Preventive Medicine Reports 30 (2022) 101995

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Preventive Medicine Reports
journal homepage: www.elsevier.com/locate/pmedr

Association between lifestyle and metabolic syndrome incidence of workers
in northern Okinawa, Japan: A cohort study
Takuji Kishimoto a, *, Miwa Churiki a, Tatsuya Miyazato a, Akihiro Yamashiro a,
Yoshitaka Nagasawa b, Hayashi Shokita cabc

Department of Health Screening, Okinawa North Medical Association Hospital, 1712-3, Umusa, Nago City, Okinawa 905-8611, Japan
Department of Endocrinology, Metabolism and Dialysis, Okinawa North Medical Association Hospital, 1712-3, Umusa, Nago City, Okinawa 905-8611, Japan
Department of Gastroenterology, Okinawa North Medical Association Hospital, 1712-3, Umusa, Nago City, Okinawa 905-8611, JapanA R T I C L E I N F O

A B S T R A C T

Keywords:
Metabolic syndrome
Lifestyle
Cohort study
Target trial emulation

In Japan, specific health checkups were implemented to prevent metabolic syndrome (MetS) and cardiovasculardiseases in April 2008. This study aimed to clarify the relationship between lifestyle factors and the MetS
incidence to understand how the disease can be prevented and to improve the public health policy.
A retrospective cohort study was conducted using the specific health checkup data of 2,781 workers. Lifestylefactors were assessed using lifestyle-related items in the general health questionnaire included in the specifichealth checkups. The hazard ratio values for the incidence of MetS according to lifestyle-related items were
determined from the data of the specific health checkup for 12 years. The Cox proportional hazard survival
model was used to evaluate hazard ratio values after adjusting for confounding factors. The limitations of thisresearch method are discussed using a target trial emulation framework which investigates problems such as
biases in observational studies.
The crude incidence rates per 1,000 person-years of MetS in women and men were 15.25 and 47.58,
respectively. Three dietary lifestyle-related factors, namely “Eating snacks and sweet beverages other thanbreakfast, lunch, and dinner,” “Eating faster than others,” and “Skipping breakfast at least three times a week,”
were identified, with the hazard ratio values 1.262 (95 % confidence interval [CI] 1.032–1.542, p = 0.023),
1.220 (95 % CI 1.032–1.442, p = 0.020) and 1.189 (95 % CI 1.012–1.397, p = 0.036), respectively.
These results suggest that lifestyle improvements related to extracted lifestyle-related items contribute to theprevention of MetS.

1. Introduction

as described by previous studies (Sakurai et al., 2012; Willi et al., 2007;
Otsuka et al., 2006; Waki et al., 2005; Sasaki et al., 2003; Craig et al.,1989). However, studies on the relationship between lifestyle-related
items and the incidence of MetS using the general health question­
naire of a specific health checkup are limited (Tsutatani et al., 2017).
The transition of the nationwide prevalence of MetS (2008–2019)
remained almost constant from 14.4 % to 15.8 %. Thus, it is important toconsider the effective utilization of lifestyle-related items in general
health questionnaires to lower the prevalence of MetS. We conducted aretrospective cohort study for 12 years on the relationship between theincidence of MetS and lifestyle-related items using the results of specifichealth checkups of workers enrolled in the Employee Health Insurance.
We also examined the limitations of the research method applied in thisstudy using the framework of target trial emulation (TTE) which

Metabolic syndrome (MetS) is defined as a combination of risk fac­
tors such as obesity, hypertension, dyslipidemia, hyperglycemia, andother metabolic disorders, and is one of the causes of cardiovasculardisease (Gami et al., 2007; Takeuchi et al., 2005). In Japan, due to thedramatic increase in cardiovascular disease, a specific health checkup toprevent MetS and cardiovascular disease has been conducted since April
2008 for people aged 40–74 years. Recently, approximately 30 million
people (29,935,810 people [2019]: 47.7 % of Japan’s 40–74 years old
population) have undergone a specific health checkup.
At the time of a specific health checkup, a general health question­
naire containing lifestyle-related items was undertaken. The questions
are based on lifestyle-related items associated with obesity and diabetes,* Corresponding author.
E-mail address: takuji.kishimoto@nagohp.com (T. Kishimoto).

