Specific dynamic variations in the peripheral blood lymphocyte subsets in COVID-19 and severe influenza A patients: a retrospective observational study

  1. Fang Qian
  2. Guiju Gao
  3. Yangzi Song
  4. Yanli Xu
  5. Aibin Wang
  6. Sa Wang
  7. Yiwei Hao
  8. Meiling Chen
  9. Xiaoyang Ma
  10. Tianwei Zhao
  11. Xiaodi Guo
  12. Zhihai Chen
  13. Fujie Zhang

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

Both COVID-19 and influenza A contribute to increased mortality among the elderly and those with existing comorbidities. Changes in the underlying immune mechanisms determine patient prognosis. This study aimed to analyze the role of lymphocyte subsets in the immunopathogenesisof COVID-19 and severe influenza A, and examined the clinical significance of their alterations in the prognosis and recovery duration.

Methods

By retrospectively reviewing of patients in four groups (healthy controls, severe influenza A, non-severe COVID-19 and severe COVID-19) who were admitted to Ditan hospital between 2018 to 2020, we performed flow cytometric analysis and compared the absolute counts of leukocytes, lymphocytes, and lymphocyte subsets of the patients at different time points (weeks 1–4).

Results

We reviewed the patients’ data of 94 healthy blood donors, 80 Non-severe-COVID-19, 19 Severe-COVID-19 and 37 severe influenza A. We found total lymphocytes (0.81 × 10 9 /L vs 1.74 × 10 9 /L, P =  0.001; 0.87 × 10 9 /L vs 1.74 × 10 9 /L, P <  0.0001, respectively) and lymphocyte subsets (T cells, CD4 + and CD8 + T cell subsets) of severe COVID-19 and severe influenza A patients to be significantly lower than those of healthy donors at early infection stages. Further, significant dynamic variations were observed at different time points (weeks 1–4).

Conclusions

Our study suggests the plausible role of lymphocyte subsets in disease progression, which in turn affects prognosis and recovery duration in patients with severe COVID-19 and influenza A.

Article activity feed

  1. Version published to 10.1186/s12879-020-05637-9
  2. Version published to 10.21203/rs.3.rs-46000/v2 on Research Square
  3. ScreenIT

    SciScore for 10.1101/2020.08.15.20175455: (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 Ethics Committee of Beijing Ditan Hospital (No. 202000601).
    Consent: The need for individual consent was waived because of the retrospective nature of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablePatients in four groups (non-severe COVID-19 and severe COVID-19, severe influenza A, and healthy controls) excluded minors younger than eighteen years old and pregnant women.
    Cell Line AuthenticationContamination: Patients were excluded if an alternative diagnosis, such as the presence of influenza B, parainfluenza, respiratory syncytial virus, adenovirus, mycoplasma, chlamydia, legionella was determined.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses: All analyses were performed using SPSS statistical software (version 26.0, IBM, Armonk, NY, USA).
    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:
    Our study has several limitations. This study was retrospective, small single-center study and only included a small sample of 99 patients with COVID-19 admitted to Beijing Ditan Hospital, which may confound the results and potentially introduce selection bias. This may limit the generalizability of the study. In addition, inconsistencies in time periods between illness onset and admission might have led to missing data which could result in observation biases in the dynamic variations in immune cells.

    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.

  4. Version published to 10.1101/2020.08.15.20175455 on medRxiv
  5. Version published to 10.21203/rs.3.rs-46000/v1 on Research Square