Environmental Health Perspectives • VOLUME 109 | NUMBER 5 | May 2001 527

Interrelations of Lead Levels in Bone, Venous Blood, and Umbilical Cord

Blood with Exogenous Lead Exposure through Maternal Plasma Lead in

Peripartum Women

Hung-Yi Chuang,1,2 Joel Schwartz,1,3 Teresa Gonzales-Cossio,4 Marlene Cortez Lugo,4,5 Eduardo Palazuelos,5

Antonio Aro,1,3 Howard Hu,1,3 and Mauricio Hernandez-Avila4,6

1Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 2Department of Occupational

Medicine, Kaohsiung Medical University Hospital, Taiwan; 3Channing Laboratory, Department of Medicine, Brigham and Women’s

Hospital and Harvard Medical School, Boston, Massachusetts, USA; 4Centro de Investigaciones en Salud Poblacional, Instituto Nacional

de Salud Publica, Cuernavaca, Morelos, Mexico; 5American British Cowdray Hospital, Mexico; 6Department of Environmental and

Occupational Health, The Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

Lead is a well-known human reproductive

toxin. For centuries, lead exposure has been

linked to adverse outcomes in pregnant

women and newborns (1). In addition, fetal

lead exposure can cause delay in the embryonic development of multiple organ systems,

including retardation of cognitive development in early childhood (2). 

Most studies that have used biomarkers

to measure fetal lead exposure have relied on

lead levels in maternal blood or umbilical

cord blood at birth. However, recent research

has cast doubt on whether either of these biomarkers reflects fetal lead exposure in a way

that adequately predicts fetal lead toxicity.

For example, in a recent epidemiologic study

of women environmentally exposed to lead

in Mexico City, maternal bone lead levels

(but not maternal blood lead or umbilical

cord blood lead levels) were related to

declines in infant birth weight (3). In a companion study, maternal bone lead levels (but

not maternal blood lead or umbilical cord

blood lead levels) were related to smaller head

circumference and shorter length at birth (4).

What parameters govern fetal lead exposure, and why is maternal bone lead the best

predictor of fetal toxicity? Theoretically, fetal

lead exposure must involve lead that is available to cross the placenta. Lead that is free to

cross the placenta, in turn, must be derived

from lead in plasma. Within the circulatory

compartment, lead is partitioned primarily

between red cells (> 99%) and plasma (<

1%). Because most lead is bound to red

cells, whole blood lead levels (the biomarker

most commonly measured) invariably reflect

red-cell lead concentrations. However, the

lead that segregates into red cells is bound

and unavailable. If the < 1% of lead that is

found in plasma were always in equilibrium

with red-cell lead levels, whole blood lead

would serve as an adequate proxy for plasma

lead levels. However, recent research suggests

that plasma lead levels can vary substantially

without corresponding variations in whole

blood lead levels (5). For example,

Hernandez-Avila et al. (6) directly measured

plasma lead among environmentally exposed

adults in Mexico City and found that the

plasma:whole blood lead ratio varied

between 0.27% and 0.7%. In multivariate

analyses, bone lead levels were found to

influence plasma lead levels independent of

the effect of whole blood lead levels. The

strong influence of bone lead on plasma lead

probably reflects the role of bone as the main

repository (> 95%) of lead in adults (5). As

further evidence of the importance of bone

as a source of circulating lead, Gulson et al.

(7,8) conducted a series of studies of women

who had emigrated from Eastern Europe to

Address correspondence to H. Hu, Channing

Laboratory, Department of Medicine, Brigham and

Women’s Hospital and Harvard Medical School,

181 Longwood Avenue, Boston, MA 02115, USA.

Telephone: (617) 525-2736. Fax: (617) 525-0362.

E-mail: howard.hu@channing.harvard.edu

We thank G. Fleischaker for research management; M. Lino for her valuable contribution in

database management and programming in

Mexico; E. Fishbeing, field coordinator in the

Mexico site; and S. Datta for valuable assistance

with computer programming. We also thank the

participants for their time and effort, without

which this project would have been impossible.

Support for this research was provided by NIEHS

401 ES0721-01 A2, NIEHS P42-ES05947 (with

funding from the U.S. Environmental Protection

Agency), NIEHS Center grant 2 P30 ES00002,

and Consejo Nacional de Cienca y Tecnologia

(CONACyT) grant 4150M9405 from CONSERVA, Department of Federal District, Mexico.

Received 29 July 1999; accepted 14 November

2000.

Articles

Recent research has raised the possibility that fetal lead exposure is not estimated adequately by

measuring lead content in maternal whole blood lead because of the variable partitioning of lead in

whole blood between plasma and red blood cells. Lead in maternal plasma may derive in large part

from maternal bone lead stores. In this study we aimed to estimate the contribution of maternal

whole blood lead, maternal bone lead levels, and environmental lead to umbilical cord blood lead

levels (as a measure of fetal lead exposure). In the model, we assumed that lead from all of these

sources reaches the fetus through the maternal plasma lead pathway. In 1994–1995, we recruited

615 pregnant women for a study of lead exposure and reproductive outcomes in Mexico City. We

gathered maternal and umbilical cord blood samples within 12 hr of each infant’s delivery and

measured maternal lead levels in cortical bone and trabecular bone by a K-X-ray fluorescence

(K-XRF) instrument within 1 month after delivery. We administered a questionnaire to assess use

of lead-glazed ceramics (LGC) to cook food and we obtained data on regional air lead levels during

the 2 months before delivery. We used structural equation models (SEMs) to estimate plasma lead

as the unmeasured (latent) variable and to quantify the interrelations of plasma lead, the other lead

biomarkers, and environmental lead exposure. In the SEM analysis, a model that allowed plasma

lead to vary freely from whole blood lead explained the variance of cord blood lead (as reflected by

a total model R2; R2 = 0.79) better than did a model without plasma lead (r 2 = 0.67). Cortical

bone lead, trabecular bone lead, use of LGC, and mean air lead level contributed significantly to

plasma lead. The exchange of lead between plasma and red blood cells was mostly in the direction

of plasma to cells. According to the final model, an increase in trabecular bone lead and cortical

bone lead was associated with increases in cord blood lead of 0.65 and 0.25 μg/dL, respectively. An

increase of 0.1 μg/m3 in air lead was associated with an increase in the mean level of fetal cord

blood lead by 0.67 μg/dL. With one additional day of LCG use per week in the peripartum period,

the mean fetal blood lead level increased by 0.27 μg/dL. Our analyses suggested that maternal

plasma lead varies independently from maternal whole blood lead and that the greatest influences

on maternal plasma lead are maternal bone lead stores, air lead exposures, and recent cooking with

LGC. The contributions from endogenous (bone) and exogenous (environmental) sources were relatively equal. Measurement of plasma and bone lead may be important in accurately assessing fetal

lead exposure and its major sources, particularly if exogenous exposures decline. Key words: blood

lead, bone lead, cord blood lead, maternal lead exposure, newborns, plasma lead. Environ Health

Perspect 109:527–532 (2001). [Online 11 May 2001]

http://ehpnet1.niehs.nih.gov/docs/2001/109p527-532chuang/abstract.html
Articles • Chuang et al.

528 VOLUME 109 | NUMBER 5 | May 2001 • Environmental Health Perspectives

Australia. In these women, the lead stored in

bone was a different isotopic ratio than lead

in their current environments. The investigators demonstrated that pregnancy and lactation were associated with large increases in

the amounts of lead released from bone. 

Together, these findings suggest that fetal

lead exposure, reflected by plasma lead levels,

may not be represented adequately by maternal whole blood lead levels and that the

importance of bone lead levels as an independent determinant of fetal lead toxicity may be

explained by the importance of bone lead as a

source of circulating lead and by its influence

on lead liberated into plasma. 

What factors influence plasma lead levels

during pregnancy, and why not simply measure plasma lead levels? Unfortunately, direct

measurement of lead in plasma is an extraordinarily challenging procedure because of several factors. Among the most important are

the potential for plasma lead to be significantly contaminated by lead in red blood cells

with even a small amount of hemolysis; the

extremely low levels of lead in plasma (< 0.1

μg/dL), which can be measured precisely and

accurately but requires special instruments;

and the potential for environmental contamination from equipment and from other

sources during phlebotomy, specimen transports, digestion procedures, and analysis.

As a consequence, few studies have investigated plasma lead levels, and, to the best of

our knowledge, none has done so during

pregnancy. Because umbilical cord blood

lead levels are the best available measure of

fetal lead exposure and because plasma lead

must be the vehicle for both exogenous (environmental) and endogenous (bone) sources

of lead that influence fetal lead exposure, the

role of plasma lead in fetal lead exposure can

be investigated directly by examining interrelationships between existing biomarkers of

lead exposure and assuming that cord blood

lead is influenced most by plasma lead. 

The aim of the current study was to use

data from a large epidemiologic study (n >

600) of lead and reproduction and to apply

structural equation models (SEMs) to estimate the effects of endogenous and exogenous lead exposure on fetal cord blood lead

through maternal plasma lead. The women

who participated in this study live in Mexico

City, where environmental lead exposure

and consequently bone lead levels have been

high (9). Measurements were available for

environmental sources of lead exposure and

levels of lead in maternal whole blood,

maternal trabecular and cortical bone, and

umbilical cord blood.

Materials and Methods

Study subjects. From 28 April 1994 to 30

June 1995, women attending any of three

hospitals (Mexican Social Security Institute,

Manuel Gea Gonzalez Hospital, or National

Institute of Perinatology) for prepartum

examination in Mexico City were recruited.

Interviewers explained the study in detail and

obtained informed consent from participants.

The study protocol was approved by the

Human Subjects Committee of the National

Institute of Public Health (Mexico City,

Mexico). Women were excluded if they had a

physician’s diagnosis of multiple fetuses,

preeclampsia, psychiatric, kidney or cardiac

diseases, gestational diabetes, history of

repeated urinary infections, seizure disorder

requiring daily medication, ingestion of corticoids, systolic blood pressure > 140 mm Hg,

or diastolic blood pressure > 90 mm Hg. 

Maternal and umbilical cord blood lead

test. Maternal and umbilical cord blood samples were gathered within 12 hr of each

infant’s delivery. Blood samples were analyzed

by atomic absorption spectrometry (PerkinElmer 3000; PerkinElmer, Norwalk, CT,

USA) at the metal laboratory of the American

British Cowdray Hospital in Mexico City.

Analyses of external blinded quality-control

samples were provided throughout the study

period by the Wisconsin State Laboratory of

Hygiene (WSLH) Cooperative Blood Lead

Proficiency Testing Program (PBPTP)

(Madison, WI, USA). On tests of blinded

specimens in the ranges of 0–8, 14–22, and

40–50 μg/dL, the laboratory performed with

accuracy within 5.0% and a coefficient of

variation (CV) < 15%, which demonstrated

good precision and accuracy of the determinations made at American British Cowdray

Hospital.

Bone lead test. At each mother’s visit to

the research center at American British

Cowdray Hospital 1 month after delivery,

maternal bone lead levels were measured on

each woman’s mid-tibia shaft (cortical) and

patella (trabecular) with a spot-source 109Cd

K-XRF instrument (ABIOMED Inc.,

Danvers, MA, USA). Thirty-minute measurements were taken at each bone site after

the skin had been washed with 50% isopropyl alcohol. The K-XRF beam collimator

was sited perpendicular to the bone surface

for the tibia and 30° in the lateral direction

for the patella. The introduction of 109Cd as

an excitation γ-ray source, with 88 keV photons emitted from approximately 4% of its

decays, provokes the emission of fluorescent

photons from the target. The resulting signal

sensed by the detector, high-purified germanium crystal, undergoes processing by the

amplifier-computer system for subsequent

display and analysis. The detection system

collects, counts, and analyzes the emitted Xrays over the wavelength spectrum, from

which the net lead signal is determined after

subtraction of Compton background counts,

by a linear least-square algorithm. The lead

fluorescence signal is then normalized to the

elastic or coherently scattered γ-ray signal,

which arises predominantly from the calcium

and phosphorus present in bone mineral.

The unit of measurement is micrograms per

gram (μg/g) of bone mineral (10). Because

the K-XRF instrument provides a continuous, unbiased estimate that oscillates around

the true bone lead value, negative value estimates are sometimes produced when true

bone lead values are close to zero. The instrument also provides an estimate of uncertainty

association with each measurement, which is

derived from a goodness-of-fit calculation of

the spectrum curve and is equivalent to a single standard deviation. Although a minimal

detectable limit calculation of twice this value

has been proposed for interpreting an individual’s bone lead estimates, statistical experiments have shown that retention of all point

estimates makes better use of the data in epidemiologic studies (11,12). Validation studies of the instrument indicated a high degree

of precision and accuracy of point estimate

and uncertainty measurement in comparison

with chemical analysis in studies of leaddoped phantoms (13).

Use of lead-glazed ceramics. Previous

studies have shown that use of low-temperature–fired lead-glazed ceramics (LGC) is a

major source of lead exposure in Mexico

City (15,19). To evaluate this exposure, we

interviewed participants in the research

office by trained personnel using a structured

questionnaire. Using pictures to help identify these LGC, we asked participants about

the frequency of LGC use in the previous

week (number of days used per week) and

about the number of years they had used

LGC to store and cook food. The product of

these two variables was used to estimate the

total lifetime days of LGC use:

Air lead concentrations. We used the air

lead data supplied by the Environmental Air

Monitoring Network of Mexico City.

Nineteen monitoring points in Mexico City

provide information on air lead levels, which

were measured four to five times per month.

