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OR I G I N A L R E S E A R CH AR T I C L E
Nightly fasting duration is not associated with the
prevalence of metabolic syndrome among non-shift
workers: The Furukawa Nutrition and Health Study
Mariko Ueda1 | Yosuke Inoue2 | Huan Hu2 | Masafumi Eguchi3 |
Zobida Islam2 | Takako Miki2,4 | Ami Fukunaga2 | Takeshi Kochi3 |
Shamima Akter2 | Isamu Kabe3 | Rie Akamatsu5 | Tetsuya Mizoue2
1Graduate School of Humanities and
Sciences, Ochanomizu University, Tokyo,
Japan
2Department of Epidemiology and
Prevention, National Center for Global
Health and Medicine, Tokyo, Japan
3Department of Health Administration,
Furukawa Electric Corporation, Tokyo,
Japan
4Department of Mental Health, Graduate
School of Medicine, The University of
Tokyo, Tokyo, Japan
5Natural Science Division, Faculty of Core
Research, Ochanomizu University, Tokyo,
Japan
Correspondence
Yosuke Inoue, Department of
Epidemiology and Prevention, National
Center for Global Health and Medicine,
1-21-1 Toyama Shinjuku-ku, Tokyo
1628655, Japan.
Email: yosuke.yoshi.yosky@gmail.com
Funding information
Industrial Health Foundation; Japan
Society for the Promotion of Science,
Grant/Award Numbers: 16H05251,
25293146, 25702006; National Center for
Global Health and Medicine, Grant/
Award Numbers: 19A-1006, 28-Shi-1206,
30-Shi-2003
Abstract
Objectives: While several experimental studies in animals and humans have
suggested the protective effect of nightly fasting duration (NFD) against car-
diometabolic risk factors, few population-based studies have been conducted.
This study aimed to investigate the association between NFD and metabolic
syndrome (MetS) among Japanese non-shift workers.
Methods: A subset of 1054 non-shift workers from the Furukawa Nutrition and
Health Study were included in this analysis. Participants completed dietary and
lifestyle surveys during a periodic checkup. NFDwas defined as the time between
dinner and breakfast and was categorized into four groups (ie, ≥12 hours,
11 hours, 10 hours, and ≤9 hours). MetS was defined as ≥3 of the following com-
ponents: high waist circumference (≥90 cm [men] and ≥80 cm [women]), high
triglycerides (≥150 mg/dL), low high-density lipoprotein cholesterol (<40 mg/dL
[men] and <50 mg/dL [women]), hypertension (systolic blood pressure≥130 mm
Hg or diastolic blood pressure ≥85 mm Hg), and high fasting glucose (fasting
plasma glucose ≥100 mg/dL or hemoglobin A1c ≥5.6%). A multivariable logistic
regressionmodel was used to examine the association between NFD andMetS.
Results: The odds ratios (95% confidence intervals) of MetS for the highest
(≥12 hours) through lowest (≤9 hours) NFD categories were 1.00 (reference),
0.83 (0.51-1.35), 0.83 (0.48-1.43), and 0.80 (0.43-1.48) (P for trend = 0.50) after
adjusting for covariates. Further analyses on the relationship between NFD
and each MetS component found no significant associations.
Conclusions: We did not find any evidence of a significant association
between NFD and MetS among non-shift workers in Japan.
1 | BACKGROUND
Metabolic syndrome (MetS) is defined as a clustering of car-
diometabolic risk factors (ie, abdominal obesity, insulin
resistance, hypertension, dysglycemia, and dyslipidemia;
Alberti et al., 2009) and is estimated to affect 20% to 25% of
the global adult population (International Diabetes
Federation, 2006). Given the rapidly increasing prevalence
of MetS in Asian countries (Pan, Yeh, & Weng, 2008) and
that Asian individuals typically have higher visceral fat
Received: 23 January 2020 Revised: 16 April 2020 Accepted: 6 May 2020
DOI: 10.1002/ajhb.23437
Am J Hum Biol. 2021;33:e23437. wileyonlinelibrary.com/journal/ajhb © 2020 Wiley Periodicals, Inc. 1 of 8
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accumulation compared to European descendants at any
given body mass index (BMI) (Park, Allison, Heymsfield, &
Gallagher, 2001), there is a need to study MetS and its com-
ponents among Asian populations.
