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Deep Learning of Left Atrial Structure and Function Provides Link to Atrial Fibrillation Risk

By James P. Pirruccello, Paolo Di Achille, Seung Hoan Choi, Shaan Khurshid, Mahan Nekoui, Sean J. Jurgens, Victor Nauffal, Kenney Ng, Sam Freesun Friedman, Kathryn L. Lunetta, Anthony Philippakis, Jennifer E Ho, Steven A. Lubitz, Patrick T Ellinor

Posted 05 Aug 2021
medRxiv DOI: 10.1101/2021.08.02.21261481

Aims: Increased left atrial (LA) volume is a known risk factor for atrial fibrillation (AF). There is also emerging evidence that alterations in LA function due to an atrial cardiomyopathy are associated with an increased risk of AF. The availability of large-scale cardiac MRI data paired with genetic data provides a unique opportunity to assess the joint genetic contributions of LA structure and function to AF risk. Methods and results: We developed deep learning models to measure LA traits from cardiovascular magnetic resonance imaging (MRI) in 40,558 UK Biobank participants and integrated these data to estimate LA minimum (LAmin), maximum (LAmax), and stroke volume (LASV), as well as emptying fraction (LAEF). We conducted a genome-wide association study (GWAS) in 35,049 participants without pre-existing cardiovascular disease, identifying 20 common genetic loci associated with LA traits. Eight of the loci associated with LA traits were previously associated with AF: the AF risk alleles were associated with an increased LA minimum volume (LAmin) and a decreased LAEF. A Mendelian randomization analysis confirmed that AF causally affects LA volume (IVW P = 6.2E-06), and provided evidence that LAmin causally affects AF risk (IVW P = 4.7E-05). In UK Biobank participants, a polygenic prediction of LAmin was significantly associated with risk for AF (HR 1.09 per SD; P = 1.6E-36) and ischemic stroke (HR 1.04 per SD; P = 4.7E-03). Conclusions: We performed the largest and highest resolution assessment of LA structure and function to date. We then identified 20 common genetic variants associated with LA volumes or LAEF, 19 of which were novel. We found that a polygenic prediction of the minimal LA volume was associated with AF and stroke. Finally, we found an inverse relation between genetic variants associated with AF risk and LAEF. Our findings provide evidence of a causal relation between LA contractile function and AF. Keywords: Left atrium, machine learning, cardiovascular disease, genetics, atrial fibrillation

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