Relationship Between the Triglyceride-glucose Index and Incident Ischemic Heart Disease Risk: A Prospective Study Using National Health Insurance Data



Background: Ischemic heart disease (IHD) without diabetes is considered an important challenge to human health and is associated with a poor prognosis as well as a lack of health awareness. Until now, the association between early insulin resistance and IHD among nondiabetic adults has been poorly understood. We prospectively investigated the relationship between the triglyceride-glucose (TyG) index, a surrogate marker of early insulin resistance, and incident IHD risk in a large cohort of nondiabetic adults using National Health Insurance data.

Methods: We assessed 16,455 participants (8,426 men and 8,029 women) without diabetes using data from a health risk assessment study (HERAS) and Korea Health Insurance Review & Assessment (HIRA) data. The participants were divided into four groups according to TyG index quartiles, calculated as ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. We prospectively assessed hazard ratios (HRs) with 95% confidence intervals (CIs) for IHD, using multivariate Cox proportional-hazards regression models, over a 50-month period that followed the baseline survey.

Results: During the follow-up period, 322 (2.0%) participants developed IHD. HRs of IHD for TyG index quartiles 1–4 were 1.00, 1.63 (95% CI, 1.06–2.49), 1.88 (95% CI, 1.23–2.87), and 2.35 (95% CI, 1.53–3.61), respectively, after adjusting for age, sex, body mass index, smoking status, alcohol intake, physical activity, high sensitivity C-reactive protein, and mean arterial blood pressure.

Conclusion: A higher TyG index precedes and significantly predicts future IHD among nondiabetic Koreans. Accordingly, a high TyG index may be a useful additional measure in assessing cardiovascular risks for apparently healthy adults in clinical practice.


Ischemic heart disease (IHD) can cause premature morbidity and mortality among middle-aged and older individuals, and it is an important challenge to human health in both developing and developed countries [1]. The burden of premature IHD in an ageing population cannot be underestimated, considering it is a factor that decreases the quality of life and increases social burden [2]. Accordingly, assessing and identifying potential risks for IHD in the preclinical stage among apparently healthy individuals is worthwhile for facilitating disease prevention and slowing the progression of IHD.

Accumulating evidence suggests that the triglyceride-glucose (TyG) index, a simple and widely accessible measure, is a novel and surrogate marker for early insulin resistance [3, 4]. Epidemiological studies conducted in the Korean population have shown that the TyG index is a better indicator of metabolic syndrome and type 2 diabetes than the homeostasis model assessment of insulin resistance (HOMA-IR) [5, 6]. Koreans are a group of East Asians with ethnic homogeneity with a lower overall body mass index (BMI) than Westerners. Regarding the total energy intake, the proportion of carbohydrate intake was much higher among Koreans than Westerners [7]. The prevalence of both hypertriglyceridemia and impaired fasting glucose was 20–30% among Korean adults, according to data from the Korea National Health and Nutrition Examination Survey (KNHANES) [8, 9].

The TyG index has been associated with subclinical atherosclerosis symptoms such as arterial stiffness and preclinical coronary arterial calcification [1012]. Meanwhile, prospective studies to evaluate the predictive value of TyG index in cardiovascular diseases (CVDs) have been focused mainly on preexisting coronary arterial disease or diabetes mortality [13, 14]. Furthermore, nondiabetic individuals with IHD tend to have a poorer prognosis than do diabetic patients without IHD [15, 16]. Therefore, we prospectively investigated the relationship between the TyG index and IHD incidence within a large-scale, community-dwelling, nondiabetic adult cohort using National Health Insurance data.


Study participants

This study is based on a health risk assessment study (HERAS) that aimed to characterize cardiovascular risk factors and explore surrogate markers of CVD in Korean adults. The study cohort consisted of 20,530 individuals aged ≥ 20 years who voluntarily visited the Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, for regular health examinations between November 2006 and June 2010. Among 20,530 participants initially assessed, 1,590 (7.7%) participants with a history of IHD or ischemic stroke, a previous diagnosis of type 2 diabetes or a fasting plasma glucose level ≥ 126 mg/dL were excluded. We also excluded participants who met at least one of the following criteria: age <30 years, missing data, current use of dyslipidaemia medication or aspirin, high-sensitivity C-reactive protein (hsCRP) level ≥ 10 mg/L (N = 2,485). After exclusion criteria were applied, 16,455 participants (8,426 men and 8,029 women) were included in our final analysis.

