International Journal ofEnvironmental Researchand Public Health

ArticleLifestyle Factors Associated with Metabolic Syndrome in
Urban Cambodia
Miharu Tamaoki 1 , Ikumi Honda 1, *, Keisuke Nakanishi 1 , Maki Nakajima 1 , Sophathya Cheam 2 ,Manabu Okawada 2 and Hisataka Sakakibara 3
1

2
3

*

Citation: Tamaoki, M.; Honda, I.;
Nakanishi, K.; Nakajima, M.; Cheam,
S.; Okawada, M.; Sakakibara, H.

Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University,
Nagoya 461-8673, Japan
Department of Pediatric, Sunrise Japan Hospital Phnom Penh, Phnom Penh 121001, Cambodia
School of Nursing, Ichinomiya Kenshin College, Ichinomiya 491-0063, Japan
Correspondence: ihonda@met.nagoya-u.ac.jp

Abstract: This study aimed to identify lifestyle factors associated with metabolic syndrome (MetS) in
urban Cambodia. In this cross-sectional study, we used existing health checkup data from a private
hospital in Phnom Penh, Cambodia. The participants comprised 5459 Cambodians aged ≥20 years
who underwent health checkups between 2017 and 2019. The harmonized diagnostic definition wasused as the MetS criteria. The prevalence of MetS was 56.6% overall, 60.4% in men and 52.6% in
women. The lifestyle factor significantly associated with MetS in both sexes were “eating quicker thanothers”, (men: odds ratio [OR]= 2.25, 95% confidence interval [CI] = 1.68–3.03, women: OR = 1.92,
95%CI = 1.41–2.60), “walking faster than others”, (men: OR = 0.78, 95% CI = 0.67–0.92, women:
OR = 0.75, 95% CI = 0.62–0.89) and “drinking alcohol” (men: OR = 1.33, 95% CI = 1.10–1.61, women:
OR = 1.33, 95% CI = 1.09–1.62). Other significant associations with MetS for men was “eating speed isnormal”, (OR = 1.73, 95%CI = 1.30–2.31), and, for women, “eating food after dinner at least 3 daysa week”, (OR = 1.25, 95%CI = 1.01–1.55), “skipping breakfast at least 3 days a week”, (OR = 0.83,
95%CI = 0.69–0.99) and “getting enough rest from sleep” (OR = 1.19, 95% CI = 1.01–1.42) were significantly associated with MetS. Lifestyle interventions through health education and guidance may beeffective in preventing MetS in Cambodia.

Lifestyle Factors Associated with
Metabolic Syndrome in Urban

Keywords: metabolic syndrome; Cambodia; lifestyle factor; non-communicable diseases

Cambodia. Int. J. Environ. Res. Public
Health 2022, 19, 10481. https://doi.org/10.3390/ijerph191710481
Academic Editor: Paul B. Tchounwou
Received: 14 July 2022
Accepted: 20 August 2022
Published: 23 August 2022
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1. Introduction
Cambodia, a lower-middle-income country, has experienced a marked increase in theprevalence of non-communicable diseases (NCDs) such as cardiovascular disease, stroke
and type 2 diabetes mellitus [1,2]. NCDs account for 77% of global deaths in low- andmiddle-income countries [3], and the causes of death within Cambodia have changed
dramatically from communicable diseases to NCDs over the past few decades. In 2019,68% of deaths were from NCDs [4]. One factor contributing to the increase in NCDs inlower-middle-income countries is changes in living environment development-associatedurbanization [5]. For example, the consumption of fast foods high in sugar, fat and salt has
become widespread, resulting in an increase in the prevalence of obesity and hypertension [6,7]. Further, physical activity has decreased due to improved transportation [8,9].
As a result, NCDs have been increasing more rapidly in urban areas than in rural areas.
Despite ongoing rapid economic development in Cambodia, the medical care system has
not fully caught up, even in urban areas, and necessary treatments for these diseases are
not easily available. Therefore, the prevention of NCDs is an urgent issue.
Metabolic syndrome (MetS) is a combination of risk factors such as elevated bloodpressure, dyslipidemia, hyperglycemia and obesity; it is a high-risk factor for NCDs [10].
The World Health Organization has identified unhealthy diets, physical inactivity,
tobacco use and the harmful use of alcohol as lifestyle factors that contribute to the risk ofInt. J. Environ. Res. Public Health 2022, 19, 10481. https://doi.org/10.3390/ijerph191710481

