Content uploaded by Nicola M Mckeown
Author content
All content in this area was uploaded by Nicola M Mckeown on Nov 09, 2015
Content may be subject to copyright.
Carbohydrate Nutrition, Insulin
Resistance, and the Prevalence of the
Metabolic Syndrome in the Framingham
Offspring Cohort
NICOLA M. MCKEOWN,
PHD
1
JAMES B. MEIGS,
MD, MPH
2
SIMIN LIU,
MD, SCD
3
EDWARD SALTZMAN,
MD
1
PETER W.F. WILSON,
MD
4
PAUL F. JACQUES,
SCD
1
OBJECTIVE — The aim of this study was to examine the relation between carbohydrate-
related dietary factors, insulin resistance, and the prevalence of the metabolic syndrome in the
Framingham Offspring Cohort.
RESEARCH DESIGN AND METHODS — We examined cross-sectional associations
between carbohydrate-related dietary factors, insulin resistance, and the prevalence of the met-
abolic syndrome in 2,834 subjects at the fifth examination (1991–1995) of the Framingham
Offspring Study. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated
using the following formula (fasting plasma insulin ⫻ plasma glucose)/22.5. The metabolic
syndrome was defined using the National Cholesterol Education Program criteria.
RESULTS — After adjustment for potential confounding variables, intakes of total dietary
fiber, cereal fiber, fruit fiber, and whole grains were inversely associated, whereas glycemic index
and glycemic load were positively associated with HOMA-IR. The prevalence of the metabolic
syndrome was significantly lower among those in the highest quintile of cereal fiber (odds ratio
[OR] 0.62; 95% CI 0.45–0.86) and whole-grain (0.67; 0.48–0.91) intakes relative to those in
the lowest quintile category after adjustment for confounding lifestyle and dietary factors. Con-
versely, the prevalence of the metabolic syndrome was significantly higher among individuals in
the highest relative to the lowest quintile category of glycemic index (1.41; 1.04 –1.91). Total
carbohydrate, dietary fiber, fruit fiber, vegetable fiber, legume fiber, glycemic load, and refined
grain intakes were not associated with prevalence of the metabolic syndrome.
CONCLUSIONS — Whole-grain intake, largely attributed to the cereal fiber, is inversely
associated with HOMA-IR and a lower prevalence of the metabolic syndrome. Dietary glycemic
index is positively associated with HOMA-IR and prevalence of the metabolic syndrome. Given
that both a high cereal fiber content and lower glycemic index are attributes of whole-grain foods,
recommendation to increase whole-grain intake may reduce the risk of developing the metabolic
syndrome.
Diabetes Care 27:538 –546, 2004
T
ype 2 diabetes is a major cause of
morbidity and mortality in the U.S.
(1), and the prevalence of this dis-
ease continues to rise (2). One subgroup
of the population at increased risk of de-
veloping type 2 diabetes are individuals
with the “metabolic syndrome,” a condi-
tion characterized by disturbed glucose
and insulin metabolism, central obesity,
mild dyslipidemia, and hypertension (3).
Recent estimates indicate that the meta-
bolic syndrome is highly prevalent in the
U.S., with an estimated 24% of the adult
population affected (4). The etiology of
this syndrome is largely unknown, but
presumably represents a complex interac-
tion between genetic, metabolic, and en-
vironmental factors, including diet (5–7).
Whereas aspects of diet have been linked
to individual metabolic features of the
syndrome (8,9), the role of diet in the eti-
ology of the metabolic syndrome is poorly
understood and limited to only a few ob-
servational studies (10,11).
There is conflicting evidence on the
influence of total carbohydrate intake on
insulin sensitivity (12) and in fact, a re-
cent dietary intervention found that after
6 months on a low-carbohydrate, high-fat
diet, insulin sensitivity improved among
obese individuals (13). However, the
source and quality of dietary carbohy-
drates may differentially optimize insulin
action and thereby affect the degree of in-
sulin resistance, which is a key underlying
metabolic feature of this syndrome. Ob-
servational studies have found that fasting
insulin concentrations are lower among
individuals reporting higher dietary fiber
(14–16) or whole-grain intakes (17,18)
after adjustment for other lifestyle and di-
etary factors. The role of high-fiber carbo-
hydrate sources, however, in influencing
insulin sensitivity in randomized feeding
studies is inconsistent. For instance, some
studies report a beneficial effect on insulin
sensitivity with a high consumption of di-
etary fiber (19) or whole-grain foods (20),
whereas others showed no effect on insu-
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
From the
1
Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts
University, Boston, Massachusetts; the
2
General Medicine Division and Department of Medicine, Massachu-
setts General Hospital and Harvard Medical School, Boston, Massachusetts; the
3
Department of Epidemiol-
ogy, Harvard School of Public Health and Division of Preventive Medicine, Brigham and Women’s Hospital
and Harvard Medical School, Boston, Massachusetts; and the
4
Department of Endocrinology, Diabetes, and
Medical Genetics, Medical University of South Carolina, Charleston, South Carolina.
Address correspondence and reprint requests to Paul Jacques, Epidemiology Program, Jean Mayer U.S.
Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA
02111-1524. E-mail: paul.jacques@tufts.edu.
Received for publication 1 August 2003 and accepted in revised form 29 September 2003.
Abbreviations: FFQ, food frequency questionnaire; HOMA-IR, homeostasis model assessment of insulin
resistance.
Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the
authors and do not necessarily reflect the views of the U.S. Department of Agriculture.
A table elsewhere in this issue shows conventional and Syste`me International (SI) units and conversion
factors for many substances.
© 2004 by the American Diabetes Association.
See accompanying editorial, p. 613.
Metabolic Syndrome/Insulin Resistance Syndrome/Pre-Diabetes
ORIGINAL ARTICLE
538 DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004
lin sensitivity (21,22). The glycemic in-
dex, a measure of the glycemic response
to carbohydrate-containing foods, has
been used to physiologically classify di-
etary carbohydrates (23). Evidence from
observational data suggests that a high di-
etary glycemic index is associated with
components of the metabolic syndrome,
such as elevated triglyceride concentra-
tions (9) and low HDL cholesterol
(24,25). Some clinical studies have dem-
onstrated that low glycemic index carbo-
hydrates improve glycemic control and
lipid profiles in individuals with (26,27)
and without type 2 diabetes (28,29). The
glycemic load, a measure of both carbo-
hydrate quality and quantity, has been
linked to increased risk of type 2 diabetes
in some (30,31) but not all observational
studies (32,33). To date, no observational
study has examined the glycemic index
and glycemic load of the diet in relation to
insulin resistance or the metabolic syn-
drome.
Dietary recommendations emphasize
the benefits of high-carbohydrate, low-fat
diets in reducing chronic diseases
(34,35). However, increasing carbohy-
drate intake may adversely affect blood
lipid and lipoprotein concentrations and
glucose metabolism (36,37), predispos-
ing some individuals to develop the
metabolic syndrome. Thereby, under-
standing the association of carbohydrate
nutrition with metabolic syndrome may
provide a strategy for early intervention in
the natural progression of type 2 diabetes.
The purpose of the present study was to
examine the relation between carbohy-
drate-related dietary factors, insulin resis-
tance, and the prevalence of the metabolic
syndrome in the Framingham Offspring
Cohort.
