Integrative Cluster Analysis Identifies Distinct Clinical–Morphological–Molecular Profiles in Late-Stage Osteoarthritis

BACKGROUND/AIMS: Late-stage osteoarthritis (OA) is a multi-faceted disease with diverse clinical, morphological, and molecular characteristics. To capture disease heterogeneity, we aimed to determine whether cluster analysis could classify late-stage osteoarthritis patients into distinct profiles based on clinical, histopathological, and molecular characteristics, and to identify the key distinguishing features of each profile.
MATERIALS AND METHODS: Seventy late-stage OA patients undergoing arthroplasty were included. Clinical data and WOMAC assessments were obtained preoperatively. During arthroplasty, samples of cartilage, subchondral bone, synovium, and synovial fluid were collected. The severity of cartilage degradation, bone marrow lesions (BMLs), and synovitis was assessed using OARSI, OABS, and Krenn classification systems. CTX-II, COMP and CTX-I synovial fluid levels were quantified by ELISA. Statistical and cluster analyses were conducted using JMP Pro V19.
RESULTS: Six distinct late-stage OA profiles were identified. The first profile exhibited long-standing, pain-predominant disease with severe cartilage degradation, low-grade synovitis, and low CTX-II and COMP levels. The second displayed pronounced functional impairment, severe BMLs, no synovitis, and elevated CTX-I. The third was characterized by mild symptoms and minimal tissue alterations, reflected in synovial fluid biomarker levels. The fourth exhibited severe pain and functional impairment accompanied by extensive tissue damage, low-grade synovitis, elevated BMI, and increased serum CRP. The fifth was defined by nocturnal pain and stiffness with BML-dominated changes, near-high-grade synovitis, and elevated COMP. The sixth showed extreme symptoms with the most severe tissue involvement and high-grade synovitis.
DISCUSSION AND CONCLUSION: Cluster analysis effectively classified OA patients into biologically and clinically meaningful profiles, delineating disease heterogeneity and supporting a profile-based understanding of OA.