Skip to main content
SpringerLink
Log in

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

  • Macrovascular Complications in Diabetes (R SHAH, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

Recent findings

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses.

Summary

Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

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. Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50.

    Article  CAS  PubMed  Google Scholar 

  2. Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes. 2017;66(2):241–55.

    Article  CAS  PubMed  Google Scholar 

  3. 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 

  4. 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 

  5. Cheng S, Shah SH, Corwin EJ, Fiehn O, Fitzgerald RL, Gerszten RE, et al. Potential impact and study considerations of metabolomics in cardiovascular health and disease: a scientific statement from the American Heart Association. Circ Cardiovasc Genet. 2017;10(2):e000032.

  6. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Walford GA, Ma Y, Clish C, Florez JC, Wang TJ, Gerszten RE, et al. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes. 2016;65(5):1424–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yamakado M, Nagao K, Imaizumi A, Tani M, Toda A, Tanaka T, et al. Plasma free amino acid profiles predict four-year risk of developing diabetes, metabolic syndrome, dyslipidemia, and hypertension in Japanese population. Sci Rep. 2015;5:11918.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ferrannini E, Natali A, Camastra S, Nannipieri M, Mari A, Adam KP, et al. Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes. 2013;62(5):1730–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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 

  12. Shah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C, et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet. 2010;3(2):207–14.

    Article  CAS  PubMed  Google Scholar 

  13. Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engstrom G, et al. A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J. 2013;34(26):1982–9.

    Article  CAS  PubMed  Google Scholar 

  14. White PJ, Newgard CB. Branched-chain amino acids in disease. Science. 2019;363(6427):582–3.

    Article  CAS  PubMed  Google Scholar 

  15. Zhao H, Zhang F, Sun D, Wang X, Zhang X, Zhang J, et al. Branched-chain amino acids exacerbate obesity-related hepatic glucose and lipid metabolic disorders via attenuating Akt2 signaling. Diabetes. 2020;69(6):1164–77.

    Article  CAS  PubMed  Google Scholar 

  16. •• White PJ, McGarrah RW, Grimsrud PA, Tso SC, Yang WH, Haldeman JM, et al. The BCKDH kinase and phosphatase integrate BCAA and lipid metabolism via regulation of ATP-citrate lyase. Cell Metab. 2018;27(6):1281–93 e7. This study demonstrated that branched-chain ketoacid dehydrogenase is an important regulator of the effects of BCAAs on glucose tolerance and elucidated some of the mechanisms responsible for the association of BCAAs and diabetes risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Qureshi W, Santaren ID, Hanley AJ, Watkins SM, Lorenzo C, Wagenknecht LE. Risk of diabetes associated with fatty acids in the de novo lipogenesis pathway is independent of insulin sensitivity and response: the Insulin Resistance Atherosclerosis Study (IRAS). BMJ Open Diabetes Res Care. 2019;7(1):e000691.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lotta LA, Scott RA, Sharp SJ, Burgess S, Luan J, Tillin T, et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLoS Med. 2016;13(11):e1002179.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wopereis S, Rubingh CM, van Erk MJ, Verheij ER, van Vliet T, Cnubben NH, et al. Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes. PLoS One. 2009;4(2):e4525.

    Article  PubMed  PubMed Central  Google Scholar 

  20. 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 

  21. •• Robbins JM, Herzig M, Morningstar J, Sarzynski MA, Cruz DE, Wang TJ, et al. Association of dimethylguanidino valeric acid with partial resistance to metabolic health benefits of regular exercise. JAMA Cardiol. 2019;4(7):636–43. A study characterizing the response of the metabolite DMGV to exercise training.

