5
Interpretation of Biomonitoring Results

INTRODUCTION

Finding chemicals in bodily fluids is evidence of contact with them through inhalation, dermal exposure, or ingestion, and it typically leads to two questions that pose important challenges in interpreting biomonitoring results and are the focus of this chapter:

  • Is the biomonitoring result in a range that is typical of the general, non-occupationally exposed population?

  • Does the biomonitoring result indicate a health risk?

This chapter describes various options for interpreting biomonitoring results with respect to those two questions and discusses how the analysis and interpretation can be used in different biomonitoring settings. The settings in which biomonitoring results may need interpretation include the workplace, the doctor’s office, screening of the general population, and study of specific subpopulations. The purpose and use of biomonitoring data may vary among those scenarios, but the options for interpreting the data are generally similar.

Other questions that are alluded to but not addressed in detail in this chapter include, how did the exposures occur? Are there means to decrease the exposures? These questions involve interpretation of biomonitoring data but also extend into risk-management issues.

Figure 5-1 is a flow diagram of the information provided in this chapter. When biomonitoring data become available, one must determine the



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Human Biomonitoring for Environmental Chemicals 5 Interpretation of Biomonitoring Results INTRODUCTION Finding chemicals in bodily fluids is evidence of contact with them through inhalation, dermal exposure, or ingestion, and it typically leads to two questions that pose important challenges in interpreting biomonitoring results and are the focus of this chapter: Is the biomonitoring result in a range that is typical of the general, non-occupationally exposed population? Does the biomonitoring result indicate a health risk? This chapter describes various options for interpreting biomonitoring results with respect to those two questions and discusses how the analysis and interpretation can be used in different biomonitoring settings. The settings in which biomonitoring results may need interpretation include the workplace, the doctor’s office, screening of the general population, and study of specific subpopulations. The purpose and use of biomonitoring data may vary among those scenarios, but the options for interpreting the data are generally similar. Other questions that are alluded to but not addressed in detail in this chapter include, how did the exposures occur? Are there means to decrease the exposures? These questions involve interpretation of biomonitoring data but also extend into risk-management issues. Figure 5-1 is a flow diagram of the information provided in this chapter. When biomonitoring data become available, one must determine the

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Human Biomonitoring for Environmental Chemicals FIGURE 5-1 Overview of interpretive options for biomonitoring data. interpretive options for evaluating them. The options include descriptive approaches that involve comparisons among biomonitoring datasets and risk-based approaches that describe the degree of risk associated with a given biomonitoring result. Throughout this chapter and in Appendix B, case studies are used to illustrate the applications of biomonitoring data to understanding of risk. The case studies are intended solely as illustrations and are not judgments about the data or risks associated with the chemicals discussed. INITIAL REVIEW OF BIOMONITORING DATA Interpreting biomonitoring results depends on the availability of various types of information, including data on exposure, toxicity, and toxicokinetics. If toxicity information is unavailable, the results cannot be put into a risk

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Human Biomonitoring for Environmental Chemicals context. If exposure information is unavailable, it may not be possible to determine where and how the exposures that produced the measured biomarker concentrations occurred. However, the starting point for interpreting biomonitoring data is an assessment of the quality of the biomonitoring data. If they are of low quality, there is little point in considering whether exposure or toxicity information is available. But high-quality biomonitoring data may be applied to a variety of interpretive options, as outlined below. Chapter 4 reviews the considerations relevant to the design of biomonitoring studies to ensure scientific quality and integrity. OVERVIEW OF INTERPRETIVE OPTIONS FOR BIOMONITORING DATA Two main options for interpreting biomonitoring results—descriptive and risk-based approaches—appear in Figure 5-1. This figure is organized from simplest to most complex approaches, with the potential for interpreting health risks also increasing from top to bottom. The expectation is that the quicker, descriptive approaches would be used first and then, depending on the level of concern and data availability, risk-based approaches would be used. The final interpretation of biomonitoring data would probably have elements of both. Descriptive Approaches The first level of analysis is purely descriptive, presenting a statistical review of the data, typically in the form of a data distribution from which percentiles of the population (such as 10th, 25th, 50th, 75th, and 90th percentiles) are easily obtained. That establishes a reference range with which individual or subgroup results can be compared. The range offers a point of comparison; individuals or subgroups may be within the range or may be subject to more or less exposure or vulnerability. A number of interpretive issues in this approach are described in this chapter. For the most part, the Centers for Disease Control and Prevention (CDC) analysis of biomonitoring results from its National Health and Nutrition Examination Survey (NHANES) is focused on the reference-range approach (CDC 2005). Another descriptive approach characterizes a chemical’s use pattern in society at large. The information is used to interpret biomonitoring data in terms of how long the chemical may have been detected in bodily fluids and whether its concentration may be going up or down with changing use. It is not uncommon for the public to consider a new biomarker as evidence of new exposure. But it is possible that exposure has been going on for de-

