Crash dieting: The effects of eating and drinking on driving performance

Previous research suggests that compared to mobile phone use, eating and drinking while driving is more common and is seen as lower risk by drivers. Nevertheless, snacking at the wheel can affect vehicle control to a similar extent as using a hands-free phone, and is actually a causal factor in more crashes. So far, though, there has not been a controlled empirical study of this problem. In an effort to fill this gap in the literature, we used the Brunel University Driving Simulator to test participants on a typical urban scenario. At designated points on the drive, which coincided with instructions to eat or drink, a critical incident was simulated by programming a pedestrian to walk in front of the car. Whilst the driving performance variables measured were relatively unaffected by eating and drinking, perceived driver workload was significantly higher and there were more crashes in the critical incident when compared to driving normally. Despite some methodological limitations of the study, when taken together with previous research, the evidence suggests that the physical demands of eating and drinking while driving can increase the risk of a crash

From abstract to impact in cardiovascular research: factors predicting publication and citation

Aims Through a 4-year follow-up of the abstracts submitted to the European Society of Cardiology Congress in 2006, we aimed at identifying factors predicting high-quality research, appraising the quality of the peer review and editorial processes, and thereby revealing potential ways to improve future research, peer review, and editorial work. Methods and results All abstracts submitted in 2006 were assessed for acceptance, presentation format, and average reviewer rating. Accepted and rejected studies were followed for 4 years. Multivariate regression analyses of a representative selection of 10% of all abstracts (n= 1002) were performed to identify factors predicting acceptance, subsequent publication, and citation. A total of 10 020 abstracts were submitted, 3104 (31%) were accepted for poster, and 701 (7%) for oral presentation. At Congress level, basic research, a patient number ≥ 100, and prospective study design were identified as independent predictors of acceptance. These factors differed from those predicting full-text publication, which included academic affiliation. The single parameter predicting frequent citation was study design with randomized controlled trials reaching the highest citation rates. The publication rate of accepted studies was 38%, whereas only 24% of rejected studies were published. Among published studies, those accepted at the Congress received higher citation rates than rejected ones. Conclusions Research of high quality was determined by study design and largely identified at Congress level through blinded peer review. The scientometric follow-up revealed a marked disparity between predictors of full-text publication and those predicting citation or acceptance at the Congres

A Case Matched Gender Comparison Transcriptomic Screen Identifies eIF4E and eIF5 as Potential Prognostic and Tractable Biomarkers in Male Breast Cancer

Purpose: Breast cancer (BC) affects both genders, but is understudied in men. Although still rare, male BC is being diagnosed more frequently. Treatments are wholly informed by clinical studies conducted in women, based on assumptions that underlying biology is similar. Experimental design: A transcriptomic investigation of male and female BC was performed, confirming transcriptomic data in silico. Biomarkers were immunohistochemically assessed in 697 MBCs (n=477, training; n=220, validation set) and quantified in pre- and post-treatment samples from a male BC patient receiving Everolimus and PI3K/mTOR inhibitor. Results: Gender-specific gene expression patterns were identified. eIF transcripts were up-regulated in MBC. eIF4E and eIF5 were negatively prognostic for overall survival alone (Log rank; p=0.013; HR=1.77, 1.12-2.8 and p=0.035; HR=1.68, 1.03-2.74, respectively), or when co-expressed (p=0.01; HR=2.66, 1.26-5.63), confirmed in the validation set. This remained upon multivariate Cox regression analysis (eIF4E p=0.016; HR 2.38 (1.18-4.8), eIF5 p=0.022; HR 2.55 (1.14-5.7); co-expression p=0.001; HR=7.04 (2.22-22.26)). Marked reduction in eIF4E and eIF5 expression was seen post BEZ235/Everolimus, with extended survival. Conclusions: Translational initiation pathway inhibition could be of clinical utility in male BC patients overexpressing eIF4E and eIF5. With mTOR inhibitors which target this pathway now in the clinic, these biomarkers may represent new targets for therapeutic intervention, although further independent validation is required

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk

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

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.Peer reviewe