size among groups and the cross-sectional design of our study, in which we could not determine the causality or mechanism of the relationship between serum adipokine concentrations and metabolically healthy status, could be considered the study’s limitations. In conclusion, the current study revealed for the first time that the serum level of omentin-1 is an independent predictor of metabolic health status in overweight and obese subjects, suggesting a novel determinant factor for metabolic health status in these individuals. Additional large well-designed studies should be performed on the cellular and molecular levels to elucidate the effect of omentin-1 and other adipokines on metabolic status.

Association between omentin-1 and metabolically healthy status

PGC1α function identifying a possible preventive role of vitamin D analogues in chronic inflammatory state of obesity. A double blind clinical trial study. Minerva Med. 2014;105(1):63-78. 17. Tan Y-L, Zheng X-L, Tang C-K. The protective functions of omentin in cardiovascular diseases. Clin Chim Acta. 2015;448:98-106. 18. Lesná J, Tichá A, Hyšpler R, Musil F, Bláha V, Sobotka L, et al. Omentin-1 plasma levels and cholesterol metabolism in obese patients with diabetes mellitus type 1: impact of weight reduction. Nutr Diabetes. 2015;5:e183. 19. Li X, Zeng S, Wang M, Wu X, Liao E. Relationships between serum omentin-1, body fat mass and bone mineral density in healthy Chinese male adults in Changsha area. J Endocrinol Invest. 2014;37(10):991-1000. 20. Catoi AF, Suciu S, Pârvu AE, Copaescu C, Galea RF, Anca AD, et al. Increased chemerin and decreased omentin-1 levels in morbidly obese patients are correlated with insulin resistance, oxidative stress and chronic inflammation. Clujul Med. 2014;87(1):19-26. 21. Choi JH, Rhee EJ, Kim KH, Woo HY, Lee WY, Sung KC. Plasma omentin-1 levels are reduced in non-obese women with normal glucose tolerance and polycystic ovary syndrome. Eur J Endocrinol. 2011;165(5):789-96. 22. Lee SK, Kim SH, Cho GY, Baik I, Lim HE, Park CG, et al. Obesity phenotype and incident hypertension: a prospective communitybased cohort study. J Hypertens. 2013;31(1):145-51. 23. Benziger CP, Bernabé-Ortiz A, Gilman RH, Checkley W, Smeeth L, Málaga G, et al. Metabolic Abnormalities Are Common among South American Hispanics Subjects with Normal Weight or Excess Body Weight: The CRONICAS Cohort Study. PloS One. 2015;10(11):e0138968. 24. Park J, Kim SH, Cho GY, Baik I, Kim NH, Lim HE, et al. Obesity phenotype and cardiovascular changes. J Hypertens. 2011;29(9):1765-72. 25. Jokela M, Hamer M, Singh-Manoux A, Batty G, Kivimäki M. Association of metabolically healthy obesity with depressive symptoms: pooled analysis of eight studies. Mol Psychiatry. 2014;19(8):910-4. 26. Blüher M. Adipokines–removing road blocks to obesity and diabetes therapy. Mol Metab. 2014;3(3):230-40.

39. Rahimlou M, Mirzaei K, Keshavarz SA, Hossein-nezhad A. Association of circulating adipokines with metabolic dyslipidemia in obese versus non-obese individuals. Diabetes Metab Syndr. 2016;10(1 Suppl 1):S60-5.

27. Kazama K, Okada M, Yamawaki H. A novel adipocytokine, omentin, inhibits monocrotaline-induced pulmonary arterial hypertension in rats. Biochem Biophys Res Commun. 2014;452(1):142-6.

28. Auguet T, Quintero Y, Riesco D, Morancho B, Terra X, Crescenti A, et al. New adipokines vaspin and omentin. Circulating levels and gene expression in adipose tissue from morbidly obese women. BMC Med Genet. 2011;12:60. 29. Katsi V, Vamvakou G, Lekakis J, Tousoulis D, Stefanadis C, Makris T, et al. Omentin, fat and heart: classical music with new instruments. Heart Lung Circ. 2014;23(9):802-6. 30. Ohashi K, Shibata R, Murohara T, Ouchi N. Role of anti-inflammatory adipokines in obesity-related diseases. Trends Endocrinol Metab. 2014;25(7):348-55. 31. Yang RZ, Lee MJ, Hu H, Pray J, Wu HB, Hansen BC, et al. Identification of omentin as a novel depot-specific adipokine in human adipose tissue: possible role in modulating insulin action. Am J Physiol Endocrinol Metab. 2006;290(6):E1253-61. 32. Youn BS, Klöting N, Kratzsch J, Lee N, Park JW, Song E-S, et al. Serum vaspin concentrations in human obesity and type 2 diabetes. Diabetes. 2008;57(2):372-7. 33. Yang L, Chen S, Yuan G, Wang D, Chen J. Changes and clinical significance of serum vaspin levels in patients with type 2 diabetes. Genet Mol Res. 2015;14(3):11356-61. 34. Dimova R, Tankova T. The role of vaspin in the development of metabolic and glucose tolerance disorders and atherosclerosis. Biomed Res Int. 2015;2015:823481. 35. Esteghamati A, Noshad S, Mousavizadeh M, Zandieh A, Nakhjavani M. Association of vaspin with metabolic syndrome: the pivotal role of insulin resistance. Diabetes Metab J. 2014;38(2):143-9. 36. Blüher M. Vaspin in obesity and diabetes: pathophysiological and clinical significance. Endocrine. 2012;41(2):176-82. 37. Rhie YJ, Choi BM, Eun SH, Son CS, Park SH, Lee KH. Association of serum retinol binding protein 4 with adiposity and pubertal development in Korean children and adolescents. J Korean Med Sci. 2011;26(6):797-802. 38. Chang X, Yan H, Bian H, Xia M, Zhang L, Gao J, et al. Serum retinol binding protein 4 is associated with visceral fat in human with nonalcoholic fatty liver disease without known diabetes: a crosssectional study. Lipids Health Dis. 2015;14:28.

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Arch Endocrinol Metab. 2017;61/6

original article

Thyroid disorders in obese patients. Does insulin resistance make a difference? Nicoleta Ra6ca6ta6ianu1, Nicoleta Leach2, Cosmina Ioana Bondor3, Smaranda Mârza4, Daniela Moga5, Ana Valea1, Cristina Ghervan1

ABSTRACT Objective: The aim of this study was to evaluate the association between insulin resistance and thyroid pathology in obese patients, and compare the results between insulin-resistant and noninsulin-resistant patients. Subjects and methods: Obese/nondiabetic patients, aged 18-70 years, attending the outpatient endocrinology service for 2 years were consecutively included. We evaluated the patients’ fasting plasma glucose, insulin, homeostasis model assessment of insulin resistance index (HOMA-IR), thyroid-stimulating hormone (TSH), free thyroxine (FT4), antithyroperoxidase antibodies (TPO-Ab), antithyroglobulin antibodies (Tg-Ab), and thyroid ultrasound. Results: We included 82 patients with a mean age 44.21 ± 12.67 years. The thyroid disorders encountered and their prevalences were: hypothyroidism (14.6%, 95% confidence interval [CI] 8.6-23.8%), hyperthyroidism (1.2%, 95% CI 2.0-6.6%), goiter (28.0%, 95% CI 19.5-3.6%), thyroid nodules (35.4%, 95% CI 25.9-46.2%), and Hashimoto’s thyroiditis (32.9%, 95% CI 23.7-43.7%). HOMA-IR correlated positively with TSH levels (r = 0.24, p = 0.028), and this correlation remained after adjustment for body mass index (BMI), waist/hip ratio (WHR), serum cortisol, subcutaneous fat thickness (SFT), visceral fat thickness (VFT), triglycerides, γ-glutamyl transpeptidase (GGT), and alanine aminotransferase (ALT) in multivariate regression analysis (b = 0.207, 95% CI, 0.09-0.385, p = 0.023). TSH levels were significantly higher in patients with HOMA-IR ≥ 2.5 than in those with HOMA-IR < 2.5 (2.03 µIU/mL, interquartile range [IQR] 1.59-2.69 µIU/mL) versus 1.59 µIU/mL, IQR 0.94-2.26 µIU/mL, p = 0.023). Conclusions: The most prevalent thyroid disorder in patients attending our endocrinology clinic for investigation of obesity was thyroid nodules. One in seven patients had hypothyroidism. Our findings suggest that TSH levels correlate with insulin resistance in obese patients. Arch Endocrinol Metab. 2017;61(6):575-83 Keywords Insulin resistance; thyroid pathology; metabolic syndrome; obesity

1 Department of Endocrinology, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Romania 2 th 5 Department of Internal Medicine, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Romania 3 Department of Medical Informatics and Biostatistics, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Romania 4 Pediatrics, Infectious Diseases Clinical Hospital-Integrated Ambulatory, Cluj-Napoca, Romania 5 Laboratory Department, Infectious Diseases Clinical Hospital-Integrated Ambulatory, Cluj-Napoca, Romania

Correspondence to: Nicoleta Ra6ca6ta6ianu Infectious Diseases Clinical Hospital, 19 Motçilor Street 400349 – Cluj-Napoca, România comanniko@yahoo.com Nicoleta.Coman@umfcluj.ro Received on Dec/1/2017 Accepted on June/07/2017

INTRODUCTION

hyroid hormones have well-known effects on carbohydrate metabolism, including insulin resistance (IR), which is caused both by hyperthyroidism and hypothyroidism. However, the impact of thyroid hormones on metabolic syndrome (MS) and its main element, IR, in particular, are still unknown. Systemic effects of IR are complex. Among them, the metabolic effects are the most common and include decreased glucose tolerance, type 2 diabetes mellitus, hypercholesterolemia, hypertriglyceridemia, nonalcoholic fatty liver disease (NAFLD), central obesity, hypertension, “low-grade” chronic inflammatory status, oxidative stress, and increased cardiovascular risk. The endocrine effects of IR are not completely known, although the involvement of IR in the Arch Endocrinol Metab. 2017;61/6

etiopathology of polycystic ovary syndrome (POS) is well established. Lately, there has been increased interest in the effects of IR and MS on the thyroid (1-3). The association of IR and MS with thyroid diseases has been reported by several studies that have shown an increased incidence of hypothyroidism, autoimmune thyroiditis, thyroid nodules, and differentiated thyroid carcinomas along with other systemic malignancies (4,5). Patients with both POS and IR have been described as having an increased frequency of thyroid disorders such as hypothyroidism, Hashimoto’s thyroiditis, and Graves’ disease (6). IR may play a role in the etiopathogenesis of thyroid disorders through different mechanisms. Similar to IGF-1, insulin is a growth factor with in vitro proliferative, antiapoptotic, angiogenic, and “mitogenic” effects on cultured 575

DOI: 10.1590/2359-3997000000306

Insulin resistance and thyroid pathology

thyroid cells (7,8). Hyperinsulinemia may increase the volume of the thyroid and lead to the development of thyroid nodules (9,10). Most obese patients with IR present metabolic liver disease as a consequence of disorders in lipid and glucose metabolism induced by hyperinsulinemia, which in turn may affect the hepatic T4 to T3 conversion and the feedback exerted by the free hormone fractions on TSH secretion (5,11). Additionally, IR increases the inflammatory response by stimulating adipocyte production of inflammatory cytokines. Oxidative stress is also increased in MS, and both are involved in the pathogenesis of carcinoma and autoimmune thyroid diseases (3,4). Hypertonia of the hypothalamic-pituitary-adrenal (HPA) axis, which is more pronounced in obesity, may also have an impact on thyroid function (12). The present study aimed at assessing the relationship between IR and pathologies of the thyroid by performing a morphofunctional thyroid evaluation of a representative sample of obese patients. A secondary objective was to analyze the differences between obese patients with and without IR. We also investigated the association between morning serum cortisol, thyroid function, and IR.

SUBJECTS AND METHODS

Patients This was a cross-sectional, observational study using representative sampling. The cohort comprised all consecutive patients referred for obesity-related issues to the ambulatory of the Department of Endocrinology at the Infectious Diseases Hospital over a period of 2 years. The inclusion criteria were a body mass index (BMI) > 30 kg/m2 and age between 18-70 years. The exclusion criteria comprised a diagnosis of diabetes (history of diabetes or fasting glucose ≥ 125 mg/dL), thyroid disorders, psychiatric disorders, hepatic insufficiency, congestive heart failure, or refusal to participate in the study. All participants underwent complete clinical, laboratory, and ultrasonographic evaluations. Prior to recruitment, informed consent was obtained from each patient. The study protocol was designed according to the ethical guidelines of the 1975 Declaration of Helsinki and approved by the Ethics Committee at Iuliu Hatçieganu University of Medicine and Pharmacy. 576

Clinical evaluation We collected the patients’ personal data, demographic characteristics (gender and age), anthropometric data (weight, height, waist circumference [WC], hip circumference [HC], waist/hip ratio [WHR]), and clinical data (blood pressure). Body weight (in kilograms) and standing height (in meters) were measured with the patients wearing lightweight clothes and no shoes. BMI (in kg/m2) was calculated by dividing each patient’s weight (in kilograms) by their squared height (in meters). Obesity was defined as a BMI > 30 kg/m2 and subdivided into 1st degree (30-34.9 kg/m2), 2nd degree (35-39.9 kg/m2), and 3rd degree (≥ 40 kg/m2) (13). With the patient standing, WC was measured at the midpoint between the lower border of the rib cage and the iliac crest at the end of expiration, whereas HC was measured at the widest point between the hip and buttocks. The WHR was considered abnormal if > 0.89. Blood pressure was measured with a mechanical sphygmomanometer. Hypertension or uncontrolled hypertension was defined as the presence of blood pressure values > 140/90 mmHg, according to the Eighth Joint National Committee.

Laboratory investigation Venous blood samples were drawn in the morning after overnight fasting. Metabolic parameters (fasting glucose, total cholesterol, HDL-cholesterol, LDLcholesterol, aspartate aminotransferase [AST], alanine aminotransferase [ALT], γ-glutamyl transpeptidase [GGT], and uric acid) were assayed on an automatic analyzer (Beckman Coulter Unicell DXC600, Beckman Coulter Inc., Fullerton, CA, USA) using standard laboratory procedures. Immunological parameters were analyzed with the same automatic analyzer (Beckman Coulter Unicell DXI 600) by ELISA method, following specifications of the kit’s protocol. Serum insulin was measured, and the degree of IR was calculated according to the homeostasis model assessment of IR (HOMA-IR) using the formula: (fasting plasma glucose [mg/dL] × fasting serum insulin [μU/mL])/405. A HOMA-IR cutoff value of 2.5 defined IR (14,15). MS was defined according to the 2009 Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Arch Endocrinol Metab. 2017;61/6

Insulin resistance and thyroid pathology

Ultrasound evaluation Each participant was evaluated with thyroid ultrasound for assessment of thyroid volume and morphology and detection of goiter, nodules, and autoimmune thyroiditis. The ultrasound evaluations were performed on a DC-N3 Doppler ultrasound system (Mindray DS USA, Inc., Mahwah, NJ, USA) equipped with a 7.5 MHz linear transducer. We also performed abdominal ultrasound evaluations to detect the occurrence of hepatic steatosis and measured the subcutaneous fat thickness (SFT) and visceral fat thickness (VFT) using the same ultrasound equipment (Mindray DC-N3 Doppler) equipped with a 5 MHz convex transducer. Hepatic steatosis was defined as the presence of a diffuse, hyperechoic, and bright liver, with an increased echotexture when compared to the kidneys, vascular blurring, and deep attenuation of the ultrasonic beam. The occurrence of nonalcoholic steatohepatitis (NASH) was defined by the presence of a combination of imaging (hepatic steatosis on ultrasound imaging) and laboratory criteria (detection of increased transaminases levels after exclusion of other causes of secondary hepatic fat accumulation, such as significant alcohol consumption [> 30 g alcohol/day for men and > 20 g/day for women], other liver diseases [hepatitis B, C, and autoimmune hepatitis], use of steatogenic medications, or hereditary disorders) (17). The VFT was measured with the ultrasound probe located 1 cm above the umbilicus on the xiphoArch Endocrinol Metab. 2017;61/6

umbilical line in both longitudinal and transverse views and defined as the distance between the linea alba and the anterior wall of the aorta. The SFT was determined as the distance between the linea alba and the skin, with the transducer located 1 cm above the umbilicus. All patients were evaluated by the same radiologist.

Statistical analysis All results were statistically analyzed using Excel (Microsoft, Redmond, WA, USA) and SPSS (SPSS, Inc., Chicago, IL, USA). The sample size was calculated based on a 0.1-0.3% prevalence of thyroid pathologies with a 95% level of confidence and ± 10% confidence interval (CI) length (3,18). Data with normal distribution were reported as mean ± standard deviation, and those without normal distribution as median (25th – 75th percentiles [Q1, Q3]). Differences between groups were analyzed with Student’s t test for independent samples in case of normally distributed quantitative variables, and the Mann-Whitney test for data without normal distribution. For qualitative data, chi-square test of Fisher’s exact test was used. Pearson’s correlation coefficient was applied to determine the linear relationship between two or more continuous quantitative variables, while Spearman’s correlation coefficient was used to assess the relationship between several continuous quantitative variables with outliers or to assess nonlinear relationships. In order to estimate the relationship between serum TSH levels and HOMA-IR values with multivariate analysis, we used multiple logistic regression. We adopted as the dependent variable the classification given by the HOMA-IR cutoff value and as independent variables all other continuous variables that correlated significantly with HOMA-IR in the univariate analysis. Considering that the relationship between HOMA-IR values and serum TSH levels was nonlinear, we adopted the classification given by the ratio between HOMA-IR values and TSH levels. Using a HOMA-IR cutoff value of 2.5, the patients were divided into two groups: obese and IR (HOMA-IR ≥ 2.5; Ob-IR) and obese without IR (HOMA-IR < 2.5; Ob-NIR).

RESULTS Baseline characteristics The study included 82 obese patients with a mean age of 44.21 ± 12.67 years, of whom 91.5% were women, 577

Atherosclerosis Society and International Association for the Study of Obesity. According to this definition, the patients were classified as having MS if at least three of the following five criteria were present: 1) WC > 94 cm in men or > 80 cm in women, 2) triglycerides > 150 mg/dL or fibrate treatment, 3) HDL-cholesterol < 40 mg/dL in men and < 50 mg/dL in women or cholesterol-lowering treatment, 4) fasting plasma glucose > 100 mg/dL or previously diagnosed type 2 diabetes mellitus, 5) blood pressure > 130/85 mmHg or antihypertensive treatment (16). Thyroid function was assessed with measurement of TSH and free T4 serum levels, while screening for Hashimoto’s thyroiditis was done with determination of antithyroid peroxidase (TPO) and antithyroglobulin antibodies. Serum cortisol was measured at 8 a.m. in all patients to assess the HPA axis and evaluate the relationship between serum cortisol levels and BMI, WC, WHR, serum insulin, HOMA-IR, and thyroid function.

Insulin resistance and thyroid pathology

and 8.5% were men. Table 1 describes the clinical, laboratory, and ultrasonographic characteristics of the cohort. Most patients (n = 48; 58.5%) had a BMI between 30 and 35 kg/m2. Overall, 22 (26.8%) had a BMI between 35 and 40 kg/m2, while 12 (14.6%) had Table 1. Clinical and laboratory characteristics of the investigated group Parameters Male gender (number; %)

7 (8.5)

Age (years)

44.21 ± 12.67

Weight (kg)

91.5 (85, 103)

Height (cm)

162 (157, 166)

BMI (kg/m²)

34.3 (32.38, 37.88)

Table 2. Frequency of thyroid disorders in the overall study cohort Parameters

Total (n = 82)

95% confidence interval (percentages)

Hypothyroidism (number; %)

12 (14.6)

8.6-23.8

Euthyroid (number; %)

69 (84.1)

74.8-90.5

1 (1.2)

0.2-6.6

Hyperthyroidism (number; %)

WC (cm)

108 (102, 116)

Goiter (number; %)

23 (28.0)

19.5-38.6

HC (cm)

111 (108, 119)

Thyroid nodules (number; %)

29 (35.4)

25.9-46.2

Hashimoto’s thyroiditis (number; %)

27 (32.9)

23.7-43.7

WHR (cm) HBP (number; %)

0.97 (0.92, 1.03) 39 (47.6)

SBP (mmHg)

130 (120, 140)

DBP (mmHg)

80 (70, 90)

Insulin (µUI/mL)

12.48 ± 5

FPG (mg/dL)

95 (90, 103)

HOMA-IR

2.68 (1.98, 3.9)

TSH (µIU/mL)

1.89 (1.2, 2.66)

FT4 (ng/dL)

0.78 (0.73, 0.88)

TPO-Ab (UI/mL) Tg-Ab (UI/mL)

1.3 (0.5, 10.6) 0.4 (0.2, 0.9)

Thyroid volume (mL)

12.3 (10.2, 13.7)

Serum cortisol at 8 a.m. (μg/dL)

11.06 (9.05, 14.8)

TG (mg/dL)

133 (104, 166)

LDL-cholesterol (mg/dL)

124.15 ± 28.89

Total cholesterol (mg/dL)

202.16 ± 39.15

HDL-cholesterol (mg/dL)

45.7 ± 9.95

Uric acid (mg/dL)

4.93 ± 1.13

ALT (U/L)

22 (18, 29)

AST (U/T)

20.5 (18, 25)

GGT (U/L)

19 (15, 28)

Hepatic steatosis (number; %) NASH (number; %) MS (number; %)

Total (n = 82)

severe obesity with a BMI above 40 kg/m2. A total of 57.3% of the patients met the criteria for MS. Table 2 shows the frequency of thyroid disorders in the cohort. Thyroid nodules were among the most frequent morphological abnormalities, while one out of seven patients presented hypothyroidism.

61 (75.3) 3 (3.7) 47 (57.3)

SFT (cm)

2.74 (2.52, 3.09)

VFT (cm)

4.99 ± 1.01

BMI: body mass index; WC: waist circumference; HC: hip circumference; WHR: waist/hip ratio; HBP: high blood pressure; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HOMA-IR: homeostasis model assessment of insulin resistance index; TSH: thyroid-stimulating hormone; FT4: free thyroxine; TPO-Ab: antithyroid peroxidase antibodies; Tg-Ab: antithyroglobulin antibodies; TG- triglycerides; LDL-cholesterol: low-density lipoprotein cholesterol; HDL-cholesterol: high-density lipoprotein cholesterol; ALT: alanine aminotransferase; AST: aspartate aminotransferase; GGT-γ: gamma glutamyl transpeptidase; NASH: nonalcoholic steatohepatitis; MS: metabolic syndrome; SFT: subcutaneous fat thickness; VFT: visceral fat thickness. The results are expressed as mean ± standard deviation (SD) or median and range (25-75%).

578

Analysis of the relationship between HOMA-IR and thyroid function There was a positive correlation between HOMA-IR values and TSH levels (r = 0.24, p = 0.028) in the overall cohort (n = 82). TSH correlated positively with serum insulin (r = 0.23, p = 0.037). In contrast, no significant correlation was found between insulin, HOMA-IR, and thyroid volume. Table 3 presents an analysis of differences between the groups with HOMA-IR ≥ 2.5 and < 2.5 (Ob-IR and ObNIR, respectively). Patients with HOMA-IR ≥ 2.5 had significantly higher TSH levels than those with HOMAIR < 2.5. Thyroid nodules were detected in 44.4% of the patients with HOMA-IR ≥ 2.5 compared with 24.3% of those with HOMA-IR < 2.5 (p = 0.058), thus showing a trend towards an association between both variables. Although patients with HOMA-IR ≥ 2.5 had an increased frequency of goiter and Hashimoto’s thyroiditis, the difference was not significant (Table 3). No correlation was found between HOMA-IR values and FT4 levels.

Analysis of the relationship between HOMA-IR and metabolic parameters As expected, HOMA-IR correlated positively with the degree of obesity, BMI (r = 0.57, p < 0.001), central adipose tissue (WC; r = 0.48, p = 0.000), WHR (r = 0.30, p = 0.006), VFT (r = 0.35, p = 0.002), and SFT (r = 0.28, p = 0.012). HOMA-IR also correlated positively with triglycerides (r = 0.25, p = 0.026), ALT (r = 0.37, p = 0.001), and GGT (r = 0.32, p = 0.003) levels, as well as with 8 a.m. cortisol levels (r = 0.24, p = 0.03). Arch Endocrinol Metab. 2017;61/6

Insulin resistance and thyroid pathology

HOMA-IR < 2.5 (n = 37)

HOMA-IR ≥ 2.5 (n = 45)

Thyroid disorders Hypothyroidism (number; %)

Table 4. Comparison of metabolic parameters between the groups with and without insulin resistance (HOMA -IR ≥ 2.5 and < 2.5, respectively) HOMA-IR < 2.5 (n = 37)

p 0.184

Male gender, n (%)

4 (10.8)

3 (6.7)

0.695

43.62 ± 12.82

0.648

25 (67.6)

36 (81.8)

0.138

1 (2.7)

2 (4.4)

1.00

12 (32.4)

25 (55.6)

0.046

15 (40.5)

32 (71.1)

0.005

33.1 (32, 36)

35.8 (33.8, 39.4)

0.000

120 (120, 130)

140 (120, 145)

0.227

DBP (mmHg), median (Q1, Q3)

80 (70, 90)

85 (70, 90)

0.719

WC (cm), median (Q1, Q3)

102 (98, 113)

112 (106, 116)

0.003

HC (cm), median (Q1, Q3)

110 (107, 117)

113 (110, 121)

0.017

WHR (cm), median (Q1, Q3)

0.95 (0.91, 1.02)

0.982 (0.922, 1.04)

0.124

Insulin (µUI/mL), mean ± SD

7.8 ± 1.9

16.33 ± 5.13

0.000

FPG (mg/dL), median (Q1, Q3)

93 (89, 99)

99 (93, 106)

0.009

HOMA-IR, median (Q1, Q3)

1.95 (1.5, 2.1)

3.59 (3.08, 4.43)

TG (mg/dL), median (Q1, Q3)

126 (92, 166)

139 (121, 165)

0.252

LDL-cholesterol (mg/dL), mean ± SD

122.16 ± 25.2

125.78 ± 31.79

0.576

Total cholesterol(mg/dL), mean ± SD

198.86 ± 36.01

204.87 ± 41.77

0.493

HDL-cholesterol (mg/dL), mean ± SD

46.28 ± 9.96

45.22 ± 10.02

0.636

Uric acid (mg/dL), mean ± SD

4.84 ± 1.08

5 ± 1.18

0.504

ALT (U/L), median (Q1, Q3)

19 (17, 25)

24 (19, 36)

0.089

AST (U/T), median (Q1, Q3)

20 (19, 23)

21 (18, 26)

0.690

GGT (U/L), median (Q1, Q3)

17 (14, 25)

19 (16, 29)

0.138

Serum cortisol (μg/dL), median (Q1, Q3)

11.11 (8.38, 14.6)

11.01 (9.63, 15.78)

0.134

2.62 (2.41, 2.86)

2.90 (2.66, 3.3)

0.000

4.74 ± 0.92

5.19 ± 1.05

0.052

9 (20.0)

Age (years), mean ± SD

33 (89.2)

36 (80.0)

Hepatic steatosis, n (%)

Hyperthyroidism (number; %)

1 (2.7)

0 (0.0)

Goiter (number; %)

8 (21.6)

15 (33.3)

0.240

Uncontrolled HBP (mmHg), n (%)

Thyroid nodules (number; %)

9 (24.3)

20 (44.4)

0.058

MS, n (%)

Hashimoto’s thyroiditis (number; %)

12 (32.4)

0.931

BMI (kg/m²), median (Q1, Q3) SBP (mmHg), median (Q1, Q3)

15 (33.3)

NASH, n (%)

TSH (µIU/mL) (median (Q1, Q3))

1.59 (0.94, 2.26)

2.03 (1.59, 2.69)

0.023

FT4 (ng/dL) (median (Q1, Q3))

0.79 (0.72, 0.88)

0.78 (0.73, 0.88)

0.860

TPO-Ab (UI/mL) (median (Q1, Q3))

1.4 (0.5, 10.1)

1.1 (0.4, 10.6)

0.593

Tg-Ab (UI/mL) (median (Q1, Q3))

0.6 (0.2, 1.1)

0.4 (0.2, 0.5)

0.226

12 (10.2, 13.7)

12.3 (10.3, 13.7)

0.755

Thyroid volume (mL) (median (Q1, Q3))

HOMA-IR: homeostasis model assessment of insulin resistance index; TSH: thyroid-stimulating hormone; FT4: free thyroxine; TPO-Ab: antithyroid peroxidase antibodies; Tg-Ab: antithyroglobulin antibodies.

Both groups also presented differences in metabolic parameters (BMI, WC, HC, SFT, VFT) and MS, which were significantly higher in patients with HOMA-IR ≥ 2.5 than in those with HOMA-IR < 2.5 (Table 4).

Analysis of the relationship between serum TSH levels and metabolic parameters Serum TSH levels correlated positively with serum triglycerides levels (r = 0.30, p = 0.006) and negatively with serum HDL-cholesterol levels (r = -0.25, p = 002). There was a positive relationship between serum TSH levels and serum morning cortisol levels (r = 0.26, p = 0.02). When we compared obese patients with and without hepatic steatosis in regards to thyroid dysfunction, we found no significant differences in TSH levels between both groups. Obese patients with NASH displayed significantly higher TSH values compared with those without NASH (Table 5). There were no significant differences in TSH levels between subjects with versus those without MS. Arch Endocrinol Metab. 2017;61/6

44.92 ± 12.64

3 (8.1)

Euthyroidism (number; %)

HOMA-IR ≥ 2.5 (n = 45)

SFT (cm), median (Q1, Q3) VFT (cm), mean ± SD

NASH: nonalcoholic steatohepatitis; HBP: high blood pressure; MS: metabolic syndrome; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; WC: waist circumference; HC: hip circumference; WHR: waist/hip ratio; FPG: fasting plasma glucose; HOMA-IR: homeostasis model assessment of insulin resistance index; TG: triglycerides; LDL-cholesterol: low-density lipoprotein cholesterol; HDL-cholesterol: high-density lipoprotein cholesterol; ALT: alanine aminotransferase; AST: aspartate aminotransferase; GGT-γ: gamma glutamyl transpeptidase; SFT: subcutaneous fat thickness; VFT: visceral fat thickness. The results are expressed as mean ± standard deviation (SD) or median and range (25-75%).

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Table 3. Comparison of thyroid parameters between the groups with and without insulin resistance (HOMA-IR ≥ 2.5 and < 2.5, respectively)

Insulin resistance and thyroid pathology

Table 5. TSH levels (expressed as median and (Q1, Q3)) according to the presence or not of hepatic steatosis, nonalcoholic steatohepatitis, and metabolic syndrome TSH

TSH

Without MS (n = 35)

With MS (n = 47)

1.75 (1.10, 2.53)

1.99 (1.34, 2.68)

0.36

Without NASH (n = 79)

With NASH (n = 3)

1.77 (1.19, 2.49)

4.76 (3.73, 4.80)

0.04

Without hepatic steatosis (n = 20)

With hepatic steatosis (n = 61)

1.61 (1.02, 2.42)

1.99 (1.29, 2.66)

0.21

TSH: thyroid-stimulating hormone; NASH: nonalcoholic steatohepatitis; MS: metabolic syndrome.

Multivariate analyses On multivariate linear analysis including the model with HOMA-IR cutoff values as a dependent variable and all other quantitative variables as independent variables, the correlation between serum TSH levels and HOMA-IR values remained (b = 0.195, 95% CI 0.0140.376, p = 0.035). The covariate variables were BMI, WHR, serum cortisol, SFT, VFT, triglycerides, GGT, and ALT. Serum cortisol also predicted statistically significant HOMA-IR (b = 1.535, 95% CI 0.2822.789, p = 0.017) along with BMI and TSH.

DISCUSSION The results of this study showed a significant positive correlation of HOMA-IR values with serum TSH levels among obese patients. Ob-IR patients had significantly higher TSH levels compared with Ob-NIR patients, although FT4 levels were comparable in both groups (Table 3). These results are consistent with others reported in the literature (19,20). Increased TSH level could thus be hypothesized to be a metabolic consequence of obesity. The mechanisms by which IR may impact the thyroid are not completely understood. Recent evidence suggests that the main suspected mechanism is a possible relationship between thyroid hormones and the adipokines released by visceral fat, especially leptin (21,22). Leptin interferes with the negative feedback regulation of thyroid hormones and stimulates the hypothalamic-pituitary-thyroid axis to increase TSH secretion (23). Production of leptin increases along with increased body fat, with visceral fat mass inducing and aggravating IR, while hyperinsulinemia stimulates the production of leptin (24). Another hypothesis is that the metabolic hepatic disorder related to IR could impact the metabolism 580

of thyroid hormones. Hepatic IR leads to increased glucose production and increased VLDL-cholesterol, aggravating hyperglycemia and hyperinsulinemia. IR increases the influx of free fatty acids (FFA) to the liver and de novo lipogenesis, leading to lipid accumulation in hepatocytes, which in the context of the inflammatory status and oxidative stress, increases lipid peroxidation and mitochondrial dysfunction, leading to NASH (25). Consequently, metabolic liver damage can lead to impaired synthesis of thyroid hormones transport proteins (thyroxine-binding globulin [TBG], albumin, and transthyretin), decreasing the transport of these hormones to target cells. Moreover, decreased type I 5’-deiodinase activity alters T4 to T3 conversion, which can stimulate TSH production by reducing the feedback of the thyroid hormones on the HPA (11,26). In support of the previous hypothesis, HOMA-IR correlated positively with liver markers (ALT and GGT) and obese patients with severe hepatic impairment (NASH) had significantly higher TSH levels than those without NASH (Table 5). Of note, we did not perform liver biopsy for histological confirmation of NASH, which may be considered one of our study’s limitations. As expected, HOMA-IR correlated positively with the degree of obesity (BMI) and visceral adipose tissue (WC, WHR, and VFT) and the Ob-IR group had significantly higher BMI, VFT, and WC values when compared with the Ob-NIR group (Table 4). This fact supports the theory that obesity and particularly its visceral component should be considered the main causative factor in MS (27). Macrophage infiltration with increased production of adipokines and cytokines (TNF-α, MCP-1, CRP, IL-1, IL-6, FFA, leptin, resistin) occurs at the level of visceral fat, leading to chronic “low-grade” inflammation and oxidative stress, which affect insulin signaling in target cells (and binding of insulin to its receptor) leading to IR and its systemic consequences (28). A comparison of both our study groups showed a significantly higher incidence of MS and uncontrolled hypertension among Ob-IR patients. Also, the OB-IR group had a higher frequency of liver disease (NAFLD/ NASH) than the Ob-NIR group (Table 4). Recent publications have suggested the occurrence of peripheral resistance to thyroid hormones secondary to the oxidative stress and inflammatory status related to IR, which would alter the transmembrane intracellular transport of thyroid hormone and its binding to nuclear receptors (29). Arch Endocrinol Metab. 2017;61/6

Other possible assumptions are that the central hypertonia of the TRH-TSH axis in obesity is an adaptation to chronically increased caloric intake aiming at resetting the basal metabolism at a higher level. This results in increased T4 levels and T4 to T3 conversion by stimulation of the type 2 deiodinase (5’D2) activity in muscle and brown tissue to boost energy expenditure. This theory might also justify the higher TSH levels observed among obese patients (26). Our study has shown a positive correlation between serum TSH and cortisol levels, both of which correlated positively with HOMA-IR. No correlation was found between cortisol and FT4 levels, which is consistent with previously reported data in the specialized literature (30). Little is known about the HPA axis hypertonia involved in obesity and its relation to the TRH-TSHthyroid axis. Elevated serum cortisol may have peripheral and hepatic effects by decreasing the hepatic synthesis of TBG, affecting T4 to T3 conversion (decreasing the type 1 deiodinase [5’D1] activity), and stimulating the conversion of T4 into inactive reverse T3 (increasing the type 3 deiodinase [5’D3] activity). Elevated cortisol may also have a central impact since cortisol has an effect on TSH depression (31,32). Moreover, the serum cortisol measurements in our obese patients demonstrated a U-shaped association with BMI: patients with overweight and first-degree obesity showed normal or low morning serum cortisol, which increased along with the obesity (33). These controversies are likely due to the increased peripheral metabolism of cortisol. Patients with obesity may display enhanced inactivation of cortisol by 5α-reductase or impaired reactivation of cortisol from cortisone by 11β-hydroxysteroid dehydrogenase type 1, resulting in activation of the HPA axis (34,35). A negative correlation between serum TSH and cortisol levels has been reported for TSH cutoff values < 2.0 µUI/mL, while a positive correlation has been observed for cutoff values > 2.0 µUI/mL (30). Similar to our results, other researchers have also reported a positive correlation of cortisol and TSH levels in euthyroid obese patients (36). Recent studies have shown that MS and its central factor (IR) are associated with increased thyroid volume, prevalence of thyroid nodules, and risk of systemic malignancies, including differentiated thyroid carcinomas (7,37). Comparing both our groups with and without IR in regards to thyroid morphology, we observed a higher Arch Endocrinol Metab. 2017;61/6

frequency of goiter (33.3% versus 21.6%) and thyroid nodules (44.4% versus 24.3%, p = 0.058) in the ObIR group, although without statistical significance. There were no significant differences in the incidence of Hashimoto’s thyroiditis in the two groups. We must note that the results obtained in our study derived from a population attending an Endocrinology Department for investigation of obesity, which explains the 10-fold higher prevalence of women over men in our cohort. As a consequence, the prevalence rates encountered cannot be generalized to the entire population of obese individuals. The female population is known to be the most interested group in investigating the causes of obesity, and multiple studies have been conducted exclusively in women (38). Additionally, thyroid disorders are considered to be more common in women (39). Due to limited financial resources, we calculated the sample size with an accurate CI of ± 10%. We consider this as a limitation of the study and recommend that further research should be conducted including a larger sample size. The proliferative effect of insulin has been shown in vitro using cultured thyroid cells via the insulin receptors and IGF-1 receptor – both overexpressed in thyroid tumors as well as in non-thyroid tumors (breast, colon, liver). Hyperinsulinemia can, thus, determine an increase in thyroid volume, occurrence of thyroid nodules, and thyroid carcinogenesis (4,8,40). Moreover, TSH is a major growth factor in the thyroid gland and a regulator of the expression other growth factors; TSH promotes the insulin/IGF-1 signaling pathway, while IGF-1 is actively involved in TSHmediated proliferation of thyrocytes (8,41). Patients with IR have higher TSH levels than those without IR and, as a consequence, a higher risk of proliferation of thyroid cells due to its morphogenic effect. In conclusion, obese patients displayed a significantly positive correlation between HOMA-IR values and TSH levels. Our findings validate the cutoff value of 2.5 for HOMA-IR in regards to TSH levels, which was higher in obese patients with IR when compared with those without IR. Although serum cortisol correlated with HOMA-IR values and serum TSH levels, the correlation of HOMA-IR and TSH remained even after adjusting the cortisol’s influence. Acknowledgements: we would like to express our gratitude to the management unit of the Infectious Diseases Clinical Hospital, Cluj-Napoca, Romania, for its assistance and support. Also, we are sincerely thankful to the journal’s anonymous reviewers for 581

Insulin resistance and thyroid pathology

their careful reading of our manuscript and their insightful comments and suggestions. Disclosure: we wish to confirm that there are no known conflicts of interest that could be perceived as prejudicing the impartiality of the research reported, and there has been no financial support for this work that could have influenced its outcome. This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. All coauthors listed below made their contribution to the submitted manuscript, as follows: Nicoleta Leach, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, 5th Department of Internal Medicine (study design); Cosmina Ioana Bondor, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Department of Medical Informatics and Biostatistics (data analysis); Smaranda Mârza, Infectious Diseases Clinical Hospital – Integrated Ambulatory – Pediatrics, competence in general ultrasonography, Cluj-Napoca (ultrasonographic investigation); Daniela Moga, Infectious Diseases Clinical Hospital – Integrated Ambulatory – Laboratory Department, Cluj Napoca (laboratory investigations); Ana Valea, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Department of Endocrinology (methods); Cristina Ghervan, Iuliu Hatçieganu University of Medicine and Pharmacy Cluj-Napoca, Department of Endocrinology (study conception). We further confirm that the order of authorship listed in the manuscript has been approved by all authors. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing, we confirm that we have followed the regulations of our institutions concerning intellectual property.

We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from comanniko@yahoo.com or Nicoleta.Coman@ umfcluj.ro

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33. Schorr M, Lawson EA, Dichtel LE, Klibanski A, Miller KK. Cortisol Measures Across the Weight Spectrum. J Clin Endocrinol Metab. 2015;100(9):3313-21. 34. Rask E, Walker BR, Söderberg S, Livingstone DE, Eliasson M, Johnson O, et al. Tissue-specific changes in peripheral cortisol metabolism in obese women: increased adipose 11betahydroxysteroid dehydrogenase type 1 activity. J Clin Endocrinol Metab. 2002;87(7):3330-6. 35. Tsilchorozidou T, Honour JW, Conway GS. Altered cortisol metabolism in polycystic ovary syndrome: insulin enhances 5alpha-reduction but not the elevated adrenal steroid production rates. J Clin Endocrinol Metab. 2003;88(12):5907-13. 36. Neslihan SA, Betül EB, Bülent B, Sonat DK. Relationship between TSH and cortisol levels in euthyroid obese subjects. Endocrine Abstracts. 2015;37 EP623. 37. Rezzónico J, Rezzónico M, Pusiol E, Pitoia F, Niepomniszcze H. High prevalence of thyroid nodules in patients with acrochordons (skin tags). Possible role of insulin-resistance. Medicina (B Aires). 2009;69(3):302-4. 38. Farishta F, Farishta S. Insulin resistance and thyroid hypofunction in obese women – A cross sectional study. Integr Obes Diabetes. 2015;1(4):101-2. 39. Vanderpump MPJ. The epidemiology of thyroid disease. British Medical Bulletin. 2011;99(1):39-51. 40. Rezzonico J, Rezzonico M, Pusiol E, Pitoia F, Niepomniszcze H. Introducing the thyroid gland as another victim of the insulin resistance syndrome. Thyroid. 2008;18(4):461-4. 41. Hegedüs L, Bonnema SJ, Bennedbaek FN. Management of simple nodular goiter: Current status and future perspectives. Endocr Rev. 2003;24(1):102-32.

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Arch Endocrinol Metab. 2017;61/6

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original article

Metastatic lymph node characteristics as predictors of recurrence/persistence in the neck and distant metastases in differentiated thyroid cancer Departamento de Endocrinologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil 2 Departamento de Endocrinologia, Instituto Nacional de Câncer (Inca), Rio de Janeiro, RJ, Brasil 3 Serviço de Cirurgia de Cabeça e Pescoço, Instituto Nacional de Câncer (Inca), Rio de Janeiro, RJ, Brasil 4 Instituto Nacional de Câncer (Inca), Rio de Janeiro, RJ, Brasil 5 Departamento de Endocrinologia, Instituto Nacional de Câncer (Inca), Rio de Janeiro, RJ, Brasil e Departamento de Medicina Nuclear, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil 1

Correspondence to: Mayara Peres Barbosa Rua Augusto Spinelli, 178/105 28610-190 – Nova Friburgo, RJ, Brasil mayperes5@yahoo.com.br Received on Aug/04/2017 Accepted on Sep/26/2017 DOI: 10.1590/2359-3997000000307

Mayara Peres Barbosa1, Denise Momesso2, Daniel Alves Bulzico2, Terence Farias3, Fernando Dias4, Roberto Araújo Lima4, Rossana Corbo5, Mario Vaisman1, Fernanda Vaisman2

ABSTRACT Objective: The aim of this study was to evaluate the association between this characteristic and outcomes in patients with lymph node metastasis in a Brazilian cohort. Subjects and methods: This study examined a retrospective cohort of adult patients diagnosed with differentiated thyroid cancer and lymph node metastases from 1998 to 2015 in two referral centers. Number, location, size and extranodal extension (ENE) of metastatic lymph nodes were assessed and correlated with response to initial therapy. Results: A greater number of metastatic nodes, larger size, presence of lateral neck disease and ENE were all associated with a lower probability of achieving an excellent response to initial therapy (p ≤ 0.05 for all these parameters). Local recurrent disease had a significant association with lymph node number (6 in the recurrence/persistence group versus 4 in the non-recurrent group; p = 0.02) and ENE (19.2 versus 7.5%, p = 0.03). Lateral neck disease was the only characteristic associated with distant metastasis and was present in 52.1% of the group without metastasis and 70.4% of the group with metastasis (p = 0.001). Conclusion: The lymph node characteristics were associated with response to initial therapy and neck recurrence/persistence, confirming the importance of the analysis of these factors in risk stratification in a Brazilian population and its possible use to tailor initial staging and long term follow-up. Arch Endocrinol Metab. 2017;61(6):584-9 Keywords Neck recurrence/persistence; thyroid cancer; lymph nodes; prognosis

INTRODUCTION Differentiated thyroid cancer (DTC) is the cancer with the highest increase in the incidence in the United States (1). Cervical lymph nodes are the most common site of metastases. In most series, the incidence varies from 20-50%, depending on tumor size, age, gender and local invasion; however, it can be found in up to 90% in countries that routinely adopt prophylactic neck dissection (2). The prognostic significance of lymph node metastases in DTC is still controversial (3). Most studies show that the presence of lymph node metastases has little impact on overall survival, being more significant in older patients despite a great impact on recurrence/ persistence rates and impairment of quality of life in all age groups (4,5). 584

In the past, the presence or absence of node metastasis and its location in the neck were the only factors analyzed to classify node disease (6). Recently, lymph node characteristics such as number, size, location and extranodal extension (ENE) have been shown to have great impacts on the risk of nodal disease recurrence/ persistence (7). In 2015, the American Thyroid Association (ATA) recognized the importance of these factors and recommended that patients be considered as low risk when there is no evidence of clinical nodal metastases (cN0) or when micrometastases (less than two millimeters) in five or fewer lymph nodes is present. Patients with clinically evident lymph nodes (cN1) and/or more than five lymph nodes, all less than three centimeters, should be classified as intermediate risk. The Committee defined high risk patients as those with lymph nodes larger than three centimeters. The presence Arch Endocrinol Metab. 2017;61/6

Prognostic impact of metastatic lymph nodes

SUBJECTS AND METHODS The study is a retrospective analysis of a cohort of patients 21 years of age or older diagnosed with DTC with lymph node metastases from 1998 to 2015. Data were obtained from the University Hospital Clementino Fraga Filho (UFRJ) and the Brazilian National Cancer Institute (Inca). Patients were followed for at least 1 year, and all were submitted to total thyroidectomy and RAI therapy. The RAI activity was decided by a multidisciplinary team based on clinical, histopathological and complementary tests. In neither institution is it routine to perform prophylactic cervical dissection. Patients with a diagnosis of medullary, anaplastic carcinoma and poorly differentiated variants such as insular, tall and columnar cells were excluded.

Laboratory studies Between 1998 and 2001, a thyroglobulin (Tg) assay with a functional sensitivity of 0.5 ng/mL was employed. From 2001 until 2010, serum Tg was quantified by an immunometric assay (Immulite) with a functional sensitivity of 0.2 ng/mL. From 2010 until the present, the functional sensitivity was reduced to 0.1 ng/mL.

Evaluation of outcomes Clinical-pathological characteristics of the patients, treatment details (surgery, RAI therapy) and postoperative follow-up (Tg, recurrence/persistence, deaths) were obtained. The characteristics of the metastatic lymph nodes were analyzed such as number, Arch Endocrinol Metab. 2017;61/6

location, size of the largest lymph node and presence of ENE. Patients were classified by AJCC/TNM (8) and ATA risk classification (2). Response to initial therapy was assessed by ATA and classified as follows: excellent response (negative imaging and suppressed Tg < 0.2 ng/mL and stimulated Tg < 1.0 ng/mL); indeterminate response (nonspecific findings on imaging studies, nonstimulated Tg detectable but < 1 ng/mL, stimulated Tg detectable but < 10 ng/mL); biochemical incomplete response (negative imaging and nonstimulated Tg > 1 ng/mL or stimulated Tg > 10 ng/mL); or structural incomplete response to therapy (structural or functional evidence of disease, with any Tg level) (2). Patients were classified in the final follow-up as having no evidence of disease when the suppressed Tg was less than 1 ng/mL, no antibodies were present, and there was no structural evidence of the disease. Patients with suppressed Tg greater than 1 ng/mL and stimulated greater than 2 ng/mL or any evidence of structural disease (complementary exams or biopsy) were classified as biochemical or structural persistence, respectively. Cervical recurrence/persistence was defined as follows: positive cytology/histology, highly suspicious lymph nodes or thyroid bed nodules on the US (hyper-vascularity, cystic areas, heterogeneous content, rounded shape and enlargement on followup), or cross-sectional imaging highly suspicious for metastatic disease. Distant metastases were assessed by cross sectional images and considered as present when there was iodine uptake and/or were highly suspicious on CTs and/or MRIs, even with no iodine uptake but with high thyroglobulin levels or if proven by biopsy. The ethical boards of both institution involved approved this study.

Statistical analysis Continuous data are presented as the mean and standard deviations with median values. For comparing nonparametric medians, the Mann-Whitney test was used, and for categories, we used Chi-square and Fisherâ&#x20AC;&#x2122;s exact tests. Analysis was performed using SPSS software (Version 20.0 for MAC; SPSS, Inc., Chicago IL).

RESULTS General characteristics of the participants are shown in Table 1. As expected, the majority were women (71.1%), and the median age was 41 years. Two hundred and eight 585

of ENE was not included as an independent factor, but the presence of more than three lymph nodes with ENE was considered a high risk feature with a 40% risk of recurrence/persistence (2). The clinical implication of this new stratification has also an impact in adjuvant radioiodine (RAI) therapy, allowing low nodal volume disease to be managed without adjuvant RAI therapy, for example (3). However, data in the literature validating this impact around the world is still scattered. The aim of this study is to evaluate the association between the characteristics of metastatic lymph nodes and the final clinical status according to response to therapy. In addition, this study aims to analyze the association of these characteristics with cervical recurrence/persistence and distant metastatic disease risks.

Prognostic impact of metastatic lymph nodes

patients (98,57%) had a diagnosis of papillary carcinoma, of which 90.9% (192) were the classical form, 6.2% (9) were follicular variant and 1.4% (3) were HĂźrthle cell variant. Only 3 patients (1,4%) were diagnosed as having follicular carcinoma. The median size of the tumors was 2.2 cm (0,3-15), 121 patients (57.3%) had tumors with extrathyroid extension, 103 (48.8%) had multifocality and 63 (29.9%) had vascular invasion. Regarding the characteristics of the lymph nodes, 131 patients (62.1%) had N1 tumors, 99 (46.9%) had more than five metastatic lymph nodes, 40 (19%) had the largest lymph node affect above or equal to 3 cm, and 15 (7.1%) patients had descriptions of extranodal extension (ENE). Ninety one patients (43.1%) had only involvement of the central compartment (N1a), and 102 (48.3%) had lateral and central compartment involvement (N1b). Twentyseven patients (12.8%) presented distant metastases in diagnosis. All patients underwent RAI treatment. The stratification of patients according to ATA 2015 risk showed 21 patients (9.9%) were low, 110 (52.1%) intermediate and 80 (38%) high risk. As also shown in Table 1, the median follow-up was 6.2 years (0.8-17). At the end of follow-up, 89 patients (42.2%) were disease-free, 74 (35.1%) maintained biochemical disease, 43 (20.4%) had structural persistence, and 5 (2.4%) died from the disease. Table 2 shows the relationship between lymph node characteristics and response to initial therapy. The number of lymph nodes, size, presence of ENE and N1b involvement presented greater association with an incomplete structural response, whereas the presence of metastatic node exclusively in the central compartment (N1a) was associated with a higher chance of having an excellent response. Patients with recurrent lymph node disease tend to be younger and have a greater number of metastatic lymph nodes, which tend to be more than 3 cm, than patients who did not have neck recurrence/persistence/ persistence of nodal disease. The involvement of lateral neck (N1b) and ENE were also more frequent in patients who recurred. The number and presence of ENE were the features that had significant associations with recurrence/persistence with a relative risk RR 1.3 (IC: 1-2.7) (Table 3). Regarding the primary tumor, vascular invasion was the only feature that was associated with lymph node recurrence/persistence. As expected, the recurrent group had a higher basal Tg and underwent a higher cumulative activity of RAI overtime. 586

Table 1. Cohort description Characteristic Gender (F:M)

N = 211

150:61

71.1:28.9

Age at diagnosis (years)

41 (21-77)

Size (cm)

2.2 (0.3-15)

146 10 51 4

69.2 4.7 24.2 1.9

Race White Black Latin Not informed Size (cm)

2.2 (0.3-15)

Total thyroidectomy

211

100

Histology PTC Invasive follicular variant of PTC Hurthle variant FTC

192 13 3 3

90.9 6.2 1.4 1.4

ETE

121

57.3

Multifocality

103

48.8

Vascular invasion

29.9

cN1

131

62.1

Lymph node staging > 5 metastatic lymph nodes > 3 cm metastatic lymph node Central compartment only (N1A) Lateral compartment (N1B) Extra nodal extension

99 40 91 102 15

46.9 19 43.1 48.3 7.1

Distant metastasis

12.8

RAI

211

100

Initial activity (mCi)

150 (30-250)

Total activity (mCi)

200 (100-1100)

21 110 80

9.9 52.1 38

6.2 (0.8-17)

Final status NED with additional therapy Biochemical persistent disease Structural persistent disease

89 74 43

42.2 35.1 20.4

Deaths

2.4

ATA 2015 risk Low Intermediate High Follow-up (years)

PTC: papillary thyroid cancer; FTC: follicular thyroid cancer; ETE: extrathyroidal extension; NED: no evidence of disease; ATA: American Thyroid Association; RAI: radioactive iodine.

Finally, as shown in the Table 4, N1b tumors and the presence of vascular invasion presented a statistically significant association with the presence of distant metastases. Other lymph node characteristics were not significant as risk factors for distant metastases. Arch Endocrinol Metab. 2017;61/6

Prognostic impact of metastatic lymph nodes

Table 2. Lymph node characteristics versus response to initial therapy Excellent response (n = 67)

Indeterminate response (n = 16)

Biochemical incomplete (n = 84)

Structural incomplete (n = 44)

p-value

> 5 metastatic lymph nodes

33.3%

37.5%

71.5%

70%

0.05

> 3 cm metastatic lymph nodes

16.7%

25%

26.6%

26.7%

0.01

Lymph node Central compartment alone (N1A)

49.3%

50%

47.6%

20.5%

0.02

Lateral compartment (N1B)

48.7%

50%

52.4%

79.5%

0.02

Extranodal extension

6.7%

6.25%

8.2%

14.3%

0.03

Characteristic

Table 3. Risk factors for lymph node structural recurrence/persistence during follow-up N = 211

LN Recurrence/persistence (n = 52)

p-value

38 (21-77)

42 (21-72)

0.08

67.3%:32.7%

71.7%:28.3%

0.3 (NS)

Age Gender (F:M)

No LN recurrence/persistence (n = 159)

cN1

66.7%

75.3%

0.4 (NS)

6 (2-33)

4 (1-30)

0.02

> 3 cm metastatic lymph node

30.5%

26%

0.08

Lymph node Central compartment (N1A)

28.5%

41.6%

0.07

Lateral compartment (N1B)

71.5%

58.4%

0.07

Extranodal extension

19.2%

7.5%

0.03

Histology PTC FV PTC Hurthle variant FTC

90.6% 7.5% 0 1.9%

86.4% 9.3% 2.9% 1.4%

Tumor size (cm)

2.5 (1.0-8.0)

2.0 (0.3-15)

0.3 (NS)

ETE

65.3%

55.8%

0.3 (NS)

Multifocality

53.1%

51.1%

0.8 (NS)

34%

20.4%

0.05

Number of metastatic lymph nodes

0.4

Vascular invasion Distant metastases RAI initial activity (mCi)

13.5%

12.6%

0.5 (NS)

150 (100-200)

150 (30-250)

0.5 (NS)

RAI cumulative activity(mCi)

350 (120-1050)

150 (100-1100)

< 0.001

Post-operative suppressed Thyroglobulin (ng/mL)

92 (< 0.1-460)

1.15 (< 0.1-280)

0.01

PTC: papillary thyroid cancer; FTC: follicular thyroid cancer; ETE: extrathyroidal extension; NED: no evidence of disease; ATA: American Thyroid Association; RAI: radioactive iodine.

Distant metastasis (n = 27)

No distant metastasis (n = 184)

RR (IC 95%)

p-value

87.5%

71.4%

1.19 (0.99-1.5)

0.06

Vascular invasion by the primary tumor

50%

28.2%

1.83 (1.19-2.82)

0.04

> 3 cm metastatic lymph nodes

22%

19.5%

1.13 (0.52-2.4)

0.7

Extra nodal extension

12%

9.1%

1.2 (0.39-4.07)

0.7

> 5 metastatic lymph nodes

59%

44.5%

1.3 (0.93-1.8)

0.1

70.4%

52.1%

1.3 (1.01-1.78)

0.03

Clinical N1

N1b

Arch Endocrinol Metab. 2017;61/6

Table 4. Risk factors for distant metastasis

587

Prognostic impact of metastatic lymph nodes

DISCUSSION The present study analyzed the characteristics of tumor and metastatic lymph nodes in patients with differentiated thyroid cancer (DTC), associating them with response to initial therapy, lymph node recurrence/ persistence and the presence of distant metastases. In this study, the number of metastatic nodes (> 5) and the presence of ENE were significantly associated with neck recurrence/persistence. In addition, their size and location showed a trend to be associated with an increased risk of neck recurrence/persistence during follow-up. Hence, the presence of lateral neck disease was also associated with a greater risk of distant metastases. Our first analysis aimed to correlate the metastatic lymph node features at diagnosis with the response to initial therapy within the first 6-24 months of follow-up. All lymph node characteristics studied, except N1a, had a high correlation with incomplete structural or biochemical responses. Other studies in the literature, which also analyzed response to therapy and lymph node characteristics, showed similar results. Jeon and cols. (10) classified their patients according to the number and size of lymph nodes (very low risk when there are 5 or less and they are less than 0.2 cm, low risk when there are 5 or less and they are 0.2 cm or greater, and high risk when there are more than 5) and concluded that these features predicted the response to initial therapy. In the study by Lango and cols. (11), the presence of ENE reduced the likelihood of an excellent response (OR 3.5 CI 95% 1.3-10; p 0.01), and increased the likelihood (OR 5 CI 1.2-21; p 0.03) of a Tg postoperative greater than 50 ng/mL. The present study also demonstrated a significant correlation between the number of metastatic lymph node and recurrent disease (p = 0.02). Similarly, Lee and cols. demonstrated that the higher the number of metastatic lymph nodes the higher the recurrence/ persistence rate: patients with 2 to 5 metastatic lymph nodes showed 2 to 3 times greater recurrence/ persistence than patients with one, and patients with 6 or more metastatic lymph nodes had a risk of 3.7 times greater (12). Ricarte-Filho and cols. demonstrated that the number of metastatic lymph nodes (greater than 3 in patients aged 45 years or less and greater than 5 in those over 45 years) was an important and independent predictor of recurrence/persistence-free survival (13). Sugitani and cols. and Ito and cols. showed that the 588

presence of 5 or more metastatic lymph nodes was an independent predictor of recurrence/persistence but only in young patients (9,14). Randolph and cols. published a meta-analysis in which the presence of less than 5 lymph nodes was associated with a median recurrence/persistence rate of 4% (3-8%), versus 19% (7-21%) in patients with more than 5 lymph nodes. The ATA 2015 guidelines did not include ENE, but the presence of more than 3 lymph nodes with ENE has been considered a high-risk characteristic with a 40% recurrence/persistence rate (1). Previous studies have not shown an association between ENE and outcomes probably due to small samples (15). However, subsequent studies have shown the importance of this characteristic in relation to recurrence/persistence (7,9,11,16,17), as our findings have demonstrated. Lango and cols. showed that patients with ENE had higher post-treatment Tg levels as well as a higher risk of persistent nodal disease and progression of systemic disease; the presence of ENE was associated with a 20% risk of persistent nodal disease (11). Leboulleux and cols. showed that more than 3 metastatic lymph nodes with ENE were associated with a high recurrence/ persistence rate in patients with DTC when compared to patients with less than 3 (18). However, the study by Ito and cols. did not demonstrate an increased risk of recurrence/persistence in patients with ENE, although the number of lymph nodes with this characteristic was not evaluated (19). In our study, 19.2% of the patients with lymph node recurrence/persistence had ENE versus 7.5% in the patients without recurrence/ persistence (p = 0.03). One of the major limitations in the ENE analysis is the interobserver variation of its definition. Du and cols. evaluated the concordance rate among 11 pathologists from the United States, Canada and Italy and the concordance ratio was 0.68. This low rate may explain the variation of studies in the analysis of the prognosis of ENE (20). Furthermore, this study also analyzed the correlation between distant metastases and metastatic lymph node characteristics. In our Brazilian cohort, the presence of lateral neck lymph node metastases at diagnosis was the most important factor associated with a greater risk of having distant metastases, as assigned by TNM (8). Similarly, Chen and cols. (21) found that the presence of a lateral metastatic lymph node was an independent risk factor for distant metastasis (OR 4.02, 95% CI 1.2512.95; p: 0.02). Lango and cols. (10) demonstrated that the impact of ENE on the risk of developing distant Arch Endocrinol Metab. 2017;61/6

Prognostic impact of metastatic lymph nodes

metastases was independent of nodal persistence (HR 4.3 CI 95%1.2-15; p: 0.02), and Yamashita and cols. (22) showed that the presence of ENE is associated with the presence of distant metastases (p < 0.0001). The absence of association between ENE and distant metastasis in our study may be due to the small number of patients in whom this characteristic was analyzed. In conclusion, this study showed association between lymph node characteristics and outcomes such as recurrence/persistence and response to initial therapy, reinforcing the importance of the analysis of these factors for stratification and therapeutic management. Disclosure: no potential conflict of interest relevant to this article was reported.

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original article

Thyroglobulin levels before radioactive iodine therapy and dynamic risk stratification after 1 year in patients with differentiated thyroid cancer Leonardo Bandeira1, Rosália do Prado Padovani1,2, Ana Luiza Ticly1, Adriano Namo Cury1, Nilza Maria Scalissi1, Marília Martins Silveira Marone2, Carolina Ferraz1

ABSTRACT Serviço de Endocrinologia, Departamento de Medicina, Irmandade da Santa Casa de Misericórdia de São Paulo (ISCMSP), São Paulo, SP, Brasil 2 Serviços de Medicina Nuclear, Irmandade da Santa Casa de Misericórdia de São Paulo (ISCMSP), São Paulo, SP, Brasil 1

Correspondence to: Carolina Ferraz Rua Conselheiro Brotero, 1539, cj. 101 01232-011 – São Paulo, SP, Brasil carolina.ferraz.endocrinologia@gmail.com Received on Jan/23/2017 Accepted on Aug/23/2017 DOI: 10.1590/2359-3997000000308

Objectives: We sought to assess the relationship between stimulated thyroglobulin (sTg) before radioactive iodine therapy (RIT), and the dynamic risk stratification 1 year after treatment, and to establish the utility of the sTg as a predictor of response to therapy in these patients. A retrospective chart review of patients with differentiated thyroid cancer (DTC) who underwent RIT after surgery and were followed for at least 1 year, was carried out. Subjects and methods: Patients were classified according to the dynamic risk stratification 1 year after initial treatment. The sTg values before RIT were compared among the groups. ROC curve analysis was performed. Results: Fifty-six patients were enrolled (mean age 44.7 ± 14.4 years, 80.7% had papillary carcinoma). Patients with excellent response had sTg = 2.1 ± 3.3 ng/mL, those with indeterminate response had sTg = 8.2 ± 9.2 ng/mL and those with incomplete response had sTg = 22.4 ± 28.3 ng/mL before RIT (p = 0.01). There was a difference in sTg between excellent and incomplete response groups (p = 0.009) while no difference was found between indeterminate and either excellent or incomplete groups. The ROC curve showed an area under the curve of 0.779 assuming a sTg value of 3.75 ng/mL. Conclusion: Our study results suggest that the higher the sTg before RIT, the greater the likelihood of an incomplete response to initial treatment. A sTg cut-off of 3.75 ng/mL was found to be a good predictor of response to initial treatment in patients with DTC. Arch Endocrinol Metab. 2017;61(6):590-9 Keywords Dynamic risk stratification; radioactive iodine therapy; thyroglobulin; thyroid cancer

INTRODUCTION

ifferentiated thyroid carcinomas (DTC) originating from follicular cells account for over 90% of thyroid cancers. DTC comprise both papillary and follicular carcinomas, with the former type being the most common type (around 80% of DTC). The incidence of DTC has increased in recent decades and is 2-4 times more frequent in women than in men, peaking at 40-50 years of age. This disparity between genders narrows progressively with age, with rates almost equal in older adults (1,2). Based on global consensus on management of DTC, initial treatment includes thyroidectomy with or without lymph node chain resection, in association with administration of I131 radioactive iodine therapy (RIT) in patients with significant risk of death or recurrence 590

(3-5). After initial therapy, the daily administration of suppression doses of levothyroxine has an important adjuvant role in high-risk patients since suppression of thyroid stimulating hormone (TSH) inhibits tumor growth and progression, thereby reducing the risk of disease recurrence and associated death (6-9). Ideally, initial staging of DTC should be carried out shortly after surgery. Several classifications have been developed for staging. Systems for classifying death risk include the MACIS and TNM, with the latter being the most widely used. More recently, new classification systems have been created for assessing risk of disease recurrence and persistence, for example the American Thyroid Association (ATA) risk stratification (4,10). With the aim of complementing initial staging and assessing response to treatment, a new classification that Arch Endocrinol Metab. 2017;61/6

Tg before RIT and dynamic risk stratification

restages patients 1 year after initial therapy was recently developed (dynamic risk stratification using response to therapy restaging system). Using Tg and anti-Tg antibodies levels, USG and other imaging methods as parameters, patients are reclassified as having excellent, indeterminate (acceptable), or incomplete (biochemical or structural) response to treatment, which then modifies subsequent therapy and follow-up (4,11). Under this classification, excellent response is defined as the presence of stimulated Tg (sTg) < 1 ng/mL (with absent anti-Tg levels) and negative imaging scans. The presence of elevated Tg (suppressed ≥ 1 ng/mL or stimulated ≥ 10 ng/mL) or rising anti-Tg levels defines incomplete response, which is either structural (local/ regional or distal disease evident on imaging exams) or biochemical (no disease evident). Indeterminate response is established in the presence of non-specific findings on cervical ultrasound, suppressed Tg < 1 ng/ mL and sTg < 10 ng/mL or stable/declining anti-Tg levels (Table 1). According to this classification, in the event of excellent response to treatment, the frequency and intensity of monitoring can be reduced and TSH target raised (0.5-2 mUI/L if initial ATA low or intermediate risk patients; 0.1-0.5 mUI/L if initial ATA high risk patients). Patients attaining an indeterminate response should be kept under approximately the same TSH target as the excellent response and undergo more frequent follow-up, for later reclassification. If biochemical or structural response, patients should be kept under suppression (TSH < 0.1 mUI/L for structural incomplete response; TSH = 0.1-0.5 mUI/L for biochemical incomplete response). In case of elevated Tg, investigation by imaging studies and more aggressive therapeutic are recommended (4,11). Serum Tg is the primary tumor marker used in follow-up of patients with DTC for detecting the disease after initial treatment (12). In recent years, the utility of Tg measured immediately before ablative therapy with I131 and after surgery (Tg before RIT) as

a prognostic marker of disease progression has been confirmed (13-17). No previous studies, however, have sought to establish the relationship between Tg before RIT and the new dynamic risk stratification. In order to better define the role of Tg before RIT as a prognostic factor, the objectives of the present study were: 1) To determine whether a relationship exists between sTg levels (TSH > 30 mUI/L) before RIT and after thyroidectomy, and the dynamic risk stratification at 1 year after therapy in patients with DTC; 2) To determine a possible cut-off for sTg before RIT and after thyroidectomy, as a predictor of prognosis.