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Human Biomonitoring for Environmental Chemicals cades and the biomarker became available only recently. Because new or increasing exposure generally prompts greater concern, the context is important. This question is best answered by analysis of biomonitoring results that span several years of sample collection. However, if biomonitoring results are available only for a single sampling round, temporal trends cannot be known. In such a case, historical data on chemical production rates and trends may be useful (if they are available). Workplace biologic reference values are another descriptive option for interpreting biomonitoring results in the general population. Such values as the Biological Exposure Index (BEI) of the American Conference of Governmental Industrial Hygienists (ACGIH) are workplace standards used to evaluate whether individual workers have received exposures that exceed a workplace air standard, such as a Threshold Limit Value (TLV). A blood or urinary biomarker is a better indication of personal exposure than an area air sample. BEIs have been used as points of reference for biomonitoring results in the general public (CDC 2005). However, because BEIs do not take into account the differing exposure patterns (continuous vs 8-hour workshift exposure) and vulnerability of the general public (including children, pregnant women, the elderly, and the ill) compared with healthy workers, using BEIs to judge community exposure and risk raises numerous interpretive issues. This chapter reviews those issues and outlines major limitations in applying adjustment factors to BEIs to derive biomarker targets relevant to the general public. Risk-Based Approaches The most data-intensive approaches are those which evaluate the risk associated with a biomonitoring result. Evaluation of risk may be a desirable outcome, given the importance of the “How risky is this blood concentration” question and the fact that the descriptive approaches only provide relative information and do not assess risk. Figure 5-2 illustrates the various risk-based options discussed in the report. In the most straightforward risk-based approach, epidemiologic studies have developed exposure-response relationships based on biomarker measurements in hair, blood, urine, or other matrices (e.g., mercury, lead) (see Figure 5-2a). The relationships can be applied directly to new biomonitoring data to determine where on the exposure-response curve any person is. That may facilitate an understanding of risk, but it does not analyze sources of exposure, so other techniques (such as environmental sampling and behavioral surveys) may be needed to assess where the exposure came from. Because human biomarkers are rarely the basis of exposure-response relationships, practitioners generally rely on more traditional risk assess-

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Human Biomonitoring for Environmental Chemicals FIGURE 5-2 Illustration of the interpretative risk-based options. ments. Those assessments characterize human exposure with a pathways analysis, accounting for concentrations in air, food, water, and soil to estimate human dose in milligrams per kilogram per day. The dose is then used to calculate risk on the basis of reference doses or cancer slope factors (see Figure 5-2b). Using existing risk assessments for interpreting biomonitoring data can help to put biomonitoring results into a broad risk

