Aims. To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design. Methods. Healthcare professionals, clinicians, and/or researchers (HCP/Rs) were surveyed, and the data were presented at a congress workshop. A second and related survey was then developed for people with joint damage caused by knee injury and/or osteoarthritis (PJDs), who were approached by a UK Charity newsletter or Oxford involvement registry. Anonymized data were collected and analyzed in Qualtrics. Results. Survey responses (n = 19 HCP/Rs, 39 PJDs) supported studies testing pharmacological agents preventing PTOA. All HCP/Rs and 30/31 (97%) PJDs supported the development of new treatments that improved or delayed knee symptoms and damage to knee structure. PJDs thought that improving structural knee damage was more important than knee symptoms. Both groups found studies more acceptable as expected future benefit and risk of PTOA increased. All drug delivery routes were acceptable. Workshop participants (around n = 60) reflected survey views. Discussions suggested that stratifying using molecular testing for likely drug response appeared to be more acceptable than using characteristics such as sex, age, and BMI. Conclusion. Our findings supported PTOA drug intervention studies, including situations where there is low risk of disease, no expected benefit of treatment, and frequent treatment administration. PJDs appeared less risk-averse than HCP/Rs. This work reinforces the benefits of consensus and involvement work in the co-creation of PTOA drug trial design. Involvement of key stakeholders, such as PJDs with different risks of OA and regulatory representatives, are critical for trial design
Osteoarthritis (OA) is a common degenerative joint disease characterized by chronic inflammatory articular cartilage degradation. Long noncoding RNAs (lncRNAs) have been previously indicated to play an important role in inflammation-related diseases. Herein, the current study set out to explore the involvement of lncRNA H19 in OA. Firstly, OA mouse models and interleukin (IL)-1β-induced mouse chondrocytes were established. Expression patterns of IL-38 were determined in the synovial fluid and cartilage tissues from OA patients. Furthermore, the targeting relationship between lncRNA H19, tumour protein p53 (TP53), and IL-38 was determined by means of dual-luciferase reporter gene, chromatin immunoprecipitation, and RNA immunoprecipitation assays. Subsequent to gain- and loss-of-function assays, the levels of cartilage damage and proinflammatory factors were further detected using safranin O-fast green staining and enzyme-linked immunosorbent assay (ELISA) in vivo, respectively, while chondrocyte apoptosis was measured using Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) in vitro.Aims
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Osteoarthritis (OA) is a common degenerative joint disease. The osteocyte transcriptome is highly relevant to osteocyte biology. This study aimed to explore the osteocyte transcriptome in subchondral bone affected by OA. Gene expression profiles of OA subchondral bone were used to identify disease-relevant genes and signalling pathways. RNA-sequencing data of a bone loading model were used to identify the loading-responsive gene set. Weighted gene co-expression network analysis (WGCNA) was employed to develop the osteocyte mechanics-responsive gene signature.Aims
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The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
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