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

ABSTRACT 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.

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Journal
Neurourology and Urodynamics
Published
2026-10-07
DOI
https://doi.org/10.1002/nau.70466
Primary Topic
Urinary Bladder and Prostate Research
Type
article
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article

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

Lijuan Ren, Jianwei Lv, Dongxiang Zheng, Hai Hui Huang et al.
Neurourology and Urodynamics
Urinary Bladder and Prostate Research
article

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

Lijuan Ren, Jianwei Lv, Dongxiang Zheng, Hai Hui Huang, 孟小鑫, 吴小芬, Baojun Wang, Yan Li, 张彩霞, 张建军, Xiao Ning, 文伟, Li Wen, Xin Zhang, Fan Fan, Long Cheng, Yuting Wu, Fei Liu, Yipeng Liu, Qingqing He, Xiande Huang, Haihong Jiang, Yi Zhang, Xi Xie, Peixian Chen, Xiangjun Lyu, Yanlan Yu, Qi Chen, Suibin Huang, Shuxin Qu, Juan Chen, Weifang Chen, Dachuang Liu, Qingwei Wang, Juan Li, Hao Huang, Jiao Liu, Peng Zhang, Guanghui Du, Xijun Weng, Huasheng Zhao, Youhui Yang
article en

Abstract

ABSTRACT 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.

Neurourology and Urodynamics
Harbin Medical University (CN), Sun Yat-sen University (CN), Guilin Medical University (CN), Shanxi Medical University (CN), Second Military Medical University (CN), Jilin University (CN), Anhui Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Sir Run Run Shaw Hospital (CN), China Rehabilitation Research Center (CN), Chinese PLA General Hospital (CN), Haikou City People's Hospital (CN), First Affiliated Hospital of Xiamen University (CN), Shaanxi Provincial People's Hospital (CN), Sun Yat-sen Memorial Hospital (CN), Ningbo No. 2 Hospital (CN), Second Hospital of Yichang (CN), Wuhan No.1 Hospital (CN), Lishui Central Hospital (CN), Gansu Provincial Hospital (CN), People's Hospital of Xinjiang Uygur Autonomous Region (CN), Fourth Affiliated Hospital of Harbin Medical University (CN), First Hospital of Shanxi Medical University (CN), Zhongshan Hospital (CN), Second Affiliated Hospital of Xi'an Jiaotong University (CN), First Affiliated Hospital of Chinese PLA General Hospital (CN), First Affiliated Hospital of Gannan Medical University (CN), Shanghai First People's Hospital (CN), Wuhan Third Hospital (CN), Huizhou Central People's Hospital (CN), The Central Hospital of Shaoyang (CN), First Hospital of China Medical University (CN), Second Affiliated Hospital of Jilin University (CN), Guangdong Province Women and Children Hospital (CN), Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN), Second Affiliated Hospital of Anhui Medical University (CN), Shanghai Pudong New Area Gongli Hospital (CN), Third People's Hospital of Hefei (CN), First Affiliated Hospital of Zhengzhou University (CN), Tongji Hospital (CN), Shanxi Provincial People’s Hospital (CN), Qilu Hospital of Shandong University (CN), Jiangsu Provincial Hospital of Traditional Chinese Medicine (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 9%
Urinary Bladder and Prostate Research
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