The Global Prevalence of Metabolic Syndrome in Connection with Sleep Duration: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The duration and quality of sleep are believed to significantly influence the onset of metabolic syndrome (MetS), but existing data lack consistency. The meta-analysis aims to evaluate the prevalence of the MetS in association with sleep duration. We conducted a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines. A comprehensive search of PubMed, Google Scholar, and Research Gate databases was performed to identify cohort and cross-sectional studies published in English between 2013 and 2023. We included studies that examined the association between sleep duration/quality and MetS, and two independent reviewers assessed study quality and bias using the Newcastle-Ottawa scale. The systematic review included 11 studies with a total of 343,669 participants, including 4 cohort studies and 7 cross-sectional studies. Sample sizes varied widely, ranging from 293 to 162,121 individuals. The studies had different follow-up periods, participant ages ranging from 10 to 80 years, and predominantly male populations. The prevalence of MetS was higher in average sleepers [52%, 95% confidence interval (CI): 40%-64%] compared with short sleepers (13%, 95% CI: 8%-18%) and long sleepers (15%, 95% CI: 9%-24%). Globally, North American countries exhibit the highest prevalence of MetS across short- (25.3%, 95% CI: 4.2%-72.4%) and long-sleeper (22.4%, 95% CI: 2.8%-74.1%) categories, whereas Asian countries experience the highest prevalence among the average sleeper category (58.7%, 95% CI: 44.1%-71.9%). Our meta-analysis indicates an elevated prevalence of MetS in average sleepers. Future research endeavors address delve into the underlying mechanisms and incorporate objective measures to understand the multifaceted connection between sleep patterns and MetS, guiding more effective preventive and management 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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".