Lifestyle Behaviors Associated With Lower Risk of Having the Metabolic Syndrome

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Shankuan Zhu, Marie-Pierre St-Onge, Stanley Heshka, and Steven B. Heymsfield
The metabolic syndrome is a cluster of risk factors that predisposes individuals to cardiovascular disease (CVD) and diabetes
and is present in almost one fourth of adult Americans. Risk factors involved with the metabolic syndrome can be altered via
modifiable lifestyle factors, such as diet, physical activity, and smoking and drinking habi

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Lifestyle Behaviors Associated With Lower Risk of Having
the Metabolic Syndrome
Shankuan Zhu, Marie-Pierre St-Onge, Stanley Heshka, and Steven B. Heymsfield
The metabolic syndrome is a cluster of risk factors that predisposes individuals to cardiovascular disease (CVD) and diabetes
and is present in almost one fourth of adult Americans. Risk factors involved with the metabolic syndrome can be altered via
modifiable lifestyle factors, such as diet, physical activity, and smoking and drinking habits. The objective of this study was
to examine the extent to which these modifiable lifestyle behaviors are associated with the risk of having the metabolic
syndrome. Data from the Third National Health and Nutrition Examination Survey (NHANES III), conducted between 1988 and
1994, were used to measure the risk of having the metabolic syndrome in healthy adult Americans who follow certain lifestyle
behaviors, such as dietary practices, levels of physical activity, smoking and drinking habits. Low physical activity level, high
carbohydrate (CHO) intake, and current smoking habits were all significantly associated with an increased risk of having the
metabolic syndrome, even after adjusting for other related covariates. Relative to physically inactive subjects, being physi-
cally active was associated with lower odds ratio (OR) (0.36, confidence interval [CI] 0.21 to 0.68, P < .01) in overweight men
and in normal weight (0.36, CI 0.18 to 0.70, P < .01) and overweight (0.61, CI 0.38 to 0.97, P < .05) women. Although the type
of CHO could not be distinguished, relative to a high CHO diet, men having a low or moderate CHO intake had a lower risk
of having the metabolic syndrome with respective ORs of 0.41 (CI 0.24 to 0.67, P < .01) and 0.44 (CI 0.25 to 0.77, P < .01); no
effect of dietary CHO was observed in women. Moderate alcohol consumption was not significantly related to the risk of
having the metabolic syndrome in men, but was associated with a lower OR in women (0.76, CI 0.61 to 0.95, P < .05).
Regression models indicate a reduced risk of having the metabolic syndrome when selected low-risk lifestyle factors are
present in combination, particularly in subjects with body mass index (BMI) < 30 kg/m
2
. According to our cross-sectional
logistic models, the risk of having the metabolic syndrome is substantially lower in individuals who are physically active,
nonsmoking, have a relatively low CHO intake and moderate alcohol consumption, and who maintain a BMI in the non-obese
range. These observations have potentially important value for public health recommendations.
©
2004 Elsevier Inc. All rights reserved.
T
HE METABOLIC SYNDROME is associated with an
increased risk of developing cardiovascular disease
(CVD) and appears in individuals as a cluster of risk factors
that have sensitivity to the cellular actions of insulin in com-
mon.
1,2
The main components of the metabolic syndrome,
including waist circumference, plasma glucose and lipid levels,
and blood pressure, are all associated with excess adiposity,
and weight loss is the primary therapeutic and preventive
measure.
1,3
A recent report based on a representative sample of the
American population suggests that the metabolic syndrome
criteria are met by approximately 1 in 4 adults.
4
Major efforts
are now under way to detect and treat the metabolic syndrome
as a means of lowering the risk of or preventing CVD in the US
population.
1
Some behaviors or lifestyle patterns are associated
with the metabolic syndrome and include physical activity,
cigarette smoking, and diet, particularly carbohydrate (CHO)
and fat intakes.
5-11
Our previous study examined the relation-
ships of these factors with the metabolic syndrome,
12
however,
the probability of having the metabolic syndrome in individuals
within different body mass index (BMI) categories and the
probability of having the metabolic syndrome in individuals
who have a combination of these risk factors remains unknown.
Accordingly, in the present study, we used cross-sectional
data to examine how different levels of each recognized life-
style factor, or combinations of these lifestyle factors, impact
on the risk of having the metabolic syndrome.
SUBJECTS AND METHODS
Subjects
Subjects over the age of 20 years from 4 ethnic groups, non-Hispanic
blacks, Mexican Americans, non-Hispanic whites, and others were
eligible for inclusion in the study. Among 18,825 eligible subjects,
5,904 subjects were excluded for lack of anthropometric, demographic,
socioeconomic, or physical examination information, and 1,480 sub-
jects were excluded due to improper fasting, defined as drinking or
eating 6 hours before blood collection. In addition, 202 women who
were pregnant or breast-feeding were also excluded. Therefore, 11,239
subjects, 5,415 men (48.2%) and 5,824 women (51.8%), were included
in the analysis.
Study Design
The relationships between metabolic syndrome risk and lifestyle-
related factors were modeled using data from a representative US
sample, the Third National Health and Nutrition Examination Survey
(NHANES III). This survey, conducted between 1988 and 1994 in 81
counties across the United States, includes 40,000 individuals over the
age of 2 months who were not institutionalized during the evaluation
period.
13
From the Injury Research Center, Department of Family and Com-
munity Medicine, Medical College of Wisconsin, Milwaukee, WI; and
the Obesity Research Center, St. Luke’s-Roosevelt Hospital, Institute of
Human Nutrition, Columbia University College of Physicians and
Surgeons, New York, NY.
Submitted February 23, 2004; accepted April 7, 2004.
Supported by Grant No. DK PO1-42618 from the National Institutes
of Health, an unrestricted grant from Pfizer Pharmaceutical (to S.Z.),
and fellowship funding from Bristol Myers Squibb-Mead Johnson and
the Canadian Institutes of Health Research (to M-P.St-O.).
Address reprint requests to Shankuan Zhu, MD, PhD, Injury Re-
search Center, and Department of Family and Community Medicine,
Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee,
WI 53226.
©
2004 Elsevier Inc. All rights reserved.
0026-0495/04/5311-0045$30.00/0
doi:10.1016/j.metabol.2004.04.017
1503
Metabolism, Vol 53, No 11 (November), 2004: pp 1503-1511

