The Effect of Body Mass Index on Atrial Fibrillation in a Patient-Level Computational Model

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a leading cause of stroke and mortality. Its growing prevalence is linked to ageing and modifiable factors such as obesity. This thesis quantifies the impact of body mass index (BMI) and weight loss on AF by integrating BMI as a dynamic risk factor into a validated patient-level stochastic model of AF progression. Based on European epidemiological data, a demographic model was first developed to generate realistic height, weight, and BMI trajectories for adults. These synthetic profiles were coupled with a lifetime AF patient-level model describing transitions between sinus rhythm, AF, stroke states, and death. Within this framework, BMI dynamically modified AF risk, allowing simulation of how obesity accelerates AF onset. The model was calibrated and validated against published clinical cohorts, reproducing the characteristic rise in AF incidence with advancing age and increasing BMI. Simulation results showed that higher BMI led to earlier and more frequent AF, while sustained weight reduction markedly lowered risk. The model reproduces known epidemiological trends and demonstrates the preventive benefit of stable weight control. This work provides both scientific and clinical value: it advances computational cardiology by embedding a modifiable metabolic factor within a mechanistic model and supports precision-medicine approaches to AF prevention. The framework can be extended to other lifestyle and metabolic determinants, offering a quantitative tool for evaluating public-health strategies and personalised risk reduction.