We used information regarding the area of

residency of participants and assigned these

data to subjects depending on their area of

residence and the date of delivery. The mean

of air lead levels for the previous 2 months

before delivery was the index of lead

exposure from ambient air for each woman.

In addition, we used the number of years of

residence in Mexico City to indicate chronic

exposure. 

Total lifetime days of LGC use

days of use per week years of use

 

 

=

× ( ) × ( ) 365 25

7

.
Estimated lead in red blood cells.

Because < 1% of lead is present in plasma

(6,16), we used whole blood lead level

divided by hematocrit (BPb/Hct) to estimate

lead concentration in red blood cells (RBC).

Out calculation is:

Data analysis. Descriptive statistics. We

examined univariate distributions of continuous variables to determine the normality of

the variables. To better attain normal distributions, maternal and cord blood lead values

were natural log-transformed. Outliers were

identified using the extreme studentized

deviate (ESD) procedure (17). Percentiles

for the procedure are approximated on the

basis of the t-distribution (18,19). To assess

the potential influence of outliers, we

repeated our SEM analyses after excluding

four outliers. We used Pearson correlation

analyses to determine the intercorrelations

among the lead biomarkers (maternal whole

blood and bone lead levels, umbilical cord

blood lead level) and use of LGC, air lead

levels, maternal age, and time of residence in

Mexico City in both years and percentage of

life span spent in Mexico City (years of residence in Mexico City divided by age).

Multiple regression analyses. As predictors of fetal cord blood lead levels, the independent influence of age, our maternal lead

biomarkers, and our external exposure parameters, including use of LGC, air lead levels,

and percentage of life span in Mexico City,

were examined. 

Structural equation models. SEMs were

used to express the relationships among variables, including measured variables and

plasma lead (latent variable) (20), and thus to

test the hypothesis that maternal bone lead

levels contribute to fetal lead exposure independent of maternal whole blood lead levels.

Structural equation modeling allows one to

estimate simultaneously a set of interrelated

regression models. For example, bone lead

must be considered an outcome in one

regression (depending on use of LGC, length

of residence in Mexico City, and the like). In

a second regression model it is a predictor of

umbilical cord lead. By combining these into

one system of equations, one gains several

advantages. First, estimates may be made

more precisely, taking into account the

covariance structure of all of the variables.

Second, downward bias that results from

putting variables on the casual pathway into

the regression (21) may be avoided. Third,

the system of equations may be compactly

written in terms of a path diagram that illustrates the multiple relationships. Finally,

unmeasured (latent) variables may be

included when there is sufficient ancillary

information. While this produces noisy estimates of the latent variable, recognizing its

existence usually increases the precision of

the estimates of the associations among the

measured variables. These models have been

well developed and described since the

1960s. Our analysis approach conceptualizes

plasma lead levels as the unmeasured,

“latent” variable through which lead levels in

the maternal patella, tibia, and venous blood

can influence umbilical cord blood lead levels

(as fetal lead exposure). 

To fit our model, we established the network of causal relationships among the variables (Figure 1). The direction of causality

among these variables is clear. All that is

unknown is the magnitude of the associations (which for some pathways may be

zero). As a second step, we constructed a

structural model with a latent variable

plasma lead level added. This allowed us to

test the hypothesis that such a model fit the

data better and that some variables may

influence cord lead through plasma rather

than erythrocyte lead. We used the covariance analysis of linear structural (CALIS)

equation in SAS package 6.12 (CALIS procedure; SAS Institute Inc., Cary, NC) for

SEM analysis (22). All these analyses used

the maximum likelihood method of parameter estimation, and all were performed on

the variance–covariance matrix (20).

We assumed that missing data occurred

completely at random (MCAR). Missing data

were the result of incomplete questionnaires

and incomplete collection of blood samples.

The most common reasons for failure to collect blood samples and questionnaires were

inconvenience and the time-consuming

nature of the interview process. In the data

analysis, we used the pairwise deletion

method to treat missing data; each correlation and covariance between two variables

was computed from pairwise-complete data,

with the exclusion of cases that had missing

values for one or both variables (23,24). 

