Methylome-wide Association Study of Atrial Fibrillation in Framingham Heart Study

DNA hypermethylation of MED1 and MED23 as early diagnostic biomarkers for unsolved issues in atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Much effort was spent to identify biomarkers useful to stratify AF patients. Mediator complex (MED) is an ancestral regulator of transcriptional mechanisms. Here, we investigated the role of methyl DNA-MED regulatory networks in AF patients.

METHODS

We analyzed the methylome of circulating CD4+T lymphocytes isolated from patients at the time of first AF diagnosis vs. healthy subjects for identifying epigenetic dysregulation of MED-related genes.

RESULTS

We identified 10 differentially methylated regions (DMRs) which were hypermethylated and annotated to 10 genes encoding for MED complex subunits in CD4+T lymphocytes of AF patients vs. healthy subjects (HS). Network-oriented analysis prioritized 6 subunits including MED1, MED13, MED15, MED17, MED23 and MED30, which enriched significantly lipid metabolism pathways and cardiopathy onset. ROC curve analysis showed that elevated methylation levels of MED1 and MED23 discriminated AF patients with an area under the curve (AUC) of 92.7 % (p < 0.001) and an AUC = 100 % (p < 0.001), respectively. Methylation levels of MED23 correlated with the presence of mitral valve disease (p < 0.05) and NT-proBNP (p < 0.05); moreover, MED23 had a not inferior diagnostic value than circulating levels of NT-proBNP (AUC = 0.923, p < 0.001).

CONCLUSIONS

For the first time, we showed that DNA methylation changes are associated with regulation of MED complex subunits in early diagnosis of AF patients. Clinically, MED1 and MED23 hypermethylation showed a diagnostic value not inferior to circulating levels of NT-proBNP suggesting early diagnostic biomarker pathogenic molecular routes underlying disease onset.

Epigenetic regulation on left atrial function and disease recurrence after catheter ablation in atrial fibrillation

BACKGROUND

Genetic variation and modifiable risk factors play a significant role in the pathogenesis of atrial fibrillation (AF). The influence of epigenetic modification on AF remains to be elucidated. We investigated the role of DNA methylation in the etiology of AF. Epigenetic evaluation was performed in 115 AF patients who underwent radiofrequency catheter ablation in a single institution. We measured methylation at approximately 850,000 bp cytosine-phosphate-guanine (CpG) sites in the 115 samples. The degree of methylation was compared across seven classification criteria: type of AF, late recurrence, impaired left atrium (LA) function, late gadolinium enhancement, LA diameter, LA volume, and flow velocity of the LA appendage.

RESULTS

The four most significantly methylated genes were DEFB104B, C3, TANC1, and TMEM9B. The DEFB104B gene (cg20223677 in the transcription start site), which encodes β-defensin 104B, was hypomethylated in three groups: AF patients with late recurrence, impaired LA function, and impaired LAA flow velocity. Enriched functional annotation of the differentially methylated datasets revealed that five out of the seven AF groups in this cohort were associated with genes involved in the cell movement of endothelial cell lines, sprouting angiogenesis by endothelial cell lines, or migration of endothelial cell lines.

CONCLUSIONS

Epigenetic profiling revealed that epigenetic modification might affect important characteristics of AF. Our results suggest that the pathogenesis of AF might be affected by not only genetic variation or modifiable factors but also by epigenetic modulation.

The combination of decitabine with multi-omics confirms the regulatory pattern of the correlation between DNA methylation of the CACNA1C gene and atrial fibrillation

Background

Studies have shown that DNA methylation of the CACNA1C gene is involved in the pathogenesis of various diseases and the mechanism of drug action. However, its relationship with atrial fibrillation (AF) remains largely unexplored.

Objective

To investigate the association between DNA methylation of the CACNA1C gene and AF by combining decitabine (5-Aza-2'-deoxycytidine, AZA) treatment with multi-omics analysis.

Methods

HepG2 cells were treated with AZA to observe the expression of the CACNA1C gene, which was further validated using gene expression microarrays. Pyrosequencing was employed to validate differentially methylated sites of the CACNA1C gene observed in DNA methylation microarrays. A custom DNA methylation dataset based on the MSigDB database was combined with ChIP-sequencing and RNA-sequencing data to explore the regulatory patterns of DNA methylation of the CACNA1C gene.

