The purpose of this study was to identify predictors of incident diabetes during follow-up of nondiabetic patients with chronic heart failure (CHF) in the Candesartan in Heart Failure Assessment of Reduction in Mortality and Morbidity (CHARM) program.
RESEARCH DESIGN AND METHODS
A total of 1,620 nondiabetic patients had full baseline datasets. We compared baseline demographic, medication, and laboratory data for patients who did or did not develop diabetes and conducted logistic regression and receiver operator characteristic curve analyses.
Over a median period of 2.8 years, 126 of the 1,620 patients (7.8%) developed diabetes. In multiple logistic regression analysis, the following baseline characteristics were independently associated with incident diabetes in decreasing order of significance by stepwise selection: higher A1C (odds ratio [OR] 1.78 per 1 SD increase; P < 0.0001), higher BMI (OR 1.64 per 1 SD increase; P < 0.0001), lipid-lowering therapy (OR 2.05; P = 0.0005), lower serum creatinine concentration (OR 0.68 per 1 SD increase; P = 0.0018), diuretic therapy (OR 4.81; P = 0.003), digoxin therapy (OR 1.65; P = 0.022), higher serum alanine aminotransferase concentration (OR 1.15 per 1 SD increase; P = 0.027), and lower age (OR 0.81 per 1 SD increase; P = 0.048). Using receiver operating characteristic curve analysis, A1C and BMI yielded areas under the curve of 0.723 and 0.712, respectively, increasing to 0.788 when combined. Addition of other variables independently associated with diabetes risk minimally improved prediction of diabetes.
In nondiabetic patients with CHF in CHARM, A1C and BMI were the strongest predictors of the development of diabetes. Other minor predictors in part reflected CHF severity or drug-associated diabetes risk. Identifying patients with CHF at risk of diabetes through simple criteria appears possible and could enable targeted preventative measures.