SUBJECTS AND METHODS A retrospective study was conducted analyzing the relationship between sTg levels before RIT and the dynamic risk stratification at 1 year after initial therapy in patients with DTC who undergone thyroidectomy. Data were collected from medical charts of patients referred for RIT after thyroidectomy at the Laboratory of Nuclear Medicine of the Santa Casa Hospital of Sao Paulo. Sixty patients were eligible for the study. The definitive diagnosis of DTC was reached based on the results of pathological examination of the surgical specimen. The study included all patients diagnosed with DTC of any histological subtype submitted to initial surgical treatment (thyroidectomy) followed by RIT. The I131 activity was administered after preparing the patient with a discontinuation of thyroid hormone and an iodine-poor diet as American Thyroid Association (ATA) recommendations (4). The exclusion criteria were patients who had partial thyroidectomy and those with positive anti-Tg antibodies. Data were collected for age, surgery type, histological type of carcinoma, initial staging by TNM and ATA classifications, I131 (RIT) activity administered, sTg level before RIT, and response to treatment at 1 year after

Excellent response

Indeterminate response

Incomplete response

- Negative imaging - Suppressed Tg < 0.2 ng/mL or stimulated Tg < 1.0 ng/mL - Absent anti-Tg levels

- Non-specific findings on imaging studies - Faint uptake in thyroid bed on RAI scanning - Suppressed Tg detectable but < 1 ng/mL - Stimulated Tg detectable but < 10 ng/mL or stable or declining anti-Tg levels

- Suppressed Tg ≥ 1 ng/mL or stimulated Tg ≥ 10 ng/mL or rising anti-Tg levels - Biochemical, if negative imaging - Structural, if evidence of disease on imaging studies

Table 1. Dynamic risk stratification (restratification)

Adapted from Haugen and cols. (4) and Tuttle and Leboeuf (11). Tg: thyroglobulin. Arch Endocrinol Metab. 2017;61/6

591

Tg before RIT and dynamic risk stratification

RIT based on the dynamic risk stratification (4,11). Tg and anti-Tg antibody analyses were performed at the same laboratory for all patients using the same assay (chemiluminescent Immulite 2000, Siemens). Statistical analyses were carried out using the statistics package SPSS version 13.0. The level of statistical significance adopted was p ≤ 0.05. Absolute (n) and relative (%) frequencies were analyzed for qualitative variables, while decimal measures (mean, standard deviation, standard error and median) were calculated for quantitative variables. Patients were divided into 3 groups according to the dynamic risk stratification (excellent, indeterminate or incomplete response) at 1 year after initial treatment (4,11). Although it is known that patients with biochemical incomplete response do have better outcomes than patients with structural incomplete response (4), those from both groups were pooled into a single incomplete response group given they need similar treatment (suppression levothyroxine therapy and more closer follow-up). The Kruskal-Wallis nonparametric test was performed to compare sTg levels before RIT and after thyroidectomy among the 3 groups. The Mann-Whitney non-parametric test was used for multiple comparisons between the specific groups. A ROC curve was built to define a cut-off value for sTg before RIT for predicting response to initial treatment after 1 year.

RESULTS Sixty patients were initially included in the study. Four patients tested positive for anti-Tg antibodies and were subsequently excluded. All participants were submitted to total thyroidectomy (TT) or totalization after partial thyroidectomy. In these cases, I131 dose and sTg measurement before RIT were performed after totalization and therefore these patients were not excluded. Among the 56 patients enrolled in the study (supplemental Table 1), 46 (80.7%) had

papillary carcinoma while the remainder had follicular carcinoma. Among patients with papillary carcinoma, 70% had the classic variant subtype, 26% the follicular variant and 4% had other more aggressive variants. Among the cases with follicular carcinoma, 30% had the Hürthle cell variant. Participant age had a range of 20-76 years, mean of 44.7 ± 14.4 years and median of 47 years. According to TNM staging (4), 51.8% of patients were classified as stage I, 3.6% stage II, 28.6% stage III and 16.1% stage IV. With regard to risk of disease recurrence/persistence by the ATA classification, 14.3% of patients had low risk, 69.6% intermediate risk and 16.1% high risk of recurrence. The I131 dose administered ranged from 100 to 250 mCi, with a mean of 184.1 ± 55.8 and median of 200 mCi. sTg value after TT and before RIT ranged from 0.5 to 81 ng/mL, with mean of 6.4 ± 13.8 and median of 0.8 ng/mL. When patients were restaged, 67.3% had an excellent response to treatment, 15.4% indeterminate and 17.3% incomplete response 1 year after initial therapy. From our initial stated ATA low risk patients, 87.5% had an excellent response to the proposed treatment while 12.5% evolved with incomplete response. Among the intermediate risk group patients 61.1% had an excellent response, 22.2% an indeterminate response and 16.7% evolved with incomplete response. Finally, the high risk group showed an excellent response in 75% of the patients while in 25% there was an incomplete response (Table 2). Table 3 shows the baseline characteristics of the dynamic risk stratification groups. Patients showing an excellent response to treatment after 1 year had a mean sTg before RIT of 2.1 ± 3.3 and median of 0.7 ng/mL; those with indeterminate response had a mean sTg before RIT of 8.2 ± 9.2 and median of 4.6 ng/mL; whereas patients with incomplete response had a mean sTg before RIT of 22.4±28.3 and median of 6.3 ng/mL (p = 0.01, Figure 1).

Dynamic risk stratification ATA Recurrence Risk

592

Excellent response

Indeterminate response

Incomplete response

Total

87.5%

12.5%

100%

Intermediate

61.1%

22.2%

16.7%

100%

High

75.0%

25.0%

100%

Total

67.3%

15.4%

17.3%

100%

Low

Arch Endocrinol Metab. 2017;61/6

Tg before RIT and dynamic risk stratification

Table 3. Baseline characteristics of the patients ± SD Excellent response (n = 37)

Indeterminate response (n = 9)

Incomplete response (n = 10)

p value

46.4 ± 14.7

38.2 ± 13.2

42.2 ± 14.3

0.443

Type of carcinoma

Papillary, n (%)

31 (83.8)

8 (88.9)

7 (70.0)

Follicular, n (%)

6 (16.2)

1 (11.1)

3 (30.0)

Age (years)

TNM Staging I, n (%)

20 (54.1)

5 (55.6)

5 (50.0)

II, n (%)

1 (2.7)

0 (0.0)

III, n (%)

11 (29.7)

2 (22.2)

3 (30.0)

IV, n (%)

5 (13.5)

2 (22.2)

2 (20.0)

ATA Classification

Low risk, n (%)

7 (18.9)

0 (0.0)

1 (10.0)

Intermediate risk, n (%)

24 (64.9)

9 (100.0)

6 (60.0)

High risk, n (%) sTg after TT and before RIT (ng/mL) RIT dose (mCi)

0.509

0.979

0,194

6 (16.2)

0 (0.0)

3 (30.0)

2.1 ± 3.3

8.2 ± 9.2

22.4 ± 28.3

0.01

177.0 ± 57.2

195.7 ± 53

210.0 ± 39.4

0.111

sTg: stimulated thyroglobulin; TT: total thyroidectomy; RIT: radioactive iodine therapy.

the excellent and indeterminate response groups (p = 0.76) and between the indeterminate and incomplete response groups (p = 0.273) there were no significant difference between sTg values. For sTg values measured before RIT, a cut-off of 3.75 ng/mL had a sensitivity for predicting poor response to treatment of 66.7% while the specificity was 85.7%. Analysis of the ROC curve showed an area under the curve of 0.779 (Figure 3).

25 p = 0.01

20 15 10 5 0

Excellent response

Indeterminate response

Incomplete response

Figure 1. Stimulated Tg before radioactive iodine therapy (mean ± SE) and dynamic risk stratification 1 year after initial treatment. Difference in Tg values among the 3 groups of dynamic risk stratification (p = 0.01).

Comparison of restaging groups revealed a difference in sTg values before RIT between the excellent and incomplete response groups (p = 0.009). Comparisons between sTg values in the indeterminate and the excellent response groups (p = 0.072) and between the indeterminate and incomplete response groups (p = 0.385) were not statistically different (Figure 2). If patients with sTg value before RIT < 1 ng/mL are excluded from the analysis (in order to avoid pulling down the Tg values), the difference in sTg before RIT between the excellent and incomplete response groups maintains significant (p = 0.007). Again, between Arch Endocrinol Metab. 2017;61/6

35 30 25 20 15 10

p = 0.009

p = 0.07

p = 0.38

5 0

Excellent response Incomplete response

Excellent response Indeterminate response

Incomplete response Indeterminate response

Figure 2. Stimulated Tg before radioactive iodine therapy (mean and SE) and comparison among groups of dynamic risk stratification 1 year after initial treatment. Difference in Tg between excellent and incomplete response groups (p = 0.009). Comparisons between indeterminate and excellent response groups (p = 0.072) and between indeterminate and incomplete response groups (p = 0.385) were not statistically significant. 593

Tg before radioactive iodine therapy

Tg before RIT and dynamic risk stratification

ROC curve 1.0

Sensitivity

0.8

0.6

0.4

0.2

0.0 0.0

0.2

0.4 0.6 1-Specificity

0.8

1.0

Figure 3. ROC curve assuming a Tg value of 3.75 ng/mL before RIT. Area under the curve = 0.779.

DISCUSSION The prevalence of DTC has risen in recent years, largely due to the increase in diagnosis of microcarcinomas (tumors up to 1 cm across at widest point) and representing the fourth most prevalent malignant neoplasm in Brazilian women (5,18-20). Studies suggest that the majority of DTC does not clinically progress, explaining the continued low death rates despite increased incidence (21,22). Although most patients with DTC have a good outcome with conventional therapy, a considerable percentage of them has unfavorable response (21). Thus, it is important to distinguish between patients requiring more aggressive treatment from those that can be spared unnecessary treatment and procedures. Consequently, it is essential to perform initial staging of the disease (5). Several systems have been developed to assess prognosis, risk of recurrence and death, to help inform decisions on post-operative therapy and the frequency and intensity of follow-up, as well as to standardize language and facilitate communication of the multi-disciplinary team involved in the follow-up of these patients (3-5). However, these classification systems are, in general, only static representations of the patient after surgery and are not modifiable during follow-up, proving useful only to guide initial therapeutic measures. In an effort to optimize the follow-up of this patient group, a dynamic risk 594

stratification/restaging was recently developed (11). This system has been incorporated into the new ATA guidelines on the management of DTC (4). sTg (in the presence of TSH > 30 mUI/l) is the best means of detecting remnant thyroid tissue after initial treatment in patients with DTC. Elevated TSH, whether endogenous or recombinant, stimulates the uptake of iodine and promotes release of more Tg from thyroid remnants and metastatic lesions (23), thereby improving the accuracy of the scan. However, despite its importance as a biochemical marker for monitoring DTC, serum Tg is not classically used as an initial prognostic value. Guidelines for DTC management indicate the use of sTg levels at between 6 and 12 months after initial treatment for the diagnosis of disease persistence and/ or recurrence (3,4,24). Thus, this diagnosis is often delayed by up to 1 year. Although the use of sTg before RIT and after surgery was initially questioned, because the remnant healthy thyroid tissue also contributes to its production, a number of studies have shown the prognostic value of sera sTg measured at this time point (13-17). The marker can be an early indicator of patient response, allowing treatment to be started immediately in the event of suspected unfavorable outcome. Heemstra and cols. assessed the prognostic value of sTg at different time points and concluded that sTg before RIT was an independent prognostic marker of remission, while Tg measured after initial therapy (at 6 months, 2 years and 5 years) had utility for predicting death due to the disease (15). Studies found that sTg before RIT predicts the presence of metastases and empirically suggests the administration of high doses of I131 if this marker is elevated (13,16). Another two studies showed the higher the values of sTg before RIT, the greater the risk of disease persistence/recurrence (14,17). We report a retrospective study analyzing the relationship between stimulated Tg levels after thyroidectomy and before RIT, and dynamic risk stratification 1 year after therapy in patients with DTC. Around 80% of patients had papillary carcinoma while the remainder had follicular carcinoma, a similar rate to that found in the literature (1). Most patients had an excellent response (67.3%) according to restaging, an expected outcome given that the majority of patients with DTC has a favorable response after initial therapy (21). A considerable number of patients, however, had an indeterminate Arch Endocrinol Metab. 2017;61/6

or incomplete response, indicating the need for closer follow-up and more aggressive therapeutic measures in these groups. In the present study, a significant difference in sTg levels before RIT was found among the restaging groups 1 year after therapy. The excellent response group had lower levels, the indeterminate group had intermediate levels of sTg and the incomplete group had higher levels (2.1 ± 3.3 vs. 8.2 ± 9.2 vs. 22.4 ± 28.3 ng/mL, p = 0.01). A statistically difference was detected between the excellent and incomplete groups (p = 0.009). Comparisons involving patients with indeterminate response revealed no statistical significance, results that might be explained by the small number of patients (n = 8) included in this group (p = 0.072 vs. excellent response group; p = 0.385 vs. incomplete response group). Thus, the higher the sTg value before RIT, the greater the likelihood of the patient having an incomplete, or even an indeterminate response to treatment 1 year after initial therapy. Analysis of the ROC curve showed good accuracy using a Tg value before RIT of 3.75 ng/mL (area under curve of 0.779), whereas ideally a diagnostic test should have an area under the curve > 0.7 to have at least moderate accuracy (25). In this analyzed cohort, another cut-off of Tg value before RIT comparing patients that evolve “better”, that means excellent, indeterminate and biochemical incomplete response, with patients that do not evolve well (structural incomplete response) can´t be done due to the low number of patients with structural disease. Further patients must be added to the latest group in order to find a new cut-off to predict structural incomplete disease. Studies assessing sTg before RIT as a prognostic value have shown different cut-off values for predicting better or worse outcomes. While Ronga and cols. (16) suggested administration of a high dose of I131, claiming a greater risk of metastasis, if the sTg value before RIT exceeds 69.7 ng/mL, other studies suggest a much lower cut-off, namely 5-10 ng/mL, in which greater levels would increase the risk of metastasis and also the rate of failed RIT ablation (26-28). Melo and cols. (17) established a cut-off of 7.2 ng/mL, in which levels of sTg before RIT lower than this had a high probability of remission after 1 year. Kim and cols. (14) suggested a lower cut-off point as an indicator of disease remission (negative predictive value of 98.4% for sTg before RIT Arch Endocrinol Metab. 2017;61/6

≤ 2 ng/mL), although their study excluded patients with metastasis. Hall and cols. (29) determined that a sTg level above 20 ng/mL is an independent predictor of disease recurrence. Other study found a greater cutoff (50 ng/mL) as a predictor of disease persistence/ recurrence (30), but this study enrolled only high-risk patients. Despite the disparity in cut-off values before RIT, most of the related medical literature sees a sTg value ≥ 10 ng/mL as a predictor of negative response to initial treatment (13,31-36). In our study, we found that a Tg value before RIT ≥ 3.75 ng/mL had good specificity (85.7%) with acceptable sensitivity (66.7%) for predicting a not so good (incomplete or indeterminate) response to initial treatment. Our results showed that sTg level before RIT can point out the response to initial therapy after 1 year. Therefore, as shown in other studies (13,14,16,17), it can be used to indicate prognostic. No previous studies, however, have attempted to associate sTg level before RIT with the new dynamic risk stratification adopted by the ATA. This study has several limitations. Firstly, although RIT was administered at the same laboratory, using the same protocols and type of preparation, the surgery and follow-up of patients was not carried out at the same center. The laboratory of Nuclear Medicine of the Santa Casa hospital of Sao Paulo, as a referral center, receives patients from a number of other centers in the region specifically to undergo RIT. Consequently, variables such as surgical ability, extent of surgery (with or without lymphadenectomy) and follow-up protocols specific to each service may represent confounding factors. Secondly, the relationship between sTg before RIT and restaging was not tested in patients submitted to partial thyroidectomy or those not receiving RIT after TT, and results reported do not apply to such cases. It is important to note that anti-Tg antibodies are associated with disease activity (4) and interfere the Tg assay (37). Accordingly, patients testing positive for antibodies were excluded. Of the original sample, 6.6% tested positive for anti-Tg, lower than the rate described in the literature (15-20%) (38,39). This disparity may have occurred because some patients were not referred for therapy straight away and, upon withdrawal of antigenic stimulus after surgery, anti-Tg levels steadily decline (40,41). Another exclusion criterion was for patients submitted to partial thyroidectomy because Tg 595

Tg before RIT and dynamic risk stratification

values for assessing response are higher in these patients given that part of the thyroid remains (42). However, all patients included were submitted to total thyroidectomy or totalization after partial thyroidectomy. In this study, it was concluded that sTg before RIT is associated with dynamic risk stratification (restaging) at 1 year after therapy in patients with DTC. Higher Tg levels were found in patients that had indeterminate, and particularly incomplete, response. Thus, the higher the Tg level before RIT and after surgery, the greater the likelihood of having an incomplete response to initial treatment. Therefore, we suggest that sTg before RIT can serve as a predictor of response to initial treatment and that a value ≥ 3.75 ng/mL represents a good cut-off for incomplete response.

8. Jonklaas J, Sarlis NJ, Litofsky D, Ain KB, Bigos ST, Brierley JD, et al. Outcomes of patients with differentiated thyroid carcinoma following initial therapy. Thyroid. 2006;16:1229-42.

Funding: this research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

13. de Rosário PW, Guimarães VC, Maia FF, Fagundes TA, Purisch S, Padrao EL, et al. Thyroglobulin before ablation and correlation with posttreatment scanning. Laryngoscope. 2005;115:264-7.

Acknowledgements: we are grateful to Heidy and Josi from NUCLIMAGEM Nuclear Medicine Lab for their assistance in the search and organization of patients records, and also to Erika Tiemi Fukunaga for her excellent statistical support.

14. Kim TY, Kim WB, Kim ES, Ryu JS, Yeo JS, Kim SC, et al. Serum thyroglobulin levels at the time of 131I remnant ablation just after thyroidectomy are useful for early prediction of clinical recurrence in low-risk patients with differentiated thyroid carcinoma. J Clin Endocrinol Metab. 2005;90:1440-145.

Disclosure: no potential conflict of interest relevant to this article was reported.

15. Heemstra KA, Liu YY, Stokkel M, Kievit J, Corssmit E, Pereira AM, et al. Serum thyroglobulin concentrations predict disease-free remission and death in differentiated thyroid carcinoma. Clin Endocrinol (Oxf). 2007;66:58-64.

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16. Ronga G, Filesi M, Ventroni G, Vestri AR, Signore A. Value of the first serum thyroglobulin level after total thyroidectomy for the diagnosis of metastases from differentiated thyroid carcinoma. Eur J Nucl Med. 1999;26:1448-52. 17. Melo M, Costa G, Ribeiro C, Carrilho F, Martins MJ, da Rocha AG, et al. Stimulated thyroglobulin at recombinant human TSHaided ablation predicts disease-free status one year later. J Clin Endocrinol Metab. 2013;98:4364-72. 18. Davies L, Welch HG. Increasing incidence of thyroid cancer in the United States, 1973-2002. JAMA. 2006;10:2164-7. 19. Veiga LH, Neta G, Aschebrook-Kilfoy B, Ron E, Devesa SS. Thyroid cancer incidence patterns in Sao Paulo, Brazil, and the U.S. SEER program, 1997-2008. Thyroid. 2013;23:748-57. 20. Schönberger J, Marienhagen J, Agha A, Rozeboom S, Bachmeier E, Schlitt H, et al. Papillary microcarcinoma and papillary cancer of the thyroid <or=1 cm: modified definition of the WHO and the therapeutic dilemma. Nuklearmedizin. 2007;46:115-20. 21. Ito Y, Miyauchi A, Inoue H, Fukushima M, Kihara M, Higashiyama T, et al. An observational trial for papillary thyroid microcarcinoma in Japanese patients. World J Surg. 2010;34:28-35. 22. Sugitani I, Toda K, Yamada K, Yamamoto N, Ikenaga M, Fujimoto Y. Three distinctly different kinds of papillary thyroid microcarcinoma should be recognized: our treatment strategies and outcomes. World J Surg. 2010;34:1222-31. 23. Pacini F, Molinaro E, Castagna MG, Agate L, Elisei R, Ceccarelli C, et al. Recombinant human thyrotropin-stimulated serum thyroglobulin combined with neck ultrasonography has the highest sensitivity in monitoring differentiated thyroid carcinoma. J Clin Endocrinol Metab. 2003;88:3668-73. 24. Gharib H, Papini E, Paschke R, Duick DS, Valcavi R, Hegedüs L, et al. American Association of Clinical Endocrinologists, Associazione Medici Endocrinologi, and European Thyroid Association Arch Endocrinol Metab. 2017;61/6

Tg before RIT and dynamic risk stratification

Medical Guidelines for Clinical Practice for the Diagnosis and Management of Thyroid Nodule. Endocr Pract. 2010;16:1-43. 25. Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007;96:644-7. 26. Robenshtok E, Grewal RK, Fish S, Sabra M. Tuttle RM. A low postoperative nonstimulated serum thyroglobulin level does not exclude the presence of radioactive iodine avid metastatic foci in intermediate-risk differentiated thyroid cancer patients. Thyroid. 2013;23:436-42. 27. Tamilia M, Al-Kahtani N, Rochon L, Hier MP, Payne RJ, Holcroft CA, et al. Serum thyroglobulin predicts thyroid remnant ablation failure with 30 mCi iodine-131 treatment in patients with papillary thyroid carcinoma. Nucl Med Commun, 2011;32:212-20. 28. Bernier MO, Morel O, Rodien P, Muratet JP, Giraud P, Rohmer V, et al. Prognostic value of an increase in the serum thyroglobulin level at the time of the first ablative radioiodine treatment in patients with differentiated thyroid cancer. Eur J Nucl Med Mol Imaging. 2005:32:1418-21. 29. Hall FT, Beasley NJ, Eski SJ, Witterick IJ, Walfish PG, Freeman JL. Predictive Value of Serum Thyroglobulin After Surgery for Thyroid Carcinoma. Laryngoscope. 2003;113:77-81. 30. Piccardo A, Arecco F, Puntoni M, Foppiani L, Cabria M, Corvisieri S, et al. Focus on High-Risk DTC Patients: high postoperative serum thyroglobulin level is a strong predictor of disease persistence and is associated to progression-free survival and overall survival. Clin Nucl Med. 2013;38:18-24. 31. Webb RC, Howard RS, Stojadinovic A, Gaitonde DY, Wallace MK, Ahmed J, et al. The Utility of Serum Thyroglobulin Measurement at the Time of Remnant Ablation for Predicting Disease-Free Status in Patients with Differentiated Thyroid Cancer: A MetaAnalysis Involving 3947 Patients. J Clin Endocrinol Metab. 2012;97:2754-63.

34. Oyen WJ, Verhagen C, Saris E, van den Broek WJ, Pieters GF, Corsten FH. Follow-up regimen of differentiated thyroid carcinoma in thyroidectomized patients after thyroid hormone withdrawal. J Nucl Med. 2000;41:643-6. 35. Lin JD, Huang MJ, Hsu BR, Chao TC, Hsueh C, Liu FH, et al. Significance of postoperative serum thyroglobulin levels in patients with papillary and follicular thyroid carcinomas. J Surg Oncol. 2002;80:45-51. 36. Heemstra KA, Liu YY, Stokkel M, Kievit J, Corssmit E, Pereira AM, et al. Serum thyroglobulin concentrations predict disease-free remission and death in differentiated thyroid carcinoma. Clin Endocrinol. 2007;66;58-64. 37. Lupoli GA, Okosieme OE, Evans C, Clark PM, Pickett AJ, Premawardhana LD, et al. Prognostic significance of thyroglobulin antibody epitopes in differentiated thyroid cancer. J Clin Endocrinol Metab. 2015;100:100-8. 38. Rahmoun MN, Bendahmane I. Anti-thyroglobulin antibodies in differentiated thyroid carcinoma patients: Study of the clinical and biological parameters. Ann Endocrinol (Paris). 2014;75:15-8. 39. Donegan D, McIver B, Algeciras-Schimnich A. Clinical Consequences of a Change in Anti-Thyroglobulin Antibody Assays During the Follow-Up of Patients with Differentiated Thyroid Cancer. Endocr Pract. 2014;20:1032-6. 40. Chiovato L, Latrofa F, Braverman LE, Pacini F, Capezzone M, Masserini L, et al. Disappearance of humoral thyroid autoimmunity after complete removal of thyroid antigens. Ann Intern Med. 2003;139:346-51. 41. Görges R, Maniecki M, Jentzen W, Sheu SN, Mann K, Bockisch A, et al. Development and clinical impact of thyroglobulin antibodies in patients with differentiated thyroid carcinoma during the first 3 years after thyroidectomy. Eur J Endocrinol. 2005;153:49-55. 42. Momesso DP, Tuttle RM. Update on differentiated thyroid cancer staging. Endocrinol Metab Clin North Am. 2014;43:401-21.

32. Valadão MM, Rosário PW, Borges MA, Costa GB, Rezende LL, Padrão EL, et al. Positive predictive value of detectable stimulated tg during the first year after therapy of thyroid cancer and the value of comparison with Tg-ablation and Tg measured after 24 months. Thyroid. 2006;1:1145-9.

33. Polachek A., Hirsch D, Tzvetov G, Grozinsky-Glasberg S, Slutski I, Singer J, et al. Prognostic value of post-thyroidectomy thyroglobulin levels in patients with differentiated thyroid cancer. J Endocrinol Invest, 2011;34:855-60.

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Tg before RIT and dynamic risk stratification

SUPPLEMENTAL MATERIAL Supplemental table 1. Summarized data from all patients.

Patients’ initials

598

Age

Histological type

TNM staging

ATA classification

sTg

Dynamic risk stratification

SNG

0,8

IndR

ICR

III

0,5

IndR

TCK

0,5

IndR

FIVS

IndR

MRRL

IndR

EJA

1,1

IndR

8,1

IndR

FSS

18,9

IndR

EFLS

III

IndR

MSFA

III

0,8

RFZ

0,5

JDJR

0,7

MGCS

2,7

LSV

1,3

MAPS

2,8

SNR

III

0,5

AVN

III

0,5

SMF

III

0,7

MCZ

III

0,5

TMC

0,6

MFBR

III

0,7

EOM

0,5

MAJJ

III

0,5

SGF

0,5

SMRL

III

0,5

DGS

III

0,5

HMBR

0,5

AMS

0,7

CFB

III

0,5

MCV

0,6

TLO

0,5

ECLH

3,6

EAEA

7,6

MJSP

1,4

DEC

1,3

LMJS

0,6

EPB

0,5

MSLP

0,7

ESS

III

6,3

CRAR

7,1

ALGCO

0,5

JCBC

0,5

Arch Endocrinol Metab. 2017;61/6

Tg before RIT and dynamic risk stratification

Patients’ initials

Age

Histological type

TNM staging

ATA classification

sTg

Dynamic risk stratification

SSR

6,2

MGBS

1,6

TNP

17,3

CAES

1,1

ACTS

IncR

MLLMS

III

0,9

IncR

JJS

III

3,9

IncR

0,5

IncR

JNJ

0,6

IncR

LHV

15,7

IncR

MCSPS

III

40,4

IncR

MSS

IncR

EFR

6,3

IncR

RNS

20,7

IncR

F: Follicular carcinoma, P: Papillary carcinoma, I: Indermediate risk, H: High risk, L: Low risk, sTg: Stimulated Tg, IndR: Indeterminate response, ER: Excellent response, IncR: Incomplete response.

Arch Endocrinol Metab. 2017;61/6

599

original article

Serum selenium and selenoprotein-P levels in autoimmune thyroid diseases patients in a select center: a transversal study Marco Aurélio Ferreira Federige1, João Hamilton Romaldini1, Ana Beatriz Pinotti Pedro Miklos1, Marcia Kiyomi Koike1, Kioko Takei1, Evandro de Souza Portes1

ABSTRACT Endocrinologia, Hospital do Servidor Público Estadual (IAMSPE), São Paulo, SP, Brasil 1

Correspondence to: Marco Aurélio Ferreira Federige Hospital do Servidor Público Estadual Av. Ibirapuera, 981, 2º andar 04029-000 – São Paulo, SP, Brasil marcofederige@gmail.com Received on Mar/13/2017 Accepted on July/31/2017 DOI: 10.1590/2359-3997000000309

Objective: Selenium (Se) supplementation has been used to help prevent the progression of Graves’ ophthalmopathy (GO) and autoimmune thyroid diseases (AITD) patients. We investigated Se serum and selenoprotein P (SePP) levels in Graves’ disease (GD) with and without GO, Hashimoto’s thyroiditis (HT) patients and in 27 control individuals (C). Subjects and methods: We studied 54 female and 19 male patients: 19 with GD without GO, 21 GD with GO, 14 with HT and 19 with HT+LT4. Se values were measured using graphite furnace atomic absorption spectrophotometry. Serum SePP levels were measured by ELISA. Results: Median Se levels were similar among all groups; GD patients: 54.2 (46.5-61.1 μg/L), GO: 53.6 (43.5-60.0 μg/L), HT: 51.9 (44.6-58.5 μg/L), HT+LT4 54.4 (44-63.4) and C group patients: 56.0 (52.4-61.5 μg/L); P = 0.48. However, serum SePP was lower in GO patients: 0.30 (0.15-1.05 μg/mL) and in HT patients: 0.35 (0.2-1.17 μg/mL) compared to C group patients: 1.00 (0.564.21 μg/mL) as well as to GD patients: 1.19 (0.62-2.5 μg/mL) and HT+LT4 patients: 0.7 (0,25-1.95); P = 0.002. Linear regression analysis showed a significant relationship between SePP and TPOAb values (r = 0.445, R2 = 0.293; P < 0.0001). Multiple regression analysis found no independent variables related to Se or SePP. Conclusion: A serum Se concentration was lower than in some other countries, but not significantly among AITD patients. The low serum SePP levels in GO and HT patients seems to express inflammatory reactions with a subsequent increase in Se-dependent protein consumption remains unclear. Arch Endocrinol Metab. 2017;61(6):600-7 Keywords Selenium; selenoprotein P; Graves’ disease; Graves’ ophthalmopathy; Hashimoto’s thyroiditis

INTRODUCTION

elenium (Se) is fundamental to cell metabolism as it is incorporated by a group of important proteins known as selenoproteins, each of which plays a critical role in thyroid metabolism. Thus it is no surprise that the thyroid gland contains the highest concentration of Se per gram of tissue (1). Se levels have been shown to be lower in patients with autoimmune thyroid diseases (AITD) and particularly in Graves’ Ophthalmopathy (GO). Se has an effect in AITD as it influences antioxidative protection through peroxidase glutathione action (GPx) and selenoproteins P (SePP), N, S and K. Se supports normal thyroid function directly in the formation and regulation of thyroid hormones through iodothyronine deiodinases (DI) and thioredoxin reductases (TRx) (1-3). Se deficiency intake can negatively influence the activity of several Se-responsive enzymes, particularly DI and a SePP

600

(4,5). Low Se serum levels are also associated with an increased risk of thyroid diseases (6). Wide variations in the amount of Se found in different foods and soils can also cause wide variations in Se serum according to the studied population (Table 1). Se is transported in the circulation mainly by SePP, which is produced in the liver, and is considered the best nutritional biomarker for Se (1,5) SePP also has an antioxidant activity (2,7). It can reduce hydroperoxides, protecting plasma proteins and endothelial cells against oxidative damage (5). SePP is found in almost all body tissues, regulating energy metabolism and insulin resistance (8-10). Furthermore, SePP serum can serve as a Se status indicator (11). In Se deficiency situations the thyroid gland appears to maintain high concentrations of Se, suggesting that there is a retention mechanism that allows maintaining normal thyroid function at the detriment of other cells and tissues (1,12). Recent Arch Endocrinol Metab. 2017;61/6

Selenium and SePP in thyroid diseases

Table 1. Worldwide serum selenium concentrations in normal subjects Country

Author (reference)

Year

Patients number

Se (µg/L)

Brazil

Saiki and cols. (30)

2007

92.7 ± 7*

Turkey

Erdal and cols. (21)

2008

83.7 ± 17.3*

England

Rayman and cols. (22)

2008

501

91.3 (89-92)***

Austria

Moncayo and cols. (23)

2008

554

90.5 ± 20.8*

Greece

Charalabopoulos and cols. (25)

2009

120

68.7 ± 4.5*

USA

Combs and cols. (20)

2011

261

142 ± 23.5*

Japan

Muzembo and cols. (2)

2013

116 (80-180)**

China

Liu and cols. (26)

2013

1205

52.6 (40-67)**

Denmark

Pedersen and cols. (12)

2013

830

96.8 ± 19.7*

Australia

McDonald and cols. (24)

2013

581

85.6 ± 0.5*

Brazil

Cardoso and cols. (27)

2015

50 ± 15*

Brazil

Present study

2015

56 (52.4-61.5)**

* Mean ± SD; ** Median and interquartile intervals; *** Geometric mean. Arch Endocrinol Metab. 2017;61/6

SUBJECTS AND METHODS Patients This study included 73 AITD patients (54 female and 19 male) from the ambulatory of Endocrinology of the Hospital do Servidor Público Estadual (HSPE) – IAMSPE and included HT patients (n = 14), HT + LT4 (n = 19), GD patients without GO (n = 19) and GD with GO patients defined as having proptosis and Clinical Activity Index (CAS) greater than 1 (n = 21). A control group (C), consisted of 27 individuals without any autoimmune disease, diabetes mellitus, thyroid disease, presenting normal liver and renal function. All individuals resided in the same location of Sao Paulo City, Brazil and were euthyroid at the time of the study. The HT patients had goiters and elevated serumTPOAb and/or TgAb. The HT + LT4 patients had elevated serum TPOAb and/or TgAb and were taking levothyroxine. The 19 GD had goiters and elevated serum TPOAb and/or TgAb but 75% had elevated serum TRAb. All were euthyroid treated with methimazole for at least a year. Among the 21 patients with GO, 10 had been treated with radioiodine therapy and using levothyroxine and the remaining 11 were being treated with methimazole. The distribution of CAS was as follows: CAS 1 (n = 3), CAS 2 (n = 11), CAS 3 (n = 6) and CAS 5 (n = 1). The inclusion criteria were: ambulatory patients with well-defined AITD diagnosis, female patients could not be pregnant or less than 12 months post-partum at the time of study. The exclusion criteria were as follows: (a) use recent of multivitamins; (b) smoking; (c) frequent alcohol intake; (d) regular consumption Brazil nuts; (e) ongoing amiodarone, antidepressants or anticonvulsants therapy; and (f) the presence of other endocrine or autoimmune diseases. All the participants responded a clinical and nutritional questionnaire about Se ingestion in the last month (attached) in order to avoid some bias in the Se measurements (see Supplement 1). The study was approved by the Research Ethics Committee of the IAMSPE (number of 533,774) and all subjects signed a consent agreement.

