an exposure-response curve for copper excess and deficiency

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An Exposure-Response Curve for Copper Excess andDeficiencyAndrea Chambers a , Daniel Krewski a , Nicholas Birkett a b , Laura Plunkett c , RichardHertzberg d , Ruth Danzeisen e , Peter J. Aggett f , Thomas B. Starr g , Scott Baker e ,Michael Dourson h , Paul Jones i , Carl L. Keen j , Bette Meek k , Rita Schoeny l & Wout Slob ma Institute of Population Health, McLaughlin Centre for Population Health Risk Assessment,University of Ottawa, Ottawa, Ontario, Canadab McLaughlin Centre for Population Health Risk Assessment and Department of Epidemiologyand Community Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canadac Integrated Biostrategies, LLC, Houston, Texas, USAd Department of Environmental and Occupational Health, Emory University, 1518 Clifton Rd.,Atlanta, Georgia, USAe Environment Program, International Copper Association, Ltd., New York, New York, USAf School of Medicine and Health, Lancaster University, Lancaster, United Kingdomg TBS Associates, Raleigh, North Carolina, USAh Toxicology Excellence for Risk Assessment, Cincinnati, Ohio, USAi Waltham Center for Pet Nutrition, Waltham on the Wolds, Leicestershire, United Kingdomj Department of Nutrition, University of California at Davis, Davis, California, USAk McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa,Ontario, Canadal U.S. Environmental Protection Agency, Washington, DC, USAm Dutch National Institute for Public Health and the Environment (RIVM), Bilthoven, TheNetherlandsPublished online: 17 Dec 2010.

To cite this article: Andrea Chambers , Daniel Krewski , Nicholas Birkett , Laura Plunkett , Richard Hertzberg , RuthDanzeisen , Peter J. Aggett , Thomas B. Starr , Scott Baker , Michael Dourson , Paul Jones , Carl L. Keen , Bette Meek ,Rita Schoeny & Wout Slob (2010) An Exposure-Response Curve for Copper Excess and Deficiency, Journal of Toxicology andEnvironmental Health, Part B: Critical Reviews, 13:7-8, 546-578, DOI: 10.1080/10937404.2010.538657

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Journal of Toxicology and Environmental Health, Part B, 13:546–578, 2010Copyright © Taylor & Francis Group, LLCISSN: 1093-7404 print / 1521-6950 onlineDOI: 10.1080/10937404.2010.538657

AN EXPOSURE-RESPONSE CURVE FOR COPPER EXCESS AND DEFICIENCY

Andrea Chambers1, Daniel Krewski1, Nicholas Birkett1,2, Laura Plunkett3, Richard Hertzberg4,Ruth Danzeisen5, Peter J. Aggett6, Thomas B. Starr7, Scott Baker5, Michael Dourson8,Paul Jones9, Carl L. Keen10, Bette Meek11, Rita Schoeny12, Wout Slob13

1Institute of Population Health, McLaughlin Centre for Population Health Risk Assessment,University of Ottawa, Ottawa, Ontario, Canada2McLaughlin Centre for Population Health Risk Assessment and Department of Epidemiology andCommunity Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, Ontario, Canada3Integrated Biostrategies, LLC, Houston, Texas, USA4Department of Environmental and Occupational Health, Emory University, 1518 Clifton Rd.,Atlanta, Georgia, USA5Environment Program, International Copper Association, Ltd., New York, New York, USA6School of Medicine and Health, Lancaster University, Lancaster, United Kingdom7TBS Associates, Raleigh, North Carolina, USA8Toxicology Excellence for Risk Assessment, Cincinnati, Ohio, USA9Waltham Center for Pet Nutrition, Waltham on the Wolds, Leicestershire, United Kingdom10Department of Nutrition, University of California at Davis, Davis, California, USA11McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa,Ontario, Canada12U.S. Environmental Protection Agency, Washington, DC, USA13Dutch National Institute for Public Health and the Environment (RIVM), Bilthoven,The Netherlands

There is a need to define exposure-response curves for both Cu excess and deficiency to assistin determining the acceptable range of oral intake. A comprehensive database has been devel-oped where different health outcomes from elevated and deficient Cu intakes were assignedordinal severity scores to create common measures of response. A generalized linear modelfor ordinal data was used to estimate the probability of response associated with dose, dura-tion and severity. The model can account for differences in animal species, the exposuremedium (drinking water and feed), age, sex, and solubility. Using this model, an optimalintake level of 2.6 mg Cu/d was determined. This value is higher than the current U.S. rec-ommended 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 protectsagainst toxicity from Cu excess. Compared to traditional risk assessment approaches, cat-egorical regression can provide risk managers with more information, including a range ofintake levels associated with different levels of severity and probability of response. To weighthe relative harms of deficiency and excess, it is important that the results be interpretedalong with the available information on the nature of the responses that were assigned toeach severity score.

Cu has vital physiological functions withinthe body, serving as a functional compo-nent of numerous metalloenzymes. It is also

The views and conclusions in this article are those of the authors and do not represent policies of or endorsement by U.S. EPA orother agencies with which the authors are affiliated. Laura Plunkett was working as a consultant for the International Copper Association.Peter Aggett was involved in the preparation of one of the studies in the Cu database (Harvey et al., 2003).

Address correspondence to Andrea Chambers, McLaughlin Centre for Population Health Risk Assessment, Institute of PopulationHealth, University of Ottawa, 1 Stewart Street, Ottawa, Ontario, K1N 6N5, Canada. E-mail: [email protected]

an important structural component of variousimportant macromolecules (Stern et al., 2007;ICPS, 2002). The World Health Organization

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COPPER EXPOSURE-RESPONSE 547

(WHO) categorizes a metal as essential when“absence or deficiency of the element fromthe diet produces either functional or structuralabnormalities and that the abnormalities arerelated to, or a consequence of, specific bio-chemical changes that can be reversed by thepresence of the essential metal” (WHO, 1996).As for all elements, too much Cu can also leadto undesirable effects.

The body is equipped with a complexregulatory system that maintains internal con-centrations of Cu within a narrow homeostaticrange; however, when these mechanisms aredisrupted, adverse health effects occur (Aggett& Fairweather-Tait, 1998). Characterizing theexposure-response relationship is an importantstep in determining the upper and lower limitsof the acceptable range of oral intake. As theshape of the exposure-response curve has notyet been characterized for Cu and may differbetween deficiency and excess, there remainssome uncertainty with respect to what levelsshould be recommended to balance the risk ofadverse health effects from both Cu excess anddeficiency (Food and Nutrition Board, 2001).

A recent review in the Journal of Toxicologyand Environmental Health described possibleexposure-response modeling strategies for Cu(Stern et al., 2007). Benchmark dose (BMD)modeling is an example of one of the moresophisticated approaches that was developedfor dose-response assessment. The BMD is amodeled point in the dose-response curve ofan adverse effect corresponding to a prede-termined increase in risk (usually in the rangeof 5–10%, adjusted for background response)when compared to the risk in controls (Sternet al., 2007). This is an empirical curve-fittingstrategy that uses all the dose-response datafor one endpoint at one point in time to char-acterize the BMD estimate and its associateduncertainty. Benchmark dose modeling doesnot, however, take into account other adversehealth effects that may occur simultaneously.

Biologically based dose-response modelingquantifies biological mechanisms to determinethe adverse effects of chemical agents, and,in the future, may serve as a potential alter-native to the use of experiments. Biologically

based dose-response models are of particu-lar interest in the risk assessment of essentialmetallic elements, since different mechanismsmay lead to adverse health outcomes fromboth states of excess and deficiency. In generalthere is a lack of understanding of the dynamicand kinetic properties of Cu in animal andhuman tissues, which limits the developmentand application of biologically based exposure-response models. The review by Stern et al.(2007) identified categorical regression as apotentially useful empirical approach to mod-eling the exposure-response relationship of Cu.Categorical regression involves the organizationof qualitatively heterogeneous response data inthe form of ordered categories of severity andthe application of regression analysis to esti-mate the probability that a particular severitycategory occurs as a function of one or moreindependent variables (e.g., dose and dura-tion of exposure). This modeling strategy canincorporate data for multiple endpoints frommultiple studies of Cu excess and deficiency(Stern et al., 2007).

In May 2008, a workshop was held inOttawa, Canada, on the health risk assess-ment of select essential metals. The focus ofthe workshop was Cu, zinc (Zn) and man-ganese (Mn). This meeting provided an oppor-tunity to discuss the limitations of modelingdose-response relationships for such essentialelements that are expected to exhibit “U-shaped”1 exposure-response curves. One ofthe series of papers from the workshop dis-cussed how a categorical regression analy-sis could be used to model the exposure-response relationship for Cu excess and defi-ciency (Krewski et al., 2010a). At this time,categorical regression has not yet been usedto model an exposure-response curve for anessential metallic element.

In order to conduct exposure-responsemodeling (e.g., categorical regression analysis),the extensive data on Cu excess and deficiency

1A “U-shaped” exposure-response curve results when thecurves for Cu deficiency and excess are plotted on a continuumfrom very low doses of Cu to high doses of copper (x axis) andthe likelihood of adverse events (y axis) increases with both verylow doses of Cu and very high doses of Cu.

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548 A. CHAMBERS ET AL.

had to be organized into an exposure-responsedatabase (Krewski et al., 2010b). These inves-tigators also presented a preliminary analysisof the Cu exposure-response database. Theoriginal database, which only included stud-ies published before 2002, was not sufficientto create an exposure-response model for Cudeficiency and excess; however, it was sug-gested that an updated database might permita more comprehensive analysis with finer strat-ification options. It was postulated that theresulting exposure-response model could thenbe used to guide the determination of anacceptable range of oral intake for Cu.

The Cu exposure-response database hasbeen updated and now includes studies pub-lished between 2002 and 2008. The purposeof the current review was to (1) provide abrief review of dietary reference values for Cu;(2) define the acceptable range of oral intakeas described by the International Programmeon Chemical Safety; (3) present the results ofthe literature review update; and (4) utilizethe updated database to construct an empiri-cal exposure-response model for Cu deficiencyand excess using categorical regression.

TYPICAL EXPOSURES ANDNUTRITIONAL REFERENCE VALUES FORCOPPER

The third National Health and NutritionExamination Survey (NHANES III) in the UnitedStates revealed that depending on the agerange, the estimated mean Cu intake from foodis 1.54–1.7 mg/d (± 0.05 standard deviation[SD]) for men and 1.13-1.18 mg/d (± 0.05SD) for women (Food and Nutrition Board,2001). NHANES III also reported that approxi-mately 15% of adults in the United States con-sume supplements containing Cu (Food andNutrition Board, 2001). While food accountsfor the majority of human daily Cu intake,drinking water might also be a significantsource, especially if there is high dissolutionfrom Cu pipes (NAS, 2000).