RESEARCH DESIGN AND
METHODS
Study population
The Framingham Offspring Study is a
longitudinal community-based study of
cardiovascular disease among the off-
spring of the original participants of the
Framingham Heart Study Cohort and
their spouses (38). In 1971, 5,135 partic-
ipants were enrolled into the study (39),
and since then, the cohort has been exam-
ined every 3 to 4 years. Between 1991 and
1995, during the fifth examination cycle
of the Framingham Offspring Study,
3,799 participants underwent a standard-
ized medical history and physical exami-
nation. Valid food frequency
questionnaire (FFQ) data were available
for 3,418 participants. Dietary informa-
tion was judged as valid if reported energy
intakes were ⱖ2.51 MJ/day (600 kcal) for
men and women or ⬍16.74 MJ/day
(4,000 kcal/day) for women and ⬍17.57
MJ/day (4,200 kcal/day) for men, respec-
tively, or if fewer than 13 food items were
left blank. Participants were excluded
from these analyses if they were taking
cholesterol-lowering medication (n ⫽
229) or if they had previously diagnosed
diabetes (n ⫽ 122) based on use of insulin
or oral hypoglycemic medication. Fur-
thermore, we excluded participants with
missing covariate information (n ⫽ 112)
and those with missing values for fasting
plasma glucose or insulin concentrations
(n ⫽ 122), reducing the final sample to
2,834 (1,290 men and 1,544 women).
Excluding participants with previously
undiagnosed diabetes (n ⫽ 118) based on
either a fasting blood glucose level (ⱖ7.0
mmol/l) or an oral glucose tolerance test
(2-h postchallenge plasma glucose level
ⱖ11.1 mmol/l) did not alter the findings
of the present study, and therefore these
participants were included in the analy-
ses. The Institutional Review Board for
Human Research at Boston University
and the Human Investigation Research
Committee of New England Medical Cen-
ter approved the protocol.
Dietary data
Usual dietary intake for the previous year
was assessed at the fifth cycle using a
semiquantitative 126-item FFQ (40). The
questionnaires were mailed to the partic-
ipants before the examination, and the
participants were asked to bring the com-
pleted questionnaire with them to their
appointment. The FFQ consisted of a list
of foods with a standard serving size and a
selection of nine frequency categories
ranging from never or ⬍1 serving/month
to ⬎6 servings/day. Participants were
asked to report their frequency of con-
sumption of each food item during the
last year. Separate questions about use of
vitamin and mineral supplements and
type of breakfast cereal most commonly
consumed were also included in the FFQ.
Nutrient intakes were calculated by mul-
tiplying the frequency of consumption of
each unit of food from the FFQ by the
nutrient content of the specified portion.
The relative validity of this FFQ has been
examined in several populations for both
nutrients and foods (40 – 42). Energy-
adjusted intake between the FFQ and
multiple diet records are moderately cor-
related for total carbohydrate and fiber in-
take. In men and women, respectively,
the correlation coefficients were 0.69 and
0.45 for total carbohydrate and 0.64 and
0.58 for fiber (40,41). Dietary exposures
included intakes of total dietary carbohy-
drate, dietary fiber, whole- and refined-
grain foods, glycemic index, and glycemic
load. In addition, the contribution of total
dietary fiber was calculated for each of the
food categories: cereals, fruits, vegetables,
and legumes.
The average dietary glycemic index
value based on a white bread standard
was calculated for each participant. A
food’s glycemic index is defined as the in-
cremental area under the blood glucose
curve induced by a specific carbohydrate-
containing food and is expressed as a per-
centage of the area produced by the same
amount of carbohydrates from a standard
source, either glucose or white bread
(23). Glycemic index values for foods in
the FFQ were obtained either from pub-
lished estimates (27), from direct testing
of food items, or imputed when necessary
by matching similar foods based on calo-
ries, carbohydrate, sucrose, fat, and di-
etary fiber content. In addition for cereals,
whenever possible, the method of pro-
cessing was taken into account.
The dietary glycemic load was calcu-
lated by multiplying the carbohydrate
content of each food by its glycemic in-
dex; this value was then multiplied by the
frequency of consumption and summed
for all food items. Each unit of dietary
glycemic load is the equivalent to1gof
carbohydrate from white bread (9,43). As
an indirect measure of validity, dietary in-
takes of glycemic index and glycemic load
estimated from the FFQ have been related
to triglyceride concentrations (9), a met-
abolic marker known to respond to car-
bohydrate intake.
Laboratory methods
As part of the fifth offspring cohort exam-
inations, blood samples were obtained
from subjects who had fasted for at least
10 h, and the blood samples were stored
at ⫺70°C. Fasting plasma glucose was
measured in fresh specimens with a hex-
okinase reagent kit. Glucose assays were
run in duplicate, and the intra-assay coef-
ficient of variation (CV) was ⬍3%. Fast-
McKeown and Associates
DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004 539
ing plasma insulin levels were determined
using the Coat-A-Count
125
I-radioimmu
-
noassay (Diagnostic Products, Los Ange-
les, CA). This is a polyclonal assay with
cross-reactivity with proinsulin at the
midcurve of 40%. The intra- and interas-
say CV ranged from 5 to 10% for concen-
trations reported here, and the lower limit
of sensitivity was 1.1 U/ml (7.9 pmol/l).
Insulin resistance (IR) was estimated us-
ing the homeostasis model assessment
(HOMA) from fasting glucose and insulin
concentrations (44) using the following
formula: HOMA-IR ⫽ (fasting plasma in-
sulin [U/ml] ⫻ fasting plasma glucose
[mmol/l])/22.5.
The HOMA-IR method has been val-
idated by comparison with results of glu-
cose clamp studies (44) and frequently
sampled intravenous glucose tolerance
tests (45,46). The correlation between
HOMA-IR and fasting insulin was high in
the present study (r ⫽ 0.94).
Lifestyle variables
Height, weight, and waist-to-hip circum-
ferences were measured with the subject
standing. BMI was calculated (kg/m
2
).
Smoking was categorized based on the
number of cigarettes smoked per day
(none, 1–15, 16 –25, ⬎25). Additional
covariate information included age, alco-
hol intake (grams/day), current multivita-
min use (yes/no), treatment for blood
pressure (yes/no), and physical activity
score (47).
Ascertainment of the metabolic
syndrome
Metabolic syndrome was defined as the
presence of three or more of the following
components, as recommended by the
Adult Treatment Panel (48): 1) abdominal
adiposity as defined by a waist circumfer-
ence of ⬎40 inches in men and ⬎35
inches in women; 2) low serum HDL cho-
lesterol (⬍40 mg/dl [1.04 mmol/l] or
⬍50 mg/dl [1.29 mmol/l] in men and
women, respectively); 3) hypertriglyceri-
demia as defined by an elevated triglycer-
ide of ⱖ150 mg/dl (ⱖ1.69 mmol/l); 4)
elevated blood pressure as defined by a
blood pressure of at least 130/85 mmHg;
and 5) abnormal glucose homeostasis as de-
fined by a fasting plasma glucose concentra-
tion of ⱖ6.1 mmol/l (110 mg/dl). In
addition, if individuals reported taking hy-
pertensive medication, they were catego-
rized as having elevated blood pressure.
