Deep learning of left atrial structure and function provides link to atrial fibrillation risk.
James Paul PirruccelloPaolo Di AchilleSeung Hoan ChoiJoel T RämöShaan KhurshidMahan NekouiSean Joseph JurgensVictor NauffalShinwan Kanynull nullKenney NgSamuel Freesun FriedmanPuneet BatraKathryn L LunettaAarno PalotieAnthony A PhilippakisJennifer E HoSteven A LubitzPatrick T EllinorPublished in: Nature communications (2024)
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.
Keyphrases
- left atrial
- atrial fibrillation
- catheter ablation
- mitral valve
- oral anticoagulants
- left ventricular
- left atrial appendage
- deep learning
- magnetic resonance imaging
- direct oral anticoagulants
- cardiovascular disease
- heart failure
- genome wide
- genome wide association study
- electronic health record
- type diabetes
- artificial intelligence
- big data
- machine learning
- coronary artery disease
- dna methylation
- blood brain barrier
- cardiovascular risk factors
- cardiovascular events