Diets Containing Egg or Whey Protein and Inulin Fiber Improve Energy Balance and Modulate Gut Microbiota in Exercising Obese Rats
Bibliographic record
Abstract
SCOPE: Dietary protein, prebiotic fiber, and exercise individually have been shown to aid in weight loss; however less is known of their combined effects on energy balance. The effects of diets high in protein and fiber, with exercise, on energy balance, hormones, and gut microbiota, were determined. METHODS AND RESULTS: Obese male rats were fed high-fat diets with high protein and fiber contents from egg protein and cellulose, egg protein and inulin, whey protein and cellulose, or whey protein and inulin, together with treadmill exercise. We found that inulin enriched diets decreased energy intake and respiratory quotient (RQ), increased energy expenditure (EE), and upregulated transcripts for cholecystokinin (CCK), peptide YY, and proglucagon in distal gut. Notably, CCK1-receptor blockade attenuated the hypophagic effects of diets and in particular whey-inulin diet, and β-adrenergic blockade reduced EE across all diets. Egg-cellulose, egg-inulin, and whey-inulin diets decreased weight gain, adiposity, and hepatic lipidosis; decreased lipogenic transcripts, improved glycemic control, and upregulated hepatic glucose metabolism transcripts; and decreased plasma insulin and leptin. Importantly, diet was linked to altered gut microbial composition and plasma metabolomics, and a subset of predicted metagenome pathways and plasma metabolites significantly correlated, with plasma butyric acid the most strongly associated to metagenome function. CONCLUSION: Combination of dietary egg or whey protein with inulin and exercise improved energy balance, glucose metabolism, upregulated anorectic hormones, and selectively modulated gut microbiota and plasma metabolites.
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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) | 0.001 | 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) | 0.001 | 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".