Humoral immunity to endemic coronaviruses in older adults increases post-COVID-19 vaccination and in correlation with their anti-SARS-CoV-2 responses
Bibliographic record
Abstract
Abstract Advanced age is an established risk factor for SARS-CoV-2 infection and severe disease. Older adults, having experienced recurrent infections with human endemic coronaviruses (HCoVs) throughout life, may have developed humoral immune responses that impact IgG targeting of both severe acute respiratory syndrome coronavirus (SARS-CoV) −2 and HCoVs. We profiled IgG responses in community-dwelling adults aged 50-87 years from the “PRospEctiVe EvaluatioN of immunity after COVID-19 vaccines” (PREVENT-COVID) prospective cohort study. Dried blood spot sampling occurred at baseline, prior to detected SARS-CoV-2 antigen exposure, and throughout the recommended vaccination schedule in Canada for SARS-CoV-2 seronegative and seropositive individuals. We define an HCoV-OC43-IgG high and HCoV-NL63-low baseline antibody landscape, which was unaffected by age or sex. Following COVID-19 vaccination, we observed increased anti HCoV-OC43 and HCoV-HKU1 IgG antibody titers, with substantial waning between doses. IgG specific to beta-coronaviruses and SARS-CoV-2 spike antigens positively correlated, strengthening with subsequent doses. In contrast, SARS-CoV-2 anti-nucleocapsid (N) antibodies associated most closely with HCoV-NL63 IgG levels. These findings suggest that HCoV-specific humoral immunity induced by COVID-19 vaccination or disease may impact susceptibility to future coronavirus infection. ACE2-binding assays revealed that individuals with greater HCoV-OC43- and HCoV-HKU1-specific IgG levels had the highest percent neutralization of the wildtype virus and SARS-CoV-2 variants. Together, our study highlights potential effects of COVID-19 vaccination on humoral immunity towards related coronaviruses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".