Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Journal: Nature communications
PMID: 38773065

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.

Authors

  • James P Pirruccello
    Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Paolo Di Achille
    Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Seung Hoan Choi
    Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA.
  • Joel T Rämö
    Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Shaan Khurshid
    Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Mahan Nekoui
    Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA.
  • Sean J Jurgens
    Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Victor Nauffal
    Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA.
  • Shinwan Kany
    Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Kenney Ng
    Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.
  • Samuel F Friedman
    Data Sciences Platform, Broad Institute, Cambridge, MA, USA.
  • Puneet Batra
    Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Kathryn L Lunetta
    Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
  • Aarno Palotie
    Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
  • Anthony A Philippakis
    Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Jennifer E Ho
    Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Steven A Lubitz
    Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Patrick T Ellinor
    Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.

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