Cohort Core

项目概要 射血分数保留的心力衰竭 (HF) (HFpEF) 是一种异质性、多方面的综合征， 是精准医疗应用的理想场所。 HFpEF 已被证明能够抵抗传统的 研究其潜在病理生理学和进行随机临床试验的技术。新 因此，正如最近发布的一份白皮书所指出的，迫切需要采取一些方法 总结了 NHLBI HFpEF 研究重点工作组的审议情况。国家 科学院精准医学报告提出“当多种分子指标应用于 与传统的临床、组织学和实验室发现相结合，它们提供了更多的机会 准确、精确的疾病描述和分类”。该范例也得到了 NHLBI 的支持 战略愿景认为组学的组合（例如基因组学、蛋白质组学和代谢组学） 结合临床和生理数据，对于增进对病理生物学的理解是必要的。 HFpEF 等复杂临床综合征的基础，并为 HeartShare 提供了基本原理。 精准医学的前景在于数据多样性。不仅仅是生物医学数据的庞大规模， 多种数据模式的分层，提供互补的观点，这将有助于识别 具有共同病理生理学的患者亚组。 HeartShare 提供了一个进行多方合作的巨大机会 HFpEF 的分层精准医学研究。 HeartShare 队列核心将对以下领域产生持久影响： HFpEF 领域通过实现以下具体目标来响应 HeartShare 请求 用于应用程序。 (1) 汇集、清理和协调多种心血管疾病的所有数据和图像 队列和 HF 试验，以便为研究界开发全面、数据丰富的资源 识别新的 HFpEF 亚型。 (2) 使用假设将机器学习和多组学分析结合起来： 通过询问先前识别的 HFpEF 亚型来改进识别和驱动方法 对这些 HFpEF 亚型的分子/病理生理学理解。 （3）将机器学习与 使用公正的方法识别和验证新型 HFpEF 的多维数据（包括多组学） 亚型。

PROJECT SUMMARY Heart failure (HF) with preserved ejection fraction (HFpEF) is a heterogeneous, multifaceted syndrome that is an ideal setting for the application of precision medicine. HFpEF has proven resistant to conventional techniques for the study of its underlying pathophysiology and conduct of randomized clinical trials. New approaches are therefore desperately needed, as indicated in a recently published white paper which summarized the deliberations of the NHLBI Working Group on Research Priorities in HFpEF. The National Academy of Sciences Precision Medicine Report proposes that “when multiple molecular indicators are used in combination with conventional clinical, histological, and laboratory findings, they offer the opportunity for a more accurate and precise description and classification of disease”. This paradigm is also supported by the NHLBI’s Strategic Vision, which argues that a combination of omics (e.g., genomics, proteomics, and metabolomics), coupled with clinical and physiological data, will be necessary to advance understanding of the pathobiological basis of complex clinical syndromes such as HFpEF, and provides the underlying rationale for HeartShare. The promise of precision medicine lies in data diversity. More than the sheer size of biomedical data, it is the layering of multiple data modalities, offering complementary perspectives, that will enable the identification of patient subgroups with shared pathophysiology. HeartShare provides an immense opportunity to conduct a multi- layered precision medicine investigation of HFpEF. The HeartShare Cohort Core will create a lasting impact on the HFpEF field by accomplishing the following specific aims, which are responsive to the HeartShare request for applications. (1) To pool, clean, and harmonize all data and images from multiple cardiovascular disease cohorts and HF trials in order to develop a comprehensive, data-rich resource for the research community to identify novel HFpEF subtypes. (2) To combine machine learning and multi-omics analyses using a hypothesis- driven approach by interrogating previously identified HFpEF subtypes in order to improve the identification and molecular/pathophysiologic understanding of these HFpEF subtypes. (3) To combine machine learning and multi-dimensional data (including multi-omics) using unbiased approaches to identify and validate novel HFpEF subtypes.