National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.
Sci Total Environ. 2011 Oct 15;409(22):4875-84. doi: 10.1016/j.scitotenv.2011.07.046. Epub 2011 Sep 8.
Biomonitoring is used in exposure and risk assessments to reduce uncertainties along the source-to-outcome continuum. Specifically, biomarkers can help identify exposure sources, routes, and distributions, and reflect kinetic and dynamic processes following exposure events. A variety of computational models now utilize biomarkers to better understand exposures at the population, individual, and sub-individual (target) levels. However, guidance is needed to clarify biomonitoring use given available measurements and models.
This article presents a biomonitoring research framework designed to improve biomarker use and interpretation in support of exposure and risk assessments.
The biomonitoring research framework is based on a modified source-to-outcome continuum. Five tiers of biomonitoring analyses are included in the framework, beginning with simple cross-sectional and longitudinal analyses, and ending with complex analyses using various empirical and mechanistic models. Measurements and model requirements of each tier are given, as well as considerations to enhance analyses. Simple theoretical examples are also given to demonstrate applications of the framework for observational exposure studies.
This biomonitoring framework can be used as a guide for interpreting existing biomarker data, designing new studies to answer specific exposure- and risk-based questions, and integrating knowledge across scientific disciplines to better address human health risks.
生物监测被用于暴露和风险评估中,以减少从源头到结果连续体中的不确定性。具体来说,生物标志物可以帮助确定暴露源、途径和分布,并反映暴露事件后的动力学和动态过程。现在有多种计算模型利用生物标志物来更好地理解人群、个体和亚个体(靶标)水平的暴露情况。然而,需要有指导意见来澄清生物监测的使用,以考虑到现有的测量和模型。
本文提出了一个生物监测研究框架,旨在改进生物标志物的使用和解释,以支持暴露和风险评估。
生物监测研究框架基于改良的从源头到结果连续体。该框架包括五个层次的生物监测分析,从简单的横断面和纵向分析开始,最后使用各种经验和机制模型进行复杂分析。给出了每个层次的测量和模型要求,以及增强分析的考虑因素。还给出了简单的理论示例,以演示该框架在观察性暴露研究中的应用。
该生物监测框架可用作解释现有生物标志物数据、设计新研究以回答特定暴露和基于风险的问题以及整合跨学科知识以更好地解决人类健康风险的指南。