Next Article in Journal
Chemical Insect Attractants Produced by Flowers of Impatiens spp. (Balsaminaceae) and List of Floral Visitors
Next Article in Special Issue
Cardioprotection and Suppression of Fibrosis by Diverse Cancer and Non-Cancer Cell Lines in a Murine Model of Duchenne Muscular Dystrophy
Previous Article in Journal
The Non-Invasive Prediction of Colorectal Neoplasia (NIPCON) Study 1995–2022: A Comparison of Guaiac-Based Fecal Occult Blood Test (FOBT) and an Anti-Adenoma Antibody, Adnab-9
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 24
10.3390/ijms242417244
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of rs1805127 and rs55742440 Variants on Atrial Remodeling in Hypertrophic Cardiomyopathy Patients with Atrial Fibrillation: A Romanian Cohort Study

by
Nicoleta-Monica Popa-Fotea
1,2,
Nicoleta Oprescu
2,
Alexandru Scafa-Udriste
1,2,* and
Miruna Mihaela Micheu
2,*
1
Department 4—Cardio-Thoracic Pathology, University of Medicine and Pharmacy Carol Davila, Eroii Sanitari Bvd. 8, 050474 Bucharest, Romania
2
Department of Cardiology, Clinical Emergency Hospital of Bucharest, Calea Floreasca 8, 014461 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(24), 17244; https://doi.org/10.3390/ijms242417244
Submission received: 11 October 2023 / Revised: 25 November 2023 / Accepted: 7 December 2023 / Published: 8 December 2023
(This article belongs to the Special Issue Advances in Cardiovascular Remodeling)
Versions Notes

Abstract

:
Atrial fibrillation (AFib) is characterized by a complex genetic component. We aimed to investigate the association between variations in genes related to cardiac ion handling and AFib in a cohort of Romanian patients with hypertrophic cardiomyopathy (HCM). Forty-five unrelated probands with HCM were genotyped by targeted next-generation sequencing (NGS) for 24 genes associated with cardiac ion homeostasis. Subsequently, the study cohort was divided into two groups based on the presence (AFib+) or absence (AFiB−) of AFib detected during ECG monitoring. We identified two polymorphisms (rs1805127 located in KCNE1 and rs55742440 located in SCN1B) linked to AFib susceptibility. In AFib+, rs1805127 was associated with increased indexed left atrial (LA) maximal volume (LAVmax) (58.42 ± 21 mL/m2 vs. 32.54 ± 6.47 mL/m2, p < 0.001) and impaired LA strain reservoir (LASr) (13.3 ± 7.5% vs. 24.4 ± 6.8%, p < 0.05) compared to those without respective variants. The rs55742440 allele was less frequent in patients with AFib+ (12 out of 25, 48%) compared to those without arrhythmia (15 out of 20, 75%, p = 0.05). Also, AFib+ rs55742440 carriers had significantly lower LAVmax compared to those who were genotype negative. Among patients with HCM and AFib+, the rs1805127 variant was accompanied by pronounced LA remodeling, whereas rs55742440’s presence was related to a milder LA enlargement.

1. Introduction

AFib is the most common cardiac arrhythmia in adults, imposing a considerable burden on both patients and healthcare providers due to substantial morbidity and mortality [1]. Globally, its prevalence is estimated to be approximately 60 million cases, resulting in over 8 million disability-adjusted life years. The estimated lifetime risk of developing AFib is around 33%, although it may vary based on patient-specific factors such as age, sex, race, and comorbidities [2]. Recently, there has been a growing recognition of AFib as a multifaceted and dynamic condition, which is characterized by a substantial genetic foundation and complex epigenetic mechanisms [3,4,5,6]. Existing evidence suggests that both common and rare genetic variants contribute to increased susceptibility to AFib especially when combined with ethnic-specific risk factors [7]. These findings carry significant clinical implications, including the potential to guide the antiarrhythmic treatments and to identify appropriate candidates for ablation therapy based on genetic background. Moreover, the incorporation of common genetic variants into AFib risk scores has demonstrated enhanced predictive accuracy and improved risk stratification [8,9]. Structural and functional alterations in atrial tissue, involving electrical, mechanical, and structural remodeling processes, contribute to AFib appearance and persistence. Electrical remodeling leads to changes in ion channel function, affecting the cardiac action potential duration and refractoriness, thereby facilitating AFib perpetuation. Mechanical remodeling, encompassing atrial dilation, fibrosis, and hypertrophy, is also crucial. Atrial dilation occurs due to increased pressure or volume overload, stretching the atrial walls. Fibrosis, characterized by excessive collagen deposition, disrupts electrical conduction pathways, and hypertrophy increases electrical and mechanical disturbances. Additionally, neurohormonal and inflammatory factors can promote atrial electrical instability and remodeling by modulating ion channel function, calcium handling, and fibrotic processes, exacerbating the arrhythmia [3,10,11]. Many of these abnormalities are also hallmark features of HCM [12]; hence, AFib is four to six times more common in HCM individuals than in the general population [13]. The presence of HCM does not necessarily entail the occurrence of AFib; nevertheless, genetic variants have the potential to modulate AFib vulnerability by modifying the structure of proteins involved in the aforesaid cellular processes. Lee et al. reported that in patients with MYH7 mutations, there was a higher incidence of AFib independently of other clinical and echocardiographic factors compared with other sarcomeric genes mutations [14]. Candidate genome-wide association studies have identified many genes possibly involved in the development of atrial fibrillation, but the genotype dependency of AFib in HCM is less studied; for example, in the case of cardiac Troponin T gene mutations, the association with AFib is dependent on the pathophysiological mechanism induced by the variant [15]. Consequently, different mutations in the same gene have different impacts on the atrial arrhythmia appearance. In this study, we focused on the association between variations in genes related to cardiac ion handling and AFib in a cohort of adult patients with HCM.

2. Results

2.1. Study Population and Echocardiography Parameters

The study cohort consisted of 25 patients who had both HCM and AFib (AFib+ group), as identified through previous ECG monitoring. None of these individuals were experiencing permanent atrial fibrillation. Additionally, there were 20 HCM subjects without detected arrhythmia (AFib− group). Table 1 provides a summary of the demographic and conventional echocardiographic data for these two clusters. All included subjects were Caucasians. The mean age of AFib+ group was 51.67 ± 14.55 years old and 80% were males, while the mean age of AFib− was 46.35 ± 15 years with 75% males. There were no significant differences in age, gender, history of sudden cardiac death (SCD), or implantable cardiac defibrillator (ICD) presence between the two groups. There was an important difference in the risk of SCD at 5 years, patients with HCM and AFib+ displaying a higher risk (5.49 ± 3.92%) compared with those with HCM and AFib− (2.83 ± 1.37%), p = 0.044. The two groups did not differ significantly in terms of LV maximal wall thickness, LVEF, LVEDD, LVESD, LVEDV and LVESV (Table 1). Patients with AFib+ had increased LAVmax and LAVpre-A compared with those without AFib. LATEF was decreased in the AFib+ group (54 ± 10.3%) in contrast with the AFib− group (60 ± 8.8%) (p = 0.041). The dysfunction of LA reservoir function was also confirmed by LA deformation analysis depicting reduced LASr (21 ± 5.6%) and pLASRr (0.8 ± 0.4 s−1) in subjects with AFib+ compared with AFib− (LASr = 25 ± 4%, p = 0.008 and pLASRr = 1.2 ± 0.67 s−1, p = 0.025).

2.2. Genes and Variants

Of the 174 genes covered by the TruSight Cardio Sequencing Kit, only 24 genes were considered in this analysis, specifically those related to cardiac ion homeostasis. The mean depth of sequence coverage across target regions was 177×. The complete list of analyzed genes and encoded proteins is depicted in Table 2. Table 3 details the pathogenic or likely pathogenic variants in sarcomeric genes identified in the cohort. Also, comprehensive data regarding all the variants identified is provided, detailing information such as genotype, rs ID, allele frequency in the gnomAD v4 database, published association with AFib, and in how many AFib+ patients these variants were found (Supplementary Table S1).

