Interpreting the literature on lead and child development: The neglected role of the “experimental system”

Neurotoxicology and Teratology, Vol. 17, No. 3, pp. 201-212, 1995 Copyright o 1995 Elsevier Science Ltd Printed in the USA. All rights reserved 0892-0362195 $9.50 + .OO

Pergamon 0892-0362(94)00081-6

OPEN PEER COMMENTARY

Interpreting the Literature on Lead and Child Development: The Neglected Role of the “Experimental System” DAVID

C. BELLINGER

Neuroepidemiology Unit, Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115 BELLINGER, D. C. Interpreting the literature on lead and child development: The neglected role of the “Experimental System.” NEUROTOXICOL TERATOL 17(3) 201-212, 1995. -Controversy over lead’s effect on children’s cognition rests in part on the assumption that if such an effect exists it can be characterized by a single estimator (e.g., the same rate of decline in IQ with increasing exposure, the same neuropsychological presentation), which will be found by any study that is valid. Accordingly, efforts to resolve inconsistencies in study findings have focused almost exclusively on data analytic issues germane to bias, in particular confounding and its statistical control. Relatively little consideration has been given to the role of effect modification, i.e., the impact on effect estimation of differences in the “experimental systems” employed in human epidemiological studies. Lack of consistency in findings could be due to differences among study cohorts in exposure/ toxicokinetic factors (e.g., dose, timing), differences in environmental characteristics (e.g., co-exposures, co-morbidity, developmental supports, assessment setting), or differences in the distribution of genetic characteristics that affect lead metabolism. Recent findings regarding lead’s impact on the development of nervous system structure and function are consistent with the hypothesis that contextual factors affect the form in which lead toxicity is expressed and may contribute to the failure to date to identify a lead-associated “behavioral signature.” Characterizing the neuropsychological effects of lead might be facilitated by greater use of a clinical “process” approach to assessment, which would permit the type of fine-grained analyses of lead-associated performance differences often employed in studies of behavioral toxicity in animal models.

and toxicity, and more comprehensive secondary prevention programs. A concern that lead may contribute, to a clinically significant extent, to the burden of developmental, neuropsychologic, and behavioral morbidity in children provided much of the impetus for the formidable investment in lead research over the past two decades. This research has been motivated largely by public health policy concerns, as lead-exposed children have rarely been selected as a “model system” for investigating the impact of a neurotoxicant on brain-behavior relationships. Rather, the characterization of lead effects has generally been viewed solely as an end in itself, the goal being to identify critical features of the dose-effect and dose-response relationships, particularly “lowest observed effect level(s)” (of-

“It turns out to be very difficult to devise a theory to describe the universe all in one go.” Hawking, S. A Brief History of Time.

THE dramatic reduction in the blood lead levels of the U.S. population since the 1970s is one of the greatest public health successes in recent decades. The mean blood lead level of young children, 15 pg/dL in the late 197Os, presently stands at approximately 4 pg/dL. Among the factors likely to be responsible are the promulgation of restrictions on certain uses of lead (e.g., gasoline, paint, plumbing materials), changes in manufacturing practices (e.g., food processing), increased public and medical education about lead sources

Portions of this paper are based on a presentation to the Neurobehavioral Teratology Society, Tucson AZ, June, 1993 and on a chapter to appear in Advances in Child Neuropsychology (Vol. 3), New York: Springer-Verlag. 201

202 ten interpreted as “thresholds”). Remarkably, little effort has been devoted to discerning how and why a child’s performance on developmental tests varies with level of lead exposure, i.e., to identify the neuropsychological underpinnings of lead’s apparent effect on indices of global function, such as IQ. To a large extent, the controversy associated with lead effects on cognition has turned on the importance assigned to inconsistencies among studies in the statistical significance of the association. Some view them as sufficient grounds for concluding that the data do not support the hypothesis of an association at levels below 25 pg/dL (109). The lack of consistency in specific aspects of the association (e.g., age at blood lead measurement reported to be most predictive of performance, type of psychologic performance most strongly associated with increased exposure) has provided additional cause for doubt. The thesis developed in this commentary is that much of the debate rests on an implicit expectation that there is a single point estimate for the “true” lead-cognition relationship that all studies, assuming they are internally valid, should find. Accordingly, the predominant focus of efforts to reconcile discrepancies in study findings has been the assessment and statistical control of confounding. Differences in study results are thus interpreted as evidence that the findings of some studies are “right” and others “wrong.” Even in the small piece of the universe pertaining to lead, nature is likely to be more complicated than this. In this review, several proposals are offered about factors that should be considered in trying to resolve some of the discrepancies in study findings. No attempt is made to review individual studies as numerous recent narrative reviews and statistical integrations are available (15,40,71,97,106). Many of the examples are drawn, however, from the set of international prospective studies of lead neurotoxicity that were begun around 1980 (Boston, Cincinnati, Cleveland, Kosovo, Port Pirie, Sydney). To frame the discussion, I offer three observations about the literature: First, there is general consistency in terms of the observation of a modest inverse association between indices of lead burden, usually blood lead, and global indices of development or neuropsychological functioning, usually IQ. Several metaanalyses, each following somewhat different procedures, support this claim (7 1,97,117), although the use of such methods to combine the results of observational studies differing in key aspects of methodology is controversial (30) and some claim should be abandoned altogether (99). Including only crosssectional studies in their analyses, Needleman and Gatsonis (71) estimated the 95% confidence interval of the correlation between blood lead and IQ to be -0.1 to -0.2; and between tooth lead and IQ to be -0.03 to -0.13. Combining crosssectional and prospective studies, Schwartz (97) estimated the decrease in IQ score for a 10 pg/dL increase in blood lead to be approximately 2.6 points (95% CI: 1.8 to 3.4). In analyses conducted by the World Health Organization (117), separate estimates were calculated according to study design (i.e., cross-sectional, prospective) and a common blood lead index was used for the prospective studies (mean lifetime blood lead). The estimates of effect size (ES) for an increase in blood lead from 10 to 20 pg/dL were remarkably similar, however (-2.0 points, 95% CI: -0.3, -3.6, for prospective studies; -2.1, 95% CI: -1.2, -3.1, for cross-sectional studies). Expert judgments about the likely probability distribution of IQ effects under specific exposure scenarios also display a surprising degree of consistency given the vituperative debate that has attended this issue (113).

