KSPEN
KSSMN
ASSMN
Articles
- Page Path
- HOME > Ann Clin Nutr Metab > Volume 16(3); 2024 > Article
- Original article Triglyceride-glucose index predicts future metabolic syndrome in an adult population, Korea: a prospective cohort study
-
Min-Su Park
-
Annals of Clinical Nutrition and Metabolism 2024;16(3):168-172.
DOI: https://doi.org/10.15747/ACNM.2024.16.3.168
Published online: December 1, 2024
Department of Surgery, School of Medicine, Kyung Hee University, Seoul, Korea
- Corresponding author: Min-Su Park, email: ikireida@hanmail.net
• Received: November 12, 2024 • Revised: November 23, 2024 • Accepted: November 23, 2024
© 2024 The Korean Society of Surgical Metabolism and Nutrition · The Korean Society for Parenteral and Enteral Nutrition
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- 27 Views
- 0 Download
Abstract
-
Purpose The triglyceride-glucose (TyG) index has been proposed as a reliable surrogate marker for insulin resistance. This study aimed to assess the utility of the TyG index in predicting the future presence of metabolic syndrome (MetS) in an adult population.
-
Methods A total of 3,241 adults aged 40–70 years were included in this cross-sectional study. MetS was diagnosed based on the modified National Cholesterol Education Program Adult Treatment Panel III criteria, which requires the presence of at least three of the following components abdominal obesity, elevated blood pressure, dysglycemia, hypertriglyceridemia, and low high-density lipoprotein cholesterol.
-
Results In comparison to the homeostasis model assessment of insulin resistance (HOMA-IR), the TyG index exhibited superior diagnostic performance, with a higher area under the receiver operating characteristic curve of 0.854 vs. 0.702 for HOMA-IR. The 95% confidence interval for the TyG index was narrower, reflecting a more consistent predictive ability. Sensitivity for the TyG index was 79.7%, while specificity was 79.3%, compared to HOMA-IR, which showed a sensitivity of 52.7% and specificity of 78.3%.
-
Conclusion The TyG index is a highly effective and robust tool for identifying individuals at risk for MetS, demonstrating superior sensitivity and predictive accuracy over HOMA-IR. This index could be a valuable clinical marker for early detection of MetS, aiding in the prevention and management of associated metabolic disorders.
Introduction
Metabolic syndrome is preceded by a subset of the following conditions: obesity, hypertension, and insulin resistance, each of which increase the risk for cardiovascular disease, type 2 diabetes, and other chronic conditions [1,2]. The rising prevalence of metabolic syndrome (MetS) worldwide has made early identification and intervention critical in preventing these associated comorbidities. Insulin resistance, a central feature of MetS, remains challenging to measure in clinical practice, often requiring complex and costly tests. Identifying reliable, easily obtainable markers of insulin resistance has therefore become a priority in clinical and epidemiological research.
The triglyceride-glucose (TyG) index, calculated from fasting triglycerides and glucose levels, has been proposed as a cost-effective and reliable surrogate marker for insulin resistance [1]. Previous studies have shown that the TyG index correlates well with more direct measures of insulin resistance, such as the hyperinsulinemic-euglycemic clamp and homeostasis model assessment of insulin resistance (HOMA-IR). Furthermore, it has been suggested that the TyG index is independently associated with an increased risk of MetS and other metabolic disorders [3,-5]. However, the ability of the TyG index to predict the future development of MetS over time remains understudied.
The primary objective of this study was to evaluate the predictive utility of the TyG index for the future development of MetS in a large cohort of adults.
Methods
The present study was approved by the Institutional Review Board of Korea Centers for Disease Control and Prevention (KBP-2017-014). This study was performed in line with the principles of the Declaration of Helsinki.
This was a prospective cohort study aimed at evaluating the predictive utility of the TyG index for the future development of MetS. It involved a comparative analysis of patients grouped according to the presence or absence of MetS, based on the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria.
The study was conducted as part of the community-based Korean Genome and Epidemiology Study, a large-scale prospective cohort study which ran from 2001 to 2014. Participants were drawn from the urban Ansan and rural Anseong regions in Korea. For the purposes of this study, participants were stratified into two subgroups based on the modified NCEP-ATP III criteria: the MetS group and the healthy control group.
