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Impact of Diet on Blood Pressure and Age-Related Changes in Blood Pressure in the US Population
Analysis of NHANES III
Ihab M. Hajjar, MD, MS;
Clarence E. Grim, MD;
Varghese George, PhD;
Theodore A. Kotchen, MD
Arch Intern Med. 2001;161:589-593.
ABSTRACT
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Background The impact of diet on blood pressure and the age-related changes in
blood pressure have been difficult to detect within one population. We designed
this analysis to study the association of major dietary factors with blood
pressure and with age-related changes in blood pressure in a representative
sample of the US population.
Methods Data were obtained on all individuals 20 years or older (n = 17 030)
surveyed in the Third National Health and Nutrition Examination Survey (NHANES
III), including demographic data, anthropometric data, dietary intake (sodium,
potassium, calcium, magnesium, protein, alcohol, and total energy) based on
24-hour recall, and blood pressure. Multivariate models relating diet to blood
pressure were constructed using stepwise regression, best subset regression,
and multiple regression.
Results Systolic blood pressure was positively associated with higher sodium,
alcohol, and protein intakes (P<.05) and negatively
associated with potassium intake (P = .003). Diastolic
blood pressure was negatively associated with potassium and alcohol intakes
(P<.001). Pulse pressure was positively associated
with sodium, protein, and alcohol intakes (P<.001).
A higher intake of calcium (P = .01) was associated
with a lower rate of rise in systolic blood pressure with age.
Conclusion A diet low in sodium, alcohol, and protein is associated with lower
systolic blood and pulse pressure. Potassium intake was associated with lower
systolic and diastolic blood pressure, whereas alcohol intake was associated
with lower diastolic blood pressure. In addition, the age-related changes
in systolic blood pressure were attenuated by higher calcium and protein intakes.
Magnesium was not associated with any changes in blood pressure.
INTRODUCTION
RESULTS OF the recently reported Dietary Approaches to Stop Hypertension
(DASH) trial document the effects of overall dietary patterns on blood pressure
levels.1 Although many specific dietary factors
have been linked with changes in blood pressure, relatively strong evidence
exists for only a few of them. Higher blood pressure levels have been associated
with high intakes of alcohol, sodium, and protein and low intakes of potassium,
calcium, and magnesium.2, 3, 4, 5, 6, 7, 8, 9, 10, 11
The effects of these nutrients on blood pressure are more apparent in studies
among several populations, as it is difficult to consistently detect the impact
of specific dietary factors on blood pressure or age-related changes in blood
pressure within one population. For example, although a relationship between
sodium consumption and blood pressure has been demonstrated in across-population
studies, at the time this article was written, this was not the case in within-population
studies. An analysis of the first National Health and Nutrition Examination
Survey (NHANES I) data concluded that a high sodium intake was associated
with lower blood pressure.4 The difficulty
of accurately measuring nutrient intakes and their limited range within a
single population contribute to the difficulty of assessing the relationship
between specific dietary factors and blood pressure within a population11, 12
Moreover, few studies have included methodological approaches to adjust
for the effect of the high correlation and interaction among different dietary
factors.13 These studies have usually assessed
the association of isolated dietary factors with blood pressure rather than
assessing multiple factors taken together.14
We designed this analysis of NHANES III to study the associations between
the major dietary factors and systolic (SBP) and diastolic (DBP) blood pressure
and pulse pressure (PP), as well as age-related changes in blood pressure.
METHODS
The NHANES III is a stratified multistage probability sample of the
US population. The sampling methods and the survey protocols have been previously
described.15 Dietary information was obtained
via a 24-hour recall questionnaire, and standardized blood pressure and anthropometric
measurements were taken by a trained observer. Of the 39 695 participants
in NHANES III, our analysis included only those participants 20 years of age
or older (n = 17 030).
Our data set included demographic information (age, sex, and ethnicity),
anthropometric information (weight, height, and body mass index [BMI], as
reported in the survey), and blood pressure (the mean of 3 readings of SBP
and DBP). We selected dietary factors commonly thought to be associated with
blood pressure, including sodium, potassium, calcium, magnesium, protein,
alcohol, and total energy consumed per day. We excluded the other macronutrients
(fat and carbohydrates) because they lacked evidence suggesting an association.
Pulse pressure was calculated by subtracting the DBP from the SBP for each
participant.
Intakes of the different dietary factors evaluated were highly correlated
with each other. A consequence of this intercorrelation (or multicollinearity)
among dietary factors was the inability to attribute an independent effect
of any single nutrient on blood pressure.16, 17
Standardization of individual dietary factors using density measures (either
as an energy ratio obtained by dividing the amount consumed by total energy
per day or a mass ratio obtained by dividing the amount consumed by the BMI
of each participant) decreased this degree of intercorrelation. We elected
to use the density measure for our statistical analysis.
