Covariation of Zinc Deficiency with COVID-19 Infections and Mortality in European Countries: Is Zinc Deficiency a Risk Factor for COVID-19?
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Abstract
Variables responsible for the differential COVID-19 pandemic severity among countries remain undefined. Zinc, a micronutrient required for immunocompetence, is found deficient in populations. We hypothesized the differential COVID-19 severity observed among European countries could be associated with the Zn-deficiency prevalence. The COVID-19 data from different stages of pandemic, i.e ., 8 April, 12 and 26 May 2020, were analyzed for covariation with the estimated Zn-deficiency. A significant, relatively stable, but negative correlation of Zn-deficiency with cases per million for the time period [ r (20): -0.4930 to -0.5335, p -value: 0.02720 to 0.0154] and a steady improvement of covariation with deaths per million [ r (20): -0.4056; p -value: 0.0760 on 26 May 2020] was observed. Considering, Zinc’s key immunomodulatory role, widespread deficiency along with the self- and prescribed intervention in different target groups, e.g. children, women, elderly, carefully planned dedicated exploratory studies to understand the basis of the observed association are advisable.
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SciScore for 10.1101/2020.06.12.20105676: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All basic statistical analysis of the data, i.e., descriptive, linear regression, calculation of best-fit trend line, Pearson correlation coefficient, and R- squared value (R) was performed in the Microsoft excel as done previously (Singh, Kaur & Singh, 2020). Microsoft excelsuggested: (Microsoft Excel, RRID:SCR_016137)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
SciScore for 10.1101/2020.06.12.20105676: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All basic statistical analysis of the data, i.e., descriptive, linear regression, calculation of best-fit trend line, Pearson correlation coefficient, and R- squared value (R) was performed in the Microsoft excel as done previously (Singh, Kaur & Singh, 2020). Microsoft excelsuggested: (Microsoft Excel, RRID:SCR_016137)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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