Risk prediction of atrial fibrillation in the community combining biomarkers and genetics

Analyses of m6A regulatory genes and subtype classification in atrial fibrillation

Objective

To explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.

Methods

We downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.

Results

The data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.

Conclusion

The m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Advances in basic and translational research in atrial fibrillation

Atrial fibrillation (AF) is a very common cardiac arrhythmia with an estimated prevalence of 33.5 million patients globally. It is associated with an increased risk of death, stroke and peripheral embolism. Although genetic studies have identified a growing number of genes associated with AF, the definitive impact of these genetic findings is yet to be established. Several mechanisms, including electrical, structural and neural remodelling of atrial tissue, have been proposed to contribute to the development of AF. Despite over a century of exploration, the molecular and cellular mechanisms underlying AF have not been fully established. Current antiarrhythmic drugs are associated with a significant rate of adverse events and management of AF using ablation is not optimal, especially in cases of persistent AF. This review discusses recent advances in our understanding and management of AF, including new concepts of epidemiology, genetics and pathophysiological mechanisms. We review the current status of antiarrhythmic drug therapy for AF, new potential agents, as well as mechanism-based AF ablation. This article is part of the theme issue 'The heartbeat: its molecular basis and physiological mechanisms'.

Association of bone morphogenetic protein 10 and recurrent atrial fibrillation after catheter ablation

AIMS

Atrial remodelling, defined as a change in atrial structure, promotes atrial fibrillation (AF). Bone morphogenetic protein 10 (BMP10) is an atrial-specific biomarker released to blood during atrial development and structural changes. We aimed to validate whether BMP10 is associated with AF recurrence after catheter ablation (CA) in a large cohort of patients.

METHODS AND RESULTS

We measured baseline BMP10 plasma concentrations in AF patients who underwent a first elective CA in the prospective Swiss-AF-PVI cohort study. The primary outcome was AF recurrence lasting longer than 30 s during a follow-up of 12 months. We constructed multivariable Cox proportional hazard models to determine the association of BMP10 and AF recurrence. A total of 1112 patients with AF (age 61 ± 10 years, 74% male, 60% paroxysmal AF) was included in our analysis. During 12 months of follow-up, 374 patients (34%) experienced AF recurrence. The probability for AF recurrence increased with increasing BMP10 concentration. In an unadjusted Cox proportional hazard model, a per-unit increase in log-transformed BMP10 was associated with a hazard ratio (HR) of 2.28 (95% CI 1.43; 3.62, P < 0.001) for AF recurrence. After multivariable adjustment, the HR of BMP10 for AF recurrence was 1.98 (95% CI 1.14; 3.42, P = 0.01), and there was a linear trend across BMP10 quartiles (P = 0.02 for linear trend).

CONCLUSION

The novel atrial-specific biomarker BMP10 was strongly associated with AF recurrence in patients undergoing CA for AF.

CLINICALTRIALS.GOV IDENTIFIER

NCT03718364; https://clinicaltrials.gov/ct2/show/NCT03718364.

Inflammatory proteomics profiling for prediction of incident atrial fibrillation

OBJECTIVE

Atrial fibrillation (AF) has emerged as a common condition in older adults. Cardiovascular risk factors only explain about 50% of AF cases. Inflammatory biomarkers may help close this gap as inflammation can alter atrial electrophysiology and structure. This study aimed to determine a cytokine biomarker profile for this condition in the community using a proteomics approach.

METHODS

This study uses cytokine proteomics in participants of the Finnish population-based FINRISK cohort studies 1997/2002. Risk models for 46 cytokines were developed to predict incident AF using Cox regressions. Furthermore, the association of participants' C reactive protein (CRP) and N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations with incident AF was examined.