Statistical methods
Statistical analyses were conducted using
SAS statistical software (version 8; SAS In-
stitute, Cary, NC). Because HOMA-IR lev-
els were positively skewed, analyses were
performed on the natural logarithm trans-
formations. Inverse transformations were
performed to provide geometric mean
HOMA-IR concentrations and their 95%
CI. Baseline characteristics of the partici-
pants were computed across quintile cat-
egories of HOMA-IR. Associations among
continuous variables were assessed by
tests for linear trend using linear regres-
sion, and for categorical variables, the
Mantel-Haenszel
2
test for trend was ap
-
plied. Statistical significance was defined
as a two-tailed P value ⱕ0.05.
To examine the relation between car-
bohydrate nutrition and HOMA-IR, we
compared geometric mean HOMA-IR
across quintile categories of energy-
adjusted carbohydrate, dietary fiber, and
source of fiber intakes, glycemic index,
and glycemic load. We tested each asso-
ciation for age and sex interactions, but no
interactions were statistically significant.
Nutrient intakes were adjusted for total
energy intake by the residual method, as
described by Willett and Stampfer (49).
We used multiple logistic regression to
calculate the odds ratios (ORs) and their
95% CIs for metabolic syndrome with in-
dividuals in the lowest quintile category
of carbohydrate, fiber type, glycemic in-
dex, glycemic load, and grain intakes as
the referent category. OR and mean
HOMA-IR were adjusted for sex, age, cig-
arette dose, total energy intake, alcohol
intake, percentage saturated and polyun-
saturated fat, multivitamin use, and phys-
ical activity. In addition, mean HOMA-IR
was also adjusted for BMI, waist-to-hip
ratio, and treatment for blood pressure.
To assess trends across quintile catego-
ries, we assigned the median intake of
each quintile category to individuals with
intakes in the category and then included
this quintile median variable as a contin-
uous factor in the linear or logistic regres-
sion models. The P for trend was the
resulting P value for the associated linear
or logistic regression coefficient.
Given that obesity is strongly corre-
lated with an individual’s underlying de-
gree of insulin resistance, we tested for
interactions between dietary factor quin-
tile categories and BMI on HOMA-IR by
introducing a multiplicative term for
overweight and the median nutrient in-
take in each model.
RESULTS — The 2,834 participants
(1,290 men and 1,544 women) in this
study ranged in age from 26 to 82 years;
their mean age was 54 ⫾ 9.8 years. The
characteristics of the study population
across quintile categories of HOMA-IR are
presented in Table 1. Higher quintile cat-
egories of HOMA-IR included a greater
proportion of men, older participants,
those with hypertension, glucose intoler-
ances, and undiagnosed diabetes. In ad-
dition, BMI, waist-to-hip ratio, and
concentrations of fasting insulin were all
higher with increasing HOMA-IR. The
prevalence of alcohol use, current smok-
ing, and estrogen replacement therapy
among postmenopausal women de-
creased across quintiles of HOMA-IR,
whereas physical activity remained con-
stant. HOMA-IR clearly captures charac-
teristics of the metabolic syndrome, with
59% of individuals in the highest quintile
category of HOMA-IR with the metabolic
syndrome. Only 3% of those participants
with a BMI ⬍25 kg/m
2
had the metabolic
syndrome compared with 32% of the par-
ticipants with a BMI ⱖ25 kg/m
2
.
The multivariate-adjusted analyses
for intakes of carbohydrates, dietary fiber,
fiber source, glycemic index and load,
and whole and refined grains are shown
in Table 2. After adjustment for potential
confounding variables, intakes of total di-
etary fiber, cereal fiber, fruit fiber, and
whole grains were associated with lower
HOMA-IR. The association between fruit
and cereal fiber and HOMA-IR remained
significant after mutual adjustment for
each other. The association between
whole-grain intake and HOMA-IR was at-
tenuated and no longer remained signifi-
cant after adjustment for cereal (lowest
versus highest quintile, 6.8 vs. 6.7, P ⫽
0.34 for trend) and fruit (6.8 vs.6.6, P ⫽
0.09) fiber. However, cereal fiber re-
mained significantly associated with
HOMA-IR after adjustment for whole
grains (6.9 vs. 6.5, P ⫽ 0.003). As the
glycemic index increased, the multivari-
ate-adjusted HOMA-IR increased from
6.4 in the lowest to 7.0 in the highest
quintile category of glycemic index. A
similar increase in HOMA-IR was ob-
served with increasing dietary glycemic
load, and these associations remained sig-
nificant after further adjustment of the
model for cereal fiber and whole-grain in-
Nutrition/insulin resistance/metabolic syndrome
540 DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004
takes. Furthermore, the associations be-
tween whole-grain and cereal fiber and
HOMA-IR were independent of glycemic
index.
Dietary intakes of total carbohydrate,
refined grains, and fiber from vegetables
and legumes were not associated with im-
proved HOMA-IR. The lack of an associ-
ation between vegetable fiber and
HOMA-IR did not change after excluding
potatoes, a high glycemic index food
source. The findings in Table 2 were es-
sentially identical when analyses were re-
peated using fasting insulin, rather than
the HOMA-IR, as a measure of insulin
resistance.
Given that obesity has a strong effect
on insulin concentrations, obesity may al-
ter the relation between the carbohydrate
source and insulin concentration. When a
continuous interaction term between BMI
and dietary factors was included in the
model, the inverse relation between
HOMA-IR and dietary fiber, cereal fiber,
and whole-grain intake became stronger
as BMI increased (P for interactions
⬍0.05). However, when specific BMI cut
points of 25 and 30 were applied to the
models, a significant interaction was only
found between HOMA-IR and whole-
grain intake (P ⫽ 0.04). The inverse asso-
ciation between whole grain and
HOMA-IR was much stronger for those
with a BMI ⱖ30 kg/m
2
(9.8 vs. 8.6, P ⫽
0.02 for trend) compared with those with
aBMI⬍30 kg/m
2
(6.1 vs. 6.1, P ⫽ 0.57
for trend).
The relation between the prevalence
of metabolic syndrome and intakes of car-
bohydrates, dietary fiber, fiber source,
glycemic index and load, and whole and
refined grains are shown in Table 2. Ce-
real fiber and whole-grain intakes were
significantly inversely associated with the
metabolic syndrome after adjustment for
sex, age, cigarette dose, total energy in-
take, saturated and polyunsaturated fat,
alcohol intake, multivitamin use, and
physical activity. A substantial reduction
in the prevalence odds of metabolic syn-
drome was observed with increasing ce-
real fiber intake and whole-grain intake.
The reduction in odds was 38% (OR 0.62;
95% CI 0.45– 0.86) and 33% (0.67;
0.48– 0.91) for the lowest relative to the
highest category of cereal fiber and whole-
grain intake, respectively. These associa-
tions remained significant after
adjustment for glycemic index. The in-
verse association between whole-grain in-
take and metabolic syndrome was largely
explained by cereal fiber, and a significant
association was no longer observed be-
tween whole-grain intake and the risk of
metabolic syndrome after adjusting for
cereal fiber (0.77; 0.55–1.09; P ⫽ 0.20).
