Amylin receptor agonism protects against metabolic side effects of acute olanzapine
Bibliographic record
Abstract
Olanzapine is a second-generation antipsychotic (SGA) used in the treatment of schizophrenia and several on- and off-label conditions. While effective in reducing psychoses, acute olanzapine treatment causes rapid hyperglycemia, insulin resistance, and dyslipidemia and these perturbations are linked to an increased risk of developing cardiometabolic disease. Increases in glucagon are central to the metabolic side effects of olanzapine. Amylin receptor activation has been shown to reduce circulating glucagon but it is unclear if targeting amylin signaling would be an effective approach to lessen olanzapine-induced hyperglycemia alone or as an adjunct with other glucose lowering medications such as glucagon-like peptide-1 (GLP1) receptor agonist liraglutide. The purpose of this study was to 1) determine if treatment with recombinant amylin or the amylin receptor agonist pramlintide is sufficient to protect against acute olanzapine-induced impairments in glucose and lipid homeostasis and 2) if there are synergistic effects of combining pramlintide and liraglutide. We hypothesized that amylin and pramlintide would confer protection against olanzapine-induced perturbations in glucose homeostasis and that these effects would be additive with liraglutide. We found that pramlintide co-treatment lowered olanzapine-induced increases in glucagon:insulin. There was an additive effect of pramlintide and liraglutide in protecting against olanzapine-induced hyperglycemia and a synergistic effect on markers of dyslipidemia. Our findings provide evidence that pramlintide, while moderately protective against some aspects of olanzapine-induced metabolic dysfunction, can be used to enhance other interventions which protect against olanzapine-induced hyperglycemia. This research was supported by a Project Grant (PJT 159538) from the Canadian Institutes of Health Research to DCW. KDM was supported by a Postgraduate Scholarship from the Natural Sciences and Engineering Research Council (NSERC) of Canada. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".