A growing body of literature identifies the association
between meal timing and MetS risk factors. Ha and
Song (2019) showed that Koreanmales who consumed ≥25%
of total energy after 9:00 p.m. had 1.48 times greater risk of
MetS compared to those who did not. Ashizawa et al. (2014)
showed that eating dinner immediately before going to bed
was significantlyassociatedwithMetS (odds ratios [OR]=1.15
[men] and 1.19 [women]) among 279 989 Japanese individ-
uals. Yoshida, Eguchi, Nagaoka, Ito, and Ogino (2018)
reported that the risk of developing MetS was 1.68 times
higher among Japanese females who ate dinner within
3 hours before going to bed and snacked after dinner com-
pared with those without these habits. It has been suggested
that meal timing and circadian rhythm misalignment may
contribute to these findings (Paoli, Tinsley, Bianco, &
Moro, 2019; Scheer, Hilton,Mantzoros, & Shea, 2009).
Despite this progress, several issues remain to be
addressed. One such example is that while previous animal
(Chaix, Lin, Le, Chang, & Panda, 2019) and human (Gabel
et al., 2018; Gill & Panda, 2015; Hutchison et al., 2019;
Lecheminant, Christenson, Bailey, & Tucker, 2013) studies
have suggested the effect of time-restricted feeding (ie, the
practice of consuming ad libitum energy within a restricted
window of time and fasting during night time; Marinac,
Natarajan, et al., 2015), few population-based epidemiologic
studies have been conducted to investigate the association
between nightly fasting duration (NFD) (ie,mealtime interval
between dinner and breakfast) and cardiometabolic risk fac-
tors, with inconsistent findings. The Korean study conducted
by Ha and Song (2019) found no association between
NFD and MetS. However, two studies using data from the
US National Health and Nutrition Examination Survey
(NHANES) examined the association between NFD and car-
diometabolic risk markers and found inverse associations
between NFD and C-reactive protein (Marinac, Sears,
et al., 2015 aswell as HbA1c (Marinac, Natarajan, et al., 2015).
To facilitate our efforts to effectively reduce disease
burden associated with MetS, this study aimed to investi-
gate the association between NFD and MetS and its com-
ponents among a working population in Japan.
2 | METHODS
2.1 | Study design
As a part of the Furukawa Nutrition and Health Study
(FUN study), an ongoing nutritional epidemiological
survey investigating health determinants among working
age population, periodic health examinations were con-
ducted at two worksites of one manufacturing company,
which are both located in the Greater Tokyo Area, in
2015 and 2016. Participants completed two question-
naires relating to (a) dietary information and (b) overall
health and lifestyle. Of 2350 workers who received a
health examination, 2067 agreed to participate (response
rate = 88%).
The study protocol was approved by the ethics com-
mittee of the National Center for Global Health and Med-
icine, Japan (No. NCGM-G-001140-16). Subjects were
allowed to withdraw their participation at any time dur-
ing and after the survey. We followed the Japanese Ethi-
cal Guidelines for Epidemiological Research for
observational studies that use existing data.
2.2 | Study participants
Of the 2067 employees who participated in the health
checkup, 1013 were excluded based on the following
criteria: missing outcomes data (n = 98); did not return
the study questionnaires (n = 21); missing breakfast
information (ie, participants who did not answer the
breakfast time questions or had breakfast less than three
times a week; n = 328); missing dinner information (ie,
participants who did not answer the dinner time ques-
tion, did not have dinner, or ate snacks after dinner more
than three times per week; n = 294); shift workers (eg,
rotating-shift workers and night-shift workers) orpartici-
pants with missing information on shift work (n = 219);
participants with a previous diagnosis of cancer, cardio-
vascular disease, nephritis, hepatitis, or pancreatitis
(n = 45); participants with total energy intake of
<600 kcal or ≥4000 kcal, which were deemed irrelevant
(n = 2); and participants with missing covariates infor-
mation (n = 6). After exclusions, 1054 participants
(925 men and 129 women, aged 18-78 years) were
included in the analyses.
