Higher BCG‐induced trained immunity prevalence predicts protection from COVID‐19: Implications for ongoing BCG trials

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Endeavors to identify potentially protective variables for COVID‐19 impact on certain populations have remained a priority. Multiple attempts have been made to attribute the reduced COVID‐19 impact on populations to their Bacillus–Calmette–Guérin (BCG) vaccination coverage ignoring the fact that the effect of childhood BCG vaccination wanes within 5 years while most of the COVID‐19 cases and deaths have occurred in aged with comorbidities. Since the supposed protection being investigated could come from heterologous ‘trained immunity’ (TI) conferred by exposure to Mycobacterium spp. (i.e., environmental and BCG), it is argued that the estimates of the prevalence of TI in populations currently available as latent tuberculosis infection (LTBI) prevalence would be a better variable to evaluate such assertions. Indeed, when we analyze the European populations (24), and erstwhile East and West Germany populations completely disregarding their BCG vaccination coverage, the populations with higher TI prevalence consistently display reduced COVID‐19 impact as compared to their lower TI prevalence neighbors. The TI estimates of the populations not the BCG coverage per se, negatively correlated with pandemic phase‐matched COVID‐19 incidences ( r (24): −0.79 to −0.57; p ‐value < .004), mortality ( r (24): −0.63 to −0.45; p ‐value < .03), and interim case fatality rates ( i ‐CFR) data. To decisively arrive at dependable conclusions about the potential protective benefit gained from BCG vaccination in COVID‐19, the ongoing or planned randomized controlled trials should consciously consider including measures of TI as: (a) all individuals immunized do not respond equally, (b) small study groups from higher background TI could fail to indicate any protective effect.

Article activity feed

  1. SciScore for 10.1101/2021.02.11.20233593: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical estimations and correlation analysis of the COVID-19 incidence and mortality with TIC or LTBI prevalence of populations (Average, Standard deviation (STDEV), Standard Error (Std. Err.), F-value, Correlation/ Pearson coefficient (r/R), regression, etc) were performed using Microsoft Excel 2019.
    Microsoft Excel
    suggested: (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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.