Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study

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Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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MARTIN Laura Rodriguez OTTENBROS Ilse VOGEL Nina KOLOSSA-GEHRING Marike SCHMIDT Phillipp ŘIHÁČKOVÁ Katarína MIGUEL Julia Molina VAREA-JIMENEZ Elena GOVARTS Eva PEDRAZA-DIAZ Susana LEBRET Erik VLAANDEREN Jelle LUIJTEN Mirjam

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Toxics
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://www.mdpi.com/2305-6304/11/3/204
Doi http://dx.doi.org/10.3390/toxics11030204
Klíčová slova chemical mixtures; human biomonitoring; network analysis; combined exposure; clustering; mixture risk assessment; HBM4EU
Přiložené soubory
Popis 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.
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