Assessment of Various Insulin Resistance Surrogate Indices in Thai People with Type 2 Diabetes Mellitus

Authors

  • Waralee Chatchomchuan Theptarin hospital https://orcid.org/0000-0003-0880-6693
  • Yotsapon Thewjitcharoen Diabetes and Thyroid center, Theptarin Hospital, Bangkok, Thailand https://orcid.org/0000-0002-2317-4041
  • Soontaree Nakasatien Diabetes and Thyroid center, Theptarin Hospital, Bangkok, Thailand
  • Ekgaluck Wanothayaroj Diabetes and Thyroid center
  • Sirinate Krittiyawong Diabetes and Thyroid center
  • Thep Himathongkam

DOI:

https://doi.org/10.15605/jafes.039.02.21

Keywords:

Insulin resistance, Surrogate Markers, HOMA-IR, Triglyceride-Glucose (TyG) index, estimated Glucose Disposal Rate (eGDR)

Abstract

Objective. To compare insulin surrogate indices with the homeostasis model assessment of insulin resistance (HOMA-IR) in Thai people with type 2 diabetes (T2D).

Methodology. A cross-sectional study of 97 individuals with T2D was done to determine the association between HOMA-IR and seven surrogate indices for insulin resistance. IR was defined as HOMA-IR ≥2.0. The indices included Waist Circumference (WC), Waist-to-Hip Ratio (WHR), Waist-to-Height Ratio (WHtR), Triglyceride-Glucose (TyG) index, estimated Glucose Disposal Rate (eGDR) calculated by WC, BMI, and WHR.

Results. A total of 97 subjects with T2D (36.1% female, mean age 61.7 ± 12.0 years, BMI 26.4 ± 3.7 kg/m2, A1C 6.9 ± 1.2%) were studied. The TyG index showed a positive association with HOMA-IR, while eGDR exhibited a negative association. TyG index had the strongest correlation with IR (r = 0.49), while various eGDR formulas showed weaker negative correlations (r = 0.12-0.25). However, subgroup analysis in individuals with T2D and coronary artery disease (CAD) showed that only eGDR-WC and eGDR-BMI demonstrated a significant correlation with triple vessel disease.

Conclusion. The TyG index was a useful and simple marker for identifying the presence of IR in Thai people with T2D. Future longitudinal studies are warranted to demonstrate the prediction value of cardiovascular outcomes.

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References

1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595-1607. https://pubmed.ncbi.nlm.nih.gov/3056758 https://doi.org/10.2337/diab.37.12.1595

2. Ginsberg HN. Insulin resistance and cardiovascular disease. J Clin Invest. 2000;106(4):453-8. https://pubmed.ncbi.nlm.nih.gov/10953019 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC380256 https://doi.org/10.1172/JCI10762

3. Reaven G. All obese individuals are not created equal: Insulin resistance is the major determinant of cardiovascular disease in overweight/obese individuals. Diab Vasc Dis Res. 2005;2(3):105-12. https://pubmed.ncbi.nlm.nih.gov/16334591 https://doi.org/10.3132/dvdr.2005.017

4. Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-9. https://pubmed.ncbi.nlm.nih.gov/3899825 https://doi.org/10.1007/BF00280883

5. Pacini G, Finegood DT, Bergman RN. A minimal-model based glucose clamp yielding insulin sensitivity independent of glycemia. Diabetes. 1982;31(5 Pt 1):432-41. https://pubmed.ncbi.nlm.nih.gov/6759258 https://doi.org/10.2337/diab.31.5.432

6. Park SY, Gautier JF, Chon S. Assessment of insulin secretion and insulin resistance in human. Diabetes Metab J. 2021;45(5):641-54. https://pubmed.ncbi.nlm.nih.gov/34610719 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497920 https://doi.org/10.4093/dmj.2021.0220

7. McAuley KA, Williams SM, Mann JI, et al. Diagnosing insulin resistance in the general population. Diabetes Care. 2001;24(3):460-4. https://pubmed.ncbi.nlm.nih.gov/11289468 https://doi.org/10.2337/diacare.24.3.460

8. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist: Hip ratio as predictors of cardiovascular risk -- a review of the literature. Eur J Clin Nutr. 2010;64(1):16-22. https://pubmed.ncbi.nlm.nih.gov/19654593 https://doi.org/10.1038/ejcn.2009.68

9. Ross R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: A consensus statement from the IAS and ICCR Working Group on visceral obesity. Nat Rev Endocrinol. 2020;16(3):177-89. https://pubmed.ncbi.nlm.nih.gov/32020062 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027970 https://doi.org/10.1038/s41574-019-0310-7