The glycemic index demonstrated a sig-
nificant positive association with preva-
lence of metabolic syndrome with 41%
increased risk in highest compared with
lowest category (1.41; 1.04 –1.91; P ⫽
Table 1—Characteristics of subjects in the Framingham Offspring Cohort across quintile categories of HOMA-IR insulin resistance
HOMA-IR
Quintile categories
P value*⬍4.83 4.84–5.71 5.72–6.79 6.79–8.64 ⬎8.64
Participants (n) 568 565 567 568 565
Characteristics
Women (%) 71 64 51 46 41 ⬍0.0001
Age (years) 52 53 54 55 56 ⬍0.0001
BMI (kg/m
2
)
24.2 25.4 26.6 28.4 31.3 ⬍0.0001
Physical activity score 34.6 34.7 34.8 35.2 34.5 0.74
Alcohol use (%) 78 75 76 73 67 ⬍0.0001
Current cigarette smoking (%) 21 20 19 20 16 0.07
Estrogen replacement therapy (women only) (%) 18 21 18 14 9 ⬍0.001
Normal glucose tolerance (%)† 94 91 90 77 50 ⬍0.0001
Impaired fasting glucose/impaired glucose
tolerance (%)†
68 9 2033⬍0.0001
Undiagnosed diabetes (%)† 011317⬍0.0001
Fasting serum insulin (U/ml) 1.9 4.3 6.8 10.0 19.3 ⬍0.0001
Insulin resistance phenotype
Abnormal waist circumference (%)‡ 13 25 31 50 72 ⬍0.0001
Low HDL cholesterol (%)§ 19 24 35 45 64 ⬍0.0001
Elevated triglycerides (%)㛳 12 19 29 41 61 ⬍0.0001
Elevated fasting glucose (%)¶ 02 2 1138⬍0.0001
Abnormal blood pressure (%)# 10 14 18 29 42 ⬍0.0001
Metabolic syndrome ⱖ3 components (%) 2 7 11 28 59 ⬍0.0001
Data are means unless otherwise indicated. Geometric means are given for levels of fasting insulin. *P values for trend for continuous variables or Mantel-Haenzel
2
for categorical variables across quintiles of HOMA-IR. †Previously undiagnosed diabetes was defined as a fasting plasma glucose concentration ⱖ126 mg/dl (7.0
mmol/l) or a 2-h postchallenge glucose concentration ⱖ200 mg/dl (11.1 mmol/l). Impaired fasting glucose was defined as a fasting plasma glucose concentration
of 110–126 mg/dl (6.1–7.0 mmol/l). Impaired glucose tolerance was defined as a 2-h postchallenge glucose concentration of 140–200 mg/dl (11.1 mmol/l). Normal
glucose tolerance was defined as a fasting glucose concentration of ⬍110 mg/dl (6.1 mmol/l) and a 2-h postchallange glucose concentration of ⬍140 mg/dl (7.8
mmol/l). ‡Waist circumference ⬎40 inches in men and ⬎35 inches in women. §Serum HDL cholesterol ⬍40 mg/dl or ⬍50 mg/dl in men and women, respectively.
㛳Triglyceride level ⱖ150 mg/dl. #Blood pressure of at least 130/85 mmHg or taking hypertensive medication. ¶Fasting plasma glucose concentration ⱖ6.1 mmol/l.
McKeown and Associates
DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004 541
0.04), whereas the glycemic load was not
significantly associated with prevalence of
the metabolic syndrome. The association
between glycemic load and prevalence of
the syndrome did not change after adjust-
ment for cereal fiber.
CONCLUSIONS — Our fi ndings
suggest that higher intakes of whole-grain
foods, dietary fiber, cereal, and fruit fiber
Table 2—Multivariate adjusted geometric mean HOMA-IR and prevalence OR of metabolic syndrome across quintiles of carbohydrate-related
dietary factors
Quintiles of carbohydrate source§
P for trendQuintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5
n 566 567 567 567 567
Total carbohydrate
Median intake (g/day) 179 207 226 244 272
Range of intake (g/day) ⬍194 195–217 218–234 235–257 ⬎258
Mean HOMA-IR* 6.8 (6.5–7.1) 6.7 (6.5–7.0) 6.6 (6.3–6.8) 6.7 (6.5–7.0) 6.9 (6.6–7.2) 0.52
OR IRS† 1.00 0.90 (0.65–1.23) 0.76 (0.53–1.09) 1.03 (0.69–1.52) 0.92 (0.57–1.49) 0.97
Dietary fiber
Median intake (g/day) 11.6 14.9 17.4 20.1 25.5
Range of intake (g/day) ⬍13.5 13.6–16.0 16.1–18.6 18.6–22.1 ⬎22.2
Mean HOMA-IR 7.0 (6.8–7.3) 6.7 (6.5–7.0) 6.7 (6.5–7.0) 6.7 (6.5–7.0) 6.4 (6.1–6.6) ⬍0.001
OR IRS 1.0 0.81 (0.61–1.09) 0.88 (0.65–1.19) 0.81 (0.59–1.07) 0.73 (0.51–1.03) 0.11
Cereal fiber
Median intake (g/day) 2.6 3.7 4.6 5.8 8.0
Range of intake (g/day) ⬍3.1 3.2–4.2 4.3–5.1 5.2–6.7 ⬎6.8
Mean HOMA-IR 6.8 (6.5–7.0) 6.9 (6.7–7.2) 6.8 (6.6–7.0) 6.6 (6.4–6.9) 6.5 (6.3–6.8) 0.02
OR IRS 1.0 0.87 (0.65–1.16) 0.88 (0.66–1.18) 0.74 (0.54–1.00) 0.62 (0.45–0.86) 0.002
Fruit fiber
Median intake (g/day) 0.7 1.7 2.8 4.2 5.8
Range of intake (g/day) ⬍1.2 1.2–2.2 2.2–3.4 3.4–5.1 ⬎5.2
Mean HOMA-IR 7.0 (6.7–7.2) 6.8 (6.5–7.0) 6.8 (6.5–7.0) 6.6 (6.4–6.8) 6.5 (6.2–6.7) ⬍0.001
OR IRS 1.0 1.07 (0.80–1.43) 0.74 (0.55–1.01) 0.89 (0.65–1.21) 0.88 (0.64–1.22) 0.36
Vegetable fiber
Median intake (g/day) 2.4 3.7 4.8 6.1 8.4
Range of intake (g/day) ⬍3.1 3.1–4.2 4.2–5.3 5.3–6.9 ⬎6.9
Mean HOMA-IR 6.7 (6.4–6.9) 6.9 (6.6–7.2) 6.7 (6.4–6.9) 6.8 (6.5–7.0) 6.8 (6.5–7.0) 0.64
OR IRS 1.0 1.08 (0.81–1.45) 1.04 (0.77–1.40) 1.00 (0.74–1.36) 1.15 (0.84–1.57) 0.51
Legume fiber
Median intake (g/day) 0.23 0.69 1.0 1.4 2.5
Range of intake (g/day) ⬍0.5 0.6–0.8 0.8–1.2 1.2–1.8 ⬎1.8
Mean HOMA-IR 6.8 (6.5–7.0) 6.8 (6.6–7.1) 6.8 (6.5–7.0) 6.7 (6.5–6.9) 6.7 (6.5–7.0) 0.58
OR IRS 1.00 0.91 (0.68–1.23) 0.90 (0.67–1.20) 1.00 (0.75–1.34) 0.96 (0.72–1.29) 0.98
Glycemic index‡
Median intake (per day) 72 76 78 81 84
Range of intake (per day) ⬍74 74–77 77–79 79–82 82–98
Mean HOMA-IR 6.4 (6.2–6.7) 6.7 (6.5–7.0) 6.8 (6.5–7.0) 6.8 (6.5–7.0) 7.0 (6.7–7.2) ⬍0.001
OR IRS 1.00 1.17 (0.86–1.59) 1.21 (0.89–1.64) 1.19 (0.88–1.62) 1.41 (1.04–1.91) 0.04
Glycemic load
Median intake (g/day) 131 158.6 174.5 190.8 220.3
Range of intake (g/day) ⬍147.0 147.1–166.3 166.4–182.3 182.3–202.1 ⬎202.2
Mean HOMA-IR 6.7 (6.4–7.0) 6.5 (6.2–6.7) 6.7 (6.5–7.0) 6.8 (6.6–7.1) 7.0 (6.7–7.3) 0.03
OR IRS 1.00 0.74 (0.53–1.02) 0.71 (0.50–1.00) 1.00 (0.69–1.46) 0.82 (0.52–1.27) 0.74
Whole grains
Median intake (serving/week) 0.90 3.5 6.4 9.5 20.4
Range of intake (serving/week) ⬍1.5 1.9–4.4 4.5–7.5 7.9–12.9 ⬎13
Mean HOMA-IR 6.8 (6.6–7.1) 6.9 (6.6–7.1) 6.7 (6.5–7.0) 6.6 (6.4–6.8) 6.6 (6.4–6.9) 0.05
OR IRS 1.0 0.81 (0.60–1.08) 1.09 (0.82–1.44) 0.82 (0.61–1.10) 0.67 (0.48–0.91) 0.01
Refined grains
Median intake (serving/week) 6.9 11.9 16.7 23.7 38.8
Range of intake (serving/week) ⬍9.7 9.7–13.9 14.0–19.8 19.9–29.3 ⬎29.3
Mean HOMA-IR 6.8 (6.6–7.1) 6.6 (6.4–6.9) 6.8 (6.6–7.1) 6.8 (6.5–7.0) 6.7 (6.5–7.0) 0.81