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NCDs [3]. Several previous studies have shown that these lifestyle factors influence thedevelopment of MetS [11–16]. Furthermore, some studies have clearly shown that lifestyleinterventions, such as nutritional management and the promotion of exercise, reduces therisk of MetS [17–20]. In addition, eating habits, such as eating speed and skipping breakfast,
walking speed and sleep, have also been reported to be associated with MetS [21–24]. Thus,
in addition to diets and physical inactivity, various other lifestyle factors are relevant andwarrant attention.
In 2010, the STEPS survey investigated lifestyle factors associated with the risk for
NCDs in Cambodia [25]. It reported that smoking, alcohol consumption and low vegetable/fruit intake were risk factors for NCDs. Other cross-sectional studies have shown anassociation between fast food intake and obesity [26]. However, there is insufficient researchon NCDs and lifestyles in Cambodia, with the existing data being reported tangentiallyas part of studies focused on different issues. Furthermore, there are no reports focusing
on MetS in Cambodia, with such studies mainly originating from developed countries
or China. Identifying the link between various lifestyle habits and MetS in Cambodia
would allow for the development of multifaceted preventive measures, which may also be
relevant to other low- and middle-income countries. Therefore, this study aimed to clarify
the lifestyle habits associated with MetS in urban Cambodia, focusing not only on smoking,alcohol consumption and exercise, but also other lifestyle habits such as eating, walking
speed and sleep.
2. Materials and Methods
2.1. Data Source and Study Population
In this cross-sectional study, we used data measured during a health checkup conducted at an international private general hospital, which is a collaborative research institution, with the permission of the collaborative research institution. This private hospital,
which opened in 2016, is located in Phnom Penh, the capital of Cambodia, and aims toprovide quality medical care equivalent to Japanese standards. The hospital targets the
middle class to the affluent population and specializes in neurosurgery, gastrointestinal
surgery and internal medicine. Most patients come from Phnom Penh or the surroundingarea, which are urban areas [25]. In 2019, 39.4% of the total population of Cambodia, or
approximately 6.14 million people, were living in urban areas [27].
The participants comprised Cambodians aged ≥20 years who underwent healthcheckups at this hospital between January 2017 and December 2019. During this period,
10,533 participants underwent health checkups at this hospital. Of these, those younger
than 20 years (n = 89) and those who had two or more health checkups during the period
were included in the analysis on their first visit, and duplicate data were excluded (n = 1675).
Those with missing information to define MetS and lifestyle items necessary for analysis
were also excluded (n = 3310). Finally, data from 5459 individuals were analyzed for thisstudy (Figure 1).

Figure 1. Flowchart of the selection of the study population.

