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Related Experiment Video

Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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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.

Journal of Geriatric Cardiology : JGC
|January 1, 2026
PubMed
Summary
This summary is machine-generated.

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.

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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.