2.3 | Anthropometric and biochemical
measurements
Several anthropometric and biochemical measurements
were recorded. Waist circumference was measured at the
umbilical level in standing position using a measuring
tape. Systolic and diastolic blood pressure were measured
with an automated sphygmomanometer (HEM-907,
Omron Health Care Co. Ltd., Kyoto, Japan). As part of
the health checkup, fasting plasma glucose (FPG) was
2 of 8 UEDA ET AL.
assayed enzymatically using Quick-auto-neo-GLU-HK
and Quick-auto-II-GLU-HK (Shino-Test Corp., Tokyo,
Japan). HbA1c levels were measured with a latex aggluti-
nation immunoassay using the Determiner HbA1c and
Determiner L HbA1c kits (Kyowa Medex Co., Ltd.,
Tokyo, Japan) at an external laboratory (Kinki Kenko
KanriCenter, Shiga, Japan). High-density lipoprotein
cholesterol (HDL-C) and low-density lipoprotein choles-
terol (LDL-C) concentrations were measured by direct
enzymatic method using the Metabolead-HDL-C and
Determiner LLDL-C (Kyowa Medex Co., Ltd). Triglycer-
ide (TG) levels were measured by an enzymatic method
using the Determiner C TG (Kyowa Medex Co., Ltd.).
2.4 | Nightly fasting duration
NFD was defined as the time between dinner and break-
fast. Participants self-reported information on dinner and
breakfast timing in response to the following questions:
What time do you usually have dinner/breakfast? The
response options for dinner time included: before 7:00 p.
m.; 7:00-7:59 p.m.; 8:00-8:59 p.m.; 9:00-9:59 p.m.;
10:00-10:59 p.m.; and 11:00 p.m. or later. The response
options for breakfast time included before 5:00 a.m.;
5:00-5:59 a.m.; 6:00-6:59 a.m.; 7:00-7:59 a.m.; 8:00-8:59
a.m.; and 9:00 a.m. or later. To calculate NFD, a mid-time
point was assigned for the interval responses, 6:30
p.m. and 4:30 a.m. were assumed for the options “before
7:00 p.m.” and “before 5:00 a.m.”, respectively, and 11:30
p.m. and 9:30 a.m. were assumed for the options “11:00
p.m. or later” and “9:00 a.m. or later”, respectively. Par-
ticipants were categorized into four groups based on
NFD: ≥12 hours, 11 hours, 10 hours, or ≤9 hours.
2.5 | Definition of MetS
MetS was defined as the presence of three or more of
the following risk factors (Alberti et al., 2009): (a) high
waist circumference (ie, waist circumference ≥90 cm in
men and ≥80 cm in women); (b) high TG (ie, serum TG
levels ≥150 mg/dL); (c) low HDL-C (ie, HDL-C
<40 mg/dL in men and <50 mg/dL in women); (d) high
blood pressure (ie, systolic blood pressure ≥130 mm Hg
or diastolic blood pressure ≥85 mm Hg); and (e) high
blood glucose (ie, FPG level ≥100 mg/dL or HbA1c
≥5.6%) (National Glycohemoglobin Standardization
Program). Participants with a history of diabetes, taking
hyperlipidemia medication, and taking antihyperten-
sive medication were considered to meet the criteria for
high blood glucose, high TG, and high blood pressure,
respectively.
2.6 | Covariables
Several covariates including job grade (high, middle, or
low), marital status (yes or no), smoking status (never-
smoked, quit, current smoker smoking <20 cigarettes/
day, or current smoker smoking ≥20 cigarettes/day),
alcohol consumption (do not drink, drink 1-3 days per
month, drink less than 1 go [i.e., Japanese traditional
unit equivalent to 23 g of ethanol] per day, drink 1 to <2
go per day, or drink 2 go or more per day), work-related
physical activities (<3 metabolic equivalents [METs]-h/
day, 3-6.9 METs-h/day, 7-19.9 METs-h/day, or ≥20
METs-h/day), leisure-time physical activities (0 METs-h/
week, 0-2.9 METs-h/week, 3-9.9 METs-h/week, or ≥10
METs-h/week), overtime work (<10 hours/month,
10-29 hours/month, or ≥30 hours/month), weekdays
sleep duration (<5 hours, 5-5.9 hours, 6-6.9 hours,
7-7.9 hours, or ≥8 hours), and the interval between din-
ner time and bedtime (<2 hours, ≥2 hours) were assessed
using a lifestyle questionnaire. Work-related and leisure-
time physical activities were expressed as the sum of their
MET value multiplied by the duration of that activity.