For adult men and women, the recom-mended dietary intake (RDI) is currently setat 0.9 mg Cu/d (Food and Nutrition Board,

2001). The RDI is defined as being equal tothe estimated average requirement (EAR) plustwice the coefficient of variation (the coeffi-cient of variation is set at 15%) to cover theneeds of 98% of individuals (the RDI is thus130% of the EAR). In North America the EARis the intake level for a nutrient at which theneeds of 50% of the population will be met(Cockell et al., 2008). Data from three stud-ies were used to set the EAR at 0.7 mg Cu/d(Turnlund et al., 1990; 1997; Milne & Nielsen,1996). No single indicator was judged to beadequate for deriving the EAR for adults. Acombination of indicators from these studieswas used, including plasma Cu, ceruloplas-min, erythrocyte superoxide dismutase activity(SOD), and platelet Cu concentrations (Foodand Nutrition Board, 2001). One study foundthat 0.4 mg Cu/d was not adequate to main-tain reference levels of serum Cu, ceruloplas-min, and SOD activity in 8 of 11 young men(Turnlund et al., 1997). In a second study,0.8 mg Cu/d did not result in a significantdecline in serum Cu, ceruloplasmin, or SODactivity (Turnlund et al., 1990). It was there-fore decided that the Cu intake needed tomaintain Cu status in half of the individualsin a group was more than 0.4 mg/d but lessthan 0.8 mg/d. Data from these two studieswere then used to construct a linear dose-response model, which suggested that half ofthe male subjects would not maintain theirCu status with a Cu intake of 0.6 mg/d. Athird study found that platelet Cu concentra-tion declined in 8 of 10 women given 0.6mg/d, but increased with Cu supplementation(Milne & Nielsen, 1996). As this study sug-gested that 0.6 mg/d may be a marginal intakelevel in over half the female population, anincrement of 0.1 mg/d was added to coverthe female population, resulting in an EAR of0.7 mg Cu/d. The Food and Nutrition Board(FNB) has stated that these indicators do notalways reflect dietary intake and that they maybe inadequate for the detection of marginalCu status (Food and Nutrition Board, 2001).For example, during pregnancy, two commonlyused indicators, serum Cu and ceruloplas-min, increase independent of diet. Similarly,

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because ceruloplasmin is an acute-phase pro-tein, both serum Cu and ceruloplasmin oftenrise with numerous disease conditions (Foodand Nutrition Board, 2001).

The FNB (2001) prescribed an upper safelimit (UL) of 10 mg Cu/d. The UL was basedlargely on a double-blind supplement studyshowing normal liver function in adults con-suming 10 mg Cu/d (Pratt et al., 1985). InNorth America an uncertainty factor of 1 isused because this was considered to be asafe no-observed-adverse-effect level (NOAEL)for most of the population. However, in theEuropean Union an uncertainty factor of 2 isused to account for the potential variability ina normal population, resulting in a UL of 5 mgCu/d. It is important to note that the UL forCu was largely based on liver toxicity endpointsand does not take into consideration less severebut clinically important responses.

ACCEPTABLE RANGE OF ORAL INTAKE

The acceptable range of oral intake (AROI)was described by the IPCS as the trough inthe U-shaped exposure-response curve (IPCS,2002). IPCS conceptualizes the lower limit ofthis range as equivalent to the RDI and thehigher limit equivalent to the LBMD2.5 (lowerconfidence limit on the benchmark dose)(IPCS, 2002). Figure 1 provides a theoretical U-shaped exposure-response curve and pictorialrepresentation of the AROI (IPCS, 2002).

Variability among individuals, characterized bythe risk of toxicity from excess and deficiency inFigure 1, may be due to differences in home-ostasis, bioavailability, age-related factors, anddietary and nutrient interactions (IPCS, 2002).The acceptable range of oral intake (the widthof the trough) varies depending on the essentialmetallic element being considered.

LITERATURE REVIEW AND SEVERITYSCORING UPDATE

Krewski et al. (2010a) published the resultsof an analysis of the original Cu exposure-response database that was based on studiespublished prior to 2002. Due to the limitednumber of studies on humans that were suit-able for an exposure-response analysis, thedatabase and analysis included both humanand animal models. This analysis concludedthat there was a need for more exposure-response data in order to permit a morecomprehensive categorical regression analy-sis. Following this analysis, the Cu databasewas updated to include citations publishedbetween January 2002 and December 2008.The initial pool of relevant citations was iden-tified from the Copper Research InformationFlow database, which contains an extensiveand up-to-date collection of publications on Cuas it relates to human health and the environ-ment. The project uses several online databases(e.g., Chemical Abstracts, Toxline, Medline,

FIGURE 1. Theoretical representation of the acceptable range of oral intake (IPCS, 2002). Reprinted with permission from the WorldHealth Organization.

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Biosis, NTIS, EMBASE) and thousands of elec-tronic journals. The Cu Research InformationFlow project is based in the Department ofEarth and Ocean Sciences at the Universityof British Columbia and is supported by theInternational Copper Association. The poolof relevant citations contained case studies,experimental studies, human health risk assess-ments, epidemiological studies, and occupa-tion exposure studies.

After the pool of relevant studies was iden-tified, a qualitative “binning exercise” wasconducted to categorize each study based onits quality and usefulness for an exposure-response assessment. This process is describedin more detail in Krewski et al (2010b). Aworking group with expertise in biostatistics,nutritional sciences, toxicology, and molecu-lar biology reviewed the studies that wereidentified and also developed a list of qualityconsiderations for human and animal studies(Table 1) as well as a list of exclusion criteria(Table 2). Details regarding the scoring of theindividual studies with respect to their qualityare available upon request.

In order to define an exposure-responserelationship that integrates multiple studiesmeasuring outcomes in different target organswith varying levels of severity, a commonresponse scale is required. Excess and defi-cient levels of Cu can lead to a wide rangeof responses with varying degrees of severitydepending on the dose and duration of expo-sure. Ordinal severity scores were defined tocreate a common measure of response. Once apool of relevant studies was identified, severity

TABLE 2. Exclusion Criteria for Human and Animal Studies

There was inadequate information to characterize the dose andduration of exposure.

The information could not be entirely attributed to the effects ofcopper alone (confounders).

Copper toxicity was considered as the outcome and not a sideeffect of an intervention involving copper exposure.

Animals or humans have features suggestive of disturbed coppermetabolism (transgenic animals, humans with genetic disease,or dietary copper deficiency).

The exposure route was not relevant for humans.The animal model is not suitable for human health risk

assessment (e.g., ruminant species, invertebrate species).There was inadequate statistical reporting of the data.

scores were assigned to the response data fromthe individual studies.

The original severity matrix for Cu wasguided by a detailed review of indicators of tox-icity from excess and deficiency (Stern et al.,2007). Table 3 presents the updated severitymatrix that was based on the most recent liter-ature review. The lowest severity level (severitylevel 0) corresponds to no changes comparedto controls. In essence, severity level 0 corre-sponds to a NOEL, in this case an ideal orabsolute in that there is not even an observ-able homeostatic response in the body’s use ofCu. Severity level 1 is associated with adap-tive responses without evidence of Cu defi-ciency or excess. Severity level 2 correspondsto early phenomena of Cu imbalance (e.g., lossof Cu-dependent enzyme function with inad-equate intakes). Severity level 3 correspondsto derangements of substrate metabolism thatare dependent on Cu metabolism (direct orindirect mechanisms). In the original severitymatrix that was used in the initial Krewski et al.

TABLE 1. Quality Considerations for Human and Animal Studies

Human studies Animal studies

The study included multiple endpoints.Copper balance studies provided adequate repletion following

the period of copper depletion.Controlled clinical study environment or design is optimal;

however, other study designs may be adequate.Accurate estimates of copper intake were available.Data were subject to adequate statistical analyses.Separate analyses have been conducted for infants, children, and

adults.

The animal species and strain was considered to be a suitablemodel for the purpose of human health risk assessment.

The exposure medium was relevant to human health riskassessment.

Standard considerations for animal study design and performancewere applied.

In the case of dietary exposure studies, pair feeding designs areoptimal; however, other study designs may also be appropriate.

The data was subject to appropriate statistical analyses.Separate analyses were conducted based on the age of the

animals in the study.

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551

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(2010) study, the highest severity score (i.e.,severity level 4) was associated with reversibleadverse effects, irreversible adverse effects,and death. Severity level 4 now correspondsto changes that could be described as grossreversible toxic effects, whereas severity level5 has been added and corresponds to irre-versible, gross toxic effects. Death was givenits own category, severity level 6. The listedresponses in Table 3 are cumulative: Thoseeffects listed in lower severity categories arepresumed to also occur in the higher cat-egories. All responses measured within eachstudy in the Cu exposure-response databasewere assigned a severity score. As most stud-ies reported multiple responses to Cu excessor deficiency, several severity scores might beassociated with one exposure level. In thesecases, the single severity score that correspondsto the most severe effect was selected to repre-sent the exposure group.

The exposure-response data and the cor-responding severity scores from the literaturereview update were added to the originalCu database. This database was designed tohold all of the (1) exposure-response data, (2)assigned severity scores, and (3) detailed infor-mation extracted from each study. Informationextracted from each study in the Cu databaseincluding animal species, exposure medium,Cu species, age, gender, study design, and doseand duration of exposure can be accessed onthe Internet (http://mclaughlincentre.ca).

EXPOSURE-RESPONSE ANALYSIS

Exposure-response data extracted from thestudies identified in the literature review wereassigned severity scores and were integratedinto the Cu exposure-response database. Theseverity scoring system utilized was describedearlier. The resulting database was used toconduct the categorical regression analysis.Typically, when data from multiple species arecombined, dose is redefined by a concentra-tion metric that will account for interspecies dif-ferences in sensitivity. Some investigators rec-ommended a default dose metric defined bymilligrams per (kilogram body weight [bw])3/4

per day, as it seems to be consistent withseveral physiological processes related to effec-tive intake and internal dose (Rhomberg &Lewandowski 2006). For this analysis, dosewas defined in milligrams per kilogram bwper day, as the CatReg program accounts forinterspecies differences beyond body weightby allowing the user to define species spe-cific model parameters. There are, however,advantages to defining a dose metric thataccounts for interspecies differences in sensi-tivity. Any differences that can be reduced oreliminated by scaling diminish the complex-ity of the model by eliminating the need tostratify model parameters by animal species.There is currently no standardized approach forevaluating and selecting a common dose met-ric. One may use a more subjective approach,such as a visual analysis of exposure-responsecurves and their corresponding data points.More objective approaches could involve theuse of statistics that assess explanatory capacityof the exposure-response curve (e.g., R2 statis-tic). When conducting a categorical regressionanalysis of the Cu database, no single dosemetric appears to be superior across differentmeasures of model fit. While the use of differ-ent dose metrics did not improve the fit of theexposure-response model and had no effecton final risk estimates, future CatReg analyseswith the Cu database should continue to con-sider alternative dose metrics that account forinterspecies differences.

Compared to animal studies, human stud-ies often provide more information on the totalamount of Cu consumed; however, experi-ments that control Cu intake with the use ofa capsule often do not provide accurate infor-mation on the amount of Cu contained in abasal diet. In such cases, information on typi-cal dietary Cu intakes is estimated from studiesthat have measured habitual dietary intake(Baker et al., 1999a). While the concentra-tion of Cu in feed is often provided in studieson animals, there is often missing informationon total feed intake. This information may beestimated from standard guidelines for exper-imental animal studies. A systematic processwas developed to ensure that any assumptions

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used to estimate feed or water consumptionlevels or body weights were documented andstandardized across studies (Chambers, 2009).