BELLINGER These meta-analyses suggest, then, that the pattern of associations observed between lead burden and IQ is not likely to have occurred if the actual effect size is zero and appropriate adjustments were made for confounding. Although metaanalysis does not address the issue of whether studies share a systematic bias away from the null hypothesis, the lack of statistically significant heterogeneity in the size of the regression coefficients (ESs) assigned to lead in cohorts with differing covariance structures is not consistent with the hypothesis that the association is attributable to residual confounding. A set of effect estimates that are consistent in magnitude, regardless of their precision (and thus their associated p values) is generally viewed as more persuasive evidence of a “true” association than are a set of ESs showing consistency in statistical significance but not in magnitude (70). Second, despite the findings of the meta-analyses, especially the absence of significant heterogeneity in the ES estimates, the reader with even a casual familiarity with this body of studies will recognize that, as in most areas of epidemiologic research, the range of study findings is striking. Among the prospective studies, for instance, some report strikingly large ESs with highly extreme p values (e.g., Boston: 12), others report smaller ESs which if the sample sizes are large enough reach statistical significance (Port Pirie: 5; Cincinnati: 33; Kosovo: 1 lo), whereas other studies report essentially null findings (Cleveland: 43; Sydney: 23). Some observers have questioned the existence of an association between lead and cognition because the ES estimate derived on the basis of one cohort does not necessarily predict accurately the ES derived on the basis of another study cohort (e.g., 27). Such a strategy ignores the analytic principle that a multiple regression coefficient, which represents the change in the dependent variable per unit change in the predictor variable (i.e., the slope), is conditional on the presence of the other predictors in the regression equation. To take this one step farther, it is conditional on adjustment for the specific ranges of the other predictors. Whereas many studies adjust for maternal IQ, social class, H.O.M.E. score, and other factors, the distributions of these variables vary substantially from cohort to cohort. The key task in integrating apparently conflicting findings is partitioning inter-study variance in ES into that which is attributable to true differences among studies in ES, and to differences among studies in either the accuracy or precision of estimation. To date, the latter has been the near exclusive focus. Third, whereas the preponderance of evidence is consistent with the hypothesis of an association between lead burden and general intellectual level, except for consistency in terms of impairment in reaction task performance, relatively little progress has been made in identifying a “behavioral signature” for lead, i.e., a coherent and reasonably specific “syndrome” of neuropsychologic deficits. The inconsistency on this point is fundamental. In some studies the neuropsychologic domain most strongly associated with lead is verbal skills, whereas in others it is nonverbal skills. In yet others the associations are roughly equivalent in magnitude (70). Several interpretations are possible. Either residual confounding or chance (i.e., a Type I error rate that is inflated by multiple comparisons) would produce variability in the specific form in which an association is expressed in different studies. Alternatively, it may be that the manner in which lead’s impact on a child’s nervous system is expressed depends on the circumstances and that our models are not yet sufficiently sophisticated to characterize the interactions governing this process. Selection of residual confounding or chance as the explana-

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tion seems premature. Debate about the impact of lead on children’s nervous system function has generally been mired at a level that might be characterized as “My biostatistician can lick your biostatistician.” As noted, the public-health orientation driving much of this research has frequently led to a near exclusive focus on methodological issues relating to internal and external study validity in seeking explanations for differences in findings, including specification of sampling frame, subject recruitment, and statistical modelling approach. Without question these issues are critical, but frequently they have garnered attention at the expense of such critical issues as the adequacy with which exposure is characterized, the theoretical framework motivating the selection of endpoints as indices of nervous system function, and factors pertaining to the circumstances of exposure and outcome assessment. These latter factors are usually treated only as extraneous or “nuisance” variables to be adjusted for and subsequently ignored. The primary focus has seemed to be whether any difference can be identified in the performance of children with greater lead burdens, with relatively less interest in the specific nature of the differences. Because it is important that the difference, whatever it is, be understandable and easily communicated, IQ-like measures have usually been chosen as primary endpoints. Everybody thinks they know what IQ is and that higher is better. Moreover, from the standpoint of psychometric properties, IQ tests are the jewels of the psychologist’s testing armamentarium. They yield a little information about each of a broad range of cognitive domains in a relatively brief period of time. Public health officials are more likely to base policy decisions on IQ differences than differences in performance on differential reinforcement of low rate or fixed-interval schedules of reinforcement. Moreover, econometricians can assign dollar values to IQ change, providing a straightforward way to compare the economic implications of alternative regulatory strategies. Thus, many study reports contain highly detailed discussions of statistical approaches and issues, but give at best only superficial consideration to explicating the neuropsychological pathology that underlies exposure-related variation in IQ. The general form of the argument outlined here is that there are good reasons to expect that all studies may not estimate the same dose-effect relationship between lead and cognition. The decline in IQ that is measured in response to a specific increase in lead burden will differ depending on the unique conditions that pertain in a particular study. It is further proposed that this variation has both statistical and subject matter determinants. Specifically, the strength and nature of the “signal” representing the dose-effect relationship vary according to the context of exposure and assessment. At the same time, however, this context also can be viewed as “noise” that may either enhance or diminish the clarity with which this signal is perceived. Table 1 presents a list of factors that may affect the toxicity of a compound. As Doull states, “Much of the controversy that is associated with the interpretation of results of supposedly comparable toxicologic investigations is the result of failure to recognize the importance of these factors and the ability of even slight changes in the experimental protocol to alter markedly the response of the test system” (ref. 38, p. 70). Although these factors are generally interpreted to pertain to investigations using nonhuman subjects, they seem no less relevant to human studies. The following discussion focuses on four general classes of factors that may be responsible for some of the inter-study differences in the behavioral/cognitive

