Thermoneutral housing and preexisting obesity do not abolish the sexually dimorphic effects of olanzapine on weight gain in mice
Bibliographic record
Abstract
OBJECTIVE: In contrast to what is seen clinically, male mice are resistant to antipsychotic-induced obesity. This is problematic as preclinical studies examining mechanisms of antipsychotic-induced metabolic dysfunction might be relevant to only half the population. This study sought to determine whether housing mice at thermoneutrality and under conditions of preexisting obesity, steps that have not been previously considered, would uncover a greater obesogenic effect of the antipsychotic olanzapine (OLZ). METHODS: C57BL6/J mice were fed a low- or high-fat diet (HFD) for 4 weeks and then switched to a control HFD or an HFD supplemented with OLZ for 6 weeks. RESULTS: Irrespective of obesity, OLZ treatment attenuated weight gain and increased energy expenditure in male mice. In females, OLZ increased food intake and potentiated weight gain in mice with preexisting obesity. CONCLUSIONS: Despite taking steps to increase clinical translatability, this study did not unmask an obesogenic effect of OLZ in male mice. Interestingly, prior studies in female mice could have been underestimating the metabolic consequences of OLZ by not considering the importance of preexisting obesity. Uncovering the mechanisms conferring resistance to weight gain in males may provide clues for approaches to counter the metabolic side effects of antipsychotics clinically.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".