Clinical, Genetic, and Proteomic Risk Factors for Pulmonary Hypertension in Heart Failure

项目总结 至少50%的心力衰竭患者会发展为肺动脉高压(HF-PH)。事实上，所有患有心力衰竭的人 左心室充盈压升高表明一定有其他因素推动 PH的发展。确定这些因素很重要，因为HF-PH的死亡率增加了50% 也没有任何治疗方法可以改善结果或防止PH的发展。有关风险的流行病学数据 目前尚缺乏HF-PH的发病因素和自然发病史。类似地，HF-PH的生物学机制是 未知，因为还没有在这个群体中进行过分子研究。除了建立 发病率和临床危险因素，这一应用的一个重要目标是识别分子特征 与HF-PH和右室(RV)代偿相关(即在 PH)。我们假设(1)使用超声心动图数据的HF-PH发生率比以前更高 基于医疗代码的报告比率(2)HF-PH和RV补偿受遗传影响，以及(3) 蛋白质生物标志物将与流行的HF-PH和RV功能相关。这些假设是基于 我们的初步结果显示：1)使用回波数据的入射率比基于医疗代码的入射率更高 2)代谢健康不良与HF-PH和RV功能障碍的关联；3)高遗传遗传率 肺压力；4)肥胖和肺压力之间的共同遗传风险；5)基因关联 胰岛素抵抗与PH之间的关系；6)HF-PH患者炎症和血管紧张性蛋白的升高 病人。开发旨在研究HF-PH自然历史的大型预期队列将是 令人望而却步的昂贵和低效。利用基于电子健康记录(EHR)的队列链接到 BIOBANKS为研究HF-PH流行病学提供了一条科学有效、经济高效的途径 和病理生理学。在目标1中，我们将建立HF-PH发病率，并检查 超声心动图PASP提取可改变的HF-PH危险因素(如肥胖、胰岛素抵抗) 对退伍军人事务部和Vanderbilt EHR中约425,000人的价值(64,000名非洲裔美国人和 85,000名妇女)。这些人中约有100,000人有重复的PASP测量，65,000人 拥有黄金标准的RHC数据。这两组人都有很好的表型，都有详细的人口统计数据， 合并症、药物暴露、实验室和临床事件。在目标2中，我们将利用退伍军人管理局资助的 百万退伍军人计划和Vanderbilt的BioVU分析总共25,000名患者的全基因组基因数据 受试者患有心力衰竭，本申请不收取任何费用。在目标3中，我们将进行蛋白质组学分析(1129个蛋白质) 发现(800名受试者)和复制(600名受试者)通过BioVU收集的HF队列。我们有 将现有资源与新的表型、基因和蛋白质组数据相结合，并组建了一个团队 执行我们目标的特定专业知识。如果我们的假设是正确的，结果可能会改善临床 HF-PH指南，并为HF-PH和RV功能障碍确定新的治疗靶点。

PROJECT SUMMARY At least 50% of people with HF develop pulmonary hypertension (HF-PH). The fact that all people with HF have elevated left ventricular filling pressures suggests that there must be additional factors that drive the development of PH. Identifying these factors is important because HF-PH carries a 50% increase in mortality and no treatments exist to improve outcomes or prevent PH development. Epidemiologic data on the risk factors and natural history of HF-PH are lacking. Similarly, the biological mechanisms underlying HF-PH are unknown because no molecular studies have been performed in this population. In addition to establishing incidence rates and clinical risk factors, an important goal of this application is to identify molecular features associated with HF-PH and right ventricular (RV) compensation (i.e. preserved RV function in the setting of PH). We hypothesize that (1) HF-PH incidence rates using echocardiographic data are higher than previously reported rates based on medical codes (2) HF-PH and RV compensation are genetically influenced, and (3) protein biomarkers will be associated with prevalent HF-PH and RV function. These hypotheses are based on our preliminary showing: 1) higher rates of incident HF-PH using echo data than rates based on medical codes alone; 2) association of poor metabolic health with HF-PH and RV dysfunction; 3) high genetic heritability of pulmonary pressure; 4) shared genetic risk between obesity and pulmonary pressure; 5) a genetic association between insulin resistance and PH; and 6) elevation of inflammatory and vascular tone proteins in HF-PH patients. Developing large, prospective cohorts designed to study the natural history of HF-PH would be prohibitively expensive and inefficient. Leveraging electronic health record (EHR)-based cohorts linked to biobanks presents a scientifically valid, cost-effective, and efficient pathway for studying HF-PH epidemiology and pathophysiology. In Aim 1, we will establish HF-PH incidence rates and examine the importance of modifiable risk factors for HF-PH (e.g. obesity, insulin resistance) by extracting echocardiographic PASP values on ~425,000 individuals in the Veterans Affairs and Vanderbilt EHRs (64,000 African Americans and 85,000 women). Approximately 100,000 of these individuals have repeat PASP measurements, and 65,000 have gold standard RHC data. Both cohorts are well phenotyped with detailed data on demographics, comorbidities, medication exposure, laboratory, and clinical events. In Aim 2, we will leverage the VA-funded Million Veterans Program and Vanderbilt's BioVU to analyze genome-wide genotyping data in a total of 25,000 subjects with HF at no cost to this application. In Aim 3, we will perform proteomic profiling (1129 proteins) in discovery (800 subjects) and replication (600 subjects) HF cohorts collected through BioVU. We have combined existing resources with new phenotypic, genotypic, and proteomic data and assembled a team with the specific expertise to execute our aims. If our hypotheses are correct, the results could improve clinical guidelines for HF-PH and identify new therapeutic targets for HF-PH and RV dysfunction.