Association between sleep disorders and preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Sleep disorders are prevalent during pregnancy and are associated with unfavorable outcomes. The meta-analysis evaluated the association between sleep disturbances and preeclampsia.Methods We systematically searched in English and Persian databases, including Web of Science, Scopus, PubMed, ProQuest, Google Scholar, SID, IRANDOC, and MagIran, for studies published up to September 12, 2024. Eligibility was restricted to observational studies including cohort, case-control, and cross-sectional designs on expectant mothers diagnosed with preeclampsia and sleep disorders. The population studied comprised pregnant mothers with preeclampsia and diagnosed sleep disorders, diagnosed using polysomnography. The common sleep disorders investigated included insomnia, poor sleep quality, breathing problems, sleep apnea, and restless legs syndrome. Two authors independently reviewed and assessed the quality of the studies using the Newcastle-Ottawa Scale. Heterogeneity was evaluated using the I2 statistic. Data were analyzed using RevMan 5, presenting results as random effects odds ratios (ORs) and standardized mean differences (SMDs), each with 95% confidence intervals (CIs).Results A total of 25 articles involving 3,992 participants were included in this analysis. Subgroup analysis showed that sleep disturbances significantly increased preeclampsia risk in pregnant women (Qualitative Sleep Disorder Indices OR = 6.79, 95% CI: 3.54–13.71; Quantitative Sleep Disorder Indices SMD = 3.91, 95% CI: 2.11–5.70, p < 0.001). Although high heterogeneity was observed among studies on sleep disorders (I2 = 82%, 96%), heterogeneity was low within studies focusing on sleep duration and quality (I2 = 0%). The meta-analysis found significantly higher systolic (29.42 mmHg) and diastolic (16.67 mmHg) blood pressure, as well as increased BMI and maternal age, in the preeclampsia group compared to controls (p < 0.01).Conclusion Sleep disorders, including sleep-disordered breathing, obstructive sleep apnea, insomnia, and poor sleep quality, significantly increase the risk of developing preeclampsia. Prioritizing the diagnosis and treatment of these sleep disorders is crucial for improving pregnancy outcomes.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".