Vitamin D Status and Immune Health Outcomes in a Cross-Sectional Study and a Randomized Trial of Healthy Young Children
Bibliographic record
Abstract
In young children, the relationship between vitamin D and biomarkers of immune function is not well elucidated. The objective was to investigate relationships between vitamin D and immune function in young children. Data were from a cross-sectional study (study 1) of healthy children 1.8–5.9 years (n = 457) and a 12 weeks trial using vitamin D fortified foods (study 2) in healthy 1.8–8.7 years old (n = 77) in Montreal, Canada. Vitamin D status and ex vivo immune function were assessed. In study 1 (male: n = 242; 53%), plasma IL-6, TNFα and CRP were significantly higher (p < 0.05) in children with 25-hydroxyvitamin D (25(OH)D) ≥ 75 nmol/L compared to <50 nmol/L. In study 2 (male: n = 40; 52%), there were no differences in illness outcomes (duration, number of reported illnesses, etc.) among groups. In a 6–8 years old sub-group, only the peripheral blood lymphocytes were higher in the 600 IU/day vitamin D group compared to control (percent of white blood cells; control: 41.6 ± 8.0%, 600 IU/d: 48.6 ± 8.5%). IL-6 production (but not other cytokines) by isolated mononuclear cells, after ex vivo mitogen stimulation, was lower in the intervention groups compared to the control group at 12 weeks. In conclusion, in healthy young children with sufficient vitamin D status, increasing vitamin D intakes does not confer additional advantage to immune function.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".