Immunomodulatory drugs have divergent effects on humoral and cellular immune responses to SARS-CoV-2 vaccination in people living with rheumatoid arthritis
Bibliographic record
Abstract
Abstract Understanding the efficacy of SARS-CoV-2 vaccination in people on immunosuppressive drugs, including those with rheumatoid arthritis (RA), is critical for their protection. Vaccine induced protection requires antibodies, CD4+ T cells, and CD8+ T cells, but it is unclear if these are equally affected by immunomodulatory drugs. Here, we determined how humoral and cellular SARS-CoV-2 vaccination responses differed between people with RA and controls, and which drug classes impacted these responses. Blood was collected from participants with RA on immunomodulatory drugs and controls after their second, third, and fourth SARS-CoV-2 vaccinations. Receptor binding domain (RBD)-specific antibodies were quantified by ELISA. Spike-specific memory T cells were quantitated using flow cytometry. Linear mixed models assessed the impact of age, sex, and immunomodulatory drug classes on SARS-CoV-2 vaccination responses. Compared to non-RA controls (n = 35), participants with RA on immunomodulatory drugs (n = 62) had lower anti-RBD IgG and spike-specific CD4+ T cell levels, but no deficits in spike-specific CD8+ T cells, following SARS-CoV-2 vaccination. Use of costimulation inhibitors was associated with lower humoral responses. JAK inhibitors were associated with fewer spike-specific CD4+ T cells. Participants with RA on immunomodulatory drugs mounted weaker responses to SARS-CoV-2 vaccination, with different drug classes impacting the cellular and humoral compartments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".