Context Components of the insulin resistance syndrome (IRS), including obesity,
glucose intolerance, hypertension, and dyslipidemia, are major risk factors
for type 2 diabetes and heart disease. Although diet has been postulated to
influence IRS, the independent effects of dairy consumption on development
of this syndrome have not been investigated.
Objective To examine associations between dairy intake and incidence of IRS, adjusting
for confounding lifestyle and dietary factors.
Design The Coronary Artery Risk Development in Young Adults (CARDIA) study,
a population-based prospective study.
Setting and Participants General community sample from 4 US metropolitan areas of 3157 black
and white adults aged 18 to 30 years who were followed up from 1985-1986 to
1995-1996.
Main Outcome Measure Ten-year cumulative incidence of IRS and its association with dairy
consumption, measured by diet history interview.
Results Dairy consumption was inversely associated with the incidence of all
IRS components among individuals who were overweight (body mass index ≥25
kg/m2) at baseline but not among leaner individuals (body mass
index <25 kg/m2). The adjusted odds of developing IRS (2 or
more components) were 72% lower (odds ratio, 0.28; 95% confidence interval,
0.14-0.58) among overweight individuals in the highest (≥35 times per week,
24/102 individuals) compared with the lowest (<10 times per week, 85/190
individuals) category of dairy consumption. Each daily occasion of dairy consumption
was associated with a 21% lower odds of IRS (odds ratio, 0.79; 95% confidence
interval, 0.70-0.88). These associations were similar for blacks and whites
and for men and women. Other dietary factors, including macronutrients and
micronutrients, did not explain the association between dairy intake and IRS.
Conclusions Dietary patterns characterized by increased dairy consumption have a
strong inverse association with IRS among overweight adults and may reduce
risk of type 2 diabetes and cardiovascular disease.
Risk of type 2 diabetes and cardiovascular disease is affected by a
number of medical and lifestyle factors. In recent years, increasing attention
has been focused on a constellation of risk factors termed the insulin resistance
syndrome (IRS), also known as the metabolic syndrome or syndrome X.1,2 In this syndrome, obesity, insulin
resistance, and hyperinsulinemia are thought to cause glucose intolerance,
dyslipidemia (low serum high-density lipoprotein cholesterol (HDL-C), and
high serum triglyceride concentrations), hypertension, and impaired fibrinolytic
capacity.3 An increasing incidence of IRS in
all racial, ethnic, and social class groups in the United States can be inferred
from the increasing prevalence of obesity4,5
and type 2 diabetes6-8
over the last 3 decades. Recently, this syndrome has been observed in youth,9-11 and age-adjusted prevalence
among adults has been estimated at 24%.12 An
increase in the prevalence of IRS may partly explain the recent plateau or
increase in cardiovascular disease rates, after several decades of decline.13
Although various environmental influences, including smoking and physical
inactivity, are known to promote insulin resistance, the effect of dietary
composition on IRS is poorly understood. For most of the past 3 decades, the
US Department of Agriculture and the American Heart Association have recommended
low-fat diets in the prevention and treatment of cardiovascular disease. Recently,
however, some have questioned these recommendations out of concern that high-carbohydrate
consumption might promote IRS.14-17
Other dietary factors that have been linked to components of IRS include the
ratios of monounsaturated or polyunsaturated to saturated fatty acids,15,18,19 dietary fiber,20,21 and glycemic index.22-24
Dairy consumption is another dietary factor that might affect IRS. Milk
intake has decreased significantly over the past 3 decades25-27
as the prevalence of obesity and type 2 diabetes has increased. Epidemiologic
and experimental studies suggest that dairy products may have favorable effects
on body weight in children28 and adults.29-31 In addition, dairy
and/or calcium may decrease the risk for hypertension,32,33
coagulopathy,34 coronary artery disease,35,36 and stroke.37,38
An inverse cross-sectional association between dairy intake and IRS was observed
in men but not in women although the influence of physical activity, fruit
and vegetable intake, and other lifestyle factors was not considered.39 The purpose of this study was to examine, in a prospective
fashion, the independent association between dairy consumption and IRS, after
taking into account physical activity level, macronutrient and fiber intake,
and other potentially confounding variables.
