Network analysis to identify communities among multiple exposure biomarkers measured at birth in three Flemish general population samples
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Introduction Humans are exposed to multiple environmental chemicals via different sources resulting in complex real-life exposure patterns. Insight into these patterns is important for applications such as linkage to health effects and (mixture) risk assessment. By providing internal exposure levels of (metabolites of) chemicals, biomonitoring studies can provide snapshots of exposure patterns and factors that drive them. Presentation of biomonitoring data in networks facilitates the detection of such exposure patterns and allows for the systematic comparison of observed exposure patterns between datasets and strata within datasets. Methods We demonstrate the use of network techniques in human biomonitoring data from cord blood samples collected in three campaigns of the Flemish Environment and Health Studies (FLEHS) (sampling years resp. 2002-2004, 2008-2009 and 2013-2014). Measured biomarkers were multiple organochlorine compounds, PFAS and metals. Comparative network analysis (CNA) was conducted to systematically compare networks between sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Results Network techniques offered an intuitive approach to visualize complex correlation structures within human biomonitoring data. The identification of groups of highly connected biomarkers, “communities”, within these networks highlighted which biomarkers should be considered collectively in the analysis and interpretation of epidemiological studies or in the design of toxicological mixture studies. Network analyses demonstrated in our example to which extent biomarker networks and its communities changed across the sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Conclusion Network analysis is a data-driven and intuitive screening method when dealing with multiple exposure biomarkers, which can easily be upscaled to high dimensional HBM datasets, and can inform mixture risk assessment approaches.