Circulation: Cardiovascular Genetics最新文献

Background: Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified.

Methods and results: We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h²_g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h²_g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h²_g in age, sex, and genomic strata of interest. The h²_g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions.

Conclusions: Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.

Background: Statins lower cholesterol by inhibiting HMG-CoA reductase, the rate-limiting enzyme of the metabolic pathway that produces cholesterol and other isoprenoids. Little is known about their effects on metabolite and lipoprotein subclass profiles. We, therefore, investigated the molecular changes associated with pravastatin treatment compared with placebo administration using a nuclear magnetic resonance-based metabolomics platform.

Methods and results: We performed metabolic profiling of 231 lipoprotein and metabolite measures in the PREVEND IT (Prevention of Renal and Vascular End-stage Disease Intervention Trial) study, a placebo-controlled randomized clinical trial designed to test the effects of pravastatin (40 mg once daily) on cardiovascular risk. Metabolic profiles were assessed at baseline and after 3 months of treatment. Pravastatin lowered low-density lipoprotein cholesterol (change in SD units [95% confidence interval]: -1.01 [-1.14, -0.88]), remnant cholesterol (change in SD units [95% confidence interval]: -1.03 [-1.17, -0.89]), and apolipoprotein B (change in SD units [95% confidence interval]: -0.98 [-1.11, -0.86]) with similar effect magnitudes. In addition, pravastatin globally lowered levels of lipoprotein subclasses, with the exception of high-density lipoprotein subclasses, which displayed a more heterogeneous response pattern. The lipid-lowering effect of pravastatin was accompanied by selective changes in lipid composition, particularly in the cholesterol content of very-low-density lipoproteinparticles. In addition, pravastatin reduced levels of several fatty acids but had limited effects on fatty acid ratios.

Conclusions: These randomized clinical trial data demonstrate the widespread effects of pravastatin treatment on lipoprotein subclass profiles and fatty acids.

Clinical trial registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT03073018.

Background: Mutations in LMNA (lamin A/C), which encodes lamin A and C, typically cause age-dependent cardiac phenotypes, including dilated cardiomyopathy, cardiac conduction disturbance, atrial fibrillation, and malignant ventricular arrhythmias. Although the type of LMNA mutations have been reported to be associated with susceptibility to malignant ventricular arrhythmias, the gene-based risk stratification for cardiac complications remains unexplored.

Methods and results: The multicenter cohort included 77 LMNA mutation carriers from 45 families; cardiac disorders were retrospectively analyzed. The mean age of patients when they underwent genetic testing was 45±17, and they were followed for a median 49 months. Of the 77 carriers, 71 (92%) were phenotypically affected and showed cardiac conduction disturbance (81%), low left ventricular ejection fraction (<50%; 45%), atrial arrhythmias (58%), and malignant ventricular arrhythmias (26%). During the follow-up period, 9 (12%) died, either from end-stage heart failure (n=7) or suddenly (n=2). Genetic analysis showed truncation mutations in 58 patients from 31 families and missense mutations in 19 patients from 14 families. The onset of cardiac disorders indicated that subjects with truncation mutations had an earlier occurrence of cardiac conduction disturbance and low left ventricular ejection fraction, than those with missense mutations. In addition, the truncation mutation was found to be a risk factor for the early onset of cardiac conduction disturbance and the occurrence of atrial arrhythmias and low left ventricular ejection fraction, as estimated using multivariable analyses.

Conclusions: The truncation mutations were associated with manifestation of cardiac phenotypes in LMNA-related cardiomyopathy, suggesting that genetic analysis might be useful for diagnosis and risk stratification.

Background: Mass spectrometry is selective and sensitive, permitting routine quantification of multiple plasma proteins. However, commonly used nanoflow liquid chromatography (LC) approaches hamper sample throughput, reproducibility, and robustness. For this reason, most publications using plasma proteomics to date are small in study size.

Methods and results: Here, we tested a standard-flow LC mass spectrometry (MS) method using multiple reaction monitoring for the application to large epidemiological cohorts. We have reduced the LC-MS run time to almost a third of the nanoflow LC-MS approach. On the basis of a comparison of the quantification of 100 plasma proteins in >1500 LC-MS runs, the SD range of the retention time during continuous operation was substantially lower with the standard-flow LC-MS (<0.05 minutes) compared with the nanoflow LC-MS method (0.26-0.44 minutes). In addition, the standard-flow LC method also offered less variation in protein measurements. However, 5× more sample volume was required to achieve similar sensitivity. Two different commercial multiple reaction monitoring kits and an antibody-based multiplexing kit were used to compare the apolipoprotein measurements in a subset of samples. In general, good agreement was observed between the 2 multiple reaction monitoring kits, but some of the multiple reaction monitoring-based measurements differed from antibody-based assays.

Conclusions: The multiplexing capability of LC-MS combined with a standard-flow method increases throughput and reduces the costs of large-scale protein measurements in epidemiological cohorts, but protein rather than peptide standards will be required for defined absolute proteoform quantification.