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Comparison of biometric data of children with high and low levels of lead in the blood.

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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).
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