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Human Biomonitoring for Environmental Chemicals context that makes maximal use of the underlying exposure and toxicology data but falls short of actually calculating risk. Another option attempts to convert biomonitoring results into a form that is directly useful for risk assessment. The chapter describes both the human pharmacokinetic (PK) modeling used to relate internal concentration to dose and the development of exposure-response relationships in animal studies that use biomarker concentrations rather than applied dose (see Figure 5-2c). Finally, the chapter describes how biomonitoring studies can augment and help to interpret traditional risk assessments. Many communication challenges stem from collection, interpretation, and reporting of biomonitoring results. This chapter indicates where communication issues arise in relation to the interpretation of biomonitoring results; these issues are explored more fully in Chapter 6. Case Examples Used in This Chapter A number of case examples are used to illustrate the feasibility of the interpretive options described in this chapter. Some of the examples are presented in the chapter, and others are presented in Appendix B. Generally, examples were selected because they have the requisite data from epidemiology, PK, or animal toxicology studies to facilitate the risk interpretation of biomonitoring results. For many other chemicals that may be the subjects of biomonitoring, those types of data are not available and thus constitute biomarker-specific data gaps. Such data gaps need to be filled case by case on the basis of the type of biomarker and the underlying database to improve our interpretation of biomonitoring results. As exemplified by the examples presented, it may be most expeditious in some cases to obtain animal PK and in others to use human PK modeling or epidemiology studies (Table 5-1). However, obtaining data may take months. Some of the recommendations presented by the committee in Chapter 7 attempt to address the biomarker data gaps through a research agenda. When data gaps are filled, there may be disagreement about how to apply the data for interpreting biomonitoring results. For example, the biomarker–toxicity relationship for methylmercury has been controversial because of the differences in results among major epidemiology studies (Appendix B). Although a national consensus has emerged after the National Research Council review of methylmercury (NRC 2000), there may not be an opportunity for such a comprehensive analysis of other biomarkers as data gaps are addressed and risk assessors use existing data. The case studies in this chapter and in Appendix B are presented to illustrate particular points and are not intended to be exhaustive in their review or analysis of a chemical.

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Human Biomonitoring for Environmental Chemicals TABLE 5-1 Overview of Major Biomarker Case Examples Used to Illustrate Interpretive Options Chemical Biomarker Interpretive Option Exemplified Where Presented PBDE PBDEs in blood and breast milk Biomonitoring studies demonstrate key data gaps; need to obtain new toxicity and exposure information Chapter 5 Organophosphates Various metabolites Comparison of subpopulation with reference range Chapter 5 Glyphosate Urinary glyphosate Use of existing risk assessment to put biomonitoring results into risk context Appendix B Permethrin Urinary carboxylic Use of existing risk assessment to put biomonitoring results into risk context acid metabolite Appendix B TCE Blood TCE Use of Bayesian techniques and bounding approaches to estimate exposure dose from non-steady-state blood concentration Appendix B PFOA Serum PFOA Use of animal toxicology and physiologically based pharmacokinetic modeling to develop biomarker-response relationship in animals Chapter 5 Lead Blood lead Use of epidemiology studies to develop biomarker-response relationship in humans Chapter 5 Mercury Blood mercury Use of epidemiology studies to develop biomarker-response relationship in humans Appendix B Chlorpyrifos Urinary TCP Use of pharmacokinetic modeling to estimate exposure dose from amount excreted in urine Appendix B Phthalates Urinary monoester metabolites Use of pharmacokinetic modeling to estimate exposure dose from amount excreted in urine Chapter 5 Dioxin Dioxin in blood or lipid Use of pharmacokinetic modeling to estimate body burden and daily dose Appendix B Styrene Urinary metabolites Use of worker urinary metabolite-exposure information to develop pharmacokinetic model applicable to general public Appendix B Abbreviations: PBDE = polybrominated diphenyl ether; TCE = trichloroethylene; PFOA = perfluorooctanoic acid; TCP = trichloro-2-pyridinol.

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Human Biomonitoring for Environmental Chemicals REFERENCE RANGES Biomonitoring results can be interpreted at different levels of complexity (Figure 5-1). The reference-range approach represents the least complex level. It is only descriptive, offering a statistical presentation of data (Tables 5-2 and 5-3) for comparison with data from other populations or individuals but with no conclusions about risk potential. However, this approach is often the first stage in the more complex risk-related analyses discussed in the remainder of the chapter. In the reference-range approach, reference ranges (or intervals),1 are established, and biomonitoring values from individuals or subgroups are compared with them. The validity and utility of biomonitoring values for use as reference ranges depends on study design and data quality, with special attention to the availability and comparability of data on the reference population in relation to the study population. The overview below focuses on two fundamental elements of the reference-range approach: establishing a reference range and interpreting biomonitoring data in comparison with it. The remainder of this section details methods, principles, and issues related to data quality and reference-population selection and comments on regulatory uses of this approach and related cautions. Overview Establishing Reference Ranges Recent biomonitoring efforts in the United States and Europe have placed a high priority on establishing reference ranges. For example, a central purpose of the Third National Report on Human Exposure to Environmental Chemicals (CDC 2005) is “to establish reference ranges that can be used by physicians and scientists to determine whether a person or group has an unusually high exposure.” The report updates and supplements two earlier reports (CDC 2001, 2003). As documented in Chapter 2, other nations and international organizations are developing comparable information. The CDC sampling plan follows a “complex, stratified, multistage, probability cluster design to select a representative sample of the civilian noninstitutionalized population of the United States.” Relevant details are 1 Poulsen et al. (1994) appear to use the term reference interval as synonymous with reference range. In a paper titled “Trace element reference values … ,” the authors emphasize that “knowledge of the reference intervals (baseline data) for the trace elements in human body fluids and tissues is of paramount importance.”

