Risk of Metabolic Syndrome among Middle-Aged Koreans from Rural and Urban Areas


Lee, S.; Shin, Y.; Kim, Y.

Nutrients 10(7): E859

2018


Metabolic syndrome (MetS) is a common global health problem. This study aims to assess nutrient intake and risk of MetS in middle-aged Koreans based in residential areas. Participants were 161,326 (142,137 in urban and 19,189 in rural) subjects enrolled in the Korea Genome and Epidemiology Study. The prevalence of MetS was much higher in rural (39.8%) than that in urban (22.5%) subjects (<i>p</i> < 0.001). The rural residents showed significantly higher blood pressure (<i>p</i> < 0.001), serum triglyceride levels (<i>p</i> < 0.001), and LDL (Low density lipoprotein)-cholesterol level (<i>p</i> < 0.001), as well as the odds ratio (OR) for MetS (OR = 1.65, 95% CI: 1.59⁻1.71), compared to urban residents. The rural subjects showed a higher consumption of carbohydrate and sodium compared to the urban subjects (<i>p</i> < 0.001). After adjusting for potential confounders, subjects in the highest quartile of carbohydrate intake had higher OR for MetS (OR = 1.23, 95% CI: 1.15⁻1.32) and those in the highest quartile of sodium intake had a higher chance of having MetS (OR = 1.11, 95% CI: 1.07⁻1.16) than did those in the lowest quartiles. Our results suggested that the higher consumption of carbohydrate and sodium in rural residents might be associated with the increased risk of MetS in this population.

nutrients
Article
Risk
of
Metabolic
Syndrome
among
Middle-Aged
Koreans
from
Rural
and
Urban
Areas
Seohyun
Lee,
Yoonjin
Shin
and
Yangha
Kim
*
Department
of
Nutritional
Science
and
Food
Management,
Ewha
Womans
University,
Seoul
03760,
Korea;
hayeeun@empas.com
(S.L.);
yjin19@hotmail.com
(Y.S.)
*
Correspondence:
yhmoon@ewha.ac.kr
;
Tel.:
+82-2-3277-3101
check
for
Received:
25
May
2018;
Accepted:
29
June
2018;
Published:
3
July
2018
updates
Abstract:
Metabolic
syndrome
(MetS)
is
a
common
global
health
problem.
This
study
aims
to
assess
nutrient
intake
and
risk
of
MetS
in
middle-aged
Koreans
based
in
residential
areas.
Participants
were
161,326
(142,137
in
urban
and
19,189
in
rural)
subjects
enrolled
in
the
Korea
Genome
and
Epidemiology
Study.
The
prevalence
of
MetS
was
much
higher
in
rural
(39.8%)
than
that
in
urban
(22.5%)
subjects
(p
<
0.001).
The
rural
residents
showed
significantly
higher
blood
pressure
(p
<
0.001),
serum
triglyceride
levels
(p
<
0.001),
and
LDL
(Low
density
lipoprotein)-cholesterol
level
(p
<
0.001),
as
well
as
the
odds
ratio
(OR)
for
MetS
(OR
=
1.65,
95%
CI:
1.59-1.71),
compared
to
urban
residents.
The
rural
subjects
showed
a
higher
consumption
of
carbohydrate
and
sodium
compared
to
the
urban
subjects
(p
<
0.001).
After
adjusting
for
potential
confounders,
subjects
in
the
highest
quartile
of
carbohydrate
intake
had
higher
OR
for
MetS
(OR
=
1.23,
95%
CI:
1.15-1.32)
and
those
in
the
highest
quartile
of
sodium
intake
had
a
higher
chance
of
having
MetS
(OR
=
1.11,
95%
CI:
1.07-1.16)
than
did
those
in
the
lowest
quartiles.
Our
results
suggested
that
the
higher
consumption
of
carbohydrate
and
sodium
in
rural
residents
might
be
associated
with
the
increased
risk
of
MetS
in
this
population.
Keywords:
metabolic
syndrome;
urban;
rural;
carbohydrate;
sodium
1.
Introduction
Metabolic
syndrome
(MetS)
is
a
major
emerging
global
health
problem.
MetS
is
associated
with
the
development
of
cardiovascular
disease
(CVD)
and
diabetes
because
it
is
primarily
associated
with
abdominal
obesity,
high
blood
glucose,
high
blood
pressure,
and
dyslipidemia.
CVD
is
a
main
cause
of
mortality
in
Korea,
and
MetS
is
an
important
risk
factor
for
CVD.
The
number
of
people
with
MetS
is
increasing
at
an
alarming
rate
worldwide.
Within
the
United
States,
23.7%
of
the
population
had
high
prevalence
of
MetS,
according
to
the
definition
of
the
National
Cholesterol
Education
Program
(NCEP)
Adult
Treatment
Panel
III
(NCEP-ATP
III)
[
].
The
number
of
individuals
with
MetS
has
also
increased
with
the
increase
of
abdominal
obesity
and
dyslipidemia
in
Korea
[2].
The
incidence
of
age-adjusted
MetS
among
participants
in
the
Korean
National
Health
and
Nutrition
Examination
Survey
(KNHNES)
from
1998
to
2007
reflected
a
steady
climb
as
shown
by
the
following
numbers:
24.9%
in
1998,
29.2%
in
2001,
30.4%
in
2005,
and
31.3%
in
2007
[2].
The
prevalence
of
MetS
has
increased
in
Korean
adults
of
all
ages,
especially
after
middle-age
[2].
Environmental
factors,
including urbanization,
and
westernization
of
lifestyle
can
significantly
affect
the
prevalence
of
MetS
among
various
population
groups.
MetS
is
affected
by
not
only
socioeconomic
factors
as
education
and
income
but
also
lifestyle
factors
such
as
diet,
smoking,
alcohol
consumption,
and
physical
activity
[
].
Diet
in
particular
has
been
reported
to
be
strongly
associated
with
the
risk
of
MetS.
Diets
rich
in
fruits,
vegetables,
and
whole
grains
have
been
reported
to
help
prevent
MetS
[
"],
whereas
meats,
fried
foods,
and
diet
soda
were
adversely
linked
with
incidence
of
Nutrients
2018,
10,
859;
doi:10.3390/nu10070859
www.mdpi.com/journal/nutrients
Nutrients
2018,
10,
859
2
of
15
MetS
[
].
In
addition,
dietary
nutrients
such
as
carbohydrates,
protein,
fat,
and
sodium
have
been
reported
to
be
related
to
the
prevalence
of
MetS
[3].
Socioeconomic
changes
and
urbanization
throughout
their
economic
growth
eventually
affected
a
nutritional
transition
within
developing
Asian
countries
such
as
South
Korea
[6],
China,
Malaysia,
and
India
[
].
Urbanization
is
related
to
increased
intake
of
energy-rich
foods
and
shift
to
a
Western-style
diet.
However,
these
changes
generally
occur
at
prominently
different
rates
across
urban-rural
residential
areas.
The
dietary
habits
of
urban
residents
are
characteristically
high
in
sugar,
fat,
and
animal
protein.
Residents
of
rural
areas,
however,
have
generally
retained
a
more
traditional
diet
of
natural
foods
than
those
in
urban
areas
[
].
Rapid
socioeconomic
transition
in
Korea
over
the
last
several
decades
brought
significant
change
in
the
lifestyle
of
the
people,
resulting
in
changes
in
MetS
prevalence
patterns.
Lifestyle
and
diet,
major
factors
affecting
MetS,
are
reported
to
be
different
in
urban
and
rural
areas
in
Korea
[9,10].
