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Aim <p>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.</p> Methods and results <p>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.</p> Conclusion <p>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.</p> Graphical Abstract <p></p>

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Advanced prediction of cardiovascular-kidney-metabolic syndrome using eight machine learning models and 24 composite indices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Minghao Li,
  • Junyuan Xiang,
  • Fang Xiao,
  • Qianxiang Wang,
  • Lin Liu,
  • Yujia Huo,
  • Chunyu Zhang,
  • Li Deng,
  • Jian Feng

摘要

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.

Graphical Abstract