Association of the cumulative triglyceride-glucose index with major adverse cardiovascular events in patients with type 2 diabetes

Shi Tai, Liyao Fu, Ningjie Zhang, Rukai Yang, Yuying Zhou, Zhenhua Xing, Yongjun Wang, Shenghua Zhou, Shi Tai, Liyao Fu, Ningjie Zhang, Rukai Yang, Yuying Zhou, Zhenhua Xing, Yongjun Wang, Shenghua Zhou

Abstract

Background: The triglyceride-glucose (TyG) index is a reliable surrogate marker of insulin resistance and is associated with major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus (T2DM). However, the long-term effect of the TyG index on the incidence of MACEs remains unclear. We aimed to investigate the association between the cumulative TyG index and the risk of MACEs in patients with T2DM.

Methods: This post-hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial assessed patients' (T2DM > 3 months) cumulative TyG index and MACE data from the study database. Five fasting blood glucose and triglyceride measurements, at baseline and the first four visits, were taken from 5695 participants who had not experienced MACEs. Cumulative exposure to the TyG index was calculated as the weighted sum of the mean TyG index value for each time interval (value × time). Multivariable-adjusted Cox proportional hazard models and restricted cubic spline analysis were used to determine the association between the cumulative TyG index and MACEs. The incremental predictive value of the cumulative TyG index was further assessed.

Results: Over a median follow-up of 5.09 years, 673 (11.82%) MACEs occurred, including 256 (4.50%) cardiovascular disease (CVD) deaths, 288 (5.06%) non-fatal myocardial infarctions (MIs), and 197 (3.46%) strokes. The risk of developing MACEs increased with the cumulative TyG index quartile. After adjusting for multiple potential confounders, the hazard ratios for the very high cumulative TyG index group versus the low group were 1.59 (95% confidence interval [CI], 1.17-2.16), 1.97 (95% CI 1.19-3.26), and 1.66 (95% CI 1.02-2.70) for overall MACEs, CVD death, and non-fatal MI, respectively. Restricted cubic spline analysis also showed a cumulative increase in the risk of MACEs with an increase in the magnitude of the cumulative TyG index. The addition of the cumulative TyG index to a conventional risk model for MACEs improved the C-statistics, net reclassification improvement value, and integrated discrimination improvement value.

Conclusions: In patients with T2DM, the cumulative TyG index independently predicts the incidence of MACEs, and monitoring the long-term TyG index may assist with optimized-for-risk stratification and outcome prediction for MACEs. Trial registration URL: http://www.

Clinicaltrials: gov . Unique identifier: NCT00000620.

Keywords: Cumulative exposure; Insulin resistance; Major adverse cardiovascular events; Triglyceride-glucose index; Type 2 diabetes.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Kaplan–Meier survival curves for MACEs and individual outcomes based on cumulative TyG index quartiles. a MACEs; b cardiovascular disease (CVD)-related death; c non-fatal myocardial infarction (MI); d non-fatal stroke. MACEs major adverse cardiovascular events, TyG triglyceride-glucose
Fig. 2
Fig. 2
Multivariable-adjusted hazard ratios for MACEs and individual outcomes based on restricted cubic spline analysis. Restricted cubic spline analysis has five knots at the 25th, 50th, 75th, and 95th percentiles of changes in the triglyceride-glucose (TyG) index. a MACEs; b cardiovascular disease (CVD)-related death; c non-fatal myocardial infarction (MI). CI confidence interval, CVD cardiovascular disease, HR hazard ratio, MACEs major adverse cardiovascular events, SD standard deviation, TyG triglyceride-glucose
Fig. 3
Fig. 3
Subgroup analyses of the relationship between cumulative triglyceride-glucose (TyG) index and MACEs. The study population was stratified by age (2 vs. ≥ 30 kg/m2). Adjustments were made for age, sex, education level, race, smoking status, drinking status, years of hypertension diagnosis, years of diabetes diagnosis, depression, body mass index, systolic blood pressure, diastolic blood pressure, heart rate, history of CVD, plasma total cholesterol, HbA1c, LDL-C, eGFR, statin, insulin, non-dihydropyridine calcium channel blockers, dihydropyridine calcium channel blockers, thiazolidinediones, and thiazide diuretics treatment at the baseline level. BMI body mass index, CI confidence interval, CVD cardiovascular disease, eGFR estimated glomerular filtration rate, HbA1c glycated hemoglobin, HR hazards ratio, LDL-C low-density lipoprotein cholesterol, MACEs major adverse cardiovascular events, SD standard deviation, TyG triglyceride-glucose