To
better
understand
the
prevalence
of
MetS
of
urban
and
rural
areas
in
Korea,
large-scale
epidemiologic
studies
of
populations
across
a
wide
range
of
communities
are
needed.
Furthermore,
it
is
meaningful
to
examine
regional
characteristics
and
the
MetS
prevalence
according
to
the
residential
areas
through
large-scale
cohort
data
because
it
could
be
useful
as
basic
data
to
inform
the
development
of
public
health
promotion
programs
and
related
policies
for
prevention
or
reduction
of
MetS
in
each
region.
The
purpose
of
this
study
was
to
investigate
nutrient
intake
and
the
risk
of
MetS
according
to
rural
and
urban
areas
using
data
of
the
Korea
Genome
and
Epidemiology
Study
(KoGES).
2.
Materials
and
Methods
2.1.
Data
Source
and
Study
Population
Baseline
examination
data
from
the
Health
Examinee
(HEXA)
cohort
and
Cardiovascular
Disease
Association
Study
(CAVAS)
cohort,
two
subprojects
of
KoGES
were
used
in
this
study.
The
KoGES
is
an
ongoing
population-based
cohort
to
investigate
genetic
and
environmental
risk
factors
and
their
interaction
with
the
main
diseases
found
in
the
Korean
population.
The
baseline
examination
of
the
HEXA
cohort
and
CAVAS
cohort
consist
of
urban-
or
rural-community
dwellers,
respectively.
The
baseline
investigation
of
the
HEXA
cohort
was
performed
at
38
community-health
examination
centers
and
training
hospitals
located
in
14
large
urban
areas
during
2004-2013,
and
the
CAVAS
cohort
was
conducted
at
11
rural
areas
during
2005-2011
in
Korea.
The
participants,
who
were
aged
40-70
years
at
baseline,
were
recruited
from
the
national
health
examinee
registry.
We
used
their
baseline
survey
and
measurement
data.
More
detailed
information
on
the
aims,
the
community
characteristics,
and
the
baseline
characteristics
is
given
elsewhere
[11].
All
participants
completed
an
interviewer-administered
questionnaire
that
included
sociodemographic
and
behavioral
characteristics
and
a
validated
food
frequency
questionnaire.
Anthropometric
measurements
and
biochemical
measurements
were
also
performed
for
all
participants.
Informed
consent
was
obtained
from
all
participants.
Initially,
201,695
subjects
(173,357
in
urban
and
28,338
in
rural)
were
recruited
at
baseline.
We
exclude
participants
who
reported
implausible
daily
energy
intake
(<800
or
>4000
kcal/day
in
men,
<500
or
>3500
kcal/day
in
women),
those
over
aged
64
years
old,
those
who
provided
insufficient
anthropometric
information,
and
those
who
did
not
respond
to
biochemical
information
(total
cholesterol,
triglyceride,
high
density
lipoprotein
(HDL)-cholesterol,
diastolic
blood pressure
(DBP),
systolic
blood
pressure
(SBP),
fasting
glucose
level).
After
exclusion,
161,326
(142,137
in
urban
and
19,189
in
rural)
were
included
in
the
final
data
analyses.
The
study
protocol
was
approved
by
the
Ethics
Committee
of
the
Korean
Health
and
Genome
Study
and
the
Institutional
Review
Board
of
the
Ewha
Womans
University
(IRB
No:135-1),
which
was
in
compliance
with
the
Declaration
of
Helsinki.
Nutrients
2018,
10,
859
3
of
15
2.2.
Dietary
Assessment
Dietary
intake
data
were
collected
through
a
semi-quantitative
food
frequency
questionnaire
(SQFFQ)
involving
106
items,
which
was
developed
and
validated
by
Korea
Centers
for
Disease
Control
and
Prevention
(KCDC)
for
KoGES.
More
detailed
information
concerning
the
protocol
and
the
results
of
a
validation
study
about
SQFFQ
is
given
elsewhere
[12].
Nutrient
intake
was
measured
using
a
database
developed
by
the
Rural
Development
and
Administration
of
Korea.
2.3.
Definition
of
Metabolic
Syndrome
Metabolic
syndrome
was
defined
based
on
the
National
Cholesterol
Education
Program
(NCEP)
Adult
Treatment
Panel
III
criteria
[ ]
as
having
three
or
more
of
the
following
components:
(i)
abdominal
obesity:
a
waist
circumference
>90
cm
in
men
and
>80
cm
in
women
(waist
circumference
criteria
was
modified
by
the
International
Obesity
Task
Force
criteria
for
Asian-pacific
populations
[14]);
(ii)
high
triglycerides
level:
>150
mg/dL
(1.70
mmol/L);
(iii)
low
HDL-cholesterol
level:
<40
mg/
dL
(1.04
mmol/L)
in
men
and
<50
mg/
dL
(1.30
mmol/L)
in
women;
(iv)
elevated
blood
pressure:
>130/85
mmHg
or
antihypertensive
medication;
(v)
elevated
fasting
blood
glucose:
>100
mg/dL
(5.60
mmol/L)
or
medication
(insulin
or
oral
agents).
2.4.
Other
Measurements
Demographic
characteristics,
socioeconomic
status,
and
health-related
factors
data
were
collected
using
a
standardized
questionnaire.
Education
status
as
present
socioeconomic
status
was
categorized
into
four
groups:
<Elementary
school,
<High
school,
<University,
>University
Monthly
income
level
was
divided
into
four
groups:
<$1000,
$1000-2000,
$2000-4000,
>$4000.
Smoking
status
and
alcohol
consumption
were
classified
as
current
or
past/never.
Regular
exercise
was
classified
as
"yes"
or
"no"
depending
on
whether
participants
regularly
exercised
enough
to
sweat
at
least
once
a
week.
Marital
status
was
categorized
by
married
or
other,
including
single,
separated,
divorced,
widowed,
and
cohabiting.
The
presences
of
CVD
and
cancer
were
assessed
by
self-reporting.
Myocardial
infarction,
angina
pectoris,
and
stroke
were
considered
CVD.
Anthropometric
characteristics
were
also
collected
using
standardized
methods.
Height,
weight,
waist
circumference,
and
hip
circumference
were
collected
using
standardized
techniques
and
calibrated
equipment.
Height
and
weight
were
measured
to
the
nearest
0.1
cm
or
0.1
kg,
respectively.
Body
mass
index
(BMI),
which
reflects
obesity
status,
was
defined
as
weight
(kg)/height
(m
2
).
Participants
were
classified
into
two
categories
based
on
BMI:
<25
kg/m
2
or
>25
kg/m
2
.
Waist
circumference
was
measured
at
the
narrowest
point
between
the
lowest
rib
and
the
right
iliac
crest
to
the
nearest
0.1
cm.
Hip
circumference
was
measured
at
the
maximal
extension
of
the
buttocks
to
the
nearest
0.1
cm.
Blood
pressure
was
recorded
after
participants
had
rested
for
more
than
10
min.
SBP
and
DBP
were
measured
two
times
in
the
right
arm
using
an
automatic
sphygmomanometer
or
standardized
mercury
depending
on
the
institution.
Blood
total
cholesterol,
HDL-cholesterol,
triglyceride
and
glucose
levels
were
measured
using
the
enzyme
method
(ADVIA
1650
and
ADVIA
1800;
Siemens
Healthineers,
Deerfield,
IL,
USA).
Low
density
lipoprotein
(LDL)-cholesterol
level
was
calculated
by
using
the
Friedewald
formula
[15]
in
individuals
with
blood
triglyceride
levels
<400
mg/dL
(<4.52
mmol/L).
2.5.
