Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation

Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation View ORCID Profile Quim Bech Vilaseca , View ORCID Profile Oliver Bundgaard Vad , View ORCID Profile Christian Paludan-Müller , View ORCID Profile Laura Andreasen , View ORCID Profile Morten Salling Olesen , View ORCID Profile Jesper Hastrup Svendsen , View ORCID Profile Pia Rengtved Lundegaard doi: https://doi.org/10.1101/2025.03.12.25323872 Quim Bech Vilaseca 1 Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Quim Bech Vilaseca Oliver Bundgaard Vad 1 Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark 2 Department of Cardiology, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oliver Bundgaard Vad Christian Paludan-Müller 2 Department of Cardiology, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Copenhagen, Denmark 3 Department of Cardiothoracic Surgery, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Paludan-Müller Laura Andreasen 2 Department of Cardiology, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura Andreasen Morten Salling Olesen 1 Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Morten Salling Olesen Jesper Hastrup Svendsen 2 Department of Cardiology, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Copenhagen, Denmark 4 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesper Hastrup Svendsen Pia Rengtved Lundegaard 1 Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pia Rengtved Lundegaard For correspondence: plundegaard{at}sund.ku.dk Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Atrial fibrillation (AF) is a common cardiac arrhythmia associated with an increased risk of stroke, heart failure, and death. Recent studies suggests that individuals with early onset of AF could be at increased risk of developing heart failure and dilated cardiomyopathy. This study aimed to identifying genetic variants in a broad panel of cardiomyopathy genes among early-onset AF individuals. Methods We conducted targeted genetic sequencing of 29 cardiomyopathy-associated genes in 478 individuals with AF onset below 45 years of age from a Danish cohort. Additionally, we analyzed whole exome sequencing data in 374,289 individuals from the UK Biobank, including 29,108 individuals with AF. The cohort was stratified by age at AF diagnosis, and individuals with pre-existing cardiomyopathy were excluded. We focused on rare, truncating variants predicted to lead to loss of function, and potentially deleterious missense variants in the UK Biobank. Results In the Danish cohort, 42 (8.8%) individuals with early-onset AF had truncating genetic variants in known cardiomyopathy genes. The UK Biobank analysis showed an inverse dose-response-like relationship between age of AF onset and prevalence of truncating variants, ranging from 3.8% in the AF onset <45 years group to 1.4% in the group without AF diagnosis. The prevalence of rare missense variants showed a similar pattern. Conclusions We identified a high prevalence of deleterious variants in cardiomyopathy-associated genes among individuals with early-onset AF. This supports recent guideline suggestions and indicates that genetic testing and surveillance for cardiomyopathy could be relevant in selected individuals with an early AF diagnosis. Introduction With a lifetime risk of up to 30%( 1 ), atrial fibrillation (AF) is the most common cardiac arrhythmia, and represents a serious burden for patients and healthcare systems( 2 ). Among individuals with AF, one in five suffers a stroke, and two in five develop heart failure (HF)( 1 ), leading to increased risk of morbidity and mortality. While AF has been extensively studied, its association with subsequent cardiovascular diseases and mortality across age groups is not entirely understood. A recent study found that the risk of cardiomyopathy and HF was highest among those with AF diagnosis early in life( 3 ). Moreover, an earlier onset of AF was associated with a considerable decrease in life expectancy compared with a later onset of the disease( 3 ). It has been suggested to be due to a shared genetic architecture of AF and cardiomyopathy( 3 ). Atrial cardiomyopathy has been suggested as both an important component in AF pathogenesis, and as a potential precursor to more severe, ventricular cardiomyopathy phenotypes( 4 , 5 ). In a cohort of 1,293 individuals with AF before age 65, Yoneda et al. reported an increased prevalence of potentially pathogenic genetic variants in genes associated with arrhythmia syndromes, cardiac structure, and cardiomyopathy( 6 ). Individuals who were carriers of such variants also exhibited an increased mortality compared to non-carriers( 7 ). In line with these findings, the newest AF guidelines from the American College of Cardiology and the American Heart Association now recommend that genetic testing could be considered in early-onset AF patients without obvious risk factors( 8 ). However, the prevalence of cardiomyopathy-associated genetic variants in early-onset AF in large