Effect of selenium supplementation on changes in HbA1c: Results from a multiple‐dose, randomized controlled trial
Bibliographic record
Abstract
AIM: To investigate the effect of selenium supplementation at different dose levels on changes in HbA1c after 6 months and 2 years in a population of low selenium status. MATERIALS AND METHODS: The Denmark PRECISE study was a single-centre, randomized, double-blinded, placebo-controlled, multi-arm, parallel clinical trial with four groups. In total, 491 volunteers aged 60 to 74 years were randomly assigned to treatment with 100, 200 or 300 μg selenium/day as selenium-enriched yeast or placebo-yeast. HbA1c measurements were available for 489 participants at baseline, 435 at 6 months, and 369 after 2 years of selenium supplementation. Analyses were performed by intention to treat. RESULTS: The mean (SD) age, plasma-selenium concentration, and blood HbA1c at baseline were 66.1 (4.1) years, 86.5 (16.3) ng/g and 36.6 (7.0) mmol/mol, respectively. During the initial 6-month intervention period, mean HbA1c (95% CI) decreased by 1.5 (-2.8 to -0.2) mmol/mol for 100 μg/d of selenium supplementation and by 0.7 (-2.0 to 0.6) mmol/mol for the 200 and 300 μg/d groups compared with placebo (P = 0.16 for homogeneity of changes across the four groups). After 2 years of selenium supplementation, HbA1c had decreased significantly in all treatment groups, with no difference between active treatment and placebo. CONCLUSIONS: Selenium supplementation in an elderly European population of low selenium status did not significantly affect HbA1c levels after 2 years. Our findings corroborate a possible U-shaped response of selenium supplementation on glucose metabolism.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".