P.1 

OAR Box 1126 

Prepped by Ollie Stewart 

Document Number: 

124)11-1-338 

Docket Number: 

A-83-22 

Printed 4/18/2008 9:58:26 AM Sheet 124 of 182 
J fh&~* 7ZHS1339 

P.2 

Environmental H 

Vol. 78, pp. 15-2. 

alth. 

198 

mpectives 

The Relationship Between Blood Lead and 

Blood Pressure in the NHANES II Survey 

by Joel Schwartz* 

A large body of experimental data has shown that lead raises blood pressure and increases 

responsiveness to a-adrenergic agonists in rats and pigeons. These studies suggest the need to look 

for a similar relationship in humans. This paper examines the robustness of the previously reported 

association between blood lead and blood pressure in adult males. The association remains strong 

and is essentially unchanged in tests that include nutritional factors and demographic factors, alone 

or together, or in tests that include insignificant terms. The relationship was not confounded by age; 

it held for all adult men in die 20-45 age group, the 40-59 age group, and the 46-74 age group. 

Interaction terms for 25 30-year age groups (20-49, 21-50, . . . , 45-74) were all insignificant, 

indicating no difference in the relationship by age. The relationship is also robust to the inclusion of a 

time trend to account for possible omitted time-varying factors, and it held in a model that controlled 

for possible site effects. Given the strong experimental evidence, die relationship is likely causal. 

Introduction 

Recent experimental evidence has demonstrated that 

moderate levels of lead cause elevations in blood 

pressure in animals (1-6). These findings show effects 

at varying doses and across species and indicate possible modes of action for lead that involve general 

mechanisms operant across all animal species. 

For instance, both Iannoconne et al. (1) and Carmignani et al. (2) have reported in vivo studies showing that lead-exposed rats had increased response to aadrenergic stimulation. These results have been 

reported in vitro by Webb et al. (4), using arteries from 

rats that had been exposed in vivo. Such activity 

would be expected to lead to increases in blood pressure. 

Skoczynska et al. (7) also reported that lead-treated rats 

had augmented and prolonged pressor responses to 

epinephrine and norepinephrine, less pronounced 

depression of arterial pressure in response to isoproterenol, and more pronounced tachycardia in response 

to isoproterenol. Skoczynska et al. also found in perfusion studies of isolated arteries that lead caused more 

pronounced vasoconstriction in response to norepinephrine. 

Of particular interest, Piccinini and Favalli (8) 

have reported that the addition of lead to the media of 

perfused isolated rat tail arteries led to contraction and 

to hyperreactivity in response to stimulation. The 

exposed arteries had higher levels of cytosolic calcium 

•Departmentof Biostatistics, School of Public Health, Harvard University, Boston, MA 02115. 

than the control group, consistent with other findings 

of lead causing a calcium leak into cells (9). Lead has 

been found to contribute to increased intracellular 

concentrations of calcium in brain capillaries (10), 

neurons (11), osteoclasts (12), and hepatocytes (13). As 

increased intracellular calcium is the trigger for 

smooth muscle contraction, these results again suggest 

a biological mechanism for an effect of low-level lead 

exposure on blood pressure. 

In addition, Goldstein and Ar (14) have reported 

that lead activates calmodulin in its role of activating 

phosphodiesterase, the enzyme that converts cyclic 

AMP into AMP. Cyclic AMP is involved in stimulating the calcium pump that removes calcium from 

the cytosol into the endoplasmic reticulum and also 

leads to the phosphorylation of myosin light-chain 

kinase, reducing its sensitivity to activation by calcium. These are actions that would be expected to reduce 

reactivity and tone, and a lead effect on cyclic AMP 

represents a potential adverse disturbance of these 

processes. 

Finally, the experimental literature suggests a 

nonlinear relationship, with effects on blood pressure 

primarily at the lower blood lead levels. Pounds (13) 

reported that low-level exposure to lead caused increased accumulation of calcium in all of the compartments of the cell, whereas at higher lead exposure, 

the two shallower compartments, including the cytosol, saturated out, and calcium accumulation continued only in the deepest compartment, thought to be 

the mitochondria. Thus, the hypothesis that lead 

increases blood pressure by disturbing cytosolic cal-
P.3 

16. S J. SCHWARTZ 

. 

cium metabolisms appears consistent with this nonlinearity in response. 

