A simple action reduces high-fat diet intake and obesity in mice
Bibliographic record
Abstract
Diets that are high in fat cause overeating and weight gain in multiple species of animals, suggesting that high dietary fat is sufficient to cause obesity. However, high-fat diets are typically provided freely to animals in obesity experiments, so it remains unclear whether high-fat diets would still cause obesity if these diets required more effort to obtain. We hypothesized that unrestricted access to high-fat diets is important for these diets to induce overeating and that requiring mice to perform small amounts of work to obtain a high-fat diet would reduce calorie intake and associated weight gain. To test this hypothesis, we developed a novel home-cage-based feeding device that provided the high-fat diet in two conditions: either freely or after mice poked their noses into a port one time-a simple action that is easy for them to do. Consistent with our hypothesis, requiring mice to nose-poke reduced high-fat diet intake and nearly completely prevented weight gain. Requiring mice to nose-poke also reduced low-fat grain-based pellet intake, confirming that this is a general mechanism governing food choice and not something specific to a high-fat diet. We conclude that unrestricted access to food promotes overeating and that requiring a simple action such as a nose-poke can reduce overeating and weight gain in mice. Our results may have implications for why overeating and obesity are common in modern food environments, which are often characterized by easy access to low-cost unhealthy foods.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".