Methods For Se determinations, blood samples were collected in trace tubes (Vacuette, Greiner BioOne Brazil tubes) and centrifuged at 1,500 g for 15 minutes. The serum was frozen at -20°C. Measurements were performed by atomic absorption spectrometry (Perkin601

have shown that in Hashimoto thyroiditis (HT) and in Graves’ disease (GD) have been associated with Se deficiency and that this disability can trigger the mechanism and progression of AITD (12). In HT patients, the predominantly cytotoxic effects are mediated by T lymphocytes where autoantibody production leads to the destruction of thyroid epithelial cells (7,13), eventually causing thyroid hypofunction in GD patients. The presence of a thyroid-stimulating antibody (TRAb) with consequent abnormal overreaction of the gland explains the elevated increase in thyroid hormone levels in the serum (14). The excessive production of ROS observed in AITD may be the geneses of the observed increase in selenoproteins consumption (7,12,13). It has been described that Se deficiency impairs GPx activity and induces apoptosis and cell death by increasing H2O2 (15). In addition, recent studies have shown that Se supplementation in HT patients improved inflammation with decrease in the concentration of the thyroid peroxidase antibody (TPOAb) and the antithyroglobulin antibody (TgAb). In GD a decrease in TRAb levels was observed. Particularly in GO patients an improvement in clinical activity was also was observed (13,14,16,17). Few studies investigated serum Se concentrations in AITD. The present study evaluated Se serum and SePP concentrations in AITD patients and the likely association with thyroid function parameters.

Selenium and SePP in thyroid diseases

Elmer model; Perker-Elemer Corp., Norwalk, CT, USA) with a graphite furnace (18). For the SePP determination, blood samples were collected in tubes without anticoagulants, centrifuged at 1,500 g for 15 minutes and the serum then aliquoted into a cryogenic tube and frozen at -80°C until analyses. SePP serum concentrations were determined by sandwich enzyme immunoassay (19) using an USCN Life Science kit (Wuhan, China). All determinations were performed in duplicate The intra-assay coefficient variation (CV) was 4.7% with the inter-assay CV at 8.2%. Blood samples for determinations of thyroid-stimulating antibody (TSH), thyroid hormones, TPOAb, TgAb and TRAb were collected in tubes without anticoagulants, centrifuged and serum aliquot at -20°C until analyses. The biochemical analyses were performed on the same day. The determinations of free thyroxine (FT4), free triiodothyronine (FT3), and TSH were performed by chemiluminescence assay (Unicel DXI 800 Beckman Coulter Inc., USA). Serum TPOAb and TgAb were determined by chemiluminescence assay (Immulite 2000, Siemens Healthcare Diagnostics Inc., UK).TRAb levels were determined only in GD and GO patients by electrochemiluminescence assay (Elecsys 2010, Roche Diagnostics, GER). Serum concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, creatinine, gamma GT, blood glucose and

triglycerides were determined by enzymatic colorimetric methods (AU 5800, Beckman Coulter Inc., USA).

Statistical analysis Results are presented as mean and standard deviation or medians with quartile intervals when appropriate. A comparison of the data groups was performed using the following methods: Student T-test, Mann-Whitney and Kruskal-Wallis tests, and when necessary, a post Dunn test. Linear regression was performed for Se or SePP and TSH, FT4, FT3, TPOAb, TgAb and TRAb variables. Multiple regressions were performed considering Se or SePP as the dependent variable, and TSH, FT4, FT3, TPOAb, TgAb and TRAb as independent variables. Statistical significance was set at P < 0.05. All analyses were performed with Systat version 13 (Systat Software Inc., San Jose, CA. USA).

RESULTS As shown in Table 2 the five groups were similar regarding age, gender, BMI, serum TSH, FT4 and TRAb. Serum FT3, TPOAb and TgAb levels was higher in GD patients (P < 0.0001). The serum biochemical characteristics of the five groups were similar regarding alanine aminotransferase, aspartate aminotransferase, creatinine, gamma-glutamyl transferase, glucose, low density lipoprotein cholesterol, high density lipoprotein cholesterol and triglycerides. Serum Se

Table 2. Clinical and laboratorial characteristics

Gender (women/men)

Age (yr.)*

C (n = 27)

HT (n = 14)

GO (n = 21)

GD (n = 19)

HT+LT4 (n = 19)

20/7

13/1

15/6

14/5

17/2

51 (39.2-66.2)

46 (30.2-64.5)

58 (44.7-66.5)

54 (48-58)

52 (46.5-55.5)

BMI (kg/m²)**

25.8 ± 3.8

26.6 ± 4.1

28.2 ± 4.49

27.1 ± 5.4

28.3 ± 5.3

TSH (mU/L)**

1.63 ± 0.72

3.00 ± 2.06

2.40 ± 2.09

1.45 ± 1.21

2.73 ± 2.39

FreeT4 (ng/dL)**

1.16 ± 0.20

1.23 ± 0.22

1.252 ± 0.279

1.278 ± 0.278

1.322 ± 0.237

FreeT3 (pg/mL)*

4.60 (3.65-5.20)

3.1 (2.8-3.45)

2.601 (2.55-3.0)

3.151 (2.57-3.37)

2.81 (2.6-3.05)

TgAb (UI/mL)*

11.9 (10.0-19.5)

71.92 (1.00-170.0)

1.02 (1.0-1.0)

1.02 (1.0-12.0)

12.252 (1.0-87.9)

TPOAb (UI/mL)

5.0 (5.0-9.70)

79.453 (47.32-209.7)

22.03 (7.0-62.75)

58.53 (16.5-405.5)3

1463 (54.5-325)

0.65 (0.30-4.86)

1.08 (0.41-3.92)

TRAb (UI/L)

* Median and interquartile intervals; 25% and 75%; ** Mean and standard deviation. 1 P < 0.0001 C vs. HT + LT4, C vs. GO, C vs. GD, 2 P < 0.0001 C vs. HT, C vs. HT + LT4, C vs. Go, C vs. GD, 3 P < 0.0001 HT vs. GO, HT vs. GD, HT + LT4 vs. GO, HT + LT4 vs. GD.

602

Arch Endocrinol Metab. 2017;61/6

Selenium and SePP in thyroid diseases

100

HT + L-T4

Figure 1. Individual serum selenium (Se) concentrations expressed as median and interquartile ranges. Control individuals (C), Hashimoto’s thyroiditis (HT), Graves’disease (GD), Graves’ Ophthalmopathy (GO) and Hashimoto’s thyroiditis levothyroxine (HT + LT4) patients. P value = 0.48; Kruskal-Wallis test.

Serum SePP (ng/mL) levels

0 C

HT + L-T4

Figure 2. Individual serum selenoprotein P (SePP) concentrations expressed as median and interquartile ranges. Control individuals (C), Hashimoto’s thyroiditis (HT), Graves’ disease (GD), Graves’ Ophthalmopathy (GO) and Hashimoto’s thyroiditis levothyroxine (HT + LT4) patients. Arch Endocrinol Metab. 2017;61/6

0 0

500

1000 1500 TPOAb (UI/mL) levels

2000

2500

Figure 3. Linear regression analysis between Selenoprotein P (SePP) and thyroid peroxidase antibody (TPOAb): r = 0.445, r2 = 0.293, p < 0.0001, n = 73.

DISCUSSION In this study, the concentration of Se found in our subject sample was lower than in populations of some countries such as Japan, the United States, Turkey, England, Austria and Denmark, but was similar to values obtained in other countries such as Greece and China (2,12,2026). Interestingly these countries are considered as Se deficient. Serum Se concentrations found in our normal subjects were similar to those obtained by Cardoso and cols. (27) in Sao Paulo, Brazil. However, our C group seems to have lower levels compared to previous studies conducted in Brazil which is reasonable for a marginally Se-deficient population. Environmental or dietary factors may explain these differences, taking into account the interval between the studies (28-31). Here, we observed lower Se levels in HT, GD, GO and HT+LT4 patients in comparison with the C group, but the differences were not statistically significant. The small numbers of studied patients may be the responsible. These data are in accordance with the results of Erdal and cols. (21), Moncayo and cols. (23) and Pedersen and cols. (12). A possible explanation may be the presence of patients with elevated serum TPOAb values (greater than 1,000 IU/mL) compared with our patients, which could lead to follicular thyroid cell damage, and consequently increased Se-dependent protein depletion resulting in low Se serum concentrations (31). The serum Se concentrations obtained in GD patients were similar to those reported by Zagrodzki and cols. (32), but lower than values obtained by Pedersen and cols. (12) in newly diagnosed (in the hyperthyroid phase) GD patients, which pointed out that inflammatory activity could be responsible for the low serum Se levels. Furthermore, all GD patients 603

Serum Se (ng/L) levels

150

10 Serum SePP (ng/mL) levels

levels were similar among the studied groups (P = 0.48) as depicted in Figure 1, 56.0 (52.4-61.5 μg /L) in C; 54.2 (46.5-61.1 μg/L) in GD patients; 53.6 (43.5-60.0 μg/L) in GO patients, 51.9 (44.6-58.5 μg/L) in HT patients and 54.4 (44- 63.4 μg/L) in HT+LT4 patients. However, SePP serum was lower (P = 0.002) in GO patients; 0.30 (0.15-1.05 μg/mL) and in HT patients; 0.35 (0.2-1.17 μg/mL) compared to C; 1.00 (0.56-4.21 μg/mL), GD; 1.19 (0.622.5 μg/mL) and HT+LT4 0.7 (0.25-1.95) patients (Figure 2). Multiple regression analysis indicated no independent variables for either Se or SePP. However, there was a significant relationship between SePP and TPOAb as shown in Figure 3 (r coefficient = 0.445, R2 = 0.293; P < 0.0001).

Selenium and SePP in thyroid diseases

were euthyroid, in treatment with methimazole, which may have reduced inflammatory processes and cellular immunity, thereby further increasing Se levels. In contrast to our study, Khong and cols. (33) observed that Se serum values were lower in GD with GO than in GD without GO patients, and the most likely cause for this difference may be the inclusion of GO patients with mild to moderate GO in our study. The main finding of this study was significantly lower serum SePP in both HT and GD with GO patients, and in HT patients the levels were lower than that obtained by Eskes and cols. (34). One possible reason for this could be the use of different methodologies. The normal values of serum SePP in GD patients may be explained by the methimazole treatment that could have influenced the thyroid autoimmune system since the drug has an immunomodulatory action and decreases free radicals produced in thyroid follicular cells. The HT + LT4 group showed no significant difference in SePP serum, probably because thyroid function stabilized, which would decrease the consumption of seleniumdependent proteins. Thus, the finding of a low serum SePP values in HT and GO patients may be a result of inflammatory reactions associated with an increase in Se consumption in order to reduce the production of free radicals generated by an immunological attack. Limitations of our study consist in the low number of patients analyzed in the GD, HT and HT + LT4 groups, of the absence of GD patients without treatment in the hyperthyroid phase, the GO patient study group and the lack of serum GPx determinations. Further studies are needed to standardize benchmarks, as well as to improve methodologies used for serum Se and SePP concentration determinations. Leo and cols. recently demonstrated that Se supplementation does not have an adjuvant role in the short-term control of hyperthyroidism (35). However, further studies are also needed in order to demonstrate the probable changes in serum SePP in GD patients before and during treatment with or without Se supplementation. It should be noted that before Se supplementation it is important to establish the serum Se levels to provide improved effectiveness in GD and GO treatment. Thus, the determination of serum Se and SePP values could be used in assessing severity and inflammatory activity in AITD patients. In conclusion, our study found lower serum Se concentrations in the C group than those in other countries, but similar in other countries such as Greece and China countries considered as having 604

marginally Se-deficient populations. Low serum SePP levels in both HT and GO patients may represent inflammatory reactions with a consequent increase in consumption of Se-dependent proteins in an attempt to prevent the production of free radicals generated by thyroid autoimmune aggression. In addition, serum SePP was related to thyroid immunity. This hypothesis may be further studied before indicating serum SePP as an effective biomarker of selenium status. Acknowledgements: the authors wish to thank Capes for funding the study. Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Duntas LH. Selenium and the thyroid: a close-knit connection. J Clin Endocrinol Metab. 2010;9:5180-8. 2. Muzembo BA, Dumavibhat N, Ngatu NR, Eitoku M, Hirota R, Kondo S, et al. Serum selenium and selenoprotein P in patients with silicosis. J Trace Elem Med Biol. 2013;27:40-4. 3. Stone CA, Kawai K, Kupka R, Fawzi WW. The role of selenium in HIV infection. Nutr Rev. 2010;68:671-81. 4. Duntas LH, Benvenga S. Selenium: an element for life. Endocrine. 2014;48:756-75. 5. Cominetti C, Bortoli MC, Abdalla DSP, Cozzolino SMF. Considerations about oxidative stress, selenium and nutrigenetics. Nutrire. 2011;36:131-53. 6. Wu Q, Rayman MP, Lv H, Schomburg L, Cui B, Gao C, et al. Low population selenium status is associated with increased prevalence of thyroid disease. J Clin Endocrinol Metab. 2015;100:4037-47. 7. Duntas LH. The evolving role of selenium in the treatment of Graves’ disease and ophthalmopathy. J Thyroid Res. 2012;2012:1-6. 8. Misu H, Ishikura K, Kurita S, Takeshita Y, Ota T, Saito Y, et al. Inverse correlation between serum levels of selenoprotein p and adiponectin in patients with type 2 diabetes. PLoS One. 2012;7:1-7. 9. Brown KM, Arthur JR. Selenium, selenoproteins and human health: a review. Public Health Nutr. 2001;4:593-9. 10. Burk RF, Hill KE, Motley AK. Selenoprotein metabolism and function: evidence for more than one function for selenoprotein P. J Nutr. 2003;133(5 Suppl 1):1517S-20S. 11. Hill KE, Wu S, Motley AK, Stevenson TD, Winfrey VP, Capecchi MR, et al. Production of selenoprotein P (Sepp1) by hepatocytes is central to selenium homeostasis. J Biol Chem. 2012;287:40414-24. 12. Pedersen IB, Knudsen N, Carle A, Schomburg L, Kohrle J, Jorgensen T, et al. Serum selenium is low in newly diagnosed Graves’ disease: A population-based study. Clin Endocrinol (Oxf). 2013;79:584-90. 13. Nordio M, Pajalich R. Combined treatment with Myo-inositol and selenium ensures euthyroidism in subclinical hypothyroidism patients with autoimmune thyroiditis. J Thyroid Res. 2013;2013:1-5. 14. Marcocci C, Kahaly GJ, Krassas GE, Bartalena L, Prummel M, Stahl M, et al. Selenium and the course of mild Graves’ orbitopathy. N Engl J Med. 2011;364:1920-31. 15. Zarković M. The role of oxidative stress on the pathogenesis of Graves’ disease. J Thyroid Res. 2012;2012:1-5. Arch Endocrinol Metab. 2017;61/6

Selenium and SePP in thyroid diseases

16. Balázs C, Kaczur V. Effect of selenium on HLA-DR expression of thyrocytes. Autoimmune Dis. 2012;1:1-5. 17. Watt T, Cramon P, Bjorner JB, Bonnema SJ, Feldt-Rasmussen U, Gluud C, et al. Selenium supplementation for patients with Graves’ hyperthyroidism (the GRASS trial): study protocol for a randomized controlled trial. Trials. 2013;14:1-10. 18. Wang HC, Peng HW, Kuo MS. Determination of beryllium and selenium in human urine and of selenium in human serum by graphite-furnace atomic absorption spectrophotometry. Anal Sci. 2001;17:527-32. 19. Saito Y, Watanabe Y, Saito E, Honjoh T, Takahashi K. Production and Application of Monoclonal. Antibodies to Human Selenoprotein P. J. Health Sci. 2001;47:346-52. 20. Combs GF, Watts JC, Jackson MI, Johnson LK, Zeng H, Scheett AJ, et al. Determinants of selenium status in healthy adults. Nutr J. 2011;10:75. 21. Erdal M, Sahin M, Hasimi A, Uckaya G, Kutlu M, Saglam K. Trace element levels in hashimoto thyroiditis patients with subclinical hypothyroidism. Biol Trace Elem Res. 2008;123:1-7. 22. Rayman MP, Thompson AJ, Bekaert B, Catterick J, Galassini R, Hall E, et al. Randomized controlled trial of the effect of selenium supplementation on thyroid function in the elderly in the United Kingdom. Am J Clin Nutr. 2008;87:370-8. 23. Moncayo R, Kroiss A, Oberwinkler M, Karakolcu F, Starzinger M, Kapelari K, et al. The role of selenium, vitamin C, and zinc in benign thyroid diseases and of selenium in malignant thyroid diseases: Low selenium levels are found in subacute and silent thyroiditis and in papillary and follicular carcinoma. BMC Endocr Disord. 2008 Jan 25;8:2. 24. McDonald C, Colebourne K, Faddy HM, Flower R, Fraser JF. Plasma selenium status in a group of Australian blood donors and fresh blood components. J Trace Elem Med Biol. 2013;27:352-4.

27. Cardoso BR, Apolinário D, da Silva Bandeira V, Busse AL, Magaldi RM, Jacob-Filho W, et al. Effects of Brazil nut consumption on selenium status and cognitive performance in older adults with mild cognitive impairment: a randomized controlled pilot trial. Eur J Nutr. 2015;55:107-16. 28. Karita K, Hamada GS, Tsugane S. Comparison of selenium status between Japanese living in Tokyo and Japanese Brazilians in São Paulo, Brazil. Asia Pac J Clin Nutr. 2001;10:197-99. 29. Da Cunha S, Albanesi Filho FM, Antelo DS, De Souza MM. Serum sample levels of selenium and copper in healthy volunteers living in Rio de Janeiro city. Sci Total Environ. 2003;301:51-4. 30. Saiki M, Jaluul O, Sumita NM, Vasconcellos MBA, Filho WJ. Trace element contents in serum of healthy elderly population of metropolitan São Paulo area in Brazil. J Trace Elem Med Biol. 2007;21:70-3. 31. de Farias CR, Cardoso BR, de Oliveira GMB, de Mello Guazzelli IC, Catarino RM, Chammas MC, et al. A randomized-controlled, double-blind study of the impact of selenium supplementation on thyroid autoimmunity and inflammation with focus on the GPx1 genotypes. J Endocrinol Invest. 2015;38:1065-74. 32. Zagrodzki P, Nicol F, Arthur JR, Słowiaczek M, Walas S, Mrowiec H, et al. Selenoenzymes, laboratory parameters, and trace elements in different types of thyroid tumor. BiolTrace Elem Res. 2010;134:25-40. 33. Khong JJ, Goldstein RF, Sanders KM, Schneider H, Pope J, Burdon KP, et al. Serum selenium status in Graves’ disease with and without orbitopathy: a case-control study. Clin Endocrinol (Oxf). 2014;80:905-10. 34. Eskes S, Endert E, Fliers E, Birnie E, Hollenbach B, Schomburg L, et al. Selenite supplementation in euthyroid subjects with thyroid peroxidase antibodies. Clin Endocrinol (Oxf). 2014;80:444-51. 35. Leo M, Bartalena L, Rotondo Dottore G, Piantanida E, Premoli P, Ionni I, et al. Effects of selenium on short-term control of hyperthyroidism due to Graves’ disease treated with methimazole: results of a randomized clinical trial. J Endocrinol Invest. 2017;40(3):281-7.

25. Charalabopoulos K, Kotsalos A, Batistatou A, Charalabopoulos A, Peschos D, Vezyraki P, et al. Serum and tissue selenium levels in gastric cancer patients and correlation with CEA. Anticancer Res. 2009;29:3465-8.

26. Liu Y, Huang H, Zeng J, Sun C. Thyroid volume, goiter prevalence, and selenium levels in an iodine-sufficient area: a cross-sectional study. BMC Public Health. 2013;13:1-7.

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SUPPLEMENT 1 Código:__________________________Data da coleta dos dados: _____/_____/_____ 1) Registro:______________________________Idade:___________________ Nome:_______________________________________________________________________________________ Data de nascimento: / / Sexo:  M F Raça:  Oriental  Caucasiano  Negro  Mestiço Naturalidade:__________________________________________________________________________________ Endereço res.: (R/Av/Al)___________________________________________________no.:______/______________ Cidade/UF:________________________Bairro:__________________________CEP:_________________________ Tel. res.: (_____)_______________cel.: (_____)_________________E-mail_________________________________ Contato 1: Nome:_____________________________________________________________________________ Telefone: (____)______________E-mail___________________________________________________________ Contato 2: Nome:_____________________________________________________________________________ Telefone: (____)______________E-mail___________________________________________________________ 2) Data do diagnóstico da doença tireoideana (qual/mês/ano):______________________________________________ 3) Tireoidopatias prévias Hipertireoidismo prévio:  Não  Sim descrição____________________________________________________ Tireoidite Hashimoto  Não  Sim Descrição____________________________________________________ Hipotireoidismo subclínico  Não  Sim Descrição _______________________________________ 4) Comorbidades Cardiovasculares IAM:  Não  Sim (qdo:_________) AVC:  Não  Sim (qdo:_________)

HAS:  Não  Sim Dislipidemia:  Não  Sim  Outras:____________________________________

Respiratórias DPOC:  Não  Sim Asma:  Não  Sim

 Outras:

Gastrointestinais Gastrite:  Não  Sim D. Celíaca:  Não  Sim

D. Crohn:  Não  Sim

Geniturinárias IRC:  Não  Sim Litíase renal:  Não  Sim  Outras:_________________________________________

ITU de repetição:  Não  Sim

Endocrinológicas DM:  Não  Sim Tempo:________ Cushing:  Não  Sim Obesidade:  Não  Sim Alt. Hipofisárias:  Não  Sim Quais: Osteoarticulares Artrite:  Não  Sim Artrose:  Não  Sim Fibromialgia:  Não  Sim

Hematológicas Anemia:  Não  Sim Coagulopatias:  Não  Sim  Outras:____________________________________________________________________________________ Imunológicas Alergia:  Não  Sim Qual?_______________________________________________________________________ Doença autoimune:  Não  Sim Qual?______________________________________________________________ Psiquiátricas Depressão:  Não  Sim Ansiedade:  Não  sim Irritabilidade:  Não  Sim  Outras:____________________________________________________________________________________ 606

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Selenium and SePP in thyroid diseases

Neoplásicas  Não  Sim/Qual?____________________________________________________________________________ 5) Hábitos e vícios: Álcool:  Não  Sim Tabagismo:  Nunca fumou  Ex-tabagista > 2 anos  Ex-tabagista < 2 anos  Tabagista atual (no. de cigarros por dia:________/tempo de tabagismo:_________) Atividade física regular:  Não  Sim ( Diária  Semanal ______x/sem) Drogas ilícitas:  Não  Sim/Qual?__________________________________________________________________ Ingesta alimentar:

Peixes Castanhas Suplemento vitamínico  Não  Não  Não  Sim/qtde:______x/semana  Sim/qtde:______x/semana  Sim/qtde:______x/dia Medicações em uso atual: Droga:____________________________dose:___________qtde:________x/dia Droga:____________________________dose:___________qtde:________x/dia Droga:____________________________dose:___________qtde:________x/dia Droga:____________________________dose:___________qtde:________x/dia Droga:____________________________dose:___________qtde:________x/dia

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original article

Laparoscopic sleeve gastrectomy in severely obese adolescents: effects on metabolic profile Departamento de Endocrinologia Pediátrica do Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICrHCFMUSP), São Paulo, SP, Brasil 2 Departamento de Nutrição Pediátrica do Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICr-HCFMUSP), São Paulo, SP, Brasil 3 Departamento de Cirurgia Pediátrica do Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICr-HCFMUSP), São Paulo, SP, Brasil 1

Correspondence to: Ruth Rocha Franco Departamento de Endocrinologia Pediátrica Instituto da Criança, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo Av. Dr. Enéas de Carvalho Aguiar, 647 05403-000 – São Paulo, SP, Brasil ruth.franco@hc.fm.usp.br Received on June/30/2016 Accepted on May/17/2017

Ruth Rocha Franco1, Marina Ybarra1, Louise Cominato1, Larissa Mattar2, Leandra Steinmetz1, Durval Damiani1, Manoel Carlos Prieto Velhote3

ABSTRACT Objective: The objective was to conduct clinical and metabolic evaluations of obese adolescents before and after laparoscopic sleeve gastrectomy (LSG) (up to 24 months). Subjects and methods: This was designed as a retrospective, descriptive series of cases study, conducted in Instituto da Criança, São Paulo, Brazil. Analysis of clinical and laboratory data from 22 obese adolescents between 14 and 19 years old submitted to LSG between 2007 and 2014. Patients had BMI > 40 kg/m2 or BMI > 35 kg/m2 with comorbidities. Anthropometric, clinical and laboratory assessments were performed: before surgery, 6, 12, 18, and 24 months after surgery. We assessed weight loss and metabolic changes up to 24 months after LSG. Results: The mean preoperative weight and BMI were 128.5 kg (SD = 23.1) and 46.5 kg/m2 (SD = 7.4), respectively. There was an average weight loss of 34.5 kg in the first 12 months’ post LSG, corresponding to a 60% excess weight loss (EWL), as well as an average reduction in BMI of 12.3 kg/m2. However, after 24 months, the average EWL was 45%, corresponding to an average weight regain (WR) of 13.3 kg (15%) within two years. LSG improved dyslipidemia in 67.8% of patients, a significant remission of hepatic steatosis 47% and 37.7% systemic arterial hypertension; type 2 diabetes remission was complete. Conclusions: LSG proved to be a safe and effective procedure and seems to be the new hope for the obesity epidemic. Arch Endocrinol Metab. 2017;61(6):608-13 Keywords Bariatric surgery; sleeve gastrectomy; obesity; adolescent

DOI: 10.1590/2359-3997000000310

INTRODUCTION

s a chronic and progressive disease, obesity is currently considered a global epidemic that causes 2.8 million deaths per year. The prevalence of overweight and obese children have increased worldwide, with an estimate of 60 million obese children in 2020 (1). The risk of becoming an obese adult is 77% for obese children and 7% for non-obese children, and it has been shown that 7.3% of boys and 5.5% of girls in the United States are extremely obese (BMI ≥ 35 kg/m2 ≥ or BMI ≥ 1.2 above the 95th percentile) (2,3). In Brazil, data from the Family Expenditure Survey 2008-2009 conducted by the Brazilian Institute for Geography and Statistics showed a significant increase of overweight children, mainly in the age group between 5 and 9 years old. The number of obese children within the same age range increased by over 300%, jumping from 4.1% in 1989 to 16.6% in 2009. The number of overweight boys more 608

than doubled between 1989 and 2009, rising from 15% to 34.8%. Among girls, this variation was even greater: 2.4 in 1989 to 11.8 in 2009 (4). The metabolic risks faced by obese children and adolescents, as well as the related comorbidities, are widely known (5). In obese adolescents with diabetes, weight loss can improve glycemic control, prevent the development of type 2 diabetes mellitus (type 2 DM) in pre-diabetic teens, reduce cardiovascular risk and improve quality of life (6). However, clinical, pharmacological and behavioral treatments have had disappointing outcomes in severely obese adults, adolescents and children. The consensus is that in adults, advanced stages of obesity only respond satisfactorily to surgical treatments, and surgical treatment for obesity in adolescents has recently been gaining acceptance (7). The current guidelines of the International Pediatric Endosurgery Arch Endocrinol Metab. 2017;61/6

Group (IPEG) recommend that surgical intervention should be considered only for extremely obese adolescents. Complete remission of type 2 DM in adolescents submitted to bariatric surgery has also been reported (8). A meta-analysis done in 2008 included 19 studies on bariatric surgery in obese adolescents with a mean age of 16.8 years and mean body mass index (BMI) of 48.8 kg/m2 showed a significant reduction in BMI when patients underwent a bypass or gastric banding (7). Lately, less invasive techniques have been proposed for the pediatric age group with published efficacy and safety (9,10). Since 2007 laparoscopic sleeve gastrectomy (LSG) is one of these procedures. Performing LSG in this age group allows for intervention before the comorbidities become more severe. LSG was initially used as part of the biliopancreatic bypass with duodenal switch (BPD-DS) (11). Later, in difficult cases, the procedure was performed in two stages and surprisingly good results were observed with the LSG alone, in spite of minimal restriction and malabsorption (12). Currently, it is an isolated procedure among the arsenal of surgical procedures. LSG is a relatively simple procedure, with low morbidity and mortality, and the ample literature on it shows that it can lead to loss of excess weight in a range of 5461% without device implantation or dissociation of the gastrointestinal tract (13). There is still a lack of consensus regarding the inclusion criteria for obese adolescents in surgical obesity treatment programs, what type of surgery would be most appropriate for this population, and how postoperative follow-up should be conducted. One of the most serious medium and long-term problems is weight regain, which varies across bariatric surgery patients (14,15). Little data exist on the metabolic changes after bariatric surgery in adolescents. The goal of this study was to conduct clinical and metabolic evaluations of obese adolescents before and after LSG during a period of 24 months in order to obtain a clearer picture of postsurgical outcomes and better understand the subgroup of patients who might benefit from this procedure.

SUBJECTS AND METHODS This is a retrospective, descriptive series of cases study. Inclusion criteria was defined as patients with age from 14 to 19 years old, had BMI ≥ 40 kg/m2 or BMI ≥ Arch Endocrinol Metab. 2017;61/6

35 kg/m2 with comorbidities and were submitted to LSG between 2007 and 2014 at Instituto da Criança da Universidade de São Paulo (Children’s Institute of the University of São Paulo) Exclusion criteria were patients with no follow-up data available. We conducted the analysis of a clinical and laboratory data. All patients attended the child obesity outpatient clinic of the Pediatric Endocrinology Department. The follow-up consists of clinical and pharmacological treatment. It includes a clinical appointment once a month for at least 6 months before surgery, followed by a multidisciplinary team including a pediatric endocrinologist, a nutritionist, a psychologist and a physical educator. Clinical treatment was composed of guidance on the lifestyle, diet and physical activity. Pharmacological treatment included the use when indicated of metformine, sibutramine and anti-depressive drugs as fluoxetine and sertraline. LSG was recommended to patients who failed to achieve significant weight loss (10% of initial weight at 6 months) through clinical treatment. Both the patients and their guardians were informed about the risks and benefits of surgery and provided informed consent. Anthropometric data such as weight (kg), height (m) and BMI (kg/m2) as well as abdominal circumference (AC) were retrieved from medical records. Weight loss and reductions in BMI were reported in absolute values and as a percentage of the initial values. Excess Weight Loss (EWL) was measured using BMI values above 25 kg/m2. Clinical and laboratory assessments were performed during the following times: before surgery, and 6, 12, 18, and 24 months after surgery. We evaluated: total cholesterol (TC), fractions [Low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C) and triglycerides (TG) (colorimetric enzyme, mg/dL)], oral glucose tolerance test (OGTT) with oral administration of 75g of glucose, fasting glycaemia (FG) (enzymatic colorimetric, mg/dL) and fasting insulinemia (electrochemiluminescence immunoassay, μU/mL), glycated hemoglobin (Hb) (ion exchange high performance liquid chromatography HPLC – Variant II Turbo – method certified by NGSP), transaminases (kinetic UV – IFCC, U/L), uric acid (enzymatic colorimetric assay, mg/dL), abdominal ultrasonography for evaluation of hepatic steatosis and echocardiogram to evaluate concentric hypertrophy of the left ventricle. 609

Sleeve gastrectomy in adolescents

RESULTS We assessed 22 obese adolescents (16 females) with a mean age of 16.89 years). The mean preoperative weight and BMI were 128.5 kg (SD 23.1) and 46.5 kg/m2 (SD 7.4), respectively. The average operation (anesthesia 610

Excess weight loss (%) – 95% CI

and surgery) time was 256 minutes. There were no open conversions or postoperative complications. One patient had a spleen injury and another had intraoperative port site bleeding. The mean hospital stay was four days (considering that patients were admitted one day before the surgery), without any readmissions or deaths. The average number of postoperative appointments was 8.9 (SD 4.0) during an average of 27.6 months of follow-up. There was an average weight loss of 34.5 kg in the first 12 months’ post LSG, corresponding to a 60% EWL, as well as an average reduction in BMI of 12.3 kg/m2. However, after 24 months, the average EWL was 45%, corresponding to an average weight regain (WR) of 13.3 kg (Figures 1 and 2). Before surgery, more than half of the patients were hypertensive and had hepatic steatosis, and two had concentric hypertrophy of the left ventricle. The baseline data and the following months data are shown in Table 1. There were also high rates of dyslipidemia and IR (Figure 3). Note that after LSG all the comorbidities 68 64 60 56 52 48 44 40 36 32 28 24 20 16 12 8 4 0 Before

6 months

12 months

18 months

24 months

Figure 1. Percent of excess weight loss (EWL) after laparoscopic sleeve gastrectomy in severely obese adolescents.

BMI (kg/m2) – 95% CI

We evaluated the following variables: insulin resistance (IR; using the homeostatic model assessment of insulin resistance, HOMA-IR ≥ 2.5), pre-diabetes (FG ≥ 100 mg/dL and < 126 mg/dL or OGTT ≥ 140 and < 200 mg/dL), type 2 DM (FG ≥ 126 mg/dL or OGTT ≥ 200 mg/dL or glycated Hb ≥ 6.5%), dyslipidemia (TC > 200 or LDL-C > 130 or HDL-C < 40 for boys and HDL-C < 45 for girls or TG > 130 mg/dL), and systemic arterial hypertension (SAH) [systolic blood pressure > 130 mmHg or diastolic blood pressure > 80 mmHg]. The resolution of comorbidities was evaluated throughout follow-up. Metabolic Syndrome was considered when any 3 of 5 were present: elevated waist circumference (> 102 cm in men; > 88 cm in women), elevated triglycerides (≥ 150 mg/dL), reduced HDL-C (< 40 mg/dL in men; < 50 mg/dL in women), elevated blood pressure (≥ 130 mmHg systolic blood pressure or ≥ 85 mmHg diastolic blood pressure), elevated fasting glucose >100 mg/dL (16). Surgical descriptions, including complications, were retrieved from the medical records. After surgery, patients were followed by the same multidisciplinary team including a pediatric endocrinologist, a nutritionist, a psychologist and a physical educator. Physical activity was encouraged and all followed a one year diet with a nutritionist who introduced gradually each type of food according to a LSG guideline (17,18) and routinely received multivitamin, vitamin B1 and vitamin D3 supplements. The LSG technique has been well standardized (14). Means were calculated for every period studied. We used a repeated measures model using generalized estimating equations (GEE) to calculate the effect over time considering a normal distribution of the response variables and the autoregressive working correlation matrix. We present the p-values for the comparisons in pairs, comparing each time to the baseline time and then to the time immediately before it. Significance was set at 5%. All analyses were conducted using the ggplot2 and geeglm packages of the R 3.1.1 software (R Core Team).

48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 Before

6 months

12 months

18 months

24 months

Figure 2. Percent of reduction in body mass index (BMI) after laparoscopic sleeve gastrectomy in severely obese adolescents. Arch Endocrinol Metab. 2017;61/6

Sleeve gastrectomy in adolescents

6.5 6.0 5.5 HOMA-IR – 95% CI

remitted or improved (Table 2). Twelve months after LSG, all metabolic markers remained stable and there was no weight regain. The course of metabolic syndrome before and after surgery is also shown in Table 2. Throughout the postoperative follow-up, there were no deficiencies in vitamin D, albumin, vitamin B12, folic acid, calcium, magnesium, phosphorus or zinc dosages (data not shown). One female patient presented iron deficiency anemia with low concentrations of iron and ferritin. No patients developed a compulsive behavior. Twelve patients underwent bone densitometry (BD) 15 months after LGV, on average. Everyone showed normal BD for age and sex.