In light of this finding of an effect and possible 

mechanism, we examined a large representative survey of the U.S. population for a relationship between 

blood lead and blood pressure. Some of these analyses 

have previously been reported (15,16). 

The Second National Health and Nutrition Examination Survey (NHANES II) was conducted between 

February 1976 and February 1980 on a sample chosen 

to be representative of the civilian, noninstitutionalized U.S. population aged 6 months to 74 years. A total 

of 20,322 persons were examined, and blood lead determinations were obtained for a representative subsample of 9932. The medical evaluations included 

medical history, physical examination, anthropometric measurements, dietary information (24-hr 

recall and food frequency), laboratory tests, ECGs, and 

radiographs. Special interview and examination protocols and specifically trained interviewers and examiners were used to ensure standardized conduct of the 

survey at each site. The nutrient intake from the 24-hr 

recall was quantified for each individual using a 

current nutrient data bank. 

Blood samples were analyzed by the Clinical Chemistry Division, Centers for Disease Control. Blood lead 

concentrations were determined by atomic absorption 

spectrophotometry using a modified Delves Cup micromethod (17). Both bench and blind quality controls 

were used. 

The analysis of the relationship between blood lead 

and blood pressure is confounded by age. Both lead and 

blood pressure increase with age. Moreover, studies of 

populations not exposed to the environment of western 

civilization, both in Africa and the Pacific Islands, as 

well as in isolated communities in Wales, show that 

blood pressure does not increase with age. Thus, age 

must be treated not as representing a marker for the 

aging process, but rather as representing unknown 

stresses, and the cumulative effect of elements of the 

western environment. Lead is one of these elements, 

and thus the confounding is real. To minimize that 

confounding we have adopted several approaches. 

First, we performed analyses in the 40- to 59-year-old 

age group, the age group with the lowest correlation 

between blood pressure and age. Second, we have 

repeated the analyses for all men aged 20 to 74, with the 

expectation that the large sample size will partly 

mitigate the confounding. Finally, we have repeated 

our analyses with interaction terms for different age 

groups, to test for the uniformity of the effect across ages 

without paying the sample size penalty that subsampling entails. 

Initial Analysis 

40- to 59-Year-Old Cohort 

The established correlates of blood pressure are age, 

sex, race, and obesity. We used Quetelets body mass 

index (BMI) (weight/height2) as the principal obesity 

measure in this analysis. For the 40- to 59-year-old age 

group we only considered white males. We forced age 

into the models in order to avoid any upward bias in 

the lead coefficients. 

The natural log of blood lead was more normally 

distributed, more significant, and gave a higher R2 

than untransformed blood lead, blood lead squared, 

blood lead + blood lead squared, the square root of blood 

lead, or blood lead to other fractional powers (we 

examined 0.15, 0.2, 0.3, and 0.4). All of the results 

reported here are for the natural log of blood lead, but 

regressions for untransformed lead gave very similar 

results. 

The initial regressions analyzed systolic and diastolic blood pressure for white males, 40 to 59 years old, 

with a model consisting of age, age squared, BMI, and 

blood lead. These regressions were done to determine 

whether blood lead levels were significantly associated 

with systolic and diastolic blood pressure after 

controlling for the well-documented correlates of blood 

pressure. Lead was statistically significant (p < 0.01) for 

both systolic and diastolic blood pressures in all OLS 

(ordinary least squares) regressions, in weighted regressions, and in weighted regressions with the 

design effects of the survey. 

Tests of Robustness 

The regression models were then expanded to incorporate additional variables, with particular attention 

directed to the stability and significance of the lead 

coefficient in the presence of nutritional factors and 

blood biochemistries. 

A large set of nutritional and biochemical variables 

from NHANES II was included in stepwise regressions. The large set was used because blood pressure is 

such a multifactorial outcome and because we desired 

to test the robustness of the lead relationship to the 

inclusion of other variables, and the reporting of significance for any of them in these analyses should not 

be taken as an endorsement of a relationship. In addition, a large set of demographic and personal history 

variables was also considered in stepwise regressions. 

