Maternal consumption of high-prebiotic fibre or -protein diets during pregnancy and lactation differentially influences satiety hormones and expression of genes involved in glucose and lipid metabolism in offspring in rats
Bibliographic record
Abstract
Risk of developing the metabolic syndrome may be influenced by nutritional environment early in life. We examined the effects of high-fibre (HF) and high-protein (HP) diets consumed during pregnancy and lactation on satiety hormones and expression of genes involved in glucose and lipid metabolism in offspring. Wistar dams were fed a control (C), HF or HP diets during pregnancy and lactation. At parturition, litters were culled to ten pups. At 21 d, all pups were weaned onto C diet. At 7, 14, 21, 28 and 35 d after birth, blood was analysed for satiety hormones and tissues for mRNA expression in offspring. No differences were observed in litter size or birth weight. At 21 d, offspring of HF dams had greater adjusted intestinal mass and lower liver weight than those of C but not of HP dams. Plasma glucose at 28 d and amylin at 7, 14 and 28 d were lower in HF v. C and HP offspring. Glucagon-like peptide-1 was higher in HP offspring than in HF offspring at 7 d but was higher in HF v. C offspring at 21 d. Offspring of HF dams had higher glucose transporter (GLUT2 and Na+-dependent glucose/galactose transporter) mRNA expression at 21 d v. C and HP offspring. In brown adipose tissue, HF and HP up-regulated uncoupling protein-1 and PPAR-γ coactivator. HP was associated with increased resistin and IL-6 mRNA expression. The present study demonstrates that maternal diet composition differentially regulates circulating satiety hormones and genes involved in glucose transport and energy metabolism in offspring. These early changes could have long-term consequences for obesity risk.
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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) | 0.000 | 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".