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Human Biomonitoring for Environmental Chemicals TABLE 5-2 Blood Concentrations for Cadmium in the U.S. Population Aged 1 Year and Older   Survey Years Geometric Mean (95% confidence interval Selected Percentilesa 50th Total, age 1 year and older 1999-2000 0.412 (0.378-0.449) 0.300 (0.300-0.400)   2001-2002 b 0.300 (<LOD-0.300) Age group 1-5 years 1999-2000 b <LOD   2001-2002 b <LOD 6-11 years 1999-2000 b <LOD   2001-2002 b <LOD 12-19 years 1999-2000 0.333 (0.304-0.336) 0.300 (<LOD-0.300)   2001-2002 b <LOD 20 years and older 1999-2000 0.468 (0.426-0.513) 0.400 (0.300-0.400)   2001-2002 b 0.300 (0.300-0.400) Sex       Male 1999-2000 0.403 (0.368-0.441) 0.400 (0.300-0.400)   2001-2002 b 0.300 (<LOD-0.300) Female 1999-2000 0.421 (0.386-0.460) 0.300 (0.300-0.400)   2001-2002 b 0.300 (0.300-0.400) Race or ethnicity Mexican Americans 1999-2000 0.395 (0.367-0.424) 0.400 (0.300-0.400)   2001-2002 b <LOD Non-Hispanic blacks 1999-2000 0.393 (0.361-0.427) 0.300 (0.300-0.400)   2001-2002 b <LOD Non-Hispanic whites 1999-2000 0.420 (0.376-0.470) 0.400 (0.300-0.400)   2001-2002 b <LOD aLOD = limit of detection, which may vary for some chemicals by year and by individual sample. bNot calculated. Proportion of results below limit of detection was too high to provide valid result. Source: CDC 2005. developed in Chapter 4. The monitored populations are in broad groups defined by age, sex, and race or ethnicity. Data are analyzed and presented in eight main categories: 6-11 years old, 12-19 years old, over 20 years old; males, females, Mexican Americans, non-Hispanic blacks, and non-Hispanic whites. Other racial groups are sampled as part of the total population but do not make up a large enough proportion of the total to provide valid estimates. Newborns and infants are not included, because of difficulties (such as parental resistance and sample size) in obtaining biomonitoring data for these age groups.