5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 Before

6 months

12 months

18 months

24 months

Figure 3. Mean HOMA-IR (homeostatic model assessment of insulin resistance) post laparoscopic sleeve gastrectomy in severely obese adolescents.

Table 1. Comparison of the baseline characteristics and the evolution after 6, 12, 18 and 24 months Baseline Variables

6 months

12 months

18 months

24 months

Mean (SD)

BMI (kg/m2)

46.3 (7.4)

34.4 (6.6)*†

34.5 (6.4)*

37.1 (6.2)*

38.4 (6.7)

WC (cm)

133.6 (13.7)

102.7 (27)*

108.4 (13.8)*

114.4 (15.2)

116.6 (13.8)

SBP (mmHg)

127.8 (12.5)

113.7 (10.4)*†

119.7 (15.4)

113.9 (8.4)

120.1 (7.8)

DBP (mmHg)

80.2 (9.9)

69.8 (6.4)

72.2 (9.2)

70 (3.1)

75.1 (8.7)†

TC (mg/dL)

163.3 (25)

149.7 (15.3)

153.7 (23.7)

135.2 (10.6)

155.8 (25.6)

LDL-C (mg/dL)

106 (21.8)

94.1 (18)

91.4 (24.6)

79.9 (12.3)*

96.2 (23.4)

HDL-C (mg/dL)

36.6 (10.9)

42.3 (8)*†

51.4 (8.4)*†

48.4 (12.8)*

50.6 (14.1)*

TG (mg/dL)

115.6 (52.4)

91.8 (38.8)

68.7 (18.8)*

84.6 (31.1)*

90.5 (43.3)

Glycaemia (mg/dL)

59.5 (44.3)

66.6 (30.6)*

49 (38.7)

60.9 (37.1)

80.3 (6.8)*

Insulinemia (μU/mL)

38.6 (29.8)

12.1 (9.4)

8.4 (3.1)*

11 (8.9)

12.1 (8.2)

†

BMI: body mass index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; TC: total cholesterol; LDL-C: low-density lipoprotein; HDL-C: high-density lipoprotein; TG: triglycerides; SD: standard deviation; N: number * statistic significant when compared to 6-month mean † statistic significant when compared to the mean immediately before.

Table 2. Prevalence of comorbidities at baseline and after 12 and 24 months of laparoscopic sleeve gastrectomy (LSG) in severely obese adolescents SAH type 2 DM

Baseline

12 months

24 months

p value

13/22 (59.1%)

3/17 (17.6%)

3/14 (21.4%)

0.023

1/22 (4.5%)

0/12 (0%)

0/13 (0%)

0.999

OGI

2/22 (9.1%)

0/12 (0%)

0/13 (0%)

0.999

LV hypertrophy

3/22 (13,6%)

0/12 (0%)

0,999

HOMA-IR > 2.5

21/22 (95.5%)

4/12 (33.3%)

6/13 (46.2%)

0.046

Hepatic steatosis

12/22 (54.5%)

2/12 (16.7%)

1/14 (7.1%)

0.027

Dyslipidemia

21/22 (95.5%)

2/12 (16.7%)

3/13 (23.1%)

0.004

Metabolic syndrome

4/22 (18.2%)

1/14 (7.1%)

0.479

SAH: systemic arterial hypertension; type 2 DM: type 2 diabetes; OGI: oral glucose intolerance; LV hypertrophy: left ventricular hypertrophy; HOMA-IR: homeostatic model assessment of insulin resistance. Arch Endocrinol Metab. 2017;61/6

611

Measure

Sleeve gastrectomy in adolescents

DISCUSSION Considering the low response to clinical treatments and the lowered life expectancy of severely obese adolescents, bariatric surgery seems to be the new hope for the obesity epidemic. However, the long-term implications of surgery, such as psychological effects, metabolic interference and the impact on growth are not yet fully understood. In this study, we conducted clinical and metabolic evaluations of obese adolescents before and after LSG during a period of at least 24 months. In our group of 22 adolescents, we observed a mean weight loss of 34.5 kg (average excess weight loss of 60% - EWL) at 12 months. After 24 months, the average weight loss was 25.8 kg (45% EWL), which means 15% of the weight regain within two years. This is similar to that reported by Nadler and cols., who evaluated 33 patients with an average EWL of 40 ± 19% after 12 months (19). On the other hand, our values were lower than those reported by Alqahtani and cols., who studied a group of 108 patients with a mean EWL of 64% at 24 months (9) and Boza and cols., who studied 54 patients and recorded a mean EWL of 96.2% after 12 months (20). From our experience this weight regain is due to a lack of compromise with diet and exercise prescription after the first year of follow-up, although we have no precise evaluation concerning this topic in this study. While we found a 25.6% weight loss at 12 months, Sachdev and cols. similarly reported a loss of 27% (21). Our data were also like those of Lennerz and cols., who observed a loss of 13.1 ± 8.2 BMI points at 18 months’ post-surgery, while in our study this figure was 11.1 (22). While WR was 15% in our study, Boza and cols. reported a WR of only 4% in 2 years and Alqahtani and cols. did not report any (9,20).The lowest weight point in our study was 12 months, in line with studies reporting 12-16 months (23). WR is a common risk, and approximately 20-30% of patients do not reach their ideal weight loss (24,25). Several factors can influence weight goals and WR after bariatric surgery. Responses vary between individuals according to type of surgery, follow-up, demographics, psychosocial factors, biological factors and factors that regulate energy intake, stock and expenditure (25). Our data showed a rapid decrease in blood glucose and a significant improvement in insulin sensitivity 612

already at 6 months postoperatively, when there was still a mean BMI of 40.1 kg/m2 (SD 5.8). Previous studies related the different types of bariatric surgery with improved glucose homeostasis together with incretins and intestinal hormones, independently of weight loss (6,26-29). This was observed when they compared weight loss by bariatric surgery to weight loss achieved with clinical treatments or purely restrictive treatments such as gastric banding, which have little or no effect on the post-prandial hormonal profile (30-32). Bariatric surgery not only reduces body fat; it also improves dyslipidemia. A study that evaluated a cohort of patients who had bariatric surgery showed improved serum lipid profiles in 70% of patients (33). In our study, dyslipidemia improved in 67.8% of patients. Still regarding comorbidities, our study revealed a significant resolution rate in two years, with approximately 47% remission of hepatic steatosis and 37.7% of SAH. Type 2 DM remission was complete. Of the 95.5% of patients with IR preoperatively, 66.6% normalized their insulin profile and kept it despite the weight regained in two years. The significant improvement in dyslipidemia also remained despite the weight regained. This shows that the metabolic condition achieved by LSG goes beyond simple weight loss, which makes the surgery more metabolic in nature and not purely restrictive, as formerly thought. Indeed, the International Hepatology Committee considers bariatric surgery a valid treatment option for non-alcoholic steatohepatitis in adolescents with morbid obesity (34). Limitations of our study are that it was a retrospective series of cases study with a small number of patients, no control group and non-standard follow-up. The same surgeon with the same surgical technique operated all patients and all exams were performed in the same laboratory. The increase in the number of patients undergoing bariatric surgery has allowed for a better understanding of the mechanisms that induce weight loss. However, it is not yet clear which physiological mechanisms are responsible for continued weight loss and metabolic improvements (35). This work shows that even with weight regain, patients show great metabolic improvement, which is maintained for up to two years after surgery. Prospective studies should be conducted comparing different surgical treatments using standardized long-term follow-ups to address these issues. Acknowledgments: we thank Mariza Kazue for bibliography research, Lucas Damiani for statistical analysis and Luiz Fernando Arch Endocrinol Metab. 2017;61/6

Sleeve gastrectomy in adolescents

Ybarra and Sergio Santoro for reviewing this article many times. This manuscript was reviewed by a professional science editor and by a native English-speaking copy editor to improve readability.

17. Parkes E. Nutritional management of patients after bariatric surgery. Am J Med Sci. 2006;331(4):207-13.

Clinical Trial Registration Number: NCT02594514.

19. Nadler EP, Barefoot LC, Qureshi FG. Early results after laparoscopic sleeve gastrectomy in adolescents with morbid obesity. Surgery. 2012;152(2):212-7.

REFERENCES 1. (WHO) WHO. Obesity and overweight 2016. Available from: http:// www.who.int/mediacentre/factsheets/fs311/en/. Access on: Feb 25, 2014. 2. Stefater MA, Jenkins T, Inge TH. Bariatric surgery for adolescents. Pediatr Diabetes. 2013;14(1):1-12. 3. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363(17):1597-607. 4. BRASIL. Pesquisa de Orçamentos Familiares 2008-2009 – Antropometria e estado nutricional de crianças, adolescentes e adultos no Brasil. Ministério do Planejamento, Orçamento e Gestão, Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, 2010. 5. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond). 2011;35(7):891-8. 6. Karamanakos SN, Vagenas K, Kalfarentzos F, Alexandrides TK. Weight loss, appetite suppression, and changes in fasting and postprandial ghrelin and peptide-YY levels after Roux-en-Y gastric bypass and sleeve gastrectomy: a prospective, double blind study. Ann Surg. 2008;247(3):401-7. 7. Treadwell JR, Sun F, Schoelles K. Systematic review and metaanalysis of bariatric surgery for pediatric obesity. Ann Surg. 2008;248(5):763-76. 8. Inge TH, Miyano G, Bean J, Helmrath M, Courcoulas A, Harmon CM, et al. Reversal of type 2 diabetes mellitus and improvements in cardiovascular risk factors after surgical weight loss in adolescents. Pediatrics. 2009;123(1):214-22. 9. Alqahtani AR, Antonisamy B, Alamri H, Elahmedi M, Zimmerman VA. Laparoscopic sleeve gastrectomy in 108 obese children and adolescents aged 5 to 21 years. Ann Surg. 2012;256(2):266-73. 10. Baltasar A, Serra C, Bou R, Bengochea M, Andreo L. Sleeve gastrectomy in a 10-year-old child. Obes Surg. 2008;18(6):733-6. 11. Hess DS, Hess DW. Biliopancreatic diversion with a duodenal switch. Obes Surg. 1998;8(3):267-82. 12. Marceau P, Biron S, Bourque RA, Potvin M, Hould FS, Simard S. Biliopancreatic Diversion with a New Type of Gastrectomy. Obes Surg. 1993;3(1):29-35. 13. Pech N, Meyer F, Lippert H, Manger T, Stroh C. Complications, reoperations, and nutrient deficiencies two years after sleeve gastrectomy. J Obes. 2012;2012:828737. 14. Till H, Bluher S, Hirsch W, Kiess W. Efficacy of laparoscopic sleeve gastrectomy (LSG) as a stand-alone technique for children with morbid obesity. Obes Surg. 2008;18(8):1047-9. 15. Allen SR, Lawson L, Garcia V, Inge TH. Attitudes of bariatric surgeons concerning adolescent bariatric surgery (ABS). Obes Surg. 2005;15(8):1192-5. 16. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735-52. Arch Endocrinol Metab. 2017;61/6

20. Boza C, Viscido G, Salinas J, Crovari F, Funke R, Perez G. Laparoscopic sleeve gastrectomy in obese adolescents: results in 51 patients. Surg Obes Relat Dis. 2012;8(2):133-7; discussion 7-9. 21. Sachdev P, Makaya T, Marven SS, Ackroyd R, Wales JK, Wright NP. Bariatric surgery in severely obese adolescents: a single-centre experience. Arch Dis Child. 2014;99(10):894-8. 22. Lennerz BS, Wabitsch M, Lippert H, Wolff S, Knoll C, Weiner R, et al. Bariatric surgery in adolescents and young adults--safety and effectiveness in a cohort of 345 patients. Int J Obes (Lond). 2014;38(3):334-40. 23. Sjostrom L, Narbro K, Sjostrom CD, Karason K, Larsson B, Wedel H, et al. Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med. 2007;357(8):741-52. 24. Karra E, Yousseif A, Batterham RL. Mechanisms facilitating weight loss and resolution of type 2 diabetes following bariatric surgery. Trends Endocrinol Metab. 2010;21(6):337-44. 25. Pedersen SD. The role of hormonal factors in weight loss and recidivism after bariatric surgery. Gastroenterol Res Pract. 2013;2013:528450. 26. Peterli R, Wolnerhanssen B, Peters T, Devaux N, Kern B, Christoffel-Courtin C, et al. Improvement in glucose metabolism after bariatric surgery: comparison of laparoscopic Roux-en-Y gastric bypass and laparoscopic sleeve gastrectomy: a prospective randomized trial. Ann Surg. 2009;250(2):234-41. 27. Vrang N, Madsen AN, Tang-Christensen M, Hansen G, Larsen PJ. PYY(3-36) reduces food intake and body weight and improves insulin sensitivity in rodent models of diet-induced obesity. Am J Physiol Regul Integr Comp Physiol. 2006;291(2):R367-75. 28. Ahren B, Larsson H, Holst JJ. Effects of glucagon-like peptide-1 on islet function and insulin sensitivity in noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1997;82(2):473-8. 29. Korner J, Bessler M, Inabnet W, Taveras C, Holst JJ. Exaggerated glucagon-like peptide-1 and blunted glucose-dependent insulinotropic peptide secretion are associated with Roux-en-Y gastric bypass but not adjustable gastric banding. Surg Obes Relat Dis. 2007;3(6):597-601. 30. Korner J, Bessler M, Cirilo LJ, Conwell IM, Daud A, Restuccia NL, et al. Effects of Roux-en-Y gastric bypass surgery on fasting and postprandial concentrations of plasma ghrelin, peptide YY, and insulin. J Clin Endocrinol Metab. 2005;90(1):359-65. 31. Santoro S. Adaptive and neuroendocrine procedures: a new pathway in bariatric and metabolic surgery. Obes Surg. 2008;18(10):1343-5. 32. Laferrere B, Teixeira J, McGinty J, Tran H, Egger JR, Colarusso A, et al. Effect of weight loss by gastric bypass surgery versus hypocaloric diet on glucose and incretin levels in patients with type 2 diabetes. J Clin Endocrinol Metab. 2008;93(7):2479-85. 33. Buchwald H, Avidor Y, Braunwald E, Jensen MD, Pories W, Fahrbach K, et al. Bariatric surgery: a systematic review and metaanalysis. JAMA. 2004;292(14):1724-37. 34. Nobili V, Vajro P, Dezsofi A, Fischler B, Hadzic N, Jahnel J, et al. Indications and limitations of bariatric intervention in severely obese children and adolescents with and without nonalcoholic steatohepatitis: ESPGHAN Hepatology Committee Position Statement. J Pediatr Gastroenterol Nutr. 2015;60(4):550-61. 35. Stefater MA, Wilson-Perez HE, Chambers AP, Sandoval DA, Seeley RJ. All bariatric surgeries are not created equal: insights from mechanistic comparisons. Endocr Rev. 2012;33(4):595-622.

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Disclosure: no potential conflict of interest relevant to this article was reported.

18. Marcason W. What are the dietary guidelines following bariatric surgery? J Am Diet Assoc. 2004;104(3):487-8.

review

Relationships between adiponectin levels, the metabolic syndrome, and type 2 diabetes: a literature review Anize Delfino von Frankenberg1,2, André F. Reis3, Fernando Gerchman1,4 1 Programa de Pós-Graduação em Endocrinologia, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil 2 Departamento de Nutrição, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brasil 3 Universidade Federal de São Paulo (Unifesp), Departamento de Medicina, Disciplina de Endocrinologia, São Paulo, SP, Brasil 4 Unidade de Metabolismo, Divisão de Endocrinologia, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brasil

ABSTRACT

Correspondence to: Anize Delfino von Frankenberg Hospital de Clínicas de Porto Alegre Rua Ramiro Barcelos, 2350, Prédio 12, 4° andar 90035-003 – Porto Alegre, RS, Brasil anize.frankenberg@gmail.com

Elevated hepatic glucose production, impaired insulin secretion, and insulin resistance – abnormalities of glucose metabolism typically found in subjects with obesity – are major factors underlying the pathogenesis of type 2 diabetes (DM2) and the metabolic syndrome (MS). Adiponectin is a major regulator of glucose and lipid homeostasis via its insulin-sensitizing properties, and lower levels seems to be associated with the development of DM2 and MS. The purpose of this review is to clarify the mechanisms whereby adiponectin relates to the development of DM2 and MS and the association between polymorphisms of the adiponectin gene, circulating levels of the hormone, and its relationships with DM2. In addition, the impact of dietary lipids in the circulating levels of adiponectin will be addressed. According to the literature, circulating adiponectin levels seem to decrease as the number of MS components increases. Lower adiponectin concentrations are associated with higher intra-abdominal fat content. Therefore, adiponectin could link intra-abdominal fat with insulin resistance and development of MS. Therapeutic strategies that target the MS and its components, such as lifestyle modification through physical activity and weight loss, have been shown to increase adiponectin concentrations. Possible roles of diets containing either low or high amounts of fat, or different types of fat, have been analyzed in several studies, with heterogeneous results. Supplementation with n-3 PUFA modestly increases adiponectin levels, whereas conjugated linoleic acid supplementation appears to reduce concentrations when compared with unsaturated fatty acid supplementation used as an active placebo. Arch Endocrinol Metab. 2017;61(6):614-22

Received on Dec/9/2016 Accepted on Aug/9/2017

Keywords Adiponectin; metabolic syndrome; type 2 diabetes

DOI: 10.1590/2359-3997000000316

INTRODUCTION

he prevalence of the metabolic syndrome (MS) and diabetes mellitus type 2 (DM2) in the North American population is 22.9% and 9.3%, respectively, according to NHANES and CDC data (1,2). Very similar prevalence figures have been reported in Brazil (3). In a recent systematic review of cross-sectional studies, the weighted average prevalence of MS was 29.8%, 20.1%, and 41.5% in the adult Brazilian population in urban, rural, and indigenous areas, respectively (3). According to the results of a study conducted with fixed-shift industry workers in the state of Rio Grande do Sul, being female, being older, and having sleep deprivation proved to be potential risk factors for MS, while having a higher education and eating a greater number of meals per day were considered protective factors against MS (4). Regarding DM2, the Multicenter Study on the Prevalence of Diabetes in Brazil estimated its prevalence the adult population at 7.6%

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in the late 1980s (5). According to 2012 data, 7.4% of respondents to a telephone survey representative of the entire adult population of all 26 state capitals and the Federal District reported a medical diagnosis of DM2 (6). Complications from DM2 result in high morbidity and mortality and an average 6-year reduction in average life expectancy when the disease is diagnosed at age 50 (7). The direct and indirect costs of DM2 amount to US$ 245 billion a year in the United States alone. Health expenditures on individuals with DM2 are increased twofold when compared to spending on individuals without the disease (1). In Brazil, DM is also considered a major public health problem. Its estimated cost per capita is US$ 1527.6/year; considering that an estimated 11.6 million Brazilians aged 20 to 79 have DM2, direct expenses related to this condition amount to approximately US$ 17 billion a year (8). Obesity is associated with development of the MS, which is characterized by a cluster of risk factors for Arch Endocrinol Metab. 2017;61/6

cardiovascular disease and DM2, such as hyperglycemia, elevated blood pressure, elevated triglycerides, low HDL cholesterol, and central obesity (9). Moreover, obesity and abdominal fat deposition cause a number of metabolic abnormalities that result in increased hepatic glucose output and decreased insulin sensitivity in skeletal muscle, liver, and adipose tissue – processes that are closely related to the pathogenesis of DM2 (10). In view of the foregoing, the present review sought to clarify the mechanisms whereby the hormone adiponectin relates to the development of MS and DM2. The association between polymorphisms of the adiponectin gene, circulating levels of the hormone it encodes, and its relationships with DM2 will also be explored. In addition, the impact of diets with different levels and types of lipids on circulating levels of adiponectin will be addressed.

ADIPONECTIN AND GLUCOSE METABOLISM Adiponectin, a hormone present mainly in adipose tissue, is encoded by the APM1 gene (chromosome 3q27). In humans, adiponectin plasma levels range from 3 to 30 µg/mL, which is among the highest plasma concentrations of a circulating protein. The adiponectin molecule is a 247-amino acid polypeptide and is secreted into circulation in three oligomeric isoforms: a lowmolecular-weight trimer, an intermediate-molecularweight hexamer and a high-molecular-weight complex (11). Some studies suggest that the high-molecularweight isoform is most biologically active, and that lower levels of this form are associated with DM and coronary artery disease (12-14). Adiponectin acts through two receptors, AdipoR1 and AdipoR2; the former is expressed at higher levels in muscle tissue, and the latter, in liver tissue. Studies have demonstrated that the AdipoR1 receptor is also present in endothelial cells (15), cardiomyocytes (16), and pancreatic beta cells (17), while AdipoR2 is present in endothelial cells (18), and both receptors are present in the hypothalamus (19). Resistance to the action of insulin resulting from obesity causes downregulation of adiponectin receptors in muscle and liver (20). Furthermore, adiponectin expression blunts increases in insulin, TNF-α, endothelin-1, and glucocorticoids, factors implicated in the pathogenesis of insulin resistance, subclinical inflammation, endothelial dysfunction, and regulation of energy metabolism, and closely related to the development of MS, DM2, and cardiovascular Arch Endocrinol Metab. 2017;61/6

disease (21). Accordingly, extensive research has shown that adiponectin levels are reduced in obesity (22,23), DM2 (22,24), and coronary artery disease (25-27). To test the in vivo effect of adiponectin on insulin sensitivity, a lipoatrophy mouse model with adiponectin deficiency was developed. In these animals, replacement of physiological doses of adiponectin improved insulin resistance (28). Adiponectin stimulated fatty acid oxidation in muscle by increasing the expression of molecules involved in the transport of fatty acids (CD36), their combustion (acetyl coenzyme A oxidase), and dissipation of energy through increased expression of type 2 uncoupling protein (UCP-2) (28). Adiponectin replacement in these animals increased PPAR-alpha expression, fatty acid oxidation, and energy consumption, causing a reduction of triglyceride levels in muscle and liver tissue (28). The reduction in triglyceride content in skeletal muscle was associated with increased translocation of GLUT-4, which led to improved insulin sensitivity (28). In another study by the same group, acute treatment (up to 6 hours) of C2C12 myoblasts with adiponectin increased the oxidation of fatty acids and stimulated glucose uptake via activation of AMPK (29), leading to a reduction in levels of enzymes that indicate hepatic gluconeogenesis. Furthermore, wild-type mice that received a diet rich in total lipids had a reduction in adiponectin levels compared to a diet rich in carbohydrates. Adiponectin replacement in these animals also improved insulin resistance and hypertriglyceridemia induced specifically by the high-fat diet (28). Additionally, studies demonstrated in wild-type and ob/ob or streptozotocininduced DM1 mouse models that an acute increase in circulating levels of adiponectin leads to a transient decrease in baseline glucose level by inhibiting enzymes involved in hepatic gluconeogenesis and hepatic glucose production rate (30,31). Based on these studies, it was demonstrated that stimulation of fatty acid oxidation in muscle and liver, increasing glucose uptake in skeletal muscle and suppressing hepatic gluconeogenesis, are potential routes through which adiponectin regulates insulin sensitivity (28,30,31). These data suggest that insulin resistance associated with a high-fat diet and obesity are partly related to a reduction in circulating adiponectin levels and that an increase of these levels would protect against the development of different components of MS, especially those related to the modulation of insulin sensitivity, body fat distribution, and lipoprotein metabolism (28). 615

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Studies have suggested a relationship between adipokines, such as letptin and resistin, and DM-related vascular complications (32,33). Chronic kidney disease is considered a long-term complication of DM, and its development has been associated with higher levels of these adipokines (34). According to an experimental study, adipokines can lead to kidney injury by regulation of endothelial dysfunction, oxidative stress, and inflammatory processes (35). A longitudinal study of 161 subjects with diabetes followed from 2002 to 2013 demonstrated that plasma adiponectin increased in patients with renal insufficiency, and that its levels were positively associated with albuminuria (36). It is interesting to highlight that, in the context of chronic kidney disease, higher levels of adiponectin have been found to predict progression to end-stage renal disease (ESRD), cardiovascular mortality, and total mortality (37,38). Adiponectin levels are known to increase with decreasing glomerular filtration rate in chronic kidney disease, as a reflection of decreased renal clearance (39). As a result, ESRD features increased adiponectin levels and AdipoR1 expression (40). Both mechanisms may explain the association between higher adiponectin levels and total and cardiovascular mortality in this scenario.

ASSOCIATION BETWEEN ADIPONECTIN GENE POLYMORPHISMS, CIRCULATING ADIPONECTIN LEVELS, AND DM2 Epidemiological studies have shown that DM2 clusters in families, suggesting a genetic contribution to its development. The cumulative risk of DM2 at age 65 was 14.8% for individuals with no family history of DM2, 22% for individuals with only one parent with DM2, and 41% for individuals whose parents are both affected by the disease (41). Recent studies have shown that a number of genetic polymorphisms are associated with the development of obesity and DM2 (42,43). Genes that modulate the metabolism of adipose tissue and, consequently, are involved in the fatty acid synthesis and metabolism pathways are important determinants of body fat distribution and insulin sensitivity, which, in turn, are also related to abnormalities in glucose metabolism and development of DM2. Genetic variants of the adiponectin gene have also been associated with resistance to insulin action and DM2 (44). 616

It is estimated that 39-46% of the variability of circulating adiponectin levels is due to genetic factors (45,46). In this regard, a recent systematic review and meta-analysis compiled data from seven studies that explored the association between three single-nucleotide polymorphisms (SNPs), -11391G→A, +45T→G, and +276G→T, and plasma level of adiponectin (25). The -11391G→A SNP was associated with higher levels of circulating adiponectin in subjects carrying the A allele compared to subjects carrying only the G allele. No association was found between the +45T→G SNP and adiponectin levels. Regarding the SNP +276G→T, adiponectin levels showed a progressive increase in homozygotes for the G allele when compared to heterozygotes and homozygotes for the T allele (25). Associations between adiponectin gene polymorphisms and risk of DM2 have also been widely explored in the literature. Among the nine chromosomal regions related to DM2, three (3q, 15q, and 20q) are found in various ethnic groups, such as the Japanese, German, and French (47). Interestingly, the 3q27 region containing the adiponectin gene once again suggests a role of adiponectin as a determinant of susceptibility to DM2. The 276 SNP in intron 2 (G vs. T) has been associated with distinct phenotypes of adiponectin levels, insulin resistance, and susceptibility to DM2. Individuals with the G/G genotype at position 267 had lower adiponectin levels and increased DM2 risk compared with T/T genotype carriers (24). Similar associations between the adiponectin gene and susceptibility to DM2 have also been demonstrated in German and French populations (48,49). Given the large number of studies that have sought associations between different polymorphisms of the adiponectin gene and DM2, a recent systematic review and meta-analysis pooled the results of more than 2,000 individuals with DM2 vs. controls for four SNPs: -11391G→A, -11377C→G, +45T→G, and +276G>T (25). No association was demonstrated between the evaluated SNPs and risk of DM2. Subsequently, another systematic review and metaanalysis of 45 studies (9,986 individuals with DM2 vs. 16,222 control subjects) only assessed polymorphism +45T→G and, through a subgroup analysis, found an association between +45T→G and risk of DM2 in studies involving Asians. However, there was no such association in studies involving Caucasians (50). Regarding insulin resistance, an association between the +276G→T SNP and insulin resistance estimated Arch Endocrinol Metab. 2017;61/6

by HOMA-IR has been observed. Resistance to insulin action was higher in individuals homozygous for the G allele compared to heterozygotes and those homozygous for the T allele, indicating higher insulin sensitivity in individuals carrying the T allele – the same allele that showed a trend toward association with higher levels of adiponectin (25). In summary, studies have suggested that genetic factors modulate circulating adiponectin levels (27). Additionally, adiponectin gene variants have been associated with higher risk of developing DM2 and MS, especially in phenotypes associated with insulin resistance (51). However, this finding remains controversial.

ADIPONECTIN AND THE METABOLIC SYNDROME Studies have suggested that expression of the APM1 gene in visceral abdominal fat is lower than in subcutaneous abdominal fat (52,53). This gene expression in adipose tissue correlates significantly with plasma levels, being higher in lean individuals and those with higher sensitivity to insulin action (23). Furthermore, the lowest concentration of adiponectin is associated more strongly with quantification of visceral abdominal fat than with subcutaneous abdominal fat, suggesting a possible relationship with MS (54). The inverse relation between adiponectin levels and criteria for MS has been described in the literature (47,55-58). It is well demonstrated that overweight individuals have lower levels of adiponectin compared to lean individuals, and that levels of this hormone decrease as BMI increases in men and women (55). In addition, higher levels of adiponectin were associated with a lower incidence of DM2 in a Japanese cohort followed for 5 years in order to better understand the factors related to development of DM. Individuals in the lowest tertile of adiponectin levels developed approximately nine times more DM2 than those individuals belonging to the highest tertile (56). Additionally, individuals with lower plasma levels of adiponectin have LDLcholesterol molecules of smaller size, lower lipoprotein lipase activity, lower HDL-cholesterol levels, and higher triglyceride levels (47,58). Regarding blood pressure, lower levels of circulating adiponectin were observed in hypertensive compared to non-hypertensive patients, even after adjusting for obesity, insulin resistance, and DM2 (57). Studies have suggested an effect of adiponectin on blood pressure homeostasis. An increase in collagen deposition promoted by increased Arch Endocrinol Metab. 2017;61/6

serum levels of procollagen type I carboxy-terminal propeptide (PICP) is associated with an acceleration of the arterial stiffening process, a phenomenon closely related to the development of hypertension (59) and MS (60). A cross-sectional study of 188 hypertensive patients without DM2 showed that higher adiponectin levels were associated with lower circulating levels of PICP (61). Reinforcing this hypothesis, lower levels of adiponectin were associated with increased arterial wall stiffness in a cohort of elderly individuals (62). An effect of adiponectin on endothelial function has also been demonstrated. Adiponectin increases gene expression and activates endothelial nitric oxide synthase through activation of AMPK (63), stimulating the synthesis of nitric oxide, an important endothelial factor and potent vasodilator (64). Additionally, it is known that the renin-angiotensin system plays an important role in regulating blood pressure and that, when activated, it perpetuates the inflammatory process in the arterial wall, increasing oxidative stress and fostering development of atherosclerosis (65,66). Through its antioxidant and anti-inflammatory effects, adiponectin inhibits the deleterious vascular effect of renin-angiotensin system activation and is closely related to dysregulation of blood pressure homeostasis in MS (67). Figure 1 shows the different mechanisms involved in the pathogenesis of MS related to hypoadiponectinemia. Cross-sectional studies have evaluated the associations between the different MS components and adiponectin levels in populations with different metabolic profiles (68-70). The results of a crosssectional study conducted in the elderly U.S. Rancho Bernardo cohort demonstrated that individuals with MS had lower circulating levels of adiponectin compared to individuals without MS (68). Furthermore, the presence of each of the MS components was associated with lower levels of adiponectin (68). Koh and cols. included only Asian individuals over the age of 40, and found that lower adiponectin levels were associated with greater waist circumference and increased levels of triglycerides, CRP, fasting glucose, and insulin. Furthermore, individuals with higher circulating adiponectin had higher HDL-cholesterol levels (69). In a study conducted by von Frankenberg and cols. in Brazil, individuals were referred to a tertiary care hospital (Hospital de Clínicas de Porto Alegre, state of Rio Grande do Sul) for screening and evaluation of glucose metabolism abnormalities and MS. A replication analysis was performed in subjects 617

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Figure 1. Mechanisms whereby a reduction in adiponectin levels is associated with the development of MS. 1: Accumulation of visceral fat reduces production of adiponectin. Tissue inflammation increases levels of C-reactive protein (CRP) and inflammatory cytokines (TNF-α and interleukin-6), activating hepatic gluconeogenesis. 2: Hepatic gluconeogenesis is activated and insulin sensitivity in muscle and liver is further reduced, resulting in increased circulating glucose levels. 3 and 4: Reduction of triglyceride oxidation from adipose tissue and dietary lipids by the liver increases levels of free fatty acids (FFA) and production of VLDL, generating an imbalance in lipid profile (increased LDL cholesterol, triglycerides [TG], and reduced HDL cholesterol). 5: Increased serum level of procollagen type I carboxy-terminal propeptide (PICP) intensifies arterial stiffness, and reduced nitric oxide production contributes to reduced vasodilation. These mechanisms, along with the pro-inflammatory environment, promote changes in blood pressure homeostasis, which contribute to the development of systemic arterial hypertension.

undergoing cardiac catheterization at another tertiary referral center (Hospital São Paulo, in the city of São Paulo). This study demonstrated that levels of total and high-molecular-weight adiponectin were lower in the presence of MS, and were reduced with each increase in the number of components of MS. Adiponectin levels were mainly determined by their relation with HDL cholesterol, triglycerides, and waist circumference. In addition, blood glucose, subclinical inflammation, and insulin resistance partially explained why adiponectin levels were lower in individuals with compared to individuals without MS (70).

EFFECT OF DIETARY LIPIDS ON CIRCULATING ADIPONECTIN LEVELS Intervention studies have shown that adiponectin levels can be partly determined by different types of diet. Given the important role of adiponectin in carbohydrate and lipid metabolism, including improving insulin sensitivity and increasing fatty acid oxidation, diets that modify the quantity and quality of lipids ingested can have an impact on the metabolism and plasma levels of adiponectin (71,72). Several studies conducted in humans aimed to show the effect of diets with high or low levels of total lipids in the regulation of adiponectin 618

(73-75). In a randomized clinical trial comparing a fatrestricted hypocaloric diet (27% fat, 52% carbohydrates, and 21% protein) vs. a high-fat diet (41% fat, 39% carbohydrates, and 20% protein), there were no changes in adiponectin levels observed at the end of 10 weeks of intervention (73). However, another study that compared a normal-lipid hypocaloric diet (30% fat) vs. a high-lipid (61% fat) diet showed 30% and 18% increases in adiponectin levels after 52 weeks of intervention respectively (75). However, providing isocaloric diets for weight maintenance with high fat (55% fat, 27% carbohydrates, 18% protein) or low fat (20% fat, 62% carbohydrates, 18 % protein) was not associated with differences in adiponectin levels after 4 weeks of intervention (74). These results suggest there is no consensus about the effect of total dietary lipid intake (low fat vs. high fat) on adiponectin levels in interventional studies conducted in humans. Greater adherence to the Mediterranean style diet, which is rich in unsaturated fats, has been associated with higher adiponectin levels (76). This relationship is possibly attributable not only to the low glycemic load and moderate alcohol consumption characteristic of this diet, but also to its high content of oilseeds and olive and fish oil, which are dietary sources of polyunsaturated fatty acids (71). The mechanisms Arch Endocrinol Metab. 2017;61/6

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Arch Endocrinol Metab. 2017;61/6

However, CLA supplementation reduced adiponectin concentrations as compared with unsaturated fatty acid supplementation as active placebo (72).