Results

Treatment of HepG2 cells with three different concentrations of AZA (2.5 µM, 5.0 µM, and 10.0 µM) resulted in 1.6, 2.5, and 2.9-fold increases in the mRNA expression of the CACNA1C gene, respectively, compared to the DMSO group, with statistical significance at the highest concentration group (p < 0.05). Similarly, AZA treatment of T47D cells showed upregulated mRNA expression of the CACNA1C gene in the gene expression microarray results (adj P < 0.05). DNA methylation microarray analysis revealed that methylation of a CpG site in intron 30 of the CACNA1C gene may be associated with AF (adj P < 0.05). Pyrosequencing of this site and its adjacent two CpG sites demonstrated significant differences in DNA methylation levels between AF and sinus rhythm groups (p < 0.05). Subsequent multivariate logistic regression models confirmed that the DNA methylation degree of these three sites and their average was associated with AF (p < 0.05). Additionally, the UCSC browser combined with ChIP-sequencing revealed that the aforementioned region was enriched in enhancer markers H3K27ac and H3K4me1. Differential expression and pathway analysis of RNA-sequencing data ultimately identified ATF7IP and KAT2B genes as potential regulators of the CACNA1C gene.

Conclusion

The DNA methylation levels at three CpG sites in intron 30 of the CACNA1C gene are associated with AF status, and potentially regulated by ATF7IP and KAT2B.

An atlas of genetic effects on the monocyte methylome across European and African populations

Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis- regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

DNA Methylation and Telomeres-Their Impact on the Occurrence of Atrial Fibrillation during Cardiac Aging

Atrial fibrillation (AF) is the most common arrhythmia in humans. AF is characterized by irregular and increased atrial muscle activation. This high-frequency activation obliterates the synchronous work of the atria and ventricles, reducing myocardial performance, which can lead to severe heart failure or stroke. The risk of developing atrial fibrillation depends largely on the patient's history. Cardiovascular diseases are considered aging-related pathologies; therefore, deciphering the role of telomeres and DNA methylation (mDNA), two hallmarks of aging, is likely to contribute to a better understanding and prophylaxis of AF. In honor of Prof. Elizabeth Blackburn's 75th birthday, we dedicate this review to the discovery of telomeres and her contribution to research on aging.

Quantitative Investigation of Methylation Heterogeneity by Digital Melting Curve Analysis on a SlipChip for Atrial Fibrillation

Methylation is an essential epigenetic modification involved in regulating gene expression and maintaining genome stability. Methylation patterns can be heterogeneous, exhibiting variations in both level and density. However, current methods of methylation analysis, including sequencing, methylation-specific PCR, and high-resolution melting curve analysis (HRM), face limitations of high cost, time-consuming workflows, and the difficulty of both accurate heterogeneity analysis and precise quantification. Here, a droplet array SlipChip-based (da-SlipChip-based) digital melting curve analysis (MCA) method was developed for the accurate quantification of both methylation level (ratio of methylated molecules to total molecules) and methylation density (ratio of methylated CpG sites to total CpG sites). The SlipChip-based digital MCA system supplements an in situ thermal cycler with a fluorescence imaging module for real-time MCA. The da-SlipChip can generate 10,656 droplets of 1 nL each, which can be separated into four lanes, enabling the simultaneous analysis of four samples. This method's clinical application was demonstrated by analyzing samples from ten healthy individuals and twenty patients with atrial fibrillation (AF), the most common arrhythmia. This method can distinguish healthy individuals from those with AF of both the paroxysmal and persistent types. It also holds potential for broader application in various research and clinical settings requiring methylation analysis.

DNA methylation and cardiovascular disease in humans: a systematic review and database of known CpG methylation sites

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death worldwide and considered one of the most environmentally driven diseases. The role of DNA methylation in response to the individual exposure for the development and progression of CVD is still poorly understood and a synthesis of the evidence is lacking.

