Major depressive disorder (MDD) involves complex interactions across multiple physiological systems, necessitating a comprehensive and integrative perspective for objective diagnosis and personalized treatment. Although multi-omics technologies provide broad molecular insights, their high cost and complexity limit clinical translation. We developed a cost-effective and practical machine learning framework using digital biomarkers from dried serum near-infrared spectroscopy (NIRS) for MDD diagnosis and antidepressant treatment outcome prediction. A total of 126 MDD patients and 86 healthy controls were enrolled. Six feature selection methods were systematically compared via rigorous nested cross-validation, and the final models were constructed using partial least squares discriminant analysis, evaluated with decision curve analysis (DCA), and deployed as a clinical diagnostic and decision-support web tool. Dried serum NIRS provided sufficient biochemical information to support the development of accurate diagnostic and treatment outcome prediction models. Competitive adaptive reweighted sampling (CARS) combined with SHapley Additive exPlanations (SHAP) emerged as the best-performing approach for identifying decisive wavelengths. The diagnostic model achieved an AUC of 0.91 (95% CI: 0.86–0.95, accuracy 0.85, sensitivity 0.87, specificity 0.83), and the treatment outcome prediction model reached an AUC of 0.90 (95% CI: 0.80–0.97, accuracy 0.84, sensitivity 0.83, specificity 0.84). DCA confirmed substantial net clinical benefit across wide probability ranges. SHAP analysis revealed that the diagnostic model was associated with multiple biomolecular components, especially proteins, whereas the treatment outcome prediction model was primarily influenced by proteins and carbohydrates. To our knowledge, this study is the first to comprehensively characterize dried-serum NIRS and demonstrate its potential utility in psychiatry, paving a promising avenue for spectral digital biomarker-based precision diagnosis and personalized treatment.
Liu et al. (Fri,) studied this question.