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Environmental Health Perspectives 

Vol. 78, pp. 53-56, 1988 

EPA AIR DOCKET 

The Relationship of Blood Lead to Systolic 

Blood Pressure in a Longitudinal Study of 

Policemen 

by Scott T. Weiss,*1, Alvaro Munoz,* Amy Stein,f 

David Sparrow,8 and Frank E. Speizer+ 

We examined the relationship of blood lead level to systolic and diastolic blood pressure in a 

longitudinal study of 89 Boston, MA, policemen. At the second examination blood lead level and 

blood pressure were measured in triplicate. Blood pressure measurements were taken in a similar 

fashion in years 3, 4, and 5. Multivariate analysis using a first-order autoregressive model revealed 

that after adjusting for previous systolic blood pressure, body mass index, age, and cigarette 

smoking, an elevated blood lead level was a significant predictor of subsequent systolic blood 

pressure. Bootstrap simulations of these models provided supporting evidence for the observed 

association. These data suggest that blood lead level can influence systolic blood pressure even 

within the normal range. 

Introduction 

A variety of epidemiologic studies have suggested a 

small but statistically significant effect of blood lead 

level on systolic blood pressure and suggest a weaker 

association with diastolic blood pressure as well (1-4). 

These studies were cross-sectional in design and involved large numbers of subjects. 

We examined the relationship between blood lead 

level and longitudinal change in blood pressure in a 

small group of policemen under observation for health 

outcomes related to environmental work exposures. 

Using computer-intensive statistical techniques, we 

demonstrated an effect of blood lead on systolic blood 

pressure similar to that seen in large cross-sectional 

surveys. 

Materials and Methods 

The populations of subjects studied has been characterized in previous reports (5-7). The men of two 

'Charles A. Dana Research Institute and the Harvard Thorndike 

' Laboratory of the Beth Israel Hospital and Department of Medicine, 

Harvard Medical School, Boston, MA 02115. 

tChanning Laboratory, Brigham and Women's Hospital and 

Department of Medicine, Harvard Medical School, Boston, MA 02215. 

^Department of Epidemiology, Johns Hopkins School of Public 

Health, Baltimore, MD 21218. 

§Normative Aging Study, VA Outpatient Clinic, Boston, MA. 

separate units of the Boston Police Department were 

seen at their respective station houses during normal 

working hours. Preliminary observations included 

measures of height, made with subjects in stocking feet, 

and weight of subjects wearing street clothing, without 

jackets add usual accessory equipment. A detailed 

questionnaire containing demographic, smoking, 

occupational, and residential history, and a modified 

version of the British Medical Research Council Respiratory Disease Questionnaire were administered by 

trained interviewers. In addition, information about 

cardiac complaints were obtained by a standard version of the Rose Angina Questionnaire (8). 

Pulmonary function testing was measured on a 

water-filled spirometer and is the subject of a separate 

report (7). A blood specimen was obtained to determine 

hematocrit concentration and to make peripheral 

smears to assess basophilic stippling. Blood lead concentration was determined on a small sample of subjects to validate their traffic exposure histories. Blood 

lead concentration was determined using the technique of atomic absorption spectrophotometry (9). 

Blood pressure was measured with a random zero 

blood pressure machine to minimize digit preference. 

With the subject seated, systolic pressure and fifthphase diastolic pressure was measured in the left arm 

to the nearest 2 mm Hg. 

The study was a longitudinal investigation with 

initial screening beginning in 1969-1970 and with 
P.3 

54 WEISS ETAL. 

observations completed in 1974-1975. Blood pressure 

(in mm Hg) was recorded in years 2 through 5. The 

mean of triplicate measures of systolic pressure and 

diastolic pressure at each visit was used for analysis. 

Age in years, body mass index (in kg/m2), and current smoking status (recorded as 1 = current, 0 = never 

or exsmoker) were available for years 1 through 5. 

