McLaughlin Centre for Population Health Risk Assessment, Institute of Population Health, University of Ottawa, Ottawa, Ontario K1N N5, Canada.
J Toxicol Environ Health A. 2010;73(2):187-207. doi: 10.1080/15287390903340781.
Characterization of the exposure-response relationship for copper (Cu) is an essential step in identifying a range of exposures that can prevent against toxicity from either excess or deficiency. Categorical regression is a exposure-response modeling technique that can be used to model data from multiple studies with diverse endpoints simultaneously by organizing the toxicity data into ordered categories of severity. This study describes how categorical regression can be used to model the exposure-response relationship for Cu and presents a preliminary analysis of the comprehensive database on Cu-induced toxicity due to either excess or deficiency. Categorical regression provides a useful tool for summarizing and describing the available data on Cu excess and deficiency, as well as in identifying data gaps in Cu exposure-response. This methodology also allows for a diverse database with considerable variability in animal species, strain, age, and study design to be analyzed in its entirety. The present application of the Cu toxicity database suggests that there is a lack of information on the potential adverse health effects from chronic exposure to Cu; there are also a limited number of studies using marginally excess and deficient levels of Cu. The database presently includes insufficient data to create a complex model that accounts for a large proportion of the heterogeneity in toxicity seen among the available studies on Cu-induced toxicity. The current Cu database is presently being updated in order to permit more comprehensive categorical regression analyses with finer stratification options. The resulting exposure-response model could be used to provide information in the determination of an acceptable range of oral intake for Cu.
铜（Cu）暴露-反应关系的特征描述是确定一系列暴露水平的重要步骤，这些暴露水平可以防止因过量或缺乏而产生毒性。分类回归是一种暴露-反应建模技术，可以通过将毒性数据组织成严重程度的有序类别，同时对具有不同终点的多项研究的数据进行建模。本研究描述了如何使用分类回归来对 Cu 的暴露-反应关系进行建模，并对由于过量或缺乏引起的 Cu 毒性的综合数据库进行了初步分析。分类回归为总结和描述 Cu 过量和缺乏的可用数据以及识别 Cu 暴露-反应中的数据空白提供了有用的工具。该方法还允许分析具有不同动物物种、品系、年龄和研究设计的多样化数据库的整体情况。Cu 毒性数据库的当前应用表明，关于慢性暴露于 Cu 可能产生的潜在不良健康影响的信息不足；使用边缘过量和缺乏 Cu 的研究数量也有限。目前的数据库中包含的数据不足以创建一个复杂的模型，该模型可以解释在 Cu 诱导的毒性的现有研究中观察到的毒性异质性的很大一部分。目前正在更新当前的 Cu 数据库，以便能够进行更全面的分类回归分析，并提供更精细的分层选项。由此产生的暴露-反应模型可用于提供关于可接受的 Cu 口服摄入量范围的信息。
