The Effect of Serum 25-Hydroxyvitamin D on Serum Ferritin Concentrations: A Longitudinal Study of Participants of a Preventive Health Program
Bibliographic record
Abstract
Various studies have suggested a role of vitamin D in inflammation. However, its effect on ferritin, a biomarker of inflammation, has received relatively little attention. Therefore, we aimed to assess the association of serum 25-hydroxyvitamin D (25(OH)D) with serum ferritin (SF) concentrations, and to examine whether temporal increases in serum 25(OH)D concentrations are paralleled by a reduction in SF concentrations. Data from a community sample of Canadian adults who participated in a preventive health program (n = 6812) were analyzed. During the follow-up, serum 25(OH)D concentrations increased from 80.7 to 115.0 nmol/L whereas SF concentrations decreased from 122.0 to 92.0 µg/L (median follow-up time was 11.67 months). Cross-sectional analyses revealed that compared to participants with 25(OH)D concentrations of <50 nmol/L, those with 25(OH)D concentrations of 75 to <100, 100 to <125, and ≥125 nmol/L had SF concentrations that were 13.00, 23.15, and 27.59 µg/L lower respectively (p < 0.001). Compared to those without temporal improvements in 25(OH)D concentrations between baseline and follow-up, participants who improved their 25(OH)D concentrations with ≥50 nmol/L decreased their SF concentrations with 5.71 µg/L. For participants for whom the increase in 25(OH)D concentrations was less than 50 nmol/L, decreases in SF concentrations were less pronounced and not statistically significant. These observations suggest that despite strong associations between 25(OH)D and SF concentrations, interventions aiming to lower SF concentrations through sun-exposure and vitamin D supplementation should target substantial increases in 25(OH)D concentrations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".