Indian Journal of Medical Biochemistry

Register      Login

VOLUME 28 , ISSUE 2 ( May-August, 2024 ) > List of Articles


Unveiling the Significance of Surrogate Markers of Insulin Resistance in Metabolic Health Assessment

Najmunnissa, Kishorkumar M Guruswamy, Jadeppa Gowda, NK Swetha, Akila Prashant, Suma M Nataraj

Keywords : Adiponectin, Homeostatic model assessment-insulin resistance, Insulin resistance, Metabolic health assessment, Surrogate markers, Triglycerides and glucose index

Citation Information : Najmunnissa, Guruswamy KM, Gowda J, Swetha N, Prashant A, Nataraj SM. Unveiling the Significance of Surrogate Markers of Insulin Resistance in Metabolic Health Assessment. Indian J Med Biochem 2024; 28 (2):45-53.

DOI: 10.5005/jp-journals-10054-0227

License: CC BY-NC 4.0

Published Online: 18-05-2024

Copyright Statement:  Copyright © 2024; The Author(s).


Recent years have evidenced an alarming increase in the incidence of diabetes mellitus (DM) and other metabolic disorders. Rapid urbanization and lifestyle changes have been the major factors for this increase. Early diagnosis is the key to better risk stratification and prompt management of these patients. Insulin resistance (IR) plays a pivotal role in the pathogenesis of various metabolic disorders. Assessing the IR in the initial stages would therefore help in early detection of patients who are susceptible to metabolic disorders. The hyperinsulinemic-euglycemic clamp technique has been the gold standard method for assessing IR. The major limitation of this technique is it is invasive and requires a specialized setup. Hence, identifying reliable surrogate markers for assessing IR is the need of the hour both in clinical and research settings. This review delves into the current knowledge of surrogate markers utilized to assess IR, providing a comprehensive overview of their strengths, limitations, and emerging trends. We explore commonly employed surrogate markers such as fasting insulin, homeostatic model assessment-insulin resistance (HOMA-IR), adiponectin, triglyceride-to-glucose index, etc. The search for accurate and cost-effective surrogate markers holds significant promise for early detection, risk stratification, and targeted interventions. This review aims to contribute to the existing knowledge on IR and highlight future directions in the quest for effective markers for IR.

