Predicting Insulin Resistance in Taiwanese Men Using Machine Learning: An Integrated Analysis of Biochemical Markers and Volatile Organic Compounds

Background: Type 2 diabetes (T2D) and insulin resistance (IR) are major global health challenges. Volatile organic compounds (VOCs) in exhaled breath offer a non-invasive window into metabolic dysregulation. This study aimed to predict HOMA-IR using machine learning (ML) by integrating biochemical markers and VOC profiles in a male cohort. Methods: This cross-sectional study included 1258 male participants from the Taiwan MJ cohort. Four ML algorithms (Elastic Net, MARS, Random Forest, and XGBoost) were trained to predict HOMA-IR. Model performance was evaluated using R2, RMSE, and MAE. SHAP analysis was used to interpret feature contributions. Results: Ensemble tree-based approaches (Random Forest and XGBoost) demonstrated better predictive performance than Elastic Net and MARS. Random Forest achieved the highest predictive performance on the test set (R2 = 0.323). SHAP analysis identified BMI (mean |SHAP| = 1.326) as the strongest predictor, followed by TG (1.005) and HDL-C (0.445). Notably, specific breath VOCs, including methanol and formic acid, ranked among the top 20 predictors, capturing distinct aspects of metabolic dysregulation orthogonal to standard blood tests. Conclusions: Integrating VOC profiles with clinical markers provides acceptable predictive performance for IR in men. While traditional metabolic markers dominate the prediction, specific VOCs capture distinct metabolic information, highlighting the potential of breath analysis as a complementary early screening tool.