Statistical
Analysis
Sociodemographic
characteristics
and
health-related
variables
are
expressed
as
means
with
their
standard
error
(continuous)
or
number
with
percentages
(categorical).
Differences
in
baseline
characteristics
were
examined
by
Student's
t
test
for
continuous
variables
or
chi-square
test
for
categorical
variables
by
dwell
region
and
sex.
The
general
linear
model
was
used
to
examine
for
significant
differences
in
mean
values
for
nutrient
intakes.
Multivariable
logistic
regression
analysis
was
used
to
assess
to
comparison
of
the
risk
of
MetS
between
rural
and
urban
areas.
The
95%
confidence
intervals
(95%
CI)
of
the
odds
ratio
(OR)
were
estimated
using
the
Wald
method.
Model
I
Nutrients
2018,
10,
859
4
of
15
was
crude
data.
Model
II
was
adjusted
for
age,
education
level,
household
income,
smoking
status,
alcohol
intake,
regular
exercise,
BMI,
and
daily
total
energy
intake.
In
this
study,
BMI
was
considered
as
a
potential
confounder
variable
due
to
it
being
an
important
risk
factor
for
MetS
and
its
components.
However,
as
BMI
has
a
high
correlation
with
waist
circumference,
the
final
model
excluded
BMI
from
OR
calculation
of
abdominal
obesity,
a
component
of
MetS.
Logistic
regression
model
was
also
used
to
analyze
whether
the
multivariate-adjusted
OR
for
MetS
was
associated
with
carbohydrate
and
sodium
intake.
The
linear
trend
test
across
increasing
categories
of
carbohydrate
and
sodium
was
conducted
as
continuous
variables
using
median
consumption
of
within
each
category.
All
the
data
were
analyzed
using
SAS
9.4
(SAS
Institute,
Cary,
NC,
USA).
All
of
the
p-values
were
two-sided,
and
statistical
significance
was
defined
as
p
<
0.05.
3.
Results
3.1.
General
Characteristics
of
Study
Population
The
sociodemographic,
anthropometric,
clinical,
and
biochemical
variables
of
the
study
subjects
are
shown
in
Tables
1
and
2.
The
mean
age
for
rural
men
and
women
was
higher
than
that
of
urban
men
and
women,
and
the
proportion
of
population
over
50
years
old
was
significantly
higher
in
rural
areas
than
in
urban.
The
rural
participants
had
lower
rates
of
education
and
lower
household
incomes.
The
proportion
of
the
male
smokers
was
higher
in
rural
than
urban
locations,
but
there
were
fewer
female
smokers
in
rural
than
in
urban
areas.
The
percentage
of
alcohol
intake
and
regular
exercise
in
rural
populations
were
lower
than
those
of
urban
dwellers.
Within
the
physical
and
blood
biochemical
parameters,
overall,
rural
subjects
had
higher
weight,
waist
circumference,
SBP,
and
DBP
than
their
urban
counterparts.
BMI
was
higher
in
rural
women
than
urban
women,
but
not
men.
Fasting
blood
glucose,
levels
of
triglyceride,
total
cholesterol,
LDL-cholesterol,
and
the
ratio
of
LDL-cholesterol
to
HDL-cholesterol
were
higher
in
rural
dwellers
compared
to
those
of
urban
populations.
On
the
other
hand,
the
HDL-cholesterol
level
was
lower
in
rural
people
than
in
urban
people.
3.2.
Nutrients
Intake
of
Study
Population
Table
3
shows
the
subjects'
daily
nutrient
consumption
by
residence
and
gender.
Total
daily
energy
intakes
of
all
participants
were
1697.7
kcal
for
rural
subjects
and
1750.8
kcal
for
urban
subjects.
Rural
residents
had
a
significantly
lower
energy
intake
compared
to
their
urban
counterparts.
Macronutrient
intakes,
including
protein
and
fat,
were
also
significantly
lower
in
rural
than
in
urban
areas.
The
ratio
of
carbohydrate
to
protein
to
fat
displayed
a
similar
pattern
without
reference
to
residence.
However,
the
individual
ratios
of
macronutrients
showed
differences
between
urban
and
rural
areas.
The
carbohydrate
portion
for
total
energy
was
significantly
higher
in
rural
populations
than
in
urban
populations;
however,
urban
subjects
consumed
more
protein
and
fat
than
rural
subjects.
Most
micronutrient
consumption
was
significantly
lower
in
rural
residents
than
those
of
urban
residents,
except
for
sodium
intake;
which
was
significantly
higher
for
both
genders
in
rural
areas
than
that
of
the
urban
areas.
Nutrients
2018,
10,
859
5
of
15
Table
1.
General
characteristics
of
rural
and
urban
Korean
adults
aged
40-64,
CAVAS
(2005-2011)
and
HEXA
(2004-2013)
cohorts.
Men
(n
=
53,704)
Women
(It
=
107,622)
Overall
(n=
161,326)
Urban
(n
=
46,680)
Rural
(It
=
7024)
p-Value
Urban
(n
=
95,457)
Rural
(it
=
12,165)
p-Value
Urban
Rural
(It
=
142,137)
(It
=
19,189)
p-Value
Age
(year)
51.68
±
0.03
54.53
±
0.08
<0.0001
51.17
±
0.02
53.65
±
0.06
<0.0001
51.34
±
0.02
53.95
±
0.05
<0.0001
40-49
18,568
(39.78)
1749
(24.90)
<0.0001
39,806
(41.70)
3612
(29.69)
<0.0001
58,374
(41.07)
5361
(27.94)
<0.0001
50-64
28,112
(60.22)
5275
(75.10)
<0.0001
55,651(58.30)
8553
(70.31)
<0.0001
83,763
(58.93)
131,828
(72.06)
<0.0001
Education
level
<0.0001
<0.0001
<0.0001
<Elementary
4051
(8.68)
2493
(35.49)
17,112
(17.93)
6761
(55.58)
21,163
(14.89)
9254
(48.23)
<High
school
21,520
(46.10)
3470
(49.40)
54,381
(56.97)
4616
(37.94)
75,901(53.40)
8086
(42.14)
<University
16,275
(34.87)
822
(11.70)
20,219
(21.18)
697
(5.73)
36,494
(25.68)
1519
(7.92)
>University
4325
(9.27)
216
(3.08)
2613
(2.74)
60
(0.49)
6938
(4.88)
276
(1.44)
Unknown
509
(1.09)
23
(0.33)
1132
(1.19)
31
(0.25)
1641
(1.15)
54
(0.28)
Income
(USD/month)
<0.0001
<0.0001
<0.0001
<1000
2275
(4.87)
1615
(22.99)
8484
(8.89)
3423
(28.14)
10,759
(7.57)
5038
(26.25)
1000-2000
7074
(15.15)
1495
(21.28)
16,640
(17.43)
145
(15.99)
23,714
(16.68)
3440
(17.93)
2000-4000
19,177
(41.08)
1180
(16.80)
35,205
(36.88)
1693
(13.92)
54,382
(38.26)
2873
(14.97)
>4000
12,601
(26.99
)
365
(5.20)
19,946
(20.90)
507
(4.17)
32,547
(22.90)
872
(4.54)
Unknown
5553
(11.90)
2369
(33.73)
15,182
(15.90)
4597
(37.79)
20,735
(14.59)
6966
(36.30)
Marital
status
0.176
<0.0001
0.657
Married
41,802
(89.55)
6299
(89.68)
79,355
(83.13)
10,061
(82.70)
121,157
(85.24)
16,360
(85.26)
Others
3043
(6.52)
478(6.81)
12,018
(12.59)
1672
(13.74)
15,061
(10.60)
2150
(11.20)
Unknown
1835
(3.93)
247(3.52)
4084
(4.28)
432
(3.55)
5919
(4.16)
679
(3.54)
Smoking
status
<0.0001
<0.0001
0.104
Past/never
30,757(65.89)
4447
(63.32)
92,770
(97.19)
11,919
(97.98)
123,527
(86.91)
16,366
(85.29)
Current
15,820
(33.89)
2572
(36.62)
2323
(2.43)
227
(1.57)
18,143
(12.76)
2799
(14.59)
Unknown
103
(0.22)
5(0.07)
364
(0.38)
19
(0.16)
467
(0.33)
24
(0.13)
Alcohol
intake
<0.0001
<0.0001
<0.0001
Past/never
11,801
(25.28)
2269
(32.30
)
63,785
(66.82)
8461
(69.55)
75,586
(53.18)
10,730
(55.92)
Current
34,800
(74.55)
4747
(67.58)
31,345
(32.84)
3667
(30.14)
66,145
(46.54)
8414
(43.85)
Unknown
79
(0.17)
8
(0.11)
327
(0.34)
37
(0.16)
406
(0.29)
45
(0.23)
Regular
exercise
<0.0001
<0.0001
<0.0001
No
20,463
(43.84)
4642
(66.09)
47,008
(49.25)
7941
(65.28)
67,471(47.47)
12,583
(65.57)
Yes
26,140
(56.00)
2363
(33.64
)
48,264
(50.56)
4206
(34.57)
74,404
(52.35)
6569
(34.23)
Unknown
77
(0.16)
19
(0.27)
185
(0.19)
18
(0.15)
262
(0.18)
37
(0.19)
Disease
History
CVD
10,441
(22.37)
1736
(24.72)
<0.0001
15,931
(16.69)
2897
(23.81)
<0.0001
26,372
(18.55)
4633
(24.14)
<0.0001
Cancer
838
(1.80)
110
(1.57)
<0.0001
3294
(3.45)
281
(2.31)
<0.0001
4132
(2.91)
391
(2.04)
<0.0001
CAVAS:
Cardiovascular
Disease
Association
Study;
HEXA:
Health
Examinee;
USD:
United
States
dollar;
CVD:
Cardiovascular
disease.