CONCLUSIONS Circulating levels of adiponectin are reduced in the presence of the MS, cardiovascular disease, and DM2, and also decrease as the number of MS components increases. The association of adiponectin with HDL cholesterol, triglycerides, and abdominal fat may partly explain the lower levels of adiponectin found in individuals with MS. Among dietary interventions, diets low in total lipids have shown no effect on circulating adiponectin. Supplementation with n-3 polyunsaturated fatty acids, however, was associated with a moderate increase in adiponectin. In contrast, conjugated linoleic acid appears to reduce adiponectin levels when compared to unsaturated fat supplementation. Funding statement: this study received financial support from the State of Rio Grande do Sul Foundation for Research Support (Fapergs PG 5989.284.18921.12062013), the Hospital de Clínicas de Porto Alegre Research and Event Incentive Fund (FIPE-HCPA 11-226), and the Brazilian National Research Council (CNPq 486802/2013-2). Disclosure: no potential conflict of interest relevant to this article was reported.

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through which the Mediterranean diet has impacts on circulating levels of adiponectin is still unknown, but several hypotheses have been raised. The omega-3 type polyunsaturated fatty acids (n-3 PUFA) found in this diet can modulate adiponectin levels by interacting with the peroxisome proliferator-activated receptors alpha (PPAR-α) and gamma (PPAR-g) (77). Activation of PPAR-α stimulated by consumption of n-3 PUFA increases expression of AdipoR1 and AdipoR2 in muscle and liver, improving sensitivity to this hormone in these tissues (78). Adiponectin then acts by reducing inflammation and oxidative stress, which ultimately leads to improved insulin sensitivity (79). Moreover, n-3 PUFAs activate PPAR-g, thus increasing adiponectin levels through a second route. In an experimental study, consumption of n-3 PUFAs was associated with a twofold increase in expression of the adiponectin gene, which occurred parallel to a twofold to threefold increase in expression of the gene which encodes PPAR-g (77). Thus, the activation of PPAR-α and PPAR-g promoted by n-3 PUFAs increases adiponectin levels and activity, which results in improvement in obesity-induced inflammation and insulin resistance (78). In addition to the n-3 PUFAs, other types of lipids have shown effectiveness in the regulation of adiponectin, among which conjugated linoleic acid (CLA) stands out. CLA can cause resistance to insulin action by reducing plasma levels of adiponectin (80). The mRNA levels of adiponectin were reduced after CLA supplementation in rats (81) and in cultured human adipocyte cells (82). Since the adiponectin gene is modulated by activation of PPAR-γ, suppression of the adiponectin gene can be partly attributed to the antagonistic effect of the trans-10, cis-12 CLA on PPAR-γ (83). Analysis of the results of clinical trials conducted across different ethnic groups, genders, metabolic profiles, and diseases provides an understanding of the effects of lipid intake on circulating levels of adiponectin. In this regard, a recent systematic review and meta-analysis performed by our group showed that, in intervention studies comparing low-fat vs. high-fat diets, there was no association of total amount of fat with circulating levels of adiponectin (72). Omega-3 PUFA supplementation modestly increased circulating concentrations of adiponectin; however, these findings should be interpreted with caution, since publication bias was identified in this meta-analysis (72).

Adiponectin and metabolic diseases

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brief report

Effect of biliopancreatic diversion on sleep quality and daytime sleepiness in patients with obesity and type 2 diabetes Mayra Mello1*, Ana Carolina J. Vasques1,2*, José C. Pareja1,3, Maria da S. de Oliveira1, Fernanda S. Novaes1, Élinton A. Chaim3, Bruno Geloneze1

ABSTRACT Objective: The poor quality of sleep and the deprivation thereof have been associated with disruption of metabolic homeostasis, favoring the development of obesity and type 2 diabetes (T2DM). We aimed to evaluate the influence of biliopancreatic diversion (BPD) surgery on sleep quality and excessive daytime sleepiness of obese patients with T2DM, comparing them with two control groups consisting of obese and normal weight individuals, both normal glucose tolerant. Subjects and methods: Forty-two women were divided into three groups: LeanControl (n = 11), ObeseControl (n = 13), and ObeseT2DM (n = 18). The LeanC and ObeseC groups underwent all tests and evaluations once. The ObeseT2DM underwent BPD and were reassessed after 12 months. Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were applied before and 12 months after BPD. Results: Before surgery, there was less daytime sleepiness in LeanC group (p = 0.013) compared with ObeseC and T2DMObese groups. The two obese groups did not differ regarding daytime sleepiness, demonstrating that the presence of T2DM had no influence on daytime sleepiness. After surgery, the daytime sleepiness (p = 0.002) and the sleep quality (p = 0.033) improved. The score for daytime sleepiness of operated T2DMObese group became similar to LeanC and lower than ObeseC (p = 0.047). Conclusion: BPD surgery has positively influenced daytime sleepiness and sleep quality of obese patients with T2DM, leading to normalization of daytime sleepiness 12 months after surgery. These results reinforce previously identified associations between sleep, obesity and T2DM in view of the importance of sleep in metabolic homeostasis, quality of life and health. Arch Endocrinol Metab. 2017;61(6):623-7 Keywords Bariatric surgery; type 2 diabetes mellitus; obesity; sleep; biliopancreatic diversion

1 Laboratório de Investigação em Metabolismo e Diabetes (LIMED), Gastrocentro, Universidade Estadual de Campinas (Unicamp), Campinas, SP, Brasil 2 Faculdade de Ciências Aplicadas, Universidade Estadual de Campinas (FCA/Unicamp), Limeira, SP, Brasil 3 Departamento de Cirurgia, Unidade de Cirurgia Diabética, Universidade Estadual de Campinas (Unicamp), Campinas, SP, Brasil

* These authors contributed equally to this manuscript Correspondence to: Ana Carolina J. Vasques Universidade Estadual de Campinas Laboratório de Investigação em Metabolismo e Diabetes Rua Carlos Chagas, 420 Cidade Universitária “Zeferino Vaz” 13081-970 – Campinas, SP, Brasil ana.vasques@fca.unicamp.br Received on Feb/23/2017 Accepted on Aug/9/2017

INTRODUCTION

he growing obesity epidemic has favored the concomitant increase in the prevalence of type 2 diabetes (T2DM) and sleep disorders (1). Partial sleep restriction decreases glucose tolerance, elevates levels of serum cortisol, reduces the release of satiety hormone leptin and increases the secretion of the hormone ghrelin, increasing hunger and appetite (2-5). Bariatric surgery is a treatment option for obese with T2DM who does not achieve adequate metabolic control with pharmacological treatment associated with changes in life style. Studies with bariatric surgery have demonstrated improvement in sleep disorders in patients after surgery due to the impact of weight reduction (5). The biliopancreatic diversion technique (BPD) has a higher percentage of T2DM remission and

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sustained weight loss in the long term (6). In light of these questions, we hypothesized that massive surgical induced weight loss as observed in BPD surgery decreases the level of daytime sleepiness and improves sleep quality after long-term weight loss, as previously demonstrated in Roux-in-Y gastrectomy and sleeve gastrectomy techniques (5,7). Given the importance of sleep in metabolic homeostasis and the association between sleep quality, obesity and T2DM, we aimed: 1) to assess daytime sleepiness and sleep quality in obese individuals with T2DM, comparing them with two control groups consisting of obese and normal weight individuals, both normal glucose tolerant (NGT); 2) to evaluate the influence of BPD surgery on sleep quality and daytime sleepiness; 3) to compare sleep quality and daytime sleepiness in operated patients with both controls. 623

DOI: 10.1590/2359-3997000000314

Bariatric surgery and sleep

SUBJECTS AND METHODS

Biliopancreatic diversion surgery (BPD)

Study design and patients

The technique used is an adaptation of the original technique (10). The procedure consists of a 60% gastric resection with a Roux-en-Y reconstruction. The residual stomach volume is around 300 ml. The small intestine is transected from 280 to 320 cm from the ileocecal valve, and its distal end is anastomosed to the remaining stomach. The proximal end of the ileum is anastomosed from 80 to 120 cm away from the ileocecal valve. The total length of intestinal absorption is reduced to 280-300 cm, whose 80-120 cm are called final common channel.

This study consisted of an experimental group and two control groups. We evaluated 42 premenopausal women who composed the following groups: • Lean Control (LeanC): 11 normal weight (BMI = 23 ± 2 kg/m2) NGT; • Obese Control (ObeseC): 13 obese (BMI = 35 ± 5 kg/m2) NGT; • Surgical (T2DMObese): 18 obese (BMI = 35 ± 5 kg/m2) with T2DM undergoing BPD surgery. The LeanC and ObeseC groups underwent all tests and evaluations once. The T2DMObese group was evaluated before and 12 months after surgery. At the reevaluation, two subjects did not answer the sleep questionnaires. Inclusion criteria were: age ≥ 20 years; weight variation < 5% in the last three months; negative for anti-GAD antibody; glycated hemoglobin < 10%; not taking insulin and corticosteroids; not using medications that alter sleep physiology; no liver and renal dysfunction and no recent history of neoplasia. The protocol was approved by the Ethics Committee of the Unicamp. All participants signed the informed consent before testing.

Clinical and biochemical evaluation Demographics, medical history and blood pressure data were evaluated and anthropometric assessment was performed consisting of BMI, neck and waist circumferences. We evaluated body composition through the tetrapolar electrical bioimpedance technique. The oral glucose tolerance test was performed in T2DMObese patients to confirm T2DM diagnosis (8). We collected blood samples at baseline and 120 minutes after ingestion of glucose solution containing 75 g of glucose for determination of glucose. The levels of total cholesterol, HDL-cholesterol and triglyceride were measured by enzymatic methods. We calculated levels of LDL cholesterol by the Friedewald equation. Glycated hemoglobin was determined by high performance liquid chromatography. The blood glucose levels were measured with the Glucose Analyzer YSI 2700. The insulin was determined by chemiluminescence. Insulin resistance was assessed by HOMA-IR (9) index using measurements of blood glucose and fasting insulin. 624

Sleep assessment We used the subjective evaluation method by individual application of two questionnaires: Pittsburgh Sleep Quality Index (PSQI) (11) and Epworth Sleepiness Scale (ESS) (12). The PSQI assesses the overall sleep quality for the last month (11), through 7 sleep components with weights distributed on a scale from 0 to 3, with a maximum overall score of 21. The sleep components are: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disorders, sleep medication use, and daytime sleepiness. The ESS assesses excessive daytime sleepiness (EDS). The score given by the respondent in all situations was summed and analyzed. A high score indicates EDS (12).

Statistical analysis Results were presented in median and interquartile range. The Kruskal-Wallis test was used to compare the three groups under study. The Duncan post hoc test was used to determine which groups differ in relation to the others. The Wilcoxon test was used to compare the T2DMObese group before and 12 months after surgery. The level of significance was p < 0.05.

RESULTS Clinical and metabolic characteristics at baseline and after intervention are shown in Table 1. Figure 1 presents the results of the subjective sleep evaluation. Before surgery, there was less daytime sleepiness in LeanC group (p = 0.013) compared with ObeseC and T2DMObese groups. The two obese groups did not differ regarding daytime sleepiness, demonstrating that the presence of T2DM had no influence on ESS (Figure 1). However, sleep quality did Arch Endocrinol Metab. 2017;61/6

Bariatric surgery and sleep

Table 1. Clinical, anthropometric and metabolic characteristics of the study groups lean control, obese control and obese type 2 diabetes Groups Characteristics

Lean Control (n = 11)

Obese Control (n = 13)

Obese T2DM Baseline (n = 18)

Obese T2DM Post BPD (n = 16)

Age (years)

29 (25 – 38)

39 (34 – 44)

46 (40 – 50)*,**

0.001

46 (40 – 50)

-----

BMI (kg/m )

23 (21 – 24)

33 (31 – 40)

36(33 – 39)

0.001

28 (26 – 30)

0.001

Waist circumference (cm)

83 (79 – 85)

105 (101 – 122)

118 (107 – 125)*

0.001

98 (94 – 106)

0.001

Neck circumference (cm)

32.7 (32.7 – 33.3)

36.9 (35.5 – 38.7)

38.8 (37.3 – 40.0)

0.001

35.0 (33.1 – 35.3)

0.036

29 (27 – 31)

40 (35 – 44)

42 (38 – 43)*

0.001

34 (31 – 36)

0.012

Systolic blood pressure (mmHg)

110 (100 – 115)

120 (110 – 130)

125 (110 – 140)

0.024

120 (100 – 120)

0.010

Diastolic blood pressure (mmHg)

70 (70 – 80)

80 (70 – 90)

80 (80 – 90)*

0.080

80 (70 – 80)

0.001

7.2 (6.5 – 8.2)

Body fat (%)

HbA1c (%)

*,**

0.001

5.0 (4.8 – 5.4)

0.001

127 (112 – 150)*,**

0.001

91 (83 – 97)

0.001

120 (111 – 136)

257 (229 – 292)

0.001

103 (81 – 175)

0.002

4.3 (4.1 – 4.6)

5.0 (4.5 – 5.5)

Plasma glucose (mg/dL)

85 (82 – 88)

92 (89 – 97)

Plasma glucose 2hPC (mg/dL)

99 (92 – 104)

Insulin (µU/l)

*,**

5.5 (4.4 – 7.8)

7 (6 – 15.4)

13 (8.6 – 18.2)

0.028

3.4 (2.0 – 12.0)

0.001

HOMA-IR

1.25 (0.9 – 1.62)

1.6 (1.3 – 3.5)

4.0 (2.6 – 5.5)*,**

0.002

0.60 (0.28 – 0.73)

0.001

Total cholesterol (mg/dL)

168 (153 – 184)

173 (159 – 208)

177 (169 – 201)

0.353

137 (117 – 154)

0.001

HDL cholesterol (mg/dL)

60 (50 – 66)

46 (42 – 58)

40 (34 – 44)*

0.001

43 (40 – 48)

0.015

LDL cholesterol (mg/dL)

87 (78 – 110)

105 (93 – 133)

109 (95 – 127)

0.091

62 (54 – 88)

0.001

Triglycerides (mg/dL)

57 (51 – 97)

103 (71 – 154)

124 (110 –185)*

0.001

107 (75 – 142)

0.005

BMI: body mass index. Data presented as median (Interquartile range p25–p75). a Kruskal-Wallis Test and Duncan post hoc Test (LeanC versus ObeseC versus preoperative Obese T2DM). b Wilcoxon Test. * p < 0.05 vs Lean C; ** p < 0.05 vs ObeseC.

p = 0.047

ESS score

p = 0.013

**p = 0.002 ***

10 5 0 Lean control

Obese control

Obese T2DM Obese T2DM post baseline BPD

p = 0.336 p = 0.033

20 PSQI global score

p = 0.121 15

***

10 5 0 Lean control

Obese control

Obese T2DM baseline

Obese T2DM post BPD

* p < 0.05 vs Lean Control; ** p < 0.05 vs Obese Control; *** p < 0.05 vs Obese T2DM at baseline.

Figure 1. Comparison of daytime sleepiness (A) and sleep quality scores (B) in the three groups before surgery, in the surgical group pre and postoperative, and in the three groups after surgery. Arch Endocrinol Metab. 2017;61/6

not differ between the three groups in the PSQI global score (p = 0.121) (Figure 1B), and also in the analysis of individual components of the score (data not shown). After surgery, T2DMObese group showed reduced daytime sleepiness and improved sleep quality. Regarding the PSQI components, the component related to sleep disorders showed significant improvement in post-BPD (p = 0.034) and there was a trend towards improvement in the component related to daytime dysfunction (p = 0.075). For the remaining components, there was no statistically significant difference. When comparing the three groups, the score for daytime sleepiness of operated T2DMObese group became similar to LeanC and lower than ObeseC (p = 0.047) (Figure 1A). For the PSQI questionnaire there was no difference in overall sleep quality (Figure 1B), although the component related to sleep disorders presented a lower score in the post-BPD patients compared with controls (p = 0.017).

DISCUSSION This study is the first to evaluate the effect of BPD technique on daytime sleepiness and sleep quality in obese individuals with T2DM. The main results 625

Bariatric surgery and sleep

were: 1) obese individuals, independent of T2DM presence, presented higher daytime sleepiness than normal weight patients with normal glucose tolerance; 2) BPD decreased daytime sleepiness and improved sleep quality; 3) operated subjects showed daytime sleepiness normalization, with scores similar to LeanC and reduced compared to ObeseC. Previous studies have shown that obese individuals have worse sleep quality compared with normal weight individuals (3,5,13,14). Approximately 50% of the obese population has daytime sleepiness independent of the presence of obstructive sleep apnea. Bidulescu and cols. (13), observed higher prevalence of sleep disorders and daytime sleepiness in obese women compared with men. Another study showed that overweight is associated with worse sleep quality assessed by PSQI, especially in adult women (14). Short sleep duration is also a risk factor for obesity because of its impact on metabolism, increasing ghrelin levels and consequently appetite, resulting in weight gain; increasing evening concentrations of cortisol and whole body insulin resistance (3,15). Clinical and animal studies support the concept that obesity can directly contribute to sleepiness. The complex interrelationships between circulating systemic hormones and neuronal signaling pathways in the central nervous system are involved in this process. Obesity and metabolic syndrome are related with atypically raised basal levels of sympathetic nervous system activity, which may have the potential to fragment sleep and contribute to daytime sleepiness (16). In this study, T2DM presence did not influence the scores for sleep quality and daytime sleepiness in obese individuals. However, studies have shown that T2DM presence is associated with worse sleep quality (17). Dixon and cols. (18), noted that the ESS scores correlated positively and significantly with elevated glucose levels. Poor sleep quality and daytime sleepiness are reported as a result of metabolic decompensation and T2DM deleterious effect on the central mechanism of breathing control. Studies have shown the influence of sleep disorders on T2DM development, making it the cause or effect of sleep disorders (15). Sleep deprivation inhibits insulin production due to elevated cortisol levels, increasing glucose levels, which can aggravate the diabetic condition (19). Improved daytime sleepiness in patients undergoing BPD has also been observed in clinical studies with different techniques in bariatric surgery. In one of these studies, preoperative excessive daytime sleepiness was 626

reduced from 30% to 5.5% twelve months after surgery. Patients with higher reduction in daytime sleepiness were those with more significant weight loss (20). In the present study, the surgery has improved sleep quality assessed by PSQI; however, it did not show a significant difference compared with the controls. The small number of patients studied may explain this fact, since the distribution of scores in the boxplot chart tends to decrease. The T2DMObese group showed lower score for the sleep disorder component in relation to ObeseC and similar to LeanC. A study (7) with gastric bypass and sleeve gastrectomy Roux-en-Y techniques showed sleep duration increase and consequent improvement in overall sleep quality twelve months after surgery. In this study, the improvement in sleep quality and duration was attributed to the significant weight loss (7). Jennings and cols. (21), demonstrated that the PSQI global score is positively correlated with BMI after adjustment for age among white women. In the present study, after BPD surgery, in addition to significant weight loss, fat accumulations in the abdominal and cervical regions were reduced. Obesity results in increased deposition of fat in the neck region, including at the base of the tongue and lining the airway, especially in the throat. When the airway becomes narrowed, respiratory distress increases and may result in obstructive sleep apnea (22). In obesity, increased neck circumference has been linked to increased risk for sleep disorders (23). Our data demonstrate decreased neck circumference after surgery, which may have contributed to improved daytime sleepiness and sleep quality. Finally, sleep loss also occurs in the presence of obstructive sleep apnea. The prevalence of obstructive sleep apnea is drastically increased in severe obesity and is around 71% in patients admitted for bariatric surgery (24). In obstructive sleep apnea patients, cortical microarousals are the main cause of sleep fragmentation, chronic sleep loss, and secondarily increased sympathetic nervous activity (25). The present study may have included patients with undiagnosed sleep apnea, and its resolution or improvement may be another of the mechanisms that explain the improvement in daytime sleepiness and sleep quality. The present study has some limitations, such as: the small number of individuals in each group; the use of a single bariatric surgery technique, preventing comparisons with other techniques more used in clinical practice; the absence of a lean control group Arch Endocrinol Metab. 2017;61/6

Bariatric surgery and sleep

with type 2 diabetes, preventing the assessment of the isolate influence of type 2 diabetes on sleep quality and daytime sleepiness; and the absence of the polysomnography test in the study protocol. In conclusion, BPD surgery has positively influenced daytime sleepiness and sleep quality, leading to normalization of daytime sleepiness 12 months after surgery. These results reinforce previously observed associations between sleep, obesity, and T2DM and demonstrate the beneficial effect of BPD on sleeping habits of obese patients with T2DM, in view of the importance of sleep in metabolic balance, quality of life and health. Acknowledgments: we thank the support provided by the São Paulo Research Foundation – Fapesp, grants No. 2008/09451-7 and No. 2008/07312-0. We are grateful to the bariatric patients who provided data for this study. Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012 Jan;70(1):3-21. 2. Pannain S, Mokhlesi B. Bariatric surgery and its impact on sleep architecture, sleep-disordered breathing, and metabolism. Best Pract Res Clin Endocrinol Metab. 2010;24(5):745-61. 3. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review. Obesity (Silver Spring). 2008;16(3):643-53. 4. Leproult R, Van Cauter E. Role of sleep and sleep loss in hormonal release and metabolism. Endocr Dev. 2010;17:11-21. 5. Katherine M, Sharkey KM, Orff HJ, Tosi C, David Harrington, Roye GD, et al. Subjective sleepiness and daytime functioning in bariatric patients with obstructive sleep apnea. Sleep Breath. 2013;17(1):267-74. 6. Buchwald H, Estok R, Fahrbach K, Banel D, Jensen MD, Pories WJ, et al. Weight and type 2 diabetes after bariatric surgery: systematic review and meta-analysis. Am J Med. 2009;122(3):248-256.e5. 7. Toor P, Kim K, Buffington CK. Sleep quality and duration before and after bariatric surgery. Obes Surg. 2012;22(6):890-5.

10. Scopinaro N, Gianetta E, Civalleri D, Bonalumi U, Bachi V. Biliopancreatic bypass for obesity: II. Initial experience in man. Br J Surg. 1979;66(9):618-20. 11. Bertolazi AN, Fagondes SC, Perin C, Schonwald SV, John AB, Miozzo ICS, et al. Validation of the Pittsburgh Sleep Quality Index in The Brazilian Portuguese language. Sleep. 2008;31:347. 12. Bertolazi AN, Fagondes SC, Perin C, Schonwald SV, John AB, Miozzo ICS, et al. Validation of the Epworth Sleepiness Scale in the Brazilian Portuguese language. Sleep. 2008;31:347-7. 13. Bidulescu A, Din-Dzietham R, Coverson DL, Chen Z, Meng Y, Buxbaum SG, et al. Interaction of sleep quality and psychosocial stress on obesity in African Americans: the Cardiovascular Health Epidemiology Study (CHES). BMC Public Health. 2010;10:581. 14. Hung HC,YangYC, Ou HY, Wu JS, Lu FH, Chang CJ.The association between self-reported sleep quality and overweight in a Chinese population. Obesity (Silver Spring). 2013;21(3):486-92. 15. Van Cauter E, Knutson KL. Sleep and the epidemic of obesity in children and adults. Eur J Endocrinol. 2008;159(1):59-66. 16. Panossian LA, Veasey SC. Daytime sleepiness in obesity: mechanisms beyond obstructive sleep apnea--a review. Sleep. 2012;35(5):605-15.. 17. Resnick HE, Redline S, Shahar E, Gilpin A, Newman A, Walter R, et al. Diabetes and sleep disturbances: findings from the Sleep Heart Health Study. Diabetes Care. 2003;26(3):702-9. 18. Dixon JB, Dixon ME, Anderson ML, Schachter L, O’Brien PE. Daytime sleepiness in the obese: not as simple as obstructive sleep apnea. Obesity (Silver Spring). 2007;15(10):2504-11. 19. Berglund G, Nilsson PM, Roost M, Engstrom G, Hedblad B. Incidence of diabetes in middle-aged men is related to sleep disturbances. Diabetes Care. 2004;27(10):2464-9. 20. Holty JEC, Parimi N, Ballesteros M, Blackwell T, Cirangle PT, Jossart GH, et al. Does surgically induced weight loss improve daytime sleepiness? Obes Surg. 2011;21(10):1535-45. 21. Jennings JR, Muldoon MF, Hall M, Buysse DJ, Manuck SB. Selfreported sleep quality is associated with the metabolic syndrome. Sleep. 2007;30:219-23. 22. Kim AM, Keenan BT, Jackson N, Chan EL Staley B, Poptani H, et al. Tongue Fat and its Relationship to Obstructive Sleep Apnea. Sleep. 2014;37(10):1639-48. 23. Ho WA, Moul DE, Krishna J. Neck Circumference-Height Ratio as a Predictor of Sleep Related Breathing Disorder in Children and Adults. J Clin Sleep Med. 2016;12(3):311-7. 24. Peromaa-Haavisto P, Tuomilehto H, Kössi J, Virtanen J, Luostarinen M, Pihlajamäki J, et al. Prevalence of Obstructive Sleep Apnoea Among Patients Admitted for Bariatric Surgery. A Prospective Multicentre Trial. Obes Surg. 2016;26(7):1384-90. 25. Stepanski EJ. The effect of sleep fragmentation on daytime function. Sleep. 2002;25(3):268-76.

8. Executive Summary: Standards of Medical Care in Diabetes – 2012. Diabetes Care. 2012;35:4-10.

9. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487-95.

Arch Endocrinol Metab. 2017;61/6

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brief report

High prevalence of insulin resistance among Brazilian chronic hepatitis C patients Livia Melo Villar1, Gabriela Cardoso Caldas1, Leticia de Paula Scalioni1, Juliana Custódio Miguel1, Elisangela Ferreira da Silva1, Vanessa Alves Marques1, Cristiane Alves Villela-Nogueira2, Lia Laura Lewis-Ximenez1, Elisabeth Lampe1

ABSTRACT Objective: This study aims to estimate the prevalence of insulin resistance (IR) among chronic hepatitis C (CHC) patients and their related laboratory and demographic data. Subjects and methods: In this study, non-diabetic CHC patients referred to Viral Hepatitis Ambulatories from Rio de Janeiro (Brazil) donated blood samples. Insulin was measured using a chemiluminescence immunoassay. IR was determined by HOMA-IR, where HOMA-IR > 2 was defined as IR. Results: A total of 214 CHC patients were recruited (123 females aged 53.6 years ± 10.9 years). IR was present in 133 patients (62.1%) and was associated in bivariate analysis to higher mean values of age (p = 0.040), triglycerides (p = 0.032), glucose (p = 0.000), insulin (p = 0.000), waist circumference (p = 0.001), and body mass index (p = 0.007); however, none of these variables were significant in the multivariate analysis. Conclusions: The high prevalence of IR was observed among CHC patients, and there was no difference in clinical or laboratory parameters when both groups were compared in the multivariate analysis. This high IR prevalence could lead to a high risk for development of cardiovascular disease and metabolic disorders. Arch Endocrinol Metab. 2017;61(6):628-32 Keywords Hepatitis C; insulin resistance; prevalence

1 Laboratório de Hepatite Viral, Instituto Oswaldo Cruz (Fiocruz), Rio de Janeiro, RJ, Brasil 2 Unidade de Hepatologia, Departamento de Clínica Médica, Hospital Universitário Clementino Fraga Filho (HUCFF), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil

Correspondence to: Livia Melo Villar Fundação Oswaldo Cruz, Laboratório de Hepatite Viral, Pavilhão Helio e Peggy Pereira, Térreo, sala B09 Av. Brasil, 4365 210360-040 – Rio de Janeiro, RJ, Brasil lvillar@ioc.ﬁocruz.br Received on July/31/2016 Accepted on July/31/2017 DOI: 10.1590/2359-3997000000315

INTRODUCTION

epatitis C virus (HCV) is a major cause of chronic liver disease, cirrhosis and hepatocellular carcinoma and is responsible for chronically infecting approximately 130 million people worldwide (1). HCV morbidity is not limited to the liver but encompasses extrahepatic conditions, including insulin resistance (IR) and type 2 diabetes mellitus (DM) (2). Chronic infection with HCV is a major risk factor for the development of insulin resistance and, consequently, for type 2 DM (3). High prevalence of type 2 DM was documented among chronic HCV cases compared to the general population or to other chronic liver diseases (2). Currently, several direct-acting antiviral agents (DAAs) were developed for the treatment of HCV infection (4), but DAAs are not widely available in low resource areas. In these regions, Peg-IFN and ribavirin (RBV) are used for antiviral therapy and result in a sustained virological response (SVR) of approximately

628

50-80% according to the HCV genotype (5). Low rates of SVR for double therapy are observed in the presence of IR or type 2 diabetes using double therapy (6,7). Some studies showed that more than 50% of HCV infected individuals have IR (8,9). In Brazil, IR prevalence varies from 20 to 80% in HCV patients (913). IR was also associated with age, waist circumference, body mass index (BMI), advanced liver fibrosis (10), HCV viral load (14), higher mean degree of steatosis (15) and higher serum levels of oxidative stress (10,11). Although some studies have reported IR prevalence among HCV individuals, the number of subjects is relatively small, and only one or two HCV genotypes were included. In addition, the relationship among IR and biochemical, haematological, and virological data is unclear. The present study was conducted to determine the prevalence of IR and related demographic and laboratory data among chronic HCV patients referred to hepatology units from Southeast Brazil. Arch Endocrinol Metab. 2017;61/6

High IR prevalence among CHC patients

Study population A total of 214 consecutive chronic HCV (CHC) patients referred to Viral Hepatitis Ambulatory (FIOCRUZ) and Hepatology Unit (Clementino Fraga Filho University Hospital, UFRJ) were recruited from January 2012 to November 2012 and were included in this study. The inclusion criteria were individuals of any gender or ethnicity who were more than 18-years-old, presented with reactive serum for anti-HCV/HCV RNA for more than 6 months and agreed to participate in the study after reading and signing the informed consent. The exclusion criteria were the presence of decompensated liver disease (ascites, bleeding from ruptured varices, or encephalopathy), other causes of liver disease (hepatitis B), severe psychiatric disorders, pregnancy or breastfeeding, pancreatitis, the presence of type 2 diabetes mellitus defined as a fasting plasma glucose concentration ≥ 126 mg/dL in two measurements prior to inclusion in the study or previous antiviral treatment. All patients were negative for hepatitis B surface antigen and anti-human immunodeficiency virus.

Anthropometric and laboratory evaluations Body mass index (BMI) was calculated as weight divided by the square of the height (kg/m2). Waist circumference was measured to the nearest 0.5 cm at the shortest point below the lower rib margin and the iliac crest. Trained staff took these measurements. Fasting blood samples were obtained for measurement of aminotransferases (alanine aminotransferase, aspartate aminotransferase), alkaline phosphatase, gamma-glutamyltransferase, glucose, total cholesterol, high density lipoprotein cholesterol (HDL cholesterol), very low density lipoproteins (VLDL), low density lipoproteins (LDL) cholesterol, triglycerides, haemoglobin, haematocrit, neutrophil and platelet counts, and thyroid stimulating hormone (TSH). Serum insulin levels were determined by a chemo–illuminescence immunoassay (Liaison Insulin, Diasorin, Pomezia, Italy) using an autoanalyser. The HOMA-IR score was calculated using the following equation: HOMAIR = fasting insulin x fasting glucose x 0.056/22.5. A HOMA-IR higher than 2.0 was Arch Endocrinol Metab. 2017;61/6

considered insulin resistance (7,15). Fibrosis grade was assessed using aminotransferase (AST) to platelet ratio index (APRI), and an index above 2 indicated the presence of advanced fibrosis (16).

Virological assays All patients had positive anti-HCV as measured using EIA (HCV Ab, Radim, Italy) and positive HCV RNA in serum. HCV RNA was detected and quantified using Cobas Taqman HCV (Roche Diagnostics, France), and HCV RNA reactive samples were submitted to the genotype using INNOLIPA HCV II (Innogenetics, Zwijndrecht, Belgium).

Data analysis Continuous variables were summarized as the mean ± standard deviation (SD) and categorical variables were described as frequencies and percentages. Comparisons between groups were made by using the Student’s t test or the Mann-Whitney U test for continuous variables and the Chi-square test or Fisher’s exact probability test for categorical data. Two-sided P-values 0.05 were considered statistically significant. Bivariate analysis was done using HOMA-IR as the dependent dichotomous variable (≤ 2 or > 2). Variables significantly associated with IR in the bivariate analysis (p < 0.05) were included in the stepwise multivariate logistic regression model. All data were entered and analysed in SPSS version 20.0.

Ethical considerations This study was conducted according to the ethical guidelines of the 2013 Declaration of Helsinki. The protocol and consent form were approved by the Ethics Committee of Fiocruz. All individuals gave their informed consent and answered a questionnaire giving information regarding risk behaviour and sociodemographic data (age, gender).

RESULTS Patients’ characteristics Table 1 shows the baseline features of the 214 CHC patients included in this study. There were 123 females (57.6%) and their mean age was 53.7 ± 10.9 years. The mean waist circumference was 93.3 cm (±11.8) and the mean BMI was 27.5 kg/m2 (± 4.2). The mean 629

SUBJECTS AND METHODS

High IR prevalence among CHC patients

HOMA-IR score was 3.4 (3.5) and a HOMA-IR level higher than 2 was found in 133 patients (62.1%). A total of 46 individuals (21.5%) were classified with an advanced grade of fibrosis using the APRI index. HCV genotype was determined among 167 individuals, 145 of them presented with genotype 1 (86.8%) followed by genotypes 3 (9.6%), 2 (2.4%), and 5 (1.2%). After inclusion in the study, 39 individuals underwent therapy with PEG interferon/ribavirin, 16 (41.0%) of whom achieved SVR. The mean values of ALT, AST, and GGT were above the normal values.