The categorical regression analysis pre-sented in this paper used studies on humans,rats, and mice. While several studies with pigand rabbits were included in the database, suchstudies were omitted from the analysis due totheir scarcity. At this time the analysis focusedon subacute, subchronic, and chronic expo-sure studies with duration expressed in days.Acute exposure studies were not included. Inthe preliminary analysis of the Cu exposure-response database that contained studies pub-lished prior to 2002 (Krewski et al., 2010a),inclusion of the acute exposure studies inthe analysis markedly increased the magni-tude of the standard errors for the parameterestimates. The analysis also defined interceptparameter estimates for each severity scorethat were not significant and eliminated theeffect of duration in the exposure-responsemodel. In the exposure-response model forCu excess, as duration of exposure increased,so did the severity of response, as would beexpected. However, including several obser-vations with high levels of severity and shortdurations of exposure (i.e., exposure within 1d or a one-time exposure) disrupted this pat-tern. Acute exposure studies that have beenincluded in the database typically administerhigh levels of Cu in drinking water and makeobservations within 1 h after the exposure.The experimental design and the outcomesof interest are different from those of studiesthat have used subacute or subchronic expo-sures.

As there were no observations on Cu defi-ciency that were assigned a severity level 5,scores 4–6 were combined in the Cu deficiencyexposure-response model. Due to the scarcityof observations within categories 5 and 6 for Cuexcess, scores 4–6 were also combined.

CatReg, a software program developed bythe U.S. Environmental Protection Agency, wasused to conduct the exposure-response analy-sis. Separate analyses were conducted to defineexposure-response curves for Cu excess anddeficiency. CatReg uses a generalized linear

model (McCullagh & Nelder, 1989) to describethe dependence of the probabilities of occur-rence of different severity categories on theexplanatory variables, namely, the concentra-tion and duration of exposure (U.S. EPA, 2000).It was assumed that the response was related tothe explanatory variables according to a user-specified functional relationship called a linkfunction. The use of a link function in CatReghas been described as “a function appliedto the exposure-response curve to transformit to a simple linear relation in concentra-tion and duration. By also transforming theobserved responses, the link function reducesthe mathematical complexity of estimating theparameters” (U.S. EPA, 2000). For the threedifferent probability functions that are avail-able (logistic, normal, and Gumbel) there arethree corresponding link functions (logit, pro-bit, and log–log). Further details on the use ofthe link functions can be found in the CatReguser manual (U.S. EPA, 2000).

An important feature of CatReg is the abil-ity to estimate effective response concentra-tion (ERCq) curves versus exposure durationfor various severity levels from the exposure-response model. By selecting an appropri-ate probability level and fixing the durationof exposure (e.g., ERC05-T100, ERC10-T100),effective doses associated with the probabil-ity of attaining each severity level may beestimated. Duration of exposure was fixed at100 d. A chronic duration of exposure wouldhave been ideal; however, there are limiteddata on humans after 100 d of exposure. It isalso important to note that if data were basedon individual exposed subjects, the probabil-ity represents the chance that an individual’sresponses will be at that level of severity orhigher, or, for homogeneous populations, theexpected fraction of the population predictedto exhibit response of a given severity level orhigher. If data are only available at the dose-group level, which is the case in this analysis, anERC10 estimate for a given severity category isthe concentration associated with a 10% prob-ability that a group exposed at that dose wouldexhibit responses of that severity category orhigher.

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Two models are available in CatReg, aparallel cumulative odds model and an unre-stricted cumulative odds model. In the caseof the parallel cumulative odds model, theintercept parameters can differ by the severitylevel, while the coefficients for concentrationand duration are the same across severity lev-els. In the unrestricted cumulative model, thecoefficients for the concentration and dura-tion of exposure are estimated separately foreach severity level (U.S. EPA, 2000). The Cuexcess and deficiency data were modeled firstwith the unrestricted cumulative odds modelfollowed by the parallel-restricted cumulativeodds model. In CatReg the “parallel.test” func-tion was used to test the joint null hypothesisthat the parameter estimates for concentrationat each severity level and the parameter esti-mates for duration at each severity level arethe same. The test is a generalized Wald-typechi-square test that all of the specified con-straints hold. A p value less than .05 was takenas evidence that the null hypothesis shouldbe rejected and that rather the more com-plex model (i.e., unrestricted cumulative odds)be used.

To select the link function and the transfor-mation options for concentration and duration,the Akaike information criterion (AIC) was usedto compare 12 different models defined by 3different link functions (logit, probit and log-log) and 4 transformation options (logarithmicor linear concentration and/or duration). Theminimum AIC identifies the “best” model bybalancing bias and variance aspects of the esti-mated dose-response relationships. The selec-tion of the link function is an empirical mod-eling decision that currently does not have abiological basis.

In the Cu database, there are groups ofobservations from the same experiment and/orthe same study. Ignoring the correlations amongobservations within these clusters could leadto estimated standard errors for the modelparameters that are biased toward zero. CatRegprovides an option for the user to specifywhether or not the data set contains any clus-ters and uses the method of generalized esti-mating equations to account for the cluster

sampling effect (Simpson et al., 1996; Diggleet al., 1994). In the current analysis, all observa-tions from the same reference and experimentwere treated as a cluster.

The effects of potentially important expla-natory variables such as animal species,exposure medium (drinking water versus feed),age, gender, and Cu solubility were assessedin CatReg by stratifying the regression param-eters on discrete levels of these variables. It isimportant to note that Cu is generally moreavailable for intestinal uptake and transfer fromwater than from food. To account for theincreased risk of toxicity from Cu in drinkingwater, the exposure-response model for theCu excess data was stratified by the exposuremedium (drinking water versus feed). However,the exposure-response model for Cu deficiencywas not stratified by exposure medium. WhenCu is administered in drinking water in Cudeficiency studies, the Cu-deficient groups aregiven purified drinking water and a diet con-taining only minimal amounts of Cu. The con-trol group is then typically given the same diet;however, the drinking water is supplementedwith adequate amounts of Cu to prevent anyresponses associated with Cu excess or defi-ciency. Therefore, whether Cu is administeredin the drinking water or the diet of the con-trol group should not impact the severity ofresponse in Cu deficiency studies.

In order to stratify the intercept, concentra-tion and/or duration parameters by age, a two-level categorical variable was defined (“young”and “mature”). Young rats and mice were lessthan or equal to 30 d of age and mature ratsand mice were older than 30 d of age. Thesedesignations were based on age categories andlife-stage estimates from the Canadian Councilon Animal Care (1984), which are based onthe estimated age at puberty. At this time, thehuman studies in the database only focus onadults (≥18 yr of age).

The potential effect of sex was alsoassessed. As there are studies in the Cuexposure-response database that do not reportresults independently for males and females,a three-level variable was created (“both,”“males,” and “females”).

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Cu salts that have low solubility include Cuhydroxide, Cu oxide, and Cu carbonate (Sternet al., 2007). Often Cu salts with low solubilityare used in Cu deficiency studies. The majorityof studies on Cu excess utilize forms of Cu withhigh solubility, and few studies use less solubleforms of Cu. Therefore, the effect of solubil-ity was only assessed in the exposure-responsemodel of the Cu deficiency data.

Stratification allows systematically differentsubsets of the data to have different valuesfor some or all of the parameters includ-ing the models’ intercept, concentration, orduration parameters (U.S. EPA, 2000). Uponstratifying the model’s intercept, concentration,and/or duration parameters, CatReg providesan option to test statistically whether the esti-mates produced for one variable (e.g., inter-cept coefficients for rats, mice, and humans)are different from each other. The test is a gen-eralized Wald-type chi-square test of the nullhypothesis that there is a common set of modelparameters across the strata.

Model selection was based on a series oflikelihood ratio tests between nested models.The goal was to produce not only an exposure-response curve that sufficiently accounts for thevariability in the data by considering differentparameters and stratification options, but alsoone that achieves this aim as simply as possi-ble. If stratifying any of the model parametersdecreased the model deviance by only a smallamount, the simpler model was used.

Observations that contribute to any lack offit of the exposure-response curve can be iden-tified by examining the individual contributionsof each data point to the deviance. Data pointsidentified as potential outliers were reviewedin terms of the corresponding study design andrange of endpoints measured. CatReg was usedto generate what the program refers to as “all-sevsplots,” which plots the ERCq lines for eachlevel of severity at a defined effective risk level(q) on one graph where the y axis is definedby concentration and the x axis by duration.These plots also present the data points cor-responding to the defined stratum. The plotswere used to examine the impact of durationin the exposure-response model. When there

were no observations that corresponded to aparticular severity score, the ERCq line wasdefined by using observations from other stratain the analysis.

Thus far, it was assumed that responses toCu excess and deficiency are similar across ani-mal species if duration of exposure is measuredin days. However, an exposure duration of 100d could be considered either a subchronic orchronic exposure in an animal study, while itmight be viewed as only a subacute exposurein a study on humans. For this reason, themodeling results were compared to those froman analysis where duration of exposure wasexpressed as a species-specific percent of lifespan. It was of interest to determine whetherrescaling the duration variable exerted substan-tial impact on our final results as characterizedby the ERC10-T100 for humans at severity level2 or greater.

Data from animal studies fill informationgaps that exist in the human study data sets.It is readily appreciated that experimental tox-icity data are gathered more easily in animalsthan humans in part because of the unique eth-ical considerations associated with conduct ofcontrolled human studies. As a result, it is notsurprising that in the current Cu database, stud-ies with rats greatly outnumbered the studieswith human subjects. To look at the impact ofcombining data from multiple animal species,three further models were defined. One modelused only the human data, the second modelused only the rat data, and the last modelused only the data on mice. The ERC10-T100estimates produced from these three separateanalyses were compared to the original analysisthat incorporated all animal species.

Studies are beginning to provide moreinformation about the exposure-responsecurves outside of the acceptable range of oralCu intake. The Cu database could be updatedperiodically to improve the precision and accu-racy of the exposure-response curves and fur-ther refine the model estimates of the bound-aries on the homeostatic range. To look at howthe most recent update has modified the riskestimates produced in the categorical regres-sion analysis, an analysis of the Cu database

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prior to the update (studies up until 2002) wascompared with an analysis of the most recentCu database (studies up until 2008).

FINAL ESTIMATES

After estimating parameters of theexposure-response models for Cu excessand deficiency, one of the challenges was mak-ing use of the results in determining the AROI.The IPCS has defined the lower limit of theAROI as representing the RDI (2002). Unlikethe RDI approach, which uses individual leveldata, the categorical regression uses grouplevel data. As long as the mean response ina dose group was significantly different fromcontrols and the change was considered tobe clinically significant, the entire dose-groupwas assigned a single severity score. Theboundary of the homeostatic range or theAROI would thus fall between the NOAEL andthe adverse effect level (AEL). In our analysis,responses associated with a severity level 0 (nodetectable response) and severity level 1 (noevidence of Cu imbalance) would lie belowthe AEL. The AEL would thus be representedby responses associated with severity level 2 orgreater. The goal then would be to minimizethe risk of responses associated with a severitylevel 2 or greater. It was expected that theexposure-response curves would cross becausethe deficiency curve starts at a probabilitylevel of 1 for zero Cu intake and descentsmonotonically toward zero as intake increases,while the excess curve starts at a probabilitylevel of zero for zero Cu intake and ascendsmonotonically to 1 for infinite Cu intake.

