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RESEARCH ARTICLE
Prevalence, sleep characteristics, and
comorbidities in a population at high risk for
obstructive sleep apnea: A nationwide
questionnaire study in South Korea
Jun-Sang Sunwoo
1
, Young Hwangbo
2
, Won-Joo Kim
3
*, Min Kyung Chu
4
, Chang-Ho Yun
5
,
Kwang Ik Yang
6
*
1Department of Neurology, Soonchunhyang University College of Medicine, Seoul Hospital, Seoul, South
Korea, 2Department of Preventive Medicine, Soonchunhyang University College of Medicine, Cheonan,
South Korea, 3Department of Neurology, Gangnam Severance Hospital, Yonsei University, College of
Medicine, Seoul, South Korea, 4Department of Neurology, Hallym University College of Medicine, Seoul,
South Korea, 5Department of Neurology, Bundang Clinical Neuroscience Center, Seoul National University
Bundang Hospital, Seongnam, South Korea, 6Sleep Disorders Center, Department of Neurology,
Soonchunhyang University College of Medicine, Cheonan Hospital, Cheonan, South Korea
*neurofan@schmc.ac.kr (KIY); kzoo@yuhs.ac (WJK)
Abstract
Objective
To determine the prevalence, sleep characteristics, and comorbidities associated with a
high risk for obstructive sleep apnea (OSA) in the Korean adult population.
Methods
We analyzed data from 2,740 subjects who responded to a nationwide questionnaire survey
of sleep characteristics. Those who qualified under two or more symptom categories of the
Berlin questionnaire were defined as “at high risk for OSA”. We investigated their socio-
demographic information, sleep habits, and medical and psychiatric comorbidities. Logistic
regression analyses were performed to identify factors and consequences significantly
associated with a high risk for OSA.
Results
The prevalence of a high risk for OSA was 15.8% (95% confidence interval [CI] 14.5–
17.2%). Multiple logistic regression analysis showed that old age (70 years, odds ratio
[OR] 2.68) and body mass index 25 kg/m
2
(OR 10.75) were significantly related with a
high risk for OSA, whereas regular physical activity (OR 0.70) had a protective effect. Sub-
jective sleep characteristics associated with a high risk for OSA were perceived insufficient
sleep (OR 1.49), excessive daytime sleepiness (OR 1.88), and insomnia (OR 3.70). In addi-
tion, hypertension (OR 5.83), diabetes mellitus (OR 2.54), hyperlipidemia (OR 2.85), and
anxiety (OR 1.63) were comorbid conditions independently associated with a high risk for
OSA.
PLOS ONE | https://doi.org/10.1371/journal.pone.0193549 February 28, 2018 1 / 14
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OPEN ACCESS
Citation: Sunwoo J-S, Hwangbo Y, Kim W-J, Chu
MK, Yun C-H, Yang KI (2018) Prevalence, sleep
characteristics, and comorbidities in a population
at high risk for obstructive sleep apnea: A
nationwide questionnaire study in South Korea.
PLoS ONE 13(2): e0193549. https://doi.org/
10.1371/journal.pone.0193549
Editor: Andrea Romigi, University of Rome Tor
Vergata, ITALY
Received: December 12, 2017
Accepted: February 13, 2018
Published: February 28, 2018
Copyright: ©2018 Sunwoo et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This study was supported by grants from
Korean Neurological Association (grant # KNA-10-
MI-03) and Soonchunhyang University Research
Fund (grant # N/A). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Conclusions
This is the first study to demonstrate the prevalence of a high risk for OSA in a nationwide
representative sample of the Korean adult population. These findings elucidate the epidemi-
ology and clinical characteristics of those at high risk for OSA.
