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McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PWF, Jacques PF. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. Diabetes Care 27, 538-546



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. We examined cross-sectional associations between carbohydrate-related dietary factors, insulin resistance, and the prevalence of the metabolic 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 x plasma glucose)/22.5. The metabolic syndrome was defined using the National Cholesterol Education Program criteria. 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. Conversely, 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. 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.
Carbohydrate Nutrition, Insulin
Resistance, and the Prevalence of the
Metabolic Syndrome in the Framingham
Offspring Cohort
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; 0.480.91) 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
Diabetes Care 27:538 –546, 2004
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
Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts
University, Boston, Massachusetts; the
General Medicine Division and Department of Medicine, Massachu-
setts General Hospital and Harvard Medical School, Boston, Massachusetts; the
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
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:
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
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
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 proles 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-
Dietary recommendations emphasize
the benets 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
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 fth 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 2.51 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 nal 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 (7.0
mmol/l) or an oral glucose tolerance test
(2-h postchallenge plasma glucose level
11.1 mmol/l) did not alter the ndings
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 fth 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 specied 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 ber in-
take. In men and women, respectively,
the correlation coefcients were 0.69 and
0.45 for total carbohydrate and 0.64 and
0.58 for ber (40,41). Dietary exposures
included intakes of total dietary carbohy-
drate, dietary ber, whole- and rened-
grain foods, glycemic index, and glycemic
load. In addition, the contribution of total
dietary ber 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
foods glycemic index is dened as the in-
cremental area under the blood glucose
curve induced by a specic 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 ber 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 fth 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-
cient of variation (CV) was 3%. Fast-
McKeown and Associates
ing plasma insulin levels were determined
using the Coat-A-Count
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
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
Smoking was categorized based on the
number of cigarettes smoked per day
(none, 115, 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
Metabolic syndrome was dened as the
presence of three or more of the following
components, as recommended by the
Adult Treatment Panel (48): 1) abdominal
adiposity as dened 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 dened by an elevated triglycer-
ide of 150 mg/dl (1.69 mmol/l); 4)
elevated blood pressure as dened by a
blood pressure of at least 130/85 mmHg;
and 5) abnormal glucose homeostasis as de-
ned by a fasting plasma glucose concentra-
tion of 6.1 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
test for trend was ap
plied. Statistical signicance was dened
as a two-tailed P value 0.05.
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 ber, and
source of ber intakes, glycemic index,
and glycemic load. We tested each asso-
ciation for age and sex interactions, but no
interactions were statistically signicant.
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, ber 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 coefcient.
Given that obesity is strongly corre-
lated with an individuals 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
had the metabolic
syndrome compared with 32% of the par-
ticipants with a BMI 25 kg/m
The multivariate-adjusted analyses
for intakes of carbohydrates, dietary ber,
ber source, glycemic index and load,
and whole and rened grains are shown
in Table 2. After adjustment for potential
confounding variables, intakes of total di-
etary ber, cereal ber, fruit ber, and
whole grains were associated with lower
HOMA-IR. The association between fruit
and cereal ber and HOMA-IR remained
signicant after mutual adjustment for
each other. The association between
whole-grain intake and HOMA-IR was at-
tenuated and no longer remained signi-
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) ber. However, cereal ber re-
mained signicantly 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-
nicant after further adjustment of the
model for cereal ber and whole-grain in-
Nutrition/insulin resistance/metabolic syndrome
takes. Furthermore, the associations be-
tween whole-grain and cereal ber and
HOMA-IR were independent of glycemic
Dietary intakes of total carbohydrate,
rened grains, and ber from vegetables
and legumes were not associated with im-
proved HOMA-IR. The lack of an associ-
ation between vegetable ber and
HOMA-IR did not change after excluding
potatoes, a high glycemic index food
source. The ndings 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
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 ber, cereal ber,
and whole-grain intake became stronger
as BMI increased (P for interactions
0.05). However, when specic BMI cut
points of 25 and 30 were applied to the
models, a signicant 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 30 kg/m
(9.8 vs. 8.6, P
0.02 for trend) compared with those with
aBMI30 kg/m
(6.1 vs. 6.1, P 0.57
for trend).
The relation between the prevalence
of metabolic syndrome and intakes of car-
bohydrates, dietary ber, ber source,
glycemic index and load, and whole and
rened grains are shown in Table 2. Ce-
real ber and whole-grain intakes were
signicantly 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 ber 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 ber and whole-
grain intake, respectively. These associa-
tions remained signicant after
adjustment for glycemic index. The in-
verse association between whole-grain in-
take and metabolic syndrome was largely
explained by cereal ber, and a signicant
association was no longer observed be-
tween whole-grain intake and the risk of
metabolic syndrome after adjusting for
cereal ber (0.77; 0.551.09; P 0.20).
