Are higher antibody levels against seasonal human coronaviruses associated with a more robust humoral immune response after SARS-CoV-2 vaccination?
Bibliographic record
Abstract
The SARS-CoV-2 belongs to the coronavirus family, which also includes common endemic coronaviruses (HCoVs). We hypothesized that immunity to HCoVs would be associated with stronger immunogenicity from SARS-CoV-2 vaccines. The study included samples from the COSRIP observational cohort study of adult paramedics in Canada. Participants provided blood samples, questionnaire data, and results of COVID-19 testing. Samples were tested for anti-spike IgG against SARS-CoV-2, HCoV-229E, HCoV-HKU1, HCoV-NL63, and HCoV-OC43 antigens. We first compared samples from vaccinated and unvaccinated participants, to determine which HCoV antibodies were affected by vaccination. We created scatter plots and performed correlation analysis to estimate the extent of the linear relationship between HCoVs and SARS-CoV-2 anti-spike antibodies. Further, using adjusted log-log multiple regression, we modeled the association between each strain of HCoV and SARS-CoV-2 antibodies. Of 1510 participants (mean age of 39 years), 94 (6.2%) had a history of COVID-19. There were significant differences between vaccinated and unvaccinated participant in anti-spike antibodies to HCoV-HKU1, and HCoV-OC43; however, levels for HCoV-229E and HCoV-NL63 were similar (suggesting that vaccination did not affect these baseline values). Among vaccinated individuals without prior COVID-19 infection, SARS-COV-2 anti-spike IgG demonstrated a weak positive relationship between both HCoV-229E (r = 0.11) and HCoV-NL63 (r = 0.12). From the adjusted log-log multiple regression model, higher HCoV-229E and HCoV-NL63 anti-spike IgG antibodies were associated with increased SARS-COV-2 anti-spike IgG antibodies. Vaccination appears to result in measurable increases in HCoV-HKU1, and HCoV-OC43 IgG levels. Anti-HCoV-229E and HCoV-NL63 antibodies were unaffected by vaccination, and higher levels were associated with significantly higher COVID-19 vaccine-induced SARS-COV-2 antibodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".