    Article  PubMed  PubMed Central  Google Scholar 

  22. •• Nayor M, Shah RV, Miller PE, Blodgett JB, Tanguay M, Pico AR, et al. Metabolic architecture of acute exercise response in middle-aged adults in the community. Circulation. 2020;142(20):1905–24. This study described the response of the circulating metabolome to exercise, demonstrating favorable effects on broad metabolic pathways.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee HJ, Jang HB, Kim WH, Park KJ, Kim KY, Park SI, et al. 2-Aminoadipic acid (2-AAA) as a potential biomarker for insulin resistance in childhood obesity. Sci Rep. 2019;9(1):13610.

    Article  PubMed  PubMed Central  Google Scholar 

  24. •• Li Q, Parikh H, Butterworth MD, Lernmark A, Hagopian W, Rewers M, et al. Longitudinal metabolome-wide signals prior to the appearance of a first islet autoantibody in children participating in the TEDDY study. Diabetes. 2020;69(3):465–76. Another study describing distinct metabolic signatures that can be identified prior to autoantibody formation in type 1 diabetes. Here, the investigators compared different types of autoantibodies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. •• Curovic VR, Suvitaival T, Mattila I, Ahonen L, Trost K, Theilade S, et al. Circulating metabolites and lipids are associated to diabetic retinopathy in individuals with type 1 diabetes. Diabetes. 2020;69(10):2217–26. A large, prospective study of individuals with type 1 diabetes, demonstrating novel metabolite associations with diabetic retinopathy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  27. Liu X, Zheng Y, Guasch-Ferré M, Ruiz-Canela M, Toledo E, Clish C, et al. High plasma glutamate and low glutamine-to-glutamate ratio are associated with type 2 diabetes: case-cohort study within the PREDIMED trial. Nutr Metab Cardiovasc Dis. 2019;29(10):1040–9.

    Article  CAS  PubMed  Google Scholar 

  28. Fahien LA, Macdonald MJ. The complex mechanism of glutamate dehydrogenase in insulin secretion. Diabetes. 2011;60(10):2450–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016;39(5):833–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Crawford SO, Hoogeveen RC, Brancati FL, Astor BC, Ballantyne CM, Schmidt MI, et al. Association of blood lactate with type 2 diabetes: the Atherosclerosis Risk in Communities Carotid MRI Study. Int J Epidemiol. 2010;39(6):1647–55.

    Article  PubMed  PubMed Central  Google Scholar 

  31. DiGirolamo M, Newby FD, Lovejoy J. Lactate production in adipose tissue: a regulated function with extra-adipose implications. FASEB J. 1992;6(7):2405–12.

    Article  CAS  PubMed  Google Scholar 

  32. O’Sullivan JF, Morningstar JE, Yang Q, Zheng B, Gao Y, Jeanfavre S, et al. Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes. J Clin Invest. 2017;127(12):4394–402.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ottosson F, Ericson U, Almgren P, Smith E, Brunkwall L, Hellstrand S, et al. Dimethylguanidino valerate: a lifestyle-related metabolite associated with future coronary artery disease and cardiovascular mortality. J Am Heart Assoc. 2019;8(19):e012846.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 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 

  35. Wang TJ, Ngo D, Psychogios N, Dejam A, Larson MG, Vasan RS, et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest. 2013;123(10):4309–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Aslam M, Aggarwal S, Sharma KK, Galav V, Madhu SV. Postprandial hypertriglyceridemia predicts development of insulin resistance glucose intolerance and type 2 diabetes. PLoS One. 2016;11(1):e0145730.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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 

  39. 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  Google Scholar 

  40. Cole LK, Vance JE, Vance DE. Phosphatidylcholine biosynthesis and lipoprotein metabolism. Biochim Biophys Acta. 2012;1821(5):754–61.

