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Record W4407286041 · doi:10.2196/62861

Long-Term Exposure to Fine Particulate Matter (PM<sub>2.5</sub>) Components and Precocious Puberty Among School-Aged Children: Cross-Sectional Study

2025· article· en· W4407286041 on OpenAlexvenueno aff
Xuelian Zhou, Xiaochi Zhang, Guannan Bai, Guanping Dong, X. Li, Ruimin Chen, Rongxiu Zheng, Chun Lin Wang, Haiyan Wei, Bingyan Cao, Yan Liang, Hui Yao, Zhe Su, Mireguli Maimaiti, Feihong Luo, Pin Li, Min Zhu, Hongwei Du, Yu Yang, Jinling Wang, Jinna Yuan, Wei Wu, Qi Zhao, Junfen Fu

Bibliographic record

VenueJMIR Public Health and Surveillance · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPrecocious pubertyEnvironmental healthCross-sectional studyMedicinePopulationDemographyBody mass indexPediatricsEndocrinologyHormone

Abstract

fetched live from OpenAlex

Background: The increasing incidence of precocious puberty is a major health challenge for Chinese children, while related risk factors remain less well explored. Exposure to ambient fine particulate matter (PM2.5) is a leading environmental hazard in China. Although certain components of PM2.5 have been reported to be endocrine disruptors for sex hormones, population-based evidence is still lacking on the association between PM2.5 exposure and precocious puberty in China. Objective: Based on a cross-sectional survey covering 30 cities in 2017 to 2019, this study was designed to explore the association between long-term exposure to PM2.5 and its 5 major components with precocious puberty in China and to check the potential modifying effects of family-related and personal factors. Methods: We included 34,105 children aged 6 to 9 years. We collected the 5-year average concentrations of PM2.5 and its 5 major components (sulfate, nitrate, ammonium, organic matter, and black carbon) in the area (at a spatial resolution of 0.1° × 0.1°) where each school was located. We used mixed effect logistic regression to estimate the effect sizes of the total mass of PM2.5 and each of its components on precocious puberty, and we examined the modifying effects of family-related and personal factors using an additional interactive term. A weighted quantile sum (WQS) regression model was applied to identify the weights of each component in explaining the effect size of the total mass of PM2.5. Results: We found that the odds ratio (OR) for precocious puberty per IQR increase in the concentration of total PM2.5 mass was 1.27 (95% CI 0.92-1.75) for the whole population, 2.12 (95% CI 1.27-3.55) for girls, and 0.90 (95% CI 0.62-1.30) for boys. Similarly, the effect sizes of the 5 major components were all substantial for girls but minimal for boys. Results of the WQS analysis showed that organic matter could explain the highest proportion of the effect of PM2.5, with the weight of its contribution being 0.71. Modification effects of family income and dietary habits were only observed in certain population subgroups. Conclusions: Long-term exposure to total PM2.5 mass was significantly associated with precocious puberty in girls, with organic matter identified as the major effect contributor. The results add evidence on the detrimental effects of PM2.5 on children's development and growth.

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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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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