Association between the Triglyceride-Glucose Index and Cardiovascular Risk and Mortality across Different Diabetes Durations: A Nationwide Cohort Study
Article information
Abstract
Background
We aimed to assess the association between triglyceride-glucose (TyG) index and cardiovascular disease (CVD) risk and mortality in a large cohort of diabetes patients.
Methods
A retrospective cohort study of 1,090,485 participants from the Korean National Health Insurance Service database was conducted. Participants were stratified into TyG quartiles.
Results
Higher TyG index quartiles were significantly associated with an increased CVD risk and mortality risk. In fully adjusted models, participants in the highest TyG quartile (Q4) had an 18% higher risk of CVD (hazard ratio [HR], 1.18; 95% confidence interval [CI], 1.13 to 1.23) and a 16% higher risk of mortality (HR, 1.16; 95% CI, 1.11 to 1.23) compared to those in the lowest quartile (Q1). The association was particularly pronounced in patients with fasting glucose ≥126 mg/dL (CVD [HR, 1.33; 95% CI, 1.29 to 1.37], mortality [HR, 1.23; 95% CI, 1.20 to 1.26]; P for interaction <0.001). Patients with a diabetes duration of ≥10 years showed the strongest association between the TyG index and CVD risk (HR, 1.44; 95% CI, 1.38 to 1.50), while the mortality risk was particularly elevated in those with a diabetes duration of less than 5 years (HR, 1.23; 95% CI, 1.18 to 1.30). Subgroup analyses revealed stronger associations between TyG index and CVD risk in younger participants, non-obese individuals, and non-smokers.
Conclusion
The TyG index is a significant predictor of CVD and mortality in diabetic patients, particularly in those with poor glycemic control or longer disease duration.
INTRODUCTION
Cardiovascular disease (CVD) is the leading cause of death globally and poses an even greater threat to individuals with diabetes mellitus (DM) [1,2]. The global rise in DM prevalence has heightened the importance of identifying accurate and practical tools for CVD risk prediction in this high-risk population [3]. Traditional risk factors, such as hyperglycemia, hypertension, and dyslipidemia, are established contributors to CVD. However, these factors alone do not fully capture the underlying metabolic disturbances—particularly insulin resistance (IR)—that are intricately linked to increased cardiovascular risk in DM patients [4].
IR is a key pathological feature of type 2 DM and plays a central role in the development of both DM and CVD. It drives a cascade of metabolic abnormalities, including hyperglycemia, dyslipidemia, and inflammation, all of which promote endothelial dysfunction and accelerate atherosclerosis [5]. In clinical practice, IR can be estimated using markers such as the homeostasis model assessment of insulin resistance (HOMA-IR), which is a reliable method used in both research and routine care [6]. Although HOMA-IR is valuable for assessing IR, its usefulness is limited in populations with preserved β-cell function or significant insulin secretion defects, where the correlation between insulin sensitivity and HOMA-IR can be less accurate [7]. Furthermore, HOMA-IR requires precise insulin measurement, which can vary depending on assay standardization, making it less robust across diverse settings.
Given these limitations, simpler and more universally applicable surrogate markers for IR have garnered increasing attention. The triglyceride-glucose (TyG) index, derived from fasting triglycerides (TG) and fasting glucose (FG) levels, has emerged as a promising alternative [8]. It is easily calculated and correlates well with IR. Recent study has demonstrated that the TyG index not only reflects IR but also predicts CVD outcomes, such as myocardial infarction (MI) and stroke, in both DM and non-DM populations [3]. Despite this growing evidence, its prognostic utility in DM patients across varying disease durations remains underexplored.
This study investigates the association between the TyG index and the risk of both CVD and mortality in a nationwide cohort of DM patients. Specifically, we will assess how the TyG index predicts CVD and mortality outcomes across different durations of DM.
METHODS
Data collection
This retrospective cohort study used data from the Korean National Health Insurance Service (NHIS) database, which is part of South Korea’s single, universal healthcare system, covering nearly the entire population. NHIS enrollees are recommended to undergo medical checkups at least once every 2 years. The NHIS database consists of a qualification database (containing information on sex, age, income, area of residence, and qualification type), a claims database (providing details on treatment specifications, consultation records, diagnosis codes according to the International Classification of Diseases 10th revision [ICD-10], and prescription records), a health checkup database (containing general health examination results and lifestyle/behavior questionnaire responses), and death information [9].