RESULTS

A systematic review of articles examining measurements of DNA cytosine methylation in CVD was conducted in accordance with PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The search yielded 5,563 articles from PubMed and CENTRAL databases. From 99 studies with a total of 87,827 individuals eligible for analysis, a database was created combining all CpG-, gene- and study-related information. It contains 74,580 unique CpG sites, of which 1452 CpG sites were mentioned in ≥ 2, and 441 CpG sites in ≥ 3 publications. Two sites were referenced in ≥ 6 publications: cg01656216 (near ZNF438) related to vascular disease and epigenetic age, and cg03636183 (near F2RL3) related to coronary heart disease, myocardial infarction, smoking and air pollution. Of 19,127 mapped genes, 5,807 were reported in ≥ 2 studies. Most frequently reported were TEAD1 (TEA Domain Transcription Factor 1) and PTPRN2 (Protein Tyrosine Phosphatase Receptor Type N2) in association with outcomes ranging from vascular to cardiac disease. Gene set enrichment analysis of 4,532 overlapping genes revealed enrichment for Gene Ontology molecular function "DNA-binding transcription activator activity" (q = 1.65 × 10^-11) and biological processes "skeletal system development" (q = 1.89 × 10^-23). Gene enrichment demonstrated that general CVD-related terms are shared, while "heart" and "vasculature" specific genes have more disease-specific terms as PR interval for "heart" or platelet distribution width for "vasculature." STRING analysis revealed significant protein-protein interactions between the products of the differentially methylated genes (p = 0.003) suggesting that dysregulation of the protein interaction network could contribute to CVD. Overlaps with curated gene sets from the Molecular Signatures Database showed enrichment of genes in hemostasis (p = 2.9 × 10^-6) and atherosclerosis (p = 4.9 × 10^-4).

CONCLUSION

This review highlights the current state of knowledge on significant relationship between DNA methylation and CVD in humans. An open-access database has been compiled of reported CpG methylation sites, genes and pathways that may play an important role in this relationship.

Atrial fibrillation: Epigenetic aspects and role of sodium-glucose cotransporter 2 inhibitors

Atrial fibrillation (AF) is the most frequent arrhythmia and is associated with substantial morbidity and mortality. Pathophysiological aspects consist in the activation of pro-fibrotic signaling and Ca²⁺ handling abnormalities at atrial level. Structural and electrical remodeling creates a substrate for AF by triggering conduction abnormalities and cardiac arrhythmias. The care of AF patients focuses predominantly on anticoagulation, symptoms control and the management of risk factors and comorbidities. The goal of AF therapy points to restore sinus rhythm, re-establish atrioventricular synchrony and improve atrial contribution to the stroke volume. New layer of information to better comprehend AF pathophysiology, and identify targets for novel pharmacological interventions consists of the epigenetic phenomena including, among others, DNA methylation, histone modifications and noncoding RNAs. Moreover, the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in diabetic and non-diabetic patients at cardiovascular risk as well as emerging evidence on the ability of SGLT2i to modify epigenetic signature in cardiovascular diseases provide a solid background to investigate a possible role of this drug class in the onset and progression of AF. In this review, following a summary of pathophysiology and management, epigenetic mechanisms in AF and the potential of sodium-glucose SGLT2i in AF patients are discussed.

Progress in molecular biology and translational science: Epigenetics in cardiovascular health and disease

Conrad Waddington's epigenetics landscape has provided a metaphorical framework for how cells progress from undifferentiated states to one of several discrete, distinct, differentiated cell fates. The understanding of epigenetics has evolved over time, with DNA methylation being the most studied epigenetic modification, followed by histone modifications and non-coding RNA. Cardiovascular diseases (CVD) are leading contributors to death worldwide, with the prevalence of CVDs increasing across the last couple of decades. Significant amount of resources being poured into researching key mechanisms and underpinnings of the various CVDs. These molecular studies looked at the genetics, epigenetics as well as the transcriptomics of various cardiovascular conditions, aiming to provide mechanistic insights. It has paved the way for therapeutics to be developed and in recent years, epi-drugs for the treatment of CVDs. This chapter aims to cover the various roles of epigenetics in the context of cardiovascular health and disease. The following will be examined in detail: the developments in basic experimental techniques used to study epigenetics, the role of epigenetics in various CVDs (hypertension, atrial fibrillation, atherosclerosis, and heart failure), and current advances in epi-therapeutics, providing a holistic view of the current concerted efforts in advancing the field of epigenetics in CVDs.

