Aim: Gender medicine is increasingly recognized as crucial to understanding how biological sex influences the manifestation and treatment of disease. In cardiac electrophysiology, sex differences have been observed in the prevalence, symptomatology and treatment of atrial fibrillation. The aim of the study is to develop and analyze a mathematical model for rabbit atrial action potential (AP) taking into account sex differences.
Methods: The Aslanidi model was taken as the starting point for this study. A global sensitivity analysis (GSA) was performed using Sobol' indices to identify the ionic conductances that most influence AP characteristics. To reduce the computational complexity, a Gaussian process emulator was developed. Experimental data on IK1 current and AP traces from male and female rabbits were integrated. Parameter optimization and Approximate Bayesian Computation with Sequential Monte Carlo methods were used to estimate model parameters and quantify uncertainties.
Results: The sensitivity analysis highlighted key conductances affecting AP properties; in particular, it showed that gCaL dominates the influence on the action potential duration (APD30, APD50, APD90), while gNa mainly controls the upstroke velocity and the AP amplitude. The resting membrane potential was found to be governed mainly by gK1 and iNaK,max. Simulated APs closely resembled experimental measurements. Parameter estimation revealed varying degrees of identifiability, with some parameters estimated with high confidence while others remained uncertain. No significant sex-related differences in ionic conductances were found, possibly due to the limited number of experimental data. However, significant differences were found in the IK1 current parameters: females exhibit higher conductance than males, indicating a higher potassium current density and differences in gating parameters were observed between the groups.
Conclusion: This study establishes a framework for investigating sex-specific cardiac electrophysiology through mathematical modeling. The results emphasize the importance of expanding experimental data collection and incorporating hormonal influences in future research.