Among 8,652 subjects, 3,241 adults aged 40–70 years were included in the study. Inclusion criteria were complete data measurements, and informed consent from the subjects. Each subject’s medical history, weight and height, waist circumference, and laboratory evaluations including serum total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein (HDL) cholesterol, fasting glucose, and serum insulin were collected. Exclusion criteria included the presence of mental disorders, malignancies, or incomplete medical record information. Participants with missing baseline data were also excluded. The participants were divided into 2 subgroups according to modified NCEPATP III criteria: MetS group (n=1,134) and healthy group (n=2,107).
The primary outcome of this study was the ability of the TyG index to predict the development of MetS over the duration of the study period from 2001 to 2014. Key performance metrics for this prediction were the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
Defining MetS
MetS was diagnosed according to the modified NCEP-ATP III criteria, which requires the presence of at least three of the following five parameters: (i) waist circumference: ≥90 cm in male and ≥80 cm in female (in accordance with the International Obesity Task Force criteria for Asian-Pacific population); (ii) triglycerides: ≥150 mg/dL; (iii) HDL-cholesterol: <40 mg/dL in male and <50 mg/dL in female; (iv) blood pressure: ≥130/85 mmHg or antihypertensive medications; and (v) fasting blood glucose: ≥110 mg/dL (fasting blood glucose ≥100 mg/dL was revised in 2005) or antidiabetic medications [6].
Clinical surrogate markers
The TyG index was calculated as ln [fasting triglycerides (mg/dL)×FPG (mg/dL)/2]. HOMA-IR was calculated as FPG (mmol/L)×fasting insulin (mIU/L)/22.5 [7].
There was no selection bias reportable.
Sample size estimation was not done since the entire target population was subjected to it.
Data were expressed as the mean±standard deviation for continuous variables and as frequencies and percentages for categorical variables. Comparisons of patient characteristics between the MetS and healthy groups were performed using the chi-square test for categorical variables and Student’s t-test for continuous variables.
The diagnostic performance of the TyG index and HOMA-IR in predicting MetS was evaluated using ROC curve analysis, with the AUROC, sensitivity, specificity, and the Youden index (YI) calculated. AUROC values were interpreted as follows: excellent (area under the curve [AUC] 0.90–1.00), good (AUC 0.80–0.89), fair (AUC 0.70–0.79), poor (AUC 0.60–0.69), or fail/no discriminatory capacity (AUC 0.50–0.59). A P-value of <0.05 was considered statistically significant. All statistical analyses were performed using MedCalc software.
Results
Among the 3,241 participants, the overall prevalence of MetS during the follow-up period from 2001 to 2014 was 34.9% (n=1,134). The participants were stratified into two groups based on the modified NCEP-ATP III criteria: the MetS group (n=1,134, 34.9%) and the healthy group (n=2,107, 65.1%). As shown in Table 1, individuals in the MetS group had a significantly higher average age compared to those in the healthy group (P<0.05). The prevalence rates of MetS were 42.4% in female and 27.4% in male. Subjects diagnosed with MetS exhibited significantly higher values for waist circumference, body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, triglycerides, HOMA-IR, and TyG index. In contrast, the MetS group had lower high-density lipoprotein cholesterol (HDL-C) levels compared to the healthy group (P<0.05). All components of MetS—hyperglycemia, elevated blood pressure, elevated triglycerides, low HDL-C, and central obesity—were significantly more pronounced in the MetS group than in the healthy group (P<0.05).
The incidence of MetS increased progressively with higher quartiles of the TyG index. Specifically, the incidence was 2.1% in the first quartile (Q1), 8.0% in the second quartile (Q2), 22.4% in the third quartile (Q3), and 41.8% in the fourth quartile (Q4). A similar trend was observed with HOMA-IR, with incidence rates of 16.5%, 15.8%, 19.6%, and 32.8% in Q1, Q2, Q3, and Q4, respectively (Table 2).
The diagnostic performance of various parameters for predicting MetS is summarized in Table 3. The AUROC for the TyG index was 0.854, significantly higher than that of HOMA-IR, which was 0.702. This finding suggests that the TyG index has higher accuracy and reliability in diagnosing MetS compared to HOMA-IR. The optimal cut-off values, sensitivity, specificity, and YI for each parameter are also shown in Table 3. The sensitivity of the TyG index was 79.7%, while its specificity was 79.3%. In contrast, HOMA-IR exhibited a sensitivity of 52.7% and a specificity of 78.3%.
