Third and fourth vaccine doses broaden and prolong immunity to SARS-CoV-2 in immunocompromised adult patients
Bibliographic record
Abstract
Abstract Background Previous studies have reported impaired humoral responses after SARS-CoV-2 mRNA vaccination in immunocompromised patients with immune-mediated inflammatory diseases (IMID), particularly those treated with anti-tumor necrosis factor (TNF) biologics. We previously reported that IMID patients exhibited greater waning of antibody and T cell responses compared to healthy controls after dose 2. Fewer data are available on the effects of third and fourth doses. Methods This observational cohort study collected plasma and peripheral blood mononuclear cells from healthy controls and untreated or treated IMID patients, pre-vaccination and after one to four doses of SARS-CoV-2 mRNA vaccine (BNT162b2 or mRNA-1273). SARS-CoV-2- specific antibody levels, neutralization, and T cell cytokine responses were measured against Wildtype (WT) and BA.1 and BA.5 variants of concern (VOCs). Results Third vaccine doses substantially restored and prolonged antibody and T cell responses in IMID patients and broadened responses against VOCs. Fourth dose effects were subtle but also prolonged antibody responses. However, IMID patients treated with anti-TNF, especially inflammatory bowel disease (IBD) patients, exhibited lower antibody responses even after the fourth dose. Although T cell IFNγ responses were maximal after one dose, IL-2 and IL-4 production increased with successive doses, and early production of these cytokines was predictive of neutralization responses at 3-4 months post-vaccination. Conclusion Our study demonstrates that third and fourth doses of the SARS-CoV-2 vaccine sustain and broaden immune responses to SARS-CoV-2, supporting the recommendation for three- and four-dose vaccination regimens in IMID patients. Funding COVID-19 Immunity Task Force and Speck family donation Conflict-of-Interest Statements Anne-Claude Gingras has received research funds from a research contract with Providence Therapeutics Holdings, Inc., for other projects, participated in the COVID-19 Immunity Task Force (CITF) Immune Science and Testing working party, chaired the CIHR Institute of Genetics Advisory Board, and chairs the SAB of the National Research Council of Canada Human Health Therapeutics Board. Vinod Chandran has received research grants from AbbVie, Amgen, and Eli Lilly and has received honoraria for advisory board member roles from AbbVie, Amgen, BMS, Eli Lilly, Janssen, Novartis, Pfizer, and UCB. His spouse is an employee of AstraZeneca. Vincent Piguet has no personal financial ties with any pharmaceutical company. He has received honoraria for speaker and/or advisory board member roles from AbbVie, Celgene, Janssen, Kyowa Kirin Co. Ltd, LEO Pharma, Novartis, Pfizer, Sanofi, UCB, and Union Therapeutics. In his role as Department Division Director of Dermatology at the University of Toronto, Dr. Piguet has received departmental support in the form of unrestricted educational grants from AbbVie, Bausch Health, Celgene, Janssen, LEO Pharma, Lilly, L’Oréal, NAOS, Novartis, Pfizer, Sandoz and Sanofi in the past 36 months. Vincent Piguet has received research grants from Sanofi, Abbvie and Novartis. Mark Silverberg has received research support, consulting fees and speaker honoraria from AbbVie, Janssen, Takeda, Pfizer, Gilead, and Amgen. All other authors have no conflicts to declare. Graphical Abstract
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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 | 0.000 | 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) | 0.001 | 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".