Patterns and Determinants of Blood Lead During Pregnancy

Irva Hertz-Picciotto1, Margaret Schramm2, Margaret Watt-Morse2, Kim Chantala3, John Anderson4 and John Osterloh5

1 Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC.
2 Department of Obstetrics and Gynecology, Magee-Womens Hospital, University of Pittsburgh, Pittsburgh, PA.
3 Carolina Population Center, University of North Carolina, Chapel Hill, NC.
4 Department of Nutrition, School of Public Health, University of North Carolina, Chapel Hill, NC.
5 Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The pattern of blood lead during pregnancy was investigated in a cohort of 195 women who, between October 1992 and February 1995, entered prenatal care at Magee-Womens Hospital in Pittsburgh, Pennsylvania, by week 13 of pregnancy. Blood was drawn as many as five times, once in each of the first two trimesters and a maximum of three times in the third trimester. Blood lead determinations were made by atomic absorption spectrophotometry. Potential sources or modifiers of lead exposure were collected by interviews, including sociodemographic, pregnancy history, occupational, and lifestyle data. Results confirmed a previously reported U-shaped curve in blood lead concentration during pregnancy as well as findings that blood lead levels increase with age, smoking, lower educational level, and African-American race and decrease with history of breastfeeding and higher intake of calcium. Additionally, interactions were found between time since last menstrual period and both maternal age and calcium. Specifically, older mothers showed steeper increases in blood lead concentrations during the latter half of pregnancy than did younger mothers, and intake of calcium had a protective effect only in the latter half of pregnancy, an effect that became stronger as pregnancy progressed. These findings provide further evidence that lead is mobilized from bone during the latter half of pregnancy and that calcium intake may prevent bone demineralization. Am J Epidemiol 2000;152:829–37.

bone and bones; calcium; lead; lead poisoning; nutrition; pregnancy

Abbreviations: BMI, body mass index; CDC, Centers for Disease Control and Prevention; LMP, last menstrual period.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Although lead has a half-life of about 45 days in the bloodstream, most of the body burden is stored in bone, where the half-life is estimated to be decades (1Go). Recent evidence suggests that lead stored in bone is not fully sequestered but rather may become bioavailable during periods of increased bone resorption, for example, in the postmenopausal period or during pregnancy. Cross-sectional studies show higher lead levels in post- than premenopausal women after adjustment for age (2GoGo–4Go). This post- versus premenopausal difference was greater among nulliparous than parous women, suggesting that prior pregnancies had depleted the lead stores in this latter group, resulting in less lead to be mobilized after menopause (2Go). Symanski and Hertz-Picciotto (4Go) showed that the postmenopausal effect was strongest in the first 4 years after menopause, when bone loss is most rapid (5Go, 6Go).

During pregnancy, substantial quantities of calcium are required for fetal bone growth. If maternal dietary sources are insufficient, bone demineralization may occur, with consequent release of lead. Patterns of blood lead during pregnancy may provide clues about this process. Rothenberg et al. (7Go), who enrolled about 200 Mexico City women at public prenatal clinics during their first trimester, found a decline in blood lead between weeks 12 and 20, followed by a rise that continued throughout the remainder of pregnancy. The initial decline is believed to be due to pregnancy-induced plasma volume expansion (fluid mass increases more than cell mass) (8Go), but the subsequent rise could be due to either increased absorption or mobilization through osteoclastic resorption of lead stored in bone, or both. In the Rothenberg et al. study, blood lead concentrations ranged from 1.0 to 35.5 µg/dl, with a geometric mean of 7.0 µg/dl.