PDF Share
  1. Hardy OT, Czech MP, Corvera S. What causes the insulin resistance underlying obesity? Curr Opin Endocrinol Diabetes Obes; 2012;19(2):81–87. DOI: 10.1097/MED.0b013e3283514e13.
  2. Bikov A, Frent SM, Meszaros M, et al. Triglyceride-glucose index in non-diabetic, non-obese patients with obstructive sleep apnoea. J Clin Med 2021;10(9):1932. DOI: 10.3390/jcm10091932.
  3. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am J Physiol Endocrinol Metab Gastrointest Physiol 1979;6:163–237. DOI: 10.1152/ajpendo.1979.237.3.E214.
  4. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 2008;6(4):299–304. DOI: 10.1089/met.2008.0034.
  5. Tabassum M, Mozaffor M, Rahman MM, et al. Triglycerides and glucose index as potential marker of metabolic syndrome. Int J Hum Heal Sci 2021;5(1):85–89. DOI:
  6. Khan SH, Sobia F, Niazi NK, et al. Metabolic clustering of risk factors: Evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr 2018;10:74. DOI: 10.1186/s13098-018-0376-8.
  7. Sesti G. Pathophysiology of insulin resistance. Best Pract Res Clin Endocrinol Metab 2006;20(4):665–679. DOI:
  8. Unger G, Benozzi SF, Perruzza F, et al. Triglycerides and glucose index: A useful indicator of insulin resistance. Endocrinol Nutr 2014;61(10):533–540. DOI: 10.1016/j.endonu.2014.06.009.
  9. Minokoshi Y, Kahn CR, Kahn BB. Tissue-specific ablation of the GLUT4 glucose transporter or the insulin receptor challenges assumptions about insulin action and glucose homeostasis. J Biol Chem 2003;278(36):33609–33612. DOI: 10.1074/jbc.R300019200.
  10. Hanhineva K, Törrönen R, Bondia-Pons I, et al. Impact of dietary polyphenols on carbohydrate metabolism. Int J Mol Sci 2010;11(4):1365–1402. DOI: 10.3390/ijms11041365.
  11. Bays H, Mandarino L, DeFronzo RA. Role of the adipocyte, free fatty acids, and ectopic fat in pathogenesis of type 2 diabetes mellitus: Peroxisomal proliferator-activated receptor agonists provide a rational therapeutic approach. J Clin Endocrinol Metab 2004;89(2):463–478. DOI:
  12. Dandona P, Aljada A, Chaudhuri A, et al. Metabolic syndrome: A comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation 2005;111(11):1448–1454. DOI: 10.1161/01.CIR.0000158483.13093.9D.
  13. Bhoi SK, Kalita J, Misra UK. Metabolic syndrome and insulin resistance in migraine. J Headache Pain 2012;13(4):321–326. DOI: 10.1007/s10194-012-0416-y.
  14. Roberts CK, Hevener AL, Barnard RJ. Metabolic syndrome and insulin resistance: Underlying causes and modification by exercise training. Compr Physiol 2013;3(1):1–58. DOI: 10.1002/cphy.c110062.
  15. Di Pino A, Defronzo RA. Insulin resistance and atherosclerosis: Implications for insulin-sensitizing agents. Endocr Rev 2019;40(6):1447–1467. DOI: 10.1210/er.2018-00141.
  16. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: An update on mechanisms and implications. Endocr Rev 2012;33(6):981–1030. DOI: 10.1210/er.2011-1034.
  17. Park KH, Kim JY, Ahn CW, et al. Polycystic ovarian syndrome (PCOS) and insulin resistance. Int J Gynecol Obstet 2001;74(3):261–267. DOI: 10.1016/s0020-7292(01)00442-8.
  18. Qureshi K, Abrams GA. Metabolic liver disease of obesity and role of adipose tissue in the pathogenesis of nonalcoholic fatty liver disease. World J Gastroenterol 2007;13(26):3540–3553. DOI: 10.3748/wjg.v13.i26.3540.
  19. Watt MJ, Miotto PM, De Nardo W, et al. The liver as an endocrine organ-Linking NAFLD and insulin resistance. Endocr Rev 2019;40(5): 1367–1393. DOI: 10.1210/er.2019-00034.
  20. Jauch EC, Saver JL, Adams HP, et al. Guidelines for the early management of patients with acute ischemic stroke: A guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2013;44(3):870–947. DOI: 10.1161/STR.0b013e318284056a.
  21. Mancia G, Fagard R, Narkiewicz K, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: The task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J 2013;34(28):2159–2219. DOI: 10.1093/eurheartj/eht151.
