Browsing Miscellaneous by Title
Now showing items 1037-1056 of 5221
-
Dairy Product Intake and Risk of Type 2 Diabetes in EPIC-InterAct: A Mendelian Randomization Study.To estimate the causal association between intake of dairy products and incident type 2 diabetes. The analysis included 21,820 European individuals (9,686 diabetes cases) of the EPIC-InterAct case-cohort study. Participants were genotyped, and rs4988235 (LCT-12910C>T), a SNP for lactase persistence (LP) which enables digestion of dairy sugar, i.e., lactose, was imputed. Baseline dietary intakes were assessed with diet questionnaires. We investigated the associations between imputed SNP dosage for rs4988235 and intake of dairy products and other foods through linear regression. Mendelian randomization (MR) estimates for the milk-diabetes relationship were obtained through a two-stage least squares regression. Each additional LP allele was associated with a higher intake of milk (β 17.1 g/day, 95% CI 10.6-23.6) and milk beverages (β 2.8 g/day, 95% CI 1.0-4.5) but not with intake of other dairy products. Other dietary intakes associated with rs4988235 included fruits (β -7.0 g/day, 95% CI -12.4 to -1.7 per additional LP allele), nonalcoholic beverages (β -18.0 g/day, 95% CI -34.4 to -1.6), and wine (β -4.8 g/day, 95% CI -9.1 to -0.6). In instrumental variable analysis, LP-associated milk intake was not associated with diabetes (hazard ratio 0.99 rs4988235 was associated with milk intake but not with intake of other dairy products. This MR study does not suggest that milk intake is associated with diabetes, which is consistent with previous observational and genetic associations. LP may be associated with intake of other foods as well, but owing to the modest associations we consider it unlikely that this has caused the observed null result.
-
Data on child complementary feeding practices, nutrient intake and stunting in Musanze District, Rwanda.Stunting prevalence in Rwanda is still a major public health issue, and data on stunting is needed to plan relevant interventions. This data, collected in 2015, presents complementary feeding practices, nutrient intake and its association with stunting in infants and young children in Musanze District in Rwanda. A household questionnaire and a 24-h recall questionnaire were used to collect the data. In total 145 children aged 5-30 months participated in the study together with their caregivers. The anthropometric status of children was calculated using WHO Anthro software [1] according to the WHO growth standards [2]. The complementary feeding practices together with households' characteristics are reported per child stunting status. The nutrient intake and food group consumption are presented per age group of children. Also, the percentage contribution of each food groups to energy and nutrient intake in children is reported. The data also shows the association between zinc intake and age groups of children. Using multiple linear regression, a sensitivity analysis was done with height-for-age z-score as the dependent variable and exclusive breastfeeding, deworming table use, BMI of caregiver, dietary zinc intake as independent variables. The original linear regression model and a detailed methodology and analyses conducted are presented in Uwiringiyimana et al. [3].
-
Dataset of near-infrared spectral data of illicit-drugs and forensic casework samples analyzed by five portable spectrometers operating in different wavelength ranges.The increasing amount of globally seized controlled substances in combination with the more diverse drugs-of-abuse market encompassing many new psychoactive substances (NPS) provides challenges for rapid and reliable on-site presumptive drug testing. Long-established colorimetric spot tests tend to fail due to the unavailability of reliable tests for novel drugs and to false-positive reactions on commonly encountered substances. In addition, handling of samples and chemicals is required. Spectroscopic techniques do not have these disadvantages as spectra are compound-specific and non-invasive tests are possible. Near-infrared (NIR) spectroscopy is a promising technique for on-scene forensic drug detection. Numerous portable devices were introduced in the market in recent years. However, most handheld spectrometers operate in different and relatively confined wavelength ranges compared to the full 780 - 2500 nm NIR wavelength range. In addition, their spectral resolution is limited compared to benchtop instruments. This dataset presents the NIR spectra of 430 forensic samples, including regularly encountered illicit-drugs, NPS, commonly used adulterants, bulking-agents and excipients, and seized casework materials (powders and tablets). Data is available from 5 different NIR spectrometers; including a benchmark high-resolution, full range 350-2500 nm laboratory grade instrument and 4 portable spectrometers operating in the ranges of 1300-2600 nm, 1550-1950 nm, 950-1650 nm and 740-1070 nm. Via this dataset, spectra of illicit-drugs become available to institutes that typically do not have access to controlled substances. This data can be used to develop chemometric detection and classification models for illicit-drugs and provide insight in diagnostic spectral features that need to be recorded for reliable detection models. Additionally, the high-resolution, full range VIS-NIR spectra of the benchmark ASD instrument can be used for in-silica predictions of spectra in a certain wavelength range to provide insight in the optimal resolution and wavelength range of a prospective portable device.
-
Decision models of prediabetes populations: a systematic review.With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarise and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, evaluate their potential use for the assessment of novel prevention strategies and discuss the knowledge gaps, challenges and opportunities. We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardised form, and double extraction, four reviewers each extracted 50% of identified studies. Twenty-nine published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n=8) and type 2 diabetes (n=17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n=7) and accounted for heterogeneity amongst individuals with prediabetes (n=7). Current prediabetes decision models have considerable limitations in terms of their quality and validity and are not equipped to evaluate stratified strategies using novel biomarkers highlighting a clear need for more comprehensive prediabetes decision models. This article is protected by copyright. All rights reserved.