Mechanistic Insights into Sex Differences in Atrial Electrophysiology and Arrhythmia Vulnerability through Sex-specific Computational Models

Atrial fibrillation (AF), the most common cardiac arrhythmia, is characterized by notable sex differences in clinical presentation, treatment response, and outcomes. Although prevalence is similar between sexes, women often experience more severe symptoms, higher rates of adverse drug effects, and reduced treatment efficacy. To investigate the underlying sex-specific AF mechanisms, we developed and validated male and female human atrial cardiomyocyte models that integrate known sex-based differences in electrophysiology and calcium (Ca ²⁺ ) handling under normal sinus rhythm (nSR) and chronic AF (cAF) conditions. While the model parameterizations were based on limited human data, and the assumptions may not capture the full spectrum of clinical variability, the models reproduced key reported sex-dependent differences in human atrial cardiomyocyte action potential (AP) and Ca ²⁺ transient (CaT) dynamics. Simulations revealed that both sexes exhibited shortened effective refractory periods and wavelengths in cAF vs. nSR. However, females were more prone to delayed afterdepolarizations (DADs), while males were more susceptible to AP duration (APD) and CaT amplitude (CaT _Amp ) alternans. Population-based modeling identified distinct parameter associations with arrhythmia mechanisms, whereby DAD vulnerability was associated with enhanced ryanodine receptor sensitivity to Ca ²⁺ (in females), and alternans in males correlated with reduced L-type Ca ²⁺ current maximal conductance. Pharmacological simulations revealed sex-specific responses to antiarrhythmic therapies. In males, multiple drug combinations proved effective in restoring APD at 90% repolarization (APD ₉₀ ), CaT _Amp , and reducing alternans susceptibility, whereas females responded to only one combination improving APD ₉₀ and CaT _Amp but with minimal impact on DAD risk. These findings underscore the need for sex-specific therapeutic strategies and support the use of computational modeling in guiding precision medicine approaches against AF.