Results

A total of 615 pregnant women had bone

lead measurements. The most common reason for which women did not have bone

lead measurements were residence outside

Mexico City and the length of time required

for the measurement. Table 1 shows the

characteristics of study subjects. Bone lead

levels were considered missing if they were

associated with excessive measurement

uncertainty (> 15 μg/g in patella and > 10

μg/g in tibia) reflecting quality assurance

procedures used previously (11,25). Thus,

only 575 women had patella lead levels and

603 had tibia lead levels; the mean (SD) values for the patella and tibia lead values were

14.24 (14.19) and 9.67 (9.21) μg/g bone

mineral, respectively, and 86 (14%) and 78

(13%) of these values, respectively, were 0 or

negative, signifying very low levels measured

with error. Among the women who used

LGC for cooking, 25% reported using LGC

≤ 3 day per week and 8% reported use ≥ 3

days per week (mean 2.92 days per week, SD

= 2.28). About 20% of the women had used

LGC for ≤ 5 years and 16% of the women

for ≥ 5 years (mean 9.03 years, SD = 9.13).

The mean number of days of lifetime LGC

use, calculated from the above variables, was

1,489 (SD = 2,010). Twenty-six women

(4%) reported smoking during pregnancy;

about 39% had smoked in the past but not

during pregnancy.

Table 2 shows the Pearson correlation

coefficient matrix. Natural log-transformed

cord blood level was significantly correlated

with all maternal lead biomarkers, use of

Pb Conc in RBC Lead in RBC

RBC volume

Lead in whole blood

Whole blood volume Hct

Lead in whole blood

Whole blood volume Hct

Lead conc in whole blood

Hct

 

 

 

 

 

 

=

= ( ) ×

×

= ( )

( ) ×

= ( )

99

1

%

Articles • Perinatal blood lead measurements

Environmental Health Perspectives • VOLUME 109 | NUMBER 5 | May 2001 529

Figure 1. Manifest model of interrelationships of bone lead and umbilical cord blood lead in peripartum

women. Standardized coefficients are presented. 

*p < 0.05.

0.1377*

0.1934*

0.1274*

0.1697*

0.0872*

0.1151*

0.0780*

0.1756*

0.1992* 0.0454

Error = 0.9563 Error = 0.5719

0.8203*

R2 = 0.673

Percentage of

life span in

Mexico City

Age

Long-term

use of ceramics

to cook food

Patella lead

level

Tibia lead

level

Maternal

whole blood

lead level

Cord blood

lead

Recent use

of ceramics

to cook

Mean of air

lead in the

2 months
LGC, air lead level, and years of residence in

Mexico City. Number of years living in

Mexico City was significantly correlated with

age (R2 coefficient = 0.45). To avoid

collinearity between these two variables, we

used percentage of life span spent in Mexico

City, which did not correlate with age (R2

coefficient = –0.057, p = 0.159). Few women

reported smoking while pregnant (n = 26).

Neither current nor past smoking was associated with any of the lead biomarkers. Thus,

we did not include smoking status in further

analyses. 

In a multiple regression analysis (data

not shown), maternal blood lead level was

highly associated with fetal cord blood lead

level. Maternal tibia lead level, patella lead

level, and the other exposure indices (including LGC use, air lead level, and percentage

of life span in Mexico City) were not significantly associated with cord blood lead levels.

However, as discussed by Breslow and Day

(21), this regression analysis was inappropriate because maternal blood lead was an

intermediate marker for sources of lead

exposure, (i.e., maternal blood lead level was

influenced by bone lead and external sources

of lead exposure). Failure to account for

these interrelationships weakened the analysis. Thus, we used SEMs to assess the interrelationships of these factors.

Figures 1 and 2 depict the results

obtained in SEMs without and with the

plasma lead level respectively. The coefficients in these two figures were standardized

to represent a 1 SD change in each exposure

pathway. In testing for overall model fits, we

found the goodness-of-fit index (GFI) and

degree-of-freedom–adjusted GFI in both

models were ≥ 0.9 (1 = perfect fit). In both

models, Bentler’s comparative fit indices

were ≥ 0.95 (1 = perfect fit). All indices for

model fit revealed an acceptable fit in both

models (20). The SEM used for the analysis

shown in Figure 1 revealed that both cortical

and trabecular bone lead levels significantly

contributed lead to the maternal blood pool

in pregnant women. The lead in patella (trabecular bone) contributed more than that in

tibia (cortical bone). Long-term use and

recent use of LGC were positively correlated

with bone and blood lead levels, respectively.

However, air lead levels measured 2 months

before delivery were only weakly associated

with whole blood lead in the model. Overall,

the coefficient of maternal whole blood lead

to cord blood lead was similar to the regression coefficient and explained about 67% of

variance in the cord blood lead level.

In the model used for the analysis in

Figure 2, we implanted a latent variable,

plasma lead, and RBC lead to represent two

components in the blood pool. First, we

observed that both bone lead measures contributed significantly to maternal plasma lead.

Second, we found that recent air lead level

was significantly associated with plasma lead

but not with whole blood lead. To compare

the coefficients in the different models, we

standardized them to represent a 1 SD change

in each blood index. The standardized coefficient for whole blood lead level was 0.8203

whereas that for plasma lead level was 0.8897.

