The effects of chemical mixtures on lipid profiles in the Korean adult population: threshold and molecular mechanisms for dyslipidemia involved

A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals,...

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Bibliographic Details
Published in:Environmental science and pollution research international 2022-06, Vol.29 (26), p.39182-39208
Main Authors: Nguyen, Hai Duc, Oh, Hojin, Kim, Min-Sun
Format: Article
Language:English
Subjects:
Adults
Apoptosis
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Bayesian analysis
Biological activity
Chemicals
Cholesterol
Data mining
Dyslipidemia
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Estrogen receptors
Estrogens
Heavy metals
High density lipoprotein
Homeostasis
Lipids
Low density lipoprotein
Mathematical models
Metabolic disorders
Metabolic syndrome
Metabolites
Mixtures
Molecular modelling
Organic compounds
Quartiles
Regression analysis
Regression models
Research Article
Sequestering
Triglycerides
VOCs
Volatile organic compounds
Waste Water Technology
Water Management
Water Pollution Control
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Summary:A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals, and lipid profiles among Korean adults (aged ≥ 18) using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). In silico toxicogenomic data-mining, we assessed molecular mechanisms linked with dyslipidemia, including genes, miRNAs, pathways, biological processes, and diseases. In the linear regression models, heavy metals, volatile organic compound metabolites, and phthalate metabolites were found to be related to HDL-C, triglycerides, LDL-C, total lipids, and total cholesterol, and significant trends were observed for these chemical quartiles ( p  
ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-022-18871-2