Data collection

Each participant completed a lifestyle and medical history questionnaire that included information regarding cigarette smoking, alcohol consumption, and physical activity.

Smoking status was defined using the following categories: non-smoker, ex-smoker, and current smoker. Questions regarding alcohol intake included information regarding consumption frequency on a weekly basis. Regular alcohol consumption was defined as alcohol consumption ≥ two times per week. Participants were asked about their level of physical exercise on a weekly basis, and regular exercise was defined as exercise ≥ three times per week. Body weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, in light indoor clothing without shoes. BMI was calculated as an individual’s weight in kilograms divided by the square of his/her height in metres (kg/m2). Systolic blood pressure and diastolic blood pressure were measured on the patient’s right arm using a standard mercury sphygmomanometer in the sitting position after 10 min of rest (Baumanometer, W.A. Baum Co Inc., Copiague, NY, USA). All blood samples were obtained from the antecubital vein after overnight fasting for 12 h. Fasting plasma glucose, total cholesterol, triglyceride, and high-density lipoprotein (HDL)-cholesterol levels were measured via enzymatic methods using a Hitachi 7600 automated chemistry analyser (Hitachi Co.; Tokyo, Japan). hsCRP concentrations were measured with a Roche/Hitachi 912 System (Roche Diagnostics, Indianapolis, IN, USA) using a latex-enhanced immunoturbidimetric method with a low limit of detection of 0.09 mg/L. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of hypertension medication. Chronic kidney disease (CKD) was defined either as renal tissue damage or reduced renal functioning, as determined by an eGFR value < 60 mL/min/1.73 m2 or proteinuria 1+ or greater [17].

Study outcomes

The primary outcome assessed was IHD, which consisted of angina pectoris (ICD-10 codes I20) or acute myocardial infarction (ICD-10 codes I21) that occurred after initial study enrolment. To define baseline and post-survey outcomes, we linked a personal, 13-digit identification number that was assigned to each subject with Korea Health Insurance Review & Assessment (HIRA) data, which is a repository of claims data collected in the process of reimbursing healthcare providers, between November 2006 and December 2010. Participants that were found to have had IHD or ischemic stroke (ICD-10 codes I20, I21, and I63) at the time of their initial assessment were excluded before the final analysis.

Statistical analysis

The TyG index values were categorised into quartiles, as follows: Q1 (≤ 8.08), Q2 (8.09–8.45), Q3 (8.46–8.85), and Q4 (≥ 8.86). All data are presented as means with standard deviations or percentages. The baseline characteristics of the study population according to the TyG index quartiles were compared using an analysis of variance (ANOVA) model for continuous variables and the chi-squared test for categorical variables. Kaplan–Meier curves were used to assess the cumulative incidence of IHD. The log-rank test was used to determine whether the distributions of cumulative IHD incidence differed among groups. Pairwise comparisons of receiver-operating characteristic (ROC) curves were used to contrast area under the ROC curve (AUC) of IHD incidence based on TyG index, fasting plasma glucose, and serum triglyceride levels. Further, AUC values were used to test the sensitivity and specificity of biomarkers for predicting IHD. For multivariate analysis, after setting the lowest TyG index value quartile as a reference group, hazard ratios (HRs) and 95% confidence intervals (CIs) for incident IHD were calculated using the Cox proportional-hazards regression model after adjusting for potential confounding variables. All analyses were performed using SAS version 9.4 software (SAS Institute Inc., Cary, NC, USA). All statistical tests were two-sided, and statistical significance was set at P < 0.05.


Table 1 shows the baseline characteristics of the study population (n = 16,455; 8,426 men and 8,029 women) according to TyG index quartiles. The mean age and BMI of the study population were 46.1 ± 9.5 years and 23.4 ± 3.0 kg/m2, respectively. The mean fasting plasma glucose concentration was determined to be 91.4 ± 9.8 mg/dL, the mean triglycerides level was 124.2 ± 84.9 mg/dL, and the mean TyG index value was 8.49 ± 0.56.

