HOH
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metabolites

Project Report

Obesity and Voiding Parameters in a Community-Based
Population of Okinawa, Japan: Kumejima Digital Health
Project (KDHP)
Asuka Ashikari 1, * , Minoru Miyazato 2 , Koshi Nakamura 3 , Kiyoto Yamashiro 4 , Takehiro Nakamura 4 ,Tsugumi Uema 4 , Moriyuki Uehara 4 , Hiroaki Masuzaki 4 , Seiichi Saito 1 , Shiro Maeda 5,6 , Hajime Ishida 7
and Masayuki Matsushita 8
1

2

3

4

5

6

Citation: Ashikari, A.; Miyazato, M.;
Nakamura, K.; Yamashiro, K.;

7

8

Nakamura, T.; Uema, T.; Uehara, M.;
Masuzaki, H.; Saito, S.; Maeda, S.;*

Department of Urology, Graduate School of Medicine, University of the Ryukyus, Nishihara 903-0215, Japan;
ssaito@med.u-ryukyu.ac.jp
Department of Systems Physiology, Graduate School of Medicine, University of the Ryukyus,
Nishihara 903-0215, Japan; miyaz929@med.u-ryukyu.ac.jp
Department of Public Health and Hygiene, Graduate School of Medicine, University of the Ryukyus,
Nishihara 903-0215, Japan; knakamur@med.u-ryukyu.ac.jp
Division of Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology (Second Department ofInternal Medicine), Graduate School of Medicine, University of the Ryukyus, Nishihara 903-0215, Japan;y.kiyoto419psfm@gmail.com (K.Y.); maystorm180@hotmail.com (T.N.); ma2.ky8.26@gmail.com (T.U.);dairiman@hotmail.co.jp (M.U.); hiroaki@med.u-ryukyu.ac.jp (H.M.)
Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine,
University of the Ryukyus, Nishihara 903-0215, Japan; smaeda@med.u-ryukyu.ac.jp
Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital,
Nishihara 903-0215, Japan
Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus,
Nishihara 903-0215, Japan; ishidaha@med.u-ryukyu.ac.jp
Department of Molecular and Cellular Physiology, Graduate School of Medicine, University of the Ryukyus,
Nishihara 903-0215, Japan; masayuki@med.u-ryukyu.ac.jp
Correspondence: ashikari@med.u-ryukyu.ac.jp; Tel.: +81-98-895-1186

et al. Obesity and Voiding Parameters
in a Community-Based Population of
Okinawa, Japan: Kumejima Digital
Health Project (KDHP). Metabolites
2022, 12, 468. https://doi.org/
10.3390/metabo12050468
Academic Editor: Nils J.
K. Færgeman
Received: 23 April 2022
Accepted: 19 May 2022
Published: 23 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Abstract: (1) Background: Evidence has accumulated regarding the etiology of lower urinary tract
symptoms associated with obesity and metabolic syndrome. Therefore, the present study aimed
to identify which subjectively and objectively measured voiding parameters were associated withobesity in a community-based population. (2) Methods: Voiding parameters on a self-administeredquestionnaire and a digital self-health monitoring system for urine excretion (s-HMSU) were compared between participants with and without obesity, defined as a body mass index ≥ 25 kg/m2 (n= 30 and 29, respectively), from a community in Okinawa, Japan. Logistic regression analysis was
employed to calculate the odds ratios of abnormalities in voiding parameters for the obese group,with the non-obese group serving as a reference. (3) Results: The obese group had odds ratios of
5.17 (95% confidence interval: 1.33–20.0) for shortened hours of undisturbed sleep (<302 min) by
s-HMSU and 7.65 (1.88–31.1) for nighttime urinary frequency by a questionnaire after adjusting forage and sex. In addition, the obese group had an adjusted odds ratio of 2.27 (0.76–6.78) for decreased
maximum bladder capacity (<212 mL) by s-HMSU. (4) Conclusion: the results of the present study
suggest that nocturia and shortened hours of undisturbed sleep are signs of obesity.
Keywords: obesity; nocturia; polyuria; hours of undisturbed sleep; maximum bladder capacity

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Lower urinary tract symptoms (LUTS) are a major problem in middle-aged andolder populations. Nocturia worsens the quality of daily living because of insomnia andalso increases the risk of falls/bone fracture, frailty, pneumonia, and cardiovascular disease [1–3]. There have been numerous reports about the association between nocturia

