Using Atrial Cardiomyopathy Metrics to Characterize Atrial Fibrillation in Endurance Athletes

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Authors: Luke W. Spencer, Paolo D’Ambrosio, Monique Ohanian, Kristel Janssens, Amy M. Mitchell, Stephanie J. Rowe, D. Flannery, Paul E. Young, Magdalena J. Soka, Renée Johnson, Chenglong Yu, Gwilym M. Morris
+38 more Paul A Lacaze, Eleni Giannoulatou, Tim Van Puyvelde, Youri Bekhuis, Max Ray, Rik Pauwels, Jarne De Paepe, Tomas Robyns, Boris Delpire, David Prior, Peter M. Kistler, Jonathan M. Kalman, Prashanthan Sanders, Adrian D. Elliott, Rik Willems, Hein Heidbuchel, Guido Claessen, Diane Fatkin, André La Gerche, Elizabeth Paratz, Maria J. Brosnan, Oscar Cullen, Stephen J. Foulkes, Kaitlin Newcomb, Kudzai Chamunorwa, Peter Hespel, Steven Dymarkowski, Tom Dresselaers, Hielko Miljoen, Kasper Favere, Bernard Paelinck, Dorien Vermeulen, Isabel Witvrouwen, Dominique Hansen, Bert Op’t Eijnde, Daisy Thijs, Peter Vanvoorden, Kristof Lefebvre
Cited by: 1
References: 39

Abstract

BackgroundAtrial fibrillation (AF) is more prevalent in endurance athletes (EAs) than in the general population, but identifying athletes at greatest risk remains challenging. ObjectivesThis study sought to develop an AF risk assessment tool from associations between AF and electrophysiologic, imaging, and genetic markers of atrial cardiomyopathy in EAs. MethodsEAs and nonathletic control subjects (NAs) underwent comprehensive phenotyping with the use of cardiopulmonary exercise testing, echocardiography, 12-lead electrocardiography (ECG), Holter monitoring for premature atrial contractions (PACs), and biochemical analysis. A genotyping array was used to determine polygenic risk scores for AF (AF-PRS). Logistic regression was used to develop an AF probability model. ResultsEAs (n = 353; median age 41 years, 74% male) had 40% larger LA volumes (42.9 vs 29.7 mL/m2; P < 0.001) than NAs (n = 97; median age 34 years, 64% male). Of the EAs, 96 (27%) had prevalent AF and demonstrated larger LA volumes (46.2 mL/m2 vs 41.9 mL/m2; P < 0.001), reduced LA strain (reservoir 26% vs 32%; P < 0.001), prolonged P-wave duration (124 vs 111 ms; P < 0.001), abnormal P-wave terminal force velocity in lead V1 (26% vs 9%; P < 0.001), and increased PACs (118 vs 15 PACs/24 h; P < 0.001) than EAs without AF. A clinical risk model incorporating these factors demonstrated excellent discrimination (AUC = 0.84). High AF-PRS increased AF odds 2.56-fold but contributed little additional predictive value. Using the model, AF prevalence increased from 4% to 86% in low to very high risk athletes, respectively. ConclusionsA combination of atrial cardiomyopathy markers was associated with high AF prevalence in EAs. A resulting quantitative AF risk identification tool provides promise for interventions aimed at preventing AF and thromboembolic complications in EAs.

Fields of Study & Subjects

Cardiovascular Effects of Exercise Cardiac Arrhythmias and Treatments Atrial Fibrillation Management and Outcomes

Publication details

Journal / source
JACC. Clinical electrophysiology
Publisher
Elsevier BV
Volume
12
Issue
9
Pages
2210-2222
Publication date
1 April 2026
Language
EN