The Cu deficiency and excess data weremodeled separately, each by monotonic func-tions of dose and duration. To create a jointU-shaped exposure-response curve for adverseresponses to either Cu excess or deficiency, thesum of the estimated conditional probabilitiesfor Cu excess and Cu deficiency need to bereduced by their product, as shown here:

P(excess or deficiency) = P(excess)+ P(deficiency)

− P(excess)× P(deficiency)(1)

The dose associated with the lowest level ofprobability of this U-shaped exposure-responsecurve represents the optimal intake level.

The original Cu database contained 79studies; 26 of those studies were on Cu excessand 53 were on Cu deficiency. After the searchupdate, 16 studies on Cu excess and 26 studieson Cu deficiency were added to the database.More specifically, as an observation in a cat-egorical regression analysis corresponds to adose group used in one experiment, 56 obser-vations were added to the original 187 for Cuexcess, and 74 observations were added tothe original 140 for Cu deficiency. Figure 2,a–f, presents plots that categorize observationsby whether they were in the original database(studies up until 2002) or added during thedatabase update. Plots are defined separatelyfor both Cu deficiency and excess and for eachanimal species (humans, rats, mice). Table 4presents the number of observations by sever-ity score and animal species. The bolded valueis the number of observations added fromthe literature review update. The number inparentheses corresponds with the total num-ber of observations. Because our definition ofthe AROI falls between severity levels 1 and2, an important contribution of the literaturereview update was the addition of 4 observa-tions on humans that were assigned a severitylevel 2 (i.e., early phenomena of accumulatedCu). Overall, the majority of observations onCu deficiency from the literature update wereclassified into severity levels 1 through 3. Evenafter the literature review update, there werestill only 11 observations assigned to higherlevels of severity.

Human StudiesThe following discussion focuses on the

studies identified in the literature reviewupdate. Studies entered into the originaldatabase are described by Stern et al. (2007)and Krewski et al. (2010b). Several addi-tional acute exposure studies were addedto the Cu exposure-response database (Arayaet al., 2003a, 2003b, 2003c, 2004). Arayaet al. (2003a) identified and confirmed an

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FIGURE 2. Copper deficiency data before and after the database update for humans (a), rats (c), and mice (e), respectively. Copperexcess data before and after the database update for humans (b), rats (d), and mice (f), respectively. Observations from dietary studies upuntil 2002 = . Observations from dietary studies post 2002 = •. Drinking-water studies up until 2002 = X. Drinking-water studiespost 2002 = X.

acute NOAEL (4 mg Cu/L) and a LOAEL(6 mg Cu/L) for Cu in drinking water. Thisstudy measured a wide range of responses to

marginally low and marginally high levels ofCu; these included serum and erythrocyte Culevels, peripheral mononuclear cell Cu, serum

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TABLE 4. Number of Observations by Animal Species and Severity Score

Severity levels

Factor 0 1 2 3 4 5 6

Copper excessHumans 12 (28) 0 4 (5) 0 6 (13) 0 0Rats 7 (55) 0 (8) 0 (3) 2 (17) 4 (46) 0 (3) 0 (4)Mice 2 (21) 0 0 2 (4) 0 (14) 0 0 (5)Pigs 8 (8) 0 3 (3) 3 (3) 0 0 0Rabbits 1 (1) 0 0 1 (1) 0 0 0

Copper deficiencyHumans 2 (5) 2 (3) 0 (3) 1 (2) 0 0 0Rats 27 (74) 6 (10) 6 (22) 21 (64) 5 (6) 0 0 (1)Mice 2 (11) 6 (0) 0 (1) 2 (2) 0 (4) 0 0Pigs 0 (1) 0 0 0 (1) 0 0 0

Note. Boldfaced values represent the number of observations added from the literature review update, and values in parenthesesrepresent the total number of observations including those identified prior to 2002.

ceruloplasmin, the non-ceruloplasmin-boundCu fraction, superoxide dismutase activity,hemoglobin, mean corpuscular volume, serumferritin, liver enzymes, and gastrointestinalsymptoms. However, other than gastrointesti-nal symptoms, no apparent detectable changeswere observed. In terms of subacute and sub-chronic exposures, only two human dietarystudies on the effects of Cu excess were addedto the database. Turnlund et al. (2004) exam-ined graded levels of Cu intake on indicesof Cu status, oxidant damage, and immunefunction, whereas O’Connor et al. (2003) stud-ied mononuclear leukocyte DNA damage andindicators of liver function.

Two new human studies on Cu deficiencywere identified in the literature update. Davisand Johnson (2002) used a lower (more defi-cient) dose of Cu than in previous studiesto investigate the effects of low and ade-quate Cu intake on Cu nutriture and putativerisk factors for colon cancer susceptibility inhealthy men. Cu deficiency was identified asa possible dietary factor that may increase therisk of colon cancer (Davis & Feng, 1999;Davis & Johnson, 2001; Bird, 1995; Pretlowet al., 1991; DiSilvestro et al., 1992; Greeneet al., 1987; Davis & Johnson, 2002; Davis &Newman, 2000).

While low dietary Cu did not affect anyhaematological indicators of Cu status, it didincrease fecal free radical production and fecalwater alkaline phosphatase activity, which are

established risk factors for colon cancer. Thesecond study (Harvey et al., 2003) is also con-sidered an important study, as it assessed threelevels of Cu intake judged to represent defi-cient, marginally deficient and adequate dosesof Cu. This study provides data on homeo-static responses to these intake levels relevantto severity categories 0 and 1.

Animal StudiesThe majority of new rat studies are within

the range of concentrations addressed in stud-ies prior to 2002; however, the newer studiesappear to utilize more marginally deficient lev-els of Cu (Goldschmith et al., 2005). Marginallydeficient and excess levels of Cu are moreinformative for defining the acceptable range oforal intake. For Cu deficiency, the most impor-tant new studies on rats are those that exam-ined graded levels of Cu deficiency (Andersenet al., 2007; Falcone et al., 2005; Johnsonet al., 2005; Li et al., 2005). The experimentconducted by Johnson et al. (2005) is particu-larly important as it assessed seven levels of Cuintake, including five marginally deficient Culevels.

Only two Cu deficiency studies on micewere added to the database, neither of whichaddress marginal levels of Cu deficiency norutilize a duration of exposure that has notbeen previously addressed in earlier studies.Only two mouse studies on Cu excess were

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added. The mouse study by Kvietkauskaiteet al. (2004) can be considered an importantaddition as it utilized multiple levels of expo-sure and included a broad range of indicatorsof Cu status including sensitive measures of Cutoxicity. At this time, there are still few obser-vations for animal species other than rats andmice. Three pig studies and one rabbit studyon Cu excess were identified in the literaturereview update (Armstrong et al., 2004; Fenget al., 2007; Alissa et al., 2004; Davis et al.,2002). There was also one study using rhesusmonkeys that examined the effects of chronicCu exposure during early life (Araya et al.,2005). This study was not added to the Cudatabase as it provided insufficient informationon average daily food intake.

COPPER EXCESS AND DEFICIENCYEXPOSURE-RESPONSE MODEL

In total, 208 observations on Cu defi-ciency and 207 observations on Cu excess wereavailable for this analysis. CatReg producedseveral error messages during the calculationof the model parameters when the log–loglink function was used. In terms of the othermodeling options available, for Cu excess, themodel with the lowest AIC used the logitlink function and took the logarithm (log10)of both concentration and duration (Table 5).Table 5 also presents the AIC values for copper

Cu deficiency. The log–log link function pro-duces the lowest AIC value; however, whenthe model using this link function was fur-ther stratified, CatReg presented several errormessages during the calculation of the modelparameters. The program documentation rec-ommends use of the logit or probit link functionwhen the log–log link function produces theseerrors messages. Consequently, the Cu defi-ciency model employed the logit link functionand log-transformed dose and duration.

Analyses were run with both the parallel-constrained cumulative odds model and themore complex unrestricted cumulative oddsmodel. Attempting to fit the unrestricted modelto the Cu excess data produced an error mes-sage in CatReg, requesting that the user simplifythe model due to incorrectly ordered severityestimates. For example, the ordered severityconstraint does not allow the estimated back-ground risk (intercept coefficient) for a severitylevel 3 response to be greater than that aseverity level 1 response.

Incorrectly ordered severity-level parame-ter estimates indicate that there may be toomany severity levels in the data (U.S. EPA,2000). A series of reduced severity scoreagglomerations were compared for the Cuexcess model. Reducing the number of severityscores to three levels was the only combinationthat resulted in correctly ordered parameterestimates for the severity scores. Severity scores

TABLE 5. Akaike Infrmation Criteria (AIC) for 24 Modeling Options Using the Copper Excess andDeficiency Exposure-Response Database

AIC

Link function Concentration Duration Deficiency Excess

Logit Linear Linear 514.3146 576.76Logit Linear Log 511.2093 574.78Logit Log Linear 514.3866 547.11Logit Log Log 511.1258 541.52Probit Linear Linear 518.7908 579.49Probit Linear Log 517.7919 579.79Probit Log Linear 518.8718 574.04Probit Log Log 517.8761 543.70Log-log Linear Linear 514.5548 NALog-log Linear Log 505.7347 NALog-log Log Linear 514.6525 NALog-log Log Log 505.8695 NA

Note. NA, not applicable; analysis could not be run with this link function.

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TABLE 6. Stratification Options in the Cumulative Odds Model of the Copper Excess and CopperDeficiency Database

Stratification option Chi-square dfd p Value

Copper excess:Intercept stratified by animal speciesa 20.98 4 <.05Intercept stratified by exposure mediumb 7.07 3 <.05Concentration stratified by animal speciesc 8.07 3 <.05Concentration stratified by ageb 11.40 2 <.05

Copper Deficiency:Intercept stratified by animal speciesc 83.62 3 <.0001Intercept stratified by ageb 11.93 2 <.01

Note. Cumulative odds model of the copper excess data uses the logit link function and stratifies theintercept by animal species and exposure medium and stratifies the concentration parameter by animalspecies and age. Cumulative odds model of the copper deficiency data uses the logit link function andstratifies the intercept by animal species.

aControlling for the exposure medium (drinking water or diet).bControlling for animal species (humans, rats or mice).cControlling for age (mature or young).ddf, Degrees of freedom.

0 and 1 were therefore combined to representlevel 0; scores 2 to 4 were combined to repre-sent level 1; and scores 5 and 6 were combinedto represent level 2.

The test for the assumption of parallelismfound no significant departures from equal-ity of the coefficients for concentration andduration across the severity scores. This sug-gests that it would be reasonable to use thesimpler parallel-constrained cumulative oddsmodel. Furthermore, when the more complexunrestricted cumulative odds model continuedto be used, model fits could not be obtainedwhile stratifying the model parameters on theother variables of interest (i.e., animal species,exposure medium, age or sex). Errors messagesin CatReg indicated that too many parameterswere being estimated for the amount of dataavailable.

The unrestricted cumulative odds modelof the Cu deficiency data allowed for all fiveseverity levels to be defined; however, thismore complex model also did not permit anyof the parameters to be stratified by the vari-ables of interest (i.e., animal species, age, sex,and Cu solubility).

