Glucose Tolerance, Lipids, and GLP‐1 Secretion in JCR:LA‐cp Rats Fed a High Protein Fiber Diet
Bibliographic record
Abstract
BACKGROUND: We have shown that individually, dietary fiber and protein increase secretion of the anorexigenic and insulinotropic hormone, glucagon-like peptide-1 (GLP-1). OBJECTIVE: Our objective was to combine, in one diet, high levels of fiber and protein to maximize GLP-1 secretion, improve glucose tolerance, and reduce weight gain. METHODS AND PROCEDURES: Lean (+/?) and obese (cp/cp) male James C Russell corpulent (JCR:LA-cp) rats lacking a functional leptin receptor were fed one of four experimental diets (control, high protein (HP), high fiber (HF, prebiotic fiber inulin), or combination (CB)) for 3 weeks. An oral glucose tolerance test (OGTT) was performed to evaluate plasma GLP-1, insulin and glucose. Plasma lipids and intestinal proglucagon mRNA expression were determined. RESULTS: Energy intake was lower with the HF diet in lean and obese rats. Weight gain did not differ between diets. Higher colonic proglucagon mRNA in lean rats fed a CB diet was associated with higher GLP-1 secretion during OGTT. The HP diet significantly reduced plasma glucose area under the curve (AUC) during OGTT in obese rats, which reflected both an increased GLP-1 AUC and higher fasting insulin. Diets containing inulin resulted in the lowest plasma triglyceride and total cholesterol levels. DISCUSSION: Overall, combining HP with HF in the diet increased GLP-1 secretion in response to oral glucose, but did not improve glucose tolerance or lipid profiles more than the HF diet alone did. We also suggest that glycemic and insulinemic response to prebiotics differ among rat models and future research work should examine their role in improving glucose tolerance in diet-induced vs. genetic obesity with overt hyperleptinemia.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".