Int. J. Environ. Res. Public Health 2022, 19, 10481

3 of 112.2. Variables
The variables assessed were demographic charactersitics (age, sex), physical measurements, blood data and lifestyles and were obtained from electronic medical record data of
the health checkups.
2.2.1. Definition of Metabolic Syndrome
The harmonized diagnostic definition from the Joint Interim Statement was used todefine MetS as a dependent variable [10]. MetS was diagnosed when three or more of the following five risk factors were met: (1) abdominal obesity (waist circumference [WC] ≥90 cm
in men and ≥80 cm in women); (2) elevated triglycerides [TG] levels (≥150 mg/dL) or on
treatment for dyslipidemia; (3) decreased high-density lipoprotein cholesterol [HDL-C]
levels (<40 mg/dL in men, <50 mg/dL in women or ongoing treatment for dyslipidemia);(4) elevated blood pressure [BP] (≥130 mmHg systolic BP, ≥85 mmHg diastolic BP) or
treatment for previously diagnosed hypertension; and (5) elevated fasting blood glucose[FBG] level (≥100 mg/dL) or treatment for previously diagnosed type 2 diabetes.
2.2.2. Lifestyle Factors
The following 10 lifestyle factors are the independent variables. Three items related toexercise, “I do exercise lightly, sweating for at least 30 min per session more than twice aweek for over a year”, “I walk or do equivalent physical activities in daily life for more than
1 h per day”, “My walking speed is faster than those of the same sex of similar age”; four
items related to eating habits, “I eat quicker than others”, “I have dinner within 2 h before
bedtime at least three days a week”, “I eat food after dinner at least three days a week”,“I skip breakfast at least three days a week”; an item related to sleep, “I get enough rest
from sleep”; an item related to alcohol consumption, “I drink alcohol”; and an item related
to smoking, “I currently smoke habitually”. Of the 10 items, the following seven items
were answered with either “Yes” or “No”: “I do exercise lightly, sweating for at least 30 min
per session more than twice a week for over a year”, “I walk or do equivalent physicalactivities in daily life for more than 1 h per day”, “My walking speed is faster than those of
the same sex of similar age”, “I have dinner within 2 h before bedtime at least three days aweek”, “I eat food after dinner at least three days a week”, “I skip breakfast at least threedays a week” and “I get enough rest from sleep”. For the question, “I eat quicker thanothers”, the answers were “Quickly”, “Normal” and “Slowly”, and for “I drink alcohol”
and “I currently smoke habitually”, the answers were “Current drinker/smoker”, “Former
drinker/smoker” and “Never”.
2.2.3. Anthropometric and Biochemical Measurements
The health checkups were conducted by medical professionals who had received
specialized training in such assessments. All anthropometric indices were measured twice
to reduce measurement error, and the average value was adopted. The data from electronic
medical records comprised details such as systolic and diastolic BP, WC, body mass index[BMI: body weight {kg}/height {m}2 ]) and blood test results (TG, HDL-C, low-density
lipoprotein cholesterol [LDL-C], FBG). Blood pressure was measured at the upper arm using
a medical automatic blood pressure monitor (ES-H55, Terumo Corporation, Tokyo, Japan)
after at least 5 min of rest. WC diameter is the diameter of the umbilical height measured
while standing and during light exhalation. Blood tests were performed using an automatedchemistry analyzer (Siemens Dimension EXL 200, Siemens Healthcare, Erlangen, Germany)at the hospital. Health checkup participants were required not to consume anything
except water at least 8 h to avoid affecting the blood test results. Therefore, participants
were instructed to finish dinner by 9:00 p.m. and avoid eating or consuming sweetened
beverages thereafter before visiting the hospital, and health checkups were conducted in
the morning.

Int. J. Environ. Res. Public Health 2022, 19, 10481

4 of 112.3. Statistical Analysis
Analyses were performed separately by sex, and descriptive statistics of the subjects
were presented. The data are presented as means and standard deviations for continuous
variables and as numbers and percentages for categorical variables. The anthropometricand hematological values of the MetS and non-MetS groups were adjusted for age, andbivariate analysis was performed. To examine the association between MetS and lifestyle
habits, multivariable logistic regression analysis was used, with the presence of metabolic
syndrome as the dependent variable and lifestyle habits as the independent variable. First,
univariate logistic regression analysis with age adjustment was performed to determine
the variables of interest for the multivariable analysis (Model 1). Multivariable logistic
regression analysis was conducted using the forced entry method with the variables forwhich p < 0.2 was obtained in the univariate logistic regression analysis as independent
variables (Model 2). The results of each of the logistic regression analyses were presented
with odds ratios and 95% confidence intervals [CI]. The Hosmer–Lemeshow goodness-of-fit
test was used to evaluate the suitability of the final model.
IBM SPSS Statistics for Windows Ver.28 (IBM Corp., Armonk, NY, USA) was used forstatistical analysis, and the significance level was set at 5% (two-tailed test).
2.4. Ethical Consideration
Ethical approval was obtained from the Cambodian National Ethics Committee for
Health Research (200NECHR). Ethical approval was also obtained from the Collaborative
Research Institution (No.20-002) and from the Ethics Review Committee of the Nagoya
University School of Health Sciences (No. 20-109). The provided existing data were
provided in fully anonymized form. Individual informed consent was waived by all Ethics
Review Committees because the opt-out consent process was used in this study.
3. Results
3.1. Characteristics of Participants
A total of 5459 (2845 [52.1%] men, 2914 [47.9%] women) participants were included in
the study. The prevalence of MetS was 60.4% in men and 52.6% in women. The statisticalanalysis for the characteristics of these participants is presented in Table 1.
Table 1. Characteristics of participants by sex.

