Causal associations of circulating biomarkers with cardiovascular disease

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT Cardiovascular diseases (CVD) comprise the global leading cause of morbidity and mortality, and in the United States, CVD account for more than one-third of all deaths, of which ~150,000 deaths per year occurs in individuals younger than 65 years. Over the past decades, hundreds of circulating biomarkers have been associated with CVD, but their relative importance and potential involvement in the actual disease processes have been less investigated. Using a very large cohort study that recently became available to the scientific community, we will deploy Mendelian randomization methods to study the causal role of biomarkers proposed to be associated with CVD. In 2006-2010, the UK Biobank recruited 502,650 participants aged 37-73 years to undergo physical measurements, detailed assessments about risk factors and future disease events, and sampling of blood, urine and saliva. Genome-wide genotyping on the UK Biobank Axiom Array (820,967 genetic markers) and measurement of 36 circulating biomarkers with relevance for CVD will be finished during 2016. We will study the associations of 36 circulating biomarkers representing coagulation and inflammation (fibrinogen, D-dimer, hsCRP, rheumatoid factor), glucose homeostasis (HbA1c, glucose, IGF-1), lipid metabolism (total cholesterol, LDL-C, HDL-C, triglycerides, ApoAI, ApoB, Lp(a)), liver function (ALT, AST, ALP, direct and total bilirubin, GGT, albumin, total protein), kidney function (creatinine, cystatin C, phosphate, urate, urea, urinary sodium, potassium, microalbumin and creatinine), reproductive system (SHBG, testosterone, oestradiol), and mineral metabolism (calcium, vitamin D) with incidence of coronary heart disease, stroke, heart failure, atrial fibrillation and type 2 diabetes in traditional observational multivariable-adjusted analyses. We will then perform genome-wide association studies (GWAS) of all 36 biomarkers to establish common genetic variation associated with respective biomarker. With a sample size of ~390,000 individuals, we will have excellent statistical power to uncover a substantial fraction of common genetic variants associated with the biomarkers. These associations will be used to develop robust instrumental variables. Finally, using instrumental variable analyses, we will study the causal roles of these circulating biomarkers for development of cardiovascular disease. The large sample size of the present study will allow for unprecedented possibilities of Mendelian randomization studies of CVD biomarkers with adequate statistical power and with low risk of pleiotropy. Knowledge about the causal roles of CVD-related biomarkers for development of coronary heart disease, stroke, heart failure, atrial fibrillation and type 2 diabetes will provide important insights regarding the etiological understanding of these diseases and accelerate new prevention strategies, including druggable targets.