OR IRS 1.0 1.13 (0.84–1.52) 1.01 (0.74–1.38) 1.03 (0.75–1.42) 0.76 (0.53–1.09) 0.05
*Geometric mean HOMA-IR adjusted for sex, age, BMI, waist-to-hip ratio, cigarette dose, total energy intake, alcohol intake, percentage saturated fat, percentage
polyunsaturated fat, multivitamin use, physical activity, and treatment for blood pressure. Results were essentially the same when the analysis was repeated using
fasting insulin rather than the HOMA-IR. †Adjusted for sex, age, cigarette dose, total energy intake, alcohol intake, percentage saturated fat, percentage polyunsat-
urated fat, multivitamin use, and physical activity. ‡Values are based on a white bread standard. §Quintile categories are based on energy-adjusted values using the
residual method, with the exception of whole and refined grains. IRS, insulin resistance syndrome.
Nutrition/insulin resistance/metabolic syndrome
542 DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004
and diets with a lower glycemic index and
glycemic load are associated with lower
insulin resistance as determined using the
HOMA method. Insulin resistance is a
common feature of and a possible con-
tributing factor to the metabolic
syndrome. However, after considering
several aspects of carbohydrate nutrition,
only whole-grain, cereal fiber, and glyce-
mic index intakes were associated with
the prevalence of the metabolic syn-
drome. The prevalence of the metabolic
syndrome was 38 and 33% less in the
highest relative to the lowest categories of
cereal fiber and whole-grain intake, re-
spectively. A high dietary glycemic index
was positively associated with the meta-
bolic syndrome; participants with the
highest glycemic index intakes were
⬃40% more likely to have the metabolic
syndrome than were participants with the
lowest dietary glycemic index. To our
knowledge, this is the first observational
study to examine associations between
different aspects of carbohydrate nutri-
tion and prevalence of the metabolic
syndrome.
Our data confirm other observational
studies that diets rich in whole-grain
foods are associated with lower insulin
concentrations (17,50). One intervention
study further supports the hypothesis that
diets rich in whole-grain foods improve
insulin sensitivity. Pereira et al. (20)
found that insulin sensitivity, as mea-
sured by the euglycemic-hyperinsuline-
mic clamp, improved after 6 weeks on a
whole-grain diet compared with a refined
grain diet, independent of change in body
weight in 11 overweight subjects. Im-
proved insulin sensitivity associated with
high whole-grain diets appear in part to
be attributed to the high dietary or cereal
fiber content of whole-grain foods. Chan-
dalia et al. (51) found that increasing di-
etary fiber intake for 6 weeks reduced
glucose and insulin concentrations in
type 2 diabetic patients. A recent con-
trolled metabolic trial found that supple-
menting a high-carbohydrate diet with
soluble fiber improved blood lipid and li-
poprotein concentrations and improved
glycemic control in pre-diabetic patients
with several metabolic abnormalities that
define the metabolic syndrome (52). In
contrast, other intervention studies have
found no effect on insulin sensitivity with
consumption of high-fiber or whole-grain
foods, particularly among older individu-
als (19,21,22). The interpretation of these
intervention studies is complicated by the
varied patient populations (e.g., obese
nondiabetic subjects, type 2 diabetic sub-
jects), the different age ranges studied,
and the short-term nature of most of these
interventions. Whereas some interven-
tion studies may have missed potential ef-
fects because of the short duration on
diets, it is also not known whether ob-
served effects in these short-term inter-
ventions would remain over time.
In the present study, fiber from cere-
als was inversely related with the preva-
lence of the metabolic syndrome, whereas
fiber from fruit, vegetable, and legumes
was not. Observational data consistently
indicate a greater protective role of fiber
from cereal than from other sources in the
development of type 2 diabetes (30 –
33,53). Adjustment for cereal fiber con-
siderably weakened the associations
between whole-grain intake and both
HOMA-IR and metabolic syndrome, sug-
gesting that the relation of whole grain
may be due in part to cereal fiber or to
factors related to cereal fiber intake. Col-
lectively, these data suggest a greater role
for cereal fiber rather than other fiber
sources in the development of insulin re-
sistance and the metabolic syndrome.
However, further experimental and longi-
tudinal studies are needed to examine if
fiber source is differentially related to
change in metabolic risk factors and the
incidence of the metabolic syndrome.
Magnesium is another component of
whole grains that may improve insulin
sensitivity. Low magnesium status has
been associated with decreased insulin
sensitivity (54), metabolic syndrome
(55), and increased risk of type 2 diabetes
(30–32). Clinical studies further support
a role for magnesium by demonstrating
that supplementation with magnesium
improved insulin sensitivity in type 2 di-
abetic patients (56,57). We previously
found that the relationship between
whole-grain intake and fasting insulin
was mediated, in part, by magnesium
(18).
Although the lack of a formal defini-
tion for the metabolic syndrome previ-
ously hampered investigation into the
role of diet in the etiology of this condi-
tion, observational studies have examined
the role of carbohydrate-related dietary
factors and individual metabolic risk fac-
tors associated with this syndrome. Wir-
falt et al. (8) found that a refined bread
food pattern was associated with hyperin-
sulinemia in women, whereas a high-fiber
bread food pattern was associated with
lower central obesity and dyslipidemia in
men. In the Framingham Offspring Co-
hort, we found that whole-grain intake
was favorably associated with several met-
abolic risk factors of this syndrome,
including central obesity, insulin sensitiv-
ity, and dyslipidemia (18).
