Skip to main content
SpringerLink
Log in

What Have Metabolomics Approaches Taught Us About Type 2 Diabetes?

  • Pathogenesis of Type 2 Diabetes and Insulin Resistance (RM Watanabe, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

Type 2 diabetes (T2D) is increasing worldwide, making identification of biomarkers for detection, staging, and effective prevention strategies an especially critical scientific and medical goal. Fortunately, advances in metabolomics techniques, together with improvements in bioinformatics and mathematical modeling approaches, have provided the scientific community with new tools to describe the T2D metabolome. The metabolomics signatures associated with T2D and obesity include increased levels of lactate, glycolytic intermediates, branched-chain and aromatic amino acids, and long-chain fatty acids. Conversely, tricarboxylic acid cycle intermediates, betaine, and other metabolites decrease. Future studies will be required to fully integrate these and other findings into our understanding of diabetes pathophysiology and to identify biomarkers of disease risk, stage, and responsiveness to specific treatments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance,•• Of major importance

  1. DeFronzo RA. Banting lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):773–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tabak AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimaki M, Witte DR. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet. 2009;373(9682):2215–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Martin BC, Warram JH, Rosner B, Rich SS, Soeldner JS, Krolewski AS. Familial clustering of insulin sensitivity. Diabetes. 1992;41:850–4.

    Article  CAS  PubMed  Google Scholar 

  4. IDF. IDF diabetes atlas. Seventh ed. Brussels, Belgium: International Diabetes Federation; 2015. p. 144.

    Google Scholar 

  5. Pauling L, Robinson AB, Teranishi R, Cary P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A. 1971;68(10):2374–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rhee EP, Gerszten RE. Metabolomics and cardiovascular biomarker discovery. Clin Chem. 2012;58(1):139–47.

    Article  CAS  PubMed  Google Scholar 

  7. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research. Moving from information to knowledge. Diabetes. 2009;58:2429–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Roberts LD, Gerszten RE. Toward new biomarkers of cardiometabolic diseases. Cell Metab. 2013;18(1):43–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, et al. Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin Chem. 2015;61(3):487–97.

    Article  CAS  PubMed  Google Scholar 

  11. Park S, Sadanala KC, Kim EK. A metabolomic approach to understanding the metabolic link between obesity and diabetes. Mol Cells. 2015;38(7):587–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010;5(12):e15234.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Gogna N, Krishna M, Oommen AM, Dorai K. Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol Biosyst. 2015;11(2):595–606.

    Article  CAS  PubMed  Google Scholar 

  14. Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab. 2013;98(6):E1060–E5.

    Article  CAS  PubMed  Google Scholar 

  15. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Menni C, Fauman E, Erte I, Perry JR, Kastenmuller G, Shin SY, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62(12):4270–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, et al. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol. 2008;4:214.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Spegel P, Malmgren S, Sharoyko VV, Newsholme P, Koeck T, Mulder H. Metabolomic analyses reveal profound differences in glycolytic and tricarboxylic acid cycle metabolism in glucose-responsive and -unresponsive clonal beta-cell lines. Biochem J. 2011;435(1):277–84.

    Article  CAS  PubMed  Google Scholar 

  19. Boucher A, Lu D, Burgess SC, Telemaque-Potts S, Jensen MV, Mulder H, et al. Biochemical mechanism of lipid-induced impairment of glucose-stimulated insulin secretion and reversal with a malate analogue. J Biol Chem. 2004;279(26):27263–71.

    Article  CAS  PubMed  Google Scholar 

  20. Lu D, Mulder H, Zhao P, Burgess SC, Jensen MV, Kamzolova S, et al. 13C NMR isotopomer analysis reveals a connection between pyruvate cycling and glucose-stimulated insulin secretion (GSIS). Proc Natl Acad Sci U S A. 2002;99(5):2708–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kelley DE, Mokan M, Simoneau JA, Mandarino LJ. Interaction between glucose and free fatty acid metabolism in human skeletal muscle. J Clin Invest. 1993;92(1):91–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Koves TR, Li P, An J, Akimoto T, Slentz D, Ilkayeva O, et al. Peroxisome proliferator-activated receptor-gamma co-activator 1alpha-mediated metabolic remodeling of skeletal myocytes mimics exercise training and reverses lipid-induced mitochondrial inefficiency. J Biol Chem. 2005;280(39):33588–98.

