Decoding Benign Prostatic Hyperplasia: Insights from Multi‐Fluid Metabolomic Analysis

Abstract With the rising incidence of benign prostatic hyperplasia (BPH) due to societal aging, accurate and early diagnosis has become increasingly critical. The clinical challenges associated with BPH diagnosis, particularly the lack of specific biomarkers that can differentiate BPH from other causes of lower urinary tract symptoms (LUTS). Here, matrix‐assisted laser desorption/ionization mass spectrometry (MALDI MS) metabolomic detection platform utilizing urine and serum samples is applied to explore metabolic information and identify potential biomarkers in designed cohort. The nanoparticle‐assisted platform demonstrated rapid analysis, minimal sample consumption, and high reproducibility. Employing a two‐step grouping screening approach, the identification of urinary metabolic patterns (UMPs) is automated to distinguish healthy individuals from LUTS group, followed by the use of serum metabolic patterns (SMPs) to accurately identify BPH cases within the LUTS cohort, achieving an area under the curve (AUC) of 0.830 (95% CI: 0.802‐0.851). Furthermore, eight BPH‐sensitive metabolic markers are identified, confirming their uniform distribution across age groups ( p > 0.05). This research contributes valuable insights for the early diagnosis and personalized treatment of BPH, enhancing clinical practice and patient care.