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Comparison of predicting value of triglyceride‑glucose indices family in cardiovascular disease risk over time: Insights from a 9-year nationwide prospective cohort study

  • Antian Chen , Yuhui Zhang EMAIL logo and Jian Zhang EMAIL logo
Published/Copyright: May 8, 2025
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Journal of Translational Internal Medicine
From the journal Journal of Translational Internal Medicine Volume 13 Issue 2

Introduction

Cardiovascular diseases (CVD) remain a leading cause of morbidity and mortality, particularly in older populations.[1] With the rising prevalence of CVD risk factors like hypertension, diabetes, and dyslipidemia, early identification and management are crucial. The Triglyceride-Glucose (TyG) indices family, comprising TyG, TyG-BMI, cumulative TyG, and cumulative TyGBMI, has emerged as a potential indicator of insulin resistance and cardiometabolic risk.[2,3] These indices, derived from fasting triglycerides, glucose levels, and BMI, have shown promise as predictors of insulin resistance, potentially surpassing traditional CVD risk factors.[4,5] However, most studies focus on individual indices, with limited research on the entire TyG indices family, especially in aging populations at higher CVD risk. This study comprehensively analyzes the TyG indices family and their association with CVD in individuals aged 45 and older, aiming to improve CVD risk stratification and inform targeted prevention strategies for aging populations.

Methods

Study design and population

The study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort study of 17,708 middle-aged and elderly adults aged 45 years and above, spanning 28 provinces.[6] CHARLS received ethical approval from the Biomedical Ethics Review Board of Peking University (IRB00001052-11015), and written consent was obtained from all participants. The CHARLS survey includes biennial follow-up waves, with blood sample tests available during the baseline and Wave 3. In this analysis, we included participants who were free from CVD at baseline and had complete data on fasting glucose, triglycerides, and BMI, resulting in a final sample size of 4800 individuals. Participants with pre-existing CVD, incomplete data, or loss to follow-up were excluded to minimize confounding and attrition bias.

Measurement of TyG indices

The TyG index was calculated as ln [TG (mg/dL) × FBG (mg/dL)/2]. TyG-BMI was calculated by multiplying the TyG index by BMI (kg/m2). To capture long-term exposure to metabolic disturbances, cumulative TyG and cumulative TyG-BMI were calculated by averaging the indices from two consecutive waves and multiplying by the time interval between the waves. These cumulative measures provide a more robust estimate of long-term metabolic dysfunction compared to single-point measurements.

Outcome assessment

CVD was determined as the primary outcome of the study and was defined as defined as self-reporting heart disease and/or stroke. Self-reporting heart disease is determined by whether answering “yes” to the question “Did your doctor tell you that you have been diagnosed with a heart attack, angina pectoris, coronary heart disease, heart failure, or other heart problem?” or selecting choices regarding the heart disease treatment “by Chinese Traditional Medicine/Western Modern Medicine/Other Treatments/None of the Above”. Similarly, stroke assessment involved participants self-reporting a “yes” to the question, “Have you been diagnosed with a stroke by a doctor? “ Additionally, those who indicated they received treatment for stroke through options such as “by Chinese Traditional Medicine/Western Modern Medicine/Physical Therapy/Acupuncture and Moxibustion/Occupational Therapy/None of the Above” were also identified as individuals with stroke.

Covariates

Several potential confounders were included in the analysis, including age, gender, residence (urban or rural), education level, marital status, smoking status, alcohol consumption, hypertension, and kidney disease. These variables were adjusted in models to isolate the independent effect of the TyG indices on CVD outcomes.

Statistical analysis

Cox proportional hazard regression models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between TyG indices and incident CVD. TyG, TyG-BMI, cumulative TyG, and cumulative TyG-BMI were divided into quartiles for comparison. Three models were developed: Model 1 adjusted for age and gender; Model 2 additionally adjusted for residence, education, marital status, smoking, and alcohol consumption; and Model 3 added adjustments for hypertension and kidney disease, both known risk factors for CVD.

Results

Baseline characteristics

The overall study is illustrated in Figure 1. Of all 11,847 participants in the baseline Wave 1, 4800 were included in the study, with more females (55.23%) than males (44.77%) (Supplementary Figure S1). The median age of the participants was 57 years. Among all participants enrolled, The BMI (23.16 in Wave 1 vs. 23.51 in Wave 3), TyG index (8.59, 8.63), and TyG-BMI (199.13 vs. 204.24) were all elevated in the follow-up of 4 years in 2015 with statistically significant differences (Supplementary Table S1).

Figure 1 Overall study design and implementation. (Created in BioRender. Chen, A. [2025] https://BioRender.com/q18u688).
Figure 1

Overall study design and implementation. (Created in BioRender. Chen, A. [2025] https://BioRender.com/q18u688).

