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Multimodal machine learning-based marker enables the link between obesity-related indices and future stroke: a
Beilei Hu1,2, Xuan Chen1,2, Tingyang Chen3
1Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
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 incorporating TyG-BMI, which achieved an AUC of 0.816 (95% CI: 0.807-0.824) in the training cohort and 0.833 (95% CI: 0.816-0.849) in the internal set for predicting stroke risk. For the external sets, the AUC was 0.803 (95% CI: 0.791-0.816) for the ELSA cohort and 0.805 (95% CI: 0.793-0.818) for the health examination cohort. The stacked ML model based on TyG-BMI showed the best performance with the highest F1 score (0.209:0.124:0.117), lowest Brier score (0.040:0.041:0.041) and model improvement (all NRI and IDI >0). The ISS score was significantly associated with stroke and stroke-related death, classifying individuals into low- and high-risk groups for death in the training cohort with and AUC of 0.891 (95% CI: 0.840, 0.935) and 0.879 (95% CI: 0.749, 0.979) for the internal validation sets.
Interpretation:
The stacked ML model incorporating TyG-BMI effectively predicts stroke risk, with the ISS score demonstrating strong performance across diverse populations. Further research is needed to assess its applicability in broader cohorts.
Funding:
None.
