TY - JOUR
T1 - A population-based phenome-wide association study of cardiac and aortic structure and function
AU - Bai, Wenjia
AU - Suzuki, Hideaki
AU - Huang, Jian
AU - Francis, Catherine
AU - Wang, Shuo
AU - Tarroni, Giacomo
AU - Guitton, Florian
AU - Aung, Nay
AU - Fung, Kenneth
AU - Petersen, Steffen E.
AU - Piechnik, Stefan K.
AU - Neubauer, Stefan
AU - Evangelou, Evangelos
AU - Dehghan, Abbas
AU - O’Regan, Declan P.
AU - Wilkins, Martin R.
AU - GUO, Yi-Ke
AU - Matthews, Paul M.
AU - Rueckert, Daniel
N1 - Funding Information:
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.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613250/
UR - https://www.scopus.com/pages/publications/85089725406
U2 - 10.1038/s41591-020-1009-y
DO - 10.1038/s41591-020-1009-y
M3 - Journal article
C2 - 32839619
AN - SCOPUS:85089725406
SN - 1078-8956
VL - 26
SP - 1654
EP - 1662
JO - Nature Medicine
JF - Nature Medicine
IS - 10
ER -
SDG 3 Good Health and Well-being