AICAR Attenuates Olanzapine‐Induced Hyperglycaemia and Insulin Resistance in C57BL6 Mice
Bibliographic record
Abstract
Olanzapine (OLZ), an antipsychotic drug used in the treatment of schizophrenia, induces undesirable side effects including hyperglycaemia, glucose intolerance, dyslipidaemia, weight gain and insulin resistance, often resulting in the development of Type 2 Diabetes (T2D). The acute effects of OLZ on blood glucose are likely caused by a reduction in insulin secretion, the development of insulin resistance and increases in liver glucose production. 5′AMP‐activated protein kinase (AMPK) is a cellular energy sensor activated during exercise that has been shown to increase insulin sensitivity and increase insulin‐independent glucose uptake. Though having some off‐target effects, 5‐aminoimidazole‐4‐carboxamide riboside (AICAR) is a pharmacological agent that can activate AMPK in vivo. The purpose of this investigation was to determine if co‐treatment with AICAR would prevent acute OLZ‐induced increases in blood glucose in C57BL6 mice. AMPK and ACC phosphorylation was increased in liver, epididymal adipose tissue and triceps muscles 30 minutes following an injection of (AICAR) (250 mg/kg, IP injection) compared to vehicle treated mice. OLZ (5 mg/kg) caused rapid (15 minutes) and sustained increases in blood glucose that was prevented with AICAR co‐treatment. Treatment with OLZ attenuated the ability of insulin (0.75 U/kg, IP) to reduce blood glucose levels and this was partially prevented by AICAR. OLZ led to an exaggerated increase in blood glucose during a pyruvate tolerance test (2 g/kg, IP) and this was mitigated by AICAR. The results of the current study provide evidence that AICAR prevents OLZ‐induced hyperglycemia by preventing the development of peripheral insulin resistance and blunting increases in liver glucose output. Support or Funding Information D.C.W. is a Tier II Canada Research Chair. This research was funded by a NSERC Discovery Grant to D.C.W. N.B. was funded by CGS‐M (CIHR).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".