Serum metabolomic profile of incident diabetes
Metabolomic profiling offers the potential to reveal metabolic pathways relevant to the pathophysiology of diabetes and improve diabetes risk prediction.
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.
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).
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.
KeywordsAmino acids Branched chain amino acids Diabetes Metabolic pathways Metabolomics
Atherosclerosis Risk in Communities
The authors thank the staff and participants of the ARIC study for their important contributions.
CMR proposed the study, planned the statistical analysis and wrote the manuscript. EB obtained the metabolomics data. BY, ZZ, PC and AT contributed to the statistical analysis. AK, LEW, JC, EB and ES provided methodological and content-related expertise. All authors provided substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; drafted the article or revised it critically for important intellectual content; and provided final approval of the version to be published. CMR is responsible for the integrity of the work as a whole.
Selected data elements of the ARIC study are available upon request through the National Health, Lung, and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC).
Duality of interest
The authors declare that there is no duality of interest associated with this manuscript.
- 9.Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E (2009) Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 81:6656–6667CrossRefPubMedGoogle Scholar
- 18.Yu B, Zheng Y, Alexander D, Morrison AC, Coresh J, Boerwinkle E. (2014) Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet 10:e1004212Google Scholar
- 21.Suhre K, Meisinger C, Doring A et al. (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953Google Scholar
- 29.Tillin T, Hughes AD, Wang Q et al (2015) Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia 58:968–979CrossRefPubMedPubMedCentralGoogle Scholar