SARS-CoV-2 antibodies and breakthrough infections in the Virus Watch cohort

  1. Robert W. Aldridge
  2. Alexei Yavlinsky
  3. Vincent Nguyen
  4. Max T. Eyre
  5. Madhumita Shrotri
  6. Annalan M. D. Navaratnam
  7. Sarah Beale
  8. Isobel Braithwaite
  9. Thomas Byrne
  10. Jana Kovar
  11. Ellen Fragaszy
  12. Wing Lam Erica Fong
  13. Cyril Geismar
  14. Parth Patel
  15. Alison Rodger
  16. Anne M. Johnson
  17. Andrew Hayward

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Abstract

A range of studies globally demonstrate that the effectiveness of SARS-CoV-2 vaccines wane over time, but the total effect of anti-S antibody levels on risk of SARS-CoV-2 infection and whether this varies by vaccine type is not well understood. Here we show that anti-S levels peak three to four weeks following the second dose of vaccine and the geometric mean of the samples is nine fold higher for BNT162b2 than ChAdOx1. Increasing anti-S levels are associated with a reduced risk of SARS-CoV-2 infection (Hazard Ratio 0.85; 95%CIs: 0.79-0.92). We do not find evidence that this antibody relationship with risk of infection varies by second dose vaccine type (BNT162b2 vs. ChAdOx1). In keeping with our anti-S antibody data, we find that people vaccinated with ChAdOx1 had 1.64 times the odds (95% confidence interval 1.45-1.85) of a breakthrough infection compared to BNT162b2. We anticipate our findings to be useful in the estimation of the protective effect of anti-S levels on risk of infection due to Delta. Our findings provide evidence about the relationship between antibody levels and protection for different vaccines and will support decisions on optimising the timing of booster vaccinations and identifying individuals who should be prioritised for booster vaccination, including those who are older, clinically extremely vulnerable, or received ChAdOx1 as their primary course. Our finding that risk of infection by anti-S level does not interact with vaccine type, but that individuals vaccinated with ChAdOx1 were at higher risk of infection, provides additional support for the use of using anti-S levels for estimating vaccine efficacy.

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  1. Version published to 10.1038/s41467-022-32265-5
  2. ScreenIT

    SciScore for 10.1101/2021.11.05.21265968: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics: The Virus Watch study has been approved by the Hampstead NHS Health Research Authority Ethics Committee.
    Sex as a biological variablenot detected.
    RandomizationWe used the Royal Mail Post Office Address File to generate a random list of residential address lists that were sent recruitment postcards, we placed social media adverts on Facebook and Twitter and sent SMS messages and letters to participants from their General Practitioners.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We measured antibody titres targeting the spike (S) protein (anti-S) in the context of seronegativity for SARS-CoV-2 anti-Nucleocapsid (anti-N) which is associated with natural infection.
    anti-Nucleocapsid
    suggested: None
    15 Individuals were included in this analysis if they underwent antibody testing (anti-N and anti-S) and had a valid result between 1st July 2021 and 24th October 2021.
    anti-N
    suggested: None
    We used the estimated regression coefficient r from the linear model to calculate the corresponding half-life value associated with anti-S waning, using the formula: Second, to investigate the use of anti-S levels as a correlate of protection, we undertook a survival analysis using a Kaplan-Meier descriptive analysis in addition to a Cox regression model with individuals entering at risk of SARS-CoV-2 infection 14 days after an antibody test from 1st July and exiting at the first of reported episode of SARS-CoV-2 breakthrough infection or at the end of study follow up on 24th October 2021.
    anti-S
    suggested: None

    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.

    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.

  3. Version published to 10.1101/2021.11.05.21265968 on medRxiv