The Coronary Artery Risk Development in Young Adults (CARDIA) Study
is a multicenter population-based prospective study of cardiovascular disease
risk factor evolution in a US cohort of black and white young adults. The
4 study centers are Birmingham, Ala; Chicago, Ill; Minneapolis, Minn; and
Oakland, Calif. Stratification was used to obtain nearly equal numbers of
individuals in each race, age group (age ranges, 18-24 and 25-30 years), and
educational level (high school diploma and <high school diploma). Participants
have been followed up for 15 years, with the present analyses including the
first 10 years and 5 clinic examinations beginning with the baseline in 1985
and including 1987, 1990, 1992, and 1995. Fifty-one percent of 5115 eligible
participants underwent the baseline examination. Participation has been excellent
at approximately 80% through 1995. More details of the CARDIA Study design
and its participants have been reported.40
From a total sample of 5115, we excluded from our analysis those who
had no year 0 or year 7 dietary data (n = 1175); had unusually high or low
dietary intake values (<800 and >8000 cal/d for men; <600 and >6000
cal/d for women), consistent with CARDIA procedures (n = 707); were pregnant
at baseline or within 180 days of year 10 clinic examination (n = 184); or
were taking medications that affect blood lipid levels (n = 87). Many participants
belonged to more than 1 of these categories, leaving 3563 study participants.
Two hundred sixty-five of these individuals had 2 or more components of the
IRS at baseline, and 141 had missing IRS data, resulting in a final sample
size of 3157. For stratified analyses, 923 of these individuals were overweight
(body mass index [BMI] ≥25 kg/m2).
Standard questionnaires were used to maintain consistency in the assessment
of demographic (age, sex, race, educational level) and behavioral (physical
activity and cigarette smoking) information across CARDIA examination visits.
The CARDIA Physical Activity History questionnaire41
queries the amount of time per week spent in leisure, occupational, and household
physical activities over the past 12 months. Physical activity level is summarized
as units of total activity averaged from the baseline and year 7 examination.
Educational level was quantified as the number of years of school completed
by the year 10 examination, and cigarette smoking status as current vs other
smoker at the baseline and year 7 examination.
The CARDIA Diet History42 queries usual
dietary practices and obtains a quantitative food frequency of the past 28
days. Starting with the Western Electric dietary history as a model, the list
of foods was expanded from 150 to approximately 700 items in the hope of developing
a dietary assessment tool that would be suitable across various populations
and ethnic groups. Liu et al43 reported on
the reliability and validity of the CARDIA Diet History in 128 young adults.
The validity correlations between mean daily nutrient intakes from the CARDIA
Diet History and means from 7 randomly scheduled 24-hour recalls were generally
above 0.50.43 The correlations of calorie-adjusted
calcium intake ranged from 0.56 to 0.69 across race and sex groups. After
correction for within-person variability, they ranged from 0.66 to 0.80.43
The University of Minnesota Nutrition Coordinating Center (NCC) tape
10 nutrient database was used at baseline44
and tape 20 at year 7.45 Foods containing dairy
were identified by matching all CARDIA food codes to the entire NCC code listings
for dairy products. We identified dairy products as any items reported during
the diet history interview that were either 100% dairy (eg, milk) or included
dairy as one of the main ingredients (eg, dips made with sour cream). We did
not include mixed dishes or recipes when the contribution of dairy to the
weight or caloric content of the item was unclear or likely to be minimal.
The most frequently consumed dairy product at the baseline examination was
milk and milk drinks, followed by butter, cream, and cheeses. Together these
items comprised approximately 90% of dairy intake. Most of the remaining products
were yogurts, dips, ice cream, and puddings and other dairy-based desserts.
Weekly frequency of consumption for each food (times per week) was used to
estimate relative intake per week for each food for each individual. In addition
to using specific commonly consumed dairy foods, such as milk, as independent
variables in our analyses, we also performed analyses for various dairy food
groups based on type of product and amount of fat. Milk was considered to
be reduced fat if it consisted of 2% milk fat whereas cheeses and desserts
were considered to be reduced fat if they had less than 15% milk fat (eg,
reduced fat sour cream). The summation of dairy intake across all foods in
the respective food groups was computed for each individual. To improve the
accuracy of estimating habitual intake, we averaged the intake reported during
the interviews of the baseline and year 7 examinations. Total dairy intake
was classified into 5 categories. To ensure sufficient numbers in each race
per dairy category, approximate quintile cut points from the dairy distribution
of the total cohort were used. Therefore, when stratified by race or baseline
overweight status, we did not have equal numbers of observations per category.
