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Glycemic Index and Serum High-Density Lipoprotein Cholesterol Concentration Among US Adults
Earl S. Ford, MD;
Simin Liu, MD
Arch Intern Med. 2001;161:572-576.
ABSTRACT
| |
Background Dietary glycemic index, an indicator of the ability of the carbohydrate
to raise blood glucose levels, and glycemic load, the product of glycemic
index and carbohydrate intake, have been positively related to risk of coronary
heart disease. However, the relationships between glycemic index and glycemic
load and high-density lipoprotein cholesterol (HDL-C) concentration in the
US population are unknown.
Methods Using data from 13 907 participants aged 20 years and older in
the Third National Health and Nutrition Examination Survey (1988-1994), we
examined the relationships between glycemic index and glycemic load, which
were determined from a food frequency questionnaire and HDL-C concentration.
Results The age-adjusted mean HDL-C concentrations for increasing quintiles
of glycemic index distribution were 1.38, 1.32, 1.30, 1.26, and 1.27 mmol/L
(P<.001 for trend). (To convert millimoles per liter to milligrams
per deciliter, divide by 0.0259.) After additional adjustment for sex, ethnicity,
education, smoking status, body mass index, alcohol intake, physical activity,
energy fraction from carbohydrates and fat, and total energy intake, the mean
HDL-C concentrations for ascending quintiles of glycemic index were 1.36,
1.31, 1.30, 1.27, and 1.28 mmol/L (P<.001 for trend). Adjusting
for the same covariates and considering glycemic index as a continuous variable,
we found a change in HDL-C concentration of -0.06 mmol/L per 15-unit
increase in glycemic index (P<.001). The multiple R2 for the model was 0.23. Similarly, the multivariate-adjusted
mean HDL-C concentrations for ascending quintiles of glycemic load distribution
were 1.35, 1.31, 1.31, 1.30, and 1.26 mmol/L (P<.001 for linear
trend). The inverse relationships between glycemic index and glycemic load
and HDL-C persisted across all subgroups of participants categorized by sex
or body mass index.
Conclusions These findings from a nationally representative sample of US adults
suggest that high dietary glycemic index and high glycemic load are associated
with a lower concentration of plasma HDL-C.
INTRODUCTION
CARDIOVASCULAR disease remains the leading cause of mortality in the
United States, despite notable declines in the cardiovascular-related mortality
rate since the 1960s.1 High-density lipoprotein
cholesterol (HDL-C) is a powerful predictor of the development of coronary
heart disease.2, 3, 4
Various factors are associated with HDL-C concentrations, including sociodemographic
characteristics, physical activity, body mass index (BMI), alcohol use, cigarette
smoking, and diet.2 In metabolic studies, Katan5 showed that increased intake of carbohydrates reduces
HDL-C concentrations. Besides the quantitative relationship between carbohydrate
intake and HDL-C concentrations, the quality of the carbohydrate characterized
by glycemic index may also affect HDL-C concentrations. For example, a recent
study6 reported that glycemic index was the
only dietary variable that was associated with HDL-C concentrations in a national
study of the adult population in England.
The glycemic index of foods reflects their tendency to affect postprandial
glucose and insulin concentrations.7 Thus,
given equal amounts of carbohydrate, food with a high glycemic index leads
to higher postprandial glucose and insulin concentrations than food with a
low glycemic index. Although it has been long known that foods affect glucose
and insulin concentrations differently, the clinical significance of the glycemic
index remains controversial.8 Current dietary
guidelines in the United States do not recommend the use of glycemic index,
although many recommendations are generally consistent with the consumption
of foods with a low glycemic index and avoidance of refined foods with a high
glycemic index.9, 10 Examples of
foods with a lower glycemic index include various legumes, pasta, and minimally
refined products.11 Examples of foods with
a higher glycemic index include potatoes, white breads with refined flour,
and refined grain cereals. Recently, several studies found that the glycemic
index is positively associated with the incidence of type 2 diabetes mellitus12, 13 and cardiovascular disease.14
Because persons consuming diets with a high glycemic index may have
lower HDL-C concentrations than persons consuming a diet with a lower glycemic
index, thus indirectly increasing the risk of coronary heart disease, we examined
data from the Third National Health and Nutrition Examination Survey (NHANES
III) to examine the relationship between glycemic index and HDL-C concentrations
in the US population.15, 16
PARTICIPANTS AND METHODS
Started in 1988 and completed in 1994, NHANES III included a representative
sample of the noninstitutionalized civilian population, which was selected
by using a multistage, stratified sampling design. Persons 60 years and older
and African American and Mexican American persons were oversampled. After
a home interview, participants were invited to attend 1 of 3 examination sessions:
morning, afternoon, or evening. For some participants who were unable to attend
the examination because of health reasons, a blood sample was obtained during
the home interview. Persons who attended the morning session were asked to
fast for 12 hours before the session. Those who attended the afternoon or
evening session were asked to fast for 6 hours.