population-based studies has not been thoroughly explored. This study included next-generation sequencing data from Danish AF patients diagnosed before the age of 45 without known cardiomyopathy and HF, and whole exome sequencing data from 374,289 individuals from the UK Biobank. Focusing on pathogenic variants in well-established cardiomyopathy genes, this study aimed to provide new insights into the burden of rare variants in cardiomyopathy-associated genes according to age of AF onset. Methods The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. All participants gave written informed consent. The study of the Danish cohort was approved by the scientific ethics committee of the Capital Region of Denmark (protocol number: H-20048862). The UK Biobank was accessed under application number 43247. The UK Biobank received ethical approval from the North West Multi-Centre Research Ethics Committee. A detailed methods section is available in the Supplemental Material . Results This study included 478 Danish individuals and 374,289 participants from the UK Biobank. Baseline characteristics of the cohorts are summarized in Table 1 . In the Danish cohort, all individuals had an AF diagnosis at time of inclusion, with a median (1 st -3 rd quartile) age at AF diagnosis of 28 (23-34) years. Among them, 85 participants (17.8%) were female. In contrast, the UK Biobank included 29,108 individuals diagnosed with AF and 345,181 participants without an AF diagnosis, who served as controls. The median (1 st -3 rd quartile) age at enrollment in the UK Biobank was 58 (51-63) years, and 201,335 participants (53.8%) were female. View this table: View inline View popup Download powerpoint Table 1. Baseline characteristics at inclusion in the Danish and UK Biobank cohorts. High prevalence of truncating variants in the Danish cohort Among 478 Danish individuals diagnosed with AF before age 45 and no known cardiovascular comorbidities, 42 individuals (8.8%) were carriers of 38 rare truncating variants in well-established cardiomyopathy-related genes ( Figure 1a ) . This cohort exhibited genetic variants in nine of the 29 selected genes. Download figure Open in new tab Figure 1. Percentage and odds ratio of participants carrying predicted Loss-of-Function (pLOF) variants in cardiomyopathy genes based on age at atrial fibrillation (AF) diagnosis. Figure 1a: Danish cohort data showing the percentage of carriers. Figure 1b: UK Biobank data stratified by age at AF onset. The highest prevalence of carriers is seen in individuals with AF onset before 45 years (red), while the lowest is seen in individuals with late-onset AF (green) or no diagnosis (purple). Figure 1c: Forest plot of UK Biobank data, displaying odds ratios (ORs), confidence intervals (CI) and p-values of carrying truncating variants, categorized by age at AF onset. Each group is compared to the reference group for statistical significance. Of the 38 variants identified, 26 were classified as variants of unknown significance (VUS), while the remaining variants were categorized as pathogenic (five variants), pathogenic/likely pathogenic (six variants), and likely pathogenic (one variant), according to ClinVar( 25 ). The TTN gene was the most frequently affected, with 29 (69.0%) of the 42 individuals harboring a truncating variant (see Supplementary Table S3 ). TTN truncating variants accounted for the majority (n = 25, 65.8%) of all pLOF identified in the cohort. LMNA followed, with truncating variants observed in three individuals (7.9%), as shown in Supplementary Table S3 . Furthermore, truncating variants were identified in two individuals for RBM20 , TPM1 , and DSC2 (5.3% per gene), respectively, whereas MYBPC3 , PKP2 , MYH7 , and SCN5A had variants in a single individual each (2.6% per gene). The distribution of disease-associated variants in this cohort revealed that 88.1% of all variants were associated with DCM genes (e.g., TTN , LMNA , SCN5A, RBM20, MYH7 and TPM1 ), 3.9% to HCM genes ( MYBPC3 and MYH7 ), and 7.9% to ARVC genes ( PKP2 and DSC2 ). Rare pLOF variants in the UK Biobank A total of 634 individuals (2.17%) from the AF-diagnosed population in the UK Biobank were identified as carriers of rare truncating variants in established cardiomyopathy genes. We observed an inverse dose-response relationship between the age of AF onset and the prevalence of rare pLOF variants, as shown in Figure 1b . The highest prevalence of carriers was found among individuals diagnosed with AF below the age 45, at 3.8%. In comparison, the prevalence was 2.6% for those with AF onset between 45 and 54 years, 2.2% for 55 to 64 years, 2.0% for those aged 65 and older, and 1.4% among individuals without an AF diagnosis. Details on the truncating variant carrier counts are presented in Supplementary Table S4 , where we report the variant count for AF patients with onset <45 years, the overall AF population, and the entire study population from UK Biobank. Additionally, percentages are reported based on the size of each group, providing