Discussion
In this study, we evaluated the predictive utility of the TyG index for identifying individuals at risk for MetS in the large cohort. Our findings suggest that the TyG index is a highly effective tool for predicting the presence of MetS, outperforming the HOMA-IR in terms of diagnostic accuracy.
The TyG index demonstrated a significantly higher AUROC of 0.854, compared to the AUROC of 0.702 for HOMA-IR. This result is consistent with previous studies that have highlighted the superior diagnostic capacity of the TyG index in detecting insulin resistance and MetS. For instance, many researchers found that the TyG index was strongly correlated with more invasive measures of insulin resistance, such as the hyperinsulinemic-euglycemic clamp, and outperformed HOMA-IR in predicting MetS risk [8]. The larger AUROC for the TyG index in our study reflects its higher sensitivity (79.7%) and specificity (79.3%) compared to HOMA-IR, which had sensitivity of 52.7% and specificity of 78.3%. These results suggest that the TyG index is not only more sensitive but also more consistent in predicting MetS, offering a reliable and practical tool for early detection in clinical settings.
MetS is characterized by a combination of risk factors including abdominal obesity, elevated blood pressure, dyslipidemia, and insulin resistance, all of which are linked to an increased risk of cardiovascular disease and type 2 diabetes [1,2]. Given that insulin resistance plays a central role in the pathogenesis of MetS, identifying simple and cost-effective markers of insulin resistance is crucial for public health [9,10]. The TyG index, derived from fasting triglycerides and glucose levels, serves as an easily accessible surrogate for insulin resistance. Our study corroborates findings from other large cohort studies, which demonstrated that the TyG index is a strong predictor of MetS, particularly in populations at risk for metabolic disorders [11,12].
Interestingly, the incidence of MetS in our study showed a clear gradient across the quartiles of the TyG index. In the highest quartile (Q4), nearly 42% of participants were diagnosed with MetS, highlighting the potential of the TyG index as a risk stratification tool. This trend was similarly observed for HOMA-IR, though the correlation was weaker, underscoring the superior performance of the TyG index.
The simplicity and low cost of calculating the TyG index make it an appealing option for widespread clinical use, particularly in primary care settings [13,14]. Unlike HOMA-IR, which requires fasting insulin measurements, the TyG index only requires fasting glucose and triglyceride levels, which are routinely measured in standard clinical practice. Moreover, as both triglycerides and glucose are frequently monitored in patients with diabetes or cardiovascular risk, the TyG index can be easily integrated into existing clinical workflows. Given its high sensitivity and specificity, the TyG index could be used for early detection of MetS, allowing for timely interventions to prevent the progression to more severe metabolic conditions, such as type 2 diabetes and cardiovascular disease.
Although our study demonstrates the utility of the TyG index in predicting MetS, there are several limitations that should be considered. First, because this study was conducted in a specific Korean population, the generalizability of the results to other ethnic groups may be limited. Future studies are needed to confirm the predictive validity of the TyG index across diverse populations. Second, while our study focused on the cross-sectional relationship between the TyG index and MetS, longer-term prospective studies are needed to establish the TyG index as a reliable predictor of MetS development over time. Finally, while the TyG index performed well in comparison to HOMA-IR, it remains unclear whether it can outperform other emerging biomarkers of metabolic risk, such as adipokines or genetic risk scores, which may also provide additional insights into MetS prediction.
The TyG index is a promising marker for predicting the presence of MetS, with superior diagnostic performance compared to HOMA-IR. Its simplicity and high sensitivity and specificity make it an attractive tool for early identification of individuals at risk for MetS. The integration of the TyG index into routine clinical practice could improve early detection, risk stratification, and management of metabolic disorders, potentially reducing the burden of cardiovascular disease and type 2 diabetes in at-risk populations.
Acknowledgments
None.
Conflict of interest
The author of this manuscript has no conflicts of interest to disclose.
Funding
None.
Data availability
Contact the corresponding author for data availability.
Supplementary materials
None.
