Associations of Blood Lead, Dimercaptosuccinic Acid-chelatable Lead, and Tibia Lead with Neurobehavioral Test Scores in South Korean Lead Workers

Brian S. Schwartz1,,–3, Byung-Kook Lee4, Gap-Soo Lee4, Walter F. Stewart1,3, Sung-Soo Lee4, Kyu-Yoon Hwang4, Kyu-Dong Ahn4, Yong-Bae Kim4, Karen I. Bolla5, David Simon3, Patrick J. Parsons6 and Andrew C. Todd7

1 Division of Occupational and Environmental Health, Department of Environmental Health Sciences, Johns Hopkins School of Hygiene and Public Health, Baltimore, MD.
2 Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.
3 Department of Epidemiology, Johns Hopkins School of Hygiene and Public Health, Baltimore, MD.
4 Institute of Industrial Medicine, Soonchunhyang University, Chonan, South Korea.
5 Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD.
6 Lead Poisoning/Trace Elements Laboratory, Wadsworth Center, New York State Department of Health, Albany, NY.
7 Department of Community and Preventive Medicine, Mount Sinai Medical Center, New York, NY.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The authors performed a cross-sectional study to evaluate associations between blood lead, tibia lead, and dimercaptosuccinic acid (DMSA)-chelatable lead and measures of neurobehavioral and peripheral nervous system function among 803 lead-exposed workers and 135 unexposed controls in South Korea. The workers and controls were enrolled in the study between October 1997 and August 1999. Central nervous system function was assessed with a modified version of the World Health Organization Neurobehavioral Core Test Battery. Peripheral nervous system function was assessed by measuring pinch and grip strength and peripheral vibration thresholds. After adjustment for covariates, the signs of the ß coefficients for blood lead were negative for 16 of the 19 tests and blood lead was a significant predictor of worse performance on eight tests. On average, for the eight tests that were significantly associated with blood lead levels, an increase in blood lead of 5 µg/dl was equivalent to an increase of 1.05 years in age. In contrast, after adjustment for covariates, tibia lead level was not associated with neurobehavioral test scores. Associations with DMSA-chelatable lead were similar to those for blood lead. In these currently exposed workers, blood lead was a better predictor of neurobehavioral performance than was tibia or DMSA-chelatable lead, mainly in the domains of executive abilities, manual dexterity, and peripheral motor strength.

cross-sectional studies; lead; lead poisoning; nervous system; adult; nervous system; neurobehavioral manifestations; neurologic manifestations; occupational exposure; spectrometry; x-ray emission

Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; DMSA, dimercaptosuccinic acid


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A 1995 review of 16 cross-sectional studies of lead exposure and neurobehavioral function in adults revealed that no studies had compared and contrasted the efficacy of different lead biomarkers, including bone lead, in the prediction of neurobehavioral decrements (1Go). Of the 16 studies, the median sample size was 49, and most of the studies relied on blood lead levels in evaluating associations with neuro-behavioral function. Since that review, six cross-sectional studies have examined associations between bone lead concentrations and neurobehavioral function in adults (2GoGoGoGoGo–7Go). These studies had a median sample size of 110 and differed widely in terms of study populations, control groups, neuro-behavioral methods, ages of study subjects, and bone and blood lead levels.

Overall, an interesting conclusion that can be drawn from these recently published studies is that blood lead may be a better predictor of neurobehavioral test scores than bone lead in cross-sectional studies. Österberg et al. (3Go) reported no associations of any measures of lead exposure, including finger bone lead, with neurobehavioral function. Bleecker et al. (2Go) reported associations of several blood lead measures with neurobehavioral test performance that were stronger and more consistent than associations with tibia lead. Hänninen et al. (4Go) reported no associations of tibia or calcaneus lead levels with neurobehavioral test scores. Payton et al. (5Go) reported significant (p < 0.05) associations of blood lead, patella lead, and tibia lead with five, zero, and one neurobehavioral measures, respectively. Stokes et al. (6Go) reported no significant associations of tibia or calcaneus lead levels with neurobehavioral test scores. Stewart et al. (7Go) reported that current and peak tibia lead levels were consistent predictors of neurobehavioral decrements in former organolead manufacturing workers.

