Center for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands.
Front Public Health. 2021 Feb 10;9:590038. doi: 10.3389/fpubh.2021.590038. eCollection 2021.
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. 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. 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. 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.
人类通过不同的来源接触到多种环境化学物质，从而导致复杂的实际暴露模式。深入了解这些模式对于将其与健康影响和（混合物）风险评估联系起来等应用非常重要。通过提供（化学物质的代谢物）的体内暴露水平，生物监测研究可以提供暴露模式的快照，并确定驱动这些模式的因素。通过将生物监测数据呈现为网络，可以检测到这种暴露模式，并允许系统地比较数据集之间和数据集中各层之间观察到的暴露模式。 我们展示了网络技术在三个 Flemish 环境与健康研究（FLEHS）研究期（分别为 2002-2004 年、2008-2009 年和 2013-2014 年）的脐带血样本中进行人体生物监测数据中的应用。测量的生物标志物是多种有机氯化合物、PFAS 和金属。比较网络分析（CNA）用于系统地比较采样期、孕期吸烟状况和母体孕前 BMI 之间的网络。 网络技术提供了一种直观的方法来可视化人体生物监测数据中的复杂相关结构。在这些网络中，高度连接的生物标志物组（“社区”）的识别突出了哪些生物标志物应该在分析和解释流行病学研究或设计毒理学混合物研究中一起考虑。我们示例中的网络分析表明，生物标志物网络及其社区在采样期、孕期吸烟状况和母体孕前 BMI 方面发生了多大程度的变化。 网络分析是处理多种暴露生物标志物时的数据驱动和直观的筛选方法，它可以轻松扩展到高维 HBM 数据集，并可以为混合物风险评估方法提供信息。