Identification of novel susceptibility methylation loci for pancreatic cancer in a two-phase epigenome-wide association study

The role of DNA methylation and its interplay with gene expression in the susceptibility to pancreatic cancer (PanC) remains largely unexplored. To fill in this gap, we conducted an integrative two-phase epigenome-wide association study (EWAS) of PanC using genomic DNA from 44 cases and 556 controls (20 local controls and 536 public controls in the Framingham Heart Study) in phase 1 and 23 cases and 22 controls in phase 2. We validated the findings using pre-diagnostic blood samples from 13 cases and 26 controls in the Women's Health Initiative (WHI) Study. We further examined gene expression in peripheral leukocytes of 47 cases and 31 controls involved in the methylation study using RNA sequencing and performed bidirectional Mendelian randomization (MR) analysis using existing single nucleotide polymorphism (SNP) data. In phase 1, we identified 2776 significantly differentially methylated CpG sites (DMPs) and 154 significantly differentially methylated regions (DMRs). In phase 2, we validated six DMPs (in or near AIM2, DGKA, STK39, and TNFSF8) and three DMRs (in or near nc886, LY6G5C, and HLA-DPB1). The DMR near nc886 was further validated in the WHI samples (P = 6.69 × 10-5). MR analysis suggested that the CpG sites cg00308130 and cg16684184 for nc886 and cg16875057 for STK39 were causally related to PanC susceptibility and that PanC influenced methylation of cg15354065 for DGKA. This first integrative EWAS of PanC provides novel insights into the role of DNA methylation and its interplay with SNPs and gene expression in the disease susceptibility.

Immune-associated pivotal biomarkers identification and competing endogenous RNA network construction in post-operative atrial fibrillation by comprehensive bioinformatics and machine learning strategies

Background

Atrial fibrillation (AF) is the most common arrhythmia. Previous studies mainly focused on identifying potential diagnostic biomarkers and treatment strategies for AF, while few studies concentrated on post-operative AF (POAF), particularly using bioinformatics analysis and machine learning algorithms. Therefore, our study aimed to identify immune-associated genes and provide the competing endogenous RNA (ceRNA) network for POAF.

Methods

Three GSE datasets were downloaded from the GEO database, and we used a variety of bioinformatics strategies and machine learning algorithms to discover candidate hub genes. These techniques included identifying differentially expressed genes (DEGs) and circRNAs (DECs), building protein-protein interaction networks, selecting common genes, and filtering candidate hub genes via three machine learning algorithms. To assess the diagnostic value, we then created the nomogram and receiver operating curve (ROC). MiRNAs targeting DEGs and DECs were predicted using five tools and the competing endogenous RNA (ceRNA) network was built. Moreover, we performed the immune cell infiltration analysis to better elucidate the regulation of immune cells in POAF.

Results

We identified 234 DEGs (82 up-regulated and 152 down-regulated) of POAF via Limma, 75 node genes were visualized via PPI network, which were mainly enriched in immune regulation. 15 common genes were selected using three CytoHubba algorithms. Following machine learning selection, the nomogram was created based on the four candidate hub genes. The area under curve (AUC) of the nomogram and individual gene were all over 0.75, showing the ideal diagnostic value. The dysregulation of macrophages may be critical in POAF pathogenesis. A novel circ_0007738 was discovered in POAF and the ceRNA network was eventually built.

Conclusion

We identified four immune-associated candidate hub genes (C1QA, C1R, MET, and SDC4) for POAF diagnosis through the creation of a nomogram and evaluation of its diagnostic value. The modulation of macrophages and the ceRNA network may represent further therapy methods.

DNA Methylation-Based Prediction of Post-operative Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF.

Methods

We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort.

Results

A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort.

Conclusions

We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Epigenetic Mechanism and Therapeutic Implications of Atrial Fibrillation

Atrial fibrillation (AF) is the most common arrhythmia attacking 1. 5–2.0% of general population worldwide. It has a significant impact on morbidity and mortality globally and its prevalence increases exponentially with age. Therapies like catheter ablation or conventional antiarrhythmic drugs have not provided effective solution to the recurrence for AF over the past decades. Over 100 genetic loci have been discovered to be associated with AF by Genome-wide association studies (GWAS) but none has led to a therapy. Recently potential involvement of epigenetics (DNA methylation, histone modification, and non-coding RNAs) in the initiation and maintenance of AF has partly emerged as proof-of-concept in the mechanism and management of AF. Here we reviewed the epigenetic features involved in AF pathophysiology and provided an update of their implications in AF therapy.

