Estimating risk from copper excess in human populations

The nutritional requirements of infants. Towards EU alignment of reference values: the EURRECA network

This paper presents a review of the current knowledge regarding the macro- and micronutrient requirements of infants and discusses issues related to these requirements during the first year of life. The paper also reviews the current reference values used in European countries and the methodological approaches used to derive them by a sample of seven European and international authoritative committees from which background scientific reports are available. Throughout the paper, the main issues contributing to disparities in micronutrient reference values for infants are highlighted. The identification of these issues in relation to the specific physiological aspects of infants is important for informing future initiatives aimed at providing standardized approaches to overcome variability of micronutrient reference values across Europe for this age group.

An exposure-response curve for copper excess and deficiency

There is a need to define exposure-response curves for both Cu excess and deficiency to assist in determining the acceptable range of oral intake. A comprehensive database has been developed where different health outcomes from elevated and deficient Cu intakes were assigned ordinal severity scores to create common measures of response. A generalized linear model for ordinal data was used to estimate the probability of response associated with dose, duration and severity. The model can account for differences in animal species, the exposure medium (drinking water and feed), age, sex, and solubility. Using this model, an optimal intake level of 2.6 mg Cu/d was determined. This value is higher than the current U.S. recommended dietary intake (RDI; 0.9 mg/d) that protects against toxicity from Cu deficiency. It is also lower than the current tolerable upper intake level (UL; 10 mg/d) that protects against toxicity from Cu excess. Compared to traditional risk assessment approaches, categorical regression can provide risk managers with more information, including a range of intake levels associated with different levels of severity and probability of response. To weigh the relative harms of deficiency and excess, it is important that the results be interpreted along with the available information on the nature of the responses that were assigned to each severity score.

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.