Abstract
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986-1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986-2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.