Methotrexate and Tumor Necrosis Factor Inhibitors Independently Decrease Neutralizing Antibodies after SARS-CoV-2 Vaccination: Updated Results from the SUCCEED Study
Bibliographic record
Abstract
Objective: SARS-CoV-2 remains the third most common cause of death in North America. We studied methotrexate and tumor necrosis factor inhibitor (TNFi) effects on neutralization responses post-COVID vaccination, in immune-mediated inflammatory disease(IMID). Methods: Prospective data and sera on adults with inflammatory bowel disease (IBD), rheumatoid arthritis (RA), spondyloarthritis (SpA), psoriatic arthritis (PsA) and systemic lupus (SLE) were collected at 6 academic centres in Alberta, Manitoba, Ontario, and Quebec between 2022-2023. Sera from two time points were evaluated for each subject. Neutralization studies were divided between 5 laboratories, and each lab’s results analyzed separately using multivariate generalized logit models (ordinal outcomes: absent, low, medium, and high neutralization). Odds ratios (ORs) for methotrexate and TNFi effects were adjusted for demographics, IMID, other biologics and immunosuppressives, prednisone, COVID vaccinations (number/type), and infections in the 6 months prior to sample. Adjusted ORs for methotrexate and TNFi were then pooled in random-effects meta-analyses (separately for ancestral, and Omicron BA1 and BA5 strains). Results: Of 479 individuals (958 samples), 292 (61%) were IBD, 141 (29.4%) RA, and the remainder PsA, SpA and SLE. Mean age was 57 (62.2 % female). For both individual labs and the meta-analyses, adjusted ORs suggested independent negative effects of TNFi and methotrexate on neutralization. The meta-analysis adjusted ORs for TNFi were 0.56 (95% confidence interval, CI 0.39, 0.81) for the ancestral strain and 0.56 (95% CI 0.39, 0.81) for BA5. The meta-analysis adjusted OR for methotrexate was 0.39 (95% CI 0.19, 0.76) for BA1. Conclusions: SARS-CoV-2 neutralization in vaccinated IMID was diminished independently by TNFi and methotrexate. As SARS-CoV-2 circulation continues, ongoing vigilance regarding optimized vaccination is required.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".