Analyzing the Effect of Commonly Used Illicit Substances on Blood Pressure Regulation

Purpose:

Cocaine, heroin, and methamphetamine are some of the most commonly used illicit substances in the US.1 These substances result in a dopamine increase that may alter blood pressure.2,3 Currently, there is limited evidence evaluating the effects of illicit substances on blood pressure.2 The purpose of this cross-sectional study was to assess the effects of cocaine, heroin, methamphetamine on blood pressure regulation among patients with established hypertension.

Methods:

We utilized data from the NHANES 2017-2018 Questionnaire. Inclusion criteria required answering “yes” to “ever been told you have high blood pressure?” Participants were excluded if they answered “no”, if it was not answered, or if a systolic blood pressure (SBP) reading was unavailable. A logistic regression model calculated odds ratios, adjusting for sex, age, BMI, and race. An independent t-test determined the significance between illicit drug use and continuous variables (age, systolic blood pressure, diastolic blood pressure) and a Chi-square determined the impact of illicit drugs on hypertension. Lack of proper blood pressure control was defined as SBP ≥ 130 mmHg.

Results:

Those that reported illicit drug use had a nearly identical odds of SBP ≥ 130 mmHg compared to those that did not use illicit drugs, but this finding was not statistically significant (adjusted OR: 1.067; 95% CI (0.779-1.461); p = 0.686). Increasing age was associated with a higher odds of SBP ≥ 130 mmHg.

Conclusion: There was overall no significant difference in blood pressure between people who have used illicit drugs and those who have not among patients with established hypertension. Potential confounders include smoking status and medications used to treat hypertension. Further research should be conducted to prove an association between illicit drug use and hypertension control.

The Impact of Cigarette Smoking on Glycemic Control in Patients with Diabetes

Emma Daly Pharm.D. Candidate, Alex DiLucia Pharm.D. Candidate, Kaitlyn Goncalves Pharm.D. Candidate, Taylor Mezini

Pharm.D. Candidate, Rithvik Pottepalem Pharm.D. Candidate, Rachel Yang Pharm.D. Candidate

Purpose:

Smoking has shown an increased risk of developing diabetes.1 However, its effects on glycemic control and HbA1c are not fully known.2,3,4,5 The study’s objective was to better determine the impact of smoking on glycemic control.

Methods:

Data was collected from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) data. A composite outcome was created using the 2015 ADA criteria for glycemic control. Adequate glycemic control was classified as A1C ≤7% and fasting blood glucose within 70-130 mg/dL. Poor glycemic control was defined as participants not meeting this criteria. Pearson Chi square and Wilcoxon rank sum tests were conducted to test the significance of characteristics between smokers and non-smokers. A multivariable logistic regression (ɑ=0.05) was used to evaluate the predictive ability of the exposure and covariates for the outcome. Analyses were performed using SPSS. Inclusion criteria included those <80 years old, told by doctor they have diabetes, answered survey questions regarding smoking, and had glycohemoglobin and fasting blood glucose levels.

Results:

39.7% of the smokers versus 33.5% of the non-smokers had adequate glycemic control (p> 0.05). The results, while statistically insignificant, showed smokers having 20% lower odds of adequate glycemic control than non-smokers (95% CI 0.408, 1.583, p > 0.05). Current insulin users had 4.8 times higher odds of adequate glycemic control than non-insulin users (95% CI 2.070, 11.034, p < 0.001). Current diabetic pill takers had 2.3 times higher odds of adequate glycemic control than non-diabetic pill takers (95% CI 1.075, 4.964, p < 0.05).

Conclusions:

These findings suggest that smoking may have a negative impact on glycemic control. In the future, it may be beneficial to conduct another study with a larger population and longer follow up period.

