Fletcher-Turek Model Averaged Profile Likelihood Confidence Intervals

We evaluate the model averaged profile likelihood confidence intervals proposed by Fletcher and Turek (2011) in a simple situation in which there are two linear regression models over which we average. We obtain exact expressions for the coverage and the scaled expected length of the intervals and use these to compute these quantities in particular situations. We show that the Fletcher-Turek confidence intervals can have coverage well below the nominal coverage and expected length greater than that of the standard confidence interval with coverage equal to the same minimum coverage. In these situations, the Fletcher-Turek confidence intervals are unfortunately not better than the standard confidence interval used after model selection but ignoring the model selection process

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact

A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

Assessing the Benefits of Public Research Within an Economic Framework: The Case of USDA's Agricultural Research Service

Evaluation of publicly funded research can help provide accountability and prioritize programs. In addition, Federal intramural research planning generally involves an institutional assessment of the appropriate Federal role, if any, and whether the research should be left to others, such as universities or the private sector. Many methods of evaluation are available, peer review—used primarily for establishing scientific merit—being the most common. Economic analysis focuses on quantifying ultimate research outcomes, whether measured in goods with market prices or in nonmarket goods such as environmental quality or human health. However, standard economic techniques may not be amenable for evaluating some important public research priorities or for institutional assessments. This report reviews quantitative methods and applies qualitative economic reasoning and stakeholder interviewing methods to the evaluation of economic benefits of Federal intramural research using three case studies of research conducted by USDA’s Agricultural Research Service (ARS). Differences among the case studies highlight the need to select suitable assessment techniques from available methodologies, the limited scope for comparing assessment results across programs, and the inherent difficulty in quantifying benefits in some research areas. When measurement and attribution issues make it difficult to quantify these benefits, the report discusses how qualitative insights based on economic concepts can help research prioritization.Agricultural Research Service, Federal intramural research, publicly funded research, Environmental Economics and Policy, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries, Productivity Analysis,

Association of N-terminal pro-brain natriuretic peptide with cognitive function and depression in elderly people with type 2 diabetes

<p>Background: Type 2 diabetes mellitus is associated with risk of congestive heart failure (CHF), cognitive dysfunction and depression. CHF itself is linked both to poor cognition and depression. The ventricular N-terminal pro-brain natriuretic peptide (NT-proBNP) is a marker of CHF, suggesting potential as a marker for cognitive impairment and/or depression. This was tested in the Edinburgh Type 2 Diabetes Study (ET2DS).</p> <p>Methodology and Principal Findings: Cross-sectional analysis of 1066 men and women aged 60–75 with type 2 diabetes. Results from seven neuropsychological tests were combined in a standardised general cognitive ability factor, ‘g’. A vocabulary-based test estimated pre-morbid cognitive ability. The Hospital Anxiety and Depression Scale (HADS) assessed possible depression. After adjustment for age and sex, raised plasma NT-proBNP was weakly associated with lower ‘g’ and higher depression scores (ß −0.09, 95% CI −0.13 to −0.03, p = 0.004 and ß 0.08, 95% CI 0.04 to 0.12, p<0.001, respectively). Comparing extreme quintiles of NT-proBNP, subjects in the highest quintile were more likely to have reduced cognitive ability (within the lowest tertile of ‘g’) and ‘possible’ depression (HADS depression ≥8) (OR 1.80; 95% CI: 1.20, 2.70; p = 0.005 and OR 2.18; 95% CI: 1.28, 3.71; p = 0.004, respectively). Associations persisted when pre-morbid ability was adjusted for, but as expected were no longer statistically significant following the adjustment for diabetes-related and vascular co-variates (β −0.02, 95% CI −0.07 to 0.03, p>0.05 for ‘g’; β 0.03, 95% CI −0.02 to 0.07, p>0.05 for depression scores).</p> <p>Conclusion: Raised plasma NT-proBNP was weakly but statistically significantly associated with poorer cognitive function and depression. The prospective phases of the ET2DS will help determine whether or not NT-proBNP can be considered a risk marker for subsequent cognitive impairment and incident depression and whether it provides additional information over and above traditional risk factors for these conditions.</p&gt