https://doi.org/10.1016/j.pmedr.2022.101995
Received 15 May 2022; Received in revised form 1 September 2022; Accepted 17 September 2022
Available online 19 September 2022
2211-3355/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

T. Kishimoto et al.

Preventive Medicine Reports 30 (2022) 101995

investigates problems such as biases in observational studies (GarcíaAlbéniz et al., 2017; Hernán and Robins, 2016).
The purpose of this study was to identify important information
related to the incidence of MetS from lifestyle-related items in thegeneral health questionnaire of the specific health checkup and to
consider the effective utilization of these items for improving the publichealth policy.

cholesterol (HDL-C) < 40 mg/dl, with or without serum triglyceride ≥150 mg/dl, or medication for hyperlipidemia. Individuals with central
obesity plus one of the three components were defined as Pre-MetS.
Healthy examinees who did not participate in the specific healthcheckup during the study period and those with missing data forlifestyle-related items were excluded. The incidence of MetS wasobserved in this cohort over 12 years.

2. Methods

2.3. Outcome variable

2.1. Specific health checkup

The outcome variable was MetS incidence, which was determined by
specific health checkups. Since the date of MetS incidence could not be
determined, the central day between the date of diagnosis of MetS andthe date of the previous specific health checkup was used as the esti­
mated date of MetS incidence. Person-years were calculated using theestimated date of MetS incidence. The person-years of those who were
not diagnosed with MetS and participated in specific health checkups
only halfway through the observation period and those who did not
develop MetS until the end of the observational period were calculated
based on the date of the last participating specific health checkup.

In Japan, under the initiative of the Japanese Ministry of Health,
Labor and Welfare, a specific health checkup has been conducted sinceApril 1, 2008, focusing on the prevention of metabolic syndrome andcardiovascular disease. Specified health checkups are conducted once a
year for people aged 40–74 years who have some kind of medical in­surance. If MetS or MetS reserve group (Pre-MetS) is diagnosed, healthguidance for 3 months (MetS: monthly guidance and evaluation byphysical measurements and blood test after 3 months; Pre-MetS: one
guidance and evaluation by physical measurements and blood test after
3 months) is carried out. In addition, when only central obesity (waist
circumference ≥ 85 cm in men, ≥ 90 cm in women) is determined, in­formation on MetS countermeasures is provided.

2.4. Explanatory variable
The explanatory variables were sex, age, and nine lifestyle-related
items selected from the general health questionnaire for specifichealth checkups. The lifestyle-related items included the following nine
items: (1) “Having an evening meal within 2 h before bedtime 3 days ormore per week,” (2) “Eating snacks and sweet beverages other thanbreakfast, lunch and dinner,” (3) “Eating faster than others,” (4)“Skipping breakfast at least three times a week,” (5) “Walking for at least
1 h/day or having equivalent physical activities,” (6) “Exercising at least
2 days/week at least 30 min each at an intensity that causes a slightsweat for at least 1 year,” (7) “Frequency of drinking,” (8) “Regular
smoker,” (9) “Feeling refreshed after a night’s sleep.”.

2.2. Study design and population
This was a retrospective cohort study using data from specific healthcheckups conducted at Okinawa North Medical Association Hospitalover 12 years, from April 1, 2008, to March 31, 2021. The Okinawa
North Medical Association Hospital is one of the core hospitals innorthern Okinawa Prefecture. Among the specific health checkup ex­
aminees, the workers who were enrolled in the Employee Health In­
surance were selected as the research participants (Fig. 1).
The Japanese MetS criteria were used as the diagnostic criteria for
MetS. Individuals with central obesity (waist circumference ≥ 85 cm inmen, ≥ 90 cm in women) plus at least two of the following three com­ponents were defined as MetS: (1) blood pressure ≥ 130/85 mmHg or
taking an antihypertensive; (2) fasting plasma glucose (FPG) ≥ 110 mg/
dl or medication for diabetes; (3) serum high-density lipoprotein

2.5. Adjusted variable
The adjusted variables were sex, age, and nine lifestyle-related items.
All explanatory variables were applied to the Cox proportional hazard
survival model and analyzed using the forced entry method andFig. 1. Study design and selection of study population. (MetS: metabolic syndrome; Pre-MetS: MetS reserve group).
2

T. Kishimoto et al.

Preventive Medicine Reports 30 (2022) 101995

backward stepwise method.

