Vaccination against the virus that causes COVID-19 triggers the body to produce antibodies that help fight future infections. But some people generate more antibodies after vaccination than others. People with lower levels of antibodies are more likely to get COVID-19 in the future. Identifying people with low antibody levels after COVID-19 vaccination is important. It could help decide who receives priority for future vaccination.

Previous studies show that people with certain health conditions produce fewer antibodies after one or two doses of a COVID-19 vaccine. For example, people with weakened immune systems. Now that third booster doses are available, it is vital to determine if they increase antibody levels for those most at risk of severe COVID-19.

Cheetham et al. show that a third booster dose of a COVID-19 vaccine boosts antibodies to high levels in 90% of individuals, including those at increased risk. In the experiments, Cheetham et al. measured antibodies against the virus that causes COVID-19 in 9,361 individuals participating in two large long-term health studies in the United Kingdom. The experiments found that UK individuals advised to shield from the virus because they were at increased risk of complications had lower levels of antibodies after one or two vaccine doses than individuals without such risk factors. This difference was also seen after a third booster dose, but overall antibody levels had large increases. People who received the Oxford/AstraZeneca vaccine as their first dose also had lower antibody levels after one or two doses than those who received the Pfizer/BioNTech vaccine first. Positively, this difference in antibody levels was no longer seen after a third booster dose. Individuals with lower antibody levels after their first dose were also more likely to have a case of COVID-19 in the following months.

Antibody levels were high in most individuals after the third dose. The results may help governments and public health officials identify individuals who may need extra protection after the first two vaccine doses. They also support current policies promoting booster doses of the vaccine and may support prioritizing booster doses for those at the highest risk from COVID-19 in future vaccination campaigns.

Immunological responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and SARS-CoV-2 vaccination vary between individuals and over time (Shrotri et al., 2021a; Shrotri et al., 2021b; Wei et al., 2021b). Within 2–4 weeks of infection, most individuals generate detectable levels of several antibody subtypes (immunoglobulin A, M, G) directed against different domains of the virus (Nucleocapsid protein, Spike protein, receptor-binding domain of Spike), which gradually decline over time (Seow et al., 2020; Brochot et al., 2020; Wei et al., 2021a; Long et al., 2020; Gaebler et al., 2021). Anti-Nucleocapsid antibodies are generated following infection but not by any current SARS-CoV-2 vaccines, while anti-Spike antibodies are generated following infection or vaccination. Levels of anti-Spike antibodies correlate with SARS-CoV-2-neutralising anti-receptor-binding domain antibody titre (Perkmann et al., 2021). A similar profile of antibody induction with subsequent waning is observed after vaccination against SARS-CoV-2 (Shrotri et al., 2021a; Shrotri et al., 2021b; Matsunaga et al., 2022; Pegu et al., 2021). Waning of antibody levels are likely correlated with observed reductions in vaccine effectiveness over time (Pouwels et al., 2021; UKHSA, 2021; Lopez Bernal et al., 2021). Reduced antibody neutralising activity and vaccine effectiveness have been observed for variants of concern in comparison to ancestral SARS-CoV-2 (UKHSA, 2021; Collie et al., 2022; Yu et al., 2022; Nemet et al., 2022; Cheng et al., 2022).

Anti-Spike antibody levels have been found to be inversely proportional to risk of future infection, and so identified as a correlate of protection (Goldblatt et al., 2022a; Perry et al., 2022; Gilbert et al., 2022; Goldblatt et al., 2022b; Feng et al., 2021; Dimeglio et al., 2022a; Dimeglio et al., 2022b; Khoury et al., 2021). Goldblatt et al. estimated protective thresholds of 154 (95% CI: 42, 559) and 171 (95% CI: 57, 519) BAU/mL for wild-type and alpha variant SARS-CoV-2 respectively and an initial estimate range of 36–490 BAU/ml for delta variant (Goldblatt et al., 2022b), while Feng et al., 2021 estimated 80% vaccine effectiveness against alpha variant for levels above 264 (95% CI: 108, 806) BAU/mL. Dimeglio et al. estimated much higher levels of more than 6000 BAU/mL were needed for protection against omicron variant BA.1, while no relationship was found between infection and antibody level for BA.2 (Dimeglio et al., 2022b).