Nutritional and Biochemical Variables 

To provide an unusually rigorous test of the independent significance of blood lead, almost all of the nutritional and biochemical variables in the NHANES 

II were included in stepwise regressions. In addition, 

to account for possible curvilinear relationships, 

squared and natural logarithmic transformations of 

these variables were also included. The variables are 

listed in Table 1. The objective was not to evaluate the 

possible association of nutritional or biochemical 

measurements with blood pressure, but rather to conservatively estimate the strength and independence of 

the relationship between blood lead and blood pressure. 

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BLOOD LEAD AND BLOOD PRESSURE 17 

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Table 1. Variables included in the stepwise regression analysis. 

Age* 

Age23 

Body mass index 

Dietary sodiumb 

Salt-shaker sodium 

Dietary sodium x 

salt-shaker sodium 

Dietary potassiumb 

Dietary sodiumpotassium ratio 

Dietary calciumb 

Dietary phosphorusb 

Dietary proteinb 

Dietary fatb 

Dietary carbohydrate15 

Dietary cholesterolb 

Dietary saturated 

fatty acidsb 

Dietary oleic acidb 

Dietary linoleic acidb 

Dietary ironb 

Dietary vitamin Ab 

Dietary vitamin Cb 

Dietary thiamineb 

Dietary riboflavinb 

Dietary niacinb 

Serum cholesterol11 

Serum vitamin Cb 

Serum ironb 

Serum transferrin saturation 

Serum zincb 

Serum copperb 

Serum albuminb 

Hemoglobinb 

Red blood cell count 

Ethanol consumption/weekb 

Cigarettes smoked/day 

Total dietary gramsb 

Total dietary caloriesb 

Cigar or pipe smoking 

"Forced into each regression to remove any possible age effects on 

blood pressure. 

bThe natural log and squared transformation of these variables were 

also included in the stepwise regression. 

Including these additional 87 variables increases the 

probability of variables being found statistically significant due to chance alone. This complicates the interpretation of nutritional and biochemical factors, but 

not the interpretation of the lead variable; it only makes 

it more difficult for lead to maintain its significance. 

The general procedure for variable selection was as 

follows: First, weighted, stepwise multiple linear 

regression was used to determine which variables 

were significantly related (p < 0.05) to blood pressure 

(using the MAXR options of the SAS procedure 

Stepwise). The MAXR procedure determines for any 

given model size (i.e., number of independent variables) the variables that explain the greatest amount of 

the variance of blood pressure. We chose the largest 

model with all variables significantly related to blood 

pressure (p < 0.05). From the 87 nutritional and biochemical variables, the weighted, stepwise regression 

selected 5 additional variables for diastolic pressure 

and 6 additional variables for systolic pressure, using 

the 5% significance test. These were used as the starting 

model for the SAS procedure SURREGR, which incorporates the survey design effects. For both systolic 

and diastolic blood pressures, one variable from the 

weighted stepwise regression failed to maintain significance at the 5% level after the design effects were 

incorporated. The final regression results for systolic 

and diastolic pressures, after accounting for the 

weighting and design effects, are given in Table 2. 

To investigate whether a threshold level, below 

which no effect of blood lead on blood pressure existed, 

piecewise linear regressions were performed using the 

SAS procedure NLIN. These segmented regressions fit 

two lines to the data. One, below the putative blood lead 

threshold T, depends on all the variables except lead. 

The other, for blood lead levels above T, includes lead. 

Table 2. Regression of diastolic and systolic blood pressures in 

white males age 40 to 59. 