In the present study, we sought to replicate the findings from Mexico City (7Go) in a population of pregnant women in the United States, where lower blood lead levels prevail (9Go, 10Go). By following the patterns of blood lead during pregnancy in a population with few sources of ongoing exposure and by analyzing factors potentially related to bone stores of lead and their mobilization, we addressed potential endogenous sources of blood lead in pregnant woman.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
This study enrolled a cohort of healthy pregnant women who initiated prenatal care during the first trimester at Magee-Womens Hospital or one of two auxiliary clinics in Pittsburgh, Pennsylvania, between October 1992 and February 1995. Women were excluded if they were less than 18 years of age, did not speak English, did not plan to carry the pregnancy to term, were not African American or White, were past 13 weeks since their last menstrual period (LMP), or had preexisting medical conditions such as chronic hypertension, diabetes, or psychoses. A total of 753 women were enrolled initially. Of these women, 183 were excluded for eligibility reasons, and 72 randomly selected White women were excluded from further data collection to maintain a 50:50 racial balance, leaving a cohort of 498 (figure 1). Blood specimens were collected once in the first and second trimesters, as many as three times in the third trimester, and at delivery. We focused on women who completed the entire protocol, delivered at Magee-Womens Hospital, and did not miscarry. Within this group, we selected a subset of women (n = 195) for blood lead determinations by stratifying on calcium status and oversampling those with a low (<1,000 mg/day) or high (>2,000 mg/day) dietary calcium intake. Since women with a low calcium intake would be most likely to mobilize minerals from bone, this sampling strategy was designed to maximize our statistical power and precision in evaluating changes in blood lead during pregnancy. The possible drawback to this strategy is that women whose calcium intake has been low over a long time period might have already mobilized their bone stores of lead, which could lead to bias and/or loss of precision in measuring changes induced by pregnancy.



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FIGURE 1. Construction of the cohort of healthy pregnant women from those who initiated prenatal care at Magee-Womens Hospital in Pittsburgh, Pennsylvania, 1992–1995.

 
Lead determinations
Blood lead concentration was determined once in each of the first and second trimesters and as many as three times in the third trimester, as well as from maternal and umbilical cord blood at delivery. Blood was drawn by venous phlebotomy into trace metal Vacutainers (Becton-Dickinson and Company, Franklin Lakes, New Jersey) containing EDTA, stored temporarily at 4°C at Magee-Womens Hospital, and sent in monthly batches via overnight delivery to the San Francisco General Hospital Metals Laboratory, University of California, San Francisco. This laboratory has 20 years of experience in blood lead analysis; participates in the Centers for Disease Control and Prevention (CDC)/American Association of Clinical Chemists blood lead proficiency testing program, with certification by the US Occupational Safety and Health Administration; and analyzes hospital-based, program-based (Childhood Lead Prevention Programs), and research samples.

Blood lead analyses were performed by graphite furnace atomic absorption spectrophotometry with deuterium background correction (11Go). Duplicate aliquots were prepared for specimens, calibrators, and controls. Initially, each duplicate preparation was analyzed twice and the quadruplicate results averaged, but differences between the means of the quadruplicate determinations and the means from the first duplicate determinations were negligible and nonsystematic; thenceforth, we proceeded with duplicate determinations only. All samples for the same woman were analyzed in the same run. Transfer tubes and reagent plasticware were precleaned with low-lead acid and water washes. Samples were prepared in a class 1,000 clean room that contained a high-efficiency particulate air filter. Quality control blood samples included spiked banked blood and CDC proficiency test samples. Quality control values were assayed by using prior repetitive analysis or CDC mean assignments. Both types of quality control blood samples were analyzed for every two to six subject samples. Interassay precision was 6 percent (coefficient of variation) at 2.0 µg/dl.

Interview data
Each woman was interviewed twice by a trained interviewer: once between 8 and 18 weeks and the second time between 31 and 41 weeks of pregnancy. The two structured questionnaires took on average 35 and 40 minutes, respectively, to complete. Most women were interviewed in person; however, since some had already spent a long day at the clinic, some were interviewed by telephone or the in-person interview was stopped and later completed by telephone (n = 49 for the first and n = 9 for the second interviews). Interviewers collected information on potential sources or modifiers of lead exposure and on factors that might confound or modify associations with maternal blood pressure, infant weight, and length of gestation. The potential sources or modifiers of lead exposure included age, prepregnancy weight, reproductive and breastfeeding histories, residential history, occupational history, use of ceramic cookware or Grecian Formula hair care products (Combe Incorporated, White Plains, New York), calcium intake (both dietary and supplemental), education and income, hobbies involving lead exposure, occupations and hobbies of persons with whom the woman lived (past and present), smoking, and alcohol consumption.

Questionnaires were reviewed for completeness prior to data entry. Data collection forms (interviews, visit forms, medical record abstractions, etc.) were entered in duplicate and were verified. Range and consistency checks were then performed. After thorough data cleaning, an analysis file was created with all relevant variables.

The date of the LMP was based on self-report in the interview. If ultrasound results and the self-report disagreed by more than 14 days, the ultrasound date was used.

