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Publikationstyp
Wissenschaftlicher Artikel
Erscheinungsjahr
2023
Identification of real-life mixtures using human biomonitoring data: a proof of concept study
Identification of real-life mixtures using human biomonitoring data: a proof of concept study
Autor:innen
Herausgeber
Quelle
Toxics
11 (2023), Heft 3
11 (2023), Heft 3
Schlagwörter
Human-Biomonitoring, Netzplantechnik
Zitation
MARTIN, Laura Rodriguez, Marike KOLOSSA-GEHRING, Ilse OTTENBROS, Phillipp SCHMIDT und Nina VOGEL, 2023. Identification of real-life mixtures using human biomonitoring data: a proof of concept study. Toxics [online]. 2023. Bd. 11 (2023), Heft 3. DOI 10.60810/openumwelt-855. Verfügbar unter: https://openumwelt.de/handle/123456789/2070
Zusammenfassung englisch
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. © 2023 by the authors