Inflammation, Oxidative Stress, and Antioxidant Micronutrients as Mediators of the Relationship Between Sleep, Insulin Sensitivity, and Glycosylated Hemoglobin
Bibliographic record
Abstract
Background Sleep deprivation and poor sleep quality contribute to increases in oxidative stress, antioxidant imbalance, and a pro-inflammatory state which may predispose to a higher risk of diabetes. Our objective was to estimate the contributions of C-reactive protein (CRP), gamma glutamyl transferase (GGT), and micronutrient antioxidants (bilirubin, carotenoids, uric acid, vitamins A, C–E?) to the relationships between sleep-fasting insulin concentration and -glycosylated hemoglobin (HbA1c). Methods Data from the 2005/06 US National Health and Nutritional Examination Survey were used ( N = 1,946; 20 y+). Sleep quality and quantity was assessed by the Sleep Disorders Questionnaire, and fasting blood was collected to quantify CRP, GGT, antioxidant micronutrients, insulin concentration, and HbA1c. The bootstrap method was used to estimate the amount of mediation or contribution of these mediators to the sleep–insulin concentration and -HbA1c relationships, which were quantified as large (≥0.25) or moderate (≥0.09). Results The sleep duration –fasting insulin relationship was mediated by GGT, carotenoids, uric acid, and vitamins C and D, whereas CRP and bilirubin were non-significant mediators of a moderate effect size. Similarly, the sleep quality –fasting insulin relationship was mediated by CRP, bilirubin and vitamin C, whereas GGT, carotenoids, uric acid, and vitamin D were non-significant large-to-moderate mediators. To a lesser degree, these micronutrients mediated for the relationship between sleep-HbA1c levels. Conclusion Several factors related to inflammation, oxidative stress, and antioxidant status were found to lie on the pathway of the sleep–insulin and –glycemic control relationships. Sleep hygiene, reduced systemic inflammation/oxidative stress, and optimal antioxidants intake are potentially beneficial targets for managing diabetes risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".