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Article

Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK)

by
David A. Jolliffe
1,2,
Sian E. Faustini
3,
Hayley Holt
1,2,4,
Natalia Perdek
2,
Sheena Maltby
2,
Mohammad Talaei
1,
Matthew Greenig
2,
Giulia Vivaldi
1,2,
Florence Tydeman
1,2,
Jane Symons
5,
Gwyneth A. Davies
6,
Ronan A. Lyons
6,
Christopher J. Griffiths
1,4,
Frank Kee
7,
Aziz Sheikh
8,
Seif O. Shaheen
1,
Alex G. Richter
3 and
Adrian R. Martineau
1,2,4,*
1
Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AB, UK
2
Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
3
Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
4
Asthma UK Centre for Applied Research, Queen Mary University of London, London E1 2AB, UK
5
Jane Symons Media, London, UK
6
Population Data Science, Swansea University Medical School, Singleton Park, Swansea SA2 8PP, UK
7
Centre for Public Health Research (NI), Queen’s University Belfast, Belfast BT12 6BA, UK
8
Usher Institute, University of Edinburgh, Edinburgh EH16 4UX, UK
*
Author to whom correspondence should be addressed.
Vaccines 2022, 10(10), 1601; https://doi.org/10.3390/vaccines10101601
Submission received: 23 August 2022 / Revised: 9 September 2022 / Accepted: 16 September 2022 / Published: 23 September 2022
(This article belongs to the Special Issue Antibody Response of Vaccines to SARS-CoV-2)
Browse Figure
Review Reports Versions Notes

Abstract

:
Antibody responses to SARS-CoV-2 vaccines vary for reasons that remain poorly understood. A range of sociodemographic, behavioural, clinical, pharmacologic and nutritional factors could explain these differences. To investigate this hypothesis, we tested for presence of combined IgG, IgA and IgM (IgGAM) anti-Spike antibodies before and after 2 doses of ChAdOx1 nCoV-19 (ChAdOx1, AstraZeneca) or BNT162b2 (Pfizer-BioNTech) in UK adults participating in a population-based longitudinal study who received their first dose of vaccine between December 2020 and July 2021. Information on sixty-six potential sociodemographic, behavioural, clinical, pharmacologic and nutritional determinants of serological response to vaccination was captured using serial online questionnaires. We used logistic regression to estimate multivariable-adjusted odds ratios (aORs) for associations between independent variables and risk of seronegativity following two vaccine doses. Additionally, percentage differences in antibody titres between groups were estimated in the sub-set of participants who were seropositive post-vaccination using linear regression. Anti-spike antibodies were undetectable in 378/9101 (4.2%) participants at a median of 8.6 weeks post second vaccine dose. Increased risk of post-vaccination seronegativity associated with administration of ChAdOx1 vs. BNT162b2 (adjusted odds ratio (aOR) 6.6, 95% CI 4.2–10.4), shorter interval between vaccine doses (aOR 1.6, 1.2–2.1, 6–10 vs. >10 weeks), poor vs. excellent general health (aOR 3.1, 1.4–7.0), immunodeficiency (aOR 6.5, 2.5–16.6) and immunosuppressant use (aOR 3.7, 2.4–5.7). Odds of seronegativity were lower for participants who were SARS-CoV-2 seropositive pre-vaccination (aOR 0.2, 0.0–0.6) and for those taking vitamin D supplements (aOR 0.7, 0.5–0.9). Serologic responses to vaccination did not associate with time of day of vaccine administration, lifestyle factors including tobacco smoking, alcohol intake and sleep, or use of anti-pyretics for management of reactive symptoms after vaccination. In a sub-set of 8727 individuals who were seropositive post-vaccination, lower antibody titres associated with administration of ChAdOx1 vs. BNT162b2 (43.4% lower, 41.8–44.8), longer duration between second vaccine dose and sampling (12.7% lower, 8.2–16.9, for 9–16 weeks vs. 2–4 weeks), shorter interval between vaccine doses (10.4% lower, 3.7–16.7, for <6 weeks vs. >10 weeks), receiving a second vaccine dose in October–December vs. April–June (47.7% lower, 11.4–69.1), older age (3.3% lower per 10-year increase in age, 2.1–4.6), and hypertension (4.1% lower, 1.1–6.9). Higher antibody titres associated with South Asian ethnicity (16.2% higher, 3.0–31.1, vs. White ethnicity) or Mixed/Multiple/Other ethnicity (11.8% higher, 2.9–21.6, vs. White ethnicity), higher body mass index (BMI; 2.9% higher, 0.2–5.7, for BMI 25–30 vs. <25 kg/m2) and pre-vaccination seropositivity for SARS-CoV-2 (105.1% higher, 94.1–116.6, for those seropositive and experienced COVID-19 symptoms vs. those who were seronegative pre-vaccination). In conclusion, we identify multiple determinants of antibody responses to SARS-CoV-2 vaccines, many of which are modifiable.

1. Introduction

Vaccination against SARS-CoV-2 represents a key tool for COVID-19 control. However, existing vaccines offer imperfect protection against disease, reflecting heterogeneity in immunogenicity [1,2]. Improved understanding of factors influencing these responses to SARS-CoV-2 vaccines could lead to discovery of effect-modifiers that could be harnessed to augment vaccine immunogenicity, and identify groups of poor responders who might benefit from more intensive vaccine dosing regimens or implementation of other protective measures [3].
Existing studies investigating determinants of vaccine immunogenicity have reported that lower antibody responses following SARS-CoV-2 vaccination associate with administration of viral vector vs. messenger RNA (mRNA) vaccines, older age, poorer general health, immunosuppression and shorter inter-dose intervals [4,5,6,7,8,9,10,11]. Higher post-vaccination antibody titres are seen in those with evidence of SARS-CoV-2 infection before vaccination [9,12,13]. However, these studies are limited in several respects: many are conducted in specific populations such as health care workers [5,9,12,13] or in individuals with a particular demographic or clinical characteristic that may influence vaccine immunogenicity [6,10,11], which may constrain generalisability of their results. Where population-based studies have been done [4,7,8], these did not compare the effect of two doses of viral vector vs. mRNA vaccines on host response; neither did they systematically ascertain participants’ pre-vaccination SARS-CoV-2 serostatus, which is an important potential confounder of associations reported. Moreover, these studies did not investigate effects of modifiable factors that have been posited to influence responses to vaccination, such as time of day of inoculation [14], nutrition [15], sleep [16], alcohol use [17], tobacco smoking [18] and peri-vaccination use of anti-pyretic analgesics [19].
We sought to address these limitations by investigating a comprehensive range of potential sociodemographic, behavioural, clinical, pharmacologic and nutritional determinants of antibody responses in a population-based cohort of UK adults (COVIDENCE UK) [20] following administration of two doses of either BNT162b2 (Pfizer-BioNTech) or ChAdOx1 nCoV-19 (Oxford-AstraZeneca; hereafter ChAdOx1) SARS-CoV-2 vaccines. We utilised a semi-quantitative assay with proven sensitivity for detection of combined IgG, IgA and IgM antibodies to the SARS-CoV-2 spike antigen [21] that has been validated as a correlate of protection against breakthrough COVID-19 in two populations [22,23].

2. Materials and Methods

2.1. Study Design and Participants

COVIDENCE UK is a prospective, longitudinal, population-based observational study of COVID-19 in the UK population (www.qmul.ac.uk/covidence accessed on 20 September 2022) [20]. Inclusion criteria were age ≥ 16 years and UK residence at enrolment, with no exclusion criteria. Participants were invited via a national media campaign to complete an online baseline questionnaire to capture information on potential symptoms of COVID-19 experienced since 1 February 2020; results of any COVID-19 tests; and details of a wide range of potential risk factors for COVID-19 and determinants of vaccine response (Table S1, Online Data Supplement). Online monthly follow-up questionnaires captured incident test-confirmed COVID-19 and vaccination details (Table S2, Online Data Supplement), and a pre-vaccination serology study was conducted to evaluate risk factors for SARS-CoV-2 infection [24]. The study was launched on 1 May 2020, and closed to enrolment on 6 October 2021. Participants in the cohort were invited via email to participate in post-vaccination antibody studies and to give additional consent for these. No incentives were offered to participants. The primary analysis presented here includes data from all participants for whom a serology result was available following administration of two doses of ChAdOx1 or BNT162b2.
COVIDENCE UK was sponsored by Queen Mary University of London and approved by Leicester South Research Ethics Committee (ref 20/EM/0117). It is registered with ClinicalTrials.gov (NCT04330599).

2.2. Statistical Analysis

Full details of statistical analyses, study procedures, outcomes and sample size calculation are provided in the Online Data Supplement. Briefly, logistic regression models were used to estimate adjusted ORs, 95% (CIs) and associated pairwise p-values for potential determinants of post-vaccination seronegativity in all participants who had not reported a SARS-CoV-2 infection prior to their 2nd vaccine dosing date. Linear regression models were used to estimate beta-coefficients, 95% CIs and associated pairwise p-values for potential determinants of log-transformed antibody titres in the subset of seropositive participants. For ease of interpretation, log-transformed estimates of antibody titres were exponentiated and a percentage increase or decrease was calculated for every one-unit increase in the potential determinant. We first estimated ORs and beta-coefficients in minimally adjusted models, and included factors independently associated with each outcome at the 10% significance level in fully adjusted models.
In a sensitivity analysis, we excluded participants from the analysis of antibody titres after two vaccine doses who reported a positive RT-PCR or lateral flow test for SARS-CoV-2 between the date of their second dose of vaccine and the date on which they provided their dried blood spot sample. We also stratified the analysis of antibody titres following two vaccine doses according to the type of vaccine received to explore whether determinants of antibody responses to two vaccine doses were consistent for ChAdOx1 vs. BNT162b2. Given reports that peri-vaccination use of antipyretic analgesics may attenuate vaccine immunogenicity [25], we also conducted an exploratory analysis to determine the influence of taking paracetamol or non-steroidal anti-inflammatory drugs (NSAIDs) to treat post-vaccination symptoms on post-vaccination antibody titres.

3. Results

A total of 15,527 participants were invited to participate in the study of antibody responses following two vaccine doses, of whom 13,005 consented to take part. Serology results were available for 9244 participants, of whom 9101 received two doses of ChAdOx1 (n = 5770) or BNT162b2 (n = 3331) for their primary vaccination course and were included in this analysis (Figure S1, Online Data Supplement). Average participant outcome data completeness was 99.5%. Participant characteristics are presented in Table 1: median age was 64.2 years at enrolment (IQR 57.1, 69.9), 71.1% were female, and 96.5% were of White ethnic origin. Post-vaccination dried blood spot samples were provided from 12 January to 18 September 2021 by participants who received their first dose of vaccine from 15 December 2020 to 10 July 2021 and their second dose from 5 January 2021 to 4 September 2021. Median time from the date of participants’ second vaccine dose to the date on which post-vaccination dried blood spot samples were provided was 8.6 weeks (IQR 6.4–10.7 weeks) and 374 of 9101 participants (4.1%) were seronegative following two doses of vaccine.

