Exposure to Metal Mixtures and Childhood Adiposity: An Examination of Periods of Heightened Susceptibility Between Gestation and Late Childhood
Bibliographic record
Abstract
INTRODUCTION: Childhood obesity is a public health concern. Studies have investigated the effects of metal mixtures on childhood obesity but none have identified periods of heightened susceptibility of exposure. We identified the periods by investigating the association of metal mixture, measured at four time points, with adiposity. MATERIALS AND METHODS: Using data from the Maternal-Infant Research on Environmental Chemicals Research Platform, we included 234 child-parent pairs. We measured whole blood metal concentrations during the first and third trimesters, early and late childhood. Outcomes were late childhood body mass index z-score (zBMI), body fat percentage (%BF) and waist circumference z-score (zWC). We used treed distributed lag mixture models (TDLMM) to investigate associations between metal mixture and adiposity. We also investigated associations using linear regression and conducted sex-specific analysis. RESULTS: Among females, arsenic was positively associated with zBMI and zWC. Regression results show that each doubling in third trimester arsenic concentrations was associated with 0.16 (95% CI: 0.02, 0.31) and 0.13 (95% CI: 0.01, 0.25) increase in zBMI and zWC, respectively. TDLMM results were similar but attenuated. We also observed negative associations between third trimester cadmium and zWC, null associations between other metals and adiposity and among males and no metal interactions. CONCLUSION: Third trimester is a period of heightened susceptibility to obesogenic effects of arsenic exposure in females.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".