The glycemic index demonstrated a sig-
nicant 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 1Characteristics of subjects in the Framingham Offspring Cohort across quintile categories of HOMA-IR insulin resistance
Quintile categories
P value*4.83 4.845.71 5.726.79 6.798.64 8.64
Participants (n) 568 565 567 568 565
Women (%) 71 64 51 46 41 0.0001
Age (years) 52 53 54 55 56 0.0001
BMI (kg/m
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 20330.0001
Undiagnosed diabetes (%) 0113170.0001
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 (%) 12 19 29 41 61 0.0001
Elevated fasting glucose (%) 02 2 11380.0001
Abnormal blood pressure (%)# 10 14 18 29 42 0.0001
Metabolic syndrome 3 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
for categorical variables across quintiles of HOMA-IR. Previously undiagnosed diabetes was dened as a fasting plasma glucose concentration 126 mg/dl (7.0
mmol/l) or a 2-h postchallenge glucose concentration 200 mg/dl (11.1 mmol/l). Impaired fasting glucose was dened as a fasting plasma glucose concentration
of 110126 mg/dl (6.17.0 mmol/l). Impaired glucose tolerance was dened as a 2-h postchallenge glucose concentration of 140200 mg/dl (11.1 mmol/l). Normal
glucose tolerance was dened 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.
Triglyceride level 150 mg/dl. #Blood pressure of at least 130/85 mmHg or taking hypertensive medication. Fasting plasma glucose concentration 6.1 mmol/l.
McKeown and Associates
0.04), whereas the glycemic load was not
signicantly 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 ber.
suggest that higher intakes of whole-grain
foods, dietary ber, cereal, and fruit ber
Table 2Multivariate 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 195217 218234 235257 258
Mean HOMA-IR* 6.8 (6.57.1) 6.7 (6.57.0) 6.6 (6.36.8) 6.7 (6.57.0) 6.9 (6.67.2) 0.52
OR IRS 1.00 0.90 (0.651.23) 0.76 (0.531.09) 1.03 (0.691.52) 0.92 (0.571.49) 0.97
Dietary ber
Median intake (g/day) 11.6 14.9 17.4 20.1 25.5
Range of intake (g/day) 13.5 13.616.0 16.118.6 18.622.1 22.2
Mean HOMA-IR 7.0 (6.87.3) 6.7 (6.57.0) 6.7 (6.57.0) 6.7 (6.57.0) 6.4 (6.16.6) 0.001
OR IRS 1.0 0.81 (0.611.09) 0.88 (0.651.19) 0.81 (0.591.07) 0.73 (0.511.03) 0.11
Cereal ber
Median intake (g/day) 2.6 3.7 4.6 5.8 8.0
Range of intake (g/day) 3.1 3.24.2 4.35.1 5.26.7 6.8
Mean HOMA-IR 6.8 (6.57.0) 6.9 (6.77.2) 6.8 (6.67.0) 6.6 (6.46.9) 6.5 (6.36.8) 0.02
OR IRS 1.0 0.87 (0.651.16) 0.88 (0.661.18) 0.74 (0.541.00) 0.62 (0.450.86) 0.002
Fruit ber
Median intake (g/day) 0.7 1.7 2.8 4.2 5.8
Range of intake (g/day) 1.2 1.22.2 2.23.4 3.45.1 5.2
Mean HOMA-IR 7.0 (6.77.2) 6.8 (6.57.0) 6.8 (6.57.0) 6.6 (6.46.8) 6.5 (6.26.7) 0.001
OR IRS 1.0 1.07 (0.801.43) 0.74 (0.551.01) 0.89 (0.651.21) 0.88 (0.641.22) 0.36
Vegetable ber
Median intake (g/day) 2.4 3.7 4.8 6.1 8.4
Range of intake (g/day) 3.1 3.14.2 4.25.3 5.36.9 6.9
Mean HOMA-IR 6.7 (6.46.9) 6.9 (6.67.2) 6.7 (6.46.9) 6.8 (6.57.0) 6.8 (6.57.0) 0.64
OR IRS 1.0 1.08 (0.811.45) 1.04 (0.771.40) 1.00 (0.741.36) 1.15 (0.841.57) 0.51
Legume ber
Median intake (g/day) 0.23 0.69 1.0 1.4 2.5
Range of intake (g/day) 0.5 0.60.8 0.81.2 1.21.8 1.8
Mean HOMA-IR 6.8 (6.57.0) 6.8 (6.67.1) 6.8 (6.57.0) 6.7 (6.56.9) 6.7 (6.57.0) 0.58
OR IRS 1.00 0.91 (0.681.23) 0.90 (0.671.20) 1.00 (0.751.34) 0.96 (0.721.29) 0.98
Glycemic index
Median intake (per day) 72 76 78 81 84
Range of intake (per day) 74 7477 7779 7982 8298
Mean HOMA-IR 6.4 (6.26.7) 6.7 (6.57.0) 6.8 (6.57.0) 6.8 (6.57.0) 7.0 (6.77.2) 0.001
OR IRS 1.00 1.17 (0.861.59) 1.21 (0.891.64) 1.19 (0.881.62) 1.41 (1.041.91) 0.04
Glycemic load
Median intake (g/day) 131 158.6 174.5 190.8 220.3
Range of intake (g/day) 147.0 147.1166.3 166.4182.3 182.3202.1 202.2
Mean HOMA-IR 6.7 (6.47.0) 6.5 (6.26.7) 6.7 (6.57.0) 6.8 (6.67.1) 7.0 (6.77.3) 0.03
OR IRS 1.00 0.74 (0.531.02) 0.71 (0.501.00) 1.00 (0.691.46) 0.82 (0.521.27) 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 1.94.4 4.57.5 7.912.9 13
Mean HOMA-IR 6.8 (6.67.1) 6.9 (6.67.1) 6.7 (6.57.0) 6.6 (6.46.8) 6.6 (6.46.9) 0.05
OR IRS 1.0 0.81 (0.601.08) 1.09 (0.821.44) 0.82 (0.611.10) 0.67 (0.480.91) 0.01
Rened grains
Median intake (serving/week) 6.9 11.9 16.7 23.7 38.8
Range of intake (serving/week) 9.7 9.713.9 14.019.8 19.929.3 29.3
Mean HOMA-IR 6.8 (6.67.1) 6.6 (6.46.9) 6.8 (6.67.1) 6.8 (6.57.0) 6.7 (6.57.0) 0.81
OR IRS 1.0 1.13 (0.841.52) 1.01 (0.741.38) 1.03 (0.751.42) 0.76 (0.531.09) 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 rened grains. IRS, insulin resistance syndrome.
