Fasting or the short‐term consumption of a ketogenic diet protects against antipsychotic‐induced hyperglycaemia in mice
Bibliographic record
Abstract
Antipsychotic (AP) medications, such as olanzapine (OLZ), are used in the treatment of schizophrenia and a growing number of 'off-label' conditions. A single dose of OLZ causes robust increases in blood glucose within minutes of treatment. The purpose of the current study was to investigate whether interventions that increase circulating ketone bodies (fasting, β-hydroxybutyrate (βHB), ketone esters or a ketogenic diet (KD)) would be sufficient to protect against the acute metabolic side effects of OLZ. We demonstrate that fasting or the short-term consumption of a KD protects against OLZ-induced hyperglycaemia, independent of alterations in whole-body insulin action, and in parallel with a blunted rise in serum glucagon. Interestingly, the effects of fasting and KDs were not recapitulated by acutely increasing circulating concentrations of ketone bodies through treatment with βHB or oral ketone esters, approaches which increase ketone bodies to physiological or supra-physiological levels, respectively. Collectively, our findings demonstrate that fasting and the short-term consumption of a KD can protect against acute AP-induced perturbations in glucose homeostasis, whereas manipulations which acutely increase circulating ketone bodies do not elicit the same beneficial effects. KEY POINTS: Antipsychotic medications cause rapid and robust increases in blood glucose. Co-treatment approaches to offset these harmful metabolic side effects have not been identified. We demonstrate that fasting or the consumption of a short-term ketogenic diet, but not treatment with β-hydroxybutyrate or oral ketone esters, protects against acute antipsychotic-induced hyperglycaemia. The protective effects of fasting and ketogenic diets were paralleled by reductions in serum glucagon, but not improvements in whole-body insulin action.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".