Prenatal exposure to persistent organic pollutants and body mass index trajectories from birth to age 12
Bibliographic record
Abstract
Prenatal exposure to persistent organic pollutants (POPs) has been linked to growth and adiposity development in children. However, only few studies considered the growth dynamic and none have tracked children into adolescence. This study included 450 mother-child pairs from the PELAGIE cohort (France). Polychlorinated biphenyls (PCBs), Organochlorine pesticides (OCs), Polybrominated diphenyl ethers (PBDEs), and Perfluoroalkyl substances (PFAS) were measured in cord blood. A latent class growth model identified four types of BMI z-score (zBMI) trajectories from birth to 13 years. Associations between each POP and zBMI trajectories were assessed using multinomial regressions. POP mixture effects were explored using Quantile G-computation and grouped Weighted Quantile Sum regressions. All analyses were stratified by sex and adjusted for confounders. In girls, all PCBs were associated with higher odds of the "Low-High" trajectory (e.g., OR (95%CI) = 3.78 (1.58; 9.05) per doubling of PCB 153). The PCB mixture, HCB and β-HCH also tended to favor this trajectory, whereas dieldrin was associated with a lower odds. In boys, the "High-High" and "Low-High" trajectories were significantly associated with the POP and PFAS mixtures (with PFuDA, PFDA and PFOA as main contributors). Our study suggests sex-specific associations of prenatal exposure to POPs mixtures with growth dynamics until adolescence known as risk factors for adult cardiovascular diseases. Although these results need to be confirmed in larger studies, girls seem more vulnerable to prenatal exposure to PCBs while boys seem more vulnerable to prenatal exposure to PFAS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".