Cannabis produces acute hyperphagia in humans and rodents via increased reward valuation for, and motivation to, acquire food
Bibliographic record
Abstract
With approximately 4% of the world's population using cannabis, there is a need to better understand its physiological effects. Cannabis consumption acutely promotes food intake ("the munchies") via delta-9-tetrahydrocannabinol-mediated activation of cannabinoid 1 receptors (CB1R); however, these appetitive effects have not been well characterized. We examined effects of cannabis vapor inhalation on energy and macronutrient intake patterns in human participants and then validated these findings in a translational rat model through which we explored behavioral and physiological mechanisms subserving this response. Vaporized cannabis acutely and robustly increased energy intake. In humans, this occurred in the first 30 min of snack and beverage access, irrespective of dose or gender. In rats, these effects were observed in the first 60 min of food access, irrespective of macronutrient content, satiation, or sex, and were a result of cannabis vapor reducing latency to eat and increasing feeding bout number. Also, cannabis vapor did not change the proportion of macronutrients consumed by human participants and abolished preexisting macronutrient-specific food preferences in rats. Our rat data indicate that cannabis vapor may override homeostatic appetite regulation by increasing motivation to eat and reducing food reward devaluation to promote energy intake. Finally, cannabis vapor did not alter circulating appetite-associated hormones, and these feeding effects were mediated by central, but not peripheral, CB1Rs. This study complements and builds upon previous literature to characterize the appetitive effects of vaporized cannabis and uses a translational approach to examine cannabis-driven energy and macronutrient intake patterns in humans and rodents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".