a comparative distribution of truncating variants for the 29 selected genes across different cohorts. Individuals with early-onset AF had a significantly higher OR for carrying a pLOF variant ( Figure 1c ). Compared with referents without AF, there was a 2.81-fold increase in OR for carrying a pLOF variant in individuals with AF onset before 45 years of age (95% CI 1.89-4.16; P <0.0001), representing the highest risk. This was followed by a 1.91-fold increase in individuals with onset between 45 and 54 years (95% CI 1.48-2.45; P<0.0001), a 1.67-fold increase with onset between 55 and 64 years (95% CI 1.44-1.95; P<0.0001), and a 1.53-fold increase with onset at or above 65 years (95% CI 1.37-1.71; P<0.0001). Furthermore, we examined the distribution of variants across the 29 investigated cardiomyopathy genes in the UK Biobank ( Figure 2 ) , and found that genes associated with DCM represented 57% of all variants present in the AF group in the UK Biobank, followed by 22% of variants related to ARVC and 21% to HCM. The majority of the truncating variants were found in TTN , representing 32.8% of all rare truncating variants identified in the AF population, followed by variants in PKP2 (10.9%) and ALPK3 (9.1%). Download figure Open in new tab Figure 2. Distribution of rare predicted Loss-of-Function (pLOF) variants in individuals diagnosed with atrial fibrillation (AF) according to genes and their association with cardiomyopathy. Chord diagram illustrating the distribution of rare pLOF variants according to genes and corresponding cardiomyopathy subtypes. The blue segments represent variants in genes associated with dilated cardiomyopathy (DCM), the red segments correspond to variants in hypertrophic cardiomyopathy (HCM)-associated genes, and the yellow segments indicate variants in genes associated with arrhythmogenic right ventricular cardiomyopathy (ARVC). Missense Variants in the UK Biobank We identified 389 individuals with AF (1.33%) from the AF-diagnosed population as carriers of rare missense variants (MTR < 5%) in the studied genes. The highest prevalence, 2.19%, was seen in individuals diagnosed below the age of 45 ( Figure 3a ), showing an inverse dose-response-like pattern. The prevalence rates for other age groups were 1.34% for 45 to 54 years, 1.31% for 55 to 64 years, 1.31% for those aged 65 and above, and 1.28% among individuals without an AF diagnosis. Supplementary Table S5 provides the count of carriers among individuals with AF across age groups. Download figure Open in new tab Figure 3. Percentage and odds ratio of participants carrying missense variants in cardiomyopathy genes based on age at atrial fibrillation (AF) diagnosis. Figure 3a: UK Biobank data showing rare missense variant carrier percentage, specifically for MTR below the 5 th percentile, stratified by age at AF onset. The highest prevalence of carriers is observed among individuals with AF onset before 45 years (red), while the lowest is seen in individuals with onset of AF at or after 65 years of age (green) or no diagnosis (purple). Figure 3b: Forest plot displaying odds ratios (ORs), confidence intervals (CI) and p-values for missense MTR below the 5 th percentile carriers in the UK Biobank. Figure 3b illustrates the OR for carrying a missense variant among different age groups of AF onset. Carriers with AF onset before 45 years of age had a 1.71-fold increase in OR for carrying a variant (95% CI 1.02-2.85; P = 0.04), compared with referents without AF. No statistically significant associations were found for groups with later onset of AF. Of the identified missense variants, 71.8% were in genes associated with HCM, 27.2% were in genes associated with DCM, and 1% were in genes associated with ARVC ( Figure 4 ). The majority of these variants were identified in the TTR gene (54%), followed by MYH7 (8.9%) and FLNC (7.4%). Download figure Open in new tab Figure 4. Distribution of rare missense variants in individuals diagnosed with atrial fibrillation (AF) according to genes and their association with different cardiomyopathies. The blue segments represent variants in genes associated with dilated cardiomyopathy (DCM), the red segments correspond to variants in hypertrophic cardiomyopathy (HCM)-associated genes, and the yellow segments indicate variants in genes associated with arrhythmogenic right ventricular cardiomyopathy (ARVC). Aggregated prevalence of genetic variants in the UK Biobank Integrating both truncating and missense variants indicated a larger aggregated percentage of carriers while following the same pattern. A total of 982 individuals (3.37%) from the AF population carried genetic variants (see Table S6 in the Supplementary ). Figure 5a shows a consistent pattern, with the highest prevalence seen in early-onset AF individuals at 5.99%. In comparison, the combined prevalence was 3.86% in individuals with AF onset between 45 and 54 years, 3.52% in the 55 to 64 years group, 3.30% in those aged 65 and above, and 2.66% among individuals without an AF