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Human Biomonitoring for Environmental Chemicals (in mg/L) (95% confidence interval)   75th 90th 95th Sample Size 0.600 (0.500-0.600) 1.00 (0.900-1.00) 1.30 (1.20-1.40) 7,970 0.400 (0.400-0.500) 0.900 (0.900-1.10) 1.30 (1.20-1.60) 8,945 0.300 (<LOD-0.300) 0.400 (0.300-0.400) 0.400 (0.300-0.400) 723 <LOD <LOD 0.300 (<LOD-0.300) 898 0.300 (<LOD-0.300) 0.400 (0.300-0.400) 0.400 (0.400-0.500) 905 <LOD <LOD 0.400 (0.300-0.400) 1,044 0.300 (0.300-0.400) 0.800 (0.600-0.900) 1.10 (0.900-1.10) 2,135 0.300 (<LOD-0.300) 0.400 (0.400-0.500) 0.800 (0.600-1.10) 2,231 0.600 (0.600-0.700) 1.00 (1.00-1.10) 1.50 (1.40-1.60) 4,207 0.600 (0.500-0.600) 1.10 (0.900-1.20) 1.60 (1.30-1.80) 4,772 0.600 (0.500-0.600) 1.00 (0.900-1.10) 1.30 (1.20-1.50) 3,913 0.400 (0.400-0.500) 0.900 (0.900-1.10) 1.40 (1.20-1.80) 4,339 0.600 (0.500-0.600) 1.00 (0.800-1.00) 1.30 (1.10-1.40) 4,057 0.500 (0.500-0.600) 1.00 (0.900-1.10) 1.40 (1.20-1.60) 4,606 0.400 (0.400-0.500) 0.700 (0.700-0.900) 1.10 (0.900-1.30) 2,742 0.300 (0.300-0.400) 0.600 (0.500-0.700) 1.00 (0.700-0.900) 2,268 0.600 (0.500-0.600) 1.00 (0.800-1.10) 1.40 (1.10-1.50) 1,842 0.400 (0.400-0.500) 1.00 (0.900-1.00) 1.40 (1.20-1.50) 2,219 0.500 (0.500-0.600) 1.00 (0.900-1.10) 1.30 (1.20-1.40) 2,716 0.500 (0.500-0.600) 0.900 (0.900-1.10) 1.40 (1.20-1.80) 3,806 As shown in Tables 5-2 and 5-3, the data on each group include survey period, geometric mean, population sample size, and the biomarker concentration at the 50th, 75th, 90th, and 95th percentiles of the population distribution. Comparison with a Reference Population At the simplest level of interpretation of biomonitoring data, a biomarker concentration found in an individual or group under study is com-

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Human Biomonitoring for Environmental Chemicals TABLE 5-3 Urine Concentrations for Cadmium in the U.S. Population Aged 6 Years and Older   Survey Years Geometric Mean (95% confidence interval Selected Percentilesa 50th Total, age 6 years and older 1999-2000 0.193 (0.169-0.220) 0.232 (0.214-0.249)   2001-2002 0.210 (0.189-0.235) 0.229 (0.207-0.255) Age group       6-11 years 1999-2000 a 0.078 (0.061-0.101)   2001-2002 0.061 (<LOD-0.081 0.077 (0.067-0.092) 12-19 years 1999-2000 0.092 (0.067-0.126) 0.128 (0.107-0.148)   2001-2002 0.109 (0.087-0.136) 0.135 (0.114-0.157) 20 years and older 1999-2000 0.281 (0.253-0.313) 0.306 (0.261-0.339)   2001-2002 0.273 (0.249-0.299) 0.280 (0.261-0.308) Sex       Male 1999-2000 0.199 (0.165-0.241) 0.227 (0.193-0.263)   2001-2002 0.201 (0.177-0.229) 0.223 (0.191-0.257) Female 1999-2000 0.187 (0.153-0.229) 0.239 (0.220-0.255)   2001-2002 0.219 (0.192-0.251) 0.234 (0.202-0.265) Race or ethnicity       Mexican Americans 1999-2000 0.191 (0.157-0.233) 0.202 (0.167-0.221)   2001-2002 0.160 (0.135-0.189) 0.181 (0.171-0.198) Non-Hispanic blacks 1999-2000 0.283 (0.208-0.387) 0.312 (0.243-0.412)   2001-2002 0.277 (0.229-0.336) 0.302 (0.257-0.354) Non-Hispanic whites 1999-2000 0.175 (0.148-0.206) 0.220 (0.194-0.246)   2001-2002 0.204 (0.179-0.231) 0.221 (0.191-0.255) aNot calculated. Proportion of results below limit of detection was too high to provide valid result. Source: CDC 2005. pared with that in a reference population. That approach depends on a suitable reference population and a body of biomonitoring data collected in comparable fashion that can serve as a reference range. (For discussion of an appropriate comparison population, see Chapter 4.) Figure 5-3 illustrates the distribution of biomarker concentrations in a generic reference population, expressed as cumulative frequency. As is commonly done in a clinical test, the 95th percentile of the distribution can be used to determine the upper limit value of this test result. However, a different percentile may be chosen, depending on the circumstances, the characteristics of the reference population, the distribution of the results, and the intended purpose of the study. It is important to be aware that a particular cut point does not represent a bright line that automatically separates the population into typical vs highly exposed, or no risk vs high risk (when, for example, BEIs or risk-based targets are used). Rather, it is a guideline to point out where in the population distribution exposures may require more detailed analysis of sources and health risks.