Introduction
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive upper
airway collapse during sleep with consequent oxygen desaturation, frequent arousals, and
sleep fragmentation [1]. Of particular importance is that untreated OSA significantly increases
the risk of cardiovascular diseases, stroke, and death [2,3]. In addition, OSA leads to neuro-
cognitive consequences including excessive daytime sleepiness, reduced cognitive perfor-
mance, and increased risk for motor vehicle and work accidents [4,5]. To prevent the health
consequences of OSA, early identification and optimal treatment of OSA is necessary. The
prevalence of OSA varies with measurement methods, diagnostic criteria, and apnea-hypop-
nea index (AHI) cutpoints [6]. Previous cohort studies with in-laboratory polysomnography
(PSG) demonstrated that the prevalence of OSA defined by AHI 5 ranged from 17 to 26% in
men and from 9 to 28% in women [7–10]. Similarly, 27% of men and 17% of women in the
Korean adult population were found to have an AHI of 5 or more [11]. Furthermore, OSA is
more prevalent in patients with resistant hypertension and cardiovascular diseases, but OSA
remains unrecognized and untreated in most patients [12,13].
PSG is considered the gold standard for diagnosis of OSA in adults [14]. However, consid-
ering the high prevalence of OSA, PSG testing of all patients suspected of having OSA is not
feasible due to significant cost and limited accessibility. OSA needs to be screened for in any
patients with OSA symptoms, such as witnessed apnea, snoring, nocturnal gasping, and unex-
plained daytime sleepiness, and those who have comorbid conditions related to a high risk of
OSA, such as obesity, heart failure, hypertension, and stroke [15]. Then, those found to be at
high risk should undergo objective sleep testing to confirm the diagnosis as well as to deter-
mine the severity of OSA. However, OSA symptoms are not adequately screened or assessed in
primary care settings [16]. Clinical questionnaires can be a convenient and efficient means of
screening individuals at high risk of OSA. The Berlin questionnaire is the most widely used
questionnaire for screening for a high risk of OSA in clinical practice [17–19], and its screen-
ing properties have been validated in several population-based studies [20–22]. The diagnostic
performance of the Berlin questionnaire was shown to have a pooled sensitivity of 0.76 and a
pooled specificity of 0.45 when predicting OSA with an AHI cutoff of 5 [14].
In the present study, we determined the prevalence of a high risk for OSA estimated by the
Berlin questionnaire in a nationwide sample representative of the Korean adult population. In
addition to the risk for OSA, we collected data about subjective sleep characteristics and
comorbid medical conditions from the study subjects. Based on this data, we determined the
factors and health consequences independently and significantly associated with a high risk
for OSA.
Methods
Subjects
A nationwide questionnaire survey for subjective sleep characteristics was performed for
adults aged 19 years. Study population sampling and the questionnaire survey were
High risk for obstructive sleep apnea in South Korea
PLOS ONE | https://doi.org/10.1371/journal.pone.0193549 February 28, 2018 2 / 14
Competing interests: The authors have declared
that no competing interests exist.
conducted by Gallup Korea and the detailed procedures have been described elsewhere [23,
24]. Briefly, Gallup Korea approached a total of 7,615 adults in 2010. The sampling areas
included all 15 administrative districts except for Jeju province: 8 provinces, 6 metropolitan
cities, and the Seoul special city. Consequently, 2,836 (37.2%) subjects responded to the ques-
tionnaire through face-to-face interviews. Among them, we excluded 96 subjects who reported
incomplete data for sleep habits (n = 42) and socio-demographic information (n = 54). All par-
ticipants provided written informed consent before responding to the survey. Data collected
from the questionnaire survey were de-identified to protect the privacy of study subjects. The
study protocol was approved by the Institutional Review Board of Soonchunhyang University
Cheonan Hospital (IRB No. 2017-03-028) and was conducted according to the Declaration of
Helsinki and the Good Clinical Practice guidelines.
Risk stratification for obstructive sleep apnea
We estimated the risk of OSA of the study population by using the Berlin questionnaire
[25]. The Korean version of the Berlin questionnaire was previously developed and its use-
fulness as a screening tool for OSA was validated in an adult population [26]. The Berlin
questionnaire consists of three symptom categories. Briefly, category 1 evaluates snoring
and sleep apnea, while category 2 addresses daytime sleepiness and fatigue. Category 3
investigates the presence of hypertension or obesity defined as body mass index (BMI) 25
kg/m
2
according to the scoring guideline of the Korean version of the Berlin questionnaire
[26,27]. Subjects who qualify for two or more symptom categories were classified as at high
risk for OSA. Conversely, those who report positive symptom categories of 1 were classi-
fied as “at low risk for OSA”.
