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Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction

Nature Cardiovascular Research volume 4, pages 293–311 (2025)Cite this article

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Abstract

Heart failure (HF) has limited therapeutic options. In this study, we differentiated the pathophysiological underpinnings of the HF subtypes—HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)—and uncovered subtype-specific therapeutic strategies. We investigated the causal roles of the human proteome and transcriptome using Mendelian randomization on more than 420,000 participants from the Million Veteran Program (27,799 HFrEF and 27,579 HFpEF cases). We created therapeutic target profiles covering efficacy, safety, novelty, druggability and mechanism of action. We replicated findings on more than 175,000 participants of diverse ancestries. We identified 70 HFrEF and 10 HFpEF targets, of which 58 were not previously reported; notably, the HFrEF and HFpEF targets are non-overlapping, suggesting the need for subtype-specific therapies. We classified 14 previously unclassified HF loci as HFrEF. We substantiated the role of ubiquitin–proteasome system, small ubiquitin-related modifier pathway, inflammation and mitochondrial metabolism in HFrEF. Among druggable genes, IL6R, ADM and EDNRA emerged as potential HFrEF targets, and LPA emerged as a potential target for both subtypes.

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Fig. 1: Schematic of study design.
Fig. 2: Robustness of MR findings against HFrEF and HFpEF.
Fig. 3: Orthogonal sources of support on efficacy.
Fig. 4: MR effect on HFpEF against the MR effect on HF risk factors, BMI and T2DM for HFpEF gene findings.
Fig. 5: On-target safety for MR findings on HFrEF and HFpEF.
Fig. 6: Flowchart for druggability annotations of MR findings for HFrEF and HFpEF to identify potential repurposing and safety signals.

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Data availability

The MVP GWAS summary statistics used in this study will be available through the database of Genotypes and Phenotypes (dbGAP) (study accession number phs001672.v11.p1). The only restriction is that use of the data is limited to health/medical/biomedical purposes and does not include the study of population origins or ancestry. Use of the data does include methods development research (for example, development and testing of software or algorithms), and requesters must agree to make the results of studies using the data available to the larger scientific community. Full summary statistics of the risk factor and safety traits that used data from the Million Veteran Program are publicly available and can be downloaded at the VA’s Centralized Interactive Phenomics Resource (CIPHER) web portal. All supporting annotation files are also available for download at CIPHER. Detailed visualizations pertaining to the Mendelian randomization and co-localization results and orthogonal sources of support are provided in the supplementary figures.

Code availability

We used publicly available software for the analyses, and all software used is listed and described in the Methods section of our paper. Statistical analyses were conducted in R version 3.6.3. Mendelian randomization analyses were conducted using the TwoSampleMR package in R (version 0.5.6, release date 25 March 2021); genetic co-localization analyses were conducted using the coloc (version 5.2.3) package in R; and standardization of GWAS summary statistics was conducted using MungeSumstats (release 3.20). Imputation was conducted using Minimac4 (version 1.0.0). The GWAS for HFrEF and HFpEF was conducted using Plink 2.0. The GWAS findings were functionally annotated using FUMA (version 1.3.8). Meta-analysis of GWAS summary statistics was prepared using METAL (version released 6 October 2020).

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Acknowledgements

We are grateful to all the MVP investigators. A list of MVP investigators can be found in the Supplementary Information.

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards MVP037 (BLR&D Merit Award BX005831), Veterans Affairs Grant I01CX001922 (principal investigator (PI): J.J.) and MVP001 (I01-BX004821). This publication does not represent the views of the Department of Veterans Affairs or the United States Government. We also acknowledge the VA Merit Grant I01-CX001025 (PIs: P.W.F.W. and K.C.).

