Multi‐Vitamin Supplementation during Pregnancy in Rats Alters the Development of Food Intake Regulation in the Offspring
Bibliographic record
Abstract
Multi‐vitamin supplements are commonly consumed by women, especially during pregnancy. Increased vitamin intake during pregnancy may alter the expression of genes regulating metabolic systems in the offspring. Thus, we tested the effect of high multi‐vitamin intake during pregnancy in rats on food intake (FI) regulation and body weight in the offspring. Two groups of pregnant Wistar rats were fed the AIN‐93 diet containing either the recommended (RV) or 10X higher vitamin (HV) content by addition of the AIN vitamin mix. In male pups (n=10/group), body weight (g) and daily FI (g/day) were measured weekly from birth. Also, 1 hr FI was measured 30 min after the pups received either a glucose (5 g/kg) or water preload given by gavage in random order and on alternate days after an overnight fast. Plasma ghrelin and GLP‐1 were measured at weaning and 15 wks. Compared to RV offspring, HV offspring were not different in birth weight, but were 5% lighter at weaning (P<0.05) and 6% heavier at 15 wks (P<0.01). At weaning, HV offspring had 32% higher fasting GLP‐1 (P<0.01) and a 150% greater decrease in FI (P<0.01) after the glucose load compared to RV offspring. However, at 15 wks, HV offspring compared to RV offspring ate 10% more throughout the day (P<0.05), had 24% higher fasting ghrelin (P<0.01), a 29% lower GLP‐1 response (P<0.01) and failed to suppress FI after the glucose preload (−3.1 ± 0.4 vs. −4.1 ± 0.5 g; P<0.05). In conclusion, HV intake during pregnancy in rats alters the development of food intake regulation in the offspring. Supported by CIHR‐INMD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".