JACC. Clinical electrophysiology · 2026
Using Atrial Cardiomyopathy Metrics to Characterize Atrial Fibrillation in Endurance Athletes.
СтатьяНазвание и аннотация: Перевод в очереди
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Spencer Luke W, D' Ambrosio Paolo, Ohanian Monique, Janssens Kristel, Mitchell Amy M, Rowe Stephanie J, Flannery M Darragh, Young Paul E, Soka Magdalena, Johnson Renee, Yu Chenglong, Morris Gwilym M, Lacaze Paul, Giannoulatou Eleni, Van Puyvelde Tim, Bekhuis Youri, Ray Max, Pauwels Rik, De Paepe Jarne, Robyns Tomas, Delpire Boris, Prior David L, Kistler Peter M, Kalman Jonathan M, Sanders Prashanthan, Elliott Adrian D, Willems Rik, Heidbuchel Hein, Claessen Guido, Fatkin Diane, Gerche Andre La, Pro@Heart Consortium
Аннотация
BACKGROUND: Atrial fibrillation (AF) is more prevalent in endurance athletes (EAs) than in the general population, but identifying athletes at greatest risk remains challenging.
OBJECTIVES: This study sought to develop an AF risk assessment tool from associations between AF and electrophysiologic, imaging, and genetic markers of atrial cardiomyopathy in EAs.
METHODS: EAs 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.
RESULTS: EAs (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.
CONCLUSIONS: A 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.
OBJECTIVES: This study sought to develop an AF risk assessment tool from associations between AF and electrophysiologic, imaging, and genetic markers of atrial cardiomyopathy in EAs.
METHODS: EAs 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.
RESULTS: EAs (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.
CONCLUSIONS: A 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.