Prepregnancy body mass index (BMI; kg/m2) was calculated by using self-reported height and prepregnancy weight. For six women with missing prepregnancy weight, we imputed values by using weight gain and prepregnancy weight in strata based on age and race, the strongest predictors of prepregnancy weight in these data. For each woman who was missing prepregnancy weight, we first calculated the mean daily weight gain as of the first prenatal visit for nonmissing women in her stratum; this value was then multiplied by days since LMP at her first prenatal visit and was subtracted from the weight recorded at that visit.

Education was coded ordinally in three levels. "Low" included those women aged 20 years or more who had less than a high school education. "Medium" included those who earned a high school diploma at any age as well as those aged less than 20 years without one. "High" was reserved for those women with some college education.

The occupational history requested start and stop dates (or ages at the beginning and end of the job), job title, work activities, and industry. This information was used to code each position by using Standard Industry Classification codes and codes published by the Labor Force Statistics Branch of the US Department of Health and Human Services (12Go). A list of Standard Industry Classification codes with any potential for lead exposure, obtained from the National Institute for Occupational Safety and Health (13Go), was reviewed to classify jobs and calculate the number of months of potential job-related lead exposure for each woman. Besides occupational history, the questionnaire included specific queries about major occupations (battery manufacturing, welding, etc.) and hobbies (jewelry making, etc.) known to involve lead exposure; the woman was asked whether she or anyone with whom she lived had ever worked in these occupations or engaged in these hobbies. We created a total number of such jobs and/or hobbies 1) for her and 2) for others with whom she lived.

A food frequency table, which included portion sizes, was used at both interviews to obtain women's current sources of calcium: these items (table 1) contain a substantial portion of the calcium in the adult US diet (14Go). Dietary intake of calcium was calculated by using DIETSYS version 3.0 software developed by Block et al. (15Go). Supplemental calcium was determined from the weekly intake of prenatal vitamins, calcium supplements, and calcium-containing antacids. The calcium content of specific brands reported was estimated by using manufacturers' product information from the 1992–1995 editions of the Physicians' Desk Reference (16Go). (A complete listing by year, brand name, and product is available from the authors or at their Web site: http://www.cpc.unc.edu/projects/lead/). Thus, daily calcium intake from the diet, antacids, calcium supplements, and prenatal vitamins was calculated. Indicators of lifetime calcium intake were constructed from self-reported milk consumption during childhood, adolescence, previous pregnancies, and lactational periods and from lifetime consumption of antacids and calcium supplements.


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TABLE 1. Food items* used to assess dietary calcium intake in a cohort of pregnant women in Pittsburgh, Pennsylvania, 1992–1995

 
Statistical analyses
Initially, we conducted simple bivariate linear regressions for each visit to screen our large pool of potential predictors of blood lead. Those factors showing some association with the outcome (broadly defined as a p value of <0.25 for more than two visits) and for which data were complete were then selected for possible inclusion in multivariate models. Factors that met this criterion included mother's age, prepregnancy BMI, smoking, caffeine intake, total calcium intake, race, education, alcohol consumption, and number of servings of canned foods per month.

Blood lead values were plotted against week of pregnancy to enable visual inspection of the longitudinal pattern. The resulting graph suggested a nonlinear relation; in regression models, we therefore examined splines as well as a simple quadratic term for time since LMP.

We next fit longitudinal models predicting all blood lead measurements taken before labor, which included as many as five determinations for each woman. A longitudinal, multivariate fixed effect model was fit by using all factors that met our inclusion criteria (described above), along with time since LMP. Longitudinal analysis accounting for intraindividual correlation was implemented with SAS software (17Go) by using both the PROC MIXED and PROC GENMOD procedures, the latter of which uses generalized estimating equations. As both procedures produced similar results, further model building was conducted with generalized estimating equations. A marginal (or "population-average") regression model was selected because we were not interested in estimating subject-specific parameters.

Several different covariance structures were examined. The two with Akaike's Information Criterion values indicating the best fit were the autoregressive first order and the exchangeable (sometimes referred to as compound symmetry) covariances. Since prenatal visits were not spaced equally, the autoregressive structure (in which the covariances decline rapidly for nonadjacent visits) seemed less plausible; therefore, exchangeable covariances were used. Variances were assumed to be homoscedastic. As residuals for some visits did not show Normality, two transformations of blood lead were tried: square root and logarithmic. The square root transformation yielded Normal errors, but since the predictive importance of the risk factors was virtually the same regardless of transformation, this paper reports results based on the log transformation. This choice was based on interpretability; it allows presentation of the percentage change in blood lead level for a given increase in the predictor variable. For comparison, we also present the model with untransformed blood lead values.