We found no evidence for an effect of
total carbohydrate intake on insulin resis-
tance or prevalence of the metabolic syn-
drome. Other observational studies have
found that total carbohydrate intake is
unrelated to fasting insulin (14) and the
risk of developing type 2 diabetes (30 –
32). Because total carbohydrate intake
fails to take into account the glycemic ef-
fect of different carbohydrate foods, the
glycemic index has been proposed to clas-
sify carbohydrate-containing foods. A
high dietary glycemic index was posi-
tively associated with both HOMA-IR and
the prevalence of the metabolic syn-
drome. This is not unexpected given that
high glycemic index foods produce
higher postprandial blood glucose con-
centrations than those with a low glyce-
mic index, which over the long term will
generate a higher insulin demand
(23,27). Two intervention studies have
found that after 4 weeks on a low glyce-
mic index diet, insulin sensitivity was im-
proved in both normal (58) patients and
those with coronary heart disease (59).
More recently, a high glycemic index diet
was associated with increased postpran-
dial insulin resistance among overweight
middle-aged men (60). Although our data
provide evidence that a high glycemic in-
dex diet is associated with a greater risk of
metabolic syndrome, they are insufficient
to examine the potential mechanisms by
which a high glycemic index diet might
affect risk of metabolic syndrome.
In our study, glycemic load was
highly correlated with total carbohydrate
intake (r ⫽ 0.92). Thereby, it is likely that
the inverse association between glycemic
load and HOMA-IR was largely explained
by the glycemic index part of the equa-
tion. Based on regression analyses, Brand-
Miller and Holt (61) reported that
carbohydrate intake alone explained 68%
of variation in glycemic load compared
with 49% explained by the glycemic in-
dex. In the present study, a higher preva-
lence of the metabolic syndrome was
found with a high dietary glycemic index,
but no association was found with the gly-
McKeown and Associates
DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004 543
cemic load. Furthermore, no difference
was found in the association between gly-
cemic load and prevalence to the meta-
bolic syndrome after adjustment for
cereal fiber intake. Stevens et al. (33) re-
ported a marginal significant association
between dietary glycemic load and diabe-
tes risk after adjustment for cereal fiber,
supporting other observational data that
found that diets with a high glycemic load
and a low cereal fiber content increase
risk of type 2 diabetes (30,31).
The FFQ has many limitations with
respect to determining carbohydrate-
related dietary intakes that may have
caused some misclassification of subjects,
in particular with respect to fiber and
whole-grain intake. For example, the as-
sumption that dark breads are largely
made from whole-grain flour would lead
to measurement error, thereby attenuat-
ing associations with cereal fiber and
whole-grain intake. Despite this potential
misclassification, significant associations
among these carbohydrate-related dietary
factors, HOMA-IR, and the metabolic
syndrome were observed. Furthermore,
the FFQ reportedly underestimates re-
fined grain intake compared with diet
records, and this may explain in part the
lack of association between refined grain
intake, insulin resistance, and the meta-
bolic syndrome (62). Interpretation of the
findings from the present study is subject
to some additional caveats. Although the
apparent protective association with
whole-grain and cereal fiber intakes per-
sisted after adjustment for lifestyle and di-
etary factors associated with a healthier
lifestyle, we cannot rule out residual con-
founding. Another potential limitation is
the use of a single measure of plasma in-
sulin and glucose to calculated HOMA-
IR. At the population level, HOMA-IR can
be used as a surrogate measure of insulin
resistance to identify those individuals
who are most insulin resistant. It is per-
haps less useful on an individual basis,
given the modest intraindividual variabil-
ity in insulin and glucose levels. Further-
more, if -cell function is failing (i.e.,
among individuals with late impaired glu-
cose tolerance, early diabetes, or estab-
lished diabetes), true insulin resistance
may be underestimated due to fasting in-
sulin levels that are pathologically low
given ambient glucose levels. However,
the findings of the present study were not
altered after removing those with newly
diagnosed diabetes, and from the outset,
we excluded individuals with established
diabetes. Finally, the cross-sectional na-
ture of this study precludes any causal in-
ferences, therefore, more observational
and experimental studies are needed be-
fore any firm conclusions can be drawn
with regard to the influence of different
aspects of carbohydrate nutrition, insulin
resistance, and the metabolic syndrome.
No specific dietary recommendations
have been advocated by health agencies
for treatment of insulin resistance or the
metabolic syndrome. A high cereal fiber
content and low glycemic index are inher-
ent attributes of most whole-grain foods.
Therefore, in terms of implementing di-
etary change, emphases should be place
on increasing dietary intakes of whole-
grain foods. Given that the metabolic syn-
drome is an identifiable and potentially
modifiable risk state for both type 2 dia-
betes and cardiovascular disease, increas-
ing whole-grain cereal fiber may reduce
the potential untoward effects of carbohy-
drate on risk of these diseases. However,
more longitudinal studies are required to
ascertain which aspects of carbohydrate
nutrition are linked to development of the
metabolic syndrome milieu.
Acknowledgments— This material is based
upon work supported in part with federal
funds from the U.S. Department of Agriculture
Agreement 58-1950-9-001 and the National
Heart, Lung, and Blood Institute’s Framing-
ham Heart Study Contract N01-HC-25195.
Dr. Meigs is supported by a Career Develop-
ment Award from the American Diabetes
Association.
References
1. Centers for Disease Control and Preven-
tion: National Diabetes Fact Sheet: General
Information and National Estimates on Dia-
betes in the United States, 2000. Atlanta,
GA, U.S. Department of Health and Hu-
man Services, Centers for Disease Control
and Prevention, 2002
2. Mokdad AH, Ford ES, Bowman BA, Dietz
WH, Vinicor F, Bales VS, Marks JS: Prev-
alence of obesity, diabetes, and obesity-
related health risk factors, 2001. JAMA
289:76–79, 2003
3. Reaven GM: Banting lecture 1988: role of
insulin resistance in human disease. Dia-
betes 37:1595–1607, 1988
4. Ford ES, Giles WH, Dietz WH: Prevalence
of the metabolic syndrome among U.S.
