Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

INTRODUCTION: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. OBJECTIVES: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). METHODS: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. RESULTS: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86). CONCLUSION: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

ViralVar: A Web Tool for Multilevel Visualization of SARS-CoV-2 Genomes (preprint)

The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. A majority of the currently available tools either require computational expertise to deploy or limit user input to pre-selected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: 1) visualization of spatiotemporal dynamics of SARS-CoV-2 lineages, and 2) structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in the pediatric, adult, and elderly population (n > 1.7 million genomes). While the spatiotemporal dynamics of variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate large-scale surveillance of circulating pathogens. The ViralVar webserver is freely available at http://viralvar.org/.

Vitamin D, vitamin D-binding protein, free vitamin D and COVID-19 mortality in hospitalized patients.

BACKGROUND: Vitamin D deficiency has been associated with worse coronavirus disease 2019 (COVID-19) outcomes, but circulating 25-hydroxyvitamin D [25(OH)D] is largely bound to vitamin D-binding protein (DBP) or albumin, both of which tend to fall in illness, making the 25(OH)D status hard to interpret. Because of this, measurements of unbound ("free") and albumin-bound ("bioavailable") 25(OH)D have been proposed. OBJECTIVES: We aimed to examine the relationship between vitamin D status and mortality from COVID-19. METHODS: In this observational study conducted in Liverpool, UK, hospitalized COVID-19 patients with surplus sera available for 25(OH)D analysis were studied. Clinical data, including age, ethnicity, and comorbidities, were extracted from case notes. Serum 25(OH)D, DBP, and albumin concentrations were measured. Free and bioavailable 25(OH)D were calculated. Relationships between total, free, and bioavailable 25(OH)D and 28-day mortality were analyzed by logistic regression. RESULTS: There were 472 patients with COVID-19 included, of whom 112 (23.7%) died within 28 days. Nonsurvivors were older (mean age, 73 years; range, 34-98 years) than survivors (mean age, 65 years; range, 19-95 years; P = 0.003) and were more likely to be male (67%; P = 0.02). The frequency of vitamin D deficiency [25(OH)D < 50 nmol/L] was similar between nonsurvivors (71/112; 63.4%) and survivors (204/360; 56.7%; P = 0.15) but, after adjustments for age, sex, and comorbidities, increased odds for mortality were present in those with severe deficiency [25(OH)D < 25 nmol/L: OR, 2.37; 95% CI, 1.17-4.78] or a high 25(OH)D (≥100 nmol/L; OR, 4.65; 95% CI, 1.51-14.34) compared with a 25(OH)D value of 50-74 nmol/L (reference). Serum DBP levels were not associated with mortality after adjustments for 25(OH)D, age, sex, and comorbidities. Neither free nor bioavailable 25(OH)D values were associated with mortality. CONCLUSIONS: Vitamin D deficiency, as commonly defined by serum 25(OH)D levels (<50 nmol/L), is not associated with increased mortality from COVID-19, but extremely low (<25 nmol/L) and high (>100 nmol/L) levels may be associated with mortality risks. Neither free nor bioavailable 25(OH)D values are associated with mortality risk. The study protocol was approved by the London-Surrey Research Ethics Committee (20/HRA/2282).

Twelve lateral flow immunoassays (LFAs) to detect SARS-CoV-2 antibodies.

BACKGROUND: There are an abundance of commercially available lateral flow assays (LFAs) that detect antibodies to SARS-CoV-2. Whilst these are usually evaluated by the manufacturer, externally performed diagnostic accuracy studies to assess performance are essential. Herein we present an evaluation of 12 LFAs. METHODS: Sera from 100 SARS-CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR) positive participants were recruited through the FASTER study. A total of 105 pre-pandemic sera from participants with other infections were included as negative samples. RESULTS: At presentation sensitivity against RT-PCR ranged from 37.4 to 79% for IgM/IgG, 30.3-74% for IgG, and 21.2-67% for IgM. Sensitivity for IgM/IgG improved ≥ 21 days post symptom onset for 10/12 tests. Specificity ranged from 74.3 to 99.1% for IgM/IgG, 82.9-100% for IgG, and 75.2-98% for IgM. Compared to the EuroImmun IgG enzyme-linked immunosorbent assay (ELISA), sensitivity and specificity ranged from 44.6 to 95.4% and 85.4-100%, respectively. CONCLUSION: There are many LFAs available with varied sensitivity and specificity. Understanding the diagnostic accuracy of these tests will be vital as we come to rely more on the antibody status of a person moving forward, and as such manufacturer-independent evaluations are crucial.

Decreasing work-related movement during a pandemic. Location analytics and the implications of the digital divide

PurposeThe purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. The purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. In response to the COVID-19 pandemic, countries around the world have issued “stay-at-home” orders and curtailed a variety of economic activities. As countries have adopted aggressive policies to limit the spread of COVID-19, varying levels of national infrastructure to provide internet access have limited some nations’ ability to reduce travel requirements. As national policies struggle to address public health issues, location analytics enabled by big data provide unique insights regarding the efficacy of digital infrastructure. These insights can provide valuable tools to public health officials and regulators in understanding how health recommendations are implemented within an economy.Design/methodology/approachThis study analyzes mobile phone movement data during the first half of 2020 and finds that countries that provided greater access to internet capabilities were better able to reduce work-related mobility.FindingsThis study’s findings indicate that greater levels of digital infrastructure may better prepare countries to adapt to societal disruptions such as COVID-19.Practical implicationsThis study’s findings demonstrate that public health controls regarding movement and person-to-person interaction are less likely to be effective in nations with weaker digital infrastructure, even after accounting for variation attributable to gross domestic product (GDP) and pandemic severity. This could limit public health options in developing countries when faced with future socially disruptive events and encourage national investment in digital infrastructure.Social implicationsThis study’s findings highlight positive externalities associated with reducing the digital divide. Developing better digital business infrastructure globally may reduce human exposure to future pandemic risks.Originality/valueThis research demonstrates the practical development implications of analysis of aggregate data widely available through mobile technology. As institutions develop techniques to ethically and effectively analyze this data, greater opportunities to support economic development may be revealed.