PDF(Pubmed)
Abstract：
Fundamental to regulatory guidelines is to identify chemicals that are implicated with adverse human health effects and inform public health risk assessors about \"acceptable ranges\" of such environmental exposures (e.g., from consumer products and pesticides). The process is made more difficult when accounting for complex human exposures to multiple environmental chemicals. Herein we propose a new class of nonlinear statistical models for human data that incorporate and evaluate regulatory guideline values into analyses of health effects of exposure to chemical mixtures using so-called \'desirability functions\' (DFs). The DFs are incorporated into nonlinear regression models to allow for the simultaneous estimation of points of departure for risk assessment of combinations of individual substances that are parts of chemical mixtures detected in humans. These are, in contrast to published so-called biomonitoring equivalent (BE) values and human biomonitoring (HBM) values that link regulatory guideline values from in vivo studies of single chemicals to internal concentrations monitored in humans. We illustrate the strategy through the analysis of prenatal concentrations of mixtures of 11 chemicals with suspected endocrine disrupting properties and two health effects: birth weight and language delay at 2.5 years. The strategy allows for the creation of a Mixture Desirability Function i.e., MDF, which is a uni-dimensional construct of the set of single chemical DFs; thus, it focuses the resulting inference to a single dimension for a more powerful one degree-of-freedom test of significance. Based on the application of this new method we conclude that the guideline values need to be lower than those for single chemicals when the chemicals are observed in combination to achieve a similar level of protection as was aimed for the individual chemicals. The proposed modeling may thus suggest data-driven uncertainty factors for single chemical risk assessment that takes environmental mixtures into account.
摘要：
监管指南的基础是识别与人类健康不利影响有关的化学品，并告知公共卫生风险评估人员此类环境暴露的“可接受范围”(例如，来自消费品和农药)。当考虑到复杂的人类暴露于多种环境化学物质时，该过程变得更加困难。在此，我们为人类数据提出了一类新的非线性统计模型，该模型使用所谓的“理想功能”（DF）将监管指南值纳入并评估了暴露于化学混合物的健康影响分析中。将DF合并到非线性回归模型中，以允许同时估计出发点，以对作为人体中检测到的化学混合物的一部分的单个物质的组合进行风险评估。这些是,与已发布的所谓的生物监测等效（BE）值和人类生物监测（HBM）值相反，这些值将单一化学物质的体内研究的监管指导值与人体内部监测的浓度联系起来。我们通过分析11种化学物质的产前浓度来说明该策略，这些化学物质具有怀疑的内分泌干扰特性和两种健康影响：出生体重和2.5年时的语言延迟。该策略允许创建混合期望度函数，即MDF,这是一组单一化学DF的一维结构；因此，它将所得的推论集中在单个维度上，以进行更强大的单自由度显著性检验。基于这种新方法的应用，我们得出结论，当组合观察化学品时，指导值需要低于单一化学品的指导值，以实现与针对单个化学品的目标相似的保护水平。因此，拟议的模型可能会为考虑环境混合物的单一化学品风险评估建议数据驱动的不确定性因素。
