Sleep extension increases IGF-I concentrations before and during sleep deprivation in healthy young men
Bibliographic record
Abstract
Sleep deprivation is known to suppress circulating trophic factors such as insulin-like growth factor (IGF)-I and brain-derived neurotrophic factor (BDNF). This experiment examined the effect of an intervention involving 6 nights of extended sleep before total sleep deprivation on this catabolic profile. In a randomized crossover design, 14 young men (age range: 26-37 years) were either in an extended (EXT; time in bed: 2100-0700 h) or habitual (HAB: 2230-0700 h) sleep condition, followed by 3 days in the laboratory with blood sampling at baseline (B), after 24 h of sleep deprivation (24h-SD), and after 1 night of recovery sleep (R). In the EXT condition compared with the HAB condition, free IGF-I levels were significantly higher at B, 24h-SD, and R (P < 0.001), and those of total IGF-I at B and 24h-SD (P < 0.05). EXT did not influence growth hormone, IGF binding protein 3, BDNF, insulin, and glucose levels. The only effect of 24 h of sleep deprivation was for insulin levels, which were significantly higher after R compared with B. In a healthy adult, additional sleep over 1 week increased blood concentrations of the anabolic factor IGF-I before and during 24 h of sleep deprivation and after the subsequent recovery night without effects on BDNF. With further research, these findings may prove to be important in guiding effective lifestyle modifications to limit physical or cognitive deficits associated with IGF-I decrease with age.
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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 | 0.000 |
| 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".