Cohort Core

队列核心

• 批准号: 10488280
• 负责人: Sanjiv J Shah
• 金额: $ 20.62万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10488280
• 关键词: Aging; COVID-19; Cardiac; Cardiovascular Diseases; Classification; Clinical; Clinical Trials; Cohort Studies; Collaborations; Communities; Complex; Coupled; Data; Data Pooling; Data Set; Development; Devices; Disease; EFRAC; Electronic Health Record; Environmental Exposure; Functional disorder; Funding; Genetic Determinism; Genome; Genomics; Goals; Grant; Heart; Heart failure; Heterogeneity; Histologic; Human Genome Project; Image; Investigation; Laboratory Finding; Machine Learning; Modality; Molecular; Molecular Target; Multiomic Data; National Heart, Lung, and Blood Institute; Paper; Pathway interactions; Pharmaceutical Preparations; Phenotype; Physiological; Proteomics; Publishing; Randomized Clinical Trials; Reporting; Request for Applications; Research; Research Priority; Resistance; Resources; Strategic vision; Structure; Syndrome; Systems Biology; Techniques; Trans-Omics for Precision Medicine; Translations; United States National Academy of Sciences; Work; cohort; collaborative trial; data management; disease classification; diverse data; exercise capacity; experience; genome wide association study; genome-wide; genomic epidemiology; heart imaging; improved; innovation; insight; member; metabolomics; multidimensional data; multiple omics; novel; novel strategies; patient subsets; precision medicine; preservation; prospective; socioeconomics; working group; Aging; COVID-19; Cardiac; Cardiovascular Diseases; Classification; Clinical; Clinical Trials; Cohort Studies; Collaborations; Communities; Complex; Coupled; Data; Data Pooling; Data Set; Development; Devices; Disease; EFRAC; Electronic Health Record; Environmental Exposure; Functional disorder; Funding; Genetic Determinism; Genome; Genomics; Goals; Grant; Heart; Heart failure; Heterogeneity; Histologic; Human Genome Project; Image; Investigation; Laboratory Finding; Machine Learning; Modality; Molecular; Molecular Target; Multiomic Data; National Heart, Lung, and Blood Institute; Paper; Pathway interactions; Pharmaceutical Preparations; Phenotype; Physiological; Proteomics; Publishing; Randomized Clinical Trials; Reporting; Request for Applications; Research; Research Priority; Resistance; Resources; Strategic vision; Structure; Syndrome; Systems Biology; Techniques; Trans-Omics for Precision Medicine; Translations; United States National Academy of Sciences; Work; cohort; collaborative trial; data management; disease classification; diverse data; exercise capacity; experience; genome wide association study; genome-wide; genomic epidemiology; heart imaging; improved; innovation; insight; member; metabolomics; multidimensional data; multiple omics; novel; novel strategies; patient subsets; precision medicine; preservation; prospective; socioeconomics; working group

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.

项目摘要 具有保留的射血分数（HFPEF）的心力衰竭（HF）是一种异质，多方面的综合征 是应用精密医学的理想环境。 HFPEF已证明对常规的抵抗力 研究其潜在的病理生理学和随机临床试验的技术。新的 因此，迫切需要方法，如最近发表的白皮书中所示 总结了HFPEF研究重点的NHLBI工作组的审议。国民 科学学院精确医学报告“当使用多个分子指标时 结合常规临床，组织学和实验室发现，它们为更多的机会提供了机会 疾病的准确描述和分类”。该范式也得到了NHLBI的支持 战略愿景，它认为Omics（例如基因组学，蛋白质组学和代谢组学）的结合 加上临床和物理数据，必须为了提高对病理学的了解 复杂的临床综合征（例如HFPEF）的基础，并为心脏占有基本原理。 精确医学的希望在于数据多样性。不仅仅是生物医学数据的巨大大小，它是 多种数据模式的分层，提供完整的观点，这将使能够识别 患者亚组具有共同的病理生理学。 Heartshare提供了一个巨大的机会，可以进行多 HFPEF的分层精密医学投资。心脏同伴核心将对 通过完成以下特定目标，HFPEF字段响应于心脏的要求 用于应用。 （1）池，清洁和协调多种心血管疾病的所有数据和图像 人群和HF试验，以为研究社区开发全面的，数据丰富的资源 识别新型的HFPEF亚型。 （2）使用假设 - 通过询问先前确定的HFPEF子类型的驱动方法，以改善识别和 对这些HFPEF亚型的分子/病理生理理解。 （3）结合机器学习和 使用无偏的方法识别和验证新型HFPEF 亚型。