Interpretable Machine Learning Identification of Dietary and Metabolic Factors for Metabolic Syndrome in Southern China: A Cross-Sectional Study

<b>Background</b>: Metabolic syndrome (MetS) is a rising public health concern in Southern China, with limited evidence available on dietary and metabolic factors. This cross-sectional study employed an interpretable machine learning (ML) approach to identify factors that could inform clinical and community interventions. <b>Methods</b>: Data were obtained from the Guangdong Nutrition Surveys conducted in 2015 and 2022, including sociodemographic information, lifestyle patterns, physical examinations, laboratory measurements and dietary intake information (collected via repeated 24-h dietary recalls). Potentially relevant variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and incorporated into seven ML models. Model performance was primarily assessed using the area under the receiver operating characteristic curve (AUC), and the contribution of identified features was interpreted through SHapley Additive exPlanations (SHAP). <b>Results</b>: This analysis included 5593 participants, of whom 1103 were classified as having MetS. After removing collinear features, the ML models retained 19 candidate variables, which were selected through LASSO regression. XGBoost achieved the best performance (AUC: 0.834; F1 score: 0.537) with a misclassification rate of 27.1%. SHAP analysis highlighted body mass index (BMI), age, and uric acid levels as major risk factors, while insoluble dietary fiber, carbohydrate and specific micronutrients exhibited protective associations. <b>Conclusions</b>: Machine learning identified key dietary and metabolic factors of MetS. Integrating these factors into clinical practice and public health initiatives may enhance early detection and support personalized prevention strategies for MetS in Southern China.