Number (%)
Age (years)
MetS n (%)
BMI
WC (cm)Systolic BP (mmHg)
Diastolic BP (mmHg)
Triglycerides (mg/dL)
FBG (mg/dL)
HbA1C (%)
HDL-C (mg/dL)
LDL-C (mg/dL)

Men

Women

2845 (52.1)
47.0 ± 14.4
1717 (60.4)
25.5 ± 3.6
88.7 ± 10.0
128.6 ± 16.4
84.5 ± 11.1
186.45 ± 150.23
110.0 ± 31.7
5.8 ± 0.98
41.9 ± 10.8
122.8 ± 34.4

2614 (47.9)
49.4 ± 15.6
1375 (52.6)
23.9 ± 4.0
80.5 ± 10.7
122.2 ± 19.0
77.8 ± 11.3
135.13 ± 101.1
103.6 ± 27.0
5.8 ± 0.95
49.2 ± 13.0
125.3 ± 36.0

Mean ± standard deviation. Abbreviation: BMI; body mass index, WC; waist circumference, BP; blood pressure,
FBG; fasting blood glucose, HbA1C; hemoglobin A1C, HDL-C; high-density lipoprotein cholesterol, LDL-C;
low-density lipoprotein cholesterol.

3.2. Characteristics of Participants with and without MetS
Table 2 shows the physical characteristics and blood test results in the MetS andnon-MetS groups. Compared to the non-MetS group, the MetS group showed significantly
higher values for BMI, abdominal circumference, blood pressure, triglycerides, blood
glucose and HbA1c and significantly lower values for HDL-C in both men and women(p < 0.001). Only women showed significantly higher LDL-C levels (p < 0.001).

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Table 2. Characteristics of participants with MetS and non-MetS.
Men (n = 2845)

Number (%)
Age (years)
BMI
WC (cm)Systolic BP (mmHg)
Diastolic BP (mmHg)
Triglycerides (mg/dL)
FBG (mg/dL)
HbA1c (%)
HDL-C (mg/dL)
LDL-C (mg/dL)

Women (n = 2614)

MetS

Non-MetS

p-Value

MetS

Non-MetS

p-Value

1717 (60.4)
49.4 ±13.7
26.7 ± 3.4
92.7 ± 9.0
133 ± 16.4
87.3 ± 11.1
232.2 ± 172.3
116.7 ± 36.8
6.0 ± 1.1
38.4 ± 9.2
129.4 ± 37.1

1128 (39.6)
43.3 ± 14.5
23.6 ± 3.0
82.7 ± 8.4
122.3 ± 14.3
80.4 ± 14.3
116.9 ± 61.0
99.9 ± 16.7
5.5 ± 0.7
47.2 ± 10.8
127.8 ± 32.7

<0.001 a<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b
0.061 b1375 (52.6)
55.6 ± 14.0
25.6 ± 3.7
85.8 ± 8.9
129.8 ± 18.6
81.5 ± 11.2
175.7 ± 115.9
112.0 ± 33.4
6.1 ± 1.1
43.6 ± 10.9
122.9 ± 37.3

1239 (47.4)
42.5 ± 14.4
22.0 ± 3.3
74.5 ± 9.2
113.7 ± 15.7
73.8 ± 12.3
90.1 ± 52.9
94.3 ± 11.7
5.4 ± 0.5
55.5 ± 12.3
120.1 ± 33.1

<0.001 a<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b<0.001 b

Mean ± standard deviation. Abbreviation: BMI; body mass index, WC; waist circumference, BP; blood pressure,
FBG; fasting blood glucose, HbA1C; hemoglobin A1C, HDL-C; high-density lipoprotein cholesterol, LDL-C;
low-density lipoprotein cholesterol. a : Student’s t-test, b : age-adjusted linear regression analysis.