Page 2

The NHANES III study used stratified, multistage probability cluster
sampling and results from each respondent were weighted using a
factor indicating sampling probability. The derived estimates of meta-
bolic syndrome risk are thus representative of the entire American
civilian population.
13
The NHANES III database includes information on socioeconomic
status, physical activity, dietary, and smoking habits, as well as anthro-
pometric and biochemical data that can be used to assess the prevalence
of the metabolic syndrome. These data were used to model the asso-
ciations between the risk of having the metabolic syndrome and life-
style-related behaviors, such as diet, physical activity, and smoking
habits. These logistic models then allowed us to examine how variation
in each specific lifestyle factor, controlling for other covariates, im-
pacted on the risk of having the metabolic syndrome. Our models also
allowed us to explore to what extent the effects of a combination of
several low-risk behaviors impact on the risk of having the metabolic
syndrome.
Definitions
Metabolic syndrome. Metabolic syndrome was defined according
to the Third Report of the National Cholesterol Education Program
expert panel on the detection, evaluation, and treatment of high blood
cholesterol in adults (NCEP ATP III) criteria, which require that 3 or
more of the following risk factors be present to establish the diagnosis
of the metabolic syndrome
1
: abdominal obesity (waist circumference
102 cm in men and 88 cm in women); triglyceride levels
1.70
mmol/L [150 mg/dL]; high-density lipoprotein (HDL) cholesterol lev-
els
1.04 mmol/L [40 mg/dL] for men or 1.30 mmol/L [50 mg/dL]
for women; high blood pressure (systolic
130 mm Hg or diastolic
85 mm Hg); and fasting plasma glucose
6.11 mmol/L [110 mg/dL].
Subjects using blood pressure medication or oral hypoglycemic agents
at the time of survey were considered as having the associated abnor-
mal risk factor.
Socioeconomic factors. Subjects were divided into 3 groups ac-
cording to completed years of schooling: 8 years, 9 to 12 years, and
12 years. Three economic status groups were also created according
to the subject’s household income for the previous year: $15,000,
$15,001 to $25,000, and
$25,000. Menopause was established in
women at the time of interview as cessation of menstruation.
Lifestyle-related factors. Physical activity, CHO, fat, and alcohol
consumption, as well as smoking habits, were chosen as lifestyle-
related factors that may be modifiable and associated with the
metabolic syndrome, while age, ethnicity, education, and household
income levels were taken as fixed demographic characteristics of the
subjects.
Physical activity intensity score was defined as the ratio of work
metabolic rate to resting metabolic rate obtained by a history of
participating in 1 of the following activities during the past month:
walking, jogging or running, bicycle riding, swimming, lifting
weights, or doing aerobics or aerobic dancing, other dancing, cal-
isthenics, or garden/yard work.
13
Physical activity level was cate-
gorized based on the subject’s physical activity intensity rating
scores: being physically inactive referred to a physical activity
intensity score of 3.5; being moderately active and active corre-
sponded to intensity rating scores of 3.6 to 14.9 and 15.0, respec-
tively, for both men and women.
12
These cutoff points were chosen
to represent the 15th and 65th percentile of physical activity for
males and 25th and 75th percentile for females.
Cutoffs used to categorize CHO and fat consumption reflected cur-
rent dietary recommendations by the US Department of Health and
Human Services: low, moderate, and high CHO intakes corresponded
to 40%, 40% to 60%, and 60% of dietary energy intake; and low,
moderate, and high fat intakes corresponded to 30%, 30% to 40%,
40% of dietary energy intake.
14
Smoking was categorized as current, past, and never smoked. Past
smokers were those who reported that they had smoked at least 100
cigarettes during their lifetime, but who did not currently smoke.
Drinking was categorized as heavy, light-to-moderate, and nondrink-
ing. According to the recommendation by the US Department of
Agriculture and Department of Health and Human Services, heavy
drinkers were subjects who answered that they “ever drank 5 or more
alcoholic beverages almost every day” or drank beer, wine, or hard
liquor more than twice a day during the past month for men and once
a day for women. Light-to-moderate drinkers had 2 alcoholic bever-
ages per day during the past month for men or 1 drink per day for
women.
15
Nondrinkers were those who did not drink any alcoholic
beverage during the past month.
Each of the lifestyle factors was graded relative to an arbitrarily
defined low-risk category according to their respective relationships
with the metabolic syndrome. Five low-risk categories were defined
during our analyses that included the following: normal weight (BMI
25 kg/m
2
) or non-obese (BMI
30 kg/m
2
); physically active; low or
moderate CHO consumption; a history of never smoking; and a light-
to-moderate alcohol intake.
Statistical Methods
The differences in baseline characteristics, expressed in the text as
the mean and 95% confidence interval (CI), between men and women
were tested using the adjusted Wald test. Statistical significance was set
at P
.05. Stata (Version 7.0 for Windows, Stata, College Station, TX)
was used to incorporate the complex sampling design of NHANES III
to produce nationally representative estimates.
The first stage of analysis focused on identifying and modeling
factors that confer a risk of the metabolic syndrome. Three logistic
regression models were used to estimate the sex-specific metabolic
syndrome odds ratio (OR) for lifestyle-related factors. In model 1, the
independent variable was the specific lifestyle factor examined. This
model tested the univariate relationship of the lifestyle factor with the
metabolic syndrome. In model 2, age, ethnicity, education, household
income, and menopausal status for women, were added into model 1 as
covariates to test whether the relationship observed in model 1 changed
after controlling for demographic characteristics. In model 3, all other
lifestyle-related factors were incorporated into model 2 as additional
covariates to verify whether these other lifestyle-related factors modi-
fied the association between the lifestyle factor examined and the
metabolic syndrome.
To test the adequacy of the final model (model 3), we entered an
additional variable that might plausibly be related to the dependent
variable, such as total caloric intake, to determine whether this made
significant contributions to the model.
16
In addition, we pooled men and
women together to test the interaction between gender and lifestyle
factor using regression model 3 to determine whether the association
between lifestyle factors and metabolic syndrome differed between
men and women.
In the second phase of the analysis, we explored the effects of a
combination of several low-risk factors on the risk of having the
metabolic syndrome. In condition 1, the odds of cases in the
low-risk groups were compared with the odds of the remaining cases
after controlling for age, ethnicity, education and income levels,
menopausal status for women, and other lifestyle-related factors that
were not involved in the low-risk groups. In conditions 2 and 3,
BMI
30 and 25 kg/m
2
were, respectively, considered as addi-
tional factors to redefine the low-risk groups. These models pro-
vided an estimate of the odds of having the metabolic syndrome
when specific low-risk lifestyle factors are present in general (con-
dition 1) or when they are present in non-obese (ie, BMI
30
kg/m
2
) or normal weight (ie, BMI
25 kg/m
2
) subjects. The
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ZHU ET AL