2.2.1. Analysis of LA Traits and Potassium Channel Variants

There were no statistically significant differences in the number of potassium channel variants identified between the two groups with a mean of 2.56 ± 0.76 variants in AFib+ and 2.8 ± 0.89 variants in AFib− (p = 0.604) (Table 4).
There was no correlation observed between the number of variants in potassium channels and the size or function of the LA within AFib+ patients. We did not observe any differences between the two groups regarding c.112A>G polymorphism (reference SNP ID: rs1805127); the variant was detected in 80% of AFib+ cases (20 out 25) and in all AFib− individuals (N = 20). But subjects with AFib+ and the c.112A>G polymorphism of KCNE1 had significantly larger LAVmax (58.42 ± 21 mL/m2) compared to those without respective polymorphism (32.54 ± 6.47 mL/m2, p < 0.001). Moreover, those with AFib+ and c.112A>G polymorphism had an impaired reservoir function (LASr = 13.3 ± 7.5%) compared to those with AFib+ but without the variant (LASr = 24.4 ± 6.8%, p < 0.05), conduit and pump function being similar regardless of the presence of genetic variants in KCNE1 gene. No additional correlations were found between other potassium channel-coding genes variants and LA traits.

2.2.2. Analysis of LA Traits and Sodium Channel Variants

There were no statistically significant differences in the number of sodium channel variants between the two subgroups: AFib+ (1.92 ± 1.41) or AFib− (2 ± 1.62 variants, p = 0.078) (Table 5).
Patients with AFib+ and two or more sodium channel variants (n = 14) had significantly larger LAVmax (86.4 ± 23.9 mL/m2) compared to those with one or no variants (n = 11) (65.34 ± 10.7 mL/m2, p = 0.023). The same observation remains valid after excluding Na channel variants with MAF < 1%; subjects with AFib+ and two or more variants in sodium channels (n = 13) had significantly higher LAVmax (62.9 ± 8.4 mL/m2) compared with those having one or no variant (n = 10) (54.32 ± 9 mL/m2), p = 0.024.
The SCN1B c.629T>C polymorphism (reference SNP ID: rs55742440, protein sequence: p.Leu210Pro) was found less frequently amid patients with AFib+ (N = 12/25, 48%) compared to AFib− (N = 15/20, 75%, p = 0.05). Moreover, AFib+ subjects harboring the c.629T>C variant had significantly lower LAVmax (24.7 ± 5.4 mL/m2) compared to those without this polymorphism (64.67 ± 9.29 mL/m2, p < 0.001). There were no additional significant correlations between other sodium channel-coding gene variants and other LA traits.

2.2.3. Analysis of LA Traits and Calcium Channel Variants

There were no statistically significant differences concerning the number of calcium channel variants between the two subgroups: AFib+ (a mean number of 5 ± 1.67) or AFib− (a mean number of 4.45 ± 1.19), p = 0.318 (Table 6). Within AFib+, no correlation was identified between the number of Ca channel variants or specific variants and LA volumes and/or LA mechanics.

3. Discussion

In this study, we investigated the influence of low-frequency and common variants within specific genes on the occurrence of AFib in a cohort of adult patients with HCM. For our analysis, we selected a list of 24 genes (Table 2) involved in ion homeostasis within cardiac cells, for which literature data have shown an association with AFib [3,5,7].
The overall demographic profile of our study group was previously documented [37]; the average age at enrollment was approximately in the fifth decade of life, with a higher proportion of male participants, analogous to findings obtained from the Romanian Registry of Hypertrophic Cardiomyopathy [38]. As showed in Table 1, there were no significant differences between the AFib+ (N = 25) and AFib− (N = 20) groups in terms of age, gender distribution, history of sudden cardiac death (SCD), presence of implantable cardiac defibrillators (ICD), and LV size and function. Predictably, we noticed a significant difference in the risk of SCD at 5 years between the two groups. Patients with HCM and AFib+ displayed an almost twofold higher risk of SCD compared to AFib− (5.49 ± 3.92% vs. 2.83 ± 1.37%). Comparable statistics were detected in a nationwide study conducted in Taiwan; after adjustment for various confounders (such as age, sex, comorbidities and medications), the presence of AFib was linked to a threefold increase in the risk of SCD, demonstrating its substantial impact on adverse cardiac events in HCM patients [39].
LA enlargement has been commonly recognized as a marker of increased risk for cardiovascular events and adverse outcomes in various cardiac conditions, including HCM [40,41]. Our data showed a notable difference between groups regarding both LA size and its function. Similar to evidence from large cohorts [42,43,44,45], AFib+ was associated with augmented LA volumes and reduced atrial performance. Specifically, patients with AFib exhibited increased LAVmax and LAVpre-A compared to AFib−. Furthermore, LATEF was found to be decreased in the AFib+ group (54 ± 10.3%) compared to the AFib− group (60 ± 8.8%, p = 0.041). Additionally, the dysfunction of LA’s reservoir function was confirmed through analysis of LA deformation. The findings revealed reduced LASr (21 ± 5.6%) and pLASRr (0.8 ± 0.4 s−1) in subjects with AFib+ compared to AFib−. Explicitly, LASr was 25 ± 4% in the AFib− group (p = 0.008) and pLASRr was 1.2 ± 0.67 s−1 (p = 0.025). The reduction in reservoir function was most likely due to the reduction in LA compliance, which was indirectly evaluated by measuring LASI. LASI showed a significant increase in the AFib+ group (1.8 ± 0.4) compared to the AFib− group (1.4 ± 0.8).

3.1. Analysis of LA Traits and Potassium Channel Variants

Potassium channels play a crucial role in cardiac electrical activity, and genetic variants in potassium channel genes can impact atrial repolarization and electrical stability, potentially increasing the risk of AFib [16,22,46]. In our study, there were no statistically significant differences found in the number of K channel variants identified between the two groups. The mean number of variants was 2.56 ± 0.76 in AFib+ and 2.8 ± 0.89 in AFib− (p = 0.604). Also, there was no correlation observed between the number of variants in potassium channels and the size or function of the LA within AFib+ patients.
Upon analyzing each detected potassium channel variant individually in relation to AFib, only a single one showed a potential relationship with the condition, namely c.112A>G polymorphism in the KCNE1 gene. The KCNE1 gene encodes the β-subunit of voltage-gated potassium channel Kv7.1, producing the slow delayed rectifier potassium current (IKs). The c.112A>G polymorphism results in the substitution of serine to glycine at position 38 (p.Ser38Gly) in the encoded protein.
Within the AFib+ group, we observed that c.112A>G positive subjects exhibited greater LA enlargement (LAVmax 58.42 ± 21 mL/m2) compared to those without respective polymorphism (LAVmax 32.54 ± 6.47 mL/m2, p < 0.001). Moreover, the presence of the aforesaid SNP was associated with impaired reservoir function (LASr = 13.3 ± 7.5%) compared to those lacking the variant (LASr = 24.4 ± 6.8%, p < 0.05). However, conduit and pump function remained similar regardless of the presence of genetic variants in the KCNE1 gene. Existing evidence advocate that AFib causally increases LA size and decreases its function [47,48,49]; at the same time, atrial remodeling enhances the vulnerability to AFib occurrence and persistence [42,50,51,52], thus perpetuating the cycle. So, we could hypothesize that patients harboring rs1805127 might carry increased AFib burden compared with those lacking it, and they also are more prone to recidivate. Higher AFib burden is associated with higher cardiovascular morbidity and mortality [53,54], so an early identification of high-risk patients is of paramount importance.
Several studies have investigated the relationship between the c.112A>G polymorphism and AFib susceptibility, but the results have been conflicting. While certain findings indicate a potential correlation between this particular genetic variation and an elevated risk of AFib [55,56,57], other studies have not found supporting evidence for such an association [58,59]. It is important to note that the exact contribution of rs1805127 to AFib risk may vary depending on the population studied and other genetic and environmental modifiers. This variability has been demonstrated by Liang et al. in their meta-analysis, which encompassed ten case-control studies involving 2099 cases and 2252 controls [60]. The overall analysis exposed that the rs1805127 polymorphism was linked to an elevated risk of AFib across all four comparisons: homozygote comparison (GG vs. AA), heterozygote comparison (GA vs. AA), dominant model (GA/GG vs. AA), and recessive model (GG vs. GA/AA). Further subgroup analyses based on ethnicity revealed that the risk remained consistent in all four comparisons among the Asian population. However, among the Caucasian population, the heightened risk of AFib was observed in the heterozygote comparison and dominant model, while no significant association was found in the homozygote comparison and recessive model.
We did not observe any differences between the groups regarding the presence of c.112A>G. Although no significant difference was found concerning the distribution of c.112A>G, the association between this polymorphism and more pronounced LA remodeling may be due to the fact that the mentioned variant is a modifier variant that only contributes to the atrial phenotype in conjunction with other genetic variants. Our sample size was too small to enable specific analysis depending on the presence of the allele in a homozygous or heterozygous state.

