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Ludwig DS, Pereira MA, Kroenke CH, et al. Dietary Fiber, Weight Gain, and Cardiovascular Disease Risk Factors in Young Adults. JAMA. 1999;282(16):1539–1546. doi:10.1001/jama.282.16.1539
Context Dietary composition may affect insulin secretion, and high insulin levels,
in turn, may increase the risk for cardiovascular disease (CVD).
Objective To examine the role of fiber consumption and its association with insulin
levels, weight gain, and other CVD risk factors compared with other major
Design and Setting The Coronary Artery Risk Development in Young Adults (CARDIA) Study,
a multicenter population-based cohort study of the change in CVD risk factors
over 10 years (1985-1986 to 1995-1996) in Birmingham, Ala; Chicago, Ill; Minneapolis,
Minn; and Oakland, Calif.
Participants A total of 2909 healthy black and white adults, 18 to 30 years of age
Main Outcome Measures Body weight, insulin levels, and other CVD risk factors at year 10,
adjusted for baseline values.
Results After adjustment for potential confounding factors, dietary fiber showed
linear associations from lowest to highest quintiles of intake with the following:
body weight (whites: 174.8-166.7 lb [78.3-75.0 kg], P<.001;
blacks: 185.6-177.6 lb [83.5-79.9 kg], P = .001),
waist-to-hip ratio (whites: 0.813-0.801, P = .004;
blacks: 0.809-0.799, P = .05), fasting insulin adjusted
for body mass index (whites: 77.8-72.2 pmol/L [11.2-10.4 µU/mL], P = .007;blacks: 92.4-82.6 pmol/L [13.3-11.9 µU/mL], P = .01) and 2-hour postglucose insulin adjusted for body
mass index (whites: 261.1-234.7 pmol/L [37.6-33.8 µU/mL], P = .03; blacks: 370.2-259.7 pmol/L [53.3-37.4 µU/mL], P<.001). Fiber was also associated with blood pressure
and levels of triglyceride, high-density lipoprotein cholesterol, low-density
lipoprotein cholesterol, and fibrinogen; these associations were substantially
attenuated by adjustment for fasting insulin level. In comparison with fiber,
intake of fat, carbohydrate, and protein had inconsistent or weak associations
with all CVD risk factors.
Conclusions Fiber consumption predicted insulin levels, weight gain, and other CVD
risk factors more strongly than did total or saturated fat consumption. High-fiber
diets may protect against obesity and CVD by lowering insulin levels.
The prevalence of cardiovascular disease (CVD), after declining steadily
since mid-century, has been stable or increasing over the past decade.1 Indeed, CVD continues to be the leading cause of death
in the United States.2 Factors known to increase
the risk of CVD include age, obesity, central distribution of body fat, smoking,
physical inactivity, hypertension, dyslipidemias, and abnormalities in blood
clotting factors. Insulin resistance associated with hyperinsulinemia is common
to many of these risk factors.
Because of the critical role of insulin in glucose homeostasis, resistance
to insulin-stimulated glucose uptake usually induces compensatory hyperinsulinemia.
Fasting insulin level is, in fact, an excellent marker for insulin resistance
in population studies.3 While this compensatory
process serves to maintain glucose tolerance, chronic hyperinsulinemia may
increase risk for CVD through a variety of mechanisms.4,5
Obesity, smoking, age, and physical inactivity may cause insulin resistance
and hyperinsulinemia, but together appear to account for, at most, 50% of
the observed individual variability.6 This
finding raises the important question as to what other factors contribute
to hyperinsulinemia and, by implication, CVD.
Diet may affect insulin levels in 3 ways: by modulating insulin secretion,
by affecting insulin action at peripheral sites, or by promoting obesity.
