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<p>This study aims to investigate the association between triglyceride-glucose (TyG) index and risk of CVD using explainable survival analysis method based on 2011 to 2020 China Health and Retirement Longitudinal Study (CHARLS) data. We enrolled 7,721 participants in a prospective cohort. A Lasso Cox model was implemented to select covariates for constructing the multivariate Cox regression model. Restricted cubic splines (RCS) analysis was performed to explore dose-response relationship. In addition, we utilized explainable machine learning methods to analyse the Cox model. During 9-year follow-up, 1,895 (24.5%) participants developed CVD. Nine variables including TyG index, age, BMI, WC, SBP, history of hypertension, liver disease and kidney disease and antihypertensive medication were retained to establish the Cox model. HRs (95% CIs) for CVD were 1.21 (1.06–1.39), 1.26 (1.10–1.44), and 1.22 (1.06–1.40) for Q2 to Q4 groups compared with Q1. Result of RCS showed that the Q1 group had lowest risk. Time-dependent feature importance analysis showed that age and history of hypertension were two most important risk factors based on Brier score and C/D AUC. All variables in the coxph model gain importance over time. Routine measurement of the TyG index may aid in the early detection and risk stratification of CVD in the aging population.</p>

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Time-dependent predictive contribution of the triglyceride-glucose index for incident cardiovascular disease in a 9-year Chinese cohort

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Meihui Zhang,
  • Nina Ma,
  • Yiya Jiang,
  • Fanqi Xu,
  • Quyige Gao,
  • Chenrui Li,
  • Yong Cai,
  • Chunhai Shao

摘要

This study aims to investigate the association between triglyceride-glucose (TyG) index and risk of CVD using explainable survival analysis method based on 2011 to 2020 China Health and Retirement Longitudinal Study (CHARLS) data. We enrolled 7,721 participants in a prospective cohort. A Lasso Cox model was implemented to select covariates for constructing the multivariate Cox regression model. Restricted cubic splines (RCS) analysis was performed to explore dose-response relationship. In addition, we utilized explainable machine learning methods to analyse the Cox model. During 9-year follow-up, 1,895 (24.5%) participants developed CVD. Nine variables including TyG index, age, BMI, WC, SBP, history of hypertension, liver disease and kidney disease and antihypertensive medication were retained to establish the Cox model. HRs (95% CIs) for CVD were 1.21 (1.06–1.39), 1.26 (1.10–1.44), and 1.22 (1.06–1.40) for Q2 to Q4 groups compared with Q1. Result of RCS showed that the Q1 group had lowest risk. Time-dependent feature importance analysis showed that age and history of hypertension were two most important risk factors based on Brier score and C/D AUC. All variables in the coxph model gain importance over time. Routine measurement of the TyG index may aid in the early detection and risk stratification of CVD in the aging population.