Evaluation of a web-based asthma self-management system: a randomised controlled pilot trial

Background Asthma is the most common chronic condition of childhood and disproportionately affects inner-city minority children. Low rates of asthma preventer medication adherence is a major contributor to poor asthma control in these patients. Web-based methods have potential to improve patient knowledge and medication adherence by providing interactive patient education, monitoring of symptoms and medication use, and by facilitation of communication and teamwork among patients and health care providers. Few studies have evaluated web-based asthma support environments using all of these potentially beneficial interventions. The multidimensional website created for this study, BostonBreathes, was designed to intervene on multiple levels, and was evaluated in a pilot trial. Methods An interactive, engaging website for children with asthma was developed to promote adherence to asthma medications, provide a platform for teamwork between caregivers and patients, and to provide primary care providers with up-to-date symptom information and data on medication use. Fifty-eight (58) children primarily from inner city Boston with persistent-level asthma were randomised to either usual care or use of BostonBreathes. Subjects completed asthma education activities, and reported their symptoms and medication use. Primary care providers used a separate interface to monitor their patients’ website use, their reported symptoms and medication use, and were able to communicate online via a discussion board with their patients and with an asthma specialist. Results After 6-months, reported wheezing improved significantly in both intervention and control groups, and there were significant improvements in the intervention group only in night-time awakening and parental loss of sleep, but there were no significant differences between intervention and control groups in these measures. Emergency room or acute visits to a physician for asthma did not significantly change in either group. Among the subgroup of subjects with low controller medication adherence at baseline, adherence improved significantly only in the intervention group. Knowledge of the purpose of controller medicine increased significantly in the intervention group, a statistically significant improvement over the control group. Conclusions This pilot study suggests that a multidimensional web-based educational, monitoring, and communication platform may have positive influences on pediatric patients’ asthma-related knowledge and use of asthma preventer medications

Placebo response characteristic in sequential parallel comparison design studies

The placebo response can affect inference in analysis of data from clinical trials. It can bias the estimate of the treatment effect, jeopardize the effort of all involved in a clinical trial and ultimately deprive patients of potentially efficacious treatment. The Sequential Parallel Comparison Design (SPCD) is one of the novel approaches addressing placebo response in clinical trials. The analysis of SPCD clinical trial data typically involves classification of subjects as ‘placebo responders’ or ‘placebo non-responders’. This classification is done using a specific criterion and placebo response is treated as a measurable characteristic. However, the use of criterion may lead to subject misclassification due to measurement error or incorrect criterion selection. Subsequently, misclassification can directly affect SPCD treatment effect estimate. We propose to view placebo response as an unknown random characteristic that can be estimated based on information collected during the trial. Two strategies are presented here. First strategy is to model placebo response using criterion classification as a starting point or the observed data, and to include the placebo response estimate into the treatment effect estimation. Second strategy is to jointly model latent placebo response and the observed data, and estimate treatment effect from the joint model. We evaluate both strategies on a wide range of simulated data scenarios in terms of type I error control, mean squared error and power. We then evaluate the strategies in presence of missing data and propose a method for missing data imputation under the non-informative missingness assumption. The data from a recent SPCD clinical trial is used to compare results of the proposed methods with reported results of the trial.2018-01-01T00:00:00

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: Rs13422522 (NYAP2; P = 8.87 × 10-11), rs12454712 (BCL2; P = 2.7 × 10-8), and rs10506418 (FAM19A2; P = 1.9 × 10-8). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci

Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loc

Identification of a novel proinsulin-associated SNP and demonstration that proinsulin is unlikely to be a causal factor in subclinical vascular remodelling using Mendelian randomisation

Background and aims Increased proinsulin relative to insulin levels have been associated with subclinical atherosclerosis (measured by carotid intima-media thickness (cIMT)) and are predictive of future cardiovascular disease (CVD), independently of established risk factors. The mechanisms linking proinsulin to atherosclerosis and CVD are unclear. A genome-wide meta-analysis has identified nine loci associated with circulating proinsulin levels. Using proinsulin-associated SNPs, we set out to use a Mendelian randomisation approach to test the hypothesis that proinsulin plays a causal role in subclinical vascular remodelling. Methods We studied the high CVD-risk IMPROVE cohort (n = 3345), which has detailed biochemical phenotyping and repeated, state-of-the-art, high-resolution carotid ultrasound examinations. Genotyping was performed using Illumina Cardio-Metabo and Immuno arrays, which include reported proinsulin-associated loci. Participants with type 2 diabetes (n = 904) were omitted from the analysis. Linear regression was used to identify proinsulin-associated genetic variants. Results We identified a proinsulin locus on chromosome 15 (rs8029765) and replicated it in data from 20,003 additional individuals. An 11-SNP score, including the previously identified and the chromosome 15 proinsulin-associated loci, was significantly and negatively associated with baseline IMTmean and IMTmax (the primary cIMT phenotypes) but not with progression measures. However, MR-Eggers refuted any significant effect of the proinsulin-associated 11-SNP score, and a non-pleiotropic SNP score of three variants (including rs8029765) demonstrated no effect on baseline or progression cIMT measures. Conclusions We identified a novel proinsulin-associated locus and demonstrated that whilst proinsulin levels are associated with cIMT measures, proinsulin per se is unlikely to have a causative effect on cIMT

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms