Association between dietary selenium intake and the prevalence of prediabetes in Newfoundland population: a cross-sectional study
Bibliographic record
Abstract
Introduction Emerging evidence highlights the role of selenium (Se) in glucose metabolism through selenoprotein-mediated antioxidant and anti-inflammatory pathways. However, population-specific data remains inconclusive. This study aims to investigate the association between dietary Se intake and prediabetes prevalence in Newfoundland, a population characterized by genetic homogeneity and high obesity rates (39.4%). Methods This cross-sectional study used data from 2,665 participants in the Complex Diseases in the Newfoundland Population: Environment and Genetics (CODING) study. Prediabetes was defined by the American Diabetes Association criteria for impaired fasting glucose (FPG: 5.6–6.9 mmol/L). Dietary Se intake was assessed using the Willett food frequency questionnaire and expressed as both absolute (μg/d) and body weight-adjusted (μg/kg/d) metrics. Multivariate logistic regression, generalized additive model regression, piecewise regression models, and subgroup stratification were employed to examine the association. Results The study revealed a significant inverse relationship between body weight-adjusted dietary Se intake (μg/kg/d) and prediabetes prevalence in the fully adjusted models, with a non-linear threshold effect observed at 1.42 μg/kg/d. Below this threshold, each 1-unit increase in dietary Se intake (μg/kg/d) reduced prediabetes risk by 69% (OR = 0.31, P < 0.001). However, such an association did not reach statistical significance beyond 1.42 μg/kg/d. Subgroup analyses demonstrated consistent inverse associations across age groups, family history of diabetes, and history of smoking. However, the association was statistically significant in females (OR = 0.10, p < 0.001) but not in males. Absolute dietary Se intake (μg/d) showed no significant correlation with prediabetes after adjustment. Discussion Weight-adjusted dietary Se intake (μg/kg/d) exhibits an inverse non-linear, threshold-dependent relationship with prediabetes risk in this high-risk population. The findings underscore the critical importance of body weight normalization in assessing Se’s metabolic effects and formulating Se guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".