Isocaloric Diets with Different Protein-Carbohydrate Ratios: The Effect on Sleep, Melatonin Secretion and Subsequent Nutritional Response in Healthy Young Men
Bibliographic record
Abstract
This study aimed to determine the short-term effect of two isocaloric diets differing in the ratio of protein−carbohydrate on melatonin levels, sleep, and subsequent dietary intake and physical activity in healthy young men. Twenty-four healthy men took part in a crossover design including two sessions of three days on isocaloric diets whether high-protein, low-carbohydrate (HPLC) or low-protein, high-carbohydrate (LPHC) followed by 24-h free living assessments. Sleep was measured by ambulatory polysomnography pre-post-intervention. Melatonin levels were assessed on the third night of each session on eight-point salivary sampling. Physical activity was monitored by accelerometry. On day 4, participants reported their 24-h ad-libitum dietary intake. LPHC resulted in better sleep quality and increased secretion of melatonin compared to HPLC. A significant difference was noted in sleep efficiency (p < 0.05) between the two sessions. This was mainly explained by a difference in sleep onset latency (p < 0.01) which was decreased during LPHC (PRE: 15.8 ± 7.8 min, POST: 11.4 ± 4.5 min, p < 0.001). Differences were also noted in sleep staging including time spent on REM (p < 0.05) and N1 (p < 0.05). More importantly, REM latency (PRE: 97.2 ± 19.9 min, POST 112.0 ± 20.7 min, p < 0.001) and cortical arousals (PRE: 7.2 ± 3.9 event/h, POST 8.5 ± 3.3 event/h) increased in response to HPLC diet but not LPHC. On day 4, 24-h ad-libitum energy intake was higher following HPLC compared to LPHC (+64 kcal, p < 0.05) and explained by increased snacking behavior (p < 0.01) especially from carbohydrates (p < 0.05). Increased carbohydrates intake was associated with increased cortical arousals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".