SummaryOsteoarthritis (OA) is a chronic degenerative disease of diarthrodial joints most commonly affecting people over the age of forty. The causes of OA are still unknown and there is much debate in the literature as to the exact sequence of events that trigger the onset of the heterogeneous disease we recognise as OA.There is currently no consensus model for OA that naturally reflects human disease. Existing ex-vivo models do not incorporate the important inter-tissue communication between joint components required for disease progression and differences in size, anatomy, histology and biomechanics between different animal models makes translation to the human model very difficult. This narrative review highlights the advantages and disadvantages of the current models used to study OA. It discusses the challenges of producing a more reliable OA-model and proposes a direction for the development of a consensus model that reflects the natural environment of human OA.We suggest that a human osteochondral plug-based model may overcome many of the fundamental limitations associated with animal and in-vitro models based on isolated cells. Such a model will also provide a platform for the development and testing of targeted treatment and validation of novel OA markers directly on human tissues.

These results indicate that changes in the bioavailability of VEGF-A sourced from ATII cells, namely the ratio of VEGF-Aa to VEGF-Ab, are critical in development of pulmonary fibrosis and may be a paradigm for the regulation of tissue repair.

Osteoarthritis (OA) is the most common chronic degenerative joint disease which causes substantial joint pain, deformity and loss of activities of daily living. Currently, there are over 500 million OA cases worldwide, and there is an urgent need to identify biomarkers for early detection, and monitoring disease progression in patients without obvious radiographic damage to the joint. We have used regression modelling to describe the association of 19 of the currently available biomarkers (predictors) with key radiographic and clinical features of OA (outcomes) in one of the largest and best characterised OA cohort (NIH Osteoarthritis Initiative). We demonstrate that of the 19 currently available biomarkers only 4 (serum Coll2-1 NO2, CS846, COMP and urinary CTXII) were consistently associated with established radiographic and/or clinical features of OA. These biomarkers are independent of one another and provide additional predictive power over, and above established predictors of OA such as age, gender, BMI and race. We also show that that urinary CTXII had the strongest and consistent associations with clinical symptoms of OA as well as radiographic evidence of joint damage. Accordingly, urinary CTXII may aid in early diagnosis of OA in symptomatic patients without radiographic evidence of OA.