Association between arsenic exposure and intrauterine growth restriction: A systematic review and meta-analysis
Bibliographic record
Abstract
Several observational studies have explored the link between arsenic (As) exposure and intrauterine growth restriction (IUGR). However, epidemiological findings have been inconsistent, with a wide range of reported heterogeneity. This study aims to systematically evaluate the association between As exposure and IUGR (SGA(Small for gestational age), PTB(Preterm birth), LBW(Preterm birth)) through a meta-analysis. We searched six databases-China National Knowledge Infrastructure, Wan Fang, VIP Database, PubMed, Web of Science, and Science Direct-for studies on As exposure and IUGR up to May 2024. After screening and data extraction, a comprehensive bias risk assessment was conducted using the Newcastle-Ottawa Scale (NOS), AHRQ (the assessment tool of the Agency for Healthcare Research and Quality), and NTP/OHAT (the assessment tool of the National Toxicology Program/Office of Health Assessment and Translation). Meta-analysis was conducted using random-effects models (I2 > 50%) or fixed-effects models (I2 < 50%) to estimate effect sizes. Subgroup analysis and meta-regression analysis were performed to identify the sources of heterogeneity. Publication bias was assessed using the Egger test, Begg test, and funnel plot. Eleven studies, including 2,183,652 participants from the Americas, Europe, Asia, and Africa, were analyzed. Results showed a significant association between As exposure and SGA (OR: 1.06, 95% CI: 1.00, 1.13), particularly in Asia (OR: 1.28, 95% CI: 1.10, 1.49). Maternal exposure to higher As levels (10-100 μg/L) was also significantly associated with SGA (OR: 1.25, 95% CI: 1.04, 1.50). Although PTB (OR: 1.03, 95% CI: 0.99, 1.07) and LBW (OR: 1.03, 95% CI: 0.97, 1.09) did not show overall significant associations, subgroup analyses revealed increased risks under specific conditions. As exposure at 1-10 μg/L significantly increased PTB risk (OR: 1.13, 95% CI: 1.06, 1.21), while exposure at 0-1 μg/L significantly increased LBW risk (OR: 1.13, 95% CI: 1.06, 1.21). This study supports a link between As exposure and increased IUGR risk, particularly SGA. Stricter public health policies are needed to reduce arsenic exposure during pregnancy. However, due to heterogeneity and potential publication bias, results should be interpreted with caution.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 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".