Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach
- Cristina Menni1,
- Eric Fauman2,
- Idil Erte1,
- John R.B. Perry1,3,4,5,
- Gabi Kastenmüller6,
- So-Youn Shin7,8,
- Ann-Kristin Petersen9,
- Craig Hyde10,
- Maria Psatha1,
- Kirsten J. Ward1,
- Wei Yuan1,
- Mike Milburn11,
- Colin N.A. Palmer12,
- Timothy M. Frayling4,
- Jeff Trimmer13,
- Jordana T. Bell1,
- Christian Gieger9,
- Rob P. Mohney11,
- Mary Julia Brosnan13,
- Karsten Suhre6,14,
- Nicole Soranzo7⇑ and
- Tim D. Spector1⇑
- 1Department of Twin Research and Genetic Epidemiology, King’s College London, London, U.K.
- 2Computational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, Massachusetts
- 3Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.
- 4Genetics of Complex Traits, Exeter Medical School, University of Exeter, Devon, U.K.
- 5Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
- 6Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
- 7Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.
- 8MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, U.K.
- 9Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- 10Clinical Research Statistics, Pfizer Worldwide Research and Development, Groton, Connecticut
- 11Metabolon Inc., Raleigh-Durham, North Carolina
- 12Biomedical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K.
- 13Cardiovascular and Metabolic Diseases, Pfizer Worldwide Research and Development, Cambridge, Massachusetts
- 14Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar
- Corresponding authors: Tim D. Spector, , and Nicole Soranzo, .
C.M. and E.F. contributed equally to this study.
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.
- © 2013 by the American Diabetes Association.
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