Detecting methylation quantitative trait loci using a methylation random field method

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Transcriptomic Profiling of Canine Atrial Fibrillation Models After One Week of Sustained Arrhythmia

[Figure: see text].

New biomarkers from multiomics approaches: improving risk prediction of atrial fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia leading to many adverse outcomes and increased mortality. Yet the molecular mechanisms underlying AF remain largely unknown. Recent advances in high-throughput technologies make large-scale molecular profiling possible. In the past decade, multiomics studies of AF have identified a number of potential biomarkers of AF. In this review, we focus on the studies of multiomics profiles with AF risk. We summarize recent advances in the discovery of novel biomarkers for AF through multiomics studies. We also discuss limitations and future directions in risk assessment and discovery of therapeutic targets for AF.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

Epigenetics in atrial fibrillation: A reappraisal

Atrial fibrillation (AF) is the most common cardiac arrhythmia and an important cause of morbidity and mortality globally. Atrial remodeling includes changes in ion channel expression and function, structural alterations, and neural remodeling, which create an arrhythmogenic milieu resulting in AF initiation and maintenance. Current therapeutic strategies for AF involving ablation and antiarrhythmic drugs are associated with relatively high recurrence and proarrhythmic side effects, respectively. Over the last 2 decades, in an effort to overcome these issues, research has sought to identify the genetic basis for AF thereby gaining insight into the regulatory mechanisms governing its pathophysiology. Despite identification of multiple gene loci associated with AF, thus far none has led to a therapy, indicating additional contributors to pathology. Recently, in the context of expanding knowledge of the epigenome (DNA methylation, histone modifications, and noncoding RNAs), its potential involvement in the onset and progression of AF pathophysiology has started to emerge. Probing the role of various epigenetic mechanisms that contribute to AF may improve our knowledge of this complex disease, identify potential therapeutic targets, and facilitate targeted therapies. Here, we provide a comprehensive review of growing epigenetic features involved in AF pathogenesis and summarize the emerging epigenomic targets for therapy that have been explored in preclinical models of AF.

Expression Profiles of Circular RNA in Human Atrial Fibrillation With Valvular Heart Diseases

Circular RNAs (circRNA) are involved in a variety of human heart diseases, however, circRNA expression profiles and circRNA-miRNA-mRNA regulatory network in human atrial fibrillation (AF) especially with valvular heart diseases (VHD) remain poorly understood. A high-throughput RNA sequencing was used to investigate the differentially expressed circRNAs in left atrial appendage from VHD patients with or without persistent AF. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential functions of the host genes of differentially expressed circRNA and their downstream targets. CircRNA-miRNA-mRNA regulatory network was constructed to identify mechanisms underlying circRNAs. qRT-PCR and sanger sequencing were further performed to validate the results. Compared with sinus rhythm (SR) patients, there were 3094 upregulated and 4472 downregulated circRNAs in AF patients respectively. The expression of 10 most differentially expressed circRNAs (circ 255-ITGA7, circ 418-KCNN2, circ 13913-MIB1, circ 44670-BARD1, circ 44782-LAMA2, circ 81906-RYR2, circ 35880-ANO5, circ 22249-TNNI3K, circ 3136-TNNI3K, circ 56186-TNNI3K) between SR and persistent AF patients were verified by qRT-PCR. In addition, specific back-splicing sites of these circRNAs was confirmed by sanger sequencing. GO and KEGG pathway analysis indicated that cAMP signal pathway and Wnt signal pathway might play important role in the development of AF in VHD patients, which might be affected by circRNAs. This study provided a preliminary landscape of circRNAs expression profiles which are involved in persistent AF due to VHD, and established the possibility for future related researches in this field.

Association Between GJA1 rs13216675 T>C Polymorphism and Risk of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Background: Rs13216675 T>C polymorphism, an SNP (single-nucleotide polymorphism) close to the gap junction protein alpha 1 (GJA1) gene, has been reported to be associated with risk of atrial fibrillation (AF); however, the results remained inconclusive. We aimed to perform a systematic review to clarify the relationship between rs13216675 and risk of AF.

Materials and methods: We systematically searched the databases of PubMed, EMBASE, Web of Science, and the Chinese National Knowledge Infrastructure up to July 15, 2020. Data were synthesized using the random-effects model. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the relationship between rs13216675 and risk of AF.

