Sleep recovery alleviates impaired glucose tolerance induced by sleep fragmentation possibly through gut microbiota in mice
Bibliographic record
Abstract
Sleep disturbance is increasingly common and has been linked to adverse metabolic outcomes. This study investigated whether sleep recovery (SR) mitigates the effects of chronic sleep fragmentation (SF) on glucose metabolism, with a focus on gut microbiota and inguinal white adipose tissue (iWAT) transcriptomics. Mice were subjected to 8 weeks of SF followed by SR. After 2 weeks of SR (SF 8w-SR 2w), glucose intolerance persisted, accompanied by significant alterations in gut microbiota composition and iWAT gene expression. Key hub genes (Ncapg, Cenpe, Ttk) and glucose metabolism-related genes (Lnpep, Pten, Apoe, Cebpb, Ido1, Ahsg) were identified. Bacterial genera were significantly altered and associated with glucose metabolism. After 8 weeks of SR (SF 8w-SR 8w), glucose tolerance was restored, although alterations in gut microbiota composition persisted. Notably, Rikenellaceae_RC9_gut_group and Defluviitaleaceae_UCG-011 remained persistently altered. These findings indicate that short-term SR is insufficient to reverse SF-induced glucose intolerance, which is associated with changes in the gut microbiota and iWAT transcriptome. Although prolonged SR improves glucose metabolism, persistent microbial alterations suggest a lasting impact of SF, underscoring the potential role of gut dysbiosis in metabolic dysfunction following sleep disturbances.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".