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A CLASSIFICATION

1

OF TOXICITY-INFLUENCING

FACTORS

Factors related to the exposure situation Dose, concentration, and volume of administration Route, rate, and site of administration Duration and frequency of exposure Time of administration (time of day, season of year, etc.) Inherent factors related to the subject Species and strain (taxonomic classification) Genetic status (littermates, siblings, multigenerational effects, etc.) Immunologic status Nutritional status Hormonal status (pregnancy, etc.) Age, sex, body weight, and maturity Central nervous system status (activity, crowding, handling, presence of other species, etc.) Presence of disease or specific organ pathology Environmental factors related to the subject Temperature, humidity, barometric pressure (hyper- and hypobaric effects) Ambient atmospheric composition Light and other forms of radiation Housing and caging effects, noise and other geographic influences Social factors Chemical factors Source: Adapted from Doull, 1980, p. 70.

manifestations of lead toxicity: exposure/toxicokinetic tors, lead-environment interactions, lead-genotype tions, and the mechanism(s) of lead neurotoxicity. EXPOSURE/TOXICOKINETIC

facinterac-

FACTORS

Compared to investigations using animal models, the amount of exposure information available in human studies is impoverished. Because the exposure of animals can be experimentally manipulated and multiple biologic indices measured often, the topography of internal dose can be mapped in detail, permitting assessment of the consequences of different exposure regimens (e.g., a pulsed dose during a specific developmental epoch vs. chronic low-level exposure). As Nelson (72) describes, behavioral expressions of toxicity may differ according to the dosing regimen chosen and different regimens may be appropriate for different study questions. In many human epidemiological studies, exposure information is limited to a single blood lead level. Even in the prospective studies, blood lead levels were measured at most on a quarterly basis but more typically only semiannually and with decreasing frequency over time. The hazards of ignoring toxicokinetic factors in interpreting human lead studies are readily apparent. In crosssectional studies, which generahy involve school-age children, all children with a given blood lead level are viewed as having equivalent lead burdens. This view rests on the very strong assumption that equivalence in this relatively late “snapshot” of the lead concentration in a body pool with a rapid turnover rate provides reliable information about a child’s exposure history. Clearly, children with the same blood lead at age 6 or 7 may have experienced exposures that differ dramatically in terms of magnitude, timing, or duration. In rats, Cory-Slechta (24) found that exposure duration of 1, 8, or 11 months resulted in equivalent blood lead levels but very different behav-

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WISC-R Full Scale IQ FIG. 1. Cumulative frequency distributions of full-scale WISC-R IQ at age 10 years for children in the Boston prospective study, stratified by blood lead category at age 2 years.

ioral expressions. The shorter duration produced an increased response rate on an FI schedule while the longer duration suppressed response rate. The exposure profiles of the prospective study cohorts differ in many respects, including prenatal and neonatal blood lead levels, the slope and timing of postnatal increases in blood lead level, and the duration of elevated levels. Mushak argued that toxicokinetic considerations generate study-specific predictions about lead effects and that, as a result, intra-study consistency in findings (i.e., concordance of “toxicokinetically predicted and observed doseeffect relationships” (ref. 67, p. 31) should be accorded greater weight than inter-study consistency in dose-effect relationships in evaluating the hypothesis that lead causes developmental impairment. Inter-study differences in exposure may have implications for relative statistical power as well. The prospective studies employed quite different subject sampling strategies. In the Boston cohort, the umbilical cord blood lead levels were measured for nearly all neonates delivered during the recruitment phase, and children with extremely low or extremely high levels were over-sampled. (The Kosovo study was the only other one in which a child’s selection for follow-up was based on prenatal lead exposure). Most other cohorts (Port Pirie, Sydney, Cincinnati) were assembled by enrolling consecutive births or women registering for prenatal care. In the Cleveland cohort, women who were positive on an alcohol screening test were oversampled and matched on several characteristics to women who were negative. These sampling frames based on factors other than lead produced a prenatal lead exposure distribution in the cohort that mirrors more closely than in the Boston (or Kosovo) cohort the distribution of exposures in the source population. The advantages of the latter approach are those associated with representative sampling (i.e., generalizability, the straightforward estimation of population attributable risk). The disadvantage is that such a sample includes relatively few individuals that have either extremely high or extremely low lead levels. Thus, assuming the same doseeffect relationship pertains in all cohorts, the association between prenatal lead exposure and outcome is estimated with lower precision (and less power) than in the Boston cohort, in which fully one-third of the children had cord blood lead