3.3. Lifestyle Factors Associated with MetS
The results of the association between MetS and lifestyles by sex are shown in Tables 3 and 4. Multivariable logistic regression analysis showed that the highest odds
ratio for both men and women was “eating quicker than others”, with odds ratios of 2.25
(95% CI = 1.68–3.03, p < 0.001) and 1.92 (95% CI = 1.41–2.60, p < 0.001), respectively. Twoother factors, “walking faster than those of the same sex of similar age “ (men: OR = 0.78,
95% CI = 0.67–0.92, p = 0.003, women: OR = 0.75, 95% CI = 0.62–0.89, p = 0.002) and“drinking alcohol” (men: OR = 1.33, 95% CI = 1.10–1.61, p = 0.004, women: OR = 1.33,
95% CI = 1.09–1.62, p = 0.004), showed significant associations with MetS. In addition, “eating speed is normal” (OR = 1.73, 95%CI = 1.30–2.31, p < 0.001) was significantly associatedwith MetS in men, while in women, “eating food after dinner at least 3 days a week”(OR = 1.25, 95% CI = 1.01–1.55, p = 0.041), “skipping breakfast at least 3 days a week”(OR = 0.83, 95% CI = 0.69–0.99, p = 0.045) and “getting enough rest from sleep” (OR = 1.19,
95% CI = 1.01–1.42, p = 0.043) were significantly associated with MetS.
Table 3. Association between metabolic syndrome and lifestyle (men).
Number (%)
MetS(n = 1717)

Non-MetS(n = 1128)

Age-Adjusted Logistic
Regression Model 1
OR (95% CI)

Exercising with light sweat for atleast 30 min per session more than
twice a week for over a yearYesNo1023 (59.6)
694 (40.4)

664 (58.9)
464 (41.1)

Walking or equivalent physicalactivity in daily life
for more than 1 h per day
YesNo

945 (55.0)
772 (45.0)

Walking faster than the same sexof similar age
YesNo
Eating quicker than others
Quickly
NormalSlowlyLifestyle Items (10 Items) Men

p-Value

Multivariable Logistic
Regression Model 2
OR (95% CI)

0.92 (0.79–1.08)
1

0.324

-

619 (54.9)
509 (45.1)

0.95 (0.81–1.11)
1

0.528

-

718 (41.8)
999 (58.2)

526 (46.6)
602 (53.4)

0.86 (0.74–1.00)
1

0.053

0.78 (0.67–0.92)
1

0.003

646 (37.6)
953 (55.5)
118 (6.9)

351 (31.1)
655 (58.1)
122 (10.8)

2.23 (1.66–2.99)
1.74 (1.32–2.31)
1<0.001<0.0012.25 (1.68–3.03)
1.73 (1.30–2.31)
1<0.001<0.001

p-ValueInt. J. Environ. Res. Public Health 2022, 19, 10481

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Table 3. Cont.
Number (%)
MetS(n = 1717)

Non-MetS(n = 1128)

Age-Adjusted Logistic
Regression Model 1
OR (95% CI)

Having dinner within 2 h before
bedtime at least 3 days a week
YesNo

909 (52.9)
808 (47.1)

562 (49.8)
566 (50.2)

1.13 (0.97–1.32)
1

0.105

1.12 (0.95–1.32)
1

0.175

Eating food after dinner≥3 days a week
YesNo

382 (22.2)
1335 (77.8)

219 (19.4)
909 (80.6)

1.23 (1.01–1.48)
1

0.036

1.19 (0.98–1.46)
1

0.086

Skipping breakfast≥3 days a weekYesNo

576 (33.5)
1141 (66.5)

385 (34.1)
743 (65.9)

1 (0.85–1.18)
1

0.974

Sufficient rest with sleep
YesNo

1069 (62.3)
648 (37.7)

