Distinct age-specific SARS-CoV-2 IgG decay kinetics following natural infection

  1. Calvin P Sjaarda
  2. Emily Moslinger
  3. Kyla Tozer
  4. Robert I. Colautti
  5. Samira Kheitan
  6. Robyn Meurant
  7. Stefanie Van Cleaf
  8. Ali Ardakani
  9. Oliver Bosnjak
  10. Abdi Ghaffari
  11. Prameet M Sheth

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Abstract

Background

Antibody responses to SARS-CoV-2 can be observed as early as 14 days post-infection, but little is known about the stability of antibody levels over time. Here we evaluate the long-term stability of anti-SARS-CoV-2 IgG antibodies following infection with SARS-CoV-2 in 402 adult donors.

Methods

We performed a multi-center study carried out at Plasma Donor Centers in the city of Heidelberg (Plasmazentrum Heidelberg, Germany) and Munich (Plasmazentrum München, Germany). We present anti-S/N and anti-N IgG antibody levels in prospective serum samples collected up to 403 days post recovery from SARS-CoV-2 infected individuals.

Results

The cohort includes 402 adult donors (185 female, 217 male; 17 - 68 years of age) where anti-SARS-CoV-2 IgG levels were measured in plasma samples collected between 18- and 403-days post SARS-CoV-2 infection. A linear mixed effects model demonstrated IgG decay rates that decrease over time (χ 2 =176.8, p<0.00001) and an interaction of time*age χ (χ 2 =10.0, p<0.005)), with those over 60+ years showing the highest baseline IgG levels and the fastest rate of IgG decay. Baseline viral neutralization assays demonstrated that serum IgG levels correlated with in vitro neutralization capacity in 91% of our cohort.

Conclusion

Long-term antibody levels and age-specific antibody decay rates suggest the potential need for age-specific vaccine booster guidelines to ensure long term vaccine protection against SARS-CoV-2 infection.

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  3. ScreenIT

    SciScore for 10.1101/2021.08.05.21259465: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.08.05.21259465v1 on medRxiv