Plasma Amino Acids and Incident Type 2 Diabetes in Patients With Coronary Artery Disease

Altered plasma amino acid levels have been implicated as markers of risk for incident type 2 diabetes; however, amino acids are also related to established diabetes risk factors. Therefore, potential for confounding and the impact from competing risks require evaluation. We prospectively followed 2,519 individuals with coronary artery disease but without diabetes. Mixed Gaussian modeling identified potential for confounding. Confounding, defined as a change in effect estimate (≥10%), was investigated by comparing amino acid-incident diabetes risk in a Cox model containing age and sex with that in models adjusted for potential confounders (BMI, estimated glomerular filtration rate, HDL cholesterol, triacylglycerol, C-reactive protein), which were further adjusted for plasma glucose, competing risks, and multiple comparisons (false discovery rate = 0.05, Benjamini-Hochberg method). Finally, component-wise likelihood-based boosting analysis including amino acids and confounders was performed and adjusted for competing risks in order to identify an optimal submodel for predicting incident diabetes. The mean age of the source population was 61.9 years; 72% were men. During a median follow-up of 10.3 years, 267 incident cases of diabetes were identified. In age- and sex-adjusted models, several amino acids, including the branched-chain amino acids, significantly predicted incident diabetes. Adjustment for confounders, however, attenuated associations. Further adjustment for glucose and multiple comparisons rendered only arginine significant (hazard ratio/1 SD 1.21 [95% CI 1.07-1.37]). The optimal submodel included arginine and asparagine. Adjustment for relevant clinical factors attenuated the amino acid-incident diabetes risk. Although these findings do not preclude the potential pathogenic role of other amino acids, they suggest that plasma arginine is independently associated with incident diabetes. Both arginine and asparagine were identified in an optimal model for predicting new-onset type 2 diabetes.