Effect of sleep disturbance on biomarkers related to the development of adverse health outcomes: A systematic review of the human literature
Bibliographic record
Abstract
Literature suggests that unrestricted and undisturbed sleep is vital for basic human function and performance; however, it is unclear as to what amount of sleep disturbance leads to dysregulation in biomarkers, which may underscore the development of adverse health effects. This systematic review aims to identify the amount of sleep disturbance that contributes to biomarker changes as a potential precursor to the development of adverse health effects. English-language comparative studies available in PubMed, Cochrane Central, EMBASE, and CINAHL databases from 1 January 1980 to 31 July 2021 were searched. Where possible, random-effects meta-analyses were used to examine the effect of sleep disturbances on adverse health effects. The risk of bias of individual studies was assessed using the Cochrane Risk of Bias Tool and the Risk of Bias of Nonrandomised Studies - of Exposures instruments and the certainty of the body of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. The search identified 92 primary studies reporting on blood pressure, hypertension, heart rate, cardiac arrhythmia, cardiac output, waist circumference, cortisol, adrenaline, noradrenaline, immune system markers, glucose, insulin, cholesterol, and triglyceride levels. Although some meta-analyses suggested there may be an association between sleep disturbances and certain outcomes, the certainty in the evidence was very low due to concerns with risk of bias, inconsistency across exposures, populations, and imprecision in the estimates of effects. Further research is needed to explore the point at which types, levels and duration of sleep disturbances may begin to increase the risk of developing adverse health outcomes to inform and tailor health interventions.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".