adults: findings from the Third National
Health and Nutrition Examination Sur-
vey. JAMA 287:356 –359, 2002
5. Groop L: Genetics of the metabolic syn-
drome. Br J Nutr 83 (Suppl. 1):S39 –S48,
2000
6. Lidfeldt J, Nyberg P, Nerbrand C, Samsioe
G, Schersten B, Agardh CD: Socio-demo-
graphic and psychosocial factors are asso-
ciated with features of the metabolic
syndrome: the Women’s Health in the
Lund Area (WHILA) study. Diabetes Obes
Metab 5:106 –112, 2003
7. Wolever TM: Dietary carbohydrates and
insulin action in humans. Br J Nutr 83
(Suppl. 1):S97–S102, 2000
8. Wirfalt E, Hedblad B, Gullberg B, Mattis-
son I, Andren C, Rosander U, Janzon L,
Berglund G: Food patterns and compo-
nents of the metabolic syndrome in men
and women: a cross-sectional study
within the Malmo Diet and Cancer co-
hort. Am J Epidemiol 154:1150 –1159,
2001
9. Liu S, Manson JE, Stampfer MJ, Holmes
MD, Hu FB, Hankinson SE, Willett WC:
Dietary glycemic load assessed by food-
frequency questionnaire in relation to
plasma high-density-lipoprotein choles-
terol and fasting plasma triacylglycerols in
postmenopausal women. Am J Clin Nutr
73:560–566, 2001
10. Pereira MA, Jacobs DR Jr, Van Horn L,
Slattery ML, Kartashov AI, Ludwig DS:
Dairy consumption, obesity, and the in-
sulin resistance syndrome in young
adults: the CARDIA Study. JAMA 287:
2081–2089, 2002
11. Mennen L, Lafay L, Feskens EJ, Novak M,
Lepinary P, Balkau B: Possible protective
effect of bread and dairy products on the
risk of the metabolic syndrome. Nutr Res
20:335–347, 2000
12. Daly ME, Vale CV, Walker M, Alberti KG,
Mathers JC: Dietary carboohydrates and
insulin sensitivity: a review of the evi-
dence and clinical implications. Am J Clin
Nutr 66:1072–1085, 1997
13. Samaha FF, Iqbal N, Seshadri P, Chicano
KL, Daily DA, McGrory J, Williams T,
Williams M, Gracely EJ, Stern L: A low-
carbohydrate as compared with a low fat
diet in severe obesity. N Engl J Med 348:
2074–2081, 2003
14. Ludwig DS, Pereira MA, Kroenke CH,
Hilner JE, Van Horn L, Slattery ML, Ja-
cobs DR Jr: Dietary fiber, weight gain, and
cardiovascular disease risk factors in
young adults. JAMA 282:1539 –1546,
1999
15. Marshall JA, Bessesen DH, Hamman RF:
High saturated fat and low starch and fi-
bre are associated with hyperinsulinaemia
in a non-diabetic population: the San Luis
Valley Diabetes study. Diabetologia
40:430– 438, 1997
16. Feskens EJ, Loeber JG, Kromhout D: Diet
and physical activity as determinants of
hyperinsulinemia: the Zutphen Elderly
Nutrition/insulin resistance/metabolic syndrome
544 DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004
study. Am J Epidemiol 140:350–360,
1994
17. Pereira MA, Jacobs DR, Slattery ML, Ruth
KJ, Van Horn L, Hilner JE, Kushi L: The
association of whole grain intake and fast-
ing insulin in a biracial cohort of young
adults: the CARDIA study. CVD Preven-
tion 1:231–242, 1998
18. McKeown NM, Meigs JB, Liu S, Wilson
PW, Jacques PF: Whole-grain intake is fa-
vorably associated with metabolic risk
factors for type 2 diabetes and cardiovas-
cular disease in the Framingham Off-
spring study. Am J Clin Nutr 76:390–398,
2002
19. Fukagawa NK, Anderson JW, Hageman
G, Young VR, Minaker KL: High-carbohy-
drate, high-fiber diets increase peripheral
insulin sensitivity in healthy young and
old adults. Am J Clin Nutr 52:524 –528,
1990
20. Pereira MA, Jacobs DR Jr, Pins JJ, Raatz
SK, Gross MD, Slavin JL, Seaquist ER: Ef-
fect of whole grains on insulin sensitivity
in overweight hyperinsulinemic adults.
Am J Clin Nutr 75:848 –855, 2002
21. Davy BM, Davy KP, Ho RC, Beske SD,
Davrath LR, Melby CL: High-fiber oat
cereal compared with wheat cereal con-
sumption favorably alters LDL-choles-
terol subclass and particle numbers in
middle-aged and older men. Am J Clin
Nutr 76:351–358, 2002
22. Juntunen KS, Laaksonen DE, Poutanen
KS, Niskanen LK, Mykkanen HM: High-
fiber rye bread and insulin secretion and
sensitivity in healthy postmenopausal
women. Am J Clin Nutr 77:385–391, 2003
23. Jenkins DJ, Wolever TM, Taylor RH,
Barker H, Fielden H, Baldwin JM, Bowl-
ing AC, Newman HC, Jenkins AL, Goff
DV: Glycemic index of foods: a physiolog-
ical basis for carbohydrate exchange. Am J
Clin Nutr 34:362–366, 1981
24. Frost G, Leeds AA, Dore CJ, Madeiros S,
Brading S, Dornhorst A: Glycaemic index
as a determinant of serum HDL-choles-
terol concentration. Lancet 353:1045–
1048, 1999
25. Ford ES, Liu S: Glycemic index and serum
high-density lipoprotein cholesterol con-
centration among us adults. Arch Intern
Med 161:572–576, 2001
26. Jimenez-Cruz A, Bacardi-Gascon M,
Turnbull WH, Rosales-Garay P, Severino-
Lugo I: A flexible, low–glycemic index
Mexican-style diet in overweight and
obese subjects with type 2 diabetes im-
proves metabolic parameters during a
6-week treatment period. Diabetes Care
26:1967–1970, 2003
27. Foster-Powell K, Holt SH, Brand-Miller
JC: International table of glycemic index
and glycemic load values: 2002. Am J Clin
Nutr 76:5–56, 2002
28. Bouche´ C, Rizkalla SW, Luo J, Vidal H,
Veronese A, Pacher N, Fouquet C, Lang V,
Slama G: Five-week, low–glycemic index
diet decreases total fat mass and improves
plasma lipid profile in moderately over-
weight nondiabetic men. Diabetes Care
25:822–828, 2002
29. Behall KM, Scholfield DJ, Yuhaniak I, Ca-
nary J: Diets containing high amylose vs
amylopectin starch: effects on metabolic
variables in human subjects. Am J Clin
Nutr 49:337–344, 1989
30. Salmeron J, Manson JE, Stampfer MJ,
Colditz GA, Wing AL, Willett WC: Di-
etary fiber, glycemic load, and risk of non-
insulin-dependent diabetes mellitus in
women. JAMA 277:472– 477, 1997
31. Salmeron J, Ascherio A, Rimm EB, Colditz
GA, Spiegelman D, Jenkins DJ, Stampfer
MJ, Wing AL, Willett WC: Dietary fiber,
glycemic load, and risk of NIDDM in
men. Diabetes Care 20:545–550, 1997
32. Meyer KA, Kushi LH, Jacobs DR Jr, Slavin
J, Sellers TA, Folsom AR: Carbohydrates,
dietary fiber, and incident type 2 diabetes
in older women. Am J Clin Nutr 71:921–
930, 2000
33. Stevens J, Ahn K, Juhaeri, Houston D, Ste-
ffan L, Couper D: Dietary fiber intake and
glycemic index and incidence of diabetes
in African-American and white adults: the
ARIC study. Diabetes Care 25:1715–
1721, 2002
34. Krauss RM, Deckelbaum RJ, Ernst N,
Fisher E, Howard BV, Knopp RH,
Kotchen T, Lichtenstein AH, McGill HC,
Pearson TA, Prewitt TE, Stone NJ, Horn
LV, Weinberg R: Dietary guidelines for
healthy American adults: a statement for
health professionals from the Nutrition
Committee, American Heart Association.
Circulation 94:1795–1800, 1996
35. Department of Agriculture, Department
of Health and Human Services: Nutrition
and Your Health: Dietary Guidelines for
Americans. Washington, DC, U.S. Govt.