Participants
Of the 2,616,505 participants who underwent health examinations between January 2015 and December 2016, individuals were excluded based on the following criteria: those taking lipid-lowering drugs (n=1,334,205), those with a history of MI before the index date (n=39,535), those with a prior stroke diagnosis (n=97,027), those with missing data (n=39,444), and those who died within 1 year after the health screening (n=15,809) to reduce the risk of reverse causality. Ultimately, 1,090,485 participants were included in the analysis and followed from baseline until December 31, 2022. This study complied with the ethical standards of the Declaration of Helsinki, and the protocol using secondary data was approved by the Eunpyeong St. Mary’s Hospital Institutional Review Board of Catholic Medical Center, Catholic University of Korea (IRB approval No. PC24ZASI0127). Researchers can access the NHIS database after obtaining approval from the official review committee. Written informed consent was waived due to the use of de-identified, previously collected data.
Demographic variables and measurement
Demographic and lifestyle data were collected using a standardized self-reported questionnaire, covering variables such as smoking status (current smoker, defined as having consumed at least five packs or 100 cigarettes and currently smoking), alcohol consumption (≥30 g/day for males and ≥20 g/day for females), and regular exercise (≥20 minutes of vigorous-intensity physical activity at least 3 days per week, or ≥20 minutes of moderate-intensity physical activity at least 5 days per week). Income level was dichotomized, with the lower 25% representing the lower-income group. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with participants seated after at least 5 minutes of rest. Body weight, height, and waist circumference (WC) were directly measured at each visit, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Laboratory tests included serum FG, alanine aminotransferase, aspartate aminotransferase, and γ-glutamyl transpeptidase, total cholesterol (TC), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and TG which were measured after an overnight fast of at least 8 hours. The presence of DM was defined as FG ≥126 mg/dL at least one claim per year under ICD-10 codes E10–14 and the prescription of anti-diabetic medication. Hypertension was defined as SBP/DBP ≥140/90 mm Hg or at least one claim per year under ICD-10 codes I10–13 or I15 and the prescription of antihypertensive agents. Dyslipidemia was defined as TC ≥240 mg/dL or at least one claim per year under ICD-10 code E78 and the prescription of lipid-lowering drugs.
Chronic kidney disease (CKD) was defined by an estimated glomerular filtration rate of <60 mL/min/1.73 m2, calculated using the Modification of Diet in Renal Disease formula: 186×(serum creatinine)−1.154×age−0.203×0.742 (if female) [10].
Definition of TyG
The TyG index was calculated using the following equation:
Definition of study outcomes
The primary outcomes of this study were CVD diagnosis and death. CVD was defined as the first occurrence of either MI (ICD-10 codes I21–I22) or stroke (ICD-10 codes I63–I64). These diagnoses were confirmed through claims for brain magnetic resonance imaging or brain computed tomography scans during hospitalization.
Statistical analysis
Continuous variables are presented as mean±standard deviation, while categorical variables are expressed as numbers and percentages. An independent t test was used to compare continuous variables, and the chi-squared test was employed for categorical variables. Annual trends in the number of cases and incidence were analyzed using the chi-square test for trends within age-gender strata. The incidence rate of thyroid cancer was calculated as the number of events divided by the total follow-up duration, expressed per 1,000 person-years. Cox proportional hazard models were used to estimate hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) to evaluate the risk of CVD and death across different TyG index quartiles. Significance was assessed using the log-rank test. Model 1 was a crude model providing unadjusted HR estimates for CVD and death. Model 2 was adjusted for age and sex, while Model 3 further adjusted for age, sex, BMI, smoking status, alcohol consumption, regular exercise, income level, hypertension, CKD, the use of three or more oral anti-diabetic agents, and insulin therapy. Model 4 was the fully adjusted model, which included all factors from Model 3 as well as TC. Subgroup analyses were performed by stratifying participants based on sex, age, obesity, smoking status, and alcohol consumption, with interaction testing conducted using the likelihood ratio test. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and a P value of <0.05 was considered statistically significant.