References

    1. American Diabetes Association Professional Practice C 10. Cardiovascular disease and risk management: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S144–S174. doi: 10.2337/dc22-S010.
    1. Wiviott SD, Raz I, Bonaca MP, Mosenzon O, Kato ET, Cahn A, Silverman MG, Zelniker TA, Kuder JF, Murphy SA, et al. Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2019;380(4):347–357. doi: 10.1056/NEJMoa1812389.
    1. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, Shaw W, Law G, Desai M, Matthews DR, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–657. doi: 10.1056/NEJMoa1611925.
    1. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M, Devins T, Johansen OE, Woerle HJ, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–2128. doi: 10.1056/NEJMoa1504720.
    1. American Diabetes Association Economic costs of diabetes in the US in 2017. Diabetes Care. 2018;41(5):917–928. doi: 10.2337/dci18-0007.
    1. Hill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, Sowers JR. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. doi: 10.1016/j.metabol.2021.154766.
    1. Grant PJ, Cosentino F, Marx N. Diabetes and coronary artery disease: not just a risk factor. Heart. 2020;106(17):1357–1364. doi: 10.1136/heartjnl-2019-316243.
    1. Moreno PR, Murcia AM, Palacios IF, Leon MN, Bernardi VH, Fuster V, Fallon JT. Coronary composition and macrophage infiltration in atherectomy specimens from patients with diabetes mellitus. Circulation. 2000;102(18):2180–2184. doi: 10.1161/01.CIR.102.18.2180.
    1. Cersosimo E, Solis-Herrera C, Trautmann ME, Malloy J, Triplitt CL. Assessment of pancreatic beta-cell function: review of methods and clinical applications. Curr Diabetes Rev. 2014;10(1):2–42. doi: 10.2174/1573399810666140214093600.
    1. Zhao Q, Zhang TY, Cheng YJ, Ma Y, Xu YK, Yang JQ, Zhou YJ. Impacts of triglyceride-glucose index on prognosis of patients with type 2 diabetes mellitus and non-ST-segment elevation acute coronary syndrome: results from an observational cohort study in China. Cardiovasc Diabetol. 2020;19(1):108. doi: 10.1186/s12933-020-01086-5.
    1. Simental-Mendia LE, Rodriguez-Moran 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.
    1. Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. 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.
    1. Lee SH, Kwon HS, Park YM, Ha HS, Jeong SH, Yang HK, Lee JH, Yim HW, Kang MI, Lee WC, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju metabolic disease cohort (CMC) study. PLoS ONE. 2014;9(2):e90430. doi: 10.1371/journal.pone.0090430.
    1. Navarro-Gonzalez D, Sanchez-Inigo L, Pastrana-Delgado J, Fernandez-Montero A, Martinez JA. Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: the vascular-metabolic CUN cohort. Prev Med. 2016;86:99–105. doi: 10.1016/j.ypmed.2016.01.022.
    1. Sanchez-Inigo L, Navarro-Gonzalez D, Fernandez-Montero A, Pastrana-Delgado J, Martinez JA. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest. 2016;46(2):189–197. doi: 10.1111/eci.12583.
    1. Li S, Guo B, Chen H, Shi Z, Li Y, Tian Q, Shi S. The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. Sci Rep. 2019;9(1):7320. doi: 10.1038/s41598-019-43776-5.
    1. Hong S, Han K, Park CY. The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC Med. 2020;18(1):361. doi: 10.1186/s12916-020-01824-2.
    1. Ding X, Wang X, Wu J, Zhang M, Cui M. Triglyceride-glucose index and the incidence of atherosclerotic cardiovascular diseases: a meta-analysis of cohort studies. Cardiovasc Diabetol. 2021;20(1):76. doi: 10.1186/s12933-021-01268-9.
    1. Li H, Zuo Y, Qian F, Chen S, Tian X, Wang P, Li X, Guo X, Wu S, Wang A. Triglyceride-glucose index variability and incident cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):105. doi: 10.1186/s12933-022-01541-5.
    1. Wang A, Tian X, Zuo Y, Chen S, Meng X, Wu S, Wang Y. Change in triglyceride-glucose index predicts the risk of cardiovascular disease in the general population: a prospective cohort study. Cardiovasc Diabetol. 2021;20(1):113. doi: 10.1186/s12933-021-01305-7.
    1. Wang X, Feng B, Huang Z, Cai Z, Yu X, Chen Z, Cai Z, Chen G, Wu S, Chen Y. Relationship of cumulative exposure to the triglyceride-glucose index with ischemic stroke: a 9-year prospective study in the Kailuan cohort. Cardiovasc Diabetol. 2022;21(1):66. doi: 10.1186/s12933-022-01510-y.
    1. Dai L, Xu J, Zhang Y, Wang A, Chen Z, Mo J, Li H, Meng X, Wu S, Wang Y. Cumulative burden of lipid profiles predict future incidence of ischaemic stroke and residual risk. Stroke Vasc Neurol. 2021;6(4):581–588. doi: 10.1136/svn-2020-000726.
