A Reassessment of the Hyperphagia/Weight-Loss Paradox During Sleep Deprivation
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep deprivation is a well-known paradigm to investigate the deleterious effects of prolonged wakefulness. Previous studies have shown that, during sleep deprivation, rats are hyperphagic but, paradoxically, lose body weight. This phenomenon has been attributed to increased metabolism. However, most previous studies have failed to account for food spillage, which may be considerable during sleep deprivation. DESIGN: In the present study, we revisited the issue of feeding changes in sleep-deprived rats and introduced different procedures to allow accurate estimation of food spillage prior to, during, and after 120 hours of sleep deprivation by a single platform technique. SETTING: Animal Sleep Research Laboratory, Psychobiology Department, Universidade Federal de São Paulo, Brazil. MEASUREMENTS AND RESULTS: The main finding was that, once corrected for spillage, food intake was not significantly increased during sleep deprivation. Increases in food removed from feeders were accompanied by proportional increases in food spillage, resulting in no net changes in food intake. Further, weight loss did occur during the sleep-deprivation period, especially in the first 24 hours, and it was actually explained by a reduction in food intake. CONCLUSION: The hyperphagia/weight-loss paradox previously seen during prolonged sleep deprivation does not necessarily occur with shorter periods of deprivation. Although we found no evidence of hyperphagia for up to 5 days of sleep deprivation in chow-fed rats, our data suggest that an impairment in the ability to increase food intake in response to increased energy expenditure contributes to the energy deficit during sleep deprivation in rats.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".