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Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

2013; American Diabetes Association; Volume: 62; Issue: 12 Linguagem: Inglês

10.2337/db13-0570

1939-327X

Cristina Menni, Eric B. Fauman, Idil Erte, John R. B. Perry, Gabi Kastenmüller, So–Youn Shin, Ann-Kristin Petersen, Craig Hyde, Maria Psatha, Kirsten Ward, Yuan Wei, Mike Milburn, Nicholette D. Palmer, Timothy M. Frayling, Jeff K. Trimmer, Jordana T. Bell, Christian Gieger, Rob Mohney, M. Julia Brosnan, Karsten Suhre, Nicole Soranzo, Tim D. Spector,

Diet and metabolism studies

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39-1.95], P = 8.46 × 10(-9)) and was moderately heritable (h(2) = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34-2.11], P = 6.52 × 10(-6)) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27-2.75], P = 1 × 10(-3)). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.