3.2. Analysis of LA Traits and Sodium Channel Variants

The association between sodium channel variants and AFib has been extensively investigated; findings revealed various alleles that are linked either to an increased susceptibility to AFib [61,62,63] or to potential protection against it [64].
In our research, the analysis of sodium channel variants did not reveal significant differences concerning the number of mutations between patients AFib+ and AFib− (1.92 ± 1.41 vs. 2 ± 1.62 variants, p = 0.078).
In the cohort AFib+, those who possessed two or more sodium channel variants (n = 14) demonstrated a statistically significant increase in LAVmax compared to subjects with either one or no variants (n = 11) (86.4 ± 23.9 mL/m2 vs. 65.34 ± 10.7 mL/m2, p = 0.023). This finding remained consistent even after excluding sodium channel variants with AF below 1% (62.9 ± 8.4 mL/m2 vs. 54.32 ± 9 mL/m2, p = 0.024), suggesting that the number of mutations harbored by an individual may play a role not only in specific mutations but also in the pathogenesis of AFib and LA remodeling. Indeed, prior evidence suggests that the effect is dose-dependent [64].
We observed a negative relationship between SCN1B c.629T>C polymorphism and AFib+. The SCN1B gene encodes the β-subunit of voltage-gated sodium channel Nav1.5. The respective SNP exhibited a lower prevalence among patients AFib+ (N = 12/25, 48%) in contrast to AFib− (N = 15/20, 75%, p = 0.05). Furthermore, within the AFib+ group, subjects possessing the aforesaid variant demonstrated markedly reduced LA volumes (24.7 ± 5.4 mL/m2) compared to individuals lacking it (64.67 ± 9.29 mL/m2, p < 0.001). Accordingly, one can postulate that rs55742440 genotype-positive subjects may exhibit a reduced AFib burden compared with those genotype-negative and/or might be less likely to experience AFib recurrence.

3.3. Strength and Limitations

To our knowledge, this study is the first of its kind to investigate the association between genetic variants and AFib in a Romanian cohort. By exploring this link in a specific population, it provides valuable insights into the genetic basis of AFib within that population. This contributes to the understanding of AFib’s genetic heterogeneity and may have implications for personalized medicine and targeted therapies in the Romanian population.
We are aware of some limitations that should be taken into consideration. Firstly, the cohort size is small, and the study was monocentric, which may affect the generalizability of the findings to larger populations, from this deriving the need for a replication study based on a larger population. Secondly, the two studied SNPs were not confirmed by Sanger sequencing. Thirdly, the lack of follow-up for AFib limits the ability to assess the long-term implications of rs1805127 and rs55742440 polymorphism. Fourthly, the analysis was exploratory in nature and did not consider the possibility that an association may be caused by multiple hypothesis testing. Furthermore, the analysis was focused solely on exonic variants, potentially overlooking other non-exonic genetic variations that could contribute to AFib. Therefore, further studies with larger cohorts, longitudinal follow-up, and broader genetic analyses would be valuable in confirming and expanding upon these findings.

4. Materials and Methods

4.1. Study Population

We have analyzed retrospectively 45 Caucasians subjects diagnosed with HCM according to the criteria of the European Society of Cardiology [65]. The diagnosis was established after a comprehensive evaluation based on clinical, imaging and genetic criteria. The study was approved by the ethical committee of Emergency Clinical Hospital, Bucharest, Romania, and inclusion in the study was preceded by the signature of a written informed consent. The cohort was further on divided into two subgroups depending on the detection or absence of AFib at the time of inclusion or at previous 24 h ECG monitoring.

4.2. Genetic Testing

The genetic testing methodology has been previously described in detail [37,66]. In summary, patients’ DNA was extracted from blood using a MagCore Genomic DNA Whole Blood Kit (RBC Bioscience, Taipei, Taiwan) and subsequently quantified using a Qubit dsDNA HS assay kit (Life Technologies, Carlsbad, CA, USA). Targeted next-generation sequencing (NGS) was performed on a MiSeq platform with the 150 bp paired-end protocol (Illumina, San Diego, CA, USA) with a TruSight Cardio Sequencing Kit (Illumina, San Diego, CA, USA). An initial amount of 50 ng of genomic DNA was used for optimal gene enrichment.

4.3. Variant Assessment

Sequencing data were analyzed using the MiSeq Reporter software v2.4 (Illumina, San Diego, CA, USA), adopting hg19/GRCh37 as a genome reference. To interrogate genomic variants, we used VariantStudio v3.0 software (Illumina, San Diego, CA, USA). Candidate variants were selected based on a user-defined filtering approach including 24 genes associated with cardiac ion channels and ion homeostasis (Table 2) and high-quality calling (PASS filter). Sequence variants passing the aforementioned filters were analyzed individually, the synonymous ones being discarded. Allele frequency (AF) was obtained from the 1000 genomes project (GRCh37 reference assembly) and Genome Aggregation Database (gnomAD v2.1.1 dataset aligned against the GRCh37 reference) (accessed on 1 April 2023). The following thresholds were considered to define variants in terms of population prevalence: AF above 5% for common variants, AF between 1 and 5% for low-frequency variants, while variants having AF below 1% were categorized as rare variants.

4.4. Echocardiographic Image Acquisition and Analysis

All subjects underwent a comprehensive 2D-transthoracic echocardiography study as previously reported in detail [67]. Echocardiography examinations were performed on VIVID E9 (GE Healthcare, Chicago, IL, USA) with a 3.5 MHz array probe, and images were analyzed offline on EchoPac, version BT13. Dedicated acquisitions in apical two and four-chamber views were acquired for left atrium (LA) measurement with a frame rate between 50 and 70 frames/s and at least three heartbeats. Conventional echocardiographic measurements were achieved according to current guidelines [68]. Left ventricular ejection fraction (LVEF) was quantified employing Simpson’s biplane method. The LA diameter was evaluated in parasternal long-axis view in the end systole, while for the two-dimensional LA volumetric analysis, LA volumes were calculated as the average from measurement in apical two and four chamber views using the disk summation algorithm. The maximal volume (LAVmax) was measured just before mitral valve opening, LA minimum volume (LAVmin) at mitral valve closure, and LA pre-A-wave volume (LAVpre-A), just one frame before atrial contraction in accordance with the American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines for chamber quantification [69]. Using the above volumes, LA total (LATEV), passive (LAPEV) and active emptying volumes (LAAEV) as well as fractions (LATEF, LAPEF, LAEI and LAAEF) were calculated as shown in a previous paper [67]. All obtained volumes were indexed to the body surface area. The LA stiffness index (LASI) was defined as E/e′m divided by LASr [70], where E is the mitral E wave velocity and e’m is the average between early-diastolic septal (e’s) and lateral (e’l) mitral annular velocities using tissue Doppler imaging (e’m = e’l + e’s)/2).
The analysis of LA function via speckle tracking was performed in accordance with the guidelines provided by the European Association of Cardiovascular Imaging [71]. LA S and SR were calculated as the average value of apical 4- and 2-C views. The endocardial border of LA was manually tracked so that the region of interest followed the LA endocardial border throughout the cardiac cycle in apical four and two-chamber views. Time-strain and time-strain rate plots were automatically created by the system. On the time-strain plot, we measured the LA strain reservoir (LASr) representing the difference between the strain value at the mitral valve opening and left-ventricular end-diastole (LVED), LA conduit strain (LAScd) as the difference between the strain at onset of atrial contraction and mitral valve opening, and LA contractile strain (LASct) representing the difference between the strain value at LVED and onset of atrial contraction. The zero-strain reference was set at LVED. On the time-strain rate plot, we measured the following: LA reservoir SR as peak systolic positive value (pLASRr), LA conduit SR as the early diastolic negative peak (pLASRcd) and LA contractile SR (pLASRct) as the late diastolic negative peak.

