Weight-to-waist circumference index enhances interpretable machine learning models for screening depressive symptoms in middle-aged and elderly adults comorbid with hypertension and diabetes.

OBJECTIVE: patients with comorbid hypertension and diabetes are at elevated risk of depressive symptoms, but simple metabolic predictors are insufficient. This study aimed to develop an interpretable machine learning model incorporating clinical data and novel composite indices for early screening of depressive symptoms. METHODS: a total of 546 participants ≥45 years with the comorbidities were enrolled from CHARLS (2011-2020). Eight composite indices (TyG, WHtR, WHT.5R, WWI, ABSI, CTI, hsCRP/HDL, log_tg) were included. Restricted cubic splines explored dose-response relationships. Ten machine learning models were compared via AUC, calibration curves, and DCA; SHAP interpreted the optimal model. RESULTS: the Gradient Boosting model performed best (validation AUC = 0.899, good calibration). SHAP identified frailty score and WWI as top predictors, with ABSI and TyG also contributing significantly. Subgroup analyses confirmed robust generalizability across gender and age in screening for depressive symptoms. CONCLUSION: novel composite indices (especially WWI, ABSI, TyG) have substantial clinical value for depression symptom screening in this population. The interpretable model provides a convenient, effective screening tool for clinical practice.