Nutrition/insulin resistance/metabolic syndrome
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 ber, 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 ber 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 rst observational
study to examine associations between
different aspects of carbohydrate nutri-
tion and prevalence of the metabolic
Our data conrm 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 rened
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
ber content of whole-grain foods. Chan-
dalia et al. (51) found that increasing di-
etary ber 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 ber improved blood lipid and li-
poprotein concentrations and improved
glycemic control in pre-diabetic patients
with several metabolic abnormalities that
dene the metabolic syndrome (52). In
contrast, other intervention studies have
found no effect on insulin sensitivity with
consumption of high-ber 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, ber from cere-
als was inversely related with the preva-
lence of the metabolic syndrome, whereas
ber from fruit, vegetable, and legumes
was not. Observational data consistently
indicate a greater protective role of ber
from cereal than from other sources in the
development of type 2 diabetes (30
33,53). Adjustment for cereal ber 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 ber or to
factors related to cereal ber intake. Col-
lectively, these data suggest a greater role
for cereal ber rather than other ber
sources in the development of insulin re-
sistance and the metabolic syndrome.
However, further experimental and longi-
tudinal studies are needed to examine if
ber 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
(3032). 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
Although the lack of a formal deni-
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 rened bread
food pattern was associated with hyperin-
sulinemia in women, whereas a high-ber
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 insufcient
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
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 ber intake. Stevens et al. (33) re-
ported a marginal signicant association
between dietary glycemic load and diabe-
tes risk after adjustment for cereal ber,
supporting other observational data that
found that diets with a high glycemic load
and a low cereal ber 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 misclassication of subjects,
in particular with respect to ber and
whole-grain intake. For example, the as-
sumption that dark breads are largely
made from whole-grain our would lead
to measurement error, thereby attenuat-
ing associations with cereal ber and
whole-grain intake. Despite this potential
misclassication, signicant associations
among these carbohydrate-related dietary
factors, HOMA-IR, and the metabolic
syndrome were observed. Furthermore,
the FFQ reportedly underestimates re-
ned grain intake compared with diet
records, and this may explain in part the
lack of association between rened grain
intake, insulin resistance, and the meta-
bolic syndrome (62). Interpretation of the
ndings from the present study is subject
to some additional caveats. Although the
apparent protective association with
whole-grain and cereal ber 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 ndings 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 rm conclusions can be drawn
with regard to the inuence of different
aspects of carbohydrate nutrition, insulin
resistance, and the metabolic syndrome.
No specic dietary recommendations
have been advocated by health agencies
for treatment of insulin resistance or the
metabolic syndrome. A high cereal ber
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 identiable and potentially
modiable risk state for both type 2 dia-
betes and cardiovascular disease, increas-
ing whole-grain cereal ber 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 Institutes Framing-
ham Heart Study Contract N01-HC-25195.
Dr. Meigs is supported by a Career Develop-
ment Award from the American Diabetes
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
289:7679, 2003
3. Reaven GM: Banting lecture 1988: role of
insulin resistance in human disease. Dia-
betes 37:15951607, 1988
4. Ford ES, Giles WH, Dietz WH: Prevalence
of the metabolic syndrome among U.S.
