Ethnic-Specific Differences in Vitamin D Status Is Associated with Adiposity
Bibliographic record
Abstract
BACKGROUND: Low circulating 25 hydroxyvitamin D [25(OH)D] concentrations are common in obesity (BMI ≥30 kg/m(2)) and a negative relationship with body fat distribution has recently been reported. Ethnic-specific differences in body fat distribution have been described with South Asians are reported to have greater visceral adipose tissue (VAT), which could influence circulating 25(OH)D concentrations. The objective of this study is to investigate the relationship between plasma 25(OH)D, adiposity, and body fat distribution in Europeans and South Asians. METHODS/PRINCIPAL FINDINGS: 187 Europeans and 192 South Asians were assessed for demographics, anthropometrics, and plasma 25(OH)D concentrations. Subcutaneous adipose tissue (SAT) and VAT were quantified by CT scan, and percent body fat by DEXA. Data were assessed by general linear models. South Asians had lower (P<0.001) plasma 25(OH)D concentrations and higher VAT (P = 0.04) than Europeans. Plasma 25(OH)D concentrations were negatively (P<0.05) associated with BMI, waist circumference, percent body fat, total adipose tissue, VAT, and SAT in unadjusted models and negatively (P<0.05) associated with VAT, SAT, and percent body fat after adjusting for BMI, ethnicity, age, and season of blood collection in males and females. When percent body fat, VAT, and SAT were included in the same model, only VAT remained negatively (P<0.05) associated with plasma 25(OH)D concentrations. Ethnicity remained significant in all models (P<0.001). CONCLUSION: Compared to other adipose tissue compartments, VAT may have a distinct role in determining plasma 25(OH)D concentrations, which may account for the lower levels in South Asians.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".