Univariate and multivariate analyses relating consumption of dietary
factors to blood pressure were performed. For the univariate analysis, weighted
linear and polynomial regression models were used.18
Adjustments were made for demographic variables and BMI. To develop the multivariate
model, a random subsample (n = 8529) was selected from the total sample and
used to choose the best set of variables to be included in the final models.
These variables included the demographic variables, BMI, dietary factors using
density measures, and dietary factor interaction variables. Standard stepwise
regression18, 19, 20
and best subset regression19, 21
models were used to select these variables. A principal component analysis
was also performed.16, 17 These
methods minimize the effect of intercorrelation on the model developed.17 The model was then tested on the remainder subsample
(n = 8501) using a multiple-weighted regression model.18
The variable inflation factor (VIF) was also calculated for each variable
to measure the effect of intercorrelation in the final model.22, 23
A VIF of 10 or less indicates a low degree of intercorrelation and, hence,
more reliable results.17 The final model developed
was applied to the overall sample (n = 17 030). Minitab Release 12.22
software (Minitab Inc, State College, Pa) was used for statistical analysis.
RESULTS
The overall sample that satisfied our selection criteria (age  20
years) included 17 030 participants. The mean ± SD age of the
overall sample was 48.8 ± 0.2 years; the population was 47% male. The
ethnic breakdown was 42% white, 28% African American, 26% Hispanic, and 4%
other. The mean ± SD BMI (calculated as weight in kilograms divided
by the square of height in meters) of the sample was 27.1 ± 0.2. The
dietary intake of sodium in the overall sample was high, whereas the daily
potassium, calcium, and magnesium intakes were low in relation to the recommended
dietary guidelines.24, 25 African
Americans consumed more sodium than the other ethnic groups, but less potassium,
calcium, and magnesium (P<.001 for each dietary
factor). Consumption of all dietary factors was higher in men than in women
(P<.001) (Table
1).
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Table 1. Differences in Daily Dietary Factor Consumption by Ethnicity
and Sex*
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There was a linear association between age and SBP and a curvilinear
association between age and both DBP and PP (P<.001
for all 3 associations) (Figure 1).
In the univariate analysis, after adjustments for age, sex, ethnicity, and
BMI, SBP was positively associated with the sodium-potassium ratio (P<.001), alcohol consumption (P<.001),
and total energy consumption (P = .007) and negatively
associated with potassium intake (P<.001). Systolic
blood pressure was not associated with the sodium, magnesium, calcium, or
protein intake. Diastolic blood pressure was negatively associated with calcium
intake (P<.001) and positively associated with
alcohol consumption (P<.001). No other associations
were detected, including total energy consumption. Pulse pressure was negatively
associated with magnesium intake (P<.001) and
positively associated with alcohol consumption (P<.001);
no other associations were found.
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Figure 1. Association between age and systolic
and diastolic blood pressure and pulse pressure in the overall sample.
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When the standardized daily intake per kilocalorie was used to measure
the dietary factors, different associations were found. Lower SBP and DBP
were associated with higher daily intakes of calcium, magnesium, and potassium
per kilocalorie, whereas higher SBP and DBP were associated with a higher
intake of alcohol per kilocalorie (P<.001 for
each association) (Table 2).
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Table 2. Univariate and Multivariate Analyses of Diet and Blood Pressure*
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In the multivariate analysis, higher SBP was associated with a higher
daily intake of sodium, protein, and alcohol (P = .048 for sodium; P < .001 for
protein and alcohol) (Table 2).
The associations of alcohol and protein intake with SBP were less prominent
in women than in men (P = .048; VIF, 1.5, for men; P < .001, VIF, 9.6, for women). Calcium potentiated
the effect of protein on SBP (P = .03; VIF, 9.4),
while potassium blunted that effect (P < .001;
VIF, 9.4) (Table 3). Lower SBP
was associated with a higher potassium intake (P
= .003). Systolic blood pressure was not associated with any other dietary
factors. In the final multivariate model for DBP, lower DBP was only associated
with the higher consumption of potassium and alcohol (P < .001 for both). In the final multivariate model for PP, higher
PP was associated with higher sodium, protein, and alcohol intake (P < .001 for all 3) but not potassium, magnesium, or calcium intake
(Table 2). The association between
alcohol and PP was lower in women (P < .001; VIF,
1.6) (Table 3). Calcium intake
was associated with an attenuated rate of rise in SBP with age. The mean (SD)
rate of increase of SBP with age was 0.65 (0.01) mm Hg per year for the lower
tertile of calcium consumption, 0.58 (0.01) mm Hg for the middle tertile,
and 0.53 (0.02) mm Hg for the upper tertile (P =
.02; VIF, 4.4) (Figure 2).
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Table 3. Interactions Identified in the Final Multivariate Model for
SBP, DBP, and PP*
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Figure 2. Association between systolic blood
pressure and age by daily calcium intake.