    Article  CAS  PubMed  Google Scholar 

  41. Dutta T, Chai HS, Ward LE, Ghosh A, Persson XM, Ford GC, et al. Concordance of changes in metabolic pathways based on plasma metabolomics and skeletal muscle transcriptomics in type 1 diabetes. Diabetes. 2012;61(5):1004–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. •• Lamichhane S, Kemppainen E, Trost K, Siljander H, Hyoty H, Ilonen J, et al. Circulating metabolites in progression to islet autoimmunity and type 1 diabetes. Diabetologia. 2019;62(12):2287–97. A comprehensive report of changes in metabolites preceding the development of type 1 diabetes, supporting the hypothesis that more widespread metabolic dysfunction is responsible for type 1 diabetes pathogenesis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lamichhane S, Ahonen L, Dyrlund TS, Kemppainen E, Siljander H, Hyoty H, et al. Dynamics of plasma lipidome in progression to islet autoimmunity and type 1 diabetes - Type 1 Diabetes Prediction and Prevention Study (DIPP). Sci Rep. 2018;8(1):10635.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Oresic M, Simell S, Sysi-Aho M, Näntö-Salonen K, Seppänen-Laakso T, Parikka V, et al. Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J Exp Med. 2008;205(13):2975–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Eid S, Sas KM, Abcouwer SF, Feldman EL, Gardner TW, Pennathur S, et al. New insights into the mechanisms of diabetic complications: role of lipids and lipid metabolism. Diabetologia. 2019;62(9):1539–49.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Patel A, MacMahon S, Chalmers J, Neal B, Billot L, Woodward M, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560–72.

    Article  CAS  PubMed  Google Scholar 

  47. Reaven PD, Emanuele NV, Wiitala WL, Bahn GD, Reda DJ, McCarren M, et al. Intensive glucose control in patients with type 2 diabetes - 15-year follow-up. N Engl J Med. 2019;380(23):2215–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hou XW, Wang Y, Pan CW. Metabolomics in diabetic retinopathy: a systematic review. Invest Ophthalmol Vis Sci. 2021;62(10):4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yun JH, Kim JM, Jeon HJ, Oh T, Choi HJ, Kim BJ. Metabolomics profiles associated with diabetic retinopathy in type 2 diabetes patients. PLoS One. 2020;15(10):e0241365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sugai M, Ohta A, Ogata Y, Nakanishi M, Ueno S, Kawata T, et al. Asymmetric dimethylarginine (ADMA) in the aqueous humor of diabetic patients. Endocr J. 2007;54(2):303–9.

    Article  CAS  PubMed  Google Scholar 

  51. Malecki MT, Undas A, Cyganek K, Mirkiewicz-Sieradzka B, Wolkow P, Osmenda G, et al. Plasma asymmetric dimethylarginine (ADMA) is associated with retinopathy in type 2 diabetes. Diabetes Care. 2007;30(11):2899–901.

    Article  PubMed  Google Scholar 

  52. Sumarriva K, Uppal K, Ma C, Herren DJ, Wang Y, Chocron IM, et al. Arginine and carnitine metabolites are altered in diabetic retinopathy. Invest Ophthalmol Vis Sci. 2019;60(8):3119–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Patel C, Rojas M, Narayanan SP, Zhang W, Xu Z, Lemtalsi T, et al. Arginase as a mediator of diabetic retinopathy. Front Immunol. 2013;4:173.

    Article  PubMed  PubMed Central  Google Scholar 

  54. •• Welsh P, Rankin N, Li Q, Mark PB, Wurtz P, Ala-Korpela M, et al. Circulating amino acids and the risk of macrovascular, microvascular and mortality outcomes in individuals with type 2 diabetes: results from the ADVANCE trial. Diabetologia. 2018;61(7):1581–91. A substudy of the ADVANCE trial demonstrating associations of baseline amino acid levels with long-term microvascular and macrovascular outcomes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rhee SY, Jung ES, Suh DH, Jeong SJ, Kim K, Chon S, et al. Plasma amino acids and oxylipins as potential multi-biomarkers for predicting diabetic macular edema. Sci Rep. 2021;11(1):9727.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zuo J, Lan Y, Hu H, Hou X, Li J, Wang T, et al. Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2021;9(1):e001443.