Subjective sleep characteristics
Subjects were asked to report sleep habits over the last month, such as wake-up time, bedtime,
sleep latency, and night sleep duration separately for weekdays and weekends. Average sleep
duration was calculated as follows: (sleep duration on weekdays ×5 + sleep duration on week-
ends ×2)/7. When subjects slept longer on weekends than on weekdays, we measured week-
end catch-up sleep by subtracting sleep duration on weekdays from sleep duration on
weekends. Chronotype was determined by measuring the mid-sleep time on free days cor-
rected for oversleep on free days (MSFsc), which was calculated based on the methods used in
a previous study [28]. We also investigated perceived insufficient sleep, unmet sleep need, the
Epworth sleepiness scale (ESS), the Pittsburgh sleep quality index (PSQI), and the insomnia
severity index (ISI) as previously described [29].
Other investigations
We investigated socio-demographic information, such as age, sex, BMI, education level, occu-
pation, income level, alcohol consumption, smoking status, and physical activity. The detailed
protocols have been described elsewhere [29]. In addition, we evaluated past medical history
of hypertension, diabetes mellitus, hyperlipidemia, myocardial infarction, angina pectoris,
other heart diseases, and stroke. Among them, stroke, myocardial infarction, angina pectoris,
and other heart diseases were combined into the category of cardiovascular diseases. Further-
more, as screening tools for psychiatric comorbidity, we used the Goldberg anxiety scale
(GAS) and the Patient Health Questionnaire-9 (PHQ-9), respectively [30,31].
High risk for obstructive sleep apnea in South Korea
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Statistical analysis
Comparisons of continuous variables between high- and low-risk groups for OSA were con-
ducted by the Student’s t-test, while comparisons of categorical variables were performed by
the Pearson’s chi-square test. Unadjusted odds ratio (OR) and 95% confidence interval (CI)
were estimated by univariable logistic regression analysis for each predictor variable. The
dependent variable was set as high risk for OSA estimated by the Berlin questionnaire. Next,
we performed multiple logistic regression analysis to identify independent associations
between predictor variables and high risk for OSA. Predictor variables with P <0.05 from the
univariable logistic regression analysis and potential confounders were included as covariates
for adjustment. In addition, a linear trend in the adjusted ORs of the predictor variables was
estimated by the likelihood ratio test. A two-tailed P <0.05 was considered statistically signifi-
cant. All statistical analyses were performed with SPSS version 18 (SPSS Inc., Chicago, IL).
Results
Prevalence of high risk for OSA
Data collected from a total of 2,740 subjects were analyzed in this study. Their mean age was
44.5 ±15.0 years and 49.9% were men. Based on the risk stratification by the Berlin question-
naire, the overall prevalence of a high risk for OSA was 15.8% (434 of 2,740; 95% CI 14.5–
17.2%). The prevalence of high-risk group of OSA in men (19.8%, 95% CI 17.7–21.9%) was
higher than that in women (11.9%, 95% CI 10.4–13.7%; P <0.001). As shown in Fig 1, the
prevalence increased with age (linear by linear association, P <0.001). When subjects were
further stratified by age, those 19–29, 30–39, and 40–49 years of age showed a higher
Fig 1. Prevalence of a high risk for obstructive sleep apnea according to age and sex. High risk for obstructive sleep
apnea was defined as positive symptom categories of 2 on the Berlin questionnaire. P<0.05 and P<0.01 for
comparisons between male and female in each age group. n = 1,368 in male and n = 1,372 in female.
https://doi.org/10.1371/journal.pone.0193549.g001
High risk for obstructive sleep apnea in South Korea
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prevalence in men than in women (P = 0.004, <0.001, and <0.001, respectively). However, a
high risk for OSA was equally distributed between men and women in those aged 60 years or
more.
Comparisons between high- and low-risk groups
The distribution of the study population and the prevalence of a high risk for OSA according
to socio-demographic variables are summarized in Table 1. Univariable analysis demonstrated
that subjects at high risk for OSA were more likely to be older, male, obese, less educated, and
low-income. Safety accidents at work were also associated with a high risk for OSA (unad-
justed OR 1.78, P = 0.021). However, shift work and physical activity did not significantly
influence the risk for OSA. Compared to never smokers, both ex-smokers (unadjusted OR
1.94, P <0.001) and current smokers (unadjusted OR 1.67, P <0.001) showed a higher pro-
portion of a high risk for OSA. Those who drank alcohol 2 days per week also had an
increased odds of a high risk for OSA (unadjusted OR 1.38, P = 0.016) compared to never
drinkers.
