Rheumatic musculoskeletal disorders (RMDs) are a group of diseases that primarily affect joints, muscles, tendons and ligaments, often causing pain, stiffness, swelling and limited mobility. Many of these diseases, such as rheumatoid arthritis and systemic sclerosis, are autoimmune diseases. Although each RMD has unique signs and symptoms, they also share genetic risk genes and molecular activation pathways, and accordingly, some of their common symptoms are treated with the same therapeutics. Unfortunately, the treatment of RMDs is still largely based on “trial and error” and new drugs are often developed in one disease and then tested to see if they can be used in other RMDs. With our project, we want to systematically compare the genetic background and cell activation in the different RMDs in order to create a new stratification of these diseases that could be helpful for both diagnosis and therapy. To this end we will use clustering models and polygenic risk scores to determine the genetic distance of the different RMDs. We will also integrate metabolomics data with matched transcriptome data and identify shared vs disease-specific pathways and their association with clinical scores. Finally, we will analyse pre-disease measurements to assess whether discrimination of the diseases is already possible at a very early time point in the course of the disease.