Nonlinear relations were explored by using splines and other transformations of the predictor variables. Interactions were also examined. Because of the changing kinetics of lead during pregnancy, we hypothesized that the predictors of lead in the early half of pregnancy would differ from the predictors of blood lead concentrations later in pregnancy. Thus, for factors thought to affect the rate of maternal bone resorption in response to fetal skeletal growth, we constructed interactions with time since LMP. A comparison across the five visits (pregnancy weeks 6–13, 14–27, 28–32, 33–36, and >=37) of coefficients in cross-sectional bivariate regressions, as well as inspection of splines, suggested interactions of time since LMP with mother's age at enrollment and with total calcium intake.

Models were run with and without two blood lead values outside the main distribution–8.45 and 5.70 µg/dl–which, based on published norms (9Go), were likely to be true measurements. This paper presents results for models that included these outliers, but their exclusion had minimal influence on parameter estimates.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Those women who did not complete the study, that is, who miscarried, dropped out, or delivered elsewhere (n = 129), were slightly more likely to be African American than White (55 vs. 49 percent) and were younger and hence less likely to have completed high school than those who did complete the study. Interview data for a subset of women who dropped out or delivered elsewhere indicated a smaller percentage who drank alcohol as compared with completers but no differences with regard to smoking, previous pregnancy history, breastfeeding, income, employment, time since LMP at entry into the study, and body mass. Among those who completed the study, the total cohort differed little from the subset for whom lead determinations were conducted (table 2). The main difference was that the subgroup with lead determinations had a higher calcium intake because we oversampled those at both extremes of this distribution.


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TABLE 2. Sociodemographic and lifestyle characteristics (%) of pregnant women who completed the full study protocol and of the subset for whom blood lead values were determined, Pittsburgh, Pennsylvania, 1992–1995

 
As shown in figure 2, 878 measurements from 195 pregnant women indicated low blood lead concentrations relative to levels at which human health effects have been demonstrated (for instance, above 10 µg/dl, lead poses a risk to cognitive development in children (18Go)). Two measurements of more than 5 µg/dl are not shown in this figure. A U-shaped curve characterizes blood lead level as a function of weeks of pregnancy; this curve was also present when the outlying observations were included.



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FIGURE 2. Scatterplot of 892 blood lead (Pb-blood) measurements taken at different times since last menstrual period from 195 pregnant women, Magee-Womens Hospital, Pittsburgh, Pennsylvania, 1992–1995. For each woman, blood lead levels were determined from three to five different blood drawings (after exclusion of values with missing reports on calcium intake, the final model included 3 women with two, 9 women with three, 56 women with four, and 127 women with five lead measurements). Two values of blood lead concentrations >5 µg/dl are not shown but were included in the models.

 
The best predictive models are presented in table 3. Each coefficient for model 1 and model 2 represents the change in the blood lead level and the change in the log blood lead level, respectively, in µg/dl for a one-unit increase in the x variable, unless otherwise indicated. The last column shows the percentage change in blood lead for a specified increase in the x variable based on model 2. As shown on the graph (figure 2), time since LMP at blood draw was associated nonlinearly with blood lead level. Splines were initially used for time since LMP, but a quadratic term centered at 20 weeks was found to give an equally good fit with fewer parameters. Mother's age at enrollment in the study also modified the effect of time since LMP; therefore, a product term was introduced to reflect this relation: the older a woman was, the steeper the increase in blood lead as pregnancy progressed through the last trimester (figure 3); concomitantly, the later in pregnancy, the greater the difference associated with older maternal age. For example, from week 20 to week 40 of pregnancy, blood lead levels in women with a low calcium intake increased 25 percent at age 18 years, 37 percent at age 23 years, 65 percent at age 33 years, and 99 percent (i.e., a doubling) at age 43 years.


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TABLE 3. Final regression models for prediction of lead and log(blood lead) in pregnancy*,{dagger}

 


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FIGURE 3. Fitted curves for blood lead (Pb-blood) levels of pregnant women aged 18 and 38 years who initiated prenatal care at Magee-Womens Hospital in Pittsburgh, Pennsylvania, in 1992–1995 and whose calcium intake levels were high (>2,000 mg/day) or low <600 mg/day). Values of other variables were held constant, as follows: education, middle level (completed high school or aged <20 years and did not complete high school); body mass index (kg/m2, 24); nonsmoker; race, African American; did not breastfeed previously.