On the other hand, the R2 values were 0.792

in the plasma model and 0.673 in the whole

blood model. Both results suggest that plasma

lead affects fetal cord blood lead stronger than

maternal whole blood lead level.

Table 3 shows estimates of fetal cord

blood changes, calculated from the final SEM

in Figure 2 with use of nonstandardized coefficients (original or natural coefficients) and

Articles • Chuang et al.

530 VOLUME 109 | NUMBER 5 | May 2001 • Environmental Health Perspectives

Table 1. Characteristics of study subjects.

Variable No. subjectsa Mean (SD) or %

Maternal blood lead, μg/dL 608 8.45 (3.94)

Maternal hematocrit 604 0.41 (0.04)

Estimated RBC lead 597 20.59 (9.54)

Umbilical cord blood lead, μg/dL 509 6.55 (3.45)

Patella lead, μg/g bone mineral 575 14.24 (14.19)

Tibia lead, μg/g bone mineral 603 9.67 (9.21)

Maternal age, year 614 24.52 (5.11)

Residence in Mexico City, year 613 20.57 (8.23)

Residence in Mexico City, % of life span 613 84.22 (29.55)

Air lead 2 months before delivery, μg/m3 522 0.21 (0.02)

Current smoker 26 4.2%

Ex-smoker 242 39.3%

Cooking in LGC in the week before delivery

No 406 66.0%

1–3 days/week 154 25.0%

4–7 days/week 49 8.0%

Unknown 6 1.0%

Cooking in LGC in lifetime 

No 354 57.6%

≤ 5 years 122 19.8%

6–10 years 36 5.9%

11–15 years 8 1.3%

> 15 years 53 8.6%

Unknown 42 6.8%

aThe numbers are not consistent because of missing data.

Table 2. Correlation coefficient matrix for lead biomarkers, use of LGC, age, residence in Mexico City, and smoking status.

Log Time P_Y

MPB Pat Tibia Age Cook PbAir Mex Mex Day Year Smoke

LogCPB 0.82 0.23 0.18 0.08 0.15 0.10 0.12 0.07 0.21 0.20 0.05

p-Value 0.0001 0.0001 0.0001 0.0638 0.0011 0.0404 0.0072 0.1045 0.0001 0.0001 0.2601

n 506 472 498 508 468 440 507 507 503 471 508

LogMPB 0.2232 0.1493 0.0473 0.1864 0.0417 0.1290 0.1184 0.2336 0.2167 0.02866

p-Value 0.0001 0.0003 0.2451 0.0001 0.3431 0.0015 0.0035 0.0001 0.0001 0.4810

n 569 596 607 565 518 606 606 602 568 607

Pat 0.2630 0.1240 0.0910 0.0082 0.1830 0.1287 0.1420 0.0702 0.0659

p-Value 0.0001 0.0029 0.0360 0.8571 0.0001 0.0020 0.0007 0.1052 0.1148

n 564 574 531 488 573 573 569 534 574

Tibia 0.1645 0.1200 –0.0550 0.2441 0.1816 0.1444 0.1129 0.0593

p-Value 0.0001 0.0045 0.2127 0.0001 0.0001 0.0004 0.0075 0.1461

n 602 558 515 601 601 597 561 602

Age 0.0506 –0.0913 0.4547 –0.0569 0.0241 0.0861 0.04342

p-Value 0.2280 0.0373 0.0001 0.1594 0.5526 0.0393 0.2828

n 570 521 613 613 609 573 614

Abbreviations: LogCPB, natural log-transformed cord blood lead; LogMPB, natural log-transformed maternal blood lead; Pat, patella lead; Tibia, tibia lead; Cook, total lifetime days of

cooking with lead-glazed ceramics; PbAir, mean of air lead 2 months before delivery; TimeMex, years living in Mexico City; P_Y Mex, percentage of life span in Mexico City; Day, days of

cooking with lead-glazed ceramics per week; Year, years of cooking with lead-glazed ceramics in lifetime; Smoke, smoking or not during pregnancy.
an antilogarithmic calculation method (cord

blood lead level, maternal blood lead level,

and RBC lead level were log-transformed in

the models). According to these estimates, an

increase of 1 μg/g in maternal patella lead

was associated with an increase in mean level

of fetal cord blood lead by 0.032 μg/dL. An

increase in maternal patella lead from the first

quartile (4.26 μg/g) to the third quartile

(23.71 μg/g) was associated with an increase

in mean level of cord blood lead by 0.65

μg/dL. The contribution from tibia (cortical

bone) was a little less than that from patella

(trabecular bone). An interquartile range

change in both bone lead measures was associated with an increase in cord blood lead by

about 1 μg/dL. One additional day of LGC

use per week in the peripartum period was

associated with an increase in mean level of

fetal blood lead by 0.27 μg/dL. Fetal blood

lead was also influenced by air lead levels in

peripartum period: Each 0.1 μg/m3 change

in air lead was associated with a 0.67 μg/dL

change in mean level of fetal cord blood lead.

