Circulation: Genomic and Precision Medicine最新文献

Background: MYBPC3 founder variants cause hypertrophic cardiomyopathy leading to heart failure and malignant ventricular arrhythmias. Exercise is typically regarded as a risk factor for disease expression although evidence is conflicting. Stratifying by type of exercise may discriminate low- from high-risk activities in these patients. Here, we evaluate the effects of exercise, stratified by high-static and high-dynamic components, on the risk of major cardiomyopathy-related events (MCEs) and cardiomyopathy penetrance among MYBPC3 founder variant carriers.

Methods: We interviewed 188 carriers (57.4% male; aged 43.0±15.0 years) on exercise participation since the age of 10 years. The exercise was quantified as the metabolic equivalent of task-h/wk before the presentation. MCE was defined as a composite of malignant ventricular arrhythmia (sustained ventricular tachycardia/fibrillation), heart failure (heart failure hospitalizations or transplantation), and septal reduction therapy. Static and dynamic exercises were defined per the Bethesda classification. Associations of exercise with MCE and cardiomyopathy penetrance were adjusted for sex and assessed using Cox regression.

Results: Overall, 43 (22.9%) subjects experienced MCE and 139 (73.9%) were diagnosed with cardiomyopathy. No association was found between overall physical activity and high-static activity with MCE (P=0.587 overall; P=0.322 high static) or cardiomyopathy penetrance (P=0.317 overall; P=0.623 high static). In contrast, high-dynamic activity was associated with malignant ventricular arrhythmia (dichotomized at the 75th percentile: adjusted hazard ratio, 3.26 [95% CI, 1.26-8.44]; P=0.015).

Conclusions: Overall exercise participation does not generally increase the risk of adverse events among MYBPC3 founder variant carriers. Nonetheless, an increased risk of malignant ventricular arrhythmia was observed among those engaged in the highest quartile of high-dynamic sports, suggesting that high-level high-intensity exercise activities should be entertained with caution.

Background: Genome-wide association studies identified a 20-Kb region of chromosome 8 (8q24.13) associated with plasma lipids, hepatic steatosis, and risk for coronary artery disease. The region is proximal to TRIB1, and given its well-established role in lipid regulation in animal models, TRIB1 has been proposed to mediate the contribution of the 8q24.13 locus to these traits. This region overlaps a gene encoding the primate-specific long noncoding RNA transcript TRIBAL/TRIB1AL (TRIB1-associated locus), but the contribution of TRIBAL to coronary artery disease risk remains untested.

Methods: Using recently available expression quantitative trait loci data and hepatocyte models, we further investigated this locus by Mendelian randomization analysis. Following antisense oligonucleotide targeting of TRIBAL, transcription array, quantitative reverse transcription polymerase chain reaction, and enrichment analyses were performed and effects on apoB and triglyceride secretion were determined.

Results: Mendelian randomization analysis supports a causal relationship between genetically determined hepatic TRIBAL expression and markers of hepatic steatosis and coronary artery disease risk. By contrast, expression data sets did not support expression quantitative trait loci relationships between coronary artery disease-associated variants and TRIB1. TRIBAL suppression reduced the expression of key regulators of triglyceride metabolism and bile acid synthesis. Enrichment analyses identified patterns consistent with impaired metabolic functions, including reduced triglyceride and cholesterol handling ability. Furthermore, TRIBAL suppression was associated with reduced hepatocyte secretion of triglycerides.

Conclusions: This work identifies TRIBAL as a gene bridging the genotype-phenotype relationship at the 8q24.13 locus with effects on genes regulating hepatocyte lipid metabolism and triglyceride secretion.

Background: Interpretation of variants of uncertain significance (VUSs) remains a challenge in the care of patients with inherited cardiovascular diseases (CVDs); 56% of variants within CVD risk genes are VUS, and machine learning algorithms trained upon large data resources can stratify VUS into higher versus lower probability of contributing to a CVD phenotype.

Methods: We used ClinVar pathogenic/likely pathogenic and benign/likely benign variants from 47 CVD genes to build a predictive model of variant pathogenicity utilizing measures of evolutionary constraint, deleteriousness, splicogenicity, local pathogenicity, cardiac-specific expression, and population allele frequency. Performance was validated using variants for which the ClinVar pathogenicity assignment changed. Functional validation was assessed using prior studies in >900 identified VUS. The model utility was demonstrated using the Catheterization Genetics (CATHGEN) cohort.

Results: We identified a top-ranked model that accurately prioritized variants for which ClinVar clinical significance had changed (n=663; precision-recall area under the curve, 0.97) and performed well compared with conventional in silico methods. This model (CVD pathogenicity predictor [CVD-PP]) also had high accuracy in prioritizing VUS with functional effects in vivo (precision-recall area under the curve, 0.58). In CATHGEN, there was a greater burden of higher CVD-PP scored VUS in individuals with dilated cardiomyopathy compared with controls (P=8.2×10^-15). Of individuals in CATHGEN who harbored highly ranked CVD pathogenicity predictor VUS meeting clinical pathogenicity criteria, 27.6% had clinical evidence of disease. Variant prioritization using this model increased genetic diagnosis in CATHGEN participants with a known clinical diagnosis of hypertrophic cardiomyopathy (7.8%-27.2%).

