Background: Metabolomic profiling offers the potential to reveal metabolic pathways relevant to diabetes pathophysiology and to improve diabetes risk prediction.
Methods: We prospectively analyzed metabolites and incident diabetes from baseline (1987-1989) through December 31, 2015 in a subset of 2,939 Atherosclerosis Risk in Communities (ARIC) Study participants with metabolomics data and without diabetes at baseline. Metabolomic profiling was conducted in stored serum specimens collected at baseline using a reverse phase, untargeted ultra-performance liquid chromatography tandem mass spectrometry approach.
Results: Among the 245 named compounds we identified, 7 metabolites were significantly associated with incident diabetes after Bonferroni correction and covariate adjustment (age, sex, race, center, batch, education, blood pressures, body mass index, lipids, smoking, physical activity, history of cardiovascular disease, eGFR, fasting glucose). These 7 metabolites consisted of a xenobiotic (erythritol) and compounds involved in amino acid metabolism [isoleucine, leucine, valine, asparagine, 3-(4-hydoxyphenyl)lactate] and glucose metabolism (trehalose). Higher levels of the metabolites were associated with an increased risk of incident diabetes, with the exception of asparagine which was associated with a lower risk of diabetes (HR per 1 SD increase: 0.78, 95% CI: 0.71, 0.85; p=4.19x10-8). The 7 metabolites improved the prediction of incident diabetes beyond fasting glucose and established risk factors (C statistic for model with vs. without 7 metabolites, respectively: 0.744 vs. 0.735; p-value for difference in C statistics=0.001).
Conclusions: Branched chain amino acids may play a role in diabetes development. Our study is the first to report asparagine as a protective biomarker of diabetes risk. The serum metabolome reflects known and novel metabolic disturbances that improve diabetes prediction.