Salsalate and/or metformin therapy confer beneficial metabolic effects in olanzapine treated female mice
Bibliographic record
Abstract
Antipsychotic medications are used in the management of schizophrenia and a growing number of off-label conditions. While effective at reducing psychoses, these drugs possess noted metabolic side effects including weight gain, liver lipid accumulation and disturbances in glucose and lipid metabolism. To counter the side effects of antipsychotics standard of care has typically included metformin. Unfortunately, metformin does not protect against antipsychotic induced metabolic disturbances in all patients and thus additional treatment approaches are needed. One potential candidate could be salsalate, the prodrug of salicylate, which acts synergistically with metformin to improve indices of glucose and lipid metabolism in obese mice. The purpose of the current investigation was to compare the effects of salsalate, metformin and a combination of both drugs, on weight gain and indices of metabolic health in female mice treated with the antipsychotic, olanzapine. Herein we demonstrate that salsalate was equally as effective as metformin in protecting against olanzapine induced weight gain and liver lipid accumulation with no additional benefit of combining both drugs. Conversely, metformin treatment, either alone or in combination with salsalate, improved indices of glucose metabolism and increased energy expenditure in olanzapine treated mice. Collectively, our findings provide evidence that dual therapy with both metformin and salsalate could be an efficacious approach with which to dampen the metabolic consequences of antipsychotic medications.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".