Values
are
expressed
as
mean
±
SE
or
as
the
number
of
cases
(%);
Categorical
variables
were
analyzed
using
the
x
2
test;
continuing
variables
were
analyzed
using
the
t-test.
Overall
data
was
adjusted
for
sex
using
logistic
regression
for
categorical
variables
and
general
linear
regression
for
continuous
variables.
Nutrients
2018,
10,
859
6
of
15
Table
2.
Anthropometric
parameters,
blood
pressure,
and
blood
profiles
of
rural
and
urban
Korean
adults.
Men
(n
=
53,704)
Women
(n
=
107,622)
Overall
(n
=
161,326)
Urban
(n
=
46,680)
Rural
(n
=
7024)
p-Value
Urban
(n
=
95,457)
Rural
(n
=
12,165)
p-Value
Urban
(n
=
142,137)
Rural
(n
=
19,189)
p-Value
Height
(cm)
169.02
±
0.03
166.68
±
0.07
<0.0001
156.60
±
0.02
154.27
±
0.05
<0.0001
160.74
±
0.01
158.40
±
0.04
<0.0001
Weight
(kg)
70.02
±
0.04
68.07
±
0.11
<0.0001
57.89
±
0.03
58.85
±
0.08
<0.0001
61.93
±
0.02
61.84
±
0.06
0.146
BMI
(kg/m
2
)
24.48
±
0.01
24.46
±
0.03
0.651
23.61
±
0.00
24.72
±
0.03
<0.0001
23.90
±
0.01
24.60
±
0.02
<0.0001
<25
27,586
(59.10)
4080
(58.09)
<0.0001
68,677
(71.95)
6978
(57.36)
<0.0001
96,263
(67.73)
11,058
(57.63)
<0.0001
>25
19,094
(40.90)
2944
(41.91)
26,780
(28.05)
5187
(42.64)
45,874
(32.27)
8131
(42.37)
WC
(cm)
85.78
±
0.03
86.34
±
0.09
<0.0001
78.20
±
0.03
82.61
±
0.08
<0.0001
80.72
±
0.02
83.74
±
0.06
<0.0001
HC
(cm)
96.14
±
0.03
94.78
±
0.07
<0.0001
93.56
±
0.02
94.26
±
0.06
<0.0001
94.42
±
0.02
94.37
±
0.04
0.332
Waist-hip
ratio
0.89
±
0.00
0.91
±
0.00
<0.0001
0.84
±
0.00
0.88
±
0.00
<0.0001
0.85
±
0.00
0.89
±
0.00
<0.0001
SBP
(mmHg)
125.34
±
0.07
126.84
±
0.20
<0.0001
120.22
±
0.05
123.75
±
0.16
<0.0001
121.92
±
0.04
124.72
±
0.11
<0.0001
DBP
(mmHg)
78.81
±
0.05
82.27
±
0.13
<0.0001
74.64
±
0.03
78.53
±
0.10
<0.0001
76.03
±
0.03
79.76
±
0.07
<0.0001
FBG
(mg/dL)
98.87
±
0.11
102.49
±
0.34
<0.0001
92.57
±
0.06
95.54
±
0.19
<0.0001
94.67
±
0.06
97.87
±
0.15
<0.0001
Triglyceride
(mg/dL)
154.74
±
0.52
173.27
±
1.53
<0.0001
112.03
±
0.24
135.98
±
0.77
<0.0001
126.42
±
0.24
148.25
±
0.65
<0.0001
Total
cholesterol
(mg/dL)
194.25
±
0.16
195.03
±
0.43
0.088
199.28
±
0.12
202.78
±
0.34
<0.0001
197.60
±
0.09
200.12
±
0.26
<0.0001
HDL-cholesterol
(mg/dL)
49.63
±
0.05
43.40
±
0.13
<0.0001
56.56
±
0.04
46.70
±
0.10
<0.0001
54.25
±
0.03
45.71
±
0.09
<0.0001
LDL-cholesterol
(mg/dL)
115.25
±
0.15
119.47
±
0.39
<0.0001
120.61
±
0.10
129.51
±
0.30
<0.0001
118.86
±
0.08
126.10
±
0.23
<0.0001
LDL/HDL
2.42
±
0.00
2.87
±
0.01
<0.0001
2.23
±
0.00
2.88
±
0.01
<0.0001
2.30
±
0.00
2.88
±
0.01
<0.0001
BMI:
Body
mass
index;
WC:
Waist
circumference;
HC:
Hip
circumference;
SBP:
Systolic
blood
pressure;
DBP:
Diastolic
blood
pressure;
FBG:
Fasting
blood
glucose;
HDL:
High
density
lipoprotein;
LDL:
Low
density
lipoprotein.
Values
are
expressed
as
means
±
SE
or
as
the
number
of
cases
(%).Categorical
variables
were
analyzed
using
the
x
2
test;
continuing
variables
were
analyzed
using
the
t-test.
Overall
data
was
adjusted
for
sex
using
logistic
regression
for
categorical
variables
and
general
linear
regression
for
continuous
variables.
Nutrients
2018,
10,
859
7
of
15
Table
3.
Daily
nutrients
intake
of
rural
and
urban
Korean
adults.