diagnosis. Download figure Open in new tab Figure 5. Prevalence and odd ratios of rare missense and predicted Loss-of-Function variants in cardiomyopathy genes according to age at atrial fibrillation diagnosis. Figure 5a: UK Biobank data on rare truncating and missense variant carrier percentages stratified by age of onset. Figure 5b: Forest plot displaying ORs, CI and p-values for the carriers of truncating and missense variants, comparing AF onset groups to the reference population in the UK Biobank. Figure 5b shows the aggregated OR of carrying either rare truncating or missense variants within the UK Biobank population across different age of AF onset groups. Discussion The present study combined data from a cohort of early-onset AF patients with large-scale clinical and genetic information from the UK Biobank, to elucidate the prevalence of genetic variants in known cardiomyopathy genes in patients with AF. We observed that early-onset AF was significantly associated with a greater prevalence of rare genetic variants in the 29 genes associated with either DCM, HCM or ARVC. Among the Danish individuals with early onset AF, more than one in twelve (8.8%) carried a rare truncating variant. The majority of these variants were in DCM-associated genes, with TTN variants accounting for 65.8% of all truncating variants. Nearly two-thirds of those identified were variants of unknown significance. TTN encodes titin, a key structural protein in the sarcomere, and truncating variants in this gene are well-established contributors to the development of DCM( 26 ) and AF. Following TTN , LMNA was the second gene with the most variants in this cohort, representing 7.9% of the variants found. Variants in LMNA , which encodes lamin A/C, are commonly associated with early-onset AF and are linked to severe complications such as DCM and sudden cardiac death( 27 – 29 ). Additionally, we identified a single nonsense variant in SCN5A . While this sodium channel is primarily susceptible to missense variants that are associated with atrioventricular block and DCM( 29 , 30 ), the nonsense variant we identified has been classified as pathogenic as it alters intracellular sodium and calcium homeostasis( 26 ). As previously reported( 4 ), we also observed two pLoF variants in RBM20 , a regulator of splicing for various cardiac genes, including TTN and TPM1 ( 4 ). TPM1 , encoding the actin-binding tropomyosin 1 protein, exhibited two variants in this Danish cohort. Additionally, one individual carried a truncating MYBPC3 variant, and another individual carried a nonsense variant in MYH7 . Notably, MYBPC3 and MYH7 are known to be involved in 70% of sarcomere gene variant-positive HCM( 31 ). Furthermore, we identified three variants in two desmosomal genes associated with ARVC, one in PKP2 and two in DSC2 . PKP2 encodes plakophilin 2, a desmosomal protein crucial in ARVC, where it disrupts intracellular calcium handling and predisposes patients to ventricular arrhythmias( 26 ). DSC2 , a known ARVC disease susceptibility gene, is essential for cardiac desmosome formation, early cardiac morphogenesis, and normal cardiac function( 32 ). Analyses in the UK Biobank offered insights into the prevalence and distribution of genetic variants across different age groups. Specifically, we found an OR of 2.8 for carrying truncating variants in the individuals with early-onset AF compared to the reference group. Previous studies have described a similar pattern( 6 ). In our study, we focused on constitutive exons with a TTN PSI >90( 33 ) and selected variants with a MAF of <1%( 34 ). This approach allows us to focus on rarer, potentially more pathogenic variants that are likely to have a greater impact on disease development. The majority of these truncating variants were found in key genes associated with DCM (e.g. TTN and RBM20 ), HCM (e.g. ALPK3, MYH6 and MYBPC3 ), and ARVC (e.g. PKP2, DSP and DSG2 ). In this cohort, variants in genes associated with DCM accounted for 60% of carriers, driven largely by the high prevalence of TTN truncating variants( 35 – 37 ). Other prominent contributors included well-established DCM-related genes such as NEXN ( 38 ), MYH7 ( 6 , 29 ), RBM20 ( 4 , 6 , 29 ) , TNNT2 ( 29 ) and SCN5A ( 26 , 29 , 30 ). In contrast to our findings from the Danish cohort, LMNA variants were relatively rare in the UK Biobank cohort. One possible explanation is that LMNA variants typically lead to early-onset DCM( 27 ), often manifesting before the age range of individuals included in the UK Biobank, which may have resulted in the underrepresentation of these variants. Beyond DCM, 16% of the truncating variant carriers harbored HCM-associated variants in genes including ALPK3 ( 39 ) , MYH7 ( 40 ), MYH6 ( 18 ) and MYBPC3 ( 41 ). The majority of variants in HCM genes were in MYH7, a sarcomeric gene encoding the β-myosin heavy chain, known to be implicated in both DCM and HCM depending on the penetrance( 40 , 41 ), accounted for a significant proportion of carriers. In total, 24% of