  57. Zhu XR, Yang FY, Lu J, Zhang HR, Sun R, Zhou JB, et al. Plasma metabolomic profiling of proliferative diabetic retinopathy. Nutr Metab (Lond). 2019;16:37.

    Article  Google Scholar 

  58. Rhee SY, Jung ES, Park HM, Jeong SJ, Kim K, Chon S, et al. Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy. Metabolomics. 2018;14(7):89.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Chen L, Cheng CY, Choi H, Ikram MK, Sabanayagam C, Tan GS, et al. Plasma metabonomic profiling of diabetic retinopathy. Diabetes. 2016;65(4):1099–108.

    Article  CAS  PubMed  Google Scholar 

  60. Kwon B, Lee HK, Querfurth HW. Oleate prevents palmitate-induced mitochondrial dysfunction, insulin resistance and inflammatory signaling in neuronal cells. Biochim Biophys Acta. 2014;1843(7):1402–13.

    Article  CAS  PubMed  Google Scholar 

  61. Wiggin TD, Sullivan KA, Pop-Busui R, Amato A, Sima AA, Feldman EL. Elevated triglycerides correlate with progression of diabetic neuropathy. Diabetes. 2009;58(7):1634–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Fridman V, Zarini S, Sillau S, Harrison K, Bergman BC, Feldman EL, et al. Altered plasma serine and 1-deoxydihydroceramide profiles are associated with diabetic neuropathy in type 2 diabetes and obesity. J Diabetes Complicat. 2021;35(4):107852.

    Article  CAS  Google Scholar 

  63. Rumora AE, Guo K, Alakwaa FM, Andersen ST, Reynolds EL, Jorgensen ME, et al. Plasma lipid metabolites associate with diabetic polyneuropathy in a cohort with type 2 diabetes. Ann Clin Transl Neurol. 2021;8(6):1292–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. O’Brien PD, Guo K, Eid SA, Rumora AE, Hinder LM, Hayes JM, et al. Integrated lipidomic and transcriptomic analyses identify altered nerve triglycerides in mouse models of prediabetes and type 2 diabetes. Dis Model Mech. 2020;13(2):dmm042101.

  65. Freeman OJ, Unwin RD, Dowsey AW, Begley P, Ali S, Hollywood KA, et al. Metabolic dysfunction is restricted to the sciatic nerve in experimental diabetic neuropathy. Diabetes. 2016;65(1):228–38.

    Article  CAS  PubMed  Google Scholar 

  66. Duran AM, Salto LM, Camara J, Basu A, Paquien I, Beeson WL, et al. Effects of omega-3 polyunsaturated fatty-acid supplementation on neuropathic pain symptoms and sphingosine levels in Mexican-Americans with type 2 diabetes. Diabetes Metab Syndr Obes. 2019;12:109–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Reidy K, Kang HM, Hostetter T, Susztak K. Molecular mechanisms of diabetic kidney disease. J Clin Invest. 2014;124(6):2333–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Roointan A, Gheisari Y, Hudkins KL, Gholaminejad A. Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: meta-analysis of profiling metabolomics studies. Nutr Metab Cardiovasc Dis. 2021;31(8):2253–72.

    Article  CAS  PubMed  Google Scholar 

  69. Qi W, Keenan HA, Li Q, Ishikado A, Kannt A, Sadowski T, et al. Pyruvate kinase M2 activation may protect against the progression of diabetic glomerular pathology and mitochondrial dysfunction. Nat Med. 2017;23(6):753–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. •• Gordin D, Shah H, Shinjo T, St-Louis R, Qi W, Park K, et al. Characterization of glycolytic enzymes and pyruvate kinase M2 in type 1 and 2 diabetic nephropathy. Diabetes Care. 2019;42(7):1263–73. A translational investigation in plasma and kidney biopsy samples demonstrating that upregulation of glycolytic enzymes is associated with preservation of kidney function in both type 1 and type 2 diabetes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhang F, Guo R, Cui W, Wang L, Xiao J, Shang J, et al. Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren Fail. 2021;43(1):980–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Afshinnia F, Rajendiran TM, Wernisch S, Soni T, Jadoon A, Karnovsky A, et al. Lipidomics and biomarker discovery in kidney disease. Semin Nephrol. 2018;38(2):127–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. •• Mutter S, Valo E, Aittomaki V, Nybo K, Raivonen L, Thorn LM, et al. Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes. Diabetologia. 2022;65(1):140–9. A prospective study of the association of urinary metabolites with the development of future diabetic nephropathy in >2000 individuals with type 1 diabetes.

