Article

Considerations for the Assessment of Substrates, Genetics and Risk Factors in Patients with Atrial Fibrillation

Abstract

Successful translation of research focussing on atrial arrhythmogenic mechanisms has potential to provide a mechanism-tailored classification and to support personalised treatment approaches in patients with AF. The clinical uptake and clinical implementation of new diagnostic techniques and treatment strategies require translational research approaches on various levels. Diagnostic translation involves the development of clinical diagnostic tools. Additionally, multidisciplinary teams are required for collaborative translation to describe genetic mechanisms, molecular pathways, electrophysiological characteristics and concomitant risk factors. In this article, current approaches for AF substrate characterisation, analysis of genes potentially involved in AF and strategies for AF risk factor assessment are summarised. The authors discuss challenges and obstacles to clinical translation and implementation into clinical practice.

Disclosure: The authors have no conflicts of interest to declare.

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Citation:Arrhythmia & Electrophysiology Review 2021; epub ahead of press.

Correspondence: Ulrich Schotten, Department of Physiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine and Life Sciences, Maastricht University; PO Box 616 6200 MD Maastricht, the Netherlands. E: schotten@maastrichtuniversity.nl

Open access:

This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.

In AF patients, the initiation and progression of AF depend on the underlying arrhythmogenic substrates, genetics and concomitant risk factors, which may impact the clinical presentation of the arrhythmia and manifestation in different characteristic AF patterns. Current research projects focus on the development of several diagnostic techniques for the assessment of arrhythmogenic substrates in patients with AF (diagnostic translation). Additionally, multidisciplinary teams (collaborative translation) try to describe genetic mechanisms, molecular pathways, electrophysiological characteristics and concomitant risk factors with the goal to provide a more mechanism-tailored classification of AF with the potential to improve AF treatment in subgroups of patients.

However, despite emerging evidence of the prognostic value of several new techniques and the potential utility to guide AF management, most of these strategies have not yet been implemented in the clinic. In this article, current approaches for AF substrate characterisation, analysis of genes potentially involved in AF and strategies for AF risk factor assessment are summarised. We discuss challenges and obstacles to clinical translation and implementation into clinical practice.

AF Pathogenesis

Large-animal models of AF have been of great importance in understanding the pathogenesis of AF. Seminal early studies demonstrated that AF is a self-perpetuating arrhythmia because it causes proarrhythmic remodelling of the atria, both electrical and structural.1 Electrical remodelling is a relatively fast process, complete within 1–2 days of AF onset.2 It is characterised by a shortening of the atrial action potential and a corresponding reduction in the atrial refractory period, allowing higher fibrillation frequencies. As such, it is a pivotal factor in the early stabilisation of AF, when anti-arrhythmic drugs are still effective. Structural remodelling, which is thought to be responsible for the further progression of AF, develops over a much slower time course of months to years and is typified by myocyte hypertrophy, deposition of fibrous tissue and expansion of adipocytes.3,4

Atrial electrical and structural remodelling occur not only as a result of AF, but also due to factors increasing the risk for AF. Atrial remodelling has been studied in large animal models of, e.g. congestive heart failure (CHF), valvular disease, hypertension, sleep apnoea and obesity.5–9 In a goat model of ‘lone AF’, increased complexity of fibrillatory conduction and a loss of efficacy of anti-arrhythmic drugs was associated with the development of endomysial fibrosis, i.e. increased thickness of collagen septa between myocytes within bundles (also called reactive or interstitial fibrosis) and typically occurring in the absence of myocyte death.10,11 In contrast, a dog model of heart failure did show myocyte death and a corresponding pattern of replacement fibrosis.12 In a direct comparison with a dog model of lone AF, the CHF model showed relatively simple fibrillation patterns and a higher efficacy of pharmacological cardioversion.13 This strongly suggests that the exact pattern of fibrosis, rather than the overall amount of fibrous tissue, determines AF complexity and stability, in a manner reminiscent of the relationship between fibrosis pattern and conduction abnormalities observed in the ventricle.14,15 In aggregate, evidence from diverging studies illustrates the complex interplay between anatomy, fibre architecture, patterns of fibrosis and other types of structural remodelling and their effect on AF pattern. For example, larger areas of (replacement) fibrosis may serve as an ‘anchor point’ for spiral waves and other macroreentrant patterns, whereas endomysial fibrosis with loss of side-to-side connections may precipitate microreentry or ‘zig-zag’ conduction even in relatively small tissue areas.16–18

Obstacles to Translation of Results from Large-animal Models of AF

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Obstacles to Clinical Translation Related to the Description of AF Pathogenesis

In contrast to the controlled circumstances and limited duration of pathogenic stimuli in animal models, AF patients often have several risk factors (including ageing) that may have progressed over a prolonged time span, contributing to the ‘translational gap’ between insights from animal model and clinical practice (Figure 1).19 One important aspect of AF pathogenesis in patients that has not been replicated in animal models is the occurrence of spontaneous AF episodes originating in the pulmonary vein region. Whether this results from differences in anatomy, tissue architecture, or pathogenesis, as mentioned above, is currently unclear, but the lack of large animal models showing spontaneous AF paroxysms limits our understanding of the underlying mechanisms of this important manifestation of AF in patients.

The ‘translational gap’ between animal studies on AF pathogenesis and patient studies extends to diverging methods used for electrophysiological mapping of the AF substrate. Most mapping studies in animals were performed in open chest situations with epicardial arrays of unipolar electrodes. Although some mapping studies in patients implemented similar techniques during cardiac surgery, most were performed using either bipolar endocardial electrodes or non-contact mapping.20,21 Unipolar electrograms allow more precise determination of local activation times and provide information on the degree of fractionation without sensitivity to the direction of propagation.22,23 Moreover, epicardial access provides higher spatiotemporal resolution and, therefore, more accuracy in the reconstruction of the complex fibrillation patterns that are associated with AF progression.

