Vitamin supplements: Are they associated with immune status?
Bibliographic record
Abstract
BACKGROUND & AIMS: Vitamins are essential nutrients, taken in very small amounts (0.01-100 mg a day). Associations between vitamin supplement intake or status and the immune system are far from consensual. Our aim was to understand the association between vitamin supplements and the immune system, namely regarding lymphocyte count and immunoglobulin levels against infectious pathogens. METHODS: Cross-sectional study using data from the first follow-up of the CoLaus|PsyCoLaus study (April 2009 to September 2012). Participants were categorized as vitamin users and non-users. Serostatus for 15 viruses, six bacteria, and one parasite was assessed. Data for inflammatory markers (hs-CRP, IL-1β, IL-6, and TNF-α) and blood elements were also collected. RESULTS: Of the initial 5064 participants, 3769 (74.5 %, mean age 58.3 ± 10.5 years, 53.6 % women) were retained for serostatus. On bivariate analysis, participants taking vitamins presented with higher positivity levels in three markers and lower positivity levels in two, but those differences were no longer statistically significant after multivariable analysis. 4489 participants (88.6 %, mean age 57.7 ± 10.5 years, 53.2 % women) had data for inflammatory markers; no association was found between vitamin supplement use and inflammatory markers both on bivariate and multivariable analysis. Finally, 3349 participants (66.1 %, mean age 57.3 ± 10.3 years, 53.1 % women) had data for blood elements; on bivariate analysis, vitamin supplement users had lower levels of haemoglobin and lymphocytes, but those differences were no longer significant after multivariable adjustment. CONCLUSION: In this cross-sectional, population-based study, we found no association between vitamin supplement use and markers of immune status.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".