4.5. Statistical Analysis

All analyses were completed using the statistical software program SPSS version 23. Data were presented as mean ± SD for continuous variables and as number and percentage for categorical counterparts. Differences between groups if normally distributed were compared with Student’s t-test or chi-square, while for non-parametric variables, a Mann–Whitney U test was used. p-values were two-tailed, and a cut-off of less than 0.05 was considered statistically significant.

5. Conclusions

In patients with HCM and AFib, comparative to genotype-negative subjects, the rs1805127 variant was linked to increased LA remodeling, while the occurrence of the rs55742440 allele was accompanied by a smaller increase in LA size, suggesting that the variation in ion channel genes may exert either a harmful or a protective effect.

Supplementary Materials

The supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms242417244/s1. References [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, M.M.M.; methodology, N.-M.P.-F. and M.M.M.; formal analysis, N.-M.P.-F., N.O., A.S.-U. and M.M.M.; data curation, M.M.M.; writing—original draft preparation, N.-M.P.-F., N.O., A.S.-U. and M.M.M.; writing—review and editing, A.S.-U. and M.M.M.; supervision, M.M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Emergency Clinical Hospital, Bucharest, Romania (approval number 2067, 26 February 2017).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hindricks, G.; Potpara, T.; Dagres, N.; Bax, J.J.; Boriani, G.; Dan, G.A.; Fauchier, L.; Kalman, J.M.; Watkins, C.L.; Dilaveris, P.E.; et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Eur. Heart J. 2021, 42, 373–498. [Google Scholar] [CrossRef]
  2. Elliott, A.D.; Middeldorp, M.E.; Van Gelder, I.C.; Albert, C.M.; Sanders, P. Epidemiology and modifiable risk factors for atrial fibrillation. Nat. Rev. Cardiol. 2023, 20, 404–417. [Google Scholar] [CrossRef]
  3. Lozano-Velasco, E.; Franco, D.; Aranega, A.; Daimi, H. Genetics and Epigenetics of Atrial Fibrillation. Int. J. Mol. Sci. 2020, 21, 5717. [Google Scholar] [CrossRef]
  4. Roselli, C.; Rienstra, M.; Ellinor, P.T. Genetics of Atrial Fibrillation in 2020: GWAS, Genome Sequencing, Polygenic Risk, and beyond. Circ. Res. 2020, 127, 21–33. [Google Scholar] [CrossRef]
  5. Andersen, J.H.; Andreasen, L.; Olesen, M.S. Atrial fibrillation—A complex polygenetic disease. Eur. J. Hum. Genet. 2021, 29, 1051–1060. [Google Scholar] [CrossRef] [PubMed]
  6. Assum, I.; Krause, J.; Scheinhardt, M.O.; Müller, C.; Hammer, E.; Börschel, C.S.; Völker, U.; Conradi, L.; Geelhoed, B.; Zeller, T.; et al. Tissue-specific multi-omics analysis of atrial fibrillation. Nat. Commun. 2022, 13, 441. [Google Scholar] [CrossRef] [PubMed]
  7. Feghaly, J.; Zakka, P.; London, B.; MacRae, C.A.; Refaat, M.M. Genetics of Atrial Fibrillation. J. Am. Heart Assoc. 2018, 7, e009884. [Google Scholar] [CrossRef] [PubMed]
  8. Okubo, Y.; Nakano, Y.; Ochi, H.; Onohara, Y.; Tokuyama, T.; Motoda, C.; Amioka, M.; Hironobe, N.; Okamura, S.; Ikeuchi, Y.; et al. Predicting atrial fibrillation using a combination of genetic risk score and clinical risk factors. Heart Rhythm. 2020, 17, 699–705. [Google Scholar] [CrossRef] [PubMed]
  9. O’Sullivan, J.W.; Raghavan, S.; Marquez-Luna, C.; Luzum, J.A.; Damrauer, S.M.; Ashley, E.A.; O’Donnell, C.J.; Willer, C.J.; Natarajan, P. Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement from the American Heart Association. Circulation 2022, 146, e93–e118. [Google Scholar] [CrossRef] [PubMed]
  10. Creemers, E.E.; Wilde, A.A.; Pinto, Y.M. Heart failure: Advances through genomics. Nat. Rev. Genet. 2011, 12, 357–362. [Google Scholar] [CrossRef]
  11. Roka, A.; Burright, I. Remodeling in Persistent Atrial Fibrillation: Pathophysiology and Therapeutic Targets—A Systematic Review. Physiologia 2023, 3, 43–72. [Google Scholar] [CrossRef]
  12. Popa-Fotea, N.M.; Micheu, M.M.; Bataila, V.; Scafa-Udriste, A.; Dorobantu, L.; Scarlatescu, A.I.; Zamfir, D.; Stoian, M.; Onciul, S.; Dorobantu, M. Exploring the continuum of hypertrophic cardiomyopathy—From DNA to clinical expression. Medicina 2019, 55, 299. [Google Scholar] [CrossRef] [PubMed]
  13. Kirchhof, P.; Benussi, S.; Kotecha, D.; Ahlsson, A.; Atar, D.; Casadei, B.; Castella, M.; Diener, H.C.; Heidbuchel, H.; Hendriks, J.; et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Europace 2016, 18, 2893–2962. [Google Scholar] [CrossRef] [PubMed]
  14. Lee, S.-P.; Ashley, E.A.; Homburger, J.; Caleshu, C.; Green, E.M.; Jacoby, D.; Colan, S.D.; Arteaga-Fernández, E.; Day, S.M.; Girolami, F.; et al. Incident Atrial Fibrillation Is Associated with MYH7 Sarcomeric Gene Variation in Hypertrophic Cardiomyopathy. Circ. Heart Fail. 2018, 11, e005191. [Google Scholar] [CrossRef] [PubMed]
  15. Pioner, J.M.; Vitale, G.; Gentile, F.; Scellini, B.; Piroddi, N.; Cerbai, E.; Olivotto, I.; Tardiff, J.; Coppini, R.; Tesi, C.; et al. Genotype-Driven Pathogenesis of Atrial Fibrillation in Hypertrophic Cardiomyopathy: The Case of Different TNNT2 Mutations. Front. Physiol. 2022, 13, 864547. [Google Scholar] [CrossRef]
  16. Macri, V.; Mahida, S.N.; Zhang, M.L.; Sinner, M.F.; Dolmatova, E.V.; Tucker, N.R.; McLellan, M.; Shea, M.A.; Milan, D.J.; Lunetta, K.L.; et al. A novel trafficking-defective HCN4 mutation is associated with early-onset atrial fibrillation. Heart Rhythm. 2014, 11, 1055–1062. [Google Scholar] [CrossRef]
  17. Olson, T.M.; Alekseev, A.E.; Liu, X.K.; Park, S.; Zingman, L.V.; Bienengraeber, M.; Sattiraju, S.; Ballew, J.D.; Jahangir, A.; Terzic, A. Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Hum. Mol. Genet. 2006, 15, 2185–2191. [Google Scholar] [CrossRef]
  18. Olesen, M.S.; Refsgaard, L.; Holst, A.G.; Larsen, A.P.; Grubb, S.; Haunsø, S.; Svendsen, J.H.; Olesen, S.-P.; Schmitt, N.; Calloe, K. A novel KCND3 gain-of-function mutation associated with early-onset of persistent lone atrial fibrillation. Cardiovasc. Res. 2013, 98, 488–495. [Google Scholar] [CrossRef]
  19. Olesen, M.S.; Bentzen, B.H.; Nielsen, J.B.; Steffensen, A.B.; David, J.-P.; Jabbari, J.; Jensen, H.K.; Haunsø, S.; Svendsen, J.H.; Schmitt, N. Mutations in the potassium channel subunit KCNE1 are associated with early-onset familial atrial fibrillation. BMC Med. Genet. 2012, 13, 24. [Google Scholar] [CrossRef]
