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

Wenjia Bai; Hideaki Suzuki; Jian Huang; Catherine Francis; Shuo Wang; Giacomo Tarroni; Florian Guitton; Nay Aung; Kenneth Fung; Steffen E. Petersen; Stefan K. Piechnik; Stefan Neubauer; Evangelos Evangelou; Abbas Dehghan; Declan P. O’Regan; Martin R. Wilkins; Yike Guo; Paul M. Matthews; Daniel Rueckert

doi:10.1038/s41591-020-1009-y

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

Wenjia Bai^*, Hideaki Suzuki, Jian Huang, Catherine Francis, Shuo Wang, Giacomo Tarroni, Florian Guitton, Nay Aung, Kenneth Fung, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Evangelos Evangelou, Abbas Dehghan, Declan P. O’Regan, Martin R. Wilkins, Yike Guo, Paul M. Matthews, Daniel Rueckert

^*Corresponding author for this work

Research output: Contribution to journal › Journal Article › peer-review

148 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1654-1662
Number of pages	9
Journal	Nature Medicine
Volume	26
Issue number	10
DOIs	https://doi.org/10.1038/s41591-020-1009-y
Publication status	Published - 1 Oct 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

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Bai, W., Suzuki, H., Huang, J., Francis, C., Wang, S., Tarroni, G., Guitton, F., Aung, N., Fung, K., Petersen, S. E., Piechnik, S. K., Neubauer, S., Evangelou, E., Dehghan, A., O’Regan, D. P., Wilkins, M. R., Guo, Y., Matthews, P. M., & Rueckert, D. (2020). A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine, 26(10), 1654-1662. https://doi.org/10.1038/s41591-020-1009-y

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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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Bai, W, Suzuki, H, Huang, J, Francis, C, Wang, S, Tarroni, G, Guitton, F, Aung, N, Fung, K, Petersen, SE, Piechnik, SK, Neubauer, S, Evangelou, E, Dehghan, A, O’Regan, DP, Wilkins, MR, Guo, Y, Matthews, PM & Rueckert, D 2020, 'A population-based phenome-wide association study of cardiac and aortic structure and function', Nature Medicine, vol. 26, no. 10, pp. 1654-1662. https://doi.org/10.1038/s41591-020-1009-y

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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, Yike

AU - Matthews, Paul M.

AU - Rueckert, Daniel

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.

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JF - Nature Medicine

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

Abstract

Bibliographical note

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