Duration of Postvaccination Neutralizing Antibodies to SARS‐CoV‐2 and Medication Effects: Results from the Safety and Immunogenicity of COVID‐19 Vaccination in Systemic Immune‐Mediated Inflammatory Diseases Cohort Study
Bibliographic record
Abstract
OBJECTIVE: In the face of the ongoing circulation of SARS-CoV-2, the durability of neutralization post-COVID-19 vaccination in immune-mediated inflammatory disease (IMID) is a key issue, as are the effects of medications. METHODS: Adults (n = 112) with inflammatory bowel disease, psoriasis/psoriatic arthritis, rheumatoid arthritis, spondylarthritis, and systemic lupus were recruited from participating Canadian medical centers from 2021 to 2023. We focused on log-transformed neutralization (lentivirus methods) as a continuous outcome, with separate models for wild-type and Omicron strains BA.1 and BA.5. RESULTS: Compared with 30 to 120 days postvaccination, subsequent periods were associated with greater neutralization in unadjusted models for wild-type, BA.1, and BA.5 strains and against the BA.1 strain in adjusted models. Rituximab was associated with lower neutralization for the BA.1 strain in adjusted models, with a similar trend for BA.5. In methotrexate users, there were trends for less neutralization of BA.1 and BA.5 in all unadjusted models, whereas in adjusted models, there was significantly lower neutralization only for the wild type. Three or more doses and Omicron-specific vaccines were both independently associated with better neutralization ability for all three strains. A COVID-19 infection within six months before sampling was associated with higher neutralization of wild type and BA.1 in adjusted analyses. Anti-tumor necrosis factor agents were associated with lower neutralization ability for BA.5 in adjusted analyses. CONCLUSION: Neutralization responses in immunosuppressed individuals with IMID were durable over time and were augmented by more than three doses and Omicron-specific vaccines. Less neutralization was seen with certain medications. Our work clarifies the joint effects of vaccine history, infection, and medications on COVID-19 immunity.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".