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.