Association between maternal exposure to environmental endocrine disruptors and the risk of congenital heart diseases in offspring: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Congenital heart disease (CHD) is the most common type of congenital malformation and the leading cause of death in newborns. Some observational studies have investigated the relationship between exposure to environmental endocrine disruptors (EEDs) and CHD in pregnant women. However, the findings of epidemiological studies in different countries and regions remain controversial and exhibit significant variations. This meta-analysis aimed to explore the relationship between exposure to EEDs and CHD in pregnant women, hoping to provide some insights into related research in different regions and further demonstrate the relationship between the two. Three databases (PubMed, Embase, and Web of Science) were searched, and 17 studies with 1373 117 participants were selected, including 3 on polycyclic aromatic hydrocarbons (PAHs), 5 on pesticides/insecticides, 4 on phthalates, 4 on alkylphenolic compounds, and 7 on heavy metals. The Newcastle–Ottawa Scale was used to evaluate the quality of the studies. Begg’s and Egger’s tests were used to determine the publication bias of the studies, and the I 2 statistics to evaluate the statistical heterogeneity among the studies. The adjusted estimates were pooled using the random-effects and fixed-effects models to explore the association between EEDs and CHD and its subtypes. Maternal exposure to PAHs [odds ratio (OR) = 1.34, 95% confidence interval (CI): 1.17–1.53)] (e.g. PAHs and tetralogy of Fallot, septal defects, and conotruncal defects)], pesticides/insecticides (OR = 1.32, 95% CI: 1.20–1.46), alkylphenolic compounds (OR = 1.46, 95% CI: 1.14–1.86), and heavy metals (arsenic, cadmium, mercury, and lead) (OR = 2.09, 95% CI: 1.53–2.86) during pregnancy was positively associated with CHD in offspring. This study found that exposure to EEDs in pregnant women was positively associated with CHD in offspring. These findings are of great significance for researchers to further study the relationship between the two.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".