We also considered intake of other food groups that may confound associations
between dairy intake and IRS. These food groups included fruits, nonstarchy
and starchy vegetables, fruit juices, soft drinks and sugar-sweetened beverages,
whole and refined grains, meat, and fish. In attempt to maximize our adjustment
for lifestyle factors that may confound associations between dairy intake
and IRS, we created a healthy propensity score based on the following lifestyle
factors, coded as 0 for unhealthy, and 1 for healthy: cigarette smoking (nonsmoker,
1), physical activity (above median total activity score, 1), fruit and vegetable
intake (≥5 servings per day, 1), whole grain intake (above median intake
level, 1), and soft drink consumption (below median intake level, 1). Thus,
this healthy propensity score had a range of 0 (least healthy) to 5 (most
healthy). We also created 2 groups among overweight individuals—those
with a healthy propensity score below 3 (490/923) and those with a healthy
propensity score of 3 or higher (433/923). Other dietary and nutrient measures
from the CARDIA Diet History used in our analyses as potential confounders
or mediators of our hypotheses included caloric intake; alcohol; fiber (grams
per 1000 cal/d); caffeine (mg/d); percentage of calories from carbohydrates,
protein, total fat, saturated and unsaturated fatty acids; and the micronutrients
from supplements and foods including calcium, magnesium, sodium, potassium,
and vitamin D.
All clinic procedures were conducted in accordance with the CARDIA Study
Manual of Operations. Participants were standing and dressed in light clothing
without shoes for anthropometric measures. Body weight was measured to the
nearest 0.2 kg with a calibrated balance beam scale. Height was measured with
a vertical ruler to the nearest 0.5 cm. Body mass index was computed as weight
in kilograms divided by height in meters squared. Waist and hips were measured
with a tape in duplicate to the nearest 0.5 cm around the minimal abdominal
girth and the maximal protrusion of the hips at the level of the symphysis
pubica, respectively. Waist-hip ratio (WHR) was computed from the average
of the 2 values for each respective measure.
Prior to each CARDIA examination participants were asked to fast and
to avoid smoking and heavy physical activity for the final 2 hours. For the
patients who did not fast for at least 8 hours prior to clinic examinations,
data on triglycerides, insulin, and glucose were considered missing. Blood
pressure was measured at each examination on the right arm using a Hawksley
random 0 sphygmomanometer (WA Baum Co, Copaigue, NY) with the participant
seated and following a 5-minute rest. Three measurements were taken at 1-minute
intervals. Systolic and diastolic blood pressures were recorded as phase I
and phase V Korotkov sounds.46 The second and
third measurements were averaged. Vacuum tubes containing no preservative
were used to draw blood for insulin and glucose. Serum was separated by centrifugation
at 4°C within 60 minutes, stored in cryovials and frozen at −70°C within 90 minutes until laboratory analysis. The radioimmunoassay for insulin
required an overnight, equilibrium incubation and used a unique antibody that
has less than 0.2% cross-reactivity to human proinsulin and its primary circulating
split form Des 31,32 proinsulin (Linco Research, St Louis, Mo). Blind analysis
of split serum samples resulted in a technical error of 16.6% of the mean,
and r = 0.98. Northwest Lipid Research Clinic Laboratory
(Seattle, Wash), which is a participant in the Centers for Disease Control
and Prevention standardization program, was used to measure all lipids. Triglyceride
levels were estimated using enzymatic procedures, and HDL-C levels were measured
according to the method of Warnick et al.47
Although not a component of IRS, we also included low-density lipoprotein
cholesterol as a separate independent variable to include a balanced view
of risk factors for cardiovascular disease.
Insulin Resistance Syndrome
Abnormal glucose homeostasis was defined as a fasting plasma insulin
concentration of at least 20 µU/mL (approximately the 90th percentile
of the fasting insulin distribution), fasting glucose concentration of at
least 110 mg/dL (6.1 mmol/L), or use of medications to control blood glucose.
Obesity was defined as a BMI of at least 30 kg/m2 or a WHR of at
least 0.85 for women or 0.90 for men. Elevated blood pressure was defined
as blood pressure of at least 130/85 mm Hg or use of antihypertensive medications.48 Dyslipidemia was defined as low HDL-C (≤35 mg/dL
[≤0.90 mmol/L]) or high triglyceride (≥200 mg/dL [≥2.26 mmol/L])
concentrations. Insulin resistance syndrome was defined as the presence of
2 or more of the 4 components: abnormal glucose homeostasis, obesity, elevated
blood pressure, and dyslipidemia. If 2 components of IRS were positive, the
individual was considered to have IRS, even if other components were missing.