adults: ndings 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,
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 Womens 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):S97S102, 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,
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
73:560566, 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:
20812089, 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
20:335347, 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 66:10721085, 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:
20742081, 2003
14. Ludwig DS, Pereira MA, Kroenke CH,
Hilner JE, Van Horn L, Slattery ML, Ja-
cobs DR Jr: Dietary ber, weight gain, and
cardiovascular disease risk factors in
young adults. JAMA 282:1539 1546,
15. Marshall JA, Bessesen DH, Hamman RF:
High saturated fat and low starch and -
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
study. Am J Epidemiol 140:350360,
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 1:231242, 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 76:390398,
19. Fukagawa NK, Anderson JW, Hageman
G, Young VR, Minaker KL: High-carbohy-
drate, high-ber diets increase peripheral
insulin sensitivity in healthy young and
old adults. Am J Clin Nutr 52:524 528,
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-ber 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 76:351358, 2002
22. Juntunen KS, Laaksonen DE, Poutanen
KS, Niskanen LK, Mykkanen HM: High-
ber rye bread and insulin secretion and
sensitivity in healthy postmenopausal
women. Am J Clin Nutr 77:385391, 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 34:362366, 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 161:572576, 2001
26. Jimenez-Cruz A, Bacardi-Gascon M,
Turnbull WH, Rosales-Garay P, Severino-
Lugo I: A exible, lowglycemic 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
26:19671970, 2003
27. Foster-Powell K, Holt SH, Brand-Miller
JC: International table of glycemic index
and glycemic load values: 2002. Am J Clin
Nutr 76:556, 2002
28. Bouche´ C, Rizkalla SW, Luo J, Vidal H,
Veronese A, Pacher N, Fouquet C, Lang V,
Slama G: Five-week, lowglycemic index
diet decreases total fat mass and improves
plasma lipid prole in moderately over-
weight nondiabetic men. Diabetes Care
25:822828, 2002
29. Behall KM, Scholeld DJ, Yuhaniak I, Ca-
nary J: Diets containing high amylose vs
amylopectin starch: effects on metabolic
variables in human subjects. Am J Clin
Nutr 49:337344, 1989
30. Salmeron J, Manson JE, Stampfer MJ,
Colditz GA, Wing AL, Willett WC: Di-
etary ber, 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 ber,
glycemic load, and risk of NIDDM in
men. Diabetes Care 20:545550, 1997
32. Meyer KA, Kushi LH, Jacobs DR Jr, Slavin
J, Sellers TA, Folsom AR: Carbohydrates,
dietary ber, 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 ber 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 94:17951800, 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 Ofce, 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 65:10271033,
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
41:279286, 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,
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:
11141136, 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
122:5165, 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 71:14551461,
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 simplied evaluations and mini-
mal model analysis (Letter). Diabetes Care
23:10381039, 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 benets
of physical activity: the Framingham
study. Arch Intern Med 139:857861,
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:
24862497, 2001
49. Willett W, Stampfer MJ: Total energy in-
take: implications for epidemiologic anal-
yses. Am J Epidemiol 124:1727, 1986
50. Fung TT, Rimm EB, Spiegelman D, Rifai
N, Toer GH, Willett WC, Hu FB: Asso-
ciation between dietary patterns and
plasma biomarkers of obesity and cardio-
McKeown and Associates
vascular disease risk. Am J Clin Nutr 73:
6167, 2001
51. Chandalia M, Garg A, Lutjohann D, von
Bergmann K, Grundy SM, Brinkley LJ:
Benecial effects of high dietary ber in-
take in patients with type 2 diabetes mel-
litus. N Engl J Med 342:13921398, 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: Benecial effects of viscous
dietary ber 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 ber 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,
DOnofrio 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, DOnofrio F: Im-
proved insulin response and action by
chronic magnesium administration in
aged NIDDM subjects. Diabetes Care 12:
265269, 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 89:365374, 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 proles of in-
sulin, glucose, non-esteried fatty acids
and triacylglycerols in middle-aged men.
Br J Nutr 89:207218, 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 69:243249, 1999
Nutrition/insulin resistance/metabolic syndrome
... The grain consumption patterns, combination profiles of grain subtypes or classification of grains play a key role in preventing chronic diseases. The miscellaneous association of grain subtype with health outcome may be due to the lack of standardized classification of grain subtypes, even for whole grains [7][8][9][10][11][12][13][14]. The current results about the effect of whole-grain intake on CMFs are mixed. ...
... The current results about the effect of whole-grain intake on CMFs are mixed. Several studies showed that refined grain intake was positively associated with the risks of major CMFs [7][8][9][10], whereas whole grain was inversely associated with the risk of CMFs [11,12] and further against CVD-related outcomes [13]. In contrast, several studies have reported no such associations with either refined grains or whole grains [14]. ...
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There is a lack of studies on the association between whole grain intake and cardiometabolic risk factors in China and the current definition of whole grains is inconsistent. This study defined whole grains in two ways, Western versus traditional, and examined their associations with the risks of major cardiometabolic factors (CMFs) among 4706 Chinese adults aged ≥18 years, who participated in surveys both in 2011 and in 2015. Diet data were collected by consecutive 3 d 24 h recalls, together with household seasoning weighing. Whole grains were defined as grains with a ratio of fiber to carbohydrate of ≥0.1, while coarse grains were defined as grains except for rice and its products, and wheat and its products. Multivariable logistic regressions were modeled to analyze the associations of intakes of whole grains and coarse grains, respectively, with risks of major CMFs including obesity-, blood pressure-, blood glucose- and lipid-related factors, which were defined by International Diabetes Federation and AHA/NHLBI criteria. After adjusting for potential confounders, the odds of elevated LDL-C decreased with the increasing intake levels of whole grains (OR 0.64, 95% CI 0.46–0.88, p-trend < 0.05). Moreover, adults with the whole grain intake of 50.00 to 150.00 g/day had 27% lower odds of overweight and obesity (OR 0.73, 95% CI 0.54–0.99) and 31% lower odds of elevated LDL-C (OR 0.69, 95% CI 0.49–0.96), as compared with non-consumers. In conclusion, given the significant nutrient profiles of whole grains and coarse grains, the adults with higher intakes of whole grains only may have a lower risk of LDL-C and overweight and obesity.
... Our study revealed that higher plasma DHPPA concentrations were associated with lower risk of MetS, which also confirmed previous epidemiological studies used dietary recording methods to estimate whole-grain intake [26][27][28][29]. In the Framingham Offspring Cohort, whole-grain intake assessed via 126-item Food frequency questionnaires (FFQs) was inversely related with the prevalence of MetS [26]. ...