Results: Seven studies involving 39,827 cases and 458,466 controls were analyzed in the meta-analysis. The overall pooled OR of rs13216675 polymorphism for AF was significant (OR = 1.10, 95% CI = 1.07–1.12, P < 0.001) under the additive genetic model. Subgroup analyses revealed that rs13216675 polymorphism was significantly associated with AF in both Asians (OR = 1.12, 95% CI = 1.07–1.17, P < 0.001) and Europeans (OR = 1.09, 95% CI = 1.06–1.12, P < 0.001). When data were stratified by control sources, rs13216675 polymorphism was significantly related to AF in studies with both population-based controls (OR = 1.09, 95% CI = 1.07–1.12) and hospital-based controls (OR = 1.12, 95% CI = 1.07–1.17). No evidence of publication bias was detected.

Conclusion: Our meta-analysis suggested that rs13216675 was significantly related to risk of AF and, therefore, might serve as a potential biological marker of AF.

Chamber-specific transcriptional responses in atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia, yet the molecular signature of the vulnerable atrial substrate is not well understood. Here, we delineated a distinct transcriptional signature in right versus left atrial cardiomyocytes (CMs) at baseline and identified chamber-specific gene expression changes in patients with a history of AF in the setting of end-stage heart failure (AF+HF) that are not present in heart failure alone (HF). We observed that human left atrial (LA) CMs exhibited Notch pathway activation and increased ploidy in AF+HF but not in HF alone. Transient activation of Notch signaling within adult CMs in a murine genetic model is sufficient to increase ploidy in both atrial chambers. Notch activation within LA CMs generated a transcriptomic fingerprint resembling AF, with dysregulation of transcription factor and ion channel genes, including Pitx2, Tbx5, Kcnh2, Kcnq1, and Kcnip2. Notch activation also produced distinct cellular electrophysiologic responses in LA versus right atrial CMs, prolonging the action potential duration (APD) without altering the upstroke velocity in the left atrium and reducing the maximal upstroke velocity without altering the APD in the right atrium. Our results support a shared human/murine model of increased Notch pathway activity predisposing to AF.

Distinct transcriptional changes occur in human left versus right atrial cardiomyocytes in atrial fibrillation, including Notch pathway activation, which alters electric properties and ploidy in murine models.

Genetics and Epigenetics of Atrial Fibrillation

Atrial fibrillation (AF) is known to be the most common supraventricular arrhythmia affecting up to 1% of the general population. Its prevalence exponentially increases with age and could reach up to 8% in the elderly population. The management of AF is a complex issue that is addressed by extensive ongoing basic and clinical research. AF centers around different types of disturbances, including ion channel dysfunction, Ca²⁺-handling abnormalities, and structural remodeling. Genome-wide association studies (GWAS) have uncovered over 100 genetic loci associated with AF. Most of these loci point to ion channels, distinct cardiac-enriched transcription factors, as well as to other regulatory genes. Recently, the discovery of post-transcriptional regulatory mechanisms, involving non-coding RNAs (especially microRNAs), DNA methylation, and histone modification, has allowed to decipher how a normal heart develops and which modifications are involved in reshaping the processes leading to arrhythmias. This review aims to provide a current state of the field regarding the identification and functional characterization of AF-related epigenetic regulatory networks

The molecular genetic basis of atrial fibrillation

As the most common cardiac arrhythmia, atrial fibrillation (AF) is a major risk factor for stroke, heart failure, and premature death with considerable associated costs. However, no available treatment options have optimal benefit-harm profiles currently, reflecting an incomplete understanding of the biological mechanisms underlying this complex arrhythmia. Recently, molecular epidemiological studies, especially genome-wide association studies, have emphasized the substantial genetic component of AF etiology. A comprehensive mapping of the genetic underpinnings for AF can expand our knowledge of AF mechanism and further facilitate the process of locating novel therapeutics for AF. Here we provide a state-of-the-art review of the molecular genetics of AF incorporating evidence from linkage analysis and candidate gene, as well as genome-wide association studies of common variations and rare copy number variations; potential epigenetic modifications (e.g., DNA methylation, histone modification, and non-coding RNAs) are also involved. We also outline the challenges in mechanism investigation and potential future directions in this article.

Genetics of Atrial Fibrillation in 2020: GWAS, Genome Sequencing, Polygenic Risk, and Beyond

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.

Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights

Accompanying the aging of populations worldwide, and increased survival with chronic diseases, the incidence and prevalence of atrial fibrillation (AF) are rising, justifying the term global epidemic. This multifactorial arrhythmia is intertwined with common concomitant cardiovascular diseases, which share classical cardiovascular risk factors. Targeted prevention programs are largely missing. Prevention needs to start at an early age with primordial interventions at the population level. The public health dimension of AF motivates research in modifiable AF risk factors and improved precision in AF prediction and management. In this review, we summarize current knowledge in an attempt to untangle these multifaceted associations from an epidemiological perspective. We discuss disease trends, preventive opportunities offered by underlying risk factors and concomitant disorders, current developments in diagnosis and risk prediction, and prognostic implications of AF and its complications. Finally, we review current technological (eg, eHealth) and methodological (artificial intelligence) advances and their relevance for future prevention and disease management.

The Joint Analysis of Multi-Omics Data Revealed the Methylation-Expression Regulations in Atrial Fibrillation

Atrial fibrillation (AF) is one of the most prevalent heart rhythm disorder. The causes of AF include age, male sex, diabetes, hypertension, valve disease, and systolic/diastolic dysfunction. But on molecular level, its mechanisms are largely unknown. In this study, we collected 10 patients with persistent atrial fibrillation, 10 patients with paroxymal atrial fibrillation and 10 healthy individuals and did Methylation EPICBead Chip and RNA sequencing. By analyzing the methylation and gene expression data using machine learning based feature selection method Boruta, we identified the key genes that were strongly associated with AF and found their interconnections. The results suggested that the methylation of KIF15 may regulate the expression of PSMC3, TINAG, and NUDT6. The identified AF associated methylation-expression regulations may help understand the molecular mechanisms of AF from a multi-omics perspective.

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

Whole Blood Gene Expression Associated With Clinical Biological Age

Background

Biologic age may better reflect an individual's rate of aging than chronologic age.

Methods

We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.

Results

Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).

Conclusion

We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.

Epigenomes in Cardiovascular Disease

Supplemental Digital Content is available in the text.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies.

Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10-7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Analysis of the microRNA signature in left atrium from patients with valvular heart disease reveals their implications in atrial fibrillation

BACKGROUND

Among the potential factors which may contribute to the development and perpetuation of atrial fibrillation, dysregulation of miRNAs has been suggested. Thus in this study, we have quantified the basal expressions of 662 mature human miRNAs in left atrium (LA) from patients undergoing cardiac surgery for valve repair, suffering or not from atrial fibrillation (AF) by using TaqMan® Low Density arrays (v2.0).

RESULTS

Among the 299 miRNAs expressed in all patients, 42 miRNAs had altered basal expressions in patients with AF. Binding-site predictions with Targetscan (conserved sites among species) indicated that the up- and down-regulated miRNAs controlled respectively 3,310 and 5,868 genes. To identify the most relevant cellular functions under the control of the altered miRNAs, we focused on the 100 most targeted genes of each list and identified 5 functional protein-protein networks among these genes. Up-regulated networks were involved in synchronisation of circadian rythmicity and in the control of the AKT/PKC signaling pathway (i.e., proliferation/adhesion). Down-regulated networks were the IGF-1 pathway and TGF-beta signaling pathway and a network involved in RNA-mediated gene silencing, suggesting for the first time that alteration of miRNAs in AF would also perturbate the whole miRNA machinery. Then we crossed the list of miRNA predicted genes, and the list of mRNAs altered in similar patients suffering from AF and we found that respectively 44.5% and 55% of the up- and down-regulated mRNA are predicted to be conserved targets of the altered miRNAs (at least one binding site in 3'-UTR). As they were involved in the same biological processes mentioned above, these data demonstrated that a great part of the transcriptional defects previously published in LA from AF patients are likely due to defects at the post-transcriptional level and involved the miRNAs.

CONCLUSIONS

Our stringent analysis permitted us to identify highly targeted protein-protein networks under the control of miRNAs in LA and, among them, to highlight those specifically affected in AF patients with altered miRNA signature. Further studies are now required to determine whether alterations of miRNA levels in AF pathology are causal or represent an adaptation to prevent cardiac electrical and structural remodeling.

An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection

Aim: Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Materials & methods: Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. Results & conclusion: TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

Cell-type deconvolution from DNA methylation: a review of recent applications

Recent advances in cell-type deconvolution approaches are adding to our understanding of the biology underlying disease development and progression. DNA methylation (DNAm) can be used as a biomarker of cell types, and through deconvolution approaches, to infer underlying cell type proportions. Cell-type deconvolution algorithms have two main categories: reference-based and reference-free. Reference-based algorithms are supervised methods that determine the underlying composition of cell types within a sample by leveraging differentially methylated regions (DMRs) specific to cell type, identified from DNAm measures of purified cell populations. Reference-free algorithms are unsupervised methods for use when cell-type specific DMRs are not available, allowing scientists to estimate putative cellular proportions or control for potential confounding from cell type. Reference-based deconvolution is typically applied to blood samples and has potentiated our understanding of the relation between immune profiles and disease by allowing estimation of immune cell proportions from archival DNA. Bioinformatic analyses using DNAm to infer immune cell proportions, part of a new field known as Immunomethylomics, provides a new direction for consideration in epigenome wide association studies (EWAS).

Transcriptome-wide association study of inflammatory biologic age

Chronic low grade inflammation is a fundamental mechanism of aging. We estimated biologic age using nine biomarkers from diverse inflammatory pathways and we hypothesized that genes associated with inflammatory biological age would provide insights into human aging. In Framingham Offspring Study participants at examination 8 (2005 to 2008), we used the Klemera-Doubal method to estimate inflammatory biologic age and we computed the difference (∆Age) between biologic age and chronologic age. Gene expression in whole blood was measured using the Affymetrix Human Exon 1.0 ST Array. We used linear mixed effect models to test associations between inflammatory ∆Age and gene expression (dependent variable) adjusting for age, sex, imputed cell counts, and technical covariates. Our study sample included 2386 participants (mean age 67A±9 years, 55% women). There were 448 genes significantly were associated with inflammatory ∆Age (P<2.8x10^-6), 302 genes were positively associated and 146 genes were negatively associated. Pathway analysis among the identified genes highlighted the NOD-like receptor signaling and ubiquitin mediated proteolysis pathways. In summary, we identified 448 genes that were significantly associated with inflammatory biologic age. Future functional characterization may identify molecular interventions to delay aging and prolong healthspan in older adults.

RIFS: a randomly restarted incremental feature selection algorithm

The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of “large p small n” into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.

Translational Perspective on Epigenetics in Cardiovascular Disease

A plethora of environmental and behavioral factors interact, resulting in changes in gene expression and providing a basis for the development and progression of cardiovascular diseases. Heterogeneity in gene expression responses among cells and individuals involves epigenetic mechanisms. Advancing technology allowing genome-scale interrogation of epigenetic marks provides a rapidly expanding view of the complexity and diversity of the epigenome. In this review, the authors discuss the expanding landscape of epigenetic modifications and highlight their importance for future understanding of disease. The epigenome provides a mechanistic link between environmental exposures and gene expression profiles ultimately leading to disease. The authors discuss the current evidence for transgenerational epigenetic inheritance and summarize the data linking epigenetics to cardiovascular disease. Furthermore, the potential targets provided by the epigenome for the development of future diagnostics, preventive strategies, and therapy for cardiovascular disease are reviewed. Finally, the authors provide some suggestions for future directions.

Update about atrial fibrillation genetics

PURPOSE OF REVIEW

Atrial fibrillation is an important cause of morbidity in the aging population. The mechanisms responsible for the triggering and maintenance of the chaotic atrial rhythm are still poorly understood. In this review, we will focus on the genetic aspects of atrial fibrillation, to understand causality, with special emphasis on recent studies published in the field.

RECENT FINDINGS

Diseases such as hypertension, valvular heart disease, and heart failure may induce atrial fibrillation, which increases the risk of stroke and sudden cardiac death. Clinical studies published in these last two decades have provided evidence that genetics play a key role in atrial fibrillation. Thus, a family history of the disease has been identified in up to 30% of clinically diagnosed patients. In those genotyped families, most carry rare genetic variants in genes associated with ionic channels, calcium handling protein, or predisposing to fibrosis, conduction system disease, and inflammatory processes.

SUMMARY

Currently, atrial fibrillation is the most common sustained arrhythmia in clinical practice. The pathophysiological mechanisms of atrial fibrillation are complex. A better understanding of the molecular basis will help improve both current risk stratification and clinical management.