Diet during the preceding 1 month was assessed using a
validated self-administered questionnaire, and energy
intake was estimated using an ad hoc computer algo-
rithm with reference to the Standard Tables of Food
Composition in Japan (Kobayashi et al., 2012). The inter-
val between dinner time and bedtime was calculated
using the information on dinner time and weekday
bedtime.
2.7 | Statistical analysis
Basic characteristics were compared by NFD categories,
using chi-squared tests for categorical variables, and anal-
ysis of variance for continuous variables. A multivariable
logistic regression model was used to estimate the OR
and corresponding 95% confidence intervals (CIs) for
having MetS. Model 1 was adjusted for age, sex, site, job
grade, and marital status. In addition to the covariates in
Model 1, Model 2 was adjusted for smoking status, alco-
hol consumption, work-related physical activity, leisure-
time physical activity, overtime work, weekday sleep
duration, energy intake (kcal/day, continuous), sodium
intake (g/1000 kcal, continuous), fruit intake
(g/1000 kcal, continuous), vegetable intake (g/1000 kcal,
continuous), fat intake (% energy, continuous), and car-
bohydrate intake (% energy, continuous). Model 3 added
adjustments for dinner time (before 8:00 p.m.; 8:00-8:59
p.m.; 9:00-9:59 p.m.; and 10:00 p.m. or later, based on the
response options for dinner time). Model 4 used the same
covariates included in model 2 and added adjustment for
UEDA ET AL. 3 of 8
interval between dinner time and bedtime (<2 hours;
≥2 hours). Model 5 also used the same covariates
included in model 2 and added adjustments for breakfast
time (before 6:00 a.m.; 6:00-6:59 a.m.; 7:00-7:59 a.m.; 8:00
a.m. or later, based on the response options for breakfast
time). Trend associations were assessed by assigning ordi-
nal numbers to categories of NFD, treating them as con-
tinuous variables.
To test the robustness of the study findings, we con-
ducted two sensitivity analyses in which (a) we excluded
the 155 participants who skip breakfast more than once
per week to understand the association among those who
eat breakfast every day and (b) we stratified the analysis
by dinner time (before 9:00 p.m. and 9:00 p.m. or later) to
see if meal timing modifies the association between NFD
and MetS.
All analyses were performed using the statistical soft-
ware Stata 15.0 (StataCorp, College Station, Texas). Two-
sided P values <.05 were considered statistically
significant.
3 | RESULTS
The characteristics of the study participants according to
NFD are shown in Table 1. The proportions of those with
an NFD of ≥12 hours, 11 hours, 10 hours, and ≤9 hours
were 23.4%, 30.5%, 26.1%, and 20.0%, respectively. The
mean age was 46.0 years (standard deviation [SD] = 9.0)
and mean BMI was 23.5 kg/m2 (SD = 3.3). Subjects with
shorter NFD were more likely to be male, married, and
current alcohol drinkers. They worked more overtime,
TABLE 1 Characteristics of the study population according to the categories of nightly fasting duration
Nightly fasting duration (hours)
Participants characteristics Total ≥12 11 10 ≤9 P-valuea
Number of subjects 1054 247 321 275 211
Age, mean [SD] 46.0 [9.0] 46.3 [10.4] 47.0 [9.5] 45.1[8.0] 45.5 [7.7] .05
Sex (men), n (%) 925 (87.8) 203 (82.2) 280 (87.2) 247 (89.8) 195 (92.4) .006
BMI (kg/m2), mean [SD] 23.5 [3.3] 23.6 [3.4] 23.6 [3.4] 23.4 [3.2] 23.5 [3.1] .92
Number of the risk components, mean [SD] 1.1 [1.2] 1.2 [1.3] 1.2 [1.2] 1.0 [1.1] 1.1[1.1] .11
Worksite (survey in April 2015), n (%) 589 (55.9) 124 (50.2) 195(60.7) 156 (56.7) 114 (54.0) .08
Job grade (low), n (%) 656 (62.2) 191 (77.3) 206 (64.2) 164 (59.6) 95 (45.0) <.001