In animal models, tissue samples for the detailed analysis of structural remodelling can be obtained under controlled conditions at different time points. In general, studies in human atrial samples remain essential to study the consequences of clinically relevant disease-related remodelling in patients. For example, recent work identified the proarrhythmic molecular and cellular mechanisms contributing to AF in patients with heart failure and sleep apnoea.24–26 However, human atrial samples are only available from patients undergoing cardiac surgery, who likely have a risk factor profile that is dominated by ischaemic heart disease and/or valve disease and may not be representative for all AF patients. Furthermore, atrial samples are often restricted to right or left atrial appendages, which may undergo remodelling distinct from regions that are considered more relevant for AF maintenance (e.g. the pulmonary veins and the left-atrial free wall). Nonetheless, right-atrial sources for AF are increasingly reported.27,28 Most importantly, human atrial samples can only provide a single snapshot of the cumulative effect of all AF risk factors, ignoring their complex temporal dynamics.29

As an alternative to histological analysis, several surrogate parameters have been developed for imaging of atrial tissue characteristics. These methods have an intrinsically lower resolution and are limited by the thinness of the atrial wall. Thus, imaging of fatty deposits does not allow the distinction of epicardial fat from intramyocardial fatty infiltrates.30 As a measure for fibrosis, imaging of regions with delayed enhancement of gadolinium has been used increasingly in recent years.31,32 Although this method has been validated histologically for large infarct scars in the thick ventricular wall, direct histological validation of delayed enhancement imaging in the atrium has been very limited.33 Therefore, the exact type and distribution of fibrous tissue (i.e. reactive versus replacement fibrosis) detected using this technique remains uncertain. Nevertheless, areas with delayed enhancement have been correlated to regions with low-voltage electrograms, and are predictive of clinical outcome.34,35 Most notably, patient-specific mathematical models incorporating information from delayed enhancement imaging have been successfully used to guide ablation strategies.35

Another strategy might be collection of blood biomarkers, representing different pathological pathways to evaluate the progression of atrial cardiomyopathy.36 Although not specific to atrial myocardial disease, various biomarkers characterising myocyte injury, inflammation and fibrosis have been linked to the occurrence and outcomes in AF. From an electrophysiological and pathophysiological point of view, biomarkers that characterise the atrial substrate may be more indicative of AF burden rather than the seemingly random time point at which AF is first documented.37

AF Genetics

Over the previous few decades, tremendous strides were made in elucidating the genetic underpinnings of AF.38 Family-based methods, primarily linkage and fine-mapping successfully detected numerous loci leading to Mendelian forms of AF. At least eighteen forms of monogenetic AF are described, including those resulting from mutations in genes that code for potassium voltage gated channels (KCNQ1, KCNE2, KCNA5 and KCNJ2) and sodium voltage-gated channel subunits (SCN1B, SCN2B, SCN3B, SCN4B and SCN5A), natriuretic peptides (NPPA), gap junctions (GJA5), nucleoporins (NUP155), ATP binding cassette transporters (ABCC9) and myosin light chains (MYL4).39–41 Causal genes have not been located for a number of other implicated regions.42 These findings generally involve rare, highly penetrant mutations with large effect sizes, but small population level risk.

The mutations identified in family-based studies demonstrate the fundamental importance of two ion handling groups of proteins that can influence AF aetiology through a variety of related molecular mechanisms. The potassium handling family of genes, for example, is involved in multiple facets of potassium induced current flows, particularly delayed rectifier outward currents (KCNQ1, KCNE2, KCNA5) and inward rectifier currents (KCNJ2, ABCC9).43,44 Mutations in β subunits of the voltage-gated sodium channels (SCN1B, SCN2B, SCN3B, SCN4B) have often been associated with decreases in sodium current density and/or late sodium currents, while those in α subunits, including SCN5A, lead to decreased channel expression and inactivation abnormalities and are associated with a considerable degree of phenotypic variability.45,46 Variants in the genes coding for both protein families alter cardiac action potentials.

Genome-wide association studies (GWAS) of AF, by contrast, delineated numerous common polymorphisms with generally small effect sizes, but affecting larger segments of the population in individuals of European ancestry and others.47–55 These GWAS have been supplemented by whole exome sequencing and exome-chip analyses that have identified additional pertinent loci.56,57

Obstacles to Clinical Translation Related to AF Genetics

Despite the success of these studies in locating a large number of loci associated with AF and AF endophenotypes, substantial progress in translating this information into clinically relevant knowledge has largely not been realised to date. In familial settings, characterising mutations in currently unaffected individuals may lead to the prospective identification of cases. These data have also proven useful in terms of helping to characterise mechanisms that contribute to the development of AF. Nevertheless, given the rarity of these mutations, family-based data typically finds limited applicability in the general population.

GWAS, in contrast, has identified many common genetic AF susceptibility variants throughout the genome. Polygenic risk scores derived from these can be used to successfully predict a person’s risk of developing AF.58 The authors of that study suggest that, “the identification of individuals at high risk should facilitate the design of efficient natural-history studies to discover early markers of disease onset and clinical trials to test prevention strategies.”

However, direct translation of GWAS data, outside of this domain has been complicated by a number of factors. These include the difficulty in determining the causal variant and the gene it affects, at most loci due to the effects of linkage disequilibrium, the fact that most GWAS variants are intergenic and the cost, in terms of both money and manpower, of following up on the large number of GWAS findings.