    Article  CAS  PubMed  Google Scholar 

  23. Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O, et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008;7(1):45–56.

    Article  CAS  PubMed  Google Scholar 

  24. Giesbertz P, Padberg I, Rein D, Ecker J, Hofle AS, Spanier B, et al. Metabolite profiling in plasma and tissues of ob/ob and db/db mice identifies novel markers of obesity and type 2 diabetes. Diabetologia. 2015;58(9):2133–43.

    Article  CAS  PubMed  Google Scholar 

  25. Kowalski GM, De Souza DP, Burch ML, Hamley S, Kloehn J, Selathurai A, et al. Application of dynamic metabolomics to examine in vivo skeletal muscle glucose metabolism in the chronically high-fat fed mouse. Biochem Biophys Res Commun. 2015;462(1):27–32.

    Article  CAS  PubMed  Google Scholar 

  26. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Salek RM, Maguire ML, Bentley E, Rubtsov DV, Hough T, Cheeseman M, et al. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol Genomics. 2007;29(2):99–108.

    Article  CAS  PubMed  Google Scholar 

  28. Connor SC, Hansen MK, Corner A, Smith RF, Ryan TE. Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes. Mol Biosyst. 2010;6(5):909–21.

    Article  CAS  PubMed  Google Scholar 

  29. Adams SH, Hoppel CL, Lok KH, Zhao L, Wong SW, Minkler PE, et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J Nutr. 2009;139(6):1073–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, et al. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care. 2009;32(9):1678–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Felig P, Marliss E, Cahill Jr GF. Plasma amino acid levels and insulin secretion in obesity. N Engl J Med. 1969;281(15):811–6.

    Article  CAS  PubMed  Google Scholar 

  32. Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen M, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care. 2013;36(3):648–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448–53. This study demonstrates how a combination of three amino acids predicts future diabetes, suggesting that amino acid profiles in patients might help in assessing disease risk in susceptible individuals.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Shin AC, Fasshauer M, Filatova N, Grundell LA, Zielinski E, Zhou JY, et al. Brain insulin lowers circulating BCAA levels by inducing hepatic BCAA catabolism. Cell Metab. 2014;20(5)):898–909. This study demonstrates how elevated BCAA levels in obesity and diabetes (which can predict T2D) are reduced after insulin treatment. Moreover, brain insulin signaling is shown as a major regulator of BCAA metabolism by inducing hepatic BCKDH (rate-limiting enzyme in BCAA degradation).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yamakado M, Tanaka T, Nagao K, Ishizaka Y, Mitushima T, Tani M, et al. Plasma amino acid profile is associated with visceral fat accumulation in obese Japanese subjects. Clin Obes. 2012;2(1–2):29–40.

    Article  CAS  PubMed  Google Scholar 

  36. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ, et al. alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One. 2010;5(5):e10883.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125(18):2222–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wurtz P, Tiainen M, Makinen VP, Kangas AJ, Soininen P, Saltevo J, et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care. 2012;35(8):1749–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Stancakova A, Civelek M, Saleem NK, Soininen P, Kangas AJ, Cederberg H, et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes. 2012;61(7):1895–902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Sekhar RV, McKay SV, Patel SG, Guthikonda AP, Reddy VT, Balasubramanyam A, et al. Glutathione synthesis is diminished in patients with uncontrolled diabetes and restored by dietary supplementation with cysteine and glycine. Diabetes Care. 2011;34(1):162–7.

    Article  CAS  PubMed  Google Scholar 

  42. Ejaz A, Martinez-Guino L, Goldfine AB, Ribas-Aulinas F, De Nigris V, Ribo S, et al. Dietary betaine supplementation increases Fgf21 levels to improve glucose homeostasis and reduce hepatic lipid accumulation in mice. Diabetes. 2016;65(4):902–12. doi:10.2337/db15-1094.

  43. Walford GA, Davis J, Warner AS, Ackerman RJ, Billings LK, Chamarthi B, et al. Branched chain and aromatic amino acids change acutely following two medical therapies for type 2 diabetes mellitus. Metabolism. 2013;62(12):1772–8.

    Article  CAS  PubMed  Google Scholar 

  44. Laferrere B, Reilly D, Arias S, Swerdlow N, Gorroochurn P, Bawa B, et al. Differential metabolic impact of gastric bypass surgery versus dietary intervention in obese diabetic subjects despite identical weight loss. Sci Transl Med. 2011;3(80):80re2. doi:10.1126/scitranslmed.3002043.