10. Lechner K, Lechner B, Crispin A, Schwarz PEH, von Bibra H. Waist-to-height ratio and metabolic phenotype compared to the Matsuda index for the prediction of insulin resistance. Sci Rep. 2021;11(1):8224. https://pubmed.ncbi.nlm.nih.gov/33859227 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050044 https://doi.org/10.1038/s41598-021-87266-z

11. 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. https://pubmed.ncbi.nlm.nih.gov/19067533 https://doi.org/10.1089/met.2008.0034

12. Williams KV, Erbey JR, Becker D, et al. Can clinical factors estimate insulin resistance in type 1 diabetes? Diabetes. 2000;49(4):626-32. https://pubmed.ncbi.nlm.nih.gov/10871201 https://doi.org/10.2337/diabetes.49.4.626

13. Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007–2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2019;14(3):e0212963. https://pubmed.ncbi.nlm.nih.gov/30845237 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405083 https://doi.org/10.1371/journal.pone.0212963

14. Jin JL, Cao YX, Wu LG, et al. Triglyceride glucose index for predicting cardiovascular outcomes in patients with coronary artery disease. J Thorac Dis. 2018;10(11):6137-46. https://pubmed.ncbi.nlm.nih.gov/30622785 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297409 https://doi.org/10.21037/jtd.2018.10.79

15. Chamroonkiadtikun P, Ananchaisarp T, Wanichanon W. The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study. Prim Care Diabetes. 2020;14(2):161-7. https://pubmed.ncbi.nlm.nih.gov/31466834 https://doi.org/10.1016/j.pcd.2019.08.004

16. Nyström T, Holzmann MJ, Eliasson B, et al. Estimated glucose disposal rate and long-term survival in type 2 diabetes after coronary artery bypass grafting. Heart Vessels. 2017;32(3):269-78. https://pubmed.ncbi.nlm.nih.gov/27401741 https://doi.org/10.1007/s00380-016-0875-1

17. Do HD, Lohsoonthorn V, Jiamjarasrangsi W, Lertmaharit S, Williams MA. Prevalence of insulin resistance and its relationship with cardiovascular disease risk factors among Thai adults over 35 years old. Diabetes Res Clin Pract. 2010;89(3):303-8. https://pubmed.ncbi.nlm.nih.gov/20466446 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2919620 https://doi.org/10.1016/j.diabres.2010.04.013

18. 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):3347-51. https://pubmed.ncbi.nlm.nih.gov/20484475 https://doi.org/10.1210/jc.2010-0288

19. Buderer NM. Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med. 1996;3(9):895-900. PMID: 8870764 https://doi.org/10.1111/j.1553-2712.1996.tb03538.x

20. Fukushima M, Usami M, Ikeda M, et al. Insulin secretion and insulin sensitivity at different stages of glucose tolerance: A cross-sectional study of Japanese type 2 diabetes. Metabolism. 2004;53(7):831-5. PMID: 15254872 https://doi.org/10.1016/j.metabol.2004.02.012

21. Zabala A, Darsalia V, Lind M, et al. Estimated glucose disposal rate and risk of stroke and mortality in type 2 diabetes: A nationwide cohort study. Cardiovasc Diabetol. 2021;20(1):202. PMID: 34615525 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495918 https://doi.org/10.1186/s12933-021-01394-4

22. Cui H, Liu Q, Wu Y, Cao L. Cumulative triglyceride-glucose index is a risk for CVD: A prospective cohort study. Cardiovasc Diabetol. 2022;21(1):22. PMID: 35144621 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830002 https://doi.org/10.1186/s12933-022-01456-1

23. Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): A prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23-33. https://pubmed.ncbi.nlm.nih.gov/36521498 https://doi.org/10.1016/S2666-7568(22)00247-1

24. Liu L, Xia R, Song X, et al. Association between the triglyceride-glucose index and diabetic nephropathy in patients with type 2 diabetes: A cross-sectional study. J Diabetes Investigation 2021;12:557-65. https://pubmed.ncbi.nlm.nih.gov/15132969 https://doi.org/10.1161/01.ATV.0000126485.80373.33

25. Lemieux I. Energy partitioning in gluteal-femoral fat: Does the metabolic fate of triglycerides affect coronary heart disease risk? Arterioscler Thromb Vasc Biol. 2004;24(5):795-7. https://doi.org/10.1161/01.atv.0000126485.80373.33.