  20. Nielsen, J.B.; Bentzen, B.H.; Olesen, M.S.; David, J.-P.; Olesen, S.-P.; Haunsø, S.; Svendsen, J.H.; Schmitt, N. Gain-of-function mutations in potassium channel subunit KCNE2 associated with early-onset lone atrial fibrillation. Biomark. Med. 2014, 8, 557–570. [Google Scholar] [CrossRef]
  21. Lundby, A.; Ravn, L.S.; Svendsen, J.H.; Haunsø, S.; Olesen, S.-P.; Schmitt, N. KCNE3 Mutation V17M Identified in a Patient with Lone Atrial Fibrillation. Cell. Physiol. Biochem. 2008, 21, 47–54. [Google Scholar] [CrossRef]
  22. Sinner, M.F.; Pfeufer, A.; Akyol, M.; Beckmann, B.M.; Hinterseer, M.; Wacker, A.; Perz, S.; Sauter, W.; Illig, T.; Näbauer, M.; et al. The non-synonymous coding IKr-channel variant KCNH2-K897T is associated with atrial fibrillation: Results from a systematic candidate gene-based analysis of KCNH2 (HERG). Eur. Heart J. 2008, 29, 907–914. [Google Scholar] [CrossRef] [PubMed]
  23. Deo, M.; Ruan, Y.; Pandit, S.V.; Shah, K.; Berenfeld, O.; Blaufox, A.; Cerrone, M.; Noujaim, S.F.; Denegri, M.; Jalife, J.; et al. KCNJ2 mutation in short QT syndrome 3 results in atrial fibrillation and ventricular proarrhythmia. Proc. Natl. Acad. Sci. USA 2013, 110, 4291–4296. [Google Scholar] [CrossRef]
  24. Calloe, K.; Ravn, L.S.; Schmitt, N.; Sui, J.L.; Duno, M.; Haunso, S.; Grunnet, M.; Svendsen, J.H.; Olesen, S.-P. Characterizations of a loss-of-function mutation in the Kir3.4 channel subunit. Biochem. Biophys. Res. Commun. 2007, 364, 889–895. [Google Scholar] [CrossRef] [PubMed]
  25. Delaney, J.T.; Muhammad, R.; Blair, M.A.; Kor, K.; Fish, F.A.; Roden, D.M.; Darbar, D. A KCNJ8 mutation associated with early repolarization and atrial fibrillation. Europace 2012, 14, 1428–1432. [Google Scholar] [CrossRef] [PubMed]
  26. Steffensen, A.B.; Refsgaard, L.; Andersen, M.N.; Vallet, C.; Mujezinovic, A.; Haunsø, S.; Svendsen, J.H.; Olesen, S.-P.; Olesen, M.S.; Schmitt, N. IKs Gain- and Loss-of-Function in Early-Onset Lone Atrial Fibrillation. J. Cardiovasc. Electrophysiol. 2015, 26, 715–723. [Google Scholar] [CrossRef]
  27. Watanabe, H.; Darbar, D.; Kaiser, D.W.; Jiramongkolchai, K.; Chopra, S.; Donahue, B.S.; Kannankeril, P.J.; Roden, D.M. Mutations in Sodium Channel β1- and β2-Subunits Associated With Atrial Fibrillation. Circ. Arrhythmia Electrophysiol. 2009, 2, 268–275. [Google Scholar] [CrossRef] [PubMed]
  28. Olesen, M.S.; Jespersen, T.; Nielsen, J.B.; Liang, B.; Møller, D.V.; Hedley, P.; Christiansen, M.; Varró, A.; Olesen, S.-P.; Haunsø, S.; et al. Mutations in sodium channel β-subunit SCN3B are associated with early-onset lone atrial fibrillation. Cardiovasc. Res. 2011, 89, 786–793. [Google Scholar] [CrossRef]
  29. Li, R.-G.; Wang, Q.; Xu, Y.-J.; Zhang, M.; Qu, X.-K.; Liu, X.; Fang, W.-Y.; Yang, Y.-Q. Mutations of the SCN4B-encoded sodium channel β4 subunit in familial atrial fibrillation. Int. J. Mol. Med. 2013, 32, 144–150. [Google Scholar] [CrossRef]
  30. Ziyadeh-Isleem, A.; Clatot, J.; Duchatelet, S.; Gandjbakhch, E.; Denjoy, I.; Hidden-Lucet, F.; Hatem, S.; Deschênes, I.; Coulombe, A.; Neyroud, N.; et al. A truncating SCN5A mutation combined with genetic variability causes sick sinus syndrome and early atrial fibrillation. Heart Rhythm. 2014, 11, 1015–1023. [Google Scholar] [CrossRef]
  31. Gakenheimer-Smith, L.; Meyers, L.; Lundahl, D.; Menon, S.C.; Bunch, T.J.; Sawyer, B.L.; Tristani-Firouzi, M.; Etheridge, S.P. Expanding the phenotype of CACNA1C mutation disorders. Mol. Genet. Genom. Med. 2021, 9, e1673. [Google Scholar] [CrossRef] [PubMed]
  32. Templin, C.; Ghadri, J.-R.; Rougier, J.-S.; Baumer, A.; Kaplan, V.; Albesa, M.; Sticht, H.; Rauch, A.; Puleo, C.; Hu, D.; et al. Identification of a novel loss-of-function calcium channel gene mutation in short QT syndrome (SQTS6). Eur. Heart J. 2011, 32, 1077–1088. [Google Scholar] [CrossRef] [PubMed]
  33. Ling, T.-Y.; Wang, X.-L.; Chai, Q.; Lu, T.; Stulak, J.M.; Joyce, L.D.; Daly, R.C.; Greason, K.L.; Wu, L.-Q.; Shen, W.-K.; et al. Regulation of cardiac CACNB2 by microRNA-499: Potential role in atrial fibrillation. BBA Clin. 2017, 7, 78–84. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, J.; Wu, H.; Chen, Q.; Xie, X.; Zou, T.; Zhu, C.; Dong, Y.; Xiang, G.; Ye, L.; Li, Y.; et al. Calcium-Mediated Oscillation in Membrane Potentials and Atrial-Triggered Activity in Atrial Cells of Casq2R33Q/R33Q Mutation Mice. Front. Physiol. 2018, 9, 392298. [Google Scholar] [CrossRef] [PubMed]
  35. Beavers, D.L.; Wang, W.; Ather, S.; Voigt, N.; Garbino, A.; Dixit, S.S.; Landstrom, A.P.; Li, N.; Wang, Q.; Olivotto, I.; et al. Mutation E169K in junctophilin-2 causes atrial fibrillation due to impaired RyR2 stabilization. J. Am. Coll. Cardiol. 2013, 62, 2010–2019. [Google Scholar] [CrossRef]
  36. Zhabyeyev, P.; Hiess, F.; Wang, R.; Liu, Y.; Wayne Chen, S.R.; Oudit, G.Y. S4153R is a gain-of-function mutation in the cardiac Ca2+ release channel ryanodine receptor associated with catecholaminergic polymorphic ventricular tachycardia and paroxysmal atrial fibrillation. Can. J. Cardiol. 2013, 29, 993–996. [Google Scholar] [CrossRef]
  37. Micheu, M.M.; Popa-Fotea, N.-M.; Oprescu, N.; Bogdan, S.; Dan, M.; Deaconu, A.; Dorobantu, L.; Gheorghe-Fronea, O.; Greavu, M.; Iorgulescu, C.; et al. Yield of Rare Variants Detected by Targeted Next-Generation Sequencing in a Cohort of Romanian Index Patients with Hypertrophic Cardiomyopathy. Diagnostics 2020, 10, 1061. [Google Scholar] [CrossRef]
  38. Ionilă, P.; Jurcuţ, R.; Ferariu, N.; Roşca, M.; Chivulescu, M.; Mursă, A.; Militaru, S.; Ionescu, A.A.; Căldăraru, C.; Fruntelată, A.G.; et al. Romanian Registry of Hypertrophic Cardiomyopathy—Overview of general characteristics and therapeutic choices at a national level. Rom. J. Intern. Med. Rev. Roum. Med. Interne 2018, 56, 203–209. [Google Scholar] [CrossRef]
  39. Liao, M.T.; Wu, C.K.; Juang, J.M.J.; Lin, T.T.; Wu, C.C.; Lin, L.Y. Atrial fibrillation and the risk of sudden cardiac arrest in patients with hypertrophic cardiomyopathy—A nationwide cohort study. EClinicalMedicine 2021, 34, 100802. [Google Scholar] [CrossRef]
