Satiety Hormone and Metabolomic Response to an Intermittent High Energy Diet Differs in Rats Consuming Long-Term Diets High in Protein or Prebiotic Fiber
Bibliographic record
Abstract
Large differences in the composition of diet between early development and adulthood can have detrimental effects on obesity risk. We examined the effects of an intermittent high fat/sucrose diet (HFS) on satiety hormone and serum metabolite response in disparate diets. Wistar rat pups were fed control (C), high prebiotic fiber (HF) or high protein (HP) diets (weaning to 16 weeks), HFS diet challenged (6 weeks), and finally reverted to their respective C, HF, or HP diet (4 weeks). At conclusion, measurement of body composition and satiety hormones was accompanied by (1)H NMR metabolic profiles in fasted and postprandial states. Metabolomic profiling predicted dietary source with >90% accuracy. The HF group was characterized by lowest body weight and body fat (P<0.05) and increased satiety hormone levels (glucagon-like peptide 1 and peptide-YY). Regularized modeling confirmed that the HF diet is associated with higher gut hormone secretion that could reflect the known effects of prebiotics on gut microbiota and their fementative end products, the short chain fatty acids. Rats reared on a HF diet appear to experience fewer adverse effects from an intermittent high fat diet in adulthood when rematched to their postnatal diet. Metabolite profiles associated with the diets provide a distinct biochemical signature of their effects.
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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 | 0.000 |
| 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".