Marital status (married), n (%) 791 (75.1) 165 (66.8) 237 (73.8) 213 (77.5) 176 (83.4) <.001
Smoking status (current smoker), n (%) 236 (22.4) 53 (21.5) 74 (23.1) 69 (25.1) 40 (19.0) .43
Alcohol consumption (current alcohol drinker), n (%) 742 (70.4) 159 (64.4) 227 (70.7) 195 (70.9) 161 (76.3) .048
Work related physical activity (≥20METs-h/d), n (%) 149 (14.1) 52 (21.1) 43 (13.4) 33 (12.0) 21 (10.0) .003
Leisure-time physical activity (≥10METs-h/w), n (%) 303 (28.8) 67 (27.1) 83 (25.9) 93 (33.8) 60 (28.4) .17
Overtime work (≥30 hours/month), n (%) 283 (26.9) 25 (10.1) 57 (17.8) 99 (36.0) 102 (48.3) <.001
Weekday sleep duration (<6 hours), n (%) 538 (51.0) 98 (39.7) 149 (46.4) 132 (48.0) 159 (75.4) <.001
Dinner time (9:00 p.m. or later), n (%) 313 (29.7) 2 (0.8) 23 (7.2) 108 (39.3) 180 (85.3) <.001
Interval between dinner time and bedtime
(<2 hours), n (%)
169 (16.0) 4 (1.6) 20 (6.2) 48 (17.4) 97 (46.0) <.001
Breakfast time (7:00 a.m. or before), n (%) 545 (51.7) 65 (26.3) 155 (48.3) 167 (60.7) 158 (74.9) <.001
Total energy intake (kcal), mean [SD] 1823 [480] 1735 [463] 1824 [499] 1826[471] 1922 [467] <.001
Sodium intake (g/1000 kcal), mean [SD] 5.9 [1.2] 6.2 [1.2] 5.9 [1.1] 5.8 [1.1] 5.7 [1.2] <.001
Fruit intake (g/1000 kcal), mean [SD] 45.0 [45.3] 44.0 [43.7] 43.5 [44.1] 44.5[40.7] 49.1 [54.0] .53
Vegetable intake (g/1000 kcal), mean [SD] 122.1 [62.9] 128.7 [70.2] 118.5 [56.6] 117.6[60.6] 125.6 [65.4] .12
Fat intake (% energy), mean [SD] 24.7 [5.6] 25.0 [5.6] 24.5 [5.8] 24.4 [5.2] 25.2 [5.8] .39
Carbohydrate intake (% energy), mean [SD] 53.3 [8.5] 53.4 [8.0] 53.4 [8.7] 53.9 [8.4] 52.4 [8.7] .29
Abbreviations: BMI, body mass index; METs, metabolic equivalents; SD, standard deviation.
aBased on chi-squared tests for categorical variables and analysis of variance for continuous variables.
4 of 8 UEDA ET AL.
had shorter sleep durations on weekdays, and ate dinner
later. They were less likely to be a low-grade worker and
were less physically active at work. In addition, they tend
to intake more energy and less sodium. Descriptive fea-
tures of each group are summarized in Table 1.
Of 1054 participants, 154 subjects met the criteria for
having MetS (14.6%). The results of multiple logistic
regression models investigating the association between
NFD and MetS are shown in Table 2. In the model
adjusted for age, sex, site, job grade, and marital status
(model 1), the ORs (95% CI) of MetS for the highest
(≥12 hours) through lowest (≤9 hours) NFD categories
were 1.00 (reference), 0.80 (0.51-1.27), 0.70 (0.42-1.16),
and 0.76 (0.44-1.31), respectively (P for trend = .25). After
additional adjustments for lifestyle factors (model 2), the
corresponding figures were 1.00 (reference), 0.83
(0.51-1.35), 0.83 (0.48-1.43), and 0.80 (0.43-1.48), respec-
tively (P for trend = .50). Similar results were observed
after the adjustment for dinner time (model 3), interval
between dinner time and bedtime (model 4), and break-
fast time (model 5).
The associations between NFD categories and each
component of MetS are shown in Table 3. No statistically
significant associations between NFD and the MetS com-
ponents were found. The sensitivity analysis in which we
excluded the 155 participants who skip breakfast more
than once per week revealed that there was not substan-
tial difference in the study results (Table S1). When we
stratified the analysis by dinner time, we did not find any
evidence of a significant association between NFD and
MetS (Table S2).
4 | DISCUSSION
In this cross-sectional study of 1054 Japanese non-shift
workers, there was no statistically significant association
between NFD and MetS or its components (ie, abdominal
obesity, insulin resistance, hypertension, dysglycemia,
and dyslipidemia).
While our study results do not support the findings of
two observational studies conducted in the United States
(NHANES), which showed an inverse association
between NFD and cardiometabolic markers (Marinac,
Natarajan, et al., 2015; Marinac, Sears, et al., 2015), they
are rather consistent with those reported in the Korean
study (Ha & Song, 2019), which, contrary to the expected
associations (ie, longer NFD linked to improved car-
diometabolic health), showed a lower prevalence of
abdominal obesity (in both sexes) and elevated TGs
TABLE 2 Multivariable-adjusted odds ratios (95% confidence interval) for the association between nightly fasting duration and
metabolic syndrome among non-shift workers in Japan (2015-2016)
Nightly fasting duration (hours)
≥12 11 10 ≤9 P for trend
Total number 247 321 275 211
Number of cases (%) 44 (17.8) 49 (15.3) 33 (12.0) 28 (13.3)
Model 1a 1.00 (ref.) 0.80 (0.51-1.27) 0.70 (0.42-1.16) 0.76 (0.44-1.31) .25
Model 2b 1.00 (ref.) 0.83 (0.51-1.35) 0.83 (0.48-1.43) 0.80 (0.43-1.48) .50
Model 3c 1.00 (ref.) 1.14 (0.65-2.00) 1.15 (0.57-2.30) 0.97 (0.40-2.38) .98
Model 4d 1.00 (ref.) 0.83 (0.51-1.35) 0.83 (0.48-1.44) 0.81 (0.41-1.59) .56
Model 5e 1.00 (ref.) 0.79 (0.48-1.32) 0.72 (0.40-1.31) 0.63 (0.31-1.29) .21
Abbreviation: METs, metabolic equivalents.
aModel 1 adjusted for age (year, continuous), sex, site, job grade (high, middle or low), marital status (married or not).
bModel 2 additionally adjusted for smoking status (never-smoked, quit, current smoker smoking <20 cigarettes/day, or current smoker
smoking ≥20 cigarettes/day), alcohol consumption (nondrinker, drinker consuming 1-3 days/month, weekly drinker consuming <1 go/day,
1-1.9 go/day, or ≥ 2 go/day; one go is equivalent to �23 g of ethanol), work related physical activity (<3 METs-h/day, 3-6.9 METs-h/day,
7-19.9 METs-h/day, or ≥20 METs-h/day), leisure-time physical activity (0 METs-h/week, 0.1-2.9 METs-h/w, 3-9.9 METs-h/week, or ≥10
METs-h/week), overtime work (<10 hours/month, 10-29 hours/month, or ≥30 hours/month), weekday sleep duration (<5 hours,
5-5.9 hours, 6-6.9 hours, 7-7.9 hours, ≥8 hours), total energy intake (kcal/day, continuous), sodium intake (g/1000 kcal, continuous), fruit
intake (g/1000 kcal, continuous), vegetable intake (g/1000 kcal, continuous), fat intake (% energy, continuous), carbohydrate intake (%
energy, continuous).
cModel 3 additionally adjusted for dinner time (before 8:00 p.m.; 8:00-8:59 p.m.; 9:00-9:59 p.m.; and 10:00 p.m. or later).
dModel 4 used the same covariates included in model 2 and additionally adjusted for interval between dinner time and bedtime (<2 hours;
≥2 hours).
eModel 5 used the same covariates included in model 2 and additionally adjusted for breakfast time (before 6:00 a.m.; 6:00-6:59 a.m.;
7:00-7:59 a.m.; 8:00 a.m. or later).
UEDA ET AL. 5 of 8
(in males) among those with NFD of 10-12 hours com-
pared with those with NFD ≥16 hours (Ha &
Song, 2019).