Men
(n
=
53,704)
Women
(n
=
107,622)
Overall
(n
=
161,326)
Urban
(ti
=
46,680)
Rural
(n
=
7024)
P
t
P
§
Urban
(n
=
95,457)
Rural
(n
=
12,165)
P
t
P
§
Urban
(n
=
142,137)
Rural
(ti
=
19,189)
P
t
P
§
Total
energy
intake
(kcal/day)
1866.43
±
2.32
1845.07
±
6.17
0.001
1692.98
±
1.61
1622.04
±
4.29
<0.0001
1750.75
±
1.32
1697.72
±
3.59
<0.0001
Carbohydrate
(g/1000
kcal)
177.17
±
0.08
182.10
±
0.20
<0.0001 <0.0001
179.72
±
0.06
187.44
±
0.15
<0.0001
<0.0001
178.87
±
0.05
185.58
±
0.12
<0.0001 <0.0001
Protein
(g/1000
kcal)
33.51
±
0.03
31.70
±
0.07
<0.0001 <0.0001
33.72
±
0.02
31.30
±
0.06
<0.0001
<0.0001
33.65
±
0.02
31.45
±
0.04
<0.0001 <0.0001
Fat
(g/1000
kcal)
16.09
±
0.03
14.39
±
0.07
<0.0001 <0.0001
15.26
±
0.02
12.45
±
0.05
<0.0001
<0.0001
15.54
±
0.02
13.13
±
0.04
<0.0001 <0.0001
CHO%
of
energy
71.80
±
0.03
74.01
±
0.09
<0.0001 <0.0001
72.57
±
0.02
76.00
±
0.06
<0.0001
<0.0001
72.32
±
0.02
75.31
±
0.05
<0.0001 <0.0001
Protein%
of
energy
13.56
±
0.01
12.87
±
0.03
<0.0001 <0.0001
13.59
±
0.01
12.67
±
0.02
<0.0001
<0.0001
13.58
±
0.01
12.74
±
0.02
<0.0001 <0.0001
Fat%
of
energy
14.64
±
0.02
13.13
±
0.06
<0.0001 <0.0001
13.83
±
0.02
11.32
±
0.05
<0.0001
<0.0001
14.10
±
0.01
11.95
±
0.04
<0.0001 <0.0001
Calcium
(mg/1000
kcal)
223.09
±
0.43
211.66
±
1.15
<0.0001 <0.0001
268.86
±
0.38
246.54
±
1.07
<0.0001
<0.0001
253.63
±
0.30
235.25
±
0.80
<0.0001 <0.0001
Phosphorus
(mg/1000
kcal)
491.09
±
0.41
476.52
±
1.06
<0.0001 <0.0001
518.47
±
0.34
495.83
±
0.94
<0.0001
<0.0001
509.36
±
0.26
489.64
±
0.71
<0.0001 <0.0001
Iron
(mg/1000
kcal)
5.38
±
0.01
5.00
±
0.02
<0.0001 <0.0001
5.87
±
0.01
5.34
±
0.02
<0.0001
<0.0001
5.70
±
0.00
5.23
±
0.01
<0.0001 <0.0001
Potassium
(mg/1000
kcal)
1200.19
±
1.69
1168.44
±
4.53
<0.0001 <0.0001
1340.89
±
1.43
1284.43
±
4.22
<0.0001
<0.0001
1294.07
±
1.12
1246.54
±
3.04
<0.0001 <0.0001
Vitamin
A
(RE/1000
kcal)
257.34
±
0.68
241.31
±
1.89
<0.0001 <0.0001
281.75
±
0.54
257.76
±
1.58
<0.0001
<0.0001
273.63
±
0.43
252.52
±
1.17
<0.0001 <0.0001
Vitamin
B1
(mg/1000
kcal)
0.58
±
0.00
0.56
±
0.00
<0.0001 <0.0001
0.57
±
0.00
0.54
±
0.00
<0.0001
<0.0001
0.57
±
0.00
0.55
±
0.00
<0.0001 <0.0001
Vitamin
B2
(mg/1000
kcal)
0.49
±
0.00
0.46
±
0.00
<0.0001 <0.0001
0.53±
0.00
0.48
±
0.00
<0.0001
<0.0001
0.52
±
0.00
0.47
±
0.00
<0.0001 <0.0001
Niacin
(mg/1000
kcal)
8.26
±
0.01
7.77
±
0.02
<0.0001 <0.0001
8.29
±
0.01
7.68
±
0.02
<0.0001
<0.0001
8.28
±
0.00
7.71
±
0.01
<0.0001 <0.0001
Vitamin
C
(mg/1000
kcal)
52.29
±
0.12
50.31
±
0.32
<0.0001 <0.0001
66.21
±
0.11
63.41
±
0.32
<0.0001
<0.0001
61.57
±
0.09
59.07
±
0.23
<0.0001 <0.0001
Zinc
(µg/1000
kcal)
4.50
±
0.00
4.22
±
0.01
<0.0001 <0.0001
4.53
±
0.00
4.22
±
0.01
<0.0001
<0.0001
4.52
±
0.00
4.22
±
0.01
<0.0001 <0.0001
Vitamin
E
(mg/1000
kcal)
4.38
±
0.01
4.08
±
0.02
<0.0001 <0.0001
4.73
±
0.01
4.38
±
0.02
<0.0001
<0.0001
4.61
±
0.00
4.28
±
0.01
<0.0001 <0.0001
Sodium
(mg/1000
kcal)
1446.04
±
3.12
1593.48
±
9.92
<0.0001 <0.0001
1459.59
±
2.37
1602.03
±
7.85
<0.0001
<0.0001
1455.08
±
1.94
1599.33
±
5.28
<0.0001 <0.0001
Folate
(µg/1000
kcal)
114.04
±
0.21
109.57
±
0.57
<0.0001 <0.0001
129.83
±
0.18
123.49
±
0.50
<0.0001
<0.0001
124.58
±
0.14
118.91
±
0.38
<0.0001 <0.0001
CHO:
Carbohydrate;
RE:
Retinol
equivalents.
Values
are
expressed
as
mean
±
SE;
f
p
value
of
the
unadjusted
data;
§
p
value
of
the
adjusted
for
age,
household
income,
education
level,
alcohol,
smoke,
exercise,
BMI,
and
daily
total
energy
intake.
Overall
d
ata
was
additionally
adjusted
for
sex
using
general
linear
regression.
Nutrients
2018,
10,
859
8
of
15
3.3.
Urban-Rural
Comparision
of
Metabolic
Syndrom
Table
shows
the
overall
prevalence
comparison
of
MetS
by
NCEP
Asian-Pacific
criteria.
The
prevalence
of
metabolic
syndrome
was
significantly
higher
in
rural
populations
than
urban
populations.
The
prevalence
of
MetS
was
39.8%
among
rural
residents
and
22.5%
among
urban
residents.
Prevalence
of
the
MetS
components
by
gender
and
residence
place
is
also
shown
in
Table
".
The
prevalence
of
the
MetS
components
showed
similar
results
to
the
prevalence
of
MetS
between
rural
and
urban
populations.
Of
all
subjects,
crude
prevalence
of
MetS
components
was
higher
in
rural
participants
than
those
of
urban
participants.
High
blood
pressure
was
the
most
common
factor
of
MetS
in
men
(52.0%
in
rural
and
48.6%
in
urban),
but
low
HDL-cholesterol
was
the
most
common
component
in
rural
women
(65.2%
in
rural
and
31.4%
in
urban),
followed
by
abdominal
obesity
(62.2%
in
rural
and
40.8%
in
urban).
Men
had
a
higher
prevalence
of
high
blood
pressure,
high
triglyceride,
and
high
blood
glucose
factors
than
women,
whereas
women
had
a
higher
prevalence
of
low
HDL-cholesterol
and
abdominal
obesity
than
men.
Table
4
presents
the
OR
for
MetS
in
rural
and
urban
areas.
