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.

Cardiac arrhythmias, including atrial fibrillation and ventricular arrhythmias, remain leading causes of morbidity and mortality worldwide. While structural, electrical, and metabolic remodeling have long been recognized as drivers of arrhythmogenesis, emerging evidence identifies inflammation—particularly inflammasome signaling—as a central orchestrator of this pathological triad. Among the various inflammasome complexes, the NLRP3 inflammasome has garnered particular attention due to its activation in cardiomyocytes, fibroblasts, and immune cells in diverse clinical contexts. NLRP3 activation precipitates a cascade of downstream events, including interleukin-1β and -18 maturation, oxidative stress amplification, calcium mishandling, and extracellular matrix remodeling, thereby fostering a proarrhythmic substrate. This review synthesizes mechanistic and translational data implicating inflammasome signaling in both atrial and ventricular arrhythmias, with a focus on cellular specificity and electrophysiological sequelae. We explore upstream triggers, such as metabolic stress, gut dysbiosis, and epicardial adipose inflammation, and delineate the downstream impact on cardiac conduction and structural integrity. Emerging therapeutic strategies—including NLRP3 inhibitors, IL-1 antagonists, colchicine, and SGLT2 inhibitors—are critically appraised for their anti-inflammatory and antifibrotic potential. By bridging molecular insights with clinical application, this review underscores the inflammasome as a unifying mechanistic hub in arrhythmia pathogenesis and a promising target for precision-guided therapy.

Aims: Asymptomatic atrial fibrillation (AF) is frequent and associated with disease progression. This meta-analysis aimed to estimate the prevalence of asymptomatic AF and identify risk factors associated with asymptomatic status. Methods and results: MEDLINE (PubMed), Scopus, Cochrane, and ClinicalTrials.gov were searched until 8 January 2025. Double-independent study selection, data extraction, and quality assessments were performed. Random-effects meta-analysis was used. Estimates are presented with the asymptomatic individuals in the nominator and the symptomatic patients in the denominator. The assessment of the prevalence of asymptomatic AF and the identification of risk factors associated with the asymptomatic status comprised the main endpoints. Thirty-seven studies (224 273 participants) were included. The prevalence of asymptomatic AF was 27% [95% confidence interval (CI) = (22%, 33%); I2 = 100%]. Risk factors positively associated with the asymptomatic status were male sex [odds ratio (OR)=1.67, 95% CI = (1.48, 1.89), P < 0.001, I2 = 85%), diabetes mellitus [OR = 1.19, 95% CI = (1.07, 1.33), P = 0.002, I2 = 87%], chronic kidney disease [OR = 1.21, 95% CI = (1.08, 1.36), P < 0.001, I2 = 80%] and stroke/transient ischemic attack [OR = 1.43, 95% CI = (1.18, 1.73), P < 0.001, I2 = 95%], while heart failure was negatively associated with asymptomatic AF [OR = 0.71, 95% CI = (0.54, 0.94), P = 0.017, I2 = 97%]. Asymptomatic status was also positively associated with permanent AF [OR = 2.13, 95% CI = (1.28, 3.55); P = 0.004; I2 = 98%] and negatively associated with catheter ablation [OR = 0.63, 95% CI = (0.44, 0.91); P = 0.012; I2 = 95%], beta-blockers [OR = 0.90, 95% CI = (0.82, 0.98); P = 0.018; I2 = 68%] and antiarrhythmics [OR = 0.53, 95% CI = (0.35, 0.79); P = 0.002; I2 = 95%]. Conclusion: Asymptomatic AF was estimated at around 27%, with large variability depending on the prevalence of risk factors associated with asymptomatic status. Asymptomatic individuals had distinct characteristics compared with symptomatic patients, regardless of symptoms’ assessment methods and rhythm/rate control interventions. Our results could inform AF screening practices to target asymptomatic individuals. Registration: PROSPERO: CRD42024578738.

Coronary microvascular dysfunction (CMD) is a key contributor to myocardial ischemia and adverse cardiovascular outcomes, particularly in individuals with metabolic disorders such as type 2 diabetes (T2D). While conventional therapies primarily target epicardial coronary disease, effective treatments for CMD remain limited. Glucagon-like peptide-1 receptor (GLP-1R) agonists have emerged as promising agents with cardiovascular benefits extending beyond glycemic control. Preclinical and clinical evidence suggests that GLP-1R activation enhances coronary microvascular function through mechanisms including improved endothelial function, increased nitric oxide bioavailability, attenuation of oxidative stress, and reduced vascular inflammation. Moreover, GLP-1R agonists have been shown to improve myocardial blood flow, myocardial perfusion reserve, and coronary endothelial function, particularly in high-risk populations. Despite these promising findings, inconsistencies remain across studies due to variability in patient populations, study designs, and imaging methodologies. This review summarizes current evidence on the role of GLP-1R agonists in myocardial perfusion, bridging mechanistic insights with clinical outcomes. Further large-scale, well-designed trials are needed to clarify their long-term impact on coronary microcirculation and explore their potential as targeted therapies for CMD.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and is regarded as a liver manifestation of metabolic syndrome. It is linked to insulin resistance, obesity, and diabetes mellitus, all of which increase the risk of cardiovascular complications. Endothelial dysfunction (EnD) constitutes the main driver in the progression of atherosclerosis and coronary artery disease (CAD). Several pathophysiological alterations and molecular mechanisms are involved in the development of EnD in patients with NAFLD. Our aim is to examine the association of NAFLD and CAD with the parallel assessment of EnD, discussing the pathophysiological mechanisms and the genetic background that underpin this relationship. This review delves into the management of the condition, exploring potential clinical implications and available medical treatment options to facilitate the deployment of optimal treatment strategies for these patients.