Associations of Maternal Urinary Arsenic Concentrations during Pregnancy with Infant and Child Mental, Psychomotor, and Cognitive Development: The HOME Study
Bibliographic record
Abstract
Background and aims Arsenic exposure during pregnancy may increase the risk for intellectual deficits in children, but limited data exists from prospective epidemiologic studies, particularly at low arsenic exposure levels. We investigated the association between prenatal urinary arsenic concentrations and childhood cognitive abilities in the Health Outcomes and Measures of the Environment (HOME) Study. Methods We used anion exchange chromatography coupled with inductively coupled plasma mass spectrometry detection to measure arsenic species content in pregnant women’s urine. The summation of inorganic arsenic (iAs), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) refers to ∑As. We longitudinally assessed children’s cognitive function (n = 260) longitudinally at 1, 2, and 3 years using Bayley Scales of Infant and Toddler Development, at 5 years using Wechsler Preschool and Primary Scale of Intelligence, and at 8 years using Wechsler Intelligence Scale for Children. Results We observed a modest decrease in mental development index and full-scale intelligence quotient at ages 3 and 5 years with each doubling of ∑As (median and IQR of 3.63 and 2.40-5.86 µg/L) with estimated score (ß) differences and 95% confidence interval (CI) of -1.8 from -4.1 to 0.5 and -2.5 from -5.1 to 0.0, respectively. This trend was stronger among children whose mothers had lower iAs methylation capacity and low urinary arsebonetaine concentrations. Conclusions Our findings suggest that exposure levels relevant to the general US population may adversely affect children’s cognitive abilities. Keywords arsenic; neurodevelopment; cognitive; Bayley Scale of Infant Development; Wechsler Preschool and Primary Scale of Intelligence; in utero exposure; Mental development index; Full scale intelligence quotient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".