We compared subjective sleep characteristics between high- and low-risk groups for OSA
(Table 2). Average sleep duration of the high-risk group (7.0 ±1.4 h) was shorter than that of
the low-risk group (7.4 ±1.2 h, P <0.001). Although weekend catch-up sleep of 1 h was less
frequently observed in the high-risk group than in the low-risk group (29.5 vs. 38.2%,
P = 0.001), there was no significant difference in the duration of weekend catch-up sleep
(1.8 ±1.1 vs. 1.8 ±1.1 h, P = 0.963). Furthermore, sleep characteristics associated with a high
risk for OSA included porlonged sleep latency, higher prevalence of perceived insufficient
sleep, excessive daytime sleepiness, poor sleep quality, and insomnia (P <0.001 for all).
Multivariable analysis for a high risk of OSA
We performed multiple logistic regression analysis to determine the factors independently
associated with a high risk for OSA. In this model, predictor variables included age, sex, BMI,
occupation, education and income level, alcohol consumption, and smoking status. Shift work
and physical activity were also included as covariates. Consequently, we identified that old age
(70 years, OR 2.68), BMI 25 kg/m
2
(OR 10.75), and regular physical activity (OR 0.70)
were significantly and independently associated with a high risk for OSA (Table 3). In addi-
tion, there was a trend towards an increased risk for OSA in people on low incomes (OR 1.39,
95% CI 0.99–1.94, P = 0.056). However, there was no significant association with other factors
including sex, education, occupation, smoking status, and alcohol consumption. There was no
significant multicollinearity among predictor variables with the variance inflation factors rang-
ing from 1.06 to 2.25.
Next, we constructed a multiple logistic regression model to evaluate consequences associ-
ated with a high risk for OSA. Predictor variables included subjective sleep characteristics
showing significant differences between the two groups, safety accidents at work, and medical
conditions such as hypertension, diabetes, hyperlipidemia, cardiovascular diseases, depression,
and anxiety. In addition, we entered socio-demographic variables to control for confounding.
As shown in Table 4, perceived insufficient sleep (OR 1.49), excessive daytime sleepiness (OR
1.88), and insomnia (subthreshold, OR 1.95; clinical OR 3.70; P for linear trend <0.001)
remained significantly associated with a high risk for OSA. Poor sleep quality (OR 1.51, 95%
CI 0.97–2.36) was likely to increase the odds of being at high risk for OSA, but it failed to reach
a significance level (P = 0.071). Moreover, the presence of hypertension (OR 5.83), diabetes
mellitus (OR 2.54), hyperlipidemia (OR 2.85), and anxiety (OR 1.63) had independent associa-
tions with a high risk for OSA. However, the associations with cardiovascular diseases, safety
High risk for obstructive sleep apnea in South Korea
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Table 1. Prevalence of high risk for obstructive sleep apnea according to socio-demographic characteristics and comorbidity (n = 2,740).
