A population-based phenome-wide association study of cardiac and aortic structure and function
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 848
- Type
- D - Journal article
- DOI
-
10.1038/s41591-020-1009-y
- Title of journal
- Nature Medicine
- Article number
- -
- First page
- 1654
- Volume
- 26
- Issue
- 10
- ISSN
- 1078-8956
- Open access status
- Exception within 3 months of publication
- Month of publication
- August
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://static-content.springer.com/esm/art%3A10.1038%2Fs41591-020-1009-y/MediaObjects/41591_2020_1009_MOESM1_ESM.pdf
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
18
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work, for the first time, cardiovascular MR images from an unprecedentedly large population-wide dataset (UK Biobank) are analysed using an AI pipeline. Output reports a comprehensive range of structural and functional phenotypes for heart and aorta across 26,893 participants and explores how these phenotypes vary according to sex, age and cardiovascular risk factors. This study is highly significant because it illustrates how population-based cardiac and aortic imaging phenotypes obtained with AI can be used to better define cardiovascular risk as well as heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
