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Metabolite profiles and the risk of developing diabetes


Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography–tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

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Figure 1: Correlation matrix for plasma metabolite levels.

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  1. Tabák, A.G. et al. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373, 2215–2221 (2009).

    Article  Google Scholar 

  2. Wilson, P.W. et al. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch. Intern. Med. 167, 1068–1074 (2007).

    Article  Google Scholar 

  3. Pan, X.R. et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 20, 537–544 (1997).

    Article  CAS  Google Scholar 

  4. Tuomilehto, J. et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N. Engl. J. Med. 344, 1343–1350 (2001).

    Article  CAS  Google Scholar 

  5. The Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 346, 393–403 (2002).

  6. Gerstein, H.C. et al. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet 368, 1096–1105 (2006).

    Article  CAS  Google Scholar 

  7. Nicholson, J.K. & Wilson, I.D. Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2, 668–676 (2003).

    Article  CAS  Google Scholar 

  8. Raamsdonk, L.M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 (2001).

    Article  CAS  Google Scholar 

  9. Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    Article  CAS  Google Scholar 

  10. An, J. et al. Hepatic expression of malonyl-CoA decarboxylase reverses muscle, liver and whole-animal insulin resistance. Nat. Med. 10, 268–274 (2004).

    Article  CAS  Google Scholar 

  11. Sabatine, M.S. et al. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875 (2005).

    Article  CAS  Google Scholar 

  12. Sapieha, P. et al. The succinate receptor GPR91 in neurons has a major role in retinal angiogenesis. Nat. Med. 14, 1067–1076 (2008).

    Article  CAS  Google Scholar 

  13. He, W. et al. Citric acid cycle intermediates as ligands for orphan G-protein–coupled receptors. Nature 429, 188–193 (2004).

    Article  CAS  Google Scholar 

  14. Newgard, C.B. et al. A branched-chain amino acid–related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

    Article  CAS  Google Scholar 

  15. Shaham, O. et al. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 4, 214 (2008).

    Article  Google Scholar 

  16. Wopereis, S. et al. Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes. PLoS ONE 4, e4525 (2009).

    Article  Google Scholar 

  17. Zhao, X. et al. Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at? Am. J. Physiol. Endocrinol. Metab. 296, E384–E393 (2009).

    Article  CAS  Google Scholar 

  18. Huffman, K.M. et al. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32, 1678–1683 (2009).

    Article  CAS  Google Scholar 

  19. Lewis, G.D., Asnani, A. & Gerszten, R.E. Application of metabolomics to cardiovascular biomarker and pathway discovery. J. Am. Coll. Cardiol. 52, 117–123 (2008).

    Article  CAS  Google Scholar 

  20. Felig, P., Marliss, E. & Cahill, G.F. Jr. Plasma amino acid levels and insulin secretion in obesity. N. Engl. J. Med. 281, 811–816 (1969).

    Article  CAS  Google Scholar 

  21. Patti, M.E., Brambilla, E., Luzi, L., Landaker, E.J. & Kahn, C.R. Bidirectional modulation of insulin action by amino acids. J. Clin. Invest. 101, 1519–1529 (1998).

    Article  CAS  Google Scholar 

  22. Krebs, M. et al. Mechanism of amino acid–induced skeletal muscle insulin resistance in humans. Diabetes 51, 599–605 (2002).

    Article  CAS  Google Scholar 

  23. Zhang, Y. et al. Increasing dietary leucine intake reduces diet-induced obesity and improves glucose and cholesterol metabolism in mice via multimechanisms. Diabetes 56, 1647–1654 (2007).

    Article  CAS  Google Scholar 

  24. Floyd, J.C. Jr. Fajans, S.S., Conn, J.W., Knopf, R.F. & Rull, J. Stimulation of insulin secretion by amino acids. J. Clin. Invest. 45, 1487–1502 (1966).

    Article  CAS  Google Scholar 

  25. Nilsson, M., Holst, J.J. & Bjorck, I.M. Metabolic effects of amino acid mixtures and whey protein in healthy subjects: studies using glucose-equivalent drinks. Am. J. Clin. Nutr. 85, 996–1004 (2007).

    Article  CAS  Google Scholar 

  26. van Loon, L.J., Saris, W.H., Verhagen, H. & Wagenmakers, A.J. Plasma insulin responses after ingestion of different amino acid or protein mixtures with carbohydrate. Am. J. Clin. Nutr. 72, 96–105 (2000).

    Article  CAS  Google Scholar 

  27. Meigs, J.B. et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N. Engl. J. Med. 359, 2208–2219 (2008).

    Article  CAS  Google Scholar 

  28. Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220–2232 (2008).

    Article  CAS  Google Scholar 

  29. Kannel, W.B., Feinleib, M., McNamara, P.M., Garrison, R.J. & Castelli, W.P. An investigation of coronary heart disease in families: the Framingham Offspring Study. Am. J. Epidemiol. 110, 281–290 (1979).

    Article  CAS  Google Scholar 

  30. Persson, M., Hedblad, B., Nelson, J.J. & Berglund, G. Elevated Lp-PLA2 levels add prognostic information to the metabolic syndrome on incidence of cardiovascular events among middle-aged nondiabetic subjects. Arterioscler. Thromb. Vasc. Biol. 27, 1411–1416 (2007).

    Article  CAS  Google Scholar 

  31. Matthews, D.R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419 (1985).

    Article  CAS  Google Scholar 

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This work was supported by US National Institutes of Health contract NO1-HC-25195, R01-DK-HL081572, the Donald W. Reynolds Foundation, the Leducq Foundation and the American Heart Association. S.C. is also supported by an award from the Ellison Foundation. J.C.F. is also supported by the Massachusetts General Hospital and a Clinical Scientist Development Award from the Doris Duke Charitable Foundation.

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Authors and Affiliations



T.J.W. conceived of the study, designed the experiments, analyzed and interpreted the data and wrote the manuscript. A.S. and E.P.R., under the direction of C.B.C., developed the metabolic profiling platform, performed mass spectrometry experiments and analyzed the data. S.A.C. and V.K.M. helped in the establishment of the metabolite profiling platform and manuscript revision. G.D.L. contributed to data analysis and manuscript generation. M.G.L., R.S.V., S.C. and E.M. helped in experimental design, performed statistical analyses and assisted in manuscript generation. C.J.O. and C.S.F. helped in experimental design and manuscript revision. P.F.J. directed the dietary analyses in the Framingham Heart Study and contributed to manuscript revision. J.C.F. assisted in the interpretation of the data and contributed to manuscript revision. O.M. and C.F. performed the replication analyses in the Malmö Diet and Cancer cohort and contributed to manuscript revision. R.E.G. conceived of the study, designed the experiments, analyzed and interpreted the data and wrote the manuscript.

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Correspondence to Thomas J Wang or Robert E Gerszten.

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Competing interests

T.J.W., R.S.V., M.G.L., V.K.M. and R.E.G. are named as co-inventors on a patent application to the US Patent Office pertaining to metabolite predictors of diabetes. J.C.F. has received consulting honoraria from Publicis Healthcare, Merck, bioStrategies, XOMA and Daiichi-Sankyo and has been a paid invited speaker at internal scientific seminars hosted by Pfizer and Alnylam Pharmaceuticals.

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Wang, T., Larson, M., Vasan, R. et al. Metabolite profiles and the risk of developing diabetes. Nat Med 17, 448–453 (2011).

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