Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dysfunction-associated steatotic liver disease (MASLD), as the hepatic phenotype of metabolic impairment, provides a critical link within this continuum. However, the association between CKM syndrome staging, MASLD prevalence, and liver fibrosis severity remains unclear. METHODS: This study included 3084 individuals with CKM stages 1-4 from the 2017-2020 National Health and Nutrition Examination Survey (NHANES). Hepatic steatosis and fibrosis were assessed through vibration-controlled transient elastography, providing both controlled attenuation and stiffness indices. To explore the links between CKM staging, MASLD prevalence, and fibrosis severity, weighted multivariable logistic regression was performed. Furthermore, predictive machine learning models were constructed, and SHapley Additive exPlanations (SHAP) were applied to clarify the relative impact of CKM components on MASLD prevalence. RESULTS: Advancing CKM stages were associated with higher prevalence of MASLD, advanced fibrosis, and cirrhosis, whereas no significant association was observed with significant fibrosis. Among machine learning models, the random forest model showed the best predictive performance (AUC = 0.809). SHAP analysis identified waist circumference, HbA1c, metabolic syndrome, triglycerides, diabetes, HDL-C, and age as key predictors. CONCLUSION: CKM stage was significantly associated with MASLD prevalence, advanced liver fibrosis, and cirrhosis. Machine learning interpretation highlighted adiposity, glycemic control, and lipid metabolism as the principal contributors, suggesting that these CKM-related metabolic domains may contribute to MASLD and fibrotic burden.