Local Geographic Variation of Periodontitis and Self-Reported Type 2 Diabetes Mellitus

AUTHORS

Tobias K. Boehm, DDS, PhD, is an associate professor and periodontist at the Western University of Health Sciences College of Dental Medicine. Conflict of Interest Disclosure: None reported.

Dalia Seleem, DDS, PhD, is an assistant professor at the Western University of Health Sciences Colleges of Dental Medicine. Conflict of Interest Disclosure: None reported.

Finosh G. Thankam, PhD, is an assistant professor in tissue engineering and regenerative medicine at the department of translational research at the Western University of Health Sciences. Conflict of Interest Disclosure: None reported.

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ABSTRACT

Tooth loss, periodontitis, Type 2 diabetes mellitus, age, race/ethnicity and gender are all correlated, and previous researchers developed mathematical models suggesting geographic disparities for these conditions for the Inland Empire region of California. By performing geospatial analysis of the medical charts from patients attending the dental center of the Western University of Health Sciences, the researchers provide further evidence for geographic health disparities to the ZIP code level in the northern half of the Inland Empire.

Key words: Geospatial health, periodontitis, Type 2 diabetes mellitus, tooth loss, demographics

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Periodontitis is a chronic inflammatory condition caused by a reciprocally reinforced interaction between polymicrobial communities inside periodontal sulci and a dysregulated host inflammatory response. [1] Type 2 diabetes mellitus (T2DM) is characterized by multiple disturbances in glucose homeostasis, including impaired insulin secretion, insulin resistance and splanchnic glucose uptake leading to chronic hyperglycemia. [2] Generally, hyperglycemic individuals exhibit 1.86 times more likely to develop periodontitis compared to nondiabetic individuals. [3] In turn, periodontitis is associated with poorer glycemic control in T2DM and with higher insulin resistance as determined by the homeostatic model assessment of insulin resistance (HOMA-IR) levels. [4] Potential mechanisms of uncontrolled T2DM exacerbating periodontitis include an altered periodontal microflora and immune dysfunction and periodontal extracellular matrix mineralization disorganization triggered by diverse pathological mediators including advanced glycosylation end products, oxidative stress and adipokines. Likewise, it is thought that bacterial irritants released from periodontal tissues and the chronic elevation of inflammatory mediators such as interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP) and oxygen radicals exacerbate diabetes in untreated periodontitis. [5]

Periodontitis experience and T2DM are common in males, Hispanics and older individuals in the U.S. as reported respectively by National Health and Nutrition Examination Surveys and National Center of Health Statistics at the Centers for Disease Control and Prevention. [6,7] Self-reported T2DM status is a valid and reliable substitute for clinical diagnosis of diabetes in epidemiologic studies, with self-reported diabetes matching the clinical diagnosis of diabetes more than 92% over repeated visits. [8] Given that demographics vary geographically, Eke et al. developed a model to predict prevalence of periodontitis down to census tract level, predicting possible local hot spots of severe periodontal disease in Los Angeles and San Bernardino counties. [9] Similarly, national epidemiologic data on diabetes risk factors have been used to predict local variations in overall T2DM experience; however, it was limited only to the county level. [10] Therefore, the researchers aimed to test the prediction of local variations of disease experience by mapping self-reported T2DM and clinically diagnosed periodontal status of patients attending a dental school clinic.

Methods

SUBJECTS

The researchers collected demographics, ZIP codes of patients’ mailing addresses, self-reported T2DM status and clinical periodontal diagnoses from the AxiUm patient record of all patients seen at the dental center of the Western University of Health Sciences between 2010 and 2013, and obtained 1,991 records with the complete dataset. Periodontal exams including radiographs were conducted by third- and fourth-year dental students supervised by a general dentist faculty according to the parameters published by the American Academy of Periodontology. [11] Demographics were recorded as provided by the patient for age, gender identification (coded as “male” if identifying as such for this study) and ethnicity (coded as “Hispanic” or “Caucasian,” not Hispanic for this study). For each of these patients, a periodontal diagnosis was assigned by a board-certified periodontist (TB) according to the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions [12] at the time of the patient visit. Because no other periodontist was available for calibration at the time, calibration was achieved with comparison of diagnoses provided by the student/ general dentist teams. The researchers coded “periodontitis” for cases meeting the 1999 criteria for “chronic periodontitis” and “severe periodontitis” as cases where the interproximal clinical attachment level equaled or exceeded 5 mm. The observed kappa for periodontal disease severity (no periodontitis; mild, moderate, severe chronic periodontitis) was 0.936 (standard error, 0.012) and the Pearson correlation coefficient was 0.968 (95% confidence interval (CI), 0.962–0.972). They also recorded the worst probing depth and the number of missing teeth excluding third molars for these patients.

