Significant reverse association between dietary selenium intake and visceral adiposity in the CODING study
Bibliographic record
Abstract
Excessive visceral adipose tissue (VAT) has proven to be an efficient predictor of cardiovascular disease (CVD), independent of body mass index (BMI) or total body fat, and reducing visceral obesity is key to addressing the current epidemic of CVD. Selenium (Se) is suggested to protect against CVD, but a direct link between dietary Se intake and VAT levels is lacking. This study investigated this relationship, emphasizing the importance of expressing Se intake relative to body weight. A total of 3244 individuals participated in the Complex Diseases in the Newfoundland population: Environment and Genetics (CODING) study. Dietary Se intake was assessed using the Willett food frequency questionnaire (FFQ). VAT mass and VAT volume were precisely measured via dual-energy X-ray absorptiometry (DXA). Visceral adiposity, as indicated by VAT mass and VAT volume, decreased significantly across increasing quartiles of dietary Se intake (µg/kg/d) in both sexes. Compared with participants in the lowest quartile, those in the highest quartile had substantially lower visceral adiposity with reduction of 50.97% in women, and 63.75% in men, respectively. Likewise, this inverse dose-dependent manner was also observed when dietary Se intake was expressed as µg/d. Moreover, a linear negative correlation between dietary Se intake (µg/kg/d) and visceral adiposity was discovered, whereas no significant correlation was found if dietary Se intake was expressed as µg/d. After controlling for possible confounders, the linear regression analyses revealed that a 10% increase in dietary Se intake (µg/kg/d) was associated with 2.73% decrease in visceral adiposity. Interestingly, this inverse correlation remained consistent across different sex, age and menopausal status subgroups, with greater associations observed in males, individuals younger than 35 years old, and women in menopausal status. This is the first study to demonstrate a significant inverse association between dietary Se intake and VAT, which is exclusively evident when intake is expressed relative to body weight (µg/kg/d). This finding resolves prior inconsistencies and suggests that ensuring adequate weight-adjusted Se intake could be a valuable nutritional strategy for reducing visceral obesity and improving CVD prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".