It is possible that prior studies did not observe associations with bone lead levels because of inadequate power, a restricted range of bone lead levels, or low bone lead levels. Herein we report findings from a South Korean cross-sectional study of neurobehavioral function in 803 lead-exposed workers and 135 controls without occupational lead exposure, comparing and contrasting associations with blood lead, tibia lead, and dimercaptosuccinic acid (DMSA)-chelatable lead levels. Study subjects had a wide range of all three lead dose measures, which allowed us to evaluate the complete dose-effect relation.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study overview and design
The results presented herein are from a cross-sectional analysis of data from the first year of a 3-year longitudinal study of the neurobehavioral, peripheral nervous system, renal, hematopoietic, and blood pressure effects of inorganic lead exposure. Enrollment began in October 1997 with the first of three annual visits. The current report is focused on neurobehavioral and peripheral nervous system effects and is based on the 803 lead-exposed workers and 135 controls without occupational lead exposure who were enrolled between October 24, 1997 and August 19, 1999. The study was reviewed and approved by institutional review boards at the Johns Hopkins School of Hygiene and Public Health (Baltimore, Maryland) and the Soonchunhyang University School of Medicine (Chonan, South Korea).

Study population
Participation in the study was voluntary, and all participants provided written informed consent. Subjects were paid approximately $30 for their participation. Lead-exposed workers were recruited from 26 different lead-using facilities (see table 2). Retired workers from three facilities who had received medical surveillance services from Soonchunhyang University for several years were also allowed to participate in the study. Routine, government-mandated industrial hygiene sampling revealed that the study plants did not contain significant amounts of other heavy metals such as cadmium. Because of economic challenges that confronted South Korea during recruitment at some of the plants, participation rates were relatively low in four of the plants (17 percent, 39 percent, 55 percent, and 58 percent; table 1). In the other lead-using facilities, participation rates generally exceeded 80 percent. Controls were recruited from employees of an air conditioner assembly plant that did not use lead or other heavy metals and from wage-earning employees of Soonchunhyang University. The participation rate was 49 percent among the air conditioner assembly plant workers, because enrollment was limited to 100 employees (based on the scope of the funded research). The 37 university employees were a convenience sample.


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TABLE 2. Characteristics of participants enrolled in a study of neurobehavioral function and lead exposure between October 1997 and August 1999, Republic of Korea

 

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TABLE 1. Distribution of participants enrolled in a study of neurobehavioral function and lead exposure between October 1997 and August 1999, Republic of Korea

 
Data collection
Data collection was completed either at the Institute of Industrial Medicine in Chonan or on the premises of the study facilities. Study subjects completed a standardized questionnaire; completed a neurobehavioral test battery consisting of examiner-administered tests; had their blood pressure measured; had their peripheral vibration threshold assessed with the Vibratron II tester (Physitemp Instruments, Inc., Clifton, New Jersey); had their grip and pinch strength assessed with the Jamar Hydraulic Hand and Pinch Gauge dynamometers, respectively (Sammons Preston, Bolingbrook, Illinois); provided a 10-ml blood specimen by venipuncture that was stored at –70šC as whole blood, plasma, and red blood cells; provided a spot urine sample; and had their tibia lead concentration measured by x-ray fluorescence. Lead-exposed workers, but not control subjects, also provided a 4-hour urine sample after oral administration of DMSA.

An occupational physician obtained each subject's medical history, and a trained psychologist and a registered nurse conducted all neurobehavioral testing. Neurobehavioral function was assessed with a modified version of the World Health Organization Neurobehavioral Core Test Battery (8Go). All study questionnaires and instructions for data collection were translated into Korean and then back-translated into English to ensure accuracy of translation.

The World Health Organization Neurobehavioral Core Test Battery consists of the Profile of Mood States for assessment of affect; a test of simple reaction time for assessment of attention and psychomotor speed (9Go); the Digit Span Test, a subtest of both the Wechsler Adult Intelligence Scale, Revised and the Wechsler Memory Scale, Revised, for assessment of verbal memory and learning (10Go, 11Go); the Santa Ana Dexterity Test for assessment of manual dexterity; the Digit Symbol Substitution Test, a subtest of the Wechsler Adult Intelligence Scale, Revised, for assessment of executive abilities (12Go); the Benton Visual Retention Test for assessment of visual memory (11Go); and the Pursuit Aiming Test II for assessment of manual dexterity (13Go). In this study, the Center for Epidemiologic Studies Depression Scale (CES-D) (14Go) was substituted for the Profile of Mood States because of difficulty translating the latter into Korean; and the Purdue Pegboard Test (15Go) (model 32020; Lafayette Instrument Company, Inc., Lafayette, Indiana) was substituted for the Santa Ana Dexterity Test because extensive experience with the former test suggested that it was a sensitive measure in lead-exposed workers (7Go). In addition, Trail-Making Tests A and B were included for assessment of executive abilities, and Raven's Colored Progressive Matrices (Psychological Corporation, San Antonio, Texas) was included for assessment of nonverbal intelligence (16Go). Simple reaction time was assessed with the Standard Reaction Time Tester (Software Science, Cincinnati, Ohio). The test consisted of 64 trials administered over 6 minutes with a static group of random interstimulus intervals between 1 second and 10 seconds. Vibration thresholds were measured using the manufacturer's recommended two-alternative forced-choice procedure for the nondominant index finger and the dominant great toe (17Go), and pinch and grip strengths were measured using standardized positions and instructions on the nondominant side (18Go).

