Inflammation is now recognized as a key risk factor for cardiovascular disease (CVD) and an increased inflammatory response being a major driver of atherosclerotic plaque development. Several studies have shown that drugs targeting pro-inflammatory mediators have the potential to improve the prognosis of patients with CVD. In addition, some patients with CVD have comorbid chronic inflammatory diseases (CID), such as inflammatory bowel disease, psoriasis, and lupus, which further worsen the prognosis of these patients. Previous studies have not comprehensively investigated how vascular and systemic inflammation mediate or modulate the occurrence of adverse cardiovascular events such as myocardial infarction, or cardiovascular death.
This study will be based on the UK Biobank database and aims to explore the possibility that the prevalence of CID and its association with CVD prognosis varies between regions with different levels of economic development and between ethnic groups and to assess the impact of environmental factors on disease risk. Extensive exploration of its multimodal data focusing on genetics, exposures, and endophenotypes will uncover genetic variants and genes associated with CVD combined with CID, revealing the complex relationship between inflammation and CVD. We will integrate multiple data dimensions, including multi-omics and disease risk factors, and construct highly predictive models using machine learning methods and polygenic risk scores to more accurately predict the onset and progression of inflammatory CVD. We also aim to reveal the biological functions and regulatory mechanisms of inflammation on CVD by combining multi-omics data from transcriptomics, epigenomics, proteomics, and metabolomics technologies to explore potential drug targets and provide new ideas and approaches for prevention and treatment.