 
Low educational level was associated with not only higher blood lead levels but also a higher prevalence of smoking (data not shown); when either of these variables was removed, the standard error for the coefficient of the remaining one was smaller. We left both factors in the model since lead is established to be present in cigarette smoke, and education is likely to be a surrogate for other factors besides smoking that are associated with lead. African-American women had higher blood lead levels than White women did. Despite higher BMI among African-American women, their higher blood level was independent of any association between BMI and blood lead. In fact, blood lead increased with BMI only for women whose BMI was less than 24 kg/m2, which falls into the category of normal body weight for height. No association with blood lead was found when prepregnancy BMI was in the overweight or obese range.

Total number of months of breastfeeding infants from previous pregnancies was inversely associated with blood lead. The magnitude of the coefficient was unchanged when we removed from analysis two women with over 40 months of breastfeeding. Although bivariate analyses suggested a similar association of parity with blood lead, the high correlation of parity and months of breastfeeding made it difficult to distinguish independent contributions from these two variables.

Higher calcium intake was associated with lower blood lead levels in the period from week 20 to the end of pregnancy. Stepwise, progressive differences were seen in four groups: from <=600 mg/day, to >600–1,000 mg/day, to >1,000–2,000 mg/day, and to >2,000 mg/day (data not shown; ordinal coding was adopted after monotonic and similar increases were established across these four categories). Thus, even well above the Recommended Dietary Allowance for calcium, some benefit was observed during the latter half of pregnancy. The contrast in blood lead levels between the highest and lowest calcium intakes is shown in figure 3 for African-American women aged 18 and 38 years, with mean, middle, or modal levels of other variables. As shown in this figure, calcium intake, maternal age, and time since LMP all influenced blood lead levels even at these low concentrations. Somewhat smaller effects are shown in table 4, which presents predicted blood lead concentrations according to smoking status, education, breastfeeding, BMI, and race. Thirty months of previous breastfeeding had a greater impact than smoking, race, or a 20-unit change in BMI.


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TABLE 4. Predicted blood lead value* at 36 weeks' gestation, by education, smoking status, months of previous breastfeeding, race, and body mass index, for a cohort of pregnant women in Pittsburgh, Pennsylvania, 1992–1995{dagger},{ddagger}

 
Higher caffeine consumption was also predictive of increased blood lead levels (model not shown) and was associated with smoking (positively) and with race (African Americans consumed far less caffeine). As a result, inclusion of caffeine decreased the smoking coefficient and increased the race coefficient.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Among studies examining lead over the course of pregnancy, many have not reported lead measurements for the same women or for all three trimesters (19GoGoGo–22Go). Knight et al. (23Go) measured blood lead throughout pregnancy, but not all women contributed data to all three trimesters. Rothenberg et al. (7Go), who did include measurements in all three trimesters for the same women in the cohort from Mexico City, demonstrated a clear U-shaped curve, with a nadir at 20 weeks. Swedish data for a similar-sized cohort showed lower blood levels but included measurements at only two time points before delivery: week 10 and week 32 (20Go, 21Go). According to our curves and those of Rothenberg et al., these two data points would be expected to show similar levels and, in fact, they do.

The women in the Mexico City study (7Go) had substantial concurrent external exposures from leaded gasoline and use of glazed ceramic cookware fired at low temperatures. Since the pregnant women we studied had no ongoing substantial exposure to environmental sources of lead, increased absorption of lead from the environment would likely have played a smaller role. Thus, the fact that a previously observed nonlinear relation over the course of pregnancy was confirmed for US women in the 1990s appears to support a role for mobilization of lead stored in the mineral matrix of the skeleton. Since lead has long been known to cross the placenta and to accumulate in numerous fetal organs, including the brain, in proportion to the growth of those organs (24Go), endogenous maternal lead could pose a risk to early fetal development.

Gulson et al. (25Go) estimated the contribution of skeletal lead to blood lead, based on isotopic characterization of blood lead samples, in 13 recent immigrants to Australia from eastern Europe. Several isotopes of lead are present in the environment, and their distribution in the body reflects the sources of that person's body burden. As eastern European lead sources have a higher ratio of 206Pb to 204Pb (25Go), the relative change in the isotopic ratio from a prepregnancy blood sample to samples taken during pregnancy may provide information about the contribution of skeletal lead (representing long-term exposure) to blood lead. On the basis of a population whose blood lead levels were similar to those in our population, Gulson et al. estimated that about 31 percent (range, 13–65 percent) of the changes in blood lead levels during pregnancy were due to skeletal stores of lead.