Variable 

Diastolic 

Age 

Age2 

Body mass index 

Log (blood lead) 

Dietary potassium 

Hemoglobin 

Albumin 

Log(dietary vitamin C) 

Systolic 

Age 

Age2 

Body mass index 

Log (blood lead) 

Albumin 

Log (dietary vitamin C) 

Log(dietary riboflavin) 

Log(dietary oleic acid) 

Log(serum vitamin C) 

Coefficient 

0.2768 

-0.0014 

1.131 

3.954 

-0.0018 

1.548 

3.587 

1.838 

1.311 

-0.0068 

1.736 

8.436 

7.088 

2.411 

-5.509 

3.992 

-3.472 

/-Statistic 

0.17 

0.10 

8.55 

2.85 

4.92 

3.90 

2.50 

4.65 

0.57 

0.30 

9.42 

3.24 

2.50 

3.84 

3.07 

2.49 

2.47 

Probability 

0.8636 

0.9321 

0.0001 

0.0080 

0.0001 

0.0005 

0.0179 

0.0001 

0.5720 

0.7706 

0.0001 

0.0028 

0.0178 

0.0005 

0.0044 

0.0183 

0.0184 

We used an iterative algorithm that chooses the 

threshold point that minimizes the sum of the squares 

of the errors over the full range of blood lead levels. 

After including the nutritional variables, the blood 

analytes, and their curvilinear transforms, blood lead 

remained significantly associated (p < 0.05) with both 

systolic and diastolic blood pressures. The coefficient of 

lead in the regressions was within 10% of the original 

value. Further, the segmented regression analysis 

indicated that there was no threshold blood lead level 

in the relationship. 

Interaction Terms 

In multiple regression analysis, another consideration is the possibility of significant interaction terms. 

To evaluate this possibility, an additional weighted, 

stepwise regression analysis was done for both systolic 

and diastolic blood pressure. The new variables 

consisted of the linear interaction terms between the 

final variables in the model (shown in Table 2) and the 

linear form of all the other variables originally 

considered for the initial stepwise regression (Table 1). 

This meant running a stepwise regression with 162 

interaction terms added to the final regression models 

for systolic and diastolic blood pressures. Using such a 

large set of variables gave a high probability that some 

variables would enter at the 5% level by chance. 

However, the purpose was not to determine if these 

variables were independently significant, but rather to 

further test the significance and independence of the 

relationship between blood pressure and blood lead. As 

expected, several interaction terms entered the systolic 

and diastolic regressions, but in each regression the 

lead coefficient varied less than 10% from its original 

value and remained significant (p < 0.015). 
P.5 

18 /. SCHWARTZ 

Marginally Insignificant Variables 

Three other analyses were done to ensure that this 

relationship was robust. First, the original weighted, 

stepwise regression was extended to include variables 

significant through the 15% level to see if marginally 

insignificant variables influenced the significance of 

lead. For both systolic and diastolic pressures, lead 

remained significant and there was little change in 

the magnitude of the coefficient Oess than 10%). 

Second, for diastolic blood pressure, all the variables 

were included that were significant between the p = 

0.05 and the p = 0.15 levels, and every possible combination of those variables (taken 1, 2, 3, 4, 5, 6, 7, and 8 

at a time) was considered. All 255 combinations were 

added to the variables that were significant at the 5% 

levels, and a regression was performed on each set. The 

coefficient of blood lead varied by only +10% to -10% 

from the value that we obtained when we only 

included significant variables, and lead was always 

significant (p < 0.01). This analysis was not repeated 

for systolic blood pressure. 

The last analysis was the most demanding test of the 

independence of the relationship between blood pressure and blood lead. Models for systolic and diastolic 

blood pressure were fit by weighted, stepwise regression 

using the original candidate variables (Table 1) but 

excluding lead. This gave all of the other variables and 

their curvilinear transformations the maximum 

opportunity to explain variation that could also be 

explained by lead. After obtaining these new, final 

models, a single regression for each outcome was run 

adding the lead variable to the variables of this model. 

For both systolic and diastolic blood pressures, lead was 

still statistically significant (p < 0.016) and the magnitude of the lead coefficient changed less than. 10% 

from those obtained in the original analyses. 

Because some people have found that small amounts 

of ethanol are associated with reduced blood pressure, 

ethanol was also modeled as a quadratic function of 

alcohol consumption, and also with two dummy variables for low and high consumption. The stepwise regression was repeated, and results did not change. The 

results of these analyses indicate that the strength and 

independence of the relationship between blood lead 

pressure was remarkably stable. 

