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.@en

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20–25% the number of patients who show no progression. This result might lead to more efficient clinical trials.@en