The seroprevalence and kinetics of IgM and IgG in the progression of COVID-19

  1. Xuzhen Qin
  2. Jun Shen
  3. Erhei Dai
  4. Haolong Li
  5. Guodong Tang
  6. Lixia Zhang
  7. Xin Hou
  8. Minya Lu
  9. Xian Wu
  10. Simeng Duan
  11. Jingjia Zhang
  12. Man-Fung Tsoi
  13. Ping Jiang
  14. Yongzhe Li

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Abstract

Background

SARS-CoV-2 is a novel coronavirus first recognized in late December 2019 that causes coronavirus disease 19 (COVID-19). Due to the highly contagious nature of SARS-CoV-2, it has developed into a global pandemic in just a few months. Antibody testing is an effective method to supplement the diagnosis of COVID-19. However, multicentre studies are lacking to support the understanding of the seroprevalence and kinetics of SARS-CoV-2 antibodies in COVID-19 epidemic regions.

Method

A multicentre cross-sectional study of suspected and confirmed patients from 4 epidemic cities in China and a cohort study of consecutive follow-up patients were conducted from 29/01/2020 to 12/03/2020. IgM and IgG antibodies elicited by SARS-CoV-2 were tested by a chemiluminescence assay. Clinical information, including basic demographic data, clinical classification, and time interval from onset to sampling, was collected from each centre.

Results

A total of 571 patients were enrolled in the cross-sectional study, including 235 COVID-19 patients and 336 suspected patients, each with 91.9%:2.1% seroprevalence of SARS-CoV-2 IgG and 92.3%:5.4% seroprevalence of SARS-CoV-2 IgM. The seroprevalence of SARS-CoV-2 IgM and IgG in COVID-19 patients was over 70% less than 7 days after symptom onset. Thirty COVID-19 patients were enrolled in the cohort study and followed up for 20 days. The peak concentrations of IgM and IgG were reached on the 10th and 20th days, respectively, after symptom onset. The seroprevalence of COVID-19 IgG and IgM increased along with the clinical classification and treatment time delay.

Conclusion

We demonstrated the kinetics of IgM and IgG SARS-CoV-2 antibodies in COVID-19 patients and the association between clinical classification and antibodies, which will contribute to the interpretation of IgM and IgG SARS-CoV-2 antibody tests and in predicting the outcomes of patients with COVID-19.

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  1. Version published to 10.1186/s12865-021-00404-0
  2. ScreenIT

    SciScore for 10.1101/2020.07.20.20157446: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Informed consent is waived because of the use of the remaining samples.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis was conducted by SPSS 12.0 (SAS Institute, Cary, NC, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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.

  3. Version published to 10.1101/2020.07.20.20157446v1 on medRxiv