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Submitted
Abstract
Genetic Risk and Biochemical Signatures for Urolithiasis Risk Prediction: A Cross-cohort Study in the United Kingdom and Hong Kong
Moderated Poster Abstract
Clinical Research
Endourology: Urolithiasis
Author's Information
9
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Hong Kong, China
Yongle Zhan ylzhan@connect.hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
Ruochen Ma ruochenm@connect.hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China *
Ruofan Shi shirf@hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
Xiaohao Ruan vincentruan96@gmail.com Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Department of Urology Shanghai China -
Chi Yao yaochimed@hotmail.com The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
Salida Ali u3010017@connect.hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
Tsun Tsun Stacia Chun stac@hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
Ada Tsui-Lin Ng ntl188@ha.org.hk Queen Mary Hospital Department of Surgery Hong Kong Hong Kong, China -
Rong Na yungna@hku.hk The University of Hong Kong Department of Surgery Hong Kong Hong Kong, China -
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Abstract Content
Urolithiasis, a multifactorial disease with high recurrence, lacks robust tools for personalized risk prediction. While genetic susceptibility and biochemical changes are implicated in pathogenesis, their combined utility remains unexplored. This study aims to evaluate the integration of polygenic risk scores (PRS) and biomarker-derived signatures in predicting risks of incident, recurrent and multifocal urolithiasis across diverse populations.
A cross-cohort study was implemented utilizing data of 480,098 participants from the UK Biobank (UKB) and 6,177 participants from the electronic health record database of Hong Kong Hospital Authority (EHR-HK). 16 biochemical markers related to urolithiasis were identified by LASSO regression. K-means clustering analysis was performed to categorize participants into biomarker-derived clusters. PRS was computed using genome-wide significant variants.
In UKB, 6,434 incident, 2,652 recurrent, and 777 multifocal urolithiasis cases were recorded respectively after a median follow-up of 13.8 years. Three biomarker signatures were identified, namely cardiovascular-skeletal (C-S), hematal-endocrine (H-E) and inflammatory-metabolic (I-M) clusters. Synergistic effects between PRS and biomarker signatures were observed: participants with top PRS quartile combined with I-M cluster had the highest risks of incident (hazard ratio [HR]=2.66, 95% confidence interval [CI]: 2.43-2.90), recurrent (3.56 [3.11-4.08]), and multifocal urolithiasis (2.94 [2.29-3.78]) compared to the lowest-risk group (PRS Q1 + C-S cluster) (Figure 1). Validation in EHR-HK with 1,304 incident, 263 recurrent, and 233 multifocal urolithiasis cases confirmed elevated urolithiasis odds for I-M (odds ratio [OR]=1.32, 95%CI: 1.19-1.47) and H-E clusters (1.11 [1.00-1.22]) compared to C-S cluster.
This cross-cohort study demonstrates that integrating PRS and biomarker-derived clusters enhances urolithiasis risk prediction, particularly for high-risk subgroups. The combination between genetic risk and biochemical signatures offers a framework for precision stratification strategies, with implications for early detection of incident, recurrent and multifocal urolithiasis in different population settings.
Urolithiasis, Polygenic risk score, Biomarker, Cluster analysis, Interaction, Risk stratification, Cohort study.
https://storage.unitedwebnetwork.com/files/1237/c9ee3566b57d5db092b876ec25e03a87.jpg
Figure 1. Risk stratification on urolithiasis based on genetic risks and biomarker clusters.
 
 
 
 
 
 
 
 
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Presentation Details
Free Paper Moderated Poster(02): Endourology Urolithiasis
Aug. 14 (Thu.)
16:12 - 16:16
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