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

心血管疾病（CVD）是类风湿性关节炎（RA）患者的主要合并症和主要死亡原因(1)。传统的心血管疾病危险因素（如吸烟、高血压）在类风湿性关节炎中发挥重要作用，但并不能解释所有的风险，大约30%的风险是由类风湿性关节炎相关因素和相关的全身炎症引起的(2)。对CVD患者血液中的生物标志物的分析已经确定了一些将全身性炎症与疾病风险联系起来的关键途径(3)。然而，它们与RA中CVD的相关性尚未得到充分研究，目前还缺乏分析来确定RA中CVD的共同或不同机制，或者不同的生物标志物模式是否能够描述相同的生物机制。该学生将应用最先进的统计技术来确定RA中CVD异常的分子机制，描述基于血液的生物标志物在确定重要RA结果中的作用，并确定关键的分子途径，以促进新的机制理解，加强患者监测并潜在地确定新的治疗靶点。转录组学、蛋白质组学和代谢组学数据集可用于招募参加CADERA（类风湿关节炎冠状动脉疾病评估）试验的患者，该试验生成多参数心血管磁共振（CMR）数据(4)。与传统危险因素（如2型糖尿病）的压倒性影响相反，研究人员招募了具有最多1个传统危险因素的RA患者，以便能够将异常归因于RA相关的心血管疾病。对于没有已知CVD病史的RA患者，您将确定(i)治疗前血液来源的生物标志物，这些生物标志物与CMR检测到的心血管异常相关（即亚临床CVD异常，包括心肌组织水肿/纤维化和血管僵硬）；（ii）与CMR亚临床异常变化（改善）相关的生物标志物；（iii）治疗期间对RA药物反应不同的生物标志物。您将识别共同表达的生物标志物网络和生物学途径，以更好地了解RA的CVD异常的分子机制。

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.