Objective: OA is suspected to be a collection of distinct subtypes, each with different aetiology and clinical characteristics. We aimed to explore the existence of different subtypes of knee OA, using cluster analysis of the data of the OA Initiative.
Methods: We used latent class cluster analysis (LCA) to cluster baseline data of 518 subjects of the OA Initiative progression cohort. Data included radiographic scores of OA features per compartment, regional quantitative MRI measures of cartilage quantity and denuded bone, and self-reported clinical scores on knee symptoms. To ensure that the clusters were found independently of OA severity, the LCA model was corrected with a measure of OA severity. The resulting clusters were compared with respect to the presence of risk factors and progression.
Results: LCA resulted in four clusters containing 47%, 27%, 15% and 12% of the subjects. Clusters 1, 2 and 4 showed OA features at the medial compartment, while cluster 3 only showed lateral OA features. Clusters 3 and 4 showed severe increases in areas of denuded bone, whereas no denuded bone was present in cluster 1. Prevalence of OA progression over 24 months was highest in clusters 3 and 4 and lowest in cluster 1. The clusters also differed significantly in BMI, knee alignment and prevalence of reported trauma.
Conclusion: LCA confirmed the existence of distinct subtypes of knee OA with clear differences in structural degradation and symptoms. The fact that subtypes also differed in risk factors suggests that different causes lead to different types of knee OA.
Keywords: cluster analysis; knee osteoarthritis; phenotype.
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