Dual‐Biofluid Metabolomics Combined With Machine Learning Enables Noninvasive Diagnosis of Drug Resistance in Benign Prostatic Hyperplasia

With global population aging, benign prostatic hyperplasia (BPH) prevalence has risen, with one-quarter of patients showing inadequate treatment response or drug resistance (DR) and no reliable noninvasive diagnostic method currently available. Current methods rely on clinical experience, delaying precision management. We developed a nanoparticle-enhanced mass spectrometry platform to analyze serum and urine metabolomics in 224 BPH patients (104 DR, 120 drug-sensitive [DS]). Using gradient-boosted decision trees (GBDT), we integrated dual-biofluid metabolic fingerprints (serum and urine) to distinguish DR/DS subgroups. The platform enabled rapid analysis (<25 s/sample, 1 µL volume) with high reproducibility (CV < 10%). SMF yielded a five-feature panel including five putatively annotated DR-associated metabolites, achieving an AUC of 0.89 for DR detection. UMF analysis yielded 3 features (AUC = 0.81). Critically, combining SMF/UMF data (8-feature panel) enhanced diagnostic performance to AUC = 0.95 (95% CI: 0.91-0.96), outperforming single-biofluid models. This dual-biofluid metabolomic approach provides a noninvasive, rapid method for BPH-DR stratification, integrating systemic (serum) and local (urine) metabolic insights. The platform's scalability and 0.95 AUC highlight its potential for clinical translation, offering a foundation for personalized BPH management and reducing reliance on invasive procedures. Notably, multicenter external validation further supported the model's discriminative performance in an independent cohort.