The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes:A Mendelian Randomization Study

Background & Aims: Previous results from observational, interventional studies and in vitro experiments suggest that certain micronutrients possess anti-viral and immunomodulatory activities. In particular, it has been hypothesized that zinc, selenium, copper and vitamin K(1) have strong potential for prophylaxis and treatment of COVID-19. We aimed to test whether genetically predicted Zn, Se, Cu or vitamin K(1) levels have a causal effect on COVID-19 related outcomes, including risk of infection, hospitalization and critical illness. Methods: We employed a two-sample Mendelian Randomization (MR) analysis. Our genetic variants derived from European-ancestry GWAS reflected circulating levels of Zn, Cu, Se in red blood cells as well as Se and vitamin K(1) in serum/plasma. For the COVID-19 outcome GWAS, we used infection, hospitalization or critical illness. Our inverse-variance weighted (IVW) MR analysis was complemented by sensitivity analyses including a more liberal selection of variants at a genome-wide sub-significant threshold, MR-Egger and weighted median/mode tests. Results: Circulating micronutrient levels show limited evidence of association with COVID-19 infection, with the odds ratio [OR] ranging from 0.97 (95% CI: 0.87–1.08, p-value = 0.55) for zinc to 1.07 (95% CI: 1.00–1.14, p-value = 0.06)—i.e., no beneficial effect for copper was observed per 1 SD increase in exposure. Similarly minimal evidence was obtained for the hospitalization and critical illness outcomes with OR from 0.98 (95% CI: 0.87–1.09, p-value = 0.66) for vitamin K(1) to 1.07 (95% CI: 0.88–1.29, p-value = 0.49) for copper, and from 0.93 (95% CI: 0.72–1.19, p-value = 0.55) for vitamin K(1) to 1.21 (95% CI: 0.79–1.86, p-value = 0.39) for zinc, respectively. Conclusions: This study does not provide evidence that supplementation with zinc, selenium, copper or vitamin K(1) can prevent SARS-CoV-2 infection, critical illness or hospitalization for COVID-19

Reply to Janssen et al. Comment on “Sobczyk, M.K.; Gaunt, T.R. The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes: A Mendelian Randomization Study. Nutrients 2022, 14, 233

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Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci

GWASs for atopic dermatitis have identified 25 reproducible loci. We attempt to prioritize the candidate causal genes at these loci using extensive molecular resources compiled into a bioinformatics pipeline. We identified a list of 103 molecular resources for atopic dermatitis etiology, including expression, protein, and DNA methylation quantitative trait loci datasets in the skin or immune-relevant tissues, which were tested for overlap with GWAS signals. This was combined with functional annotation using regulatory variant prediction and features such as promoter‒enhancer interactions, expression studies, and variant fine mapping. For each gene at each locus, we condensed the evidence into a prioritization score. Across the investigated loci, we detected significant enrichment of genes with adaptive immune regulatory function and epidermal barrier formation among the top-prioritized genes. At eight loci, we were able to prioritize a single candidate gene (IL6R, ADO, PRR5L, IL7R, ETS1, INPP5D, MDM1, TRAF3). In addition, at 6 of the 25 loci, our analysis prioritizes less familiar candidates (SLC22A5, IL2RA, MDM1, DEXI, ADO, STMN3). Our analysis provides support for previously implicated genes at several atopic dermatitis GWAS loci as well as evidence for plausible additional candidates at others, which may represent potential targets for drug discovery

Methods and Techniques for Skin Research:From genome wide association studies towards mechanistic understanding with case studies in dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. Genome-wide association studies (GWAS) have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start due to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights three key aspects of genetic variant interpretation – prioritizing causal genes, cell types and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation

ObjectiveSpinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques.DesignWe applied two-sample MR to investigate the causal relationships between genetic liability for select risk factors and spinal stenosis. Next, we examined the genetic relationship between OA and spinal stenosis with linkage disequilibrium score regression and Causal Analysis Using Summary Effect estimates MR method. Finally, we used multivariable MR (MVMR) to explore whether OA and body mass index (BMI) mediate the causal pathways identified.ResultsOur analysis revealed strong evidence for the effect of higher BMI (odds ratio [OR] = 1.54, 95%CI: 1.41-1.69, p-value = 2.7 × 10−21), waist (OR = 1.43, 95%CI: 1.15-1.79, p-value = 1.5 × 10−3) and hip (OR = 1.50, 95%CI: 1.27-1.78, p-value = 3.3 × 10−6) circumference on spinal stenosis. Strong evidence of causality was also observed for higher bone mineral density (BMD): total body (OR = 1.21, 95%CI: 1.12-1.29, p-value = 1.6 × 10−7), femoral neck (OR = 1.35, 95%CI: 1.09-1.37, p-value = 7.5×10−7), and lumbar spine (OR = 1.38, 95%CI: 1.25-1.52, p-value = 4.4 × 10−11). We detected high genetic correlations between spinal stenosis and OA (rg range: 0.47-0.66), with Causal Analysis Using Summary Effect estimates results supporting a causal effect of OA on spinal stenosis (ORallOA = 1.6, 95%CI: 1.41-1.79). Direct effects of BMI, BMD on spinal stenosis remained after adjusting for OA in the MVMR.ConclusionsGenetic susceptibility to anthropometric risk factors, particularly higher BMI and BMD can increase the risk of spinal stenosis, independent of OA status. These results may inform preventative strategies and treatments