Global, regional, and national burden of atrial fibrillation and atrial flutter from 1990 to 2021: sex differences and global burden projections to 2046-a systematic analysis of the Global Burden of Disease Study 2021

Epigenetic Drivers of Atrial Fibrillation: Mechanisms, Biomarkers, and Therapeutic Targets

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia, associated with significant morbidity, mortality, and healthcare burdens. Despite therapeutic advances, recurrence rates remain high, particularly in persistent AF, underscoring the need for deeper mechanistic insight. Epigenetic regulation-comprising DNA methylation, histone modifications, chromatin remodeling, RNA methylation, and non-coding RNAs-has emerged as a key contributor to the structural, electrical, and inflammatory remodeling underlying AF. These mechanisms operate at the interface of genetic susceptibility and environmental exposure, offering a dynamic framework for understanding disease progression. Systemic stressors such as aging, obesity, diabetes, hypertension, hypoxia, and alcohol have been shown to induce epigenetic reprogramming in atrial tissue, further promoting atrial cardiomyopathy and arrhythmogenesis. Additionally, circulating epigenetic markers, particularly microRNAs, are being investigated for their potential in AF diagnosis, risk stratification, and therapeutic monitoring. Therapeutic strategies targeting epigenetic pathways-ranging from histone deacetylase inhibitors and miRNA-based therapeutics to CRISPR/dCas9-mediated epigenome editing-are under investigation. Additionally, sodium-glucose cotransporter 2 inhibitors may indirectly influence epigenetic programs and miRNA expression relevant to atrial remodeling. While promising, these approaches require further validation in terms of safety, delivery specificity, and long-term efficacy. High-resolution epigenomic mapping and integrative multi-omic approaches may enhance understanding of AF heterogeneity and enable personalized treatment strategies. This review provides an integrated appraisal of epigenetic mechanisms in AF and outlines their emerging diagnostic and therapeutic relevance.

Atrial Cardiomyopathy in Atrial Fibrillation: A Multimodal Diagnostic Framework

Atrial fibrillation (AF) is increasingly recognized as the clinical manifestation of an underlying atrial disease process rather than a purely electrical disorder. This evolving paradigm has given rise to the concept of atrial cardiomyopathy (AtCM), encompassing structural, electrical, contractile, and molecular remodeling of the atrial myocardium that contributes to AF initiation, maintenance, and progression. Although consensus definitions of AtCM now exist, its integration into clinical practice remains limited, with AF management still largely guided by arrhythmic patterns rather than substrate characterization. This review synthesizes current diagnostic strategies for AtCM within the context of AF, emphasizing a multimodal approach. We outline advances in cardiac imaging-including echocardiography, cardiac magnetic resonance, and computed tomography-for detailed assessment of atrial morphology, function, and fibrosis. Electroanatomic mapping is discussed as a key invasive tool for substrate localization, while electrocardiographic indices such as P-wave morphology and dispersion serve as accessible surrogates of electrical remodeling. In parallel, we examine the role of circulating biomarkers and emerging genomic, transcriptomic, and epigenomic markers in refining disease phenotyping. Despite promising progress, significant challenges remain. Standardization of imaging protocols, validation of biomarker thresholds, and integration of artificial intelligence tools are needed to enhance clinical utility. A diagnostic framework informed by atrial substrate assessment may support more tailored therapeutic decision-making in AF. Future research should prioritize the harmonization of diagnostic criteria and explore how substrate profiling in AF may refine risk stratification and improve clinical outcomes.

Atrial Cardiomyopathy in Atrial Fibrillation: Mechanistic Pathways and Emerging Treatment Concepts

Atrial fibrillation (AF) is increasingly recognized not merely as an arrhythmia, but as a clinical manifestation of atrial cardiomyopathy (AtCM)-a progressive, multifaceted disease of the atrial myocardium involving structural, electrical, mechanical, and molecular remodeling. AtCM often precedes AF onset, sustains its perpetuation, and contributes to thromboembolic risk independently of rhythm status. Emerging evidence implicates diverse pathophysiological drivers of AtCM, including inflammation, epicardial adipose tissue, metabolic dysfunction, oxidative stress, ageing, and sex-specific remodeling. The NLRP3 inflammasome has emerged as a central effector in atrial inflammation and remodeling. Gut microbial dysbiosis, lipid dicarbonyl stress, and fibro-fatty infiltration are also increasingly recognized as contributors to arrhythmogenesis. AtCM is further linked to atrial functional valve regurgitation and adverse outcomes in AF. Therapeutically, substrate-directed strategies-ranging from metabolic modulation and immunomodulation to early rhythm control-offer promise for altering the disease trajectory. This review synthesizes mechanistic insights into AtCM and discusses emerging therapeutic paradigms that aim not merely to suppress arrhythmia but to modify the underlying substrate. Recognizing AF as a syndrome of atrial disease reframes management strategies and highlights the urgent need for precision medicine approaches targeting the atrial substrate.

Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, associated with significant morbidity, mortality, and healthcare burden. Despite advances in AF management, challenges persist in early detection, risk stratification, and treatment optimization, necessitating innovative solutions. Artificial intelligence (AI) has emerged as a transformative tool in AF care, leveraging machine learning and deep learning algorithms to enhance diagnostic accuracy, improve risk prediction, and guide therapeutic interventions. AI-powered electrocardiographic screening has demonstrated the ability to detect asymptomatic AF, while wearable photoplethysmography-based technologies have expanded real-time rhythm monitoring beyond clinical settings. AI-driven predictive models integrate electronic health records and multimodal physiological data to refine AF risk stratification, stroke prediction, and anticoagulation decision making. In the realm of treatment, AI is revolutionizing individualized therapy and optimizing anticoagulation management and catheter ablation strategies. Notably, AI-enhanced electroanatomic mapping and real-time procedural guidance hold promise for improving ablation success rates and reducing AF recurrence. Despite these advancements, the clinical integration of AI in AF management remains an evolving field. Future research should focus on large-scale validation, model interpretability, and regulatory frameworks to ensure widespread adoption. This review explores the current and emerging applications of AI in AF, highlighting its potential to enhance precision medicine and patient outcomes.