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Record W4298003567 · doi:10.3390/vaccines10101601

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

2022· article· en· W4298003567 on OpenAlexfundno aff
David A. Jolliffe, Sian Faustini, Hayley Holt, Natalia Perdek, Sheena Maltby, Mohammad Talaei, Matthew Greenig, Giulia Vivaldi, Florence Tydeman, Jane Symons, Gwyneth A. Davies, Ronan A Lyons, Chris Griffiths, Frank Kee, Aziz Sheikh, Seif O. Shaheen, Alex Richter, Adrian R. Martineau

Bibliographic record

VenueVaccines · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersMedical Research CouncilEconomic and Social Research CouncilRosetrees TrustAsthma and Lung UKBritish Heart FoundationNational Institute for Health and Care ResearchCancer Research UKArthritis SocietyVasculitis UKBritish Lung FoundationBarts CharityDiabetes UKUK Research and InnovationQueen Mary University of London
KeywordsVirologyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakAntibodyPopulationAntibody responseMedicineImmunologyEnvironmental healthOutbreakInfectious disease (medical specialty)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.432
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2022
Admission routes1
Has abstractyes

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