Effects of metformin on insulin resistance and metabolic disorders in tumor-bearing rats with advanced cachexia
Bibliographic record
Abstract
Metformin (MET) is widely used in the correction of insulin (INS) resistance and metabolic abnormalities in type 2 diabetes. However, its effect on INS resistance and metabolic disorders associated with cancer cachexia is not established. We investigated the MET effects, isolated or associated with INS, on INS resistance and metabolic changes induced by Walker-256 tumor in rats with advanced cachexia. MET (500 mg·kg−1, oral) and MET + INS (1.0 IU·kg−1, s.c.) were administered for 12 days, starting on the day of tumor cell inoculation. Tumor-bearing rats showed adipose and muscle mass wasting, body mass loss, anorexia, decreased Akt phosphorylation in retroperitoneal and mesenteric adipose tissue, peripheral INS resistance, hypoinsulinemia, reduced INS content and secretion from pancreatic islets, and also inhibition of glycolysis, gluconeogenesis, and glycogenolysis in liver. MET and MET + INS treatments did not prevent these changes. It can be concluded that treatments with MET and MET + INS did not prevent the adipose and muscle mass wasting and body mass loss of tumor-bearing rats possibly by not improving INS resistance. Therefore, MET, used for the treatment of INS resistance in type 2 diabetes, is not effective in improving INS resistance in the advanced stage of cancer cachexia, evidencing that the drug does not have the same beneficial effect in these 2 diseases.
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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 | 0.001 | 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".