RESULTS

In 10 744 participants (mean age of 50.9 years, 51.3% women), 1246 cases of incident AF were observed (40.5% women). The main analyses, adjusted for participants' sex and age, suggested that higher concentrations of macrophage inflammatory protein-1β (HR=1.11; 95% CI 1.04, 1.17), hepatocyte growth factor (HR=1.12; 95% CI 1.05, 1.19), CRP (HR=1.17; 95% CI 1.10, 1.24) and NT-proBNP (HR=1.58; 95% CI 1.45, 1.71) were associated with increased risk of incident AF. In further clinical variable-adjusted models, only NT-proBNP remained statistically significant.

CONCLUSION

Our study confirmed NT-proBNP as a strong predictor for AF. Observed associations of circulating inflammatory cytokines were primarily explained by clinical risk factors and did not improve risk prediction. The potential mechanistic role of inflammatory cytokines measured in a proteomics approach remains to be further elucidated.

Common genetic variants improve risk stratification after the atrial switch operation for transposition of the great arteries

BACKGROUND

Clinical factors are used to estimate late complication risk in adults after atrial switch operation (AtrSO) for transposition of the great arteries (TGA), but heterogeneity in clinical course remains. We studied whether common genetic variants are associated with outcome and add value to a clinical risk score in TGA-AtrSO patients.

METHODS AND RESULTS

This multicenter study followed 133 TGA-AtrSO patients (aged 28 [IQR 24-35] years) for 13 (IQR 9-16) years and examined the association of genome-wide single-nucleotide polymorphisms (SNPs) with a composite endpoint of symptomatic ventricular arrhythmia, heart failure hospitalization, ventricular assist device implantation, heart transplantation, or mortality. Thirty-two patients (24%) reached the endpoint. The genome-wide association study yielded one genome-wide significant (p < 1 × 10^-8) locus and 18 suggestive loci (p < 1 × 10^-5). A genetic risk score constructed on the basis of independent SNPs with p < 1 × 10^-5 was associated with outcome after correction for the clinical risk score (HR = 1.26/point increase [95%CI 1.17-1.35]). Risk stratification improved with a combined risk score (clinical score + genetic score) compared to the clinical score alone (p = 2 × 10^-16, C-statistic 0.95 vs 0.85). In 51 patients with a clinical intermediate (5-20%) 5-year risk of events, the combined score reclassified 32 patients to low (<5%) and 5 to high (>20%) risk. Stratified by the combined score, observed 5-year event-free survival was 100%, 79% and 31% for low, intermediate, and high-risk patients, respectively.

CONCLUSIONS

Common genetic variants may explain some variation in the clinical course in TGA-AtrSO and improve risk stratification over clinical factors alone, especially in patients at intermediate clinical risk. These findings support the hypothesis that including genetic variants in risk assessment may be beneficial.

A polygenic risk score predicts atrial fibrillation in cardiovascular disease

AIMS

Interest in targeted screening programmes for atrial fibrillation (AF) has increased, yet the role of genetics in identifying patients at highest risk of developing AF is unclear.

METHODS AND RESULTS

A total of 36,662 subjects without prior AF were analyzed from four TIMI trials. Subjects were divided into quintiles using a validated polygenic risk score (PRS) for AF. Clinical risk for AF was calculated using the CHARGE-AF model. Kaplan-Meier event rates, adjusted hazard ratios (HRs), C-indices, and net reclassification improvement were used to determine if the addition of the PRS improved prediction compared with clinical risk and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Over 2.3 years, 1018 new AF cases developed. AF PRS predicted a significant risk gradient for AF with a 40% increased risk per 1-SD increase in PRS [HR: 1.40 (1.32-1.49); P < 0.001]. Those with high AF PRS (top 20%) were more than two-fold more likely to develop AF [HR 2.45 (1.99-3.03), P < 0.001] compared with low PRS (bottom 20%). Furthermore, PRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, ranging from a 3-year incidence of 1.3% in patients with low clinical and genetic risk to 8.7% in patients with high clinical and genetic risk. The subgroup of patients with high clinical risk, high PRS, and elevated NT-proBNP had an AF risk of 16.7% over 3 years. The C-index with the CHARGE-AF clinical risk score alone was 0.65, which improved to 0.67 (P < 0.001) with the addition of NT-proBNP, and increased further to 0.70 (P < 0.001) with the addition of the PRS.