levels greater than the 90th percentile of the source population and one-third had levels below the 10th percentile. The correlation between successive measures of lead burden also varies among cohorts. In some (e.g., Port Pirie, Cincinnati), the phenomenon of “blood lead tracking” effectively precluded the identification of performance effects associated with levels measured at specific ages. Under the plausible hypothesis that stage of brain development at the time of exposure contributes to both the severity of toxicity and the manner in which it is expressed (92), heterogeneity in the exposure profiles of children in a cohort will produce heterogeneity in the magnitude and form of neuropsychological presentation. Using reasonably detailed exposure histories available for clinically lead-poisoned children, Shaheen (98) investigated the hypothesis, developed on the basis of Luria’s model, that language skills, which depend on early maturing secondary cortical areas, are affected primarily by early exposure to lead (before age 2 years), while spatialsymbolic skills, which depend on later maturing tertiary areas, are affected primarily by later exposure (after age 2 years). Although her sample was limited to 18 lead-poisoned children and matched controls, the data generally supported the hypothesis, suggesting that failure to take age-at-exposure into account may contribute to the present confusion over the heterogeneity in apparent lead effects in different cohorts. Animal studies provide clear evidence on this point. Differences in the timing of lead exposure (i.e., age at onset) contribute to complex differences in behavioral presentation in mice (36), rats (18), and primates (58,86,88,90,91). The series of studies by Rice and colleagues also suggest that the impact of age at exposure may differ according to behavioral endpoint (e.g., multiple fixed interval-fixed ratio schedule of reinforcement, spatial delayed alternation, nonspatial discrimination reversal, spatial discrimination reversal). Furthermore, the effects appear not to be a simple additive function that depends only on exposure duration. In some meta-analyses, the raw data are IQ declines observed in different studies for a given increase in blood lead (e.g., 10 pg/dL). This is based on the assumption that the dose-effect relationship is linear (i.e., the impact on performance is the same whether the increase is from 0 to 10 rg/dL or from 20 to 30 pg/dL) so that differences among studies in the blood lead range sampled will have no impact on the magnitude of the estimated slope (although a decrease in the precision of estimation might be expected due to the inverse relationship between blood lead level and the coefficient of variation). This assumption may be inappropriate. Schwartz (97) found an inverse association between the slope and the average blood lead level within a cohort, suggesting a steeper rate of IQ decline in cohorts with lower blood lead levels. Even within the data of some cohorts, the slope of the doseeffect relationship seems to be steeper at lower bIood lead levels (e.g., Port Pirie: Fig. 1 in ref. 5). Numerous examples of nonlinear or even U-shaped functions in toxicology are discussed by Davis and Svensgaard (28), supporting the hypothesis that the portion of the exposure range in which the preponderance of individuals in a cohort lie may affect the magnitude of the estimated slope. LEAD-ENVIRONMENT

INTERACTlONS

In most efforts to synthesize the data from human epidemiological studies, the cohorts of children in different studies are viewed as equivalent and the studies treated essentially as simple replicates. The underlying assumption is that not only are

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cohort differences in exposure patterns irrelevant but that any effects of lead are the same among children with widely differing life circumstances. The contexts within which the putative effects are produced and assessed are viewed as relatively unimportant. Another way to express this assumption is that if within a particular cohort the distribution of some characteristic is related both to exposure and outcome, statistical adjustment for this characteristic will produce an “adjusted” estimate of the slope of the lead-IQ relationship that will be the same as the adjusted slope estimated from the data of another cohort, even if the distribution of the characteristic differs between cohorts. If the characteristic is a true effect modifier and its distribution within cohorts is not identical, it does not follow that adjustment for it will produce the same estimated slope. In other words, there may be a different “true” slope for the lead-cognition relationship associated with the unique constellation of factors that describes each cohort. Greater consideration must be given to the possibility that aspects of the developmental context serve not only to confound the lead-IQ association but to modify it as well. In some studies, a statistical interaction has been identified such that the magnitude of the lead coefficient (i.e., slope) differs across levels of a third variable (usually social class or sex). The relevance of this point to the interpretation of inter-study consistency in findings is straightforward. When there is relatively little overlap between two cohorts in the distribution of a factor that modifies the magnitude of the lead coefficient, a phenomenon that can be termed “cross-study effect modification” results. Conceptually, this is equivalent to stratifying a cohort in which a significant lead x social class interaction is observed into two separate “studies” on the basis of social class. The difference in the magnitude of the lead coefficient derived in each “study” will necessarily be statistically significant, even if social class were controlled in the analysis of the data for each portion of the cohort. In this circumstance, social class may or may not be a confounder but it clearly is an effect modifier. The different prospective study cohorts can be viewed in these terms, i.e., as representing different strata or levels of effect modifiers, or even as different “cells” in a larger hypothetical study design (8). This situation can exist for any characteristic, illustrating the complex assumptions that underlie the development and interpretation of multivariable regression models. The lack of attention to effect modification in interpreting human epidemiological studies contrasts sharply with the approach typically taken to reconciling conflicting animal data. In commenting on the response of lead-exposed rats to the stress of a novel environment, Barrett and Livesey noted that “ . . . The nature of this response depends on the test apparatus used (open field vs. T maze), age of subjects (pre and immediately post weaned vs. adults), previous experience (continuous placement in the apparatus vs. experience at one age) and level of stimuli (no noise and noise conditions)” (ref. 6, p. 117). Similarly complex interactions have been reported by others (36,37). Petit and Alfano (78) showed that exposure of mice to an enriched environment affected the expression of lead effects although in a way that varied with the specific task and exposure characteristics. Such context-dependence of effect is frequently found in behavioral pharmacology (e.g., variations in brightness and home cage site within the laboratory affect rats’ behavioral response to d-amphetamine, ref. 44).

