Associations of endocrine disrupting chemical biomarkers and their mixture with vitamin D biomarker concentrations in childhood: The HOME Study
Bibliographic record
Abstract
BACKGROUND: Children are universally exposed to endocrine disrupting chemicals (EDCs) which may disrupt the vitamin D system through several mechanisms, including competitive receptor binding. Current epidemiologic evidence is limited, especially in children. We cross-sectionally investigated the short-term associations of 24 EDC biomarkers with 3 vitamin D biomarkers measured at ages 8 and 12 years. METHODS: Among 236 children from the Health Outcomes and Measures of the Environment Study, we quantified serum concentrations of 4 per-/poly-fluoroalkyl substances (PFAS), 5 polybrominated diphenyl ethers (PBDEs), and 3 vitamin D biomarkers and urinary metabolites of 4 organophosphate esters (OPE), 9 phthalates/replacements, and 2 environmental phenols at ages 8 (n = 180) and 12 (n = 187) years. Using linear regression models with generalized estimating equations, we estimated cross-sectional covariate-adjusted associations of interquartile range (IQR)-scaled log2 EDCs with vitamin D biomarkers. We used g-computation models to estimate effects of class-based and overall mixtures. RESULTS: A simultaneous IQR increase in all 24 EDCs was associated with 6.6 ng/mL (95% CI: 2.7, 10.6) higher total 25-dihydroxyvitamin D [total 25(OH)D]. Class-based mixtures of PFAS (β: 3.1; 95% CI: 1.3, 5.0), PBDEs (β: 2.1; 95% CI: 0.3, 3.9) and OPEs (β: 2.6; 95% CI: 0.3, 4.8) were associated with higher total 25(OH)D whereas environmental phenols (β: 0.8; 95% CI: -0.8, 2.4) and phthalates/replacements (β: -0.8; 95% CI: -3.3, 1.8) were not. Results for 24,25-dihydroxyvitamin D were similar. The PBDE mixture was associated with 4.0 pg/mL (95% CI: 0.4, 7.6) higher 1α,25-dihydroxyvitamin D. DISCUSSION: Findings suggest that EDCs may alter the childhood vitamin D system. Associations with higher vitamin D biomarker levels may indicate competitive receptor binding and altered cellular transport with potential adverse downstream health impacts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".