Circulating cytokines and lymphocyte subsets in patients who have recovered from COVID-19

  1. Hasichaolu
  2. Xinri Zhang
  3. Xin Li
  4. Xin Li
  5. Dongyan Li

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Abstract

To investigate the immune status of people who previously had COVID-19 infections, we recruited patients 2 weeks post-recovery and analyzed circulating cytokines and lymphocyte subsets. We measured levels of total lymphocytes, CD4 + T cells, CD8 + T cells, CD19 + B cells, CD56 + NK cells, and the serum concentrations of interleukin (IL)-1, IL-4, IL-6, IL-8, IL-10, transforming growth factor beta (TGF-β), tumor necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ) by flow cytometry. We found that in most post-recovery patients, levels of total lymphocytes (66.67%), CD3 + T cells (54.55%), CD4 + T cells (54.55%), CD8 + T cells (81.82%), CD19 + B cells (69.70%), and CD56 + NK cells(51.52%) remained lower than normal, whereas most patients showed normal levels of IL-2 (100%), IL-4 (80.88%), IL-6 (79.41%), IL-10 (98.53%), TNF-α (89.71%), IFN-γ (100%) and IL-17 (97.06%). Compared to healthy controls, 2-week post-recovery patients had significantly lower absolute numbers of total lymphocytes, CD3 + T cells, CD4 + T cells, CD8 + T cells, CD19 + B cells, and CD56 + NK cells, along with significantly higher levels of IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and IL-17. Among post-recovery patients, T cells, particularly CD4 + T cells, were positively correlated with CD19 + B cell counts. Additionally, CD8 + T cells positively correlated with CD4 + T cells and IL-2 levels, and IL-6 positively correlated with TNF-α and IFN-γ. These correlations were not observed in healthy controls. By ROC curve analysis, post-recovery decreases in lymphocyte subsets and increases in cytokines were identified as independent predictors of rehabilitation efficacy. These findings indicate that the immune system has gradually recovered following COVID-19 infection; however, the sustained hyper-inflammatory response for more than 14 days suggests a need to continue medical observation following discharge from the hospital. Longitudinal studies of a larger cohort of recovered patients are needed to fully understand the consequences of the infection.

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  1. Version published to 10.1101/2020.07.22.20160259v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.22.20160259: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: These subjects were used as healthy controls (HCs) from whom serum samples were collected after obtaining informed consent.
    IRB: This study was approved by the ethics committee of The First Hospital of Shanxi Medical University.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableIn total, 68 recovered patients, including 48 males, aged 21-49 years-old, and 20 females, aged 24-66 years-old, along with 28 healthy controls, including 8 males, aged 21-53 years-old, and 20 females, aged 22-53 years-old were included.

    Table 2: Resources

    Antibodies
    SentencesResources
    Further, the included patients would be excluded if they fulfilled any of the following testing criteria: positive in HBsAg, HCV antibody, or HIV antibody; creatinine above 120 μmol/L, creatine kinase above 500 U/L
    HCV
    suggested: None
    HIV
    suggested: None
    10 μL of each monoclonal antibody were added to tube A (CD45-FITC/CD4-RD1/ CD8-ECD/CD3-PC5 antibody) or tube B (CD45-FITC/CD56-RD1/ CD19-ECD/CD3-PC5 antibody).
    CD45-FITC/CD4-RD1/ CD8-ECD/CD3-PC5
    suggested: None
    CD45-FITC/CD56-RD1/ CD19-ECD/CD3-PC5
    suggested: None
    Software and Algorithms
    SentencesResources
    Fluorescent detection was then performed on a calibrated flow cytometer for each sample in sequence. 2.6. Statistics: All statistical analyses were performed using EXCEL (Microsoft) and SPSS Statistics version 21.0 software.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: We detected the following sentences addressing limitations in the study:
    There are some limitations in this study. Firstly, due to the lack of clinical data during the early infection stage, continuous observational data from the same cases are absent, and potential influence by early events are not considered. Secondly, the sample size was relatively small in comparison with Wuhan where the disease originated, which may have some impact on the statistical results. In future experiments, we will conduct follow-up studies in the patients who recovered from COVID-19, and determine a quantitative basis for intervention of rehabilitation measures. This will help treat the diseases at an earlier stage by promoting medical intervention in a timely manner. Moreover, it may be beneficial to analyze if a particular population has an added immunological advantage while combating the virus.

    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.

  3. Version published to 10.1101/2020.07.22.20160259v1 on medRxiv
  4. Version published to 10.1155/2020/7570981