Total or saturated fat intake has been reported to act in each of these ways,
but the magnitude and significance of these effects continue to be debated.7 Moreover, both total and saturated fat intake, as
a percentage of total energy consumption, have been declining since the 1960s,8 although myocardial infarction incidence has been
stable in the past decade.1 Similarly, the
effects of dietary carbohydrate on insulin sensitivity are controversial.9
Several lines of evidence suggest that dietary fiber may play a key
role in the regulation of circulating insulin levels. Dietary fiber reduces
insulin secretion by slowing the rate of nutrient absorption following a meal.10,11 Experimentally, insulin sensitivity
increases12 and body weight decreases13 on high-fiber diets. In addition, epidemiological
analyses have suggested that fiber protects against hypertension, hyperlipidemia,
The purpose of this study was to test the hypothesis that fiber consumption
is independently and inversely associated with insulin levels, weight gain,
and other CVD risk factors among adults, and to compare fiber with fat and
other major dietary components.
The Coronary Artery Risk Development in Young Adults (CARDIA) Study
is a multicenter population-based study of CVD risk factor evolution in young
adults in 4 US areas—Birmingham, Ala; Chicago, Ill; Minneapolis, Minn;
and Oakland, Calif. Stratification was used to obtain nearly equal numbers
of blacks and whites, younger (18-24 years) and older (25-30 years) individuals,
and those with more (>high school) and less (≤high school) education. A
total of 5111 (51% of eligible participants) attended the baseline examination
(year 0) in 1985-1986, with higher participation rates for men, whites, and
highly educated individuals.17 The cohort has
been followed for 10 years to date, with follow-up examinations at years 2
(1987-1988), 5 (1990-1991), 7 (1992-1993), and 10 (1995-1996).
A total of 3609 participants attended the year 0, 7, and 10 clinic examinations
and completed the diet history at year 7. In accord with previous CARDIA articles,
we excluded individuals with extreme values of dietary intake (<3347 and
>33,472 kJ/d [<800 and >8000 kcal/d] for men and <2510 and >25,104 kJ/d
[<600 and >6000 kcal/d] for women; n = 101). We then hierarchically excluded
women who were lactating or pregnant at the baseline examination or within
180 days of the year 10 examination (n = 222), individuals having diabetes
(use of medications to control blood glucose or a fasting blood glucose concentration
of >7.77 mmol/L [140 mg/dL]) at examination years 0 or 10 (n = 64), individuals
taking medication for blood pressure or lipid control (for analyses of fasting
and 2-hour insulin, blood pressure, triglycerides, high-density lipoprotein
cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]; n =
129), and those who had not fasted for at least 8 hours prior to the clinic
visit (for analyses of levels of fasting and 2-hour insulin, triglycerides,
HDL-C, and LDL-C; n = 331). As a result of these exclusions and missing data
(omitted rather than imputed) for covariates or dependent variables, the final
sample size available for these analyses varied from 1801 (2-hour insulin)
to 2909 (body weight).
Most CVD risk factors used as dependent variables here were measured
at all CARDIA examinations. To model risk factors prospectively over the maximum
time period that the cohort has been observed, dependent variables were taken
from the year 10 examination and adjusted for year 0 values (except 2-hour
insulin, which was available at year 10 only, and fibrinogen, which was available
at year 5 only). Diet, as described below, was measured at years 0 and 7.
At the time that year 0 dietary data were collected, the nutrient database
reported only crude fiber values and the overall fiber database was incomplete.
By the time of the year 7 data collection, the database included total dietary
fiber for all entries. For this reason, diet from year 7 was used for the
independent variables for these analyses, reflecting the most accurate dietary
assessment of this population.
The CARDIA diet history is an interviewer-administered quantitative
food frequency questionnaire including approximately 700 foods.18
Sex- and energy-adjusted 1-month test-retest correlations of macronutrients
tended to be lower for blacks (0.27-0.58) than whites (0.54-0.82).19 Validity correlations between mean daily nutrient
intakes from the CARDIA diet history and means from 7 randomly scheduled 24-hour
recalls ranged from 0.50 to 0.86 in white men to 0.04 to 0.53 in black women.
The University of Minnesota Nutrition Coordinating Center nutrient database
was used to estimate nutrient intake (NCC Nutrient Database, Version 20, October
1991, Nutrition Coordinating Center, University of Minnesota, Minneapolis).