Due to the modeling constraints associatedwith the use of the unrestricted cumulativemodel, the simpler cumulative odds model wasfit to the Cu excess and deficiency data. In

this model the parameter estimates for con-centration and duration are constrained to beequal across all levels of severity. For the Cuexcess data, a series of stratification optionswere compared where the intercept, con-centration, and/or duration parameters werestratified by animal species, exposure medium(drinking water versus diet), age, and sex. Thefinal model was selected based on the simpleststratification scenario that produced an accept-able fit to the data (i.e., the best fitting simplemodel); this model stratified the intercept byanimal species and the exposure medium andstratified the concentration parameter by ani-mal species and age. Stratifying the modelparameters by sex or solubility did not improvethe fit of the exposure-response curve to thedata.

Table 6 presents the results from a gen-eralized Wald-type chi-square test of the nullhypothesis that the parameter estimates thatwere permitted to differ by strata are in factequal. Animal species, exposure medium, andage all appear to be important explanatoryvariables in the exposure-response model forCu excess. For Cu deficiency, a series of strat-ification options were also compared wherethe intercept, concentration, and/or durationparameter were stratified by animal species,age, sex, and solubility. Similar to the selection

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of the model for Cu excess, the final modelwas selected based on the simplest stratifica-tion scenario that produced the best fit to thedata (i.e., best fitting simple model); this modelstratified the intercept by animal species andage. Stratification by sex and solubility did notsignificantly improve the fit of the exposure-response model for Cu deficiency. Table 6 alsopresents the results from a generalized Wald-type chi-square test of the null hypothesis thatthe parameter estimates that have been strati-fied in the Cu deficiency model are equal.

Table 7 presents the parameter estimatesin the cumulative odds model of the Cuexcess data where the intercept has beenstratified by animal species and the expo-sure medium and the duration parameter hasbeen stratified by animal species and age.Table 8 presents the same information for thecumulative odds model of Cu deficiency thatstratifies the intercept by animal species andage. In Tables 7 and 8, the parameter esti-mates correspond to the parameters specifiedin the parallel cumulative odds mode (Krewskiet al., 2010a). Intercept parameters are iden-tified by the INTERCEPT label. Stratum labelshave been added to each intercept parameter.

For example, HU:F:INTERCEPT refers to thehuman dietary stratum. HU:F:INTERCEPT inTable 7 is the reference group and thusno parameter is estimated. Its intercept isdefined by SEV1, SEV2, SEV3, and SEV4.The other intercept parameters are incre-ments relative to the reference parameter. ForHU:W:INTERCEPT, the estimated intercept forseverity category 1 is the sum of SEV 1 andHU:W:INTERCEPT, 5.88 + 1.97 = 7.85. TheZ test for HU:W:INTERCEPT is a test of the nullhypothesis that human drinking water studieshave the same intercept as human dietary stud-ies. Concentration and duration slope param-eters are identified by the CONC and TIMElabels. The LG10 component indicates that thedata has been log transformed to the base 10.The Z value for each of these parameters pro-vides a test of whether the probability of effectin the defined stratum significantly increases asdose or duration of exposure increases.

It is important to note that for Cu defi-ciency, the duration parameter does not havea significant effect in the exposure-responsemodel. However, while duration was not signif-icant in the final model, it was retained in theanalysis to plot ERC10 curves by concentration

TABLE 7. Parameter Estimates, Standard Errors, Z-Test Statistics, and p Values for and Analysis of the Copper ExcessDatabase Using the Cumulative Odds Model

Parameter Estimate Std. error Z test p Value

SEV1 5.8797 3.1609 1.8601 .0629SEV2 5.4416 3.2080 1.6963 .0898SEV3 5.0383 3.2206 1.5644 .1177SEV4 4.0248 3.2062 1.2553 .2094HU:F:INTERCEPT 0.0000 0.0000 NA NAHU:W:INTERCEPT 1.9743 1.2831 1.5387 .1239MU:F:INTERCEPT −19.1012 7.6620 −2.4930 .0127MU:W:INTERCEPT −15.6647 5.9865 −2.6167 .0089RT:F:INTERCEPT −13.8327 3.1243 −4.4274 <.0001RT:W:INTERCEPT −12.9416 3.2232 −4.0152 <.0001HU:2:LG10CONC 9.7482 2.8460 3.4252 .006MU:1:LG10CONC 5.8122 3.7392 1.5544 .1201MU:2:LG10CONC 3.8369 2.4670 1.5537 .1203RT:1:LG10CONC 3.2419 0.4016 8.0731 <.0001RT:2:LG10CONC 2.4122 0.3361 7.17777 <.0001LG10TIME 2.5437 0.6976 3.6463 <.001

Note. Cumulative odds model uses the logit link function. Concentration (mg/kg bw/d) and duration (d) havebeen log transformed to the base 10. Std, standard; SEV, severity level; LG10, log transformed to the base 10;CONC, concentration coefficient; TIME, duration coefficient; HU, humans; RT, rats; MU, mice; F, dietary studies;W, drinking water studies; 1, young animal (≤30 d of age); 2, mature animal (>30 d of age for rodents and ≥18 yrfor humans).

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TABLE 8. Parameter Estimates, Standard Errors, Z-Test Statistics and p Values for an Analysis of the CopperDeficiency Database Using the Cumulative Odds Model

Parameter Estimate Std. error Z test p Value

SEV1 −9.7115 1.7215 −5.6414 <.0001SEV2 −10.5141 1.7354 −6.0585 <.0001SEV3 −11.7843 1.7663 −6.6720 <.0001SEV4 −15.8934 1.9502 −8.1498 <.0001HU:2:INTERCEPT 0.0000 0.0000 NA NAMU:1:INTERCEPT 9.2461 1.7256 5.3583 <.0001MU:2:INTERCEPT 7.6482 1.0245 7.4655 <.0001RT:1:INTERCEPT 6.7146 0.7683 8.7391 <.0001RT:2:INTERCEPT 4.6963 0.6322 7.4285 <.0001LG10CONC −5.2314 0.5517 −9.4817 <.0001LG10TIME 0.2247 0.9321 0.2410 .8095

Note. Cumulative odds model uses the logit link function. Concentration (mg/kg bw/d) and duration (d) logtransformed (log10). Std, standard; NA, not applicable; SEV, severity level; LG10, log transformed to the base 10;CONC, concentration coefficient; TIME, duration coefficient; HU, humans; MU, mice; RT, rats; 2, mature animals(>30 d of age) or adult humans (≥18 yr of age); 1 = young animals (≤30 d of age).

and duration. CatReg does not plot horizontalERC10 lines that have no dependence of riskupon the duration of exposure.

The cumulative odds model of the Cu defi-ciency data was used to plot ERC10 curves ateach level of severity for humans (Figure 3a),mature rats (Figures 3b), and mature mice(Figure 3c). These figures emphasize the min-imal impact of duration in the exposure-response model for Cu deficiency. It is evidentthat the ERC10 curves for severity levels 1and 2 are often well beyond the range of theavailable data for the rat and mouse strata,whereas the ERC10 lines for severity levels 3and 4 are often extrapolated well beyond therange of the available data for the human stra-tum. While all of the human, rat, and mouseobservations are used to estimate the parame-ters for the exposure-response model and plotthe ERC10 curves, only the observations corre-sponding to a given stratum specific ERC10 plotare depicted in each panel in Figure 3. As somestrata do not have observations available at allseverity scores, information from other strata isbeing used to define these ERC10 curves. Forexample, for the human stratum, the ERC10curve for severity level 4 is an extrapolationentirely outside the range of the available data,as there are no severity level 4 observations inthis human stratum. Indeed, only the youngrat stratum has observations at all levels of

severity. For Cu excess, CatReg can use twosources of information to define the humanexposure-response curve for severity level 4.First, it can use the rat and mouse exposure-response curves for severity level 4 and themagnitude of interspecies differences informedby differences in exposure-response curves atother levels of severity where data are avail-able for both humans and animals. Second, itcan use the human drinking water exposure-response curves for severity level 4 and thedifferences between exposure-response curvesfor the diet and drinking water strata at otherlevels of severity.

Figure 4 presents a similar series of plotsfor the cumulative odds model of the Cuexcess data. As might be expected, the parallelERC10 curves in all of the strata have negativeslopes; i.e., higher concentrations are requiredto achieve the estimated 10% response proba-bility at shorter exposure durations. In Figure 4,a–c, it appears as though all the data is clumpedat the left-hand side of each figure. The dura-tion spans past 1000 d of exposure becausethere is one observation in Figure 4a aroundthis time point. The x and y axis in each stratumare influenced by all the data points in the anal-ysis. At this time, CatReg will only allow one toeither restrict the range for the concentrationor duration axis and not both simultaneously.Beyond where the majority of the data lie

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0.00

20.

010

0.05

00.

200

1.00

0

Sev 2

Sev 4

Human ERC10 Lines for all Severity Levels

0 100 200 300 400Duration (d)

Con

cent

ratio

n(m

g/kg

bw

/d)

(a)

Sev 1

Sev 3

0 100 200 300 400

Sev 1

Rat ERC10 Lines for all Severity Levels

Duration (d)

0.01

0.02

0.05

0.10

0.20

0.50

1.00

2.00

Con

cent

ratio

n(m

g/kg

bw

/d)

(b)

Sev 2

Sev 3

Sev 4

0.01

0.02

0.05

0.10

0.20

0.50

1.00

2.00

Mouse ERC10 Lines for all Severity Levels

0 100 200 300 400

Duration (d)

Con

cent

ratio

n(m

g/kg

bw

/d)

(c)

Sev 1

Sev 2

Sev 3

Sev 4

FIGURE 3. ERC10 line for all severity levels for humans (a), mature rats (b), and mature mice (c). Cumulative odds model of the copperdeficiency data with the logit link function transforms concentration (mg/kg bw/d) and duration (d) to the base 10. Intercept stratified byanimal species and concentration by age. Data points are represented as: = severity level 0, = severity level 1, = severity level2, = severity level 3, = severity level 4. Note that severity level 2 or greater is considered serious or adverse.

on the x-axis, the curves continue using thepatterns established for shorter durations ofexposure; however, uncertainty increases withduration due to a lack of data past 200 d ofexposure. This is why interpretations of theCatReg results are limited to sections of theexposure-response curves where the majorityof the data lie.

One qualitative approach to assessingmodel fit is to check whether the ERC10 curvefor each severity level is below (Cu excess) orabove (Cu deficiency) the majority of its cor-responding observations. For Cu excess data(Figure 4), all ERC10 lines for severity levels 1 to4 are below the majority of their corresponding

observations. For Cu deficiency data (Figure 3),the ERC10 curves for severity levels 1 to 4fall above the majority of their correspondingobservations.

