The use of categorical regression in modeling copper exposure-response relationships

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.