3.1. Determinants of Seronegativity following a Primary Course of SARS-CoV-2 Vaccination

Table 2 presents results of minimally adjusted and multivariable analyses to identify factors that were independently associated with the absence of detectable anti-spike IgG/A/M following two doses of ChAdOx1 or BNT162b2. After adjusting for age and sex, 32 independent variables associated with post-vaccination serostatus with p < 0.10 and were fitted in a fully adjusted model. Nine factors in the fully adjusted model remained independently associated with post-vaccination serostatus (pairwise p-value below the multiple comparison test (MCT) threshold of 0.024, and/or p for trend <0.05). Seven of these were associated with higher risk of post-vaccination seronegativity: receiving ChAdOx1 vs. BNT162b2 vaccine (aOR 6.62, 95% CI 4.21–10.42); shorter interval between first and second vaccine doses (aOR 1.60, 1.20–2.14, for 6–10 weeks vs. >10 weeks, and aOR 2.56, 1.22–5.37, for <6 weeks vs. >10 weeks); receiving the second vaccine dose during the first quarter (Q1), third quarter (Q3) or the final quarter (Q4) of the year (aOR 1.08, 0.61–1.92, for Q1 vs. Q2, aOR 12.64, 2.21–72.27, for Q3 vs. Q2, and aOR 21.83, 1.74–273.33, for Q4 vs. Q2; older age (aOR per 10-year increase in age 1.68, 1.42–2.00), poorer self-assessed general health (aOR per level of declining health, 1.26, 1.09–1.45); immunodeficiency disorder (aOR 6.48, 2.53–16.59), and use of systemic immunosuppressants (aOR 3.71, 2.41–5.69). Two factors were associated with lower risk of post-vaccination seronegativity: pre-vaccination seropositivity for SARS-CoV-2 (aOR 0.53, 0.32–0.89, for individuals who were seropositive without COVID-19 symptoms pre-vaccination vs. those who were seronegative pre-vaccination, and aOR 0.16, 0.04–0.56, for individuals who were seropositive with COVID-19 symptoms pre-vaccination vs. those who were seronegative pre-vaccination) and regular consumption of vitamin D supplements (aOR 0.66, 0.51–0.87). No associations were seen for other independent variables investigated, including time of day of inoculation, nutrition, sleep, alcohol use, tobacco smoking or socioeconomic deprivation.

3.2. Determinants of Post-Vaccination Antibody Titres in Subset of Individuals Who Were Seropositive following a Primary Course of SARS-CoV-2 Vaccination

To identify factors influencing the magnitude of antibody responses to COVID-19 vaccination, we investigated determinants of SARS-CoV-2 antibody titres in the subset of 8727 participants who were seropositive following their second COVID-19 vaccine dose. Results of this analysis are presented in Table 3. A total of 22 factors associated with antibody titres after adjusting for age and sex with p < 0.10 and were fitted in a fully adjusted model. Eight factors remained independently associated with antibody titres in the fully adjusted model (pairwise p-values below the MCT threshold of 0.025, and/or P for trend < 0.05). Six of these independently associated with lower antibody titres: receipt of ChAdOx1 vs. BNT162b2 vaccine (43.4% lower titres, 95% CI 41.8–44.8, Figure 1); longer duration between date of second vaccine dose and date of sampling (7.4% lower, 3.0–11.6, for 5–8 weeks vs. 2–4 weeks; 12.7% lower, 8.2–16.9, for 9–16 weeks vs. 2–4 weeks, and 7.8% lower, 1.3–16.1, for >16 weeks vs. 2–4 weeks); shorter interval between first and second vaccine doses (10.4% lower, 3.7–16.7, for <6 weeks vs. >10 weeks; 5.9% lower, 3.3–8.3, for 6–10 weeks vs. >10 weeks); receiving the second vaccine dose during the first quarter (Q1) or the final quarter (Q4) of the year (8.1% lower, 3.2–12.7, for Q1 vs. Q2, and 47.7% lower, 11.4–69.1, for Q4 vs. Q2); older age (3.3% lower per 10-year increase in age, 2.1–4.6), and presence of hypertension (4.1% lower, 1.1–6.9). Two factors independently associated with higher post-vaccination antibody titres: South Asian ethnicity (16.2% higher, 3.0–31.1, vs. White ethnicity) or Mixed/Multiple/Other ethnicity (11.8% higher, 2.9–21.6, vs. White ethnicity), and pre-vaccination seropositivity for SARS-CoV-2 (39.8% higher, 34.7–45.0, for individuals who were seropositive without COVID-19 symptoms pre-vaccination vs. those who were seronegative pre-vaccination, and 105.1% higher, 94.1–116.6, for individuals who were seropositive with COVID-19 symptoms pre-vaccination vs. those who were seronegative pre-vaccination).
All the above determinants of post-vaccination antibody titres remained significant when we performed a sensitivity analysis excluding 23 individuals who reported RT-PCR- or lateral flow test-confirmed SARS-CoV-2 infection after their second vaccine dose, but before their serology sampling date (data not shown).

3.3. Stratification of Antibody Responses by Vaccine Type

To explore whether determinants of antibody responses to vaccination were consistent for ChAdOx1 vs. BNT162b2, we stratified the analysis of post-vaccination antibody titres according to type of vaccine received. Tables S3 and S4 (Online Data Supplement) present the results of these analyses. Seven factors associated independently with post-vaccination antibody titres in ChAdOx1 recipients, but not in BNT162b2 recipients: lower titres in this group were associated with active cancer, consumption of selenium supplements and use of statins, Angiotensin Converting Enzyme (ACE) inhibitors and proton pump inhibitors (PPI), while South Asian and Mixed/Multiple/Other ethnic origin associated with higher titres. Conversely, three factors associated independently with lower post-vaccination antibody titres in BNT162b2 recipients, but not in ChAdOx1 recipients: older age, hypertension and use of systemic immunosuppressant medication. To test whether vaccine type modified the effects of these independent variables on post-vaccination antibody titres, we fitted all significant factors in our main multivariable model and included an interaction term for vaccine type. Three factors showed a small but statistically significant interaction: hypertension (Pinteraction = 0.035), regular consumption of selenium supplements (Pinteraction = 0.021) and use of systemic immunosuppressant medication (Pinteraction = 0.046; Figure S2, Online Data Supplement).

3.4. Influence of Post-Vaccination Paracetamol/NSAIDs on Antibody Response to Primary Course of Vaccination

Results of an exploratory analysis to determine the influence of taking paracetamol or NSAIDs to treat post-vaccination symptoms are presented in Table S5. After fitting post-vaccination paracetamol/NSAID use into the main multivariable model we found a significant positive association between this factor and post-vaccination antibody titres. We then reasoned that this association could be confounded by an association between presence or severity of post-vaccination symptoms and higher post-vaccination antibody titres. After additionally correcting for report of post-vaccination symptoms (i.e., headache, fever, local soreness), the association between post-vaccination paracetamol/NSAID use and post-vaccination titres was rendered statistically non-significant.

4. Discussion

We present results of a large population-based study examining a comprehensive range of potential determinants of combined IgG, IgA and IgM responses to two very widely used SARS-CoV-2 vaccines. A major finding was that 5.8% of participants did not have detectable IgG/A/M anti-spike antibodies following two doses of ChAdOx1, as compared with 1.2% of those who had received two doses of BNT162b2 (aOR 6.62, 95% CI 4.21 to 10.42). Other risk factors for post-vaccination seronegativity included shorter inter-dose interval, administration during colder months, older age, poorer general health, immunodeficiency disorder and use of systemic immunosuppressive medication. By contrast, pre-vaccination SARS-CoV-2 seropositivity and regular use of vitamin D supplements at the time of vaccination were associated with lower risk of post-vaccination seronegativity. Post-vaccination antibody titres were higher in participants of South Asian and mixed/other ethnic origin vs. White participants, and in those who were overweight vs. normal weight; these associations were independent of pre-vaccination SARS-CoV-2 serostatus. Post-vaccination use of paracetamol or NSAIDs was not associated with lower antibody responses to vaccination, after adjustment for post-vaccination symptoms, and determinants of post-vaccination anti-spike titres were not substantively different for participants who received two doses of ChAdOx1 vs. BNT162b2.
Our findings confirm previous reports of higher antibody responses to BNT162b2 vs. ChAdOx1 [4,13,26]. Pre-existing immunity to adenovirus vectors may be one of the factors underlying this difference [27], and experiments to compare the prevalence of adenovirus neutralising antibody in pre-vaccination serum of participants who did vs. did not develop anti-spike antibodies in response to ChAdOx1 are planned.
Our results with respect to the adverse influence of older age, shorter inter-dose interval, poorer general health, immunodeficiency and immunosuppressants on post-vaccination antibody responses are also broadly consistent with those of others [4,5,6,7,8,9]–as is the finding that pre-vaccination SARS-CoV-2 infection associates with higher antibody titres after vaccination [9,12,13]. However, we also report a number of novel findings. The independent association between regular use of vitamin D supplements and reduced risk of post-vaccination seronegativity echoes results of a previous study where we showed that high-dose vitamin D replacement enhances antigen-specific cellular responses following vaccination against varicella zoster virus [28]; an intervention study nested within a randomised controlled trial of vitamin D supplementation for prevention of COVID-19 (ClinicalTrials.gov identifier NCT04579640) is currently on-going to explore this finding further. With respect to season, we found that anti-S titres were significantly higher for participants who received their second vaccine dose in Q1 vs. Q2. Seasonal variations in human immune responses are well recognised, with studies in Europeans showing a more pro-inflammatory transcriptomic profile in winter vs. summer [29,30]. Linder and colleagues reported a stronger immune response to rubella vaccine in children vaccinated in winter vs. summer in Israel [31], while Martins and colleagues found that children in Guinea-Bissau who were vaccinated against measles during the rainy season had higher antibody levels than those vaccinated in the dry season [32]. However, other investigators have found no differences in antibody responses according to season of vaccination against tetanus, diphtheria or measles or rubella [33,34]. Further research is needed to investigate the mechanisms by which season of inoculation may influence vaccine immunogenicity.
We also found that White ethnicity and lower BMI both associated with lower post-vaccination antibody titres, independently of pre-vaccination SARS-CoV-2 serostatus. Ethnic variation in SARS-CoV-2 vaccine immunogenicity has not been widely studied to date: one study has reported lower antibody responses to vaccination in Jewish vs. non-Jewish health care workers in Israel [9], while a population-based UK study reported higher antibody titres in ‘non-Whites’ vs. Whites. However, pre-vaccination serostatus was not available for all participants in these studies; this omission is potentially important, because unvaccinated people of South Asian ethnic origin were at higher risk of SARS-CoV-2 infection in the pre-vaccination era [20,24]. We demonstrate for the first time that higher anti-spike titres among vaccinated people of South Asian origin are not attributable to the higher rates of pre-vaccination SARS-CoV-2 infection that we have previously reported [20,24]. The reasons for this phenomenon require further investigation: ethnic variation in recognition of SARS-CoV-2 T cell epitopes is recognised [35], and it may be that analogous variation in B cell epitopes underlies the ethnic differences in antibody responses to vaccination seen here. By contrast with ethnicity, several studies have investigated the impact of BMI on post-vaccination antibody titres. Their results are inconsistent, with one reporting lower antibody responses in those with BMI > 30 kg/m2 [4], another reporting higher responses in those with higher BMI [36] and two reporting no association [37,38]. Of note, studies investigating antibody responses to influenza vaccination report higher titres initially in obese participants, but a greater decline thereafter [39]. This finding highlights the importance of longer term follow-up to elucidate the influence of BMI on humoral responses to vaccination.
We also report some important null results. The lack of association between time of day of vaccination and degree of antibody response conflicts with findings of studies which variously report higher anti-spike responses in those vaccinated earlier [40] or later [41] in the day. We also show no association between pre- or post-vaccination use of paracetamol or NSAIDs and antibody responses, after adjustment for incidence of post-vaccination symptoms. This provides reassurance that use of these medications to manage reactive post-vaccination symptoms does not compromise SARS-CoV-2 vaccine immunogenicity. We also show no association between higher titres and socioeconomic deprivation, as reported by others [4,7]; this may reflect the fact that other studies did not adjust for pre-vaccination serostatus, which is more likely to be positive in socioeconomically deprived populations. There was no evidence of association for lifestyle factors hypothesised to influence vaccine responses including dietary factors, use of micronutrient supplements other than vitamin D, sleep, alcohol use and tobacco smoking.
Our study has several strengths. We utilised a CE-marked semi-quantitative assay with high sensitivity and specificity that targets three different types of antibody [21], and that we have validated as a correlate of protection against breakthrough disease [22,23]. Its large size affords power to detect determinants of having undetectable anti-spike antibodies after SARS-CoV-2 vaccination–a relatively rare, but potentially clinically important outcome. The population-based nature of this study, and the fact that we investigated effects of two major vaccines utilising differing technology (one live vector, one mRNA) enhances the generalisability of our findings. Very detailed characterisation of participants allowed us to investigate a wide range of potential determinants of vaccine immunogenicity, and to adjust for multiple confounders, including pre-vaccination serostatus.
Our study also has some limitations. We did not study cellular responses to vaccination, and these may be discordant with humoral responses [10,26,42,43,44]. Methods to detect SARS-CoV-2-specific T cell responses have been used by other groups [45,46] We also studied humoral responses at an early time point only; a high early peak in antibody titres following vaccination is not necessarily an indicator of durable response [39]. As with any observational study, we cannot exclude the possibility that some of the associations we report might be explained by residual or unmeasured confounding. However, we have minimised the risk of this by adjusting for a comprehensive list of putative determinants of vaccine immunogenicity. COVIDENCE UK is also a self-selected cohort, and thus certain demographics—such as people < 30 years old, people of lower socioeconomic status, and non-White ethnic groups—are under-represented. This may have limited our power to investigate antibody responses in some sub-groups, such as participants of Black ethnicity (who are at higher risk of SARS-CoV-2 infection [47,48,49] and adverse outcomes [50] than White people).
In conclusion, this large population-based study reports on the influence of a comprehensive range of potential sociodemographic, behavioural, clinical, pharmacologic and nutritional determinants on antibody responses to two major SARS-CoV-2 vaccines.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vaccines10101601/s1. Table S1. Baseline questions; Table S2. Monthly follow-up questions; Table S3. Determinants of antibody titres following administration of two doses of ChAdOx1 nCoV-19 vaccine; Table S4. Determinants of antibody titres following administration of two doses of BNT162b2 vaccine; Table S5. Influence of post-vaccination use of paracetamol/NSAIDs on antibody titres; Figure S1. Study profile; Figure S2. Post-vaccination SARS-CoV-2 antibody titres by vaccine type vs. diagnosis of hypertension (A), prescribed immunosuppressant medications (B) or consumption of selenium supplements (C) [51,52].