Dietary and nutrient measures from the CARDIA diet history used in these analyses
included energy intake (kJ/d), alcohol intake (mL/d), dietary fiber (g/4184
kJ/d, according to the Association of Official Analytical Chemists, United
States Department of Agriculture, Human Nutrition Information Service, Gaithersburg,
Md, 1988), and percentage of daily energy intake from saturated fats, unsaturated
(monounsaturated plus polyunsaturated) fats, carbohydrates (excluding dietary
fiber), and protein.
Prior to each examination, participants were asked to fast for 12 hours
and to avoid smoking and heavy physical activity for 2 hours. Blood pressure
was obtained prior to other clinical procedures with a standardized random-zero
mercury sphygmomanometer (W. A. Baum Co, Copaigue, NY). Body weight (to the
nearest 0.2 kg with a calibrated balance beam scale) and height (to the nearest
0.5 cm with a vertical ruler) were measured with subjects standing and dressed
in light clothing without shoes. Body mass index (BMI) was computed as weight
in kilograms divided by height in meters squared. Waist and hip circumferences
were measured with a tape to the nearest 0.5 cm around the minimal abdominal
girth and the maximal protrusion of the hips at the level of the pubic symphysis,
The insulin radioimmunoassay required an overnight, equilibrium incubation
and used a unique antibody that has less than 0.2% cross-reactivity to human
proinsulin and its primary circulating split form Des 31,32 proinsulin (Linco
Research, St Louis, Mo). Northwest Lipid Research Clinic Laboratory (Seattle,
Wash), which is a participant in the Centers for Disease Control and Prevention
standardization program, was used to measure all lipids. Triglyceride levels
were estimated using enzymatic procedures, and HDL-C levels were measured
according to the method of Warnick et al.20
Low-density lipoprotein cholesterol levels were calculated with the formula
devised by Friedewald et al21 for individuals
with triglyceride concentrations less than 4.52 mmol/L (400 mg/dL). Fibrinogen
analysis was performed at the University of Vermont Thrombosis Center (Burlington)
by clot formation rate using a semiautomated modification of the Clauss method.22 To determine stimulated insulin levels, subjects
drank a carbonated solution containing 75 g of dextrose after an overnight
fast. Blood was obtained 2 hours later and assayed as described above.
We used SAS software (release 6.12, SAS Institute, Cary, NC) for all
statistical analyses. Race-specific general linear models were computed to
adjust least squares means of CVD risk factors (dependent variables) according
to quintiles of dietary factors (independent variables). Quintile cutpoints
for dietary factors were based on distributions of the entire cohort, resulting
in similar levels of intake between blacks and whites within each quintile.
Although the number of blacks and whites were not evenly distributed across
quintiles, no quintile for any dietary factor contained less than 84 individuals
of either race. Dependent variables were body weight; waist-to-hip ratio;
systolic and diastolic blood pressure; HDL-C; LDL-C; fibrinogen; and the natural
logarithms (to generate a near-normal distribution) of fasting insulin, 2-hour
insulin, and triglycerides. To express insulin (pmol/L [µU/mL]) and
triglyceride (mmol/L [mg/dL]) concentrations according to their natural scale,
geometric means were computed by exponentiating the adjusted least squares
means. Covariates included in the models as potential confounders were age,
sex, CARDIA field center, education (high school graduate vs <high school
graduate at year 7), energy intake (kJ/d [kcal/d] at year 7), vitamin supplementation
use (yes/no at year 7), cigarette smoking (classified as never, former, quitter,
starter, or current at years 0 and 10), alcohol intake (mean mL/d at years
0, 7, and 10), and total physical activity (mean units at years 0 and 7).23 Since obesity causes insulin resistance and hyperinsulinemia,
we also adjusted the associations between dietary components and insulin levels
for BMI (mean of years 0 and 10). To examine whether the associations between
dietary components and risk factors may be mediated by insulin level, we adjusted
for fasting insulin in additional models. Joint associations of dietary fiber
and total fat with 10-year weight gain were modeled as the interaction of
the tertile distributions for these dietary components (resulting in 9 least
squares means) with terms for their main effects included in the models.
