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

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.

Automated summary

Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals. Human biomonitoring approaches, such as network analysis, can provide valuable information on real-life exposure patterns. Applying network analysis to HBM datasets from different countries showed the existence of densely correlated groups of biomarkers, which is relevant for regulatory risk assessment and mixture exposure experiment design.