Variables Categories Total No. High risk of OSA Unadjusted OR (95% CI)
No. (%)
Age, yr <30 524 50 (9.5) 1.00
30–39 590 75 (12.7) 1.38 (0.95–2.02)
40–49 589 86 (14.6) 1.62 (1.12–2.35)
50–59 511 105 (20.5) 2.45 (1.71–3.52)
60–69 390 80 (20.5) 2.45 (1.67–3.58)
70 136 38 (27.9) 3.68 (2.29–5.91)
Sex Female 1372 163 (11.9) 1.00
Male 1368 271 (19.8) 1.83 (1.48–2.26)
BMI, kg/m
2
<18.5 126 2 (1.6) 0.21 (0.05–0.85)
18.5–25 1969 141 (7.2) 1.00
25 645 291 (45.1) 10.66 (8.46–13.43)
Education Middle school 494 117 (23.7) 1.60 (1.24–2.07)
High school 1200 195 (16.3) 1.00
College 1046 122 (11.7) 0.68 (0.53–0.87)
Occupation Unemployed 1008 139 (13.8) 1.00
Self-employment 432 96 (22.2) 1.79 (1.34–2.38)
Sales and service 471 67 (14.2) 1.04 (0.76–1.42)
Manual labor 316 60 (19.0) 1.47 (1.05–2.04)
Office work 513 72 (14.0) 1.02 (0.75–1.39)
Shift work No 2168 340 (15.7) 1.00
Yes 145 25 (17.2) 1.12 (0.72–1.75)
Accident at work No 2651 412 (15.5) 1.00
Yes 89 22 (24.7) 1.78 (1.09–2.92)
Income level Low 786 167 (21.2) 1.68 (1.33–2.14)
Middle 1181 163 (13.8) 1.00
High 689 91 (13.2) 0.95 (0.72–1.25)
Alcohol drinking None 955 147 (15.4) 1.00
1/week 1153 160 (13.9) 0.89 (0.70–1.13)
2/week 632 127 (20.1) 1.38 (1.06–1.80)
Smoking Never 1661 213 (12.8) 1.00
Ex-smoker 343 76 (22.2) 1.94 (1.44–2.59)
Current 736 145 (19.7) 1.67 (1.32–2.10)
Physical activity None 1444 242 (16.8) 1.00
1–2/week 563 85 (15.1) 0.88 (0.68–1.16)
3/week 733 107 (14.6) 0.85 (0.66–1.09)
Hypertension No 2387 283 (11.9) 1.00
Yes 353 151 (42.8) 5.56 (4.35–7.10)
Diabetes mellitus No 2605 382 (14.7) 1.00
Yes 135 52 (38.5) 3.65 (2.54–5.24)
Hyperlipidemia No 2660 404 (15.2) 1.00
Yes 80 30 (37.5) 3.35 (2.10–5.33)
Cardiovascular diseasesNo 2653 408 (15.4) 1.00
Yes 87 26 (29.9) 2.35 (1.46–3.76)
Depression
†
No 2611 391 (15.0) 1.00
Yes 129 43 (33.3) 2.84 (1.94–4.16)
Anxiety
†
No 2422 342 (14.1) 1.00
(Continued)
High risk for obstructive sleep apnea in South Korea
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accidents, and depression were not significant. The variance inflation factors of all of the pre-
dictor variables included in this model ranged between 1.06 and 2.45, suggesting that there
were no significant problems with multicollinearity.
Discussion
Our data collected from a nationwide, population-based survey demonstrated that 15.8% of
adults were at high risk of OSA based on the Berlin questionnaire. Previous data showed that
the prevalence of high risk group of OSA was 12.4% in Korean adults [27], which is slightly
lower than that found in our study. However, that prior study only targeted South Gyeongsang
province, which is one of the 8 provinces in Korea. Accordingly, our data is the first to demon-
strate the prevalence of a high risk for OSA in a nationwide representative sample of the
Korean adult population. Furthermore, we thoroughly investigated the association of a high
risk for OSA with various sleep characteristics and comorbidities, which is another strength of
this study.
Several studies using the Berlin questionnaire have been conducted in other countries. Data
from the Norwegian and the United States populations showed prevalence of a high risk for
Table 1. (Continued )
Variables Categories Total No. High risk of OSA Unadjusted OR (95% CI)
No. (%)
Yes 318 92 (28.9) 2.48 (1.89–3.24)
Data for shift work and income level were available in 2,313 and 2,656 subjects, respectively. Unadjusted odds ratio was calculated by univariable logistic regression
analysis for each predictor variable. Abbreviations: BMI, body mass index; OR, odds ratio.
Cardiovascular diseases include myocardial infarction, stroke, angina, and other heart disease.
†
Depression was defined as the Patient Health Questionnaire-9 score of 10, and anxiety was defined as the Goldberg Anxiety Scale score of 5.
https://doi.org/10.1371/journal.pone.0193549.t001
Table 2. Comparison of subjective sleep characteristics between high- and low-risk groups for obstructive sleep apnea.