  22. Zhao X, An X, Yang C, et al. The crucial role and mechanism of insulin resistance in metabolic disease. Front Endocrinol (Lausanne) 2023;14:1149239. DOI: 10.3389/fendo.2023.1149239.
  23. Ormazabal V, Nair S, Elfeky O, et al. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol 2018;17(1):122. DOI: 10.1186/s12933-018-0762-4.
  24. Gutch M, Kumar S, Razi SM, et al. Assessment of insulin sensitivity/resistance. Indian J Endocrinol Metab 2015;19(1):160–164. DOI: 10.4103/2230-8210.146874.
  25. Miles PDG, Li S, Hart M, et al. Mechanisms of insulin resistance in experimental hyperinsulinemic dogs. J Clin Invest 1998;101(1): 202–211. DOI: 10.1172/JCI1256.
  26. Tam CS, Xie W, Johnson WD, et al. Defining insulin resistance from hyperinsulinemic-euglycemic clamps. Diabetes Care 2012;35(7): 1605–1610. DOI: 10.2337/dc11-2339.
  27. Yokoyama H, Emoto M, Fujiwara S, et al. Quantitative insulin sensitivity check index and the reciprocal index of homeostasis model assessment in normal range weight and moderately obese type 2 diabetic patients. Diabetes Care 2014;26(8):2426–2432. DOI: 10.2337/diacare.26.8.2426.
  28. Kishida K, Funahashi T, Shimomura I. Adiponectin as a routine clinical biomarker. Best Pract Res Clin Endocrinol Metab 2023;28(1):119–130. DOI:
  29. Achari AE, Jain SK. Adiponectin, a therapeutic target for obesity, diabetes, and endothelial dysfunction. J Mol Sci 2017;18(6):1321. DOI:
  30. Schöndorf T, Maiworm A, Emmison N, et al. Biological background and role of adiponectin as marker for insulin resistance and cardiovascular risk. Clin Lab 2005;51(9–10):489—494. PMID: 16285470.
  31. Qu HQ, Li Q, Rentfro AR, et al. The definition of insulin resistance using HOMA-IR for americans of mexican descent using machine learning. PLoS One 2011;6(6):e21041. DOI: 10.1371/journal.pone.0021041.
  32. Tahapary DL, Pratisthita LB, Fitri NA, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr 2022;16(8):102581. DOI: 10.1016/j.dsx.2022.102581.
  33. Horáková D, Štěpánek L, Janout V, et al. Optimal homeostasis model assessment of insulin resistance (HOMA-IR) cut-offs: A cross-sectional study in the Czech population. Medicina (Kaunas) 2019;55(5):158. DOI:
  34. Zhang X, Li J, Zheng S, et al. Fasting insulin, insulin resistance, and risk of cardiovascular or all-cause mortality in non-diabetic adults: A meta-analysis. Biosci Rep 2017;37(5):BSR20170947. DOI:
  35. Sasaki N, Ozono R, Higashi Y, et al. Association of insulin resistance, plasma glucose level, and serum insulin level with hypertension in a population with different stages of impaired glucose metabolism. J Am Heart Assoc 2020;9(7):e015546. DOI: 10.1161/JAHA.119.015546.
  36. Laakso M. How good a marker is insulin level for insulin resistance? Am J Epidemiol 1993;137(9):959–965. DOI: 10.1093/oxfordjournals.aje.a116768.
  37. Olefsky J, Farquhar JW, Reaven G. Relationship between fasting plasma insulin level and resistance to insulin mediated glucose uptake in normal and diabetic subjects. Diabetes 1973;22(7):507–513. DOI: 10.2337/diab.22.7.507.
  38. Muniyappa R, Lee S, Chen H, et al. Current approaches for assessing insulin sensitivity and resistance in vivo: Advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 2008;294(1): E15–E26. DOI: 10.1152/ajpendo.00645.2007.
  39. Quon MJ. Limitations of the fasting glucose to insulin ratio as an index of insulin sensitivity. J Clin Endocrinol Metab 2001;86(10):4615–4617. DOI: 10.1210/jcem.86.10.7952.
  40. Khan HA, Sobki SH, Ekhzaimy A, et al. Biomarker potential of C-peptide for screening of insulin resistance in diabetic and non-diabetic individuals. Saudi J Biol Sci 2018;25(8):1729–1732. DOI: 10.1016/j.sjbs.2018.05.027.
  41. Kitabchi AE, Umpierrez GE, Miles JM, et al. Hyperglycemic crises in adult patients with diabetes. Diabetes Care 2009;32(7):1335–1343. DOI: 10.2337/dc09-9032.
  42. Chen H, Sullivan G, Yue LQ, et al. QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 2003;284(4):E804–812. DOI: 10.1152/ajpendo.00330.2002.
