Recent preclinical and observational cohort studies have implicated imbalances in gut microbiota composition as a contributor to atrial fibrillation (AF). The gut microbiota is a complex and dynamic ecosystem containing trillions of microorganisms, which produces bioactive metabolites influencing host health and disease development. In addition to host-specific determinants, lifestyle-related factors such as diet and drugs are important determinants of the gut microbiota composition. In this review, we discuss the evidence suggesting a potential bidirectional association between AF and gut microbiota, identifying gut microbiota-derived metabolites as possible regulators of the AF substrate. We summarize the effect of gut microbiota on the development and progression of AF risk factors, including heart failure, hypertension, obesity, and coronary artery disease. We also discuss the potential anti-arrhythmic effects of pharmacological and diet-induced modifications of gut microbiota composition, which may modulate and prevent the progression to AF. Finally, we highlight important gaps in knowledge and areas requiring future investigation. Although data supporting a direct relationship between gut microbiota and AF are very limited at the present time, emerging preclinical and clinical research dealing with mechanistic interactions between gut microbiota and AF is important as it may lead to new insights into AF pathophysiology and the discovery of novel therapeutic targets for AF.

Introduction: Recent research has unravelled an increasing list of cardiac conditions and risk factors that may be responsible for the abnormal underlying atrial substrate that predisposes to atrial fibrillation (AF). Atrial fibrosis has been demonstrated as the pivotal structural abnormality underpinning conduction disturbances that promote AF in different disease models. Despite the advancement in our discoveries of the molecular mechanisms involved in the profibrotic milieu, targeted therapeutics against atrial fibrosis remain lacking. Areas covered: This review is focused on detailing the key molecular signalling pathways that contribute to atrial fibrosis including: angiotensin II, transforming growth factor (TGF- ß1), connective tissue growth factor (CTGF) and endothelin-1. We also discussed the potential therapeutic options that may be useful in modulating the abnormal atrial substrate. In addition, we examined the new paradigm of AF care in lifestyle and risk factor management that has been shown to arrest and reverse the atrial remodelling process leading to improved AF outcomes. Expert commentary: The future of AF care is likely to require an integrated approach consisting of aggressive risk factor management in addition to the established paradigm of rate and rhythm management and anticoagulation. Translational studies on molecular therapeutics to combat atrial fibrosis is urgently needed.

Arrhythmogenic right ventricular cardiomyopathy (ARVC), an inherited heart muscle abnormality, is a major cause of sudden cardiac death (SCD). However, the burden of SCD and risk factors in ARVC are not clearly described. Thus, we estimated the rates and predictors of SCD in ARVC in a meta-analysis. PubMed, Embase, and Web of Science were searched through 7 April 2021. Prospective studies reporting SCD from ARVC cohorts were included. Data were independently extracted by two reviewers and pooled in a random-effects meta-analysis. Fifty-two studies (n = 5485 patients) with moderate-to-low risk of bias were included. The pooled annualized rates of SCD were 0.65 per 1000 [95% confidence interval 0.00–6.43, I² 0.00%] in those with an implantable cardioverter-defibrillator (ICD) and 7.21 (2.38–13.79, I² 0.0%) in non-ICD cohorts: 7.14 in probands and 8.44 for 2010 Task Force Criteria (TFC). Multivariable predictors of life-threatening arrhythmic events including SCD were: age at presentation [adjusted hazard ratio 0.98 (0.97–0.99)], male sex [2.08 (1.29–3.36)], right ventricular (RV) dysfunction [6.99 (2.17–22.49)], QRS fragmentation [6.55 (3.33–12.90)], T-wave inversion [1.12 (1.02–1.24)], syncope at presentation [2.83 (2.40–4.08)], previous non-sustained ventricular tachyarrhythmia [2.53 (1.44–4.45)], and the TFC score [1.96 (1.02–3.76)], (P < 0.05). Predictors of appropriate ICD therapy were RV dysfunction, syncope, and inducible ventricular arrhythmia (P < 0.01). This meta-analysis demonstrates a high burden of SCD in ARVC patients, especially among probands and ARVC defined by the modified TFC. Better strategies are required to improve patient management and prevent SCD in ARVC. PROSPERO ID: CRD42020211761.

To summarize data on atrial fibrillation (AF) detection rates and predictors across different rhythm monitoring strategies in patients with cryptogenic stroke (CS) or embolic stroke of undetermined source (ESUS). MEDLINE, Embase, and Web of Science were searched to identify all published studies providing relevant data through July 6, 2020. Random-effects meta-analysis method was used to pool estimates. We included 47 studies reporting on a pooled population of 8,215 patients with CS or ESUS. Using implantable cardiac monitor (ICM), the pooled rate of AF was 12.2% (95% CI 9.4–15.0) at 3 months, 16.0% (95% CI 13.2–18.8) at 6 months, 18.7% (95% CI 15.7–21.7) at 12 months, 22.8% (95% CI 19.1–26.5) at 24 months, and 28.5% (95% CI 17.6–39.3) at 36 months. AF rates were significantly higher in patients with ESUS vs CS (22.0% vs 14.2%; p < 0.001) at 6 months, and in studies using Reveal LINQ vs Reveal XT ICM (19.1% vs 13.0%; p = 0.001) at 12 months. Using mobile cardiac outpatient telemetry (MCOT), the pooled rate of AF was 13.7% (95% CI 10.2–17.2) at 1 month. Predictors of AF detection with ICM included older age, CHA2DS2-VASc score, left atrial enlargement, P wave maximal duration and prolonged PR interval. The yield of ICM increases with the duration of monitoring. More than a quarter of patients with CS or ESUS will be diagnosed with AF during follow-up. About one in seven patients had AF detected within a month of MCOT, suggesting that a non-invasive rhythm monitoring strategy should be considered before invasive monitoring.