Page 3

regression models for low-risk groups evaluated in condition 1 were
additionally adjusted for BMI.
RESULTS
Subject Characteristics
Women in the sample were significantly (P
.001) older
(45.6 years; CI, 44.5 to 46.7) than men (43.8 years; CI, 43.0 to
44.6). Men had a significantly higher (P
.05) BMI (26.7
kg/m
2
; CI, 26.5 to 26.9) than women (26.4 kg/m
2
; CI, 26.3 to
26.6).
The overall prevalence of the metabolic syndrome was
greater (P
.05) in men (23.0%) than in women (21.9%). The
prevalence of the metabolic syndrome in men increased from
5.3% in the normal weight group to 21.9% and 58.8% in the
overweight and obese groups, respectively. Similar results were
observed in women, with the prevalence of the metabolic
syndrome increasing from 5.5% to 27.5% and 48.8% when
going from normal weight to overweight and obese groups,
respectively.
Physical Activity and Risk
The ORs for being diagnosed with the metabolic syndrome
as a function of physical activity level are organized according
to sex and BMI in Table 1. The metabolic syndrome ORs were
lower with greater levels of physical activity in men in all 3
regression models. When stratified by BMI, the risk of having
the metabolic syndrome was significantly lower in physically
active men compared with inactive men only in the overweight
group (model 3, OR
0.36; CI, 0.21 to 0.68; P
.01). The
risk of having the metabolic syndrome in moderately active
men was also lower in the overweight group (model 3, OR
0.58; CI, 0.39 to 0.88; P
.05). In obese men, the ORs of
having the metabolic syndrome were lower with greater levels
of physical activity in the univariate regression model (model 1,
OR
0.55; CI, 0.31 to 0.97; P
.05), although the ORs
became nonsignificant after adjusting for covariates in models
2 and 3.
Physical activity in women overall was not significantly
associated with the risk of having the metabolic syndrome in
models 2 and 3. In normal weight and overweight women,
being physically active was associated with a lower risk of
having the metabolic syndrome (OR
0.36; CI, 0.18 to 0.70;
P
.01 for normal weight and OR
0.61; CI, 0.38 to 0.97;
P
.05 for overweight women) compared with being inactive.
In obese women, moderate physical activity was associated
with a 21% lower risk of having the metabolic syndrome
compared with inactivity (model 3, OR
0.79; CI, 0.64 to
0.99; P
.05).
CHO Intake and Risk
The relationships between dietary CHO intake and the risk
of being diagnosed with metabolic syndrome across sex and
BMI categories are shown in Table 2. Overall, a low or
moderate CHO intake, compared with a high CHO intake,
was associated with a
50% lower OR of having the met-
abolic syndrome in men. In normal weight men, compared
with a high CHO intake, a low CHO intake was associated
with a significantly lower risk of having the metabolic
syndrome in model 1; this was not significant after adjusting
for covariates in models 2 and 3. A moderate dietary CHO
Table 1. Metabolic Syndrome ORs According to Physical Activity Level
BMI* (kg/m
2
)
Physical Activity
All
25
25-29.9
30
OR† (CI)‡
P Value
OR (CI)
P Value
OR (CI)
P Value
OR (CI)
P Value
Men
Model 1
Active
0.41 (0.31-0.54)
.001
0.61 (0.28-1.34)
.212
0.27 (0.18-0.40)
.001
0.55 (0.31-0.97)
.05
Moderately active
0.91 (0.73-1.14)
.421
1.01 (0.53-1.94)
.969
0.66 (0.48-0.89)
.01
1.08 (0.69-1.71)
.727
Inactive (reference) 1.00
1.00
1.00
1.00
Model 2
Active
0.54 (0.36-0.80)
.01
0.99 (0.42-2.32)
.974
0.35 (0.20-0.63)
.01
0.64 (0.36-1.13)
.122
Moderately active
0.76 (0.56-1.04)
.086
0.95 (0.46-1.97)
.882
0.57 (0.38-0.85)
.01
1.09 (0.68-1.75)
.721
Inactive
1.00
1.00
1.00
1.00
Model 3
Active
0.58 (0.39-0.85)
.01
1.09 (0.48-2.50)
.831
0.36 (0.21-0.68)
.01
0.67 (0.39-1.17)
.151
Moderately active
0.79 (0.58-1.08)
.138
0.99 (0.50-1.97)
.973
0.58 (0.39-0.88)
.05
1.11 (0.66-1.85)
.689
Inactive (reference) 1.00
1.00
1.00
1.00
Women
Model 1
Active
0.25 (0.18-0.37)
.001
0.10 (0.05-0.23)
.001
0.32 (0.21-0.51)
.001
0.69 (0.34-1.39)
.286
Moderately active
0.69 (0.56-0.84)
.01
0.77 (0.51-1.17)
.212
0.69 (0.48-0.99)
.05
0.81 (0.63-1.05)
.106
Inactive (reference) 1.00
1.00
1.00
1.00
Model 2
Active
0.70 (0.48-1.01)
.057
0.32 (0.17-0.62)
.01
0.56 (0.37-0.85)
.01
0.80 (0.38-1.68)
.537
Moderately active
0.85 (0.69-1.04)
.105
0.94 (0.64-1.37)
.738
0.73 (0.48-1.12)
.143
0.78 (0.63-0.97)
.05
Inactive (reference) 1.00
1.00
1.00
1.00
Model 3
Active
0.76 (0.51-1.13)
.164
0.36 (0.18-0.70)
.01
0.61 (0.38-0.97)
.05
0.82 (0.38-1.79)
.611
Moderately active
0.88 (0.72-1.08)
.214
1.01 (0.69-1.47)
.978
0.77 (0.50-1.17)
.209
0.79 (0.64-0.99)
.05
Inactive (reference) 1.00
1.00
1.00
1.00
NOTE. Model 1 includes as a predictor only for specific lifestyle-related risk factor; model 2 adds age, race, education and income levels, and
menopausal for women as predictors; and model 3 includes model 2 and other modifiable factors.
*BMI represents body-mass index; in the ALL group, the model was also adjusted for BMI as a continuous variable.
†ORs represent the metabolic syndrome odds ratios for modifiable lifestyle factors.
‡CI represents 95% confidence interval, ORs
1.96 SE.
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METABOLIC SYNDROME AND LIFESTYLE