Demographic and Personal History 

Variables 

We next considered the possibility that inclusion of 

the variables shown in Table 3 would affect the stability 

of the lead regression. Hypertension medication and 

low salt diet were tested to see if the response to lead was 

altered by these factors. The inclusion of these factors 

and interaction terms between them and lead did not 

change the lead coefficient appreciably, and the 

interaction terms were insignificant. As hypertension 

medication is a marker for high blood pressure, its 

itiuie o. rnuiuiuinuun vunuuieVariables 

Demographic 

Family income 

Poverty index 

Region of the country 

Season of the year 

Degree of urbanization 

Residence inside central city 

Educational level 

Hypertension 

Hypertensive medication 

Low salt diet 

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Other personal history variables 

Tricept skinfold 

Subscapular skinfold 

Recreational exercise 

Work-related exercise 

Recent weight loss 

Family history of hypertension 

Kidney disease 

Serum creatinine 

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inclusion in a model is not very informative. All of the 

subsequent analyses were performed with and without the inclusion of hypertension medication. 

The other variables in Table 3 were tested in two 

ways. First, the stepwise regression procedure was 

repeated using the variables in Table 3 and only the 

nutritional and biochemical variables that had been 

significant at the 15% level in the previous analysis. 

Again, lead was selected for both systolic and diastolic 

blood pressure (p < 0.005) and its coefficient was within 

10% of the original model that contained only age, 

age2 and body mass index. 

The variables in Table 3 were then added to all of 

those in Table 1, including their curvilinear transforms, and the stepwise process was repeated, yielding 

the same results. Finally, we re-ran the stepwise 

regression using all of the variables in Tables 1 and 3 

except for lead, allowing maximum opportunity for 

colinear factors to enter. Lead was then inserted in the 

resulting models for systolic and diastolic blood pressure. It was still significant (p < 0.01) with less than a 

10% change in its coefficient. In addition, smoking 

and drinking were forced into the regression models, 

and lead remained significant (p < 0.01), with no 

noticeable change in its coefficient. 

Previous analyses have shown that about half of the 

lead in people in the NHANES II sample came from 

gasoline (18,19). Tetraethyl lead has little cadmium 

contamination, so confounding with cadmium is 

unlikely. However, occupationaliy exposed workers are 

also exposed to cadmium, so we repeated the regression 

excluding all persons in job categories cited by NIOSH 

as having potential lead exposure. Lead remained 

significant for both systolic and diastolic blood 

pressure (p <0.01). 

Principal Components Analyses 

Because nutritional variables are often colinear witi 

each other and may in fact only be important it 

complexes, a second approach was taken to dealing witi 

nutritional and serological variables. Principal com 

ponents analysis was done to find the linear combine 

tions of the nutritional and serological factors tha 

explained the greatest amount of their common var 

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BLOOD LEAD AND BLOOD PRESSURE 19 

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ation. These principal components were then entered 

into the stepwise regression to allow those factors to 

explain a greater amount of the variation in blood 

pressure before colinearity prevents any more variables from entering. For instance, the first five principal components of the variables in Table 1 explain 50% 

of their variation. When these principal components 

were used instead of the raw variables and the stepwise 

regression was repeated, lead again was significantly 

associated with both systolic and diastolic blood 

pressure (p < 0.01), and its coefficient was changed by 

less than 10% from its original value. 

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BLOOD LEAD LEVEL (ug/dl) 

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Although all of these analyses make it clear that 

colinearity is not the cause of lead's correlation with 

blood pressure, variance inflation factors were 

computed for our final models. No significant variable 

had a variance inflation factor above 1.4. 

To ensure that the significance of lead in the regression was not due to the presence of a few influential 

observations, influence diagnostic procedures were 

run. Residuals were plotted for all the observations, 

and the largest residuals were clustered near the middle of the data, where their influence is slight. Cook's D 

statistics, a standard measure of an observation's influence on a regression, were also computed for each 

observation. The highest Cook's D was 0.029 and the 

second highest was 0.023, both of which are very small. 

