Multimodal machine learning-based marker enables the link between obesity-related indices and future stroke: a prospective cohort study

作者：Beilei Hu, Xuan Chen, Tingyang Chen, Xu Tong, Yungang Cao, Jing Sun, Xuan-Yu Chen, Songfang Chen, Keyang Chen · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103331 · 被引用次数：12 · 研究领域：Artificial Intelligence in Healthcare、Acute Ischemic Stroke Management、Machine Learning in Healthcare

Background: Obesity is a significant risk factor for stroke. However, body mass index is insufficient in assessing fat distribution and there is a need for a better indicator to predict stroke risk. Additionally, early detection and prognosis prediction for stroke and mortality are crucial for pre-emptive interventions. We examined to evaluate the utility of obesity-related indices in a stacked machine learning (ML) model by developing an in-silico quantitative marker (ISS) to predict stroke risk. Methods: This is a prospective cohort study utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) (2011-2018) and a health examination cohort in China (2017-2024), English Longitudinal Study of Ageing (ELSA) (2004-2014) in the UK. A total of 13,324 participants from CHARLS were included in the cross-sectional analysis. For model development and internal and external validation, 10,044 participants from CHARLS, 3698 from ELSA, and 6884 from the second affiliated hospital of Wenzhou medical university were included. Stacked ML models with optimal obesity indices to detect the risk of stroke were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC). Findings: Triglyceride-Glucose-Body Mass Index (TyG-BMI) and TyG were two optimal predictors and outperformed BMI (AUC = 0.821) in the cross-sectional study. In the longitudinal cohort, the model with the highest AUC was the stacked ML model inc...