Association of Biological and Self-Reported Stress Measures with Cardiovascular Disease and Risk Factors Among Adults with Type 2 Diabetes

Valerie Gideon, Emily Brodie

Abstract


Purpose and Background/Significance:  Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are prevalent comorbid health conditions and are among the leading causes of premature disability and mortality.  Approximately two-thirds of US adults with TD2M die from CVD.  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 analysis is to test a model of the relationships among T2DM control, depression, and stress measures as predictors of CVD risk factors/events in a sample of adults (n=45) with T2DM and depressive symptoms. It is hypothesized that, after controlling for socio-demographic and clinical characteristics, there will be: (a) a positive association between biological and self-report stress measures with self-reported cardiovascular events and risk factors; (b) a positive association between biological and self-report stress measures with socio-demographic and clinical characteristics; (c) A positive relationship between socio-demographic and clinical characteristics with self-reported cardiovascular events/ risk factors.

 

Methods: A secondary analysis will be performed using baseline data from a randomized pilot study of a patient-centered decision support intervention to improve patient decision-making about managing depressive symptoms in context of T2DM.  Standardized measures include 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).  A multivariate logistic regression analysis will be used to model the relationships of glycemic control, depression, and stress in predicting the odds of CVD risk factors/events, after controlling for socio-demographic and clinical characteristics.

Results:  The bivariate correlation matrix revealed statistical significance between PHQ-9 and DDS (R= 0.550, p=.00) and DDS and A1c (R=.314, p=.035).  Smoking status and A1c were also found to have a significant relationship (R=.299, p=.046).  After controlling for socio-demographic and clinical characteristics, a logistic regression model showed PHQ-9 (p=.031), DDS (p=.047) were found to be significant predictors for CVD events and risk factors.  Salivary α-amylase was trending towards significance (p=.082) and when α-amylase was used as a binary predictor, the significance is clearer (p=.051) showing that increased stress predicts a higher risk of CVD events and risk factors.  Smoking status remained to be a huge predictor of CVD events and risk factors (p=.031)

 

Conclusion: This analysis will contribute to a better understanding of the relationships among CVD risk factors/events, glycemic control and potentially modifiable psychological factors. This knowledge can improve healthcare interventions to reduce the morbidity and mortality associated with CVD and T2DM.


Keywords


Diabetes; Cardiovascular Disease; Stress; Depression

Full Text: PDF

Refbacks

  • There are currently no refbacks.