686 (60.8)
442 (39.2)

1.05 (0.90–1.23)
10.53

Alcohol drinking habit
Current drinker
Former drinker
Never

1241 (72.3)
90 (5.2)
386 (22.5)

811 (71.9)
49 (4.3)
268 (23.8)

1.34 (1.11–1.62)
1.04 (0.70–1.54)
1

0.002
0.854

1.33 (1.10–1.61)
1.03 (0.69–1.53)
1

0.004
0.895

Smoking status
Current smoker
Former smoker
Never

169 (9.8)
236 (13.8)
1312 (76.4)

94 (8.3)
102 (9.1)
932 (82.6)

1.13 (0.86–1.48)
1.02 (0.78–1.34)
1

0.388
0.86

Lifestyle Items (10 Items) Men

p-Value

Multivariable Logistic
Regression Model 2
OR (95% CI)

p-Value

OR: odds ratio; 95% CI: 95% confidence interval. Model 1: adjusted for age, Model 2: adjusted for age andvariables; p < 0.2 (Model 1). Model 2: Hosmer–Lemeshow p = 0.007.

Table 4. Association between metabolic syndrome and lifestyle (women).
Number (%)

p-Value

Multivariable Logistic
Regression Model 2
OR (95% CI)

0.99 (0.81–1.15)
1

0.694

-

650 (52.5)
589 (47.5)

0.92 (0.77–1.09)
1

0.312

-

448 (32.6)
927 (67.4)

504 (40.7)
735 (59.3)

0.81 (0.68–0.97)
1

0.022

0.75 (0.62–0.89)
1

0.002

Eating quicker than others
Quickly
NormalSlowly401 (29.2)
820 (59.6)
154 (11.2)

292 (23.6)
795 (64.2)
152 (12.2)

1.82 (1.34–2.46)
1.31 (0.99–1.73)
1<0.0010.056

1.92 (1.41–2.60)
1.28 (0.97–1.28)
1<0.001
0.082

Having dinner within 2 h before
bedtime at least 3 days a week
YesNo

706 (51.3)
669 (48.7)

645 (52.1)
594 (47.9)

0.96 (0.81–1.13)
1

0.591

Eating food after dinner≥3 days a week
YesNo

302 (22.0)
1073 (78.0)

219 (19.2)
909 (80.8)

1.24 (1.01–1.53)
1

0.043

1.25 (1.01–1.55)
1

0.041

Lifestyle Items (10 Items)
Women

MetS
(n = 1375)

Non-MetS(n = 1239)

Age-Adjusted Logistic
Regression Model 1
OR (95% CI)

Exercising with light sweat for atleast 30 min per session more than
twice a week for over a year
YesNo

683 (49.7)
692 (50.3)

543 (43.8)
696 (56.2)

Walking or equivalent physicalactivity in daily life formore than 1 h per day
YesNo

755 (54.9)
620 (45.1)

Walking faster than the same sexof similar age
YesNo

p-ValueInt. J. Environ. Res. Public Health 2022, 19, 10481

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Table 4. Cont.
Number (%)
Lifestyle Items (10 Items)
Women

MetS
(n = 1375)

Non-MetS(n = 1239)

Age-Adjusted Logistic
Regression Model 1
OR (95% CI)

Skipping breakfast≥3 days a weekYesNo

424 (30.8)
951 (69.2)

436 (35.2)
803 (64.8)

0.84 (0.70–1.00)
1

0.053

0.83 (0.69–0.99)
1

0.045

Sufficient rest with sleep
YesNo

811 (59.0)
564 (41.0)

691 (55.8)
548 (44.2)

1.19 (1.01–1.42)
1

0.043

1.19 (1.01–1.42)
1

0.043

Alcohol drinking habit
Current drinker
Former drinker
Never

355 (25.8)
19 (1.4)1001 (72.8)

414 (33.4)
7 (0.6)818 (66.0)

1.33 (1.09–1.61)
1.96 (0.78–4.97)
1

0.004
0.155

1.33 (1.09–1.62)
1.95 (0.77–4.94)
1

0.004
0.159

Smoking status
Current smoker
Former smoker
Never

7 (0.5)5 (0.4)
1363 (99.1)

2 (0.2)2 (0.2)1235 (99.6)

2.97 (0.56–15.91)
1.17 (0.18–7.54)
1

0.203
0.87

-

p-Value

Multivariable Logistic
Regression Model 2
OR (95% CI)

p-Value

OR: odds ratio; 95% CI: 95% confidence interval. Model 1: adjusted for age, Model 2: adjusted for age andvariables; p < 0.2 (Model 1). Model 2: Hosmer–Lemeshow p = 0.059.