  40. Nistri, S.; Olivotto, I.; Betocchi, S.; Losi, M.A.; Valsecchi, G.; Pinamonti, B.; Conte, M.R.; Casazza, F.; Galderisi, M.; Maron, B.J.; et al. Prognostic Significance of Left Atrial Size in Patients With Hypertrophic Cardiomyopathy (from the Italian Registry for Hypertrophic Cardiomyopathy). Am. J. Cardiol. 2006, 98, 960–965. [Google Scholar] [CrossRef]
  41. Minami, Y.; Haruki, S.; Yashiro, B.; Suzuki, T.; Ashihara, K.; Hagiwara, N. Enlarged left atrium and sudden death risk in hypertrophic cardiomyopathy patients with or without atrial fibrillation. J. Cardiol. 2016, 68, 478–484. [Google Scholar] [CrossRef]
  42. Lim, D.J.; Ambale-Ventakesh, B.; Ostovaneh, M.R.; Zghaib, T.; Ashikaga, H.; Wu, C.; Watson, K.E.; Hughes, T.; Shea, S.; Heckbert, S.R.; et al. Change in left atrial function predicts incident atrial fibrillation: The Multi-Ethnic Study of Atherosclerosis. Eur. Heart J. Cardiovasc. Imaging 2019, 20, 979–987. [Google Scholar] [CrossRef] [PubMed]
  43. Patel, R.B.; Delaney, J.A.; Hu, M.; Patel, H.; Cheng, J.; Gottdiener, J.; Kizer, J.R.; Marcus, G.M.; Turakhia, M.P.; Deo, R.; et al. Characterization of cardiac mechanics and incident atrial fibrillation in participants of the Cardiovascular Health Study. JCI Insight 2020, 5, e141656. [Google Scholar] [CrossRef] [PubMed]
  44. Hauser, R.; Nielsen, A.B.; Skaarup, K.G.; Lassen, M.C.H.; Duus, L.S.; Johansen, N.D.; Sengeløv, M.; Marott, J.L.; Jensen, G.; Schnohr, P.; et al. Left atrial strain predicts incident atrial fibrillation in the general population: The Copenhagen City Heart Study. Eur. Heart J. Cardiovasc. Imaging 2022, 23, 52–60. [Google Scholar] [CrossRef]
  45. Huber, M.P.; Pandit, J.A.; Jensen, P.N.; Wiggins, K.L.; Patel, R.B.; Freed, B.H.; Bertoni, A.G.; Shah, S.J.; Heckbert, S.R.; Floyd, J.S. Left Atrial Strain and the Risk of Atrial Arrhythmias From Extended Ambulatory Cardiac Monitoring: MESA. J. Am. Heart Assoc. 2022, 11, e026875. [Google Scholar] [CrossRef] [PubMed]
  46. Chen, S.; Zhang, L.; Bryant, R.M.; Vincent, G.M.; Flippin, M.; Lee, J.C.; Brown, E.; Zimmerman, F.; Rozich, R.; Szafranski, P.; et al. KCNQ1 mutations in patients with a family history of lethal cardiac arrhythmias and sudden death. Clin. Genet. 2003, 63, 273–282. [Google Scholar] [CrossRef] [PubMed]
  47. Makarious, D.; Bhat, A.; Khanna, S.; Chen, H.; Drescher, A.; Stephens, M.; Fernandez, F.; Gan, G.; Tan, T. Correlation between Atrial Fibrillation Burden and Changes in Indices of Left Atrial Size and Function. Heart Lung Circ. 2020, 29, S174. [Google Scholar] [CrossRef]
  48. Boriani, G.; Vitolo, M.; Diemberger, I.; Proietti, M.; Valenti, A.C.; Malavasi, V.L.; Lip, G.Y.H. Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes. Cardiovasc. Res. 2021, 117, 1–21. [Google Scholar] [CrossRef]
  49. van de Vegte, Y.J.; Siland, J.E.; Rienstra, M.; van der Harst, P. Atrial fibrillation and left atrial size and function: A Mendelian randomization study. Sci. Rep. 2021, 11, 8431. [Google Scholar] [CrossRef]
  50. Sardana, M.; Lessard, D.; Tsao, C.W.; Parikh, N.I.; Barton, B.A.; Nah, G.; Thomas, R.C.; Cheng, S.; Schiller, N.B.; Aragam, J.R.; et al. Association of Left Atrial Function Index with Atrial Fibrillation and Cardiovascular Disease: The Framingham Offspring Study. J. Am. Heart Assoc. 2018, 7, e008435. [Google Scholar] [CrossRef]
  51. Bajraktari, G.; Bytyçi, I.; Henein, M.Y. Left atrial structure and function predictors of recurrent fibrillation after catheter ablation: A systematic review and meta-analysis. Clin. Physiol. Funct. Imaging 2020, 40, 1–13. [Google Scholar] [CrossRef]
  52. Vaziri, S.M.; Larson, M.G.; Benjamin, E.J.; Levy, D. Echocardiographic predictors of nonrheumatic atrial fibrillation: The Framingham Heart Study. Circulation 1994, 89, 724–730. [Google Scholar] [CrossRef]
  53. Ganesan, A.N.; Chew, D.P.; Hartshorne, T.; Selvanayagam, J.B.; Aylward, P.E.; Sanders, P.; McGavigan, A.D. The impact of atrial fibrillation type on the risk of thromboembolism, mortality, and bleeding: A systematic review and meta-analysis. Eur. Heart J. 2016, 37, 1591–1602. [Google Scholar] [CrossRef]
  54. Chen, L.Y.; Chung, M.K.; Allen, L.A.; Ezekowitz, M.; Furie, K.L.; McCabe, P.; Noseworthy, P.A.; Perez, M.V.; Turakhia, M.P. Atrial Fibrillation Burden: Moving Beyond Atrial Fibrillation as a Binary Entity: A Scientific Statement From the American Heart Association. Circulation 2018, 137, e623–e644. [Google Scholar] [CrossRef] [PubMed]
  55. Fatini, C.; Sticchi, E.; Genuardi, M.; Sofi, F.; Gensini, F.; Gori, A.M.; Lenti, M.; Michelucci, A.; Abbate, R.; Gensini, G.F. Analysis of minK and eNOS genes as candidate loci for predisposition to non-valvular atrial fibrillation. Eur. Heart J. 2006, 27, 1712–1718. [Google Scholar] [CrossRef] [PubMed]
  56. Prystupa, A.; Dzida, G.; Myśliński, W.; Małaj, G.; Lorenc, T. MinK gene polymorphism in the pathogenesis of lone atrial fibrillation. Kardiol. Pol. 2006, 64, 1205–1211. [Google Scholar] [PubMed]
  57. Yao, J.; Ma, Y.T.; Xie, X.; Liu, F.; Chen, B.D. Association of KCNE1 genetic polymorphisms with atrial fibrillation in a Chinese Han population. Genet. Test. Mol. Biomark. 2012, 16, 1343–1346. [Google Scholar] [CrossRef] [PubMed]
  58. Zeng, Z.; Tan, C.; Teng, S.; Chen, J.; Su, S.; Zhou, X.; Wang, F.; Zhang, S.; Gu, D.; Makielski, J.C.; et al. The single nucleotide polymorphisms of IKs potassium channel genes and their association with atrial fibrillation in a Chinese population. Cardiology 2007, 108, 97–103. [Google Scholar] [CrossRef]
  59. Xu, L.X.; Yang, W.Y.; Zhang, H.Q.; Tao, Z.H.; Duan, C.C. Study on the correlation between CETP TaqIB, KCNE1 S38G and eNOS T-786C gene polymorphisms for predisposition and non-valvular atrial fibrillation. Chin. J. Epidemiol. 2008, 29, 486–492. [Google Scholar]
  60. Liang, C.; Li, X.; Xu, Y.; Chen, Q.; Wu, Y.; Wang, W.; Li, W.; Qiu, M. KCNE1 rs1805127 Polymorphism Increases the Risk of Atrial Fibrillation: A Meta-Analysis of 10 Studies. PLoS ONE 2013, 8, e68690. [Google Scholar] [CrossRef]
  61. Jabbari, J.; Olesen, M.S.; Yuan, L.; Nielsen, J.B.; Liang, B.; Macri, V.; Christophersen, I.E.; Nielsen, N.; Sajadieh, A.; Ellinor, P.T.; et al. Common and rare variants in SCN10A modulate the risk of atrial fibrillation. Circ. Cardiovasc. Genet. 2015, 8, 64–73. [Google Scholar] [CrossRef] [PubMed]