... Our study revealed that higher plasma DHPPA concentrations were associated with lower risk of MetS, which also confirmed previous epidemiological studies used dietary recording methods to estimate whole-grain intake [26][27][28][29]. In the Framingham Offspring Cohort, whole-grain intake assessed via 126-item Food frequency questionnaires (FFQs) was inversely related with the prevalence of MetS [26]. Sahyoun et al. also found that whole-grain intake, which was estimated using 3-d food records, was favorably associated with the risk of MetS [29]. ...
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Purpose Whole-grain intake assessed through self-reported methods has been suggested to be inversely associated with the metabolic syndrome (MetS) risk in epidemiological studies. However, few studies have evaluated the association between whole-grain intake and MetS risk using objective biomarkers of whole-grain intake. The aim of this study was to examine the association between plasma 3-(3,5-Dihydroxyphenyl)-1-propanoic acid (DHPPA), a biomarker of whole-grain wheat and rye intake, and MetS risk in a Chinese population. Methods A case–control study of 667 MetS cases and 667 matched controls was conducted based on baseline data of the Tongji-Ezhou Cohort study. Plasma DHPPA concentrations were assessed by high-performance liquid chromatography–tandem mass spectrometry. The MetS was defined based on criteria set by the Joint Interim Statement. Results Plasma DHPPA was inversely associated with MetS risk. After adjustment for age, sex, body mass index, smoking status, alcohol drinking status, physical activity and education level, the odds ratios (ORs) for MetS across increasing quartiles of plasma DHPPA concentrations were 1 (referent), 0.86 (0.58–1.26), 0.77 (0.52–1.15), and 0.59 (0.39–0.89), respectively. In addition, the cubic spline analysis revealed a potential nonlinear association between plasma DHPPA and MetS, with a steep reduction in the risk at the lower range of plasma DHPPA concentration. Conclusion Our study revealed that individuals with higher DHPPA concentrations in plasma had lower odds of MetS compared to those with lower DHPPA concentrations in plasma. Our findings provided further evidence to support health benefits of whole grain consumption.
... Factors associated with the metabolic syndrome include high blood pressure, high insulin levels, overweight (especially, around the abdomen), high levels of triglycerides, and low levels of HDL or highdensity lipoproteins, that is, "good cholesterol." High intake of fiber may offer protective benefits from this syndrome (McKeown et al., 2004), diverticular disease, and constipation. Eating lentils, particularly the insoluble component of fiber, was associated with about a 40% lower risk of diverticular disease (Aldoori et al., 1998). ...
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Grain legumes or pulses, including lentil (Lens culinaris Medik), have gained increasing popularity among consumers and food processors in recent years. This trend has been driven by the consumers opting for plant‐based proteins and environmentally sustainable food sources. Global production of lentils has more than doubled since 2001 (from 3.15 to 6.58 million metric tons in 2020), which signifies the commercial importance of this nutrient‐dense legume. As per the USDA's nutrients data (2022), lentil contains 24.6% protein, 63.4% carbohydrates, 2.7% ash content, and 1.1% total fat. High amount of dietary fiber, slowly digestible starch and potassium, and low sodium in lentil align well with consumer choices for healthy foods. Many studies have reported on the health benefits of consuming lentils, especially being effective in reducing various health conditions, such as hypertension, cardiovascular diseases, diabetes mellitus, and cancer. The relatively higher protein and lower carbohydrates content of lentil compared with cereal grains can help in expanding the utilization of lentils and lentil‐based ingredients to develop new products. Combined with high dietary fiber, resistant starch, and bioactive polyphenolic content, the demonstrated nutritional benefits can fill the ever‐increasing demand for plant‐based proteins well beyond traditional consumption of lentils in developing countries. This article reviews the composition and nutrient profile of raw and processed lentils, effect of various processing methods on composition and nutrient profile, and health benefits of lentils.
... In a cross-sectional analysis of the Framingham Offspring Study by McKeown et al. with 2834 participants, total carbohydrate intake was not associated with HOMA-IR [30], conflicting with the present findings. On the other hand, whole grain intake and fiber consumption were significantly related to lower levels of insulin resistance. ...
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The main goal of this investigation was to evaluate the relationships between several macronutrients and insulin resistance in 5665 non-diabetic U.S. adults. A secondary objective was to determine the extent to which the associations were influenced by multiple potential confounding variables. A cross-sectional design and 8 years of data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used to answer the research questions. Ten macronutrients were evaluated: total carbohydrate, starch, simple carbohydrate, dietary fiber, total protein, total fat, saturated, polyunsaturated, monounsaturated, and total unsaturated fat. The homeostatic model assessment (HOMA), based on fasting glucose and fasting insulin levels, was used to index insulin resistance. Age, sex, race, year of assessment, physical activity, cigarette smoking, alcohol use, and waist circumference were used as covariates. The relationships between total carbohydrate intake (F = 6.7, p = 0.0121), simple carbohydrate (F = 4.7, p = 0.0344) and HOMA-IR were linear and direct. The associations between fiber intake (F = 9.1, p = 0.0037), total protein (F = 4.4, p = 0.0393), total fat (F = 5.5, p = 0.0225), monounsaturated fat (F = 5.5, p = 0.0224), and total unsaturated fat (F = 6.5, p = 0.0132) were linear and inversely related to HOMA-IR, with 62 degrees of freedom. Starch, polyunsaturated fat, and saturated fat intakes were not related to HOMA-IR. In conclusion, in this nationally representative sample, several macronutrients were significant predictors of insulin resistance in U.S. adults.
