| Title: | Deep learning of left atrial structure and function provides link to atrial fibrillation risk |
| Journal: | Nature Communications |
| Published: | 21 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38773065/ |
| DOI: | https://doi.org/10.1038/s41467-024-48229-w |
| URL: | https://www.nature.com/articles/s41467-024-48229-w.pdf |
Publication 12016
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Abstract
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.</p>
18 Authors
- James P. Pirruccello
- Paolo Di Achille
- Seung Hoan Choi
- Joel T. Rämö
- Shaan Khurshid
- Mahan Nekoui
- Sean J. Jurgens
- Victor Nauffal
- Shinwan Kany
- Kenney Ng
- Samuel F. Friedman
- Puneet Batra
- Kathryn L. Lunetta
- Aarno Palotie
- Anthony A. Philippakis
- Jennifer E. Ho
- Steven A. Lubitz
- Patrick T. Ellinor
1 Application
| Application ID | Title |
|---|---|
| 7089 | Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank |
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