Comparing Renal Function of Patients with Diabetes That Are Insulin-Dependent Versus Insulin-Naïve

Elizabeth DelVecchio PharmD. Candidate, Alexander Linn PharmD. Candidate, Lydia Maskell PharmD. Candidate, Lucas Nicolau PharmD. Candidate, Ariana Toledo PharmD. Candidate

Purpose

Diabetes mellitus (DM) is the leading cause of chronic kidney disease (CKD).1 Being one of the principal causes of death worldwide,2 CKD can progress to dialysis, transplantation, and cardiovascular disease.3 Studies suggest that hyperinsulinemia enhances renal reuptake of sodium while decreasing insulin-induced vasodilation, promoting hypertension and kidney damage.4 Insulin therapy is a mainstay of DM treatment to prevent hyperglycemia by binding to insulin receptors, stimulating glucose uptake, and inhibiting glucose production.5 The study objective was to evaluate the relationship between insulin use and the presence of CKD.

Methods:

The 2017-2018 National Health and Nutrition Examination Survey data was utilized. Patients were categorized into insulin-dependent or insulin-naïve. Covariates were current age, age of diabetes diagnosis, gender, smoked at least 100 cigarettes, and health insurance. The outcome of interest in the binary logistic regression was presence (CrCl<90) or absence of CKD (CrCl≥90). The multinomial logistic regression evaluated prediction of CKD stage placement compared to the reference category, stage one. Statistical analyses included Wilcoxon rank sum, Chi-squared and Fisher’s exact tests.

Results

There were 210 insulin-dependent and 552 insulin-naïve patients. There were 116 (55.2%) patients in the exposure group and 245 (44.4%) in the control group with CrCl<90 (p=0.007). Insulin use was not significantly associated with increased odds of having CKD (aOR 1.419, 95% CI 0.937-2.150, p=.099). In the multinomial regression, insulin use was associated with increased odds of CKD stage 3 compared to CKD stage 1 (aOR 2.281, 95% CI 1.297-4.012, p=.004) and CKD stage 4+5 compared to CKD stage 1 (aOR 4.235, 95% CI 1.476-12.151, p=.007). Stages 4 and 5 were combined due to limited sample size.

Conclusions

Due to limited sample size and varying significance of our findings, further analysis is required to fully understand the relationship between insulin and CKD development.

The Incidence of Hypertension in Those with Depressive Symptoms as Defined by PHQ-9 Score

Felitte

Purpose:

About 116 million people in the US have hypertension1. Psychosocial factors can increase blood pressure2. The role that depression plays in the incidence of hypertension is unclear, but studies have shown a positive correlation3,4 . The PHQ-9 scoring tool used to identify symptoms and aid in a diagnosis of depression is a reliable and valid tool for analysis5. The objective of the study is to analyze the relationship between symptoms of depression based on PHQ-9 score and the incidence of hypertension.

Methods:

Data was collected using the National Health and Nutrition Examination Survey administered by the Center for Disease Control and Prevention. The Blood Pressure and Cholesterol data from 2017-2018 was used to identify patients with hypertension and patients with a PHQ-9 score ≥5. Hypertension was defined as a systolic blood pressure greater than 130 mmHg, a diastolic blood pressure greater than 80 mmHg, or taking medication for the treatment of hypertension1 .

Results:

Data was analyzed from 5,090 patients. There were 1,318 patients who reported a PHQ-9 score ≥5, with 727 having hypertension and 591 not having hypertension. There were 3,772 with a PHQ-9 score <5, with 2,120 having hypertension and 1,652 not having hypertension. Patients with a PHQ-9 score ≥5 were 4.6% less likely to have hypertension. Other factors contributing to increased incidence of hypertension were increasing age, obesity, and smoking status. Being female had a protective effect against hypertension.

Conclusions:

Patients with a PHQ-9 score ≥5 had a slightly decreased odds of having hypertension, however it can be reasonably concluded that there is no significant correlation between PHQ-9 score and the incidence of hypertension. Limitations include cross sectional data, lack of information on baseline characteristics, and incomplete past medical history. Future investigations will look into relationships between other mental health disorders and cardiovascular conditions.