3. Results

2.6. Statistical analysis

3.1. Study participants and crude incidence rate of MetS

The significance of MetS prevalence by age group was determinedusing Pearson’s chi-square test. The observed person-years were calcu­
lated from the estimated incidence dates, and the crude incidence rate
per 1,000 person-years was calculated. Hazard ratio values were
calculated using the Cox proportional hazard survival model. Applying
explanatory variables such as sex, age, and nine lifestyle-related items to
the Cox proportional hazard survival model, the hazard ratio values ofthe explanatory variables by the forced entry method, and the hazard
ratio values of the explanatory variables remaining by the backward
stepwise method were calculated. A level of α = 0.05 was used todetermine the significance of the models and variables. All statisticalanalyses were performed using the IBM SPSS Statistics ver. 28 (IBMCorp., Armonk, NY, USA).

Based on the specific health checkup examinees (4,880) among
workers enrolled in the Employee Health Insurance from April 1, 2008,
to March 31, 2009, a total of 3,192 (65.4 %) were healthy, 792 (16.2 %)had Pre-MetS, and 896 (18.4 %) had MetS. The prevalennce rates of
MetS were 4.7 %, 27.3 %, and 18.4 % for women, men, and the total
population, respectively (Table 1). Sex differences in MetS prevalence
rate appeared to be due to sex differences in the prevalence rates of MetS
components (obesity, hypertension, hyperlipidemia, and hyperglyce­mia). The prevalence rates of men and women in the MetS components
were obesity (men: 48.5 %; women: 29.4 %), hypertension (men: 33.5%; women: 21.4 %), and hyperlipidemia (men: 30.4 %; women: 26.5 %)and hyperglycemia (men: 18.9 %; women: 6.7 %). After screening, 2,781
participants in Healthy group were included in the study cohort (Fig. 1).
In the study cohort, the total observed person-years were 22,181 personyears, the incident case number was 644 (women: 194; men: 450), andthe crude incidence rate per 1,000 person-years was 29.03 (women:
15.25; men: 47.58) (Table 1).

Ethical approval
This study was approved after being deliberated by the institutional
review board of Okinawa North Medical Association Hospital. We
confirmed the consent of the participants to anonymize personal infor­
mation and use data, such as the results of the health checkup for
research, in writing and on the website.

3.2. Hazard ratio value of lifestyle-related items for MetS
Table 2 shows the hazard ratio values for MetS for sex-, age-, and thenine lifestyle-related items. First, we examined the forced entry method,and found significant hazard ratio values for sex, age, and the twolifestyle-related items. Next, sex, age, and three lifestyle-related items
related to dietary habits remained significant after the analysis using theTable 1
MetS rate in specific health checkup examinees (4,880) during the observation initiation period (April 1, 2008-March 31, 2009) and crude incidence rate per 1,000
person- years of MetS in the study cohort (2,781)
MetS rate in specific health checkup examinees (4,880) during April 1, 2008 - March
31, 2009
Women**

Age

Healthy

Pre-MetS

MetS

Total

40-49

822
92.4%
755
85.3%
120
80.5%
7
70.0%

45
5.1%
78
8.8%
15
10.1%
1
10.0%

23
2.6%
52
5.9%
14
9.4%
2
20.0%

890
100.0%
885
100.0%
149
100.0%
10
100.0%

Total

1,704
88.1%

139
7.2%

91
4.7%

1,934
100.0%

40-49

675
54.2%
686
49.1%
117
41.9%
10
41.7%

278
22.3%
302
21.6%
68
24.4%
5
20.8%

293
23.5%
409
29.3%
94
33.7%
9
37.5%

1,246
100.0%
1,397
100.0%
279
100.0%
24
100.0%

Total

1,488
50.5%

653
22.2%

805
27.3%

2,946
100.0%

40-49

1,497
70.1%
1,441
63.1%
237
55.4%
17
50.0%

323
15.1%
380
16.7%
83
19.4%
6
17.6%

316
14.8%
461
20.2%
108
25.2%
11
32.4%

2,136
100.0%
2,282
100.0%
428
100.0%
34
100.0%

3,192
65.4%

792
16.2%

896
18.4%

4,880
100.0%

50-59
60-69
70-74

Men**

50-59
60-69
70-74

Total**

50-59
60-69
70-74
Total

CIR of MetS in the study cohort (2,781)

