Association of Biological and Self-Reported Stress Measures with Cardiovascular Disease and Risk Factors Among Adults with Type II Diabetes Mellitus
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Publisher:The Ohio State University
Series/Report no.:The Ohio State University. College of Nursing Honors Theses; 2014
Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are prevalent comorbid health conditions in adults, affecting roughly 65% of U.S. adult population, and are among the leading causes of premature disability and mortality. An underlying inflammatory mechanism is hypothesized in the development and progression of CVD. Poor T2DM control, depression, and stress are inflammatory processes that are associated with an increased risk of CVD events, including hypertension, myocardial infarction, stroke, and kidney disease. The purpose of this thesis was to test a model of the relationships between T2DM control, depression, and stress measures as predictors of CVD risk factors and events in a sample of adults (N=45) with T2DM and depressive symptoms. It was hypothesized that, after controlling for socio-demographic characteristics and clinical characteristics, there would be: (a) a positive association between biological and self-report stress measures with self-reported cardiovascular events and risk factors, and; (b) A1c and smoking status would be positively related to biological and self-report distress measures and to self-reported CVD risk factors/events. A secondary analysis was done using baseline (pre-intervention) data from a randomized trial of a 12-week patient-centered decision support intervention (n=45) to improve patient decision-making about managing depressive symptoms in context of T2DM. Standardized measures included glycemic control (A1c), depression (Patient Health Questionnaire-9), self-reported and biomarker stress measures (Diabetes Distress Scale; salivary α-amylase), and a dichotomized self-report measure of four CVD risk factors and events (0 = no risk factors/events; 1 = at least one risk factor/event). Regression analysis was used to model relationships of glycemic control, depression, and, in predicting CVD risk factors/ events (hypertension, heart disease, kidney disease, stroke), after initially controlling for socio-demographic characteristics (age, gender, race) and smoking status. Unadjusted bivariate relationships were statistically significant for PHQ-9 and DDS (r = .550, p < .05), DDS and A1c (r = .314, p < .05), and smoking status and A1c (r = .299, p < 05). A logistic regression analysis revealed three statistically significant and positive predictors of CVD risk factors events (all p < .05): PHQ-9, DDS, and smoking status. In the first regression model, salivary α-amylase was used as a continuous variable and trended towards significance (p = .082), and when used as a binary predictor, approached statistical significance (p = .051) in predicting CVD risk factors/events. This study contributes to knowledge about relationships between potentially modifiable psychological factors (depression, stress), risky health behaviors (smoking), and glycemic control in predicting CVD risk factors/events. This knowledge can inform improved healthcare interventions to reduce the morbidity and mortality associated with cardiovascular disease and diabetes.
Academic Major: Nursing
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