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.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".