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

心血管事件的分子预测因素和慢性冠状动脉疾病的恢复力

• 批准号: 10736587
• 负责人: JONATHAN D NEWMAN
• 金额: $ 77.75万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10736587
• 关键词: Acceleration; Algorithms; American; Apoptosis; Area; Biological; Biological Markers; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Chronic; Clinical; Cohort Studies; Collaborations; Coronary Angiography; Coronary Arteriosclerosis; Data; Development; Disease; Event; Fatty Acids; Future; GDF15 gene; Genes; Genetic; Goals; Heterogeneity; Inflammation; Inflammatory; Injury; Interferons; Ischemia; Knowledge; Lipolysis; Longevity; Modeling; Molecular; Molecular Target; Muscle Cells; Myocardial Infarction; National Heart, Lung, and Blood Institute; Outcome; Participant; Pathway interactions; Patients; Performance; Phenotype; Population; Prevention; Probability; Provider; Public Health; Quality of life; Research; Residual state; Risk; Risk Assessment; Risk Factors; Severities; Severity of illness; Signal Pathway; Signal Transduction; Testing; Time; Trans-Omics for Precision Medicine; Transcript; Troponin; Validation; adipokines; adjudication; biomarker identification; biomarker performance; candidate marker; candidate validation; cardiovascular risk factor; clinical risk; cohort; differential expression; disorder risk; fatty acid metabolism; fatty acid oxidation; high risk; improved; improved outcome; innovation; molecular modeling; multiple omics; novel; novel marker; patient population; personalized risk prediction; polygenic risk score; predictive modeling; prevent; pro-brain natriuretic peptide (1-76); programs; promote resilience; protective factors; research clinical testing; resilience; resilience factor; risk prediction; risk stratification; transcriptome; transcriptomics; Acceleration; Algorithms; American; Apoptosis; Area; Biological; Biological Markers; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Chronic; Clinical; Cohort Studies; Collaborations; Coronary Angiography; Coronary Arteriosclerosis; Data; Development; Disease; Event; Fatty Acids; Future; GDF15 gene; Genes; Genetic; Goals; Heterogeneity; Inflammation; Inflammatory; Injury; Interferons; Ischemia; Knowledge; Lipolysis; Longevity; Modeling; Molecular; Molecular Target; Muscle Cells; Myocardial Infarction; National Heart, Lung, and Blood Institute; Outcome; Participant; Pathway interactions; Patients; Performance; Phenotype; Population; Prevention; Probability; Provider; Public Health; Quality of life; Research; Residual state; Risk; Risk Assessment; Risk Factors; Severities; Severity of illness; Signal Pathway; Signal Transduction; Testing; Time; Trans-Omics for Precision Medicine; Transcript; Troponin; Validation; adipokines; adjudication; biomarker identification; biomarker performance; candidate marker; candidate validation; cardiovascular risk factor; clinical risk; cohort; differential expression; disorder risk; fatty acid metabolism; fatty acid oxidation; high risk; improved; improved outcome; innovation; molecular modeling; multiple omics; novel; novel marker; patient population; personalized risk prediction; polygenic risk score; predictive modeling; prevent; pro-brain natriuretic peptide (1-76); programs; promote resilience; protective factors; research clinical testing; resilience; resilience factor; risk prediction; risk stratification; transcriptome; transcriptomics

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.

项目摘要 慢性冠状动脉疾病（CAD）的最新风险评估仅部分捕获 心血管事件（CVE），使实质性的“残留风险”未经解决。当前的风险评估 不完全捕获对CAD的弹性，被定义为当代算法高风险的CAD，但没有 疾病。这种“剩余保护”突出了新颖的弹性因素可防止 卡德。在这种情况下，了解与剩余风险和弹性有关的因素至关重要 风险预测并帮助临床医生和患者做出更好的治疗决定。我们的总体假设 是多种“ OMICS”方法可以识别CAD中残留风险和弹性的分子特征。 从历史上看，CAD的OMICS研究受到1）表型异质性的限制 - 对可变定义的依赖 CAD和CVE，有偏见的结果和限制预测； 2）风险同质性 - 限制身份证明 新的途径和限制普遍性。我们通过利用独特的访问来克服这些限制 LANDMARK NHLBI CAD策略试验和一项与对齐核LAB的队列研究确认测试，分子 数据和调整后的CVE。总的来说，这些研究涵盖了CAD风险连续性 - 这是 评估生物标志物和分子特征的性能以及克服先前的局限性。初步的 支持我们假设的数据显示：1）死亡/心肌的实质性，无法解释的残余风险（> 30％） 具有风险因素和CAD严重程度的临床模型的梗塞，2）炎症的生物标志物，心肌细胞损伤 延伸提高模型性能，3）炎症的新型转录组模块 干扰素信号传导进一步的改进预测。来自估算的转录组的新初步数据 没有CAD的“弹性”患者表现出脂肪酸代谢的途径和基因失调。我们的 总体目标是利用这些具有里程碑意义研究的精心型参与者来改善CVE 预测并更好地了解CAD的弹性。我们提出以下特定目标。目标1：改进 CAD患者CVE的预测。我们将测试和验证（1A）候选生物标志物， CAD和（1B）转录组学的多基因风险评分，以改善CVE预测超出临床模型 危险因素和最新测试（Core-LAB确认CAD和缺血的严重程度）。目标2：识别 生物标志物和对CAD弹性的分子特征。我们将测试（2A）候选人的关联 没有CAD目的地的弹性患者中的生物标志物和（2B）转录组学是疾病的高可能性 通过CAD的临床和多基因风险评分。申请人认为，该提议具有创新性和离开 通过使用精心调整的CVE和CAD风险的患者的表型从现状中 频谱和意义重大，因为它将加速个性化的风险分层和治疗，尤其是 适用于大量CAD和CVE的患者。最终，从 该应用程序有可能改善数百万使用CAD的美国人的护理和成果。