UK Biobank participants diagnosed with AF and carriers of genetic variants had truncating variants in genes previously linked to ARVC, with PKP2 being the most frequent. Additional variants were found in other ARVC-associated genes such as DSP , DSG2 , and TMEM43 ( 26 ), all of which encode desmosomal proteins. Defects in the structure of desmosomes result in cardiac myocyte detachment and cell death, leading to fibrofatty tissue infiltration and electrical instability( 45 ). To elucidate a more detailed genetic landscape of AF across age groups, we also examined rare missense variants, applying an MTR score to focus on highly conserved regions. While the overall prevalence of missense variants showed a similar trend to that of truncating variants, their impact was less pronounced, with early-onset AF patients having a 1.7-fold increased OR of carrying these variants compared to the reference group. Missense variants were predominantly found in genes associated with HCM, accounting for 71% of carriers. MYH7 had the highest prevalence of missense variants in this population, emphasizing the relevance of both missense and truncating variants in MYH 7. A higher prevalence of rare missense variants was observed in the TTR gene compared to truncating variants. TTR encodes the protein transthyretin, and mutations in this gene have been associated with cardiac amyloidosis (ATTR-CA) and HCM( 46 ). Another significant gene in the missense carrier group was FLNC . A previous study showed that FLNC missense variants were predominantly associated with HCM, while truncating variants were rather strongly associated with DCM( 47 ). The analysis of missense variants across cardiomyopathies revealed notable differences compared to truncating variants. Truncating variants were most frequently found in genes associated with DCM, whereas HCM and ARVC genes had a similar prevalence of missense and truncating variants. In contrast, disease-associated missense variants were most commonly found in HCM genes, followed by DCM genes, while missense variants in ARVC-associated genes were rare. These findings hint at distinct genetic mechanisms underlying each variant type and their respective roles in specific cardiomyopathies. In summary, our findings indicate that individuals with very early onset of AF have a high prevalence of potentially pathogenic variants in cardiomyopathy-associated genes. Thus, these individuals may benefit from more thorough clinical monitoring to aid in identifying individuals at an elevated risk of developing cardiomyopathy and subsequent HF. While systematic genetic screening for rare variants is unlikely to be implemented in the near future, incorporating genetic data into clinical practice could improve cardiomyopathy risk stratification in individuals with AF. Limitations The findings from this study should be interpreted with the following limitations in mind. First, the Danish cohort only included individuals with very early-onset AF, which is not representative of the typical patient with AF. This selection may partly explain the higher prevalence of truncating variants observed in the Danish cohort compared to individuals with early-onset AF in the UK Biobank. Additionally, a healthy-volunteer bias in the UK Biobank may lead to an underrepresentation of individuals with pathogenic genetic variants, potentially limiting the generalizability of these findings. Furthermore, targeted sequencing of the Danish cohort did not include ALPK3 , ACTC1 , TNNC1 , and PLN , which may affect the comprehensiveness of our variant analysis. Moreover, not all variants may be equally pathogenic or penetrant. As participants had to survive until inclusion in the respective cohorts, this could introduce immortal time bias and result in the underrepresentation of certain variants compared to others. Finally, both cohorts were of white European ancestry, and it cannot be assumed that the findings can be generalized to other population groups. Conclusion By using two different AF cohorts, we found that a significant number of individuals with early onset of AF carried rare pathogenic variants in hallmark cardiomyopathy genes. Prevalence of truncating variants ranged from one in twelve, to one in fifteen of individuals with AF onset below age 45. These findings support that screening and clinical surveillance for cardiomyopathy might be relevant for individuals with early-onset AF. Data Availability Due to privacy concerns, data underlying the Danish AF cohort cannot be shared. Data from the UK Biobank is available for bona-fide researchers through application to https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access Funding This work was funded by grants from Skibsreder Per Henriksen, R. og hustrus fond (OBV, JHS), the research foundation of Rigshospitalet (OBV), the Novo Nordisk Foundation (MSO), sygeforsikring “danmark” Sundhedsdonationer (grant no. 2022-0243) (PRL) and The John and Birthe Meyer Foundation (PRL), and Direktør Ib Henriksens Fond. OBV was