Machine learning based model for predicting cardiovascular disease using dynamic triglyceride-glucose index:a longit-udinal study cohort CHARLS database
Yi YANG1
Zen-Gao YANG2
Hong-Hong ZHANG3
Zheng-Feng WU3
Hai-Jing ZHAO3
Yue ZHU3
Yu-Han MA4
Yu-Qi LIU5
1.Department of Faculty of Engineering and Informati2.Department of Cardiology,the Sixth Medical Centre,3.Department of Cardiology,the Sixth Medical Centre,4.Department of Cardiology,the Sixth Medical Centre,5.National Key Laboratory of Kidney Diseases,Beijing
摘要：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 con-trols.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 mod-els,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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论文发表日期：2025-11-30
在线出版日期：2026-01-09（本平台首次上网日期，不代表文献的发表时间）
页数：11( 930-940 )