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.
Insights
Monitoring dynamic Triglyceride-glucose (TyG) index changes is crucial for predicting cardiovascular disease (CVD) risk in older adults. Stable high TyG levels significantly increase CVD and stroke risk, highlighting the need for targeted interventions.
Area of Science:
- Endocrinology
- Cardiology
- Public Health
Background:
- Cardiovascular disease (CVD) poses a significant global health burden, especially in aging populations.
- Insulin resistance, indicated by the Triglyceride-glucose (TyG) index, is a key factor in CVD development.
- The China Health and Retirement Longitudinal Study (CHARLS) provides valuable data for investigating CVD risk factors in older Chinese adults.
Purpose of the Study:
- To examine the association between dynamic changes in the Triglyceride-glucose (TyG) index and the risk of cardiovascular disease (CVD) in Chinese adults aged 45 and older.
- To compare the predictive power of dynamic TyG index changes versus static abnormal glucose metabolism for CVD events.
- To identify demographic subgroups with differential risks associated with TyG index dynamics.
Main Methods:
- Utilized five waves of the China Health and Retirement Longitudinal Study (CHARLS) data, including 5,625 participants with complete TyG index and CVD data.
- Categorized participants into four groups based on TyG index changes from 2011 to 2015: low-low, low-high, high-low, and high-high.
- Employed Cox proportional hazards models and machine learning algorithms (random forest, XGBoost, etc.) to analyze CVD risk and predictive performance.
Main Results:
- A stable high TyG index was significantly associated with increased risk of incident CVD (HR=1.18) and stroke (HR=1.45) compared to a stable low TyG index.
- Dynamic TyG index changes demonstrated a greater predictive value for CVD than abnormal glucose metabolism alone, particularly for stroke.
- Subgroup analyses revealed consistently elevated risks in the stable high TyG group, especially among individuals under 65, females, those with higher education, lower BMI, and higher depression scores.
Conclusions:
- Dynamic changes in the Triglyceride-glucose (TyG) index are significantly correlated with cardiovascular disease (CVD) risks in middle-aged and older adults.
- Monitoring TyG index trends offers a valuable approach for predicting and managing cardiovascular health.
- Implementing targeted interventions based on TyG index dynamics is essential for reducing CVD incidence in this population.
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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