Is prenatal arsenic exposure associated with salivary cortisol in infants in Arica, Chile? An exploratory cohort study
Bibliographic record
Abstract
INTRODUCTION: In animal models, gestational exposure to inorganic arsenic has been associated with higher corticosterone concentration and consequent impairment of stress control in offspring. An equivalent association relating cortisol, a glucocorticoid hormone, in humans has not been previously studied. OBJECTIVE: The aim of the study was to explore the association between prenatal inorganic arsenic exposure and salivary cortisol in infants from Arica, Chile. MATERIAL AND METHODS: A cohort study of 168 mother-child dyads was recruited. In the 2nd trimester of pregnancy, urinary inorganic arsenic was assessed; 18-24 months after delivery, salivary cortisol was measured in the children. Maternal cortisol, maternal depression, stress, and socio-economic status were also evaluated. RESULTS: The adjusted association was estimated with multiple linear regression after evaluating confounding through a directed acyclic graph. Median urinary inorganic arsenic in pregnant women was 14.1 µg/L (IQR: 10.4-21.7) while salivary cortisol in the children was 0.17 µg/L (IQR: 0.11-0.38). Among children from the highest income families (> 614 USD/month), arsenic exposure was associated with salivary cortisol. Children in the third quartile of arsenic exposure had -0.769 units of the logarithm of salivary cortiso, compared with those in the first quartile (p = 0.045). CONCLUSIONS: In this sample, prenatal exposure to arsenic was associated with salivary cortisol (third quartile of inorganic arsenic), only in infants belonging the highest income strata (> 614 USD). More studies are needed to confirm these preliminary results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".