Author Contributions

A.R.M. wrote the study protocol, with input from D.A.J., M.T. and S.O.S.; H.H., M.T., J.S., G.A.D., R.A.L., C.J.G., F.K., A.S. and A.R.M. contributed to questionnaire development and design; D.A.J. managed the study, with input from A.R.M., N.P., H.H. and S.M.; H.H., J.S., A.R.M. and S.O.S. supported recruitment; S.E.F. and A.G.R. developed, validated, and performed laboratory assays; D.A.J., M.T., H.H., M.G., G.V. and F.T. contributed to data management; Statistical analyses were done by D.A.J., with input from A.R.M.; D.A.J. and A.R.M. wrote the first draft of the report. All authors critically revised it before submission. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Barts Charity (refs MGU0466, MGU0459 and MGU0570), Rosetrees Trust and The Bloom Foundation (ref M771), The Fischer Family Trust, The Exilarch’s Foundation, UK National Institute for Health and Care Research Clinical Research Network (refs 52255 and 52257), and the UK Research and Innovation Industrial Strategy Challenge Fund (ref MC_PC_19004).

Institutional Review Board Statement

COVIDENCE UK was sponsored by Queen Mary University of London and approved by Leicester South Research Ethics Committee (ref 20/EM/0117). It is registered with ClinicalTrials.gov (NCT04330599).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

De-identified participant data will be made available upon reasonable request to the corresponding author, subject to terms of Research Ethics Committee approval and Sponsor requirements.

Acknowledgments

We thank all participants of COVIDENCE UK, and the following organisations who supported study recruitment: Asthma UK, the British Heart Foundation, the British Lung Foundation, the British Obesity Society, Cancer Research UK, Diabetes UK, Future Publishing, Kidney Care UK, Kidney Wales, Mumsnet, the National Kidney Federation, the National Rheumatoid Arthritis Society, the North West London Health Research Register (DISCOVER), Primary Immunodeficiency UK, the Race Equality Foundation, SWM Health, the Terence Higgins Trust, and Vasculitis UK.

Conflicts of Interest

J.S. declares receipt of payments from Reach plc for news stories written about recruitment to, and findings of, the COVIDENCE UK study. A.S. is a member of various UK and Scottish Government COVID-19 Advisory Groups, was an unremunerated member of Astra-Zeneca’s Thrombotic Thrombocytopenic Task Force, and holds COVID-19 research grants from UKRI (MRC), HDRUK, National Core Studies, CSO, GSK and NIHR. A.R.M. declares receipt of funding in the last 36 months to support vitamin D research from the following companies who manufacture or sell vitamin D supplements: Pharma Nord Ltd., DSM Nutritional Products Ltd., Thornton & Ross Ltd. and Hyphens Pharma Ltd. A.R.M. also declares support for attending meetings from the following companies who manufacture or sell vitamin D supplements: Pharma Nord Ltd and Abiogen Pharma Ltd. ARM also declares participation on the Data and Safety Monitoring Board for the Chair, DSMB, VITALITY trial (Vitamin D for Adolescents with HIV to reduce musculoskeletal morbidity and immunopathology). A.R.M. also declares unpaid work as a Programme Committee member for the Vitamin D Workshop. A.R.M. also declares receipt of vitamin D capsules for clinical trial use from Pharma Nord Ltd., Synergy Biologics Ltd. and Cytoplan Ltd. All other authors declare no competing interests. R.A.L. and A.S. are members of the Welsh Government COVID-19 Technical Advisory Groups and holds COVID-19 research grants from UKRI (MRC), HDRUK, National Core Studies, and HCRW.

