A maternal high-protein diet predisposes female offspring to increased fat mass in adulthood whereas a prebiotic fibre diet decreases fat mass in rats
Bibliographic record
Abstract
The negative effects of malnourishment in utero have been widely explored; the effects of increased maternal macronutrient intake are not known in relation to high fibre, and have been inconclusive with regard to high protein. In the present study, virgin Wistar dams were fed either a control (C), high-protein (40 %, w/w; HP) or high-prebiotic fibre (21·6 %, w/w; HF) diet throughout pregnancy and lactation. Pups consumed the C diet from 3 to 14·5 weeks of age, and then switched to a high-fat/sucrose diet for 8 weeks. A dual-energy X-ray absorptiometry scan and an oral glucose tolerance test were performed and plasma satiety hormones measured. The final body weight and the percentage of body fat were significantly affected by the interaction between maternal diet and offspring sex: weight and fat mass were higher in the female offspring of the HP v. HF dams. No differences in body weight or fat mass were seen in the male offspring. There was a significant sex effect for fasting and total AUC for ghrelin and fasting GIP, with females having higher levels than males. Liver TAG content and plasma NEFA were lower in the offspring of high-prebiotic fibre dams (HF1) than in those of high-protein dams (HP1) and control dams (C1). Intestinal expression of GLUT2 was decreased in HF1 and HP1 v. C1. The maternal HP and HF diets had lasting effects on body fat and hepatic TAG accumulation in the offspring, particularly in females. Whereas the HP diet predisposes to an obese phenotype, the maternal HF diet appears to reduce the susceptibility to obesity following a high-energy diet challenge in adulthood.
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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 | 0.001 | 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) | 0.003 | 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".