The mean BMI, mean arterial pressure, total cholesterol, and hsCRP values were highest and the mean HDL-cholesterol levels were lowest in the highest TyG index quartile group. The greatest proportions of current smokers and alcohol drinkers were members of the fourth TyG index quartile, whereas the proportion of individuals who participated in regular exercise was highest in the first TyG index quartile. The higher TyG index groups had a significantly elevated cumulative incidence of IHD over a 50-month period that followed the baseline survey (log-rank test, P < 0.001) (Figure 1).

Using a pairwise comparison of ROC analyses of incident IHD, the AUC of TyG index data was significantly higher than that of fasting plasma glucose (P = 0.016), and was marginally significant when compared with the AUC produced using serum triglyceride level data (P = 0.058). The sensitivity, specificity, AUC of TyG index for classifying IHD was 80.8%, 37.2%, and 0.613, respectively (Table 2).

Table 3 shows results of the multivariate Cox proportional-hazards regression analysis for the prediction of IHD according to TyG index quartile. A total of 322 individuals (2.0%, 322/16,455) developed IHD during the follow up period. The incidence rate (per 1,000 person years) of IHD increased proportionally as TyG index quartile increased. Compared with the first TyG index quartile, the HRs of incident IHD for the second, third, and fourth quartiles increased in a dose-responsive manner. The HRs of incident IHD were 1.63 (95% CI, 1.06–2.49), 1.88 (95% CI, 1.23–2.87), and 2.35 (95% CI, 1.53–3.61) for the second, third and fourth TyG index quartiles, respectively, after adjusting for age, sex, BMI, smoking status, alcohol intake, physical activity, mean arterial blood pressure, hsCRP, and CKD (Model 3).


Among community dwelling Korean adults without diabetes, we found that elevated TyG index values were positively and independently associated with IHD incidence in this large-scale, prospective cohort study that included a 50-month follow-up. Our study showed that the association between TyG index and IHD persisted after further adjustment for lifestyle factors, inflammation, and mean arterial blood pressure.

In a prospective study that considered patients with stable coronary artery disease, Jin et al. showed that the TyG index may be a useful predictive marker of cardiovascular events [13]. Ma et al., in a longitudinal study of 766 patients who underwent percutaneous coronary intervention, reported that subgroups within the top tertile of the TyG index had a 2.17-fold higher risk of adverse cardiovascular outcomes over a median follow-up period of 30 months, compared with the referent first tertile [14]. To our knowledge, one previous longitudinal study has examined the relationship between the TyG index and incident IHD within a population-based cohort. Sanchez-Inigo et al. performed a study that included 5,014 Caucasian men and women with a mean age of 55.51 ± 13.68 and 53.72 ± 12.84, respectively. The work revealed a positive association between the TyG index and CVD throughout a median period of 10 years. However, the study performed by Sanchez-Inigo et al. included an older age group, and also included patients with diabetes and current users of anti-aggregation medication [18]. That study did not identify a relationship between the TyG index and incident CVD in participants with type 2 diabetes at baseline, which may have been due to effects of medication or the adoption of heathier habits by participants.

Meanwhile, some studies have reported that the TyG index is associated with subclinical coronary atherosclerosis not only in diabetic patients but also in the general population [11, 19]. From an epidemiological standpoint, nondiabetic individuals with myocardial infarction have been reported to have a poorer prognosis than diabetic patients without myocardial infarction [15, 16, 20, 21]. To predict future CVD, a health risk assessment over a 5-year period has become as important as that over a 10-year period over the past few decades [22, 23]. This study revealed for the first time an association between the TyG index and incident IHD among apparently healthy individuals using an assessment period that did not exceed five years in an East Asian population, despite the fact that the incidence of IHD was relatively low. Some possible explanations for the observed association deserve consideration. The TyG index has been known to be one of best indices that can be used for identifying individuals with early insulin resistance [3]. In a prospective study of non-obese Chinese adults, Zhang et al. suggested that the TyG index may be valuable for predicting type 2 diabetes [24]. Further, TyG index was determined to be superior to well-known predictive biomarkers of type 2 diabetes such as HOMA-IR in a study that assessed 5,354 Korean subjects without diabetes with a mean age of 61.6 years and a mean BMI of 24.2 kg/m2 [25]. Additionally, the TyG index has been suggested to be a useful surrogate marker of overall metabolic health status according to KNHANES, a nationwide survey representing the entire Korean population [26]. Finally, chronic inflammation could contribute to the association between the TyG index and IHD. In the present study, serum hsCRP levels gradually increased with the TyG index quartile, which supports the idea that TyG index is closely linked to underlying low-grade inflammation.

Some strengths and limitations require careful consideration and may affect the interpretation of the results of the present study. A major strength of the work was that we conducted a prospective cohort study using a large number of Korean individuals that was linked to HIRA data, derived from the universal coverage system in Korea. As a result, there was a very low chance that data was missing [27]. This study had some limitations that should also be acknowledged. First, because the study cohort was composed of volunteers that visited a clinic for health promotion screenings conducted at a single hospital, patients appeared to be slightly healthier than most community-based cohorts previously assessed. Second, some diabetic individuals may have been included in the study population because glycated haemoglobin A1c and 2-h oral glucose tolerance tests were not performed at the beginning of the study.


In conclusion, an elevated TyG index precedes and significantly predicts future IHD among community dwelling nondiabetic Koreans. Moreover, the TyG index was found to be a more powerful predictive indicator of IHD than fasting glucose or triglyceride levels alone. Accordingly, a high TyG index may be a useful additional measure in assessing cardiovascular risks for apparently healthy adults in clinical practice. Large-scale prospective studies are necessary to elucidate the mechanism for the association between the TyG index and IHD.


IHD: ischemic heart disease; TyG: triglyceride glucose; HERAS: health risk assessment study; HIRA: Korea Health Insurance Review & Assessment; HRs: hazard ratios; CIs: confidence intervals; HOMA-IR: homeostasis model assessment of insulin resistance; BMI: body mass index; KNHANES: Korea National Health and Nutrition Examination Survey; CVD: cardiovascular diseases; hsCRP: high sensitivity C-reactive protein; CKD: chronic kidney disease; ANOVA: analysis of variance; ROC: receiver-operating characteristic; AUC: area under the receiver-operating characteristic curve.


Ethics approval and consent to participate

This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Yonsei University College of Medicine, Seoul, Korea.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed in the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.


This study was supported by a 2010 Grant from the Korean Academy of Medical Sciences.

Authors’ contributions

BP, YJL, and DHJ designed the study; BP, YJL, and DHJ assisted with data acquisition and interpretation; BP, YJL and HSL performed statistical analyses; BP, YJL, HSL, and DHJ contributed to the discussion; BP and YJL drafted the manuscript; and DHJ revised the manuscript. All authors read and approved the final manuscript.


The authors would like to thank the Health Insurance Review and Assessment Services (HIRA) for their cooperation.