References

  1. Bergwerk, M.; Gonen, T.; Lustig, Y.; Amit, S.; Lipsitch, M.; Cohen, C.; Mandelboim, M.; Levin, E.G.; Rubin, C.; Indenbaum, V.; et al. COVID-19 Breakthrough Infections in Vaccinated Health Care Workers. N. Engl. J. Med. 2021, 385, 1474–1484. [Google Scholar] [CrossRef]
  2. de la Monte, S.M.; Long, C.; Szczepanski, N.; Griffin, C.; Fitzgerald, A.; Chapin, K. Heterogeneous Longitudinal Antibody Responses to COVID-19 mRNA Vaccination. Clin. Pathol. 2021, 14, 2632010X211049255. [Google Scholar] [CrossRef] [PubMed]
  3. Zimmermann, P.; Curtis, N. Factors That Influence the Immune Response to Vaccination. Clin. Microbiol. Rev. 2019, 32, e00084-18. [Google Scholar] [CrossRef] [PubMed]
  4. Wei, J.; Stoesser, N.; Matthews, P.C.; Ayoubkhani, D.; Studley, R.; Bell, I.; Bell, J.I.; Newton, J.N.; Farrar, J.; Diamond, I.; et al. Antibody responses to SARS-CoV-2 vaccines in 45,965 adults from the general population of the United Kingdom. Nat. Microbiol. 2021, 6, 1140–1149. [Google Scholar] [CrossRef] [PubMed]
  5. Payne, R.P.; Longet, S.; Austin, J.A.; Skelly, D.T.; Dejnirattisai, W.; Adele, S.; Meardon, N.; Faustini, S.; Al-Taei, S.; Moore, S.C.; et al. Immunogenicity of standard and extended dosing intervals of BNT162b2 mRNA vaccine. Cell 2021, 184, 5699–5714.e11. [Google Scholar] [CrossRef]
  6. Amirthalingam, G.; Bernal, J.L.; Andrews, N.J.; Whitaker, H.; Gower, C.; Stowe, J.; Tessier, E.; Subbarao, S.; Ireland, G.; Baawuah, F.; et al. Serological responses and vaccine effectiveness for extended COVID-19 vaccine schedules in England. Nat. Commun. 2021, 12, 7217. [Google Scholar] [CrossRef]
  7. Kertes, J.; Gez, S.B.; Saciuk, Y.; Supino-Rosin, L.; Stein, N.S.; Mizrahi-Reuveni, M.; Zohar, A.E. Effectiveness of mRNA BNT162b2 Vaccine 6 Months after Vaccination among Patients in Large Health Maintenance Organization, Israel. Emerg. Infect. Dis. 2021, 28, 338–346. [Google Scholar] [CrossRef]
  8. Papadopoli, R.; De Sarro, C.; Palleria, C.; Gallelli, L.; Pileggi, C.; De Sarro, G. Serological Response to SARS-CoV-2 Messenger RNA Vaccine: Real-World Evidence from Italian Adult Population. Vaccines 2021, 9, 1494. [Google Scholar] [CrossRef]
  9. Abu Jabal, K.; Ben-Amram, H.; Beiruti, K.; Batheesh, Y.; Sussan, C.; Zarka, S.; Edelstein, M. Impact of age, ethnicity, sex and prior infection status on immunogenicity following a single dose of the BNT162b2 mRNA COVID-19 vaccine: Real-world evidence from healthcare workers, Israel, December 2020 to January 2021. Eurosurveillance 2021, 26, 2100096. [Google Scholar] [CrossRef]
  10. Fernandez-Ruiz, M.; Almendro-Vazquez, P.; Carretero, O.; Ruiz-Merlo, T.; Laguna-Goya, R.; San Juan, R.; Lopez-Medrano, F.; Garcia-Rios, E.; Mas, V.; Moreno-Batenero, M.; et al. Discordance Between SARS-CoV-2-specific Cell-mediated and Antibody Responses Elicited by mRNA-1273 Vaccine in Kidney and Liver Transplant Recipients. Transplant. Direct 2021, 7, e794. [Google Scholar] [CrossRef]
  11. Parry, H.; McIlroy, G.; Bruton, R.; Ali, M.; Stephens, C.; Damery, S.; Otter, A.; McSkeane, T.; Rolfe, H.; Faustini, S.; et al. Antibody responses after first and second COVID-19 vaccination in patients with chronic lymphocytic leukaemia. Blood Cancer J. 2021, 11, 136. [Google Scholar] [CrossRef] [PubMed]
  12. Manisty, C.; Otter, A.D.; Treibel, T.A.; McKnight, A.; Altmann, D.M.; Brooks, T.; Noursadeghi, M.; Boyton, R.J.; Semper, A.; Moon, J.C. Antibody response to first BNT162b2 dose in previously SARS-CoV-2-infected individuals. Lancet 2021, 397, 1057–1058. [Google Scholar] [CrossRef]
  13. Eyre, D.W.; Lumley, S.F.; Wei, J.; Cox, S.; James, T.; Justice, A.; Jesuthasan, G.; O’Donnell, D.; Howarth, A.; Hatch, S.B.; et al. Quantitative SARS-CoV-2 anti-spike responses to Pfizer-BioNTech and Oxford-AstraZeneca vaccines by previous infection status. Clin. Microbiol. Infect. 2021, 27, 1516.e7–1516.e14. [Google Scholar] [CrossRef]
  14. Long, J.E.; Drayson, M.T.; Taylor, A.E.; Toellner, K.M.; Lord, J.M.; Phillips, A.C. Morning vaccination enhances antibody response over afternoon vaccination: A cluster-randomised trial. Vaccine 2016, 34, 2679–2685. [Google Scholar] [CrossRef]
  15. Rayman, M.P.; Calder, P.C. Optimising COVID-19 vaccine efficacy by ensuring nutritional adequacy. Br. J. Nutr. 2021, 126, 1919–1920. [Google Scholar] [CrossRef] [PubMed]
  16. Benedict, C.; Cedernaes, J. Could a good night’s sleep improve COVID-19 vaccine efficacy? Lancet Respir. Med. 2021, 9, 447–448. [Google Scholar] [CrossRef]
  17. Calina, D.; Hartung, T.; Mardare, I.; Mitroi, M.; Poulas, K.; Tsatsakis, A.; Rogoveanu, I.; Docea, A.O. COVID-19 pandemic and alcohol consumption: Impacts and interconnections. Toxicol. Rep. 2021, 8, 529–535. [Google Scholar] [CrossRef] [PubMed]
  18. Ferrara, P.; Ponticelli, D.; Aguero, F.; Caci, G.; Vitale, A.; Borrelli, M.; Schiavone, B.; Antonazzo, I.C.; Mantovani, L.G.; Tomaselli, V.; et al. Does smoking have an impact on the immunological response to COVID-19 vaccines? Evidence from the VASCO study and need for further studies. Public Health 2022, 203, 97–99. [Google Scholar] [CrossRef] [PubMed]
  19. Prymula, R.; Siegrist, C.A.; Chlibek, R.; Zemlickova, H.; Vackova, M.; Smetana, J.; Lommel, P.; Kaliskova, E.; Borys, D.; Schuerman, L. Effect of prophylactic paracetamol administration at time of vaccination on febrile reactions and antibody responses in children: Two open-label, randomised controlled trials. Lancet 2009, 374, 1339–1350. [Google Scholar] [CrossRef]
  20. Holt, H.; Talaei, M.; Greenig, M.; Zenner, D.; Symons, J.; Relton, C.; Young, K.S.; Davies, M.R.; Thompson, K.N.; Ashman, J.; et al. Risk factors for developing COVID-19: A population-based longitudinal study (COVIDENCE UK). Thorax 2021, 77, 900–912. [Google Scholar] [CrossRef]
  21. Morley, G.L.; Taylor, S.; Jossi, S.; Perez-Toledo, M.; Faustini, S.E.; Marcial-Juarez, E.; Shields, A.M.; Goodall, M.; Allen, J.D.; Watanabe, Y.; et al. Sensitive Detection of SARS-CoV-2-Specific Antibodies in Dried Blood Spot Samples. Emerg. Infect. Dis. 2020, 26, 2970–2973. [Google Scholar] [CrossRef] [PubMed]
  22. Shields, A.M.; Faustini, S.E.; Kristunas, C.A.; Cook, A.M.; Backhouse, C.; Dunbar, L.; Ebanks, D.; Emmanuel, B.; Crouch, E.; Kroger, A.; et al. COVID-19: Seroprevalence and Vaccine Responses in UK Dental Care Professionals. J. Dent. Res. 2021, 100, 1220–1227. [Google Scholar] [CrossRef] [PubMed]
  23. Vivaldi, G.; Jolliffe, D.A.; Faustini, S.E.; Holt, H.; Perdek, N.; Talaei, M.; Tydeman, F.; Chambers, E.S.; Cai, W.; Li, W.; et al. Correlation between post-vaccination titres of IgG, IgA and IgM anti-Spike antibodies and protection against breakthrough SARS-CoV-2 infection: A population-based longitudinal study (COVIDENCE UK). medRxiv 2022. [Google Scholar] [CrossRef]
  24. Talaei, M.; Faustini, S.E.; Holt, H.; Jolliffe, D.A.; Vivaldi, G.; Greenig, M.; Perdek, N.; Maltby, S.; Bigogno, C.M.; Symons, J.; et al. Determinants of pre-vaccination antibody responses to SARS-CoV-2: A population-based longitudinal study (COVIDENCE UK). BMC Med. 2022, 20, 87. [Google Scholar] [CrossRef]
  25. Saleh, E.; Moody, M.A.; Walter, E.B. Effect of antipyretic analgesics on immune responses to vaccination. Hum. Vaccin. Immunother. 2016, 12, 2391–2402. [Google Scholar] [CrossRef]
  26. Parry, H.; Bruton, R.; Stephens, C.; Brown, K.; Amirthalingam, G.; Otter, A.; Hallis, B.; Zuo, J.; Moss, P. Differential immunogenicity of BNT162b2 or ChAdOx1 vaccines after extended-interval homologous dual vaccination in older people. Immun. Ageing 2021, 18, 34. [Google Scholar] [CrossRef]
  27. Fausther-Bovendo, H.; Kobinger, G.P. Pre-existing immunity against Ad vectors: Humoral, cellular, and innate response, what’s important? Hum. Vaccin. Immunother. 2014, 10, 2875–2884. [Google Scholar] [CrossRef] [PubMed]
  28. Chambers, E.S.; Vukmanovic-Stejic, M.; Turner, C.T.; Shih, B.B.; Trahair, H.; Pollara, G.; Tsaliki, E.; Rustin, M.; Freeman, T.C.; Mabbott, N.A.; et al. Vitamin D3 replacement enhances antigen-specific immunity in older adults. Immunother. Adv. 2020, 1, ltaa008. [Google Scholar] [CrossRef]
  29. Dopico, X.C.; Evangelou, M.; Ferreira, R.C.; Guo, H.; Pekalski, M.L.; Smyth, D.J.; Cooper, N.; Burren, O.S.; Fulford, A.J.; Hennig, B.J.; et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 2015, 6, 7000. [Google Scholar] [CrossRef] [PubMed]
  30. Wyse, C.; O’Malley, G.; Coogan, A.N.; McConkey, S.; Smith, D.J. Seasonal and daytime variation in multiple immune parameters in humans: Evidence from 329,261 participants of the UK Biobank cohort. iScience 2021, 24, 102255. [Google Scholar] [CrossRef]
  31. Linder, N.; Abudi, Y.; Abdalla, W.; Badir, M.; Amitai, Y.; Samuels, J.; Mendelson, E.; Levy, I. Effect of season of inoculation on immune response to rubella vaccine in children. J. Trop. Pediatr. 2011, 57, 299–302. [Google Scholar] [CrossRef] [PubMed]
  32. Martins, C.; Garly, M.L.; Bale, C.; Rodrigues, A.; Njie-Jobe, J.; Benn, C.S.; Whittle, H.; Aaby, P. Measles virus antibody responses in children randomly assigned to receive standard-titer edmonston-zagreb measles vaccine at 4.5 and 9 months of age, 9 months of age, or 9 and 18 months of age. J. Infect. Dis. 2014, 210, 693–700. [Google Scholar] [CrossRef]
  33. Abreu, T.C.; Boshuizen, H.; Mollema, L.; Berbers, G.A.M.; Korthals, A.H. Association between season of vaccination and antibody levels against infectious diseases. Epidemiol. Infect. 2020, 148, e276. [Google Scholar] [CrossRef] [PubMed]
  34. Moore, S.E.; Collinson, A.C.; Fulford, A.J.; Jalil, F.; Siegrist, C.A.; Goldblatt, D.; Hanson, L.Å.; Prentice, A.M. Effect of month of vaccine administration on antibody responses in The Gambia and Pakistan. Trop. Med. Int. Health 2006, 11, 1529–1541. [Google Scholar] [CrossRef] [PubMed]
  35. Bose, T.; Pant, N.; Pinna, N.K.; Bhar, S.; Dutta, A.; Mande, S.S. Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups? Virus Res. 2021, 305, 198579. [Google Scholar] [CrossRef]
  36. Mishra, S.K.; Pradhan, S.K.; Pati, S.; Sahu, S.; Nanda, R.K. Waning of Anti-spike Antibodies in AZD1222 (ChAdOx1) Vaccinated Healthcare Providers: A Prospective Longitudinal Study. Cureus 2021, 13, e19879. [Google Scholar] [CrossRef]
  37. Uysal, E.B.; Gumus, S.; Bektore, B.; Bozkurt, H.; Gozalan, A. Evaluation of antibody response after COVID-19 vaccination of healthcare workers. J. Med. Virol. 2021, 94, 1060–1066. [Google Scholar] [CrossRef]
  38. Lee, S.W.; Moon, J.Y.; Lee, S.K.; Lee, H.; Moon, S.; Chung, S.J.; Yeo, Y.; Park, T.S.; Park, D.W.; Kim, T.H.; et al. Anti-SARS-CoV-2 Spike Protein RBD Antibody Levels After Receiving a Second Dose of ChAdOx1 nCov-19 (AZD1222) Vaccine in Healthcare Workers: Lack of Association With Age, Sex, Obesity, and Adverse Reactions. Front. Immunol. 2021, 12, 779212. [Google Scholar] [CrossRef]
  39. Sheridan, P.A.; Paich, H.A.; Handy, J.; Karlsson, E.A.; Hudgens, M.G.; Sammon, A.B.; Holland, L.A.; Weir, S.; Noah, T.L.; Beck, M.A. Obesity is associated with impaired immune response to influenza vaccination in humans. Int. J. Obes. 2012, 36, 1072–1077. [Google Scholar] [CrossRef]
  40. Zhang, H.; Liu, Y.; Liu, D.; Zeng, Q.; Li, L.; Zhou, Q.; Li, M.; Mei, J.; Yang, N.; Mo, S.; et al. Time of day influences immune response to an inactivated vaccine against SARS-CoV-2. Cell Res. 2021, 31, 1215–1217. [Google Scholar] [CrossRef]
  41. Wang, W.; Balfe, P.; Eyre, D.W.; Lumley, S.F.; O’Donnell, D.; Warren, F.; Crook, D.W.; Jeffery, K.; Matthews, P.C.; Klerman, E.B.; et al. Time of Day of Vaccination Affects SARS-CoV-2 Antibody Responses in an Observational Study of Health Care Workers. J. Biol. Rhythms 2022, 37, 124–129. [Google Scholar] [CrossRef] [PubMed]
  42. Picchianti-Diamanti, A.; Aiello, A.; Lagana, B.; Agrati, C.; Castilletti, C.; Meschi, S.; Farroni, C.; Lapa, D.; Najafi Fard, S.; Cuzzi, G.; et al. ImmunosuppressiveTherapies Differently Modulate Humoral- and T-Cell-Specific Responses to COVID-19 mRNA Vaccine in Rheumatoid Arthritis Patients. Front. Immunol. 2021, 12, 740249. [Google Scholar] [CrossRef] [PubMed]
  43. Farroni, C.; Picchianti-Diamanti, A.; Aiello, A.; Nicastri, E.; Lagana, B.; Agrati, C.; Castilletti, C.; Meschi, S.; Colavita, F.; Cuzzi, G.; et al. Kinetics of the B- and T-Cell Immune Responses After 6 Months From SARS-CoV-2 mRNA Vaccination in Patients With Rheumatoid Arthritis. Front. Immunol. 2022, 13, 846753. [Google Scholar] [CrossRef]
  44. Tortorella, C.; Aiello, A.; Gasperini, C.; Agrati, C.; Castilletti, C.; Ruggieri, S.; Meschi, S.; Matusali, G.; Colavita, F.; Farroni, C.; et al. Humoral- and T-Cell-Specific Immune Responses to SARS-CoV-2 mRNA Vaccination in Patients With MS Using Different Disease-Modifying Therapies. Neurology 2022, 98, e541–e554. [Google Scholar] [CrossRef] [PubMed]
  45. Petrone, L.; Petruccioli, E.; Vanini, V.; Cuzzi, G.; Najafi Fard, S.; Alonzi, T.; Castilletti, C.; Palmieri, F.; Gualano, G.; Vittozzi, P.; et al. A whole blood test to measure SARS-CoV-2-specific response in COVID-19 patients. Clin. Microbiol. Infect. 2021, 27, 286-e7. [Google Scholar] [CrossRef]
  46. Petruccioli, E.; Najafi Fard, S.; Navarra, A.; Petrone, L.; Vanini, V.; Cuzzi, G.; Gualano, G.; Pierelli, L.; Bertoletti, A.; Nicastri, E.; et al. Exploratory analysis to identify the best antigen and the best immune biomarkers to study SARS-CoV-2 infection. J. Transl. Med. 2021, 19, 272. [Google Scholar] [CrossRef]
  47. Ward, H.; Atchison, C.; Whitaker, M.; Ainslie, K.E.C.; Elliott, J.; Okell, L.; Redd, R.; Ashby, D.; Donnelly, C.A.; Barclay, W.; et al. SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic. Nat. Commun. 2021, 12, 905. [Google Scholar] [CrossRef]
  48. Chadeau-Hyam, M.; Bodinier, B.; Elliott, J.; Whitaker, M.D.; Tzoulaki, I.; Vermeulen, R.; Kelly-Irving, M.; Delpierre, C.; Elliott, P. Risk factors for positive and negative COVID-19 tests: A cautious and in-depth analysis of UK biobank data. Int. J. Epidemiol. 2020, 49, 1454–1467. [Google Scholar] [CrossRef]
  49. Rozenfeld, Y.; Beam, J.; Maier, H.; Haggerson, W.; Boudreau, K.; Carlson, J.; Medows, R. A model of disparities: Risk factors associated with COVID-19 infection. Int. J. Equity Health 2020, 19, 126. [Google Scholar] [CrossRef]
  50. Mathur, R.; Rentsch, C.T.; Morton, C.E.; Hulme, W.J.; Schultze, A.; MacKenna, B.; Eggo, R.M.; Bhaskaran, K.; Wong, A.Y.S.; Williamson, E.J.; et al. Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: An observational cohort study using the OpenSAFELY platform. Lancet 2021, 397, 1711–1724. [Google Scholar] [CrossRef]
  51. Cook, A.M.; Faustini, S.E.; Williams, L.J.; Cunningham, A.F.; Drayson, M.T.; Shields, A.M.; Kay, D.; Taylor, L.; Plant, T.; Huissoon, A.; et al. Validation of a combined ELISA to detect IgG, IgA and IgM antibody responses to SARS-CoV-2 in mild or moderate non-hospitalised patients. J. Immunol. Methods 2021, 494, 113046. [Google Scholar] [CrossRef] [PubMed]
  52. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B (Methodol.) 1995, 57, 289–300. [Google Scholar] [CrossRef]
Vaccines 10 01601 g001 550
Figure 1. Antibody titres following two doses of vaccine by type of vaccine (A), age (B), ethnicity (C) and pre-vaccination SARS-CoV-2 status (D). For C, South Asian indicates people who self-identified their ethnic origin as Indian, Pakistani, or Bangladeshi, and Black indicates people who self-identified their ethnic origin as Black, African, Caribbean or Black British. p values from Mann–Whitney test (A) and Kruskal–Wallis tests (B–D). Anti-S, anti-spike; IgGAM, immunoglobulin G, A or M. Dotted line = limit of detection.
Figure 1. Antibody titres following two doses of vaccine by type of vaccine (A), age (B), ethnicity (C) and pre-vaccination SARS-CoV-2 status (D). For C, South Asian indicates people who self-identified their ethnic origin as Indian, Pakistani, or Bangladeshi, and Black indicates people who self-identified their ethnic origin as Black, African, Caribbean or Black British. p values from Mann–Whitney test (A) and Kruskal–Wallis tests (B–D). Anti-S, anti-spike; IgGAM, immunoglobulin G, A or M. Dotted line = limit of detection.
Vaccines 10 01601 g001
Table
Table 1. Participant characteristics, by vaccine type and overall (n = 9101).
Table 1. Participant characteristics, by vaccine type and overall (n = 9101).
CharacteristicChAdOx1
(n = 5770)		BNT162b2
(n = 3331)		Overall
(n = 9101)
AgeMedian age, years (IQR)63.5 (57.0–68.9)65.5 (57.4–71.6)64.2 (57.1–69.9)
Age range, years17.4 89.416.6 90.516.6 90.5
Sex, n (%)Male1671 (29.0)956 (28.7)2627 (28.9)
Female4099 (71.0)2375 (71.3)6474 (71.1)
Ethnicity, n (%) 1White5565 (96.4)3222 (96.7)8787 (96.5)
South Asian119 (2.1)60 (1.8)179 (2)
Black/African/Caribbean/Black British62 (1.1)38 (1.1)100 (1.1)
Mixed/Multiple/Other24 (0.4)10 (0.3)34 (0.4)
Nation of residence 2England5177 (89.8)2906 (87.3)8083 (88.8)
Northern Ireland39 (0.7)78 (2.3)117 (1.3)
Scotland358 (6.2)199 (6.0)557 (6.1)
Wales194 (3.4)147 (4.4)341 (3.8)
Body mass index, kg/m2, n (%) 3<252845 (49.3)1596 (47.9)4441 (48.8)
25–301881 (32.6)1097 (32.9)2978 (32.7)
>301034 (17.9)636 (19.1)1670 (18.3)
Highest educational level attained, n (%) 4Primary/Secondary636 (11)401 (12)1037 (11.4)
Higher/Further (A levels)857 (14.9)441 (13.2)1298 (14.3)
College2540 (44)1500 (45)4040 (44.4)
Post-graduate1736 (30.1)984 (29.5)2720 (29.9)
Quantiles of IMD rank, n (%) 5Q1 (most deprived)1172 (20.3)786 (23.6)1958 (21.5)
Q21417 (24.6)782 (23.5)2199 (24.2)
Q31537 (26.6)868 (26.1)2405 (26.4)
Q4 (least deprived)1639 (28.4)893 (26.8)2532 (27.8)
Tobacco smoking, n (%)Non-current/never smoker5546 (96.1)3194 (95.9)8740 (96)
Current smoker224 (3.9)137 (4.1)361 (4)
Alcohol consumption/week, units, n (%)None1505 (26.1)896 (26.9)2401 (26.4)
1–72018 (35)1212 (36.4)3230 (35.5)
8–141186 (20.6)695 (20.9)1881 (20.7)
15–21593 (10.3)310 (9.3)903 (9.9)
22–28263 (4.6)127 (3.8)390 (4.3)
>28205 (3.6)91 (2.7)296 (3.3)
Self-assessed general healthExcellent1183 (20.5)654 (19.6)1837 (20.2)
Very good2336 (40.5)1315 (39.5)3651 (40.1)
Good1441 (25.0)925 (27.8)2366 (26.0)
Fair637 (11.0)344 (10.3)981 (10.8)
Poor173 (3.0)93 (2.8)266 (2.9)
Pre-vaccination anti-spike IgG/A/M serostatusNegative3789 (65.7)1770 (53.1)5559 (61.1)
Positive696 (12.1)325 (9.8)1021 (11.2)
Unknown1285 (22.3)1236 (37.1)2521 (27.7)
Post-vaccination anti-spike IgG/A/M serostatusNegative334 (5.8)40 (1.2)374 (4.1)
Positive5436 (94.2)3291 (98.8)8727 (95.9)
Median inter-dose interval, weeks (IQR) 11.0 (10.0–11.2)10.7 (9.5–11.1)11.0 (9.8–11.1)
Median time from date of second vaccine dose to date of sampling, weeks (IQR) 7.6 (5.7–9.6)10.1 (8.3–13.1)8.6 (6.4–10.7)
Abbreviations: IQR, inter-quartile range; s.d., standard deviation; IMD, index of multiple deprivation; Ig, Immunoglobulin. (1) Ethnicity not reported for n = 1 who received BNT162b2 vaccine. (2) Nation of residence not reported for n = 1 who received BNT162b2 vaccine and n = 2 who received ChAdOx1 vaccine. (3) BMI not reported for n = 10 who received BNT162b2 vaccine and n = 2 who received ChAdOx1 vaccine. (4) Level of education not reported for n = 5 who received BNT162b2 vaccine and n = 1 who received ChAdOx1 vaccine. (5) IMD rank not reported for n = 2 who received BNT162b2 vaccine and n = 5 who received ChAdOx1 vaccine.
Table
Table 2. Determinants of post-vaccination seronegativity, all participants (n = 9101).
Table 2. Determinants of post-vaccination seronegativity, all participants (n = 9101).
Predictorn Seronegative (%)Minimally Adjusted Odds Ratio (95% CI) 1Pairwise p Value Fully Adjusted Odds Ratio (95% CI) 2Pairwise p ValueP for Trend
Vaccine type and timing
Vaccine typeChAdOx1334/5770 (5.8)5.49 (3.94, 7.66)<0.0016.62 (4.21, 10.42)<0.001 *-
BNT162b240/3331 (1.2)Referent
Time from date of second vaccine dose to date of sampling, weeks (IQR)<23/34 (8.8)2.85 (0.80, 10.20)0.11
2–419/593 (3.2)Referent
5–8127/3878 (3.3)0.87 (0.53, 1.42)0.57
9–16213/4155 (5.1)1.27 (0.78, 2.07)0.33
>1612/441 (2.7)0.68 (0.33, 1.43)0.31
Inter-dose interval, weeks<625/474 (5.3)1.52 (0.99, 2.34)0.0542.56 (1.22, 5.37)0.013 *<0.001
6–10142/2959 (4.8)1.47 (1.17, 1.83)0.0011.60 (1.20, 2.14)0.001 *
>10207/5668 (3.7)Referent
Time of second vaccine doseBefore 12 p.m.148/3692 (4.0)Referent
12 p.m.–2 p.m.74/1561 (4.7)1.21 (0.91, 1.61)0.19
2 p.m.–5 p.m.103/2573 (4.0)1.01 (0.78, 1.31)0.92
After 5 p.m.30/941 (3.2)0.84 (0.57, 1.26)0.41
Quarter of second vaccine doseQ156/1318 (4.2)1.02 (0.76, 1.37)0.871.08 (0.61, 1.92)0.790.005 †
Q2313/7755 (4.0)Referent
Q32/13 (15.4)4.10 (0.89, 18.77)0.0712.64 (2.21, 72.27)0.004 *
Q43/15 (20.0)5.82 (1.60, 21.17)0.00721.83 (1.74, 273.33)0.017 *
Socio-demographic factors
Age, years<303/83 (3.6)Referent <0.001
30–39.996/192 (3.1)0.87 (0.21, 3.57)0.850.96 (0.09, 10.65)0.97
40–49.9911/595 (1.8)0.51 (0.14, 1.85)0.300.53 (0.06, 4.97)0.58
50–59.9962/2266 (2.7)0.75 (0.23, 2.44)0.630.88 (0.10, 7.41)0.90
60–69.99152/3706 (4.1)1.11 (0.35, 3.55)0.861.64 (0.20, 13.67)0.65
70–79.99131/2077 (6.3)1.69 (0.52, 5.42)0.382.86 (0.34, 24.18)0.33
≥809/182 (4.9)1.25 (0.33, 4.76)0.743.38 (0.25, 46.30)0.36
SexFemale234/6474 (3.6)Referent
Male140/2627 (5.3)1.33 (1.07, 1.66)0.011.32 (0.98, 1.77)0.06-
EthnicityWhite368/8787 (4.2)Referent
Mixed/Multiple/Other4/179 (2.2)0.59 (0.22, 1.59)0.30
South Asian1/100 (1.0)0.27 (0.04, 1.94)0.19
Black/African/Caribbean/
Black British		1/34 (2.9)0.85 (0.12, 6.28)0.88
BMI, kg/m2<25164/4441 (3.7)Referent 0.71
25–30135/2978 (4.5)1.20 (0.95, 1.52)0.131.15 (0.85, 1.55)0.37
>3074/1670 (4.4)1.30 (0.98, 1.72)0.071.05 (0.70, 1.56)0.82
Highest educational level attainedPrimary/Secondary54/1036 (5.2)1.12 (0.80, 1.57)0.50
Higher/further (A levels)56/1298 (4.3)0.99 (0.72, 1.38)0.97
College150/4041 (3.7)0.86 (0.67, 1.10)0.23
Post-graduate114/2720 (4.2)Referent
Quantiles of IMD rankQ1 (most deprived)85/1958 (4.3)1.20 (0.89, 1.61)0.24
Q294/2199 (4.3)1.12 (0.84, 1.49)0.45
Q396/2405 (4.0)1.01 (0.76, 1.35)0.92
Q4 (least deprived)99/2532 (3.9)Referent
Lifestyle factors
Tobacco smokingNo363/8740 (4.2)Referent
Yes11/361 (3.0)0.77 (0.42, 1.42)0.40
VapingNo364/8881 (4.1)Referent
Yes9/196 (4.6)1.16 (0.59, 2.29)0.67
Alcohol, units/weekNone118/2401 (4.9)Referent 0.81
1–7103/3230 (3.2)0.63 (0.48, 0.82)0.0010.71 (0.50, 1.01)0.06
8–1484/1880 (4.5)0.85 (0.64, 1.14)0.291.10 (0.76, 1.60)0.61
15–2140/903 (4.4)0.83 (0.57, 1.20)0.311.00 (0.63, 1.60)0.99
22–2817/391 (4.3)0.78 (0.46, 1.32)0.351.15 (0.61, 2.17)0.68
>2812/296 (4.1)0.71 (0.38, 1.30)0.270.60 (0.25, 1.46)0.26
Light exercise, hours/week0–4140/2824 (5.0)1.52 (1.18, 1.96)0.0011.19 (0.86, 1.67)0.300.31
5–9120/2987 (4.0)1.20 (0.93, 1.56)0.171.09 (0.79, 1.50)0.61
≥10114/3268 (3.5)Referent
Vigorous exercise, hours/week0155/3458 (4.5)1.19 (0.91, 1.56)0.20
1–3132/3387 (3.9)1.04 (0.79, 1.37)0.79
≥487/2230 (3.9)Referent
Sleep, hours/night≤543/797 (5.4)1.26 (0.87, 1.81)0.221.29 (0.81, 2.05)0.290.54
693/2215 (4.2)0.93 (0.70, 1.23)0.600.91 (0.63, 1.32)0.62
7133/3750 (3.6)0.77 (0.59, 1.00)0.050.84 (0.60, 1.17)0.29
≥8105/2336 (4.5)Referent
Self-assessed general healthExcellent52/1837 (2.8)Referent 0.002
Very good136/3652 (3.7)1.31 (0.95, 1.82)0.101.57 (1.03, 2.40)0.036
Good100/2365 (4.2)1.54 (1.09, 2.16)0.011.87 (1.19, 2.95)0.007 *
Fair60/981 (6.1)2.35 (1.60, 3.44)<0.0012.00 (1.15, 3.47)0.014 *
Poor26/266 (9.8)3.85 (2.35, 6.28)<0.0013.12 (1.39, 6.96)0.006 *
Anxiety or depressionNo288/6910 (4.2)Referent
Yes86/2187 (3.9)1.00 (0.78, 1.29)0.98
Food choiceNone362/8607 (4.2)Referent
Vegetarian9/380 (2.4)0.59 (0.30, 1.15)0.12
Vegan3/114 (2.6)0.63 (0.20, 2.01)0.44
Medical conditions
Heart disease 3No351/8707 (4.0)Referent
Yes23/394 (5.8)1.26 (0.81, 1.97)0.30
Arterial disease 4No340/8572 (4.0)Referent
Yes34/529 (6.4)1.45 (1.00, 2.11)0.050.50 (0.23, 1.09)0.08-
HypertensionNo246/6902 (3.6)Referent
Yes128/2199 (5.8)1.56 (1.25, 1.95)<0.0010.89 (0.58, 1.37)0.61-
Immunodeficiency disorder 5No364/9042 (4.0)Referent
Yes10/59 (16.9)4.62 (2.32, 9.23)<0.0016.48 (2.53, 16.59)<0.001 *-
Major neurological condition 6No350/8833 (4.0)Referent
Yes24/268 (9.0)2.23 (1.44, 3.44)<0.0011.79 (0.82, 3.91)0.15-
CancerNever330/8128 (4.1)Referent
Past (cured or in remission)40/888 (4.5)1.04 (0.74, 1.47)0.80
Present (active)4/85 (4.7)1.00 (0.36, 2.77)0.99
AsthmaNo303/7652 (4.0)Referent
Yes71/1449 (4.9)1.30 (1.00, 1.70)0.050.99 (0.69, 1.41)0.94-
COPDNo364/8905 (4.1)Referent
Yes10/196 (5.1)1.19 (0.62, 2.27)0.60
Diabetic statusNo diabetes317/8334 (3.8)Referent 0.06
Pre-diabetes16/296 (5.4)1.38 (0.82, 2.31)0.231.12 (0.57, 2.22)0.74
Type 1 diabetes4/69 (5.8)1.59 (0.57, 4.41)0.371.87 (0.52, 6.75)0.34
Type 2 diabetes34/385 (8.8)2.26 (1.56, 3.28)<0.0012.02 (0.94, 4.33)0.07
Atopy 7No281/6794 (4.1)Referent
Yes93/2307 (4.0)1.01 (0.80, 1.29)0.92
Pre-vaccination SARS-CoV-2 statusSeronegative226/5422 (4.2)Referent <0.001
Seropositive, asymptomatic 18/722 (2.5)0.58 (0.35, 0.94)0.030.53 (0.32, 0.89)0.015 *
Seropositive, symptomatic2/264 (0.8)0.20 (0.05, 0.81)0.020.16 (0.04, 0.56)0.004 *
Nutritional supplements
MultivitaminNo299/7200 (4.2)Referent
Yes75/1901 (3.9)0.98 (0.75, 1.26)0.86
Vitamin ANo371/9053 (4.1)Referent
Yes3/48 (6.3)1.56 (0.48, 5.04)0.46
Vitamin CNo346/8192 (4.2)Referent
Yes28/909 (3.1)0.73 (0.49, 1.08)0.11
Vitamin DNo204/4455 (4.6)Referent
Yes170/4646 (3.7)0.80 (0.65, 0.98)0.030.66 (0.51, 0.87)0.003 *-
ZincNo357/8664 (4.1)Referent
Yes17/437 (3.9)0.94 (0.57, 1.55)0.82
SeleniumNo372/9003 (4.1)Referent
Yes2/98 (2.0)0.49 (0.12, 2.01)0.32
IronNo368/8816 (4.2)Referent
Yes6/285 (2.1)0.53 (0.23, 1.19)0.12
ProbioticsNo359/8528 (4.2)Referent
Yes15/573 (2.6)0.64 (0.38, 1.09)0.10
Omega-3 fatty acidsNo336/7975 (4.2)Referent
Yes38/1126 (3.4)0.80 (0.57, 1.12)0.19
Cod liver oilNo341/8279 (4.1)Referent
Yes33/822 (4.0)0.93 (0.64, 1.34)0.68
GarlicNo364/8907 (4.1)Referent
Yes10/194 (5.2)1.20 (0.63, 2.30)0.57
Medications
Beta-2 adrenergic agonistsNo334/8275 (4.0)Referent
Yes40/826 (4.8)1.23 (0.87, 1.72)0.24
Beta blockersNo334/8398 (4.0)Referent
Yes40/703 (5.7)1.35 (0.96, 1.89)0.091.01 (0.62, 1.65)0.96-
StatinsNo277/7337 (3.8)Referent
Yes97/1764 (5.5)1.31 (1.02, 1.68)0.030.92 (0.64, 1.32)0.65-
ACE inhibitorsNo320/8122 (3.9)Referent
Yes54/979 (5.5)1.30 (0.96, 1.76)0.091.12 (0.70, 1.80)0.64-
Proton pump inhibitorsNo287/7723 (3.7)Referent
Yes87/1378 (6.3)1.69 (1.32, 2.16)<0.0010.91 (0.63, 1.33)0.63-
H2-receptor antagonistsNo373/9040 (4.1)Referent
Yes1/61 (1.6)0.39 (0.05, 2.80)0.35
Inhaled corticosteroidsNo349/8505 (4.1)Referent
Yes25/596 (4.2)1.01 (0.66, 1.53)0.98
BronchodilatorsNo333/8240 (4.0)Referent
Yes41/861 (4.8)1.20 (0.85, 1.67)0.30
Systemic ImmunosuppressantsNo327/8630 (3.8)Referent
Yes47/471 (10.0)2.86 (2.08, 3.95)<0.0013.71 (2.41, 5.69)<0.001 *-
Angiotensin receptor blockersNo331/8493 (3.9)Referent
Yes43/608 (7.1)1.75 (1.25, 2.43)<0.0010.98 (0.58, 1.68)0.95-
SSRI antidepressantsNo350/8533 (4.1)Referent
Yes24/568 (4.2)1.11 (0.73, 1.70)0.62
Non-SSRIs antidepressantsNo352/8726 (4.0)Referent
Yes22/375 (5.9)1.57 (1.01, 2.46)0.050.73 (0.35, 1.50)0.39-
Calcium channel blockersNo314/8100 (3.9)Referent
Yes60/1001 (6.0)1.45 (1.09, 1.94)0.011.00 (0.65, 1.55)0.99-
Thiazide diureticsNo359/8765 (4.1)Referent
Yes15/336 (4.5)1.04 (0.61, 1.76)0.89
Vitamin K antagonistsNo370/9027 (4.1)Referent
Yes4/74 (5.4)1.17 (0.42, 3.23)0.76
SGLT-2 inhibitorsNo369/9054 (4.1)Referent
Yes5/47 (10.6)2.56 (1.01, 6.54)0.052.99 (0.92, 9.65)0.07-
AnticholinergicsNo354/8656 (4.1)Referent
Yes20/445 (4.5)1.08 (0.67, 1.72)0.76
MetforminNo353/8827 (4.0)Referent
Yes21/274 (7.7)1.83 (1.16, 2.90)0.010.61 (0.24, 1.55)0.30-
BisphosphonatesNo362/8912 (4.1)Referent
Yes12/189 (6.3)1.72 (0.94, 3.12)0.080.77 (0.31, 1.89)0.57-
Anti-platelet drugsNo328/8453 (3.9)Referent
Yes46/648 (7.1)1.67 (1.21, 2.32)0.0022.79 (1.06, 7.38)0.038-
Sex hormone therapyNo347/8376 (4.1)Referent
Yes27/725 (3.7)1.04 (0.69, 1.56)0.87
Aspirin 8No341/8593 (4.0)Referent
Yes33/508 (6.5)1.47 (1.01, 2.14)0.040.37 (0.14, 1.01)0.05-
Paracetamol 8No345/8677 (4.0)Referent
Yes29/424 (6.8)1.77 (1.19, 2.62)0.0050.84 (0.47, 1.51)0.56-
BCG vaccinatedNo44/1075 (4.1)Referent
Yes280/7166 (3.9)0.99 (0.71, 1.37)0.13
Abbreviations: CI, confidence interval; BMI, body mass index; IMD, index of multiple deprivation; COPD, chronic obstructive pulmonary disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; COVID-19, coronavirus disease 2019; ACE, angiotensin-converting enzyme; H2, histamine 2; SSRI, selective serotonin reuptake inhibitor; BCG, Bacille Calmette-Guérin; SGLT2, sodium-glucose cotransporter-2. (1) Adjusted for age and sex only. (2) Adjusted for age, sex, vaccine type, inter-dose interval, quarter of second vaccination, BMI, alcohol intake, light exercise, sleep, self-assessed general health, arterial disease, hypertension, immunodeficiency disorder, major neurological condition, asthma, diabetic status, pre-vaccination SARS-CoV-2 status and pre-vaccination use of vitamin D supplements, beta blockers, statins, ACE inhibitors, proton pump inhibitors, systemic immunosuppressants, angiotensin receptor blockers, non-SSRI antidepressants, calcium channel blockers, SGLT2 inhibitors, metformin, bisphosphonates, anti-platelet drugs use, aspirin and paracetamol. (3) Heart disease defined as coronary artery disease or heart failure. (4) Arterial disease defined as ischaemic heart disease, peripheral vascular disease or cerebrovascular disease. (5) Immunodeficiency defined as HIV, primary immune deficiency or other immunodeficiency. (6) Major neurological conditions defined as stroke, transient ischaemic attack, dementia, Parkinson’s disease, multiple sclerosis or motor neurone disease. (7) Atopy defined as atopic eczema/dermatitis and/or hay fever/allergic rhinitis. (8) Chronic use prior to vaccination (i.e., distinct from acute post-vaccination use for treatment of reactogenic symptoms). † Global p value presented for this non-scalar categorical variable. * Below multiple comparisons testing critical p-value threshold of 0.024.
Table
Table 3. Determinants of post-vaccination antibody titres, subset of seropositive participants (n = 8727).
Table 3. Determinants of post-vaccination antibody titres, subset of seropositive participants (n = 8727).
PredictorMedian IgGAM Ratio (IQR)Minimally Adjusted % Difference (95% CI) 1p ValueFully Adjusted % Difference (95% CI) 2Pairwise p Value P for Trend
Vaccine typeChAdOx12.39 (1.76, 3.30)−39.35 (−40.65, −38.02)<0.001−43.31 (−44.8, −41.78)<0.001 *-
BNT162b23.96 (3.15, 4.86)Referent
Time from second vaccine dose to sampling, weeks<22.96 (2.23, 13.62)36.68 (11.82, 67.05)0.0021.98 (−17.97, 26.79)0.86<0.001
2–42.86 (2.06, 4.05)Referent
5–82.81 (1.95, 3.99)−0.59 (−5.37, 4.43)0.81−7.41 (−11.62, −3.00) 0.001 *
9–163.13 (2.10, 4.24)7.54 (2.33, 13.01)0.004−12.68 (−16.94, −8.20)<0.001 *
>162.93 (2.03, 4.43)7.14 (−0.08, 14.89)0.05−7.83 (−16.12, 1.28)0.09
Inter−dose interval, weeks<6 2.91 (1.94, 4.12)−1.23 (−6.38, 4.21)0.65−10.43 (−16.69, −3.70)0.003 *<0.001
6–102.89 (2.00, 4.01)−5.12 (−7.51, −2.66)<0.001−5.85 (−8.3, −3.34)<0.001 *
>102.99 (2.06, 4.21)Referent
Time of second vaccine doseBefore 12 p.m.2.92 (2.02, 4.10)Referent 0.96 †
12 p.m.–2 p.m.2.91 (2.00, 4.07)−1.12 (−4.39, 2.26)0.51−2.87 (−6.00, 0.36)0.08
2 p.m.–5 p.m.3.03 (2.06, 4.21)2.88 (−0.01, 5.86)0.051.15 (−1.61, 3.99)0.42
After 5 p.m.2.98 (2.07, 4.07)0.54 (−3.45, 4.70)0.79−2.39 (−6.2, 1.58)0.24
Quarter of second vaccine doseQ13.44 (2.42, 4.43)15.46 (11.70, 19.34)<0.001−8.07 (−12.69, −3.22)0.001 *0.004 †
Q22.88 (1.99, 4.09)Referent
Q33.17 (1.36, 4.47)−3.37 (−30.38, 34.12)0.8431.63 (−22.10, 122.40)0.30
Q41.93 (1.28, 3.24)−24.08 (−44.54, 3.92)0.09−47.69 (−69.11, −11.39)0.016 *
Age, years<303.79 (2.48, 4.85)Referent <0.001
30–39.993.43 (2.34, 4.55)−12.71 (−24.56, 1.00)0.07−5.94 (−20.45, 11.21)0.47
40–49.993.13 (2.16, 4.31)−16.59 (−26.77, −5.01)0.006−4.12 (−17.60, 11.58)0.59
50–59.993.10 (2.14, 4.30)−16.58 (−26.32, −5.55)0.0040.70 (−13.04, 16.61)0.93
60–69.992.85 (1.98, 4.05)−22.79 (−31.75, −12.65)<0.001−5.39 (−18.27, 9.53)0.46
70–79.992.92 (1.96, 4.03)−23.08 (−32.09, −12.87)<0.001−10.54 (−22.81, 3.69)0.14
≥802.80 (1.84, 3.78)−24.71 (−35.06, −12.72)<0.001−19.62 (−34.77, −0.95)0.040
SexFemale3.01 (2.07, 4.19)Referent
Male2.87 (1.95, 4.04)−3.17 (−5.68, −0.58)0.02−2.48 (−5.05, 0.17)0.07-
EthnicityWhite2.94 (2.02, 4.11)Referent <0.001 †
Mixed/Multiple/Other3.35 (2.24, 4.73)12.78 (3.75, 22.59)0.00511.83 (2.85, 21.59)0.009 *
South Asian3.46 (2.58, 4.64)16.26 (4.10, 29.85)0.00816.21 (3.02, 31.10)0.015 *
Black/African/
Caribbean/Black British		3.54 (2.39, 5.46)26.76 (4.79, 53.33)0.01512.31 (−6.94, 35.54)0.23
BMI, kg/m2<252.90 (2.03, 4.03)Referent 0.038
25–302.96 (2.04, 4.15)3.62 (0.91, 6.40)0.0092.89 (0.18, 5.67)0.037
>303.17 (2.02, 4.36)4.87 (1.56, 8.29)0.0042.62 (−0.85, 6.21)0.14
Highest educational level attainedPrimary/Secondary3.02 (1.93, 4.29)1.05 (1.01, 1.10)0.0181.73 (−2.36, 5.99)0.410.37
Higher/further (A levels)2.98 (2.07, 4.14)1.03 (1.00, 1.07)0.071.59 (−2.14, 5.45)0.41
College2.99 (2.07, 4.11)1.04 (1.01, 1.07)0.0093.31 (0.52, 6.17)0.020 *
Post-graduate2.89 (1.98, 4.10)Referent
Quantiles of IMD rankQ1 (most deprived)3.06 (2.12, 4.24)2.28 (−1.10, 5.79)0.19
Q22.97 (1.98, 4.19)0.53 (−2.68, 3.85)0.75
Q32.90 (2.04, 4.01)−1.34 (−4.42, 1.84)0.41
Q4 (least deprived)2.95 (2.01, 4.14)Referent
Tobacco smokingNo2.95 (2.03, 4.13)Referent
Yes3.20 (2.24, 4.20)1.22 (−4.65, 7.46)0.69
VapingNo2.95 (2.03, 4.13)Referent
Yes3.37 (2.18, 4.37)5.47 (−2.73, 14.37)0.20
Alcohol, units/weekNone2.97 (2.05, 4.20)Referent 0.71
1–73.03 (2.06, 4.14)−0.68 (−3.63, 2.35)0.661.36 (−1.66, 4.48)0.38
8–142.94 (2.02, 4.16)−1.87 (−5.21, 1.58)0.280.9 (−2.57, 4.48)0.62
15–212.91 (1.98, 4.01)−3.31 (−7.46, 1.03)0.130.3 (−3.97, 4.76)0.89
22–282.65 (1.91, 3.94)−6.32 (−11.90, −0.39)0.04−4.21 (−9.79, 1.71)0.16
>282.84 (2.03, 4.18)−3.06 (−9.56, 3.90)0.380.01 (−6.59, 7.07)1.00
Light exercise, hours/week0–43.07 (2.10, 4.26)5.22 (2.21, 8.31)0.0010.18 (−2.81, 3.26)0.910.92
5–92.95 (2.02, 4.13)1.29 (−1.54, 4.20)0.38−1.02 (−3.77, 1.82)0.48
≥102.88 (1.99, 4.06)Referent
Vigorous exercise, hours/week03.02 (2.05, 4.21)4.97 (1.82, 8.21)0.0021.34 (−1.82, 4.6)0.410.45
1–32.98 (2.05, 4.12)3.09 (−0.01, 6.28)0.050.01 (−2.96, 3.06)1.00
≥42.86 (1.99, 4.06)Referent
Sleep, hours/night≤53.07 (2.13, 4.29)5.03 (0.31, 9.98)0.040.64 (−3.96, 5.45)0.790.94
63.00 (2.04, 4.19)1.12 (−2.18, 4.52)0.51−1.20 (−4.41, 2.11)0.47
72.92 (2.00, 4.08)−1.82 (−4.66, 1.11)0.22−2.48 (−5.28, 0.39)0.39
≥82.96 (2.05, 4.13)Referent
Self−assessed general healthExcellent2.92 (2.05, 4.07)Referent 0.27
Very good2.92 (2.01, 4.09)−0.20 (−3.32, 3.02)0.90−2.35 (−5.36, 0.75)0.14
Good3.03 (2.06, 4.23)4.73 (1.17, 8.42)0.009−0.86 (−4.34, 2.74)0.63
Fair3.07 (2.02, 4.15)2.38 (−2.07, 7.03)0.30−1.96 (−6.45, 2.74)0.41
Poor2.95 (2.04, 4.39)5.60 (−2.04, 13.84)0.16−6.73 (−14.02, 1.17)0.09
Anxiety or depressionNo2.95 (2.03, 4.13)Referent
Yes2.99 (2.03, 4.17)0.68 (−2.06, 3.50)0.63
Food choiceNone2.96 (2.03, 4.13)Referent
Vegetarian2.92 (2.04, 4.13)−2.98 (−8.46, 2.83)0.31
Vegan2.61 (2.00, 4.33)−2.04 (−11.76, 8.74)0.70
Heart disease 3No2.97 (2.03, 4.13)Referent
Yes2.76 (1.93, 4.17)1.33 (−4.46, 7.48)0.66
Arterial disease 4No2.96 (2.04, 4.13)Referent
Yes2.96 (1.94, 4.14)2.02 (−3.09, 7.40)0.45
HypertensionNo2.98 (2.06, 4.16)Referent
Yes2.89 (1.92, 4.08)−2.62 (−5.32, 0.14)0.06−4.09 (−6.94, −1.14)0.007 *-
Immunodeficiency disorder 5No2.96 (2.03, 4.13)Referent
Yes2.85 (1.98, 3.96)−0.17 (−14.65, 16.77)0.98
Major neurological condition 6No2.96 (2.04, 4.13)Referent
Yes2.90 (1.84, 4.10)−0.74 (−7.55, 6.57)0.84
CancerNever2.95 (2.04, 4.13)Referent
Past (cured or in remission)2.99 (2.00, 4.15)1.24 (−2.72, 5.35)0.55
Present (active)2.93 (1.89, 3.82)−5.33 (−16.23, 7.00)0.38
AsthmaNo2.97 (2.03, 4.14)Referent
Yes2.91 (2.06, 4.10)−0.74 (−3.88, 2.51)0.65
COPDNo2.96 (2.03, 4.13)Referent
Yes3.06 (1.98, 4.12)2.97 (−5.06, 11.67)0.48
Diabetic statusNo diabetes2.97 (2.04, 4.14)Referent
Pre-diabetes2.68 (1.87, 3.93)−4.13 (−10.31, 2.46)0.21
Type 1 diabetes3.13 (1.95, 4.10)−1.81 (−14.31, 12.52)0.79
Type 2 diabetes2.95 (1.88, 4.30)0.77 (−5.09, 6.98)0.80
Atopy 7No2.95 (2.02, 4.14)Referent
Yes2.97 (2.05, 4.11)−0.14 (−2.80, 2.59)0.92
Pre−vaccination SARS−COV−2 statusSeronegative2.74 (1.92, 3.80)Referent <0.001
Seropositive, asymptomatic3.62 (2.40, 5.01)37.37 (31.83, 43.13)<0.00139.77 (34.73, 45.00)<0.001 *
Seropositive, symptomatic5.50 (4.17, 12.62126.30 (112.07, 141.5)<0.001105.06 (94.13, 116.60)<0.001 *
MultivitaminNo2.97 (2.03, 4.13)Referent
Yes2.91 (2.05, 4.17)−0.64 (−3.46, 2.26)0.66
Vitamin ANo2.96 (2.03, 4.13)Referent
Yes2.90 (1.94, 3.93)−4.11 (−18.56, 12.91)0.61
Vitamin CNo2.95 (2.02, 4.11)Referent
Yes3.13 (2.10, 4.26)4.07 (0.10, 8.19)0.041.47 (−2.42, 5.52)0.46-
Vitamin DNo2.98 (2.05, 4.12)Referent
Yes2.94 (2.02, 4.15)−0.87 (−3.16, 1.49)0.47
ZincNo2.96 (2.03, 4.13)Referent
Yes2.91 (2.07, 4.18)1.48 (−3.92, 7.18)0.60
SeleniumNo2.96 (2.03, 4.13)Referent
Yes2.57 (1.85, 4.06)−6.21 (−16.16, 4.92)0.26
IronNo2.96 (2.04, 4.13)Referent
Yes2.80 (1.88, 4.10)−3.18 (−9.42, 3.49)0.34
ProbioticsNo2.97 (2.03, 4.13)Referent
Yes2.87 (2.08, 4.27)0.52 (−4.18, 5.45)0.83
Omega−3 fatty acidsNo2.95 (2.02, 4.13)Referent
Yes3.01 (2.12, 4.21)3.08 (−0.51, 6.80)0.092.09 (−1.48, 5.79)0.26-
Cod liver oilNo2.96 (2.04, 4.13)Referent
Yes2.97 (2.00, 4.12)1.15 (−2.90, 5.38)0.58
GarlicNo2.95 (2.03, 4.13)Referent
Yes3.06 (2.26, 4.42)6.51 (−1.83, 15.55)0.13
Beta−2 adrenergic agonistsNo2.96 (2.03, 4.13)Referent
Yes2.94 (2.05, 4.13)0.83 (−3.21, 5.04)0.69
Beta blockersNo2.97 (2.04, 4.13)Referent
Yes2.87 (1.94, 4.11)−1.17 (−5.47, 3.32)0.60
StatinsNo2.98 (2.06, 4.17)Referent
Yes2.86 (1.91, 4.01)−2.52 (−5.51, 0.57)0.11
ACE inhibitorsNo2.97 (2.05, 4.14)Referent
Yes2.89 (1.85, 4.09)−1.85 (−5.55, 2.00)0.34
Proton pump inhibitorsNo2.97 (2.05, 4.13)Referent
Yes2.91 (1.92, 4.16)0.26 (−3.00, 3.63)0.88
H2−receptor antagonistsNo2.96 (2.03, 4.13)Referent
Yes3.02 (2.09, 4.20)1.57 (−11.84, 17.01)0.83
Inhaled corticosteroidsNo2.96 (2.03, 4.13)Referent
Yes2.88 (2.03, 4.11)−0.44 (−5.04, 4.39)0.86
BronchodilatorsNo2.96 (2.03, 4.13)Referent
Yes2.97 (2.08, 4.18)2.06 (−1.95, 6.24)0.32
Systemic immunosuppressantsNo2.97 (2.04, 4.14)Referent
Yes2.80 (1.99, 3.91)−4.33 (−9.40, 1.02)0.11
Angiotensin receptor blockers No2.96 (2.04, 4.14)Referent
Yes2.94 (1.93, 4.03)−2.33 (−6.89, 2.45)0.33
SSRI antidepressantsNo2.95 (2.03, 4.11)Referent
Yes3.17 (2.08, 4.33)5.15 (0.17, 10.39)0.040.18 (−4.69, 5.29)0.94-
Non−SSRIs antidepressantsNo2.95 (2.03, 4.13)Referent
Yes2.98 (2.01, 4.18)2.24 (−3.66, 8.51)0.46
Calcium channel blockers No2.96 (2.05, 4.14)Referent
Yes2.94 (1.92, 4.11)−1.54 (−5.23, 2.29)0.43
Thiazide diureticsNo2.97 (2.04, 4.14)Referent
Yes2.75 (1.84, 3.95)−5.56 (−11.27, 0.52)0.07−2.77 (−8.88, 3.76)0.40-
Vitamin K antagonistsNo2.96 (2.03, 4.14)Referent
Yes2.94 (1.89, 3.81)−5.97 (−17.55, 7.23)0.36
SGLT2 inhibitorsNo2.95 (2.03, 4.13)Referent
Yes3.93 (2.44, 4.86)10.26 (−6.90, 30.57)0.26
AnticholinergicsNo2.96 (2.03, 4.13)Referent
Yes3.04 (2.11, 4.18)3.08 (−2.38, 8.84)0.27
MetforminNo2.95 (2.03, 4.12)Referent
Yes3.20 (2.02, 4.46)6.77 (−0.44, 14.50)0.070.36 (−6.45, 7.66)0.92-
BisphosphonatesNo2.96 (2.03, 4.13)Referent
Yes2.73 (1.99, 4.16)−1.09 (−8.99, 7.50)0.80
Anti−platelet drugsNo2.97 (2.04, 4.14)Referent
Yes2.87 (1.88, 4.05)0.49 (−4.12, 5.32)0.84
Sex hormone therapyNo2.95 (2.03, 4.13)Referent
Yes3.06 (2.08, 4.18)0.53 (−3.77, 5.03)0.81
Aspirin 8No2.97 (2.04, 4.13)Referent
Yes2.84 (1.89, 4.11)0.01 (−5.09, 5.39)1.00
Paracetamol 8No2.95 (2.03, 4.13)Referent
Yes3.03 (1.99, 4.23)2.81 (−2.82, 8.76)0.34
BCG vaccinatedNo2.86 (2.05, 4.07)Referent
Yes3.00 (2.05, 4.17)2.77 (−0.92, 6.59)0.65
Abbreviations: IQR, inter-quartile range; CI, confidence interval; BMI, body mass index; Ig, immunoglobulin; IMD, index of multiple deprivation; COPD, chronic obstructive pulmonary disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; COVID-19, coronavirus disease 2019; ACE, angiotensin-converting enzyme; H2, histamine 2; SSRI, selective serotonin reuptake inhibitor; BCG, Bacille Calmette-Guérin; SGLT2, sodium-glucose cotransporter-2. (1) Adjusted for age and sex only. (2) Adjusted for age, sex, vaccine type, time from second dose to sampling, inter-dose interval, time of second vaccination, quarter of second vaccination, ethnicity, BMI, educational level, alcohol intake, light exercise, vigorous exercise, sleep, self-assessed general health, hypertension, pre-vaccination SARS-CoV-2 status and pre-vaccination use of vitamin C supplements, omega-3 fatty acid supplements, SSRI antidepressants, thiazide diuretics and metformin. (3) Heart disease defined as coronary artery disease or heart failure. (4) Arterial disease defined as ischaemic heart disease, peripheral vascular disease or cerebrovascular disease. (5) Immunodeficiency defined as HIV, primary immune deficiency or other immunodeficiency. (6) Major neurological conditions defined as stroke, transient ischaemic attack, dementia, Parkinson’s disease, multiple sclerosis or motor neurone disease. (7) Atopy defined as atopic eczema/dermatitis and/or hay fever/allergic rhinitis. (8) Chronic use prior to vaccination (i.e., distinct from acute post-vaccination use for treatment of reactogenic symptoms). † Global p value presented for this non-scalar categorical variable. * Below multiple comparisons testing critical p-value threshold of 0.025.
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MDPI and ACS Style

