The Relationship Between Maternal Exposure to Endocrine-Disrupting Chemicals and the Incidence of Congenital Heart Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Congenital heart diseases are among the most common birth defects, significantly impacting infant health. Recent evidence suggests that exposure to endocrine-disrupting chemicals may contribute to the incidence of congenital heart diseases. This study systematically reviews and analyzes the association between maternal endocrine-disrupting chemicals exposure and congenital heart diseases. Methodology: This systematic review and meta-analysis followed the Cochrane Handbook and PRISMA guidelines. We included studies assessing the link between maternal exposure to various endocrine-disrupting chemicals and the incidence of congenital heart diseases without restricting the study design or exposure assessment methods. Data were extracted from four databases, including PubMed, Scopus, Web of Science, and Cochrane Library, up to June 2024. Quality assessment of observational studies was conducted using the Newcastle–Ottawa Scale. Statistical analysis was performed using RevMan software version 5.3, presenting results as odds ratios with 95% confidence intervals. Results: Fifty-nine studies were included in the meta-analysis. The pooled analysis revealed a significant association between maternal endocrine-disrupting chemical exposure and the incidence of congenital heart diseases when measured using human samples (odds ratio = 1.63, 95% confidence interval [1.35–1.97], p < 0.00001). Notably, exposure to heavy metals, polycyclic aromatic hydrocarbons, and perfluoroalkyl compounds was strongly associated with congenital heart diseases. However, non-sample-based methods showed no significant overall correlation (odds ratio = 1.08, 95% confidence interval [0.93–1.26], p = 0.30), except for housing renovation compounds, which were linked to a higher incidence of congenital heart diseases. Conclusions: Maternal exposure to specific endocrine-disrupting chemicals, particularly heavy metals and polycyclic aromatic hydrocarbons, significantly increases the risk of congenital heart diseases. These findings underscore the need for preventive measures to reduce endocrine-disrupting chemicals exposure during pregnancy and further research to elucidate the underlying mechanisms.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| 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".