Several clinical variables contribute to variation in antibody response following vaccination. Lower antibody levels following both first and second vaccinations have been observed in individuals with particular comorbidities (including cancer, renal disease, and hepatic disease; Parry et al., 2021; Monin et al., 2021; Kearns et al., 2021), individuals using immunosuppressant medications (Shrotri et al., 2021a; Shrotri et al., 2021b; Parry et al., 2021; Monin et al., 2021), and individuals identified from electronic health records data as of moderate or high risk of COVID-19 complications (according to UK government prior ‘Shielded Patient List’ criteria of conditions, ongoing treatments, and medications) (Shrotri et al., 2021a; Shrotri et al., 2021b; GOV.UK, 2022b). Studies testing for associations between antibody response and non-clinical factors such as socio-demographics have been more limited. Here, the use of longitudinal studies, with broader catalogues of bio-social data, are well suited to such investigations.

Here, we aimed to examine variables associated with variation in post-vaccination antibody levels, and the subsequent likelihood of post-vaccination infection. We measured the antibody levels of participants from two population-based longitudinal cohorts during the time of the UK vaccination roll-out: TwinsUK (in April-May 2021 and November 2021-January 2022) (Verdi et al., 2019) and Avon Longitudinal Study of Parents and Children (ALSPAC) (Fraser et al., 2013; Boyd et al., 2013) (April-May 2021 only). We aimed firstly to assess the relationship between anti-Spike antibody levels (identified as a correlate of protection against infection), measured after first or second vaccination in April-May 2021, and an outcome of subsequent post-vaccination infection over the following 6–9 months (identified through further serological evidence and/or self-reported COVID-19 from data collected in TwinsUK between November 2021 and January 2022). Secondly, we used peri-pandemic and historical data to investigate associations with an outcome of having relatively low antibody levels following first, second (ALSPAC and TwinsUK), or third (TwinsUK only) vaccination, for multiple socio-demographic, physical and mental health characteristics, prior SARS-CoV-2 infection, and vaccination history. Finally, we used twin-pair analysis within TwinsUK to probe genetic and environmental contributions to antibody level variation.

In this study we used SARS-CoV-2 anti-Nucleocapsid and anti-Spike antibody testing, and questionnaire data collected at multiple timepoints during and before the COVID-19 pandemic, to investigate associations with antibody response to vaccination in TwinsUK and ALSPAC longitudinal population-based cohorts.

Firstly, we observed large non-linear increases in antibody levels between first, second, and third vaccination, both at the median and 10th percentile levels where risk of infection is heightened, with a relatively narrowed antibody level distribution after third vaccination producing a more even response across the sampled population. Secondly, individuals with lower levels of anti-Spike antibodies following first vaccination were at higher risk of future SARS-CoV-2 infection at any subsequent time, including after further vaccinations, providing further indication of anti-Spike antibody levels as a correlate of protection. Thirdly, the following groups all had higher odds of having lower antibody levels following vaccination: those on the UK ‘Shielded Patient List’; those with lower self-rated health; those who received AZD1222 (Oxford/AstraZeneca) vaccine for first and second vaccination; those sampled at longer time since second vaccination and third vaccination; those prescribed immunosuppressant medication (in TwinsUK) or with self-reported immunosuppression (in ALSPAC). These findings were consistent across multiple rounds of vaccination and/or in both cohorts. Individuals with evidence of SARS-CoV-2 infection prior to sampling were less likely to have lower antibody levels, consistent with previous studies that postulating that the quantity and quality of antibody response were linked to the total number of exposures to SARS-CoV-2 (Walls et al., 2022; Bates et al., 2022). Finally, in analyses exploiting the twin-pair design of the TwinsUK cohort, we found that genetic factors influenced antibody level variation (considered only after third vaccination), with smaller differences in antibody levels within genetically identical MZ pairs compared with DZ pairs. Twin-pair regression models showed that association between antibody levels and ‘Shielded Patient List’ status was independent of genetic and other shared factors, after explicit adjustment for key vaccination and infection variables.

Longitudinal antibody testing within TwinsUK at Q4 highlighted the effectiveness of third vaccination at both increasing absolute levels of antibodies and reducing variability in post-vaccination antibody levels evident after earlier doses. Even among sub-groups associated with having the lowest antibody levels and/or higher risk of severe COVID-19, such as shielding, frail, and/or immunosuppressed individuals, over 75% of individuals had levels above 6000 BAU/mL (Supplementary file 4), the minimum level estimated to give partial protection against omicron BA.1 variant (Dimeglio et al., 2022b). Moreover, although individuals receiving AZD1222 vaccine (versus BNT162b2 [Pfizer BioNTech]) were more likely to have lower antibody levels after first and second vaccination, this disparity was no longer evident after third vaccination, consistent with lower vaccine effectiveness and increased post-vaccination infection after first or second vaccination following AZD1222 versus BNT162b2 (UKHSA, 2021; Katikireddi et al., 2022; Stouten et al., 2022), but only minor differences after third vaccination (UKHSA, 2021; Menni et al., 2022).

Notably, health-related variables associated with lower antibody levels were more general (self-rated poor health, immunosuppression indicators) and/or collective measures with wide-ranging criteria (e.g., ‘Shielded Patient List’, very frail, multimorbidity), rather than specific factors such as individual comorbidities (e.g., rheumatoid arthritis). These more general and collective measures may contain more specific risk factors for which we did not have data or sufficient sample size to detect, or could suggest that variation in post-vaccination antibody levels between individuals may originate from a wide range of variables in combination. Of the several variables associated with antibody levels, only serology-based evidence of prior SARS-CoV-2 infection was directly associated (here, negatively associated) with subsequent post-vaccination infection between April-May 2021 and November 2021-January 2022 (majority sampled before the January 2022 UK omicron variant peak). We found no consistent associations of lower antibody levels with age or employment status, but a very strong age gradient (lower incidence with older age) and lower likelihood among retired (versus employed) individuals of post-vaccination infection. These results are consistent with risk of infection being a complex combination of SARS-CoV-2 case prevalence, individual immune response to vaccination, and individual level of exposure. Given the relaxation of measures across many countries, groups previously less exposed, for example, due to shielding guidance, may become more at risk.

We also acknowledge limitations of this work. Both TwinsUK and ALSPAC (Generation 0) participants are disproportionately older, female, and more likely of white ethnicity, in comparison to the UK population. Geographically, TwinsUK (based in London) is skewed towards lower deprivation areas in south east England and ALSPAC (based in Bristol) towards south west England. Consequently, the generalisability of our findings to non-white UK and international populations, in addition to our ability to detect associations with smaller effect sizes, is limited. Our analyses are subject to selection biases due to use of multiple and varying data collections that rely on voluntary participation. This may cause collider bias and affect findings as outlined elsewhere (Griffith et al., 2020; Munafò et al., 2018). For example, indicators of poorer health have been associated with lower response to COVID-19 questionnaires in ALSPAC (Fernández-Sanlés et al., 2021), which may bias the observed results. Acknowledging the potential effects of biases, the replication of multiple associations with lower antibody levels across compositionally varied TwinsUK and ALSPAC cohorts and across multiple rounds of vaccination support the robustness of our findings. It is these replicated findings that we chose to discuss primarily.

In conclusion, our results highlight the large boost across the antibody level distribution produced by third vaccination, and suggest that measurement of anti-Spike antibodies after first SARS-CoV-2 vaccination may have potential use as an early indicator to identify individuals at higher risk of a future SARS-CoV-2 infection, particularly in the many countries where vaccination roll-out is at an earlier stage. Individuals who previously met UK ‘Shielded Patient List’ criteria had consistently lower antibody responses to vaccination than other participants, highlighting the importance of continuing to inform such individuals of their personal risk of SARS-CoV-2 infection, despite the UK government decision to end shielding guidance in April 2021 (GOV.UK, 2021). This result should inform prioritisation of vaccination towards these individuals in any future immunisation campaigns.

Data from all analyses presented in figures and tables herein are tabulated and available as a supplementary spreadsheet file. Original antibody test data are available within the UK Longitudinal Linkage Collaboration upon application (see https://ukllc.ac.uk/apply/). UK LLC houses COVID-19 related datasets from over 20 UK longitudinal population studies (see https://ukllc.ac.uk/datasets/). Original TwinsUK data are available to researchers on application. Access to original TwinsUK data is managed by the TwinsUK Resource Executive Committee (see https://twinsuk.ac.uk/resources-for-researchers/access-our-data/) and access to original ALSPAC data via an online proposal system (see http://www.bristol.ac.uk/media-library/sites/alspac/documents/researchers/data-access/ALSPAC_Access_Policy.pdf). This is to ensure privacy and protect against misuse. ALSPAC study data were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies (doi:https://doi.org/10.1016/J.JBI.2008.08.010). The study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool on the study website (http://www.bristol.ac.uk/alspac/researchers/our-data/). Analysis code is openly available via GitHub at: https://github.com/nathan-cheetham/NCS_SARSCOV2_Antibody_Study (copy archived at swh:1:rev:7103349369b7a4f00d6cf73f780283ba86fe8570).

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Author details

1. Nathan J Cheetham

   Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   nathan.cheetham@kcl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2259-1556

2. Milla Kibble

   1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
   3. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   received payment for attending the Health and Safety Executive (HSE) symposium. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0003-1130-4010

3. Andrew Wong

   MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

   Contribution

   Conceptualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Richard J Silverwood

   Centre for Longitudinal Studies, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Anika Knuppel

   MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Dylan M Williams

   1. MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom
   2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Olivia KL Hamilton

   MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5874-0058

8. Paul H Lee

   Department of Health Sciences, University of Leicester, Leicester, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5729-6450

9. Charis Bridger Staatz

   Centre for Longitudinal Studies, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   received an ESRC and NIHR funded Grant, and MRC Funded Studentship. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-2872-6968

10. Giorgio Di Gessa

    Department of Epidemiology and Public Health, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6154-1845

11. Jingmin Zhu

    Department of Epidemiology and Public Health, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8325-7589

12. Srinivasa Vittal Katikireddi

    MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    participates on the Scottish Government Expert Reference Group on Ethnicity and COVID-19, and UK Scientific Advisory Group on Emergencies (SAGE) subgroup on Ethnicity. The author has no other competing interests to declare

13. George B Ploubidis

    Centre for Longitudinal Studies, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Ellen J Thompson

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Ruth CE Bowyer

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    3. AI for Science and Government, The Alan Turing Institute, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Xinyuan Zhang

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Golboo Abbasian

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Maria Paz Garcia

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    is a member of the King's College London Health Faculties Research Ethics Subcommittee (Purple), and a Chair of the TwinsUK Volunteer Advisory Panel. The author has no other competing interests to declare

19. Deborah Hart

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

20. Jeffrey Seow

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

21. Carl Graham

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

22. Neophytos Kouphou

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

23. Sam Acors

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6428-7707

24. Michael H Malim

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7699-2064

25. Ruth E Mitchell

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

26. Kate Northstone

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

27. Daniel Major-Smith

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6467-2023

28. Sarah Matthews

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

29. Thomas Breeze

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

30. Michael Crawford

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

31. Lynn Molloy

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

32. Alex SF Kwong

    1. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    2. Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

33. Katie Doores

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

34. Nishi Chaturvedi

    MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

    Contribution

    Funding acquisition, Methodology, Project administration, Writing – review and editing

    Competing interests

    received payment for clinical trials of a diabetes drug from AstraZeneca. Nishi Chaturvedi is Chair of British Heart Foundation Fellowships Committee, a member of Diabetes UK research committee and a member of NWO Gravitational Awards Committee. The author has no other competing interests to declare

35. Emma L Duncan

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Guy’s & St Thomas’s NHS Foundation Trust, London, United Kingdom

    Contribution

    Funding acquisition, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8143-4403

36. Nicholas J Timpson

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    N.J.Timpson@bristol.ac.uk

    Competing interests

    No competing interests declared

37. Claire J Steves

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Guy’s & St Thomas’s NHS Foundation Trust, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    claire.j.steves@kcl.ac.uk

    Competing interests

    received payment for consultancy work for Zoe Ltd. The author has no other competing interests to declare

    "This ORCID iD identifies the author of this article:" 0000-0002-4910-0489

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