Women

Men

Total

Age

Number of participants

Person-years

Incident cases

CIR

40-49

729

7,109

80

11.25

50-59

661

5,056

100

19.78

60-69

92

550

13

23.66

70-74

5

10

1

99.64

Total

1,487

12,724

194

15.25

40-49

596

4,774

234

49.02

50-59

597

4,114

186

45.21

60-69

93

536

24

44.78

70-74

8

33

6

181.85

Total

1,294

9,457

450

47.58

40-49

1,325

11,882

314

26.43

50-59

1,258

9,170

286

31.19

60-69

185

1,085

37

34.09

70-74

13

43

7

162.68

Total

2,781

22,181644

29.03**: P <0.001 by Pearson’s chi-square test, relationship between age and metabolic syndrome determination
Abbrevations; MetS: Metabolic Syndrome; Pre-MetS: MetS reserve group; CIR: crude incidence rate per 1,000 person-years
3T. Kishimoto et al.

Preventive Medicine Reports 30 (2022) 101995

Table 2
Hazard ratio values of lifestyle-related items for MetS by forced entry method and backward stepwise method of Cox regression hazard servival model
lifestyle-related
item

Number ofparticipants

Personyears

Incident
cases

CIR

Hazardratio†

95% confidence
interval

Sex
Woman
Man

1,487
1,294

12,724
9,457

194
450

130.464347.759

1.000
2.912

2.407

ー

3.522

P<
0.001

1.020

1.007

ー

1.033

0.002

Age
for every 1 year
increase

PvalueHaving an
evening meal
within 2hours before
bedtime 3days or more
per week
no
yes

1,866
915

15,119
7,062

414
230

221.865251.366

1.000
1.032

0.871

ー

1.222

0.719

Eating snacks
and sweet
beverages
other thanbreakfast,
lunch anddinner
no
yes

2,274
507

18,239
3,942

522
122

229.551240.631

1.000
1.268

1.032

ー

1.558

0.024

2,004

16,019441

220.060

1.000

Eating fasterthan others
No (general orslow)
Yes (fast)

95% confidence
intervalPvalue

++

1.000
3.119

2.631

ー

3.697

P<
0.001

++

++

1.019

1.007

ー

1.032

0.002

++

+

1.000
1.262

1.032

ー

1.542

0.023

+

1.220

1.032

ー

1.442

0.020

+

1.000
1.189

1.012

ー

1.397

0.036

+

1.000

777

6,162

203

261.261

1.242

1.049

ー

1.472

0.012

Skipping
breakfast atleast threetimes a week
no
yes

1,869
912

14,950
7,231

403
241

215.623264.254

1.000
1.157

0.980

ー

1.367

0.086

Walking for atleast 1 hour/
day or having
equivalent
physical
activities
no
yes

1,315
1,466

10,507
11,674301
343

228.897
233.970

1.000
0.915

0.779

ー

1.075

0.280

Exercising atleast 2 days/
week at least
30 minutes
each at an
intensity thatcause a slightsweat for atleast 1 year
no
yes

2,102
679

17,080
5,101

469
175

223.121257.732

1.000
1.055

0.880

ー

1.265

0.562

2,176

17,539452

207.721

1.000

605

4,642

192

317.355

1.073

0.894

ー

1.286

0.450

Regular smoker
no
yes

1,825
956

15,014
7,168

349
295

191.233
308.577

1.000
1.116

0.938

ー

1.329

0.216

Feelingrefreshed
after a night’s
sleep
no
yes

957
1,824

7,676
14,505203441

212.121241.776

1.000
1.053

0.889

1.246

0.551

Frequency ofdrinking
No (never orsometime)
Yes (everyday)