The patient’s diabetes status was recorded by third- or fourth-year dental students during the initial visit following a questionnaire asking if patients had diabetes or blood sugar anomalies. Students further questioned patients if the answer was yes, checking a box for Type 1 or Type 2 diabetes if the patient was able to confirm a previous diagnosis of these conditions and “other” if the response did not specifically indicate either condition. Examples of “other” conditions that students listed were “unknown,” “don’t know,” “pregnancy related” or “gestational.” These “other” conditions were omitted in further analysis.

The researchers aimed to test the prediction of local variations of disease experience by mapping self-reported T2DM and clinically diagnosed periodontal status of patients.

This study was approved by the Western University of Health Sciences Institutional Review Board as exempt (12/IRB/019 and the addition of geographic analysis was approved April 21, 2015.)

STATISTICAL ANALYSIS

Data were tabulated using Microsoft Excel (Redmond, Wash.) and reformatted as needed for statistical analysis for the R statistical package (Vienna). Correlation of periodontitis with diabetes was evaluated using chi square analysis. Differences in proportions of males, Caucasians, Hispanics, periodontitis and severe periodontitis were evaluated with the proportion test. Age, pocket depths and missing teeth were found to be nonparametrically distributed as visualized using histograms and determined by the Shapiro-Wilk test. Consequently, the Mann-Whitney U test was used to assess whether nondiabetic and diabetic groups were different.

Geographic distribution of periodontitis and diabetes was mapped according to percentage of patients with these conditions for ZIP codes (based on 2015 census zoning data as found in the file cb_2015_us_zcta510_500k published by the U.S. Census Bureau). A choropleth (heat map) was produced using the dlpyr, rgdal, ggplot2, ggmap, 13 rgeos, maptools, RColorBrewer and scales packages in R and the U.S. Census Bureau’s ZIP Code Tabulation Areas. The researchers set a limit of a minimum of 10 patients from each displayed ZIP code tabulation area disease average, as a power calculation indicated that nine individuals would be sufficient to detect a fivefold increase in severe periodontitis (alpha 0.05, power 80%).

Logit regression was performed using the glm function of the “aod” package in R after transforming the data in the following fashion for a model that regarded the following as risk factors for severe periodontitis: age, identifying as male, Hispanic or Type 2 diabetic and reporting a residence in a ZIP code where more than 20% of patients experienced severe periodontitis. Different permutations of risk factors were also tested and the same approach for modeling the risk for periodontitis was used.

Results

To study whether the sample provided by the dental school clinic population could provide a useful test for the predicted local variation, the researchers determined whether the sample followed the known periodontitis-diabetes correlation (TABLE 1). The dental school clinic patient population is generally older and more female than the average demographics reported by the U.S. Census Bureau for Los Angeles, Riverside and San Bernardino counties, and the ethnic composition is similar to the ranges reported for these counties (male 49% to 50%, Hispanic 49% to 55%, white alone, non-Hispanic 26% to 35%). [14] As displayed in TABLE 1, the clinic population follows the known epidemiologic pattern for T2DM, with an increase in males and a significantly older age and a larger number of Hispanic patients. As expected, patients with T2DM experienced significantly more periodontitis, severe periodontitis and tooth loss excluding third molars and displayed worse probing depths. Not adjusting for age and other risk factors, self-reported Type 2 diabetics in these patient populations were 1.7-fold more likely to experience periodontitis and 1.8-fold more likely to experience severe periodontitis, which is in accordance with the risk ratios published in the literature.

