Understanding the key factors associated with and predicting pain in arthritis is essential in order to better identify those at greatest risk, to inform treatment choices and to develop preventative and treatment strategies to reduce the burden or arthritis pain in society.
Our cohorts and cross-sectional datasets elucidate incidence, prevalence, natural history and impact of joint pain. We explore risk factors for the development and progression of arthritis pain and identify features that can explain differences in pain severity and treatment responses between individuals. This research should help us to identify those groups of people for whom changing their treatment will be particularly helpful. Genetic and biomarker studies are identifying molecular, cellular and neuorphysiological pathways that contribute to arthritis pain and help us to understand how individuals respond to different treatments.
Our extensive biorepository collections of blood, urine, DNA, synovial fluid, joint and spinal tissues, paired with clinical data, provide a key resource for addressing these research questions. Linkage between cohorts and biorepositories enables us to place molecular findings in a population context, and to explore at the molecular level mechanistic hypotheses that emerge from findings in populations.
Knee Pain and related health In the Community (KPIC) cohort
Investigating Musculoskeletal Health and Wellbeing (IMH&W) cohort
Joint Tissue Repository