The anti-inflammatory cytokine response characterized by elevated interleukin-10 is a stronger predictor of severe disease and poor outcomes than the pro-inflammatory cytokine response in coronavirus disease 2019 (COVID-19)

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Objectives

Severe coronavirus disease 2019 (COVID-19) is associated with a dysregulated immune state. While research has focused on the hyperinflammation, little research has been performed on the compensatory anti-inflammatory response. The aim of this study was to evaluate the anti-inflammatory cytokine response to COVID-19, by assessing interleukin-10 (IL-10) and IL-10/lymphocyte count ratio and their association with outcomes.

Methods

Adult patients presenting to the emergency department (ED) with laboratory-confirmed COVID-19 were recruited. The primary endpoint was maximum COVID-19 severity within 30 days of index ED visit.

Results

A total of 52 COVID-19 patients were enrolled. IL-10 and IL-10/lymphocyte count were significantly higher in patients with severe disease (p<0.05), as well as in those who developed severe acute kidney injury (AKI) and new positive bacterial cultures (all p≤0.01). In multivariable analysis, a one-unit increase in IL-10 and IL-10/lymphocyte count were associated with 42% (p=0.031) and 32% (p=0.013) increased odds, respectively, of severe COVID-19. When standardized to a one-unit standard deviations scale, an increase in the IL-10 was a stronger predictor of maximum 30-day severity and severe AKI than increases in IL-6 or IL-8.

Conclusions

The hyperinflammatory response to COVID-19 is accompanied by a simultaneous anti-inflammatory response, which is associated with poor outcomes and may increase the risk of new positive bacterial cultures. IL-10 and IL-10/lymphocyte count at ED presentation were independent predictors of COVID-19 severity. Moreover, elevated IL-10 was more strongly associated with outcomes than pro-inflammatory IL-6 or IL-8. The anti-inflammatory response in COVID-19 requires further investigation to enable more precise immunomodulatory therapy against SARS-CoV-2.

Article activity feed

  1. SciScore for 10.1101/2020.09.28.20203398: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Institutional Review Board of the University of Cincinnati and performed under a waiver of informed consent.
    Consent: This study was approved by the Institutional Review Board of the University of Cincinnati and performed under a waiver of informed consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was performed using Prism 8 (GraphPad Software, San Diego, CA, USA) and R (version 4.0.2, R Foundation for Statistical Computing, Vienna, Austria) with a p<0.05 considered statistically significant.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.