Association among blood BPDE-DNA adduct, serum interleukin-8 (IL-8) and DNA strand breaks for children with pulmonary diseases

Interactions between Benzo(a)pyrene exposure and genetic polymorphisms of AhR signaling pathway on missed abortion

Benzo(a)pyrene (BaP) is an environmental pollutant widely exposed to human beings. While the relationship between BaP and missed abortion is few understood. To explore the association between missed abortion and BaP, genetic polymorphisms of AhR pathway, we recruited 112 cases women with missed abortion and 137 controls women with normal pregnancy from Shanxi, China. The BPDE-DNA adducts level in the case group was higher than that in the control group (P < 0.001). The subjects were categorized according to the tertiles of BPDE-DNA adduct concentrations: T1 ( Collapse

Key Words

• AhR signaling pathway
• BaP
• genetic polymorphisms
• missed abortion

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MESH Headings

• Pregnancy
• Humans
• Female
• DNA Adducts
• Benzo(a)pyrene/toxicity
• 7,8-Dihydro-7,8-dihydroxybenzo(a)pyrene 9,10-oxide/metabolism
• Abortion, Missed
• Signal Transduction
• Polymorphism, Genetic

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Affiliation(s)

Sha Liu Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
Mei Han Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
Jiayu Zhang Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
Jingru Ji Department of Obstetrics, The First Affiliated Hospital, Shanxi Medical University, Taiyuan, Shanxi, China
Yanfei Wu Department of Obstetrics, The First Affiliated Hospital, Shanxi Medical University, Taiyuan, Shanxi, China Department of Chinese Medicine, The First Affiliated Hospital, Shanxi Medical University, Taiyuan, Shanxi, China
Junni Wei Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China

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Construction of Models To Predict the Effectiveness of E-Waste Control through Capture of Volatile Organic Compounds and Metals/Metalloids Exposure Fingerprints: A Six-Year Longitudinal Study

The significant health implications of e-waste toxicants have triggered the global tightening of regulation on informal e-waste recycling sites (ER) but with disparate governance that requires effective monitoring. Taking advantage of the opportunity to implement e-waste control in the Guiyu ER since 2015, we investigated the temporal variations in levels of oxidative DNA damage, 25 volatile organic compound metabolites (VOCs), and 16 metals/metalloids (MeTs) in urine in 918 children between 2016 and 2021 to demonstrate the effectiveness of e-waste control in reducing population exposure risks. The hazard quotients of most MeTs and levels of 8-hydroxy-2'-deoxyguanosine in children decreased significantly during this time, indicating that e-waste control effectively reduces the noncarcinogenic risks of MeT exposure and levels of oxidative DNA damage. Using mVOC-derived indexes as a feature, a bagging-support vector machine algorithm-based machine learning model was constructed to predict the extent of e-waste pollution (EWP). The model exhibited excellent performance with accuracies >97.0% in differentiating between slight and severe EWP. Five simple functions established using mVOC-derived indexes also had high accuracy in predicting the presence of EWP. These models and functions provide a novel human exposure monitoring-based approach for assessing e-waste governance or the presence of EWP in other ERs.

A review of statistical strategies to integrate biomarkers of chemical exposure with biomarkers of effect applied in omic-scale environmental epidemiology

Humans are exposed to a growing list of synthetic chemicals, some of them becoming a major public health concern due to their capacity to impact multiple biological endpoints and contribute to a range of chronic diseases. The integration of endogenous (omic) biomarkers of effect in environmental health studies has been growing during the last decade, aiming to gain insight on the potential mechanisms linking the exposures and the clinical conditions. The emergence of high-throughput omic platforms has raised a list of statistical challenges posed by the large dimension and complexity of data generated. Thus, the aim of the present study was to critically review the current state-of-the-science about statistical approaches used to integrate endogenous biomarkers in environmental-health studies linking chemical exposures with health outcomes. The present review specifically focused on internal exposure to environmental chemical pollutants, involving both persistent organic pollutants (POPs), non-persistent pollutants like phthalates or bisphenols, and metals. We identified 42 eligible articles published since 2016, reporting 48 different statistical workflows, mostly focused on POPs and using metabolomic profiling in the intermediate layer. The outcomes were mainly binary and focused on metabolic disorders. A large diversity of statistical strategies were reported to integrate chemical mixtures and endogenous biomarkers to characterize their associations with health conditions. Multivariate regression models were the most predominant statistical method reported in the published workflows, however some studies applied latent based methods or multipollutant models to overcome the specific constraints of omic or exposure of data. A minority of studies used formal mediation analysis to characterize the indirect effects mediated by the endogenous biomarkers. The principles of each specific statistical method and overall workflow set-up are summarized in the light of highlighting their applicability, strengths and weaknesses or interpretability to gain insight into the causal structures underlying the triad: exposure, effect-biomarker and outcome.