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In comparing the performance of lead-exposed primates from her lab and from the University of Wisconsin Primate Center lab on spatial delayed alternation tasks, Rice (87) identified two seemingly minor differences in method that seemed to affect the pattern of lead-related effects observed: (a) the delay intervals were constant within a session but increased between sessions in her lab, whereas delays of 5, 10, 20, and 40 s were given in a counterbalanced order within each session in the Wisconsin lab, and (b) errors were corrected in her lab but not in the Wisconsin lab. Other data from Rice’s lab indicate that for some primate behaviors the manner in which lead effects are expressed depends on the animals’ previous behavioral testing histories (89). Early experience on learning tasks, a form of environmental enrichment that seems analogous to the provision of increased academic stimulation to children, did not produce a general improvement in performance, however, as the degree of similarity between the early and later tasks was critical. Conditions that result in some children being exposed to lead may result in exposure to other neurotoxicants as well. Co-exposure to the roundworm toxocura canis, whose distribution in human populations is related to socioeconomic status (118), may alter the form in which the impact of lead on a variety of behavioral endpoints is expressed (35). For some endpoints, co-exposure to cadmium and lead reduces the adverse impact seen when each metal is administered alone (68), an antagonistic effect that depends on the developmental epoch of administration and on dose. Disulfiram may potentiate lead neurotoxicity by altering lead kinetics (76). These animal data are potentially relevant to human studies because the prospective study cohorts differ substantially in terms of demographic characteristics (and hence the distribution of developmental risks other than lead). These differences can be seen as analogous to the differences often viewed as critical elements of “the experimental system” in animal stud. . res, i.e., genotype, animal housing and handling practices, pre- and post-exposure environments, and behavioral testing history. To provide several examples, the prospective cohorts cover a substantial portion of the sociodemographic range. In the Boston cohort, the mean maternal IQ is 121 (9) but is only 74 and 75 in the Cleveland (42) and Cincinnati cohorts (32), respectively. Approximately half of the families in the Boston cohort are in Hollingshead social class I (highest) (lo), whereas 96% of families in the Cleveland cohort are in class V (lowest) (41). Scores on the Home Observation for Measurement of the Environment (HOME), an index of rearing environment quality, were nearly 1 SD higher in the Boston cohort (9) than in the Cincinnati cohort (31). The typical approach to taking account of such factors is to include them in a regression equation, holding them “statistically constant” when assessing the relationship between indices of lead exposure and child function. Simulation studies indicate that this approach may not control for the possible confounding influence of these variables to the same extent in different cohorts if the degree of collinearity among these variables and the exposure index is not the same in all cohorts (11). Moreover, it does not address at all the phenomenon of “cross-study effect modification,” which as noted will occur when (a) the lead-outcome association varies in magnitude at different levels of these variables and (b) study cohorts differ in the distributions of the variables. A key question for research on lead (as well as other developmental neurotoxicants) then becomes, “What characteristics of a cohort increase the likelihood of detecting an association?“. Because early biologic insults frequently have greater

206 impact or are more enduring among “at risk” children (4,114), an assumption is frequently made that any effects of lead on cognition will be more evident among children whose development is jeopardized by other sociocultural or medical adversities. Under these circumstances the strength of the lead “signal” may well be increased, but it is important to note that the strength of the “noise” may be increased as well, reducing the likelihood that the signal can be measured with sufficient precision to support the inference of a “significant” lead effect (i.e., a potential Type II error). At the same time, in a cohort of “at risk” children, the typical co-occurrence (i.e., collinearity) of lead exposure and other developmental risk factors may introduce bias away from the null hypothesis, leading IQ variance that reflects residual confounding to be misattributed to lead (a potential Type I error). Thus, although biological considerations suggest that disadvantaged cohorts may provide the best opportunity to assess lead effects, statistical considerations suggest that the opposite may sometimes be true and that a cohort that consists solely of advantaged children may in fact provide the better opportunity. Any subtle impact lead has on cognition may be easier to detect among children who, growing up in high-functioning families who provide optimal developmental supports, are performing near their peak potential. This also suggests that in observational studies the impact of lead may be most evident at the higher end of a performance distribution, where the adverse impact of other risk factors will be reduced. These issues frequently arise in the epidemiologic study of “weak” associations in free-ranging human populations (39, 46). The approach sometimes taken to reducing the influence of very strong determinants of an outcome is to select a sampling frame that identifies as eligible only individuals who are overall at relatively low-risk for the outcome. For example, researchers interested in whether air pollution increases the individual’s risk of lung pathology restricted their study cohort to religious groups that discourage smoking, thus, removing by design a powerful determinant of lung disease (65). The inverse association observed in the Cincinnati cohort between prenatal lead exposure and birth weight was most prominent among women who did not smoke cigarettes, a known risk factor for low birth weight (17). Some findings from the Boston prospective study further illustrate these proposals. As expected from the skewed socioeconomic distribution of the families in this cohort, the average intellectual performance of the children at school-age was more than 1 SD above the expected population mean. Figure 1 presents cumulative IQ frequency distributions at age 10 years (unadjusted), classifying children into 4 strata corresponding to blood lead level at 2 years of age (5 pg/dL increments). The distributions clearly differ from one another, lining up from left to right in a dose-effect fashion at nearly every decile. Nevertheless, the differences are more striking at higher IQs than at lower IQs, where the impact of other risk factors is also expressed and may obscure an association between IQ and lead. This does not necessarily contradict the finding reported in several studies of a significant statistical interaction between social class and lead, such that the association is stronger among children from lower socioeconomic strata (lo,3 1,32, 52,115). The interplay between the biologic and statistical forces identified above may find expression in different features of the dose-effect relationship. In the Boston study, for instance, the blood lead level at which a decline in scores was first evident was lower for the less advantaged children (below the median social class), suggesting that their biologic vulnera-

BELLINGER bility is in some sense greater than that of more advantaged children. Nevertheless, the absolute difference between the Mental Development Index scores at ages 18 and 24 months of the children in the highest and lowest cord blood lead categories (i.e., the magnitude of the ES) was greater for the children in the higher SES stratum than it was for children in the lower SES stratum (Fig. 2) (10). Findings were similar when analyses of the relationship between 2-year blood lead level and IQ at 10 years were stratified by social class (Fig. 3). LEAD-GENOTYPE

INTERACTIONS

The possibility of genetic differences in susceptibility to lead has received relatively little consideration compared to other toxicants (74). Given the widespread recognition of individual differences in clinical response to lead in humans, as well as substantial evidence of individual differences in primates’ behavioral responses (85), additional work is needed to clarify some of these issues. Several possible mechanisms have been suggested, including differences in the activity of leadbinding proteins in erythrocytes (19) or brain (48), and polymorphisms for glucose-6-phosphate dehydrogenase (64), cytochrome P-450 (70), and amino levulinic acid dehydratase (ALA-D) (111). The prevalence of elevated blood lead levels is reported to be greater among individuals with the allele associated with reduced ALA-D activity (i.e., the l-2 or 22 isozyme phenotype) (2). In the only study examining the relationship of ALA-D polymorphism to lead neurotoxicity, individuals who were heterozygotic (i.e., l-2) tended to achieve better scores on neuropsychological tests than individuals with the most common phenotype (i.e., l-l), even controlling for differences in lead burden (14). The prevalences of the genetic variants differ according to ethnic and social group, providing a potential explanation for differences in the ESs among study cohorts that differ in these respects. MECHANISM(S)

OF LEAD NEUROTOXICITY

For some neurotoxicants, knowledge of the site and mechanism of action permits the formulation of specific predictions about the form in which toxicity will be expressed. For example, MPTP, a contaminant of heroin production, selectively destroys dopaminergic neurons in the substantia nigra and, not surprisingly, results in a clinical presentation virtually identical to that of Parkinson’s disease (69). Unfortunately, the primary neuropathological lesion underlying lead neurotoxicity cannot be characterized so neatly. Silbergeld cogently summarizes the state of knowledge as follows, “there is as yet no unifying hypothesis of the fundamental neurobiological mechanisms of lead toxicity” (ref. 100, p. 90). She hypothesizes that several mechanisms are likely, distinguishing those that are “neurodevelopmental” (i.e., affecting the development of cell : cell connections) from those that are “neuropharmacologic” (affecting ionic aspects of cell : cell interactions) (101). The overgrowth of neuronal synapses in the early postnatal period and the subsequent “pruning” or deletion of synapses by a process of “competitive interaction between extrinsic afferents” (ref. 82, p. 234) is believed to be one mechanism by which the CNS is organized to meet the needs imposed by a particular environment (7581). Often-used synapses are strengthened and inappropriate synapses and incorrectly targeted projections are eliminated. This selection process is thought to be experience- or activity-dependent. For instance, activity-blocking studies in the rat using tetrodotoxin suggest that over 90% of retinal ganglion cells with incorrect projections (topographic targeting errors) are eliminated (as com-

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1A. 6monfb.s

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Umbii//a/ Cord Hood Lead Group FIG. 2. Adjusted Mental 12, 18, and 24 months for circles: above median) and “high”: > 10 pg/dL). Error

Development Index scores (Bayley Scales of Infant Development) at ages 6, children stratified by family social class (open circles: below median; closed by umbilical cord blood lead level (“low”: < 3 pg/dL; “mid”: 6 to 7 pg/dL; bars represent 1 SE. Source: (see ref. 10).

pared to 60% of the total original population) (73). Synaptic density and number of synapses per neuron in some areas of the frontal cortex, an area which both human and primate studies suggest may be an important target for lead, peaks at around 2 years of age (55), a period which some human epidemiological studies suggest is critical in terms of exposureoutcome predictions (15). Goldstein (49,50) proposed that lead may disrupt this “tuning” or “matching” process, producing a nervous system that appears grossly normal but in which the connections are “poorly chosen,” perhaps producing functional impairment. Although this hypothesis is speculative, several lines of evidence are consistent with aspects of it. Lead’s facilitation of the unstimulated or spontaneous release of neurotransmitter and inhibition of stimulated release (3) may uncouple the contingency between environmental salience and the likelihood that a synapse will survive. A disruptive effect of lead on desialylation of the morphoregulator neural cell adhesion mol-

130, Blood

q n q

c! I! 3

120

at

lead

24

ecule in rat cerebellum (21,83,84) provides a mechanism by which fiber outgrowth and routing and synaptic elaboration may be altered. Lead-induced effects on glial cell differentiation may disrupt their functioning as pathway or trophic supports for the organization and maintenance of neuronal connections and for the regulation of amino acids and ions in the neuronal microenvironment (22,93,103,107). In radioactively labeled ligand binding studies of receptor ontogeny for catecholaminergic, cholinergic, and indolaminergic transmitters in the rat, Rossouw and colleagues (95) reported that changes in receptor densities and/or affinities varied with age at lead exposure. Neurotransmitters contribute to the morphological organization of the brain during development via their varied trophic roles (60). Several aspects of lead’s activity as a “neuropharmacologic” toxicant, many involving calcium-dependent aspects of neurotransmission and signal transduction (80), are also relevant to the process by which neuronal projections and connec-

months

~5.0 pg/dL 5.0-9.9 >9.9

T

/lg/dL

pg/dL

-I-

T

Low (l-56) Socio-Economic

High (57-66) Strata

FIG. 3. Adjusted full-scale WISC-R IQ scores at age 10 years for children in the Boston prospective study stratified by social class and blood lead category at age 2 years. Error bars represents 1 SE.

