Abstract

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.

Original languageEnglish
Article number204
Number of pages31
JournalToxics
Volume11
Issue number3
DOIs
Publication statusPublished - Mar 2023

Bibliographical note

Funding Information:
This research was carried out under the HBM4EU project. The HBM4EU project has received co-funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 733032. The work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857560. This publication reflects only the author’s views, and the European Commission is not responsible for any use that may be made of the information it contains. K.Ř. thanks the RECETOX RI (No LM2018121) financed by the Ministry of Education, Youth and Sports, and OP RDE—project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) and the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_003/0000469) for supportive background. GerES V received funding by the German Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection. The 3xG study is commissioned and co-financed by NIRAS/ONDRAF (Belgian National Agency for Radioactive Waste and enriched Fissile Material), STORA (Study and Consultation Radioactive Waste Dessel) and MONA (Mols Overleg Nucleair Afval). BIOAMBIENT.ES received funding from the Spanish Ministry of Agriculture, Food and Environment (MAGRAMA) and the Instituto de Salud Carlos III (ISCIII) (SEG 1251/07 and SEG 1210/10).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • chemical mixtures
  • human biomonitoring
  • network analysis
  • combined exposure
  • clustering
  • mixture risk assessment
  • HBM4EU

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