SARS-COV-2 MRNA VACCINE IMMUNOGENICITY IN SLE
Bibliographic record
Abstract
PV152 / #774 Poster Topic: AS17 - Miscellaneous Background/Purpose The ACR recommends SARS-CoV-2 vaccination for all patients with rheumatic diseases, but the impact of immune suppressing medications on SARS-CoV-2 immunogenicity remains poorly understood. This study sought to answer how SLE patients, on a variety of medications, responded to the two-dose primary SARS-CoV-2 mRNA vaccine. We also evaluated the degree to which additional vaccine doses and natural infection impacted the development of SARS-CoV-2 anti-spike antibodies. Methods Using biobanked serum from before and following vaccination, we investigated anti-spike antibody development in 87 adult SLE patients and 16 adult healthy controls who had each received 2 doses of a SARS-CoV-2 mRNA vaccine 14-180 days prior a clinic visit. ELISA was used to assess the amount of vaccination strain (D614G) anti-spike antibodies being produced, reported as area under the curve (AUC); AUC <2 was considered a nonresponder, 2-6 blunted response, and >6 full response. The dates of primary vaccine doses and subsequent vaccine doses were determined based on the state immunization registry. Dates of COVID infection were documented by patient recall and confirmed by chart review. Medication hold strategies were determined by chart review. Results As seen in Table 1, healthy controls had a higher AUC (mean 7.95, median 7.72, range 5.88-10.60) compared to the 87 SLE patients (mean 6.23, median 7.09, range 0.34-11.5; p=0.0002). The responses of 23 SLE patients who were receiving no immunosuppressive medications (mean 7.36, median 7.96, range 0.34-10.5) were not different, as a group, from those of healthy controls (p=0.3). Among SLE patients, neither disease activity nor prednisone dose (0-60 mg; p=0.8) were associated with AUC. Compared to healthy controls (p=0.0008) or SLE patients not taking immunosuppressants (p=0.008), SLE patients treated with rituximab (n=3, mean 4.90, range 3.75-5.70) or mycophenolate (n=20, mean 5.01, median 5.68, range 0.50-10.34) had significantly reduced antibody production. Of the 6 MMF primary nonresponders (Table 2), 1 experienced COVID infection and 3 received additional vaccine doses over the subsequent year. While the patient experiencing natural infection demonstrated a robust anti-spike response (7.97) following infection, 2 of the 3 primary MMF nonresponders did not mount an antibody response, even following 3 rd , 4 th , or 5 th doses (AUCs all <1). One primary MMF nonresponder, however, demonstrated a robust response following her 3 rd vaccine dose, which was administered with instructions to hold the next 7 doses (3.5-days) of MMF; this patient’s AUC rose to 9.74 following her 3 rd primary dose. Table 1. Amount of vaccination strain SARS-CoV-2 anti-spike antibody produced by health controls and by SLE patients who had each received two doses of a SARS-CoV-2 mRNA vaccine described as area under the curve (AUC). HCQ=hydroxychlotoqume; MMF=mycophenolate or mycophenolic acid; MTX=methotrexate; AZA=azathioprine; Belim=belimumab; RTX=rituximab; pred=prednisone. A Bonferroni correction for multiple comparisons suggests a p-value of ≤0.006 as the cutoff for statistical significance (significant p-values are bolded). Table 2. Of the 6 primary MMF non-responders, 4 received subsequent vaccine doses (n=3) or documented infection (n=1). Among those who received additional vaccine doses, 2 remained non-responders while the 3 rd developed a robust response in the setting of MMF being (7 held doses immediately following vaccination). Conclusions We herein demonstrate that treatment with rituximab and mycophenolate significantly blunts the humoral immunogenicity of SARS-CoV-2 mRNA vaccines. Primary MMF nonresponders can go on to develop a robust humoral response following natural COVID infection and may be more likely to respond to mount a humoral response to subsequent SARS-CoV-2 immunization if MMF doses are held around the time of vaccine administration. These findings suggest that holding a small number of MMF doses during the days immediately following vaccination could be a practical and safe strategy for enhancing vaccine immunogenicity.
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 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 | 0.000 | 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) | 0.002 | 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".