Laboratory methods
Hemoglobin was assayed by the cyanmethemoglobin method (model Ac-T 8; Beckman Coulter, Inc., Fullerton, California), and hematocrit was measured by the capillary centrifugation method (19Go). Urinary creatinine level was measured using a Sigma kit (Sigma Chemical Company, St. Louis, Missouri) and a Beckman DU-7 spectrophotometer (Beckman Instruments, Inc., Fullerton, California) (20Go).

Blood lead was measured with a Zeeman background-corrected atomic absorption spectrophotometer (model Z-8100; Hitachi Ltd., Tokyo, Japan) using the standard addition method of the US National Institute for Occupational Safety and Health (21Go) at the Institute of Industrial Medicine, a certified reference laboratory for lead measurement in South Korea. Tibia lead was assessed, in micrograms of lead per gram of bone mineral (hereafter referred to as µg/g), with a 30-minute measurement at the left midtibia shaft using 109Cd K-shell x-ray fluorescence, as previously described (22GoGo–24Go). X-ray fluorescence can provide negative point estimates of bone lead concentrations; however, all point estimates were retained in the statistical analyses, including negative values, because this method minimizes bias and does not require censoring of data (25Go).

Four-hour urinary lead excretion after oral administration of DMSA at 10 mg/kg was used to measure DMSA-chelatable lead. Urinary lead levels were measured in the laboratories of the Wadsworth Center at the New York State Department of Health (Albany, New York). Urinary lead concentrations were determined by electrothermal atomization atomic absorption spectrometry (model 4100ZL; PerkinElmer Instruments, Inc., Norwalk, Connecticut) using previously published methods (26Go).

Statistical analysis
The primary goals of the analysis were to: 1) compare test scores among lead-exposed workers and controls, controlling for covariates; and 2) compare and contrast blood lead, tibia lead, and DMSA-chelatable lead as predictors of test scores, controlling for covariates. Linear regression was used to compare test scores in lead-exposed subjects and controls, controlling for age, gender, and education (<=8, 9–11, and >=13 years vs. 12 years (high school graduates formed the reference group)).

To evaluate associations between the three lead biomarkers (i.e., blood lead, tibia lead, and DMSA-chelatable lead) and test performance, we used linear regression modeling. For ease of interpretation, all outcomes were standardized so that a higher test score always indicates better performance. Each of the 19 outcomes was modeled separately, evaluating one lead biomarker or a combination of biomarkers. Results from regression analyses with DMSA-chelatable lead were very similar to those for blood lead, so we provide only the regression results (ß coefficients and standard errors) for blood lead and tibia lead obtained from four different models, controlling for age, gender, and education: blood lead or tibia lead alone (model 1); blood lead and tibia lead together (model 2); blood lead or tibia lead with job duration (model 3); and blood lead, tibia lead, and job duration together (model 4). Models of the vibration threshold measures also controlled for height, and models of the pinch and grip strength measures also controlled for body mass index. In analyses with lead biomarkers, only current or former lead workers were included in the models. X-ray fluorescence provides an estimate of bone lead concentration measurement uncertainty; in linear regression modeling, we included tibia lead in the models both with (weighted as the inverse of the measurement variance) and without weighting for this measurement uncertainty.

A covariate was not retained in the final regression models if it was neither a significant predictor of test scores nor a confounder of the relations between lead variables and test scores. These included tobacco and alcohol consumption, former lead exposure versus current exposure, type of lead-using facility, blood lead adjusted for hematocrit, DMSA-chelatable lead adjusted for urinary creatinine, and quadratic terms for the lead biomarkers (for assessment of nonlinear relations). Graphic methods were used to evaluate linear regression assumptions of normality, homoscedasticity, and linearity and to evaluate the presence of influential points.

