Plasma ApoE elevations are associated with NAFLD:The PREVEND Study

Non-alcoholic fatty liver disease (NAFLD) is featured by increased plasma very low density lipoproteins (VLDL). The extent to which plasma apolipoprotein E (ApoE) levels are elevated in NAFLD is unclear. We determined whether plasma ApoE is elevated in subjects with suspected NAFLD. Plasma ApoE and genotypes were determined in 6,762 participants of the Prevention of Renal and Vascular End-Stage Disease (PREVEND) cohort. A Fatty Liver Index (FLI) >= 60 was used as a proxy of NAFLD. A total of 1,834 participants had a FLI >= 60, which coincided with increased triglycerides, non-HDL cholesterol, ApoB and ApoE (all P<0.001). In multivariable linear regression analysis, plasma ApoE levels were positively associated with an elevated FLI when taking account of ApoE genotypes and other clinical and laboratory covariates (fully adjusted model: beta = 0.201, P<0.001). Stratified analysis for ApoE genotypes (ApoE epsilon 3 epsilon 3 homozygotes, ApoE epsilon 2 carriers, and ApoE epsilon 3 epsilon 4 and epsilon 4 epsilon 4 carriers combined), also showed positive associations of plasma ApoE levels with an elevated FLI in each group (all P<0.001). In conclusion, it is suggested that NAFLD is characterized by increased plasma ApoE levels, even when taking account of the various ApoE geno-types. Increased plasma ApoE may contribute to altered VLDL metabolism and to increased atherosclerosis susceptibility in NAFLD

Data in support of a central role of plasminogen activator inhibitor-2 polymorphism in recurrent cardiovascular disease risk in the setting of high HDL cholesterol and C-reactive protein using Bayesian network modeling

AbstractData is presented that was utilized as the basis for Bayesian network modeling of influence pathways focusing on the central role of a polymorphism of plasminogen activator inhibitor-2 (PAI-2) on recurrent cardiovascular disease risk in patients with high levels of HDL cholesterol and C-reactive protein (CRP) as a marker of inflammation, “Influences on Plasminogen Activator Inhibitor-2 Polymorphism-Associated Recurrent Cardiovascular Disease Risk in Patients with High HDL Cholesterol and Inflammation” (Corsetti et al., 2016; [1]). The data consist of occurrence of recurrent coronary events in 166 post myocardial infarction patients along with 1. clinical data on gender, race, age, and body mass index; 2. blood level data on 17 biomarkers; and 3. genotype data on 53 presumptive CVD-related single nucleotide polymorphisms. Additionally, a flow diagram of the Bayesian modeling procedure is presented along with Bayesian network subgraphs (root nodes to outcome events) utilized as the data from which PAI-2 associated influence pathways were derived (Corsetti et al., 2016; [1])

Apolipoprotein A-II Influences Apolipoprotein E-Linked Cardiovascular Disease Risk in Women with High Levels of HDL Cholesterol and C-Reactive Protein

Background: In a previous report by our group, high levels of apolipoprotein E (apoE) were demonstrated to be associated with risk of incident cardiovascular disease in women with high levels of C-reactive protein (CRP) in the setting of both low (designated as HR1 subjects) and high (designated as HR2 subjects) levels of high-density lipoprotein cholesterol (HDL-C). To assess whether apolipoprotein A-II (apoA-II) plays a role in apoE-associated risk in the two female groups. Methodology/Principal: Outcome event mapping, a graphical data exploratory tool; Cox proportional hazards multivariable regression; and curve-fitting modeling were used to examine apoA-II influence on apoE-associated risk focusing on HDL particles with apolipoprotein A-I (apoA-I) without apoA-II (LpA-I) and HDL particles with both apoA-I and apoA-II (LpA-I:A-II). Results of outcome mappings as a function of apoE levels and the ratio of apoA-II to apoA-I revealed within each of the two populations, a high-risk subgroup characterized in each situation by high levels of apoE and additionally: in HR1, by a low value of the apoA-II/apoA-I ratio; and in HR2, by a moderate value of the apoA-II/apoA-I ratio. Furthermore, derived estimates of LpA-I and LpA-I:A-II levels revealed for high-risk versus remaining subjects: in HR1, higher levels of LpA-I and lower levels of LpA-I:A-II; and in HR2 the reverse, lower levels of LpA-I and higher levels of LpA-I:A-II. Results of multivariable risk modeling as a function of LpA-I and LpA-I:A-II (dichotomized as highest quartile versus combined three lower quartiles) revealed association of risk only for high levels of LpA-I:A-II in the HR2 subgroup (hazard ratio 5.31, 95% CI 1.12-25.17, p = 0.036). Furthermore, high LpA-I: A-II levels interacted with high apoE levels in establishing subgroup risk. Conclusions/Significance: We conclude that apoA-II plays a significant role in apoE-associated risk of incident CVD in women with high levels of HDL-C and CRP

