Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts
Ari V Ahola-Olli,
Aki S. Havulinna,
Olli T. Raitakari,
Posted 08 Jan 2019
bioRxiv DOI: 10.1101/513648 (published DOI: 10.1007/s00125-019-05001-w)
Posted 08 Jan 2019
Objective: Advances in metabolomics now allow high-throughput biomarker profiling of large population studies. We aimed to identify circulating metabolic biomarkers predictive of type 2 diabetes in young adults. Methods: Nuclear magnetic resonance metabolomics was used to quantify 229 metabolic measures in 11,896 individuals from four Finnish cohorts (mean age 33 years, range 24-45). Associations between baseline metabolites and risk of type 2 diabetes onset during 8-15 years of follow-up (392 incident cases) were assessed by logistic regression adjusted for sex, age, body mass index, and fasting glucose. Results: Out of 229 metabolic measures, 113 were associated with incident diabetes in meta-analysis of the four cohorts (P<0.0009; odds ratios per 1-SD: 0.59-1.50). Among the strongest predictors of diabetes risk were branched-chained and aromatic amino acids (odds ratios 1.31-1.33), triglyceride fractions within the largest very-low-density lipoprotein particles (VLDL; odds ratios 1.33-1.50)), as well as linoleic omega-6 fatty acids (odds ratio 0.75) and free cholesterol in large high-density lipoprotein particles (HDL; odds ratio 0.59). A biomarker score comprised of phenylalanine, free cholesterol in large HDL, and the ratio of cholesteryl esters to total lipids in large VLDL was predictive of the risk for future diabetes in an independent validation cohort (odds ratio 10.1 [95% confidence intervals 4.2-24.1] comparing individuals in upper vs lower fifth of biomarker score). Adjustment for routine lipids and insulin attenuated the odds ratio to 5.8 [2.2-15.1]. Conclusions: Metabolic aberrations across multiple molecular pathways are predictive of the long-term risk of type 2 diabetes in young adults. Comprehensive metabolic profiling may potentially help targeting preventive interventions for young asymptomatic individuals at increased risk for type 2 diabetes.
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