Jolliffe, D.A.; Faustini, S.E.; Holt, H.; Perdek, N.; Maltby, S.; Talaei, M.; Greenig, M.; Vivaldi, G.; Tydeman, F.; Symons, J.; et al. Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK). Vaccines 2022, 10, 1601. https://doi.org/10.3390/vaccines10101601

AMA Style

Jolliffe DA, Faustini SE, Holt H, Perdek N, Maltby S, Talaei M, Greenig M, Vivaldi G, Tydeman F, Symons J, et al. Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK). Vaccines. 2022; 10(10):1601. https://doi.org/10.3390/vaccines10101601

Chicago/Turabian Style

Jolliffe, David A., Sian E. Faustini, Hayley Holt, Natalia Perdek, Sheena Maltby, Mohammad Talaei, Matthew Greenig, Giulia Vivaldi, Florence Tydeman, Jane Symons, and et al. 2022. "Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK)" Vaccines 10, no. 10: 1601. https://doi.org/10.3390/vaccines10101601

APA Style

Jolliffe, D. A., Faustini, S. E., Holt, H., Perdek, N., Maltby, S., Talaei, M., Greenig, M., Vivaldi, G., Tydeman, F., Symons, J., Davies, G. A., Lyons, R. A., Griffiths, C. J., Kee, F., Sheikh, A., Shaheen, S. O., Richter, A. G., & Martineau, A. R. (2022). Determinants of Antibody Responses to SARS-CoV-2 Vaccines: Population-Based Longitudinal Study (COVIDENCE UK). Vaccines, 10(10), 1601. https://doi.org/10.3390/vaccines10101601

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