Vitamin A and E Nutritional Status in Relation to Leptin, Adiponectin, IGF-I and IGF-II in Early Life - a Birth Cohort Study
Bibliographic record
Abstract
The metabolic health effects of vitamin A and E nutritional status in early life are largely unknown. We assessed whether vitamin A and vitamin E nutritional status may affect circulating leptin, adiponectin, insulin-like growth factor (IGF)-I and IGF-II levels in early life in humans. In a singleton birth cohort (n = 248), vitamin A and E nutritional status in fetuses/newborns were assessed by cord plasma concentrations of retinol, β-carotene, α- and γ-tocopherols. The primary outcomes were cord plasma leptin, adiponectin, IGF-I and IGF-II concentrations. Cord plasma retinol was significantly positively correlated to IGF-I in girls (r = 0.42, P < 0.0001) but not in boys (r = 0.14, P = 0.11). Adjusting for maternal and newborn's characteristics, one log unit increase in cord plasma retinol was associated with a 28.0% (95% CI: 11.1-47.5%) increase in IGF-I in girls (P < 0.001) but not in boys (P = 0.75). One log unit increment in cord plasma α-tocopherol was associated with a 6.6% (0.4-12.3%) decrease in adiponectin (P = 0.04), while one log unit increment in cord plasma γ-tocopherol was associated with a 21.2% (4.7-34.8%) decrease in leptin (P = 0.01). There may be a sex-specific association between retinol and IGF-I, a negative association between α-tocopherol and adiponectin, and a negative association between γ-tocopherol and leptin in early life in humans.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".