Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

Albert Henry1,2,3, María Gordillo-Marañón1,2, Chris Finan1,2,4

  • 1Institute of Cardiovascular Science (A.H., M.G.-M., C.F., A.F.S., A.D.H.), University College London, United Kingdom.

Circulation
|March 18, 2022
PubMed

Insights

This study identified 44 proteins linked to heart failure (HF), with 8 showing a causal relationship. Adrenomedullin emerged as a promising therapeutic target for HF treatment.

Area of Science:

  • Cardiovascular disease research
  • Proteomics and genomics
  • Drug discovery and development

Background:

  • Heart failure (HF) is a widespread condition with incompletely understood disease mechanisms.
  • Identifying disease-associated proteins with genetic evidence can reveal new therapeutic targets for HF.

Purpose of the Study:

  • To investigate observational and causal associations between 90 cardiovascular proteins and incident heart failure.
  • To identify druggable protein targets for potential HF therapies.

Main Methods:

  • Meta-analysis of 4 population studies (3019 participants, 732 HF events) to assess protein-HF associations.
  • Mendelian randomization using cis-protein quantitative loci from large GWAS (>30,000 individuals) to determine causal effects.
  • Multiverse sensitivity analysis and cross-trait analysis to ensure robustness and explore mechanisms.

Main Results:

  • 44 of 90 proteins were associated with incident HF risk (P<6.0x10^-4).
  • 8 proteins demonstrated a robust causal association with HF: CSF-1, Gal-3, KIM-1 (positively associated), and ADM, CHI3L1, CTSL1, FGF-23, MMP-12 (protective).
  • 7 of these proteins were druggable, with ADM and Gal-3 already in clinical trials.

Conclusions:

  • Identified 44 circulating proteins associated with incident HF, with 8 showing causal links.
  • Highlighted 7 druggable proteins, particularly adrenomedullin (ADM), as promising therapeutic targets for HF.
  • Demonstrated a method for integrating genomic and proteomic data to prioritize targets for complex diseases.
Abstract

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