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Overall, the final models explained 40.9% of the SBP variability, 16.7%
of the DBP variability, and 40.9% of the PP variability in the sample. Age
alone contributed about 35.3% and diet about 2% of the SBP variability. About
6% of the DBP variability was explained by age and 3% by diet, whereas about
39% of the PP variability was explained by age and 2% by diet.
COMMENT
The statistical approach of this analysis, specifically, the multistep
multivariate analysis and the standardization to the individual's total energy
intake, led to more valid results than those previously reported, and the
models in this analysis were more stable. Consistent with previous results
in other populations,13 we found a high degree
of multicollinearity among the different dietary factors evaluated in NHANES
III. Our approach was able to detect and correct for this prevalent but unnoticed
phenomenon of multicollinearity. By including multiple factors together, evaluating
their interactions, and adjusting for their high degree of interdependence,
we were able to develop models that allowed us to detect specific associations
between dietary factors and blood pressure in addition to identifying patterns
of dietary associations with blood pressure. In particular, we were able to
study the impact of diet on age-related changes in blood pressure by including
age and diet interaction measures in our models.
Our analysis of NHANES III documents that higher sodium, protein, and
alcohol intakes are associated with higher SBP and PP. These associations
with sodium and protein intake were apparent only in the multivariate model,
whereas the associations of alcohol intake with SBP and PP were observed in
both the univariate and multivariate models. Our finding of an association
between sodium intake and SBP is in accordance with animal models, across-population
surveys, and intervention trials.6, 10, 26, 27, 28, 29
Earlier studies provided conflicting results about the association between
protein intake and SBP,30, 31, 32
while our analysis demonstrates a positive association. This association is
blunted by a higher potassium intake and magnified by a higher calcium intake.
Our analysis also adds evidence for a positive association between alcohol
intake and SBP,6, 8, 17, 19, 33, 34, 35
although the converse was true in the univariate analysis, highlighting the
importance of adjusting for covariates. The associations of higher PP and
lower DBP with higher alcohol intake have not been described previously. Furthermore,
our analysis suggests that the associations between alcohol intake and SBP
and PP are more prominent in men than in women. Similarly, the Belgian Interuniversity
study showed that alcohol consumption is associated with higher SBP only in
men.36 In accordance with prior evidence,4, 7, 37, 38, 39, 40
we observed that higher potassium consumption was associated with lower SBP
and DBP. Our study shows the importance of potassium intake for blood pressure
levels in the population.
Until the present study, no relationship between dietary factors and
PP has been reported. Recent evidence suggests that PP is more predictive
of cardiovascular events, especially coronary events, than either SBP or DBP
or even mean arterial pressure.41 The relationship
of PP to cardiovascular events is present even in the absence of an elevated
SBP, and the incidence of all these events is decreased by measures that reduce
PP.41 Our observations suggest that this risk
factor may be favorably modified by reducing dietary intakes of sodium, protein,
and alcohol.
Our results suggest that the age-related changes in blood pressure within
the US population can be modified by dietary manipulations. Higher calcium
and protein intakes attenuated the age-related rise in SBP, whereas higher
sodium, alcohol, and protein intakes increased the rate of the age-related
change in DBP. These observations remain to be confirmed using longitudinal
data.
Consistent with previous studies,11, 37
blood pressure and dietary factors in our sample were associated less strongly
than blood pressure and demographic factors or BMI. Despite this smaller effect,
the impact of diet in the general population is significant, since small changes
in blood pressure in a large population would have a significant effect. In
addition, this magnitude of the effect of specific nutrients on blood pressure
within the general population is not dissimilar from results of clinical trials.2
In summary, this is one of the largest analyses to investigate the association
of diet and blood pressure within a population, especially within older age
groups. The results have important clinical and public health implications.
Since dietary habits are potentially modifiable, the manipulation of diet
could have a significant impact not only on blood pressure levels but also
on the rise in blood pressure with age. More specifically, our results suggest
that a diet low in sodium, protein, and alcohol and rich in potassium and
calcium would affect the blood pressure levels in the general population,
including PP. Furthermore, our study shows that, since the impact of an individual
nutrient on blood pressure is modified by the intake of other nutrients, it
is important to assess the overall diet rather than any single nutrient in
isolation when measuring the impact of diet on blood pressure.
AUTHOR INFORMATION
Accepted for publication September 14, 2000.
From the Division of Geriatrics, Department of Medicine, University
of South Carolina/Palmetto Health Alliance, Columbia (Dr Hajjar), and the
Departments of Medicine (Drs Grim and Kotchen) and Biostatistics (Dr George),
Medical College of Wisconsin, Milwaukee.
Corresponding author: Ihab M. Hajjar, MD, MS, University of South
Carolina/Palmetto Health Alliance, 9 Medical Park Dr, #230, Columbia, SC 29203
(e-mail: ihab.hajjar{at}rmh.edu).
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