Metabolites 2022, 12, 468. https://doi.org/10.3390/metabo12050468

https://www.mdpi.com/journal/metabolites

Metabolites 2022, 12, 468

2 of 8and obesity/metabolic syndrome (MetS) based on large-scale, community-based, epidemiological studies [4–6]. There has also been accumulating evidence regarding how theetiology of LUTS, such as an overactive bladder or benign prostatic hyperplasia, is related
to obesity/MetS [4,7–10]. However, evidence is scarce on how nocturia itself is related
to obesity/MetS. In addition, which specific voiding parameters that are associated withobesity/MetS remain unclear.
The etiology of nocturia is multifactorial and now considered to consist of the following
three pathological conditions: overproduction of urine (global/nocturnal polyuria), reduction of bladder capacity, and sleep disorders [11]. As the therapeutic approach for nocturia
depends on its cause, the frequency volume chart (FVC) is useful for diagnosing distinct
etiologies [12]. Using this self-reported FVC, concrete void parameters, such as nighttime
urinary frequency, maximum bladder capacity (MBC), and hours of undisturbed sleep(HUS), can be measured. However, paper-based FVC is annoying and time-consuming
for patients with nocturia, and is usually difficult to complete. We recently developed
the digital self-health monitoring system of urine excretion (s-HMSU) as an alternative to
FVC [13,14]. We believe that the s-HMSU’s ability to automatically record each voiding
time and measure the volume of voided urine might be useful in the development ofdigital health approaches. Therefore, in the present study, we aimed to identify which voiding parameters, including self-reported LUTS questionnaire items and s-HMSU-derived
voiding parameters, are associated with obesity by comparing these parameters between
individuals with and without obesity, recruited as part of the Kumejima Digital Health
Project (KDHP).
2. Results
The non-obese and obese groups included 29 and 30 participants, respectively. Theage and male-to-female ratio showed no statistically significant differences between thetwo groups (Table 1). Clinical characteristics, including body mass index (BMI), abdominal
circumference, blood pressure, and triglyceride levels, were significantly higher in theobese group than in the non-obese group (p < 0.05). Table 2 shows the relevant urinary
tract symptoms based on a self-administered questionnaire for the non-obese and obese
groups. The nighttime urinary frequency (Q2 in the Core Lower Symptoms Score (CLSS))
was significantly higher in the obese group than in the non-obese group (once and zero,
respectively; p = 0.012), with 51.7% for zero, 34.5% for once, and 6.9% for two or three times
in the non-obese group, and 23.3%, 56.7%, and 20.0% in the obese group. Mid-nocturnal
insomnia (Q2 in the Quick Inventory Depressive Symptomatology-Japanese (QIDS-J))
was significantly worsened in the obese group than that in the non-obese group, with it
defined as either “I wake up more than once a night and stay awake for 20 min or more,
frequently more than half a week (score 3)” or “I wake up at least once a night, but I go
back to sleep easily (score 2)” (p = 0.0058, Table 2). Table 3 shows the voiding parameters
measured using the 24 h voided volume monitoring system for each group. Hours ofundisturbed sleep (HUS) tended to worsen in the obese group than in the non-obese
group, although the difference was not statistically significant. The average urine flowrate (mL/s) was not significantly different between the groups. When LUTS and voiding
parameters were treated as dichotomous variables, the obese group had odds ratios of 5.17
(95% confidence interval (CI): 1.33–20.0, p = 0.0175) for shortened HUS (<302 min) and 7.65
(95% CI: 1.88–31.1, p = 0.0045) for nighttime urinary frequency (≥1 time; Q2 in CLSS) after
adjusting for age and sex, with the non-obese group serving as a reference (Table 4). In
addition, the obese group had adjusted odds ratios of 2.45 (95% CI: 0.67–9.14, p = 0.1724) fornighttime urinary frequency, documented by the 24 h voided volume monitoring system,and 2.27 (95% CI 0.76–6.78, p = 0.1780) for decreased MBC (<212 mL).

Metabolites 2022, 12, 468

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Table 1. Characteristics of study participants grouped according to the absence or presence of obesity.