If 3 components were negative, the individual was not considered to have IRS
even if the fourth was missing. In all other cases of missing components,
the IRS status was considered missing and the individual was not included
in our analyses. To ensure true incident cases, baseline (year 0) cases of
IRS were excluded from all analyses. When the outcome variable was an individual
component of IRS (eg, obesity), we excluded from the analysis the baseline
cases of this particular component.
All analyses were performed using SAS statistical software version 8
(SAS Institute, Cary, NC). General linear regression models were used to compare
the incidence of components of IRS and of IRS itself across categories of
dairy intake. We used multiple logistic regression to evaluate associations
between dairy consumption and the odds of developing IRS during the 10-year
study after excluding all individuals who had IRS at baseline. Odds ratios
(ORs) and their 95% confidence intervals (CIs) were computed for the second
through fifth category of the respective dairy food group with the first category
(lowest intake) as the referent group. A linear trend across categories was
tested with contrast statements using orthogonal polynomial coefficients.49 In addition to assessing the influence of potential
confounders, models were constructed to evaluate micronutrients and macronutrients
as possible physiologic mediators of the association between dairy intake
and IRS. We evaluated whether adding these variables to the final regression
models attenuated the strength of the association between dairy intake and
risk factors and disease outcomes. We tested 2-way interactions based on a
priori hypotheses about potential differences in the association between dairy
intake and IRS by race and sex, and by baseline overweight (BMI <25 kg/m2vs ≥25 kg/m2) status. Statistical significance was set
at P = .05.
Table 1 presents total dairy
intake and specific dairy food groups by race. Dairy intake was higher in
whites than in blacks (P<.001), and this difference
was generally consistent across the dairy subgroups. One exception was when
dairy was classified according to amount of fat; whites tended to consume
more reduced fat dairy products than blacks, whereas the reverse was true
for higher fat dairy products. We also observed differential dairy intake
according to baseline BMI, with overweight individuals consuming dairy products
at a lower frequency than their normal-weight counterparts (P<.001). These differences were larger for blacks than for whites.
We observed a small decline in dairy intake of approximately 13% from the
year 0 to the year 7 examination (−3.1 times per week, 95% CI, −3.7 to −2.4]), but change in dairy intake was not associated with baseline
BMI (Pearson correlation coefficient, 0.02). Thus, any potential misclassification
related to dairy consumption over time should not affect lean and obese individuals
differently.
Demographic, lifestyle, and dietary correlates of dairy intake are shown
in Table 2, with adjustment for
age, sex, race, caloric intake, and study center. In comparison, the baseline
characteristics of the 1552 participants excluded from the present analyses
were generally similar although 64% were black and 46% were white. Higher
dairy consumers were much less likely to be black and somewhat more likely
to be women. Notably, dairy consumption was positively associated with whole
grain, fruit, vegetable, saturated fat intake, and inversely associated with
sugar-sweetened soft drink intake.
Ten-year cumulative incidence rates of each IRS component, as well as
the IRS itself, are shown in Table 3
stratified by race and overweight status (BMI ≥25 kg/m2). Incidence
rates for all components were higher for individuals who were overweight at
baseline. Blacks have higher rates of each component with the exception of
dyslipidemia. The incidence of IRS (developing 2 or more components over 10
years) was nearly 4-fold higher in overweight blacks and nearly 5-fold higher
in overweight whites compared with their normal-weight counterparts.
Figure 1 shows 10-year cumulative
incidence of the 4 components of IRS by dairy categories stratified by baseline
overweight status and adjusted for age, sex, race, caloric intake, study center,
and baseline BMI as a continuous variable within each BMI category. There
was a consistent reduction in incidence for each of the 4 components with
increasing categories of dairy intake for overweight individuals only. Associations
between dairy intake and these IRS components were much weaker and less consistent
in normal-weight individuals. Although not part of IRS, we found no association
between dairy intake and incidence of high LDL-C (≥140 mg/dL [3.6 mmol/L],
data not shown).
With IRS as the dependent variable, there was an interaction between
dairy intake and baseline overweight status (P =
.03). No association was observed between dairy intake and IRS incidence in
those who were not overweight at baseline. Among overweight individuals of
either race, incidence of IRS decreased by more than 50% from lowest to highest
categories of dairy consumption. This association behaved in a dose-response
manner and was monotonic for blacks. Associations between dairy intake and
IRS were similar between races and sexes (P value
for interaction terms >.10).