Because the clinic population exhibited the typical periodontitis-diabetes comorbidity and demographics-disease relationships, the researchers first mapped demographic patient characteristics for ZIP code tabulation areas and noted whether these corresponded to the publicly available demographic maps for the same areas (FIGURE 1). They noted that the pattern for age (FIGURE 1A), gender (FIGURE 1B) and either self-identified Caucasian or Hispanic identity (FIGURES 1C and D) resembled census bureau data maps, suggesting that the dental school clinic population captured a reasonable snapshot of the surrounding communities.

To determine whether the prediction of local variances of diabetic prevalence was supported by our data, they mapped the self-reported condition of “T2DM” to ZIP code tabulation areas (FIGURE 2). As seen in this map, experience of T2DM varied widely, supporting the hypothesis that T2DM prevalence may differ significantly between geographic locations. Even though the diabetes map does not precisely match any demographic map, it resembled the map for age, confirming the relationship between age and T2DM.

To understand whether the prediction of local variance of periodontitis prevalence was supported by our data, they mapped diagnoses of “(chronic) periodontitis,” “severe periodontitis,” average worst probing depth and the number of missing teeth (FIGURE 3). The geographic diversity in disease experience of our patients was readily visible, with clusters of severe periodontitis (FIGURE 3A), periodontitis in general (FIGURE 3B), pocketing (FIGURE 3C) and tooth loss (FIGURE 3D) in all neighborhoods. The patterns that do not match suggest that occurrence of pocketing, periodontitis diagnosis, severe periodontitis and tooth loss are not related in our patient population. Similarly, none of these patterns match the T2DM pattern, suggesting that there is no relationship contrary to the rough correlation between T2DM and periodontal findings displayed in TABLE 1. This suggests that the difference in periodontitis experience between T2DM and nondiabetics is likely not predicted by self-reported diabetes status.

The logit regression analyses identified that 15 ZIP codes where more than 20% of patients experienced severe periodontitis of demographic factors and considered a mailing address of these ZIP codes a risk for (severe) periodontitis as well as age, identifying as male, Hispanic or having T2DM (TABLE 2). For severe periodontitis, age and being male were the strongest predictors for a diagnosis of severe periodontitis. “Hispanic” and the ZIP code were also predictive to a significant degree, whereas self-reported T2DM was not (TABLE 2A). For diagnosis of periodontitis, age was most predictive and being male and Hispanic were other significant predictors. In contrast, ZIP code or self-reported T2DM were not predictive. This suggests that the difference in periodontal disease activity seen among nondiabetic patients of the dental center and self-reported T2DM can be predicted by age and ethnicity alone.

Discussion

The present study confirmed the possibility of local variations in the prevalence of T2DM and periodontal status. The researchers demonstrated that the dental school clinic patients reporting a history of T2DM were likely to be older and experience periodontitis of increased severity, worse probing depths and increased tooth loss. However, it appears that the effect of T2DM on the clinical presentation is easily masked by age and ethnicity and that clinical studies evaluating the relationship between periodontitis and diabetes may benefit from an age and ethnicity matched case-control setup.

However, understanding the existence of local variations in disease prevalence warrants further investigations involving representative sample populations from local areas. Also, the researchers are not aware of any obvious demographic or environmental risk factor that could explain the wide variance in observed disease prevalence, and this needs further research. Moreover, it is possible that small sample sizes (10 or more) could exacerbate disease prevalence by chance, although it seems likely that the extremes of periodontal disease prevalence were based on power analysis.

Studies reporting the relationship of periodontitis to T2DM often report odds ratios. The odds ratio of periodontitis in T2DM in this study is 6.02 (95% CI 3.77 to 9.61) and that of severe periodontitis is 2.03 (95% CI 1.37 to 3.01). This odds ratio is similar to the odds ratio of periodontitis observed in diabetic Pima Indians of Arizona (2.81, 95% CI1.91–4.13), [15] where diagnosis of periodontitis was determined by attachment loss as in this study. Increased odds for periodontal disease were reported in multiple studies that used different measures such as the Community Index of Periodontal Treatment Needs, probing depths only or combinations of bleeding on probing, probing depths and attachment loss. [16–19] Regarding tooth loss, our data replicates earlier findings of increased tooth loss in T2DM. [20–25]

