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Record W4416439073 · doi:10.1016/j.envres.2025.123316

Associations of endocrine disrupting chemical biomarkers and their mixture with vitamin D biomarker concentrations in childhood: The HOME Study

2025· article· en· W4416439073 on OpenAlexaff
Katherine M. Marquess, Jordan R. Kuiper, Taylor Etzel, Bruce P. Lanphear, Andrew N. Hoofnagle, Antonia M. Calafat, Julianne Cook Botelho, María Ospina, Andreas Sjödin, Lesliam Quirós-Alcalá, Kim M. Cecil, Aimin Chen, Yingying Xu, Kimberly Yolton, Heidi J. Kalkwarf, Joseph M. Braun, Jessie P. Buckley

Bibliographic record

VenueEnvironmental Research · 2025
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsSimon Fraser University
FundersNational Institute of Environmental Health SciencesJohns Hopkins Bloomberg School of Public HealthNational Institutes of Health
KeywordsVitamin D and neurologyBiomarkerEndocrine systemvitamin D deficiencyVitaminCalcitriol receptorAdverse effect

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.345
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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