Table 4 includes ORs for
IRS according to categories of total dairy intake. Because of the interaction
between dairy intake and overweight status described above, we only present
models for individuals who were overweight or obese at baseline. Model 1 includes
basic demographic factors and BMI. We observed a substantial reduction in
the odds of IRS over the 10-year period with increasing category of dairy
intake. The reduction in odds was 71% (OR, 0.29, 95% CI, 0.14-0.58) for the
highest category of dairy intake relative to the lowest category (P value for linear trend across all quintiles <.001). Among those
who were not overweight or obese at baseline, the OR of IRS for those in the
highest category of dairy intake was 0.72 (95% CI, 0.39-1.34, P for trend = .22).
In the adjusted models 2 and 3A involving overweight or obese individuals,
we observed little evidence of confounding by other lifestyle and dietary
factors. Confidence intervals became wider in model 3A due to the inclusion
of many dietary variables, most of which are themselves nonsignificant predictors
of IRS. Adjustment for the healthy propensity score revealed very similar
findings (OR for highest vs lowest category of dairy intake, 0.37; 95% CI,
0.18-0.79). In model 3B, we evaluated several macronutrients and micronutrients
as possible mediators of the association between dairy intake and IRS. Results
for models 2 and 3B are very similar, suggesting that these factors do not
explain the inverse association between dairy intake and IRS incidence. Finally,
in model 3C, we performed stepwise logistic regression for all dietary variables
included in models 3A and 3B while forcing all demographic and nondietary
lifestyle factors into the model. Other than dairy, fiber and protein were
the only dietary variables with significant associations with IRS. Of particular
note is the strong inverse association between dietary fiber intake and IRS
(OR for each 3 g/1000–cal increment in fiber [ap proximate interquartile
range], 0.66; 95% CI, 0.53-0.80). However, fiber was not a confounder of the
association between diary and IRS. The strong and independent joint association
of dairy and fiber intake with odds of IRS is shown in Figure 2. Odds of IRS for individuals in the lowest tertiles of
both fiber and dairy were nearly 7-fold higher than those in the highest tertiles
of both fiber and dairy intake. Dietary protein demonstrated a positive association
with IRS incidence (OR for each 1% caloric increment of protein, 1.12; 95%
CI, 1.04-1.21), and this association was due to animal rather than vegetable
protein (data not shown). Although dietary calcium appeared to be inversely
associated with IRS incidence in a model without dairy intake (OR for each
600-mg increment [approximate interquartile range], 0.79: 95% CI, 0.61-1.03),
the association between calcium intake and IRS was entirely explained by adding
dairy intake to the model (OR, 0.99; 95% CI, 0.76-1.29).
To examine the extent to which weight gain explains the association
between dairy and IRS, we added 10-year weight gain to the final model as
a continuous variable. In this model, the OR for the highest category of dairy
intake compared with the lowest category was 0.33 (95% CI, 0.16-0.72). This
finding was similar when adjusting for weight gain as quintiles or deciles.
We also stratified the sample by 10-year weight gain, based on a median split.
In both weight gain strata, the odds of IRS were lowest for those with highest
dairy intakes although CIs became wide because of the imbalance in the number
of cases between these 2 groups.
As shown in Figure 3, the
association between dairy intake and IRS incidence was very similar for both
races and sexes. With the same covariates as in model 3C of Table 4, the odds of IRS associated with an increment of 1 daily
eating occasion of dairy was 0.96 (95% CI, 0.73-1.28) for black men, 0.70
(95% CI, 0.54-0.91) for black women, 0.74 (95% CI, 0.59-0.93) for white men,
and 0.62 (95% CI, 0.46-0.84) for white women.
In Table 5, ORs of the components
of IRS and of IRS itself are shown for 1 daily increment (7/wk) of total dairy
intake and of specific types of dairy. Odds were generally lower, and in most
cases considerably reduced, with increasing intake of all types of dairy products.
Inverse associations were observed for both reduced-fat and high-fat dairy
products. Odds of obesity, abnormal glucose homeostasis, and elevated blood
pressure were lower by nearly 20% for each daily eating occasion of total
dairy products, and odds of IRS were lower by 21%. When a BMI of 30 kg/m2 and a WHR of 0.90 for men and 0.85 for women were evaluated separately,
odds of both were lower (OR, 0.81 for BMI; OR, 0.89 for WHR) with each daily
increment of total dairy. The association between dairy intake and dyslipidemia
was somewhat weaker. However, dairy intake appeared to be inversely associated
with the odds of elevated triglyceride levels (OR, 0.79 for 1 daily increment
of total dairy; 95% CI, 0.67-0.94) but not with low HDL-C (OR, 0.99; 95% CI,
0.87-1.12).
We observed inverse associations between frequency of dairy intake and
the development of obesity, abnormal glucose homeostasis, elevated blood pressure,
and dyslipidemia in young overweight black and white men and women. The 10-year
incidence of the IRS was lower by more than two thirds among overweight individuals
in the highest category of dairy consumption (≥5/d) compared with those
in the lowest category (<1.5/d). These associations were not confounded
by other lifestyle factors or dietary variables that are correlated with dairy
intake and did not differ materially by race or sex.
The main limitation of our study is its observational nature. Therefore,
we cannot rule out residual confounding, and we cannot conclude that increased
dairy intake reduced the incidence of IRS in a causal manner. The strengths
of the study include its longitudinal design, allowing us to exclude participants
with existing IRS at baseline and to compare the 10-year cumulative incidence
of IRS across dairy categories from the average of 2 comprehensive diet history
interviews. Self-reported diet averaged over time should be a better estimate
of habitual intake than a single measure.50
Remaining errors in the measure of diet are likely to bias associations toward
the null hypothesis (no association), resulting in an underestimation of the
true magnitude of the association. Indeed, we observed somewhat stronger associations
between dairy intake and IRS incidence when modeling the average dairy intake
compared with the year 0 and year 7 dairy intake separately, although these
differences were not large and do not materially affect the results or conclusions
(data not shown). The diet history method was chosen for use in the CARDIA
study because of its comprehensiveness, interviewer-administered format, suitable
time-frame for capturing habitual diet without exacerbating recall error,
and applicability to populations differing in social and cultural characteristics.
Although saturated fat contained in dairy products may raise LDL-C levels,
there are several mechanisms by which dairy intake may protect against insulin
resistance, obesity, and cardiovascular disease. Many single-nutrient studies,
but not all,51 suggest that calcium, potassium,
and magnesium may lower the risk of hypertension,32,33
coronary heart disease,35,36 stroke,37,38 or type 2 diabetes.52
Other studies have suggested an intracellular role of calcium or other components
of dairy products in body weight regulation,30
a hypothesis supported by several,28-31
but not all,53 observational and experimental
studies. In our study, the inverse association between calcium intake and
IRS was entirely explained by dairy intake whereas the association between
dairy consumption and IRS was not materially affected by adjustment for the
intake of calcium or any other nutrients. It is also possible that the lactose,
protein, and fat in dairy foods may enhance satiety and reduce the risk of
overweight and obesity relative to other high-carbohydrate foods and beverages.
However, adjustment for these nutrients also had no meaningful effect on the
associations between dairy intake and the risk factors of the present study.
Alternative explanations for a possible effect of dairy on the development
of IRS include alterations in dietary patterns associated with dairy intake
(eg, low glycemic index22-24),
presence in dairy of unrecognized biologically active components, or residual
confounding by recognized dietary or lifestyle factors. Further observational
and experimental work is needed to examine these possibilities.
The association between dairy intake and IRS was not observed in individuals
who were not overweight (BMI <25 kg/m2) at baseline of this
10-year study, perhaps because these individuals were protected from insulin
resistance and obesity by other lifestyle or genetic factors. Other epidemiologic
studies of coronary disease or lipid levels have reported similar interactions
between overweight status and dietary patterns related to insulin sensitivity.22
Changing dietary patterns may play an important role in the epidemics
of obesity4,5 and type 2 diabetes,6,7 as well as the plateauing or increase
in heart disease rates13 in the United States
in recent years. Trends in dietary intake behaviors over the past few decades
have revealed decreasing intake of dairy products, especially milk, and increasing
amounts of soda consumption and snacking among children and adolescents.25-27,54 In
summary, our study suggests that dietary patterns characterized by increased
dairy consumption may protect overweight individuals from the development
of obesity and the IRS, which are key risk factors1,2
for type 2 diabetes and cardiovascular disease. Indeed, other major clinical
trials and official nutritional recommendations would appear to be supportive
of this dietary pattern.55,56
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