Moreover, of the 10 observations with the largest Cook's 

D statistics, 6 had positive residuals and 4 had negative 

residuals, indicating that the most influential observation's split almost evenly on how they would influence the lead coefficient. Figures 1 and 2 show the 

relationship between blood lead and both systolic and 

diastolic blood pressure in this population, after adjusting for all of the other significant covariates. 

5 15 25 35 

BLOOD LEAD LEVEL (ug/dl) 

FIGURE 1. Adjusted diastolic blood pressure and adjusted blood lead 

levels for white males age 40 to 59. Both blood pressure and blood 

lead have been adjusted by regression for the effects of age, age2, 

body mass, and other significant variables listed in Tables 2 and 3. 

Each point represents the mean blood pressure and mean blood 

lead for 24 consecutive observations, sorted in increasing order of 

blood lead. 

FIGURE 2. Adjusted systolic blood pressure and adjusted blood lead 

levels for white males age 40 to 59. Both blood pressure and blood 

lead have been adjusted by regression for the effects of age, age2, 

body mass, and other significant variables listed in Tables 2 and 3. 

Each point represents the mean blood pressure and mean blood 

lead for 24 consecutive observations, sorted in increasing order of 

blood lead. 

Results for All Adult Men 

The 40- to 59-year-old age group represents about onethird of adult males and is the only group where the 

confounding of age and blood lead can be eliminated 

unambiguously. Additional regressions were performed, however, to confirm the relationship in the 

full population of adult men (> 20 years of age). Again, 

our analysis began by looking at the basic variables 

clearly linked to blood pressure: age, race, and body 

mass. Age was again modeled with both a linear and 

quadratic term to account for the nonlinear dependence. In this analysis, blacks were included, and a 

dummy variable for race was included in the regression because the sample size was now large enough to 

permit separating the race effect. Lead was once more 

found to be a significant predictor of both systolic and 

diastolic blood pressure (p < 0.01). 

Tests of Robustness 

We next included all of the variables in Table 1 and 

Table 3, along with their square and log transforms, in 

a stepwise regression analysis, to test whether 

inclusion of these other variables could affect the leadblood pressure relationship. Lead was selected as significant for both systolic and diastolic blood pressure 

(p < 0.01). This result held whether or not hypertensive 

medication was included in the stepwise regression. 

Figures 3 and 4 show the relationship between blood 

lead and both systolic and diastolic blood pressure in 

all adult men after controlling for all covariates. 

To assure that lead was not acting for a variable that 

did not enter the regression because it was colinear 

with lead, we re-ran the stepwise regression using all of 

the variables in Tables 1 and 3 except for lead. Lead was 

then inserted in the resulting model and its significance checked. Lead was significantly related to 

both systolic and diastolic blood pressure (p < 0.01). 
P.7 

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BLOOD LEAD LEVEL (ug/dl) 

FIGURE 3. Adjusted diastolic blood pressure and adjusted blood lead 

levels for males age 20 to 74. Both blood pressure and blood lead 

have been adjusted by regression for the effects of age, age2, body 

mass, and other significant variables listed in Tables 2 and 3. Each 

point represents the mean blood pressure and mean blood lead for 

50 consecutive observations, sorted in increasing order of blood 

lead. 

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BLOOD LEAD LEVEL (ug/dl) 

FIGURE 4. Adjusted systolic blood pressure and adjusted blood lead 

levels for males age 20 to 74. Both blood pressure and blood lead 

have been adjusted by regression for the effects of age, age2, body 

mass, and other significant variables listed in Tables 2 and 3. Each 

point represents the mean blood pressure and mean blood lead for 

24 consecutive observations, sorted in increasing order of blood 

lead. 

Interactions with Hypertension or 

Hypertensive Medication 

To assure that a spurious correlation was not occurring due to an interaction of lead levels with hypertensive medication or due to kidney disease elevating 

both blood pressure and blood lead levels, several additional analyses were performed. The stepwise regressions were repeated excluding all persons on hypertensive medication. Again, all variables in Table 1 were 

considered, not to determine whether they were true 

predictors of blood pressure, but to allow maximum 

possibility for another factor to explain the correlation 

between blood pressure and lead. Lead was significantly related to both systolic and diastolic blood pressure (p < 0.01). Serum creatinine was forced into the 

regression, and lead remained significant (p < 0.01). 

