Enhancing endogenous levels of GLP1 dampens acute olanzapine induced perturbations in lipid and glucose metabolism
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. Pharmacological agonists of the glucagon-like peptide-1 (GLP1) receptor have been shown to offset weight-gain associated with chronic SGA administration and mitigate the acute metabolic side effects of SGAs. The purpose of this study was to determine if increasing endogenous GLP1 is sufficient to protect against acute olanzapine-induced impairments in glucose and lipid homeostasis. Male C57BL/6J mice were treated with olanzapine, in the absence or presence of an oral glucose tolerance test (OGTT), and a combination of compounds to increase endogenous GLP1. These include the non-nutritive sweetener allulose which acts to induce GLP1 secretion but not other incretins, the DPPiv inhibitor sitagliptin which prevents degradation of active GLP1, and an SSTR5 antagonist which relieves inhibition on GLP1 secretion. We hypothesized that this cocktail of agents would increase circulating GLP1 to supraphysiological concentrations and would protect against olanzapine-induced perturbations in glucose and lipid homeostasis. We found that 'triple treatment' increased both active and total GLP1 and protected against olanzapine-induced perturbations in lipid and glucose metabolism under glucose stimulated conditions and this was paralleled by an attenuation in the olanzapine induced increase in the glucagon:insulin ratio. Our findings provide evidence that pharmacological approaches to increase endogenous GLP1 could be a useful adjunct approach to reduce acute olanzapine-induced perturbations in lipid and glucose metabolism.
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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 | 0.002 |
| 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".