Printing Office, 2000 (Home and Garden
Bulletin no. 232)
36. Jeppesen J, Schaaf P, Jones C, Zhou MY,
Chen YD, Reaven GM: Effects of low-fat,
high-carbohydrate diets on risk factors for
ischemic heart disease in postmenopausal
women. Am J Clin Nutr 65:1027–1033,
1997
37. Mittendorfer B, Sidossis LS: Mechanism
for the increase in plasma triacylglycerol
concentrations after consumption of
short-term, high-carbohydrate diets. Am J
Clin Nutr 73:892– 899, 2001
38. Dawber T, Meadors G, Moore FJ: Epide-
miological approaches to heart disease:
the Framingham Study. Am J Public Health
41:279–286, 1951
39. Feinleib M, Kannel WB, Garrison RJ, Mc-
Namara PM, Castelli WP: The Framing-
ham Offspring study: design and
preliminary data. Prev Med 4:518 –525,
1975
40. Rimm EB, Giovannucci EL, Stampfer MJ,
Colditz GA, Litin LB, Willett WC: Repro-
ducibility and validity of an expanded
self-administered semiquantitative food
frequency questionnaire among male
health professionals. Am J Epidemiol 135:
1114–1136, 1992
41. Hu FB, Rimm E, Smith-Warner SA, Fes-
kanich D, Stampfer MJ, Ascherio A,
Sampson L, Willett WC: Reproducibility
and validity of a semiquantitative food
frequency questionnaire. Am J Epidemiol
122:51–65, 1985
42. Salvini S, Hunter DJ, Sampson L,
Stampfer MJ, Colditz GA, Rosner B, Wil-
lett WC: Food-based validation of a di-
etary questionnaire: the effects of week-
to-week variation in food consumption.
Int J Epidemiol 18:858 –867, 1989
43. Liu S, Willett WC, Stampfer MJ, Hu FB,
Franz M, Sampson L, Hennekens CH,
Manson JE: A prospective study of dietary
glycemic load, carbohydrate intake, and
risk of coronary heart disease in U.S.
women. Am J Clin Nutr 71:1455–1461,
2000
44. Matthews DR, Hosker JP, Rudenski AS,
Naylor BA, Treacher DF, Turner RC: Ho-
meostasis model assessment: insulin re-
sistance and beta-cell function from
fasting plasma glucose and insulin con-
centrations in man. Diabetologia 28:412–
419, 1985
45. Taniguchi A, Nagasaka S, Fukushima M,
Sakai M, Nagata I, Doi K, Tanaka H,
Yoneda M, Tokuyama K, Nakai Y: Assess-
ment of insulin sensitivity: comparison
between simplified evaluations and mini-
mal model analysis (Letter). Diabetes Care
23:1038–1039, 2000
46. Hermans MP, Levy JC, Morris RJ, Turner
RC: Comparison of insulin sensitivity
tests across a range of glucose tolerance
from normal to diabetes. Diabetologia 42:
678– 687, 1999
47. Kannel WB, Sorlie P: Some health benefits
of physical activity: the Framingham
study. Arch Intern Med 139:857–861,
1979
48. Expert Panel on Detection, Evaluation,
and Treatment of High Blood Cholesterol
in Adults: Executive Summary of the
Third Report of the National Cholesterol
Education Program (NCEP) Expert Panel
on Detection, Evaluation, and Treatment
of High Blood Cholesterol in Adults
(Adult Treatment Panel III). JAMA 285:
2486–2497, 2001
49. Willett W, Stampfer MJ: Total energy in-
take: implications for epidemiologic anal-
yses. Am J Epidemiol 124:17–27, 1986
50. Fung TT, Rimm EB, Spiegelman D, Rifai
N, Tofler GH, Willett WC, Hu FB: Asso-
ciation between dietary patterns and
plasma biomarkers of obesity and cardio-
McKeown and Associates
DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004 545
vascular disease risk. Am J Clin Nutr 73:
61–67, 2001
51. Chandalia M, Garg A, Lutjohann D, von
Bergmann K, Grundy SM, Brinkley LJ:
Beneficial effects of high dietary fiber in-
take in patients with type 2 diabetes mel-
litus. N Engl J Med 342:1392–1398, 2000
52. Vuksan V, Sievenpiper JL, Owen R, Swil-
ley JA, Spadafora P, Jenkins DJ, Vidgen E,
Brighenti F, Josse RG, Leiter LA, Xu Z,
Novokmet R: Beneficial effects of viscous
dietary fiber from Konjac-mannan in sub-
jects with the insulin resistance syn-
drome: results of a controlled metabolic
trial. Diabetes Care 23:9 –14, 2000
53. Montonen J, Knekt P, Jarvinen R, Aromaa
A, Reunanen A: Whole-grain and fiber in-
take and the incidence of type 2 diabetes.
Am J Clin Nutr 77:622– 629, 2003
54. Ma J, Folsom AR, Melnick SL, Eckfeldt
JH, Sharrett AR, Nabulsi AA, Hutchinson
RG, Metcalf PA: Associations of serum
and dietary magnesium with cardiovascu-
lar disease, hypertension, diabetes, insu-
lin, and carotid arterial wall thickness: the
ARIC study: Atherosclerosis Risk in Com-
munities Study. J Clin Epidemiol 48:927–
940, 1995
55. Guerrero-Romero F, Rodriguez-Moran
M: Low serum magnesium levels and met-
abolic syndrome. Acta Diabetol 39: 209 –
213, 2002
56. Paolisso G, Sgambato S, Gambardella A,
Pizza G, Tesauro P, Varricchio M,
D’Onofrio F: Daily magnesium supple-
ments improve glucose handling in el-
derly subjects. Am J Clin Nutr 55:1161–
1167, 1992
57. Paolisso G, Sgambato S, Pizza G, Passari-
ello N, Varricchio M, D’Onofrio F: Im-
proved insulin response and action by
chronic magnesium administration in
aged NIDDM subjects. Diabetes Care 12:
265–269, 1989
58. Goff LM, Frost GS, Hamilton G, Thomas
EL, Dhillo WS, Dornhorst A, Bell JD: Car-
bohydrate-induced manipulation of insulin
sensitivity independently of intramyo-
cellular lipids. Br J Nutr 89:365–374, 2003
59. Frost G, Keogh B, Smith D, Akinsanya K,
Leeds A: The effect of low-glycemic car-
bohydrate on insulin and glucose re-
sponse in vivo and in vitro in patients with
coronary heart disease. Metabolism 45:
669– 672, 1996
60. Brynes AE, Edwards CM, Ghatei MA,
Dornhorst A, Morgan LM, Bloom SR,
Frost GS: A randomised four-intervention
crossover study investigating the effect of
carbohydrates on daytime profiles of in-
sulin, glucose, non-esterified fatty acids
and triacylglycerols in middle-aged men.
Br J Nutr 89:207–218, 2003
61. Brand-Miller JC, Holt SH: Glycemic load
values (Letter). Am J Clin Nutr 77:994 –
995, 2003
62. Hu FB, Rimm E, Smith-Warner SA, Fes-
kanich D, Stampfer MJ, Ascherio A,
Sampson L, Willett WC: Reproducibility
and validity of dietary patterns assessed
with a food-frequency questionnaire.
Am J Clin Nutr 69:243–249, 1999
Nutrition/insulin resistance/metabolic syndrome
546 DIABETES CARE, VOLUME 27, NUMBER 2, FEBRUARY 2004