  45. Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220–32.

    Article  CAS  PubMed  Google Scholar 

  46. Floyd Jr JC, Fajans SS, Conn JW, Knopf RF, Rull J. Stimulation of insulin secretion by amino acids. J Clin Invest. 1966;45(9):1487–502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. van Loon LJ, Saris WH, Verhagen H, Wagenmakers AJ. Plasma insulin responses after ingestion of different amino acid or protein mixtures with carbohydrate. Am J Clin Nutr. 2000;72(1):96–105.

    PubMed  Google Scholar 

  48. Newgard CB, Matschinsky FM. Substrate control of insulin release. Comprehensive Physiology. 2011;125–151. doi:10.1002/cphy.cp070205.

  49. Nilsson M, Holst JJ, Bjorck IM. Metabolic effects of amino acid mixtures and whey protein in healthy subjects: studies using glucose-equivalent drinks. Am J Clin Nutr. 2007;85(4):996–1004.

    CAS  PubMed  Google Scholar 

  50. Patti ME, Brambilla E, Luzi L, Landaker EJ, Kahn CR. Bidirectional modulation of insulin action by amino acids. J Clin Invest. 1998;101:1519–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014;10(12):723–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Macotela Y, Emanuelli B, Bang AM, Espinoza DO, Boucher J, Beebe K, et al. Dietary leucine—an environmental modifier of insulin resistance acting on multiple levels of metabolism. PLoS One. 2011;6(6):e21187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. She P, Reid TM, Bronson SK, Vary TC, Hajnal A, Lynch CJ, et al. Disruption of BCATm in mice leads to increased energy expenditure associated with the activation of a futile protein turnover cycle. Cell Metab. 2007;6(3):181–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Muoio DM, Newgard CB. Mechanisms of disease: molecular and metabolic mechanisms of insulin resistance and beta-cell failure in type 2 diabetes. Nat Rev Mol Cell Biol. 2008;9(3):193–205.

    Article  CAS  PubMed  Google Scholar 

  55. Morino K, Petersen KF, Shulman GI. Molecular mechanisms of insulin resistance in humans and their potential links with mitochondrial dysfunction. Diabetes. 2006;55 Suppl 2:S9–S15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Boden G, Shulman GI. Free fatty acids in obesity and type 2 diabetes: defining their role in the development of insulin resistance and beta-cell dysfunction. Eur J Clin Invest. 2002;32 Suppl 3:14–23.

    Article  CAS  PubMed  Google Scholar 

  57. Lowell BB, Shulman GI. Mitochondrial dysfunction and type 2 diabetes. Science. 2005;307(5708):384–7.

    Article  CAS  PubMed  Google Scholar 

  58. Samuel VT, Petersen KF, Shulman GI. Lipid-induced insulin resistance: unravelling the mechanism. Lancet. 2010;375(9733):2267–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Dube JJ, Amati F, Toledo FG, Stefanovic-Racic M, Rossi A, Coen P, et al. Effects of weight loss and exercise on insulin resistance, and intramyocellular triacylglycerol, diacylglycerol and ceramide. Diabetologia. 2011;54(5):1147–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Itani SI, Ruderman NB, Schmieder F, Boden G. Lipid-induced insulin resistance in human muscle is associated with changes in diacylglycerol, protein kinase C, and IkappaB-alpha. Diabetes. 2002;51(7):2005–11.

    Article  CAS  PubMed  Google Scholar 

  61. Adams JM, Pratipanawatr T, Berria R, Wang E, DeFronzo RA, Sullards MC, et al. Ceramide content is increased in skeletal muscle from obese insulin-resistant humans. Diabetes. 2004;53(1):25–31.

    Article  CAS  PubMed  Google Scholar 

  62. Oh DY, Talukdar S, Bae EJ, Imamura T, Morinaga H, Fan W, et al. GPR120 is an omega-3 fatty acid receptor mediating potent anti-inflammatory and insulin-sensitizing effects. Cell. 2010;142(5):687–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Virtanen JK, Mursu J, Voutilainen S, Uusitupa M, Tuomainen TP. Serum omega-3 polyunsaturated fatty acids and risk of incident type 2 diabetes in men: the Kuopio Ischemic Heart Disease Risk Factor study. Diabetes Care. 2014;37(1):189–96.

