A pragmatic randomized trial comparing tablet computer informed consent to traditional paper-based methods for an osteoporosis study

AbstractObjectiveMethods to improve informed consent efficiency and effectiveness are needed for pragmatic clinical trials. We compared informed consent using a tablet computer to a paper approach to assess comprehension and satisfaction of patients and clinic staff for a future osteoporosis clinical trial.MethodsNine community-based practices identified and recruited patients to compare the informed consent processes (tablet vs. paper) in a mock osteoporosis clinical trial. The tablet informed consent included an animation summarizing the trial, complete informed consent document, and questions to assess and reinforce comprehension of the study. Participants were women age ≥55 years with ≥1 year of alendronate use. We surveyed participants to assess comprehension and satisfaction and office staff for satisfaction and perceived time demands.ResultsThe nine practices enrolled 33 participants. There was not a significant difference in comprehension between the tablet vs. paper informed consent [mean (SD) tablet: 12.2 (1.0) vs. paper: 11.4 (1.7)]. Office staff preferred the tablet to the paper informed consent for identifying potential study participants (two-sided t-test p = 0.02) despite an increased perceived time spent to complete the tablet process [tablet: 28.3 min (SD 16.3) vs. paper: 19.0 min (SD 6.9); p = 0.08].ConclusionsAlthough, there were no significant differences in participant satisfaction and comprehension with the tablet informed consent compared to a paper informed consent, patients and office staff trended towards greater satisfaction with the tablet informed consent. Larger studies are needed to further evaluate the utility of electronic informed consent in pragmatic clinical trials

Carbon cycle uncertainty in the Alaskan Arctic

Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (o) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0±9.2 kgCm−2), then gross primary production (GPP) (0.22±0.50 kgCm−2 yr−1), ecosystem respiration (Re) (0.23±0.38 kgCm−2 yr−1), net primary production (NPP) (0.14±0.33 kgCm−2 yr−1), autotrophic respiration (Ra) (0.09±0.20 kgCm−2 yr−1), heterotrophic respiration (Rh) (0.14±0.20 kgCm−2 yr−1), net ecosystem exchange (NEE) (−0.01±0.19 kgCm−2 yr−1), and CH4 flux (2.52±4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region

Global carbon budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019)

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.Molecular Epidemiolog

Evaluation of thermoplastic polyester molding resins. [Valox 420; Celanex 3300; Tenite 6H91; LNP WF-1006]

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Epigenetic age acceleration in adolescence associates with BMI, inflammation and risk score for middle age cardiovascular disease

Background‘Accelerated ageing’, assessed by adult DNA methylation predicts cardiovascular disease (CVD). Adolescent accelerated aging might predict CVD earlier. We investigated whether epigenetic age acceleration (assessed age 17-years) associated with adiposity/CVD-risk measured (ages 17, 20, 22-years), and projected CVD by middle-age.MethodsDNA methylation measured in peripheral blood provided 2 estimates of epigenetic age acceleration; intrinsic (IEAA, (preserved across cell types) and extrinsic (EEAA, dependent on cell admixture and methylation levels within each cell type).Adiposity was assessed by anthropometry, ultrasound and DEXA (ages 17, 20, 22 years). CVD-risk factors (lipids, HOMA-IR, blood pressure, inflammatory markers) were assessed at age 17-years. CVD development by age 47 years was calculated by Framingham algorithms. Results are presented as regression coefficients/5-year epigenetic age acceleration (IEAA/EEAA) for adiposity, CVD-risk factors and CVD development.ResultsIn 995 participants (49.6% female, age 17.3+/-0.6 years), EEAA (/5-years) was associated with increased BMI of 2.4% (95%CI 1.2-3.6%) and 2.4% (0.8-3.9%) at 17 and 22 years, respectively. EEAA was associated with increases of 23% (3-33%) in hsCRP, 10% (4-17%) in interferon-gamma induced protein (IP-10) and 4% (2-6%) in tumour necrosis factor receptor 2 (sTNFR2), adjusted for BMI and HOMA-IR. EEAA(/5-years) results in a 4% increase in hard endpoints of CVD by 47 years old and a 3% increase, after adjustment for conventional risk factors.ConclusionsAccelerated epigenetic age in adolescence was associated with inflammation, BMI measured 5 years later, and probability of middle-age CVD. Irrespective whether this is cause or effect, assessing epigenetic age might refine disease prediction