For presentation, only the lowest and highest quintiles of dietary intake
are shown in the tables; however, graded linear trends were generally observed
across all 5 quintiles for those associations that were statistically significant
(data not shown). A linear trend across quintiles was tested with contrast
statements using orthogonal polynomial coefficients.24
In separate models, interaction terms between sex and the dietary factors
were entered to determine if associations were generally similar between men
and women within each race. Of 120 possible interactions with sex between
the risk factors and the 6 dietary components, only 4 had P values of <.05 (fiber, total fat, and unsaturated fat with fasting
insulin in blacks [all more strongly associated in men (P<.02) than women (P>.18)] and carbohydrate
with LDL-C in whites). Six interactions would be expected by chance alone,
an observation that justifies pooling men and women within race.
Table 1 shows energy- and
sex-adjusted descriptive characteristics of the CARDIA cohort according to
lowest and highest quintiles of the dietary factors. Associations with age
were positive for fiber, inverse for carbohydrate, weak for fat, and null
for protein. Women consumed more fiber and carbohydrate but less protein and
fat (whites only) than men. Cigarette smoking was inversely associated with
carbohydrate and fiber intake and positively associated with fat intake (whites
only). Physical activity was inversely associated with dietary fat, but positively
associated with fiber, carbohydrate, and protein. Alcohol intake showed inverse
associations with dietary carbohydrate and dietary fat. Finally, vitamin supplementation
use was positively associated with dietary fiber, carbohydrate (whites only),
and protein, and inversely associated with dietary fat.
Mean body weight and waist-to-hip ratio at year 10, adjusted for baseline
values, demographic characteristics, and lifestyle behaviors, are shown in Table 2 according to lowest and highest
quintiles of dietary intake. Body weight was inversely associated with fiber
and carbohydrate and positively associated with protein intake in whites.
The mean difference in body weight across quintiles for fiber was considerably
larger (− 3.65 kg [8.1 lb], P<.001) than
for carbohydrate (−1.40 kg [3.1 lb], P = .04)
or protein (+2.03 kg [4.5 lb], P<.001). Neither
total nor saturated fat intake was associated with body weight in whites.
In blacks, dietary fiber was also strongly associated with body weight (−3.60
kg [8.0 lb], P = .001), while total fat (+1.62 kg
[3.6 lb], P = .03) and carbohydrate (−1.58
kg [3.5 lb], P = .03) were more modestly associated.
In models including pairs of dietary components (not shown), fiber remained
independently associated with body weight after adjustment for carbohydrate
(−3.56 kg [7.9 lb], P< .001) or protein
(−3.65 kg [8.1 lb], P<.001) in whites, and
fat (−3.24 kg [7.2 lb], P<.001) or carbohydrate
(−3.33 kg [7.4 lb], P = .004) in blacks. In
contrast, the associations of body weight with carbohydrate in whites (−1.04
kg [2.3 lb], P = .14) and blacks (−0.81 kg
[1.8 lb], P = .184), and with fat in blacks (+0.50
kg [1.1 lb], P = .41) were substantially attenuated
by adjustment for fiber.
We also examined the independent and joint associations of fiber and
fat intake with 10-year weight gain in white and black men and women (Figure 1). At all levels of fat intake, individuals
eating the most fiber gained less weight than those eating the least fiber.
In whites, the largest mean difference was found when comparing those with
the lowest fiber and fat consumption to those with the greatest fiber and
fat consumption (9.59 kg vs 5.72 kg [21.3 vs 12.7 lb], P = .003). Fiber intake was significantly associated with waist-to-hip
ratio in whites (0.813-0.801, P = .004) and blacks
(0.809-0.799, P = .05), although dietary fat was
not (Table 2).
As shown in Table 2, after
adjusting for BMI and other confounding variables, both dietary fiber (mean
difference of −5.6 pmol/L [0.8 µU/mL] from lowest to highest quintiles, P = .007) and saturated fat (+4.2 pmol/L [0.6 µU/mL], P = .05) were associated with fasting insulin in whites.
