Resolving Causal Influences Among Correlated Risk Biomarkers for Coronary Artery Disease

项目摘要/摘要 流行病学研究表明，许多生物标志物之间存在相关性(定义为 疾病严重程度或存在的可测量指标)和冠状动脉疾病的风险 (CAD)。然而，尚不清楚这些生物标记物中的许多是否代表了CAD的因果过程。 推断生物标记物与冠心病的因果关系有可能识别可能导致 冠心病发生发展的病理生理过程。 最近，我们开发了一种方法，称为多表型孟德尔随机化，它可以解开 在一组相关的生物标志物中对疾病的因果影响。我们将我们的方法应用于等离子体 甘油三酯，并表明SNP对甘油三酯的影响大小与其对甘油三酯的影响大小呈线性关系 冠心病，考虑相同SNP对血浆低密度脂蛋白的潜在影响 胆固醇(低密度脂蛋白胆固醇)和/或高密度脂蛋白胆固醇(高密度脂蛋白胆固醇)。这一发现后来得到了证实 通过其他研究。总而言之，这些结果表明，血浆甘油三酯可能捕捉到了 可能会促进动脉粥样硬化和冠心病。 我们建议在以前工作的基础上，通过推断32个变量之间的因果关系来扩展 来自电子病历的心脏代谢特征、245种代谢物和2,000种临床表型 亚临床CAD内表型。在目标1中，我们将评估当前孟德尔随机化方法和 改进方法以允许检测多效性(或检测违反基本假设 孟德尔随机化)，可以改善这些方法的统计特性。在目标2中，我们将推断 32种心脏代谢物特征和245种代谢物特征与亚临床动脉粥样硬化和 用于CAD的心脏结构和功能内表型。在目标3中，我们将执行一个名为 全表型孟德尔随机法推断冠心病性状与2,000个临床表型的因果关系 电子病历(EMR)。 这项提议是创新的，因为我们正在利用新的方法进行因果推理，以及 心脏代谢特征、代谢物、EMR临床表型和亚临床CAD的详细信息库 疾病特征。我们建议使用以下资源：1)新的因果推理方法，说明 多效性；2)广泛的心脏代谢特征(总共32个)和代谢物(总共245个)；3)亚临床 CAD结果(42个亚临床动脉粥样硬化和54个心脏结构和功能特征)；以及4)EMR 来自大规模西奈山生物群生物库和英国生物库的表型(&gt；2000)。 该建议有可能揭示亚临床CAD疾病结局的新的因果风险生物标记物。 它可以为开发防治冠心病的新疗法提供新的途径。

PROJECT SUMMARY / ABSTRACT Epidemiological studies have shown correlations among numerous biomarkers (defined as measurable indicators of the severity or presence of a disease state) and risk for coronary artery disease (CAD). However, it's unknown whether many of these biomarkers represent causal processes for CAD. Inferring causality of a biomarker with CAD has the potential to identify risk factors that may lead to pathophysiological processes for the development of CAD. Recently, we developed a method, called Multi-Phenotype Mendelian Randomization, that disentangles causal influences for a disease among a set of correlated biomarkers. We applied our method to plasma triglycerides and showed that the effect size of a SNP on triglycerides is linearly related to its effect size on CAD, before and after accounting for the same SNP's potential effect on plasma low-density lipoprotein cholesterol (LDL-C) and/or high-density lipoprotein cholesterol (HDL-C). This finding has since been validated by other studies. Together, these results suggest that plasma triglycerides may capture causal processes that may promote atherosclerosis and CAD. We propose to expand on our prior work by inferring causal relationships between a wide range of 32 cardiometabolic traits, 245 metabolites and >2,000 clinical phenotypes from electronic medical records with subclinical CAD endophenotypes. In Aim 1, we will evaluate current Mendelian randomization methods and refine the approach to allow for detection of pleiotropy (or detection of violation of a basic assumption of Mendelian randomization), which can improve statistical properties of these methods. In Aim 2, we will infer causality of a wide range of 32 cardiometabolic and 245 metabolite traits with subclinical atherosclerosis and cardiac structure and function endophenotypes for CAD. In Aim 3, we will perform a novel framework called Phenome-Wide Mendelian Randomization to infer causality of CAD traits with >2,000 clinical phenotypes from electronic medical records (EMR). The proposal is innovative because we are utilizing novel approaches for causal inference, along with a detailed repository of cardiometabolic traits, metabolites, EMR clinical phenotypes, and subclinical CAD disease traits. We propose to use the following resources: 1) new causal inference approach that accounts for pleiotropy; 2) extensive set of cardiometabolic traits (32 in total) and metabolites (245 in total); 3) subclinical CAD outcomes (42 subclinical atherosclerosis and 54 cardiac structure and function traits); and 4) EMR phenotypes from large-scale Mount Sinai's BioMe Biobank and UK Biobank (>2,000). This proposal has the potential to reveal new causal risk biomarkers for subclinical CAD disease outcomes. It can provide new avenues for the development of new therapeutics for the prevention and treatment of CAD.