The cumulative odds models of the Cuexcess and the Cu deficiency data wereused to produce ERC10-T100 estimates forseverity level 2 or greater for each stratum(Table 9). After accounting for interspeciesdifferences in body weight, the ERC10-T100estimates for dietary studies on Cu deficiencyare 8-fold greater for rats than humans and18-fold higher for mice than humans. TheERC10-T100 estimates for dietary studies onCu excess are 50-fold greater for rats than

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0 200 400 600 800 1000

1e–0

11e

+00

1e+0

11e

+02

1e+0

3

Duration (Days)

Con

cent

ratio

n (m

g/kg

bw

/d)

Sev 3

Sev 2Sev 1

Sev 4

Mice ERC10 Lines for all Severity Levels(c)

0.01

0.02

0.05

0.10

0.20

0.50

1.00

Sev 2

0 200 400 600 800 1000Duration (Days)

(a)Humans ERC10 Lines for all Severity Levels

Con

cent

ratio

n (m

g/kg

bw

/d)

Sev 4Sev 3

Sev 1

0 200 400 600 800 1000Duration (Days)

1e–0

11e

–03

1e+0

11e

+03

Con

cent

ratio

n (m

g/kg

bw

/d)

(b)Rat ERC10 Lines for all Severity Levels

Sev 4

Sev 2Sev 1

Sev 3

Levels

FIGURE 4. ERC10 line for all severity levels for humans (a), mature rats (b), and mature mice (c). Cumulative odds model of the copperexcess data with the logit link function transforms concentration (mg/kg bw/d) and duration (d) to the base 10. Intercept stratified byanimal species and the exposure medium and concentration stratified by animal species and age. Data points are represented as: ◦ =severity level 0, � = severity level 1, � = severity level 2, �= severity level 3, = severity level 4. Note that severity level 2 or greateris severe.

humans and 824-fold higher for mice thanhumans. The estimates can also be comparedbased on the exposure medium. Less Cu isrequired to produce the same level of severitywhen consumed in drinking water than inthe diet. The ERC10-T100 at severity level 2or greater for human dietary studies is 0.05mg/kg bw/d (90% CI 0.03, 0.08) and forhuman drinking water studies 0.03 mg/kgbw/d (90% CI 0.02, 0.05).

SENSITIVITY ANALYSIS

No modifications were made to theduration variable to account for interspecies

differences in physiologic time. When durationwas defined as percent of life span, the distribu-tion of data highlighted the lack of human sub-chronic and chronic exposure studies. Humandata tend to cluster primarily between dura-tions of 0 to 5% life span. In terms of theERC10-T100 estimates for humans for severitylevel 2 or greater, when percent of life spanis incorporated into the model, the originalhuman ERC10-T100 value for Cu deficiencydoes not change. When the model definesduration in days the ERC10-T100 is 0.031 (90%CI 0.022, 0.045) and when the model definesduration in percent of life span the ERC10-T100 is 0.032 (90% CI 0.022, 0.045). For Cu

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TABL

E9.

Extra

Risk

Con

cent

ratio

n(E

RC)E

stim

ates

(mg/

kgbw

/d)

atPr

obab

ility

Leve

l.10

forS

ever

ityLe

vel2

orG

reat

erW

ith90

%C

onfid

ence

Inte

rval

s(C

I)by

Anim

alSp

ecie

s,Ex

posu

re(E

xp.)

Med

ium

,and

Age

Usin

gth

eC

oppe

r(C

u)D

efici

ency

and

Exce

ssD

atab

ase

ERC

10-T

100

(90%

CI)

Cu

defic

ienc

yaC

uex

cess

b

Age

Exp.

med

ium

Hum

ans

Rats

Mic

eH

uman

sRa

tsM

ice

Mat

ure

Feed

0.03

(0.0

2,0.

05)

0.25

(0.1

5,0.

42)

0.91

(0.4

7,1.

74)

0.05

(0.0

3,0.

08)

2.51

(1.2

0,5.

25)

41.1

9(2

.52,

674.

43)

Wat

er—

——

0.03

(0.0

2,0.

05)

1.07

(0.2

0,5.

62)

5.25

(0.2

4,11

5.83

)Yo

ung

Feed

—0.

61(0

.41,

0.89

)1.

84(0

.55,

6.11

)—

1.99

(1.1

3,3.

50)

11.6

8(1

.89,

72.3

3)

a Cu

defic

ienc

ym

odel

uses

the

cum

ulat

ive

odds

optio

nan

dth

elo

gitl

ink

func

tion.

Con

cent

ratio

n(m

g/kg

bw/d)

and

dura

tion

(d)l

ogtra

nsfo

rmed

toth

eba

se10

.Int

erce

ptis

stra

tified

byan

imal

spec

ies

and

age.

bC

uex

cess

mod

elus

esth

ecu

mul

ativ

eod

dsop

tion

and

the

logi

tlin

kfu

nctio

n.C

once

ntra

tion

(mg/

kgbw

/d)

and

dura

tion

(d)h

ave

been

log

trans

form

edto

the

base

10.I

nter

cept

isst

ratifi

edby

anim

alsp

ecie

san

dth

eex

posu

rem

ediu

m.C

once

ntra

tion

para

met

eris

stra

tified

byan

imal

spec

ies

and

age.

565

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TABLE 10. Comparison of the Combined Analysis with the Species-Specific Analysis: Extra RiskConcentration (ERC) Estimates (mg/kg bw/d) at Probability Level .10 Are Defined for 100 d (T) with90% Confidence Intervals (CI) at Severity Level 2 or Greater for Copper Excess

ERC10-T100 (90% CI)

Stratum Combined analysisa Species-specific analysis

Humansb 0.05 (0.03, 0.08) 0.04 (0.01, 0.21)Mature ratsc 2.51 (1.20, 5.25) 3.56 (1.53, 8.28)Mature miced 41.19 (2.52, 674.43) —

aOriginal model in the combined analysis contains all data on humans, rats, and mice. Cu excessmodel uses the cumulative odds option and the logit link function. Concentration (mg/kg bw/d) andduration (d) have been log-transformed to the base 10. Intercept is stratified by animal species andthe exposure medium. Concentration parameter is stratified by animal species and age.

bIn the species-specific model with human data, the model’s intercept was stratified by theexposure medium.

cIn the species-specific model with rat data, the intercept was stratified by the exposure mediumand the concentration parameter was stratified by age.

dMice only model could not be defined due to limited data.

excess, the original ERC10-T100 value usingdays for duration is 0.047 mg/kg bw/d (90%CI 0.028, 0.078). Using percent of life spanfor duration, the resulting ERC10-T100 valueis 0.045 (90% CI 0.027, 0.075). The risk esti-mates do not appear to be greatly influencedby changes to the scale used to measure theduration of exposure.

To investigate the impact of combiningstudies with different animal species in a com-mon analysis, a separate species-specific anal-ysis was conducted. Table 10 presents theERC10 estimates for severity level 2 or greaterfor each animal species in a species-specificanalysis as well as a combined analysis for Cuexcess. Table 11 presents the same informa-tion for Cu deficiency. For Cu excess there

were insufficient human data to run a modelwith 5 levels of severity (severity level 0 toseverity level 4). Severity levels 1 and 2 aswell as severity levels 3 and 4 needed to becombined in order to run the model. For bothCu deficiency and excess, there were insuffi-cient data on mice to run the analysis. Overall,the ERC10-T100 estimates produced in thespecies-specific analysis are close to the ERC10estimates generated in the combined analysis(Tables 10 and 11). Comparing the combinedanalysis to the species-specific analysis for Cuexcess, the ERC10 estimates for rats increasedfrom 2.51 mg/kg bw/d (95% CI 1.20, 5.25) to3.56 mg/kg bw/d (95% CI 1.53, 8.28) and theERC10 estimates for humans decreased from0.05 mg/kg bw/d (95% CI 0.03, 0.08) to 0.04

TABLE 11. Comparison of the Combined Analysis with the Species-Specific Analysis: Extra RiskConcentration (ERC) Estimates (mg/kg bw/d) at Probability Level .10 Are Defined for 100 d (T) With90% Confidence Intervals (CI) at Severity Level 2 or Greater for Copper Deficiency

ERC10-T100 (90% CI)

Stratum Combined analysisa Species-specific analysis

Humansb 0.03 (0.02, 0.05) 0.02 (0.01, 0.02)Mature ratsc 0.25 (0.15, 0.42) 0.24 (0.13, 0.42)Mature miced 0.91 (0.47, 1.74) —

aOriginal model in the combined analysis contains all data on humans, rats and mice. For copperdeficiency, the intercept is stratified by animal species and age.

bHuman only model for copper deficiency combines severity scores 1–2 and 3–6 and stratifies theintercept by means of exposure.

cIn the species-specific model with rat data, the intercept is stratified by age.dMice-only model could not be defined due to limited data.

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mg/kg bw/d (95% CI 0.01, 0.21). For Cu defi-ciency, the ERC10 estimates for rats decreasedfrom 0.25 mg/kg bw/d (95% CI 0.15, 0.42) to0.24 mg/kg bw/d (95% CI 0.13, 0.42) and theERC10 estimates for humans decreased from0.03 mg/kg bw/d (95% CI 0.02, 0.05) to 0.02mg/kg bw/d (95% CI 0.01, 0.02).

Prior to the database update, there were136 observations and 187 observations fromCu deficiency and excess studies, respectively.After the Cu database update, 73 observationswere added for Cu deficiency and 55 obser-vations were added for Cu excess. There wasonly a small change in the estimate for Cu defi-ciency since the database update (Table 12).The ERC10-T100 estimate for severity level 2or greater before the update was 0.032 mg/kgbw/d (90% CI 0.021, 0.049 mg/kg bw/d)which decreased to 0.031 mg/kg bw/d (90%CI 0.022, 0.045 mg kg bw/d) after the update.There was a larger change in the estimates forCu excess. The ERC10-T100 for severity level 2or greater before the update was 0.076 mg/kgbw/d (90% CI 0.038, 0.152 mg/kg bw/d),which fell to 0.047 mg/kg bw/d (90% CI 0.028,0.078 mg/kg bw/d) after the literature reviewupdate. Consequently, the database updateended up narrowing the acceptable range oforal Cu intake defined by the ERC10-T100estimates for severity level 2 or greater.

Based on the various results from sensitiv-ity analyses, the final Cu deficiency and excessmodels utilized exposure duration expressed indays and all of the available data on humans,rats and mice in a combined analysis. Theparallel cumulative odds models defined bythese specifications produced an ERC10-T100

estimate at 2.1 mg/d (90% CI, 1.4, 3.5) forseverity level 2 or greater for Cu deficiency and3.5 mg/d (90% CI, 2.1, 5.6) for severity level 2or greater for Cu excess.

Figure 5, a–d, presents plots of the proba-bility curves for severity levels 1 to 3 for both Cudeficiency and excess. Equation (1) was used tocreate the U-shaped exposure-response curvefor toxicity from deficiency or excess (repre-sented by the dotted curves in Figure 5, b–d).The resulting trough in the U-shaped curve orthe AROI is quite narrow. The probability levelat the lowest point in the U-shaped curve forseverity level 2 or greater is 0.11. The corre-sponding dose at this probability level is 0.037mg/kg bw/d. This estimate can be convertedto a dose in mg/d if we assume an averagehuman bodyweight of 70 kg. Therefore, theoptimal intake level to protect the populationfrom severity level 2 or greater responses asso-ciated with both Cu deficiency and excess isapproximately 2.6 mg/d. The probability levelat the lowest point in the U-shaped curve forseverity level 3 or greater is 0.04, which cor-responds to 0.0315 mg/kg bw/d. The optimalintake level to protect the population fromseverity level 3 or greater responses associatedwith both Cu deficiency and excess is approx-imately 2.2 mg/d. It is important to note thatgroup data, and not individual subject data,were used in this analysis, which complicatesthe interpretation of the final risk estimates. Ifthe data were for individual exposed subjects,then the probability curves would representestimates of individual risk. However, with dataonly available at the group level, as is the casein this analysis, p represents the probability that

TABLE 12. Comparison of Original and Updated Copper Database: Extra Risk Concentration (ERC)Estimates (mg/kg bw/d) at Probability Level .10 Are Defined for 100 d (T) With 90% ConfidenceIntervals (CI) at Severity Level 2 or Greater

ERC10-T100 (90% CI)

Stratum Deficiencya Excessb

Pre-database update 0.032 (0.021, 0.049) 0.076 (0.038, 0.152)Database update 0.031 (0.022, 0.045) 0.047 (0.028, 0.078)

aCopper deficiency models stratify intercept by animal species and age.bCopper excess models stratify intercept by animal species and exposure medium and the

concentration parameter by animal species and age.