Current cigarette smokers were defined as subjects who 

smoked as many as one cigarette a day in the study 

year. Blood lead values (in ug/100 mL) were collected 

only in year 2. Based on the distribution of blood lead 

values in our sample and in that of the United States 

population (10), blood lead values were divided into 

high (> 30 ug/100 mL) and low (> 20 and < 30 ug/100 

mL) groups for purposes of the regression analysis. 

This gave comparable numbers of subjects in each 

category. These two groups were compared with our 

reference group in which values were < 20 ug/100 mL. 

To examine the relationship of blood lead concentration to change in blood pressure over the 4 years of 

the study, a Markov type autoregressive model was 

used (11). In this model, blood pressure (systolic or 

diastolic) at time r(Ft) is related to blood pressure at t-1 

(yt_i) and the levels of other covariates at time t or at 

previous time points. Specifically, the model takes the 

form 

y, M+C(rl_1)+B1A11+B2X2t + ...+et 

This model has several advantages for the analysis of 

longitudinal data sets. Specifically, the model uses the 

data efficiently since any individual for whom 

complete data are available for any two consecutive 

years (t and t-1) will contribute data to the model. 

Secondly, the model does not impose a particular shape 

(i.e., linearity) on the relationship between the dependent and independent variables. Finally, this model 

can be fitted using ordinary statistical software packages. The model assumes that the residual e's are 

independent with constant variance (02) both within 

and between individuals. An additional assumption is 

that the relationship between the outcome variable and 

the independent variables is the same for all individuals (fixed effects model). 

A cross-validation was. undertaken to determine if 

the magnitude and significance of the regression 

coefficients obtained for the blood lead variables were 

the result of the disproportionately large contribution 

of values from a few individuals (12). To determine if 

an individual was an outlier, we used a modification 

of Tukey's fences applied to the cross-validation exercise 

(13). After the full cross-validation, we determined the 

subject whose exclusion has the largest influence on 

any regression coefficient (measured in units of 

interquartile range away from either the first or the 

third quartiles of the distribution of the values of all 

the regression coefficients obtained from the crossvalidation). This individual was then removed and 

the cross-validation repeated. This process was continued until all regression coefficients resulting from 

a cross-validation exercise were within five interquartile range units away from either the first or the 

third quartiles. 

To determine the variability of the regression coefficients, without the assumption that the residuals are 

normally distributed, a bootstrap analysis was performed (14). In this analysis, we generated 1000 

(bootstrap) samples equal in size to the original sample 

by randomly sampling with replacement from the 

original pool of individuals. The distribution of the 

coefficients for the bootstrap samples can be considered 

as though they were coming from real samples, and 

thus they provide a measure of the statistical precision 

of the original estimates on the regression coefficients. 

Results 

Table 1 presents the cross-sectional data for the variables used in the longitudinal regression analysis. In 

the second year of the study, when blood lead was 

initially measured, the average subject was a normotensive, middle-aged man who was overweight. 

Roughly half of all subjects were cigarette smokers, and 

one-quarter of all subjects had a concentration of blood 

lead > 30 ug/100 mL. 

A total of 95 men had blood lead determinations out 

of a total of 314 (30%). Information for six men was 

missing for all covariates. The other 89 men were 

entirely comparable to men without blood lead measurements for all covariates (17). 

We modeled the level of current systolic blood presTable 1. Cross sectional data for blood pressure, age, body mass index, blood lead, 

and smoking for Boston policemen by study year. 