mentored through the AFGen fellowship for early-career researchers (2024-2025) which was funded by AFGen grant R01HL092577. Disclosures JHS reports grants and speaker fee from Medtronic not related to the current study and is a member of an advisory board in Medtronic And Vital Beats. M.S.O. has received speaker fee from Johnson & Johnson Institute not related to the current study. The remaining authors report no conflicts of interest. Non-Standard Abbreviations and Acronyms AF Atrial Fibrillation DCM Dilated Cardiomyopathy HCM Hypertrophic Cardiomyopathy ARVC Arrhythmogenic Right Ventricular Cardiomyopathy HF Heart failure pLoF Predicted Loss-of-Function MTR Missense Tolerance Ratio OR Odds Ratio CI Confidence Interval MAF Minor Allele Frequency VUS Variant of Uncertain Significance PSI Percent Spliced In ACMG American College of Medical Genetics and Genomics ACC/AHA American College of Cardiology and the American Heart Association Acknowledgments The UK Biobank was accessed under application number 43247. The authors would like to thank all participants from both cohorts for their participation. Our study integrates publicly available data based on the gnomAD and TOPMed project, and we would like to acknowledge the investigators and participants of these studies. References 1. ↵ Vinter N , Cordsen P , Johnsen SP , Staerk L , Benjamin EJ , Frost L , et al. Temporal trends in lifetime risks of atrial fibrillation and its complications between 2000 and 2022: Danish, nationwide, population based cohort study . BMJ . 2024 Apr 17 ; e077209 . 2. ↵ Odutayo A , Wong CX , Hsiao AJ , Hopewell S , Altman DG , Emdin CA . Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: systematic review and meta-analysis . BMJ . 2016 Sep 6 ; i4482 . 3. ↵ Paludan-Müller C , Vad OB , Stampe NK , Diederichsen SZ , Andreasen L , Monfort LM , et al. Atrial fibrillation: age at diagnosis, incident cardiovascular events, and mortality . European Heart Journal . 2024 Jun 28 ; 45 ( 24 ): 2119 – 29 . OpenUrl PubMed 4. ↵ Vad OB , Angeli E , Liss M , Ahlberg G , Andreasen L , Christophersen IE , et al. Loss of Cardiac Splicing Regulator RBM20 Is Associated With Early-Onset Atrial Fibrillation . JACC: Basic to Translational Science . 2024 Feb ; 9 ( 2 ): 163 – 80 . OpenUrl PubMed 5. ↵ Vad OB , Monfort LM , Paludan-Müller C , Kahnert K , Diederichsen SZ , Andreasen L , et al. Rare and Common Genetic Variation Underlying Atrial Fibrillation Risk . JAMA Cardiol . 2024 Aug 1 ; 9 ( 8 ): 732 . OpenUrl PubMed 6. ↵ Yoneda ZT , Anderson KC , Quintana JA , O’Neill MJ , Sims RA , Glazer AM , et al. Early-Onset Atrial Fibrillation and the Prevalence of Rare Variants in Cardiomyopathy and Arrhythmia Genes . JAMA Cardiol . 2021 Dec 1 ; 6 ( 12 ): 1371 . OpenUrl CrossRef PubMed 7. ↵ Yoneda ZT , Anderson KC , Ye F , Quintana JA , O’Neill MJ , Sims RA , et al. Mortality Among Patients With Early-Onset Atrial Fibrillation and Rare Variants in Cardiomyopathy and Arrhythmia Genes . JAMA Cardiol . 2022 Jul 1 ; 7 ( 7 ): 733 . OpenUrl PubMed 8. ↵ Joglar JA , Chung MK , Armbruster AL , Benjamin EJ , Chyou JY , Cronin EM , et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines . Circulation [Internet] . 2024 Jan 2 [cited 2025 Feb 20]; 149 ( 1 ). Available from: https://www.ahajournals.org/doi/10.1161/CIR.0000000000001193 9. Rix TA , Riahi S , Overvad K , Lundbye-Christensen S , Schmidt EB , Joensen AM . Validity of the diagnoses atrial fibrillation and atrial flutter in a Danish patient registry . Scandinavian Cardiovascular Journal . 2012 Jun ; 46 ( 3 ): 149 – 53 . OpenUrl CrossRef PubMed 10. Li H , Durbin R . Fast and accurate long-read alignment with Burrows–Wheeler transform . Bioinformatics . 2010 Mar 1 ; 26 ( 5 ): 589 – 95 . OpenUrl CrossRef PubMed Web of Science 11. Van Der Auwera GA , Carneiro MO , Hartl C , Poplin R , Del Angel G , Levy-Moonshine A , et al. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline . CP in Bioinformatics [Internet] . 2013 Oct [cited 2025 Feb 20]; 43 ( 1 ). Available from: https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/0471250953.bi1110s43 12. Ahlberg G , Refsgaard L , Lundegaard PR , Andreasen L , Ranthe MF , Linscheid N , et al. Rare truncating variants in the sarcomeric protein titin associate with familial and early-onset atrial fibrillation . Nat Commun . 2018 Oct 17 ; 9 ( 1 ): 4316 . OpenUrl CrossRef PubMed 13. Fry A , Littlejohns TJ , Sudlow C , Doherty N , Adamska L , Sprosen T , et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population . American Journal of Epidemiology . 2017 Nov 1 ; 186 ( 9 ): 1026 – 34 . OpenUrl CrossRef PubMed 14. Sudlow C , Gallacher J , Allen N , Beral V , Burton P , Danesh J , et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age . PLoS Med . 2015 Mar 31 ; 12 ( 3 ): e1001779 . OpenUrl CrossRef PubMed 15. Backman JD , Li AH , Marcketta A , Sun D , Mbatchou J , Kessler MD , et al. Exome sequencing and analysis of 454,787 UK Biobank participants . Nature . 