    Article  CAS  PubMed  Google Scholar 

  64. Cao H, Gerhold K, Mayers JR, Wiest MM, Watkins SM, Hotamisligil GS. Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell. 2008;134(6):933–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121(4):1402–11. This article shows a relationship between lipid acyl chain content and diabetes risk (lower carbon number and double bond content was linked to increased risk), suggesting lipid profiles may identify high-risk individuals.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Riserus U, Willett WC, Hu FB. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009;48(1):44–51.

    Article  CAS  PubMed  Google Scholar 

  67. Yore MM, Syed I, Moraes-Vieira PM, Zhang T, Herman MA, Homan EA, et al. Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell. 2014;159(2):318–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Illig T, Gieger C, Zhai G, Romisch-Margl W, Wang-Sattler R, Prehn C, et al. A genome-wide perspective of genetic variation in human metabolism. Nat Genet. 2010;42(2):137–41. The authors performed a GWAS of 163 metabolic traits in one large human cohort (with replication in a second one) and identified loci explaining 5.6–36.3 % of variance in metabolite concentrations.

    Article  CAS  PubMed  Google Scholar 

  69. Xie W, Wood AR, Lyssenko V, Weedon MN, Knowles JW, Alkayyali S, et al. Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes. Diabetes. 2013;62(6):2141–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Tannock GW. The normal microflora: new concepts in health promotion. Microbiol Sci. 1988;5(1):4–8.

    CAS  PubMed  Google Scholar 

  71. Menni C, Zhai G, MacGregor A, Prehn C, Romisch-Margl W, Suhre K, et al. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. 2013;9(2):506–14.

    Article  CAS  PubMed  Google Scholar 

  72. Roberts LD, Bostrom P, O’Sullivan JF, Schinzel RT, Lewis GD, Dejam A, et al. Beta-aminoisobutyric acid induces browning of white fat and hepatic beta-oxidation and is inversely correlated with cardiometabolic risk factors. Cell Metab. 2014;19(1):96–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lawton KA, Berger A, Mitchell M, Milgram KE, Evans AM, Guo L, et al. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008;9(4):383–97.

    Article  CAS  PubMed  Google Scholar 

  74. Nicholson JK, Wilson ID. Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov. 2003;2(8):668–76.

    Article  CAS  PubMed  Google Scholar 

  75. Nicholson JK, Holmes E, Lindon JC, Wilson ID. The challenges of modeling mammalian biocomplexity. Nat Biotechnol. 2004;22(10):1268–74.

    Article  CAS  PubMed  Google Scholar 

  76. Wu H, Tremaroli V, Backhed F. Linking microbiota to human diseases: a systems biology perspective. Trends Endocrinol Metab. 2015;26(12):758–70.

    Article  CAS  PubMed  Google Scholar 

  77. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480–4.

    Article  CAS  PubMed  Google Scholar 

  78. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102(31):11070–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Remely M, Dworzak S, Hippe B, Zwielehner J, Aumüller E, Brath H, et al. Abundance and diversity of microbiota in type 2 diabetes and obesity. Diabetes and Metabolism. 2013;4:3.

    Google Scholar 

  80. Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 2010;5(2):e9085.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Wu X, Ma C, Han L, Nawaz M, Gao F, Zhang X, et al. Molecular characterisation of the faecal microbiota in patients with type II diabetes. Curr Microbiol. 2010;61(1):69–78.

    Article  CAS  PubMed  Google Scholar 

  82. Zhang H, DiBaise JK, Zuccolo A, Kundrna D, Braidotti M, Yu Y, et al. Human gut microbiota in obesity and after gastric bypass. Proc Natl Acad Sci U S A. 2009;106(7):2365--70. doi: 10.1073/pnas.0812600106.

  83. Holmes E, Li JV, Athanasiou T, Ashrafian H, Nicholson JK. Understanding the role of gut microbiome-host metabolic signal disruption in health and disease. Trends Microbiol. 2011;19(7):349–59.

    Article  CAS  PubMed  Google Scholar 

  84. Patti ME, Houten SM, Bianco AC, Bernier R, Larsen PR, Holst JJ, et al. Serum bile acids are higher in humans with prior gastric bypass: potential contribution to improved glucose and lipid metabolism. Obesity (Silver Spring). 2009;17(9):1671–7. doi:10.1038/oby.2009.102.

