# Characterization of Cardiac Fat in Atrial Fibrillation Patients Prior to Ablation Treatment
## 4 Discussion
Our results demonstrate significant EAT and PAT area differences in normal-weight patients between age groups 40 and 50 years and over 60 years, suggesting that epicardial fat naturally increases with age. Indeed, previous studies have found EAT to be 22% thicker in patients aged 65 years and older, corroborating the notion of age-related increase [22; 31]. This could be partially attributed to the hormonal changes that come with aging or to medications that manage comorbid conditions. With respect to anatomical and functional parameters, we observed that patients' LV mass significantly increased with obesity and was positively correlated with EAT and PAT. However, when indexed by BSA, LV mass was reduced among obese patients and had no association with cardiac fat. Our data suggest eccentric and not concentric LV changes, indicating increased preload rather than afterload, which might be consistent with increased BMI. This implies that the relationship between LV mass and cardiac fat is influenced by body size adjustment, suggesting that LV mass may be linked to larger body size rather than solely cardiac hypertrophy. This aligns with findings by [PERSON] et al. and [PERSON] et al., who propose that in the context of CAD, the influence of obesity on cardiac remodeling may overcome the local consequences of both EAT and PAT [32; 33]. The central obesity and visceral adipose tissue may raise LV afterload, eventually resulting in LV remodeling as a compensatory mechanism, which comprises an increase in LV diameter, volume, and mass [32]. Thus, it seems that obesity's impact on LV mass and cardiac structure may extend beyond local cardiac fat deposits. EAT and PAT in our patient population also exhibited negative correlations with functional aspects such as LV-Cardiac Index (CI), which reflects cardiac output (CO) adjusted by BSA [34]. Although, to our knowledge, no clear EAT/PAT-CI relationship in the setting of AF has been established, it is recognized that higher BMI is associated with an enlarged left atrium, subsequently increasing stroke volume and CO [3]. Prolonged elevated output leads to LV enlargement and hypertrophy, eventually resulting in diastolic dysfunction and systolic impairment [3]. Indeed, AF is known to increase the risk of heart failure along with EAT [15; 16; 35]. Obese patients with AF tend to be younger than patients with a normal BMI, which was also the case in our patient population. The impact of age may explain our findings, given that age is a key predictor of all-cause mortality in AF [3]. Moreover, obese patients tend to have more comorbidities, warranting stricter treatment strategies for rhythm control and anticoagulation, potentially influencing their outcomes [3]. Thus, the roots of the obesity paradox likely stem from the complex interactions of several contributing factors, requiring further exploration in future studies. In our study, EAT did not show an association with AF recurrence. However, other studies have demonstrated that EAT can be associated with AF recurrence [36]. Our study has certain limitations that warrant consideration. Firstly, this was a single-center investigation, and our findings may not fully represent broader populations. We were limited to the records captured by our local registry. The normal BMI group served as a control group and reference for other BMI groups. An appropriate control match was not conducted. We focused solely on area measurements of EAT and PAT and did not consider volume measurements. The automate machine learning model is only able to quantify EAT and PAT following similar patterns to those used in the training dataset. The latter limited our capacity to define and quantify regions not included in the model. Additionally, due to the constraints of our available data, we were unable to analyze the potential effects of underweight individuals (BMI \(<\) 18.5).