We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep learning of left atrial structure and function provides link to atrial fibrillation risk.
- Authors
Pirruccello, James P.; Di Achille, Paolo; Choi, Seung Hoan; Rämö, Joel T.; Khurshid, Shaan; Nekoui, Mahan; Jurgens, Sean J.; Nauffal, Victor; Kany, Shinwan; FinnGen; Ng, Kenney; Friedman, Samuel F.; Batra, Puneet; Lunetta, Kathryn L.; Palotie, Aarno; Philippakis, Anthony A.; Ho, Jennifer E.; Lubitz, Steven A.; Ellinor, Patrick T.
- 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. In this study, a deep learning-based model of left atrial size in UK Biobank enabled genome-wide association studies in 35,049 healthy participants. Several lines of evidence, including the PITX2 locus, linked left atrial dysfunction to atrial fibrillation risk.
- Subjects
UNITED Kingdom; DEEP learning; LEFT heart atrium; CARDIAC magnetic resonance imaging; ATRIAL fibrillation; GENOME-wide association studies; GASTRIC emptying
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-48229-w