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

Author:

Menni Cristina1,Fauman Eric2,Erte Idil1,Perry John R.B.1345,Kastenmüller Gabi6,Shin So-Youn78,Petersen Ann-Kristin9,Hyde Craig10,Psatha Maria1,Ward Kirsten J.1,Yuan Wei1,Milburn Mike11,Palmer Colin N.A.12,Frayling Timothy M.4,Trimmer Jeff13,Bell Jordana T.1,Gieger Christian9,Mohney Rob P.11,Brosnan Mary Julia13,Suhre Karsten614,Soranzo Nicole7,Spector Tim D.1

Affiliation:

1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, U.K.

2. Computational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, Massachusetts

3. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.

4. Genetics of Complex Traits, Exeter Medical School, University of Exeter, Devon, U.K.

5. Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan

6. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany

7. Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.

8. MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, U.K.

9. Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany

10. Clinical Research Statistics, Pfizer Worldwide Research and Development, Groton, Connecticut

11. Metabolon Inc., Raleigh-Durham, North Carolina

12. Biomedical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K.

13. Cardiovascular and Metabolic Diseases, Pfizer Worldwide Research and Development, Cambridge, Massachusetts

14. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar

Abstract

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.

Publisher

American Diabetes Association

Subject

Endocrinology, Diabetes and Metabolism,Internal Medicine

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