    Article  CAS  PubMed  Google Scholar 

  74. Ekstrand AV, Groop PH, Grönhagen-Riska C. Insulin resistance precedes microalbuminuria in patients with insulin-dependent diabetes mellitus. Nephrol Dial Transplant. 1998;13(12):3079–83.

    Article  CAS  PubMed  Google Scholar 

  75. Action to Control Cardiovascular Risk in Diabetes Study G, Gerstein HC, Miller ME, Byington RP, Goff DC Jr, Bigger JT, et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545–59.

    Article  Google Scholar 

  76. Group AC, Patel A, MacMahon S, Chalmers J, Neal B, Billot L, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560–72.

    Article  Google Scholar 

  77. Haas AV, McDonnell ME. Pathogenesis of cardiovascular disease in diabetes. Endocrinol Metab Clin N Am. 2018;47(1):51–63.

    Article  Google Scholar 

  78. Ottosson F, Smith E, Gallo W, Fernandez C, Melander O. Purine metabolites and carnitine biosynthesis intermediates are biomarkers for incident type 2 diabetes. J Clin Endocrinol Metab. 2019;104(10):4921–30.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ottosson F, Smith E, Fernandez C, Melander O. Plasma metabolites associate with all-cause mortality in individuals with type 2 diabetes. Metabolites. 2020;10(8):315.

  80. Ottosson F, Smith E, Melander O, Fernandez C. Altered asparagine and glutamate homeostasis precede coronary artery disease and type 2 diabetes. J Clin Endocrinol Metab. 2018;103(8):3060–9.

    Article  PubMed  Google Scholar 

  81. Contrepois K, Wu S, Moneghetti KJ, Hornburg D, Ahadi S, Tsai MS, et al. Molecular choreography of acute exercise. Cell. 2020;181(5):1112–30. e16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ho JE, Larson MG, Vasan RS, Ghorbani A, Cheng S, Rhee EP, et al. Metabolite profiles during oral glucose challenge. Diabetes. 2013;62(8):2689–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Morris C, O’Grada C, Ryan M, Roche HM, Gibney MJ, Gibney ER, et al. Identification of differential responses to an oral glucose tolerance test in healthy adults. PLoS One. 2013;8(8):e72890.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Fiamoncini J, Rundle M, Gibbons H, Thomas EL, Geillinger-Kastle K, Bunzel D, et al. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements. FASEB J. 2018;32(10):5447–58.

    Article  CAS  PubMed  Google Scholar 

  85. •• Li-Gao R, Hughes DA, van Klinken JB, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, et al. Genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Diabetes. 2021;70(12):2932–46. Genetic analyses of the individual responses to a mixed meal tolerance test, identifying genetic variants associated with the post-meal response to lipid handling.

    Article  CAS  PubMed  Google Scholar 

  86. Shah SH, Crosslin DR, Haynes CS, Nelson S, Turer CB, Stevens RD, et al. Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia. 2012;55(2):321–30.

