期刊
DIABETES
卷 62, 期 12, 页码 4270-4276出版社
AMER DIABETES ASSOC
DOI: 10.2337/db13-0570
关键词
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资金
- Wellcome Trust
- European Community
- National Institute for Health Research (NIHR) Clinical Research Facility at Guy's and St. Thomas' National Health Service (NHS) Foundation Trust
- NIHR Biomedical Research Centre based at Guy's and St. Thomas' NHS Foundation Trust
- King's College London
- ERC
- Wellcome Trust [092447/Z/10/Z, WT098051, WT091310]
- Oak Foundation
- European Community [257082, HEALTH-F5-2011-282510]
- Pfizer Worldwide Research and Development
- Medical Research Council [MC_UU_12013/1] Funding Source: researchfish
- MRC [MC_UU_12013/1] Funding Source: UKRI
- Wellcome Trust [092447/Z/10/Z] Funding Source: Wellcome Trust
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 x 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 x 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 x 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.
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