A predictive model for metabolic syndrome in a community-based population with sleep apnea: a secondary prevention screening tool using simple and accessible indicators.

Objective: To establish a secondary prevention screening model for predicting metabolic syndrome (MetS) based on community obstructive sleep apnea (OSA) screening, using simple and easily accessible indicators, to help early identification of high-risk individuals and improve prognosis and reduce mortality. Methods: This study enrolled adults newly diagnosed with OSA from community settings in China, collecting comprehensive demographic and lifestyle data. To identify key predictive variables, least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. Nine machine learning algorithms, such as logistic regression, random forest, and support vector machine (SVM), were then used to build predictive models, with each undergoing rigorous training, hyperparameter tuning, and evaluation on stratified training, validation, and test datasets. Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, specificity, F1 score, calibration curves, and clinical decision curve analysis (DCA). To improve interpretability, Shapley additive explanations (SHAP) analysis was applied to quantify each predictor's contribution to the model's output. Results: Among the nine machine learning algorithms evaluated, the logistic regression model exhibited superior performance. The finalized model achieved an AUC of 0.814 on the test dataset, demonstrating strong discriminative ability. Key performance metrics included a sensitivity of 0.794, specificity of 0.647, accuracy of 0.693, and an F1 score of 0.617. Feature importance analysis highlighted body mass index (BMI), age, and gender as the most significant predictors of MetS. Calibration curves and clinical DCA further confirmed the model's reliability, showing close alignment between predicted probabilities and observed outcomes, thus affirming its clinical utility. External validation reinforced the model's robustness, yielding an AUC of 0.818, with consistent discrimination and well-calibrated predictions. Conclusion: This study successfully developed a MetS prediction model based on community environment. The model relies solely on simple, easily obtainable self-reported indicators and demonstrates good predictive performance. This model, as a primary screening tool, enables residents to assess their MetS risk status independently, without relying on complex biochemical tests or the assistance of specialized medical personnel.