Improving causal inference in rheumatic diseases research by integrating observational and genetic epidemiology

Rheumatic and musculoskeletal diseases (such as rheumatoid and osteoarthritis) are the leading causes of disability and loss of work productivity in developed countries. Their impact on individuals and societies will increase with ageing populations. Traditional research methods to identify risk factors for, and health-related consequences of, rheumatic diseases have inherent weaknesses. For example, body fat correlates with arthritis risk, but these research methods cannot tell us whether one causes the other and which way around. Untangling cause and effect is important because, for example, reducing body fat can only prevent arthritis if body fat causes arthritis, not if arthritis changes body fat. Inability to determine cause and effect is the same reason that the majority of molecules linked to arthritis do not translate into effective drug treatments. Newer scientific methods that borrow strength from genetic data can address these weaknesses and make existing research findings more relevant to public health policy and drug-development to prevent or treat rheumatic disease. We propose three objectives to address this overarching aim. We will investigate risk factors that can be modified to prevent rheumatic diseases (Aim 1) and, in reverse, whether rheumatic diseases influence risk factors or other diseases (Aim 2). We will incorporate genetic data to improve accuracy of disease definitions (Aim 3), which will help all future UK biobank studies of these diseases and is required to achieve Aims 1 and 2.