Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

Long-term cardiovascular outcomes after orlistat therapy in patients with obesity: a nationwide, propensity-score matched cohort study.

AIMS: The rising prevalence of obesity and its associated comorbidities represent a growing public health issue; in particular, obesity is known to be a major risk factor for cardiovascular disease. Despite the evidence behind the efficacy of orlistat in achieving weight loss in patients with obesity, no study thus far has quantified its long-term effect on cardiovascular outcomes. The purpose of this study is to explore long-term cardiovascular outcomes after orlistat therapy. METHODS AND RESULTS: A propensity-score matched cohort study was conducted on the nation-wide electronic primary and integrated secondary healthcare records of the Clinical Practice Research Datalink (CPRD). The 36 876 patients with obesity in the CPRD database who had completed a course of orlistat during follow-up were matched on a 1:1 basis with equal numbers of controls who had not taken orlistat. Patients were followed up for a median of 6 years for the occurrence of the primary composite endpoint of major adverse cardiovascular events (fatal or non-fatal myocardial infarction or ischaemic stroke), and a number of secondary endpoints including primary endpoint components individually, the occurrence of new-onset heart failure, coronary revascularization, new chronic kidney disease stage III+ (CKD3+), and all-cause mortality. During the median study follow-up of 6 years, the occurrence of major adverse cardiovascular events was lower in the orlistat cohort [hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.66-0.83, P < 0.001]. Patients who took orlistat experienced lower rates of myocardial infarction (HR 0.77; 95% CI 0.66-0.88, P < 0.001) and ischaemic stroke (HR 0.68; 95% CI 0.56 to -0.84, P < 0.001) as well as new-onset heart failure (HR 0.79; 95% CI 0.67-0.94, P = 0.007). There was no differences in revascularization rates (HR 1.12; 95% CI 0.91-1.38, P = 0.27), but a lower rate of both CKD3+ development (HR 0.78; 95% CI 0.73-0.83, P < 0.001) and mortality (HR 0.39, 95% CI 0.36 to -0.41, P < 0.001) was observed. CONCLUSION: In this nation-wide, propensity-score matched study, orlistat was associated with lower rates of overall major adverse cardiovascular events, new-onset heart failure, renal failure, and mortality. This study adds to current evidence on the known improvements in cardiovascular risk factor profiles of orlistat treatment by suggesting a potential role in primary prevention.

Robust Inference for Mediated Effects in Partially Linear Models.

We consider mediated effects of an exposure, X on an outcome, Y, via a mediator, M, under no unmeasured confounding assumptions in the setting where models for the conditional expectation of the mediator and outcome are partially linear. We propose G-estimators for the direct and indirect effects and demonstrate consistent asymptotic normality for indirect effects when models for the conditional means of M, or X and Y are correctly specified, and for direct effects, when models for the conditional means of Y, or X and M are correct. This marks an improvement, in this particular setting, over previous 'triple' robust methods, which do not assume partially linear mean models. Testing of the no-mediation hypothesis is inherently problematic due to the composite nature of the test (either X has no effect on M or M no effect on Y), leading to low power when both effect sizes are small. We use generalized methods of moments (GMM) results to construct a new score testing framework, which includes as special cases the no-mediation and the no-direct-effect hypotheses. The proposed tests rely on an orthogonal estimation strategy for estimating nuisance parameters. Simulations show that the GMM-based tests perform better in terms of power and small sample performance compared with traditional tests in the partially linear setting, with drastic improvement under model misspecification. New methods are illustrated in a mediation analysis of data from the COPERS trial, a randomized trial investigating the effect of a non-pharmacological intervention of patients suffering from chronic pain. An accompanying R package implementing these methods can be found at github.com/ohines/plmed.

Causal graphs for the analysis of genetic cohort data.

The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.