  85. Mutch DM, Fuhrmann JC, Rein D, Wiemer JC, Bouillot JL, Poitou C, et al. Metabolite profiling identifies candidate markers reflecting the clinical adaptations associated with Roux-en-Y gastric bypass surgery. PLoS One. 2009;4(11):e7905.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Dumas ME, Barton RH, Toye A, Cloarec O, Blancher C, Rothwell A, et al. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc Natl Acad Sci U SA. 2006;103(33):12511–6.

    Article  CAS  Google Scholar 

  87. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31.

    Article  PubMed  Google Scholar 

  88. Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe. 2008;3(4):213–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Tremaroli V, Karlsson F, Werling M, Stahlman M, Kovatcheva-Datchary P, Olbers T, et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 2015;22(2):228–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Vrieze A, Van Nood E, Holleman F, Salojarvi J, Kootte RS, Bartelsman JF, et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology. 2012;143(4):913–6. e7.

    Article  CAS  PubMed  Google Scholar 

  91. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472(7341):57–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Tang WH, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X, et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med. 2013;368(17):1575–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Organ CL, Otsuka H, Bhushan S, Wang Z, Bradley J, Trivedi R, et al. Choline diet and its gut microbe-derived metabolite, trimethylamine N-oxide, exacerbate pressure overload-induced heart failure. Circ Heart Fail. 2016;9(1):e002314.

    Article  CAS  PubMed  Google Scholar 

  95. Sas KM, Karnovsky A, Michailidis G, Pennathur S. Metabolomics and diabetes: analytical and computational approaches. Diabetes. 2015;64(3):718–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Ferrara CT, Wang P, Neto EC, Stevens RD, Bain JR, Wenner BR, et al. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLoS Genet. 2008;4(3):e1000034.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol. 2010;6:401.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Gille C, Bolling C, Hoppe A, Bulik S, Hoffmann S, Hubner K, et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol. 2010;6:411.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Bordbar A, Feist AM, Usaite-Black R, Woodcock J, Palsson BO, Famili I. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst Biol. 2011;5:180.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Nogiec C, Burkart A, Dreyfuss JM, Lerin C, Kasif S, Patti ME. Metabolic modeling of muscle metabolism identifies key reactions linked to insulin resistance phenotypes. Mol Metab. 2015;4(3):151–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Shlomi T, Cabili MN, Ruppin E. Predicting metabolic biomarkers of human inborn errors of metabolism. Mol SystBiol. 2009;5:263.

    Google Scholar 

  102. Kuc S, Koster MP, Pennings JL, Hankemeier T, Berger R, Harms AC, et al. Metabolomics profiling for identification of novel potential markers in early prediction of preeclampsia. PLoS One. 2014;9(5):e98540.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Maitre L, Fthenou E, Athersuch T, Coen M, Toledano MB, Holmes E, et al. Urinary metabolic profiles in early pregnancy are associated with preterm birth and fetal growth restriction in the Rhea mother-child cohort study. BMC Med. 2014;12:110.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. de Seymour JV, Conlon CA, Sulek K, Villas Boas SG, McCowan LM, Kenny LC, et al. Early pregnancy metabolite profiling discovers a potential biomarker for the subsequent development of gestational diabetes mellitus. Acta Diabetol. 2014;51(5):887–90.

    Article  PubMed  CAS  Google Scholar 

  105. Ciborowski M, Zbucka-Kretowska M, Bomba-Opon D, Wielgos M, Brawura-Biskupski-Samaha R, Pierzynski P, et al. Potential first trimester metabolomic biomarkers of abnormal birth weight in healthy pregnancies. Prenat Diagn. 2014;34(9):870–7.

    Article  CAS  PubMed  Google Scholar 

  106. Heazell AE, Bernatavicius G, Warrander L, Brown MC, Dunn WB. A metabolomic approach identifies differences in maternal serum in third trimester pregnancies that end in poor perinatal outcome. Reprod Sci. 2012;19(8):863–75.

    Article  PubMed  CAS  Google Scholar 

  107. Bahado-Singh RO, Akolekar R, Mandal R, Dong E, Xia J, Kruger M, et al. First-trimester metabolomic detection of late-onset preeclampsia. Am J Obstet Gynecol. 2013;208(1):58–e1-7.

    PubMed  Google Scholar 

  108. Horgan RP, Broadhurst DI, Walsh SK, Dunn WB, Brown M, Roberts CT, et al. Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. J Proteome Res. 2011;10(8):3660–73.

