Medication count, including statin or metformin use, is not associated with influenza vaccine responses in older adults
Bibliographic record
Abstract
BACKGROUND: Vaccination helps prevent infections and associated sequelae, especially in older adults. Since the degree to which vaccine responses are influenced by medications is not well described, we examined the association of medication count, including statin or metformin use, with influenza vaccine responsiveness. METHODS: A secondary analysis of data from 542 participants aged 65 years and older randomized to receive either the standard or high dose vaccine during four influenza seasons (2015-2018) was conducted. Medication counts, as well as statin and metformin usage, were self-reported. Associations between vaccine antibody titer responses and medication usage were estimated using mixed model linear regression. RESULTS: Participants reported taking a median of five medications, with 45 % (n = 244) of participants using a statin and 12 % (n = 65) metformin. Medication use was higher with frailty, chronic condition count and in males. Our data showed that medication use was not broadly associated with vaccine responsiveness, which held after adjusting for frailty and number of chronic conditions, and when investigated within sex and vaccine dose strata. However, within diabetics, the strong boosting effect of the high-dose vaccine was significantly dampened for metformin users (n = 64) as compared to non-users (n = 30), but still remained slightly higher than standard-dose responses. This dampening effect was evident in analyses where all antigen-specific responses were combined (i.e. maxRBA, p = 0.002), and for responses against seasonal A/H3N2 (p = 0.007) and B (p = 0.03) antigens. CONCLUSION: Vaccine responses varied little with medication usage among older adults. However, our findings suggest metformin may be associated with reductions in the high-dose vaccine response within diabetics.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".