The
crude
OR
for
MetS
was
significantly
higher
in
rural
populations
(OR
=
2.26,
95%
CI:
2.19-2.33)
compared
to
the
urban
subjects.
The
overall
crude
OR
for
all
the
individual
components
of
MetS
were
also
significantly
higher
in
rural
subjects
than
in
urban
residents.
Even
after
multivariate
adjustment,
urban-rural
areas
were
associated
with
OR
for
MetS
and
MetS
components
such
as
abdominal
obesity,
high
triglyceride,
and
low
LDL-cholesterol.
In
this
study,
higher
intake
of
carbohydrate
or
sodium
was
associated
with
increase
of
OR
for
MetS.
Table
5
presents
the
OR
for
MetS
by
quartile
of
carbohydrate
and
sodium
consumption.
The
crude
OR
for
MetS
was
significantly
higher
in
the
highest
carbohydrate
(OR
=
1.07,
95%
CI:
1.01-1.13,
p
for
trend
0.003
in
men;
OR
=
1.09,
95%
CI:
1.05-1.14,
p
for
trend
<0.0001
in
women)
and
sodium
(OR
=
1.20,
95%
CI:
1.14-1.27,
p
for
trend
<0.0001
in
men;
OR
=
1.14,
95%
CI:
1.10-1.19,
p
for
trend
<0.0001
in
women)
consumption
category
than
those
in
the
lowest
carbohydrate
and
sodium
intake
category.
After
adjusting
for
potential
confounder
variables,
the
overall
OR
for
MetS
also
increased
with
the
highest
consumption
of
carbohydrate
(OR
=
1.23,
95%
CI:
1.14-1.33,
p
for
trend
<0.0001)
and
sodium
(OR
=
1.11,
95%
CI:
1.06-1.16,
p
for
trend
<0.0001)
compared
to
those
in
the
lowest
category
of
carbohydrate
and
sodium
consumption.
Nutrients
2018,
10,
859
9
of
15
Table
4.
Prevalence
and
odds
ratio
(95%
CI)
of
metabolic
syndrome
and
components
of
rural
and
urban
Korean
adults.
Men
(n
=
53,704)
Women
(n
=
107,622)
Overall
(n
=
161,326)
Urban
(n
=
46,680)
Rural
(n
=
7024)
p-Value
Urban
(n
=
95,457)
Rural
(n
=
12,165)
p-Value
Urban
(n
=
142,137)
Rural
(n
=
19,189)
p-Value
Metabolic
syndrome
Prevalence
(n
(%))*
12,217
(26.17)
2684
(38.21)
<0.0001
19,803
(20.75)
4951
(40.70)
<0.0001
32,020
(22.53)
7635
(39.79)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
1.75
(1.66-1.84)
<0.0001
Ref.
2.62
(2.52-2.73)
<0.0001
Ref.
2.26
(2.19-2.33)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
1.68
(1.58-1.80)
<0.0001
Ref.
1.73
(1.65-1.81)
<0.0001
Ref.
1.65
(1.59-1.72)
<0.0001
Abdominal
obesity
Prevalence
(n
(%))
13,849
(29.7)
2353
(33.5)
<0.0001
38,904
(40.8)
7566
(62.2)
<0.0001
52,753
(37.1)
9919
(51.7)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
1.19
(1.13-1.26)
<0.0001
Ref.
2.39
(2.30-2.49)
<0.0001
Ref.
1.87
(1.82-1.93)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
1.15
(1.08-1.22)
<0.0001
Ref.
1.63
(1.56-1.70)
<0.0001
Ref.
1.36
(1.32-1.41)
<0.0001
High
blood
pressure
Prevalence
(n
(%))
22,669
(48.6)
3652
(52.0)
<0.0001
32,122
(33.7)
5108
(42.0)
<0.0001
54,791
(38.6)
8760
(45.7)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
1.15
(1.09-1.21)
<0.0001
Ref.
1.43
(1.37-1.48)
<0.0001
Ref.
1.32
(1.28-1.36)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
1.00
(0.94-1.06)
0.998
Ref.
0.98
(0.94-1.03)
0.421
Ref.
0.97
(0.93-1.00)
0.044
High
triglyceride
Prevalence
(n
(%))
18,082
(38.7)
3158
(45.0)
<0.0001
18,897
(19.8)
3700
(30.4)
<0.0001
36,979
(26.0)
6858
(35.7)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
1.29
(1.23-1.36)
<0.0001
Ref.
1.77
(1.70-1.85)
<0.0001
Ref.
1.56
(1.51-1.61)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
1.25
(1.18-1.33)
<0.0001
Ref.
1.28
(1.22-1.34)
<0.0001
Ref.
1.23
(1.18-1.27)
<0.0001
High
blood
glucose
Prevalence
(n
(%))
15,407
(33.0)
2701
(38.5)
<0.0001
17,892
(18.7)
2800
(23.0)
<0.0001
33,299
(23.4)
5501
(28.7)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
1.27
(1.20-1.34)
<0.0001
Ref.
1.30
(1.24-1.36)
<0.0001
Ref.
1.28
(1.24-1.33)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
1.11
(1.05-1.18)
0.000
Ref.
0.92
(0.88-0.97)
0.002
Ref.
0.96
(0.95-1.02)
0.478
Low
HDL
cholesterol
Prevalence
(n
(%))
8857
(19.0)
2897
(41.2)
<0.0001
29,973
(31.4)
7934
(65.2)
<0.0001
38,830
(27.3)
10,831(56.4)
<0.0001
Model
I
(OR,
95%
Cl)
Ref.
3.00
(2.84-3.16)
<0.0001
Ref.
4.10
(3.94-4.26)
<0.0001
Ref.
3.66
(3.55-3.78)
<0.0001
Model
II
(OR,
95%
Cl)
Ref.
3.11
(2.92-3.31)
<0.0001
Ref.
3.44
(3.29-3.59)
<0.0001
Ref.
3.25
(3.14-3.37)
<0.0001
Ref.:
Reference
category;
OR:
Odds
ratio;
CI:
Confidence
intervals.
Metabolic
syndrome:
The
presence
of
three
or
more
of
the
following
components,
(i)
Abdominal
obesity:
defined
by
Asian-Pacific
guideline,
waist
circumference
>90
for
men
and
>80
cm
for
women;
(ii)
High
blood
pressure:
blood
pressure
>130/85
mmHg
or
medication;
(iii)
High
triglyceride:
>150
mg/dL;
(iv)
High
blood
glucose:
fasting
glucose
>100
mg/dL
or
medication;
(v)
Low
HDL
cholesterol:
<40
for
men
and
<50
mg/dL
for
women.*
p-Values
for
prevalence
of
MetS
were
calculated
by
x
test.
Model
I,
unadjusted
data;
Model
II,
Model
I
adjusted
for
age,
household
income,
education
level,
alcohol,
smoke,
exercise,
BMI
(not
adjusted
for
abdominal
obesity
OR),
and
daily
total
energy
intake.
Overall
data
was
additionally
adjusted
for
sex
using
logistic
regression.
Nutrients
2018,
10,
859
10
of
15
Table
5.
Adjusted
odds
ratio
(95%
CI)
of
the
metabolic
syndrome
according
to
carbohydrate
and
sodium
consumption.
Quartile
of
Carbohydrate
Consumption
(g/Day)
p-Trend
I
Quartile
of
Sodium
Consumption
(mg/Day)
p-Trend
I
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Men
(n
=
53,704)
n
9152
13,994
14,396
16,162
11,211
12,644
13,967
15,882
Median
226.92
285.01
329.33
412.45
1142.02
1954.54
1748.86
4061.51
Model
I
Ref.
