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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 >Correlation patterns between multiple chemicals measured through human biomonitoring to describe changes over time
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Correlation patterns between multiple chemicals measured through human biomonitoring to describe changes over time

机译：通过人类生物监测测量的多种化学物质之间的相关模式，以描述随时间的变化

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While mixture toxicology and mixture risk assessment have been on the research and policy agenda for decades, assessment of the actual exposure of the population to mixtures of chemicals has received rather little attention. Within the HBM4EU project, individual human biomonitoring (HBM) data of multiple chemicals was analysed to better understand the patterns in correlation and network structures over time. These analyses are informative in understanding the common exposure patterns over time to combinations of chemicals. Three Flemish birth cohorts (FLEHSI-II-III) were included; recruited in ～2003, 2008 and 2013 from the general population. Heavy metals (Cd, Pb), p,p'-DDE, HCB and PCBs (PCB138, PCB153, PCB180) were measured in cord blood at all three points in time. Additionally, in 2008 and 2013 the heavy metals (As, Cu, Mn, Tl, MeHg (in hair)) and PFASs (PFOA, PFOS) were measured. Besides the Flemish data, preliminary comparative results of the cross-sectional population-representative German Environmental Survey for Children and Adolescents (GerES V) will be presented. At first, correlation structures within and across the datasets were assessed visually with Circos plots. Next, partial correlations were calculated to measure strength and direction between two chemicals whilst controlling for the effect of the other chemicals in the data. Based on the partial correlations, the best undirected graph networks were estimated using sparsity parameters. 'Undirected graphs' is an approach to describe the conditional independence among many variables. Within the networks, groups of closely related chemicals were identified (communities), which can provide insight into the existence of groupings. Changes over time were assessed with differentia) network analysis, assessing the similarities and differences between network structures and the communities. Here, differences are changes in correlation strength and direction between chemicals.
机译：尽管混合物毒理学和混合物风险评估已经在研究和政策议程上讨论了数十年，但是评估人群实际接触化学混合物的实际情况却很少受到关注。在HBM4EU项目中，分析了多种化学物质的个人人体生物监测（HBM）数据，以更好地了解一段时间内相关性和网络结构的模式。这些分析有助于理解随着时间推移对化学物质组合的常见暴露方式。包括三个佛兰芒出生队列（FLEHSI-II-III）; 〜2003年，2008年和2013年从一般人群中招募。在所有三个时间点测量脐带血中的重金属（Cd，Pb），p，p'-DDE，HCB和PCBs（PCB138，PCB153，PCB180）。此外，在2008年和2013年，对重金属（As，Cu，Mn，Tl，MeHg（在头发中））和PFAS（PFOA，PFOS）进行了测量。除弗拉芒语数据外，还将提供具有代表性的横断面人口代表性德国儿童和青少年环境调查（GerES V）的初步比较结果。首先，使用Circos图直观地评估数据集内和数据集之间的相关结构。接下来，计算部分相关性以测量两种化学物质之间的强度和方向，同时控制数据中其他化学物质的影响。基于偏相关，使用稀疏性参数估计最佳无向图网络。 “无向图”是一种描述许多变量之间的条件独立性的方法。在网络中，确定了密切相关的化学品组（社区），这可以提供对分组的存在的见解。通过差异网络分析评估随时间的变化，评估网络结构与社区之间的异同。在此，差异是化学物质之间的相关强度和方向的变化。

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