All test scores were reviewed by a neuropsychologist before the modeling was carried out. Several tests had scores or results that were not thought to be physiologically plausible; thus, for these variables, only a subset of values was used (simple reaction time, mean: ¾500 ms; simple reaction time, standard deviation: <=300 ms; Trail-Making Test A: <=200 seconds; Trail-Making Test B: <=400 seconds). These limits were defined before modeling, and as a result, 10, 6, 3, and 11subjects were excluded for these four tests, respectively. No study subjects were excluded from the analysis for other reasons (e.g., on the basis of medical conditions). Data for five neurobehavioral outcome variables (standard deviation of simple reaction time, Trail-Making Test A, Trail-Making Test B, Pursuit Aiming Test (incorrect responses), and CES-D) were skewed and thus were log-transformed to better approximate normality.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Demographic data and dose measures
Compared with controls without occupational lead exposure, lead-exposed subjects were older, had lower educational levels, and had a lower proportion of male subjects (table 2). The majority of both controls and exposed subjects were current users of tobacco and alcohol products. There was a wide range of blood lead, tibia lead, and DMSA-chelatable lead levels among the lead workers (table 3). The corresponding values among control subjects were low. Among lead workers, tibia lead was moderately correlated (Pearson's r) with blood lead (r = 0.42), DMSA-chelatable lead (r = 0.43), and job duration (r = 0.40) (all p values < 0.01). Blood lead was highly correlated with DMSA-chelatable lead (r = 0.81).


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TABLE 3. Summary data for selected biologic measures in lead-exposed subjects and controls without occupational lead exposure, Republic of Korea, 1997–1999

 
Comparison of lead-exposed subjects with unexposed controls
Exposed subjects had worse performance than controls on all tests except Colored Progressive Matrices, the Pursuit Aiming Test (incorrect responses), and the CES-D (table 4). After adjustment for age, gender, and education (and height or body mass index for the peripheral nervous system measures), exposed subjects performed significantly (p < 0.05) worse than controls on the following tests: simple reaction time, standard deviation of simple reaction time, Digit Span, Benton Visual Retention, Colored Progressive Matrices, Digit Symbol Substitution, Purdue Pegboard (dominant hand, nondominant hand, both hands, and assembly), peripheral vibration threshold for the great toe, and grip strength. Controls performed significantly worse than lead-exposed workers on the Pursuit Aiming Test (incorrect responses), the test of pinch strength, and the CES-D.


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TABLE 4. Neurobehavioral test scores, by cognitive domain, in lead-exposed subjects and controls without occupational lead exposure, Republic of Korea, 1997–1999

 
Lead biomarkers and neurobehavioral and peripheral nervous system measures
On average, test scores were worse with increasing age and lower educational level and were worse in women. Scores were generally better with longer job duration. After data were controlled for covariates, higher DMSA-chelatable lead levels were associated (p < 0.10) with worse performance on Trail-Making Test B, the Pursuit Aiming Test (correct and incorrect responses), the Purdue Pegboard Test (dominant hand, nondominant hand, both hands, and assembly), and the test of pinch strength. After the addition of blood lead to these models, DMSA-chelatable lead was no longer associated with any of these tests. Moreover, when both blood lead and DMSA-chelatable lead were included in the same models together, blood lead was the more consistent predictor of worse test performance. Thus, in subsequent models we evaluated only blood lead, tibia lead, and job duration.

After adjustment for covariates, the signs of the regression coefficients for blood lead were negative for 16 of the 19 tests (table 5, model 1). Blood lead was a significant predictor of poorer scores on Trail-Making Test B, the Pursuit Aiming Test (correct and incorrect responses), the Purdue Pegboard Test (dominant hand, nondominant hand, both hands, and assembly), and the test of pinch strength. The addition (to model 1) of tibia lead alone (table 5, model 2), job duration alone (table 5, model 3), and tibia lead and job duration together (table 5, model 4) did not substantially alter these associations. On average (table 5, model 1), for the eight tests significantly associated with blood lead levels, an increase in blood lead of 5 µg/dl was equivalent in its effects on test scores to an increase of 1.05 years in age; thus, increases in blood lead levels of 15, 25, and 35 µg/dl were equivalent to increases of 3.15, 4.20, and 5.25 years in age, respectively.


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TABLE 5. Linear regression modeling{dagger} of relations of neurobehavioral and peripheral nervous system measures with blood lead levels, Republic of Korea, 1997–1999

 
In contrast, after adjustment for the same covariates, tibia lead (table 6) was significantly associated with worse performance only on the CES-D and the measure of vibration threshold in the dominant great toe (regression results shown in table 6, model 1 and figures 1GoGo4 are unweighted for tibia lead measurement uncertainty). The signs of the regression coefficients for tibia lead were negative for 14 of the 19 tests. These associations were not significantly altered by the addition of blood lead to model 1 (table 6, model 2). In contrast, after the addition of job duration to model 1, tibia lead was now significantly associated with five measures (table 6, model 3); three other outcomes had p values between 0.05 and 0.10. In a model containing tibia lead, blood lead, and job duration together (table 6, model 4), the associations were similar to those from models containing tibia lead alone (table 6, model 1). There were no significant changes in associations in models in which tibia lead was weighted for measurement uncertainty (data not shown).


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TABLE 6. Linear regression modeling{dagger} of relations of neurobehavioral and peripheral nervous system measures with tibia lead levels, Republic of Korea, 1997–1999

 


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FIGURE 1. Associations of blood lead, tibia lead, job duration, and age with scores (seconds) on Trail-Making Test B (TRLBT) among 803 lead-exposed workers in South Korea, October 1997–August 1999. The scatterplots are for the regression models presented in table 5, adjusted for age, gender, and education. Blood lead, tibia lead, and job duration were entered into the regression models separately. The associations with age are from the models containing blood lead. The solid lines are the adjusted regression lines; the dotted lines are estimations made by a smoothing method (32Go) using the S-PLUS statistical software function Lowess (MathSoft, Inc., Cambridge, Massachusetts), with adjustment for covariates.

 


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FIGURE 2. Associations of blood lead, tibia lead, job duration, and age with scores (number of correct responses) on the Pursuit Aiming Test (correct responses) (PATR) among 803 lead-exposed workers in South Korea, October 1997–August 1999. The scatterplots are for the regression models presented in table 5, adjusted for age, gender, and education. Blood lead, tibia lead, and job duration were entered into the regression models separately. The associations with age are from the models containing blood lead. The solid lines are the adjusted regression lines; the dotted lines are estimations made by a smoothing method (32Go) using the S-PLUS statistical software function Lowess, with adjustment for covariates.

 


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FIGURE 3. Associations of blood lead, tibia lead, job duration, and age with scores (number of pieces) on the Purdue Pegboard Test (assembly) (PEGASM) among 803 lead-exposed workers in South Korea, October 1997–August 1999. The scatterplots are for the regression models presented in table 5, adjusted for age, gender, and education. Blood lead, tibia lead, and job duration were entered into the regression models separately. The associations with age are from the models containing blood lead. The solid lines are the adjusted regression lines; the dotted lines are estimations made by a smoothing method (32Go) using the S-PLUS statistical software function Lowess, with adjustment for covariates.

 


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FIGURE 4. Associations of blood lead, tibia lead, job duration, and age with scores (kg) on a test of pinch strength (PINCH) among 803 lead-exposed workers in South Korea, October 1997–August 1999. The scatterplots are for the regression models presented in table 5, adjusted for age, gender, education, and body mass index. Blood lead, tibia lead, and job duration were entered into the regression models separately. The associations with age are from the models containing blood lead. The solid lines are the adjusted regression lines; the dotted lines are estimations made by a smoothing method (32Go) using the S-PLUS statistical software function Lowess, with adjustment for covariates.

 
For many of the tests, increasing job duration was associated with better test performance–for example, in models with blood lead, the signs of 14 out of 19 regression coefficients for job duration were positive (data not shown). Examination of plots suggested that test scores of lead workers improved with increasing job duration up to approximately 15 years and then declined (figures 1GoGo4). Because of concerns about a possible survivor effect (i.e., individuals who are susceptible to the central nervous system effects of lead are more likely to terminate employment), additional exploratory analyses were performed. We conducted regression analyses to evaluate whether job duration, divided into quartiles (<=3.0, 3.1–6.9, 7.0–12.4, and >12.4 years), modified the relations between tibia lead and test scores. These analyses did not reveal consistent associations of tibia lead with test scores in subjects grouped by quartile of job duration (data not shown). Regression analyses were also repeated after removal of subjects with the highest cumulative lead exposures (i.e., tibia lead concentrations greater than 150 µg/g). After this exclusion, tibia lead was still not associated with test scores.

It is possible that the chronic effects of lead exposure, as assessed by relations of tibia lead levels with test scores, could be obscured by the larger acute effects of lead exposure, as assessed by relations of blood lead levels with test scores. To address this possibility, we used regression models to evaluate whether tibia lead modified the relations between blood lead and test scores. Tibia lead did not modify these relations (data not shown). Similarly, the relations between tibia lead and neurobehavioral test scores were not significantly different among subjects with high and low blood lead levels.

We performed additional regression analyses to assess whether the associations of blood lead with test scores differed between plants with high levels of participation and those with low levels of participation. These analyses did not suggest that differential levels of participation affected the study results.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Evidence reported to date supports the conclusion that blood lead levels in adults with occupational exposure to lead are associated with decrements in neurobehavioral performance in cross-sectional studies. Tests most often associated with blood lead levels include reaction time, block design, Santa Ana Dexterity, grooved pegboard, Digit Symbol Substitution, and embedded figures, representing the cognitive domains of manual dexterity, visuomotor skills, psychomotor speed, and verbal memory (1Go). In the current study, associations were observed between blood lead levels and the cognitive domains of executive abilities, manual dexterity, and peripheral motor strength.

In contrast, evidence does not support an association between bone lead levels and neurobehavioral function. Only one published article has reported consistent associations of tibia lead levels, both current and extrapolated peak levels, with neurobehavioral test scores, in former organolead manufacturing workers without current exposure to lead (7Go). Bleecker et al. (2Go), Österberg et al. (3Go), and Hänninen et al. (4Go) all studied subjects with current or past occupational lead exposure; all included 80 or fewer subjects; and each had a range of bone lead levels from 0 to approximately 100 µg/g. Stokes et al. (6Go) reported on young adults with past high environmental exposure to lead and currently low tibia lead levels. Stewart et al. (7Go) reported on former workers with past exposure to organic or inorganic lead; this limits inferences that can be made regarding the responsible neurotoxicant (organic lead, inorganic lead, or both). Payton et al. (5Go) reported on older men with generally low current blood and tibia lead levels, and theirs was the largest study of adults, with 141 subjects.

In comparisons of lead-exposed workers with controls, lead workers performed worse than controls in all of the domains represented by tests associated with blood lead levels, as well as psychomotor speed, verbal memory and learning, visual memory, nonverbal intelligence, and peripheral nervous system sensory and strength testing. This suggests that the cognitive deficits associated with lead may be broad-based. Concerning the magnitude of the effect, for the eight tests significantly associated with blood lead levels, an increase in blood lead of 5 µg/dl was equivalent to an increase of 1.05 years in age, which suggests that the magnitudes of the associations with blood lead levels are not small. Figures 1GoGo4 indicate that, for at least some of the tests (e.g., Purdue Pegboard (assembly) and Trail-Making Test B), there may be a "threshold," as evidenced by the Lowess lines, up to a blood lead level of approximately 18 µg/dl. After that level, both the regression lines and the Lowess lines indicate a decline in test scores with increasing blood lead levels. These data suggest that the current occupational blood lead limit of 40 µg/dl may not be a level at which neurobehavioral decrements can be prevented.

The current data are consistent with prior evidence in that blood lead levels were consistent predictors of decrements in neurobehavioral test scores while tibia lead levels were not. There are a number of possible explanations for these findings. First, it is possible that blood lead measures a pool of lead that is more relevant to the health outcomes under study than that measured by tibia lead. Second, a single blood lead measurement may be more representative of lead in blood than is a single tibia lead measurement of lead in tibia, mainly because of issues regarding the homogeneity of the distribution of lead in the pool and the sampling of the pool. Third, it is possible that blood lead is an estimate of the more recent "dose" of the brain to lead while tibia lead is an estimate of the cumulative dose of the brain to lead, and that neurobehavioral test scores are more affected by recent lead dose than by cumulative lead dose. Fourth, associations of tibia lead with test scores in cross-sectional studies are more likely to be obscured by problems such as selection bias than are associations with blood lead levels.

We are concerned that the last explanation may be the most likely, in that test scores tended to be better with increasing job duration and associations of tibia lead with the outcome measures were highly influenced by inclusion of job duration in the regression models. Blood lead thus appears to have advantages over bone lead in the evaluation of neurobehavioral function in cross-sectional studies, but it is possible that tibia lead may emerge as a better predictor of decline in neurobehavioral function in longitudinal studies.

DMSA-chelatable lead is thought to be a measure of bioavailable lead burden (27GoGo–29Go). Analyses performed in our study suggested that associations of DMSA-chelatable lead with neurobehavioral test scores were similar to those for blood lead, and these two measures were highly correlated. Controlling for 4-hour creatinine clearance in the linear regression models did not alter associations with DMSA-chelatable lead, nor did dividing DMSA-chelatable lead by urinary creatinine concentration.

Blood lead levels were predictors of worse performance on the Purdue Pegboard Test, the Pursuit Aiming Test, Trail-Making Test B, and the test of pinch strength. All of these measures require responses with the hands, evaluating dexterity, speed, and strength; however, other neurobehavioral measures with large peripheral components, including reaction time and vibration thresholds, were not associated with blood lead levels. These findings are similar to those of Stollery et al. (30Go), who concluded that the sensory and motor (rather than cognitive) requirements of the neuro-behavioral tests were most affected by lead. The results of the current study are consistent with this hypothesis. However, results regarding controls suggested that a larger group of cognitive domains may be affected.

As was discussed by Ingraham and Aiken (31Go), the correlation between neuropsychological tests would lower the expected number of abnormal results if lead had no effect on neurobehavioral test performance. These authors presented a model based on a binomial probability distribution for evaluating the expected number of abnormal test results in an assessment battery and argued that this probability, based on the assumption of test independence, can serve as an upper bound in situations where multiple tests are part of an intercorrelated test battery. We calculated the probability of obtaining by chance our finding of at least seven out of 15 central nervous system tests' having associated p values less than or equal to 0.05; this probability was less than 0.000001 (31Go), suggesting that the study findings are very unlikely to be due to chance.

We do not think that our findings were likely to be due to confounding or bias. Although test scores tended to improve with increasing job duration, suggesting a "survivor bias," we feel that this is likely to have biased our study results toward the null. Although test scores were assessed at only one point in time for each subject, both recent and lifetime cumulative measures of lead absorption were assessed in associations with test scores. Causal inferences should not be made from this cross-sectional study; however, the body of literature assessing associations of blood lead levels with neurobehavioral test scores is now quite extensive and consistent.


    ACKNOWLEDGMENTS
 
This research was supported by grant ES07198 (Dr. Schwartz) from the National Institute of Environmental Health Sciences.


    NOTES
 
Reprint requests to Dr. Brian S. Schwartz, Division of Occupational and Environmental Health, Johns Hopkins School of Hygiene and Public Health, 615 North Wolfe Street, Room 7041, Baltimore, MD 21205 (e-mail: bschwart{at}jhsph.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Balbus-Kornfeld JM, Stewart W, Bolla KI, et al. Cumulative exposure to inorganic lead and neurobehavioral test performance in adults: an epidemiologic review. Occup Environ Med 1995;52:2–12.[Abstract]
  2. Bleecker ML, Lindgren KN, Ford DP. Differential contribution of current and cumulative indices of lead dose to neuropsychological performance by age. Neurology 1997;48:639–45.[Abstract]
  3. Österberg K, Börjesson J, Gerhardsson L, et al. A neurobehavioral study of long-term occupational inorganic lead exposure. Sci Total Environ 1997;201:39–51.[ISI][Medline]
  4. Hänninen H, Aitio A, Kovala T, et al. Occupational exposure to lead and neuropsychological dysfunction. Occup Environ Med 1998;55:202–9.[Abstract]
  5. Payton M, Riggs KM, Spiro A, et al. Relations of bone and blood lead to cognitive function: The VA Normative Aging Study. Neurotoxicol Teratol 1998;20:19–27.[ISI][Medline]
  6. Stokes L, Letz R, Gerr F, et al. Neurotoxicity in young adults 20 years after childhood exposure to lead: the Bunker Hill experience. Occup Environ Med 1998;55:507–16.[Abstract]
  7. Stewart WF, Schwartz BS, Simon D, et al. Neurobehavioral function and tibial and chelatable lead levels in 543 former organolead workers. Neurology 1999;52:1610–17.[Abstract/Free Full Text]
  8. Johnson BL, Baker EL, Batawi M, et al. Prevention of neurotoxic illness in working populations. New York, NY: John Wiley and Sons, Inc, 1987.
  9. Wilkinson RT, Houghton D. Field test of arousal: a portable reaction timer with data storage. Hum Factors 1982;24:487–93.[ISI][Medline]
  10. Cassito MG, Camerino D, Hanninen WK. International Collaboration to Evaluate the WHO Neurobehavioral Core Test Battery. In: Johnson BL, ed. Advances in neurobehavioral toxicology: applications in environmental and occupational health. Boca Raton, FL: CRC Press, 1990:203–23.
  11. Benton A. Contributions to neuropsychological assessment. New York, NY: Oxford University Press, 1983.
  12. Wechsler D. The Wechsler Adult Intelligence Scale–Revised manual. New York, NY: The Psychological Corporation, 1981.
  13. Fleischman EA. Dimensional analysis of psychomotor abilities. J Exp Psychol 1954;48:437–54.[ISI]
  14. Radloff LS, Rae DS. Susceptibility and precipitating factors in depression: sex differences and similarities. J Abnorm Psychol 1979;88:174–81.[ISI][Medline]
  15. Haaland KY, Cleeland CS, Carr D. Motor performance after unilateral hemisphere damage in patients with tumor. Arch Neurol 1977;34:556–9.[Abstract]
  16. Raven J. The Raven Progressive Matrices: a review of national norming studies and ethnic and socio-economic variation within the United States. J Educ Meas 1989;16:1–16.
  17. Arezzo JC. Quantitative sensory testing of vibration threshold–Vibratron II. Rationale and methods. Clifton, NJ: Physitemp Instruments, Inc, 1992.
  18. Mathiowetz V, Weber K, Volland G, et al. Reliability and validity of grip and pinch strength evaluations. J Hand Surg [Am] 1984;9:222–6.[Medline]
  19. Thomas WJ, Collins TM. Comparison of venipuncture blood counts with microcapillary measurements in screening for anemia in one-year-old infants. J Pediatr 1982;101:32–5.[ISI][Medline]
  20. Heinegard D, Tiderstrom G. Determination of serum creatinine by a direct colorimetric method. Clin Chim Acta 1973;43:305–10.[ISI][Medline]
  21. Kneip TJ, Crable JV. Methods for biological monitoring: a manual for assessing human exposure to hazardous substances. Washington, DC: American Public Health Association, 1988:199–201.
  22. Todd AC, McNeill FE. In vivo measurements of lead in bone using a Cd spot source. In: Human body composition studies. New York, NY: Plenum Press, 1993:299–302.
  23. Todd AC, McNeill FE, Palethorpe JE, et al. In vivo x-ray fluorescence of lead in bone using K x-ray excitation with 109Cd sources: radiation dosimetry studies. Environ Res 1992;57:117–32.[ISI][Medline]
  24. Schwartz BS, Stewart WF, Todd AC, et al. Predictors of DMSA-chelatable lead levels and bone lead levels in organolead manufacturing workers. Occup Environ Med 1999;56:22–9.[Abstract]
  25. Kim R, Aro A, Rotnitzky A, et al. K x-ray fluorescence measurements of bone lead concentration: the analysis of low-level data. Phys Med Biol 1995;40:1475–85.[ISI][Medline]
  26. Parsons PJ, Slavin W. Electrothermal atomization atomic absorption spectrometry for the determination of lead in urine: results of an interlaboratory study. Spectrochim Acta Part B 1999;54:853–64.[ISI]
  27. Lee B-K, Schwartz BS, Stewart W, et al. Urinary lead excretion after DMSA and EDTA: evidence for differential access to lead storage sites. Occup Environ Med 1995;52:13–19.[Abstract]
  28. Schwartz BS, Lee B-K, Stewart W, et al. {delta}-Aminolevulinic acid dehydratase genotype modifies 4-hour urinary lead excretion after oral administration of dimercaptosuccinic acid. Occup Environ Med 1997;54:241–6.[Abstract]
  29. Batuman V. Lead nephropathy, gout, and hypertension. Am J Med Sci 1993;305:241–7.[ISI][Medline]
  30. Stollery BT, Banks HA, Broadbent DE, et al. Cognitive functioning in lead workers. Br J Ind Med 1989;46:698–707.[ISI][Medline]
  31. Ingraham LJ, Aiken CB. An empirical approach to determining criteria for abnormality in test batteries with multiple measures. Neuropsychology 1996;10:120–4.[ISI]
  32. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979;74:829–36.[ISI]
Received for publication September 25, 1999. Accepted for publication June 12, 2000.