Effect of combining rosiglitazone with either metformin or insulin on β-cell mass and function in an animal model of Type 2 diabetes characterized by reduced β-cell mass at birth
Bibliographic record
Abstract
BACKGROUND: Interventions that preserve or increase β-cell mass may also prevent Type 2 diabetes. Rosiglitazone alone, as well as in combination with metformin, prevents diabetes in people with high, yet non-diabetic glucose levels. These effects may be mediated through changes in β-cell mass. In the present study, the effect of combining rosiglitazone with metformin and/or insulin on β-cell mass and glucose levels was examined in a rat model of Type 2 diabetes. METHODS: Diabetes-prone pups were randomized to receive rosiglitazone alone or in combination with metformin and/or insulin starting at 4 weeks of age. β-Cell mass and glucose homeostasis were examined in adulthood. RESULTS: Rosiglitazone treatment reduced insulin resistance and partially restored β-cell mass in animals with reduced β-cell mass at birth. The addition of metformin to rosiglitazone decreased insulin resistance and reduced weight gain, but had no additional effect on β-cell mass. Conversely, the addition of insulin had no additional effect on these outcomes. Although the combination of rosiglitazone and metformin did not affect β-cell mass at 26 weeks of age, it did result in reduced body weight and insulin resistance. CONCLUSION: The results of the present study suggest that the addition of metformin to rosiglitazone improves the metabolic profile through an effect on insulin resistance and not β-cell mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".