Page 4

intake in normal weight men, however, was accompanied by
a significantly lower risk of having the metabolic syndrome
in all 3 regression models compared with a high CHO intake.
In overweight men, the metabolic syndrome OR was lower
in both low (model 3, OR
0.33; CI, 0.19 to 0.57; P
.001) and moderate CHO intake groups (model 3, OR
0.44; CI, 0.23 to 0.87; P
.05). There was no significant
relationship between CHO intake and the risk of having the
metabolic syndrome in obese men.
A moderate dietary CHO intake in normal weight women
was associated with a lower risk of having the metabolic
syndrome only in model 1. CHO intake had no impact on the
risk of having the metabolic syndrome in models 2 and 3 for all
3 BMI categories in women.
Fat intake did not have a significant impact on the risk of
having the metabolic syndrome in models 2 and 3. However, in
model 1, for the overall BMI group, compared with a low-fat
intake, a high-fat intake was associated with a lower risk of
having the metabolic syndrome for both men (OR
0.44; CI,
0.21 to 0.91; P
.05) and women (OR
0.58; CI, 0.41 to
0.81; P
.01).
Alcohol Consumption
Overall, in men, light-to-moderate alcohol consumption was
accompanied by a lower risk of having the metabolic syndrome
compared with never drinking in model 1. This lower risk was
no longer significant after controlling for the covariates in
models 2 and 3 (Table 3). Heavy drinking in overweight men
had a negative impact on the risk of having the metabolic
syndrome (OR
1.37; CI, 1.04 to 1.79; P
.05).
In women, light-to-moderate and heavy alcohol consumption
was accompanied by a significantly lower metabolic syndrome
risk in all 3 models tested. Also, in obese women, heavy
drinking was accompanied by a lower OR (0.56; CI, 0.32 to
0.98; P
.05). In overweight women, reported light-to-mod-
erate drinking tended (model 3, OR
0.64; CI, 0.38 to 1.07;
P
.088) to be associated with a lower OR of having the
metabolic syndrome.
Smoking
Subjects who reported never smoking did not have a signif-
icantly lower risk of having the metabolic syndrome in the
univariate regression analysis compared with those who cur-
rently smoked. In the adjusted models, never smoking was
accompanied by lower metabolic syndrome ORs in both men
and women compared with current smoking (Table 4). In
normal weight and overweight women, never smoking was
associated with a significantly lower risk of having the meta-
bolic syndrome compared with current smoking (model 3,
OR
0.51; CI, 0.28 to 0.94; P
.05 and OR
0.47; CI, 0.25
to 0.88; P
.05, for normal weight and overweight women,
respectively).
When an additional variable, such as total daily caloric
intake, was included in model 3, the significance of the mod-
ifiable lifestyle factors already in the model did not change, nor
did the magnitude of the coefficients for the additional variable
reach significance in any of the sex or BMI groups ( coeffi-
cients ranged between 0.00004 and 0.0001, P values ranged
between .08 and .80).
Table 2. Metabolic Syndrome ORs for Carbohydrate Intake
BMI* (kg/m
2
)
Carbohydrate Intake
All
25
25-29.9
30