RESULTS
The baseline characteristics according to TyG quartiles
A total of 1,090,485 participants were analyzed with a mean follow-up of 5.64 years. The mean age was 56.96±12.47 years in total participants, with younger participants (<40 years) more prevalent in higher TyG quartiles (6.37% in Q1, 13.86% in Q4) (Table 1). Conversely, participants aged ≥50 years were concentrated in lower quartiles (78.85% in Q1, 55.94% in Q4). Men comprised 68.55% of the cohort. Most participants had new-onset DM or disease duration of less than 5 years. The proportion of participants using three or more oral anti-diabetic agents was 15.06%, decreasing from 16.32% in Q1 to 13.9% in Q4. Insulin use also decreased from 8.34% in Q1 to 4.35% in Q4.
The Baseline Characteristics according to TyG Quartiles
Obesity prevalence increased from 34.5% in Q1 to 60.48% in Q4, along with increases in body weight, BMI, and WC. SBP and DBP also increased, while hypertension prevalence remained similar across quartiles. The prevalence of hypertension and chronic CKD was lower in Q4. Regular exercise was more common in Q1, while smoking and high-risk alcohol consumption were more frequent in Q4. Lipid profiles and FG levels changed with higher TyG quartiles; HDL-C levels decreased from 56.83 mg/dL in Q1 to 46.3 mg/dL in Q4, while FG, TC, and TG levels increased.
Association between TyG quartiles and CVD risk or mortality
The relationship between the TyG index and CVD outcomes, including MI, stroke, and all-cause mortality, was analyzed across quartiles in the overall cohort (Table 2). A higher TyG index was consistently associated with an increased risk of CVD. In the fully adjusted model (model 4), individuals in the highest quartile (Q4) had a HR of 1.28 (95% CI, 1.25 to 1.31) for CVD compared to the lowest quartile (Q1), with a clear upward trend observed across quartiles.
Association between TyG Index Quartiles and CVD Outcomes
A higher TyG index also showed a significant association with increased risk of MI or stroke after adjustment for all covariates. The HR for MI in Q4 was 1.22 (95% CI, 1.17 to 1.26), and for stroke, it was 1.34 (95% CI, 1.29 to 1.38), compared to Q1.
CVD mortality also increased progressively across TyG quartiles. However, a significant association with CVD mortality was observed only in the highest quartile (Q4), with an HR of 1.19 (95% CI, 1.16 to 1.22) in the fully adjusted model.
Association between TyG quartiles and CVD risk according to DM duration
The association between TyG index quartiles and CVD incidence was analyzed across different durations of DM, using Q1 as the reference group (Table 3, Fig. 1A). The incidence rate of CVD per 1,000 person-years varied across TyG quartiles and DM duration categories. Among patients with new-onset DM, the incidence rate was 6.03 in Q1 and 6.25 in Q4. For patients with a DM duration of less than 5 years, the incidence rate ranged from 9.21 in Q1 to 9.06 in Q4, while for those with a DM duration of 5 to 9 years, it increased from 11.83 in Q1 to 13.14 in Q4. In patients with a DM duration of 10 years or more, the IR was 14.91 in Q1 and 19.47 in Q4.
Association between TyG Index Quartiles and CVD Risk according to DM Duration Categories
(A) Association between triglyceride-glucose (TyG) quartiles and cardiovascular disease (CVD) risk according to diabetes mellitus (DM) duration. (B) Association between TyG quartiles and CVD mortality according to DM duration.
CVD risk increased with both higher TyG quartiles and longer DM duration. In fully adjusted model with age, sex, BMI, income, smoking status, alcohol consumption, physical activity, hypertension, CKD, and TC level (model 4), CVD risk in new-onset DM was highest in TyG quartile (Q4) (HR, 1.18; 95% CI, 1.13 to 1.23). In patients with a DM duration of less than 5 years, the adjusted HR (aHR) for CVD risk was 1.23 (95% CI, 1.16 to 1.29) in Q4. Among those with a DM duration of 5 to 10 years, aHR for CVD risk was 1.34 (95% CI, 1.27 to 1.41) in Q4. For patients with a DM duration of 10 years or more, aHR for CVD risk was highest in Q4 at 1.44 (95% CI, 1.38 to 1.50).
Additional analyses, using the lowest quartile of the TyG index in individuals with new-onset DM as the reference group, further supported these findings (Supplemental Table S1). In these analyses, the HR for CVD in the highest TyG quartile among patients with a DM duration of 10 years or more increased to 3.24 (95% CI, 3.09 to 3.39). Even after full adjustment, the HR in this group remained significantly elevated at 1.87 (95% CI, 1.78 to 1.97; P for interaction <0.001).
