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Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping

Nature Medicine volume 30, pages 1749–1760 (2024)Cite this article

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Abstract

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77–6.19; P = 1.78 × 10−4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

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Fig. 1: Study flow chart.
Fig. 2: Multi-organ T1 time association with disease.
Fig. 3: Multi-organ T1 time genome-wide association results.
Fig. 4: Genetic correlation of multi-organ T1 time.
Fig. 5: All-cause mortality stratified by number of organs with T1 time in the top quintile.

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Data availability

UKB data are made available to researchers from research institutions with genuine research inquiries, following IRB and UKB approval. Genome-wide association analysis summary statistics are available from the Downloads page of the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/). Genome Reference Consortium Human Build 37 (GRCh37) data are publicly available at https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13/. Genome Reference Consortium Human Build 38 (GRCh38) data are publicly available at https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/. Single-cell data for each organ are available via: Liver, (Gene Expression Omnibus (GEO) accession number GSE185477); Pancreas, https://doi.org/10.6084/m9.figshare.12173232 (ref. 108); Heart, processed single-cell data are available at https://singlecell.broadinstitute.org/single_cell/study/SCP1849/ and raw sequence data are available for authorized users at the database of Genotypes and Phenotypes, under accession number phs001539.v4.p1; Kidney, https://doi.org/10.6084/m9.figshare.21587670.v1 (ref. 109) (single-cell data) and https://doi.org/10.6084/m9.figshare.21587679.v1 (ref. 110) (single-nucleus data). Source data are provided with this paper. All other data are contained within the article and its supplementary information.

Code availability

Code used to ingest, for QC and to train machine learning models is available at https://github.com/broadinstitute/ml4h under an open-source BSD license.

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Acknowledgements

This research has been conducted using the UKB resource under application number 7089. We acknowledge the contributions of the UKB participants without whom this work would not have been possible. National Institutes of Health (NIH) T32HL007604 grant to V.N.; grants from the NIH (1RO1HL092577, 1R01HL157635 and 5R01HL139731), the American Heart Association (18SFRN34230127, 961045) and the European Union (MAESTRIA 965286) to P.T.E.; Scholar award from the Sarnoff Cardiovascular Research Foundation and NIH grant K08HL159346 to J.P.P.; NIH 1R01HL139731, NIH R01HL157635 and AHA 18SFRN34250007 grants to S.A.L.; NIH 5T32HL007208-42 grant to M.C.H.; Walter Benjamin Fellowship from the Deutsche Forschungsgemeinschaft (521832260) to S.K.

Author information

Author notes

  1. Matthew C. Hill

    Present address: Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

  2. These authors contributed equally: Victor Nauffal, Marcus. D. R. Klarqvist.

Authors and Affiliations

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

    Victor Nauffal

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

    Victor Nauffal, Matthew C. Hill, Danielle F. Pace, Seung Hoan Choi, Joel T. Rämö, James P. Pirruccello, Shinwan Kany, Cody Hou, Anthony A. Philippakis & Patrick T. Ellinor

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

    Marcus D. R. Klarqvist, Danielle F. Pace, Paolo Di Achille, Pulkit Singh, Anthony A. Philippakis & Puneet Batra

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

    James P. Pirruccello

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

    James P. Pirruccello

  6. Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany

    Shinwan Kany

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

    Kenney Ng

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

    Anthony A. Philippakis

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

    Steven A. Lubitz & Patrick T. Ellinor

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

    Patrick T. Ellinor

Authors
  1. Victor Nauffal
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  2. Marcus D. R. Klarqvist
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  3. Matthew C. Hill
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  4. Danielle F. Pace
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  5. Paolo Di Achille
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  6. Seung Hoan Choi
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  7. Joel T. Rämö
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  8. James P. Pirruccello
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  9. Pulkit Singh
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  10. Shinwan Kany
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  11. Cody Hou
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  12. Kenney Ng
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  13. Anthony A. Philippakis
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  14. Puneet Batra
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  15. Steven A. Lubitz
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  16. Patrick T. Ellinor
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Contributions

V.N. and P.T.E. conceived the study. M.D.R.K. ingested and prepared the MRI data. V.N., M.D.R.K., P.D.A. and J.T.R., performed QC. M.D.R.K. and D.F.P. trained machine learning models. V.N. and M.D.R.K. performed the main analyses. V.N., M.D.R.K. and P.T.E. wrote the paper. All other authors contributed to the analysis plan or provided critical revisions.

Corresponding author

Correspondence to Patrick T. Ellinor.

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Competing interests

M.D.R.K., P.D.A and P.B. are supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. P.B. has consulted for Novartis and Prometheus Biosciences. P.B. is now employed by Flagship Pioneering. P.D.A. is now employed by Google. S.A.L. is now employed by Novartis. S.A.L. received sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit and IBM, and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, Blackstone Life Sciences and Invitae previously. P.T.E. receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb, Pfizer and Novo Nordisk; and has also served on advisory boards or consulted for MyoKardia and Bayer AG. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks David Brenner, Gerhard-Paul Diller, Yingkun Guo and Katalin Susztak for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–25.

Reporting Summary

Supplementary Tables 1–18

Spreadsheet-based supplementary tables combined into one workbook with multiple tabs.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Nauffal, V., Klarqvist, M.D.R., Hill, M.C. et al. Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping. Nat Med 30, 1749–1760 (2024). https://doi.org/10.1038/s41591-024-03010-w

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