Hazardratio‡4+

T. Kishimoto et al.

Preventive Medicine Reports 30 (2022) 101995+: P < 0.05; ++: P < 0.01
Hazard ratio†: by forced entry method; Hazard ratio‡: by backward stepwise method
Abbrevations; MetS: Metabolic Syndrome; CIR: crude incidence rate per 1,000 person-years

backward stepwise method considering the influence of confounding
factors. The hazard ratio values of sex, age, “Eating snacks and sweet
beverages other than breakfast, lunch, and dinner,” “Eating faster thanothers,” and “Skipping breakfast at least three times a week,” were 3.119(95 % confidence interval [CI] 2.631–3.697, p < 0.001), 1.019 (95 % CI
1.007–1.032, p = 0.002), 1.262 (95 % CI 1.032–1.542, p = 0.023),
1.220 (95 % CI 1.032–1.442, p = 0.020) and 1.189 (95 % CI
1.012–1.397, p = 0.036), respectively.

effects on waist circumference, body mass index (BMI), MetS, and cir­
culatory disease (Yokoyama et al., 2016; Kutsuma et al., 2014; Wata­nabe et al., 2014). Kobayashi F et al. showed that skipping breakfast
enhances the post-lunch and post-dinner blood glucose elevation
response in healthy individuals (Kobayashi et al., 2014). In patients with
type 2 diabetes, skipping breakfast is associated with impaired insulin
response and low intact glucagon-like peptide-1 (iGLP-1), which en­
hances the post-lunch and post-dinner blood glucose elevation response.
It has been shown to cause hyperglycemic conditions over time (Jaku­
bowicz et al., 2015). These studies suggest a link between skipping
breakfast and MetS.
The number of examinees in the specific health checkup increased
from 20,192,502 (2008) to 29,935,810 (2019) nationwide. Accordingly,
those who fall under MetS and Pre-MetS have a system in which healthguidance is provided, and lifestyle-related items are used for healthguidance. However, those in the healthy group did not receive healthguidance to prevent MetS incidence using lifestyle-related items. For
instance, in 2019, approximately 70 % of the 29,935,810 examinees in
the healthy group (about 20 million) completed specific health exami­
nations without receiving health guidance. The implementation ofhealth guidance for the prevention of MetS incidence using the lifestylerelated items extracted in this study for this healthy group can beeffective in reducing the constant MetS prevalence rate.

4. Discussion
The purpose of this study was to extract items that are statisticallysignificantly related to the incidence of MetS from lifestyle-related itemsin the general health questionnaire of specific health checkups and to
consider the effective utilization of the extracted lifestyle-related items.
Three dietary lifestyle-related factors were identified, and their effective
utilization was considered for improving the public health policy.
The prevalence of MetS at the start of observation (April 1, 2008 –March 31, 2009) was 18.4 %. According to the Japanese Ministry of
Health, Labor, and Welfare, the nationwide prevalence of MetS in 2008
was 14.4 % (number of specific health checkup examinees: 20,192,502;
the number with MetS: 2,907,018), and the prevalence of MetS in thisstudy is one of the highest in Japan. The transition of nationwide MetS
prevalence (2008–2019) remained almost constant from 14.4 % to 15.8%. With the dramatic increase in the global prevalence of MetS in recent
years, the fact that the prevalence of MetS in Japan remains constant
may be inferred as an outcome of specific health checkups (GBD 2015
Obesity collaborators et al., 2017; Wang et al., 2007).
Three dietary lifestyle-related items were identified as statisticallysignificant factors for the incidence of MetS. According to a retrospective
cohort study on the relationship between lifestyle-related items and theincidence of MetS in specific health checkups, “Having an evening meal
within 2 h before bedtime 3 days or more per week” in men and “Fre­quency of drinking” in women were considered as statistically signifi­
cant lifestyle-related items (Tsutatani et al., 2017). The reasons for thedifference in our results were thought to be due to the use of theabdominal circumference, which is one of the adjusted variables in the
MetS diagnostic criteria, as well as the characteristics of the study
population (in our research, the workers at the business establishment).
Several cross-sectional studies have reported an association between
snack foods and MetS. Thorp A A et al. showed that TV viewing time andsnack food consumption were independently and jointly associated withMetS in Australian women (Thorp et al., 2013). In Japanese workers,
Yoshida J et al. recognized that “snacks after dinner” and “dinner
immediately before bed” had a significant odds ratio for obesity, which
is a component of MetS (Yoshida et al., 2018). Mimiran P et al. con­ducted an 8.9-year prospective cohort study in the Iranian population,
showing snack foods, especially candies and chocolate, increased therisk of MetS among individuals with a low physical activity level (Mir­miran et al., 2021). These studies suggest that snack food intake, alongwith other lifestyle habits, promotes overconsumption of calories and is
a risk of developing MetS.
There are several studies on the relationship between the extracted
lifestyle-related item “Eating faster than others” and MetS. Tanihara S
et al. showed that fast eating speed increased serum insulin resistanceand fasting blood glucose levels, thereby increasing the risk of MetS
(Tanihara et al., 2011). Moreover, some studies have shown that fasteating speed causes binge eating and increases postprandial blood
glucose (Teo et al., 2020; Tanihara et al., 2011; Rei et al., 2006; Matsuki
et al., 2003). These studies suggest that fast eating speed can be a highrisk factor for MetS.
Several studies have shown that skipping breakfast has unhealthy

