Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques
Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Indigenous (First Nations) region from Quebec, Canada (n=526). Using principa...
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ftdoajarticles:oai:doaj.org/article:94b95607788241189018b226858ae38b 2023-05-15T16:17:07+02:00 Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques Eric N. Liberda Aleksandra M. Zuk David S. Di Robert J. Moriarity Ian D. Martin Leonard J.S. Tsuji 2021-07-01T00:00:00Z https://doi.org/10.1016/j.envadv.2021.100054 https://doaj.org/article/94b95607788241189018b226858ae38b EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2666765721000259 https://doaj.org/toc/2666-7657 2666-7657 doi:10.1016/j.envadv.2021.100054 https://doaj.org/article/94b95607788241189018b226858ae38b Environmental Advances, Vol 4, Iss , Pp 100054- (2021) Thyroid Machine learning Contaminants Indigenous Exposure BKMR Environmental sciences GE1-350 article 2021 ftdoajarticles https://doi.org/10.1016/j.envadv.2021.100054 2022-12-31T06:31:44Z Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Indigenous (First Nations) region from Quebec, Canada (n=526). Using principal component analysis (PCA) to reduce the number of variables, we generated varimax rotated principal component (PC) loadings of contaminants on these uncorrelated synthetic axes. Associations with levels of thyroid hormones (TSH, free T4, and total T3) were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for covariates. Additionally, Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). No significant observations were observed for free T4. BKMR provided additional insight into the PCA results and suggested that nickel, and not cadmium, was responsible for driving the observed effects with this effect remaining when evaluating the entire mixture. BKMR analysis did not support the association of TSH with organic contaminants that were found in the PCA regression. Our findings reinforced other studies which showed that metals such as nickel may alter thyroid hormone levels and highlighted how complex environmental mixtures interact with each other. These observations represent an important step to determining how complex mixtures of contaminants can be assessed in human populations, especially those living a subsistence lifestyle who may have high body burdens of contaminants, and to help understand the resultant net effect of exposures on ... Article in Journal/Newspaper First Nations Directory of Open Access Journals: DOAJ Articles Canada Environmental Advances 4 100054 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Thyroid Machine learning Contaminants Indigenous Exposure BKMR Environmental sciences GE1-350 |
spellingShingle |
Thyroid Machine learning Contaminants Indigenous Exposure BKMR Environmental sciences GE1-350 Eric N. Liberda Aleksandra M. Zuk David S. Di Robert J. Moriarity Ian D. Martin Leonard J.S. Tsuji Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
topic_facet |
Thyroid Machine learning Contaminants Indigenous Exposure BKMR Environmental sciences GE1-350 |
description |
Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Indigenous (First Nations) region from Quebec, Canada (n=526). Using principal component analysis (PCA) to reduce the number of variables, we generated varimax rotated principal component (PC) loadings of contaminants on these uncorrelated synthetic axes. Associations with levels of thyroid hormones (TSH, free T4, and total T3) were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for covariates. Additionally, Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). No significant observations were observed for free T4. BKMR provided additional insight into the PCA results and suggested that nickel, and not cadmium, was responsible for driving the observed effects with this effect remaining when evaluating the entire mixture. BKMR analysis did not support the association of TSH with organic contaminants that were found in the PCA regression. Our findings reinforced other studies which showed that metals such as nickel may alter thyroid hormone levels and highlighted how complex environmental mixtures interact with each other. These observations represent an important step to determining how complex mixtures of contaminants can be assessed in human populations, especially those living a subsistence lifestyle who may have high body burdens of contaminants, and to help understand the resultant net effect of exposures on ... |
format |
Article in Journal/Newspaper |
author |
Eric N. Liberda Aleksandra M. Zuk David S. Di Robert J. Moriarity Ian D. Martin Leonard J.S. Tsuji |
author_facet |
Eric N. Liberda Aleksandra M. Zuk David S. Di Robert J. Moriarity Ian D. Martin Leonard J.S. Tsuji |
author_sort |
Eric N. Liberda |
title |
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
title_short |
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
title_full |
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
title_fullStr |
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
title_full_unstemmed |
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
title_sort |
complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doi.org/10.1016/j.envadv.2021.100054 https://doaj.org/article/94b95607788241189018b226858ae38b |
geographic |
Canada |
geographic_facet |
Canada |
genre |
First Nations |
genre_facet |
First Nations |
op_source |
Environmental Advances, Vol 4, Iss , Pp 100054- (2021) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2666765721000259 https://doaj.org/toc/2666-7657 2666-7657 doi:10.1016/j.envadv.2021.100054 https://doaj.org/article/94b95607788241189018b226858ae38b |
op_doi |
https://doi.org/10.1016/j.envadv.2021.100054 |
container_title |
Environmental Advances |
container_volume |
4 |
container_start_page |
100054 |
_version_ |
1766002956124028928 |