The White Dwarf in EM Cygni: Beyond The Veil

We present a spectral analysis of the FUSE spectra of EM Cygni, a Z Cam DN system. The FUSE spectrum, obtained in quiescence, consists of 4 individual exposures (orbits): two exposures, at orbital phases phi ~ 0.65 and phi ~ 0.90, have a lower flux; and two exposures, at orbital phases phi =0.15 and 0.45, have a relatively higher flux. The change of flux level as a function of the orbital phase is consistent with the stream material (flowing over and below the disk from the hot spot region to smaller radii) partially masking the white dwarf. We carry out a spectral analysis of the FUSE data, obtained at phase 0.45 (when the flux is maximual, using the codes TLUSTY and SYNSPEC. Using a single white dwarf spectral component, we obtain a white dwarf temperature of 40,000K, rotating at 100km/s. The white dwarf, or conceivably, the material overflowing the disk rim, shows suprasolar abundances of silicon, sulphur and possibly nitrogen. Using a white dwarf+disk composite model, we obtain that the white dwarf temperature could be even as high as 50,000K, contributing more than 90% of the FUV flux, and the disk contributing less than 10% must have a mass accretion rate reaching 1.E-10 Msun/yr.In both cases, however, we obtain that the white dwarf temperature is much higher than previously estimated.Comment: accepted for publication in ApJ, 3 Tables, 12 Figures (including color figures), 33 pages in present format (possibly 10 pages in ApJ format

Body mass index and risk of non-alcoholic fatty liver disease: two electronic health record prospective studies

Context: The relationship between rising body mass index (BMI) and prospective risk of non-alcoholic fatty liver disease (NAFLD) / non-alcoholic steatohepatitis (NASH) is virtually absent. Objective: Determine the extent of the association between BMI and risk of future NAFLD diagnosis, stratifying by sex and diabetes. Design: Two prospective studies using Humedica and THIN with 1.54 and 4.96 years of follow-up respectively. Setting: Electronic health record databases Participants: Patients with had a recorded BMI measurement between 15–60kg/m2, and smoking status, and one year of active status prior to baseline BMI. Patients with a diagnosis or history of chronic diseases were excluded. Interventions: None Main Outcome Measure: Recorded diagnosis of NAFLD/NASH during follow-up (Humedica ICD-9 code 571.8, and read codes for NAFLD and NASH in THIN). Results: Hazard ratios (HR) were calculated across BMI categories using BMI of 20–22.5kg/m2 as the reference category, adjusting for age, sex and smoking status. Risk of recorded NAFLD/NASH increased linearly with BMI and was approximately 5-fold higher in Humedica (HR=4.78, 95% CI 4.17–5.47) and 9-fold higher in THIN (HR=8.93, 7.11–11.23) at a BMI of 30–32.5 kg/m2 rising to around 10-fold higher in Humedica (HR=9.80, 8.49–11.32) and 14-fold higher in THIN (HR=14.32, 11.04–18.57) in the 37.5–40 kg/m2 BMI category. Risk of NAFLD/NASH was approximately 50% higher in men, and approximately double in those with diabetes. Conclusions: These data quantify the consistent and strong relationships between BMI and prospectively recorded diagnoses of NAFLD/NASH and emphasize the importance of weight reduction strategies for prevention and management of NAFLD

Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study

Objective: To investigate the association between active commuting and incident cardiovascular disease (CVD), cancer, and all cause mortality. Design: Prospective population based study. Setting: UK Biobank. Participants: 263 450 participants (106 674 (52%) women; mean age 52.6), recruited from 22 sites across the UK. The exposure variable was the mode of transport used (walking, cycling, mixed mode v non-active (car or public transport)) to commute to and from work on a typical day. Main outcome measures: Incident (fatal and non-fatal) CVD and cancer, and deaths from CVD, cancer, or any causes. Results: 2430 participants died (496 were related to CVD and 1126 to cancer) over a median of 5.0 years (interquartile range 4.3-5.5) follow-up. There were 3748 cancer and 1110 CVD events. In maximally adjusted models, commuting by cycle and by mixed mode including cycling were associated with lower risk of all cause mortality (cycling hazard ratio 0.59, 95% confidence interval 0.42 to 0.83, P=0.002; mixed mode cycling 0.76, 0.58 to 1.00, P<0.05), cancer incidence (cycling 0.55, 0.44 to 0.69, P<0.001; mixed mode cycling 0.64, 0.45 to 0.91, P=0.01), and cancer mortality (cycling 0.60, 0.40 to 0.90, P=0.01; mixed mode cycling 0.68, 0.57 to 0.81, P<0.001). Commuting by cycling and walking were associated with a lower risk of CVD incidence (cycling 0.54, 0.33 to 0.88, P=0.01; walking 0.73, 0.54 to 0.99, P=0.04) and CVD mortality (cycling 0.48, 0.25 to 0.92, P=0.03; walking 0.64, 0.45 to 0.91, P=0.01). No statistically significant associations were observed for walking commuting and all cause mortality or cancer outcomes. Mixed mode commuting including walking was not noticeably associated with any of the measured outcomes. Conclusions: Cycle commuting was associated with a lower risk of CVD, cancer, and all cause mortality. Walking commuting was associated with a lower risk of CVD independent of major measured confounding factors. Initiatives to encourage and support active commuting could reduce risk of death and the burden of important chronic conditions