  1. Smith SC, Jr., Jackson R, Pearson TA, Fuster V, Yusuf S, Faergeman O, et al. Principles for national and regional guidelines on cardiovascular disease prevention: a scientific statement from the World Heart and Stroke Forum. Circulation. 2004;109(25):3112-21.
  2. Yusuf S, Reddy S, Ôunpuu S, Anand S. Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation. 2001;104(22):2746-53.
  3. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovascular diabetology. 2014;13(1):146.
  4. Park B, Lee H-S, Lee Y-J. Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among non-obese adults: A 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort. Translational Research. 2020.
  5. Lee S-H, Kwon H-S, Park Y-M, Ha H-S, Jeong SH, Yang HK, 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.
  6. Shin K-A. Triglyceride and glucose (TyG) index is a clinical surrogate marker for the diagnosis of metabolic syndrome. Biomedical Science Letters. 2017;23(4):348-54.
  7. Ha K, Kim K, Chun OK, Joung H, Song Y. Differential association of dietary carbohydrate intake with metabolic syndrome in the US and Korean adults: data from the 2007–2012 NHANES and KNHANES. European journal of clinical nutrition. 2018;72(6):848-60.
  8. Kim B-Y, Won JC, Lee JH, Kim H-S, Park JH, Ha KH, et al. Diabetes Fact Sheets in Korea, 2018: an appraisal of current status. Diabetes & metabolism journal. 2019;43(4):487-94.
  9. Roh E, Ko S-H, Kwon H-S, Kim NH, Kim JH, Kim CS, et al. Prevalence and management of dyslipidemia in Korea: Korea National Health and Nutrition Examination Survey during 1998 to 2010. Diabetes & metabolism journal. 2013;37(6):433-49.
  10. Lee SB, Ahn CW, Lee BK, Kang S, Nam JS, You JH, et al. Association between triglyceride glucose index and arterial stiffness in Korean adults. Cardiovasc Diabetol. 2018;17(1):41.
  11. Kim MK, Ahn CW, Kang S, Nam JS, Kim KR, Park JS. Relationship between the triglyceride glucose index and coronary artery calcification in Korean adults. Cardiovasc Diabetol. 2017;16(1):108.
  12. Won KB, Park EJ, Han D, Lee JH, Choi SY, Chun EJ, et al. Triglyceride glucose index is an independent predictor for the progression of coronary artery calcification in the absence of heavy coronary artery calcification at baseline. Cardiovasc Diabetol. 2020;19(1):34.
  13. Jin JL, Cao YX, Wu LG, You XD, Guo YL, Wu NQ, et al. Triglyceride glucose index for predicting cardiovascular outcomes in patients with coronary artery disease. J Thorac Dis. 2018;10(11):6137-46.
  14. Ma X, Dong L, Shao Q, Cheng Y, Lv S, Sun Y, et al. 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.
  15. Evans JM, Wang J, Morris AD. Comparison of cardiovascular risk between patients with type 2 diabetes and those who had had a myocardial infarction: cross sectional and cohort studies. Bmj. 2002;324(7343):939-42.
  16. Lee CD, Folsom AR, Pankow JS, Brancati FL. Cardiovascular events in diabetic and nondiabetic adults with or without history of myocardial infarction. Circulation. 2004;109(7):855-60.
  17. Levey AS, Eckardt KU, Tsukamoto Y, Levin A, Coresh J, Rossert J, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 2005;67(6):2089-100.
  18. Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest. 2016;46(2):189-97.
  19. Lee EY, Yang HK, Lee J, Kang B, Yang Y, Lee SH, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155.
  20. Hu FB, Stampfer MJ, Solomon CG, Liu S, Willett WC, Speizer FE, et al. The impact of diabetes mellitus on mortality from all causes and coronary heart disease in women: 20 years of follow-up. Arch Intern Med. 2001;161(14):1717-23.
  21. Cho E, Rimm EB, Stampfer MJ, Willett WC, Hu FB. The impact of diabetes mellitus and prior myocardial infarction on mortality from all causes and from coronary heart disease in men. J Am Coll Cardiol. 2002;40(5):954-60.
  22. Pylypchuk R, Wells S, Kerr A, Poppe K, Riddell T, Harwood M, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet. 2018;391(10133):1897-907.
  23. Panagiotakos D, Pitsavos C, Chrysohoou C, Palliou K, Lentzas I, Skoumas I, et al. Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study. Nutr Metab Cardiovasc Dis. 2009;19(4):253-63.
  24. Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, et al. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc Diabetol. 2017;16(1):30.
  25. Lee SH, Kwon HS, Park YM, Ha HS, Jeong SH, Yang HK, 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.
  26. Lee SH, Han K, Yang HK, Kim MK, Yoon KH, Kwon HS, et al. Identifying subgroups of obesity using the product of triglycerides and glucose: the Korea National Health and Nutrition Examination Survey, 2008-2010. Clin Endocrinol (Oxf). 2015;82(2):213-20.
  27. Kim JA, Yoon S, Kim LY, Kim DS. Towards Actualizing the Value Potential of Korea Health Insurance Review and Assessment (HIRA) Data as a Resource for Health Research: Strengths, Limitations, Applications, and Strategies for Optimal Use of HIRA Data. J Korean Med Sci. 2017;32(5):718-28.


