Sleep Disturbances and Metabolic Syndrome in Shift Workers: A Systematic Review
Bibliographic record
Abstract
Background: Poor sleep has been identified as a strong risk factor for metabolic syndrome. Shift workers, who often experience reduced and misaligned sleep due to nighttime work schedules, are particularly susceptible to both sleep disturbances and metabolic syndrome. However, the interplay among shift work, sleep disturbances, and metabolic syndrome remains insufficiently explored. This systematic review aimed to critically appraise, compare, and synthesize the current evidence on the pathways linking these factors. Methods: A comprehensive literature search was conducted across major electronic databases and peer-reviewed journals specializing in metabolic disorders and sleep disorders. Two independent reviewers screened titles, abstracts, and full texts for relevance. Methodological quality was assessed using the Newcastle–Ottawa Scale. Results: Out of 4,982 studies identified, 15 met the predefined inclusion criteria, encompassing diverse occupational groups with fixed and rotating shift patterns and totaling 37,147 participants. Most studies demonstrated a positive association between shift work and sleep disturbances, particularly among fixed night shift workers. Longer durations of night shift exposure were linked to increased risk of metabolic syndrome. Notably, reduced sleep quantity was more strongly associated with metabolic syndrome than impaired sleep quality. The methodological quality of the included studies was moderate to high. Conclusion: This review highlights a consistent association between shift work, sleep disturbances, and metabolic syndrome. Shift work appears to impact both sleep health and metabolic outcomes independently. These findings underscore the need for targeted interventions and longitudinal studies to further elucidate causal pathways and inform occupational health strategies.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".