Conclusions: We present a cardiac-specific model for prioritizing variants underlying CVD syndromes with high performance in discriminating the pathogenicity of VUS in CVD genes. Variant review and phenotyping of individuals carrying VUS of pathogenic interest support the clinical utility of this model. This model could also have utility in filtering variants as part of large-scale genomic sequencing studies.

Background: Though epidemiological studies show increased cardiovascular disease (CVD) risks among individuals with psychiatric disorders, findings on sex differences in comorbidity have been inconsistent.

Methods: This genetic epidemiology study examined the sex-specific association between the genetic risk of 3 psychiatric disorders (major depression [MD], schizophrenia, and bipolar disorder), estimated using polygenic scores (PGSs), and risks of 3 CVDs (atrial fibrillation [AF], coronary artery disease [CAD], and heart failure [HF]) in 345 169 European-ancestry individuals (UK Biobank), with analyses replicated in an independent BioVU cohort (n=49 057). Mediation analysis was conducted to determine whether traditional CVD risk factors could explain any observed sex difference.

Results: In the UK Biobank, a 1-SD increase in PGS_MD was significantly associated with the incident risks of all 3 CVDs in females after multiple testing corrections (hazard ratio [HR]_AF-female=1.04 [95% CI, 1.02-1.06]; P=1.5×10^-4; HR_CAD-female=1.07 [95% CI, 1.04-1.11]; P=2.6×10^-6; and HR_HF-female=1.09 [95% CI, 1.06-1.13]; P=9.7×10^-10), but not in males. These female-specific associations remained even in the absence of any psychiatric disorder diagnosis or psychiatric medication use. Although mediation analysis demonstrated that the association between PGS_MD and CVDs in females was partly mediated by baseline body mass index, hypercholesterolemia, hypertension, and smoking, these risk factors did not explain the higher risk compared with males. The association between PGS_MD and CAD was consistent between females who were premenopausal and postmenopausal at baseline, while the association with AF and HF was only observed in the baseline postmenopausal cohort. No significant association with CVD risks was observed for the PGS of schizophrenia or bipolar disorder. The female-specific positive association of PGS_MD with CAD risk was replicated in BioVU.

Conclusions: Genetic predisposition to MD confers a greater risk of CVDs in females versus males, even in the absence of any depression diagnosis. This study warrants further investigation into whether genetic predisposition to depression could be useful for improving cardiovascular risk prediction, especially in women.

Background: No disease-specific therapy currently exists for arrhythmogenic right ventricular cardiomyopathy (ARVC), a progressive cardiogenetic condition conferring elevated risk for ventricular arrhythmias, heart failure, and sudden cardiac death. Emerging gene therapies have the potential to fill this gap. However, little is known about how adults with ARVC, or any other inherited cardiomyopathy or arrhythmia syndrome, appraise the risks and benefits of gene therapy research and which considerations may influence their decisions about clinical trial participation.

Methods: Twenty adults with clinically diagnosed and gene-positive ARVC participated in semi-structured interviews that explored perceptions of gene therapy and hypothetical decision-making for gene therapy clinical trial participation. Interview transcripts were qualitatively coded and analyzed.

Results: Participants expressed enthusiasm for gene therapy with varied levels of personal interest in trial participation. Although clinical severity appeared to be associated with an elevated interest in early trial participation, participants anticipated weighing both personal and trial-specific factors including life stage, trial stage, risks, benefits, participation burden, study leadership, and anticipated cost of future gene therapy. Adaptation to living with ARVC and involvement in the ARVC patient community were also relevant to decision-making about trial participation. Potential ethical concerns included unquestioning trust in clinical teams collaborating on industry-led trials and vulnerability of those recently diagnosed or with high perceived severity of ARVC symptoms.

Conclusions: Several characteristics of the individual and trial warrant consideration during the informed consent process. Insights from this study may affect trial planning and communication with participants who have inherited cardiac conditions.

Background: Myocardial infarction (MI) is a complex disease caused by both lifestyle and genetic factors. This study aims to investigate the predictive value of genetic risk, in addition to traditional cardiovascular risk factors, for recurrent events following early-onset MI.

Methods: The Italian Genetic Study of Early-Onset Myocardial Infarction is a cohort study enrolling patients with MI before 45 years. Monogenic variants causing familial hypercholesterolemia were identified, and a coronary artery disease polygenic score (PGS) was calculated. Ten-fold cross-validated Cox proportional hazards models were fitted sequentially including all clinical variables, the PGS, and monogenic variants on the composite outcome of cardiovascular death, recurrent MI, stroke, or revascularization.