... A higher percentage of energy intake was associated with a higher incidence of MetS, mostly due to abdominal obesity and hypertriglyceridemia in old adults [31,32]. The prevalence of MetS has increased rapidly in Asia in recent years, and several studies have demonstrated stronger associations between dietary carbohydrate intake and metabolic disease [33,34]. The 2007-2012 National Health and Nutrition Examination Survey studies showed that a high carbohydrate intake is associated with metabolic abnormalities (Ha et al., 2018). ...
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Background Participation in exercise, and dietary and nutritional intakes have an impact on the risk and prevalence of metabolic syndrome (MetS), but these effects may differ according to whether a person lives alone or in a multi-person household. We analyzed differences in physical activity (PA) levels and energy intake according to household-type and MetS presence among young adults, to investigate the relationships among these factors. Methods Data of 3974 young adults (aged > 19 years and < 40 years) were obtained from the Korean National Health and Nutrition Examination Survey (2016–2018). We analyzed PA levels (occupational and recreational PA, and transport) and energy intake (total, carbohydrate, protein, and fat). Results Logistic regression data showed that low PA levels and higher energy intake were associated with MetS incidence and its components in young adults, after adjusting for body mass index, smoking, household-type, and sex. Overall, there was no significant difference in PA level between the MetS and non-MetS group. The total energy intake was higher in the MetS than in the non-MetS group ( p < 0.05). These results were similar to those found in multi-person households. In single-person households, the MetS group had significantly lower PA levels ( p < 0.01) and total energy intake ( p < 0.05) than the non-MetS group. Conclusions We found significant association among low PA levels, high energy intake, and MetS components in young Korean adults, but with patterns differing according to household type. Energy intake was higher in young adults with than those without MetS, who lived in multi-person households, while young adults with MetS who lived alone had lower PA levels and lower energy intake than those without MetS. These findings highlight the need for different approaches of implementing PA and nutrition strategies according to the type of household in order to prevent MetS.
... In this study, energy intake was stratified into categories defined by the Korean nutrient intake standards 28 . However, given that smokers consume fewer essential nutrients such as vitamins, calcium, and potassium than non-smokers, it is likely that smokers meet their energy requirements by eating foods that adversely affect insulin resistance 29 . These findings may account for the increase in the insulin resistance risk that we observed in continuous-smokers and past-smokers. ...
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Insulin resistance can be affected directly or indirectly by smoking. This cross-sectional study aimed at examining the association between smoking patterns and insulin resistance using objective biomarkers. Data from 4043 participants sourced from the Korea National Health and Nutrition Examination Survey, conducted from 2016 to 2018, were examined. Short-term smoking patterns were used to classify participants according to urine levels of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and cotinine as continuous-smokers, past-smokers, current-smokers, and non-smokers. Insulin resistance was calculated using the triglyceride-glucose index from blood samples and was defined as either high or low. Multiple logistic regression analysis was performed to investigate the association between smoking behavior and insulin resistance. Men and women who were continuous-smokers (men: odds ratio [OR] = 1.74, p = 0.001; women: OR = 2.01, p = 0.001) and past-smokers (men: OR = 1.47, p = 0.033; women: OR = 1.37, p = 0.050) were more likely to have high insulin resistance than their non-smoking counterparts. Long-term smokers (≥ 40 days) are at an increased risk of insulin resistance in short-term smoking patterns. Smoking cessation may protect against insulin resistance. Therefore, first-time smokers should be educated about the health benefits of quitting smoking.
... Since then, research has been extended to the role of a diet producing a low glycemic response (i.e. low GI and GL) in the prevention and management of metabolic syndrome [3], risk for type 2 diabetes [4], overweight and obesity [5], cardiovascular disease [6], and many other health problems, such as cancer [7] and age-related macular degeneration [8]. ...
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Background The glycemic index (GI) reflects body responses to different carbohydrate-rich foods. Generally, it cannot be simply predicted from the composition of the food but needs in vivo testing. Methods Healthy adult volunteers with normal body mass index were recruited. Each volunteer was asked to participate in the study center twice in the first week to consume the reference glucose (50 g) and once a week thereafter to consume the study fruit juices in a random order. The study fruit juices were Florida orange juice, Tangerine orange juice, Blackcurrant mixed juice, and Veggie V9 orange carrot juice which were already available on the market. The serving size of each fruit juice was calculated to provide 50 g of glycemic carbohydrate. The fasting and subsequent venous blood samplings were obtained through the indwelling venous catheters at 0, 15, 30, 45, 60, 90, and 120 min after the test drink consumption and immediately sent for plasma glucose and insulin. GI and insulin indices were calculated from the incremental area under the curve of postprandial glucose of the test drink divided by the reference drink. Glycemic load (GL) was calculated from the GI multiplied by carbohydrate content in the serving size. Results A total of 12 volunteers participated in the study. Plasma glucose and insulin peaked at 30 min after the drink was consumed, and then started to decline at 120 min. Tangerine orange juice had the lowest GI (34.1 ± 18.7) and GL (8.1 g). Veggie V9 had the highest GI (69.6 ± 43.3) but it was in the third GL rank (12.4 g). The insulin responses correlated well with the GI. Fructose to glucose ratio was inversely associated with GI and insulin responses for all study fruit juices. Fiber contents in the study juices did not correlate with glycemic and insulin indices. Conclusions The GIs of fruit juices were varied but consistently showed a positive correlation with insulin indices. Fruit juices with low GI are a healthier choice for people with diabetes as well as individuals who want to stay healthy since it produces more subtle postprandial glucose and insulin responses.
