Paternal Lead Exposure and Pregnancy Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Paternal lead exposure has emerged as a potential contributor to adverse pregnancy outcomes, yet its impact remains underexplored compared to maternal exposure. This systematic review and meta-analysis synthesize evidence on the association between paternal lead exposure and pregnancy outcomes to inform public health interventions and future research. To evaluate the association between paternal lead exposure and adverse pregnancy outcomes, including spontaneous abortion, low birth weight, preterm birth, small-for-gestational-age, and congenital anomalies. A systematic search of PubMed, Scopus, and Google Scholar was conducted up to August 2024. Observational studies examining paternal lead exposure (⩾15 µg/dL) and its effects on pregnancy outcomes were included. Data synthesis adhered to PRISMA 2020 guidelines, and study quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using a random-effects model to compute pooled odds ratios (ORs) with 95% confidence intervals (CIs). Eleven studies were included in the systematic review, with 7 contributing to the meta-analysis. The pooled OR for congenital anomalies associated with paternal lead exposure was statistically significant (OR = 2.09, 95% CI: 2.09-3.35; P < .01), indicating a strong association. However, no significant associations were observed for other outcomes: spontaneous abortion (OR = 1.11, 95% CI: 0.75-1.64), low birth weight (OR = 0.98, 95% CI: 0.68-1.39), preterm birth (OR = 1.57, 95% CI: 0.61-4.05), and small-for-gestational-age infants (OR = 0.92, 95% CI: 0.78-1.09). Heterogeneity was low for most outcomes, except for spontaneous abortion ( I 2 = 39%) and preterm birth ( I 2 = 52%). This study highlights a significant association between paternal lead exposure and congenital anomalies, emphasizing the need for occupational and environmental regulations targeting lead exposure among men of reproductive age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".