Impact of age and SARS-CoV-2 breakthrough infection on humoral immune responses after three doses of COVID-19 mRNA vaccine
Bibliographic record
Abstract
ABSTRACT Background Longer-term immune response data after three doses of COVID-19 mRNA vaccine remain limited, particularly among older adults and following Omicron breakthrough infection. Methods We quantified wild-type- and Omicron-specific serum IgG levels, ACE2 displacement activities and live virus neutralization up to six months post-third dose in 116 adults aged 24-98 years who remained COVID-19-naïve or experienced their first SARS-CoV-2 infection during this time. Results Among 78 participants who remained COVID-19-naïve throughout follow-up, wild-type- and Omicron BA.1-specific IgG concentrations were comparable between younger and older adults, though BA.1-specific responses were consistently significantly lower than wild-type-specific responses in both groups. Wild-type- and BA.1-specific IgG concentrations declined at similar rates among COVID-19-naïve younger and older adults, with median half-lives ranging from 69-78 days. Antiviral antibody function declined substantially over time in COVID-19-naïve individuals, particularly older adults: by six months, BA.1-specific neutralization was undetectable in 96% of older adults, versus 56% of younger adults. SARS-CoV-2 infection, experienced by 38 participants, boosted IgG levels and neutralization above those induced by vaccination alone. Nevertheless, BA.1-specific neutralization remained significantly lower than wild-type, with BA.5-specific neutralization lower still. Conclusions Our findings underscore the immune benefits of third COVID-19 mRNA vaccine doses in adults of all ages, but rapid decline of Omicron-specific neutralization activity in COVID-19-naïve individuals, particularly among older adults, demonstrates the need for fourth doses within 3-6 months to maintain systemic responses. Individuals who experienced SARS-CoV-2 breakthrough infection post-third vaccine dose however can likely delay a fourth dose beyond this timeframe.
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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) | 0.001 | 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".