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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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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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.
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.
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.
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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.
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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