2021 Nov 25 ; 599 ( 7886 ): 628 – 34 . OpenUrl CrossRef PubMed 16. Hershberger RE , Givertz MM , Ho CY , Judge DP , Kantor PF , McBride KL , et al. Genetic Evaluation of Cardiomyopathy—A Heart Failure Society of America Practice Guideline . Journal of Cardiac Failure . 2018 May ; 24 ( 5 ): 281 – 302 . OpenUrl CrossRef PubMed 17. Jordan E , Peterson L , Ai T , Asatryan B , Bronicki L , Brown E , et al. Evidence-Based Assessment of Genes in Dilated Cardiomyopathy . Circulation . 2021 Jul 6 ; 144 ( 1 ): 7 – 19 . OpenUrl CrossRef PubMed 18. ↵ Biddinger KJ , Jurgens SJ , Maamari D , Gaziano L , Choi SH , Morrill VN , et al. Rare and Common Genetic Variation Underlying the Risk of Hypertrophic Cardiomyopathy in a National Biobank . JAMA Cardiol . 2022 Jul 1 ; 7 ( 7 ): 715 . OpenUrl PubMed 19. James CA , Jongbloed JDH , Hershberger RE , Morales A , Judge DP , Syrris P , et al. International Evidence Based Reappraisal of Genes Associated With Arrhythmogenic Right Ventricular Cardiomyopathy Using the Clinical Genome Resource Framework . Circ: Genomic and Precision Medicine . 2021 Jun ; 14 ( 3 ): e003273 . OpenUrl 20. Schiabor Barrett KM , Cirulli ET , Bolze A , Rowan C , Elhanan G , Grzymski JJ , et al. Cardiomyopathy prevalence exceeds 30% in individuals with TTN variants and early atrial fibrillation . Genetics in Medicine . 2023 Apr ; 25 ( 4 ): 100012 . OpenUrl PubMed 21. Sherry ST . dbSNP: the NCBI database of genetic variation . Nucleic Acids Research . 2001 Jan 1 ; 29 ( 1 ): 308 – 11 . OpenUrl CrossRef PubMed Web of Science 22. Cingolani P , Platts A , Wang LL , Coon M , Nguyen T , Wang L , et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w 1118 ; iso-2; iso-3 . Fly . 2012 Apr ; 6 ( 2 ): 80 – 92 . OpenUrl CrossRef PubMed Web of Science 23. Jaganathan K , Kyriazopoulou Panagiotopoulou S , McRae JF , Darbandi SF , Knowles D , Li YI , et al. Predicting Splicing from Primary Sequence with Deep Learning . Cell . 2019 Jan ; 176 ( 3 ): 535 – 548 .e24. OpenUrl CrossRef PubMed 24. RStudio Team . RStudio: Integrated Development Environment for R [Internet] . Boston, MA : RStudio, PBC .; 2020 . Available from: http://www.rstudio.com/ 25. ↵ Sun KY , Bai X , Chen S , Bao S , Zhang C , Kapoor M , et al. A deep catalogue of protein-coding variation in 983,578 individuals . Nature . 2024 Jul 18 ; 631 ( 8021 ): 583 – 92 . OpenUrl CrossRef PubMed 26. ↵ Lukas Laws J , Lancaster MC , Ben Shoemaker M , Stevenson WG , Hung RR , Wells Q , et al. Arrhythmias as Presentation of Genetic Cardiomyopathy . Circulation Research . 2022 May 27 ; 130 ( 11 ): 1698 – 722 . OpenUrl PubMed 27. ↵ Izquierdo I , Rosa I , Bravo SB , Guitián E , Pérez-Serra A , Campuzano O , et al. Proteomic identification of putative biomarkers for early detection of sudden cardiac death in a family with a LMNA gene mutation causing dilated cardiomyopathy . Journal of Proteomics . 2016 Oct ; 148 : 75 – 84 . OpenUrl PubMed 28. Tobita T , Nomura S , Fujita T , Morita H , Asano Y , Onoue K , et al. Genetic basis of cardiomyopathy and the genotypes involved in prognosis and left ventricular reverse remodeling . Sci Rep . 2018 Jan 31 ; 8 ( 1 ): 1998 . OpenUrl CrossRef PubMed 29. ↵ McNally EM , Mestroni L . Dilated Cardiomyopathy . 30. ↵ Paludan-Müller C , Larsen S , Ahlberg G , Andreasen L , Monfort LM , Svendsen JH , et al. Clinical Implications of SCN10A Loss-of-Function Variants in 169 610 Exomes Representing the General Population . Circ: Genomic and Precision Medicine [Internet] . 2022 Feb [cited 2025 Feb 27]; 15 ( 1 ). Available from: https://www.ahajournals.org/doi/10.1161/CIRCGEN.121.003574 31. ↵ Tudurachi BS , Zăvoi A , Leonte A , Țăpoi L , Ureche C , Bîrgoan SG , et al. An Update on MYBPC3 Gene Mutation in Hypertrophic Cardiomyopathy . IJMS . 2023 Jun 22 ; 24 ( 13 ): 10510 . OpenUrl PubMed 32. ↵ Heuser A , Plovie ER , Ellinor PT , Grossmann KS , Shin JT , Wichter T , et al. Mutant Desmocollin-2 Causes Arrhythmogenic Right Ventricular Cardiomyopathy . The American Journal of Human Genetics . 2006 Dec ; 79 ( 6 ): 1081 – 8 . OpenUrl CrossRef PubMed Web of Science 33. ↵ Schafer S , De Marvao A , Adami E , Fiedler LR , Ng B , Khin E , et al. Titin-truncating variants affect heart function in disease cohorts and the general population . Nat Genet . 2017 Jan ; 49 ( 1 ): 46 – 53 . OpenUrl CrossRef PubMed 34. ↵ Choi SH , Weng LC , Roselli C , Lin H , Haggerty CM , Shoemaker MB , et al. Association Between Titin Loss-of-Function Variants and Early-Onset Atrial Fibrillation . JAMA . 2018 Dec 11 ; 320 ( 22 ): 2354 . OpenUrl CrossRef PubMed 35. ↵ Haggerty CM , Damrauer SM , Levin MG , Birtwell D , Carey DJ , Golden AM , et al. Genomics-First Evaluation of Heart Disease Associated With Titin-Truncating Variants . Circulation . 2019 Jul 2 ; 140 ( 1 ): 42 – 54 . OpenUrl CrossRef PubMed 36. Jolfayi AG , Kohansal E , Ghasemi S , Naderi N , Hesami M , MozafaryBazargany M , et al. Exploring TTN variants as genetic insights into cardiomyopathy pathogenesis and potential emerging clues to molecular mechanisms in cardiomyopathies . Sci Rep . 