4. Discussion
This study focused on the lifestyle habits of Cambodians living in urban Cambodia
and is the first to show an association with MetS. The findings of this study indicated thatthe lifestyle factors significantly associated with MetS were walking faster than those of thesame sex of similar age, eating quicker than others, eating food after dinner at least 3 daysa week, skipping breakfast at least 3 days a week and getting enough rest through sleep.
These related lifestyles were similar to the results obtained in previous studies in Westernand developed countries, implying the possibility of the effectiveness of interventions
through health education and health guidance for the prevention of MetS.
The prevalence of MetS was 56.6% (men: 60.4%, women: 52.6%), which comprised
the majority and showed a high value. The study hospital was targeting the middle to theaffluent class. Since most of the participants are urban residents, urbanization most likely
affects their lifestyles. The high prevalence of MetS among the participants may also reflecturbanization. Furthermore, physical measurements and blood test data for both men andwomen indicated that those with MetS had poorer health than those without. These results
suggest the necessity of lifestyle interventions for MetS.
The category with the highest odds ratio was “eating quicker than others” for both
men and women. The association of eating speed with MetS has been shown in many
previous studies, and the results were consistent in Cambodia. In a cohort study of Japaneseparticipants, fast eaters had a higher risk of MetS than those who did not [28]. This study
also revealed that fast eating was significantly associated with MetS components suchas abdominal circumference and HDL-C. Studies in China and Korea have also reported
similar findings [29,30]. The reasons why eating too fast is a risk for MetS include a delayin the brain’s perception of satiety, resulting in overeating and increased total energy
intake [31,32], as well as its association with impaired insulin resistance, which is known tocause type 2 diabetes and visceral obesity [33]. Controlling the eating rate in Cambodia
may reduce the occurrence of MetS, since these causes are physiological factors rather thanvariations across cultural backgrounds.
Alcohol consumption was associated with MetS, as in the previous study [30,34–36]. In
this study, the answers were divided into three categories: current alcohol drinkers, quittersand never drinkers; however, many previous studies have focused on the frequency ofdrinking and changes in the amount and quantity of alcohol consumed [36–38]. These studies reported that those who drank more frequently, those who drank in greater quantities
and those with increased alcohol consumption were more likely to have MetS. In contrast,

