Genetic testing in early-onset atrial fibrillation.

Atrial fibrillation (AF) is a globally prevalent cardiac arrhythmia with significant genetic underpinnings, as highlighted by recent large-scale genetic studies. A prominent clinical and genetic overlap exists between AF, heritable ventricular cardiomyopathies, and arrhythmia syndromes, underlining the potential of AF as an early indicator of severe ventricular disease in younger individuals. Indeed, several recent studies have demonstrated meaningful yields of rare pathogenic variants among early-onset AF patients (∼4%-11%), most notably for cardiomyopathy genes in which rare variants are considered clinically actionable. Genetic testing thus presents a promising opportunity to identify monogenetic defects linked to AF and inherited cardiac conditions, such as cardiomyopathy, and may contribute to prognosis and management in early-onset AF patients. A first step towards recognizing this monogenic contribution was taken with the Class IIb recommendation for genetic testing in AF patients aged 45 years or younger by the 2023 American College of Cardiology/American Heart Association guidelines for AF. By identifying pathogenic genetic variants known to underlie inherited cardiomyopathies and arrhythmia syndromes, a personalized care pathway can be developed, encompassing more tailored screening, cascade testing, and potentially genotype-informed prognosis and preventive measures. However, this can only be ensured by frameworks that are developed and supported by all stakeholders. Ambiguity in test results such as variants of uncertain significance remain a major challenge and as many as ∼60% of people with early-onset AF might carry such variants. Patient education (including pretest counselling), training of genetic teams, selection of high-confidence genes, and careful reporting are strategies to mitigate this. Further challenges to implementation include financial barriers, insurability issues, workforce limitations, and the need for standardized definitions in a fast-moving field. Moreover, the prevailing genetic evidence largely rests on European descent populations, underscoring the need for diverse research cohorts and international collaboration. Embracing these challenges and the potential of genetic testing may improve AF care. However, further research-mechanistic, translational, and clinical-is urgently needed.

Multisite Validation of a Functional Assay to Adjudicate SCN5A Brugada Syndrome-Associated Variants.

BACKGROUND: Brugada syndrome is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging, and ≈79% of SCN5A missense variants in ClinVar are currently classified as variants of uncertain significance. Automated patch clamp technology enables high-throughput functional studies of ion channel variants and can provide evidence for variant reclassification. METHODS: An in vitro SCN5A-Brugada syndrome automated patch clamp assay was independently performed at Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute. The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. Odds of pathogenicity values were derived from the experimental results according to ClinGen Sequence Variant Interpretation recommendations. The calibrated assay was then used to study SCN5A variants of uncertain significance observed in 4 families with Brugada syndrome and other arrhythmia phenotypes associated with SCN5A loss-of-function. RESULTS: Variant channel parameters generated independently at the 2 research sites showed strong correlations, including peak INa density (R2=0.86). The assay accurately distinguished benign controls (24/25 concordant variants) from pathogenic controls (23/24 concordant variants). Odds of pathogenicity values were 0.042 for normal function and 24.0 for abnormal function, corresponding to strong evidence for both American College of Medical Genetics and Genomics/Association for Molecular Pathology benign and pathogenic functional criteria (BS3 and PS3, respectively). Application of the assay to 4 clinical SCN5A variants of uncertain significance revealed loss-of-function for 3/4 variants, enabling reclassification to likely pathogenic. CONCLUSIONS: This validated high-throughput assay provides clinical-grade functional evidence to aid the classification of current and future SCN5A-Brugada syndrome variants of uncertain significance.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Recurrence After Atrial Fibrillation Ablation and Investigational Biomarkers of Cardiac Remodeling.

BACKGROUND: Recurrence after atrial fibrillation (AF) ablation remains common. We evaluated the association between recurrence and levels of biomarkers of cardiac remodeling, and their ability to improve recurrence prediction when added to a clinical prediction model. METHODS AND RESULTS: Blood samples collected before de novo catheter ablation were analyzed. Levels of bone morphogenetic protein-10, angiopoietin-2, fibroblast growth factor-23, insulin-like growth factor-binding protein-7, myosin-binding protein C3, growth differentiation factor-15, interleukin-6, N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were measured. Recurrence was defined as ≥30 seconds of an atrial arrhythmia 3 to 12 months postablation. Multivariable logistic regression was performed using biomarker levels along with clinical covariates: APPLE score (Age >65 years, Persistent AF, imPaired eGFR [<60 ml/min/1.73m2], LA diameter ≥43 mm, EF <50%; which includes age, left atrial diameter, left ventricular ejection fraction, persistent atrial fibrillation, and estimated glomerular filtration rate), preablation rhythm, sex, height, body mass index, presence of an implanted continuous monitor, year of ablation, and additional linear ablation. A total of 1873 participants were included. A multivariable logistic regression showed an association between recurrence and levels of angiopoietin-2 (odds ratio, 1.08 [95% CI, 1.02-1.15], P=0.007) and interleukin-6 (odds ratio, 1.02 [95% CI, 1.003-1.03]; P=0.02). The area under the receiver operating characteristic curve of a model that only contained clinical predictors was 0.711. The addition of any of the 9 studied biomarkers to the predictive model did not result in a statistically significant improvement in the area under the receiver operating characteristic curve. CONCLUSIONS: Higher angiopoietin-2 and interleukin-6 levels were associated with recurrence after atrial fibrillation ablation in multivariable modeling. However, the addition of biomarkers to a clinical prediction model did not significantly improve recurrence prediction.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

BACKGROUND: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing. METHODS: The SDY Case Registry is a National Institutes of Health/Centers for Disease Control and Prevention surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases < 20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015 to 2019. The cohort included 211 children (median age 0.33 year; range 0-20 years), determined to have died suddenly and unexpectedly and from whom DNA biospecimens for DNA extractions and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex- and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy, and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, pathogenic and likely pathogenic genetic variation was identified using a Bayesian-based artificial intelligence (AI) tool. RESULTS: The SDY cohort was 43% European, 29% African, 3% Asian, 16% Hispanic, and 9% with mixed ancestries and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy, or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, potentially damaging variants in epilepsy, cardiomyopathy, and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death. CONCLUSIONS: While damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.