4.1. Limitations
The limitations of this research method were examined using a target
trial emulation framework. Among the items to be considered in target
trial emulation, the following four items were examined: (1) eligibility,(2) treatment assignment, (3) time zero (timing to start follow-up), and(4) causal contrasts of interest. In a target trial (an ideal randomized
controlled trial), participants are first screened based on a series of
eligibility criteria, assigned to an intervention group (treatment
assignment), and then followed up (time zero). Finally, the outcomes areevaluated (Fig. 2).
In this study, eligibility was performed after treatment assignment
and at time zero. If some individuals were lost to follow-up (411 in thisstudy), there might be differences in the rate of follow-up loss between
groups with and without lifestyle-related items. For example, whencomparing the incidence of MetS by the presence or absence of “Eating
faster than others,” there may be more dropouts in the group who
responded “Yes” to “Eating faster than others.” This finding suggests that
there is a potential selection bias in this study. However, the direction inwhich this selection bias affects the increase or decrease in the hazard
ratio value is unclear. Considering that the dropout rate in this study waslow (12.9 %, 411/3,192), the magnitude of the effect of this selection
bias seems to be relatively small.
Intention-to-treat and per-protocol effects were examined for causal
contrasts of interest in a target trial. It is unclear how long the lifestylerelated items in this study will be maintained after time zero, and it is
impossible to evaluate the per-protocol effect. In this study, a modified
intention-to-treat analysis was used, excluding those who had only
baseline-specific health checkup data. Since the validity of the modified
intention-to-treat analysis in a target trial has been confirmed, theanalysis in this study appears to be valid (Abraha and Montedori(2010)).
5. Conclusion
A retrospective cohort study was conducted using the results of a 12year health checkup to clarify the relationship between lifestyle-related
items in the questionnaire and MetS incidence. We identified three5

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Preventive Medicine Reports 30 (2022) 101995

Fig. 2. Time relationship among the main items of the target trial emulation framework.

lifestyle-related items that were significantly associated with the inci­
dence of MetS. These items are like to be effective for the prevention of
MetS incidence and the reduction in the prevalence rate of MetS to
provide health guidance focusing on healthy groups that are not in the
Pre-MetS or MetS conditions at the time of a specific health checkup.

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CRediT authorship contribution statement
Takuji Kishimoto: Conceptualization, Data curation, Writing –original draft. Miwa Churiki: . Tatsuya Miyazato: Data curation.
Akihiro Yamashiro: Data curation. Yoshitaka Nagasawa: Validation.
Hayashi Shokita: Validation.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The authors do not have permission to share data.
Acknowledgments
The authors would like to thank the staff of Okinawa North Medical
Association Hospital for their involvement in specific health checkups.
We deeply appreciate Dr. Yoshihisa Nakamura, Dr. Tomoko Oshiro, andDr. Yasuko Kishimoto for their support in health screening activities.
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