Prenatal and Early Postnatal Dentine Mn, Zn and Pb and Childhood Behavior
Bibliographic record
Abstract
Background: Metal exposure alters neurodevelopmental outcomes; little is known about critical windows of susceptibility when exposure exerts the strongest effect.Objective: To examine associations between prenatal and early postnatal manganese (Mn), zinc (Zn) and lead (Pb) and childhood behavior.Methods: 153 subjects enrolled in a Mexico City birth cohort study provided deciduous teeth. We estimated weekly prenatal and postnatal dentine Mn, Zn and Pb concentrations in teeth using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICPMS) and measured behavior at ages 6-16 years using the Behavior Assessment System for Children, 2nd edition (BASC-2). We used distributed lag models and lagged weighted quantile sum regression to identify the role of individual and mixed metals on behavioral symptoms controlling for maternal education and gestational age.Results: Prenatal dentine Mn appears protective against behavioral problems, specifically hyperactivity and attention. Postnatal dentine Mn is associated with increased internalizing problems, specifically anxiety. At 6 months (mo), a 1-unit (unit = 1SD of log concentration) increase in Mn associated with a 0.18-unit (unit =1SD of BASC-2 score) and 0.25-unit increase in the BASC-2 anxiety score, respectively. Postnatal Pb is associated with higher anxiety symptoms. At 12 mo, a 1-unit increase in Pb is associated with a 0.4-unit increase in anxiety. Examined as a mixture, we observe two windows of susceptibility to increased anxious symptoms: the first window (0-8 mo) is driven by Mn, the second window (8-12 mo) is driven by the mixture and dominated by Pb. A 1-unit increase in the mixture is associated with a 0.7-unit increase in SD of anxiety score.Conclusions: Prenatal dentine Mn may be protective, while postnatal Mn may increase risk for adverse behaviors. In combination, Mn, Zn and Pb may have an adverse impact on behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".