High levels of nonblocking anti-interferon and anticytokine autoantibodies in individuals with mRNA vaccine–induced systemic allergic reactions
Bibliographic record
Abstract
Background: Hypersensitivity reactions following coronavirus 2019 disease (COVID-19) mRNA vaccination, although rare, have raised public concern and contributed to vaccine hesitancy. The underlying mechanisms and cofactors that increase the risk of these adverse reactions remain poorly understood. Objective: We aimed to investigate whether the presence of autoantibodies, particularly anticytokine autoantibodies (ACAs), correlates with systemic adverse events (SAEs) in response to mRNA COVID-19 vaccines. Methods: We analyzed serum samples from 2 independent cohorts of individuals who experienced convincing SAEs after receiving their first dose of COVID-19 mRNA vaccine, including 16 individuals at the National Institutes of Health (NIH) and 18 at Stanford University. Individuals enrolled in the NIH cohort received subsequent vaccine doses under medical supervision. The control groups included vaccine-tolerant individuals. Bead-based autoantigen arrays were used to detect autoantibodies, whereas cell-based assays were used to assess the functional blocking activity of specific antibodies. Results: Autoantibody positivity was detected in 81.2% of the NIH cohort and 38.8% of the Stanford cohort. Elevated levels of antibodies against IFN-λ1 were associated with repeated SAEs in the NIH cohort. Other notable targets included IL-1A, IL-4, IL-6, IL-11, IL-17, TNF-α, and IFN-γ. Despite elevated autoantibody levels, functional blocking activity was not detected in reporter assays. Conclusion: Our findings reveal a potential link between cytokine-targeting autoantibodies, especially anti-IFN-λ1, and systemic adverse responses to mRNA vaccination. These results suggest a role for immune dysregulation in individuals with hypersensitivity to mRNA vaccines and highlight the need for further investigation to improve vaccine safety and tolerance.
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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 | 0.001 | 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".