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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要最先进的慢性冠状动脉疾病 (CAD) 风险评估仅部分捕捉到以下风险：心血管事件（CVE），目前的风险评估也留下了大量的“残余风险”。不完全捕捉 CAD 的恢复能力，当代算法将其定义为高风险人群，但没有这种“剩余保护”凸显了防止疾病发展的新弹性因素。在这种情况下，了解与残余风险和弹性相关的因素对于个性化至关重要。风险预测并帮助婴儿和患者做出更好的治疗决策。多“组学”方法可以识别 CAD 中残余风险和恢复力的分子特征。从历史上看，CAD 的组学研究受到 1) 表型异质性的限制——依赖变量定义 CAD 和 CVE、偏差结果和限制预测；2) 风险同质性——约束识别；我们通过利用独特的途径克服了这些限制。具有里程碑意义的 NHLBI CAD 策略试验和队列研究与一致的核心实验室确认测试、分子总的来说，这些研究涵盖了 CAD 风险连续体——这一特征对于风险评估至关重要。评估生物标志物和分子特征的性能并克服先前的局限性。支持我们假设的数据显示：1) 死亡/心肌损伤的大量、无法解释的残余风险 (>30%) 具有危险因素和 CAD 严重程度的临床模型的梗塞，2) 炎症、肌细胞损伤的生物标志物和膨胀改善模型性能，3）炎症和炎症的新转录组模块干扰素信号传导进一步改善了预测转录组的新初步数据。没有 CAD 的“有弹性”的患者表现出脂肪酸代谢途径和基因失调。总体目标是利用这些里程碑式研究中表型良好的参与者来改善 CVE 我们提出以下具体目标：提高预测能力并更好地了解 CAD 的恢复能力。预测患有 CAD 的患者的 CVE。我们将测试和验证 (1a) 候选生物标志物， CAD 多基因风险评分和 (1b) 转录组学可改善 CVE 预测，超越临床模型风险因素和最先进的测试（核心实验室确认了 CAD 和缺血的严重程度）。我们将测试 (2a) 候选者的生物标志物和分子特征。尽管患病概率很高，但无 CAD 的恢复力患者的生物标志物和 (2b) 转录组学申请人认为，该提案具有创新性且与众不同。通过使用来自 CAD 风险患者的仔细判定的 CVE 和表型，从现状出发谱并且意义重大，因为它将加速个性化风险分层和治疗，尤其是对于大量处于 CAD 和 CVE 中等风险的患者，最终产生的知识。该应用程序有可能改善数百万患有 CAD 的美国人的护理和治疗结果。