The British Heart Foundation funded the manual analysis to create a cardiovascular MRI reference standard for the UK Biobank imaging resource in 5,000 CMR scans (https://www.bhf.org.uk/; PG/14/89/31194). This work acknowledges the support of the National Institute for Health and Care Research Barts Biomedical Research Centre (NIHR203330), a delivery partnership of Barts Health NHS Trust, Queen Mary University of London, St. George’s University Hospitals NHS Foundation Trust and St. George’s University of London. This article is supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded by the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). Views expressed are those of the authors and not necessarily those of the AI4VBH Consortium members, the NHS, Innovate UK or UKRI. This work was funded by UKRI Programme Grant MC_UU_00002/18. S.E.P. acknowledges support from the ‘SmartHeart’ EPSRC Programme Grant (https://www.nihr.ac.uk/; EP/P001009/1). N.A. acknowledges support from the Medical Research Council for his Clinician Scientist Fellowship (MR/X020924/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. A list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Million Veteran Program (MVP) Coordinating Center, Veterans Affairs Healthcare System, Boston, MA, USA

    Danielle Rasooly, Gina M. Peloso, Hesam Dashti, Brian R. Ferolito, Daniel Golden, Andrea R. V. R. Horimoto, Nicole M. Kosik, Kelly Cho, J. Michael Gaziano, Alexandre C. Pereira & Juan P. Casas

  2. Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Danielle Rasooly, Hesam Dashti, Daniel Golden, Andrea R. V. R. Horimoto, Kelly Cho, J. Michael Gaziano & Alexandre C. Pereira

  3. Integrative Data Analysis Unit, Health Data Science Centre, Human Technopole, Milan, Italy

    Claudia Giambartolomei

  4. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    Gina M. Peloso

  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Hesam Dashti

  6. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Cambridge, UK

    Maik Pietzner & Claudia Langenberg

  7. Computational Medicine, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany

    Maik Pietzner & Claudia Langenberg

  8. Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

    Maik Pietzner & Claudia Langenberg

  9. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

    Eric H. Farber-Eger

  10. Departments of Medicine (Cardiology), Biomedical Informatics and Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

    Quinn Stanton Wells

  11. Istituto Italiano di Tecnologia, CHT@Erzelli, Genova, Italy

    Giorgio Bini, Gabriele Proietti & Gian Gaetano Tartaglia

  12. Dipartimento di Fisica Via Dodecaneso, Genova, Italy

    Giorgio Bini

  13. ICREA - Institució Catalana de Recerca I Estudis Avançats, Barcelona, Spain

    Gian Gaetano Tartaglia

  14. Atlanta VA Health Care System, Decatur, GA, USA

    Peter W. F. Wilson, Lawrence S. Phillips & Yan V. Sun

  15. Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Peter W. F. Wilson

  16. Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Lawrence S. Phillips

  17. William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK

    Patricia B. Munroe, Steffen E. Petersen & Nay Aung

  18. Barts Heart Centre, St. Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK

    Steffen E. Petersen & Nay Aung

  19. Department of Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK

    Andrew R. Leach

  20. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    John Whittaker

  21. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA

    Yan V. Sun

  22. Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA

    Yan V. Sun

  23. Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil

    Alexandre C. Pereira

  24. Cardiology Section, VA Providence Healthcare System, Providence, RI, USA

    Jacob Joseph

  25. Warren Alpert School of Medicine, Brown University, Providence, RI, USA

    Jacob Joseph

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  1. Danielle Rasooly
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  2. Claudia Giambartolomei
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  3. Gina M. Peloso
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  13. Gian Gaetano Tartaglia
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  14. Nicole M. Kosik
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  15. Peter W. F. Wilson
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  19. Kelly Cho
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  20. J. Michael Gaziano
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VA Million Veteran Program

  • Peter W. F. Wilson
  • , Kelly Cho
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Contributions

D.R., J.P.C. and J.J. conceptualized the project. D.R., C.G., G.M.P., H.D., B.R.F., D.G., G.B., G.P., G.G.T. and N.A. contributed to formal analysis. D.R., C.G., G.M.P., B.R.F., D.G., G.B., G.P., G.G.T., N.A., A.R.V.R.H., M.P., E.H.F.-E. and Q.S.W. provided data curation. D.R., C.G., G.M.P., H.D., B.R.F., N.M.K., P.W.F.W., L.S.P., P.B.M., S.E.P., K.C., J.M.G., A.R.L., J.W., C.L., N.A., Y.V.S., A.C.P., J.P.C. and J.J. contributed to investigation. D.R., C.G., J.P.C. and J.J. wrote the original draft, and all authors contributed to the review and editing of the manuscript.

Corresponding authors

Correspondence to Danielle Rasooly or Jacob Joseph.

Ethics declarations

Competing interests

S.E.P. provides consultancy to Circle Cardiovascular Imaging, Inc. J.W. holds membership of scientific advisory boards/consultancy for Relation Therapeutics and Silence Therapeutics and ownership of GlaxoSmithKline shares. J.P.C. is employed full-time by the Novartis Institute of Biomedical Interest (his major contributions to this project were while employed at the VA Boston Healthcare System). The remaining authors declare no competing interests.

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Peer review information

Nature Cardiovascular Research thanks Ruth Frikke-Schmidt, Scott Ritchie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Venn diagram illustrating the overlap of genes among pQTLs and eQTLs.

Figure A shows the overlap of genes among ARIC (1,342 genes), Fenland (1,383 genes), and deCODE (1,493 genes). Figure B shows the overlap of genes among eQTLGen (8,054 genes) and GTEx v8 (11,985 genes).

Extended Data Fig. 2 MR effect on atrial fibrillation against the MR effect on HFpEF and HFrEF for genes that pass the Bonferroni threshold only.

The error bars indicate the 95% confidence interval of the MR beta coefficient. The best-fit line derived through ordinary least squares is shown in black. The sample size used to derive statistics for the MR effects on HFpEF is 27,579 participants with HFpEF, 27,799 participants with HFrEF, and 367,267 control individuals. The sample size used to derive statistics for the MR effects on atrial fibrillation is 91,703 cases and 776,176 controls. All results are shown for MR on atrial fibrillation analyses that have passed p-value \(< 5\times {10}^{-5}\). The predicted model for HFpEF is y = -0.0016 + 0.98x (left plot) and for HFrEF is y = -0.003 + 0.65x (right plot). The correlation between the MR betas for atrial fibrillation and HFpEF is 0.99 and between the MR betas for atrial fibrillation and HFrEF is 0.94. Data are presented as mean values +/- 1.96*standard error. All tests were two-sided without adjustment for multiple testing.

Extended Data Fig. 3 MR effect on atrial fibrillation against the MR effect on HFpEF and HFrEF for genes that pass the Bonferroni threshold and including HFpEF suggestive genes.

The plot is shown for HFpEF findings that pass the Bonferroni-adjusted threshold of p-value \(< 2.06\times {10}^{-6}\) (in red) and for findings that pass the false discovery rate (FDR) of 5% (p-value \(< 6.80\times {10}^{-4}\)) (in blue). The sample size used to derive statistics for the MR effects on HFpEF is 27,579 participants with HFpEF, 27,799 participants with HFrEF, and 367,267 control individuals. The sample size used to derive statistics for the MR effects on atrial fibrillation is 91,703 cases and 776,176 controls. The predicted model is y = 0.0079 + 0.83x. The correlation between the MR betas for atrial fibrillation and HFpEF is 0.93. The errors bars represent the 95% confidence interval, defined as 1.96*standard error. All tests were two-sided without adjustment for multiple testing.

Extended Data Fig. 4 The number of unique and shared genes among the present study categorized as HFpEF/HFrEF MR, and prior studies on HF GWAS, HFpEF/HFrEF GWAS, HF MR, and cardiomyopathy presented as a Venn diagram and as an upset plot sorted by cardinality.

In Extended Data Fig. 3A, the percentage presented in parentheses represents the proportion of the total data set that falls within each section of the Venn diagram. Sets with no genes are not annotated.

Extended Data Fig. 5 Schematic for two-step Mendelian randomization study design for assessing mediation.

The causal effect of Lp(a) levels on the mediator, coronary artery disease (CAD), \({\beta }_{1},\) was determined via two-sample MR. The causal effect of the mediator, CAD, on HFpEF and HFrEF, \({\beta }_{2},\) was determined via two-sample MR. The instrumental variables utilized in the second step are independent of those utilized in the first step. The total effect, \({\beta }_{0}\), represents the effect of Lp(a) levels on HFrEF or HFpEF. The indirect effect of the mediator, CAD, was calculated as \({\beta }_{1}* {\beta }_{2}\) and the proportion mediated was calculated as the indirect effect divided by the total effect,\({(\beta }_{1}* {\beta }_{2})/{\beta }_{0}\).

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Rasooly, D., Giambartolomei, C., Peloso, G.M. et al. Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction. Nat Cardiovasc Res 4, 293–311 (2025). https://doi.org/10.1038/s44161-025-00609-1

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