Serum metabolomic profile of incident diabetes

Abstract

Aims/hypothesis: Metabolomic profiling offers the potential to reveal metabolic pathways relevant to the pathophysiology of diabetes and improve diabetes risk prediction.

Methods: We prospectively analysed known metabolites using an untargeted approach in serum specimens from baseline (1987-1989) and incident diabetes through to 31 December 2015 in a subset of 2939 Atherosclerosis Risk in Communities (ARIC) study participants with metabolomics data and without prevalent diabetes.

Results: Among the 245 named compounds identified, seven metabolites were significantly associated with incident diabetes after Bonferroni correction and covariate adjustment; these included a food additive (erythritol) and compounds involved in amino acid metabolism [isoleucine, leucine, valine, asparagine, 3-(4-hydoxyphenyl)lactate] and glucose metabolism (trehalose). Higher levels of metabolites were associated with increased risk of incident diabetes (HR per 1 SD increase in isoleucine 2.96, 95% CI 2.02, 4.35, p = 3.18 × 10^-8; HR per 1 SD increase in trehalose 1.16, 95% CI 1.09, 1.25, p = 1.87 × 10^-5), with the exception of asparagine, which was associated with a lower risk of diabetes (HR per 1 SD increase in asparagine 0.78, 95% CI 0.71, 0.85, p = 4.19 × 10^-8). The seven metabolites modestly improved prediction of incident diabetes beyond fasting glucose and established risk factors (C statistics 0.744 vs 0.735, p = 0.001 for the difference in C statistics).

Conclusions/interpretation: 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 prediction of diabetes.

Keywords: Amino acids; Branched chain amino acids; Diabetes; Metabolic pathways; Metabolomics.

Fig. 1

Plot of −log₁₀ p values for the adjusted association between serum metabolites and incident diabetes mellitus (DM); adjusted for the covariates in model 3: age, sex, race, centre, batch, education level, systolic blood pressure, diastolic blood pressure, BMI, HDL-cholesterol, LDL-cholesterol, smoking status, physical activity level, history of cardiovascular disease, eGFR and fasting glucose. The width of each category of metabolites (super-pathway) reflects the number of metabolites within that category that were detected by the untargeted metabolomic approach in this study population