The odds ratio reported in this study for T2DM is comparable to the odds ratios reported for other well-known risk factors such as smoking and risk indicators such as age and gender. Generally, odds ratios are adjusted for each risk factor to remove influences from other risk factors or indicators. For example, Tomar et al. (2000) reports an adjusted odds ratio of 3.97 for periodontitis in current cigarette smokers, 5.88 in heavy cigarette smokers and 1.68 in former smokers (TABLE 3). [26] It is difficult to compare odds ratios, as they are directly comparable across studies because the adjustment mechanisms are different. For example, the NHANESbased studies [16,26] adjust for education level and income, which was not possible in this study. Moreover, the NHANES subject population is selected to mimic national averages, whereas the local dental school patient population has higher levels of periodontitis (52%) compared to the national average, which also makes odds ratios less comparable. Last, periodontitis definitions vary across studies, [16,26,27] which adds to the observed variance between previously reported odds ratios.

A limitation of this study is that the researchers chose to limit the data from 2010 to 2013 because changes in clinic protocols resulted in a much larger number of providers entering periodontal diagnoses. This resulted in much increased heterogeneity in data, as some providers did not determine attachment levels, lacked calibration in periodontal diagnosis or did not utilize the clinic form used to collect diagnostic data.

Heterogeneity was a problem also for attempting to correlate radiographic bone level with diabetes, demographic characteristics and ZIP codes. In most cases, periodontal exams included a full-mouth radiographic series, and only occasionally were vertical bitewings ordered. Therefore, it was not possible to obtain reliable radiographic bone level measurements for all subjects.

Likewise, HbA1c data was too heterogenous to be useful for this study, as routine HbA1c testing on the day of the dental exam is not part of our comprehensive oral exam protocol at the dental center. While a significant number of patients could report their latest HbA1c level, we felt that the self-reported data was not reliable and of questionable validity because the testing date and reporting accuracy varied widely. For the values that were reported by patients, it appeared that most patients in our clinic were able to achieve a modest control of their glucose levels with HbA1c levels below 9% and glucose levels below 200 mg/dL. This corresponds to the observed odds ratio for T2DM in this study, similar to the odds ratio of 1.56 reported by Tsai et al. [16] for “better controlled” diabetics.

It is unknown how much socioeconomic status played a role as risk indicator in our study population because income level and education level are never recorded during the patient intake process at the dental center. Previous studies [16,27] indicate that there is an inverse relationship between severe periodontitis and attained education level and income.

A standard classification scheme for periodontitis has been endorsed by the American Academy of Periodontology and the European Federation of Periodontology. [28] Consequently, the periodontitis definition in this study does not precisely match the current classification scheme of periodontitis stages. In this study, the “severe periodontitis” approximates Stages III and IV in the new disease classification and may include cases of periodontally healthy, but reduced periodontium. Likewise, the “periodontitis” definition in this study includes Stage I type of periodontitis, along with cases that are now considered “healthy” if the bleeding-on-probing percentage of sites is less than 10%.

This study suggests that the geographical disparities in oral health extrapolated from national epidemiological data likely reflect reality. With a network of calibrated dental practices, it may be possible to chart oral diseases and health conditions to the neighborhood level in real time for the entire state of California or beyond. Data of this type could assist dentists in selecting locations for practice. Moreover, this type of data could assist development of initiatives that could address public health disparities at locations where market mechanisms are not supportive of conventional private practice.

Conclusion

This study provides evidence that patients with a history of T2DM exhibited worse levels of periodontal disease and tooth loss compared to a general nondiabetic patient population, and identifies age, gender and ethnicity as risk indicators for periodontitis. In addition, the data in this study provides evidence for geographic variations in periodontal disease and suggests that a patient’s mailing address ZIP code may be used by dentists as a risk indicator for diagnosis of periodontitis in portions of the Inland Empire of California. However, the study also emphasizes the importance of adjusting for age, gender and ethnicity when determining the effect of T2DM on periodontal disease.

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THE CORRESPONDING AUTHOR, Tobias K. Boehm, DDS, PhD, can be reached at tboehm@westernu.edu.