Risk of MI or stroke according to TyG index and DM duration
The impact of TyG index quartiles on MI incidence varied according to DM duration. In new-onset diabetes, the incidence rate increased from 2.86 to 3.23 per 1,000 person-years, with an aHR of 1.18 (95% CI, 1.11 to 1.26) for Q4 vs. Q1. In patients with less than 5 years of DM, the incidence rate remained stable (4.44 vs. 4.35), but the aHR rose to 1.12 (95% CI, 1.04 to 1.21). In the 5- to 9-year group, the incidence rate showed an increase (5.66 to 5.93), with an aHR of 1.17 (95% CI, 1.08 to 1.26). For those with a DM duration of ≥10 years, the incidence rate increased from 6.80 to 9.14, with a corresponding aHR of 1.40 (95% CI, 1.32 to 1.50).
Stroke incidence showed a similar pattern. In new-onset DM, the incidence rate was stable (3.40 in Q1 vs. 3.23 in Q4), but the aHR for Q4 was 1.17 (95% CI, 1.02 to 1.24). For patients with less than 5 years of DM, the incidence rate remained steady (5.16 vs. 5.07), but the aHR rose to 1.32 (95% CI, 1.23 to 1.42). In the 5- to 9-year group, the incidence rate increased from 6.68 to 7.73, with an aHR of 1.49 (95% CI, 1.40 to 1.60). For those with ≥10 years of DM, the IR rose from 8.78 to 11.30, and the aHR reached 1.49 (95% CI, 1.40 to 1.57) (Supplemental Table S2).
Association between TyG index and CVD mortality according to DM duration
The association between TyG index quartiles and mortality was evaluated across different diabetes durations, using the lowest quartile (Q1) as the reference (Table 4, Fig. 1B). In new-onset DM, the IR dropped from 8.75 in Q1 to 5.11 in Q4. Similarly, for DM duration less than 5 years, the IR fell from 13.67 to 8.03, for 5–9 years from 17.64 to 12.02, and for ≥10 years from 22.90 to 18.97, indicating a clear downward trend in incidence rates as TyG index quartiles increased.
Association between TyG Index Quartiles and CVD Mortality according to DM Duration Categories
Despite these reductions in incidence, the aHR for mortality consistently increased with higher TyG quartiles in all DM duration groups. In new-onset DM, Q4 had the highest mortality risk (aHR, 1.09; 95% CI, 1.04 to 1.13). The aHR increased to 1.17 (95% CI, 1.11 to 1.23) for those with DM duration <5 years, 1.23 (95% CI, 1.18 to 1.30) for 5–9 years, and 1.29 (95% CI, 1.24 to 1.34) for ≥10 years, indicating a clear trend of increasing risk with longer disease duration.
Further analysis confirmed these findings, showing that in patients with ≥10 years of DM, the aHR for CVD mortality in Q4 was markedly higher (aHR, 2.17; 95% CI, 2.08 to 2.27), and remained elevated even after adjusting for all covariates (aHR, 1.15; 95% CI, 1.10 to 1.20; P interaction <0.001) (Supplemental Table S3).
Association between TyG index and CVD or CVD mortality according to glycemic control
The association between the TyG index and CVD or CVD mortality was assessed across subgroups stratified by glycemic control, including FG levels, insulin use, and the number of oral anti-diabetic drugs (OADs) (Supplemental Table S4).
CVD risk
There was no significant difference according to insulin use or number of OADs. Non-insulin users had an aHR for CVD in Q4 vs. Q1 of 1.27 (95% CI, 1.24 to 1.31), which was comparable to insulin users (aHR, 1.31; 95% CI, 1.22 to 1.41; P for interaction=0.596). Similarly, patients on fewer than three OADs showed an aHR of 1.27 (95% CI, 1.24 to 1.31), which was consistent with those on three or more agents (aHR, 1.29; 95% CI, 1.22 to 1.35; P for interaction=0.832). Patients with FG levels ≥126 mg/dL showed a stronger association between the TyG index and CVD (aHR for Q4 vs. Q1, 1.33; 95% CI, 1.29 to 1.37) compared to those with FG levels <126 mg/dL (aHR, 1.13; 95% CI, 1.05 to 1.22; P for interaction <0.001).