Serum HDL-C was measured enzymatically (Hitachi 704 Analyzer; Boehringer
Mannheim Diagnostics, Indianapolis, Ind) after precipitation of other lipoproteins
with a manganese chloride–heparin solution. Details about quality-control
procedures have been published elsewhere.16
Values are reported in millimoles per liter; to convert to milligrams per
deciliter, divide by 0.0259.
Dietary variables were created from responses to a food frequency questionnaire
administered to participants to assess their usual diet over the past month.
Respondents were asked how often over the past month they had eaten specified
food items. If the frequency of consumption was reported as never, the value
was recorded as zero. Based on previous work,11, 14
we determined glycemic index values for foods reported on the food frequency
questionnaire. For example, glycemic index values were 102% for potato, 100%
for white bread, 55% for apple, and 13% for broccoli. In particular, using
the method described by Liu,17, 18
we calculated dietary glycemic load by multiplying the carbohydrate content
of each food by its glycemic index value; this value was then multiplied by
the frequency of consumption and summed for all food items.11
Values for the carbohydrate content of each food were obtained from the US
Department of Agriculture food composition tables.19
We calculated the average glycemic index for each participant by dividing
each person's glycemic load scores by their total daily intake of carbohydrates.
Because individual food portion size was not collected in NHANES III, we applied
standard serving sizes according to values published in the US Department
of Agriculture food composition tables.19 Previous
studies20, 21, 22 showed
that additional queries on portion size for participants provided few changes
in participants' ranking according to their relative intake of foods and nutrients.
We included other variables in the analyses: age, sex, race or ethnicity
(white, African American, Mexican American, or other), education (years of
attendance), smoking status (current, former, or never), BMI (calculated as
weight in kilograms divided by the square of height in meters), alcohol consumption
(drinks per month), leisure-time physical activity, energy fraction from protein
and carbohydrates, and total energy intake. Alcohol consumption was determined
from responses to a food frequency questionnaire. For regression models, we
created a physical activity index by summing the products of the frequency
of participation in 1 of 9 specific activities or up to 4 additional self-reported
activities by the metabolic equivalent level for each reported activity. The
energy fraction from protein and carbohydrates and the total energy intake
were calculated from a single 24-hour dietary recall.
We limited the analyses to participants 20 years or older who attended
the medical examination and included 13 907 participants (6825 men and
7082 women) with complete information in our analyses. We treated glycemic
index, carbohydrate intake, and glycemic load as either continuous or categorical
variables. Means or percentages for HDL-C concentration and baseline characteristics
were calculated for quintiles of glycemic index and glycemic load. To examine
the significance of means or percentages of these variables by quintiles of
glycemic index and glycemic load, we performed tests for linear trend. Least
squares–adjusted means of HDL-C concentration were calculated using
analysis of covariance. Tests for linear trend were performed using regression
analysis by assigning the median values of glycemic index or glycemic load
for each of the quintiles. All analyses were done using computer software
(version 7.5, Software for the Statistical Analysis of Correlated Data [SUDAAN];
Research Triangle Park, NC) to obtain proper variance estimates because of
the complex sampling design.
RESULTS
For glycemic index, the rounded quintiles were 75% or less, 76% to 79%,
80% to 83%, 84% to 87%, and 88% or higher. For glycemic load, the quintiles
were 98 or less, 99 to 127, 128 to 157, 158 to 198, and 199 or higher. As
the glycemic index increased, decreasing trends were noted for age, white
race, education, and BMI (Table 1).
The percentage of men, current smokers, and physically inactive participants
was directly associated with increasing glycemic index. Except for education,
current smoking, and physical inactivity, the relationships between glycemic
load and the other variables were similar to those described for glycemic
index (Table 2).
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Table 1. Unadjusted Means and Percentages of Selected Covariates by
Quintiles of Glycemic Index*
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Table 2. Unadjusted Means and Percentages of Selected Covariates by
Quintiles of Glycemic Load*
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As glycemic index increased, the unadjusted mean serum HDL-C concentration
decreased 8%, from 1.38 to 1.27 mmol/L (Table 3). After adjustment for age, sex, race or ethnicity, education,
smoking status, BMI, alcohol intake, physical activity, energy fraction from
protein and carbohydrates (quintiles), and total energy intake (quintiles),
this relationship changed little. Eliminating participants with glucose concentrations
of 7 mmol/L or higher or those with previously diagnosed diabetes mellitus
did not change our results materially. The multiply adjusted HDL-C concentrations
were 1.37, 1.33, 1.32, 1.29, and 1.29 mmol/L for quintiles 1 through 5 of
glycemic index, respectively (P<.001 for trend).
The decrease in HDL-C concentration with increasing glycemic index was more
pronounced among men (unadjusted 7% decrease; adjusted 8% decrease) than women
(unadjusted 5% decrease; adjusted 2% decrease). The decrease in percentage
of HDL-C concentration was similar for participants with a BMI less than 25
and 25 or higher. Adjusting for the same covariates and considering glycemic
index as a continuous variable, we found a change in HDL-C concentration of -0.06
mmol/L per 15-unit increase in glycemic index (P<.001).
The multiple R2 for the model was 0.23.