Age (years)

Total (N = 59)

Non-Obesity (N = 29)

Obesity (N = 30)

p-Value

54.2 ± 12.9

56.1 ± 12.9

52.3 ± 12.7

0.2552Sex, n (%)

0.5221

Female26 (44.1)

14 (48.3)

12 (40.0)Male

33 (55.9)

15 (51.7)

18 (60.0)

Body mass index (kg/m2 )25.5 ± 3.77

22.9 ± 1.88

28.0 ± 3.39

<0.0001

Abdominal circumference (cm)

88.8 ± 10.00

82.3 ± 6.73

95.1 ± 8.4<0.0001

Systolic blood pressure (mmHg)

135.0 ± 19.8

128.2 ± 19.6

141.8 ± 17.9

0.0079

Diastolic blood pressure (mmHg)

80.2 ± 13.5

73.0 ± 10.8

87.3 ± 12.3

<0.0001

Fasting glucose (mg/dL)

84.5 (80–92)

84 (80–89)

85 (79.5–92.5)

0.5743

HbA1c (%)

5.6 ± 0.35

5.6 ± 0.40

5.6 ± 0.31

0.4177

Triglyceride (mg/dL)

104 (81–153)

101 (68–123.5)

119.5 (86.25–244)

0.0243

Total cholesterol (mg/dL)

200.3 ± 25.8

196.2 ± 28.4

204.3 ± 22.7

0.2334

HDL-C (mg/dL)

59.4 ± 16.3

63.0 ± 17.7

56.0 ± 14.3

0.1013

LDL-C (mg/dL)

111.8 ± 28.6

112.4 ± 25.6

111.2 ± 31.6

0.8678Creatinine (mg/dL)

0.73 ± 0.17

0.72 ± 0.14

0.73 ± 0.19

0.8624

Unpaired t-test, Mann–Whitney U test, or chi-square test was used to compare each characteristic. HbA1c,
hemoglobin A1c; HDL-C, high density lipoprotein-cholesterol; and LDL-C, low density lipoprotein-cholesterol.

Table 2. Self-reported urinary tract symptoms of participants grouped by obese status.
Non-Obesity (N = 29)

Obesity (N = 30)

p-Value

Daytime urinary frequency, CLSS Q1

0 (0–1)0 (0–1)0.6890

Nighttime urinary frequency, CLSS Q2

0 (0–1)1 (0.75–1)

0.012

Urinary urgency, CLSS Q3

0 (0–1)0 (0–1)0.7072Urinary urgency incontinence, CLSS Q4

0 (0)

0 (0)

0.7078Stress urinary incontinence, CLSS Q50 (0)0 (0–0.25)

0.6193

Weak urinary stream, CLSS Q6

0 (0–1)0 (0–1)

0.9229Straining, CLSS Q7

0 (0)

0 (0)

0.3713

Feeling of incomplete emptying, CLSS Q8

0 (0–1)0 (0–1)

0.7612

Bladder pain, CLSS Q9

0 (0)

0 (0)

0.2918Urethral pain, CLSS Q10

0 (0)

0 (0)

0.1325Mid-nocturnal insomnia, QIDS-J Q2

2 (1–2)3 (1–3)0.0058Median (interquartile range). The Mann–Whitney U test was used to compare each symptom. CLSS, Core Lower
Urinary Tract Symptoms Score; QIDS-J, Quick Inventory of Depressive Symptomatology-Japanese Score; QIDS-J,
Quick Inventory Depressive Symptomatology-Japanese. CLSS, Q1: 0, < 7; 1, 8–9 times; 2, 10–14 times; and 3, 15
times or more; Q2: 0, 0 times; 1, 1 time; 2, 2–3 times; and 3, 4 times or more; and Q3–Q10: 0, none; 1, occasionally;
2, sometimes; and 3, always; QIDS-J Q2 (mid-nocturnal insomnia): 0, I do not wake up at night; 1, I have a restless,light sleep with a few brief awakenings each night; 2, I wake up at least once a night, but I go back to sleep easily;and 3, I wake up more than once a night and stay awake for 20 min or more, frequently more than half a week.

Metabolites 2022, 12, 468

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Table 3. Voiding parameters measured by 24 h voided volume monitoring system of participantsgrouped by obese status.
Non-Obesity (N = 29)

Obesity (N = 30)

p-Value

1708.3 ± 705.9
6.0 (5–7)
1.0 (0–1)
587.3 (408.4–861.5)
190.4 (124.1–405.8)
24.1 (18.0–38.0)
226.9 (171.6–315.6)
330 (198.0–416.0)
23.6 (19.0–25.4)

1925.5 ± 765.8
6.0 (4.75–8)
1.0 (0–2)
535.1 (355.5–662.1)
195.6 (93.3–283.7)
25.2 (12.9–32.9)
186.1 (128.6–293.6)
211.5 (45.75–411.0)
18.8 (14.0–25.2)

0.26240.67890.24140.39580.29550.67120.15410.15410.5956

Water intake (mL/day)
Daytime urinary frequency (times)
Nighttime urinary frequency (times)
Daytime urinary volume (mL)
Nocturnal urinary volume (mL)NPi (%)MBC (mL)
HUS (min)Average urine flow rate (mL/s)

Mean ± standard deviation, median (interquartile range) unpaired t-test, or Mann–Whitney U test was used tocompare each parameter. Npi, nocturnal polyuria index; MBC, maximum bladder capacity; and HUS, hours ofundisturbed sleep.