Baseline HOMA-IR and demographic and laboratory data When IR was evaluated according to the demographic and laboratory data (Table 1), significant differences were found between the two groups according to age (p = 0.040), waist circumference (p = 0.001), BMI (p = 0.007), weight categories (p = 0.009), glucose (p = 0.000), triglycerides (p = 0.032) and insulin (p = 0.000) in the bivariate analysis. Variables included in the multivariate analysis model were age, waist circumference, BMI, and

Table 1. Demographic and laboratory data among Brazilian HCV patients according to insulin resistance (IR) status Variable

All patients

Without IR N = 81

With IR N = 133

Bivariate analysis P value

Multivariate analysis odds ratio (IC 95%) P value

Age (years)a

53.7 ± 10.9

55.64 ± 10.40

52.47 ± 11.16

0.040

0.954 (0.909 – 1.002) 0.063

123/91

47/34

76/57

0.899

Waist circumference (cm) Female gender Male gender

93.30 ± 11.80 92.48 ± 11.26 94.24 ± 12.49

87.96 ± 11.29 88.44 ± 10.37 87.47 ± 12.51

95.80 ± 11.24 94.23 ± 11.27 98.00 ± 10.97

0.001

1.056 (0.997 – 1.119) 0.062

BMI (kg/m2)a

27.50 ± 4.20

25.90 ± 3.80

28.30 ± 4.20

0.007

1.199 (0.872 – 1.650) 0.265

Weight categories Healthy weight Overweight Obese

57 108 49

25 37 8

32 71 41

0.009

4.572 (0.176 – 118.99) 0.374

Glucose (mg/dL)b

90.00 (32.00 – 355.00)

87.00 (32.00 – 24.00)

92.5 (60.00 – 355.00)

0.000

Gender (Female/Male) a

VLDL (mg/dL)

18.00 (7.00 – 115.00)

16.30 (7.00 – 46.80)

19.80 (40.00 – 115.00)

0.059

LDL (mg/dL)b

103.00 (14.00 – 1077.00)

97.85 (35.00 –1077.00)

107.00 (14.00 – 996.00)

0.154

90.00 (34.00 – 573.00)

82.00 (34.00 – 273.00)

99 (40.00 – 573.00)

0.032

1.011 (0.999 – 1.023) 0.071

173.00 (74.00 – 1169.00)

165.50 (100.00 –1169.00)

177.00 (74.00 – 1069.00)

0.099

49.60 (3.00 –130.00)

49.30 (4.00 – 130.00)

49.80 (3.00 – 120.00)

0.844

Triglycerides (mg/dL)

Total cholesterol (mg/dL)b HDL (mg/dL)b Insulin (mU/mL)b

11.60 (2.00 – 75.00)

6.00 (1.70 – 12.60)

14.70 (6.00 – 75.00)

0.000

ALT (U/L)b

52.00 (0.00 – 297.00)

56.00 (12.00 – 269.00)

51.00 (0.00 – 297.00)

0.350

AST (U/L)

62.00 (10.00 – 325.00)

69.50 (21.30 – 244.00)

61.00 (10.40 – 325.00)

0.274

66.15 (10.00 – 1142.00)

73.00 (10.00 – 343.00)

63.00 (14.00 –1142.00)

0.399

115.00 (6.00 – 95.00)

114.00 (6.00 – 501.00)

117.00 (10.00–695.00)

0.538

1.70 (0.07 – 91.40)

1.62 (0.37 – 77.70)

1.75 (0.07 – 91.40)

0.360

13.90 ± 1.50

13.84 ± 1.39

13.96 ± 1.53

0.574

GGT (U/L)

Alkaline phosphataseb TSH (mlU/L)

Haemoglobina

Haematocrita Platelet (x 103/mm3)a HCV RNA (Ul/mL)b Fibrosis grade Low (APRI < 2), n (%) Advanced (APRI > 2), n (%) a

41.60 ± 4.50

41.25 ± 3.57

41.92 ± 4.95

0.297

188.80 ± 80.10

179.52 ± 78.52

194.54 ± 80.85

0.185

9.80 x 104 (25 – 1.00 x 109)

6.00 x 104 (25 – 3.00 x 107)

2.70 x 105 (28 – 1.00 x 109)

0.108

168 (78.50) 46 (21.50)

62 (76.54) 19 (23.45)

106 (79.70) 27 (20.30)

0.703

Mean ± SD; b Median, minimum and maximum; NI: not included.

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Arch Endocrinol Metab. 2017;61/6

High IR prevalence among CHC patients

DISCUSSION HCV infection has been related to extra-hepatic manifestations, such as diabetes and IR. The present study demonstrated a high prevalence of IR (62.1%) compared to previous studies conducted in Europe (42-46%) (7,17), North America (40%) (18), Asia (51%) (8) and Brazil (53% and 61%) (9,15). Recently, IR prevalence in the Brazilian Longitudinal Study of Adult Health was found to vary from 12 to 22%, a rate that is lower than that found in the present study (19). In this study, HOMA cut off was 2.0 like used in similar studies that have shown an association of IR and HOMA higher than 2 (20-23). The high prevalence of IR found in the present study could be the result of the large number of HCV individuals who were included. Since the role of insulin resistance and hyperglycaemia as predictors of SVR with DAA HCV treatment has not been clearly established, the high prevalence of IR observed in the present study could have an impact in SVR with DAA treatment. The efficacy of direct-acting antivirals overcame some of the predictors of therapeutic response, but IR and HCV remain important issues to be addressed in the clinical context of the patients. In the bivariate analysis, IR was associated with age and anthropometric factors (BMI, waist circumference and weight categories); however, this association was not found in the multivariate analysis. Some studies also showed the association of age, waist circumference, and BMI to IR among HCV patients (9,10,11,13,18). IR was also associated with higher mean values of triglycerides and is most likely the result of the characteristics of the HCV replication cycle. The HCV life cycle is associated with cholesterol and lipogenesis pathways in hepatocytes causing enhanced lipogenesis, impaired degradation and impaired export (24). Some biochemical parameters, such as ALT, AST and, GGT were above the normal values, most likely as a result of chronic liver inflammation. Miyajima and cols. (16) also showed higher mean values of glucose, ALT, AST and GGT among HCV chronic patients compared to non-infected individuals from Japan. In the present work, HOMA was not associated with HCV viral load, genotype or SVR. Although Arch Endocrinol Metab. 2017;61/6

some studies have shown an improvement in HOMA values among HCV patients presenting with SVR (7) and a high viral load among those presenting with IR (14), other studies have not found any such association (17,25). This lack of association could be the result of the low prevalence of SVR, high viral load and predominance of genotype 1 in the present cohort. One limitation of this study is the low number of non-1 HCV genotypes; other studies that evaluated HOMAIR in HCV patients also included 75 to 80% of HCV genotype 1 (9,14). IR was not associated with fibrosis grade as was observed in studies conducted in HCV patients from Northeast and South regions of Brazil (9,14); this could be because of the small number of patients with an advanced grade of fibrosis that was included in these studies. This study present some limitations: absence of control variables such as, obesity, physical activity and diet; lack of evaluation of patient’s diet routine and performance of physical activity and absence of other anthropometric parameters, such as body composition criteria or laboratory evaluation in combination with image, due to financial constraints. In conclusion, a high prevalence of insulin resistance was observed among HCV patients, demonstrating that this population may be at increased risk for developing type 2 diabetes mellitus. In the bivariate analysis, IR was associated with demographic (age), anthropometric (BMI and waist circumference) and laboratory (glucose, insulin and triglycerides) parameters. Although these variables were not significant in the multivariate analysis, these data suggest a high risk for development of cardiovascular disease and metabolic disorders among HCV patients. Acknowledgements: the authors would like to thank the health personnel of the Viral Hepatitis Laboratory for their help in collecting and processing blood samples. Funding statement: this research was supported by the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (Faperj), Brazilian National Counsel of Technological and Scientific Development (CNPq) and the Oswaldo Cruz Foundation (Fiocruz). Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. World Health Organization (WHO). Hepatitis C. Available from: <http://www.who.int/mediacentre/factsheets/fs164/en>. Accessed on: July 31, 2016.

631

triglycerides; none of these variables was significantly associated with HOMA-IR. Most individuals with IR had higher mean values of GGT and HCV viral load, but this was not statistically significant.

High IR prevalence among CHC patients

2. Knobler H, Malnick S. Hepatitis C and insulin action: an intimate relationship. World J Hepatol. 2016;8(2):131-8. 3. Das GC, Hollinger FB. Molecular pathways for glucose homeostasis, insulin signaling and autophagy in hepatitis C virus induced insulin resistance in a cellular model. Virology. 2012;434(1):5-17. 4. Zhang X. Direct anti-HCV agents. Acta Pharm Sin B. 2016;6(1): 26-31. 5. Ghany MG, Strader DB, Thomas DL, Seeff LB. American Association for the Study of Liver Diseases. Diagnosis, management, and treatment of hepatitis C: an update. Hepatology. 2009;49(4): 1335-74. 6. Laurito MP, Parise ER. Association between insulin resistance and sustained virologic response in hepatitis C treatment, genotypes 1 versus 2 and 3: systematic literature review and meta-analysis. Braz J Infect Dis. 2013;17(5):555-63. 7. Del Campo JA, Ampuero J, Rojas L, Conde M, Rojas A, Maraver M, et al. Insulin resistance predicts sustained virological response to treatment of chronic hepatitis C independently of the IL28b rs12979860 polymorphism. Aliment Pharmacol Ther. 2013;37(1):74-80. 8. Kiran Z, Zuberi BF, Anis D, Qadeer R, Hassan K, Afsar S. Insulin resistance in non-diabetic patients of chronic Hepatitis C. Pak J Med Sci. 2013;29(1):201-4. 9. Mello V, Cruz T, Nuñez G, Simões MT, Ney-Oliveira F, Braga H, et al. Peripheral insulin resistance during treatment of chronic hepatitis C with peguilated interferon plus ribavirin. J Med Virol. 2006;78(11):1406-10. 10. Oliveira AC, Parise ER, Catarino RM, Lanzoni V, Leite-Mor MM, Simon KA, et al. Insulin resistance and not steatosis is associated with modifications in oxidative stress markers in chronic hepatitis C, non-3 genotype. Free Radic Res. 2009;43(12):1187-94. 11. Souza AFM, Pace FHL, Chebli JMF, Ferreira LE. Insulin resistance in non-diabetic patients with chronic hepatitis C: what does it mean? Arq Bras Endocrinol Metab. 2011;55(6):412-8. 12. Oliveira LP, Jesus RP, Boulhosa RS, Mendes CM, Lyra AC, Lyra LG. Metabolic syndrome in patients with chronic hepatitis C virus genotype 1 infection who do not have obesity or type 2 diabetes. Clinics (Sao Paulo). 2012;67(3):219-23.

15. Péres DP, Cheinquer H, Wolf FH, Cheinquer N, Falavigna M, Péres LD. Prevalence of insulin resistance in chronic hepatitis C genotype 1 and 3 patients. Ann Hepatol. 2013;12(6):871-5. 16. Miyajima I, Kawaguchi T, Fukami A, Nagao Y, Adachi H, Sasaki S, et al. Chronic HCV infection was associated with severe insulin resistance and mild atherosclerosis: a population-based study in an HCV hyperendemic area. J Gastroenterol. 2013;48(1):93-100. 17. Serfaty L, Forns X, Goeser T, Ferenci P, Nevens F, Carosi G, et al. Insulin resistance and response to telaprevir plus peginterferon α and ribavirin in treatment-naive patients infected with HCV genotype 1. Gut 2012;61(10):1473-80. 18. Ramcharran D, Wahed AS, Conjeevaram HS, Evans RW, Wang T, Belle SH, et al. Associations between serum lipids and hepatitis C antiviral treatment efﬁcacy. Hepatology. 2010;52(3):854-63. 19. Benseñor IM, Goulart AC, Molina Mdel C, de Miranda EJ, Santos IS, Lotufo PA. Thyrotropin Levels, Insulin Resistance, and Metabolic Syndrome: A Cross-Sectional Analysis in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Metab Syndr Relat Disord. 2015;13(8):362-9. 20. Eslam M, Aparcero R, Mousa YI, Grande L, Shaker Y, Ali A, et al. Insulin resistance impairs viral dynamics independently of ethnicity or genotypes. J Clin Gastroenterol. 2012;46(3):228-34. 21. Romero-Gómez M, Del Mar Viloria M, Andrade RJ, Salmerón J, Diago M, Fernández-Rodríguez CM, et al. Insulin resistance impairs sustained response rate to peginterferon plus ribavirin in chronic hepatitis C patients. Gastroenterology. 2005;128(3): ‑636-41. 22. Eslam M, Aparcero R, Kawaguchi T, Del Campo JA, Sata M, Khattab MA, et al. Meta-analysis: insulin resistance and sustained virological response in hepatitis C. Aliment Pharmacol Ther. 2011;34(3):297-305. 23. Eslam M, Kawaguchi T, Del Campo JA, Sata M, Khattab MA, Romero-Gomez M. Use of HOMA-IR in hepatitis C. J Viral Hepat. 2011;18(10):675-84. 24. Chang ML. Metabolic alterations and hepatitis C: from bench to bedside. World J Gastroenterol. 2016;22(4):1461-76. 25. Fattovich G, Covolo L, Pasino M, Perini E, Rossi L, Brocco G, et al. The homeostasis model assessment of the insulin resistance score is not predictive of a sustained virological response in chronic hepatitis C patients. Liver Int. 2011;31(1):66-74.

13. Oliveira LP, de Jesus RP, Boulhosa RS, Onofre T, Mendes CM, Vinhas L, et al. Factors Associated with Insulin Resistance in Patients with Chronic HCV Genotype 1 Infection without Obesity or Type 2 Diabetes. J Am Coll Nutr. 2016;35(5):436-42.

14. Michalczuk MT, Kappel CR, Birkhan O, Bragança AC, Alvares-daSilva MR. HOMA-AD in Assessing Insulin Resistance in Lean Noncirrhotic HCV Outpatients. Int J Hepatol. 2012;2012:576584.

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case report

Growth hormone deficiency with advanced bone age: phenotypic interaction between GHRH receptor and CYP21A2 mutations diagnosed by sanger and whole exome sequencing Fernanda A. Correa1, Marcela M. França1, Qing Fang2, Qianyi Ma2, Tania A. Bachega1, Andresa Rodrigues1, Bilge A. Ozel2, Jun Z. Li2, Berenice B. Mendonca1, Alexander A. L. Jorge3, Luciani R. Carvalho1, Sally A. Camper2, Ivo J. P. Arnhold1

SUMMARY Isolated growth hormone deficiency (IGHD) is the most common pituitary hormone deficiency and, clinically, patients have delayed bone age. High sequence similarity between CYP21A2 gene and CYP21A1P pseudogene poses difficulties for exome sequencing interpretation. A 7.5 year-old boy born to second-degree cousins presented with severe short stature (height SDS -3.7) and bone age of 6 years. Clonidine and combined pituitary stimulation tests revealed GH deficiency. Pituitary MRI was normal. The patient was successfully treated with rGH. Surprisingly, at 10.8 years, his bone age had advanced to 13 years, but physical exam, LH and testosterone levels remained prepubertal. An ACTH stimulation test disclosed a non-classic congenital adrenal hyperplasia due to 21-hydroxylase deficiency explaining the bone age advancement and, therefore, treatment with cortisone acetate was added. The genetic diagnosis of a homozygous mutation in GHRHR (p.Leu144His), a homozygous CYP21A2 mutation (p.Val282Leu) and CYP21A1P pseudogene duplication was established by Sanger sequencing, MLPA and whole-exome sequencing. We report the unusual clinical presentation of a patient born to consanguineous parents with two recessive endocrine diseases: non-classic congenital adrenal hyperplasia modifying the classical GH deficiency phenotype. We used a method of paired read mapping aided by neighbouring mis-matches to overcome the challenges of exomesequencing in the presence of a pseudogene. Arch Endocrinol Metab. 2017;61(6):633-6

1 Unidade de Endocrinologia do Desenvolvimento, Laboratório de Hormônios e Genética Molecular LIM42, Disciplina de Endocrinologia, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, SP, Brasil 2 Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA 3 Unidade de Endocrinologia Genética, Laboratório de Endocrinologia Celular e Molecular LIM25, Disciplina de Endocrinologia, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, SP, Brasil

Correspondence to: Fernanda A. Correa Unidade de Endocrinologia do Desenvolvimento, Laboratório de Hormônios e Genética Molecular LIM42, Hospital das Clínicas, Disciplina de Endocrinologia, Faculdade de Medicina da Universidade de São Paulo Av. Dr. Enéas de Carvalho Aguiar, 255 05403-000 – São Paulo, SP, Brasil feracorrea@uol.com.br Received on Feb/11/2016 Accepted on May/9/2017

INTRODUCTION

solated growth hormone deficiency (IGHD) is the most common pituitary hormone deficiency; it can be congenital or acquired. Although the most distinctive clinical manifestation is growth failure, there are many other clinical features including a delayed bone age. Amongst the congenital cases, a genetic aetiology can be established in about 10% of patients, with a higher prevalence in familial (34%) compared with sporadic (4%) IGHD. The main genetic causes to date are deleterious mutations in the genes encoding Arch Endocrinol Metab. 2017;61/6

growth hormone (GH), (GH1) or the receptor for GHRH (GHRHR) (1). Congenital adrenal hyperplasia is a genetically heterogeneous disorder, most frequently caused by recessive, loss of function mutations in the 21-hydroxylase enzyme, encoded by CYP21A2, and it can have a wide spectrum of clinical manifestations. The non-classic form can be asymptomatic or associated with signs of postnatal androgen excess: rapid growth, advanced bone age, precocious pubarche, menstrual abnormalities, hirsutism, acne, and/or infertility (2,3). 633

DOI: 10.1590/2359-3997000000311

GHD with concomitant non-classic CAH

Non-classic CAH due to 21-hydroxylase deficiency is caused mainly by recombination between CYP21A2 and a nearly identical pseudogene, CYP21A1P (4). Here we report the unusual presentation of a boy with IGHD and advanced bone age due to GHRHR and non-classic CYP21A2 mutations. To our knowledge, the association of these two conditions has not been reported previously. Furthermore, the pitfalls of Sanger and whole-exome sequencing (WES) to reach the genetic diagnosis are discussed.

day) was added to his treatment. At 12.2 years of age pubic hair was Tanner stage 2 and testicular length had increased to 2.5 cm indicating onset of central puberty. At 19.5 years, his adult height was 166.5 cm, below his target height of 178.5 cm, possibly due to his early bone age advancement due to CAH (Figure 2).

CASE REPORT A boy born at term by vaginal delivery (length 50 cm, weight 3,400 g) to second-degree cousins presented at 7.5 years with severe short stature (102.5 cm, SDS-3.7), high-pitched voice, blue sclera and prominent forehead. Genital examination revealed Tanner stage I, absence of pubic hairs, normal penile length (4 cm) and prepubertal testes (length 1.5 cm). Bone age was 6 years according to the Greulich and Pyle method. Basal cortisol (14.8 µg/dL) and FT4 (1.2 ng/dL) were normal. Clonidine stimulation test resulted in a peak GH of 0.6 ng/mL indicating GH deficiency. A combined (insulin, TRH, GnRH) pituitary stimulation tests, performed as part of a research protocol, also showed a peak GH of 0.6 ng/mL and a peak cortisol of 16.1 mcg/dL, initially interpreted as partial ACTH deficiency. Pituitary MRI was normal: anterior lobe 4.6 mm height (normal for age 4.5 ± 0.6 mm) (5), normal pituitary stalk and topic posterior pituitary. The patient was successfully treated with rGH (33 mcg/ kg/day) with a first-year growth velocity of 11.7 cm/ year. Surprisingly, at 10.8 years of age he presented with advanced bone age (13 years), (Figure 1), despite absence of signs of puberty and prepubertal serum LH and testosterone levels. Growth velocity at this point was 8.6 cm/year. An ACTH-stimulation test showed respectively, basal and peak levels, cortisol 6.1 and 18.8 mcg/dL, 17 hydroxyprogesterone 9.4 and 52.0 ng/mL and androstenedione 1.2 and 2.0 ng/mL, indicating non-classic 21-hydroxylase deficiency. The spectrum of clinical manifestations in non-classic CAH is wide and there is no perfect genotype-phenotype correlation (6). We probably found the advanced bone age prior to pubarche because we were checking it periodically due to IGHD. In common clinical settings, pubarche is usually the presenting sign in boys with non-classic CAH. Cortisone acetate (15 mg/m2/ 634

Figure 1. Advanced bone age (13 years) in a patient with chronological age of 10.8 years with isolated growth hormone deficiency.

Figure 2. Growth curve. Treatment with recombinant human GH (rhGH) and corstisona acetate are shown. Bone age was determined by the Greulich and Pyle criteria. Growth chart was drawn using Growth analyser 3.5 (Ed. Dutch Growth Foundation, PO Box 23068, 3001 KB, Rotterdam, The Netherlands). Arch Endocrinol Metab. 2017;61/6

GENETIC TESTING AND DISCUSSION At first, we performed Sanger-sequencing. No mutations in GH1, GHRH, or GHRHR were found (7). Using specific primers for the active CYP21A2 gene (8), a homozygous c.844G>T, p.Val282Leu mutation (previously known as p.Val281Leu) was found (both parents were heterozygous) (Figure 3A). The p.Val282Leu mutation in CYP21A2 is the most commonly found mutation in patients with non-classic CAH (4) and leads to a mild mutant that retains 2050% of 21-hydroxylase activity (9). In order to establish the unidentified genetic cause of IGHD, WES was performed. Briefly, we aligned sequence reads to the 1000 Genomes Phase 1 reference mapped to GRCh37 using BWA v0.5.9 (10) and removed duplicate read pairs using PICARD v1.74. We performed realignment, recalibration and variant calling using GATK v3.3 (11) and applied GATK VQSR filter (12) to remove low-quality variants. Variant annotation was retrieved by using ANNOVAR (13) revealing a

Figure 3. Panel A: Sanger sequencing of CYP21A2 with specific primers showing the mutation c.844G>T (p.Val282Leu) in homozygous state in the patient and in heterozygous state in the parents. Panel B: The Integrative Genomics Viewer (IGV) showing Alignment without special bioinformatics treatment suggesting heterozygous state of the CYP21A2 c.844G>T (p.Val282Leu) mutation. Panel C: Diagram of the CYP21A2 and CYP21A1P genes. The p.Val282Leu mutation and genomic position is marked as red. The four mis-matches that were used for pair read mapping between CYP21A2 and CYP21A1P sequences are in blue. Arch Endocrinol Metab. 2017;61/6

homozygous c.431C>T, p.Leu144His mutation in GHRHR. This mutation is indeed detected in the homozygous state by Sanger sequencing, but it was originally overlooked by the first Sanger sequencing, patient 5 of reference (7). This alteration was not seen when the sequencing data were initially read but after the WES it could be found in the original Sanger sequencing. Here we report the correct GHRHR findings for this patient. This mutation has been previously described in unrelated patients with IGHD from Sergipe/Brazil, Pakistan, Spain and the United States, and has been reported to lead to reduced cAMP production after GHRH stimulation with normal cellsurface localization of the receptor, suggesting a defect in ligand binding (14). To explore a possible founder effect we analyzed the C/T polymorphisms at positions -261 and -235 of the GHRHR promoter, away 7333 and 7359 bp, respectively, from the c.431C>T mutation. Our patient was homozygous C at position -235 and homozygous C at position -261. This haplotype is identical to that of the previously reported patients with p.Leu144His from north-eastern Brazil and Spain but different from the patient from north-eastern United States (15). Therefore, the present patient is not related to the previously reported patient from the United States; however, a common ancestor for both families from Brazil and that of Spain, who share the same haplotype, cannot be excluded. Interestingly, another homozygous GHRHR mutation, c.57+1G>A, found in Itabaianinha in the Northeastern Brazilian state of Sergipe, affects the largest kindred of patients with IGHD due to GHRHR mutations reported to date (16). As we already had the WES done, we decided to analyse CYP21A2 by this method. At first, interpretation of WES indicated the CYP21A2 p.Val282Leu mutation in heterozygous state (Figure 3B). This was in disagreement with the clinical diagnosis and Sanger sequencing. MLPA, was performed and, revealed CYP21A1P and C4B duplication. To resolve this genepseudogene twist in exome-sequencing, we proposed a method of paired read mapping aided by neighbouring four mis-matches using the exome-sequencing data and manually sorted out the real genotype for the variant of interest at CYP21A2 (Figure 3C). For the CYP21A2 mutation, there were 44 paired reads supporting the T allele and 0 supporting the G allele. We concluded that the genotype at CYP21A2 c.844 position is T/T. For the corresponding CYP21A1P mutation, the 635

GHD with concomitant non-classic CAH

evidence showed 78 paired reads supporting T and 73 supporting G; thus, it should be G/T at corresponding CYP21A1P position. We believe that these difficulties may happen when analyzing mutations in genes with pseudogenes and highly homologous sequences and this methodology can be useful to overcome this limitation. Ectopic posterior pituitary lobe and an interrupted stalk on MRI are increasingly being used for the diagnosis of GHD. However, it should be noted that all patients with GHD reported to date with mutations in GH1 and GHRHR (as well as in PROP1) have had a normal stalk and topic posterior lobe, as the present case did (17,18). We conclude that in patients with IGHD and advanced bone age clinicians should search for an additional diagnosis. Patients born to consanguineous parents may have more than one genetic disease leading to unusual phenotypes and treatment outcomes. Wholeexome sequencing was able to establish the genetic cause of IGHD but initially presented difficulties in diagnosing the genotype of CYP21A2/CYP21A1P. This report reveals the strengths and challenges of each sequencing technology and its applications. Funding: this work was supported by the National Council of Technological and Scientific Development – Brazil: grant number CNPq-PQ 305743/2011-2 to BBM; grant number CNPq-PQ 307922/2013-8 to IJPA and grant number CNPqPQ 304678/2012-0 to AALJ and by São Paulo Research Foundation – Fapesp: grant number 2013/03236-5 to AALJ, and by the National Institutes of Health – United States of America: grant number R01HD030428 to SAC. Disclosure: no potential conflict of interest relevant to this article was reported.

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Alatzoglou KS, Webb EA, Le Tissier P, Dattani MT. Isolated growth hormone deficiency (GHD) in childhood and adolescence: recent advances. Endocr Rev. 2014;35(3):376-432.

4. Speiser PW. Nonclassic adrenal hyperplasia. Rev Endocr Metab Disord. 2009;10(1):77-82. 5. Argyropoulou M, Perignon F, Brunelle F, Brauner R, Rappaport R. Height of normal pituitary gland as a function of age evaluated by magnetic resonance imaging in children. Pediatr Radiol. 1991;21(4):247-9. 6. Moura-Massari VO, Bugano DD, Marcondes JA, Gomes LG, Mendonca BB, Bachega TA. CYP21A2 genotypes do not predict the severity of hyperandrogenic manifestations in the nonclassical form of congenital adrenal hyperplasia. Horm Metab Res. 2013;45(4):301-7. 7.

Marui S, Trarbach EB, Boguszewski MC, França MM, Jorge AA, Inoue H, et al. GH-releasing hormone receptor gene: a novel splice-disrupting mutation and study of founder effects. Horm Res Paediatr. 2012;78(3):165-72.

8. Billerbeck A, Mendonca B, Pinto E, Madureira G, Arnhold I, Bachega T. Three novel mutations in CYP21 gene in Brazilian patients with the classical form of 21-hydroxylase deficiency due to a founder effect. J Clin Endocrinol Metab. 2002;87(9):4314-7. 9.

Tusie-Luna MT,Traktman P, White PC. Determination of functional effects of mutations in the steroid 21-hydroxylase gene (CYP21) using recombinant vaccinia virus. J Biol Chem. 1990;265(34):20916-22.

10. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754-60. 11. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297-303. 12. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43(5):491-8. 13. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. 14. Salvatori R, Fan X, Phillips JA, Espigares-Martin R, Martin De Lara I, Freeman KL, et al. Three new mutations in the gene for the growth hormone (gh)-releasing hormone receptor in familial isolated GH deficiency type IB. J Clin Endocrinol Metab. 2001;86(1):273-9. 15. Salvatori R, Aguiar-Oliveira MH, Monte LV, Hedges L, Santos NL, Pereira RM, et al. Detection of a recurring mutation in the human growth hormone-releasing hormone receptor gene. Clin Endocrinol (Oxf). 2002;57(1):77-80. 16. Salvatori R, Hayashida CY, Aguiar-Oliveira MH, Phillips JA, Souza AH, Gondo RG, et al. Familial dwarfism due to a novel mutation of the growth hormone-releasing hormone receptor gene. J Clin Endocrinol Metab. 1999;84(3):917-23. 17. Osorio MG, Marui S, Jorge AA, Latronico AC, Lo LS, Leite CC, et al. Pituitary magnetic resonance imaging and function in patients with growth hormone deficiency with and without mutations in GHRH-R, GH-1, or PROP-1 genes. J Clin Endocrinol Metab. 2002;87(11):5076-84. 18. Alatzoglou KS, Turton JP, Kelberman D, Clayton PE, Mehta A, Buchanan C, et al. Expanding the spectrum of mutations in GH1 and GHRHR: genetic screening in a large cohort of patients with congenital isolated growth hormone deficiency. J Clin Endocrinol Metab. 2009;94(9):3191-9.

2. Araújo RS, Mendonca BB, Barbosa AS, Lin CJ, Marcondes JA, Billerbeck AE, et al. Microconversion between CYP21A2 and CYP21A1P promoter regions causes the nonclassical form of 21-hydroxylase deficiency. J Clin Endocrinol Metab. 2007;92(10):4028-34.

3. White PC, Speiser PW. Congenital adrenal hyperplasia due to 21-hydroxylase deficiency. Endocr Rev. 2000;21(3):245-91.

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case report

More than kin, less than kind: one family and the many faces of diabetes in youth Luciana F. Franco1, Renata Peixoto-Barbosa1,2, Renata P. Dotto1, José Gilberto H. Vieira1, Magnus R. Dias-da-Silva1, Luiz Carlos F. Reis1, Fernando M. A. Giuffrida1,2, Andre F. Reis1

SUMMARY Identification of the correct etiology of diabetes brings important implications for clinical management. In this report, we describe a case of a 4-year old asymptomatic girl with diabetes since age 2, along with several individuals in her family with different etiologies for hyperglycemia identified in youth. Genetic analyses were made by Sanger sequencing, laboratory measurements included HbA1c, lipid profile, fasting C-peptide, pancreatic auto-antibodies (glutamic acid decarboxylase [GAD], Islet Antigen 2 [IA-2], and anti-insulin). We found a Gly178Ala substitution in exon 5 of GCK gene in three individuals co-segregating with diabetes, and type 1 diabetes was identified in two other individuals based on clinical and laboratory data. One individual with previous gestational diabetes and other with prediabetes were also described. We discuss difficulties in defining etiology of hyperglycemia in youth in clinical practice, especially monogenic forms of diabetes, in spite of the availability of several genetic, laboratory, and clinical tools. Arch Endocrinol Metab. 2017;61(6):637-42

1 Disciplina de Endocrinologia, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brasil 2 Departamento de Ciências da Vida, Universidade do Estado da Bahia (UNEB), Salvador, BA, Brasil

Correspondence to: Fernando M. A. Giuffrida Universidade do Estado da Bahia, Departamento de Ciências da Vida Rua Silveira Martins, 2555 41150-000 – Salvador, BA, Brasil fernando.giuffrida@me.com Received on Jan/25/2017 Accepted on Aug/23/2017

INTRODUCTION

dentification of the exact etiology of diabetes brings important implications for clinical management, therapeutic choice, medical follow-up, screening of clinical complications, and prognosis, as well as genetic counseling when applicable. Currently, in addition to more common forms such as type 1 and type 2 diabetes, molecular diagnosis of monogenic forms of diabetes has gained momentum due to the ever growing availability of centers performing genetic testing. In this report, we describe a family in whom several different types of diabetes have been diagnosed, underscoring the caveats of correctly identifying the etiology of diabetes.

FAMILY PRESENTATION The proband is a 4-year old asymptomatic girl (subject 21 on the pedigree chart, Figure 1), who was diagnosed with hyperglycemia in a routine visit with a pediatrician (fasting plasma glucose [FPG] 6.43 mmol/L) when she was 2 years old. Her current weight is 17 kg and height is 98 cm. She was a full-term newborn (birth weight 2450 g) with healthy neuropsychomotor Arch Endocrinol Metab. 2017;61/6

development. Further glucose testing showed FPG 6.21 mmol/L, hemoglobin A1c (HbA1c) 6%, C-peptide 0.67 ng/mL, and negative pancreatic autoantibodies (glutamic acid decarboxylase [GAD], Islet Antigen 2 [IA-2], and anti-insulin all below detection threshold for each assay). Her mother (subject 19) had had a previous history of gestational diabetes at age 30, identified at 25 weeks gestational age, with progression to normoglycemia after pregnancy. Her father (subject 18) has shown hyperglycemia since age 20 without any symptoms or medication use. Her paternal grandfather (subject 9) was diagnosed with diabetes at age 20 and has been on insulin treatment since then; his monozygotic twin (subject 8) presented with prediabetes years ago, without specific treatment. Also, on the paternal side of the family, there is a 41-year old second cousin (subject 14), who was diagnosed with diabetes at age 8, also in use of insulin. Her paternal grandmother (subjected 10) is 57 years old with a history of hyperglycemia since 22 years old, identified in a routine scan, as part of a pre-employment assessment. She is asymptomatic, in use of metformin. Table 1 summarizes the main clinical and laboratory characteristics of family members. 637

DOI: 10.1590/2359-3997000000312

One family and the many faces of diabetes

III

NN 8

NN 20

NM 22

NM 20

NN 32

NM 2 Hyperglycemic Normoglycemic NN: normal-normal; NM: normal-mutated; regarding GCK Gly178Ala substitution. Normal type numbers refers to consecutive patient numbering as described in the text. Bold type numbers enclosed in borders describe age at diagnosis. The arrow indicates the proband. Individuals 10, 18, and 21 are GCK-MODY; individuals 9 and 14 have type 1 diabetes; individuals 8 has prediabetes; individual 19 has previous gestational diabetes.

Figure 1. Pedigree of the studied family. Hyperglycemic and normoglycemic individuals depicted as black and white circles/squares, respectively.