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FIGURE 5. (a) Probability curves for copper deficiency and copper excess for severity levels 1 to 3. (b–d) Probability curves for copperdeficiency and excess for severity levels 1, 2, and 3, respectively. Each figure (b–d) also presents the summative probability curves definedby Eq. (1). This curve is represented by the dashed lines in figures b-d.

a group of average size in the Cu databasewould exhibit a mean response of a givenseverity level or greater. Essentially the CatRegmodel is predicting a probability of 0.11 that aresponse of category 2 severity or greater, fromeither excess or deficiency, may occur and bedetected reliably if 2.6 mg/d is given to a groupof subjects (of the average group size in the Cudatabase) for 100 d.

DISCUSSION

This study illustrated how categoricalregression, which combines data from differentsources and uses a common severity scale, canbe used as an analytical approach to criticallyevaluate data on Cu excess and deficiency

and define a range of dietary intakes thatmeet the nutritional requirements of a healthypopulation as well as avoid adverse healtheffects from elevated Cu intake. As illustratedin the analysis, considerable variability in theCu exposure-response database in terms ofthe study design, animal species, sex, and agemight be accounted for by stratifying param-eters in the exposure-response model. Theanalysis of the Cu database has estimated anoptimal intake level of 2.6 mg/d for severitylevel 2 or greater.

Stratification OptionsAnimal species Animal species has an

important effect on the exposure-responsemodels for both Cu excess and deficiency. The

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stratum-specific ERC10-T100 estimates definedby the model of the Cu excess data demon-strate that compared to humans, rats and miceseem to be less sensitive to adverse healtheffects when dose is expressed in milligramsper kilogram bw per day. Smaller differenceswere found for Cu deficiency. For Cu excess,the ERC10-T100 was 50 times higher in ratsthan in humans. While the analysis does sug-gest that there are large interspecies differencesin sensitivity to Cu excess, we cannot be certainof the relative magnitude of these differences.Current estimates are based on an empiricalmodeling approach that has not incorporatedbiologically based information. It is importantto note that the differences in the ERC10 esti-mates observed among animal species can alsobe a result of differences in the design of animalversus human studies and differences in thetypes of responses under investigation. Relativedifferences in sensitivity could be confirmed inadditional experimental studies that use mul-tiple animal species, similar doses of Cu, andcommon response outcomes. Understandingthe biological mechanisms that underlie thesedifferences in sensitivity is also essential.

Duration For humans, generalizationsregarding the impact of duration of exposurefrom subchronic and chronic exposures cannot be made at this time due to a lack of datapast 100 d of exposure. For rats and mice,duration does seem to exert an importantimpact on the exposure-response curve forCu excess, as a lower ERC10 is required toproduce the same response probability asduration increases. With the data set currentlyavailable, duration seems to exert minimaleffects on the exposure-response curve forCu deficiency data. Intuitively, one wouldpredict that duration should have an effect. Itis recognized that if there were more chronicstudies, there might be sufficient power todetect an impact of duration.

Exposure medium The exposure medium(drinking water versus feed) had a significanteffect on the exposure-response model of theCu excess data. A lower dose was required toproduce the same level of severity when Cuwas administered in drinking water compared

to Cu administered in feed or a capsule. Atthis time, there is a lack of human data doc-umenting any negative effects due to chronicintake of high amounts of Cu in drinking water.Additional research on the effects of chronicintake of Cu in drinking water is needed asthere have been some concerns raised aboutthe potential chronic adverse health effects ofhigh levels of Cu in drinking water.

A difficulty with the data extracted fromexisting human drinking water studies is thatthere are currently no apparent observationsthat were classified into severity levels 1 to 3.Human studies that examined adverse healtheffects from Cu excess have found either noeffects (severity level 0) or responses that havebeen classified as severity level 4. For humans,the impact of the exposure medium on therisk of adverse health effects from elevatedCu intake can not be adequately assessed atthis time. More research is required to bet-ter understand the potential importance of Cuexposure via drinking water with respect to Cubalance and long-term health outcomes. Suchstudies should ideally include a broad range ofsensitive markers of Cu imbalance and use achronic duration of exposure.

Compared to studies on humans, thereare a greater number of rat and mouse stud-ies that evaluated the effects of subacute andsubchronic exposures to excess Cu in drink-ing water. These studies measured a broadrange of sensitive markers of Cu toxicity. Datasuggest that there is greater sensitivity to Cuexcess when Cu is administered in drinkingwater, compared to the same dose adminis-tered in diet.

Age For Cu deficiency, there was only asmall difference in the ERC10-T100 estimatesbetween young and mature rats and mice.The difference between the ERC10-T100 esti-mates for young and mature rats was 0.36mg/kg bw/d. The difference for young andmature mice was 0.93 mg/kg bw/d. The dif-ference was more pronounced for Cu excess,where weanling rats and mice were more sensi-tive than mature rats and mice. The differencebetween the ERC10-T100 estimates for youngand mature rats was 41.19 mg/kg bw/d, and

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29.51 mg/kg bw/d for young and mature mice.These results are consistent with other find-ings showing that young rats absorb Cu in aconcentration-dependent fashion with limitedfeedback control or saturability (Coudray et al.,2006; Varada et al., 1993).

Sex Sex did not appear to exert a sig-nificant effect in either the cumulative oddsmodels for Cu excess or that for deficiency. Forhumans, this is consistent with the finding that,on a body-weight basis, men and women havesimilar Cu requirements (Johnson et al., 1992).In animal studies, however, differences wereobserved between males and females (Shiraishiet al., 1993; Nederbragt, 1985; Fuentealbaet al., 2000; Linder et al., 1979; Bremneret al., 1981; Bureau et al., 2003; Farquharson& Robins, 1988). The lack of a significant effectof sex in the exposure-response model maybe due to inconsistencies found in the litera-ture. In some studies males appear to be moresensitive to Cu toxicity from excess (Shiraishiet al., 1993; Nederbragt, 1985), whereas inother studies females appear to be more sen-sitive (Fuentealba et al., 2000; Linder et al.,1979; Bremner et al., 1981; Bureau et al.,2003; Farguharson & Robins,1988). AlthoughFuetealba et al. (2000) suggested that theimpact of sex on liver Cu accumulation inrats depends on the strain used, the cur-rent exposure-response models are not ableto take into consideration the animal strain.Furthermore, the impact of sex was shown tovary depending on the target organ of observedtoxicity. At this time, there are insufficient dataavailable to incorporate target organ and ani-mal strain in the exposure-response model.

Cu Exposure-Response CurvesSeparate exposure-response curves for Cu

excess and deficiency were placed on thesame axis to identify the location where thecurves cross. The crossing of the curves appearto demonstrate that as soon as the toxicresponse from Cu deficiency decreases to a lowlevel, with increasing dose of copper, the toxicresponse from copper excess increases. Basedon this analysis alone, one cannot conclude

whether the flat part in the trough of theU-shaped exposure-response curve does notexist. It is recommended that future updatesto the categorical regression analysis exploredifferent modeling options that will allow theCu deficiency and excess data to be ana-lyzed simultaneously to confirm the width ofthe trough in the U-shaped exposure-responsecurve.

Comparison of Results with CurrentRegulatory ValuesThe optimal (minimum risk) intake level for

severity level 2 was estimated to be approxi-mately 2.6 mg Cu/d. In the United States andCanada, the current recommendations for Cuintake among adults range from 0.9 mg Cu/d(RDI) to 10 mg Cu/d (tolerable UL). The opti-mal intake level (minimum risk intake level forseverity level 2 or greater) (2.6 mg/d) is belowthe tolerable UL for Cu (10 mg Cu/d) estab-lished by the Food and Nutrition Board (2001).One of the issues with the current upper intakelevel is that it is based solely on the NOAEL formarkers of liver function identified in a singlestudy (Pratt et al., 1985). If every study in theCu database (which considers a broad range ofmarkers of Cu excess and deficiency) applieda traditional risk assessment approach where aNOAEL identified from a single study is dividedby an uncertainty factor, this would more thanlikely result in a reference dose that is moresimilar to the results generated in the categor-ical regression. This would be due to the factthat the studies in the Cu database consider lesssevere but still clinically important responses toelevated Cu intake, whereas the tolerable UL isbased exclusively on liver toxicity.

The current RDI for Cu is 0.9 mg/d. Thereare several reasons why the optimal dose thatminimizes the risk of severity level 2 or greaterresponses to deficiency or excess is higher thanthe current RDI for Cu. The categorical regres-sion approach takes into consideration morestudies on Cu deficiency, beyond the threestudies used to set the EAR. The Cu databasecontains several studies that found responsesassociated with severity level 2 or greater that

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are relatively close to the current RDI (0.9 mgCu/d). For example, in the study by Klevayet al. (1986), 0.8 mg Cu/d for 150 d was asso-ciated with severity level 3 responses, namely,increased plasma glucose levels and decreasedinsulin response, as compared to controls.

Limitations in the AnalysisIt is important to recognize the various lim-

itations of the categorical regression analysis.First, the quality of the final model parame-ter estimates is influenced by the quality ofthe data coming from the individual studies inthe Cu exposure-response database. All stud-ies on rats and mice and a few studies onhumans employed a controlled experimentaldesign. When deciding on which studies toinclude in the Cu database, the original groupof experts in toxicology and risk assessmentassessed not only the utility of each experimentfor an exposure-response analysis but also thescientific quality of the study (Krewski et al.,2010b). While controlled experimental designsare considered to be the most rigorous ofthe research design methods, alternative studydesigns were considered due to the limiteddata that were available across a wide rangeof doses and durations of exposure, especiallyamong human studies. For example, two casestudies were included in the Cu database, oneinvolving an acute and accidental overdose ofCu, and one involving a report of cirrhosis froma chronic exposure to 45 mg Cu/d in supple-ment form. Case reports involving only a singleindividual clearly have limited generalizability;however, they are the only studies availableshowing the potential effects of adverse long-term elevated Cu intake or short-term effectsof massive Cu ingestion in humans.

