Background: Lower limb malalignment increases the risk of unicompartmental knee osteoarthritis (KOA). This study investigates the association between knee cartilage quality, assessed via MRI-based T2 mapping, and lower limb malalignment. It also examines whether cartilage quality is more influenced by bony or intra-articular malalignment. Methods: In this cross-sectional analysis of 156 knees from the IMI-APPROACH cohort, tibiofemoral cartilage T2 values were measured using high-resolution MRI, distinguishing superficial and deep layers. Malalignment was categorized into entire leg, bony, and intra-articular malalignment (via the Joint Line Convergence Angle). Correlations between T2 values and alignment were assessed using Spearman's rho. A subgroup analysis evaluated cartilage quality in constitutional malalignment (malalignment without intra-articular deviation). Results: Cartilage T2 values were significantly associated with alignment. Varus knees showed significantly longer T2 in the superficial medial cartilage (ρ = –0.2, p = 0.04), and valgus knees in the lateral compartment (ρ = 0.1, p = 0.35). Associations were strongest for intra-articular malalignment (ρ = 0.3, p < 0.01). In constitutional varus, a non-significant medial T2 prolongation was observed (ρ = –0.2, p = 0.28); no changes were found in constitutional valgus. Conclusion: Lower limb malalignment, particularly intra-articular malalignment, is associated with compartment-specific lower cartilage quality, as reflected by longer T2 values. Distinguishing between bony and intra-articular malalignment, rather than overall limb alignment, should be a focus of future studies on malalignment. Future research should explore whether constitutional malalignment and early cartilage alterations may trigger cartilage degeneration and KOA progression.

Objective: To analyze and synthesize the information available from five pivotal, large-scale, multicenter, observational studies (CHECK, OAI, FNIH Biomarkers Consortium, IMI-APPROACH, and MOST) focusing on knee osteoarthritis (OA), which can be used to elucidate disease progression, risk factors, and the effectiveness of potential interventions. Design: For this narrative review, a comprehensive literature search and data extraction from official web pages and scientific databases were conducted to compare methodologies, in- and exclusion criteria, outcomes, and cohort characteristics across the studies. Thematic, comparative, and qualitative analyses were employed to identify trends, commonalities, and disparities among the findings. Results: The studies collectively enhanced understanding of the onset and progression of knee OA, and in several of the studies, hip OA, emphasizing the importance of both systemic and local risk factors. Advanced imaging and biomarkers are important components in all the cohorts, with the goal of aiding early diagnosis and tracking disease progression. All cohorts evaluated unique markers generally not available in the other cohorts, while other factors overlap, suggesting possibilities for combining or cross-validating between cohorts. Conclusions: The collaborative efforts of major OA research significantly advance our understanding of knee OA. These studies highlight the importance of a multifaceted approach, integrating advanced imaging, biomarkers, and longitudinal data to tackle the complexities of OA. By synthesizing findings and addressing knowledge gaps such as heterogeneity of patients and used measurements, and use of novel pain measures, future research can develop more effective diagnostic tools and treatments, ultimately enhancing the quality of life for OA patients.

Background/Objectives: Tenosynovitis is a common feature of psoriatic arthritis (PsA) and is typically assessed using semi-quantitative magnetic resonance imaging (MRI) scoring. However, visual scoring s variability. This study evaluates a fully automated, deep-learning approach for ankle tenosynovitis segmentation and volume-based quantification from MRI in psoriatic arthritis (PsA) patients. Methods: We analyzed 364 ankle 3T MRI scans from 71 PsA patients. Four tenosynovitis pathologies were manually scored and used to create ground truth segmentations through a human–machine workflow. For each pathology, 30 annotated scans were used to train a deep-learning segmentation model based on the nnUNet framework, and 20 scans were used for testing, ensuring patient-level disjoint sets. Model performance was evaluated using Dice scores. Volumetric pathology measurements from test scans were compared to radiologist scores using Spearman correlation. Additionally, 218 serial MRI pairs were assessed to analyze the relationship between changes in pathology volume and changes in visual scores. Results: The segmentation model achieved promising performance on the test set, with mean Dice scores ranging from 0.84 to 0.92. Pathology volumes correlated with visual scores across all test MRIs (Spearman ρ = 0.52–0.62). Volume-based quantification captured changes in inflammation over time and identified subtle progression not reflected in semi-quantitative scores. Conclusions: Our automated segmentation tool enables fast and accurate quantification of ankle tenosynovitis in PsA patients. It may enhance sensitivity to disease progression and complement visual scoring through continuous, volume-based metrics.

Objective: Fibulin-3 is a glycoprotein highly expressed in osteoarthritic cartilage and inhibits angiogenesis and chondrocyte differentiation. Recent studies have indicated that fibulin-3 has potential value as a biomarker in osteoarthritis. The aim of the present study is to examine the role of 3 fibulin-3 peptides (Fib3-1, Fib3-2, and Fib3-3) and a type II collagen degradation product in a rat osteoarthritis model with systemic metabolic alterations combined with local cartilage damage. Design: Forty, 12-week-old male, Wistar rats were randomly divided over 2 groups: a standard or a high-fat diet inducing metabolic dysregulation. After 12 weeks, articular cartilage damage was induced on the femoral condyles (groove model), in 1 knee joint in 14 rats of each diet group. At endpoint, blood was collected and serum was isolated. Enzyme-linked immunosorbent assay on all selected fibulin-3 fragments was performed from serum samples in addition to immunohistochemical analysis for Fib3-3. Results: Serum concentrations of Fib3-3 were increased by 29.9%, when cartilage damage was induced in addition to a high-fat diet. Fib3-3 was also associated with an increased histological total joint degeneration (r = 0.435) and cartilage degeneration (r = 0.435). Immunostainings demonstrated increased Fib3-3 in the superficial cartilage of animals with high-fat diet and/or cartilage damage. Conclusions: In the rat groove model combined with high-fat diet–induced metabolic dysregulation an increased Fib3-3 concentration was observed systemically, which is associated with local joint degeneration. This suggests that systemic Fib3-3 concentrations can indicate the status of joint degeneration and function as a biomarker in osteoarthritis.