Table 4. Odds ratios of voiding abnormalities for obesity (vs. non-obesity).

Nighttime frequency(≥score 1), CLSS Q2
Mid-nocturnal
insomnia (≥score 2),
QIDS-J Q2Nighttime frequency(≥1 time), s-HMSU
Increased Npi(>33%), s-HMSU
Decreased MBC(<212 mL), s-HMSU
Shortened HUS(<302 min), s-HMSU

Cases in
Non-Obesity

Cases inObesity

Odds Ratio (95% Confidence Interval)
for Obesity (vs. Non-Obesity)
Age and
p-Value
Sex-Adjusted(N = 29)(N = 30)

Crude

12

23

4.11
(1.32–12.80)

0.01497.65(1.88–31.14)

0.004513

20

1.73 (0.53–5.64)

0.3626

1.82 (0.55–6.08)

0.330118

22

1.68 (0.56–5.07)

0.35652.45 (0.67–9.14)

0.172411

70.50 (0.16–1.54)

0.22690.57 (0.17–1.89)

0.360212

17

1.85 (0.66–5.21)

0.24222.27 (0.76–6.78)

0.17801118

2.45 (0.86–7.00)

0.09285.17(1.33–20.03)

0.0175

p-Value

A logistic regression model was used to calculate an odds ratio of each voiding abnormality for obesity, with
non-obesity serving as a reference. CLSS, Core Lower Urinary Tract Symptoms Score; QIDS-J, Quick Inventory
Depressive Symptomatology-Japanese; s-HMSU, self-health monitoring system for urine excretion; Npi, nocturnal
polyuria index; MBC, maximum bladder capacity; and HUS, hours of undisturbed sleep. CLSS Q2: 0, 0 times; 1, 1
time; 2, 2–3 times; and 3, 4 times or more; QIDS-J Q2: 0, I do not wake up at night; 1, I have a restless, light sleep
with a few brief awakenings each night; 2, I wake up at least once a night, but I go back to sleep easily; and 3, I
wake up more than once a night and stay awake for 20 min or more, frequently more than half a week.

3. Discussion
The results of the current study indicate the following: (1) Self-reported nighttime
urinary frequency (Q2 in CLSS) was greater in the obese group than in the non-obese group,
while that measured by s-HMSU tended to be higher in the obese group than in the nonobese group. (2) HUS measured by s-HMSU was significantly shorter in the obese groupthan in the non-obese group. (3) Mid-nocturnal insomnia obtained from the self-reported
questionnaire (QIDS-J Q2) was significantly worse in the obese group. These novel findings
suggest that nighttime urinary frequency and shorted HUS are closely related to, and may
be a notable sign of, obesity.
Of the three major causes of nocturia (nocturnal polyuria, bladder storage dysfunction,and insomnia) [11], nocturnal polyuria accounts for approximately 80% of the causes [11,12].
In the present study, the nocturnal polyuria index (Npi) was broadly similar and not very
high in the non-obese and obese groups (24.1% vs. 25.2%). Day–night reversal, such as
decreased secretion of arginine vasopressin and increased blood pressure at night, may bepresent in older patients with nocturnal polyuria [15,16]. Nocturnal polyuria also results

