Obesity research & clinical practice · 2026
Interpretable machine learning and incremental prognostic model for all-cause and cardiovascular mortality in obesity with MASLD population: Comorbidity insights from triglyceride-glucose and its obesity-related derivatives.
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Nie Limin, Li Yuanyuan, Wang Qun, Luo Yanchao, Zhou Jun, Zhang Yuxin, Du Rina, Liu Lu, Li Bin
Аннотация
OBJECTIVE: To investigate comorbid correlation and prognostic value of triglyceride-glucose (TyG) and its obesity-related derivatives for all-cause and cardiovascular mortality in the obesity with metabolic dysfunction-associated fatty liver disease (MASLD), and develop incremental predictive model using machine learning and SHapley Additive exPlanations (SHAP) analysis.
METHODS: 4807 adults with obesity and MASLD enrolled from National Health and Nutrition Examination Survey 1999-2018. Kaplan-Meier, Cox regression, and restricted cubic splines (RCS) were used to examine associations. Machine learning and incremental prognostic model were developed to predict mortality, with SHAP interpreting feature importance.
RESULTS: Both TyG and TyG-WHtR were associated with all-cause mortality after full adjustment, whereas TyG-WHtR showed a more robust association with cardiovascular mortality. TyG-WHtR showed the strongest association: each 1-unit increase linked to 31.7% and 46.7% higher risk of all-cause and cardiovascular mortality respectively in the fully adjusted model, with RCS showing an S-shaped relationship. Time-varying hazard ratio patterns suggested that the associations of TyG and TyG-WHtR with mortality were stronger in the early follow-up period and attenuated over time. XGBoost performed best with SHAP analysis identified age, fasting plasma glucose (FPG), male as primary factors. The incorporation of TyG and TyG-WHtR into predictive models modestly improved the prediction performance for mortality outcomes.
CONCLUSIONS: TyG-WHtR showed a stronger time-dependent association with mortality in obesity-MASLD comorbidity, while TyG mainly associated with all-cause mortality. S-shaped curve for TyG-WHtR indicates the critical need for early intervention. Integrating TyG-WHtR into XGBoost prognostic models enhances risk identification, while SHAP values clarify age, FPG, male as key drivers.
METHODS: 4807 adults with obesity and MASLD enrolled from National Health and Nutrition Examination Survey 1999-2018. Kaplan-Meier, Cox regression, and restricted cubic splines (RCS) were used to examine associations. Machine learning and incremental prognostic model were developed to predict mortality, with SHAP interpreting feature importance.
RESULTS: Both TyG and TyG-WHtR were associated with all-cause mortality after full adjustment, whereas TyG-WHtR showed a more robust association with cardiovascular mortality. TyG-WHtR showed the strongest association: each 1-unit increase linked to 31.7% and 46.7% higher risk of all-cause and cardiovascular mortality respectively in the fully adjusted model, with RCS showing an S-shaped relationship. Time-varying hazard ratio patterns suggested that the associations of TyG and TyG-WHtR with mortality were stronger in the early follow-up period and attenuated over time. XGBoost performed best with SHAP analysis identified age, fasting plasma glucose (FPG), male as primary factors. The incorporation of TyG and TyG-WHtR into predictive models modestly improved the prediction performance for mortality outcomes.
CONCLUSIONS: TyG-WHtR showed a stronger time-dependent association with mortality in obesity-MASLD comorbidity, while TyG mainly associated with all-cause mortality. S-shaped curve for TyG-WHtR indicates the critical need for early intervention. Integrating TyG-WHtR into XGBoost prognostic models enhances risk identification, while SHAP values clarify age, FPG, male as key drivers.