# Integrative 'omics for biomarkers and biology of early-stage heart failure

> **NIH NIH F31** · DUKE UNIVERSITY · 2024 · $48,974

## Abstract

ABSTRACT
Early-stage heart failure (HF), defined broadly as asymptomatic structural heart disease, is a growing public
health concern and has been receiving greater attention in clinical settings. Early-stage HF is a risk factor for
advanced HF, but there is heterogeneity in this risk. While there are known biomarkers and biological
mechanisms of advanced HF, little is known about the potential entity of early-stage HF that is distinct from
underlying HF risk factors. Additionally, individual -omics platforms have been applied to discover novel biology
and biomarkers of advanced HF, but multi-omics data integration methods have not yet been applied in this
space. Through preliminary analyses, I have discovered novel proteins for early-stage HF that are distinct from
underlying risk factors and advanced HF. With these initial results, I hypothesize that molecular features of
cardiac inflammation are significant biomarkers for early-stage HF, and that we can optimize biomarker and
biological mechanism discovery by applying integrative -omics techniques. The broad objective of this proposal
is to discover novel biomarkers and biological mechanisms underpinning early-stage HF that have prognostic
capabilities for advanced HF. Understanding how early-stage HF is distinct from underlying risk factors and
advanced HF at a molecular and clinical level could enable the development of novel diagnostic tests and
preemptive treatment measures to forestall adverse late-stage outcomes. I propose to accomplish this by
utilizing state-of-the-art machine learning methodologies for the analysis of multi-omics and clinical data from
large-scale population cohorts. First, I will develop and validate an automated computable phenotype for the
identification of early-stage HF using clinical data and imaging-derived measures of cardiac structure and
function. I will then apply this computable phenotype in the UK Biobank to identify significant protein and
metabolite features of predicted early-stage HF cases using separate single-omics analyses. After identifying
candidate biomarkers, I will assess their causality and biological relevance by performing Mendelian
Randomization and pathway enrichment analysis. To test the impact of data integration on model predictive
ability, I will apply a modified version of MiNet, an interpretable pathway-associated deep neural network for
diseas prediction. Using MiNet, I will integrate proteomics and metabolomics data to predict Stage B HF cases
versus Stage A HF controls and compare the neural network’s performance to those from the single-omics
models. From the trained deep learning model, I will compare pathway node activation in the neural network to
the single-omics pathway analysis results to assess whether multi-omics integration affects the richness of
biological findings from a model. With this work, I will improve knowledge of early-stage heart failure
biomarkers and biology; I will also expand upon the application of evol...

## Key facts

- **NIH application ID:** 10994115
- **Project number:** 1F31HL175914-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Kalyani Kottilil
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994115

## Citation

> US National Institutes of Health, RePORTER application 10994115, Integrative 'omics for biomarkers and biology of early-stage heart failure (1F31HL175914-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10994115. Licensed CC0.

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