Harnessing the power of multi-omics to understand the molecular drivers of cardiovascular disease risk in patients with rheumatoid arthritis

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Cardiovascular disease (CVD) is a major comorbidity and leading cause of death in patients with rheumatoid arthritis (RA) (1). Traditional CVD risk factors (e.g. smoking, high blood pressure) play an important role in RA but do not account for all the risk, with approximately 30% risk resulting from RA-related factors and associated systemic inflammation (2). Analysis of biomarkers in the blood of patients with CVD has identified a number of key pathways linking systemic inflammation to disease risk (3). However, their relevance to CVD in RA has not been fully investigated and analyses to identify shared or distinct mechanisms of CVD in RA or whether different biomarker modalities are able to describe the same biological mechanisms are currently lacking.This studentship will apply state-of-the-art statistical techniques to identify molecular mechanisms underpinning CVD abnormalities in RA, characterise the role of blood-based biomarkers in determining important RA outcomes and identify the key molecular pathways to facilitate new mechanistic understanding, enhance patient monitoring and potentially identify new targets for treatments.Transcriptomic, proteomic and metabolomic datasets are available for patients recruited to the CADERA (Coronary Artery Disease Evaluation in Rheumatoid Arthritis) trial where multi-parametric cardiovascular magnetic resonance (CMR) data were generated (4). Patients with RA but a maximum of 1 traditional risk factor were recruited to be able to attribute abnormalities to RA associated CVD as opposed to overwhelming effects of traditional risk factors such as type 2 diabetes mellitus. In patients with RA and no known CVD history you will identify (i) pre-treatment blood derived biomarkers that correlate with cardiovascular abnormalities as detected by CMR (i.e. sub-clinical CVD abnormalities including myocardial tissue oedema/fibrosis and vascular stiffness) and (ii) biomarkers that correlate with change (improvement) in CMR subclinical abnormalities and (iii) biomarkers that vary on-treatment in response to RA medication. You will identify co-expressed biomarker networks and biological pathways to better understand the molecular mechanisms of CVD abnormalities of RA.