0.98
(0.92-1.04)
1.01
(0.95-1.07)
1.07
(1.01-1.13)
0.003
Ref.
1.09
(1.03-1.15)
1.13
(1.07-1.20)
1.20
(1.14-1.27)
<0.0001
Model
II
Ref.
1.01
(0.94-1.08)
1.05
(0.97-1.13)
1.06
(0.95-1.18)
0.281
Ref. 1.10
(1.03-1.17)
1.13
(1.06-1.21)
1.19
(1.11-1.28)
<0.0001
Women
(n
=
107,622)
n
31,179
26,338
25,936
24,169
29,120
27,688
26,365
24,449
Median
211.04
284.55
329.58
404.83
1113.12 1940.32
2730.04
3999.38
Model
I
Ref.
1.21
(1.17-1.26)
1.14
(1.09-1.19)
1.09
(1.05-1.14)
<0.0001
Ref.
1.02
(0.98-1.06)
1.06
(1.02-1.10)
1.14
(1.10-1.19)
<0.0001
Model
II
Ref.
1.21
(1.15-1.28)
1.25
(1.17-1.33)
1.37
(1.25-1.50)
<0.0001
Ref.
1.03
(0.98-1.08)
1.05
(1.00-1.10)
1.09
(1.03-1.14)
0.0004
Overall
(n
=
161,326)
n
40,331
40,332
40,332
40,331
40,331
40,332
40,332
40,331
Median
215.03
284.72
329.48
407.65
1121.41
1944.78
2736.81
4024.33
Model
I
Ref.
1.14
(1.10-1.17)
1.10
(1.07-1.14)
1.10
(1.06-1.14)
<0.0001
Ref.
1.04
(1.01-1.08)
1.08
(1.05-1.12)
1.16
(1.12-1.20)
<0.0001
Model
II
Ref.
1.14
(1.09-1.18)
1.16
(1.11-1.22)
1.23
(1.15-1.32)
<0.0001
Ref.
1.05
(1.01-1.09)
1.07
(1.03-1.11)
1.11
(1.07-1.16)
<0.0001
Ref.:
Reference
category;
OR:
Odds
ratio;
CI:
Confidence
intervals.
Metabolic
syndrome:
The
presence
of
three
or
more
of
the
following
components,
(i)
Abdominal
obesity:
defined
by
Asian-Pacific
guideline,
waist
circumference
>90
for
men
and
>80
cm
for
women;
(ii)
High
blood
pressure:
blood
pressure
>130/85
mmHg
or
medication;
(iii)
High
triglyceride:
>150
mg/dL;
(iv)
High
blood
glucose:
fasting
glucose
>100
mg/dL
or
medication;
(v)
Low
HDL
cholesterol:
<40
for
men
and
<50
mg/dL
for
women.
Model
I,
adjusted
for
residential
area;
Model
II,
adjusted
for
age,
residential
area,
household
income,
education
level,
alcohol,
smoke,
exercise,
BMI,
and
daily
total
energy
intake.
t
Linear
trends
across
categories
of
carbohydrate
or
sodium
consumption
were
tested
using
the
median
consumption
value
for
each
category
as
an
ordinal
variable.
Overall
data
were
additionally
adjusted
for
sex.
Nutrients
2018,
10,
859
11
of
15
4.
Discussion
The
present
study
investigated
nutrient
intake
status
and
prevalence
of
MetS
in
urban
and
rural
populations
of
middle-aged
Koreans
using
two
large
cohort
studies.
To
examine
the
characteristics
of
the
populations
between
urban
and
rural
areas,
we
analyzed
the
sociodemographic
parameters,
anthropometric
measurements,
blood
biochemical
parameters,
and
nutrients
intake
of
the
subjects
of
each
region.
The
rural
subjects
were
older
than
the
urban
subjects.
It
has
been
reported
that
the
average
age
of
Koreans
is
higher
in
rural
areas
than
urban
areas
[9,10,16],
and
the
proportion
of
the
aging
population
is
also
higher
in
rural than
urban
locations
[10].
The
age
of
the
members
of
a
society
is
one
of
the
most
important
determinants
of
socioeconomic
change
and
could
affect
various
characteristics,
such
as
household
income
of
the
society.
In
the
current
study,
level
of
education
and
household
income,
as
well
as
sociodemographic
characteristics,
were
significantly
lower
in
rural
residents
than
urban
people
for
both
genders.
These
results
were
consistent
with
the
results
of
a
study
on
urban
and
rural
areas
of
people
older
than
55
years
old
in
13
provinces
of
Korea
[10].
Metabolic
syndrome
is
an
important
public
health
concern
in
Korea,
and
the
prevalence
of
MetS
among
Koreans
has
been
increasing
[1.
Global
prevalence
of
MetS
in
the
adult
population
is
approximated
to
be
20-25%
[17]
and
the
prevalence
is
estimated to
be
11.9-37.1%
in
Asia-Pacific
countries
[
].
Our
results
showed
that
the
prevalence
of
MetS
in
urban
populations
was
similar
to
the
global
average.
However,
the
proportion
of
MetS
prevalence
for
people
in
rural
areas
was
noticeably
higher,
about
40%,
which
is
the
highest
level
among
Asia-Pacific
countries
[18].
It
has
been
reported
that
the
annual
cost
of
healthcare
is
associated
with
chronic
diseases
[19].
According
to
the
Korea
Institute
for
Health
and
Social
Affairs
report,
the
annual
cost
of
the
healthcare
for
MetS-related
diseases
was
about
3.8%
higher
in
rural
areas
compare
to
urban
areas
in
2011
[20].
The
MetS
prevalence
generally
occurs
at
significantly
different
rates
according
to
age,
socioeconomic
environment,
residential
area,
and
dietary
nutritional
status
[
].
Aging
is
one
of
the
well-known
risk
factors
of
MetS
[
].
Our
results
showed
that
rural
subjects
were
older
than
the
urban
subjects,
and
the
MetS
prevalence
was
higher
in
rural
dweller
than
urban
people.
Li
et
al.
[ ]
also
reported
that
the
prevalence
and
the
risk
for
MetS
were
higher
in
older
adults
in
China.
Low
socioeconomic
status,
as
determined
by
the
level
of
education
and
income
[
],
has
been
shown
to
be
possibly
linked
with
a
higher
prevalence
of
MetS
[22,23].
A
recent
Chinese
study
showed
that
the
prevalence
and
the
risk
for
MetS
were
higher
in
those
with
low
level
of
education
in
2014-2015
[21].
Our
results
indicated
an
inverse
relationship
of
education
level
or
home
economics
to
the
prevalence
of
MetS.
Rural
residency
indicated
a
relatively
lower
level
of
education
and
household
income
than
urban
populations
and
reflected
a
higher
prevalence
of
MetS.
Physical
activity
may
be
the
crucial
factor
in
the
aetiology
of
MetS.
Physical
inactivity
has
an
independent
effect
on
the
components
of
MetS
[24].
It
has
been
reported
that
vigorous
and
moderate
physical
activity
is
related
to
a
reduced
risk
of
MetS
in
white
European
people
[25].
Our
findings
showed
that
rural
dwellers
exercise
less
frequently
than
urban
people.
Dietary
nutrient
intake
has
been
an
important
indicator
of
metabolic
disorders
such
as
diabetes
mellitus,
obesity,
dyslipidemia,
and
MetS
[3].
In
this
study,
there
were
differences
in
intakes
of
nutrients
such
as
protein,
vitamins,
and
iron
between
rural
and
urban
areas.
The
intake
levels
of
nutrients
except
carbohydrate
and
sodium
were
lower
in
rural
populations
compared
to
urban
populations.
The
carbohydrate
and
sodium
were
consumed
in
significantly
higher
amounts
by
rural
dwellers.
Thus,
we
focused
to
analyses
the
OR
for
MetS
based
on
carbohydrate
and
sodium
consumption.
Carbohydrate-rich
diets
are
known
to
be
the
major
cause
of
aggravation
of
glucose
intolerance
[26].
In
addition,
high
carbohydrate
intake
is
known
to
be
associated
with
lipid
abnormalities,
such
as
elevated
triglyceride
and
reduced
HDL-cholesterol
levels
[27].
We
showed
higher
fasting
blood
glucose
levels
in
rural
dwellers
with
relatively
high
percentage
of
caloric
intake
from
carbohydrate
than
in
urban
subjects.
In
blood
lipid
profiles,
triglyceride
and
LDL-cholesterol
levels
were
higher
in
people
in
rural
areas
compared
to
those
in
urban
areas,
but
the
HDL-cholesterol
level
was
lower
in
rural
subjects
than
urban
people
were.
Moreover,
we
found
that
the
risk
of
MetS
was
positively
associated
with
Nutrients
2018,
10,
859
12
of
15
increasing
carbohydrate
intake.
The
high
proportion
of
carbohydrate
intake
is
a
major
characteristic
of
the
Korean
diet
because
rice,
a
main
source
of
carbohydrate,
is
the
staple
food
for
Koreans.
Although
the
dietary
habits
of
contemporary
Koreans
have
changed
due
to
urbanization
and
westernization,
it
has
been
reported
that
rural
residents
tend
to
retain
a
more
traditional
Korean
diet
consisting
of
carbohydrate-rich
foods
compared
to
urban
people
[10].
The
present
study
showed
that
sodium
intake
in
rural
areas
was
higher
than
urban
areas,
and
blood
pressure
was
also
higher
in
rural
areas
than
in
urban
areas.
A
high-sodium
diet
has
been
related
with
various
metabolic
diseases
such
as
hypertension
and
CVD
[28].
High
sodium
consumption
was
also
positively
associated
with
increasing
prevalence
of
MetS
[29].
Our
results
showed
that
the
prevalence
of
MetS
in
rural
populations
was
higher
than
in
urban
people.
In
addition,
OR
for
MetS
also
increased
with
increasing
sodium
intake.
This
observation
is
consistent
with
the
results
of
sodium
intake
and
the
risk
of
MetS
in
Koreans
[30].
Kimchi,
soups,
and
stews
were
the
main
sources
of
sodium
intake
for
the
Korean
population
[31],
and
they
are
major
elements
of
the
traditional
Korean
food
structure.
According
to
a
traditional
Korean-diet
study,
rural
people
has
higher
proportion
of
a
traditional
diet
pattern,
consuming
rice-based
staple
food
and
kimchi,
than
metropolitan
people
[32].
The
higher
prevalence
of
MetS
in
urban
residents
compared
to
the
rural
population
have
been
reported
in
Eastern
China
[33],
India
[34],
and
Malaysia
[35].
Conversely,
other
studies
have
reported
that
the
prevalence
of
MetS
in
rural
areas
was
higher
[9,36].
Our
findings
showed
that
the
prevalence
and
the
OR
of
the
MetS
were
significantly
higher
in
rural
populations
than
those
in
urban
areas.
The
difference
in
the
prevalence
of
MetS
between
urban
and
rural
areas
might
be
attributed
to
the
difference
in
the
demographic
and
sociocultural
characteristics
of
those
areas
[q7];
thus,
the
disparity
of
the
Mets-related
factors
such
as
age,
physical
inactivity,
and
nutrient
intake
between
urban
and
rural
inhabitants
in
each
country
can
affect
different
outcomes
for
the
prevalence
of
MetS
in
rural
and
urban
across
countries.
The
urban
people
in
Eastern
China,
reported
to
have
the
higher
prevalence
of
MetS,
showed
higher
consumption
of
fat
with
less
physical
activity
compared
to
rural
residents
[
].
Our
results
showed
that
rural
people
consume
more
carbohydrates
and
sodium
and
less
regular
exercise
than
urban
residents
do.
Prevention
is
the
most
critical
strategy
to
reduce
the
MetS
and
its
outcomes.
Improper
nutrition
and
inadequate
physical
activity
are
the
main
causes
of
MetS,
so
health
education
to
improve
lifestyle
may
be
an
important
part
in
establishing
public
health
policy.
To
reduce
the
risk
of
MetS
in
Korea,
diverse
efforts
by
the
community
as
well
as
various
national
health
promotion
programs
are
required.
Our
findings
support
the
need
for
targeted
efforts
to
develop
and
implement
MetS
prevention
programs
for
urban
and
rural
residents
in
Korea.
The
present
study
was
a
cross-sectional
analysis
and
thus
has
the
limitation
that
a
temporal
relationship
between
individual
nutrient
consumption
and
prevalence
of
MetS
could
not
be
established.
Future
studies
will
require
long-term
follow-up
studies
of
nutrient
intake
and
MetS
to
better
understand
the
direct
causes
of
varying
MetS
prevalence
in
different
regions
in
Korea.
Another
limitation
of
this
study
was
that
the
participants
were
recruited
from
11
rural
and
14
urban
communities
to
secure
representative
samples,
but
only
volunteers
took
part
in
the
study.
Therefore,
collected
data
has
been
contained
selection
bias
between
study
participants
and
non-participants.
Consequently,
our
findings
should
be
applied
carefully
to
the
whole
rural
and
urban
Korean
population.
5.
Conclusions
In
summary,
this
study
showed
that
the
prevalence
and
risk
of
MetS
in
the
middle-aged
Korean
population
were
higher
in
rural
dwellers
compared
to
urban
populations.
These
differences
by
residential
areas
paralleled
higher intake
of
carbohydrate
and
sodium
in
rural
residents
than
urban
dwellers.
Moreover,
the
MetS-related
factors
such
as
education
level,
household
income,
and
rate
of
regular
exercise
were
lower
in
rural
people
than
those
of
urban
subjects.
Our
findings
suggested
that
the
difference
in
sociocultural
environmental
factors
and
lifestyle,
such
as
nutrient
intake,
might
Nutrients
2018,
10,
859
13
of
15
partially
contribute
to
the
difference
in
the
prevalence
and
risk
of
MetS
between
urban
and
rural
populations
across
regions
in
Korea.
Author
Contributions:
Y.K.
and
S.L.
conceived
and
designed
the
study.
S.L.
performed
the
statistical
analysis
and
wrote
the
manuscript.
Y.K.,
S.L.,
and
Y.S.
interpreted
and
discussed
the
data.
Y.K.
and
S.L.
refined
the
final
draft
and
revised
the
manuscript.
All
authors
read
and
approved
the
final
manuscript.
Funding:
This
research
received
no
external
funding.
Acknowledgments:
This
work
was
supported
by
the
Bio
&
Medical
Technology
Development
Program
of
the
National
Research
Foundation
(NRF)
funded
by
the
Ministry
of
Science
&
ICT
(NRF-2012M3A9C4048761)
and
Brain
Korea
21
Plus
Project
of
the
National
Research
Foundation
funded
by
the
Ministry
of
Education
of
Korea
(22A20130012143).
Data
in
this
study
were
from
the
Korean
Genome
and
Epidemiology
Study
(KoGES;
4851-302),
National
Research
Institute
of
Health,
Centers
for
Disease
Control
and
Prevention,
Ministry
for
Health
and
Welfare,
Republic
of
Korea.
Conflicts
of
Interest
The
authors
declare
no
conflict
of
interest.
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