Year 2 Year 3 Year 4 Year 5 

Parameter 

Systolic pressure, mm Hg 

Diastolic pressure, mm Hg 

Age, years 

Body mass index, kg/m2 

Blood lead, ug/100 mL 

<20 

20-29 

>30 

Current smokers, % 

n 

246 

246 

267 

253 

36 

33 

26 

182 

Mean 

126.7 

84.8 

46.6 

27.8 

40.4 

37.1 

22.5 

46.1% 

SD 

20.1 

13.8 

8.5 

3.2 

n 

199 

198 

200 

200 

213 

Mean 

126.6 

83.9 

47.3 

28.1 

47.8% 

SD 

17.6 

10.7 

9.0 

3.3 

« 

149 

167 

192 

189 

206 

Mean 

128.7 

82.7 

48.1 

28.1 

49.9% 

SD 

17.6 

11.3 

8.5 

2.9 

H 

194 

194 

193 

185 

192 

Mean 

130.9 

85.4 

49.3 

28.0 

48.4% 

SD 

17.3 

10.6 

8.1 

3.2 

_ 

L 
P.4 

BLOOD LEAD AND SPECIFIC BLOOD PRESSURE 55 

sure at time t as a function of previous systolic blood 

pressure at f-1, other independent variables known to 

influence blood pressure and blood lead (Table 2). 

Seventy individuals provided 162 pairs of data 

(consecutive examinations) for this regression. There 

was a statistically significant association (p = 0.036) 

between a high (> 30 ^g/100 mL) level of blood lead 

and subsequent systolic blood pressure. Similar 

modeling was performed for diastolic blood pressure, 

but no significant association was found (15). 

To investigate whether the observations noted above 

were the product of a few influential points, an iterative 

cross-validation analysis was undertaken. For systolic 

pressure, three individuals were excluded so that all 

regression coefficients resulting from a cross-validation were within five interquartile range units away 

from either the first or the third quartile. The regression was repeated without the data of these subjects, and 

the results are presented in Table 3. Although the 

association betwen high systolic pressure and high 

blood lead level noted above is only of borderline significance (p = 0.097), the magnitude and direction of 

the observed relationships are essentially unchanged. 

On the other hand, the effect of the other covariates 

(age, body mass index, and smoking) is more consistent with known effects of these variables on blood 

pressure. In summary, the exclusion of influential 

points improved the relationship between systolic 

pressure and the independent variables (prior systolic 

Table 2. Relationship of systolic blood pressure in Boston 

policemen at time t to prior systolic blood pressure, body 

mass index, age, smoking, and blood lead.3 

Variable Coefficient SE 

Intercept 125.383 

Prior blood pressure, 127 mm Hg 0.518 

Body mass index, 28 kg/m2 0.580 

Age, 47 years 0.176 

Smokerb 2.324 

Blood lead, low: 0.224 

Blood lead, highd 5.804 

0.059 

0.343 

0.132 

2.082 

2.251 

2.748 

< 0.001 

0.093 

0.185 

0.266 

0.921 

0.036 

"Regression was done on 162 pairs contributed by 70 subjects. 

b

l - yes; 0 = no. 

<Low = 20-29 ug/100 mL. 

dHigh = > 30 ug/100 mL. 

Table 3. Relationship of systolic blood pressure in Boston 

policemen at time t to independent variables excluding 

influential points." 

Variable Coefficient 

Intercept 125.357 

Prior blood pressure, 127 mm Hg 0.497 

Body mass index, 28 kg/m2 0.655 

Age, 47 years 0.210 

Smokerb 4.446 

Blood lead, low* -1.415 

Blood lead, highd 4.467 

SE 

0.064 

0.330 

0.130 

2.066 

2.233 

2.672 

P 

< 0.001 

0.049 

0.108 

0.033 

0.527 

0.097 

a 

< 

_i 

a 

o 

o 

- 1 CD 

* 

_ l 

cc 

o 

u. 

r-CIE FFI 

UI 

O 

O 

FlGU 

degression was done on 155 pairs contributed by 67 subjects. 

b

l » yes; 0 = no. 

cLow = 20-29 pg/100 mL. 

dHigh = > 30 ug/100 mL 

pressure, body mass index, age, smoking status) and 

did not dramatically change the relationship between 

systolic pressure and high blood lead. This provides 

further support for the observed association of these 

variables. 

In an attempt to estimate the variability (without the 

normality assumption) of the parameter estimates for 

blood lead, bootstrap simulations of the model were 

performed for systolic pressure (Fig. 1). For this purpose, we generated 1000 separate random samples by 

sampling with replacement from the 70 subjects who 

provided data on systolic pressure. Figure 1 suggests 

that the coefficient for high blood lead is greater than 

zero with a mean value of 5.8 (C. I. 90%, 1.5-11.5 mm 

Hg). This bootstrapping simulation confirms the 

association between high blood lead and high systolic 

pressure without the need to assume normality of the 

residuals. 

