Machine learning based model for predicting cardiovascular disease using dynamic triglyceride-glucose index: a
Yi Yang1, Zen-Gao Yang2,3, Hong-Hong Zhang2,4
1Department of Faculty of Engineering and Information Technology of University of Technology Sydney, Syndey, Australia.
Background:
Cardiovascular disease (CVD) remains a major health challenge globally, particularly in aging populations. Using data from the China Health and Retirement Longitudinal Study (CHARLS), this study examines the Triglyceride-glucose (TyG) index dynamics, a marker for insulin resistance, and its relationship with CVD in Chinese adults aged 45 and older.
Methods:
This reanalysis utilized five waves of CHARLS data with multistage sampling. From 17,705 participants, 5,625 with TyG index and subsequent CVD data were included, excluding those lacking 2011 and 2015 TyG data. TyG derived from glucose and triglyceride levels, CVD outcomes via self-reports and records. Participants divided into four groups based on TyG changes (2011-2015): low-low, low-high, high-low, high-high TyG groups.
Results:
Adjusting for covariates, stable high group showed a significantly higher risk of incident CVD compared to stable low group, with an HR of 1.18 (95% CI: 1.03-1.36). Similarly, for stroke risk, stable high group had a HR of 1.45 (95% CI: 1.11-1.89). Survival curves indicated that individuals with stable high TyG levels had a significantly increased CVD risk compared to controls. The dynamic TyG change showed a greater risk for CVD than abnormal glucose metabolism, notably for stroke. However, there was no statistical difference in single incidence risk of heart disease between stable low and stable high group. Subgroup analyses underscored demographic disparities, with stable high group consistently showing elevated risks, particularly among < 65 years individuals, females, and those with higher education, lower BMI, or higher depression scores. Machine learning models, including random forest, XGBoost, CoxBoost, Deepsurv and GBM, underscored the predictive superiority of dynamic TyG over abnormal glucose metabolism for CVD.
Conclusions:
Dynamic TyG change correlate with CVD risks. Monitoring these changes could predict and manage cardiovascular health in middle-aged and older adults. Targeted interventions based on TyG index trends are crucial for reducing CVD risks in this population.
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