Osteoarthritis (OA) is a chronic degenerative joint disease that affects more than 500 million people worldwide. It most commonly affects the knees, hips and hands, causing pain and stiffness. The disease starkly reduces the quality of life of patients – 80% suffer from movement limitations, while 25% face substantial challenges even with normal activities. End stage disease leads to joint failure and the need for prosthesis surgery. Meeting current and future challenges imposed by OA is of critical importance in achieving a vision of healthy and physically active ageing.
Despite the widespread prevalence of OA, we face many limitations in its diagnosis and treatment. OA diagnosis currently relies on the presence of clinical symptoms (joint pain, stiffness, etc.), sometimes accompanied by changes on joint X-rays. Furthermore, we currently lack disease-modifying treatments for OA, with clinical interventions primarily focusing on symptom management. This generalised approach has limited effect, since it fails to take into account the complexity of OA, which often has different causes, clinical features, and outcomes in different patients.
This project envisions the development of refined diagnostic approaches that identify OA subtypes and indicators of its progression, at earlier stages of the disease. This approach may enable personalised interventions that offer patients a better chance for preserving joint function and reducing pain. Towards this, the project brings together a new constellation of internationally leading researchers in the fields of OA research, medical imaging, orthopaedics and rheumatology. Using advanced structural imaging, molecular analysis and deep-learning algorithms, the team hopes to make a breakthrough in the identification of early biomarkers for disease diagnostics and personalized treatment of OA. Success in this field has the potential to improve the lives of the many millions of OA sufferers worldwide.