OR† (CI)‡
P Value
OR (CI)
P Value
OR (CI)
P Value
OR (CI)
P Value
Men
Model 1
Low
0.69 (0.50-0.98)
.05
0.34 (0.14-0.76)
.05
0.50 (0.29-0.86)
.05
0.79 (0.37-1.65)
.514
Moderate
0.65 (0.46-0.92)
.05
0.40 (0.24-0.68)
.01
0.59 (0.35-0.98)
.05
0.71 (0.35-1.43)
.323
High (reference)
1.00
1.00
1.00
1.00
Model 2
Low
0.57 (0.39-0.84)
.01
0.47 (0.20-1.10)
.81
0.53 (0.29-0.97)
.05
0.80 (0.41-1.55)
.495
Moderate
0.57 (0.38-0.86)
.01
0.53 (0.32-0.86)
.05
0.56 (0.32-0.98)
.05
0.67 (0.36-1.27)
.212
High (reference)
1.00
1.00
1.00
1.00
Model 3
Low
0.41 (0.24-0.67)
.01
0.48 (0.12-1.95)
.298
0.33 (0.19-0.57)
.001
0.68 (0.28-1.67)
.354
Moderate
0.44 (0.25-0.77)
.01
0.46 (0.25-0.84)
.05
0.44 (0.23-0.87)
.05
0.52 (0.25-1.09)
.082
High (reference)
1.00
1.00
1.00
1.00
Women
Model 1
Low
0.84 (0.59-1.20)
.318
0.76 (0.39-1.46)
.396
0.61 (0.34-1.11)
.103
0.77 (0.49-1.22)
.264
Moderate
0.98 (0.76-1.27)
.884
0.66 (0.48-0.92)
.05
0.89 (0.54-1.46)
.630
0.96 (0.72-1.28)
.794
High (reference)
1.00
1.00
1.00
1.00
Model 2
Low
0.83 (0.60-1.15)
.264
1.17 (0.58-2.36)
.645
0.99 (0.55-1.79)
.983
0.81 (0.50-1.32)
.389
Moderate
0.91 (0.69-1.19)
.471
0.89 (0.68-1.16)
.374
1.04 (0.61-1.77)
.882
0.94 (0.66-1.35)
.744
High (reference)
1.00
1.00
1.00
1.00
Model 3
Low
0.92 (0.65-1.31)
.653
1.78 (0.57-5.54)
.309
1.20 (0.54-2.64)
.649
0.71 (0.40-1.25)
.227
Moderate
0.96 (0.69-1.34)
.813
1.16 (0.70-1.92)
.564
1.13 (0.57-2.27)
.715
0.85 (0.54-1.36)
.497
High (reference)
1.00
1.00
1.00
1.00
NOTE. Model 1 includes as a predictor only for specific lifestyle-related risk factor; model 2 adds age, race, education and income levels, and
menopausal for women as predictors; and model 3 includes model 2 and other modifiable factors.
*BMI represents body-mass index; in the ALL group the model was also adjusted for BMI as a continuous variable.
†ORs represent the metabolic syndrome odds ratios for modifiable lifestyle factors.
‡CI represents 95% confidence interval, ORs
1.96 SE.
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Table 3. Metabolic Syndrome ORs for Alcohol Consumption
BMI* (kg/m
2
)
Alcohol Intake
All
25
25-29.9
30
OR† (CI)‡
P Value
OR (CI)
P Value
OR (CI)
P Value
OR (CI)
P Value
Men
Model 1
Light-moderate
0.57 (0.45-0.74)
.001
0.52 (0.29-0.96)
.05
0.59 (0.42-0.83)
.01
0.70 (0.43-1.12)
.131
Heavy
1.07 (0.80-1.43)
.637
1.15 (0.55-2.40)
.694
1.37 (1.05-1.78)
.05
1.46 (0.76-2.80)
.243
Never (reference) 1.00
1.00
1.00
1.00
Model 2
Light-moderate
0.82 (0.61-1.09)
.169
0.84 (0.43-1.66)
.605
0.75 (0.56-1.00)
.05
0.78 (0.47-1.27)
.303
Heavy
1.22 (0.89-1.68)
.210
1.05 (0.50-2.19)
.898
1.43 (1.11-1.84)
.01
1.43 (0.77-2.68)
.249
Never (reference) 1.00
1.00
1.00
1.00
Model 3
Light-moderate
0.89 (0.64-1.23)
.457
0.79 (0.39-1.60)
.498
0.82 (0.61-1.10)
.178
0.79 (0.45-1.40)
.409
Heavy
1.27 (0.89-1.81)
.180
1.03 (0.50-2.15)
.931
1.37 (1.04-1.79)
.05
1.56 (0.84-2.92)
.154
Never (reference) 1.00
1.00
1.00
1.00
Women
Model 1
Light-moderate
0.44 (0.37-0.53)
.001
0.40 (0.24-0.55)
.001
0.46 (0.34-0.62)
.001
0.65 (0.46-0.91)
.05
Heavy
0.60 (0.46-0.79)
.01
0.71 (0.33-1.49)
.351
0.69 (0.42-1.15)
.148
0.63 (0.38-1.05)
.076
Never (reference) 1.00
