Utilising large joint structural phenotypes of osteoarthritis for enhanced disease prediction and novel treatment target discovery

Osteoarthritis is a common disabling disease with a growing prevalence that currently affects 500 million people worldwide. Rather than being a homogenous condition, multiple structural osteoarthritis phenotypes are thought to exist: hypertrophic, atrophic, malalignment and mixed. Disease modifying therapeutics for osteoarthritis are lacking despite numerous therapies being trialled. One reason for this might be the heterogenous nature of osteoarthritis, with potentially effective treatments trialled on everyone rather than a specific phenotype leading to null results. I aim to: i. Develop an image-based classifier for structural large joint osteoarthritis phenotypes at the knee and hip. I will create this using already derived measures of joint shape and radiographic osteoarthritis (termed endophenotypes) in UK Biobank (n~76,000). ii. Apply the classifier to the DMOAD Trial Bank, a repository of osteoarthritis therapeutic trials, to identify phenotype-specific treatment effects. iii. Leverage the Genetics of Osteoarthritis Endophenotypes Working Group (GO-EWG), a consortium of 11 studies that contain osteoarthritis endophenotypes and genetic data, to identify novel drug targets for osteoarthritis and provide a basis for drug discovery using a multiomics approach. iv. Develop a multimodal predictor of osteoarthritis disease progression using the image-based biomarkers developed in (i) and genetic and proteomic risk scores for osteoarthritis progression.