CVD mortality
The association between the TyG index and CVD mortality showed consistent results across subgroups stratified by glycemic control. There was no significant difference based on insulin use or the number of OADs. In those with FG levels ≥126 mg/dL, the aHR for Q4 vs. Q1 was 1.23 (95% CI, 1.20 to 1.26), while no significant association was observed in the <126 mg/dL group (aHR, 0.97; 95% CI, 0.90 to 1.05; P for interaction <0.001).
Subgroup analyses
Subgroup analyses were performed based on age, sex, obesity, smoking status, alcohol consumption, and regular exercise. Across all subgroups, a higher TyG index was consistently associated with an increased CVD risk.
In sex-specific analyses, both men and women with longer DM duration in the highest TyG quartile had elevated CVD risks (men: aHR, 1.35; women: aHR, 1.51; P interaction=0.085) (Supplemental Fig. S1). Age-stratified analysis showed that younger patients (20–39 years) had a significantly higher CVD risk in Q4 compared to older patients (40–49 or ≥50 years) regardless to DM duration (Supplemental Fig. S2). When stratified by obesity status, non-obese individuals showed a stronger association between the TyG index and CVD risk. The highest TyG quartile was associated with a higher CVD risk in non-obese individuals (aHR, 1.24) and those with obesity (aHR, 1.09; P for interaction=0.009) (Supplemental Fig. S3). Non-smokers and non-drinkers exhibited a stronger association between higher TyG index levels and CVD risk. For individuals in the highest quartile (Q4) with longer DM duration, non-smokers (aHR, 1.45), non-drinkers (aHR, 1.42) and those who engaged in regular exercise (aHR, 1.51) showed a greater increase in CVD risk compared to smokers and drinkers (Supplemental Figs. S4-S6).
DISCUSSION
This large, population-based cohort study demonstrates a significant association between the TyG index and the risk of CVD and mortality in patients with DM. Our findings reveal that higher TyG index quartiles are strongly predictive of increased risk for MI, stroke, and mortality. The results show that the TyG index is a robust predictor of adverse outcomes across various stages of DM, including new-onset and long-standing DM cases. The association between the TyG index and these outcomes was stronger in patients with longer DM duration, higher FG levels (≥126 mg/dL), younger individuals, and non-obese populations. These results support the TyG index as a simple and effective tool for assessing metabolic risk and CVD outcomes, independent of traditional CVD risk factors.
The TyG index, derived from TG and FG, serves as a surrogate marker for IR. IR is a key pathophysiological process in type 2 DM and contributes directly to the development of atherosclerosis through multiple pathways [12]. IR contributes to atherosclerosis development through dyslipidemia, endothelial dysfunction, inflammation, and oxidative stress [13-15]. Elevated TG levels and hyperglycemia reflect an advanced metabolic state characterized by impaired lipid metabolism, chronic inflammation, and increased production of reactive oxygen species, which together promote endothelial damage and atherosclerosis progression [16,17].
Previous studies have consistently demonstrated a strong association between an elevated TyG index and increased risks of DM and CVD [18-20]. However, the role of the TyG index in predicting CVD outcomes in patients with established DM remains controversial. Our study shows that the highest TyG quartile (Q4) was associated with the greatest risk of CVD outcomes, including MI and stroke. This aligns with findings from previous Taiwan study, which demonstrated that a high TyG index was associated with a 1.52-fold increased risk of CVD risk, including MI and stroke in patients with DM [21]. Conversely, Sanchez-Inigo et al. [22] reported no significant association between TyG index and 10-year CVD risk. In those with a longer duration of DM, the cumulative burden of chronic hyperglycemia and IR exacerbates cardiovascular risk [23]. Prolonged exposure to elevated glucose and TG levels leads to persistent vascular inflammation, endothelial dysfunction, and arterial stiffness, promoting plaque formation and rupture, thus increasing the risk of MI and stroke [24]. The progressive nature of IR in long-standing DM further compounds this metabolic dysfunction, as reflected in the higher HRs for CVD in patients with elevated TyG index levels.
Our study investigated the relationship between the TyG index and cardiovascular outcomes by stratifying patients based on glycemic control. Unlike previous studies, which primarily focused on overall diabetes populations, our analysis highlights the stronger association of the TyG index with CVD and mortality risk in patients with FG levels ≥126 mg/dL, compared to those with levels <126 mg/dL. These findings indicate the compounding effects of hyperglycemia on IR and its downstream consequences on CVD risk. In contrast, the predictive value of the TyG index remained consistent across subgroups defined by insulin use or the number of OAD, suggesting its reliability as a marker of metabolic risk regardless of treatment intensity.