Metabolites 2022, 12, 468

5 of 8from chronic kidney disease, heart failure, hypertension, hyperglycemia, obstructive sleep
apnea, and leg edema [10,17–19], suggesting a late marker of MetS. In the present study,
creatinine levels were broadly normal in both the non-obese and obese groups. Therefore,
organ damage, such as renal function, might not have occurred in our participants, whichmight not have affected nocturnal polyuria in the present study. These results also suggestthat nocturia may be an early sign of obesity/Mets.
The HUS is defined as the time from falling asleep to first awakening, which may
be another key factor affecting nocturia-related sleep disturbances and quality of life [20].
Taking sufficient time for slow-wave sleep during non-rapid eye movement sleep, which
emerges just after falling asleep, leads to not only a high quality of sleep but also to better
glucose metabolism, hormone release, immunity, and memory [21]. It is recommended
that HUS should be achieved for at least 3 h to ensure good quality of sleep [20,22]. In
the present study, the median value of HUS in the non-obese group was >5 h while thecorresponding median value in the obese group was <4 h. Over half of the non-obese
individuals did not wake up at night. These results suggest a close association between
obesity and nocturia/shortened HUS in our participants.
In Japan, MetS is defined as an abdominal circumference ≥85 cm in men and ≥90 cm
in women, accompanied by any two or more of the three disorders (high blood glucose,dyslipidemia, and high blood pressure) [23]. In the present study, eight participants andone participant (27% vs. 3%) met the criteria for MetS in the obese and non-obese groups,
respectively. Thus, our participants were relatively young and had a relatively low rate of
MetS even in the obese group. However, nighttime urinary frequency in CLSS and HUS bys-HMSU was significantly different between the two groups and there was a high OR for
obesity in these two parameters. The results of the present observational study indicate that
nocturia (≥1 time) and shorter HUS could be clear symptoms of obesity, including MetS.
In the present study, MBC in the obese group tended to be lower than that in the nonobese group. The reduced bladder capacity commonly occurs with aging (i.e., overactive
bladder) [24,25]. In a male rat model of obesity/type 2 diabetes, Kending et al. reported
that the urine volume per void was decreased and the number of voids was increased in
16-week-old type 2 diabetes rats compared to controls; however, in 27-week-old type 2
diabetes rats, both parameters were increased, accompanied by a change in the responseto adenosine triphosphate in the bladder [26]. This indicated changes in bladder function
according to the duration of diabetes. Moreover, an animal experimental study reported
that atherosclerosis-induced chronic bladder ischemia induced detrusor overactivity byoxidative stress and proinflammatory cytokines in a rat model of iliac arterial endothelial
injury [27,28]. Hypertension could also affect the pelvic arterial blood flow, resulting in theloss of the smooth muscle of the bladder with a decrease in bladder compliance [29,30].
Thus, bladder ischemia, derived from pelvic arterial sclerosis, could be a cause of the
reduced bladder capacity in the obese participants.
Our KDHP conducted an interventional study using digital health approaches forcomprehensive lifestyle modifications among individuals with obesity and/or MetS, whichis increasing in Japan [31]. In this previous study, nighttime urinary frequency did not havea significant change between baseline and 6 months in the intervention group, although it
significantly worsened at 6 months in the non-intervention group compared to baseline(1.0 ± 0.7 vs. 1.5 ± 0.5, p < 0.05) [14]. Therefore, s-HMSU might be beneficial for monitoring
nocturia, which could also prevent its worsening. As supported by this relevant intervention study, our study suggests an association between obesity and nocturia in s-HMSU,
including nighttime urinary frequency and HUS. Unlike self-reported and paper-based
FVC, digital health approaches, such as the use of s-HMSU, could be convenient and usefulfor the management of nocturia.
This study had several limitations. First, the number of participants in the present
study was small, which could have affected a wide range of confidence intervals. Second,
the single-day monitoring period of the s-HMSU was insufficient considering that the recommended period for FVC monitoring is ≥ 3 days [13]. Third, only the CLSS questionnaire