  62. Cruz, D.; Pinto, R.; Freitas-Silva, M.; Nunes, J.P.; Medeiros, R. GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: Pathophysiological implications. Pharmacogenomics 2019, 20, 765–780. [Google Scholar] [CrossRef] [PubMed]
  63. Patel, K.K.; Venkatesan, C.; Abdelhalim, H.; Zeeshan, S.; Arima, Y.; Linna-Kuosmanen, S.; Ahmed, Z. Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Hum. Genom. 2023, 17, 47. [Google Scholar] [CrossRef]
  64. Andreasen, L.; Nielsen, J.B.; Darkner, S.; Christophersen, I.E.; Jabbari, J.; Refsgaard, L.; Thiis, J.J.; Sajadieh, A.; Tveit, A.; Haunsø, S.; et al. Brugada syndrome risk loci seem protective against atrial fibrillation. Eur. J. Hum. Genet. 2014, 22, 1357–1361. [Google Scholar] [CrossRef] [PubMed]
  65. Elliott, P.M.; Anastasakis, A.; Borger, M.A.; Borggrefe, M.; Cecchi, F.; Charron, P.; Hagege, A.A.; Lafont, A.; Limongelli, G.; Mahrholdt, H.; et al. 2014 ESC guidelines on diagnosis and management of hypertrophic cardiomyopathy: The task force for the diagnosis and management of hypertrophic cardiomyopathy of the European Society of Cardiology (ESC). Eur. Heart J. 2014, 35, 2733–2779. [Google Scholar] [CrossRef] [PubMed]
  66. Micheu, M.M.; Popa-Fotea, N.; Oprescu, N.; Dorobantu, M.; Ratiu, A.C.; Ecovoiu, A.A. NGS data validated by Sanger sequencing reveal a puzzling small deletion of MYBPC3 gene associated with hypertrophic cardiomyopathy. Rom Biotechnol Lett. 2019, 24, 91–99. [Google Scholar] [CrossRef]
  67. Popa-Fotea, N.-M.; Micheu, M.M.; Oprescu, N.; Alexandrescu, A.; Greavu, M.; Onciul, S.; Onut, R.; Petre, I.; Scarlatescu, A.; Stoian, M.; et al. The Role of Left-Atrial Mechanics Assessed by Two-Dimensional Speckle-Tracking Echocardiography to Differentiate Hypertrophic Cardiomyopathy from Hypertensive Left-Ventricular Hypertrophy. Diagnostics 2021, 11, 814. [Google Scholar] [CrossRef]
  68. Lang, R.M.; Badano, L.P.; Mor-Avi, V.; Afilalo, J.; Armstrong, A.; Ernande, L.; Flachskampf, F.A.; Foster, E.; Goldstein, S.A.; Kuznetsova, T.; et al. Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J. Am. Soc. Echocardiogr. 2015, 28, 1–39.e14. [Google Scholar] [CrossRef]
  69. Thomas, L.; Marwick, T.H.; Popescu, B.A.; Donal, E.; Badano, L.P. Left Atrial Structure and Function, and Left Ventricular Diastolic Dysfunction: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 2019, 73, 1961–1977. [Google Scholar] [CrossRef]
  70. MacHino-Ohtsuka, T.; Seo, Y.; Tada, H.; Ishizu, T.; MacHino, T.; Yamasaki, H.; Igarashi, M.; Xu, D.; Sekiguchi, Y.; Aonuma, K. Left atrial stiffness relates to left ventricular diastolic dysfunction and recurrence after pulmonary vein isolation for atrial fibrillation. J. Cardiovasc. Electrophysiol. 2011, 22, 999–1006. [Google Scholar] [CrossRef]
  71. Badano, L.P.; Kolias, T.J.; Muraru, D.; Abraham, T.P.; Aurigemma, G.; Edvardsen, T.; D’Hooge, J.; Donal, E.; Fraser, A.G.; Marwick, T.; et al. Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: A consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur. Heart J. Cardiovasc. Imaging 2018, 19, 591–600. [Google Scholar] [CrossRef]
  72. Smith, J.G.; Almgren, P.; Engström, G.; Hedblad, B.; Platonov, P.G.; Newton-Cheh, C.; Melander, O. Genetic polymorphisms for estimating risk of atrial fibrillation: A literature-based meta-analysis. J. Intern. Med. 2012, 272, 573–582. [Google Scholar] [CrossRef] [PubMed]
  73. Zeng, Z.Y.; Pu, J.L.; Tan, C.; Teng, S.Y.; Chen, J.H.; Su, S.Y.; Zhou, X.Y.; Zhang, S.; Li, Y.S.; Wang, F.Z.; et al. The association of single nucleotide polymorphism of slow delayed rectifier K+ channel genes with atrial fibrillation in Han nationality Chinese. Zhonghua Xin Xue Guan Bing Za Zhi. 2005, 33, 987–991. [Google Scholar] [PubMed]
  74. Chen, L.Y.; Goh, J.M.; Wong, R.C.; Hsu, L.F.; Foo, D.; Benditt, D.G.; Ling, L.H.; Heng, C.K. Comprehensive mutation scanning of KCNQ1 in 111 Han Chinese patients with lone atrial fibrillation. Heart Asia 2010, 2, 126–128. [Google Scholar] [CrossRef] [PubMed]
  75. Husser, D.; Ueberham, L.; Hindricks, G.; Büttner, P.; Ingram, C.; Weeke, P.; Shoemaker, M.B.; Adams, V.; Arya, A.; Sommer, P.; et al. Rare variants in genes encoding the cardiac sodium channel and associated compounds and their impact on outcome of catheter ablation of atrial fibrillation. PLoS ONE 2017, 12, 0183690. [Google Scholar] [CrossRef] [PubMed]
  76. McNair, W.P.; Sinagra, G.; Taylor, M.R.; Di Lenarda, A.; Ferguson, D.A.; Salcedo, E.E.; Slavov, D.; Zhu, X.; Caldwell, J.H.; Mestroni, L. Familial Cardiomyopathy Registry Research Group. SCN5A mutations associate with arrhythmic dilated cardiomyopathy and commonly localize to the voltage-sensing mechanism. J. Am. Coll. Cardiol. 2011, 57, 2160–2168. [Google Scholar] [CrossRef]
  77. An, R.; Liu, J.; Zhang, J.; Yao, F.; Tian, D.; Liang, F.; Li, W.; Li, D.; Wang, Y.; Yan, S.; et al. Risk factors and SCN5A-H558R polymorphism for atrial fibrillation in Tibetans living at different altitudes. Medicine 2022, 101, e31778. [Google Scholar] [CrossRef]
  78. Malakootian, M.; Jalilian, M.; Kalayinia, S.; Hosseini Moghadam, M.; Heidarali, M.; Haghjoo, M. Whole-exome sequencing reveals a rare missense variant in DTNA in an Iranian pedigree with early-onset atrial fibrillation. BMC Cardiovasc. Disord. 2022, 22, 37. [Google Scholar] [CrossRef]
Table 1. General and echocardiographic characteristics of study cohort illustrated comparatively for AFib+ and AFib− subjects.
Table 1. General and echocardiographic characteristics of study cohort illustrated comparatively for AFib+ and AFib− subjects.
VariableAFib+ (N = 25)AFib– (N = 20)p-Value
Age at inclusion, years51.67 ± 14.5546.35 ± 150.27
Male gender, N (%)20 (80%)15 (75%)0.73
History of SCD, N (%)10 (40%)4 (20%)0.27
ICD, N (%)3 (12%)4 (20%)0.43
5-years SCD risk score5.49 ± 3.92%2.83 ± 1.37%0.044
Echocardiography
Maron 13 (12%)4 (20%)
Maron 23 (12%)2 (10%)0.43
Maron 319 (76%)13 (65%)
Maron 401 (5%)
LV maximal wall thickness, mm20.19 ± 3.4520.9 ± 6.330.65
LV mass indexed, g/m2142.5 ± 44.11119.52 ± 41.370.15
LVEDD, cm41 ± 7.3341 ± 9.20.99
LVESD, cm25.7 ± 9.224.8 ± 10.790.79
LVEDV, ml106.4 ± 35.21119.15 ± 45.960.37
LVESV, ml56 ± 31.8161.84 ± 38.810.64
