Dietary Vitamin A and Visceral Adiposity: A Modulating Role of the Retinol-Binding Protein 4 Gene
Bibliographic record
Abstract
BACKGROUND/AIMS: Visceral fat (VF) compared with subcutaneous fat (SF) is more closely associated with cardiometabolic disease. Dietary vitamin A (retinol) may reduce adiposity through its effects on adipogenesis differentially in VF and SF, and this effect may be modulated by retinol-binding protein-4 (RBP4). We investigated whether intake of vitamin A is associated with either VF or SF, and whether this association is moderated by the RBP4 genotype (rs10882272, C/T) previously associated with circulating retinol levels. METHODS: This was a cross-sectional association study in a sample of 947 adolescents from a French-Canadian founder population. VF and SF were quantified with magnetic resonance imaging, and vitamin A intake was assessed with a 24-hour food recall. All participants were genotyped to determine their RBP4 variant. RESULTS: Dietary intake of vitamin A was negatively associated with VF; however, it was not associated with SF. These relationships were independent of age, sex, height and energy intake, and were modulated by the RBP4 variant. The T allele promoted adiposity-reducing effects of vitamin A in VF and adiposity-enhancing effects in SF, while the C allele had adiposity-reducing effects in both VF and SF. CONCLUSIONS: Dietary vitamin A may reduce abdominal adiposity and promote visceral to subcutaneous body fat redistribution during adolescence in an RBP4-dependent manner. These observational findings provide the basis for future interventional studies, which together with genetic information may inject further causality in the association between dietary vitamin A intake and abdominal adiposity.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".