It is recognized that there are limitationswith current biomarkers used to detect statesof Cu deficiency and excess. It is importantto recognize that severity level 2 responses forCu deficiency have been assigned to changesin blood lipids (Reiser et al., 1987), indexesof immune response (Kelley et al., 1995),and bone resorption (Baker et al., 1999b).Responses at severity level 2 for Cu excess

have been associated with changes in levelsof serum potassium (Pratt et al., 1985) andchanges in indexes of Cu status (erythrocyteSOD), immune response, and oxidant stress(Turnlund et al., 2004). These were consideredto be important physiological changes; how-ever, not all of these responses have been iden-tified or validated as optimal early biomarkersof elevated and deficient Cu status. Futureresearch is needed to identify and validate sen-sitive and early biomarkers of Cu status. Oneof the issues with traditional markers of Cudeficiency (e.g., ceruloplasmin, superoxide dis-mutase activity, plasma Cu) is that they arecontrolled by strong homeostatic mechanismsand are influenced by other biological factors(e.g., inflammation, pregnancy, disease) (Milne,1998; Stern et al., 2007). Biomarkers thathave been used to detect severely Cu deficientstates may not be sensitive enough to detectmarginal Cu deficiencies (Louro et al., 2001;Stern et al., 2007). In Harvey and McArdle’s(2008) review of the reliability and robust-ness of current biomarkers for Cu status, theycame to the conclusion that the Cu chaper-one for superoxide dismutase (CCS) may bethe most promising potential biomarker; how-ever, the reliability of this biomarker has yet tobe established. At this time, liver Cu concen-trations are considered to be the most reliablebiomarker for Cu excess; however, it is diffi-cult and invasive to measure these in humans(Milne 1998, Stern et al., 2007; Uauy et al.,2008). Refining and decreasing the uncer-tainty of the Cu exposure-response curves out-side of the acceptable range of oral intakewill be advanced through future research thatidentifies and evaluates reliable and sensitivebiomarkers that can detect early disruptions inCu status.

It is important to consider the extent towhich dose spacing in the relevant studiesimpacted the results. The database contains asignificant amount of exposure-response datathat is derived from the control groups of exper-imental studies. These observations correspondto Cu intake arising from consumption of a reg-ular diet. There is also a significant amount ofdata from animal studies at more extreme levels

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of exposure. When data clusters at low andhigh levels of exposure, this does not result inoptimal conditions to characterize the slopesof the exposure-response curve around themargins of the acceptable range of oral intake.

There was considerable effort requiredto construct the comprehensive Cu databasefor the purposes of this analysis. Classifyingresponses into severity categories is a challeng-ing task and requires considerable expert judg-ment. A number of studies measured a widerange of responses, and reported statisticallysignificant differences compared to controls.A key challenge is deciding whether statisti-cally significant differences are also clinicallysignificant. The physiologic implications of theobserved changes are not always known.

There are many gaps in our knowledgeregarding the dynamic and kinetic propertiesof Cu in animal and human tissues, and thishas limited the development of biologicallybased exposure-response models (Stern et al.,2007). An important question is whether onecould actually define a more complex empir-ical model that took into consideration moredetailed information about the real biologicalsystem with the exposure-response data that isavailable in our database. There were severallimitations in the data that prevented the useof more sophisticated modeling approaches.The current model defined in CatReg is with-out doubt overly simplistic. However, in theanalysis there was insufficient data to supportmore complex empirical models. This led toerrors when attempting to run the unrestrictedcumulative odds model that did not constrainERC10 curves to be parallel across severityscores. When there are limited data across dif-ferent severity levels and ranges of exposure,model selection options become limited, whichnecessitates the use of simpler models andstricter assumptions. While the CatReg modelsallowed parameters to be stratified, essentiallythe process is a large meta-analysis of studieswith disparate health end points, biological sys-tems, and study designs. This requires the useof a complex model with several levels of strati-fication to sufficiently account for the variabilityin the database.

Empirical modeling approaches use math-ematical models to fit data, often with littleor no biological rationale. Because there aresubstantial data gaps in the current exposure-response database, the interpretation of resultsneed to be limited to the range of availabledata. For example, one should not use ERC10estimates at chronic exposure durations forhumans because there are minimal human datapast 100 d of exposure. As one extrapolatesbeyond the available data, the extrapolationsbecome more and more model dependent.Because data from multiple studies and mul-tiple species can be combined in a commoncategorical regression analysis, extrapolationsof model results might be regarded as moredefensible than those that could be made fromBMD analyses of data from single studies; how-ever, there is no clear evidence that this isthe case.

The substantial gaps in the Cu databaseforced some extrapolations. For example,exposure-response curves for higher levels ofseverity for the human data were estimatedfrom exposure-response data from lower lev-els of severity. More questionable, perhaps,was the use of short-term data to estimateexposure-response relationships for subchronicexposure situations. In order not to extrapolatebeyond the available data, optimal intake lev-els were estimated at 100 d. Most importantly,the sparseness of chronic data precluded char-acterization of the acceptable chronic exposurerange suitable for use in regulatory decisionmaking. As there is some evidence that pro-longed marginal Cu deficiency may impairneurological function and elevate the risk ofdeveloping a range of diseases including heartdisease and osteoporosis (IPCS, 1998; Klevay,1980; Strain, 1994), it is essential that weobtain more information on more marginal andlong-term exposures to Cu deficiency.

Future Research InitiativesTo improve the analysis and establish more

confidence in the results, there is a need formore studies on the effects of marginally excessand deficient levels of Cu, and a need for the

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measurement of a broad range of relevant andsensitive markers of disrupted Cu homeostasis.At this time, there is also a lack of informa-tion on the chronic effects in humans. Whilea categorical regression analysis was shownto be a useful empirical approach for mod-eling a diverse collection of studies on Cudeficiency and excess, Stern et al. (2007) com-ment: “Ideally, detailed information regardingCu uptake, binding, distribution, metabolism,and excretion would be coupled with mech-anistic models of how various organ systemsrespond to variation in their Cu status.” Animproved understanding of Cu metabolism andhomeostasis is also needed to derive moreprecise estimates of dietary requirements. Asmore data are added to the Cu database, theremay be the potential for the development oforgan-specific exposure-response models.

Future initiatives with the Cu databasecould also involve a focused literature reviewupdate to identify and incorporate studies thatused subjects with perturbed Cu metabolism(e.g., mutant mice) to evaluate potential dif-ferences in risk to excess and deficient levelsof Cu. There have been studies on rodentgroups with some form of genetic abnormalitywhich increases their sensitivity to the effectsof Cu imbalances (Sparks et al., 2006). Datafrom these types of experiments have not beenincluded in the current database.

Differences in sensitivity due to geneticvariation must be considered in the interpre-tation of the risk estimates. The presence ofinherited diseases of Cu homeostasis in bothhumans and animals demonstrates the poten-tial for significant variation in risk of deficiencyand excess in the population. There are sev-eral disorders of Cu metabolism that leadto Cu deficiency and toxicity at levels thatwould be tolerable in the general population(IPCS, 1998). Significant advancements havebeen made to understand Wilson’s disease (Cuexcess) and Menkes’ disease (Cu deficiency),two genetic disorders of Cu metabolism.Menkes’ disease (an X-linked syndrome) hasbeen estimated to occur at a frequency of1/200,000 live births and Wilson’s disease(an autosomal recessive disorder) has been

estimated to have an incidence of 1/30,000and a carrier frequency of approximately 1:90(Stern et al., 2007). There is now evidencefrom animal studies that suggests Wilson’s dis-ease carriers, in the human population, maybe at increased risk of Cu loading for chronicdurations of exposure (Cheah et al., 2007).Polymorphisms at loci for known copper trans-porters and the heterozygous state for thesetransporters may contribute to significant vari-ation in the population with respect to suscep-tibility to Cu deficiency and excess. To inves-tigate the impact of genetic variation on theresults of the exposure-response analysis, fur-ther updates of the Cu database might considerthe inclusion of data from studies using trans-genic or knockout rats and mice. This informa-tion would be useful for understanding modeof action and incorporating more biologicallybased considerations into the dose-responseanalyses.

The Cu database also does not includeexperiments on pregnant animals. At this time,there is insufficient information to define anacceptable range of oral intake for pregnantwomen; however, including available develop-mental data study may allow us to explore theimpact of pregnancy on risks from Cu excessand deficiency.

It is important to recognize that the cur-rent Cu database includes only experimentswherein Cu was assessed alone, without anyother dietary modifications. Several experi-ments have used both Cu and Zn supplemen-tation to explore the impact of the dietaryinteraction between these essential elements.Intakes of high amounts of dietary Zn can resultin a reduction in Cu absorption from the gut,and precipitate signs of Cu deficiency. Recently,Nations et al. (2008) has suggested that Zn-induced Cu deficiencies may also be a con-sequence of the excess use of denture creamshigh in Zn. This may be a particularly importantpublic health issue as approximately 15% of theU.S. population reports taking a dietary supple-ment containing zinc (Briefel et al., 2000). Cuabsorption was also reported to be influencedby other dietary factors, including iron andfructose (Uriu-Adams et al., 2010). Interactions

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between Cu and other dietary factors thuscontribute to further variability in the optimalintake levels of Cu in the general population. Asadditional studies looking at these interactionsbecome available, a stratified analysis definedby the presence and absence of exposure inter-actions could be used to explore the impact ofthese studies.

Further initiatives should also focus onimproving the statistical models used to con-duct exposure-response analyses. The currentmodels available in CatReg can only definethe exposure-response curves for Cu deficiencyand excess separately. Before developing a sta-tistical technique that can incorporate Cu defi-ciency and excess data in a combined analysis,there is a need to consider whether mech-anisms of toxicity due to Cu deficiency andexcess are independent or interrelated at thebiological level (Stern et al., 2007).

CONCLUSION

An expert panel from multiple scientificdisciplines developed a severity scoring sys-tem and a Cu exposure-response database. Acategorical regression analysis was undertakento optimize the use of the available data onthe adverse health effects from excess anddeficiency. Study design, animal species, sex,and age were considered with stratum-specificparameters in the exposure-response models.The exposure-response models for Cu defi-ciency and excess at severity level 2 or greateryield an optimal intake level at 2.6 mg Cu/d.This estimate provides further support to theEuropean Union’s voluntary risk assessment ofcopper, which stated that current intake rec-ommendations of around 1 mg/d may be toolow (European Chemicals Agency, 2009). Theresults of this analysis should not be interpretedwithout considering the limitations that havebeen outlined. To weigh the relative harm ofdeficiency and excess, it is important that theresults be interpreted within the context ofthe information available on the adverse healtheffects assigned to each severity score. Whilea biologically based exposure-response modelfor Cu would be ideal, data required to support

such a model are currently unavailable. Withthat said, recommendations that minimize thegeneral public’s risk for Cu deficiency as well asexcess are needed.

The analysis has demonstrated that theimpact of considering multiple response out-comes when defining an exposure-responsecurve for Cu excess and deficiency. Integrationof a broad range of responses of differentlevels of severity in the categorical regressionanalysis produced an AROI that is narrowerthan current recommendations for Cu intakeincluding the RDI (0.9 mg/d) and the toler-able UL (10 mg/d). It is recommended thatfuture risk assessments on Cu consider multipleresponse outcomes when setting recommenda-tions. This study has contributed new informa-tion about the utility of categorical regressionas an empirical modeling approach to defineexposure-response curves for essential metal-lic elements. As categorical regression is able toincorporate a broad range of responses to Cuexcess and deficiency, it offers a way to makemore efficient use of the available data whenmaking risk management decisions. Categoricalregression may also be a useful empirical mod-eling approach that can be used to defineexposure-response curves for other micronutri-ent requirements where a suitable body of dataexists.

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an exposure-response curve for copper excess and deficiency

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