Search

Background and AimsCardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits.Methods and ResultsWe conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high-density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N=356,574–456,823). Multiple loci reached genome-wide levels of significance (N=145-333) for each trait, but only four loci (in/nearVEGFA, GRB14-COBLL1, KLF14, andRGS19-OPRL1)were associated with risk across all seven traits (P−8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses.GRB14-COBLL1showed the most consistent replication, revealing nominally significant associations (PConclusionsOur analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.

Introduction In observational studies, the association between the Dietary Approaches to Stop Hypertension (DASH) diet and incident heart failure has been inconsistent. It was hypothesized that higher DASH diet concordance has a protective effect on heart failure in a multi-ethnic cohort. Methods The Multi-Ethnic Study of Atherosclerosis cohort includes men and women of multiple ethnicities who were aged 45–84 years and free of clinical cardiovascular disease at baseline. Participants were recruited between 2000 and 2002 from six U.S. communities and followed for incident cardiovascular health events through 2015 for the purpose of this data set. Diet was measured using food-frequency questionnaires. Cox proportional hazards analysis was used to investigate the associations of the DASH diet concordance with incident heart failure in 2017–2018. Results During a median 13 years of follow-up, 179 of 4,478 participants developed heart failure, corresponding to a rate of 3.4 per 1,000 person years. Heart failure incidence rates did not vary significantly by DASH quintile for the population as a whole. In participants younger than 75 years, highest DASH concordance was associated with a lower risk of incident heart failure compared with those in the lowest quintile (hazard ratio=0.4, 95% CI=0.2, 0.9 vs all participants hazard ratio=1.0, 95% CI=0.2, 0.9) after adjusting for demographics, energy consumption, and known cardiovascular confounders. Conclusions This study supports the hypothesis that DASH is beneficial in heart failure prevention within the individuals aged less than 75 years subgroup, an idea that to date was substantiated only by much smaller studies or in less diverse patient populations.

Objective To describe the utility of twin studies for attention-deficit/hyperactivity disorder (ADHD) research and demonstrate their potential for the identification of alternative phenotypes suitable for genomewide association, developmental risk assessment, treatment response, and intervention targets. Method Brief descriptions of the classic twin study and genetic association study methods are provided, with illustrative findings from ADHD research. Biometrical genetics refers to the statistical modeling of data gathered from one or more group of known biological relation; it was apparently coined by Francis Galton in the 1860s and led to the "Biometrical School" at the University of London. Twin studies use genetic correlations between pairs of relatives, derived using this theoretical framework, to parse the individual differences in a trait into latent (unmeasured) genetic and environmental influences. This method enables the estimation of heritability, i.e., the percentage of variance due to genetic influences. It is usually implemented with a method called structural equation modeling , which is a statistical technique for fitting models to data, typically using maximum likelihood estimation. Genetic association studies aim to identify those genetic variants that account for the heritability estimated in twin studies. Measurements other than those used for the clinical diagnosis of the disorder are popular phenotype choices in current ADHD research. It is argued that twin studies have great potential to refine phenotypes relevant to ADHD. Results Prior studies have consistently found that the majority of the variance in ADHD symptoms is due to genetic factors. To date, genomewide association studies of ADHD have not identified replicable associations that account for the heritable variation. Possibly, the application of genomewide association studies to these alternative phenotypic measurements will assist in identifying the pathways from genetic variants to ADHD. Conclusion Power to detect associations should be improved by the study of highly heritable endophenotypes for ADHD and by reducing the number of phenotypes to be considered. Therefore, twin studies are an important research tool in the development of endophenotypes, defined as alternative, more highly heritable traits that act at earlier stages of the pathway from genes to behavior. Although genetic variation in liability to ADHD is likely polygenic, the proposed approach should help to identify improved alternative measurements for genetic association studies.

Objective Symptoms of overactivity form part of the DSM-IV criteria for the combined or hyperactive-impulsive subtypes of attention-deficit/hyperactivity disorder (ADHD); yet little data exist that would quantify the nature of the overactivity component. We aimed to quantify the ability of four different measures of motion sensor data, taken from actigraphs, and the intraindividual variability (IIV) in these measures, to distinguish ADHD cases from controls. Furthermore, we aimed to investigate the degree of shared familial influences on these measures and the ADHD diagnosis. Method Receiver operating characteristic analysis and multivariate structural equation modeling were used on actigraph data collected during a cognitive testing session in a sample of 116 ADHD combined-type probands, 119 of their siblings, and 218 control siblings (age range 6–18 years). Results Three measures of actigraph data—the number of movements made, the magnitude of these movements, and the IIV in the magnitude of movements—yielded an area under the curve of up to 0.8, indicating an ability to distinguish between cases and controls. The latter two of these measures showed significant shared familial vulnerability with an ADHD diagnosis, with high ADHD-actigraph familial correlations. Conclusions The actigraph data support the DSM-IV conceptualization of including overactivity as one of the core features within ADHD combined subtype. The magnitude of movements made, and the IIV of these movements, may be suitable candidates for future molecular genetic studies seeking to identify polymorphisms associated with the risk for ADHD. Further research should investigate if these findings generalize to a more naturalistic, homelike setting.