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Minor allele of GJA1 gene polymorphism is associated with higher heart rate during atrial fibrillation.

ABSTRACT: Atrial fibrillation (AF) tachycardia causes heart failure and requires more attention. The genetic background of individual heart rate (HR) variations during AF are unclear. We hypothesized that HR-associated single nucleotide polymorphisms (SNPs) reported in Genome-Wide Association Studies (GWAS) are also associated with HR during AF. We enrolled patients with persistent AF (311 for screening and 146 for replication) who underwent AF ablation and were genotyped for the 21 h-associated SNPs reported in GWAS. The patients underwent 24-h Holter monitoring before AF ablation and electrophysiological study after AF ablation during sinus rhythm. Only the GJA1 SNP rs1015451 (T>C) was significantly associated with total HR (TT 110,643?±?17,542 beats/day, TC 116,350?±?19,060 beats/day, CC 122,163?±?25,684 beats/day, P?=?8.5?×?10^-4). We also confirmed this significant association in the replication set. The intra-atrial conduction was faster in AF patients with the GJA1 minor allele than in those without it. Multivariate analysis revealed the presence of a GJA1 SNP rs1015451 additive model, female gender, lower left ventricular ejection fraction, and higher 1:1 atrioventricular nodal conduction were independently associated with higher HR during AF. The GJA1 SNP might be a new genetic marker for AF tachycardia.

SUBMITTER: Okamura S

PROVIDER: S-EPMC7844413 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Minor allele of GJA1 gene polymorphism is associated with higher heart rate during atrial fibrillation.

Okamura Sho S Onohara Yuko Y Ochi Hidenori H Tokuyama Takehito T Hironobe Naoya N Okubo Yosaku Y Ikeuchi Yoshihiro Y Miyauchi Shunsuke S Chayama Kazuaki K Kihara Yasuki Y Nakano Yukiko Y

Scientific reports 20210128 1

Atrial fibrillation (AF) tachycardia causes heart failure and requires more attention. The genetic background of individual heart rate (HR) variations during AF are unclear. We hypothesized that HR-associated single nucleotide polymorphisms (SNPs) reported in Genome-Wide Association Studies (GWAS) are also associated with HR during AF. We enrolled patients with persistent AF (311 for screening and 146 for replication) who underwent AF ablation and were genotyped for the 21 h-associated SNPs repo ...[more]

PMID: 33510344

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Project description:BackgroundScreening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden.MethodsWe calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale-MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases.ResultsIrregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method.ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.

Project description:BackgroundThe association between heart rate and 1-year clinical outcomes in heart failure (HF) patients with atrial fibrillation (AF), and whether this association depends on left ventricular ejection fraction (LVEF), are unclear. We investigated the relationship between discharge heart rate and 1-year clinical outcomes after discharge among hospitalized HF patients with AF, and further explored this association that differ by LVEF level.MethodsIn this analysis, we enrolled 1760 hospitalized HF patients with AF from the China Patient-centered Evaluative Assessment of Cardiac Events Prospective Heart Failure study from August 2016 to May 2018. Patients were categorized into three groups with low (<65 beats per minute [bpm]), moderate (65-85 bpm), and high (≥86 bpm) heart rate measured at discharge. Cox proportional hazard models were employed to explore the association between heart rate and 1-year primary outcome, which was defined as a composite outcome of all-cause death and HF rehospitalization.ResultsAmong 1760 patients, 723 (41.1%) were women, the median age was 69 (interquartile range [IQR]: 60-77) years, median discharge heart rate was 75 (IQR: 69-84) bpm, and 934 (53.1%) had an LVEF <50%. During 1-year follow-up, a total of 792 (45.0%) individuals died or had at least one HF hospitalization. After adjusting for demographic characteristics, smoking status, medical history, anthropometric characteristics, and medications used at discharge, the groups with low (hazard ratio [HR]: 1.32, 95% confidence interval [CI]: 1.05-1.68, P = 0.020) and high (HR: 1.34, 95% CI: 1.07-1.67, P = 0.009) heart rate were associated with a higher risk of 1-year primary outcome compared with the moderate group. A significant interaction between discharge heart rate and LVEF for the primary outcome was observed (P for interaction was 0.045). Among the patients with LVEF ≥50%, only those with high heart rate were associated with a higher risk of primary outcome compared with the group with moderate heart rate (HR: 1.38, 95% CI: 1.01-1.89, P = 0.046), whereas there was no difference between the groups with low and moderate heart rate. Among the patients with LVEF <50%, only those with low heart rate were associated with a higher risk of primary outcome compared with the group with moderate heart rate (HR: 1.46, 95% CI: 1.09-1.96, P = 0.012), whereas there was no difference between the groups with high and moderate heart rate.ConclusionsAmong the overall HF patients with AF, both low (<65 bpm) and high (≥86 bpm) heart rates were associated with poorer outcomes as compared with moderate (65-85 bpm) heart rate. Among patients with LVEF ≥50%, only a high heart rate was associated with higher risk; while among those with LVEF <50%, only a low heart rate was associated with higher risk as compared with the group with moderate heart rate.Trail registrationClinicaltrials.gov; NCT02878811.

Dataset Information

Minor allele of GJA1 gene polymorphism is associated with higher heart rate during atrial fibrillation.

Publications

Minor allele of GJA1 gene polymorphism is associated with higher heart rate during atrial fibrillation.

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