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

  1. Tim D. Spector1
  1. 1Department of Twin Research and Genetic Epidemiology, King’s College London, London, U.K.
  2. 2Computational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, Massachusetts
  3. 3Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.
  4. 4Genetics of Complex Traits, Exeter Medical School, University of Exeter, Devon, U.K.
  5. 5Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
  6. 6Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
  7. 7Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.
  8. 8MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, U.K.
  9. 9Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
  10. 10Clinical Research Statistics, Pfizer Worldwide Research and Development, Groton, Connecticut
  11. 11Metabolon Inc., Raleigh-Durham, North Carolina
  12. 12Biomedical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K.
  13. 13Cardiovascular and Metabolic Diseases, Pfizer Worldwide Research and Development, Cambridge, Massachusetts
  14. 14Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar
  1. Corresponding authors: Tim D. Spector, tim.spector{at}, and Nicole Soranzo, ns6{at}
  1. C.M. and E.F. contributed equally to this study.

  2. M.J.B., K.S., N.S., and T.D.S. contributed equally to this study.


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 (h2 = 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.

  • Received April 12, 2013.
  • Accepted July 15, 2013.

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  1. Diabetes vol. 62 no. 12 4270-4276
  1. All Versions of this Article:
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