Replacing Mycophenolate Mofetil by Everolimus in Kidney Transplant Recipients to Increase Vaccine Immunogenicity: Results of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Vaccine immunogenicity is reduced in kidney transplant recipients (KTRs), especially in those using mycophenolate mofetil (MMF). Whether replacement of MMF by everolimus improves vaccine immunogenicity is unknown. METHODS: KTRs were randomized 1:1 to continue MMF or switch to everolimus. Participants received one coronavirus disease 2019 (COVID-19) booster vaccination and two herpes zoster (HZ) vaccinations at 6, 10 and 14 weeks postrandomization. Primary outcome was the neutralizing antibody response 28 days after COVID-19 vaccination. Secondary outcomes included antibody and T-cell responses 28 days after COVID-19 and HZ vaccination, and safety. RESULTS: In 110 KTRs, COVID-19 vaccination resulted in comparable Omicron XBB.1.5 neutralizing antibody titers in the everolimus versus MMF group (308 [74.4-1314] vs 327 [115-897]; P = .83), whereas severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Spike-specific T-cell responses were slightly lower with everolimus (118 [32.1-243] vs 228 [113-381] spot-forming cells [SFCs]/106 peripheral blood mononuclear cells [PBMCs]; P = .02). HZ vaccination led to higher varicella zoster virus (VZV) glycoprotein E (gE)-specific immunoglobulin G titers with everolimus (2192 [888-4523] vs 1101 [440-2078] 50% endpoint titer; P = .004), while VZV gE-specific T-cell responses were similar (85.0 [27.5-155] vs 115 [50.0-258] SFCs/106 PBMCs; P = .24). Besides known side effects, everolimus led to more bacterial infections (27.3% vs 11.1%; P = .03). CONCLUSIONS: Six weeks' replacement of MMF by everolimus in KTRs does not improve COVID-19 booster vaccine immunogenicity, whereas 10 weeks' replacement enhances humoral HZ vaccine immunogenicity. While replacing MMF by everolimus may improve vaccine responses, its timing and potential risks require careful consideration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".