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Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study
Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one p...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058482/ https://www.ncbi.nlm.nih.gov/pubmed/36976969 http://dx.doi.org/10.3390/toxics11030204 |
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author | Rodriguez Martin, Laura Ottenbros, Ilse Vogel, Nina Kolossa-Gehring, Marike Schmidt, Phillipp Řiháčková, Katarína Juliá Molina, Miguel Varea-Jiménez, Elena Govarts, Eva Pedraza-Diaz, Susana Lebret, Erik Vlaanderen, Jelle Luijten, Mirjam |
author_facet | Rodriguez Martin, Laura Ottenbros, Ilse Vogel, Nina Kolossa-Gehring, Marike Schmidt, Phillipp Řiháčková, Katarína Juliá Molina, Miguel Varea-Jiménez, Elena Govarts, Eva Pedraza-Diaz, Susana Lebret, Erik Vlaanderen, Jelle Luijten, Mirjam |
author_sort | Rodriguez Martin, Laura |
collection | PubMed |
description | Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called “communities”, within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments. |
format | Online Article Text |
id | pubmed-10058482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100584822023-03-30 Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study Rodriguez Martin, Laura Ottenbros, Ilse Vogel, Nina Kolossa-Gehring, Marike Schmidt, Phillipp Řiháčková, Katarína Juliá Molina, Miguel Varea-Jiménez, Elena Govarts, Eva Pedraza-Diaz, Susana Lebret, Erik Vlaanderen, Jelle Luijten, Mirjam Toxics Article Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called “communities”, within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments. MDPI 2023-02-22 /pmc/articles/PMC10058482/ /pubmed/36976969 http://dx.doi.org/10.3390/toxics11030204 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rodriguez Martin, Laura Ottenbros, Ilse Vogel, Nina Kolossa-Gehring, Marike Schmidt, Phillipp Řiháčková, Katarína Juliá Molina, Miguel Varea-Jiménez, Elena Govarts, Eva Pedraza-Diaz, Susana Lebret, Erik Vlaanderen, Jelle Luijten, Mirjam Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title | Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title_full | Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title_fullStr | Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title_full_unstemmed | Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title_short | Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study |
title_sort | identification of real-life mixtures using human biomonitoring data: a proof of concept study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058482/ https://www.ncbi.nlm.nih.gov/pubmed/36976969 http://dx.doi.org/10.3390/toxics11030204 |
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