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Table 3. Mean Concentrations of High-Density Lipoprotein Cholesterol
(HDL-C) According to Quintiles of Glycemic Index*
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The pattern of decreases in HDL-C concentration with increases in glycemic
load were similar to those described for glycemic index except that the sex
differences noted for glycemic index were less pronounced (Table 4). The multiple R2 for
the model was 0.20. Eliminating participants with glucose concentrations of
7 mmol/L or higher or those with previously diagnosed diabetes mellitus did
not change our results materially. The multiply adjusted HDL-C concentrations
were 1.36, 1.31, 1.32, 1.31, and 1.29 mmol/L for quintiles 1 through 5 of
glycemic load, respectively (P = .007 for trend).
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Table 4. Mean Concentrations of High-Density Lipoprotein Cholesterol
(HDL-C) According to Quintiles of Glycemic Load*
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COMMENT
In this representative sample of US adults, we found that a lower quality
of carbohydrate intake as characterized by a high glycemic index was associated
with a lower HDL-C concentration, independent of factors known to be associated
with HDL-C concentrations and the amount of carbohydrate intake. Our results
are consistent with findings from a survey conducted by Frost et al6 in a national sample of adults in England. They recently
reported that glycemic index and HDL-C concentration were inversely related
among 1420 British participants aged 18 to 64 years in a cross-sectional study.
The percentage decrease in HDL-C concentration among men in their study was
similar to that observed in our study. They found a stronger inverse relationship
between glycemic index and HDL-C concentration among women than men, however.
These differences are likely due to differences in the demographic compositions
of the 2 samples and to the use of different dietary instruments.
Because our results were derived from a cross-sectional study, we cannot
conclude that changes in the glycemic index and glycemic load can cause changes
in HDL-C concentrations. The food frequency questionnaire that we used was
an abbreviated one and did not include questions about portion size, which
inevitably led to some misclassification of participants' dietary intake.
Consequently, we probably underestimated the magnitude of the decreases in
HDL-C concentration with increases in glycemic index or glycemic load. We
may have failed to account for relevant confounders or incompletely adjusted
for selected confounders. Finally, the apparent association between glycemic
index or glycemic load and HDL-C concentration may be due to another nutrient.
Although it has long been known that diet can affect circulating concentrations
of HDL-C, much remains to be learned about how specific aspects of diet can
affect HDL-C concentrations. Thus, the recent research that showed that the
quality of the dietary carbohydrate composition can affect HDL-C concentrations
independent of the quantity of carbohydrate intake suggests a new pathway
through which a diet with a low glycemic index may lower the risk of coronary
heart disease. A summary of observational studies3
found that for every decrease of about 0.026 mmol/L in HDL-C concentration,
the risk of coronary heart disease increased by 1.9% to 2.3% in men and 3.2%
in women. Thus, an increase in the adjusted HDL-C concentration of about 8%
in men and 2% in women could theoretically reduce coronary heart disease by
about 8% in men and 3.5% in women. In the United States in 1996, about 242 036
men and 234 088 women died of coronary heart disease, and about 1.1 million
Americans were estimated to have had myocardial infarction or fatal coronary
heart disease.23 Therefore, the potential reductions
in coronary heart disease events and mortality due to changes in HDL-C concentrations,
such as those we found, are not trivial.
Although metabolic studies in patients with diabetes mellitus or hyperlipidemia
have shown lower total cholesterol concentrations when carbohydrates with
a low glycemic index are substituted for those with a high glycemic index,
findings on HDL-C concentrations have been less consistent.24, 25, 26, 27, 28
Therefore, the recent findings in 2 national surveys in the United States
in the present study and England6 need to be
confirmed by prospective studies or clinical metabolic trials of healthy persons.
Because of the long-standing controversy about the potential etiologic role
of glycemic index in the pathogenesis of several chronic diseases and the
short-term nature of metabolic data, data from large prospective cohorts that
directly relate dietary carbohydrates, glycemic index, and glycemic load to
the subsequent development of type 2 diabetes mellitus and coronary heart
disease will be most informative. In the final analysis, the ultimate criterion
by which to judge the clinical utility of the glycemic index or that of any
other classification schemes of foods is its ability to predict disease outcomes.
If dietary carbohydrate composition influences HDL-C concentrations, recommendations
about the best dietary sources of carbohydrates may need to be refined. In
light of recent data suggesting that dietary carbohydrate composition may
affect the risk of type 2 diabetes mellitus and coronary heart disease, it
may be prudent to consider substituting foods with a low glycemic index for
ones with a high glycemic index as a means of lowering the risk of developing
these conditions.
AUTHOR INFORMATION
Accepted for publication July 28, 2000.
From the Division of Nutrition and Physical Activity, National Center
for Chronic Disease Prevention and Health Promotion, Centers for Disease Control
and Prevention, Atlanta, Ga (Dr Ford); and the Division of Preventive Medicine,
Harvard Medical School, Boston, Mass (Dr Liu).
Corresponding author and reprints: Earl S. Ford, MD, Division of
Nutrition and Physical Activity, National Center for Chronic Disease Prevention
and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford
Hwy, MS K26, Atlanta, GA 30341 (e-mail: esf2{at}cdc.gov).
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