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Record W4412845682 · doi:10.1080/19390211.2025.2539876

Yeast Beta-Glucan Enhances Antibody Response Following Influenza Vaccination – A Double-Blind, Randomized, Placebo-Controlled Pilot Trial

2025· article· en· W4412845682 on OpenAlexaff
Melissa L. Moreno, Carmelo Nieves, Kaylan B Hebert, Daniela Rivero‐Mendoza, James Colee, Thomas A. Tompkins, Wendy J. Dahl

Bibliographic record

VenueJournal of Dietary Supplements · 2025
Typearticle
Languageen
FieldMedicine
TopicHerbal Medicine Research Studies
Canadian institutionsLallemand (Canada)
Fundersnot available
KeywordsVaccinationMedicinePlaceboImmunologyAntibody titerTiterAntibodyHemagglutination assayAntigenInternal medicine

Abstract

fetched live from OpenAlex

Yeast beta-glucans demonstrate immune-modulating effects; however, few studies have explored the potential of yeast beta-glucans to enhance immune response to vaccination. This pilot study aimed to assess the adjuvant effect of a yeast beta-glucan supplementation on antibody titer response to influenza vaccination. Adults (n = 90; 70.7 ± 10.1 years) were recruited over two vaccination seasons and randomized to receive 500 mg of beta-glucan or placebo (500 mg cellulose) daily in a double-blind study design. Pre- and 4 wk post-vaccination serum influenza-specific antibody titers were assessed using an optimized Hemagglutination Inhibition (HI) assay. Plasma cytokines 24 h post-vaccination were quantified by immunoassay. Cold and flu symptoms, using the Modified Jackson Criteria, fever, and self-perceived fatigue were monitored daily. Linear mixed models were used to test for differences in the fixed effects of time, treatment, and their interactions. In season 1 (Fall 2022), despite a baseline suggesting seroprotection for the Influenza A (H3N2 A/Wisconsin/67/2005) in 92% of the beta-glucan group and 74% of the placebo group, the post-vaccination antibody titer response (Δ = 95.8) favored beta-glucan over placebo (p = 0.037). Influenza B/Austria/1359417/2021 antigen demonstrated poor detection; 7 of the 10 HI detectible antibody responses seen were in the beta-glucan group. In season 2 (Fall 2023), the Influenza A (H1N1 A/Victoria/4897/2022) antigen demonstrated poor detection (14%), which precluded further cohort analyses. Of the cytokines, interferon-gamma (IFN-γ) increased similarly in both groups after vaccination, not supporting the adjuvant action of beta-glucan at the cellular level. Reported cold and flu symptoms were low in both groups and did not differ. Overall, the findings suggest that yeast beta-glucan supplementation may elicit a greater change in antibody titer to seasonal influenza vaccination. However, confirmation is needed with a larger sample of older adults and with follow-up to assess protection from disease. Clinical trial registry number and website: https://clinicaltrials.gov/study/NCT05074303.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.060
GPT teacher head0.433
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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