There are several possible interpretations of the null
study findings. First, this study examined a relatively
short NFD range compared to those examined in the pre-
vious studies (Ha & Song, 2019; Marinac, Natarajan,
et al., 2015); it is possible that the range was not above
the threshold at which a positive effect on metabolic
health emerges. While 34.8% of participants of the US
study had a fasting duration of ≥13.5 hours, only 23.4%
of the participants in the present study had a fasting
duration of ≥12.0 hours. Secondly, the participants in the
present study were on average much leaner (mean
BMI = 23.5 kg/m2) compared to those in the US study
(mean BMI = 28.2 kg/m2). Furthermore, the US study
included only women participants, with the majority
being non-Hispanic white. These differences among
TABLE 3 Multivariable-adjusted odds ratios (95% confidence interval) for the association between nightly fasting duration and each
component of metabolic syndrome among non-shift workers in Japan (2015-2016)
Nightly fasting duration (hours)
≥12 11 10 ≤9 P for trend
Total number (%) 247 321 275 211
High waist circumference
Number of cases (%) 63 (25.5) 68 (21.2) 52 (18.9) 47 (22.3)Model 1a 1.00 (ref.) 0.83 (0.56-1.25) 0.79 (0.51-1.21) 0.99 (0.62-1.56) .84
Model 2b 1.00 (ref.) 0.87 (0.57-1.33) 0.88 (0.56-1.41) 1.02 (0.60-1.71) .96
High triglycerides
Number of cases (%) 62 (25.1) 87 (27.1) 53 (19.3) 46 (21.8)
Model 1a 1.00 (ref.) 1.04 (0.70-1.53) 0.72 (0.47-1.11) 0.81 (0.51-1.28) .14
Model 2b 1.00 (ref.) 1.03 (0.68-1.55) 0.75 (0.47-1.19) 0.86 (0.52-1.43) .30
Low HDL cholesterol (%)
Number of cases (%) 17 (6.9) 24 (7.5) 17 (6.2) 10 (4.7)
Model 1a 1.00 (ref.) 1.17 (0.61-2.26) 0.99 (0.49-2.01) 0.82 (0.36-1.89) .57
Model 2b 1.00 (ref.) 1.25 (0.60-2.60) 1.25 (0.56-2.78) 1.01 (0.38-2.69) .98
High blood pressure (%)
Number of cases (%) 84 (34.0) 136 (42.4) 86 (31.3) 66 (31.3)
Model 1a 1.00 (ref.) 1.33 (0.91-1.95) 0.94 (0.62-1.40) 0.92 (0.59-1.42) .34
Model 2b 1.00 (ref.) 1.29 (0.87-1.92) 0.90 (0.59-1.39) 0.87 (0.53-1.41) .28
High blood glucose (%)
Number of cases (%) 66 (26.7) 86 (26.8) 76 (27.6) 56 (26.5)
Model 1a 1.00 (ref.) 0.95 (0.63-1.44) 1.26 (0.82-1.93) 1.18 (0.74-1.87) .27
Model 2b 1.00 (ref.) 1.04 (0.67-1.60) 1.52 (0.96-2.41) 1.28 (0.76-2.15) .14
Note: High waist circumference: ≥90 cm in men, ≥80 cm in women; high triglycerides: triglycerides levels ≥150 mg/dL or under medication;
low HDL cholesterol: <40 mg/dL in men, <50 mg/dL in women or under medication; high blood pressure: systolic blood
pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or under medication; high blood glucose: ≥100 mg/dL or HbA1c ≥ 5.6%, his-
tory of diabetes or under medication.
Abbreviations: HDL, high density lipoprotein; METs, metabolic equivalents.
aModel 1 adjusted for age (year, continuous), sex, site, job grade (high, middle or low), marital status (married or not).
bModel 2 additionally adjusted for smoking status (never-smoked, quit, current smoker smoking <20 cigarettes/day, or current smoker
smoking ≥20 cigarettes/day), alcohol consumption (nondrinker, drinker consuming 1-3 days/month, weekly drinker consuming <1 go/day,
1-1.9 go/day, or ≥2 go/day; one go is equivalent to �23 g of ethanol), work related physical activity (<3 METs-h/day, 3-6.9 METs-h/day,
7-19.9 METs-h/day, or ≥20 METs-h/day), leisure-time physical activity (0 METs-h/week, 0.1-2.9 METs-h/w, 3-9.9 METs-h/week, or ≥10
METs-h/week), overtime work (<10 hours/month, 10-29 hours/month, or ≥30 h/month), weekday sleep duration (<5 hours, 5-5.9 hours,
6-6.9 hours, 7-7.9 hours, ≥8 hours), total energy intake (kcal/day, continuous), sodium intake (g/1000 kcal, continuous), fruit intake
(g/1000 kcal, continuous), vegetable intake (g/1000 kcal, continuous), fat intake (% energy, continuous), carbohydrate intake (% energy,
continuous).