Fasting glucose (mmol/L)

HbA1c (% / mmol/mol)

fasting C-peptide (ng/mL)

Pancreatic autoantibodies

Creatinine (mg/dL)

treatment

4.9

5.5 / 37

5.4

3.4

1.5

1.2

0.9

None

25.4

6.0

5.8 / 40

6.9

5.3

3.4

1.3

1.2

1.0

Diet

Type 1 diabetes

28.7

12.4

6.3 / 45

< 0.1

3.7

1.9

1.5

0.5

0.9

NPH + Lispro

GCK-MODY

23.8

6.4

6.6 / 49

1.1

6.2

4.1

1.8

0.5

0.6

Metformin

Normoglycemic

28.1

4.9

5.7 / 39

6.2

3.9

1.0

2.8

1.0

None

Type 1 diabetes

32.8

11.4

6.4 / 46

4.1

2.3

1.6

0.6

0.8 NPH + Lispro insulin

Normoglycemic

21.1

4.2

4.8 / 29

3.9

1.9

1.8

0.4

0.7

None

Normoglycemic

32.4

5.3

5.5 / 37

3.8

5.1

3.3

1.1

1.4

1.1

None

Normoglycemic

26.9

5.1

6.1 / 36

4.3

2.8

1.1

0.9

None

< 0.1 IA2 +

triglycerides (mmol/L)

Hypertension

HDL-cholesterol (mmol/L)

Age at diagnosis of diabetes (years)

LDL-cholesterol (mmol/L)

Age

Prediabetes

Total cholesterol (mmol/L)

Gender

Normoglycemic

Dyslipidemia

Possible/definite etiology

BMI (kg/m2)

Subject

Table 1. Clinical characteristics of studied individuals

GCK-MODY

27.2

7.2

6.1 / 43

1.1

4.6

2.9

1.3

0.5

0.9

None

Previous GDM

24.5

5.0

5.6 / 38

5.6

3.8

1.4

0.9

0.7

None

Normoglycemic

21.7

5.0

4.7 / 28

4.3

2.4

1.7

0.6

0.8

None

GCK-MODY

6.2

6.0 / 42

0.67

4.0

2.4

1.1

0.6

Diet

GDM: gestational diabetes mellitus; F: female; M: male; NA: not applicable; NT: not tested; * BMI was not calculated due to age, please see text for weight and height data. Proband data (subject 21) in boldtype. To convert total cholesterol, HDL-cholesterol, and LDL-cholesterol to mg/dL, divide values by 0.0259; to convert triglycerides to mg/dL, divide values by 0.0113; to convert glucose to mg/dL, divide values by 0.0555.

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One family and the many faces of diabetes

METHODS GCK, HNF1A, and HNF4A genes were analyzed by Sanger sequencing, using previously described primers and parameters (2). Pathogenicity of mutations was assessed by the American College of Medical Genetics and Genomics (ACMG) guidelines (3). HNF1B genotyping was performed with previously described methods (4). Hemoglobin A1c (HbA1c) was analyzed by High-Performance Liquid Chromatography (HPLC). C-peptide was analyzed by chemiluminometric assay. GAD antibodies have been assessed by enzyme-linked immunosorbent assay (ELISA); IA-2 and anti-insulin autoantibodies were assessed by radioimmunoassay. Diabetes and prediabetes were diagnosed according to the American Diabetes Association criteria (5). Arterial hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, or current use of antihypertensive medication and history of hypertension (6). Subjects were considered to have dyslipidemia either if levels of LDL-cholesterol were ≥ 4.14 mmol/L, HDL-cholesterol ≤ 1.04 mmol/L, triglycerides ≥ 2.26 mmol/L, or if they were using lipid lowering Arch Endocrinol Metab. 2017;61/6

medications (statin/fibrates) (6). The remaining clinical and laboratory data were collected from each patient’s medical records. All patients or relatives responsible for the patients have provided informed consent, with approval by Hospital São Paulo’s Ethics Committee.

DISCUSSION In this article we have described a family with multiple types of diabetes/hyperglycemia, diagnosed in infancy or early adulthood, underscoring the issue of etiological definition of diabetes in this age group, commonly dealt with in clinical practice. As far as we know, this is the first Brazilian report with these features. The correct differentiation of monogenic forms from type 1 diabetes has significant clinical impact in treating the two most important differential diagnoses. Some subtypes of monogenic diabetes can be treated with sulfonylureas, like those caused by mutations in genes such as HNF1A and HNF4A (7), whereas others such as GCK-MODY may not need medical treatment at all (8). This is in contrast with type 1 diabetes, in which insulin therapy is required. A correct definition of diabetes etiology is often challenging, despite the availability of biochemical tests, pancreatic autoantibodies assessment, and molecular biology tools. In this regard, Patel and cols., in the United Kingdom, have recently proposed a genetic score with 30 variants to stratify the risk of development of type 1 diabetes. The study has suggested that the genetic score has shown good discriminative power to distinguish type 1 diabetes from monogenic forms (9) and in another analysis, the score was also useful to differentiate young type 2 diabetes (10). A meta-analysis by the same group assessed which clinical parameters could be relevant in the etiological differential diagnosis of the many types of diabetes. Age at diagnosis (< 35 years) and the period for insulin requirement (until 6 months after diagnosis) seem to be useful in distinguishing between type 1 and type 2 diabetes (11). In this context, the use of biochemical markers could also be important. C-peptide is produced in concentrations equimolar to insulin and thus correlates with pancreatic residual function. C-peptide is more helpful 3 to 5 years after diagnosis, when its persistence may indicate type 2 diabetes or monogenic diabetes. Besides, undetectable levels at anytime in disease progression suggest type 1 diabetes. Normal reference values of C-peptide are variable in literature, but fasting 639

The atypical clinical presentation of hyperglycemia in the proband, along with her family background and absence of immunologic markers specific for type 1 diabetes, raised the hypothesis of monogenic diabetes – more specifically Maturity-Onset Diabetes of the Young (MODY) caused by a glucokinase (GCK) mutation. Genetic testing showed a Gly178Ala substitution in exon 5. The same mutation was found in the proband’s father (subject 18) and paternal grandmother (subject 10), and is depicted in Figure 1 as normal-mutated (NM) to demonstrate its heterozygous pattern. Her mother, paternal grandfather, grandfathers’s twin brother, two second cousins, and normoglycemic uncles (subjects 8, 9, 11, 14, 17, 19, and 20) did not presented any GCK mutations and are depicted as normal-normal (NN). The clear co-segregation of the mutation with mild non-progressive hyperglycemia strongly suggests it to have a causal effect (Figure 1). This mutation has been described for the first time in medical literature in this family. It was reported briefly in a recent publication by the Brazilian Monogenic Diabetes Study Group (BRASMOD), a multicenter group of researchers in the field of Monogenic Diabetes (1), but personal clinical data are presented here for the first time.

One family and the many faces of diabetes

C-peptide < 0.6 ng/mL usually indicates marked impairment of beta-cell function (12). C-peptide can be measured either in fasting or after a stimulus (with a mixed meal or glucagon) to detect beta-cell residual function. Some authors showed that urinary C-peptidecreatinine ratio (UCPCR) is a useful alternative method to measure C-peptide that can discriminate HNF1A/ HNF4A MODY from long-duration type 1 diabetes, although this test is not routinely performed in our clinic (13). In the present family all GCK-MODY subjects presented fasting C-peptide above 0.6 ng/mL, while those with type 1 diabetes (either confirmed by detection of autoantibodies or not) showed undetectable values (Table 1), suggesting it as a good discriminator between both diabetes subtypes. Current methods of genetic tests for MODY mutations are expensive and time consuming. Patients should be carefully selected by clinical criteria for testing, as this can significantly increase the positivity detection rate (14). In order to enhance accuracy in the recruitment of suspected cases of MODY, a team of researchers developed software based in specific clinical data that calculates the patient’s probability of having a positive molecular test for MODY mutations. This software uses data such as age, insulin requirement within 6 months after diagnosis, and familial history of diabetes. Moving on to genetic testing is suggested when the score is greater than 25% (15). Of note, all patients with GCK-MODY in the present family had a score above this cutoff point (data not shown). Other strategies for patient selection and mutation screening have been proposed. Shepherd and cols., investigating the prevalence of monogenic diabetes in U.K. pediatric clinics in patients younger than 20 years old, used a systematic approach of biomarker screening with UCPCR, islet autoantibodies (GAD and IA-2), and targeted genetic testing. They found 2.5% of patients to have monogenic diabetes. Authors suggested that this biomarker screening algorithm could be a rational and practical approach to identifying pediatric patients who are most suitable for genetic testing (16). Therefore, an attempt for establishing diagnosis or rendering a provisional etiology in each case is discussed as follows. The proband (subject 21) had a typical clinical presentation of GCK-MODY with mild hyperglycemia and no symptoms, along with classic laboratory findings like detectable levels of C-peptide and absence of pancreatic auto-immunity. This raises the question 640

of which parent she has inherited her mutation from. If on the one hand her mother (subject 19) had previous gestational diabetes, on the other hand, her father (subject 18) has also had mild non-progressive hyperglycemia for several years. Her father also showed a clinical pattern of GCK-MODY, being diagnosed at age 20 without clinical manifestations. The proband’s mother, previously normoglycemic, had had a history of gestational diabetes identified at 25 weeks of pregnancy. Her oral glucose tolerance test [OGTT] showed glucose values of 4.2, 9.2, and 9.1 mmol/L in fasting, at 1, and 2 hours, respectively (5), and she was managed only with diet. After delivery, blood glucose returned to normal levels as shown by a further OGTT. She was unaware of any other cases of diabetes in her family. The inherited mutation could have reflected on birthweight, partially explaining why macrosomia did not occur (17). Several studies show that weight of children born with GCK mutations is usually ~700g lower compared to siblings who did not inherited the mutation. This finding is more robust when the mother also shows hyperglycemia caused by a GCK mutation, but it could be extrapolated to the gestational diabetes context. This is possibly due to lower insulin secretion during intrauterine life (18). Her paternal grandmother (subject 10) had a typical presentation of GCK-MODY with mild non-progressive asymptomatic hyperglycemia, identified in routine pre-employment testing when she was 22. Genetic testing successfully confirmed the GCK mutation in the grandmother and ruled it out in the grandfather, in whom clinical features are more resemblant of other forms of hyperglycemia. Besides typical clinical GCK-MODY presentation and familial cosegregation, Gly178Ala mutation is Likely Pathogenic according to ACMG guidelines, presenting 2 Moderate Criteria and 3 Supporting Criteria (3). Besides, this variant has not been found in population databases of exome sequences (19). While the proband’s paternal grandfather (subject 9) has a history of diabetes since his early youth, his monozygotic twin brother (subject 8) shows a prediabetes pattern that could resemble GCK-MODY (see below). He could have inherited not only the GCK-MODY mutation but also genetic predisposition to type 1 diabetes, which would hypothetically result in a more severe clinical presentation prevailing, but this was not the case as demonstrated by negative genetic testing in him. Furthermore, he had a history of diabetes diagnosed at age 20, presenting with polydipsia, Arch Endocrinol Metab. 2017;61/6

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Arch Endocrinol Metab. 2017;61/6

(< 0.5 UI/mL), but IA-2 was positive (1.2 U/mL; normal range < 0.8 U/mL), strongly suggesting type 1A diabetes, thus the most likely etiology in this individual is autoimmune.

CONCLUSION Abnormal glucose levels are the common element present in different diseases such as GCK-MODY, prediabetes, type 1 diabetes, and gestational diabetes, especially in children, teenagers, and young adults. As a consequence, it can be challenging to correctly define a precise diabetes etiology. Improvement in the understanding of diabetes depends on thorough analysis of clinical data, genetic testing, and laboratory findings. Despite all recent advancements in molecular diagnosis, the definition of a clinical screening model for monogenic diabetes is still a challenge that demands the systematic study of several families in different populations. In conclusion, the accurate molecular and clinical diagnosis has significant impact on clinical management of a multifaceted disease such as diabetes, especially when running on the same family. Acknowledgements: for AFR, Grant Fapesp 2015-05123-9 (Fundação de Amparo à Pesquisa do Estado de São Paulo, São Paulo, Brazil). We are indebted to Fleury Laboratory (São Paulo, Brazil) for performing pancreatic autoantibodies assays (JGHV). Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Giuffrida FMA, Moisés RS, Weinert LS, Calliari LE, Manna Della T, Dotto RP, et al.; Brazilian Monogenic Diabetes Study Group (BRASMOD). Maturity-onset diabetes of the young (MODY) in Brazil: Establishment of a national registry and appraisal of available genetic and clinical data. Diabetes Res Clin Pract. 2017;123:134-42. 2. Lehto M, Wipemo C, Ivarsson SA, Lindgren C, Lipsanen-Nyman M, Weng J, et al. High frequency of mutations in MODY and mitochondrial genes in Scandinavian patients with familial earlyonset diabetes. Diabetologia. 1999;42(9):1131-7. 3. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al.; on behalf of the ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405-24. 4. Dotto RP, Giuffrida FMA, Franco L, Mathez ALG, Weinert LS, Silveiro SP, et al. Unexpected finding of a whole HNF1B gene deletion during the screening of rare MODY types in a series

641

requiring insulin since diagnosis, and showing undetectable levels of C-peptide. Thus a diagnosis of type 1 diabetes is strongly suggested. Negative GAD and IA-2 antibodies could simply reflect long disease duration, with the reduction of antibody titers that is usually observed in type 1 diabetes. A more rare condition could be the presence of an HNF1A or even HNF4A mutation in subject 9 and his twin brother with prediabetes. HNF1A-MODY presents mostly as overt diabetes, a phenotype markedly different from GCKMODY. Genetic testing for these MODY-subtypes has been carried out in subject 9 with negative results. Coexistence of different MODY subtypes (HNF1A and GCK) has been described in a family in whom clinical predominance of the most severe subtype, i.e. HNF1A, occurred. A targeted NGS panel with several MODY genes could be an important tool in such cases (20,21). An interesting feature of subject 9 is the presence of persistent proteinuria and hematuria, but with a normal retinal examination, together with normal creatinine clearance (102 mL/min/m2) and normal urinary tract ultrasound. This finding could suggest etiologies other than diabetes for nephropathy (22). Type 2 diabetes could be considered, but insulin requirement being present since diagnosis undermines this hypothesis. His twin brother (subject 8) presents with prediabetes diagnosed both by fasting glucose and HbA1c several years ago (approximately 10 years). No pancreatic autoimmunity has been demonstrated. This individual (subject 8) could either have type 2 diabetes-related prediabetes or preclinical type 1 diabetes. The absence of autoimmunity balances the scale toward the first hypothesis. Like his brother he has also presented with hematuria for several years and persistent proteinuria (around 0.85 g/L), renal cysts at ultrasound, and stable creatinine clearance over the last few years (ranging from 90 to 110 mL/min). One hypothesis for explaining the etiology of hyperglycemia with renal alterations, especially renal cysts, could be an HNF1B mutation, which has also been tested with negative results, consequently excluding this MODY subtype also in his twin brother (4,23). On the paternal grandfather’s side of the family, there is a 41-year old second-cousin (subject 14) without diabetic complications and a typical type 1 diabetes presentation, i.e., diagnosis at age 8, insulin treatment since then, and undetectable C-peptide. DKA was seen upon diagnosis and other episodes occurred during his life as well. GAD65 autoantibodies were negative

One family and the many faces of diabetes

of Brazilian patients negative for GCK and HNF1A mutations. Diabetes Res Clin Pract. 2016;116:100-4. 5. American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2016; 39 (Suppl 1):S13-22. 6. American Diabetes Association. 9. Cardiovascular Disease and Risk Management. Diabetes Care. 2017;40(Suppl 1):S75-87. 7. Pearson ER, Starkey BJ, Powell RJ, Gribble FM, Clark PM, Hattersley AT. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet. 2003;362(9392):1275-81. 8. Giuffrida FMA, Reis AF. Genetic and clinical characteristics of maturity-onset diabetes of the young. Diabetes Obes Metab. 2005;7(4):318-26. 9. Patel KA, Oram RA, Flanagan SE, De Franco E, Colclough K, Shepherd M, et al. Type 1 Diabetes Genetic Risk Score: A Novel Tool to Discriminate Monogenic and Type 1 Diabetes. Diabetes. 2016;65(7):2094-9. 10. Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, et al. A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults. Diabetes Care. 2016;39(3):337-44. 11. Shields BM, Peters JL, Cooper C, Lowe J, Knight BA, Powell RJ, et al. Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature. BMJ Open. 2015; 5(11):e009088. 12. Jones AG, Hattersley AT. The clinical utility of C-peptide measurement in the care of patients with diabetes. Diabet Med. 2013;30(7):803-17. 13. Besser REJ, Shepherd MH, McDonald TJ, Shields BM, Knight BA, Ellard S, et al. Urinary C-peptide creatinine ratio is a practical outpatient tool for identifying hepatocyte nuclear factor 1-{alpha}/ hepatocyte nuclear factor 4-{alpha} maturity-onset diabetes of the young from long-duration type 1 diabetes. Diabetes Care. 2011;34(2):286-91.

16. Shepherd M, Shields B, Hammersley S, Hudson M, McDonald TJ, Colclough K, et al. Systematic Population Screening, Using Biomarkers and Genetic Testing, Identifies 2.5% of the U.K. Pediatric Diabetes Population With Monogenic Diabetes. Diabetes Care. 2016;39(11):1879-88. 17. Chakera AJ, Steele AM, Gloyn AL, Shepherd MH, Shields B, Ellard S, et al. Recognition and Management of Individuals With Hyperglycemia Because of a Heterozygous Glucokinase Mutation. Diabetes Care. 2015;38(7):1383-92. 18. Spyer G, MacLeod KM, Shepherd M, Ellard S, Hattersley AT. Pregnancy outcome in patients with raised blood glucose due to a heterozygous glucokinase gene mutation. Diabet Med. 2009;26(1):14-8. 19. ExAC Browser (Beta) | Exome Aggregation Consortium. Available from: exac.broadinstitute.org. Accessed on: June 10, 2017. 20. López-Garrido MP, Herranz-Antolín S, Alija-Merillas MJ, Giralt P, Escribano J. Co-inheritance of HNF1a and GCK mutations in a family with maturity-onset diabetes of the young (MODY): implications for genetic testing. Clin Endocrinol (Oxf). 2013;79(3):342-7. 21. Codner E, Rocha A, Deng L, Martínez-Aguayo A, Godoy C, Mericq V, et al. Mild fasting hyperglycemia in children: high rate of glucokinase mutations and some risk of developing type 1 diabetes mellitus. Pediatr Diabetes. 2009;10(6):382-8. 22. American Diabetes Association. 9. Microvascular Complications and Foot Care. Diabetes Care. 2016;39 (Suppl 1):S72-80. 23. Bellanné-Chantelot C, Clauin S, Chauveau D, Collin P, Daumont M, Douillard C, et al. Large genomic rearrangements in the hepatocyte nuclear factor-1beta (TCF2) gene are the most frequent cause of maturity-onset diabetes of the young type 5. Diabetes. 2005;54(11):3126-32.

14. Hattersley AT, Patel KA. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60(5):769-77.

15. Shields BM, McDonald TJ, Ellard S, Campbell MJ, Hyde C, Hattersley AT. The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes. Diabetologia. 2012;55(5):1265-72.

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case report

Coexistence of diffuse large B-cell lymphoma and papillary thyroid carcinoma in a patient affected by Hashimoto’s thyroiditis Maria Trovato1,2, Giuseppe Giuffrida1, Antonino Seminara3, Simone Fogliani4, Vittorio Cavallari2, Rosaria Maddalena Ruggeri1, Alfredo Campennì5

SUMMARY Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. On the contrary, primary thyroid lymphoma (PTL) is a rare disease, accounting for 2% to 5% of all thyroid malignancies. Despite several cases in which both PTC and PTL arise in the setting of Hashimoto’s thyroiditis (HT), the coexistence of both tumors in HT patients is very rare. Herein we report the case of a 66-year-old woman with long-standing nodular HT under replacement therapy, who presented with a fast, painless enlargement in the right anterior side of the neck. Thyroid ultrasonography demonstrated increased growth of a hypoechoic nodule in the right lobe measuring 32 x 20 mm. A total thyroidectomy was performed, and histology revealed a diffuse large B-cell lymphoma (DLBCL) on a background of florid HT. Moreover, a unifocal papillary microcarcinoma, classical variant (7 mm, pT1aNxMx), was discovered. The patient was then treated with chemotherapy for the PTL, but she did not undergo radioactive iodine ablation treatment for the microPTC as per guidelines. Two years after surgery, the patient had no evidence of recurrence of either malignancy. This rare case highlights the importance of monitoring HT patients with nodular lesions, especially if they have long-standing disease. In addition, PTL should be considered for differential diagnosis in elder HT patients who present with sudden thyroid enlargement. Arch Endocrinol Metab. 2017;61(6):643-6

1 Department of Clinical and Experimental Medicine, Unit of Endocrinology, University of Messina, Messina, Italy 2 Department of Human Pathology, University of Messina, Messina, Italy 3 Azienda Sanitaria Provinciale, Messina, Italy 4 Unit of Radiology, Hospital of Milazzo, Messina, Italy 5 Department of Biomedical Sciences and Morphological and Functional Images, Unit of Nuclear Medicine, University of Messina, Messina, Italy

Correspondence to: Rosaria Maddalena Ruggeri Dipartimento di Medicina Clinica e Sperimentale, UOC di Endocrinologia Padiglione H, 4 piano, Policlinico Universitario “G. Martino” 98125 Messina, Italy rmruggeri@unime.it Received on Aug/11/2015 Accepted on Nov/16/2015 DOI: 10.1590/2359-3997000000313

apillary thyroid carcinoma (PTC) is the most common type of thyroid cancer, and its incidence has been increasing in the last few decades, with a large prevalence of small tumors (1). The occurrence of PTC in patients affected by Hashimoto’s thyroiditis (HT) is a well-known event reported by literature (2): a long-standing HT could lead to high TSH levels, thus becoming a growth factor for malignancies, but on the other hand HT could be a sort of protective agent against the aggressiveness of PTC. In fact, in HT patients the neoplasia is generally discovered at a younger age; it is characterized by smaller nodules and a less advanced TNM stage, without local or systemic invasion (3,4). Obviously, even other factors could contribute to a better prognosis, such as the more frequent ultrasonographic controls that HT patients undergo, so as to allow an early diagnosis of Arch Endocrinol Metab. 2017;61/6

malignancy. Conversely, primary thyroid lymphoma (PTL) is a rare disease that accounts for 5% of all thyroid tumors: in 70% of cases it appears as diffuse large B-cell lymphoma (DLBCL), which usually presents with a more aggressive course and has a worse outcome. The other 30% comprises mucosa associated lymphoid tissue (MALT) lymphomas, which are indolent in most cases and have a better response to systemic treatment (5,6). Some cases of association between HT and both PTC and MALT lymphomas have been described (79), while a DLBCL in the context of coexisting HT and PTC is very rare (10).

PATIENT Herein we describe the case of a 66-year-old woman, with no family history of thyroid cancer, affected by HT under replacement therapy with L-thyroxine (125 643

INTRODUCTION

Papillary thyroid carcinoma and diffuse large B-cell lymphoma in a female patient with Hashimoto’s thyroiditis

micrograms per day) and long-standing nodular goiter. She was referred to our division for a recent painless enlargement in the right anterior side of the neck, complaining of mild and intermittent dysphagia to solid and liquid foods; she denied any voice change or dyspnea, while physical examination demonstrated an enlarged thyroid gland with a palpable, firm nodular lesion in the right lobe, moving with deglutition. Thyroid ultrasonography (US) demonstrated the growth of a hypoechoic nodule in the right lobe, measuring 32 x 20 mm (the maximum diameter was 18 mm at the previous control, almost 12 months before). US-guided fine needle aspiration biopsy (FNAB) of the right-sided nodule and cytological examination revealed atypical epithelial cells and lymphocytic infiltration, concluding for indeterminate lesion (THYR3). The patient was referred to total thyroidectomy. Histology was compatible with a DLBCL, revealing large atypical lymphocytes with irregular nuclei, condensed chromatin and small nucleoli on a background of florid HT, characterized by lymphocytic aggregates with germinal centers and thyroid follicles of various sizes with dense colloid and Hurtle cell changes (Figure 1). Immunohistochemistry of the atypical lymphocytes confirmed CD20, anti-BCL2 and anti-BCL6 positivity, with monoclonal lambda chains, so the neoplasia was staged as 1A. Moreover, a unifocal papillary microcarcinoma, classic variant (7 mm, pT1aNxMx), was discovered. Staging studies for the PTL were performed, including total-body CT and bone marrow biopsy, showing no evidence of systemic disease or metastases. The patient was then treated with chemotherapy for the PTL, while she did not undergo radioactive iodine ablation treatment for the microPTC as per guidelines (11). At the last control in our hospital, two years after surgery, the patient had no evidence of recurrence of either malignancy.

DISCUSSION In patients affected by HT, thyroid architecture is usually altered by the chronic inflammation, often presenting so-called “pseudo-nodules”; nonetheless, the presence of real nodules is also a common finding in our clinical practice, especially in iodine-deficient areas (12,13). The histology of these lesions is widely variable, ranging from nodules with benign features to malignant ones, deriving from thyroid cells or lymphocytes. PTC is the most frequent thyroid tumor, 644

Figure 1. Cytological (panels AB) and histological (panels CE) features of papillary thyroid microcarcinoma and diffuse large B-cell lymphoma occurring in the context of HT. Immunohistochemical characterization of diffuse large B-cell lymphoma (panels FJ). Panel A: Moderate amount of lymphocytes, filamentous cell debris, and a cellular formation composed by polygonal follicular cells (May-Grünwald Giemsa, MGG; total magnification x100). Panel B: High magnification highlights the presence of areas of moderate anisokaryosis in a cluster of follicular cells; one cell presents also a small intranuclear inclusion (black arrow) (MGG, total magnification x400). Panel C: Unifocal papillary microcarcinoma (7 mm in size, pT1aNxMx) with no aggressive histology (hematoxylin and eosin, H&E; total magnification x200). Panel D: Diffuse large B-cell lymphoma (DLBLC), Stage 1A (H&E, total magnification x200). Panel E: Hashimoto’s thyroiditis associated with parenchimatous goiter (H&E, total magnification x200). Panels F, G, H, I, and J: Immunoreactions for cytokeratin, CD20, lambda monoclonal chains, BCL2 and BCL6, respectively (F and G, total magnification x200; G, H, and I, total magnification x400). Arch Endocrinol Metab. 2017;61/6

Papillary thyroid carcinoma and diffuse large B-cell lymphoma in a female patient with Hashimoto’s thyroiditis

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simultaneous malignancies, PTC and PTL (this one as DLBCL), originating, respectively, from follicular cells and lymphocytes. To the best of our knowledge, this is the second patient in which a DLBCL had been reported in the context of Hashimoto’s thyroiditis in association with PTC (10). Of note, a DLBCL has a more aggressive course and a worse prognosis compared to MALT lymphomas. Data concerning the optimal management of such an association of neoplastic diseases are scanty in the literature. In most cases of PTL, however, the diagnosis is made after a rapid growth of a thyroid-related mass often associated with compressive symptoms, e.g., hoarseness or dyspnea, and the patients are referred to surgery. The management of one does not affect the management of the other neoplasm (7); the prognosis, too, does not seem to be worsened by the coexistence of the two diseases, but rather is more likely affected by the one having the worse stage (7-9). Thus, when PTC and PTL coexist in the same patient, a careful staging of both diseases is mandatory, and treatment has to prioritize the tumor with the worse prognosis and/or the worse stage at diagnosis. In conclusion, considering the higher risk of neoplasia in HT patients with long-standing disease and nodular lesions, our case highlights the importance of regular follow-up in order to reach an early diagnosis: in particular, a sudden thyroid enlargement in elder patients with HT should lead physicians to consider PTL in the differential diagnosis. Funding: this work was not supported by any grant. Disclosure: no potential conflict of interest relevant to this article was reported.

REFERENCES 1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11-30. 2. Lee JH, Kim Y, Choi JW, Kim YS. The association between papillary thyroid carcinoma and histologically proven Hashimoto’s thyroiditis: a meta-analysis. Eur J Endocrinol. 2013;168(3):343-9. 3. Zhang L, Li H, Ji QH, Zhu YX, Wang ZY, Wang Y. The clinical features of papillary thyroid cancer in Hashimoto’s thyroiditis patients from an area with a high prevalence of Hashimoto’s disease. BMC Cancer. 2012;12:610. 4. Zhang Y, Dai J, Wu T, Yang N, Yin Z. The study of the coexistence of Hashimoto’s thyroiditis with papillary thyroid carcinoma. J Cancer Res Clin Oncol. 2014;140(6):1021-6. 5. Walsh S, Lowery AJ, Evoy D, McDermott EW, Prichard RS. Thyroid lymphoma: recent advances in diagnosis and optimal management strategies. Oncologist. 2013;18(9):994-1003. 6. Ahmed T, Kayani N, Ahmad Z, Haque MN. Non-Hodgkin’s thyroid lymphoma associated with Hashimoto’s thyroiditis. J Pak Med Assoc. 2014;64(3):342-4.

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and several studies have shown a significant association with HT (2), which could be explained with the progressive rise in TSH levels caused by long-standing thyroiditis; in fact, TSH is a known growth factor for thyroid nodules. In a recently published prospective study, TSH ≥ 1 µIU/ml was an independent predictor of thyroid cancer together with antithyroglobulin antibodies (TgAb), whose role is actually unknown: the authors suggest a possible, specific tumorigenic inflammatory response (14). In addition, in a study by Wirtschafter and cols., mRNA expression for the RET/ PTC 1 and RET/PTC 3 oncogenes was found even in a large number of HT patients without PTC, showing molecular genetic evidence of cancer in this population and thus highlighting their higher risk of developing a clinically expressive disease (15). However, most of the data from the literature do not clearly demonstrate a causal relationship between the two conditions, and it also has been hypothesized that the progressive increase of serum TSH in HT patients, rather than autoimmune thyroiditis per se, may play a major role in the association of PTC with HT (16,17). On the other hand, several papers report a better prognosis for PTC if it co-occurs with HT, which somehow could play a protective role against the aggressiveness of neoplasia: thyroid peroxidase antibodies (TPOAb) could probably drive a cytotoxic response against the inflammation (2-4,14). HT, however, is also a risk factor for PTL, as cellular changes due to chronic antigenic stimulation could evolve to malignancy, as demonstrated by the finding of clonal B cells, generally present in lymphomas, in HT patients (18). The onset of PTL generally happens many years after the diagnosis of HT, but Watanabe and cols. reported some cases of PTL discovered on average 18 months after the diagnosis of HT (19). In a unifying pathogenetic hypothesis, in such patients autoimmunity may exert tumorigenic actions in two ways: first, because of the chronic antigenic stimulation lymphocytes could gradually gain monoclonality for heavy chains with progression toward DLBCL; second, inflammation could produce tumorigenic compounds, e.g., cyclooxygenase-2, which has been detected in thyroid epithelial neoplasms and HT (20). At the same time, some protective factors could interfere with the tumor. In particular, as in Yamakawa and cols., the activation of the complement system, usually higher in HT patients, could prevent the lysis of neoplastic cells, thus explaining the chances of a better outcome (21). In the case we describe, we detected two distinct and

Papillary thyroid carcinoma and diffuse large B-cell lymphoma in a female patient with Hashimoto’s thyroiditis

7. Nam YJ, Kim BH, Lee SK, Jeon YK, Kim SS, Jung WJ, et al. Co-occurrence of papillary thyroid carcinoma and mucosaassociated lymphoid tissue lymphoma in a patient with longstanding hashimoto thyroiditis. Endocrinol Metab (Seoul). 2013;28(4):341-5. 8. Cheng V, Brainard J, Nasr C. Co-occurrence of papillary thyroid carcinoma and primary lymphoma of the thyroid in a patient with long-standing Hashimoto’s thyroiditis. Thyroid. 2012;22(6):647-50. 9. Melo GM, Sguilar DA, Petiti CM, Eichstaedt AG, Caiado RR, Souza RA. Concomitant thyroid Malt lymphoma and papillary thyroid carcinoma. Arq Bras Endocrinol Metab. 2010;54:425-8. 10. Xie S, Liu W, Xiang Y, Dai Y, Ren J. Primary thyroid diffuse large B-cell lymphoma coexistent with papillary thyroid carcinoma: A case report. Head Neck. 2015;37(9):E109-14. 11. American Thyroid Association (ATA) Guidelines Taskforce on Thyroid Nodules and Differentiated Thyroid Cancer, Cooper DS, Doherty GM, Haugen BR, Kloos RT, Lee SL, Mandel SJ, et al. Revised American Thyroid Association management guidelines for patients with thyroid nodules and differentiated thyroid cancer. Thyroid. 2009;19(11):1167-214. 12. Carlé A, Krejbjerg A, Laurberg P. Epidemiology of nodular goitre. Influence of iodine intake. Best Pract Res Clin Endocrinol Metab. 2014;28(4):465-79.

15. Wirtschafter A, Schmidt R, Rosen D, Kundu N, Santoro M, Fusco A, et al. Expression of the RET/PTC fusion gene as a marker for papillary carcinoma in Hashimoto’s thyroiditis. Laryngoscope. 1997;107(1):95-100. 16. Fiore E, Latrofa F, Vitti P. Iodine, thyroid autoimmunity and cancer. Eur Thyroid J. 2015;4(1):26-35. 17. Fiore E, Rago T, Latrofa F, Provenzale MA, Piaggi P, Delitala A, et al. Hashimoto’s thyroiditis is associated with papillary thyroid carcinoma: role of TSH and of treatment with L-thyroxine. Endocr Relat Cancer. 2011;18(4):429-37. 18. Moshynska OV, Saxena A. Clonal relationship between Hashimoto thyroiditis and thyroid lymphoma. J Clin Pathol. 2008;61(4): 438-44. 19. Watanabe N, Noh JY, Narimatsu H, Takeuchi K, Yamaguchi T, Kameyama K, et al. Clinicopathological features of 171 cases of primary thyroid lymphoma: a long-term study involving 24553 patients with Hashimoto’s disease. Br J Haematol. 2011;153(2):236-43. 20. Cornetta AJ, Russell JP, Cunnane M, Keane WM, Rothstein JL. Cyclooxygenase-2 expression in human thyroid carcinoma and Hashimoto’s thyroiditis. Laryngoscope. 2002;112(2):238-42. 21. Yamakawa M, Yamada K, Tsuge T, Ohrui H, Ogata T, Dobashi M, et al. Protection of thyroid cancer cells by complement-regulatory factors. Cancer. 1994;73(11):2808-17.

13. Ruggeri RM, Campennì A, Sindoni A, Baldari S, Trimarchi F, Benvenga S. Association of autonomously functioning thyroid nodules with Hashimoto’s thyroiditis: study on a large series of patients. Exp Clin Endocrinol Diabetes. 2011;119(10):621-7.

14. Azizi G, Keller JM, Lewis M, Piper K, Puett D, Rivenbark KM, et al. Association of Hashimoto’s thyroiditis with thyroid cancer. Endocr Relat Cancer. 2014;21(6):845-52.

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Arch Endocrinol Metab. 2017;61/6

Acknowledgements

I would like to heartily thank and acknowledge the excellent work and commitment performed by the Associate Editors and Reviewers.

Marcello D. Bronstein, MD. PhD Editor-in-Chief, Archives of Endocrinology and Metabology

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