Chronic Treatment With Psilocybin Decreases Changes in Body Weight in a Rodent Model of Obesity
Bibliographic record
Abstract
Background: There are currently relatively few effective pharmacological treatments for obesity, and existing ones may be associated with limiting side-effects. In the search for novel anti-obesity agents, drugs that modify central serotonergic systems have historically proven to be effective in promoting weight loss. Psilocin, which is rapidly metabolized from psilocybin, is an agonist at multiple serotonin receptors. In the present study we assessed the effects of psilocybin and a positive control (metformin) on changes in body weight in a rat model of obesity. Methods: Five groups of adult male rats were pre-conditioned with a cafeteria diet until obese (>600 g) and then treated with either psilocybin (0.1, 1, or 5 mg/kg, i.p.), metformin (300 mg/kg, p.o.) or vehicle control. Treatments were for 27 consecutive weekdays, and body weights and high calorie food intake were recorded daily. Fasting glucose levels were recorded after 11 days of treatment. At the end of treatment rats completed a glucose tolerance test, and multiple fat pads were dissected out to assess adiposity. Results: The medium dose psilocybin group had to be terminated from the study prematurely. Both the low and high dose psilocybin groups caused a significant decrease in changes in body weight compared to controls. The metformin group produced a greater decrease in change in body weight than either psilocybin groups or controls. Both high dose psilocybin and metformin decreased consumption of the high calorie diet, and exhibited decreased central adiposity. Conclusion: Psilocybin demonstrated modest but significant effects on weight gain. Further study is recommended.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".