    Article  CAS  PubMed  Google Scholar 

  109. Huynh J, Xiong G, Bentley-Lewis R. A systematic review of metabolite profiling in gestational diabetes mellitus. Diabetologia. 2014;57(12):2453–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Metzger BE, Phelps RL, Freinkel N, Navickas IA. Effects of gestational diabetes on diurnal profiles of plasma glucose, lipids, and individual amino acids. Diabetes Care. 1980;3(3):402–9.

    Article  CAS  PubMed  Google Scholar 

  111. Scholtens DM, Muehlbauer MJ, Daya NR, Stevens RD, Dyer AR, Lowe LP, et al. Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care. 2014;37(1):158–66.

    Article  CAS  PubMed  Google Scholar 

  112. Pappa KI, Vlachos G, Theodora M, Roubelaki M, Angelidou K, Antsaklis A. Intermediate metabolism in association with the amino acid profile during the third trimester of normal pregnancy and diet-controlled gestational diabetes. Am J Obstet Gynecol. 2007;196(1):65–e1-5.

    Article  PubMed  CAS  Google Scholar 

  113. Diaz SO, Pinto J, Graca G, Duarte IF, Barros AS, Galhano E, et al. Metabolic biomarkers of prenatal disorders: an exploratory NMR metabonomics study of second trimester maternal urine and blood plasma. J Proteome Res. 2011;10(8):3732–42.

    Article  CAS  PubMed  Google Scholar 

  114. Dudzik D, Zorawski M, Skotnicki M, Zarzycki W, Kozlowska G, Bibik-Malinowska K, et al. Metabolic fingerprint of gestational diabetes mellitus. J Proteomics. 2014;103:57–71.

    Article  CAS  PubMed  Google Scholar 

  115. Pinto J, Almeida LM, Martins AS, Duarte D, Barros AS, Galhano E, et al. Prediction of gestational diabetes through NMR metabolomics of maternal blood. J Proteome Res. 2015;14(6):2696–706.

    Article  CAS  PubMed  Google Scholar 

  116. Cetin I, de Santis MS, Taricco E, Radaelli T, Teng C, Ronzoni S, et al. Maternal and fetal amino acid concentrations in normal pregnancies and in pregnancies with gestational diabetes mellitus. Am J Obstet Gynecol. 2005;192(2):610–7.

    Article  CAS  PubMed  Google Scholar 

  117. Lehmann R, Friedrich T, Krebiehl G, Sonntag D, Haring HU, Fritsche A, et al. Metabolic profiles during an oral glucose tolerance test in pregnant women with and without gestational diabetes. Exp Clin Endocrinol Diabetes. 2015;123(7):483–38.

    Article  CAS  PubMed  Google Scholar 

  118. Prentice KJ, Luu L, Allister EM, Liu Y, Jun LS, Sloop KW, et al. The furan fatty acid metabolite CMPF is elevated in diabetes and induces beta cell dysfunction. Cell Metab. 2014;19(4):653–66.

    Article  CAS  PubMed  Google Scholar 

  119. Dani C, Bresci C, Berti E, Ottanelli S, Mello G, Mecacci F, et al. Metabolomic profile of term infants of gestational diabetic mothers. J Matern Fetal Neonatal Med. 2014;27(6):537–42.

    Article  CAS  PubMed  Google Scholar 

  120. Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008;359(1):61–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Wang G, Divall S, Radovick S, Paige D, Ning Y, Chen Z, et al. Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA. 2014;311(6):587–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Dessi A, Atzori L, Noto A, Visser GH, Gazzolo D, Zanardo V, et al. Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome. J Matern Fetal Neonatal Med. 2011;24 Suppl 2:35–9.

    Article  PubMed  CAS  Google Scholar 

  123. Ivorra C, Garcia-Vicent C, Chaves FJ, Monleon D, Morales JM, Lurbe E. Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med. 2012;10:142. doi:10.1186/1479-5876-10-142.

  124. Alexandre-Gouabau MC, Courant F, Moyon T, Kuster A, Le GG, Tea I, et al. Maternal and cord blood LC-HRMS metabolomics reveal alterations in energy and polyamine metabolism, and oxidative stress in very-low birth weight infants. J Proteome Res. 2013;12(6):2764–78.

    Article  CAS  PubMed  Google Scholar 

  125. Leunissen RW, Kerkhof GF, Stijnen T, Hokken-Koelega A. Timing and tempo of first-year rapid growth in relation to cardiovascular and metabolic risk profile in early adulthood. JAMA. 2009;301(21):2234–42.

