A population-based phenome-wide association study of cardiac and aortic structure and function
File(s)
Accepted version
Author(s)
Type
Journal Article
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.
Date Issued
2020-10-01
Date Acceptance
2020-07-07
Citation
Nature Medicine, 2020, 26, pp.1654-1662
ISSN
1078-8956
Publisher
Nature Research
Start Page
1654
End Page
1662
Journal / Book Title
Nature Medicine
Volume
26
Copyright Statement
© 2020 Springer Nature Limited.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
British Heart Foundation
Wellcome Trust
British Heart Foundation
National Institute for Health Research
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Identifier
https://www.nature.com/articles/s41591-020-1009-y
Grant Number
EP/P001009/1
RE/18/4/34215
206046/Z/17/Z
NH/17/1/32725
RDB02 79560
RDC04
RDB02
RG/19/6/34387
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
Medicine, Research & Experimental
Research & Experimental Medicine
HEALTH-CARE PROFESSIONALS
LEFT-VENTRICULAR MASS
CARDIOVASCULAR-DISEASE
BIRTH-WEIGHT
MENDELIAN RANDOMIZATION
RISK-TAKING
HEART
PATHOPHYSIOLOGY
TRACKING
OUTCOMES
Age Factors
Anatomy, Cross-Sectional
Aorta
Biological Specimen Banks
Cardiovascular Diseases
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Heart
Heart Function Tests
Humans
Image Processing, Computer-Assisted
Machine Learning
Magnetic Resonance Imaging
Male
Myocardium
Phenomics
Phenotype
Polymorphism, Single Nucleotide
Sex Factors
Structure-Activity Relationship
United Kingdom
Myocardium
Aorta
Heart
Humans
Cardiovascular Diseases
Genetic Predisposition to Disease
Magnetic Resonance Imaging
Heart Function Tests
Anatomy, Cross-Sectional
Age Factors
Sex Factors
Structure-Activity Relationship
Phenotype
Polymorphism, Single Nucleotide
Image Processing, Computer-Assisted
Biological Specimen Banks
Female
Male
Genome-Wide Association Study
Machine Learning
United Kingdom
Phenomics
Immunology
11 Medical and Health Sciences
Publication Status
Published
Date Publish Online
2020-08-24