Molecular predictors of cardiovascular events and resilience in chronic coronary artery disease

项目摘要 慢性冠状动脉疾病(CAD)的最新风险评估仅能部分捕获 心血管事件(CVE)，留下相当大的“残余风险”未得到解决。目前的风险评估也 未完全捕获对CAD的恢复能力，当代算法将其定义为高风险-但没有 疾病。这种“残留保护”突出了新的复原力因素，可防止 CAD。在这种情况下，了解与剩余风险和弹性相关的因素以实现个性化至关重要 风险预测，并帮助临床医生和患者做出更好的治疗决策。我们最重要的假设 多组学方法可以识别冠心病残余风险和恢复力的分子特征。 从历史上看，CAD的组学研究受限于1)表型异质性--对变量定义的依赖 CAD和CVE的偏差结果和极限预测；以及2)风险同质性-约束识别 新的途径和有限的概括性。我们利用独特的访问权限来克服这些限制 具有里程碑意义的NHLBI CAD策略试验和具有一致核心的队列研究-实验室确认的测试，分子 数据和已判决的CVE。总的来说，这些研究跨越了CAD风险连续体-这是一个关键的特征 评估生物标志物和分子特征的性能并克服先前的限制。初步 支持我们假设的数据显示：1)死亡/心肌梗死的大量、无法解释的残余风险(&gt；30%) 危险因素和冠心病严重程度的临床模型：2)炎症、心肌细胞损伤的生物标志物 和扩张改善了模型的性能，以及3)新的炎症和转录组模块 干扰素信号进一步提高了预测性。来自推测的转录组的新的初步数据 没有冠心病的“弹性”患者表现出脂肪酸代谢的异常途径和基因。我们的 总体目标是利用来自这些里程碑式研究的表型良好的参与者来改善CVE 预测并更好地了解对CAD的弹性。我们提出了以下具体目标。目标1：改进 冠心病确诊患者的心血管事件预测。我们将测试和验证(1a)候选生物标记物， 冠心病和(1b)转录本的多基因风险评分可改善CVE的预测，超越临床模型 风险因素和最先进的检测(核心实验室证实了冠心病和缺血的严重性)。目标2：确定 冠心病恢复力的生物标志物和分子特征。我们将测试(2a)个候选人的关联性 尽管疾病概率很高，但没有冠心病的恢复力患者的生物标记物和(2b)转录组 以冠心病的临床和多基因风险评分为标准。在申请人看来，这项建议是创新的， 从现状出发，使用仔细判断的CVE和来自冠心病风险患者的表型 频谱并具有重要意义，因为它将加快个性化风险分层和处理-尤其是 适用于大量处于CAD和CVE中等风险的患者。归根结底，从 这一应用具有改善数百万美国冠心病患者的护理和预后的潜力。

PROJECT ABSTRACT State-of-the-art risk assessments in chronic coronary artery disease (CAD) only partially capture risk for cardiovascular events (CVEs), leaving substantial ‘residual risk’ unaddressed. Current risk assessments also incompletely capture resilience to CAD, defined as those at high risk by contemporary algorithms—but without disease. This ‘residual protection’ highlights novel resiliency factors protective against the development of CAD. In this context, it is crucial to understand factors related to both residual risk and resiliency to personalize risk prediction and help clinicians and patients make better treatment decisions. Our overarching hypothesis is that a multi ‘omics’ approach can identify molecular features of residual risk and resilience in CAD. Historically, omics studies of CAD were limited by 1) phenotypic heterogeneity—reliance on variable definitions of CAD and CVEs, biasing results and limiting prediction; and 2) risk homogeneity—constraining identification of novel pathways and limiting generalizability. We overcome these limitations by leveraging unique access to landmark NHLBI CAD strategy trials and a cohort study with aligned core-lab confirmed testing, molecular data, and adjudicated CVEs. Collectively, these studies span the CAD risk continuum—a feature critical to assessing performance of biomarkers and molecular features and overcoming prior limitations. Preliminary data supporting our hypothesis show: 1) substantial, unexplained residual risk (>30%) for death/myocardial infarction with a clinical model of risk factors and CAD severity, 2) biomarkers of inflammation, myocyte injury and distension improve model performance, and 3) novel transcriptome modules of inflammation and interferon signaling further improve prediction. New preliminary data from the imputed transcriptome of ‘resilient’ patients without CAD demonstrates dysregulated pathways and genes of fatty acid metabolism. Our overall goal is to leverage well-phenotyped participants from these landmark studies to improve CVE prediction and better understand resilience to CAD. We propose the following specific aims. Aim 1: Improve prediction of CVEs in patients with established CAD. We will test and validate (1a) candidate biomarkers, polygenic risk scores for CAD and (1b) transcriptomics to improve CVE prediction beyond a clinical model of risk factors and state-of-the-art testing (core-lab confirmed severity of CAD and ischemia). Aim 2: Identify biomarkers and molecular features of resilience to CAD. We will test the association of (2a) candidate biomarkers and (2b) transcriptomics among resilient patients without CAD despite a high probability of disease by clinical and polygenic risk scores for CAD. In the applicant’s opinion, this proposal is innovative and departs from the status quo by using meticulously adjudicated CVEs and phenotype from patients across the CAD risk spectrum and is significant because it will accelerate personalized risk stratification and treatment—especially for the large number of patients at intermediate risk for CAD and CVEs. Ultimately, knowledge generated from this application has the potential to improve the care and outcomes for millions of Americans with CAD.