Comparison of Anemia Prevalence in Normal vs Overweight/Obese BMI in Patients with Chronic Kidney Disease

Background:

With obesity prevalence increasing nationally, patients are at risk for associated complications, including chronic kidney disease (CKD). Anemia commonly occurs with both obesity and CKD, though the effect of BMI on anemia prevalence in patients with CKD remains unclear.

Objective:

To evaluate the association between BMI and the prevalence of anemia in individuals with chronic kidney disease.

Methods:

This cross-sectional study utilized data from the National Health and Nutrition Exam Survey (NHANES) to evaluate 1,601 patients with CKD stages 2-5 (CrCl < 90 mL/min) between the years of 2015-2016. The exposure of interest was body mass index (BMI) and the study cohort was divided into two exposure groups; normal BMI (18.5-24.9 kg/m2) and overweight/obese BMI (>25 kg/m2). The outcome of interest was anemia, defined as a hemoglobin level < 12 g/dL in women and < 13 g/dL in men. Categorical variables were presented as frequency (%) and compared using a Pearson’s chi-square test. A logistic regression was performed, producing an odds ratio to compare exposure groups using 95% confidence intervals.

Results:

Of the 1,601 patients included in the study, 595 (37.2%) were included in the normal BMI cohort and 1,006 (62.8%) were included in the overweight/obese cohort. Comparing these two cohorts, frequency of anemia was 16% vs 14.9% (P=0.571) for normal vs overweight/obese. The adjusted odds ratio (OR) for anemia in the overweight/obese vs normal weight exposure groups was 1.603, 95% CI [0.762, 3.373] (P=0.214).

Conclusion:

Our results suggest that there is no statistically significant association between BMI and anemia prevalence in patients diagnosed with CKD stage 2 or higher.

Evaluating the Effect of Beta Blockers on A1c in Patients with Diabetes

Katie Famiglietti Pharm.D. Candidate, Jessica Irish Pharm.D. Candidate, Kelsey Marques Pharm.D. Candidate, Zeke Pacheco Pharm.D. Candidate, Russell Scarpa Pharm.D. Candidate

Purpose:

People with diabetes are twice as likely to develop cardiovascular disease and may require treatment with beta blockers (BB).1 However, many BB may worsen glycemic control.2,3 Conversely, a subset of BB may improve glycemic control.3,4 Hemoglobin A1c is a biomarker used to assess glycemic control.5 The objective of our research was to evaluate if BB have an effect on A1c in patients with diabetes compared to those not taking BB.

Methods:

Data was collected from the cross-sectional 2017-2018 National Health and Nutrition Examination Survey. The total number of participants was 806. Inclusion criteria was diagnosis of diabetes and participants lacking A1c were excluded. The exposure was BB use (n=98) and the outcome was above-goal A1c, defined as A1c ≥ 7%. An adjusted odds ratio was generated from a logistic regression adjusted for age, gender, race, BMI, anemia, and bilirubin. The outcome between exposure groups was compared using a Chi-square test.

Results:

Taking beta blockers was not associated with different A1c in patients with diabetes (aOR 1.028, 95% CI 0.667-1.585, p=0.900). Population demographics were similar between comparison groups, and there was no difference in the outcome before adjusting for covariates (p=979).

Conclusions:

This study shows that BB use does not affect A1C in patients with diabetes. Previous studies have shown worsened glycemic control with some BBs and improved control with others. It is possible these effects were lost due to our study combining these agents as one comparator. Other limitations include lack of doses, misclassification due to the limited medication list, and the inability to evaluate causality over time. Strengths include a large and diverse study population, and direct generalizability to the U.S. The clinical implication is that beta blockers may be prescribed without affecting A1c.

Cross-sectional study of the effects of prescription NSAIDs versus no prescription NSAIDs use on the Serum Creatinine of patients with arthritis.