BELLINGER

208 tions are selected. The calcium ion flux affects synaptic efficacy and thus neuronal adhesive interactions (96). Protein kinase C, which is exquisitely sensitive to lead (62), modulates NMDA receptor currents, affecting long-term potentiation (LTP) and other forms of synaptic plasticity that may underlie learning and memory (16,61). Lead blocks the NMDA subtype of glutamate receptors in rat hippocampal pyramidal cells (1,108), inhibits, in an age-dependent manner, binding to the NMDA receptor in rat cerebral cortex and hippocampus (51), and increases sensitivity to NMDA neurotoxicity in the rat (26,79). Lead reduces the impact of MK-801, an NMDA antagonist, on rats’ performance on a multiple schedule of repeated learning (20). NMDA-mediated processes are probably not the only mechanisms by which lead may affect LTP, however (54). Topics that warrant additional investigation include whether lead toxicity is related to excitatory amino acid neurotoxicity (53), apoptosis (i.e., the timing and accuracy of programmed cell death as it contributes to the evolution of appropriate neuronal cytoarchitecture) (75) and the recently described phenomenon of long-term depression (66). RELEVANCE

TO

HUMAN

STUDIES

Apart from providing biologic plausibility for a link between lead exposure and cognitive deficits, what implications do these proposed mechanisms have for the interpretation of human epidemiological studies and the effort to identify brainbehavior relationships? An assumption made frequently is that tests of specific neuropsychological domains will be more sensitive to lead effects than are the more global IQ tests (87). If all exposure parameters and their interactions with situational factors were known, it is almost certainly the case that batteries of domain-specific lead-sensitive tests could be assembled. In the absence of such knowledge, however, global tests may provide the best opportunity to detect, albeit not to characterize in detail, any impact of lead on cognition. An implicit context-dependence of lead’s impact on neuronal organization underlies the integration proposed by Goldstein. That is, if lead disrupts the matching process by which the CNS is organized to meet environmentally specified substrate needs, the form of the resulting “organizational lesion” will perforce be environment-dependent. As described earlier, the contexts within which exposure occurs, i.e., the developmental micro-environments, differ for children in the different prospective cohorts, as do in fact the micro-environments of children within the same cohort. Because so few of the factors that contribute to the developmental microenvironment have been examined in terms of their impact on the lead-cognition association, it is not surprising that little success has been achieved in identifying a “behavioral signature” for lead. Indeed, these considerations imply that to some extent any leadassociated neurodevelopmental impairment will be idiopathic. This does not mean that there is no lead-associated behavioral signature but rather that there may not be a single signature that is produced under all conceivable exposure scenarios broadly conceived to include host characteristics. If neuropsychological presentation does differ by scenario, exposurerelated group differences will be most apparent on tests that sample and average performance over a broad range of domains, thus dampening the impact of heterogeneity in the expression of toxicity. The IQ test is a prime example of such a measure and, indeed, meta-analyses suggest that study findings using this endpoint are reasonably consistent. The stronger psychometric properties of IQ tests may be another reason why they tend to yield more consistent findings

than do more targeted neuropsychological tests. Having the goal of identifying “lowest observed adverse effect levels” (LOAELs), most studies have recruited samples in which a substantial proportion of the children have lead levels near or below the current screening targets. Any lead effects at such levels will necessarily be subtle, perhaps too subtle to be detected using domain-specific tests that in many cases were developed largely for use in a clinical setting where the goal is to discriminate learning-disabled from nonlearning-disabled or brain-damaged from nonbrain-damaged children. As a result, these tests may not be as sensitive as IQ tests to small lead-associated performance variations within the normal range, despite the limitations of IQ as a characterization of cognitive functioning (59). Findings from the Boston prospective study illustrate this point. We observed a large and highly significant association between blood lead level at age 2 years and WISCR full-scale IQ at age 10 years (ES: 5.8 IQ points/l0 pg/dL increase) (12). Our efforts to identify the neuropsychological underpinnings of this association were unsuccessful, however, as no clear lead-related pattern was discernable in children’s scores on a variety of standard neuropsychological tests (e.g., Wisconsin Card Sorting Test, Rey-Osterreith Complex Figure, Developmental Test of Visual-Motor Integration, California Verbal Learning Test-Children) (104). Nevertheless, despite our inability to identify the neuropsychological mechanism(s) at work, it was clear that IQ did not capture the entirety of the apparent lead effect. Figure 4 shows the mean adjusted scores on the brief form of the Kaufman-Test of Educational Achievement at 10 years of age for children classified by 2year blood lead level. The children are classified, as well, by concurrently measured full-scale IQ score. The dose-effect relationship between lead and academic achievement is apparent even among children with IQ scores nearly 2 SDS above the expected population mean. A superior IQ seems not to preclude an adverse impact of lead on cognition. One implication of this is that identifying the neuropsychological pathology underlying lead neurotoxicity may require a different strategy than does identifying dose-effect relationships. One approach would be to develop new and more sensitive tests of specific neuropsychological functions. Drawbacks to this formidable task include the time and effort required to construct tests that have sufficient reliability and validity (especially face validity) that they would be accepted by those charged with regulatory policy formulation. An alternative is to continue to use stock neuropsychological tests but to study cohorts of children with exposures well above those defining LOAELs, on the assumption that any lead-associated deficits in this group will be large enough to be detected with the limited sensitivity of “off-the-shelf’ tests. An additional assumption is that the neuropsychological mechanisms underlying deficits apparent around the LOAEL are the same as those underlying deficits associated with higher exposures. Our group recently pursued this approach in an effort to identify specific aspects of attention that are most strongly associated with lead exposure (14). We found that aspects pertaining to executive function (e.g., planning, shifting) were more strongly associated with lead than were lower-level attentional functions such as vigilance. Use of this strategy provides no assurance of success, however. Studying a cohort of 28 children, some of whom had blood lead levels persistently greater than 60 pg/dL, Faust and Brown concluded that “The significant difference in overall performance was due to the consistency with which the control group outperformed the experimental and not because of large but select differences” (ref. 45, p. 626), and that “the experimental group evidence[d] sub-

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LEAD, AND THE EXPERIMENTAL Adjusted