1.00
1.00
1.00
Model 2
Light-moderate
0.78 (0.64-0.95)
.05
0.75 (0.50-1.12)
.158
0.75 (0.53-1.08)
.118
0.85 (0.62-1.16)
.295
Heavy
0.70 (0.50-0.97)
.05
0.90 (0.43-1.88)
.780
0.83 (0.55-1.27)
.381
0.59 (0.34-1.01)
.054
Never (reference) 1.00
1.00
1.00
1.00
Model 3
Light-moderate
0.76 (0.61-0.95)
.05
0.70 (0.44-1.14)
.148
0.72 (0.49-1.05)
.089
0.86 (0.63-1.18)
.346
Heavy
0.62 (0.44-0.87)
.01
0.70 (0.31-1.58)
.374
0.64 (0.38-1.07)
.088
0.56 (0.32-0.98)
.05
Never (reference) 1.00
1.00
1.00
1.00
NOTE. Model 1 includes as a predictor only for specific lifestyle-related risk factor; model 2 adds age, race, education and income levels, and
menopausal for women as predictors; and model 3 includes model 2 and other modifiable factors.
*BMI represents body-mass index; in the ALL group the model was also adjusted for BMI as a continuous variable.
†ORs represent the metabolic syndrome odds ratios for modifiable lifestyle factors.
‡CI represents 95% confidence interval, ORs
1.96 SE.
Table 4. Metabolic Syndrome ORs for Smoking Habit
BMI* (kg/m
2
)
Smoking Status
All
25
25-29.9
30
OR† (CI)‡
P Value
OR (CI)
P Value
OR (CI)
P Value
OR (CI)
P Value
Men
Model 1
Never
0.96 (0.76-1.21)
.694
0.69 (0.32-1.49)
.333
0.66 (0.39-1.12)
.120
0.86 (0.49-1.53)
.598
Previous
1.92 (1.49-2.47)
.001
1.10 (0.54-2.24)
.798
1.50 (0.87-2.58)
.136
1.06 (0.64-1.76)
.814
Current (reference) 1.00
1.00
1.00
1.00
Model 2
Never
0.62 (0.45-0.86)
.01
0.65 (0.30-1.42)
.267
0.61 (0.37-1.00)
.051
0.86 (0.44-1.68)
.645
Previous
0.76 (0.55-1.05)
.095
0.54 (0.27-1.10)
.088
0.87 (0.52-1.46)
.586
0.78 (0.43-1.41)
.394
Current (reference) 1.00
1.00
1.00
1.00
Model 3
Never
0.63 (0.45-0.90)
.05
0.57 (0.26-1.24)
.149
0.62 (0.37-1.05)
.076
0.89 (0.45-1.74)
.717
Previous
0.78 (0.57-1.08)
.128
0.52 (0.26-1.04)
.065
0.93 (0.58-1.49)
.744
0.76 (0.39-1.46)
.395
Current (reference) 1.00
1.00
1.00
1.00
Women
Model 1
Never
1.08 (0.83-1.40)
.571
0.84 (0.43-1.63)
.593
0.87 (0.52-1.45)
.593
1.13 (0.67-1.93)
.630
Previous
1.51 (1.15-2.00)
.01
1.35 (0.81-2.24)
.240
1.18 (0.63-2.22)
.596
1.78 (1.05-3.01)
.05
Current (reference) 1.00
1.00
1.00
1.00
Model 2
Never
0.63 (0.45-0.88)
.01
0.55 (0.29-1.01)
.055
0.51 (0.27-0.95)
.05
0.94 (0.56-1.57)
.799
Previous
0.77 (0.54-1.10)
.141
0.81 (0.45-1.46)
.465
0.66 (0.31-1.41)
.277
1.24 (0.75-2.05)
.391
Current (reference) 1.00
1.00
1.00
1.00
Model 3
Never
0.58 (0.41-0.81)
.01
0.51 (0.28-0.94)
.05
0.47 (0.25-0.88)
.05
0.90 (0.54-1.51)
.685
Previous
0.76 (0.53-1.07)
.116
0.78 (0.42-1.46)
.422
0.66 (0.32-1.36)
.250
1.27 (0.75-2.14)
.362
Current (reference) 1.00
1.00
1.00
1.00
NOTE. Model 1 includes as a predictor only for specific lifestyle-related risk factor; model 2 adds age, race, education and income levels, and
menopausal for women as predictors; and model 3 includes model 2 and other modifiable factors.
*BMI represents body-mass index; in the ALL group the model was also adjusted for BMI as a continuous variable.
†ORs represent the metabolic syndrome odds ratios for modifiable lifestyle factors.
‡CI represents 95% confidence interval, ORs
1.96 SE.
1507
METABOLIC SYNDROME AND LIFESTYLE