When included in the same model, fiber remained significantly associated with
fasting insulin (−4.2 pmol/L [0.6 µU/mL], P = .03) whereas fat did not (+1.4 pmol/L [0.2 µU/mL], P = .38) (not shown). In blacks, fiber was the only dietary factor
associated with fasting insulin level (−9.7 pmol/L [1.4 µU/mL], P = .01). Fiber, but neither total nor saturated fat, was
associated with 2-hour insulin in both races (whites: −26.4 pmol/L [3.8
µU/mL], P = .03; blacks: −110.4 pmol/L
[15.9 µU/mL], P<.001).
In white men and women, fiber was associated with systolic blood pressure
(mean difference from lowest to highest quintiles: −2.2 mm Hg, P = .01), diastolic blood pressure (−2.7 mm Hg, P<.001), triglycerides (−0.09 mmol/L [8 mg/dL], P = .05), HDL-C (+0.06 mmol/L [2.5 mg/dL], P = .005), LDL-C (−0.12 mmol/L [4.8 mg/dL], P = .06), and fibrinogen (−0.47 µmol/L [16 mg/dL], P = .005) (Table 3).
With the exception of diastolic blood pressure, these associations were substantially
attenuated after adjustment for fasting insulin (systolic blood pressure: −1.2
mm Hg, P = .16; triglycerides: −0.007 mmol/L
[0.6 mg/dL], P = .93; HDL-C: +0.033 mmol/L [1.3 mg/dL], P = .10; LDL-C: −0.111 mmol/L [4.3 mg/dL], P = .35; and fibrinogen: −0.21 µmol/L [7.2
mg/dL], P = .38). In contrast, no form of dietary
fat was significantly associated with any of these CVD risk factors. Dietary
components generally correlated poorly with blood pressure and levels of lipids
and fibrinogen in blacks; however, the direction of association was the same
for each risk factor in blacks and whites for dietary fiber only.
The prevalence of CVD remains high in the United States2
despite reductions in total and saturated fat intake to levels near government
recommendations.8 Furthermore, the rates of
obesity and type 2 diabetes have increased dramatically, raising serious concern
for public health in the next century. While many factors undoubtedly contribute
to this problem, our study underscores the potential importance of dietary
fiber to CVD risk. These findings are consistent with the Health Professionals'
Follow-up Study in which the modest associations between dietary fat and myocardial
infarction incidence were largely attenuated by adjustment for dietary fiber,25 whereas dietary fiber remained significantly associated
with myocardial infarction incidence even after adjustment for saturated fat.15
We believe that the strong inverse associations between dietary fiber
and multiple CVD risk factors—excessive weight gain, central adiposity,
elevated blood pressure, hypertriglyceridemia, low HDL-C, high LDL-C, and
high fibrinogen—are mediated, at least in part, by insulin levels. Dietary
fiber exerts a major effect on the glycemic, and therefore the insulinemic,
response to carbohydrate in a meal.10,11
Fiber was shown, for example, to account for about 40% of the variance in
glycemic index (a measure of the rate of carbohydrate absorption26)
among 18 starchy foods.27 Due to its inherently
high glycemic index, a low-fiber diet would tend to stimulate relatively more
insulin secretion than a high-fiber diet. In this study, the highest insulin
levels after adjustment for BMI were indeed found among individuals with the
lowest fiber intake.
High circulating insulin levels, in turn, may cause hypertension, dyslipidemia,
abnormalities in blood clotting factors, and perhaps direct vascular injury,4,5 components of the so-called syndrome
X.4 Moreover, a recent meta-analysis of 12
prospective studies concluded that insulin concentration had a positive and
statistically significant association with CVD incidence.28
However, methodological inconsistencies among studies preclude an estimate
of the magnitude of this association. Recently, a nested case-control study
of ischemic heart disease revealed that individuals with fasting insulin concentrations
above the median level had 5.5 times the odds of developing heart disease
than those without elevated insulin levels after controlling for age, lifestyle
factors, BMI, systolic blood pressure, medication use, and family history
of heart disease.29 In this study, fasting
insulin level was found to attenuate the associations between fiber and blood
pressure, lipids, and fibrinogen, which provides support for an intermediary
role of insulin.
