Hemoglobin A1c and Cardiovascular Disease Incidence in Type 1 Diabetes: An Application Of Joint Modeling Of Longitudinal And Time-To-Event Data In The Pittsburgh Epidemiology Of Diabetes Complications (EDC) Study

Abstract

Type 1 diabetes (T1D) is associated with increased cardiovascular disease (CVD) risk, but hyperglycemia, measured by HbA1c (%), which characterizes T1D has itself been an inconsistent CVD predictor. However, only baseline HbA1c or a summary measure (e.g., mean over follow-up) is usually analyzed. Joint models allow longitudinal repeated covariates, modeled using random effects, and time-to-event data to be modeled simultaneously. Data are from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study, an ongoing, prospective cohort study of childhood-onset T1D followed beginning in 1986–1988 that has repeatedly reported little association between baseline or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (42.1%) developed CVD over 25 years. In joint models, each one percent increase in HbA1c trajectory was associated with a 1.3-fold increased CVD risk (95% Confidence Interval (CI): 1.07, 1.45), after adjusting for baseline values of other CVD risk factors, and a 1.13-fold increased risk (95% CI: 0.99, 1.32) after adjusting for updated means of other CVD factors. These findings, which support the need for good glycemic control to prevent CVD in T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.