As a plausibility check on this model, we

note that Crocetti et al. (26) showed that

gasoline use contributed to about 9 μg/dL

of blood lead levels in U.S. children in

1976, a time when air lead concentrations

in U.S. urban areas were typically 1–1.25

μg/m3, a result quite consistent with the air

lead–blood lead ratio we see.

Discussion

Our study suggests that incorporating

plasma lead as a latent variable in models

used to analyze the relationship of bone lead

and exogenous lead exposure with cord

blood lead reveals important relationships.

In our analysis with SEMs, maternal plasma

lead level had a stronger association (standardized coefficient = 0.8897) than that of

whole blood lead (standardized coefficient =

0.8203) to fetal cord blood lead level, and

the fit of the model that incorporated

plasma lead (R2 = 79%; Figure 2) was substantially better than the fit of the model

that omitted plasma lead (R2 = 67%; Figure

1). Moreover, the model demonstrated a

significant contribution of lead from the

skeletal system to plasma during pregnancy,

a contribution that is independent of the

influence of maternal RBC lead. One can

infer that plasma lead is an important biomarker for fetal lead exposure and that

maternal whole blood lead levels do not

accurately capture the influence of bone lead

on fetal lead exposure. Use of a biologically

plausible model for the pathways of lead

kinetics greatly increased our ability to separate out the contributions from different

sources. For example, lead in both cortical

and trabecular bones was released into

blood during pregnancy. In a standard multiple regression analysis, we cannot easily

separate the contribution from each pathway to maternal blood lead level and study

their effects on fetal cord blood lead by way

of maternal blood or plasma lead levels

because of correlation and collinearity

between patella and tibia lead levels.

Four sources of lead contributed significantly to plasma lead. Recent use of LGC and

lead in trabecular bone (patella) had the largest

and the second largest standardized coefficients, respectively—results indicating that

these two sources had the greatest influence on

plasma lead levels. The greater contribution of

trabecular than of cortical bone probably

reflects the greater vascularity and higher

turnover rate of trabecular bones. Use of LGC,

a documented source of lead exposure in

women and children in Mexico City (15), was

a source of food contamination and a path of

lead ingestion in this study; lead absorbed by

the gastrointestinal tract is quickly detectable

in the plasma. The standardized coefficient for

air lead level was the smallest of these four

pathways, but this finding was in part a reflection of the relatively narrow distribution of air

lead levels to which these women were

exposed, which, in turn, is a reflection of the

effort in Mexico over the last 10 years to phase

out the use of leaded gasoline. Using original

coefficients (normal scale) of SEM in Figure 2,

a change of 0.1 μg/m3 in air lead level would

have a sizable effect on fetal cord blood lead

level (0.67 μg/dL). 

The effect of RBC lead on plasma lead

detected in our model revealed that most lead

moved from plasma to RBC and less from

RBC to plasma. The coefficient ratio of lead

movement into/out of RBC was about 6.

The kinetic equilibrium between RBC lead

and plasma lead has been studied previously

(7,27,28). Both Marcus (27) and deSilva

(28) demonstrated a nonlinear relationship

between plasma lead and blood lead with a

kinetic transfer ratio (into/out of RBC) ranging from 214 to 373. Plots in these studies

revealed a J-shaped curve for plasma versus

blood lead. However, their analyses were on a

normal scale, while ours used natural logarithm-transformed RBC and cord blood lead

levels. Exponentiation of our ratio gives a

value of 403, which is similar to the results

obtained by their investigations. Because we

log-transformed both variables, the shape of

the relationship in our model followed a

power law and incorporated much of the

nonlinearity in the models used by Marcus

and deSilva.

Recent studies with a lead isotope composition method have proved that maternal

skeletal lead is mobilized during pregnancy,

makes major contributions to the maternal

blood pool, and is transferred to the fetus

(7,8). In these studies, a higher 206Pb/204Pb

ratio reflected the abundance of the 206Pb

Articles • Perinatal blood lead measurements

Environmental Health Perspectives • VOLUME 109 | NUMBER 5 | May 2001 531

Table 3. Changes in cord blood lead level associated with specified changes in endogenous and environmental factors, according to the final structural equation model with normal scale unit.