The association between dietary fiber and body weight is of particular
interest. The high insulin levels associated with low-fiber diets may promote
excessive weight gain by several mechanisms, involving alterations in adipose
tissue physiology, shunting of metabolic fuels from oxidation to storage,
and increased appetite. For example, prior insulin treatment of normal rats
increased insulin-stimulated glucose utilization in white adipose tissue,
but decreased utilization in muscle. These changes were associated with increased
food intake and weight gain.30 In humans, high
glycemic index meals have been shown to induce a sequence of acute hormonal
changes that diminish availability of metabolic fuels in the postabsorptive
period and cause overeating.31 Indeed, hyperinsulinemia
has been associated with excessive weight gain in some,32,33
but not all,33 prospective epidemiological
studies. Interestingly, white men and women consuming diets presumed to be
lowest in glycemic index (high fiber, high fat) gained the least amount of
weight over 10 years, whereas those consuming diets presumed to be highest
in glycemic index (low fiber, low fat) gained the most during this period.
While fiber may also influence body weight by mechanisms independent
of insulin (ie, the low energy density of fiber promoting satiety and decreased
weight gain), such alternative explanations cannot fully account for our findings.
Were energy density to be of primary importance, fat consumption should be
tightly associated with body weight; consistent with some but not all previous
studies,34 we found no association in whites
and a modest association in blacks that was explained by fiber intake. Moreover,
fiber remained strongly associated with insulin levels after correction for
BMI, suggesting that the higher insulin levels in individuals consuming low-fiber
diets did not result from excessive weight gain alone. Thus, fiber may have
a dual role in the prevention of hyperinsulinemia by decreasing circulating
insulin levels directly and by preventing obesity with its associated insulin
resistance, effects that may be especially important for individuals consuming
low-fat (and therefore high-carbohydrate) diets.
Several methodological issues should be addressed. First, we recognize
that this study, like all observational studies, cannot prove causality. For
example, high-fiber and low-fat diets are typically associated with other
healthful lifestyle patterns. However, the conclusions reached here are likely
to be correct for several reasons: the data have been adjusted for all commonly
accepted, potential confounding variables; dietary fiber is related to CVD
risk by a plausible physiological mechanism; it seems improbable that reverse
causality would apply in the general population (ie, the presence of CVD risk
factors caused lower fiber intake); and these results are consistent with
numerous studies demonstrating improvements in CVD risk factors on either
high-fiber or low glycemic index diets.14,15,35,36
Second, 1 dietary component (eg, fat) could be less precisely or accurately
measured than another, and therefore the relative importance of this component
with respect to CVD risk factors could be underestimated. However, this study
used a standardized, validated diet history designed specifically to quantitate
dietary fat.18 Third, a relatively large spread
was observed in dietary fiber intake among individuals in this study; our
findings would not necessarily apply to other populations with different patterns
of fiber consumption. Fourth, the discrepancies in diet/CVD risk factor associations
between whites and blacks found here probably reflect the lower observed validity
of the CARDIA diet history among blacks,19
although the possibility of actual racial differences remains.
This study did not examine the effects of fiber type (eg, soluble or
insoluble), source (eg, whole grain, refined grain, vegetable, or fruit),
or form (eg, intact or processed). These variables, together with other biologically
active constituents associated with fiber (eg, magnesium, vitamin E), may
affect the insulin response to ingested carbohydrate10,37-39
as well as CVD risk15,25,40
in important ways. It remains to be determined whether improvement in certain
CVD risk factors observed on diets rich in fruits, vegetables, and whole grains
are directly attributable to the high fiber content (and lower glycemic index),
to related properties (eg, antioxidants, phytochemicals), or both.
In summary, dietary fiber was inversely associated with insulin levels,
weight gain, and other risk factors for CVD in young adults. The findings
from this investigation, together with those of the Health Professionals'
Follow-up Study,15,25 raise the
interesting possibility that fiber may play a greater role in determining
CVD risk than total or saturated fat intake. Long-term interventional studies
are needed to examine the effects of high-fiber and low glycemic index diets
in the prevention of obesity and CVD.
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