Discussion 

This longitudinal analysis demonstrates that blood 

lead levels at the upper range of normal are associated 

with mild elevations in systolic blood pressure in 

normotensive working men. The powerful statistical 

techniques used in this analysis have allowed us to 

estimate an effect of blood lead on blood pressure quite 

similar to that observed in large cross-sectional 

surveys (2-4). It is worth noting that our modeling 

approach would have allowed for repeated measurements of blood lead. Greater precision in the measure of 

exposure should have enhanced the statistical power of 

the analysis. 

Selection bias is unlikely to account for the observed 

20 

15 

10 

-5 

-10 

t i . 

• • mmm m • • 

4 •• •• •• 

-10 - 5 0 5 10 15 

COEFFICIENTS FOR HIGH BLOOD LEAD 

20 

FIGURE 1. Plot of 1000 bootstrap coefficients for high and low blood lead 

effect on systolic blood pressure. One dot may represent multiple 

data points. (*) Coefficient for blood lead including all subjects, 

Table 2; (A) coefficient for blood lead excluding influential subjects. 

Table 3. 
P.5 

56 WEISS ETAL. 

relationships, as subjects with blood lead were essentially similar subjects without blood lead (17). In 

addition, no appreciable selective loss to follow-up could 

be observed in this cohort (Table 1). 

In addition to possible bias, the small number of 

subjects could influence the precision of the regression 

coefficients. The cross-validation and subsequent study 

of influential data points (Table 3) provide an estimate 

of the smoking effect more consistent with published 

data than that observed with all the data (Table 2). In 

addition, the dose-response relationship for low and 

high blood lead is more internally consistent when the 

influential data points are excluded (Table 3). 

The influential points were excluded in a blinded 

fashion, i.e., without regard to the magnitude or directionality of their effect on the parameter estimates. 

Although the exclusions do influence statistical significance, the parameter estimates for the effect of high 

blood lead on systolic pressure are similar in both 

analyses (Tables 2 and 3). This suggests that our results 

are not driven by data from a few individuals, an 

important consideration more likely in a small data 

set. 

The bootstrap analysis assesses the statistical precision for the effect of blood lead on blood pressure and 

indicates that the 90% confidence interval for the 

parameter estimate for the effect of high blood lead (i.e., 

> 30 ug/100 mL) on systolic pressure ranges 1.5 to 11 

mm Hg. What remains unclear is the reason for the 

elevation in blood lead in these men. 

These 90% confidence limits encompass all of the 

point estimates from larger cross-sectional surveys. 

Indeed, the estimate of a 5 mm Hg increase in systolic 

blood pressure with high blood lead is almost identical 

to that observed in NHANES with 8000 subjects (3,4). 

Previous investigations by Pocock et al. (15) and 

Shaper et al. (16) have suggested a role for both alcohol 

consumption and cigarette smoking as environmental sources of lead exposure other than drinking 

water. We tested alcohol in our model and could find 

no independent effect of alcohol consumption on blood 

pressure either cross-sectionally or longitudinally. The 

fact that blood lead contributed independently to our 

model (Tables 2 and 3) when cigarette smoking was 

included suggests that blood lead level itself, rather 

than cigarette smoking-induced blood lead elevation, 

has an influence on systolic pressure. 

Recently, calcium intake has been shown to influence blood pressure and blood lead (18). We have no 

information on calcium for this cohort and thus 

could not examine this relationship. 

Clearly, further epidemiologic and physiologic 

work is necessary to elucidate the mechanisms for the 

blood lead-blood pressure relationship. However, there 

seems to be remarkable consistency in the epidemiologic data, suggesting a small but consistent increase 

in systolic blood pressure with elevated blood lead 

levels. 

This work is supported in part by Grant # HL19170 from the 

Division of Lung Diseases, National Heart Lung and Blood Institute. 

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