Prenatal PM2.5 exposure and hypertensive disorders of pregnancy: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose: This study endeavors to unravel the association between PM2.5 exposure and hypertensive disorders of pregnancy (HDP) via a comprehensive review of epidemiological studies. Methods: Pertinent studies investigating the association between PM2.5 exposure and HDP were retrieved from PubMed, Embase, Web of Science, and the Cochrane Library until June 20, 2024. In addition, one article was identified through an updated search on September 1, 2025. Our study utilized the Newcastle-Ottawa Scale (NOS) and the Journal of Biomedical Informatics (JBI) scale for eligible study quality assessment. Statistical analyses were enabled by R 4.3.2 and Stata 15.1. Results: Fifteen studies were encompassed, involving 78,427 patients. The meta-analysis revealed the following rates and 95% confidence intervals (CIs): preeclampsia (PE) at 3% (2.9-3.7%); gestational hypertension (GH) at 3% (1.9-4.4%); and HDP at 11.2% (2.1-26%). For the entire pregnancy period, analysis showed a positive association between PM2.5 and PE (OR: 1.11, 95% CI: 1.06, 1.15). In different pregnancy periods, analysis revealed a positive association of PM2.5 with PE (OR: 1.02, 95% CI: 1.01, 1.03). At PM2.5 levels in the third quartile (Q3), analysis showed a positive association with PE (OR: 1.12, 95% CI: 1.07, 1.18). Similarly, at PM2.5 levels in the fourth quartile (Q4), the association was significant (OR: 1.18, 95% CI: 1.12, 1.24). For PM2.5 levels in the second quartile (Q2), a positive association with HDP prevalence was observed (OR: 1.11, 95% CI: 1.00, 1.23). Other analyses suggested that PM2.5 is a risk factor for HDP, though our results lacked statistical significance. Conclusion: Our study indicates that PM2.5 is a significant risk factor for HDP. Due to several limitations, it was anticipated that future large-scale, multicenter, prospective studies will provide further confirmation of these findings.
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 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".