Vitamin D, leptin and impact on immune response to seasonal influenza A/H1N1 vaccine in older persons
Bibliographic record
Abstract
BACKGROUND: Influenza-related complications are highest in the elderly. Vaccine efficacy is lower due to immunosenescence. Vitamin D's immunomodulatory role was studied in the context of vaccine response. METHODS: We evaluated the effect of baseline 25-(OH) D on vaccine-induced immunological response in a cohort of 159 healthy subjects ages 50-74 in Rochester, MN, who received one dose of seasonal trivalent 2010-2011 influenza vaccine, containing A/California/H1N1- like virus. We examined correlations between 25-(OH) D, leptin, and leptin-related gene SNPs to understand the role of leptin and vitamin D's effects. RESULTS: The median (IQR) baseline for total 25-(OH) D was 44.4 ng/mL (36.6-52.2 ng/mL). No correlation was observed with age. No correlation between 25-(OH) D levels and humoral immune outcomes existed at any timepoint. There was a weak positive correlation between 25-(OH) D levels and change (Day 75-Day 0) in influenza-specific granzyme-B response (r=0.16, p=0.04). We found significant associations between 3 SNPs in the PPARG gene and 25-(OH) D levels (rs1151996, p=0.01; rs1175540, p= 0.02; rs1175544, p=0.03). CONCLUSION: Several SNPs in the PPARG gene were significantly associated with baseline 25-(OH) D levels. Understanding the functional and mechanistic relationships between vitamin D and influenza vaccine-induced immunity could assist in directing new influenza vaccine design.
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) | 0.001 | 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".