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Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress
BACKGROUND: Exposure to aldehydes has been linked to adverse health outcomes such as inflammation and oxidative stress, but research on the effects of these compounds is limited. This study is aimed at assessing the association between aldehyde exposure and markers of inflammation and oxidative stre...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313561/ https://www.ncbi.nlm.nih.gov/pubmed/37286832 http://dx.doi.org/10.1007/s11356-023-27717-4 |
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author | Zang, Xiaodong Qin, Wengang Xiong, Yingying Xu, Anlan Huang, Hesuyuan Fang, Tao Zang, Xiaowei Chen, Mingwu |
author_facet | Zang, Xiaodong Qin, Wengang Xiong, Yingying Xu, Anlan Huang, Hesuyuan Fang, Tao Zang, Xiaowei Chen, Mingwu |
author_sort | Zang, Xiaodong |
collection | PubMed |
description | BACKGROUND: Exposure to aldehydes has been linked to adverse health outcomes such as inflammation and oxidative stress, but research on the effects of these compounds is limited. This study is aimed at assessing the association between aldehyde exposure and markers of inflammation and oxidative stress. METHODS: The study used data from the NHANES 2013–2014 survey (n = 766) and employed multivariate linear models to investigate the relationship between aldehyde compounds and various markers of inflammation (alkaline phosphatase (ALP) level, absolute neutrophil count (ANC), and lymphocyte count) and oxidative stress (bilirubin, albumin, and iron levels) while controlling for other relevant factors. In addition to generalized linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) analyses were applied to examine the single or overall effect of aldehyde compounds on the outcomes. RESULTS: In the multivariate linear regression model, each 1 standard deviation (SD) change in propanaldehyde and butyraldehyde was significantly associated with increases in serum iron levels (beta and 95% confidence interval, 3.25 (0.24, 6.27) and 8.40 (0.97, 15.83), respectively) and the lymphocyte count (0.10 (0.04, 0.16) and 0.18 (0.03, 0.34), respectively). In the WQS regression model, a significant association was discovered between the WQS index and both the albumin and iron levels. Furthermore, the results of the BKMR analysis showed that the overall impact of aldehyde compounds was significantly and positively correlated with the lymphocyte count, as well as the levels of albumin and iron, suggesting that these compounds may contribute to increased oxidative stress. CONCLUSIONS: This study reveals the close association between single or overall aldehyde compounds and markers of chronic inflammation and oxidative stress, which has essential guiding value for exploring the impact of environmental pollutants on population health. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-023-27717-4. |
format | Online Article Text |
id | pubmed-10313561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-103135612023-07-02 Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress Zang, Xiaodong Qin, Wengang Xiong, Yingying Xu, Anlan Huang, Hesuyuan Fang, Tao Zang, Xiaowei Chen, Mingwu Environ Sci Pollut Res Int Research Article BACKGROUND: Exposure to aldehydes has been linked to adverse health outcomes such as inflammation and oxidative stress, but research on the effects of these compounds is limited. This study is aimed at assessing the association between aldehyde exposure and markers of inflammation and oxidative stress. METHODS: The study used data from the NHANES 2013–2014 survey (n = 766) and employed multivariate linear models to investigate the relationship between aldehyde compounds and various markers of inflammation (alkaline phosphatase (ALP) level, absolute neutrophil count (ANC), and lymphocyte count) and oxidative stress (bilirubin, albumin, and iron levels) while controlling for other relevant factors. In addition to generalized linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) analyses were applied to examine the single or overall effect of aldehyde compounds on the outcomes. RESULTS: In the multivariate linear regression model, each 1 standard deviation (SD) change in propanaldehyde and butyraldehyde was significantly associated with increases in serum iron levels (beta and 95% confidence interval, 3.25 (0.24, 6.27) and 8.40 (0.97, 15.83), respectively) and the lymphocyte count (0.10 (0.04, 0.16) and 0.18 (0.03, 0.34), respectively). In the WQS regression model, a significant association was discovered between the WQS index and both the albumin and iron levels. Furthermore, the results of the BKMR analysis showed that the overall impact of aldehyde compounds was significantly and positively correlated with the lymphocyte count, as well as the levels of albumin and iron, suggesting that these compounds may contribute to increased oxidative stress. CONCLUSIONS: This study reveals the close association between single or overall aldehyde compounds and markers of chronic inflammation and oxidative stress, which has essential guiding value for exploring the impact of environmental pollutants on population health. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-023-27717-4. Springer Berlin Heidelberg 2023-06-07 2023 /pmc/articles/PMC10313561/ /pubmed/37286832 http://dx.doi.org/10.1007/s11356-023-27717-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Zang, Xiaodong Qin, Wengang Xiong, Yingying Xu, Anlan Huang, Hesuyuan Fang, Tao Zang, Xiaowei Chen, Mingwu Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title | Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title_full | Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title_fullStr | Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title_full_unstemmed | Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title_short | Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
title_sort | using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313561/ https://www.ncbi.nlm.nih.gov/pubmed/37286832 http://dx.doi.org/10.1007/s11356-023-27717-4 |
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