LVEF, (%)51.6 ± 13.9652.53 ± 12.180.83
LAD, mm41.61 ± 6.239.4 ± 7.620.34
Volumetric variables
LAVmax (mL/m2)38.47 ± 13.728.67 ± 9.270.007
LAVmin (mL/m2)19 ± 6.817 ±4.60.247
LAVpre-A (mL/m2)24 ± 2.320 ± 7.40.029
LATEF (%)54 ± 10.360 ± 8.80.041
LAEI (%)167 ± 60.3172 ± 50.40.763
LAPEF (%)26 ± 625 ± 3.90.504
LAAEF (%)41 ± 12.646 ± 14.50.231
LAEF (kdyne)10 ± 5.711.6 ± 80.456
Deformation variables
LASr (%)21 ± 5.625 ± 40.008
pLASRr (s−1)0.8 ± 0.41.2 ± 0.670.025
LASI1.8 ± 0.41.4 ± 0.80.036
LAScd (%)17 ± 3.518 ± 9.50.659
pLASRcd (s−1)−0.4 ± 0.2−0.6 ± 1.10.432
LASct (%)10 ± 1.210.5 ± 4.50.634
pLASRct (s−1)−1.3 ± 0.6−1.4 ± 0.40.508
ICD, implantable cardiac defibrillator; LAD, left atrial diameter; LAAEF, left-atrial active ejection fraction; LAEF, left-atrial ejection force; LAEI, left-atrial ejection index; LAPEF, left-atrial passive ejection fraction; LAScd, left-atrial strain during conduit phase; LASct, left-atrial strain during contractile phase; LASI, left-atrial stiffness index; LASr, left-atrial strain during reservoir phase; pLASRr, peak strain rate during reservoir phase; pLASRcd, peak strain rate during conduit phase; pLASRct, peak strain rate during contractile phase; LATEF, left-atrial total ejection fraction; LAVmax, left-atrial volume during systole; LAVmin, left-atrial volume during diastole; LAVpre-A, left-atrial volume before P-wave; LV, left ventricle; LVEDD, left ventricle end-diastolic diameter; LVEDV, left ventricle end-diastolic volume; LVEF, left ventricle ejection fraction; LVESD, left ventricle end-systolic diameter; LVESV, left ventricle end-systolic volume; SCD sudden cardiac death.
Table 2. List of the 24 genes analyzed in our study, encoding proteins and references that relate these with atrial fibrillation.
Table 2. List of the 24 genes analyzed in our study, encoding proteins and references that relate these with atrial fibrillation.
Potassium Channel-Related GenesEncoding Protein (UniProt ID)References
ABCC9ATP-binding cassette subfamily C member 9 (O60706)[7]
HCN4Potassium/sodium hyperpolarization-activated cyclic nucleotide gated potassium channel 4 (Q9Y3Q4)[16]
KCNA5Potassium voltage-gated channel subfamily A member 5 (P22460)[17]
KCND3Potassium voltage-gated channel subfamily D member 3 (Q9UK17)[18]
KCNE1Potassium voltage-gated channel subfamily E member 1 (P15382)[19]
KCNE2Potassium voltage-gated channel subfamily E member 2 (Q9Y6J6)[20]
KCNE3Potassium voltage-gated channel subfamily E member 3 (Q9Y6H6)[21]
KCNH2Potassium voltage-gated channel subfamily H member 2 (Q12809)[22]
KCNJ2Inward rectifier potassium channel 2 (P63252)[23]
KCNJ5G protein-activated inward rectifier potassium channel 4 (P48544)[24]
KCNJ8ATP-sensitive inward rectifier potassium channel 8 (Q15842)[25]
KCNQ1Potassium voltage-gated channel subfamily KQT member 1 (P51787)[26]
Sodium channel-related genesEncoding Protein
SCN1BSodium channel subunit beta-1 (Q07699)[27]
SCN2BSodium channel subunit beta-2 (O60939)[27]
SCN3BSodium channel subunit beta-3 (Q9NY72)[28]
SCN4BSodium channel subunit beta-4 (Q8IWT1)[29]
SCN5ASodium channel protein type 5 subunit alpha (Q14524)[30]
Calcium channel-/calcium homeostasis-related genesEncoding Protein
CACNA1CVoltage-dependent L-type calcium channel subunit alpha-1C (Q13936)[31]
CACNA2D1Voltage-dependent calcium channel subunit alpha-2/delta-1 (P54289)[32]
CACNB2Voltage-dependent L-type calcium channel subunit beta-2 (Q08289)[33]
CALM1Calmodulin-1 (P0DP23)
CASQ2Calsequestrin-2 (O14958)[34]
JPH2Junctophilin-2 (Q9BR39)[35]
RYR2Ryanodine receptor 2 (Q92736)[36]
Table 3. List of pathogenic or likely pathogenic variants in sarcomeric genes identified in the cohort.
Table 3. List of pathogenic or likely pathogenic variants in sarcomeric genes identified in the cohort.
GeneHGVScHGVSpdbSNP IDClinVar IDCarrier Patient
MYBPC3c.3294G>Ap.Trp1098Terrs767039057520341AFib+
MYBPC3c.772G>Ap.Glu258Lysrs39751607442792AFib+
MYH7c.2389G>Ap.Ala797Thrrs321871642901AFib+
MYH7c.715G>Ap.Asp239Asnrs39751626443100AFib+
TNNI3c.557G>Ap.Arg186Glnrs39751635743395AFib+
TPM1c.574G>Ap.Glu192Lysrs19947631531882AFib−
Table 4. The number of nonsynonymous variants identified in potassium channel-coding genes in the two groups.
Table 4. The number of nonsynonymous variants identified in potassium channel-coding genes in the two groups.
GeneAFib+ (N = 25)AFib− (N = 20)
ABCC901
HCN413
KCNA531
KCND301
KCNE12020
KCNE200
KCNE300
KCNH2148
KCNJ200
KCNJ52519
KCNJ801
KCNQ112
AFib, atrial fibrillation.
Table 5. The number of nonsynonymous variants identified in sodium channel-coding genes in the two groups.
Table 5. The number of nonsynonymous variants identified in sodium channel-coding genes in the two groups.
GeneAFib+ (N = 25)AFib− (N = 20)
SCN1B3126
SCN2B02
SCN3B00
SCN4B01
SCN5A159
AFib, atrial fibrillation.
Table 6. The number of nonsynonymous variants identified in calcium channel-coding genes in the two groups.
Table 6. The number of nonsynonymous variants identified in calcium channel-coding genes in the two groups.
GeneAFib+ (N = 25)AFib− (N = 20)
CACNA1C7753
CACNA2D101
CACNB21610
CALM100
CASQ21412
JPH263
RYR2913
AFib, atrial fibrillation.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Popa-Fotea, N.-M.; Oprescu, N.; Scafa-Udriste, A.; Micheu, M.M. Impact of rs1805127 and rs55742440 Variants on Atrial Remodeling in Hypertrophic Cardiomyopathy Patients with Atrial Fibrillation: A Romanian Cohort Study. Int. J. Mol. Sci. 2023, 24, 17244. https://doi.org/10.3390/ijms242417244

AMA Style

Popa-Fotea N-M, Oprescu N, Scafa-Udriste A, Micheu MM. Impact of rs1805127 and rs55742440 Variants on Atrial Remodeling in Hypertrophic Cardiomyopathy Patients with Atrial Fibrillation: A Romanian Cohort Study. International Journal of Molecular Sciences. 2023; 24(24):17244. https://doi.org/10.3390/ijms242417244

Chicago/Turabian Style

Popa-Fotea, Nicoleta-Monica, Nicoleta Oprescu, Alexandru Scafa-Udriste, and Miruna Mihaela Micheu. 2023. "Impact of rs1805127 and rs55742440 Variants on Atrial Remodeling in Hypertrophic Cardiomyopathy Patients with Atrial Fibrillation: A Romanian Cohort Study" International Journal of Molecular Sciences 24, no. 24: 17244. https://doi.org/10.3390/ijms242417244

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop