Combining sitagliptin/metformin with a functional fiber delays diabetes progression in Zucker rats
Bibliographic record
Abstract
Our primary objective was to determine whether administering the viscous and fermentable polysaccharide PolyGlycopleX (PGX) with metformin (MET) or sitagliptin/metformin (S/MET) reduces hyperglycemia in Zucker diabetic fatty (ZDF) rats more so than monotherapy of each. Glucose tolerance, adiposity, satiety hormones and mechanisms related to dipeptidyl peptidase 4 activity, gut microbiota and, hepatic and pancreatic histology were examined. Male ZDF rats (9-10 weeks of age) were randomized to: i) cellulose/vehicle (control, C); ii) PGX (5% wt/wt)/vehicle (PGX); iii) cellulose/metformin (200 mg/kg) (MET); iv) cellulose/S/MET (10 mg/kg+200 mg/kg) (S/MET); v) PGX (5%)+MET (200 mg/kg) (PGX+MET); vi) cellulose/sitagliptin/MET (5%)+(10 mg/kg+200 mg/kg) (PGX+S/MET) for 6 weeks. PGX+MET and PGX+S/MET reduced glycemia compared with C and singular treatments (P=0.001). Weekly fasted and fed blood glucose levels were lower in PGX+MET and PGX+S/MET compared with all other groups at weeks 4, 5, and 6 (P=0.001). HbA1c was lower in PGX+S/MET than C, MET, S/MET, and PGX at week 6 (P=0.001). Fat mass was lower and GLP1 was higher in PGX+S/MET compared with all other groups (P=0.001). β-cell mass was highest and islet degeneration lowest in PGX+S/MET. Hepatic lipidosis was significantly lower in PGX+S/MET compared with PGX or S/MET alone. When combined with PGX, both MET and S/MET markedly reduce glycemia; however, PGX+S/MET appears advantageous over PGX+MET in terms of increased β-cell mass and reduced adiposity. Both combination treatments attenuated diabetes in the obese Zucker rat.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".