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Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators

Chronic inflammation is closely related to chronic inflammatory diseases, autoimmune diseases and cancer. Few studies have evaluated the effects of exposure to multiple chemical combinations on immunoinflammatory related indicators and their possible molecular mechanisms. This study explored the eff...

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Autores principales: Liu, Yong, Zhang, Zhihui, Han, Dongran, Zhao, Yiding, Yan, Xiaoning, Cui, Shengnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9725172/
https://www.ncbi.nlm.nih.gov/pubmed/36483254
http://dx.doi.org/10.3389/fpubh.2022.980987
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author Liu, Yong
Zhang, Zhihui
Han, Dongran
Zhao, Yiding
Yan, Xiaoning
Cui, Shengnan
author_facet Liu, Yong
Zhang, Zhihui
Han, Dongran
Zhao, Yiding
Yan, Xiaoning
Cui, Shengnan
author_sort Liu, Yong
collection PubMed
description Chronic inflammation is closely related to chronic inflammatory diseases, autoimmune diseases and cancer. Few studies have evaluated the effects of exposure to multiple chemical combinations on immunoinflammatory related indicators and their possible molecular mechanisms. This study explored the effect of exposure to various chemicals on immune-inflammatory biomarkers and its molecular mechanism. Using data from 1,723 participants in the National Health and Nutrition Examination Survey (NHANES, 2011–2012), the aim was to determine the association between chemical mixtures and immunoinflammatory biomarkers [including White blood cell (Wbc), neutrophil (Neu), lymphocytes (Lym), and Neutrophil-to-lymphocyte ratio (NLR)] using linear regression model, weighted quantile sum regression (WQSR) model, and bayesian nuclear machine regression (BKMR) model. Meanwhile, functional enrichment analysis and protein–protein interaction network establishment were performed to explore the molecular mechanism of inflammation induced by high-weight chemicals. In the linear regression model established for each single chemical, the four immunoinflammatory biomarkers were positively correlated with polycyclic aromatic hydrocarbons (PAHs), negatively correlated with perfluoroalkyl substances (PFASs), and positively or negatively correlated with metallic and non-metallic elements. WQSR model showed that cadmium (Cd), perfluorooctane sulfonic acid (PFOS) and perfluorodecanoic acid (PFDE) had the highest weights. In BKMR analysis, the overall effect of chemical mixtures was significantly associated with Lym and showed an increasing trend. The hub genes in high-weight chemicals inflammation-related genes were interleukin-6 (IL6), tumor necrosis factor (TNF), and interleukin-1B (IL1B), etc. They were mainly enriched in inflammatory response, Cytokine-cytokine receptor interaction, Th17 cell differentiation and IL-17 signaling pathway. The above results show that exposure to environmental chemical cocktails primarily promotes an increase in Lym across the immune-inflammatory spectrum. The mechanism leading to the inflammatory response may be related to the activation of IL-6 amplifier by the co-exposure of environmental chemicals.
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spelling pubmed-97251722022-12-07 Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators Liu, Yong Zhang, Zhihui Han, Dongran Zhao, Yiding Yan, Xiaoning Cui, Shengnan Front Public Health Public Health Chronic inflammation is closely related to chronic inflammatory diseases, autoimmune diseases and cancer. Few studies have evaluated the effects of exposure to multiple chemical combinations on immunoinflammatory related indicators and their possible molecular mechanisms. This study explored the effect of exposure to various chemicals on immune-inflammatory biomarkers and its molecular mechanism. Using data from 1,723 participants in the National Health and Nutrition Examination Survey (NHANES, 2011–2012), the aim was to determine the association between chemical mixtures and immunoinflammatory biomarkers [including White blood cell (Wbc), neutrophil (Neu), lymphocytes (Lym), and Neutrophil-to-lymphocyte ratio (NLR)] using linear regression model, weighted quantile sum regression (WQSR) model, and bayesian nuclear machine regression (BKMR) model. Meanwhile, functional enrichment analysis and protein–protein interaction network establishment were performed to explore the molecular mechanism of inflammation induced by high-weight chemicals. In the linear regression model established for each single chemical, the four immunoinflammatory biomarkers were positively correlated with polycyclic aromatic hydrocarbons (PAHs), negatively correlated with perfluoroalkyl substances (PFASs), and positively or negatively correlated with metallic and non-metallic elements. WQSR model showed that cadmium (Cd), perfluorooctane sulfonic acid (PFOS) and perfluorodecanoic acid (PFDE) had the highest weights. In BKMR analysis, the overall effect of chemical mixtures was significantly associated with Lym and showed an increasing trend. The hub genes in high-weight chemicals inflammation-related genes were interleukin-6 (IL6), tumor necrosis factor (TNF), and interleukin-1B (IL1B), etc. They were mainly enriched in inflammatory response, Cytokine-cytokine receptor interaction, Th17 cell differentiation and IL-17 signaling pathway. The above results show that exposure to environmental chemical cocktails primarily promotes an increase in Lym across the immune-inflammatory spectrum. The mechanism leading to the inflammatory response may be related to the activation of IL-6 amplifier by the co-exposure of environmental chemicals. Frontiers Media S.A. 2022-11-22 /pmc/articles/PMC9725172/ /pubmed/36483254 http://dx.doi.org/10.3389/fpubh.2022.980987 Text en Copyright © 2022 Liu, Zhang, Han, Zhao, Yan and Cui. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Liu, Yong
Zhang, Zhihui
Han, Dongran
Zhao, Yiding
Yan, Xiaoning
Cui, Shengnan
Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title_full Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title_fullStr Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title_full_unstemmed Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title_short Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
title_sort association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9725172/
https://www.ncbi.nlm.nih.gov/pubmed/36483254
http://dx.doi.org/10.3389/fpubh.2022.980987
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