Effect of protein source in diets fed during gestation and lactation on food intake regulation in male offspring of Wistar rats
Bibliographic record
Abstract
We hypothesized that protein source in the nutritionally adequate AIN-93G diets fed during gestation, lactation, and weaning influences food intake (FI) regulation in male offspring of Wistar rats. Pregnant rats were fed the recommended casein-based (C) or soy protein-based (S) diet during gestation (experiment 1) or during gestation and lactation (experiment 2). Pups (n = 12 per group) weaned to C or S diets were followed for 9 wk (experiment 1) or 14 wk (experiment 2). At termination, body weight was 5.4% and 9.4% higher, respectively, in offspring of dams fed the S diet. Altered FI regulation was shown by failure of devazepide (a CCK-A receptor blocker) to block FI reduction after protein preloads in offspring of S diet-fed dams, whereas it had a strong effect on offspring of C diet-fed dams (P < 0.005). Similarly, naloxone (an opioid receptor blocker) blocked FI reduction more after casein than after soy protein preloads (P < 0.01). In experiment 2, offspring of dams fed the S diet had higher hypothalamic gene expression of agouti related protein at weaning (P < 0.05), and higher FI was found throughout postweaning (P < 0.0001). FI reduction after protein preloads at week 7 and after glucose preloads at week 13 was greater in offspring of C diet-fed dams (P < 0.05). Plasma insulin at weaning and insulin, ghrelin, and glucagon-like peptide-1 at week 15 were higher in offspring of S diet-fed dams (all P < 0.05). In conclusion, nutritionally complete C and S diets consumed during gestation and lactation differ in their effects on body weight and FI regulation in the offspring. Extending the diet from gestation alone to throughout gestation and lactation exaggerated the adverse effects of the S diet. However, the diet consumed postweaning had little effect on the outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".