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Human Biomonitoring for Environmental Chemicals Carlo analysis, but ultimately the variability in fractional excretion and creatinine clearance needs to be understood to characterize population exposure to urinary biomarkers. A major factor governing variability in biomonitoring results is inter-individual differences in metabolic clearance. Genetic polymorphisms can affect the activity or inducibility of Phase I and Phase II metabolic enzymes, potentially affecting both the activation and detoxification of xenobiotics (Perera 2000; Eaton 2000). Biomonitoring results for parent compounds in blood or metabolites in urine or blood will be influenced by these differences. This can be a large factor if the enzyme systems involved are highly variable across the population. For example, a polymorphism in the CYP2D6 gene has a large influence on the clearance of certain drugs, CYP2E1 is inducible by exposure to alcohol, and glutathione conjugation to epoxides can be affected by null polymorphisms in several glutathione transferases (Thier et al. 2003; Ingelman-Sundberg 2005; Kessova and Cederbaum 2003). The design of biomonitoring studies should include an evaluation of the dominant clearance pathways for the chemical being monitored. If these pathways are modulated by genetic polymorphisms, then genotype probes should be considered when collecting the biomonitoring samples. This would be consistent with the increasing use of genotyping methods in environmental epidemiology studies (Nebert et al. 1996). This can decrease uncertainty and assist in data interpretation, pointing out whether a high biomonitoring result may have been from high intake or slow clearance. These can have very different risk implications. Another kind of uncertainty is related to the utility of occupational reference values for comparisons with general population biomonitoring results. The workplace targets are inappropriate for a general population that includes infants, the elderly, and the infirm. The committee’s attention to those limitations and uncertainties is important for two reasons. First, full disclosure of limiting factors gives scientists and the public a fuller understanding of the reliability and credibility of biomonitoring results. It provides risk assessors with information needed to “characterize” risk conclusions fully, as called for by the National Research Council risk-assessment paradigm (NRC 1983; 1994). Second, and equally important, the kinds of uncertainty define data gaps for immediate attention and related long-term research needs. CONCLUSIONS This chapter identifies a variety of approaches for interpreting biomonitoring results, ranging from descriptive to risk-based. The descriptive approaches are useful as a first step in analyzing biomonitoring data, but they do not describe the level of risk. That requires the risk-based approaches

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Human Biomonitoring for Environmental Chemicals described in the chapter. Although the methods presented are feasible, minimum data are required to exercise the various interpretive options. These minimal data are lacking in the case of numerous chemicals, so priorities need to be set in selecting biomarkers for expansion of the database to enable assessment of risk. The committee drew the following conclusions about descriptive approaches: Descriptive approaches are important in laying a foundation that risk-based approaches can build from, and in some cases they are the only type of analysis needed. The reference-range approach is a critical data layer that summarizes the biomonitoring dataset and enables comparisons between segments of the population and times. Although they do not provide information about risk, simple comparisons between an individual’s biomarker concentration and the population distribution may be all that is needed to answer key questions about the need for personal action. Workplace biologic exposure targets (such as BEIs) provide another point of reference that may be of some use in assessing the relative degree of individual or group exposure outside the workplace. Risk-based approaches try to determine how much risk is associated with a given biomarker result. Those approaches and their interpretive power vary widely with the extent of information available on a chemical and its biomarker. The committee drew the following conclusions about risk-based approaches: The biomarkers of greatest utility for interpreting risk are those for which biomarker-toxicity relationships have been developed in humans, as in the case of lead and mercury. If such relationships are not available, biomonitoring data may be interpreted by converting them to human exposure dose with the aid of PK models. That can be done in different ways depending on the chemical and the type of biomarker (for example, parent chemical and metabolite). For persistent lipid-soluble compounds, conversion of blood or adipose tissue biomonitoring results to body burden and intake dose is feasible even with simple one-compartment models, although multicompartment physiologic models can provide a more flexible and improved tool for estimating dose. Approaches for less lipid-soluble and nonpersistent chemicals can depend on whether a blood or urinary biomarker is available.