Associations between TyG indices and CVD risk

The study assessed the impact of 10-unit increments in these indices on CVD risk over 4 and 9 years. The TyG index showed a 10% non-significant risk increase at 4 years (HR = 1.100, 95% CI: 0.965–1.254, P = 0.153) and a significant 19.2% at 9 years (HR = 1.192, 95% CI: 1.081–1.315, P < 0.001) (Supplementary Figure S2). The TyG-BMI increments correlated with a 0.5% risk increase at both time points (HR = 1.005, 95% CI: 1.003–1.007, P < 0.001; HR = 1.006, 95% CI: 1.004–1.007, P < 0.001) (Supplementary Figure S3). The cumulative TyG index showed an increasing trend (HR = 1.050, 95% CI: 0.998–1.105, P = 0.062) and a significant 8.3% increase at 9 years (HR = 1.083, 95% CI: 1.042–1.125, P < 0.001) (Supplementary Figure S4). The cumulative TyG-BMI consistently elevated CVD risk by 0.2% (HR = 1.002, 95% CI: 1.001–1.003, P < 0.001) (Supplementary Figure S5). Individuals categorized in the higher quartiles for the TyG index, TyG-BMI, and cumulative TyG index, demonstrate a progressively elevated risk of developing cardiovascular diseases. Specifically, those in Q4, representing the highest range of index values, face a greater risk when compared to their counterparts in Q1, Q2, and Q3, who have lower index values. Restricted cubic spline (RCS) regression revealed nonlinear associations between TyG-BMI and cumulative TyG-BMI levels and CVD risk , whereas the associations of TyG and cumulative TyG with CVD risk appeared more linear (Supplementary Figure S6).

Discussion

This study is the first to comprehensively compare the predictive value of the TyG indices family for long-term CVD risk in a middle-aged and older population. The findings demonstrate that both single-point and cumulative measures of the TyG indices are strongly associated with incident CVD, with cumulative indices providing enhanced predictive value. Among these, the cumulative TyG-BMI emerged as the most powerful predictor of CVD risk, reflecting the cumulative burden of insulin resistance and obesity over time.

The results align with previous research showing the TyG index as a simple, yet robust, marker of insulin resistance and CVD risk.[7,8] However, this study extends these findings by introducing the concept of cumulative indices, which capture long-term exposure to metabolic disturbances and offer greater predictive precision. This is particularly important in aging populations, where cumulative metabolic stress may drive the progression of CVD.[9] These findings underscore the potential of TyG indices family as accessible and cost-effective tools for CVD risk stratification in clinical practice.

Conclusion

This study introduces the concept of the TyG indices family and highlights the TyG index and cumulative TyG-BMI as strong long-term predictors of CVD by comparing the TyG indices family. This study identified these indices as novel tools for predicting CVD risk, providing valuable insights into long-term health. These findings contribute to CVD risk stratification and may guide early intervention strategies in clinical practice. Further research shall explore the use of these indices in diverse populations to optimize prevention efforts and validate their applicability. Additionally, future studies should explore the potential combination of the TyG indices with other clinical or genetic markers that could also enhance CVD risk assessment.

Supplementary Information

Supplementary materials are only available at the official site of the journal (www.intern-med.com).

Address for Correspondence: Yuhui Zhang and Jian Zhang, Heart Failure Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, No. 167 North Lishi Road, Xicheng District, Beijing 100037, China.

Funding statement: This work was supported by grants from the Beijing Natural Science Foundation (grant number 7222143), the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (grant number 2020-I2M-1-002), and the High-level Hospital Clinical Research Fund of Fuwai Hospital (grant number 2022-GSP-GG-9).

Acknowledgements

Figure 1 was created using BioRender.com.

  1. Author Contributions

    Antian Chen wrote the manuscript. Yuhui Zhang and Jian Zhang supervised this project. All authors have read the final manuscript and approved it for publication.

  2. Ethical Approval

    The CHARLS study received ethical approval from the Biomedical Ethics Review Board of Peking University in China (IRB00001052-11015).

  3. Informed Consent

    Written consent was obtained prior to the inclusion of all study participants.

  4. Conflict of Interest

    The authors declare no conflict of interest.

  5. Use of Large Language Models, AI and Machine Learning Tools

    None declared.

  6. Data Availability Statement

    All data generated or analyzed during this study are included in this published article and the supplement.

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Published Online: 2025-05-08

© 2025 Antian Chen, Yuhui Zhang, Jian Zhang, published by De Gruyter on behalf of the SMP

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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https://doi.org/10.1515/jtim-2025-0017
Creative Commons
BY-NC-ND 4.0

Articles in the same Issue

  1. Perspective
  2. Crosstalk between cardiac dysfunction and outcome of liver cirrhosis: Perspectives from evidence-based medicine and holistic integrative medicine
  3. Telemedicine strategies in older patients with cardiovascular diseases
  4. Clinical significance of portal vein thrombosis in patients with decompensated cirrhosis: A still matter of debate
  5. Review Article
  6. Eupatilin unveiled: An in-depth exploration of research advancements and clinical therapeutic prospects
  7. Original Article
  8. Benzo[b]fluoranthene is involved in idiopathic membranous nephropathy by inducing podocyte injury
  9. A novel and safe protocol for patients with severe comorbidity who undergo haploidentical hematopoietic stem cell transplantation: A single-center prospective study
  10. Deciphering the role of ELAVL1: Insights from pan-cancer multiomics analyses with emphasis on nasopharyngeal carcinoma
  11. Nano particle loaded EZH2 inhibitors: Increased efficiency and reduced toxicity for malignant solid tumors
  12. An automated human–machine interaction system enhances standardization, accuracy and efficiency in cardiovascular autonomic evaluation: A multicenter, open-label, paired design study
  13. Rapid Communication
  14. Comparison of predicting value of triglyceride‑glucose indices family in cardiovascular disease risk over time: Insights from a 9-year nationwide prospective cohort study
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