6 of 8 UEDA ET AL.
participants may account for the discrepancies in the
NFD-cardiometabolic risk factor association.
While this study used robust statistical methods,
adjusting for a range of covariates, there were some limi-
tations. First, self-reported dinner and breakfast time
(at hourly intervals) were used to calculate NFD, which
is subject to introducing social desirability bias and mis-
classification bias. The use of self-reported surveys is less
precise than NFD measurements used in some other
studies (Antoni, Robertson, Robertson, & Johnston, 2018;
Gabel et al., 2018; Gill & Panda, 2015; Ha & Song, 2019;
Hutchison et al., 2019; Jamshed et al., 2019;Marinac,
Natarajan, et al., 2015; Marinac, Sears, et al., 2015). Sec-
ond, we excluded those who ate snacks after dinner as
we did not collect information on the timing of snacking.
These excluded participants might have been biased in
terms of health-related lifestyle. Third, while the analysis
adjusted for dinner time, energy intake from dinner or
during the evening was not considered. In the US studies
(Marinac, Natarajan, et al., 2015; Marinac, Sears,
et al., 2015), energy intake after 10:00 p.m. was adjusted
for since higher energy intake in the evening (5:00 p.m.-
12:00 a.m.) was reported to be associated with a higher
risk of obesity (Wang et al., 2014). Lastly, given the cross-
sectional nature of this study, causality could not be
determined and reverse causality may have biased the
study results, as participants aware of their MetS risks
could have avoided unhealthy eating habits, such as late
evening meals.
Future study may need to focus on possible biological
mechanisms linking NFD and MetS or other related car-
diovascular indicators (eg, inflammatory markers and
arterial stiffening). In addition, it might be also important
to examine if NFD is specifically associated with any spe-
cific combinations of MetS components.
5 | CONCLUSION
This study found no significant association between NFD
and MetS among non-shift workers at a manufacturing
company in Japan. Further population-based research,
specifically prospective cohort studies, is needed to exam-
ine the association between NFD and MetS.
ACKNOWLEDGMENTS
We thank Hiroko Tsuruoka, Rie Ito, and Akiko Makabe
(Furukawa Electric Corporation) and Yuriko Yagi
(National Center for Global Health and Medicine) for
assistance with data collection. This study was supported
by the Industrial Health Foundation, JSPS KAKENHI
Grant Numbers JP25293146, JP25702006, JP16H05251,
and the Grant of National Center for Global Health and
Medicine (28-Shi-1206, 30-Shi-2003, 19A-1006).
CONFLICT OF INTEREST
M.E., T.K., and I.K. are health professionals at the
manufacturing company where this study was conducted.
The remaining authors declare no potential conflict of
interest.
AUTHOR CONTRIBUTIONS
M.U., Y.I., H.H., Z.I., T. Miki, A.F., S.A., R.A, T. Mizoue:
conceived the study; H.H., M.E., Z.I., T. Miki, T.K., S.A.,
I.K., T. Mizoue: collected data; M.U., Y.I.: analyzed the
data and drafted the manuscript; H.H., Z.I., T. Miki, A.F.,
R.A., T. Mizoue: provided critical comments on the
manuscript.
ORCID
Yosuke Inoue https://orcid.org/0000-0002-7690-3447
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SUPPORTING INFORMATION
Additional supporting information may be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Ueda M, Inoue Y, Hu H,
et al. Nightly fasting duration is not associated
with the prevalence of metabolic syndrome among
non-shift workers: The Furukawa Nutrition and
Health Study. Am J Hum Biol. 2021;33:e23437.
https://doi.org/10.1002/ajhb.23437
8 of 8 UEDA ET AL.
https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome.html
https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome.html
https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome.html
https://doi.org/10.1002/ajhb.23437
	Nightly fasting duration is not associated with the prevalence of metabolic syndrome among non-shift workers: The Furukawa ...
	1 BACKGROUND
	2 METHODS
	2.1 Study design
	2.2 Study participants
	2.3 Anthropometric and biochemical measurements
	2.4 Nightly fasting duration
	2.5 Definition of MetS
	2.6 Covariables
	2.7 Statistical analysis
	3 RESULTS
	4 DISCUSSION
	5 CONCLUSION
	ACKNOWLEDGMENTS
	 CONFLICT OF INTEREST
	 AUTHOR CONTRIBUTIONS
	REFERENCES