Next, the stepwise regression was repeated including 

hypertensive medication as a variable. Lead was again 

selected for both systolic and diastolic blood pressure 

(P < 0.01), and an interaction term between lead and 

hypertensive medication was not significant. Finally, 

all men with diastolic blood pressure above 90 mm Hg 

were excluded from the sample, and the stepwise 

regression was repeated. Lead was again chosen as significantly related to both systolic and diastolic blood 

pressure (p< 0.01). 

Other Age Groups 

The most robust way to check for a differential lead 

effect in different age groups is to use interaction terms, 

thereby maintaining sample size. We used dummy 

variables for the age groups 20 to 39 and 60 to 74 and 

interaction terms between them and lead. The interaction terms were highly insignificant (p > 0.35), 

indicating the same magnitude effect in all ages. 

As a further check, we separately ran the stepwise 

regression, using all of the variables in Tables 1 and 3, 

for the age groups 20 to 45 and 45 to 74. The larger age 

groups were used to compensate for the increased 

colinearity with age. Lead was chosen as significant 

for both age groups, for both systolic and diastolic blood 

pressure. Next, the regression for all adult males was 

repeated, but an interaction term for the 25 sequential 

age groups (20-49, 21-50 45-74) was tested in 50 

separate regressions (one for each age group, for both 

diastolic and systolic blood pressure). Lead was significant in each regression, and the interaction term 

was insignificant, again indicating a uniform relationship in all age groups. Finally, the regressions for 

both systolic and diastolic blood pressure were repeated 

in each of the 25 age groups, and lead was significant 

in all 50 regressions. Figure 5 shows the coefficients of 

diastolic blood pressure plotted against the median age 

for each of the 25 subgroups. The slope appears lower, 

although insignificantly so, in the youngest age 

groups. For the remaining 20 age groups, the slope 

oscillates about a mean of approximately 3.4 (on the log 

scale), indicating that a drop in blood lead levels from 

18 u.g/dL to 10 Mg/dL would reduce diastolic blood 

pressure by 2 mm Hg. 

Time Trends 

The introduction of unleaded gasoline in 1975 

created a time trend in blood lead levels in the United 

States. This downward trend accelerated in 1978 when 

further regulations restricted the amount of lead in 

leaded gasoline. Since the NHANES II blood lead data 

was collected from February 1976 to February 1980, 

there is a time trend in the blood lead data. If there were 

a time trend in blood pressure because of another factor, 

and that factor were omitted from the analysis, this 

would confound the relationship. Examination of the 

blood pressure levels in the National Health Survey in 

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MEDIAN AGE 

FIGURE 5. Coefficient of log blood lead (in a regression for diastolic 

blood pressure) versus median age for the 25 subgroups. 

the 1960s, in the NHANES I from 1971 to 1975, and in 

the NHANES II, show that levels in NHANES II were 

the same as in the 1960s but somewhat lower than in 

NHANES I. Thus, no clear trend in blood pressure over 

time is evident. The data are qualitatively suggestive of 

the relationship we have found within the NHANES II 

survey, since gasoline lead use peaked during the 

period of the NHANES I survey. Nevertheless, it is important to make sure that the relationship we have 

identified is not confounded. 

Other Covariates That Might Change Over 

Time. Time trends in other causal factors of blood 

pressure will only confound the analysis if those 

factors are omitted, and the first approach is to include 

them. Obesity, exercise, diet, serum cholesterol levels, 

smoking, etc., are all included in the model; changes in 

these factors will not confound the lead relationship. 

The dietary variables are sensitive enough to measure, 

e.g., changes in saturated versus polyunsaturated fats 

in the diet. Increased use of hypertensive medication 

over time was similarly controlled for, and in fact, no 

major external determinant of blood pressure has been 

omitted from the analysis, making confounding 

unlikely. 

