Research on epicardial adipose tissue as a metabolic therapeutic target in AF: focus on GLP-1 receptor agonists

Epicardial adipose tissue (EAT), a metabolically active visceral fat depot anatomically contiguous with the myocardium, has emerged as a critical mediator and promising metabolic therapeutic target in atrial fibrillation (AF), particularly in the context of obesity and diabetes mellitus. Pathological expansion and dysfunction of EAT promote AF through paracrine and vasocrine secretion of pro-inflammatory and pro-fibrotic cytokines, release of extracellular vesicles carrying arrhythmogenic cargo, direct infiltration, and modulation of local electrophysiology and autonomic signaling, thereby creating a substrate for atrial cardiomyopathy, fibrosis, electrical remodeling, and AF initiation/persistence. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), beyond their glucoregulatory and weight-loss benefits, exhibit potential cardioprotective effects that may be relevant to AF. Notably, GLP-1 receptors are expressed in both human EAT and atrial tissue. Preclinical and clinical studies suggest that GLP-1RAs may reduce EAT volume/thickness, potentially exceeding overall weight loss, attenuate EAT inflammation and fibrosis, improve cardiomyocyte calcium handling, mitigate oxidative stress, and suppress pulmonary vein ectopy, thereby potentially reducing AF susceptibility and recurrence post-ablation. While evidence from genetic studies, meta-analyses, and specific cardiovascular outcome trials (CVOTs) supports an association between certain GLP-1RAs and reduced AF risk, conflicting data exist regarding drug-specific effects, underscoring the need for further mechanistic and outcome research. Targeting EAT modulation via GLP-1RAs represents a compelling strategy to disrupt the obesity-diabetes-AF axis, although challenges remain in elucidating precise molecular mechanisms, standardizing EAT assessment, understanding response heterogeneity, and defining the clinical role of specific GLP-1RAs within AF management algorithms.