Ventricular Tachyarrhythmias after Cardiac Arrest in Public versus at Home

Background The incidence of ventricular fibrillation or pulseless ventricular tachycardia as the first recorded rhythm after out-of-hospital cardiac arrest has unexpectedly declined. The success of bystander-deployed automated external defibrillators (AEDs) in public settings suggests that this may be the more common initial rhythm when outof-hospital cardiac arrest occurs in public. We conducted a study to determine whether the location of the arrest, the type of arrhythmia, and the probability of survival are associated. Methods Between 2005 and 2007, we conducted a prospective cohort study of out-of-hospital cardiac arrest in adults in 10 North American communities. We assessed the frequencies of ventricular fibrillation or pulseless ventricular tachycardia and of survival to hospital discharge for arrests at home as compared with arrests in public. Results Of 12,930 evaluated out-of-hospital cardiac arrests, 2042 occurred in public and 9564 at home. For cardiac arrests at home, the incidence of ventricular fibrillation or pulseless ventricular tachycardia was 25% when the arrest was witnessed by emergency-medical-services (EMS) personnel, 35% when it was witnessed by a bystander, and 36% when a bystander applied an AED. For cardiac arrests in public, the corresponding rates were 38%, 60%, and 79%. The adjusted odds ratio for initial ventricular fibrillation or pulseless ventricular tachycardia in public versus at home was 2.28 (95% confidence interval [CI], 1.96 to 2.66; P\u3c0.001) for bystanderwitnessed arrests and 4.48 (95% CI, 2.23 to 8.97; P\u3c0.001) for arrests in which bystanders applied AEDs. The rate of survival to hospital discharge was 34% for arrests in public settings with AEDs applied by bystanders versus 12% for arrests at home (adjusted odds ratio, 2.49; 95% CI, 1.03 to 5.99; P=0.04). Conclusions Regardless of whether out-of-hospital cardiac arrests are witnessed by EMS personnel or bystanders and whether AEDs are applied by bystanders, the proportion of arrests with initial ventricular fibrillation or pulseless ventricular tachycardia is much greater in public settings than at home. The incremental value of resuscitation strategies, such as the ready availability of an AED, may be related to the place where the arrest occurs. (Funded by the National Heart, Lung, and Blood Institute and others.

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping

Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: a pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10−8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD

Generalized estimating equations for genome-wide association studies using longitudinal phenotype data

Many longitudinal cohort studies have both genome-wide measures of genetic variation and repeated measures of phenotypes and environmental exposures. Genome-wide association study analyses have typically used only cross-sectional data to evaluate quantitative phenotypes and binary traits. Incorporation of repeated measures may increase power to detect associations, but also requires specialized analysis methods. Here we discuss one such method – generalized estimating equations (GEE) – in the contexts of analysis of main effects of rare genetic variants and analysis of gene-environment interactions. We illustrate the potential for increased power using GEE analyses instead of cross-sectional analyses. We also address challenges that arise, such as the need for small-sample corrections when the minor allele frequency of a genetic variant and/or the prevalence of an environmental exposure is low. To illustrate methods for detection of gene-drug interactions on a genome-wide scale, using repeated measures data, we conduct single-study analyses and meta-analyses across studies in three large cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium – the Atherosclerosis Risk in Communities (ARIC) study, the Cardiovascular Health Study (CHS), and the Rotterdam Study (RS)

Genome-wide Association Study of Susceptibility to Particulate Matter–Associated QT Prolongation

BACKGROUND: Ambient particulate matter (PM) air pollution exposure has been associated with increases in QT interval duration (QT). However, innate susceptibility to PM-associated QT prolongation has not been characterized. OBJECTIVE: To characterize genetic susceptibility to PM-associated QT prolongation in a multi-racial/ethnic, genome-wide association study (GWAS). METHODS: Using repeated electrocardiograms (1986–2004), longitudinal data on PM<10 μm in diameter (PM10), and generalized estimating equations methods adapted for low-prevalence exposure, we estimated approximately 2.5×106 SNP×PM10 interactions among nine Women’s Health Initiative clinical trials and Atherosclerosis Risk in Communities Study subpopulations (n=22,158), then combined subpopulation-specific results in a fixed-effects, inverse variance-weighted meta-analysis. RESULTS: A common variant (rs1619661; coded allele: T) significantly modified the QT-PM10 association (p=2.11×10−8). At PM10 concentrations >90th percentile, QT increased 7 ms across the CC and TT genotypes: 397 (95% confidence interval: 396, 399) to 404 (403, 404) ms. However, QT changed minimally across rs1619661 genotypes at lower PM10 concentrations. The rs1619661 variant is on chromosome 10, 132 kilobase (kb) downstream from CXCL12, which encodes a chemokine, stromal cell-derived factor 1, that is expressed in cardiomyocytes and decreases calcium influx across the L-type Ca2+ channel. CONCLUSIONS: The findings suggest that biologically plausible genetic factors may alter susceptibility to PM10-associated QT prolongation in populations protected by the U.S. Environmental Protection Agency’s National Ambient Air Quality Standards. Independent replication and functional characterization are necessary to validate our findings. https://doi.org/10.1289/EHP34

Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10(–15)) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10(–8)) and mtDNA replication (p = 1.2 × 10(–7)). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10(–4)). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00439-021-02394-w

The challenges of genome-wide interaction studies: Lessons to learn from the analysis of HDL blood levels

Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP6SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value, 1 · 1028 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30, 011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP6SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol

OBJECTIVE LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (b 5 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P 5 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P 5 0.04). CONCLUSIONS These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications

Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations : the cohorts for heart and aging research in genomic epidemiology

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, transethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N = 78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P <5 x 10(-8)), we found suggestive evidence (P <5 x 10(-6)) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions.Peer reviewe