Cohort Core

项目总结 心力衰竭(HF)伴保留射血分数(HFpEF)是一种异质性的、多方面的综合征 是精准医学应用的理想环境。HFpEF已被证明对传统的 研究其潜在病理生理学和进行随机临床试验的技术。新的 因此，正如最近发表的一份白皮书所指出的那样，迫切需要各种方法。 总结了NHLBI关于HFpEF研究优先事项的工作组的审议情况。《国家报》 美国科学院精密医学研究院的一份报告提出，当多个分子指示剂用于 结合传统的临床、组织学和实验室结果，它们提供了更多 准确和准确地描述和分类疾病“。这个范例也得到了NHLBI的支持 战略远景，它认为组学(例如基因组学、蛋白质组学和代谢组学)的组合， 再加上临床和生理数据，将是必要的，以促进对病理生物学的了解 基于复杂的临床症状，如HFpEF，并提供了HeartShare的基本原理。 精准医疗的希望在于数据的多样性。它不仅仅是生物医学数据的绝对大小，它是 多个数据模式的分层，提供互补的视角，从而能够识别 具有共同病理生理的患者亚组。HeartShare提供了一个巨大的机会来进行多项 HFpEF分层精准医学调查。心脏共享队列核心将对以下方面产生持久影响 通过实现以下特定目标来实现HFpEF字段，这些目标响应心脏共享请求 适用于应用程序。(1)汇集、清理和协调来自多种心血管疾病的所有数据和图像 队列和心力衰竭试验，以便为研究界开发一个全面的、数据丰富的资源 确定新的HFpEF亚型。(2)使用一个假设将机器学习和多组学分析结合起来- 通过询问先前确定的HFpEF亚型来驱动方法，以改进识别和 对这些HFpEF亚型的分子/病理生理了解。(3)将机器学习与 使用无偏见的方法识别和验证新的HFpEF的多维数据(包括多组学) 子类型。

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.