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Human Biomonitoring for Environmental Chemicals Urinary biomarkers can be related to exposure dose in a straightforward manner for chemicals that are excreted rapidly in urine. This approach requires the collection of data describing the percentage of dose excreted each day in urine and percentages excreted by different metabolic and elimination pathways. There can be important variability and uncertainty in those factors and in the normalization of the biomarker result (per gram of creatinine). Furthermore, there may be environmental sources of the urinary biomarker that can confound an estimation of parent-chemical dose based on the metabolite in urine. It is also possible that the urinary metabolites may exist as breakdown products in the environment. An alternative interpretive approach is to leave the human biomonitoring result as is but develop applied dose-biomarker relationships in animals. That requires obtaining animal PK data to support PBPK modeling or the collection of animal biomarker information in study designs that mimic key toxicology datasets. RECOMMENDATIONS Improved interpretation of biomonitoring results will require the expansion of the database typically available on many chemicals. The following recommendations will help in the evaluation of exposure and risk associated with biomonitoring results in general. More specific recommendations can be made case by case after an individual chemical’s database is reviewed. Increase the use of biomarkers in environmental epidemiology studies. Develop biomarkers suitable for determining internal dose-response or excreted dose-response relationships in animal studies with confirmation of biomarker applicability to humans. Improve animal toxicology study designs to incorporate use of validated biomarkers to characterize biomarker-response relationships that can be used to interpret human biomonitoring data. Expand use of exposure assessment in the biomonitoring study protocol to identify exposure sources and allow a pathway-exposure analysis that could help to interpret biomonitoring data. Research is needed on various aspects of chemicals mixtures beginning with better reporting from population-based biomonitoring studies on the number and diversity of chemicals found in subjects. New bioassays are needed that explore the health outcomes of environmentally relevant mixtures (that is chemicals and amounts found in human tissues). PBPK models also need to be expanded to better understand chemical-chemical interactions.

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Human Biomonitoring for Environmental Chemicals Research the factors governing human excretion of chemicals in urine and breast milk and how it can affect biomarker results: How breast-milk content changes over the course of the lactational period can affect excretion of toxicants into breast milk. How uncertainties and variability in creatinine clearance can affect urinary biomarker results and their extrapolation to external dose. Identify to what extent exposures to chemical degradation products in the environment contribute to metabolite levels measured in urine samples, as certain urinary metabolites may exist as breakdown products in the environment. Add a wider variety of media to biomonitoring studies, especially media that will provide information about early life stages. For example, biomonitoring of breast milk can inform about exposures during infancy, and biomarkers in cord blood and meconium can inform about fetal exposure. Include in utero exposures and young children in biomonitoring designs because they are a substantial source of population variability in exposure and susceptibility. Improve human dosimetry models to simulate life stages and population groups (for example, those with polymorphisms) that have not been biomonitored; this may allow extension of biomonitoring results to vulnerable groups that are difficult to identify or sample. Incorporate metabolic-trait determination into biomonitoring studies (for example, genotyping or phenotyping of metabolic traits) to understand how the traits can affect biomonitoring results. Expand modeling approaches and case examples in which non-steady-state biomonitoring data are simulated to explore the exposure conditions responsible for biomonitoring results; this may provide exposure estimates that can be used in risk assessment (for example, Bayesian inference techniques and population behavior-exposure models). Increase research emphasis on the low and high ends of the biomarker distribution to discover what leads to these tails and thus enhance the development of exposure interventions if warranted. REFERENCES ACGIH (American Conference of Governmental Industrial Hygienists). 2003. TLVs and BEIs: Threshold Limit Values for Chemical Substances and Biological Exposure Indices. American Conference of Governmental Industrial Hygienists, Cincinnati, OH. ACGIH (American Conference of Governmental Industrial Hygienists). 2005. TLVs and BEIs: Based on the Documentation of the Threshold Limit Values for Chemical Substances and Physical Agents & Biological Exposure Indices. American Conference of Governmental Industrial Hygienists, Cincinnati, OH.

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