Other evidence from our analysis may support the interpretation that the late pregnancy rise in blood lead results, at least partially, from transfer of lead from bone into the circulating blood. For example, the greater effect of time since LMP on blood lead among older mothers would logically follow, since those born earlier were exposed to leaded gasoline for more years of their lives. While older women might preferentially absorb more lead than younger women do, we are not aware of toxicokinetics data supporting this type of effect in women aged 35–44 years. Estimates suggest that if absorption of dietary lead doubled, blood lead would increase about 10 percent (0.2 µg over a 2 µg level) (25Go); the increase we observed between weeks 20 and 40 of pregnancy was of greater magnitude in women more than 35 years of age (figure 3).

Another of our findings, the protection associated with increased calcium intake during the latter half of pregnancy, is consistent with the hypothesized bone kinetics, although it could also reflect decreased absorption of lead. In lactating women, calcium excretion was reduced and bone resorption was increased, while intestinal absorption did not increase (26Go). The protective effect we observed from breastfeeding of previous infants may have resulted from partial unloading of bone stores of maternal lead during prior pregnancies and lactation, thus depleting the reservoir from which to draw lead during the current pregnancy. However, a recent study of lactation showed neither a change in blood lead during the first 6 months postpartum nor any association between bone changes and blood lead changes (27Go). In contrast, immigrants to Australia appear to show greater mobilization of skeletal lead during lactation than during late pregnancy (28Go).

We attempted to explore whether some part of the increase in blood lead after week 20 of pregnancy could be an artifact associated with an increase in hematocrit, but very few women had hematocrit measurements toward the end of pregnancy. Using the data available, which included an early third trimester measurement (weeks 28–32) for about half of the 195 women, we found that adjustment for hematocrit did not alter the substantive findings. Interestingly, a study of primates demonstrated a reduction in first trimester bone mobilization (29Go). If applicable to humans, this result would imply that not all of the blood lead decrease in the early half of pregnancy is attributable to the increase in plasma volume.

This study, like all epidemiologic investigations, has some limitations. Many of our variables were based on self-reports. Thus, the lack of association between blood lead and BMI above the value of 24 may be a result of poor quality data for prepregnancy weight if women who weighed less provided more accurate reports than heavier women did. Calcium intake was also measured with error, since food frequency information is only approximately correct. Nevertheless, the broad categories we created for calcium intake likely provided a reasonable rank-ordering of subjects (particularly given the wide range of intake), thus allowing estimation of an impact on blood lead. Another limitation stems from the fact that few women reported drinking alcohol during pregnancy, using Grecian Formula hair care products, having had occupations or hobbies that could have involved lead exposure, or living with persons who had such occupations or hobbies; consequently, our power to detect associations with these factors was very low.

Our results confirm established predictors of blood lead in nonoccupationally exposed populations, including smoking, lower socioeconomic status, African-American race, and older age (4Go, 9Go, 10Go, 30GoGo–32Go). Our ability to observe these associations even at very low exposures supports the quality of our blood lead determinations and confirms that high-quality blood lead determinations can distinguish real trends from noise even at today's low exposures. It also indicates that even as exogenous sources of lead exposure have declined, known risk factors continue to have measurable effects. Another point of concern is that while breastfeeding infants from previous births was protective for the mother and hence for the fetus of the current pregnancy, the implication for her previous children, who absorbed some of her lead burden, is one of potential harm.

In conclusion, we confirmed a U-shaped pattern of blood lead concentration across pregnancy. Our research adds further evidence that lead stored in the skeleton may be accessible during pregnancy, particularly and increasingly during the third trimester. The associations we observed with smoking, socioeconomic status, and age suggest that much of the so-called background levels of lead may be preventable. Finally, our study indicates that calcium intake may provide some protection not only near the Recommended Dietary Allowance but well above it.


    ACKNOWLEDGMENTS
 
This work was supported by grant 1 R01-ES05738 from the National Institute of Environmental Health Sciences.


    NOTES
 
Reprint requests to Dr. Irva Hertz-Picciotto, Department of Epidemiology, CB#7400, School of Public Health, McGavran-Greenberg Hall, University of North Carolina, Chapel Hill, NC 27599 (e-mail: ihp{at}unc.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication July 23, 1999. Accepted for publication January 12, 2000.