Sex-Specific Digital Phenotyping of Lower Urinary Tract Symptoms: A Cross-Sectional Study of 1586 Adults From 35 Centers in China.

BACKGROUND AND PURPOSE: Lower urinary tract symptoms (LUTS) represent a complex clinical syndrome with significant sexual dimorphism. Traditional "one-size-fits-all" management often overlooks intra-sex phenotypic heterogeneity. This study utilized unsupervised machine learning to identify sex-specific digital phenotypes of LUTS within a large-scale clinical cohort to provide a foundation for precision urological care. MATERIALS AND METHODS: We analyzed a multicenter database of 1586 treatment-seeking adults (1098 females, 488 males) across 35 centers. Baseline clinical characteristics and LUTS parameters from standardized voiding diaries and validated scales were collected. K-means clustering was performed separately by sex to identify phenotypic subtypes, with the optimal number of clusters determined by the Elbow Method and visualized via Principal Component Analysis (PCA). RESULTS: Significant sex-specific disparities were observed: women reported a higher symptomatic burden across almost all domains but maintained better voiding satisfaction (p < 0.001), while men exhibited poorer sleep quality (p < 0.001). Clustering identified a consistent three-subtype framework for both genders: Subtype 1 (Mild/Compensated): Characterized by the lowest symptom burden and highest tolerance. Subtype 2 (Storage-Dominant with Sleep Fragmentation): Found in older populations, defined by severe nocturia, reduced bladder capacity, and profound sleep disruption. Subtype 3 (Voiding-Dominant): Characterized by prominent dysuria and incomplete emptying. CONCLUSIONS: This exploratory sex-stratified clustering study identified symptom-based phenotypic patterns among treatment-seeking Chinese adults with LUTS. These findings support the potential value of a subtype-plus-sex framework for LUTS assessment, but prospective validation using objective functional measures and treatment outcomes is needed.