    1. 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. doi: 10.1186/s12933-022-01456-1.
    1. Tai S, Fu L, Zhang N, Zhou Y, Xing Z, Wang Y. Impact of baseline and trajectory of triglyceride-glucose index on cardiovascular outcomes in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2022;13:858209. doi: 10.3389/fendo.2022.858209.
    1. Gerstein HC, Miller ME, Byington RP, Goff DC, Jr, Bigger JT, Buse JB, Cushman WC, Genuth S, Ismail-Beigi F, Action to Control Cardiovascular Risk in Diabetes Study Group et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545–2559. doi: 10.1056/NEJMoa0802743.
    1. ACCORD Study Group Nine-year effects of 3.7 years of intensive glycemic control on cardiovascular outcomes. Diabetes Care. 2016;39(5):701–708. doi: 10.2337/dc15-2283.
    1. Chen Z, Mo J, Xu J, Wang A, Dai L, Cheng A, Yalkun G, Meng X, Zhao X, Li H, et al. Effects of individual and integrated cumulative burden of blood pressure, glucose, low-density lipoprotein cholesterol, and C-reactive protein on cardiovascular risk. Eur J Prev Cardiol. 2022;29(1):127–135. doi: 10.1093/eurjpc/zwaa052.
    1. Li C, Zhu Y, Ma Y, Hua R, Zhong B, Xie W. Association of cumulative blood pressure with cognitive decline, dementia, and mortality. J Am Coll Cardiol. 2022;79(14):1321–1335. doi: 10.1016/j.jacc.2022.01.045.
    1. Tao LC, Xu JN, Wang TT, Hua F, Li JJ. 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.
    1. Pool LR, Ning H, Wilkins J, Lloyd-Jones DM, Allen NB. Use of long-term cumulative blood pressure in cardiovascular risk prediction models. JAMA Cardiol. 2018;3(11):1096–1100. doi: 10.1001/jamacardio.2018.2763.
    1. Zhang Y, Pletcher MJ, Vittinghoff E, Clemons AM, Jacobs DR, Jr, Allen NB, Alonso A, Bellows BK, Oelsner EC, Zeki Al Hazzouri A, et al. Association between cumulative low-density lipoprotein cholesterol exposure during young adulthood and middle age and risk of cardiovascular events. JAMA Cardiol. 2021;6(12):1406–1413. doi: 10.1001/jamacardio.2021.3508.
    1. Schonmann Y. Cardiovascular risk assessment: baseline snapshots or accumulated burden? Eur J Prev Cardiol. 2022;29(1):125–126. doi: 10.1093/eurjpc/zwaa092.
    1. Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, Martinez-Abundis E, Ramos-Zavala MG, Hernandez-Gonzalez SO, Jacques-Camarena O, Rodriguez-Moran M. 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.
    1. Zhang Y, Ding X, Hua B, Liu Q, Gao H, Chen H, Zhao XQ, Li W, Li H. Predictive effect of triglycerideglucose index on clinical events in patients with type 2 diabetes mellitus and acute myocardial infarction: results from an observational cohort study in China. Cardiovasc Diabetol. 2021;20(1):43. doi: 10.1186/s12933-021-01236-3.
    1. Ma X, Dong L, Shao Q, Cheng Y, Lv S, Sun Y, Shen H, Wang Z, Zhou Y, Liu X. Triglyceride glucose index for predicting cardiovascular outcomes after percutaneous coronary intervention in patients with type 2 diabetes mellitus and acute coronary syndrome. Cardiovasc Diabetol. 2020;19(1):31. doi: 10.1186/s12933-020-01006-7.
    1. Yang Q, Vijayakumar A, Kahn BB. Metabolites as regulators of insulin sensitivity and metabolism. Nat Rev Mol Cell Biol. 2018;19(10):654–672. doi: 10.1038/s41580-018-0044-8.
    1. Molina MN, Ferder L, Manucha W. Emerging role of nitric oxide and heat shock proteins in insulin resistance. Curr Hypertens Rep. 2016;18(1):1. doi: 10.1007/s11906-015-0615-4.
    1. Nishikawa T, Kukidome D, Sonoda K, Fujisawa K, Matsuhisa T, Motoshima H, Matsumura T, Araki E. Impact of mitochondrial ROS production in the pathogenesis of insulin resistance. Diabetes Res Clin Pract. 2007;77(Suppl 1):S161–164. doi: 10.1016/j.diabres.2007.01.071.
    1. Jia G, DeMarco VG, Sowers JR. Insulin resistance and hyperinsulinaemia in diabetic cardiomyopathy. Nat Rev Endocrinol. 2016;12(3):144–153. doi: 10.1038/nrendo.2015.216.
    1. Gerrits AJ, Koekman CA, van Haeften TW, Akkerman JW. Platelet tissue factor synthesis in type 2 diabetic patients is resistant to inhibition by insulin. Diabetes. 2010;59(6):1487–1495. doi: 10.2337/db09-1008.
    1. Sanchez-Garcia A, Rodriguez-Gutierrez R, Mancillas-Adame L, Gonzalez-Nava V, Diaz Gonzalez-Colmenero A, Solis RC, Alvarez-Villalobos NA, Gonzalez-Gonzalez JG. 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.
    1. Liu H, Liu J, Liu J, Xin S, Lyu Z, Fu X. Triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, a simple but effective indicator in predicting type 2 diabetes mellitus in older adults. Front Endocrinol (Lausanne) 2022;13:828581. doi: 10.3389/fendo.2022.828581.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere