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Genetics of myocardial interstitial fibrosis in the human heart and association with disease

Nature Genetics volume 55, pages 777–786 (2023)Cite this article

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Abstract

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.

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Fig. 1: Overview of the automated pipeline for native myocardial T1 time measurement at the IVS using machine learning.
Fig. 2: Change in native myocardial T1 time associated with prevalent cardiovascular, metabolic and systemic inflammatory diseases compared to healthy controls.
Fig. 3: Adjusted cumulative incidence of cardiovascular events stratified by native myocardial T1 time.
Fig. 4: Genome-wide and transcriptome-wide association analyses.
Fig. 5: Multi-omic examination of human cardiac fibroblast activation.

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Data availability

The UKB data are made available to researchers from research institutions with genuine research inquiries according to institutional review board and UKB approval. The genome-wide association analysis summary statistics are available from the downloads page of the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/). Raw and processed next-generation sequencing data have been deposited at the NCBI Gene Expression Omnibus under accession no. GSE225336. The GRCh37 data are publicly available at https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13/. The GRCh38 data are publicly available at https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/. The Genotype-Tissue Expression v.8 datasets are publicly available at https://www.gtexportal.org/home/datasets. All other data are contained in the article and its supplementary information or are available upon reasonable request from the corresponding authors. Source data are provided with this paper.

Code availability

The code used to download, quality-control and train the machine learning models is available at https://github.com/broadinstitute/ml4h under an open-source BSD license.

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Acknowledgements

We acknowledge the contributions made by the UKB participants without whom this work would not have been possible, as well as the following financial support: National Institutes of Health (NIH) grant no. T32HL007604 to V.N.; NIH grant nos. 1R01HL092577 and K24HL105780, American Heart Association (AHA) grant no. 18SFRN34110082, Foundation Leducq grant no. 14CVD01 and MAESTRIA grant no. 965286 to P.T.E.; a Scholar award from the Sarnoff Cardiovascular Research Foundation and NIH grant no. K08HL159346 to J.P.P.; NIH 1R01HL139731, NIH R01HL157635, and AHA grant no. 18SFRN34250007 to S.A.L.; AHA Postdoctoral fellowship no. 18SFRN34110082 to L.-C.W.; and NIH grant no. 5T32HL007208-42 to M.C.H. Some of the artwork incorporated into Fig. 5a was created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Victor Nauffal, Paolo Di Achille, Marcus D. R. Klarqvist, Jonathan W. Cunningham, Matthew C. Hill.

  2. These authors jointly supervised this work: Patrick T. Ellinor, Steven A. Lubitz.

Authors and Affiliations

  1. Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA

    Victor Nauffal & Jonathan W. Cunningham

  2. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Victor Nauffal, Jonathan W. Cunningham, Matthew C. Hill, James P. Pirruccello, Lu-Chen Weng, Valerie N. Morrill, Seung Hoan Choi, Shaan Khurshid, Mahan Nekoui, Carolina Roselli, Anthony A. Philippakis, Patrick T. Ellinor & Steven A. Lubitz

  3. Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Paolo Di Achille, Marcus D. R. Klarqvist, Samuel F. Friedman, Anthony A. Philippakis & Puneet Batra

  4. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Matthew C. Hill & Lu-Chen Weng

  5. Cardiology Division, Massachusetts General Hospital, Boston, MA, USA

    James P. Pirruccello

  6. Division of Cardiology, University of California San Francisco, San Francisco, CA, USA

    James P. Pirruccello

  7. Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA

    Shaan Khurshid, Patrick T. Ellinor & Steven A. Lubitz

  8. Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    Carolina Roselli

  9. Center for Computational Health, IBM Research, Cambridge, MA, USA

    Kenney Ng

  10. Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Anthony A. Philippakis

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  1. Victor Nauffal
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Contributions

V.N., J.W.C., P.T.E. and S.A.L. conceived the study. M.D.R.K. and P.D.A. downloaded and prepared the cMRI data. V.N., M.D.R.K., P.D.A. and J.W.C. performed the quality control. M.D.R.K. trained the machine learning models. V.N., M.D.R.K. and P.D.A. performed the main analyses. M.C.H. performed the in vitro experiments. V.N., M.D.R.K., P.D.A., J.W.C., M.C.H., P.T.E. and S.A.L. wrote the paper. J.P.P., L.-C.W., V.N.M., S.H.C., S.K., S.F.F., M.N., C.R., K.N., A.A.P. and P.B. contributed to the analysis plan or provided critical revisions.

Corresponding authors

Correspondence to Patrick T. Ellinor or Steven A. Lubitz.

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Competing interests

M.D.R.K., P.D.A., S.F.F. and P.B. are supported by grants from Bayer and IBM with regard to applying machine learning in cardiovascular disease. P.B. serves as a consultant for Novartis and Prometheus Biosciences and is employed by Flagship Pioneering as of 4 January 2023. C.R. is supported by a grant from Bayer to the Broad Institute, which is focused on the development of therapeutics for cardiovascular disease. S.A.L. is employed by Novartis Institutes for Biomedical Research as of 18 July 2022. S.A.L. received sponsored research support from Bristol Myers Squibb, Pfizer, Bayer, Boehringer Ingelheim, Fitbit and IBM, and has previously consulted for Bristol Myers Squibb, Pfizer, Bayer, Blackstone Life Sciences and Invitae. P.T.E. receives sponsored research support from Bayer, Novartis, MyoKardia and Quest. L.-C.W. receives sponsored research support from IBM to the Broad Institute. The other authors declare no competing interests.

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Nauffal, V., Di Achille, P., Klarqvist, M.D.R. et al. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat Genet 55, 777–786 (2023). https://doi.org/10.1038/s41588-023-01371-5

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