Metabolites 2022, 12, 468

6 of 8was employed to evaluate LUTS. Fourth, we had no data on benign prostatic hyperplasia
and pelvic organ prolapse, which also causes LUTS in older men and women, respectively.
Finally, our study population was comprised of residents of a single remote southern island
in Japan. Therefore, caution is needed when generalizing the results of the present study
and further studies are necessary to investigate the relationship between obesity and eachvoiding parameter in a larger general population. Despite these limitations, nocturia andshortened HUS may be signs of obesity.
4. Materials and Methods
4.1. Study Design and ParticipantsThis observational study was conducted as part of the KDHP between June 2017and December 2019 [14]. Individuals aged ≥ 20 years were recruited in a community- or
hospital-based health checkup between 2017 and 2019 on Kumejima Island, Okinawa, Japan.
The total number of individuals ≥ 20 years in Kumejima was 6334 in 2018. The essential
inclusion criteria were that participants were able to install the urine monitoring system inthe toilet bowl at home and use it for the study period. A total of 62 individuals agreed toparticipate in this study and were classified into two groups of 32 and 30 individuals withand without obesity, respectively, according to the presence or absence of obesity, definedas BMI ≥ 25 kg/m2 [32]. After, 30 and 29 individuals with and without obesity, respectively,
completed the survey for the study and were finally included in the subsequent analyses.
4.2. Urinary Tract Symptoms Questionnaire
Participants were asked to fill out a questionnaire including the CLSS and the QIDS-J
score [33]. Nighttime urinary frequency (Q2 in CLSS), urinary urgency (Q3 in CLSS), andmid-nocturnal insomnia (Q2 in QIDS-J) were used to assess nocturia.
4.3. 24 h Voided Volume Monitoring System
To measure the 24 h voided volume, we employed the s-HMSU system (Symax Inc.,Tokyo, Japan) [13,14]. Calibration was performed before monitoring according to theshape, volume, and temperature of each toilet bowl. The s-HMSU system includes ameasurement sensor, server, and user terminal. The sensor was installed in the toilet bowel
and the volume of urine on every voiding occasion was calculated for each participant.
Data automatically collected through the s-HMSU included daytime urinary frequency,nighttime urinary frequency, daytime urine volume (mL), nocturnal urine volume (mL),
MBC (mL), and HUS (min). HUS was defined as the time from falling asleep to the firstawakening to void [34]. NPi (a percentage) was calculated as nocturnal urine volume(mL)/(daytime urine volume [mL] + nocturnal urine volume [mL]) × 100. The s-HMSU
also recorded the start and end times of voiding; accordingly, the average urine flow rate
was calculated as a voided volume per void (mL)/voiding time (seconds).
4.4. Other Data Collection
Other data collected during the health checkup included age, sex, anthropometricindices, blood pressure, and blood chemistry results in the fasting state. Body height
and weight were measured and BMI was calculated as weight (kg)/height squared (m2 ).
Fasting blood glucose, hemoglobin A1c (HbA1c), serum total cholesterol, low-density
lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), triglycerides, and creatinine levels were measured using standard methods.
4.5. Statistical Analysis
Continuous variables are expressed as mean and standard deviation or median andinterquartile range. Basic clinical characteristics and voiding parameters were compared
between the obese and non-obese groups using the unpaired t-test and Mann–Whitney U
test. The chi-square test was used for categorical comparisons. Finally, logistic regression
analysis was employed to calculate the odds ratio (95% CI) of each voiding abnormality

Metabolites 2022, 12, 468

7 of 8for obesity, with non-obesity serving as a reference. The model incorporated age and sex
as covariates. The cutoff value of NPi in the chi-square test was adopted as 33% because
nocturnal polyuria in middle-aged and older people is defined as >33% of NPi [11,25].
In contrast, the median of MBC (212 mL) and HUS (302 min) in all cases were adopted
as cutoff values. All statistical analyses were performed using JMP® Pro version 15.0.0
software (SAS Institute Inc., Cary, NC, USA). All probability values were two-tailed andthe significance level was set at p < 0.05.
5. Conclusions
This study demonstrated an association between obesity and nocturia in a communitybased population. Nocturia and a reduction in HUS could be signs of obesity.
Author Contributions: The contributions of each author are as follows: (1) Substantial contributions
to the conception and design: M.M. (Minoru Miyazato), A.A., K.N., K.Y., T.N., T.U., M.U. and H.M.
(2) Critical drafting and revision of the article for important intellectual content: M.M. (Minoru
Miyazato), A.A., K.N., K.Y., T.N., T.U., M.U., H.M., S.S., S.M., H.I. and M.M. (Masayuki Matsushita).
(3) All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by a grant for regional development projects in the Okinawa
remote islands as part of the 2017–2020 KDHP and by a grant from the Taiju Life Social Welfare
Foundation in 2019.
Institutional Review Board Statement: All procedures performed in studies involving humanparticipants were in accordance with the ethical standards of the institutional committee (the Ethics
Committee of the University of the Ryukyus, #1170, 21 October 2017) and with the Declaration of
Helsinki of 1975, revised in 2013, or with comparable ethical standards.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The datasets analyzed in the current study are available from thecorresponding author upon reasonable request.
Acknowledgments: The authors thank Symax Inc. (Maria Tsuruoka CEO) for the provision of ans-HMSU system and technical assistance. We also thank Maya Kodama, Miwa Taira, and Shigeko
Iizuka for their research assistance, as well as Junko Hirata and other staff of Kumejima Town Hallfor their assistance with participant recruitment.
Conflicts of Interest: The authors declare no conflict of interest.

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