Irina Fofanova PharmD. Candidate, Hannah Igoe PharmD. Candidate, Hyejin (Erica) Lee PharmD. Candidate, Lillian Luong PharmD. Candidate, Jasmine Takach PharmD. Candidate

Purpose:

Non-steroidal anti-inflammatory drugs (NSAIDs) used for arthritis can affect kidney function through COX inhibition and the prostaglandin pathway.1,2 This study aims to assess the effect of prescription NSAID use has on kidney function, through serum creatinine (SCr), in patients with arthritis.

Methods:

The data utilized was obtained from the 2017-2018 NHANE survey. The study included 780 participants who reported an arthritis diagnosis. Individuals who did not report a diagnosis of arthritis or had missing data were excluded. The exposure was reported use of any prescription NSAID. The outcome was elevated SCr levels, defined as a SCr greater than 1.3mg/dL for men and 1.1mg/dL for women. A logistic regression analysis was completed to assess predictive power of the exposure to the outcome.

Results:

After adjusting for the covariates age, gender, BMI, smoking status, and arthritis type, there was no statistically significant difference in the odds of elevated SCr in patients with arthritis taking prescription NSAIDs compared to those not taking prescription NSAIDs (adjusted OR = 0.695, 95% CI: 0.23 - 2.069, p = 0.384). Those aged 61 - 70 and 2'71 had statistically significant increased odds of an elevated SCr compared to those aged :S40 with adjusted OR= 5.709 (95% CI 1.303 - 25.016, P=0.021) and adjusted OR=14.374 (95% CI: 3.272 - 63.143, P= <0.001) respectively.

Conclusion: Our results were not statistically significant which correlates with results of other studies on the topic. Limitations of the study are data on indication, dose, and duration of the NSAID was not available and SCr is not a reliable measure of kidney function since variables such as dehydration and muscle mass can affect SCr. Future research should be done prospectively to be able to track kidney function over time.

Examining the Relationship Between Poorly Controlled Diabetes Mellitus and Fracture Risk in Osteoporosis Patients

Thu Le PharmD. Candidate, Aliaa Mohamed PharmD. Candidate, Elisa Piraino PharmD. Candidate, Elizabeth Shulman PharmD. Candidate, Collin Smith PharmD. Candidate

Purpose:

Diabetes mellitus (DM) and osteoporosis are two chronic conditions impacting 37.3 million and 10 million American adults, respectively 1,2 . Adults with DM are at increased risk of fractures, despite DM individuals having higher bone mineral density (BMD)3,4. However, the relationship between adults specifically with poorly controlled DM and fracture risk is not abundantly defined in current literature. Our objective was to evaluate the relationship between poorly controlled DM and fracture risk in osteoporosis patients.

Methods:

This cross-sectional study utilized data collected from the 2017-2018 National Examination Survey (NHANES) dataset. Patients 50 years and older with a diagnosis of both DM and osteoporosis were included. For the exposure, poorly-controlled DM defined as A1C > 7.0% and fasting blood glucose (BG) > 130 mg/dL compared to controlled DM A1C ≤ 7.0% and BG ≤ 130 mg/dL5. The outcome was measured by the occurrence of hip or wrist fracture4 . Multivariable logistic regression was conducted using SPSS. Chi-Square tests were used to obtain p-values for categorical data with values greater than five.

Results:

The number of poorly-controlled DM and controlled DM patients were 46 and 66 respectively. Of the poorly controlled DM group, 30 (65.2%) patients experienced the outcome of hip or wrist fractures while only 35 (53.0%) patients of the controlled group experienced the outcome (p-value = 0.199). In patients with osteoporosis, poorly controlled DM participants were 68.7% more likely (OR = 1.687) to experience fractures compared to controlled DM, however these results were statistically insignificant (p-value = 0.317).

Conclusion:

Our results suggest a statistically insignificant association between poorly-controlled diabetes and increased fracture outcomes. Additional studies with stronger causative design and larger sample size are needed to further examine whether poorly-controlled DM and fracture outcomes for those with osteoporosis are related.