Physiological serum 25-hydroxyvitamin D concentrations are associated with improved thyroid function—observations from a community-based program
Bibliographic record
Abstract
PURPOSE: Vitamin D deficiency has been associated with an increased risk of hypothyroidism and autoimmune thyroid disease. Our aim was to investigate the influence of vitamin D supplementation on thyroid function and anti-thyroid antibody levels. METHODS: We constructed a database that included 11,017 participants in a health and wellness program that provided vitamin D supplementation to target physiological serum 25-hydroxyvitmain D [25(OH)D] concentrations (>100 nmol/L). Participant measures were compared between entry to the program (baseline) and follow-up (12 ± 3 months later) using an intent-to-treat analysis. Further, a nested case-control design was utilized to examine differences in thyroid function over 1 year in hypothyroid individuals and euthyroid controls. RESULTS: More than 72% of participants achieved serum 25(OH)D concentrations >100 nmol/L at follow-up, with 20% above 125 nmol/L. Hypothyroidism was detected in 2% (23% including subclinical hypothyroidism) of participants at baseline and 0.4% (or 6% with subclinical) at follow-up. Serum 25(OH)D concentrations ≥125 nmol/L were associated with a 30% reduced risk of hypothyroidism and a 32% reduced risk of elevated anti-thyroid antibodies. Hypothyroid cases were found to have higher mean serum 25(OH)D concentrations at follow-up, which was a significant positive predictor of improved thyroid function. CONCLUSION: The results of the current study suggest that optimal thyroid function might require serum 25(OH)D concentrations above 125 nmol/L. Vitamin D supplementation may offer a safe and economical approach to improve thyroid function and may provide protection from developing thyroid disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".