... Weight loss and regular moderate intensity physical activity/exercise are significant factors for IR prevention and/or treatment [12]. From a dietary perspective, dietary fiber, cereal fiber, fruit fiber, whole grains, full-fat dairy products [13][14][15][16], magnesium, and calcium lower IR, whereas high glycemic index (GI) and glycemic load (GL) foods, saturated fat, salt (deficiency or excess), and alcohol (>30 g/day) increase IR [17]. T2DM development stems from various genetic and/or environmental factors and is characterized by deficient pancreatic β-cell insulin secretion and decreased sensitivity/responsiveness of insulin-sensitive tissues to insulin [18]. ...
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As years progress, we are found more often in a postprandial than a postabsorptive state. Chrononutrition is an integral part of metabolism, pancreatic function, and hormone secretion. Eating most calories and carbohydrates at lunch time and early afternoon, avoiding late evening dinner, and keeping consistent number of daily meals and relative times of eating occasions seem to play a pivotal role for postprandial glycemia and insulin sensitivity. Sequence of meals and nutrients also play a significant role, as foods of low density such as vegetables, salads, or soups consumed first, followed by protein and then by starchy foods lead to ameliorated glycemic and insulin responses. There are several dietary schemes available, such as intermittent fasting regimes, which may improve glycemic and insulin responses. Weight loss is important for the treatment of insulin resistance, and it can be achieved by many approaches, such as low-fat, low-carbohydrate, Mediterranean-style diets, etc. Lifestyle interventions with small weight loss (7–10%), 150 min of weekly moderate intensity exercise and behavioral therapy approach can be highly effective in preventing and treating type 2 diabetes. Similarly, decreasing carbohydrates in meals also improves significantly glycemic and insulin responses, but the extent of this reduction should be individualized, patient-centered, and monitored. Alternative foods or ingredients, such as vinegar, yogurt, whey protein, peanuts and tree nuts should also be considered in ameliorating postprandial hyperglycemia and insulin resistance. This review aims to describe the available evidence about the effects of diet, chrononutrition, alternative dietary interventions and exercise on postprandial glycemia and insulin resistance.
Background The Nordic Nutrition Recommendations (NNR) are developed to promote public health and to prevent food-related diseases such as obesity and cardiovascular diseases. Objective To investigate the nutrient intake and adherence to the NNR in a Swedish cohort with abdominal obesity. Design Dietary intake data were collected using 3-day food diaries and anthropometry and clinical chemistry parameters were measured at baseline of a long-term intervention studying weight-loss management. Results Eighty-seven subjects with abdominal obesity successfully completed a 3-day food diary. Twelve of these subjects were excluded for further analysis due to implausible low-energy reporting. The remaining 75 subjects (76% females) had mean age of 52.3 ± 10.1 years and a mean body mass index of 34.3 ± 3.1 kg/m ² . Mean total fat intake (41.2 ± 7.0E%) was exceeded by 56% of the sample size compared to the maximum recommended intake (RI) of 40E%, whereas mean carbohydrate intake (40.4 ± 8.0E%) was lower than the RI (45–60E%). The intake of saturated fatty acids was high compared to the NNR with only 2 women and none of men reported intakes within the RI of <10 E%. Adherence to the RI for dietary fibre was very low (16.0% and 13.3% when expressed as g/d and g/MJ, respectively). Analyses of micronutrient intake showed lowest adherences for vitamin D and sodium. Conclusions The nutrient intake in our subjects compared to NNR was rather low with a high total fat intake, particularly too high intake of saturated fatty acids, high salt consumption, and very low dietary fibre and vitamin D intake. More effort is clearly needed to promote healthy dietary habits among subjects with obesity.
Nonalcoholic fatty liver disease (NAFLD) is associated with high carbohydrate (HC) intake. We investigated whether the relationship between carbohydrate intake and NAFLD is mediated by interactions between gut microbial modulation, impaired insulin response, and hepatic de novo lipogenesis (DNL). Stool samples were collected from 204 Korean subjects with biopsy-proven NAFLD (n = 129) and without NAFLD (n = 75). The gut microbiome profiles were analyzed using 16S rRNA amplicon sequencing. Study subjects were grouped by the NAFLD activity score (NAS) and percentage energy intake from dietary carbohydrate. Hepatic DNL-related transcripts were also analyzed (n = 90). Data from the Korean healthy twin cohort (n = 682), a large sample of individuals without NAFLD, were used for comparison and validation. A HC diet rather than a low carbohydrate diet was associated with the altered gut microbiome diversity according to the NAS. Unlike individuals from the twin cohort without NAFLD, the abundances of Enterobacteriaceae and Ruminococcaceae were significantly different among the NAS subgroups in NAFLD subjects who consumed an HC diet. The addition of these two microbial families, along with Veillonellaceae, significantly improved the diagnostic performance of the predictive model, which was based on the body mass index, age, and sex to predict nonalcoholic steatohepatitis in the HC group. In the HC group, two crucial regulators of DNL (SIRT1 and SREBF2) were differentially expressed among the NAS subgroups. In particular, kernel causality analysis revealed a causal effect of the abundance of Enterobacteriaceae on SREBF2 upregulation and of the surrogate markers of insulin resistance on NAFLD activity in the HC group. Consuming an HC diet is associated with alteration in the gut microbiome, impaired glucose homeostasis, and upregulation of hepatic DNL genes, altogether contributing to NAFLD pathogenesis.
