Associations between paediatric obesity, chemical mixtures and environmental factors, in a national cross‐sectional study of Canadian children
Bibliographic record
Abstract
BACKGROUND: Whilst single chemical exposures are suspected to be obesogenic, the combined role of chemical mixtures in paediatric obesity is not well understood. OBJECTIVES: We aimed to evaluate the potential associations between chemical mixtures and obesity in a population-based sample of Canadian children. METHODS: We ascertained biomonitoring and health data for children aged 3-11 from the cross-sectional Canadian Health Measures Survey from 2007 to 2019. Several chemicals of interest were measured in blood or urine and paediatric obesity was defined based on measured anthropometrics. Using quantile-based G computational analysis, we quantified the effects of three chemical mixtures selected a priori. Models were adjusted for sociodemographic and environmental factors identified through a directed acyclic graph. Results are presented through adjusted relative risks (RR) with 95% confidence intervals (95% CI). RESULTS: We included 9147 children. Of these, 24.1% were overweight or obese. Exposure to the mixture of bisphenol A, acrylamide, glycidamide, metals, parabens and arsenic increased the risk of childhood overweight or obesity by 45% (95% CI 1.09, 1.93), obesity by 109% (95% CI 1.27, 3.42) and central obesity by 82% (95% CI 1.30, 2.56). CONCLUSIONS: Our findings support the role of early childhood chemical exposures in paediatric obesity and the potential combined effects of chemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".