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.

Center for Sustainability Environment and Health National Institute for Public Health and the Environment 3720 BA Bilthoven The Netherlands

Centre for Health Protection National Institute for Public Health and the Environment 3720 BA Bilthoven The Netherlands

German Environment Agency 14195 Berlin Germany

Health Flemish Institute for Technological Research 2400 Mol Belgium

Institute for Risk Assessment Sciences Utrecht University 3584 CM Utrecht The Netherlands

National Centre for Environmental Health Instituto de Salud Carlos 3 28220 Majadahonda Spain

RECETOX Faculty of Science Masaryk University 625 00 Brno Czech Republic

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