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首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >HBM4EU Mixtures, Human Biomonitoring and Human Health Risk: Methods to Describe Mixture Exposures in the European Population Based on Human Biomonitoring Data
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HBM4EU Mixtures, Human Biomonitoring and Human Health Risk: Methods to Describe Mixture Exposures in the European Population Based on Human Biomonitoring Data

机译：HBM4EU混合物，人类生物监测和人类健康风险：基于人类生物监测数据描述欧洲人群中混合物暴露的方法

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Humans are exposed to a myriad of concurrent and protracted environmental, occupational, dietary, lifestyle and consumer product exposures. These mixtures of exposure can form an almost infinite number of different combinations of chemicals, which makes the risk assessment extremely challenging. The term mixture (in the context of human biomonitoring) is used to describe any combination of exposure of internal dose biomarkers that has been measured in one or more biological matrices of a person during a single time point. These biomarkers include the chemical substances themselves, as well as their metabolites. The mixture might include compounds from multiple substances groups, but also from multiple compounds from the same substance group. One of our main research questions is "What patterns can we observe amongst body burdens of different substances within individuals, and can we identify hotspots or risk groups with high body burdens of mixtures and/or high mixture risks?" Understanding the correlation patterns, latent structures and functional networks of these mixtures of exposure could provide information on the occurrence of mixtures, common sources and routes of exposure. Such occurrence information is pivotal in mixture risk assessment. Distributions of (patterns) in biomarker mixtures across Europe will be described based on available human biomonitoring datasets. These distributions can be stratified by determinants such as country, region, sampling period, personal characteristics, etc. Using network visualization tools and differential network analyses, we explored similarities and differences in network structures in available human biomonitoring datasets. During this presentation the methods of the network visualization and differential network analysis will be presented.
机译：人类面临着无数同时，长期的环境，职业，饮食，生活方式和消费品暴露。这些暴露的混合物可以形成几乎无限数量的化学物质不同组合，这使风险评估极具挑战性。术语混合物（在人类生物监测的情况下）用于描述内部剂量生物标志物暴露的任何组合，这些组合已在一个时间点的一个或多个人的生物基质中进行了测量。这些生物标志物包括化学物质本身及其代谢产物。混合物可能包含来自多个物质组的化合物，也可能包含来自相同物质组的多种化合物。我们的主要研究问题之一是“我们可以在个体中不同物质的身体负担中观察到什么模式，并且我们可以确定高混合物负担和/或高混合物风险的热点或危险人群吗？”了解这些暴露混合物的相关模式，潜在结构和功能网络可以提供有关混合物发生，常见暴露源和途径的信息。此类发生信息对于混合物风险评估至关重要。将基于可用的人类生物监测数据集来描述欧洲生物标志物混合物中（图案）的分布。这些分布可以通过国家，地区，采样时间，个人特征等决定因素进行分层。使用网络可视化工具和差异网络分析，我们探索了可用的人类生物监测数据集中网络结构的异同。在本演示中，将介绍网络可视化和差分网络分析的方法。

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