Results: During a 19.9-year follow-up, 847 (50.7%) patients experienced recurrent events. Each 1-SD higher PGS was associated with a 21% higher hazard of recurrent events (hazard ratio, 1.21 [95% CI, 1.13-1.31]; P=4.04×10^-6). Except for secondary prevention, PGS was the strongest determinant of recurrent event risk (C index, 0.56 [95% CI, 0.54-0.58]) compared with clinical risk factors. Overall, predictive performance of clinical risk factors (C index, 0.69 [95% CI, 0.67-0.71]) improved after adding the PGS (C index, 0.69 [95% CI, 0.68-0.71]; P=0.006). When dividing the population by PGS quintiles, the highest fifth had a 57% higher hazard of recurrent events than the lowest fifth (hazard ratio, 1.57 [95% CI, 1.26-1.96]; P=5.57×10^-5).

Conclusions: When compared with other clinical risk factors, PGS was the strongest predictor of event recurrence among patients with an early-onset MI. Though the discriminative power of recurrent event prediction in this cohort was modest, the addition of PGS significantly improved discrimination.

Background: Ascending thoracic aortic dilation is a complex heritable trait that involves modifiable and nonmodifiable risk factors. Polygenic scores (PGS) are increasingly used to assess risk for complex diseases. The degree to which a PGS can improve aortic diameter prediction in diverse populations is unknown. Presently, we tested whether adding a PGS to clinical prediction algorithms improves performance in a diverse biobank.

Methods: The analytic cohort comprised 6235 Penn Medicine Biobank participants with available echocardiography and clinical data linked to genome-wide genotype data. Linear regression models were used to integrate PGS weights derived from a genome-wide association study of thoracic aortic diameter performed in the UK Biobank and were compared with the performance of the previously published aorta optimized regression for thoracic aneurysm (AORTA) score.

Results: Cohort participants had a median age of 61 years (IQR, 53-70) and a mean ascending aortic diameter of 3.36 cm (SD, 0.49). Fifty-five percent were male, and 33% were genetically similar to an African reference population. Compared with the AORTA score, which explained 30.6% (95% CI, 29.9%-31.4%) of the variance in aortic diameter, AORTA score+UK Biobank-derived PGS explained 33.1%, (95% CI, 32.3%-33.8%), the reweighted AORTA score explained 32.5% (95% CI, 31.8%-33.2%), and the reweighted AORTA score+UK Biobank-derived PGS explained 34.9% (95% CI, 34.2%-35.6%). When stratified by population, models including the UK Biobank-derived PGS consistently improved upon the clinical AORTA score among individuals genetically similar to a European reference population but conferred minimal improvement among individuals genetically similar to an African reference population. Comparable performance disparities were observed in models developed to discriminate cases/noncases of thoracic aortic dilation (≥4.0 cm).

Conclusions: We demonstrated that inclusion of a UK Biobank-derived PGS to the AORTA score confers a clinically meaningful improvement in model performance only among individuals genetically similar to European reference populations and may exacerbate existing health care disparities.

Background: Atrial fibrillation GWAS (genome-wide association studies) identified significant associations for rs1152591 and linked variants in the SYNE2 gene encoding Nesprin-2, which connects the nuclear membrane with the cytoskeleton.

Methods: Reporter gene vector transfection and CRISPR-Cas9 editing were used to identify the causal variant regulating the expression of SYNE2α1. After SYNE2 knockdown or SYNE2α1 overexpression in human stem cell-derived cardiomyocytes, nuclear phenotypes were assessed by imaging and atomic force microscopy. Gene expression was assessed by RNAseq and gene set enrichment analysis. Fura-2 AM staining assessed calcium transients. Optical mapping assessed action potential duration and conduction velocity.

Results: The risk allele of rs1152591 had lower promoter and enhancer activity and was significantly associated with lower expression of the short SYNE2α1 isoform in human stem cell-derived cardiomyocytes, without an effect on the expression of the full-length SYNE2 mRNA. SYNE2α1 overexpression had dominant negative effects on the nucleus with its overexpression or SYNE2 knockdown leading to increased nuclear area and decreased nuclear stiffness. Gene expression results from SYNE2α1 overexpression demonstrated both concordant and nonconcordant effects with SYNE2 knockdown. SYNE2α1 overexpression had a gain of function on electrophysiology, leading to significantly faster calcium reuptake and decreased assessed action potential duration, while SYNE2 knockdown showed both shortened assessed action potential duration and decreased conduction velocity.

Conclusions: rs1152591 was identified as a causal atrial fibrillation variant, with the risk allele decreasing SYNE2α1 expression. Downstream effects of SYNE2α1 overexpression include changes in nuclear stiffness and electrophysiology, which may contribute to the mechanism for the risk allele's association with AF.