The impact of age on the association between the TyG index and CVD risk remains uncertain. A study from China highlighted the predictive value of the TyG index for CVD risk among adults aged 45 years or above [25]. Additionally, research conducted on individuals aged 65 and older demonstrated strong associations between the TyG index and both all-cause and CVD mortality [26]. In comparison, the association between a higher TyG index and CVD risk or mortality was particularly strong in younger individuals in our study. Mechanistically, younger DM patients in the higher TyG quartiles may experience more pronounced metabolic disturbances at an earlier age, leading to accelerated vascular damage [27]. The early onset of IR could result in longer exposure to elevated glucose and lipid levels, which accelerates the development of atherosclerotic plaques and increases CVD risk [28]. This is reflected in our finding that younger individuals (aged 20 to 39 years) with the highest TyG index had nearly double the risk of CVD compared to those in the lowest quartile. These results were consistent with those of a recent study based on United States population data [29]. Liu and Liang [29] demonstrated a positive association between the TyG index and CVD risk for individuals under 65 years of age with prediabetes or DM (odds ratio,1.65; 95% CI, 1.20 to 2.25). Although our study focused on a DM population, our results align with previous research in non-DM cohorts, which also found a stronger association between the TyG index and CVD incidence in younger individuals [30]. These findings suggest that the TyG index may serve as an early indicator of metabolic disturbances in younger individuals, where traditional risk factors such as hypertension and hyperlipidemia are less prevalent. In older populations, the cumulative effect of these conventional risk factors may reduce the sensitivity of TyG index in predicting cardiovascular risk associated with IR. Additionally, when used to assess IR in both DM and non-DM individuals, the TyG index was more reliable than the HOMA-IR [8].
Both men and women exhibited a consistent association between higher TyG index levels and CVD risk, with a slightly stronger trend observed in women, aligning with findings from a previous Chinese study that focused on new-onset DM [31]. This suggests that while the metabolic disturbances reflected by the TyG index impact both sexes, women may experience a more pronounced effect due to factors like increased hepatocellular lipid content contributing to higher IR [32]. However, unlike the previous study, our analysis included individuals with varying durations of DM. Despite the broader range of DM duration in our study, the TyG index remained a strong predictor of CVD risk across both sexes.
The previous research utilizing National Health and Nutrition Examination Survey data demonstrated that a strong association between the TyG index and CVD events was particularly pronounced in obese individuals, where a high TyG index increased the risk of CVD by 63% and all-cause mortality by 32%, primarily in obese young and middle-aged adults [33]. In contrast, our study demonstrates that the TyG index is a strong predictor, particularly in non-obese DM individuals. Our finding that the TyG index was a stronger predictor of CVD risk in non-obese individuals may be partly explained by the complex metabolic roles of adipose tissue and the phenomenon known as the ‘obesity paradox,’ where adipose tissue, particularly through adipokines, exerts protective effects on glucose and lipid metabolism, reducing oxidative stress and inflammation [34,35]. In non-obese individuals, lower levels of adiponectin may result in reduced protection against IR and metabolic disturbances, making them more vulnerable to CVD risk [36]. In new-onset and early-stage DM patients, non-obese individuals in the highest TyG quartile exhibited a higher cardiovascular risk compared to the obese individuals. For patients with a longer duration of DM (≥ 10 years), the predictive power of the TyG index was consistent across both obese and non-obese individuals, indicating that prolonged IR and hyperglycemia lead to similarly elevated CVD risk, regardless of BMI.
Our findings also demonstrated that non-smokers and non-drinkers exhibited a stronger relationship between higher TyG index levels and CVD risk. This could be due to the fact that smoking and alcohol consumption independently contribute to CVD risk, potentially diluting the relative contribution of IR.