26. Farhan S, Redfors B, Maehara A, et al. Relationship between insulin resistance, coronary plaque, and clinical outcomes in patients with acute coronary syndromes: An analysis from the PROSPECT study. Cardiovasc. Diabetol. 2021;20(1):10. https://pubmed.ncbi.nlm.nih.gov/33413366 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791845 https://doi.org/10.1186/s12933-020-01207-0

27. Laakso M. Cardiovascular disease in type 2 diabetes from population to man to mechanisms: The Kelly West Award Lecture 2008. Diabetes Care. 2010;33(2):442-9. https://pubmed.ncbi.nlm.nih.gov/20103560 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2809299 https://doi.org/10.2337/dc09-0749

28. Xuan J, Juan D, Yuyu N, Anjing J. Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: Findings from a cross-sectional study. BMC Cardiovasc Disord. 2022;22(1):378. https://pubmed.ncbi.nlm.nih.gov/35987992 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392437 https://doi.org/10.1186/s12872-022-02817-0

29. da Silva A, Caldas APS, Hermsdorff HHM, et al. Triglyceride glucose index is associated with symptomatic coronary artery disease in patients in secondary care. Cardiovasc Diabetol. 2019;18(1):89. https://pubmed.ncbi.nlm.nih.gov/31296225 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6625050 https://doi.org/10.1186/s12933-019-0893-2

30. Strisciuglio T, Izzo R, Barbato E, et al. Insulin resistance predicts severity of coronary atherosclerotic disease in non-diabetic patients. J Clin Med. 2020;9(7):2144. https://pubmed.ncbi.nlm.nih.gov/32646007 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408744 https://doi.org/10.3390/jcm9072144

31. Wadström BN, Pedersen KM, Wulff AB, et al. Elevated remnant cholesterol and atherosclerotic cardiovascular disease in diabetes: A population-based prospective cohort study. Diabetologia. 2023;66(12):2238-49. https://pubmed.ncbi.nlm.nih.gov/37776347 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627991 https://doi.org/10.1007/s00125-023-06016-0

32. Gabani M, Shapiro MD, Toth PP. The role of triglyceride-rich lipoproteins and their remnants in atherosclerotic cardiovascular disease. Eur Cardiol. 2023;18:e56. https://pubmed.ncbi.nlm.nih.gov/37860700 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583159 https://doi.org/10.15420/ecr.2023.16

33. Chan JC, Malik V, Jia W, et al. Diabetes in Asia: Epidemiology, risk factors, and pathophysiology. JAMA. 2009;301(20):2129-40. https://pubmed.ncbi.nlm.nih.gov/19470990 https://doi.org/10.1001/jama.2009.726

34. Suraamornkul S, Kwancharoen R, Ovartlarnporn M, Rawdaree P, Bajaj M. Insulin clamp-derived measurements of insulin sensitivity and insulin secretion in lean and obese asian type 2 diabetic patients. Metab Syndr Relat Disord. 2010;8(2):113-8. https://pubmed.ncbi.nlm.nih.gov/20059360 https://doi.org/10.1089/met.2009.0030

35. Yamada C, Mitsuhashi T, Hiratsuka N, Inabe F, Araida N, Takahashi E. Optimal reference interval for homeostasis model assessment of insulin resistance in a Japanese population. J Diabetes Investig. 2011;2(5):373-6. https://pubmed.ncbi.nlm.nih.gov/24843516 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019305 https://doi.org/10.1111/j.2040-1124.2011.00113.x36. Goh LPW, Sani SA, Sabullah MK, Gansau JA. The prevalence of insulin resistance in Malaysia and indonesia: An updated systematic review and meta-analysis. Medicina (Kaunas). 2022;58(6):826. https://pubmed.ncbi.nlm.nih.gov/35744089 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227905 https://doi.org/10.3390/medicina58060826

37. 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. https://pubmed.ncbi.nlm.nih.gov/35939943 https://doi.org/10.1016/j.dsx.2022.102581

38. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487-95. https://pubmed.ncbi.nlm.nih.gov/15161807 https://doi.org/10.2337/diacare.27.6.1487

39. Buchanan TA, Watanabe RM, Xiang AH. Limitations in surrogate measures of insulin resistance. J Clin Endocrinol Metab. 2010;95(11):4874-6. https://pubmed.ncbi.nlm.nih.gov/21051585 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2968734 https://doi.org/10.1210/jc.2010-2167

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Published

2024-09-13

How to Cite

Chatchomchuan, W., Thewjitcharoen, Y. ., Nakasatien, S. ., Wanothayaroj, E. ., Krittiyawong, S., & Himathongkam, T. . (2024). Assessment of Various Insulin Resistance Surrogate Indices in Thai People with Type 2 Diabetes Mellitus. Journal of the ASEAN Federation of Endocrine Societies, 39(2), 33–40. https://doi.org/10.15605/jafes.039.02.21

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