These problems are exacerbated by the typically small effect sizes of these variants, which complicates the task of prioritisation for follow-up. However, it is worth noting that variants with small effects may implicate genes which may be potent therapeutic targets. An excellent example is the 3-hydroxy-3-methylglutaryl-coA reductase (HMGCR) gene. Common GWAS variants in this gene are associated with LDL cholesterol levels, albeit with a comparatively modest effect size of ~0.06 mmol/l.59 The statin drugs, which act by inhibiting the protein product of the HMGCR gene, on the other hand, have large effects on serum LDL.60 This example – and others – suggests that validation and functional characterisation of GWAS findings may provide both mechanistic and clinically actionable insights into AF.

Moreover, the value of GWAS, as well as genome- and exome-wide DNA sequencing studies, can be further exploited in several ways. The first is to use more precisely defined clinical phenotypes to discern genetic variants that may be associated with etiologically distinct forms of AF. The second is to perform genome-wide studies on novel endophenotypes, such as tissue characteristics (i.e. fibrosis, capillary rarefaction and fatty infiltration), sequential voltage mapping, unexplored ECG parameters, etc. Although it may not be feasible to ascertain sample sizes on the same scale as previously studied traits, these studies may yield findings that help to elucidate the genetic complexities of AF.

Other forms of analysis may also shed light on AF. An important branch of emerging research examines the transcriptomics involved, either causally or in response to AF. Previous studies have suggested that transcriptional profiles are associated with AF; however, these studies have typically been hampered by the use of microarrays, in lieu of next-generation RNA sequencing, small sample sizes, limited analysis of selected transcripts and lack of replication.61 On-going studies, involving large numbers of atrial biopsies and state-of-the-art RNA sequencing methods, will allow unprecedented opportunities to evaluate the transcriptional landscape of AF, related endophenotypes (such as PR interval and P wave characteristics), markers of atrial remodelling and AF risk factors. As evidence continues to accumulate implicating the crucial role of non-coding RNA (including long non-coding RNA, microRNA, circular RNA, small nucleolar RNA and other species) on transcriptional regulation, RNA sequencing should yield considerable insight into not just genes involved in AF but the mechanisms of their control. These analyses will provide valuable information on the causes and consequences of AF, AF aetiology and AF biomarkers.

RNA sequencing data will also provide valuable clues to the role of AF-associated genetic variants.62 Moreover, the integration of genomic and transcriptomic data will allow for more precise classification of AF patients – a goal considered crucial in the field.63 The application of machine-learning methods, such as random forests, will also yield more precise phenotypes for further genomic and transcriptomic discovery (Figure 2).

Leveraging Data Analysis and Integration to Elucidate AF Mechanisms and Classify Patients

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A Complex and Dynamic Substrate for AF Impacting AF Patterns

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AF Risk Factors

Several cardiovascular risk factors contribute to AF initiation and progression (see the AF Pathogenesis section). In the current guidelines of the European Society of Cardiology for AF diagnosis and management, the importance of identification and management of established AF-promoting risk factors and unhealthy lifestyle is addressed and clearly recommended.63 In addition to non-modifiable factors such as advancing age and sex, hypertension, obesity, excessive alcohol consumption, sleep apnoea and endurance exercise, in particular, are separately mentioned.64 For all these modifiable AF risk factors, meta-analyses of observational studies or randomised intervention studies support the recommendations for clinical management of these conditions.65–69 Aggressive management of AF risk factors combined with lifestyle changes has been shown to control sinus rhythm in AF patients.65 Despite these described positive responses to risk factor management programmes in AF, other studies could not confirm these beneficial effects, suggesting, that there are potentially non-weight responsive forms of AF in some patients.70–73

Previous work showed that idiopathic AF patients develop cardiovascular disease more often, at a younger age and with a more severe disease profile compared to healthy sinus rhythm controls, suggesting the presence of subclinical comorbidities.74 Similarly, in a recent retrospective study of young AF patients (mean age 46 years), only 11% of patients were free of AF risk factors or comorbidities, whereas 44% had hypertension and 25% had a family history of AF.75

Despite the available evidence and the recommendations in current AF guidelines, the optimal approach to assess AF risk factors and specific lifestyle components remains unclear. In clinical practice, the presence of AF risk factors is generally considered a binary variable. However, AF risk factors (with the exception of the genetic component) may be dynamic, which results in a temporally variable exposure of the organism to arrhythmogenic conditions that may contribute to variability in the frequency, duration and timing of paroxysmal AF episodes.76 In addition to the structural remodelling process, which generally develops and progresses slowly, the exposure to variable AF risk factors may also contribute to temporal patterns of paroxysmal AF episodes by transient acute arrhythmogenic mechanisms.77 This may result in fluctuations in triggers and components of the substrate that can be rapidly modified such as post-translational modifications of proteins or autonomic nervous system activation, etc.19 The concept of a dynamic substrate for AF is summarised in Figure 3.

Each AF risk factor has different dynamic components with distinct time courses. Advancing age is a slowly progressive AF-risk factor. Long-term untreated hypertension also has a strong progressive component. Frequent alcohol intake is an important risk factor for new-onset AF. Furthermore, binge drinking, can also trigger paroxysmal AF episodes, known as the ‘holiday heart syndrome’.78

Intense activity, sleep apnoea, excessive alcohol intake and surgery have major transient AF-promoting components that are due to modulation of systemic regulators such as activation of the autonomic nervous system in the case of exercise, intrathoracic pressure changes in sleep apnoea or inflammation following cardiac surgery. 8,79,80 These transient effects are partially or fully reversible and are modulated by variability in risk-factor severity.

The VARIOSA-AF study is an example how transient AF-promoting factors can influence AF patterns: there is considerable night-to-night variability in sleep apnoea severity, which impacts AF risk associated with sleep apnoea severity in the preceding night.81,82 Additionally, over time these variable risk factors may also promote progressive structural remodelling processes. In rats, cumulative exposure to transient sleep apnoea-related conditions resulted in AF-substrates and was associated with increased AF-susceptibility.83 Of note, AF risk is not just determined by risk factors with clinical manifestation but also by variability in sub-clinical risk factors. In AF-patients without overt risk factors, the inter-visit variability of metabolic parameters showed a close association with AF risk.84 Additionally, the combination of risk factors may be important as well. For example, rats with spontaneous hypertension and obesity show more pronounced structural remodelling and AF susceptibility than lean hypertensive rats.85

mHealth Technology Infrastructure and Assessment of Lifestyle Components

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A binary assessment of AF risk factors, as currently practised clinically, may not adequately reflect the contribution of the respective risk factor on AF mechanisms and progression. Under circumstances of variable risk factors, the development of the AF substrate will depend critically on the cumulative exposure to the arrhythmogenic conditions (the risk factor burden represents the time exposed to a particular risk factor).

Despite convincing evidence of the need for risk factor management in AF patients, it remains unclear how best to assess substrates and risk factors and guide treatment, risk factor management and lifestyle modification.64 Established risk factors are often assessed only once in a structured way at the time point when patients present for the first time in the AF-clinic (spot-assessment of risk factors). However, several AF risk factors may show a high visit-to-visit or even day-to-day variability and lifestyle components, such as physical activity, diet and sleep behaviours, may be variable over time. Therefore, clinically relevant risk factors will be missed if the assessment is only performed once. Importantly, this variability does not just complicate the detection of AF risk factors but may also have a prognostic implication. High visit-to-visit variability in risk factors is associated with increased risk of incident new-onset AF, worse cardiovascular outcome and increased mortality.86 Additionally, lifestyle components are often self-reported or assessed once by questionnaires. Lifestyle, though, may vary (such as seasonal variation), again resulting in a dynamic exposure to lifestyle-related conditions which may critically impact the timepoint and extent of incident AF episodes (Figure 4).

Risk factor assessment, therefore, requires a longitudinal and remote structured monitoring infrastructure. Additionally, longitudinal documentation of risk factors during a modification programme may allow monitoring of the response to the intervention and adaptation and guidance, as required, to optimise the results. Recent technological advances have considerably expanded the options for non-invasive, longitudinal assessment of AF and potential underlying risk factors. Implantable loop recorders and a variety of wearables enable longitudinal or (near) continuous AF monitoring, making it possible to directly link specific clinical conditions to the occurrence of AF. Furthermore, longitudinal risk factor monitoring is becoming increasingly feasible. For example, some pacemakers can perform continuous monitoring of sleep disordered breathing, and implantable pulmonary arterial pressure monitors provide haemodynamic information that is used in remote monitoring of HF patients.74,87 Finally, some blood-based biomarkers have been associated with AF.37 Repeated biomarker collection could potentially provide information about progressive atrial remodelling, although at present there are no biomarkers that specifically reflect individual AF mechanisms.

mHealth apps and smart technologies, such as activity trackers, Bluetooth-linked balances, blood pressure devices and apps to assess diet, may allow a widespread and affordable infrastructure for longitudinal risk factor identification and monitoring (Figure 4).88 Several mHealth applications are available for the management of AF.89 In addition to the assessment of risk factors, mHealth infrastructures and apps can also be helpful in applying dedicated in-app coaching to improve lifestyle and control risk factors by behavioural changes.90–93

Translation from Experimental and Clinical Research to Mechanism-tailored AF Treatment

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Obstacles to Clinical Translation Related to AF Risk Factors

Implementation of infrastructure for longitudinal assessment of variables such as risk factors, lifestyle components, or rate and rhythm information require adaptation of existing care coordination and clinical pathways.94 An important element for embedding mHealth in clinical practice is the accessibility of the recordings by other healthcare professionals. For this, a connection with the patients’ electronic healthcare record is crucial. Additionally, many available devices and apps lack scientific validation and are written at excessively high reading-grade levels challenging users with limited health literacy.

Although technologies for longitudinal monitoring of lifestyle components and AF risk factors are available and may represent an interesting tool in future research projects, legal considerations and missing reimbursement models are still blocking wide implementation in existing clinical pathways. A multidisciplinary effort by regulatory agencies, healthcare organisations and app sellers is required to improve relevance, scientific validity and readability of AF apps for AF patients. Additionally, discussions with insurance companies about reimbursement of mHealth infrastructures and with different stakeholders to agree on security and privacy regulations are initiated in different countries.

Bridging the Translation Gap

Successful translation to develop a mechanism-tailored classification of AF with the potential to improve treatment in subgroups of patients will require translational research approaches on various levels (Figure 5). Diagnostic translation involves development of clinical diagnostic tools allowing the identification of mechanisms of AF. Examples are biomarkers for fibrosis or non-invasive electrophysiological markers of the degree of electrophysiological changes in the atria. Additionally, several large ongoing studies are attempting to establish associations between the clinical profile of AF patients derived from deep phenotyping with leading AF mechanisms identified on the tissue level. This approach requires multidisciplinary teams studying genetic mechanisms, molecular pathways and electrophysiological characteristics on the tissue level in clinically well characterised patients (collaborative translation). Such approaches recently became feasible with advances in non-invasive or minimally invasive characterisation of AF patients (non-invasive electrophysiology, longitudinal risk factor assessment, AF-burden and patterns using mHealth technologies, wearables or implantable loop recorders) and the development of high throughput histological techniques and -omics approaches, such as RNA sequencing.

The development of a new mechanism-tailored classification AF will be essentially an iterative process, where particular investigational steps informed by initial outcomes are repeated to refine the classification. The application of machine learning methods will result in more precise phenotypes for further clinical investigations. This will, in turn, lead to a more exact AF taxonomy. Both diagnostic and collaborative translation aim at the development of a mechanism-based subclassification of AF potentially allowing for better treatment of subclasses of AF patients. Supplementing these efforts with other current technological innovations, such as epigenetic sequencing, proteomics, metabolomics and microbiomics, will further the goal of developing mechanistic insights into AF pathology. The implementation of these methods, especially in conjunction with genomic and transcriptomic data, will further aid efforts to define molecular sub-types of AF aetiology and pathology.

Clinical Perspective

  • The best way how to assess substrates, genetics and risk factors in patients with AF remains unclear.
  • A successful translation of research focussing on atrial arrhythmogenic mechanisms has the potential to provide a mechanism-tailored classification and to support personalised treatment approaches in patients with AF.
  • The clinical uptake and clinical implementation of new diagnostic techniques and treatment strategies require translational research approaches.

References

  1. Schotten U, Verheule S, Kirchhof P, Goette A. Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. Physiol Rev 2011;91:265–325.
    Crossref | PubMed
  2. Wijffels MCEF, Kirchhof CJHJ, Dorland R, Allessie MA. Atrial fibrillation begets atrial fibrillation: a study in awake chronically instrumented goats. Circulation 1995;92:1954–68.
    Crossref | PubMed
  3. Ausma J, Litjens N, Lenders MH, et al. Time course of atrial fibrillation-induced cellular structural remodeling in atria of the goat. J Mol Cell Cardiol 2001;33:2083–94.
    Crossref | PubMed
  4. Venteclef N, Guglielmi V, Balse E, et al. Human epicardial adipose tissue induces fibrosis of the atrial myocardium through the secretion of adipo-fibrokines. Eur Heart J 2015;36:795–805.
    Crossref | PubMed
  5. Li D, Fareh S, Leung T, Nattel S. Promotion of atrial fibrillation by heart failure in dogs: atrial remodeling of a different sort. Circulation 1999;100:87–95.
    Crossref | PubMed
  6. Verheule S, Wilson EE, Everett TH, et al. Alterations in atrial electrophysiology and tissue structure in a canine model of chronic atrial dilatation due to mitral regurgitation. Circulation 2003;107:2615–22.
    Crossref | PubMed
  7. Kistler PM, Sanders P, Dodic M, et al. Atrial electrical and structural abnormalities in an ovine model of chronic blood pressure elevation after prenatal corticosteroid exposure: implications for development of atrial fibrillation. Eur Heart J 2006;27:3045–56.
    Crossref | PubMed
  8. Linz D, Schotten U, Neuberger HR, et al. Negative tracheal pressure during obstructive respiratory events promotes atrial fibrillation by vagal activation. Heart Rhythm 2011;8:1436–43.
    Crossref | PubMed
  9. Abed HS, Samuel CS, Lau DH, et al. Obesity results in progressive atrial structural and electrical remodeling: implications for atrial fibrillation. Heart Rhythm 2013;10:90–100.
    Crossref | PubMed
  10. Verheule S, Tuyls E, Gharaviri A, et al. Loss of continuity in the thin epicardial layer due to endomysial fibrosis increases the complexity of atrial fibrillatory conduction. Circ Arrhythm Electrophysiol 2013;6:202–11.
    Crossref | PubMed
  11. Weber KT, Pick R, Jalil JE, et al. Patterns of myocardial fibrosis. J Mol Cell Cardiol 1989(21 Suppl 5):121–31.
    Crossref | PubMed
  12. Cardin S, Li D, Thorin-Trescases N, et al. Evolution of the atrial fibrillation substrate in experimental congestive heart failure: angiotensin-dependent and -independent pathways. Cardiovasc Res 2003;60:315–25.
    Crossref | PubMed
  13. Li D, Benardeau A, Nattel S. Contrasting efficacy of dofetilide in differing experimental models of atrial fibrillation. Circulation 2000;102:104–12.
    Crossref | PubMed
  14. Burstein B, Nattel S. Atrial fibrosis: mechanisms and clinical relevance in atrial fibrillation. J Am Coll Cardiol 2008;51:802-809.
    Crossref | PubMed
  15. Kawara T, Derksen R, de Groot JR, et al. Activation delay after premature stimulation in chronically diseased human myocardium relates to the architecture of interstitial fibrosis. Circulation 2001;104:3069–75.
    Crossref | PubMed
  16. Vandersickel N, Watanabe M, Tao Q, et al. Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern. PLoS Comput Biol 2018;14:e1006637.
    Crossref | PubMed
  17. Spach MS, Boineau JP. Microfibrosis produces electrical load variations due to loss of side-to-side cell connections: a major mechanism of structural heart disease arrhythmias. Pacing Clin Electrophysiol 1997;20:397–413.
    Crossref | PubMed
  18. Koura T, Hara M, Takeuchi S, et al. Anisotropic conduction properties in canine atria analyzed by high-resolution optical mapping: preferential direction of conduction block changes from longitudinal to transverse with increasing age. Circulation 2002;105:2092–8. 
    Crossref | PubMed
  19. Heijman J, Linz D, Schotten U. Dynamics of Atrial Fibrillation Mechanisms and Comorbidities. Annu Rev Physiol 2021;83:83–106.
    Crossref | PubMed
  20. de Groot NM, Houben RP, Smeets JL, et al. Electropathological substrate of longstanding persistent atrial fibrillation in patients with structural heart disease: epicardial breakthrough. Circulation 2010;122:1674–82.
    Crossref | PubMed
  21. Allessie MA, de Groot NM, Houben RP, et al. Electropathological substrate of long-standing persistent atrial fibrillation in patients with structural heart disease: longitudinal dissociation. Circ Arrhythm Electrophysiol 2010;3:606–15.
    Crossref | PubMed
  22. Gardner P, Ursell P, Fenoglio J, et al. Electrophysiologic and anatomic basis for fractionated electrograms recorded from healed myocardial infarcts. Circulation 1985;72:596–611.
    Crossref | PubMed
  23. de Bakker JM. Electrogram recording and analyzing techniques to optimize selection of target sites for ablation of cardiac arrhythmias. Pacing Clin Electrophysiol 2019;42:1503–16.
    Crossref | PubMed
  24. Molina CE, Abu-Taha IH, Wang Q, et al. Profibrotic, electrical, and calcium-handling remodeling of the atria in heart failure patients with and without atrial fibrillation. Front Physiol 2018;9:1383.
    Crossref | PubMed
  25. Aistrup GL, Arora R, Grubb S, et al. Triggered intracellular calcium waves in dog and human left atrial myocytes from normal and failing hearts. Cardiovasc Res 2017;113:1688–99.
    Crossref | PubMed
  26. Lebek S, Pichler K, Reuthner K, et al. Enhanced CaMKII-dependent late INa induces atrial proarrhythmic activity in patients with sleep-disordered breathing. Circ Res 2020;126:603–15.
    Crossref | PubMed
  27. Hocini M, Nault I, Wright M, et al. Disparate evolution of right and left atrial rate during ablation of long-lasting persistent atrial fibrillation. J Am Coll Cardiol 2010;55:1007–16.
    Crossref | PubMed
  28. Spitzer SG, Karolyi L, Rammler C, et al. treatment of recurrent nonparoxysmal atrial fibrillation using focal impulse and rotor mapping (FIRM)-guided rotor ablation: early recurrence and long-term outcomes. J Cardiovasc Electrophysiol 2017;28:31–8.
    Crossref | PubMed
  29. Wong CX, Ganesan AN, Selvanayagam JB. Epicardial fat and atrial fibrillation: current evidence, potential mechanisms, clinical implications, and future directions. Eur Heart J 2017;38:1294–302.
    Crossref | PubMed
  30. McGann C, Akoum N, Patel A, et al. Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI. Circ Arrhythm Electrophysiol 2014;7:23–30.
    Crossref | PubMed
  31. Sohns C, Marrouche NF. Atrial fibrillation and cardiac fibrosis. Eur Heart J 2020;41:1123–31.
    Crossref | PubMed
  32. Zghaib T, Keramati A, Chrispin J, et al. Multimodal examination of atrial fibrillation substrate: correlation of left atrial bipolar voltage using multi-electrode fast automated mapping, point-by-point mapping, and magnetic resonance image intensity ratio. JACC Clin Electrophysiol 2018;4:59–68.
    Crossref | PubMed
  33. Chen J, Arentz T, Cochet H, et al. Extent and spatial distribution of left atrial arrhythmogenic sites, late gadolinium enhancement at magnetic resonance imaging, and low-voltage areas in patients with persistent atrial fibrillation: comparison of imaging vs. electrical parameters of fibrosis and arrhythmogenesis. Europace 2019;21:1484–93.
    Crossref | PubMed
  34. Cochet H, Dubois R, Yamashita S, et al. Relationship between fibrosis detected on late gadolinium-enhanced cardiac magnetic resonance and re-entrant activity assessed with electrocardiographic imaging in human persistent atrial fibrillation. JACC Clin Electrophysiol 2018;4:17–29.
    Crossref | PubMed
  35. Boyle PM, Zghaib T, Zahid S, et al. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng 2019;3:870–9.
    Crossref | PubMed
  36. Goette A, Kalman JM, Aguinaga L, et al. EHRA/HRS/APHRS/SOLAECE expert consensus on atrial cardiomyopathies: definition, characterization, and clinical implication. Europace 2016;18:1455–90.
    Crossref | PubMed
  37. Linz D, Elliott AD, Marwick TH, et al. Biomarkers and new-onset atrial fibrillation to assess atrial cardiomyopathy. Int J Cardiol 2017;248:208–10.
    Crossref | PubMed
  38. Mahida S. Genetic discoveries in atrial fibrillation and implications for clinical practice. Arrhythm Electrophysiol Rev 2014;3:69–75.
    Crossref | PubMed
  39. Chen YH, Xu SJ, Bendahhou S, et al. KCNQ1 gain-of-function mutation in familial atrial fibrillation. Science 2003;299:251–4.
    Crossref | PubMed
  40. Yang Y, Xia M, Jin Q, et al. Identification of a KCNE2 gain-of-function mutation in patients with familial atrial fibrillation. Am J Hum Genet 2004;75:899–905.
    Crossref | PubMed
  41. Olson TM, Alekseev AE, Liu XK, et al. Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Hum Mol Genet 2006;15:2185–91.
    Crossref | PubMed
  42. Brugada R, Tapscott T, Czernuszewicz GZ, et al. Identification of a genetic locus for familial atrial fibrillation. N Engl J Med 1997;336:905–11.
    Crossref | PubMed
  43. Jeevaratnam K, Chadda KR, Huang CL, Camm AJ. Cardiac potassium channels: physiological insights for targeted therapy. J Cardiovasc Pharmacol Ther 2018;23:119–29.
    Crossref | PubMed
  44. Grant AO. Cardiac ion channels. Circ Arrhythm Electrophysiol 2009;2:185–94. Circ Arrhythm Electrophysiol 2009;2:185–94.
    Crossref | PubMed
  45. Dehghani-Samani A, Madreseh-Ghahfarokhi S, Dehghani-Samani A. Mutations of voltage-gated ionic channels and risk of severe cardiac arrhythmias. Acta Cardiol Sin 2019;35:99–110.
    Crossref | PubMed
  46. Ter Bekke RMA, Isaacs A, Barysenka A, et al. Heritability in a SCN5A-mutation founder population with increased female susceptibility to non-nocturnal ventricular tachyarrhythmia and sudden cardiac death. Heart Rhythm 2017;14:1873–81. 
    Crossref | PubMed
  47. Ellinor PT, Lunetta KL, Albert CM, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet 2012;44:670–5.
    Crossref | PubMed
  48. Ellinor PT, Lunetta KL, Glazer NL, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet 2010;42:240–4.
    Crossref | PubMed
  49. Gudbjartsson DF, Arnar DO, Helgadottir A, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature 2007;448:353–7.
    Crossref | PubMed
  50. Roselli C, Chaffin MD, Weng LC, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet 2018;50:1225–33.
    Crossref | PubMed
  51. Lee JY, Kim TH, Yang PS, et al. Korean atrial fibrillation network genome-wide association study for early-onset atrial fibrillation identifies novel susceptibility loci. Eur Heart J 2017;38:2586–94.
    Crossref | PubMed
  52. Benjamin EJ, Rice KM, Arking DE, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet 2009;41:879–81.
    Crossref | PubMed
  53. Low SK, Takahashi A, Ebana Y, et al. Identification of six new genetic loci associated with atrial fibrillation in the Japanese population. Nat Genet 2017;49:953–58.
    Crossref | PubMed
  54. Christophersen IE, Rienstra M, Roselli C, et al. Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nat Genet 2017;49:946–52.
    Crossref | PubMed
  55. Nielsen JB, Thorolfsdottir RB, Fritsche LG, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet 2018;50:1234–9.
    Crossref | PubMed
  56. Weng LC, Hall AW, Choi SH, et al. Genetic determinants of electrocardiographic p-wave duration and relation to atrial fibrillation. Circ Genom Precis Med 2020;13:387–95.
    Crossref | PubMed
  57. Lin H, van Setten J, Smith AV, et al. Common and rare coding genetic variation underlying the electrocardiographic PR interval. Circ Genom Precis Med 2018;11:e002037.
    Crossref | PubMed
  58. Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 2018;50:1219–24.
    Crossref | PubMed
  59. Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010;466:707–13.
    Crossref | PubMed
  60. Stancu C, Sima A. Statins: mechanism of action and effects. J Cell Mol Med 2001;5:378–87.
    Crossref | PubMed
  61. Deshmukh A, Barnard J, Sun H, et al. Left atrial transcriptional changes associated with atrial fibrillation susceptibility and persistence. Circ Arrhythm Electrophysiol 2015;8:32–41.
    Crossref | PubMed
  62. Hsu J, Gore-Panter S, Tchou G, et al. Genetic control of left atrial gene expression yields insights into the genetic susceptibility for atrial fibrillation. Circ Genom Precis Med 2018;11:e002107.
    Crossref | PubMed
  63. Fabritz L, Guasch E, Antoniades C, et al. Expert consensus document. Defining the major health modifiers causing atrial fibrillation: a roadmap to underpin personalized prevention and treatment. Nat Rev Cardiol 2016;13:230–7.
    Crossref | PubMed
  64. Hindricks G, Potpara T, Dagres N, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS). Eur Heart J 2021;42:373–498.
    Crossref | PubMed
  65. Abed HS, Wittert GA, Leong DP, et al. Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. JAMA 2013;310:2050–60.
    Crossref | PubMed
  66. Pathak RK, Middeldorp ME, Meredith M, et al. Longterm effect of goal-directed weight management in an atrial fibrillation cohort: a long-term follow-up study (LEGACY). J Am Coll Cardiol 2015;65:2159–69.
    Crossref | PubMed
  67. Voskoboinik A, Kalman JM, De Silva A, et al. Alcohol abstinence in drinkers with atrial fibrillation. N Engl J Med 2020;382:20–8.
    Crossref | PubMed
  68. Elliott AD, Linz D, Mishima R, et al. Association between physical activity and risk of incident arrhythmias in 402 406 individuals: evidence from the UK Biobank cohort. Eur Heart J 2020;41:1479–86.
    Crossref | PubMed
  69. Linz D, McEvoy RD, Cowie MR, et al. Associations of obstructive sleep apnea with atrial fibrillation and continuous positive airway pressure treatment: a review. JAMA Cardiol 2018;3:532–40.
    Crossref | PubMed
  70. Mohanty S, Mohanty P, Natale V, et al. Impact of weight loss on ablation outcome in obese patients with longstanding persistent atrial fibrillation. J Cardiovasc Electrophysiol 2018;29:246–53.
    Crossref | PubMed
  71. Yaeger A, Cash NR, Parham T, et al. Nurse-led limited risk factor modification program to address obesity and obstructive sleep apnea in atrial fibrillation patients. J Am Heart Assoc 2018;7:e010414.
    Crossref | PubMed
  72. Yaeger A, Keenan BT, Cash NR, et al. Impact of a nurse-led limited risk factor modification program on arrhythmia outcomes in patients with atrial fibrillation undergoing catheter ablation. J Cardiovasc Electrophysiol 2020;31:423–31.
    Crossref | PubMed
  73. Yew Ding W, Kozhuharov N, Hao Chin S, et al. Feasibility of weight loss in obese atrial fibrillation patients attending a specialist arrhythmia clinic and its impact on ablation outcomes. J Arrhythm 2020;36:984–90.
    Crossref | PubMed
  74. Weijs B, de Vos CB, Tieleman RG, et al. The occurrence of cardiovascular disease during 5-year follow-up in patients with idiopathic atrial fibrillation. Europace 2013;15:18–23.
    Crossref | PubMed
  75. De With RR, Marcos EG, Van Gelder IC, et al. Atrial fibrillation progression and outcome in patients with young-onset atrial fibrillation. Europace 2018;20:1750–7.
    Crossref | PubMed
  76. Wineinger NE, Barrett PM, Zhang Y, et al. Identification of paroxysmal atrial fibrillation subtypes in over 13,000 individuals. Heart Rhythm 2019;16:26–30.
    Crossref | PubMed
  77. De With RR, Erküner Ö, Rienstra M, et al. Temporal patterns and short-term progression of paroxysmal atrial fibrillation: data from RACE V. Europace 2020;22:1162–72.
    Crossref | PubMed
  78. Linz D, Crijns HJGM. Alcohol abstinence in drinkers with atrial fibrillation. N Engl J Med 2020;382:1768.
    Crossref | PubMed
  79. Guasch E, Benito B, Qi X, et al. Atrial fibrillation promotion by endurance exercise: demonstration and mechanistic exploration in an animal model. J Am Coll Cardiol 2013;62:68–77.
    Crossref | PubMed
  80. Dobrev D, Aguilar M, Heijman J, et al. Postoperative atrial fibrillation: mechanisms, manifestations and management. Nat Rev Cardiol 2019;16:417–36.
    Crossref | PubMed
  81. Linz D, Brooks AG, Elliott AD, et al. Nightly variation in sleep apnea severity as atrial fibrillation risk. J Am Coll Cardiol 2018;72:2406–7.
    Crossref | PubMed
  82. Linz D, Brooks AG, Elliott AD, et al. Variability of sleep apnea severity and risk of atrial fibrillation: the VARIOSA-AF study. JACC Clin Electrophysiol 2019;5:692–701.
    Crossref | PubMed
  83. Linz B, Hohl M, Lang L, et al. Repeated exposure to transient obstructive sleep apnea-related conditions causes an atrial fibrillation substrate in a chronic rat model. Heart Rhythm 2021;18:455–64.
    Crossref | PubMed
  84. Lee SR, Choi EK, Han KD, et al. Effect of the variability of blood pressure, glucose level, total cholesterol level, and body mass index on the risk of atrial fibrillation in a healthy population. Heart Rhythm 2020; 17:12–9.
    Crossref | PubMed
  85. Hohl M, Lau DH, Müller A, et al. Concomitant obesity and metabolic syndrome add to the atrial arrhythmogenic phenotype in male hypertensive rats. J Am Heart Assoc 2017;6:e006717.
    Crossref | PubMed
  86. Linz D, Baumert M, Desteghe L, et al. Nightly sleep apnea severity in patients with atrial fibrillation: potential applications of long-term sleep apnea monitoring. Int J Cardiol Heart Vasc 2019;24:100424.
    Crossref | PubMed
  87. Dickinson MG, Allen LA, Albert NA, et al. Remote monitoring of patients with heart failure: a white paper from the Heart Failure Society of America Scientific Statements Committee. J Card Fail 2018;24:682–94.
    Crossref | PubMed
  88. Hermans ANL, van der Velden RMJ, Gawalko M, et al. On-demand mobile health infrastructures to allow comprehensive remote atrial fibrillation and risk factor management through teleconsultation. Clin Cardiol 2020;43:1232–9.
    Crossref | PubMed
  89. Lane DA, McMahon N, Gibson J, et al. Mobile health applications for managing atrial fibrillation for healthcare professionals and patients: a systematic review. Europace 2020;22:1567–78.
    Crossref | PubMed
  90. Guo Y, Chen Y, Lane DA, et al. Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: mAF App trial. Am J Med 2017;130:1388-1396.e6.
    Crossref | PubMed
  91. Linz D, Pluymaekers NAHA, Hendriks JM. TeleCheck-AF for COVID-19. Eur Heart J 2020;41:1954–5.
    Crossref | PubMed
  92. Pluymaekers NAHA, Hermans ANL, van der Velden RMJ, et al. Implementation of an on-demand app-based heart rate and rhythm monitoring infrastructure for the management of atrial fibrillation through teleconsultation: TeleCheck-AF. Europace 2021;23:345–52.
    Crossref | PubMed
  93. Gawałko M, Duncker D, Manninger M, et al. The European TeleCheck-AF project on remote app-based management of atrial fibrillation during the COVID-19 pandemic: centre and patient experiences. Europace 2021;23:1003–15.
    Crossref | PubMed
  94. van der Velden RM, Hermans AN, Pluymaekers NA, et al. Coordination of a remote mHealth infrastructure for atrial fibrillation management during COVID-19 and beyond: TeleCheck-AF. Int J Care Coordination 2020 23:65–70.
    Crossref
  95. Verheule S, Tuyls E, van Hunnik A, et al. Fibrillatory conduction in the atrial free walls of goats in persistent and permanent atrial fibrillation. Circ Arrhythm Electrophysiol 2010;3:590–6.
    Crossref | PubMed