Modeling Time Trend. Because blood lead levels 

fell at a much faster rate in the last 2 years of the 

NHANES II survey, the slope of the correlation between 

blood lead and an omitted time-dependent covariate 

would have changed in the second half of the survey. If 

the correlation between blood lead and blood pressure 

was due to blood lead correlating with this omitted 

covariate because both of them have a time trend, then 

the slope of the blood lead-blood pressure relationship 

would also change in the second half. We tested this by 

entering an interaction term in our model. The interaction term was highly insignificant (p = 0.75 systolic, 

p = 0.79 diastolic), indicating that the major change in 

the decline of blood lead over time was not accompanied 

by any change in the relationship with blood pressure. 

Next, we inserted a term for time into our model for 

all adult men and re-estimated. Lead was again significant as a predictor of both systolic and diastolic 

blood pressure (p < 0.05). We concluded that an omitted 

time trend is not confounding this analysis. 

Geographical Variation 

Since the survey took data in 64 locations, it is possible that there are design effects in the data. The 

SURREGR regression package that we used accounts 

for the design effects by estimating the covariance 

using a Taylor series approximation for the offdiagonal effects. An alternative is to assume a different 

intercept for each sampling unit. This also examines 

the time trend issue in a different way. As the sites were 

sampled at different times, the different site intercepts 

represent either site or time differences. As the 64 PSUs 

were randomly selected from candidate PSUs to 

represent the U. S. population, we have used a random 

effects model (20). Using this model to include site 

effects, we found that lead was still a significant 

predictor of both systolic and diastolic blood pressure 

(p < 0.05) for both all adult males and the 40- to 59-yearold age group. The regression coefficients are reduced 

about 25% in these models. We conclude that the 

relationship is not confounded by either site or time 

effects. 

Conclusions 

There is a robust relationship between low-level lead 

exposure and blood pressure in the NHANES II data. 

This relationship holds whether we only examine the 

age group with minimal age confounding or whether 

we examine all adult men. It is not confounded by any 

plausible dietary, demographic, or anthropometric 

variables. It holds after controlling for the time trend 

in the data and may be the cause of that trend. 

The animal evidence clearly indicates a causal relationship between lead exposure and blood pressure in 

several species. The animal data also show that lead 

interferes with cellular calcium metabolism in ways 

that would be expected to increase blood pressure; these 

mechanisms also regulate blood pressure in humans. 

Essentially all of the recent human studies have 

found a positive association between blood lead and 

blood pressure. In most of the studies the association 

has been significant, in some of the others the magnitude of the insignificant association was similar to 

the magnitude in the significant findings. Although 

most of the studies have been in men, recently Rabinowitz et al. (21) have reported that umbilical cord 

blood lead was significantly associated with both blood 

pressure at delivery and the presence of pregnancy 

hypertension in a sample of 3200 live births in Boston 

with low lead levels (mean blood lead = 6.9 fig/dL). 

Given these results and the strength of the animal 

models, the conclusion that there is a causal association 

seems warranted. 

Small changes in blood pressure, even at levels below 

those defined as hypertension, were associated with 
P.9 

22 /. SCHWARTZ 

increased risk of heart attacks, strokes, and deaths in the 

Framingham study (22). While studies such as these 

can only inferentially implicate lead in these serious 

outcomes, the likelihood that lowering blood pressure 

reduces those risks is considered high enough to 

warrant giving drugs with serious side effects to persons with diastolic blood pressure above 90 mm Hg. It 

seems prudent to also recommend that reduction in 

exposure to a toxic substance that raises blood pressure 

is also warranted. Moreover, while these data do not 

allow a direct assessment of the effect of lead on cardiovascular deaths, there are two recent studies that 

addressed that issue. Khera et al. (23) found significantly higher levels of blood and urine lead in 

hospital outpatients being treated for hypertension or 

cardiovascular disease than in controls. Voors et al. 

(24) found a highly significant association between 

lead levels in the aorta of an autopsy series and whether 

the patient had died of cardiovascular disease. Again, it 

seems likely that measures to reduce lead exposure in 

the U. S. population will lead to small, but important, 

reductions in cardiovascular risk. 

1 

2. 

3. 

5. 

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