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 the effects of methotrexate and tumor necrosis factor inhibitor (TNFi) on neutralization responses after COVID-19 vaccination in immune-mediated inflammatory disease (IMID). Methods: Prospective data and sera of adults with inflammatory bowel disease (IBD), rheumatoid arthritis (RA), spondyloarthritis (SpA), psoriatic arthritis (PsA), and systemic lupus (SLE) were collected at six academic centers in Alberta, Manitoba, Ontario, and Quebec between 2022 and 2023. Sera from two time points were evaluated for each subject. Neutralization studies were divided between five laboratories, and each lab’s results were analyzed separately using multivariate generalized logit models (ordinal outcomes: absent, low, medium, and high neutralization). Odds ratios (ORs) for the effects of methotrexate and TNFi were adjusted for demographics, IMID, other biologics and immunosuppressives, prednisone, COVID-19 vaccinations (number/type), and infections in the 6 months prior to sampling. The adjusted ORs for methotrexate and TNFi were then pooled in random-effects meta-analyses (separately for the ancestral strains and the Omicron BA1 and BA5 strains). Results: Of 479 individuals (958 samples), 292 (61%) were IBD, 141 (29.4%) were RA, and the remainder were PsA, SpA, and SLE. The mean age was 57 (62.2% female). For both the individual labs and the meta-analyses, the 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.004 | 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".