Yeast Beta-Glucan Enhances Antibody Response Following Influenza Vaccination – A Double-Blind, Randomized, Placebo-Controlled Pilot Trial
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".