Longitudinal changes in cardiovascular disease risk factors and their effects on mortality in the Tecumseh Community Health Study.

Abstract

A study of the development of cardiovascular disease (CVD) is incomplete without information about individual change in risk factors over time. Single measurements cannot capture intrinsic individual variation; secular changes, intervening events, or changed behaviors. Changes in one risk factor may influence changes in values of others. The objective of this dissertation was to take advantage of the longitudinal nature of the Tecumseh Community Health Study and its repeated assessments of the same individuals over time to examine the dynamics of change in a constellation of risk factors for CVD (blood pressure, glucose, total serum cholesterol, smoking, body mass index (BMI)) and the relationship of those changes to subsequent cardiovascular mortality. Data were evaluated using repeated measures mixed effects models (1) to estimate the stability of blood pressure and glucose over time and (2) to examine the effects of changes in body composition and other CVD risk factors on the prediction of blood pressure or glucose and change in those risk factors. The association of blood pressure, blood glucose, serum total cholesterol, BMI and smoking, as well as time-dependent changes in those variables with the risk of CVD mortality was analyzed using Cox Proportional Hazards models. Blood pressure was moderately stable over time but glucose had low levels of tracking. BMI and change-in-BMI were significant predictors of change in blood pressure and glucose. Non-proportional hazards for CVD mortality were demonstrated for these risk factors except BMI. Increases in pack-years, drops in diastolic blood pressure, and weight loss were all significant predictors of mortality beyond attained values of the set of risk factors. Results from Cox models using only baseline risk factors were similar to time-dependent (TD) models, although glucose was a significant predictor of CVD mortality only in the TD models. Information on individual changes in potential CVD risk factors over time can provide insights into understanding the etiololy of CVD and targeting of prevention strategies.