Page 6

Sex Differences
There were no significant interaction effects with physical
activity or smoking habits in relation to the metabolic syn-
drome between genders in the overall BMI group. However,
the interaction between alcohol intake and gender was signif-
icant with heavy drinking (OR
1.90; CI, 1.13 to 3.19; P
.05) indicating a greater OR for the metabolic syndrome in
heavy drinking men compared with women. There was also a
trend for an interaction between CHO intake and gender with
an OR of 0.70 (CI, 0.45 to 1.07; P
.096) at a moderate CHO
intake level.
Combinations of Lifestyle Behaviors
The odds of having the metabolic syndrome when a combi-
nation of low-risk lifestyle factors and BMI are present are
shown in Fig 1 for men (Fig 1A) and women (Fig 1B). The
low-risk factors identified included being physically active,
having a low or moderate CHO intake for men or a light-to-
moderate alcohol consumption for women, and never smoking.
Four sets of combined factors are presented within each gender
category and the respective sets include ORs for the 3 condition
models.
Being physically active, compared with being physically
inactive, regardless of BMI, was associated with a lower OR of
having the metabolic syndrome by 31% in men (OR
0.69;
CI, 0.51 to 0.94; P
.05) and 17% in women (OR
0.83; CI,
0.59 to 1.18; P
.293). Furthermore, being physically active
combined with a BMI below 30 kg/m
2
or below 25 kg/m
2
was
associated with a substantially lower risk of having the meta-
bolic syndrome: 71% (OR
0.29; CI, 0.21 to 0.40; P
.001)
and 84% (OR
0.16; CI, 0.09 to 0.27; P
.001) in normal
weight and overweight men and 79% (OR
0.21; CI, 0.15 to
0.29; P
.001) and 94% (OR
0.06; CI, 0.03 to 0.13; P
.001) in normal weight and overweight women (Fig 1).
If, in addition to being physically active, men consumed a
low or moderate CHO diet and women had a light-to-moderate
alcohol intake, the metabolic syndrome OR was reduced to
0.62 (CI, 0.47 to 0.83; P
.01) and 0.72 (CI, 0.51 to 1.02; P
.062), respectively. If individuals had BMIs 30 or 25 kg/m
2
,
the respective ORs for having the metabolic syndrome were
even lower in both men (0.27, CI, 0.20 to 0.37; P
.001; 0.13,
CI, 0.07 to 0.25; P
.001, for BMI
30 and 25 kg/m
2
,
respectively) and women (0.14, CI, 0.07 to 0.25; P
.001;
0.04, CI, 0.02 to 0.12; P
0.001, for BMI
30 and 25 kg/m
2
,
respectively).
When being physically active and never smoking were com-
bined, metabolic syndrome ORs were 0.61 (CI, 0.35 to 1.06;
P
.076) for men and 0.79 (CI, 0.53 to 1.19; P
.255) for
women, in general. With BMIs
30 or 25 kg/m
2
, the respec-
tive ORs for having the metabolic syndrome were again even
lower in men (0.30, CI, 0.17 to 0.54; P
.001; 0.22, CI, 0.10
to 0.46; P
.001) and women (0.19, CI, 0.11 to 0.31; P
.001; 0.08, CI, 0.03 to 0.23; P
.001).
When individuals had all of the following low-risk lifestyle
behaviors, being physically active, having a low or moderate
CHO intake for men or light-to-moderate alcohol consumption
for women, and being a nonsmoker, the metabolic syndrome
ORs were further reduced in both men (OR
0.52; CI, 0.27 to
0.99; P
.05) and women (OR
0.54; CI, 0.30 to 0.97; P
.05). Subjects who exhibited all of these low-risk behaviors
combined and who had a BMI
30 or 25 kg/m
2
had a
substantially lower risk of being diagnosed with the metabolic
syndrome, 73% and 85% lower in men and 90% and 94% lower
in women, respectively.
DISCUSSION
The metabolic syndrome, a prevalent within-individual clus-
tering of CVD risk factors, is affected by lifestyle behaviors
Fig 1. ORs of having the metabolic syndrome with low-risk be-
haviors or lifestyle. Factors: physically active, low and moderate CHO
intake (for men), light-to-moderate alcohol consumption (for
women), and nonsmoking, overall (white bar) and with additional
conditions of normal weight (BMI < 25 kg/m
2
, black bar) and over-
weight (BMI < 30 kg/m
2
, gray bar). In each case, the reference group
for the comparison is with subjects who do not qualify for that
low-risk behavior or lifestyle category. PA, physically active; CHO,
low and moderate carbohydrate intakes; Alc, light-to-moderate alco-
hol consumption; SMK, smoking.
1508
ZHU ET AL

Page 7

including physical activity, diet, smoking, and drinking hab-
its.
1,12
In the present study, we developed logistic regression
models examining the independent impact of these lifestyle
factors on the odds of having the metabolic syndrome within
different BMI categories. Our models reveal that the likelihood
of being diagnosed with the metabolic syndrome is lower in
individuals who exhibit specific lifestyle behaviors either alone
or in combination, particularly if their BMI is maintained in the
non-obese range.
Lifestyle Factors and Risk Modification
Physical activity level. Of the examined lifestyle factors,
the most strongly associated with a lower risk of having the
metabolic syndrome was physical activity. Our findings are in
agreement with recent epidemiologic studies that link greater
physical activity with a reduced risk of having the metabolic
syndrome
17-22
and coronary heart disease.
23
However, in contrast to others,
24
the observed significant
lower OR between physical activity level and metabolic syn-
drome in univariate analysis persisted after controlling for age,
ethnicity, BMI, socioeconomic status, as well as when addi-
tionally controlling for smoking, drinking, and dietary habits. It
is known that a lower level of physical activity is associated
with low socioeconomic status and a higher mortality from
CVD.
8
In addition, low education level may increase CVD risk
through unhealthy dietary habits,
25-27
higher rates of smoking,
and overweight.
28
Physical activity could exert its protective effects on the
metabolic syndrome through improvements in plasma lipid
concentrations, particularly through increases in HDL choles-
terol concentrations,
29-33
decreases in triglyceride concentra-
tions,
34
or both.
22,35,36
In addition, physical activity has been
shown to lower blood pressure,
22,37
improve glucose toler-
ance
38,39
and insulin sensitivity,
40,41
and reduce the risk of
being diagnosed with type 2 diabetes.
9,42
CHO intake. In addition to physical activity level, our
models reveal a relatively low risk of having the metabolic
syndrome in men who consume a low or moderate CHO diet.
High-CHO intake has been shown to be associated with lower
HDL cholesterol and higher triglyceride concentrations, 2 cri-
teria for the diagnosis of the metabolic syndrome.
43,44
Parks
and Hellerstein
44
reported that the effects of high CHO con-
sumption on triglyceride concentrations appear to be greater in
men than women, which may, in part, explain the different
gender effects of CHO intake on metabolic syndrome risk.
However, our analyses did not discriminate between simple and
complex CHO and therefore it is not known whether the
association between CHO and the odds of having the metabolic
syndrome is due to high intakes of simple sugars consumed in
the American population and may not be indicative of high
complex CHO consumption.
Alcohol intake. A “J-shaped” relation exists between al-
cohol intake and CVD risk with minimal risk of CVD at
light-to-moderate alcohol intakes.
45-47
Light-to-moderate al-
cohol consumption may lower CVD risk by increasing HDL
cholesterol concentrations, inhibiting low-density lipopro-
tein cholesterol oxidation, decreasing blood pressure and
insulin levels, and increasing insulin sensitivity, thus im-
proving factors that have been associated with the metabolic
syndrome.
45-47
In accord with earlier studies, we also ob-
served a lower risk of having the metabolic syndrome in
light-to-moderate drinkers. With heavy drinking, men had an
increased risk of having metabolic syndrome, consistent
with a J-shaped effect of alcohol on CVD risk. However,
compared with nondrinkers, an even lower metabolic syn-
drome OR was observed in heavy drinking women than in
their light-to-moderate drinking counterparts. The reason for
this gender difference is uncertain and requires replication
and further investigation.
Combined measures. Our logistic models suggest that the
majority of Americans would not be diagnosed with the
metabolic syndrome if they had appropriate lifestyle behav-
iors, such as being physically active, having a low CHO
intake for men or light-to-moderate drinking habits for
women, and not smoking, and had a BMI
30 kg/m
2
. These
results are in agreement with earlier studies that found that,
in women, combining several lifestyle behaviors, including
maintaining a BMI
25 kg/m
2
, was associated with a 90%
lower risk of having type 2 diabetes compared with women
without these behaviors.
9
Walkins et al
48
reported that ex-
ercise combined with weight loss was a more effective
treatment compared with exercise alone for hyperinsulin-
emia and lowering of diastolic blood pressure in patients
with the metabolic syndrome.
Our models show that having a combination of several
“low-risk” behaviors in nonsmokers, such as being physically
active, consuming a diet low or moderate in CHO (for men) and
light-to-moderate in alcohol (for women), substantially lowers
the risk of having the metabolic syndrome by approximately
45%. If BMI was in the normal weight or non-obese range, this
reduction in the risk of having the metabolic syndrome would
increase to approximately 85% or 73%, respectively, for men
and 94% or 90%, respectively for women.
Study Limitations
The purpose of developing these logistic models was to
estimate the potential effects of lifestyle changes on the risk
of developing the metabolic syndrome. Ideally, such models
are developed from an experimental or cohort longitudinal
database and not from cross-sectional data. As a result,
causal pathways underlying the observed associations be-
tween the lifestyle factors examined here and the metabolic
syndrome cannot be inferred. Longitudinal studies are
needed to confirm the effects of lifestyle modifications on
the metabolic syndrome. Nevertheless, using NHANES data
enabled us to assess the strength of the relationships between
these factors and the metabolic syndrome in a representative
sample of the US population and to gauge the potential for
population benefits. Regression models further adjusting for
various potential confounding factors allowed us to assess
the effects of the covariates, such as age, ethnicity, educa-
tion and income levels, and menopausal status, on the rela-
tionships between lifestyle factors and metabolic syndrome
(results not presented). When an additional covariate was
added into the final model, the coefficients for the covariate
1509
METABOLIC SYNDROME AND LIFESTYLE

Page 8

were small and did not reach statistical significance indicat-
ing a good fit of the final model to the data.
Conclusions
Although the cross-sectional nature of the present study does
not allow causality to be inferred, the associations between
“healthy” behaviors and the metabolic syndrome observed sug-
gest that combining certain lifestyle behaviors places an indi-
vidual at lower risk of having the metabolic syndrome. Whether
the adoption of such behaviors by individuals will result in a
lowering of the prevalence of the metabolic syndrome remains
unknown. Nevertheless, our results lead us to posit that this
may be the case and thus have important value for public health
recommendations.
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METABOLIC SYNDROME AND LIFESTYLE

Информация о работе Lifestyle Behaviors Associated With Lower Risk of Having the Metabolic Syndrome