# Cohort Core

> **NIH NIH U54** · NORTHWESTERN UNIVERSITY · 2022 · $206,222

## Abstract

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.

## Key facts

- **NIH application ID:** 10488280
- **Project number:** 5U54HL160273-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Sanjiv J Shah
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $206,222
- **Award type:** 5
- **Project period:** 2021-09-13 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488280

## Citation

> US National Institutes of Health, RePORTER application 10488280, Cohort Core (5U54HL160273-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10488280. Licensed CC0.

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