CONCLUSION

In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.

Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods

AIMS

To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables.

METHODS AND RESULTS

In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. In multivariable-adjusted Cox-regression analysis, NT-proBNP was the strongest circulating predictor of incident AF [hazard ratio (HR) per standard deviation (SD), 1.93 (95% CI, 1.82-2.04); P < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13-1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10-1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02-1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index.

CONCLUSION

Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively.

Predictors of atrial fibrillation in patients with ischemic stroke of undetermined etiology

Aim    To identify a complex of predictors and to create a mathematical model for prognosis of atrial fibrillation (AF) in patients with ischemic stroke of undetermined etiology. Material and methods    The study included 981 patients with ischemic stroke. Effects of the following factors were evaluated: gender, a history of stroke, a history of thromboembolism, presence of diabetes mellitus, grade of arterial hypertension, functional class (FC) of chronic heart failure (CHF), age, data of blood biochemistry, and data of coagulogram. The prognostic model was constructed using the binary logistic regression. The value of area under the ROC curve for the proposed prognostic model was calculated.Results    The main predictors of AF in patients with ischemic stroke of undetermined etiology were CHF FC, a history of stroke, age, gender, values of cholesterol and prothrombin index, which were included into the final prognostic model. The sensitivity of the developed model was 83.5 % and the specificity was 85.5 %. The area under the ROC curve corresponding to the interrelation between the prognosis of AF and the regression function value was 0.921±0.012 with 95 % confidence interval: 0.898-0.944.Conclusion    According to the results of the study, the probability of AF in patients with ischemic stroke increased with CHF progression, recurrent stroke, older age, female gender, and reduced prothrombin index and cholesterol level.

A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

Background and Objectives: Recurrence of atrial fibrillation (AF) within six months after sinus rhythm restoration with direct current cardioversion (DCC) is a significant treatment challenge. Currently, the factors influencing outcome are mostly unknown. Studies have found a link between genetics and the risk of AF and efficacy of rhythm control. The aim of this study was to examine the association between eight single-nucleotide variants (SNVs) and the risk of AF development and recurrence after DCC. Materials and Methods: Regarding the occurrence of AF, 259 AF cases and 108 controls were studied. Genotypes for the eight SNVs located in the genes CAV1, MYH7, SOX5, KCNN3, ZFHX3, KCNJ5 and PITX2 were determined using high-resolution melting analysis and confirmed with Sanger sequencing. Six months after DCC, a telephone interview was conducted to determine whether AF had recurred. A polygenic risk score (PRS) was calculated as the unweighted sum of risk alleles. Multivariate regression analyses were performed to assess SNV and PRS association with AF occurrence and recurrence after DCC. Results: The risk allele of rs2200733 (PITX2) was significantly associated with the development of AF (p = 0.012, OR = 2.31, 95% CI = 1.206–4.423). AF recurred in 60% of patients and the allele generally associated with a decreased risk of AF of rs11047543 (SOX5) was associated with a greater risk of AF recurrence (p = 0.014, OR = 0.223, 95% CI = 0.067–0.738). A PRS of greater than 7 was significantly associated (p = 0.008) with a higher likelihood of developing AF after DCC (OR = 4.174, 95% CI = 1.454–11.980). Conclusions: A higher PRS is associated with increased odds of AF recurrence after treatment with DCC. PITX2 (rs2200733) is significantly associated with an increased risk of AF. The protective allele of rs11047543 (SOX5) is associated with a greater risk of AF recurrence. Further studies are needed to predict the success of rhythm control and guide patient selection towards the most efficacious treatment.