2024 Mar 4 ; 14 ( 1 ): 5313 . OpenUrl PubMed 37. ↵ Akhtar MM , Lorenzini M , Cicerchia M , Ochoa JP , Hey TM , Sabater Molina M , et al. Clinical Phenotypes and Prognosis of Dilated Cardiomyopathy Caused by Truncating Variants in the TTN Gene . Circ: Heart Failure [Internet] . 2020 Oct [cited 2025 Feb 20]; 13 ( 10 ). Available from: https://www.ahajournals.org/doi/10.1161/CIRCHEARTFAILURE.119.006832 38. ↵ Hermida A , Ader F , Millat G , Jedraszak G , Maury P , Cador R , et al. NEXN Gene in Cardiomyopathies and Sudden Cardiac Deaths: Prevalence, Phenotypic Expression, and Prognosis . Circ: Genomic and Precision Medicine [Internet] . 2024 Feb [cited 2025 Feb 20]; 17 ( 1 ). Available from: https://www.ahajournals.org/doi/10.1161/CIRCGEN.123.004285 39. ↵ Lopes LR , Garcia-Hernández S , Lorenzini M , Futema M , Chumakova O , Zateyshchikov D , et al. Alpha-protein kinase 3 ( ALPK3 ) truncating variants are a cause of autosomal dominant hypertrophic cardiomyopathy . European Heart Journal . 2021 Aug 21 ; 42 ( 32 ): 3063 – 73 . OpenUrl CrossRef PubMed 40. ↵ Jansen M , De Brouwer R , Hassanzada F , Schoemaker AE , Schmidt AF , Kooijman-Reumerman MD , et al. Penetrance and Prognosis of MYH7 Variant-Associated Cardiomyopathies . JACC: Heart Failure . 2024 Jan ; 12 ( 1 ): 134 – 47 . OpenUrl PubMed 41. ↵ Alfares AA , Kelly MA , McDermott G , Funke BH , Lebo MS , Baxter SB , et al. Results of clinical genetic testing of 2,912 probands with hypertrophic cardiomyopathy: expanded panels offer limited additional sensitivity . Genetics in Medicine . 2015 Nov ; 17 ( 11 ): 880 – 8 . OpenUrl CrossRef PubMed 42. Chiu C , Bagnall RD , Ingles J , Yeates L , Kennerson M , Donald JA , et al. Mutations in Alpha-Actinin-2 Cause Hypertrophic Cardiomyopathy . Journal of the American College of Cardiology . 2010 Mar ; 55 ( 11 ): 1127 – 35 . OpenUrl FREE Full Text 43. Mohapatra B , Jimenez S , Lin JH , Bowles KR , Coveler KJ , Marx JG , et al. Mutations in the muscle LIM protein and α-actinin-2 genes in dilated cardiomyopathy and endocardial fibroelastosis . Molecular Genetics and Metabolism . 2003 Sep ; 80 ( 1–2 ): 207 – 15 . OpenUrl CrossRef PubMed Web of Science 44. Carruth ED , Qureshi M , Alsaid A , Kelly MA , Calkins H , Murray B , et al. Loss-of-Function FLNC Variants Are Associated With Arrhythmogenic Cardiomyopathy Phenotypes When Identified Through Exome Sequencing of a General Clinical Population . Circ: Genomic and Precision Medicine [Internet] . 2022 Aug [cited 2025 Feb 20]; 15 ( 4 ). Available from: https://www.ahajournals.org/doi/10.1161/CIRCGEN.121.003645 45. ↵ Vimalanathan AK , Ehler E , Gehmlich K . Genetics of and pathogenic mechanisms in arrhythmogenic right ventricular cardiomyopathy . Biophys Rev . 2018 Aug ; 10 ( 4 ): 973 – 82 . OpenUrl CrossRef PubMed 46. ↵ Kanelidis AJ , Slivnick JA , Campagna R , Smith B , Kalantari S , Sarswat N , et al. ATTR Gene Variants in HCM . JACC: Case Reports . 2024 Mar ; 29 ( 6 ): 102236 . OpenUrl PubMed 47. ↵ Verdonschot JAJ , Vanhoutte EK , Claes GRF , Helderman-van Den Enden ATJM , Hoeijmakers JGJ , Hellebrekers DMEI , et al. A mutation update for the FLNC gene in myopathies and cardiomyopathies . Human Mutation . 2020 Jun ; 41 ( 6 ): 1091 – 111 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 13, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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Share Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation Quim Bech Vilaseca , Oliver Bundgaard Vad , Christian Paludan-Müller , Laura Andreasen , Morten Salling Olesen , Jesper Hastrup Svendsen , Pia Rengtved Lundegaard medRxiv 2025.03.12.25323872; doi: https://doi.org/10.1101/2025.03.12.25323872 Share This Article: Copy Citation Tools Prevalence of deleterious cardiomyopathy variants in early-onset atrial fibrillation Quim Bech Vilaseca , Oliver Bundgaard Vad , Christian Paludan-Müller , Laura Andreasen , Morten Salling Olesen , Jesper Hastrup Svendsen , Pia Rengtved Lundegaard medRxiv 2025.03.12.25323872; doi: https://doi.org/10.1101/2025.03.12.25323872 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (566) Allergy and Immunology (863) Anesthesia (295) Cardiovascular Medicine (4408) Dentistry and Oral Medicine (443) Dermatology (379) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15201) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6568) Geriatric Medicine (666) Health Economics (994) Health Informatics (4508) Health Policy (1365) Health Systems and Quality Improvement (1606) Hematology (537) HIV/AIDS (1262) Infectious Diseases (except HIV/AIDS) (15901) Intensive Care and Critical Care Medicine (1103) Medical Education (619) Medical Ethics (144) Nephrology (665) Neurology (6571) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (967) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1686) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5419) Public and Global Health (9200) Radiology and Imaging (2190) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe63c28fa7006e7',t:'MTc3OTIyNjU2Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();