Structural phenotypes of osteoarthritis are clinically and genetically distinct: findings from 59,539 UK Biobank participants
Journal:
medRxiv
Published Date:
Feb 10, 2026
Abstract
OBJECTIVES Osteoarthritis is a heterogeneous disease, with diverse structural patterns likely reflecting distinct genetic drivers. Robust, data-driven methods to identify and characterise such phenotypes are lacking. This study leveraged the UK Biobank to define machine learning-derived structural osteoarthritis phenotypes and evaluate their clinical and genetic profiles. METHODS Machine learning models were applied to knee and hip DXA scans to derive osteophyte area, minimum joint space width, and B-scores (a combined shape vector predictive of osteoarthritis). Imaging and demographic features were clustered using k-means to classify individuals with at least one osteoarthritis feature. Phenotypes were compared with healthy controls for associations with joint pain and total joint replacement (TJR). Genetic correlations, osteoarthritis risk loci, and polygenic risk scores were analysed to define shared and distinct genetic mechanisms between phenotypes. RESULTS Among 59,539 participants (mean age 65 years; 53% female), nine reproducible phenotypes were identified, spanning joint-specific and multi-joint patterns. Hypertrophic and end-stage knee phenotypes showed the highest odds of pain (OR 7.8 [95% CI 7.1,8.7], 13.4 [9.5,19.0]) and TJR (66.0 [46.6,93.5], 127.6 [72.6,224.1]). A novel increased-cartilage phenotype was associated with greater odds of hip (3.5 [2.4,5.2]) and knee replacement (4.1 [2.6,6.6]). Distinct genetic architectures were observed; increased- and atrophic-cartilage phenotypes were inversely genetically correlated (rg -0.46 [-0.9,-0.2]) with opposing effects at DOT1L and COL27A1. CONCLUSIONS Machine learning revealed nine reproducible osteoarthritis structural phenotypes with divergent clinical and genetic signatures. These findings demonstrate that simple imaging and demographic data can stratify patients into biologically distinct phenotypes likely to require tailored treatments.