Table 1. Baseline characteristics of the study population according to TyG index quartiles



Quartile of TyG index




n = 16,456


n = 4,140


n = 4,119


n = 4,096


n = 4,100

P value

TyG index

8.49 ± 0.56

≤ 8.08





Age (years)

46.1 ± 9.5

44.0 ± 8.9

46.0 ± 9.5

47.5 ± 9.7

46.8 ± 9.4

< 0.001

Male sex (%)






< 0.001

Body mass index (kg/m2)

23.4 ± 3.0

21.6 ± 2.5

22.7 ± 2.7

23.9 ± 2.8

25.2 ± 2.8

< 0.001

Systolic blood pressure (mmHg)

121.9 ± 15.5

115.0 ± 13.9

119.8 ± 14.8

124.1 ± 14.9

128.8 ± 14.8

< 0.001

Diastolic blood pressure (mmHg)

76.2 ± 10.1

71.5 ± 9.1

74.7 ± 9.5

77.7 ± 9.6

80.9 ± 9.7

< 0.001

Mean arterial pressure (mmHg)

91.5 ± 11.5

86.0 ± 10.3

89.8 ± 10.9

93.2 ± 11.0

96.9 ± 11.0

< 0.001

Fasting plasma glucose (mg/dl)

91.4 ± 9.8

85.8 ± 7.8

90.2 ± 8.3

92.9 ± 9.1

96.8 ± 10.3

< 0.001

Total cholesterol (mg/dL)

190.3 ± 33.3

175.7 ± 29.6

185.6 ± 29.7

195.5 ± 32.1

204.4 ± 34.4

< 0.001

Triglyceride (mg/dL)

124.2 ± 84.9

58.9 ± 10.7

87.3 ± 11.6

123.5 ± 17.9

227.9 ± 109.2

< 0.001

HDL-cholesterol (mg/dL)

53.2 ± 12.6

61.3 ± 12.4

55.8 ± 11.9

50.7 ± 10.7

45.1 ± 9.2

< 0.001

High sensitivity C-reactive protein (mg/L)

1.0 ± 1.3

0.7 ± 1.2

0.9 ± 1.3

1.2 ± 1.4

1.3 ± 1.4

< 0.001

Current smoker (%)






< 0.001

Alcohol drinking (%)






< 0.001

Regular exercise (%)






< 0.001

Hypertension (%)






< 0.001

Chronic kidney disease (%)







Data are expressed as the mean ± SD or percentage


Table 2. TyG index versus fasting glucose and serum triglyceride levels for predicting ischemic heart disease


Pairwise comparison of AUC


TyG index for classifying ischemic heart disease



95% CI

P value


Sensitivity (%)

Specificity (%)


P value

TyG index vs fasting plasma glucose


0.01 to 0.08







TyG index vs serum triglyceride levels


0.00 to 0.01







Fasting plasma glucose vs serum triglyceride levels


0.00 to 0.07







TyG index








< 0.001

Fasting plasma glucose








< 0.001

Serum triglyceride levels








< 0.001

ROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve; TyG index, triglyceride-glucose index


Table 3. Hazard ratios and 95% confidence intervals for new-onset ischemic heart diseases according to TyG index quartiles


TyG index quartiles



n = 4,140


n = 4,119


n = 4,096


n = 4,100

New cases of ischemic heart disease, n





Mean follow-up, years

2.4 ± 1.1

2.4 ± 1.1

2.4 ± 1.1

2.4 ± 1.1

Pearson-years of follow-up





Incidence rate/1000 person -years





Model 1

1.00 (reference)

1.41 (0.96–2.08)

1.67 (1.15–2.42)

2.13 (1.48–3.06)

Model 2

1.00 (reference)

1.61 (1.05–2.48)

1.85 (1.21–2.81)

2.29 (1.50–3.51)

Model 3

1.00 (reference)

1.63 (1.06-2.49)

1.88 (1.23-2.87)

2.35 (1.53-3.61)

Model 1: adjusted for age and sex

Model 2: adjusted for age, sex, body mass index, smoking status, alcohol intake, and physical activity

Model 3: adjusted for age, sex, body mass index, smoking status, alcohol intake, physical activity, high sensitivity C-reactive protein, and mean arterial blood pressure, C-reactive protein level, and chronic kidney disease