BMC cardiovascular disorders · 2026
Advanced prediction of cardiovascular-kidney-metabolic syndrome using eight machine learning models and 24 composite indices.
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Li Minghao, Xiang Junyuan, Xiao Fang, Wang Qianxiang, Liu Lin, Huo Yujia, Zhang Chunyu, Deng Li, Feng Jian
Аннотация
AIM: This study aimed to construct and validate a machine learning classifier for cross-sectionally stratifying existing cardiovascular-kidney-metabolic (CKM) syndrome stages using routine composite inflammatory, metabolic and anthropometric indices, and to interpret core driving biomarkers via SHAP analysis.
METHODS AND RESULTS: We analyzed data from 12,106 participants from the National Health and Nutrition Examination Survey (NHANES). Among 24 initial biomarkers, 10 were selected after addressing multicollinearity and applying feature selection. Several machine learning algorithms were evaluated, with the LightGBM model demonstrating the highest performance (ROC AUC: 0.88). External validation using the China Health and Retirement Longitudinal Study (CHARLS) dataset confirmed the model's generalizability (ROC AUC: 0.84). SHapley Additive exPlanations (SHAP) analysis revealed that metabolic markers-specifically eGDR, METS_VF, TyG, and TyG_BMI-were consistently the strongest predictors across CKM stages, whereas inflammatory indicators showed more limited utility.
CONCLUSION: Key metabolic composite indices exhibit significant associations with CKM staging and may serve as practical, clinically feasible tools for risk stratification. The model's robust performance across distinct populations (U.S. and China) supports its potential clinical utility. Further validation in diverse populations and prospective studies is needed to confirm their predictive value and translational potential.
METHODS AND RESULTS: We analyzed data from 12,106 participants from the National Health and Nutrition Examination Survey (NHANES). Among 24 initial biomarkers, 10 were selected after addressing multicollinearity and applying feature selection. Several machine learning algorithms were evaluated, with the LightGBM model demonstrating the highest performance (ROC AUC: 0.88). External validation using the China Health and Retirement Longitudinal Study (CHARLS) dataset confirmed the model's generalizability (ROC AUC: 0.84). SHapley Additive exPlanations (SHAP) analysis revealed that metabolic markers-specifically eGDR, METS_VF, TyG, and TyG_BMI-were consistently the strongest predictors across CKM stages, whereas inflammatory indicators showed more limited utility.
CONCLUSION: Key metabolic composite indices exhibit significant associations with CKM staging and may serve as practical, clinically feasible tools for risk stratification. The model's robust performance across distinct populations (U.S. and China) supports its potential clinical utility. Further validation in diverse populations and prospective studies is needed to confirm their predictive value and translational potential.