Table 1
Characteristics of the participants according to presence of metabolic syndrome
Table 2
Prevalence of metabolic syndrome according to HOMA-IR and TyG index quartile
| Quartile |
TyG index incidence of MetS (%) |
HOMA-IR incidence of MetS (%) |
|---|---|---|
| Q1 | 2.1 | 16.5 |
| Q2 | 8.0 | 15.8 |
| Q3 | 22.4 | 19.6 |
| Q4 | 41.8 | 32.8 |
HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose; MetS = metabolic syndrome; Q1 = first quartile (HOMA-IR<0.59, TyG index<7.95); Q2 = second quartile (0.59≤HOMA-IR<0.93, 7.95≤TyG index<8.41); Q3 = third quartile (0.93≤HOMA-IR<1.43, 8.41≤TyG index<8.86); Q4 = fourth quartile (1.43≤HOMA-IR, 8.86≤TyG index).
Table 3
The AUROC, optimal cut-off values, sensitivity and specificity of the clinical parameters for predicting MetS
- 1. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med 2011;9:48.ArticlePubMedPMCPDF
- 2. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 2017;11:215-25. ArticlePubMedPMCPDF
- 3. Ramdas Nayak VK, Satheesh P, Shenoy MT, Kalra S. Triglyceride glucose (TyG) index: a surrogate biomarker of insulin resistance. J Pak Med Assoc 2022;72:986-8. ArticlePubMed
- 4. Jog KS, Eagappan S, Santharam RK, Subbiah S. Comparison of novel biomarkers of insulin resistance with homeostasis model assessment of insulin resistance, its correlation to metabolic syndrome in south indian population and proposition of population specific cutoffs for these indices. Cureus 2023;15:e33653. ArticlePubMedPMC
- 5. Son DH, Lee HS, Lee YJ, Lee JH, Han JH. 2022;Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr Metab Cardiovasc Dis 32:596-604. ArticlePubMed
- 6. Sharma K, Poudyal S, Subba HK, Khatiwada S. Metabolic syndrome and life style factors among diabetes patients attending in a teaching hospital, Chitwan. PLoS One 2023;18:e0286139. ArticlePubMedPMC
- 7. Lee SH, Kwon HS, Park YM, Ha HS, Jeong SH, Yang HK, et al. 2014;Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study. PLoS One 9:e90430. ArticlePubMedPMC
- 8. Kurniawan LB. 2024;Triglyceride-glucose index as a biomarker of insulin resistance, diabetes mellitus, metabolic syndrome, and cardiovascular disease: a review. EJIFCC 35:44-51.PubMedPMC
- 9. Li M, Chi X, Wang Y, Setrerrahmane S, Xie W, Xu H. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther 2022;7:216.ArticlePubMedPMCPDF
- 10. Roberts CK, Hevener AL, Barnard RJ. Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. Compr Physiol 2013;3:1-58. ArticlePubMedPMCPDF
- 11. Nabipoorashrafi SA, Seyedi SA, Rabizadeh S, Ebrahimi M, Ranjbar SA, Reyhan SK, et al. The accuracy of triglyceride-glucose (TyG) index for the screening of metabolic syndrome in adults: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis 2022;32:2677-88. ArticlePubMed
- 12. Yu X, Wang L, Zhang W, Ming J, Jia A, Xu S, et al. Fasting triglycerides and glucose index is more suitable for the identification of metabolically unhealthy individuals in the Chinese adult population: a nationwide study. J Diabetes Investig 2019;10:1050-8. ArticlePubMedPDF
- 13. Nevárez-Sida A, Guerrero-Romero F. The triglycerides and glucose index: a cost-effectiveness analysis compared with the homeostatic model assessment for insulin resistance. Value Health Reg Issues 2023;37:49-52. ArticlePubMed
- 14. Wan H, Cao H, Ning P. 2024;Superiority of the triglyceride glucose index over the homeostasis model in predicting metabolic syndrome based on NHANES data analysis. Sci Rep 14:15499. ArticlePubMedPMCPDF
References
Figure & Data
REFERENCES
Citations
Citations to this article as recorded by 
Cite
XML DownloadTriglyceride-glucose index predicts future metabolic syndrome in an adult population, Korea: a prospective cohort study