    Article  CAS  PubMed  Google Scholar 

  87. •• Chen ZZ, Liu J, Morningstar J, Heckman-Stoddard BM, Lee CG, Dagogo-Jack S, et al. Metabolite profiles of incident diabetes and heterogeneity of treatment effect in the Diabetes Prevention Program. Diabetes. 2019;68(12):2337–49. A report from the Diabetes Prevention Program illustrating how heterogeneity in metabolic profiles may predict response to pharmacologic versus lifestyle treatments.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Shantavasinkul PC, Muehlbauer MJ, Bain JR, Ilkayeva OR, Craig DM, Newgard CB, et al. Improvement in insulin resistance after gastric bypass surgery is correlated with a decline in plasma 2-hydroxybutyric acid. Surg Obes Relat Dis. 2018;14(8):1126–32.

    Article  PubMed  PubMed Central  Google Scholar 

  89. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Chung WK, Erion K, Florez JC, Hattersley AT, Hivert MF, Lee CG, et al. Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2020;63(9):1671–93.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Zhou G, Myers R, Li Y, Chen Y, Shen X, Fenyk-Melody J, et al. Role of AMP-activated protein kinase in mechanism of metformin action. J Clin Invest. 2001;108(8):1167–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. den Ouden H, Pellis L, Rutten G, Geerars-van Vonderen IK, Rubingh CM, van Ommen B, et al. Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes. Metabolomics. 2016;12:27.

    Article  Google Scholar 

  93. Dahabiyeh LA, Mujammami M, Arafat T, Benabdelkamel H, Alfadda AA, Abdel Rahman AM. A metabolic pattern in healthy subjects given a single dose of metformin: a metabolomics approach. Front Pharmacol. 2021;12:705932.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Kappel BA, Lehrke M, Schutt K, Artati A, Adamski J, Lebherz C, et al. Effect of empagliflozin on the metabolic signature of patients with type 2 diabetes mellitus and cardiovascular disease. Circulation. 2017;136(10):969–72.

    Article  CAS  PubMed  Google Scholar 

  95. Mulder S, Hammarstedt A, Nagaraj SB, Nair V, Ju W, Hedberg J, et al. A metabolomics-based molecular pathway analysis of how the sodium-glucose co-transporter-2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes. Diabetes Obes Metab. 2020;22(7):1157–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Packer M. Autophagy-dependent and -independent modulation of oxidative and organellar stress in the diabetic heart by glucose-lowering drugs. Cardiovasc Diabetol. 2020;19(1):62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Dutta T, Kudva YC, Persson XM, Schenck LA, Ford GC, Singh RJ, et al. Impact of long-term poor and good glycemic control on metabolomics alterations in type 1 diabetic people. J Clin Endocrinol Metab. 2016;101(3):1023–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Knebel B, Strassburger K, Szendroedi J, Kotzka J, Scheer M, Nowotny B, et al. Specific metabolic profiles and their relationship to insulin resistance in recent-onset type 1 and type 2 diabetes. J Clin Endocrinol Metab. 2016;101(5):2130–40.

    Article  CAS  PubMed  Google Scholar 

  99. Nayor M, Brown KJ, Vasan RS. The molecular basis of predicting atherosclerotic cardiovascular disease risk. Circ Res. 2021;128(2):287–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Nayor M, Shah SH, Murthy V, Shah RV. Molecular aspects of lifestyle and environmental effects in patients with diabetes: JACC Focus Seminar. J Am Coll Cardiol. 2021;78(5):481–95.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

Dr. Nayor is supported by NIH grants K23-HL138260 and R01-HL156975 and from a Career Investment Award from the Department of Medicine, Boston University School of Medicine.

Author information

Authors and Affiliations

  1. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Daniel Gonzalez Izundegui

  2. Sections of Cardiology and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA

    Matthew Nayor

Authors
  1. Daniel Gonzalez Izundegui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Nayor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Nayor.

Ethics declarations

Human and Animal Rights

All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Macrovascular Complications in Diabetes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Izundegui, D.G., Nayor, M. Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms. Curr Diab Rep 22, 65–76 (2022). https://doi.org/10.1007/s11892-022-01449-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11892-022-01449-0

Keywords

Search

Navigation