Compositional Features of HDL Particles Interact with Albuminuria to Modulate Cardiovascular Disease Risk

Lipoproteins containing apolipoprotein B modify associations of elevated urinary albumin excretion (UAE) with cardiovascular disease (CVD). Additionally, it is known that elevated UAE alters high-density lipoprotein functionality. Accordingly, we examined whether HDL features might also modify UAE-associated CVD. Multivariable Cox proportional-hazards modeling was performed on participants of the PREVEND (Prevention of Renal and Vascular Endstage Disease) study at the baseline screening with standard lipid/lipoprotein analyses and, three-to-four years later (second screen), with nuclear magnetic resonance lipoprotein analyses focusing on HDL parameters including HDL particle (HDL-P) and apolipoprotein A-I concentrations. These were used with UAE and derived measures of HDL apoA-I content (apoA-I/HDL-C and apoA-I/HDL-P) in risk models adjusted for gender, age, apoB, diabetes, past CVD history, CRP and GFR. Interaction analysis was also performed. Baseline screening revealed significant associations inverse for HDL-C and apoA-I and direct for apoA-I/HDL-C. The second screening demonstrated associations inverse for HDL-P, large HDL-P, medium HDL-P, HDL size, and apoA-I/HDL-P. Significant interactions with UAE included apoA-I/HDL-C at the baseline screening, and apoA-I/HDL-P and medium HDL-P but not apoA-I/HDL-C at the second screening. We conclude that features of HDL particles including apoA-I/HDL-P, indicative of HDL apoA-I content, and medium HDL-P modify associations of elevated UAE with CVD risk

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

This article contains supporting information online at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113561119/-/DCSupplemental.Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Integrative Biolog

Origins Space Telescope: Baseline mission concept

The Origins Space Telescope will trace the history of our origins from the time dust and heavy elements permanently altered the cosmic landscape to present-day life. How did galaxies evolve from the earliest galactic systems to those found in the Universe today? How do habitable planets form? How common are life-bearing worlds? To answer these alluring questions, Origins will operate at mid-and far-infrared (IR) wavelengths and offer powerful spectroscopic instruments and sensitivity three orders of magnitude better than that of the Herschel Space Observatory, the largest telescope flown in space to date. We describe the baseline concept for Origins recommended to the 2020 US Decadal Survey in Astronomy and Astrophysics. The baseline design includes a 5.9-m diameter telescope cryocooled to 4.5 K and equipped with three scientific instruments. A mid-infrared instrument (Mid-Infrared Spectrometer and Camera Transit spectrometer) will measure the spectra of transiting exoplanets in the 2.8 to 20 μm wavelength range and offer unprecedented spectrophotometric precision, enabling definitive exoplanet biosignature detections. The far-IR imager polarimeter will be able to survey thousands of square degrees with broadband imaging at 50 and 250 μm. The Origins Survey Spectrometer will cover wavelengths from 25 to 588 μm, making wide-area and deep spectroscopic surveys with spectral resolving power R ∼ 300, and pointed observations at R ∼ 40,000 and 300,000 with selectable instrument modes. Origins was designed to minimize complexity. The architecture is similar to that of the Spitzer Space Telescope and requires very few deployments after launch, while the cryothermal system design leverages James Webb Space Telescope technology and experience. A combination of current-state-of-the-art cryocoolers and next-generation detector technology will enable Origins\u27 natural background-limited sensitivity

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages