Comparison of biometric data of children with high and low levels of lead in the blood.код для вставкиСкачать
AMERlCAN JOURNAL OF PHYSICAL ANTHROPOLOGY 69:107-116 (1986) Comparison of Biometric Data of Children With High and Low Levels of Lead in the Blood M.-C. LAUWERS, R.C. HAUSPIE, C. SUSANNE, AND J. VERHEYDEN Laboratory of Anthropogenetics, Vrije Universiteit Brussel, 1050 Brussels (M.CL., R. C.H., C.S.), Provinciaal Instituut uoor Hygiene, 2000 Antwerp (J.V),and National Fund for Scientific Research, Belgium (R.C.H.) KEY WORDS profiles Anthropometric data, Blood lead levels, Biometric ABSTRACT This paper deals with a biometric study of 312 boys and girls, aged 2.5-16 years, living in a n area with a long history of pollution by lead. The aim was to search for eventual relationships between ten biometric variables and measures of lead absorption in the bodies, i.e. the amount of lead in the blood (PbB), of these children. Standardized values of the biometric variables were compared in the highPbB and low-PbB categories, by multivariate analysis of variance. Comparison of the vectors of the ten biometric variables reveals a significant difference between the two categories of PbB levels. We found some evidence that the younger children (below 8 years of age) are more likely to absorb lead in the body and are more vulnerable to the effects of subclinical lead intoxication than their older counterparts. The differences between the averages of biometric variables in the two PbB categories are consistently (although not significantly) greater among younger children. This trend disappeared in the older age group. These results confirm data from the literature that young children are especially at risk. It can be concluded that there is a subtle, but significant, influence of lead absorption on the biometric profiles of children and that this effect is probably more important in children below 8 years of age. Since ancient times, it is known that lead is toxic to humans (Nriagu, 1983). The risk of exposure to lead substances has increased considerably during the past century, mainly as a consequence of the industrial revolution in Western countries. It is now well known that acute and chronic intoxication by high levels of lead may cause severe disturbances of the hematopoietic and nervous system. Various effects on bone, liver, kidney, and gastrointestinal functions, the immunodefense mechanism, and hormonal secretion have also been reported (Damstra, 1977; Gerber et al., 1980). The effects of long-term exposure to low levels of lead are less well understood but are, nevertheless, not without interest since large parts of the population may be involved. Children may even run a higher risk, since they store lead in the body at a faster rate than do adults (Duggan, 0 1986 ALAN R. LISS, INC. 1983), and they have lifestyles favoring extensive contact with contaminated soil, dust, and nonedible materials (Lourie et al., 1963; Katagiri et al., 1983). The neuropsychological effects in children, such as behavioral and intellectual deficiencies, have been critically reviewed by Bornschein et al. (1980). Abnormalities in fine-motor control and behavior (negativism, distractibility, and constant need for attention) have been observed in children with elevated blood lead levels, but without overt symptoms of lead poisoning (de la Burde and Choate, 1972). More recently, Needleman et al. (1979) gave evidence of reduced intellectual performance in children with elevated dentine lead levels. The frequency of nonadaptive classroom behavior Received October 10, 1983; revised March 15, 1985; accepted July 23,1985. 108 M.-C. LAUWERS, R.C. HAUSPIE, C. SUSANNE, AND J. VERHEYDEN also increased in a fashion dose-related to the amount of lead accumulated in the teeth. In view of this wide range of physiological and metabolic effects of lead intoxication, and of the fact that communitywide exposure to high concentration of lead, such as from nearby smelters, is associated with increased morbidity in children (Rabinowitz and Needleman, 1984), it is not unlikely that chronic lead poisoning might also affect the process of normal growth and development. Much of our knowledge, here, comes from laboratory experiments with animals exposed to various conditions of lead intoxication. It has been shown that chronic lead poisoning of rats causes neurological damage (Pentschew and Garro, 1966; Bornschein et al., 1980; Fox et al., 19791, as well as reduced fertility (Grant et al., 1980),increased rates of fetal and postnatal deaths (Gerber et al., 1980), and retardation of fetal and postnatal growth (Baernstein and Grand, 1942; Michaelson, 1973; Michaelson and Sauerhoff, 1974; Wapnir et al., 1977; Aviv et al., 1977, 1980; Mykkanen et al., 1982). Similar findings have been reported in mice (Gerber and Maes, 1978),rabbits (Lorenzo et al., 19781, chickens (Gilani, 19731, hamsters (Carpenter and Ferm, 1977), and dogs (Penumarthy et al., 1980). Kimmel et al. (1980) and Grant et al. (1980) observed growth retardation of weight in rats with blood lead levels similar to those observed in humans under long-term exposure to lead (18-37 pg/lOO ml blood). Information about possible effects of lead intoxication on human growth and development is very scarce. However, some evidence from the literature on impaired growth and development in children exposed to a leadcontaminated environment has been presented by Antal et al. (1968).Epidemiological studies have also reported teratogenic effects of lead intoxication, such as retardation of fetal and postnatal growth (Gerber et al., 1980), whereas others have noted a significantly decreased birth weight of babies born to women working in and living around a smelter (Nordstrom et al., 1978). While these studies suffer from the lack of control on the amount of lead actually absorbed by the children, Habercam et al. (1974) measured the amount of lead in the blood and teeth in 18 preadolescent children and concluded that about 22% of the growth deficit in height and weight was attributable to lead. The aim of the present study is to compare biometric data in two groups of children and adolescents with high and low amounts of absorbed lead, irrespective of the source or means of uptake. Since it is, indeed, the amount of lead circulating and metabolized in the body that puts a child's health at risk, we have used the concentrations of lead in the blood as a criterion to classify subjects into two groups, rather than using indirect measures of exposure, such as concentrations of lead in the air or distance from the leademitting source. The socioeconomic background has been controlled, since differences in this factor in the two groups might obscure eventual relationships between the biometric profile of the subjects and the amount of absorbed lead. MATERIALS AND METHODS The present study concerns children living in Hoboken, a suburban village near Antwerp, where pollution by heavy metals of industrial origin has been a problem during at least the past 25 years (Roels et al., 1979; Claeys-Thoreau et al., 1983). The children under study resided a median distance of 1.7 km from a metallurgic plant; some, however, lived a s close as a few hundred meters. The data come from two surveys that have been set up independently since 1978. The present sample is made up of subjects common to both of these surveys. One survey consists of a large-scale mixed-longitudinal study of child growth, held in the area during the period 1979-1980. The subjects were measured on one or several occasions during the regular medical examinations of schoolchildren in the two medical health centers of the village. Since these medical examinations are compulsary for all Belgian schoolchildren (and hence also for those of Hoboken), we can assume that the original survey consists of a representative sample of children living in that area. The other survey has been conducted since 1978 by the Provincial Institute of Hygiene, the agency responsible for semiannual examinations of concentrations of lead in blood samples of children living in Hoboken, especially those living in the vicinity of the metallurgic plant. There were 312 children (150 boys and 162 girls), aged 2.5-16 years, for whom biometric data a s well as measures of blood lead content were available. The following anthropometric measurements were collected by a single trained measurer (R.C.H.), using standardized equipment, following the techniques described by Martin and Saller (1957) 109 BIOMETRTC DATA AND BLOOD LEAD LEVELS and Twiesselmann (1969): stature, weight, total arm length, biacromial and bicristal diameter, upper arm circumference (relaxed), thigh circumference, head length and breadth, and bizygomatic diameter. Blood lead concentrations (PbB) were measured by atomic absorption spectrophotometry. Hematology examinations preceded the biometric examinations by an average of 9.5 months. In 23% of the cases more than four samples were analyzed, in 7% four samples were analyzed, in 9%, three samples, in 4%, two samples and in 58%, one sam le. When more than one sample was anayzed, the mean PbB level was calculated. Figure 1 gives the frequency distribution of PbB. The numerical description of the sample is summarized in Table 1. Information about social background was drawn from the medical records available in the medical health centers, and socioeconomic status was assigned from the occupation of the father and mother. The classification system of Graffar (1956) was adopted to rank the occupations from 1to 5 where 1 is the highest social class, 5 the lowest. This system does not include housewives. The information on the profession of the fathers was missing in 25 cases (8%)and of the mothers in 15 cases (5%). There were also 172 housewives among the mothers. Hence we were left with 287 subjects who could be classified according to the occupational status of the father and 125 according to the occupational status of the mother. The frequencies of the occupational groups are shown in Table 2. The calculations were performed at the Brussels University Computer Center, using P 69 1 h 1 Age at anthropometric examination (years) Distance between residence and metallurgic plant SPSS programs (Nie et al., 1975). Multivariate analysis of variance (MANOVA)was applied in order to compare the set of ten biometric variables in groups of children with low and with high levels of lead in the blood. For this purpose we defined, within the total 10.25 12.99 2.76 15.90 2.97 0.54 16.88 -1.58 1.14 -3.29 3.44 27.00 41.22 11.00 57.00 0.76 1.70 -1.71 17.29 (km) Time lapse between anthropometric examination and PbB examination (years) PbB (pg/100 ml total blood) 60 PbB (rs/lOO ml) Percentiles 50 10 (median) 90 Minimum Maximum 5.88 50 Fig. 1. Frequency distributions of PbB (lead in the blood) concentration (pg/lOOml) in the total sample of 312 children. TABLE 1. Numerical description of the sample (N = 312; 150 boys and 162 girls) Variable 40 110 M.-C. LAUWERS, R.C. HAUSPIE, C. SUSANNE, AND J. VERHEYDEN RESULTS TABLE 2. Number of subjects in each class of the different factors under study The first question we addressed was the relationship of the amount of lead absorbed to socioeconomicstatus. Since this factor may 1 2 4 _-___5 Total also influence biometric values, it might conOccupationof 6 14 109 150 8 287 found possible relationships between lead abfather sorption and biometric variables. The Occupationof 1 11 37 45 31 125 mother associations were tested by means of the x2 _____ statistic calculated in the various 2 x n conSee text for definition of the factors. tingency tables. The results are shown in Table 3. The table PbB x occupation of range of PbB levels (0-60 pgI100 ml), two mother had expected frequencies of less than categories (0-30 pgfi00 mi and 40-60 pgI100 5, but greater than 1, in 2 of 6 cells. Lewontin ml). These categories have been chosen ac- and Felsenstein (1965) and Slakter (1966) cording to the data from the literature indi- point out that in such situations the x2 critecating that a rise of the blood lead content rion can still be used. Since a t least in the above 40 pg/lOO ml is considered to be a level present analysis there is no significant assoleading to various hematological and neuro- ciation between the incidence of high lead logical disturbances in children (Hernberg, levels and socioeconomic factors, we can as1980). Because there is a wide variation in sume that the results of the further analysis individual sensitivity and no definite cutoff of the relationship between the lead parampoint can be defined (Hernberg, 19801, those eter and the biometric profile of the children children with intermediate levels of PbB from are not confounded by this factor. 30 to 40 pg/lOO ml, were omitted from the Simultaneous multivariate comparison of analysis. the vector of the ten biometric variables beSince the biometric data of males and fe- tween the two PbB categories was performed males of varying ages were pooled, anthro- by multivariate analysis of variance (lower pometric measurements were converted to part of Table 4) and revealed a significant standardized values, using appropriate ref- difference between the two categories of blood erence data for the Belgian population lead levels milks’ A = 0.917; P = 0.042). (Twiesselmann, 1969). The standardized Box’s M-statistic did not show heterogeneity value (z) of a measurement is obtained as of the dispersion matrix, and we assumed that the vectors of biometric variables had z = (x - m)/s, multivariate normal distributions. Univariate analyses of variance (ANOVA) were also x being the observed value for t h a t measure- applied to test separately the differences of ment, and m and s being, respectively, the the ten biometric variables between classes mean and standard deviation of that mea- of high and low levels of PbB. In Figure 2, surement in the reference population. Since the biometric profiles of the ten variables are the reference data give only yearly means depicted for the two PbB categories. Table 4 and standard deviations, and the ages of the (upper part) shows the mean standardized subjects rarely coincide with one of the mid- values of the ten biometric variables in the points of the age intervals in the reference two classes, together with the respective unipopulation, the values of m and s were as- variate F-statistic a s well as the level (a)and signed to correspond exactly to each subject’s power (1 - 6 ) of the test. Bartlett’s test for age, by linear interpolationbetweenadjacent homogeneity of variances applied for each means and standard deviations. variable in the two PbB groups showed no Class 3 TABLE 3. x2 statistics and levels ofsignificance in the analysis o f contingency tables, testing the association between the leud parameter (PbBI and socioeconomic factors (bv occunation of father and mother) Tables PbB x occupation of father PbB X occupation of mother Degrees offreedom 2 2 N x2 Probability 211 92 1.94 5.63 0.38 0.06 See Materials and Methods section for definition of categories. 111 BIOMETRIC DATA AND BLOOD LEAD LEVELS TABLE 4. Univariate and multivariate analyses of variance of a vector of ten biometric variables in subgroups of high and low levels of PbB PbB Oead in blood) Weight Stature Total arm length Biacromial diam. Bicristal diam. Upper arm circum. Thigh circum. Head length Head breath Bizygomatie diam. Means in subgroup low PbB level (N = 189) Means in subgroup high PbB level (N = 38) 0.079 > 0.157 > -0.129 > 0.035 > -0.104 > -0.006 > -0.127 > 0,152 > -0.555 > -0.629 > -0.104 -0.072 -0.540 0.019 -0.269 -0.111 -0.142 -0.117 -0.885 -0.865 Wilks'd Box's M-statistic Univariate F-statistic (df:1,225) Level (a) Power (1 -P) 1.142 1.824 5.738 0.008 0.924 0.431 0.009 2.364 3.777 1.980 0.286 0.178 0.017" 0.928 0.337 0.512 0.923 0.126 0.053 0.161 0.281 0.472 0.181 0.061 0.251 0.159 0.059 0.472 0.326 0.390 Value F-statistic df P. 0.917 72.039 1.935 1.178 10,216 55,14504 0.042* 0.180 Subgroup means of each variable expressed in standardized values, univariate F-statistics, level (a)and power (1-6)of the test, multivariate test of significance (Wilks' A), of homogeneity of dispersion matrix (Box's M-statistic) and Forrespondingdegrees of freedom (do. P 5 0.05. - Biometric profile in function of PbB Low level PbB *-* High level PbB \ L-x ~ I 1 I 2 I 3 I 4 I 5 I 6 7 I I I 8 9 10 VARIAI3l.E CODE Fig. 2. Biometric profiles (means of standardized values) for ten different anthropometric variables: weight (l),stature (2),total arm length (3),biacromial diameter (4),bicristal diameter (51, relaxed upper arm circumference (6),thigh circumference (7),head length (8),head breadth (9),and bizygomatic diameter (lo),as a function of low-level PbB (0-30 ag/100 ml total blood) and highlevel PbB (40-60pg/lOO ml). significant departures from homoscedasticity. The means of subjects with high blood lead levels are consistently below those with low blood lead levels. Only total arm length differs significantly between the high and low PbB categories. The power of the various F tests shows that the probability of correctly rejecting the false null hypothesis, i.e., of detecting the true alternate hypothesis varies between 6% and 47%. Linear discriminant analysis was aplied to the ten biometric variables in the two PbB categories in an attempt to find the directions or dimensions along which the major differencesbetween the two categories occur. The results are shown in Table 5. According to van Knippenberg and Siero (1980),we have represented the correlations between the discriminant function (or canonical variate) and the discriminating variables, rather than the loadings on the discriminant function themselves. The latter are strongly dependent on the intercorrelations between the variables, whereas the former allow a more stable interpretation of the discriminant axis. The results are ordered by magnitude of the absolute value of that correlation. They show that total arm length has the greatest discriminating power in differentiating the two J 112 M.-C. LAUWERS, R.C. HAUSPIE, C. SUSANNE, AND J. VERHEY3EN TABLE 5. Results of linear discriminant analysis of ten biornetric uariables in two categories of PbB: Correlations between the canonical uariate and the discriminating variables (ordered by magnitude) Total arm length Head breadth Head length Bizygomatic diameter Stature Weight Bicristal diameter Upper arm circumference Thigh circumference Biacromial diameter -0.534 -0.433 -0.342 -0.313 -0.301 -0.238 -0.214 -0.146 -0.021 -0.020 variables in two age groups. From graphical inspection of the plot of PbB values as a function of age, it seemed that around 8 years a positive trend changes into a more random relationship. After division of the total sample into two subsamples below and above 8 years and performance of a linear regression analysis, it appeared that the regression coefficient was significantly positive below the age of 8 years (PbB = 10.89 3.40 x age; P (slope) = 0.00002). Above 8 years, the regression coefficient was negative but not significant (PbB = 30.99 - 0.40 x age; P (slope) = 0.07673. In view of this, a MANOVA was applied in children below 8 years of age and in children older than 8 years. The results are shown in Table 6. Figure 3 shows the biometric profiles for the two age groups. Bartlett’s test showed significant heterogeneity of the variances only for stature in the children older than 8 years of age. Box’s Mstatistic for homogeneity of the dispersion matrix was not significant in either of the two age groups, nor was Wilks’ A, indicating that the hypothesis of a common vector of the ten biometric variables could not be rejected in the two age groups. However, it should be noted that in the age group below 8 years of age the means in the high PbB category are consistently below those in the low PbB category, whereas this trend disappears in the age group above 8 years (Fig. 3). The absolute differences between the means in the two PbB categories are considerably greater in the group below 8 years of age than in the analysis with all ages pooled together (Table 4,Fig. 3). The fact that the multivariate test is not significant in the latter analysis is probably due to the serious reduction of the sample size in the group below 8 years of age (N = 56) compared to the overall analysis (N = 227). It is also important to notice that there is a relatively greater proportion of high blood lead levels among the younger children than among the older ones (x2 [ l dfJ = 19.91; P 5 O.OOOl), confirming the findings by other authors that, under similar conditions of exposure to lead, young children are more likely to present high blood lead levels than their older counterparts. + PbB categories. The head and face measurements contribute relatively more to the group differences than do stature and weight. The variables showing the least difference between the two groups are the circumferences and biacromial diameter. These results are in good agreement with those obtained by the MANOVA. One can see that the correlations of the biometric variables shown in Table 5 are inversely proportional to the probabilities of their univariate F-statistics shown in Table 4. The proportions of males and females were analyzed in the two categories of blood lead levels, but the x2 test did not reveal a significant difference in the distribution of the two sexes among the two PbB-level categories (x2 = 0.073). Univariate two-factor ANOVAs were applied for each biometric variable separately in order to test simultaneously the effects of sex and PbB. A significant effect of PbB (P = 0.0181, but not of sex, was found for arm length, a result that was already shown in Table 4,for both sexes pooled together. The F-value for head breadth was almost significant (P = 0.0511, which is also in agreement with the results shown in Table 4. There was a significant effect of sex, but not of PbB, for biacromial diameter (P = 0.003) and for bicristal diameter (P = 0.0211, males having lower mean values than females for both variables. There was no interaction between the two factors for any of the ten biometric variables. Several authors have shown that young children more readily absorb lead than their older counterparts (Darrow and Schroeder, 1974; Duggan, 1983) and that there is eviDISCUSSION dence that young children are also more sensitive to the harmful effects of absorbed lead The results of the present study support (McCabe, 1979). In view of these findings, it the hypothesis that children with elevated was of interest to analyze the effects of differ- blood lead levels show slightly but signifient PbB levels on the means of the biometric cantly disturbed physical growth and devel- - circumference diameter length diameter diameter circumference breath length (N sub and Univariate 6. TABLE >>>> group 35) PbB in 0.189 of analyses > - <> < (N sub age’ years variables of biometric eight >< 1,169) Syears (d.f.: 2.1705.011 55.2534 0.000 0.974 0.460 0.204 0.107 0.309 0.041 F-statistic Univariate A), 10,160d.f 1.050 (Wilks of > in sub of levels low and high sub (N high Means -1.016 -0.840 -0.495 -0.026 -0.126 = level F-statistic groups 0.049 0.034 0.1620.153 0.178 1.062 1.430 group Age 17) PbB in of significance of test multivariate lowMeans -0.657 -0.077 -0.132< 0.073 0.180 0.918 -0.100< Value-0.5781 0.026 0.066 =level 76.930 0.154% PbB expressed 154) group in probabilities, and0.792 0.168 0.206 0.122 0.515 0.348 0.057 0.127 0.076 Prob. Rob. 0.051 0.042* 0.017* group Age 21) PbB in of variance < values, 8children freedom of years a univariate (d.f.: below (d.f.). vector 2.464 4.338 1.637 55.5841 4.002 2.402 10,45d.f.0.898 0.430 6.062 3.773 3.265 Univariate F-statistic 1,54) andof ten F-statistics above 0.839 1.508 standardized sub (Nhigh -0.742 -0.298 degrees -0.514 -0.875 -0.253 -0.921 -0.096 -0.333=level Means -0.189 F-statistic in corresponding >>>> lowMeansmultivariate and -0.502 0.105 -0.359 59.236 0.497 0.073 0.017 0.173 Value 0.454 0.749 expressed 0.132>=level 0.410> M-statistic) variable of (Box’s each 0.05. M-statistic matrices means Total Head Head *€’rob.‘SubBox’s Wilks Thigh Weight Stature dispersion Bicristal Upperarm Biacromial Bizygomatic Q group A arm 114 M.-C. LAUWERS, R.C. HAUSPIE, C. SUSANNE, AND J. VERHEYDEN 1- . . . . - Biometric profile AGE < 0 prs 1- in function of PbB . . . . Lon level PbB +-* High level PbB I 1 7 - Biometric profile in function of PbB AGE > 0 prs Low level PbB *-* High level PbB \ . \ \ I I I I I I I L I opment. This confirms the findings of Habercam et al. (1974), who noted a significant negative correlation between lead in the blood and height and weight of 18 preadolescent black children. Our study does not differentiate the various possible sources of lead contamination (industrial pollution, motor exhausts, or others), but demonstrates only that, once the amount of lead in the blood of a child has risen t o a level about 40 pgI100 ml blood, there is a tendency to reduced growth and development. It has already been shown that low levels of lead may lead to neurological damage and mental deficiency in children (for review of the literature see Bornschein et al., 1980).However, from what is known about the pathological effects of clinical lead poisoning on the level of heme synthesis (Moore et al., 1980),on the function of the kidney (Choie and Richter, 1980) and the thyroid (Sandstead et al., 19691, and on the pituitary and adrenal function (Sandstead et al., 1970; Damstra, 1977), as well as various other effects (Singhal and Thomas, 1980), it is not unthinkable that lead could also impair the physiological processes leading to normal growth. This impairment of growth could be due to a direct negative action of lead on bone formation in the growing