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A population-based phenome-wide association study of cardiac and aortic structure and function

Nature Medicine volume 26, pages 1654–1662 (2020)Cite this article

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Abstract

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

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Fig. 1: Automated CMR image analysis pipeline.
Fig. 2: Associations of selected imaging phenotypes with sex and age.
Fig. 3: Regression coefficients for cardiac and aortic imaging phenotypes on demographics (blue), anthropometrics (green) and cardiovascular risk factors (red).
Fig. 4: Associations of cardiac and aortic imaging phenotypes with common diseases.
Fig. 5: Results from the PheWAS.

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Data availability

The raw imaging data and non-imaging participant characteristics are available from UK Biobank via a standard application procedure at http://www.ukbiobank.ac.uk/register-apply. The image analysis code is available at https://github.com/baiwenjia/ukbb_cardiac. For PheWAS, category ID numbers and field ID numbers are defined in ukb_field_categories.py and included in perform_phenome_wide_association.py. The associations between imaging phenotypes and non-imaging phenotypes can be browsed at https://heartvis.doc.ic.ac.uk.

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Acknowledgements

We would like to thank H. Gao, D. Schneider-Luftman, T. J. W. Dawes and A. Kolbeinsson for fruitful discussion. This research was conducted using the UK Biobank Resource under Application Number 18545, using methods developed under Application Number 18545 or 2964. Images were reproduced with kind permission of UK Biobank. We wish to thank all UK Biobank participants and staff. This work is supported by the SmartHeart EPSRC Programme Grant (EP/P001009/1) and the Imperial BHF Centre of Excellence Grant (RE/18/4/34215). H.S. is supported by a research fellowship from the Uehara Memorial Foundation and the Grants-in-Aid program from the Japan Society for the Promotion of Science (20K07776). S.E.P. acknowledges support from the NIHR Barts Biomedical Research Centre and S.E.P. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825903 (euCanSHare project). S.E.P., S.N. and S.K.P. acknowledge the British Heart Foundation for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource in 5000 CMR scans (PG/14/89/31194). A.D. is funded by the Wellcome Trust seed award (206046/Z/17/Z). D.P.O. is supported by the Medical Research Council (MC-A651-53301) and British Heart Foundation (NH/17/1/32725, RG/19/6/34387, RE/18/4/34215). P.M.M. gratefully acknowledges support from the Edmond J. Safra Foundation and Lily Safra, the Imperial College Healthcare Trust Biomedical Research Centre, the EPSRC Centre for Mathematics in Precision Healthcare and the UK Dementia Research Institute.

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  1. These authors jointly supervised this work: Paul M. Matthews, Daniel Rueckert.

Authors and Affiliations

  1. Data Science Institute, Imperial College London, London, UK

    Wenjia Bai, Shuo Wang, Florian Guitton & Yike Guo

  2. Department of Brain Sciences, Imperial College London, London, UK

    Wenjia Bai, Hideaki Suzuki & Paul M. Matthews

  3. Department of Cardiovascular Medicine, Tohoku University Hospital, Sendai, Japan

    Hideaki Suzuki

  4. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Hideaki Suzuki

  5. MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Jian Huang, Evangelos Evangelou & Abbas Dehghan

  6. UK Dementia Research Institute, Imperial College London, London, UK

    Jian Huang, Abbas Dehghan & Paul M. Matthews

  7. National Heart and Lung Institute, Imperial College London, London, UK

    Catherine Francis & Martin R. Wilkins

  8. Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK

    Giacomo Tarroni & Daniel Rueckert

  9. CitAI Research Centre, Department of Computer Science, City University of London, London, UK

    Giacomo Tarroni

  10. NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK

    Nay Aung, Kenneth Fung & Steffen E. Petersen

  11. NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    Stefan K. Piechnik & Stefan Neubauer

  12. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

    Evangelos Evangelou

  13. MRC London Institute of Medical Sciences, Imperial College London, London, UK

    Declan P. O’Regan

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Contributions

W.B. developed the analysis pipeline and wrote the manuscript; H.S. performed manual quality control of image segmentations and provided advice on clinical research; J.H. performed Mendelian randomization; C.F. provided support on genetic data analysis; S.W. developed the data visualization website; G.T. and Y.G. provided advice and support on the computational methodology; F.G. provided support on data management; H.S., N.A., K.F., S.E.P., S.K.P., S.N., E.E., A.D., D.P.O. and M.R.W. provided advice and support on clinical research; W.B., P.M.M. and D.R. conceived and designed the study. All authors read, contributed to revision and approved the manuscript.

Corresponding author

Correspondence to Wenjia Bai.

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Competing interests

S.E.P. acknowledges consultancy fees from Circle Cardiovascular Imaging. D.R. acknowledges consultancy fees from Circle Cardiovascular Imaging, Heartflow and IXICO. P.M.M. acknowledges consultancy fees from Roche, Adelphi Communications, Celgene and Biogen. He has received honoraria or speakers’ honoraria from Novartis, Biogen and Roche and has received research or educational funds from Biogen, Novartis, GlaxoSmithKline and Nodthera. He is a member of the scientific advisory board to the board of Ipsen Pharmaceuticals. The remaining authors declare no competing interests.

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Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The conditional plots of imaging phenotypes against birth weight.

The dark line denotes the conditional plot of an imaging phenotype against birth weight, with other variables (sex, age, sex * age, weight, height, SBP, DBP, current smoking status, alcohol intake, vigorous PA frequency, high cholesterol and diabetes) set to their mean. The grey area denotes the 95% confidence interval. n = 12,169 subjects were analysed with available birth weight information. The p-values were calculated from two-sided t-tests.

Extended Data Fig. 2 Mediation model for LVM, brain volume and fluid intelligence score.

The relationship between LVM and fluid intelligence score (path c) is 26% (difference between c and c’) mediated by total brain volume. n = 18,369 subjects were analysed with available fluid intelligence information. The values are depicted as regression coefficient (two-sided t-test p-value) for standardised imaging phenotypes.

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Bai, W., Suzuki, H., Huang, J. et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat Med 26, 1654–1662 (2020). https://doi.org/10.1038/s41591-020-1009-y

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