Clinical utility of creatinine- and cystatin C-based definition of renal function for risk prediction of primary cardiovascular events in patients with diabetes.

<bold>Objective: </bold>To assess the cardiovascular risk of diabetic subjects with chronic kidney disease (CKD) based on different estimated glomerular filtration rate (eGFR) equations and to evaluate which definition of CKD best improves cardiovascular risk prediction of the Framingham Cardiovascular Risk Score (Framingham-CV-RS).<bold>Research Design and Methods: </bold>CKD was defined as eGFR <60 mL/min/1.73 m(2), estimated by the creatinine-based Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations and a cystatin C-based equation (CKD-CysC). Cox regression was used to estimate hazard ratios (HRs) of subjects with CKD for incident cardiovascular events in a cohort of 1,153 individuals with diabetes (baseline age 50-74 years). Furthermore, the CKD definitions were added individually to a reference model comprising the Framingham-CV-RS variables and HbA(1c), and measures of model discrimination and reclassification were assessed.<bold>Results: </bold>During 5 years of follow-up, 95 individuals had a primary cardiovascular event. Crude HRs were increased for all CKD definitions. However, after adjusting for established cardiovascular risk factors, HRs for both creatinine-based CKD definitions were attenuated to point estimates of 1.03, whereas the HRs for the cystatin C-based CKD definition remained significantly increased (HR 1.75 [95% CI 1.07-2.87]). Extension of the reference model by the different CKD definitions resulted in an increase in the c statistic only when adding CKD-CysC (from 0.638 to 0.644) along with a net reclassification improvement of 8.9%.<bold>Conclusions: </bold>Only the cystatin C-based CKD definition was an independent risk predictor for cardiovascular events in our diabetic study cohort and indicated a potentially better clinical utility for cardiovascular risk prediction than creatinine-based equations. [ABSTRACT FROM AUTHOR]