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

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.

Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept

“…Concentrations of biomarkers were natural-log-transformed because HBM distributions are typically skewed. Network analyses were performed as previously described [ 8 , 9 ].…”

“…Network analysis is a data-driven approach that identifies dependencies between biomarkers of exposure (for the parent chemical and/or metabolite(s) thereof) measured in the same individual at the same point in time [ 8 ]. Recent application of network analysis on multiple datasets from different HBM surveys across Europe [ 9 ] confirmed the various opportunities this analysis has to offer with regard to mixture risk assessment. Firstly, network analysis allows for the identification of groups of exposure biomarkers (communities) that are more closely related than others, i.e., the real-life mixtures.…”