Associations between metabolic-inflammatory biomarkers and

Background: This study investigated the association between metabolic-inflammatory markers and Methods: Data from 2,924 NHANES participants and 1,021 patients from the Second Hospital of Jilin University were analyzed. Associations between metabolic-inflammatory markers and HP were assessed using multivariable regression. Eleven machine learning models were compared for predictive performance, evaluated by AUC, accuracy, sensitivity, specificity, precision, F1 score, and Kappa statistic. Interpretability was assessed via SHAP values, calibration plots, confusion matrices, and decision curve analysis. Results: In NHANES, the TyG index was independently associated with HP infection (OR = 1.25, 95% CI 1.06-1.48, Conclusion: The TyG index and TyG/HDL-C ratio were independently associated with HP infection. Among machine learning models, XGBoost demonstrated the most stable and generalizable performance (AUC 0.77 in both training and validation), whereas RF and GP (AUC 0.97 → 0.75) exhibited overfitting. These results suggest that XGB provides a more reliable framework for infection risk prediction, though the cross-sectional design precludes causal inference.