K-TEA

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score

Medium

IQ Strata -

SYSTEM

~5.0 ug/dL Blood

@

(tertiles)

5.0-9.9 ug/dL lead

High (‘126)

(115-126)

a

s9.9 ug/dL

at 24 months

FIG. 4. Adjusted Battery Composite scores from the Kaufman-Test of Educational Achievement (Brief Form) at age 10 years for children in the Boston prospective study stratified bv full-scale WISC-R IO tertile and blood lead category at age 2 years. Error bars repres&t 1 SE.

heterogeneity” (p. 628). If clinically based neuropsychological tests are to be helpful in defining any “behavioral signature” lead may have, relatively large sample sizes will still be needed even in cohorts consisting of children with high lead burdens. In planning assessment batteries for use in future studies, several modifications of the current approach should be considered. It would be desirable to use neuropsychological assessments that convey information about the process of a child’s learning rather than simply its product (34). In addition to determining whether a child knows a particular answer, we would like to determine how he or she attends to and processes information and achieves “new” learning, especially learning that is cumulative. Studies of an animal model of maternal PKU suggest that efficiency of transfer of learning is a more sensitive index of impairment than is the rate at which isolated problems are learned (105). Many items on IQ tests are scored simply as “pass” or “fail.” Although there is a long tradition in child psychology of examining a child’s errors to gain insight into the organization of cognitive development, with Piaget’s clinical studies providing the best example, this approach has not been applied in human lead studies. The goal would be to use tasks that allow the type of “microanalysis” CorySlechta (25) carried out on rats’ performance on an FI schedule to identify the bases of exposure-related performance differences. Because the overall rate of responding on this schedule integrates performance during periods of both responding and pausing, she dissected it into its component parts, finding that the time rats spent pausing after food delivery or during the interval was not affected by lead exposure but that after animals started to respond, those that were lead-exposed did so at a much higher rate. The rigid test administration procedures required by a research protocol proscribe “limit testing” by which a child’s skills and knowledge may be probed. Two children with identical full-scale IQ scores may have little in common in terms of their relative strengths and weaknesses. Qualitative aspects stantial

of their behavior may vary as well. One child may breeze through the test, responding quickly and accurately while the other’s work is equally accurate but effortful and fragile, and would deteriorate under time pressure, in a setting with many competing stimuli or when the child is tired. It would be informative to apply assessment methods that explore these aspects of children’s performance (e.g., 56), as primate (87) and human studies (116) suggest that lead-related performance differences are greatest when longer delays are introduced between a cue and the chance to respond, in the presence of distracting or irrelevant cues, or as task difficulty (e.g., signal rate) increases. Studies using latent learning paradigms indicate that a lead-exposed rat’s ability to acquire information in a nonappetitive context may be impaired even though ability to acquire a simple visual discrimination is not (63). Thus, lead exposure may have attentional or motivational effects that reduce an animal’s ability to “pick up” information incidentally, i.e., information that is not immediately relevant. Clearly this is an important way knowledge is accumulated, yet it is not a process directly tapped by intelligence tests. Animal studies also suggest that lead-associated effects may be unexpressed until elicited by specific environmental or organismic conditions or, as Cory-Slechta phrases it, “under conditions requiring behavioral transitions” (ref. 24, p. 440). The adaptation of experimental paradigms developed in animal studies is a potentially fruitful avenue to explore in human studies. Paule and colleagues (77) assembled a complex operant battery for use with 3- to 11-year-old children, which includes such tasks as delayed match-to-sample and incremental repeated acquisition. The use of such tests with children is not yet widespread in behavioral toxicology studies, however. CONCLUSION

Discussions about the association between lead and IQ have focused disproportionately on methodologic determinants of the accuracy and precision of estimation, with insuf-

210

BELLINGER

ficient consideration given to the influence of the experimental system on estimation. Some aspects of this system affect the magnitude of the effect size, whereas others affect its precision and thus the “signal-to-noise ratio” and the likelihood of statistical significance. My purpose is not to ignore the critical contributions of differences in internal validity to variability in study results. Rather, it is to advocate modeling the approach taken to interpreting human epidemiological studies on that taken to interpreting animal studies and to draw attention to aspects of the experimental system that animal studies suggest may materially affect study findings. If, indeed, the association between lead and cognition is quite sensitive to contextual factors, it is “weak” in the epidemiologic sense. In contrast, the link between cigarette smoking and lung cancer is so robust (an odds ratio of approximately 10) that it is readily apparent in populations that differ in the nature and extent of confounding by other exposures. This seems not to be true of lead. As with many other developmental neurotoxicants such as cocaine and alcohol (47), exposure to lead tends to account only for small amounts of variance in children’s cognitive performance. No single study provides a definitive answer to the question, “Under what

environmental conditions do different patterns of lead exposure produce measurable impairments at which ages in which behavioral endpoints in which types of populations?“. Each study contributes information about only a limited number of cells in the hypothetical design defined by these factors. In spite of differences in the experimental systems of lead studies, meta-analyses suggest a surprising degree of consistency in the estimate of effect size relating lead burden to children’s overall function (i.e., IQ). From the standpoint of developmental neurotoxicity, however, such a global characterization is not the end-all of knowledge about a toxicant’s nervous system impact. To advance the field beyond this, we must begin to explicate the role of the experimental system as well as the behavioral mechanisms underlying the IQ effect. Although available data provide a solid empirical foundation for current public health policy, they do not provide very satisfactory answers to the most fundamental questions about the impact of lead on a child’s nervous system. Now that the basic policy issues seem largely settled, it is time to rethink our assessment goals and strategies and our interpretational approaches so that we may gain greater insight into the pathophysiology of lead’s behavioral toxicity in children.

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