Recent advances in the genetics of atrial fibrillation: from rare and common genetic variants to microRNA signaling

Besides traditional risk factors, atrial fibrillation (AF) also shares a strong genetic component. Here, we review the genetics of AF including monogenic forms of AF, heritability of AF, complex genetic risk of AF, and the role of microRNAs in AF pathophysiology. Thirtytwo mutations (17 genes) have been reported to cause familial AF. Mutations in cardiac ion channel genes or their subunits alter electrical properties and thereby lead to AF. Recently, also non-ion channel gene mutations have been identified to cause familial AF. Twin and community-based studies suggested AF to be heritable also on the population level. The AF risk in the offspring of an affected first-degree relative ranged between 2- to 5-fold, depending on the age of onset. Thereby, the risk of AF increases gradually the earlier the youngest relative of an AF patient developed the arrhythmia. African Americans bear a lesser risk of AF compared to individuals of European ancestry. Their risk rises with increasing European admixture. Genome wide association studies have revealed loci on chromosomes 4q25, 16q21 and 1q21 conferring risk of AF. Very recently, another consortial effort has identified a novel locus on chromosome 1, intronic to IL6R. IL6R encodes the a subunit of the interleukin 6 receptor. MicroRNAs were shown to regulate gene expression, and are increasingly reported to modify AF. A hallmark of AF pathophysiology is electrical and structural remodeling. MicroRNAs are involved in this process by regulating gene expression of cardiac ion channels, calcium handling proteins, transcription factors, and extracellular matrix related proteins

Catch-up-ESUS - follow-up in embolic stroke of undetermined source (ESUS) in a prospective, open-label, observational study: study protocol and initial baseline data

Introduction. So far there is no uniform, commonly accepted diagnostic and therapeutic algorithm for patients with embolic stroke of undetermined source (ESUS). Recent clinical trials on secondary stroke prevention in ESUS did not support the use of oral anticoagulation. As ESUS comprises heterogeneous subgroups including a wide age-range, concomitant patent foramen ovale (PFO), and variable probability for atrial fibrillation (AF), an individualised approach is urgently needed. This prospective registry study aims to provide initial data towards an individual, structured diagnostic and therapeutic approach in ESUS patients. Methods and analysis. The open-label, investigator-initiated, prospective, single-centre, observational registry study (Catch-up-ESUS) started in 01/2018. Consecutive ESUS patients ≥18 years who give informed consent are included and will be followed up for 3 years. Stratified by age <60 or ≥60 years, the patients are processed following a standardised diagnostic and treatment algorithm with an interdisciplinary design involving neurologists and cardiologists. Depending on the strata, patients receive a transesophageal echocardiogram; all patients receive an implantable cardiac monitor. Patients <60 years with PFO and without evidence of concomitant AF are planned for PFO closure within 6 months after stroke. The current diagnostic and therapeutic workup of ESUS patients requires improvement by both standardisation and a more individualised approach. Catch-up-ESUS will provide important data with respect to AF detection and PFO closure and will estimate stratified stroke recurrence rates after ESUS. Ethics and dissemination. The study has been approved by the responsible ethics committee at the Ludwig Maximilian University, Munich, Germany (project number 17–685). Catch-Up-ESUS is conducted in accordance with the Declaration of Helsinki. All patients will have to give written informed consent or, if unable to give consent themselves, their legal guardian will have to provide written informed consent for their participation. The first observation period of the registry study is 1 year, followed by the first publication of the results including follow-up of the patients. Further publications will be considered according the predefined individual follow-up dates of the stroke patients up to 36 months

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

Objective We sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk. Methods We evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors. Results We observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with p 0.1). Conclusion: s Genetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF

Development and external validation of predictive models for prevalent and recurrent atrial fibrillation: a protocol for the analysis of the CATCH ME combined dataset

