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Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human popu...

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Autores principales: Loh, Miranda M., Schmidt, Phillipp, Christopher de Vries, Yvette, Vogel, Nina, Kolossa-Gehring, Marike, Vlaanderen, Jelle, Lebret, Erik, Luijten, Mirjam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223739/
https://www.ncbi.nlm.nih.gov/pubmed/37235224
http://dx.doi.org/10.3390/toxics11050408
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author Loh, Miranda M.
Schmidt, Phillipp
Christopher de Vries, Yvette
Vogel, Nina
Kolossa-Gehring, Marike
Vlaanderen, Jelle
Lebret, Erik
Luijten, Mirjam
author_facet Loh, Miranda M.
Schmidt, Phillipp
Christopher de Vries, Yvette
Vogel, Nina
Kolossa-Gehring, Marike
Vlaanderen, Jelle
Lebret, Erik
Luijten, Mirjam
author_sort Loh, Miranda M.
collection PubMed
description Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as ‘communities’, reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept.
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spelling pubmed-102237392023-05-28 Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept Loh, Miranda M. Schmidt, Phillipp Christopher de Vries, Yvette Vogel, Nina Kolossa-Gehring, Marike Vlaanderen, Jelle Lebret, Erik Luijten, Mirjam Toxics Article Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as ‘communities’, reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept. MDPI 2023-04-26 /pmc/articles/PMC10223739/ /pubmed/37235224 http://dx.doi.org/10.3390/toxics11050408 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
Loh, Miranda M.
Schmidt, Phillipp
Christopher de Vries, Yvette
Vogel, Nina
Kolossa-Gehring, Marike
Vlaanderen, Jelle
Lebret, Erik
Luijten, Mirjam
Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title_full Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title_fullStr Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title_full_unstemmed Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title_short Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept
title_sort toxicity weighting for human biomonitoring mixture risk assessment: a proof of concept
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223739/
https://www.ncbi.nlm.nih.gov/pubmed/37235224
http://dx.doi.org/10.3390/toxics11050408
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