Triglyceride-glucose index predicts future metabolic syndrome in an adult population, Korea: a prospective cohort study
Characteristics of the participants according to presence of metabolic syndrome
| Variable | Non-MetS | MetS | P-value | |
|---|---|---|---|---|
| (N=2,107) | (N=1,134) | |||
| Age (yr) | 49.13±7.81 | 52.69±7.21 | <0.001 | |
| Male (%) | 1,108 (52.5) | 434 (38.2) | <0.001 | |
| Body mass index (kg/m2) | 23.3±2.6 | 26.8±2.8 | <0.001 | |
| Waist circumference (cm) | 78.05±7.50 | 89.25±7.06 | <0.001 | |
| Systolic blood pressure (mmHg) | 115.99±15.14 | 134.38±17.28 | <0.001 | |
| Diastolic blood pressure (mmHg) | 77.90±10.15 | 88.88±10.54 | <0.001 | |
| Total cholesterol (mg/dL) | 186.3±31.5 | 198.3±26.4 | <0.001 | |
| HDL cholesterol (mg/dL) | 48.32±10.12 | 39.00±6.92 | <0.001 | |
| Triglyceride (mg/dL) | 122.30±59.71 | 227.58±136.89 | <0.001 | |
| Fasting glucose (mg/dL) | 83.05±13.23 | 93.00±26.39 | <0.001 | |
| HOMA-IR | 1.38±0.78 | 2.10±1.36 | <0.001 | |
| TyG index | 8.44±0.42 | 9.12±0.54 | <0.001 | |
| Metabolic syndrome component | ||||
| High blood pressure | 520 (24.6) | 883 (77.8) | <0.001 | |
| Impaired fasting glucose | 84 (3.9) | 250 (22.0) | <0.001 | |
| High triglyceride | 389 (18.5) | 877 (77.3) | <0.001 | |
| Low HDL cholesterol | 851 (40.3) | 1,018 (89.7) | <0.001 | |
| Abdominal obesity | 292 (13.8) | 921 (81.2) | <0.001 |
Values are presented as mean±standard deviation or number (%).
MetS = metabolic syndrome; HDL = high density lipoprotein; HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose.
Prevalence of metabolic syndrome according to HOMA-IR and TyG index quartile
| Quartile | TyG index incidence of MetS (%) |
HOMA-IR incidence of MetS (%) |
|---|---|---|
| Q1 | 2.1 | 16.5 |
| Q2 | 8.0 | 15.8 |
| Q3 | 22.4 | 19.6 |
| Q4 | 41.8 | 32.8 |
HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose; MetS = metabolic syndrome; Q1 = first quartile (HOMA-IR<0.59, TyG index<7.95); Q2 = second quartile (0.59≤HOMA-IR<0.93, 7.95≤TyG index<8.41); Q3 = third quartile (0.93≤HOMA-IR<1.43, 8.41≤TyG index<8.86); Q4 = fourth quartile (1.43≤HOMA-IR, 8.86≤TyG index).
The AUROC, optimal cut-off values, sensitivity and specificity of the clinical parameters for predicting MetS
| AUROC | SE.AUC | Lower limit | Upper limit | z | Sensitivity | Specificity | Cut-off | P-value | |
|---|---|---|---|---|---|---|---|---|---|
| HOMA-IR | 0.702 | 0.009 | 0.684 | 0.721 | 21.682 | 52.7 | 78.3 | 1.783 | <0.001 |
| TyG index | 0.854 | 0.007 | 0.841 | 0.867 | 53.866 | 79.7 | 79.3 | 8.719 | <0.001 |
AUROC = area under the receiver operating characteristic curve; MetS = metabolic syndrome; SE.AUC = standard error. area under the curve; HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose.
Table 1
Characteristics of the participants according to presence of metabolic syndrome
Values are presented as mean±standard deviation or number (%). MetS = metabolic syndrome; HDL = high density lipoprotein; HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose.
Table 2
Prevalence of metabolic syndrome according to HOMA-IR and TyG index quartile
HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose; MetS = metabolic syndrome; Q1 = first quartile (HOMA-IR<0.59, TyG index<7.95); Q2 = second quartile (0.59≤HOMA-IR<0.93, 7.95≤TyG index<8.41); Q3 = third quartile (0.93≤HOMA-IR<1.43, 8.41≤TyG index<8.86); Q4 = fourth quartile (1.43≤HOMA-IR, 8.86≤TyG index).
Table 3
The AUROC, optimal cut-off values, sensitivity and specificity of the clinical parameters for predicting MetS
AUROC = area under the receiver operating characteristic curve; MetS = metabolic syndrome; SE.AUC = standard error. area under the curve; HOMA-IR = homeostasis model assessment-insulin resistance; TyG = triglyceride-glucose.