  43. Chen H, Sullivan G, Quon MJ. Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Diabetes 2005;54(7):1914–1925. DOI: 10.2337/diabetes.54.7.1914.
  44. Tao LC, Xu JN, Wang TT, et al. Triglyceride-glucose index as a marker in cardiovascular diseases: Landscape and limitations. Cardiovasc Diabetol 2022;21(1):68. DOI: 10.1186/s12933-022-01511-x.
  45. Yuan G, Al-Shali KZ, Hegele RA. Hypertriglyceridemia: Its etiology, effects and treatment. CMAJ 2007;176(8):1113–1120. DOI: 10.1503/cmaj.060963.
  46. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 2010;95(7):3347–3351. DOI: 10.1210/jc.2010-0288.
  47. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, et al. Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: A systematic review. Int J Endocrinol 2020;2020:4678526. DOI: 10.1155/2020/4678526.
  48. Lee J, Kim B, Kim W, et al. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the U.S. population. Sci Rep 2021;11(1):2366. DOI:
  49. Huang R, Cheng Z, Jin X, et al. Usefulness of four surrogate indexes of insulin resistance in middle-aged population in Hefei, China. Ann Med 2022;54(1):622–632. DOI: 10.1080/07853890.2022.2039956.
  50. Rajappa M, Sridhar MG, Balachander J, et al. Lipoprotein ratios as surrogate markers for insulin resistance in south indians with normoglycemic nondiabetic acute coronary syndrome. ISRN Endocrinol 2014;2014:981524. DOI: 10.1155/2014/981524.
  51. Oliveri A, Rebernick RJ, Kuppa A, et al. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat Genet 2024;56(2):212–221. DOI: 10.1038/s41588-023-01625-2.
  52. Perumalsamy S, Huri HZ, Abdullah BM, et al. Genetic markers of insulin resistance and atherosclerosis in type 2 diabetes mellitus patients with coronary artery disease. Metabolites 2023;13(3):427. DOI: 10.3390/metabo13030427.
  53. Lee S, Ahn J, Park J, et al. Genetic Diversity of Insulin Resistance and Metabolic Syndrome. In: Genetic Variation. IntechOpen; 2021. Available from:
  54. Lu M, Li P, Bandyopadhyay G, et al. Characterization of a novel glucokinase activator in rat and mouse models. PLoS One 2014;9(2):e88431. DOI: 10.1371/journal.pone.0088431.
  55. Kawashima Y, Nagai H, Konno R, et al. Single-shot 10k proteome approach: Over 10,000 protein identifications by data-independent acquisition-based single-shot proteomics with ion mobility spectrometry. J Proteome Res 2022;21(6):1418–1427. DOI: 10.1021/acs.jproteome.2c00023.
  56. Stahelin RV. Lipid binding domains: More than simple lipid effectors. J Lipid Res 2009;50(Suppl):S299–S304. DOI: 10.1194/jlr.R800078-JLR200.
  57. Hu YH, Meyer K, Lulla A, et al. Gut microbiome and stages of diabetes in middle-aged adults: CARDIA microbiome study. Nutr Metab 2023;20(1):3. DOI: 10.1186/s12986-022-00721-0.
  58. Takeuchi T, Kubota T, Nakanishi Y, et al. Gut microbial carbohydrate metabolism contributes to insulin resistance. Nature 2023;621(7978):389–395. DOI:
  59. Ripoche M, Bouchard C, Irace-Cima A, et al. Current and future distribution of Ixodes scapularis ticks in Québec: Field validation of a predictive model. PLoS One 2022;17(2):e0263243. DOI: 10.1371/journal.pone.0263243.
  60. Macalli M, Navarro M, Orri M, et al. A machine learning approach for predicting suicidal thoughts and behaviors among college students. Sci Rep 2021;11(1):11363. DOI:
  61. Rashid MM, Askari MR, Chen C, et al. Artificial intelligence algorithms for treatment of diabetes. Algorithms 2022;15(9):299. DOI:
  62. Jacobs PG, Herrero P, Facchinetti A, et al. Artificial intelligence and machine learning for improving glycemic control in diabetes: Best practices, pitfalls, and opportunities. IEEE Rev Biomed Eng 2024;17:19–41. DOI: 10.1109/RBME.2023.3331297.
  63. Tsai SF, Yang CT, Liu WJ, et al. Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: A prospective cohort study. EClinicalMedicine 2023;58:101934. DOI: 10.1016/j.eclinm.2023.101934.
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.