Abstract
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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
206,07 € per year
only 17,17 € per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others
References
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).
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).
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).
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).
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).
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).
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).
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).
Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).
An, J. et al. Hepatic expression of malonyl-CoA decarboxylase reverses muscle, liver and whole-animal insulin resistance. Nat. Med. 10, 268–274 (2004).
Sabatine, M.S. et al. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875 (2005).
Sapieha, P. et al. The succinate receptor GPR91 in neurons has a major role in retinal angiogenesis. Nat. Med. 14, 1067–1076 (2008).
He, W. et al. Citric acid cycle intermediates as ligands for orphan G-protein–coupled receptors. Nature 429, 188–193 (2004).
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).
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).
Wopereis, S. et al. Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes. PLoS ONE 4, e4525 (2009).
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).
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).
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).
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).
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).
Krebs, M. et al. Mechanism of amino acid–induced skeletal muscle insulin resistance in humans. Diabetes 51, 599–605 (2002).
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).
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).
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).
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).
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).
Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220–2232 (2008).
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).
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).
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).
Acknowledgements
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.
Author information
Authors and Affiliations
Contributions
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.
Corresponding authors
Ethics declarations
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.
Supplementary information
Supplementary Text and Figures
Supplementary Figure 1, Supplementary Tables 1–4 and Supplementary Methods (PDF 121 kb)
Rights and permissions
About this article
Cite this article
Wang, T., Larson, M., Vasan, R. et al. Metabolite profiles and the risk of developing diabetes. Nat Med 17, 448–453 (2011). https://doi.org/10.1038/nm.2307
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nm.2307