    Article  CAS  PubMed  Google Scholar 

  126. Barker DJ, Osmond C, Forsen TJ, Kajantie E, Eriksson JG. Trajectories of growth among children who have coronary events as adults. New England Journal of Medicine. 2005;353(17):1802–9.

    Article  CAS  PubMed  Google Scholar 

  127. Isganaitis E, Rifas-Shiman SL, Oken E, Dreyfuss JM, Gall W, Gillman MW, et al. Associations of cord blood metabolites with early childhood obesity risk. Int J Obes (Lond). 2015;39(7):1041–8.

    Article  CAS  Google Scholar 

  128. Rzehak P, Hellmuth C, Uhl O, Kirchberg FF, Peissner W, Harder U, et al. Rapid growth and childhood obesity are strongly associated with lysoPC(14:0). Ann Nutr Metab. 2014;64(3–4):294–303.

    Article  PubMed  CAS  Google Scholar 

  129. Stevens A, Bonshek C, Whatmore A, Butcher I, Hanson D, De Leonibus C, et al. Insights into the pathophysiology of catch-up compared with non-catch-up growth in children born small for gestational age: an integrated analysis of metabolic and transcriptomic data. Pharmacogenomics J. 2014;14(4):376–84.

    Article  CAS  PubMed  Google Scholar 

  130. Perng W, Gillman MW, Fleisch AF, Michalek RD, Watkins SM, Isganaitis E, et al. Metabolomic profiles and childhood obesity. Obesity (Silver Spring). 2014;22(12):2570–8.

    CAS  Google Scholar 

  131. Mihalik SJ, Michaliszyn SF, de las Heras J, Bacha F, Chace DH, et al. Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care. 2012;35(3):605–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Butte NF, Liu Y, Zakeri IF, Mohney RP, Mehta N, Voruganti VS, et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am J Clin Nutr. 2015;102(2):256–67.

    Article  CAS  PubMed  Google Scholar 

  133. McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, et al. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes. 2013;8(1):52–61.

    Article  CAS  PubMed  Google Scholar 

  134. Wahl S, Yu Z, Kleber M, Singmann P, Holzapfel C, He Y, et al. Childhood obesity is associated with changes in the serum metabolite profile. Obes Facts. 2012;5(5):660–70.

    Article  CAS  PubMed  Google Scholar 

  135. Michaliszyn SF, Sjaarda LA, Mihalik SJ, Lee S, Bacha F, Chace DH, et al. Metabolomic profiling of amino acids and beta-cell function relative to insulin sensitivity in youth. J Clin Endocrinol Metab. 2012;97(11):E2119–E24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

AG-F gratefully acknowledges support from the American Diabetes Association (mentored research fellowship to MEP). AMB acknowledges research support from the NIH (T32 DK 007260) and the Harold Whitworth Pierce Charitable Trust Postdoctoral Fellowship. EI acknowledges grant support from K99R00 HD064793 and Boston Nutrition and Obesity Research Center. MEP acknowledges research support from the NIH, American Diabetes Association, Bristol-Myers Squibb, Janssen, Nuclea, Medimmune, and Joslin DRC grant P30 DK036836.

Author information

Authors and Affiliations

  1. Research Division, 1 Joslin Place, Boston, MA, 02215, USA

    Alba Gonzalez-Franquesa, Alison M. Burkart, Elvira Isganaitis & Mary-Elizabeth Patti

Authors
  1. Alba Gonzalez-Franquesa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alison M. Burkart
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elvira Isganaitis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mary-Elizabeth Patti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mary-Elizabeth Patti.

Ethics declarations

Conflict of Interest

Alba Gonzalez-Franquesa, Alison M. Burkart, Elvira Isganaitis, and Mary-Elizabeth Patti declare no conflict of interest related to this manuscript.

Human and Animal Rights and Informed Consent

This article does not report any new studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical Collection on Pathogenesis of Type 2 Diabetes and Insulin Resistance

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gonzalez-Franquesa, A., Burkart, A.M., Isganaitis, E. et al. What Have Metabolomics Approaches Taught Us About Type 2 Diabetes?. Curr Diab Rep 16, 74 (2016). https://doi.org/10.1007/s11892-016-0763-1

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11892-016-0763-1

Keywords

Search

Navigation