Variables Cord blood lead change (μg/dL)

Patella lead change

1 μg/g of bone mineral 0.032

Interquartile (4.26–23.71 μg/g of bone mineral) 0.65

Tibia lead change

1 μg/g of bone mineral 0.022

Interquartile (4.08–15.40 μg/g of bone mineral) 0.25

Cooking with LGC

1 day/week 0.27

Interquartile (1–3 days/week) 0.56

Air lead level change

0.1 μg/m3 0.67

Figure 2. Latent model of interrelationships of bone lead and umbilical cord blood lead in peripartum

women to estimate the effect of plasma lead. Standardized coefficients are presented. 

*p < 0.05. 

0.1377*

0.1934*

0.1274*

0.1697*

0.0872*

0.1151*

0.1023*

0.1732*

0.1853* 0.0745*

Error = 0.4566

0.8897*

R2 = 0.7915 R2 = 0.3245

Error = 0.8219

0.1438 0.8650*

Percentage of

life span in

Mexico City

Age

Long-term use

of ceramics

to cook food

Patella lead

level

Tibia lead

level

Plasma

lead level

Cord blood

lead

Recent use

of ceramics

to cook

Mean of air

lead in the

2 months

RBC lead
isotope in the country where the women

grew up and was different from the isotopic

ratio (lower) in their current country of residence. The increased isotopic ratio in blood

when these women were pregnant reflected

the bone lead (with higher isotopic ratio)

released into blood. The skeletal contribution

of lead to blood lead level, based on Gulson’s

(30) study of nine pregnant immigrant

women, produced a mean increase of 31% in

the 206Pb/204Pb ratio (range, 9–65%). These

studies provided evidence for significant

increase of skeletal lead mobilization during

pregnancy. However, the amount of lead

contributed to plasma (i.e., the partitioning

of lead in the blood pool) was indirectly measured using unique samples (29). In addition,

only a small number of subjects were willing

to undergo tibial XRF measurements because

of concern over past radiation exposure (30). 

Multiple linear regression models may

not permit a full appreciation of the influence of maternal bone lead levels on the fetal

cord blood lead levels through maternal

blood or plasma lead levels, because they

involve the use of one dependent variable

and multiple independent variables in a single equation. Because maternal blood lead is

an intermediate marker for the source of lead

exposure, the model may be overcontrolled

by the inclusion of all independent variables

with equal roles in the matrix (20). For

example, bone lead must be released into

maternal plasma to influence the fetal cord

blood lead levels; however, in a multiple

regression model, the influence of bone lead

levels on the fetal cord blood lead levels cannot be revealed after controlling for maternal

blood lead. In the SEM, plasma lead can be

inserted as a latent variable in a set of relative

equations, and covariance matrix and maximal likelihood estimation can be used to calculate coefficients that indicate the association

between plasma lead and the other observed

variables. 

The results of our study suggest that

plasma lead level is an important biomarker

for endogenous and exogenous lead exposure

in pregnant women and their fetuses.

Insertion of plasma lead level as a latent variable in the SEM allowed us to examine simultaneously the association between maternal

bone lead and external lead exposures on fetal

cord blood lead without bias. However, this

method cannot offer an actual plasma lead

value. Although currently difficult, plasma

lead measurement may be necessary to

elucidate particular lead toxicity issues, such

as the mobilization of lead from bone and

fetal lead exposure during pregnancy.

The results of our study also suggest that

if one wants to predict fetal lead exposure (as

signified by cord blood lead levels) before

pregnancy occurs, measuring maternal bone

lead levels as well as maternal blood lead levels and current environmental sources of

lead exposure may provide a powerful set of

tools. Our analysis using cross-sectional epidemiologic data certainly has limitations from

a biokinetics point of view because we do not

have repeated measurements with which to

measure dynamic relationships and are unable

to glean from our data any immediate biokinetic relationships, such as those between fetal

RBC lead and fetal plasma lead. Moreover, a

simple cord blood lead level alone is likely to

have limited ability to represent fetal lead

exposure throughout pregnancy. Nevertheless,

from a clinical and epidemiologic point of

view, maternal bone and blood lead levels

and environmental data remain the only

tools available with which to assess individual

risks before or early in pregnancy. Such risk

assessments, in turn, are important, given the

attention that is now starting to be directed

toward developing strategies for preventing

maternal bone lead mobilization during pregnancy using approaches such as nutritional

supplementation (31).

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Articles • Chuang et al.

532 VOLUME 109 | NUMBER 5 | May 2001 • Environmental Health Perspectives