Background: Atrial fibrillation (AF) is caused by different mechanisms but current treatment strategies do not target these mechanisms. Stratified therapy based on mechanistic drivers and biomarkers of AF have the potential to improve AF prevention and management outcomes. We will integrate mechanistic insights with known pathophysiological drivers of AF in models predicting recurrent AF and prevalent AF to test hypotheses related to AF mechanisms and response to rhythm control therapy. Methods: We will harmonise and combine baseline and outcome data from 12 studies collected by six centres from the United Kingdom, Germany, France, Spain, and the Netherlands which assess prevalent AF or recurrent AF. A Delphi process and statistical selection will be used to identify candidate clinical predictors. Prediction models will be developed in patients with AF for AF recurrence and AF-related outcomes, and in patients with or without AF at baseline for prevalent AF. Models will be used to test mechanistic hypotheses and investigate the predictive value of plasma biomarkers. Discussion: This retrospective, harmonised, individual patient data analysis will use information from 12 datasets collected in five European countries. It is envisioned that the outcome of this analysis would provide a greater understanding of the factors associated with recurrent and prevalent AF, potentially allowing development of stratified approaches to prevention and therapy management

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Abstract Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation

Development and external validation of predictive models for prevalent and recurrent atrial fibrillation:a protocol for the analysis of the CATCH ME combined dataset

Background: Atrial fibrillation (AF) is caused by different mechanisms but current treatment strategies do not target these mechanisms. Stratified therapy based on mechanistic drivers and biomarkers of AF have the potential to improve AF prevention and management outcomes. We will integrate mechanistic insights with known pathophysiological drivers of AF in models predicting recurrent AF and prevalent AF to test hypotheses related to AF mechanisms and response to rhythm control therapy. Methods: We will harmonise and combine baseline and outcome data from 12 studies collected by six centres from the United Kingdom, Germany, France, Spain, and the Netherlands which assess prevalent AF or recurrent AF. A Delphi process and statistical selection will be used to identify candidate clinical predictors. Prediction models will be developed in patients with AF for AF recurrence and AF-related outcomes, and in patients with or without AF at baseline for prevalent AF. Models will be used to test mechanistic hypotheses and investigate the predictive value of plasma biomarkers. Discussion: This retrospective, harmonised, individual patient data analysis will use information from 12 datasets collected in five European countries. It is envisioned that the outcome of this analysis would provide a greater understanding of the factors associated with recurrent and prevalent AF, potentially allowing development of stratified approaches to prevention and therapy management

Independent susceptibility markers for atrial fibrillation on chromosome 4q25

Background-: Genetic variants on chromosome 4q25 are associated with atrial fibrillation (AF). We sought to determine whether there is more than 1 susceptibility signal at this locus. Methods and results-: Thirty-four haplotype-tagging single-nucleotide polymorphisms (SNPs) at the 4q25 locus were genotyped in 790 case and 1177 control subjects from Massachusetts General Hospital and tested for association with AF. We replicated SNPs associated with AF after adjustment for the most significantly associated SNP in 5066 case and 30 661 referent subjects from the German Competence Network for Atrial Fibrillation, Atherosclerosis Risk In Communities Study, Cleveland Clinic Lone AF Study, Cardiovascular Health Study, and Rotterdam Study. All subjects were of European ancestry. A multimarker risk score composed of SNPs that tagged distinct AF susceptibility signals was constructed and tested for association with AF, and all results were subjected to meta-analysis. The previously reported SNP, rs2200733, was most significantly associated with AF (minor allele odds ratio 1.80, 95% confidence interval 1.50 to 2.15, P=1.2×10) in the discovery sample. Adjustment for rs2200733 genotype revealed 2 additional susceptibility signals marked by rs17570669 and rs3853445. A graded risk of AF was observed with an increasing number of AF risk alleles at SNPs that tagged these 3 susceptibility signals. Conclusions-: We identified 2 novel AF susceptibility signals on chromosome 4q25. Consideration of multiple susceptibility signals at chromosome 4q25 identifies individuals with an increased risk of AF and may localize regulatory elements at the locus with biological relevance in the pathogenesis of AF