A major strength of our study is that it is the first to investigate the association between the TyG index and CVD outcomes across different durations of DM. By stratifying our cohort based on DM duration, we were able to demonstrate that the TyG index remains a strong predictor of CVD events not only in individuals with new-onset DM but also in those with long-standing DM. The large sample size and the use of a nationwide cohort provide a robust, representative dataset that minimizes selection bias and allows for the generalization of our results to a broader DM population. The longitudinal nature of the study, with a mean follow-up period of 5.64 years, offers a comprehensive view of how the TyG index predicts long-term CVD outcomes. Additionally, by incorporating subgroup analyses based on age, sex, obesity, and lifestyle factors, our study adds nuance to the understanding of how IR contributes to CVD risk across different demographic and clinical subgroups.
Despite these strengths, our study also has certain limitations. One significant limitation is the reliance on a single point measurement of the TyG index and other clinical variables, which limits our ability to assess changes in metabolic status over time. Longitudinal measurements could provide more dynamic insights into the progression of IR and CVD risk. The absence of glycosylated hemoglobin data, while mitigated by FG levels, remains a limitation for assessing overall glycemic control. Additionally, the study population is composed primarily of South Korean individuals, which may limit the generalizability of the findings to other ethnic groups with different genetic backgrounds and environmental exposures. Another limitation of our study is that we focused exclusively on MI and stroke as primary CVD outcomes, without including other important CVD events such as heart failure, peripheral artery disease, or arrhythmias. While MI and stroke represent significant and clinically relevant endpoints, expanding the scope to include other CVD events could provide a more comprehensive assessment of the TyG index’s predictive value for broader CVD risk.
In conclusion, our study highlights the TyG index as a strong predictor of cardiovascular events in both new-onset and long-standing DM patients with poor glycemic control. The TyG index effectively identifies cardiovascular risk across various subgroups, including age, sex, obesity, and lifestyle factors, emphasizing its relevance beyond traditional risk factors. The TyG index should be considered a valuable tool for identifying high-risk diabetic patients and guiding early interventions. Longitudinal studies should further investigate its prognostic utility across broader populations and explore potential interventions aimed at reducing TyG index levels to mitigate cardiovascular risk in DM individuals.
Supplementary Material
Supplemental Table S1.
Association between TyG Index Quartiles and CVD Risk Stratified by DM Duration
Supplemental Table S2.
Association between TyG Index Quartiles and MI or Stroke according to DM Duration Categories
Supplemental Table S3.
Association between TyG Index Quartiles and CVD Mortality Stratified by DM Duration
Supplemental Table S4.
Association between TyG Index Quartiles and CVD or CVD Mortality according to Glycemic Control
Supplemental Fig. S1.
Sex: Hazard ratios were calculated separately for males and females across all triglyceride-glucose (TyG) index quartiles. (A) New-onset, (B) <5 years, (C) 5–9 years, and (D) ≥10 years.
Supplemental Fig. S2.
Age: Hazard ratio for different age groups (20–39, 40–49, and ≥50 years) were evaluated. (A) New-onset, (B) <5 years, (C) 5–9 years, and (D) ≥10 years. TyG, triglyceride-glucose.
Supplemental Fig. S3.
Obesity: Hazard ratios were compared between individuals with and without obesity. (A) New-onset, (B) <5 years, (C) <10 years, and (D) ≥10 years. TyG, triglyceride-glucose.
Supplemental Fig. S4.
Smoking status: Hazard ratios for smokers and non-smokers were evaluated across triglyceride-glucose (TyG) quartiles. (A) New-onset, (B) <5 years, (C) <10 years, and (D) ≥10 years.
Supplemental Fig. S5.
Alcohol consumption: Hazard ratios for drinkers and non-drinkers were compared. (A) New-onset, (B) <5 years, (C) <10 years, and (D) ≥10 years. TyG, triglyceride-glucose.
Supplemental Fig. S6.
Regular exercise: The impact of regular exercise on hazard ratios was analyzed, comparing individuals who engaged in regular exercise and those who did not. (A) New-onset, (B) <5 years, (C) 5–9 years, and (D) ≥10 years. TyG, triglyceride-glucose.
Notes
CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.
ACKNOWLEDGMENTS
This study was performed using the database from the National Health Insurance System and the results do not necessarily represent the opinion of the National Health Insurance Corporation.
AUTHOR CONTRIBUTIONS
Conception or design: J.K., K.D.H., E.Y.L., S.H.L., D.J.L., H.S.K., J.L. Acquisition, analysis, or interpretation of data: J.L, K.D.H., E.Y.L., S.H.L., D.J.L., H.S.K., J.L. Drafting the work or revising: J.K., J.L. Final approval of the manuscript: J.L.