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Insulin resistance is associated with diabetes mellitus, ischemic heart disease, and hypertension both independently and as part of syndrome X. Environmental influences on SI are incompletely understood. Exercise has a strong beneficial effect and obesity a strong adverse effect. The balance of evidence suggests that a high-fat diet is likely to reduce insulin sensitivity but the effects of dietary carbohydrates are more controversial. Extensive studies in animals showed a detrimental effect of diets very high in fructose or sucrose, particularly in association with induction of hypertriglyceridemia. The more limited studies in humans had conflicting results, partly because of heterogeneity of design. Certain groups of subjects may be more sensitive to adverse effects of high intakes of dietary sucrose or fructose. More carefully controlled studies in humans are needed to provide evidence on which to base public health policies with respect to dietary carbohydrates and SI.
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Objective: To examine prospectively the relationship between glycemic diets, low fiber intake, and risk of non-insulin-dependent diabetes mellitus. Design: Cohort study. Setting: In 1986, a total of 65173 US women 40 to 65 years of age and free from diagnosed cardiovascular disease, cancer, and diabetes completed a detailed dietary questionnaire from which we calculated usual intake of total and specific sources of dietary fiber, dietary glycemic index, and glycemic load. Main outcome measure: Non-insulin-dependent diabetes mellitus. Results: During 6 years of follow-up, 915 incident cases of diabetes were documented. The dietary glycemic index was positively associated with risk of diabetes after adjustment for age, body mass index, smoking, physical activity, family history of diabetes, alcohol and cereal fiber intake, and total energy intake. Comparing the highest with the lowest quintile, the relative risk (RR) of diabetes was 1.37 (95% confidence interval [CI], 1.09-1.71, P trend=.005). The glycemic load (an indicator of a global dietary insulin demand) was also positively associated with diabetes (RR= 1.47; 95% CI, 1.16-1.86, P trend=.003). Cereal fiber intake was inversely associated with risk of diabetes when comparing the extreme quintiles (RR=0.72, 95% CI, 0.58-0.90, P trend=.001). The combination of a high glycemic load and a low cereal fiber intake further increased the risk of diabetes (RR=2.50, 95% CI, 1.14-5.51) when compared with a low glycemic load and high cereal fiber intake. Conclusions: Our results support the hypothesis that diets with a high glycemic load and a low cereal fiber content increase risk of diabetes in women. Further, they suggest that grains should be consumed in a minimally refined form to reduce the incidence of diabetes.
Associations between intake of specific nutrients and disease cannot be considered primary effects of diet if they are simply the result of differences between cases and noncases in body size, physical activity, and metabolic efficiency. Epidemiologic studies of diet and disease should therefore be directed at the effect of nutrient intakes independent of total caloric intake in most instances. This is not accomplished with nutrient density measures of dietary intake but can be achieved by employing nutrient intakes adjusted for caloric intake by regression analysis. While pitfalls in the manipulation and interpretation of energy intake data in epidemiologic studies have been emphasized, these considerations also highlight the usefulness of obtaining a measurement of total caloric intake. For instance, if a questionnaire obtained information on only cholesterol intake in a study of coronary heart disease, it is possible that no association with disease would be found even if a real positive effect of a high cholesterol diet existed, since the caloric intake of cases is likely to be less than that of noncases. Such a finding could be appropriately interpreted if an estimate of total caloric intake were available. The relationships between dietary factors and disease are complex. Even with carefully collected measures of intake, consideration of the biologic implications of various analytic approaches is needed to avoid misleading conclusions.
Total energy intake deserves special consideration in nutritional epidemiology for three reasons: firstly, the level of energy intake may be a primary determinant of disease; secondly, individual differences in total energy intake produce variation in intake of specific nutrients unrelated to dietary composition because the consumption of most nutrients is positively correlated with total energy intake; and, thirdly, when energy intake is associated with risk of disease but is not a direct cause, associations with specific nutrients may be distorted (confounded) by total energy intake. Before examining these three issues in detail, this chapter discusses the physiologic aspects of energy utilization and the determinants of variation in energy intake in epidemiologic studies.
Several studies suggest that markers of insulin resistance and the metabolic syndrome (the cluster of cardiovascular risk factors with insulin resistance) are related to the dietary intakes. Most of these investigations were focused on nutrient intake. We examined whether specific types of food were associated with the presence of the metabolic syndrome.Habitual intake of meat, fish, bread and dairy products was assessed in 2537 women and 2439 men by a food frequency questionnaire. The metabolic syndrome was defined by the presence of at least two of the following factors in the upper (or lower in the case of HDL cholesterol) sex-specific quartile: fasting glucose, serum triglycerides, HDL cholesterol and diastolic blood pressure.There was no association between the intake of meat or fish and the metabolic syndrome. Bread and dairy intake were both inversely related to the frequency of the metabolic syndrome in men, but not in women. Men who ate more than 50 g of bread per day or more than 1 portion of dairy products per day had at least a 40% lower prevalence of the metabolic syndrome.In conclusion the results of our study suggest that in men, a high consumption of bread or dairy products may be related to the risk of the metabolic syndrome.