I I I I I I I I I I child. There are numerous reports mentioning or studying the storage of lead in bones (Chisolm, 1971; Repko and Corum, 1979; Gloag, 1980).Ninety percent of the total body burden of lead resides in the skeleton (Schroeder and Tipton, 1968; cited in Repko and Corum, 1979).In conditions of long-term, moderate exposures, lead is stored in the long bones of the skeleton, but in rapid high-absorption conditions, the flat bones may be used as well (Hardy et al., 1971; cited in Repko and Corum, 1979). However, little or no information is available about a negative influence of this stored lead or even of the circulating lead in the body on bone formation in children. In contrast, much is known of the active interaction of lead with calcium metabolism, bone formation, and its hormonal control mechanisms in experimental animals (Kato et al., 1977; Gruden and Buden, 1977; Norimatsu and Talmage, 1979; Anderson and Danylchuk, 1980) and in bone organ cultures (Rosen, 1983). Our present epidemiological results are a t least compatible with this hypothesis, since there is no evidence that variation in factors such as socioeconomic background would have artifactually produced the observed relationships in this survey. BIOMETRIC DATA AND BLOOD LEAD LEVELS The frequency of elevated blood lead levels (40-60 pg/lOO ml) was not statistically different in boys (17.5%)and in girls (16.1%),indicating that both sexes have been exposed in much the same way to lead in the enyironment, or at least that they have absorbed equal amounts. There was also no evidence of any sex difference in the relationship between PbB and the biometric profile, since no significant interaction could be detected between the effects of the PbB and sex 04the biometric variables. The significant effect of sex observed in the two-factor ANOVAs for the two transverse diameters simply indicate that for some unknown reason, in the population of Hoboken, boys haye relatively (in terms of standardized values) smaller shoulders and hips than girls, irrespective of the amount of lead in their blood. The present study has also indicated that the age of exposure is probably an important factor influencing effects of chronic exposure to environmental lead. The fact that below 8 years of age there is a significantly higher proportion of children with high blood lead levels than among children above 8 years of age confirms data from the literature that young children are particularly at risk (Ziegler et al., 1978; Walter et al., 1980). These authors state that the higher risk for lead intoxication in children is attributable to 1) the greater risk of exposure to lead-containing substances, 2) the greater rate of lead absorption, and 3) the greater sensitivity of children t o toxic effects of lead. The latter statement is in agreement with our observation that, although not statistically significant, the differences in biometric values between the two PbB categories among the younger children were nevertheless more pronounced than in the older ones or in the total group. In fact, lead is a poison that in varying doses interferes with all biochemical and physiological systems in humans (Repko and Corum, 1979). Increased lead absorption imposes a change in the normal functioning of organisms and thus probably in growth too. ACKNOWLEDGMENTS We are grateful to the personnel of the health control centers Gemeentelijk Gezondheidscentrum Hoboken and Gezondheidscentrum Vera Vita, who made it possible to do the anthropometric measurements and to consult the medical files. Thanks to Hilde Carsau, Greet De Graef, Kris Eilers, Hilde 115 Schoeters, and Michel van Geel. One of the authors (M.-C.L)was the recipient of a grant of the Institute for the Encouragement of Scientific Research in Industry and Agriculture (J.W.O.R.L.,Belgium). LITERATURE CITED Anderson, C, and Danylchuk, KD (1980) Haversian boneremodelling rates in the beagle after cessation of exposure to chronic low doses of lead. J. Environ. Pathol. 3:413-422. Antal, A, Timaru, J, Muncaci, E, Ardevan, E, Ionescu, A, and Sandulache, L (1968)Les variations de la reactivite de Yorganisme et de l’etat de sante des enfants en rapport avec la pollution de l’air communal. Atmos. Environ. 2~383-392. 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