Free 25-Hydroxyvitamin D:Impact of Vitamin D Binding Protein Assays on Racial-Genotypic Associations

CONTEXT: Total 25-hydroxyvitamin D (25OHD) is a marker of vitamin D status and is lower in African Americans than in whites. Whether this difference holds for free 25OHOD (f25OHD) is unclear, considering reported genetic-racial differences in vitamin D binding protein (DBP) used to calculate f25OHD.OBJECTIVES: Our objective was to assess racial-geographic differences in f25OHD and to understand inconsistencies in racial associations with DBP and calculated f25OHD.DESIGN: This study used a cross-sectional design.SETTING: The general community in the United States, United Kingdom, and The Gambia were included in this study.PARTICIPANTS: Men in Osteoporotic Fractures in Men and Medical Research Council studies (N = 1057) were included.EXPOSURES: Total 25OHD concentration, race, and DBP (GC) genotype exposures were included.OUTCOME MEASURES: Directly measured f25OHD, DBP assessed by proteomics, monoclonal and polyclonal immunoassays, and calculated f25OHD were the outcome measures.RESULTS: Total 25OHD correlated strongly with directly measured f25OHD (Spearman r = 0.84). Measured by monoclonal assay, mean DBP in African-ancestry subjects was approximately 50% lower than in whites, whereas DBP measured by polyclonal DBP antibodies or proteomic methods was not lower in African-ancestry. Calculated f25OHD (using polyclonal DBP assays) correlated strongly with directly measured f25OHD (r = 0.80-0.83). Free 25OHD, measured or calculated from polyclonal DBP assays, reflected total 25OHD concentration irrespective of race and was lower in African Americans than in US whites.CONCLUSIONS: Previously reported racial differences in DBP concentration are likely from monoclonal assay bias, as there was no racial difference in DBP concentration by other methods. This confirms the poor vitamin D status of many African-Americans and the utility of total 25OHD in assessing vitamin D in the general population.</p

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Ethical Stem Cell Research Succeeds

<jats:p /

Science

<jats:p /

Science

<jats:p /

Modelling the demand for new nitrogen fixation by terrestrial ecosystems

Abstract. Continual input of reactive nitrogen (N) is required to support the natural turnover of N in terrestrial ecosystems. This “N demand” can be satisfied in various ways including biological N fixation (BNF) (the dominant pathway under natural conditions), lightning-induced abiotic N fixation, N uptake from sedimentary substrates, and N deposition from natural and anthropogenic sources. We estimated the global new N fixation demand (NNF), i.e. the total new N input required to sustain net primary production (NPP) in non-agricultural terrestrial ecosystems regardless of its origin, using a N-enabled global dynamic vegetation model (DyN-LPJ). DyN-LPJ does not explicitly simulate BNF; rather, it estimates total NNF using a mass balance criterion and assumes that this demand is met from one source or another. The model was run in steady state, and then in transient mode driven by recent changes in CO2 concentration and climate. A range of values for key stoichiometric parameters was considered, based on recently published analyses. Modelled NPP, and C:N ratios of litter and soil organic matter, were consistent with independent estimates. Modelled geographic patterns of ecosystem NNF were similar to other analyses, but actual estimated values exceeded recent estimates of global BNF. The results were sensitive to a few key parameters: the fraction of litter carbon respired to CO2 during decomposition, and plant type-specific C:N ratios of litter and soil. The modelled annual NNF increased by about 15% during the course of the transient run, mainly due to increasing CO2 concentration. The model did not overestimate recent terrestrial carbon uptake, suggesting that the increase in NNF demand has so far been met. Rising CO2 is further increasing the NNF demand, while the future capacity of N sources to support this is unknown. </jats:p

Science

<jats:p /

Science

<jats:p /