Int. J. Environ. Res. Public Health 2022, 19, 10481

8 of 11some studies reported that light alcohol consumption reduces the risk of MetS [39]. Generally, alcohol consumption is considered to increase appetite, resulting in excessive energy
intake and leading to obesity [40]. While this study did not examine the amount of alcohol
consumed, the result suggests that excessive energy intake due to alcohol consumption
may be associated with MetS.
Other significant associations were found only for women with “eating food after
dinner at least 3 days a week”, “skipping breakfast at least 3 days a week” and “getting
enough rest with sleep”, suggesting that women need to be more careful about their eating
habits than men to avoid developing MetS and comorbid conditions.
According to previous studies, skipping breakfast is a risk factor for MetS and its
components [41–43]. This is because skipping breakfast causes people to overeat on an
empty stomach, leading to blood sugar spikes, in turn causing abdominal obesity andincreased cholesterol [44]. Conversely, missing breakfast tended to lower the risk of MetS
in a South Korean study, consistent with our results [45]. Other studies have reported thatthe group with missed breakfast had lower total energy intake [46,47]. The results of thisstudy may also be attributed simply to a reduction in total energy intake due to the absenceof breakfast. Similarly, the significant result of eating an evening meal after dinner as a risk
for MetS may also be related to the increased total energy intake. Eating foods other thanthe main meal has been noted to lead to excess energy intake [48,49], and it is consideredthat energy tends to be stored more if consumed after dinner since there is less opportunity
for expending energy.
Regarding the association of MetS with sleep, sleep duration and sleep quality reportedly affect MetS. Oversleeping, insufficient sleep and poor-quality sleep are reportedly
associated with the risk of MetS [50–53]. Herein, we did not investigate sleep durationor quality but only asked about subjective assessments. Therefore, there is not enoughinformation to interpret why there was a positive association between women who felt
well rested through sleep and MetS. Nevertheless, the present results are interesting andshould be considered for the duration and quality of sleep among Cambodians.
In this study, there was no association between exercise and MetS. However, “fasterwalking speed compared to the same sex at the same age” was negatively associated withMetS in both men and women. Walking speed is sometimes used to indicate exercise
habits and physical capability. People who watched TV for shorter hours reportedly had
faster walking speeds than those who watched TV for longer hours [54] and that walking
speed can be used as an indicator of physical frailty and physical independence [55,56].
This is because walking speed correlates with both physical activity and exercise tolerance.
The present results also suggest that walking speed reflects physical activity and exercise
tolerance and thus could be a low-risk factor for MetS.
This study has several limitations. First, a causal relationship between MetS andlifestyle cannot be stated because this is a cross-sectional study. Second, the results may not
be generalizable as the participants were from a single institution. Furthermore, there is apossibility of sampling bias, such as lifestyle bias, due to the study hospital targeted the
middle class to affluent population. Fourth, there is a risk of cognitive bias in the answers
to the lifestyle questions since they are the subjective responses of the participants. In
addition, there is a risk of observer bias, such as measurement error among measurers, as
the data were not specifically obtained for research purposes. Another limitation is thatthe Hosmer–Lemeshow p-value for men was 0.007, which was a poor fit of the model, andthere may have been other factors influencing the results. Finally, we were able to clarify
the relationship between previous studies’ lifestyle habits and MetS in Cambodia because
the present study used existing health examination questionnaire items, but we may nothave been able to derive sufficient information on lifestyle factors unique to Cambodia.

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9 of 11

5. Conclusions
Our results indicated that lifestyles significantly associated with MetS among Cambodians living in urban areas of Cambodia were eating quicker than others, walking fasterthan those of the same sex of similar age and drinking alcohol in both men and women.
In addition, there were significant associations with normal eating speed for men only
and, for women only, skipping breakfast at least three days per week, eating food after
dinner at least three days per week and getting enough rest through sleep. Further studies
are needed to examine lifestyle factors to derive more suitable content for Cambodians’
living environment, which may lead to efforts for the prevention of MetS tailored to thecharacteristics of Cambodians.
Author Contributions: Conceptualization M.T. and I.H.; methodology M.T. and K.N.; data collection,
S.C. and M.O.; formal Analysis, M.T.; writing—original draft preparation, M.T.; writing—review andediting, M.T., K.N., M.N., I.H. and H.S.; supervision: I.H. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the guidelinesof the Declaration of Helsinki and was approved by the Cambodian National Ethics Committee for
Health Research (200NECHR, approved on 27 August 2021). Ethical approval was also obtained fromthe Collaborative Research Institution (No.20-002, approved on 25 August 2020) and from the Ethics
Review Committee of the Nagoya University School of Health Sciences (No. 20-109, approved on
1 July 2020). All participants in the study were fully anonymized, and their data were de-identified.
Informed Consent Statement: Instead of consent forms, an opt-out agreement was provided by acollaborating institution for the use of existing data in this study.
Data Availability Statement: The datasets generated during and/or analyzed during the current
study are available from the corresponding author on reasonable request.
Acknowledgments: This work was financially supported by JST SPRING, Grant Number JPMJSP2125.
The author M.T. would like to take this opportunity to thank the Interdisciplinary Frontier NextGeneration Researcher Program of the Tokai Higher Education and Research System.
Conflicts of Interest: The authors declare no conflict of interest.

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