Variables High risk (n = 434) Low risk (n = 2306) P
Sleep duration, h
Average 7.0 ±1.4 7.4 ±1.2 <0.001
Weekday 6.8 ±1.5 7.2 ±1.2 <0.001
Weekend 7.3 ±1.7 7.8 ±1.5 <0.001
Weekend catch-up sleep 1 h 128 (29.5) 881 (38.2) 0.001
Sleep latency, min 27.9 ±27.4 23.6 ±22.4 0.002
MSFsc, h3.8 ±1.8 3.9 ±1.5 0.201
Perceived insufficient sleep 175 (40.3) 666 (28.9) <0.001
Excessive daytime sleepiness 97 (22.4) 228 (9.9) <0.001
Poor sleep quality 155 (35.7) 419 (18.2) <0.001
Insomnia severity index (ISI) <0.001
Normal 306 (70.5) 1961 (85.0)
Subthreshold insomnia 79 (18.2) 266 (11.5)
Clinical insomnia 49 (11.3) 79 (3.4)
Data are presented as mean ±standard deviation or number (%). Excessive daytime sleepiness was defined as the Epworth sleepiness scale score of >10, and poor sleep
quality was defined as the Pittsburgh sleep quality index score of >5. We defined insomnia as follows: subthreshold (ISI score 8–14) and clinical insomnia (ISI
score 15). Abbreviations: MSFsc, mid-sleep time on free days corrected for oversleep on free days (local time in hours after midnight).
Chronotype data were available in 2,736 subjects.
https://doi.org/10.1371/journal.pone.0193549.t002
High risk for obstructive sleep apnea in South Korea
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OSA of 24.3% and 26%, respectively [20,21], which is higher than that reported in the present
study. Because excess body weight is the strongest risk factor for OSA [32], the differences in
prevalence of obesity among the study populations might account for the discrepancies in the
prevalence results. Consistent with this, BMI >30 kg/m
2
was noted in 25% and 14.8% of the
screening samples in the United States and Norwegian studies, respectively, whereas only 2.1%
(57 of 2,740) were identified in our study. Another possible explanation is a different age distri-
bution among the study populations, considering that old age is a significant risk factor of
OSA [33]. In this regard, the mean age of the screening samples was 44.5 years in this study,
which is younger than the 47.8 and 49 years in previous studies. However, the prevalence of
Table 3. Risk factors associated with high risk of obstructive sleep apnea.
Variables Adjusted OR (95% CI)
Age, yr (vs. 19–29)
30–39 0.97 (0.60–1.55)
40–49 1.08 (0.67–1.75)
50–59 1.50 (0.90–2.49)
60–69 1.13 (0.62–2.06)
70 2.68 (1.24–5.82)
BMI, kg/m
2
(vs. 18.5–25)
<18.5 0.30 (0.07–1.25)
25 10.75 (8.21–14.06)
Physical activity (vs. none)
1–2/week 0.72 (0.51–1.01)
3/week 0.70 (0.51–0.97)
Adjusted odds ratios were calculated by multivariable logistic regression analysis. Covariates included sex, education,
occupation, income level, shift work, alcohol consumption, and smoking status. Abbreviations: OR, odds ratio; CI,
confidence interval; BMI, body mass index.
P<0.05
P<0.01.
https://doi.org/10.1371/journal.pone.0193549.t003
Table 4. Sleep characteristics and comorbidity associated with high risk of obstructive sleep apnea.
Variables Adjusted OR (95% CI)
Perceived insufficient sleep 1.49 (1.06–2.10)
Excessive daytime sleepiness 1.88 (1.27–2.77)
Insomnia
Subthreshold 1.95 (1.23–3.10)
Clinical 3.70 (1.75–7.85)
Hypertension 5.83 (3.91–8.69)
Diabetes mellitus 2.54 (1.46–4.42)
Hyperlipidemia 2.85 (1.36–5.95)
Anxiety 1.63 (1.03–2.59)
The multivariable logistic regression model was adjusted for age, sex, body mass index, education, occupation, shift
work, safety accidents, income level, alcohol consumption, smoking status, physical activity, average sleep duration,
sleep latency, weekend catch-up sleep (1 h), poor sleep quality, depression, and cardiovascular diseases.
Abbreviations: OR, odds ratio; CI, confidence interval.
P<0.05
P<0.01.
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High risk for obstructive sleep apnea in South Korea
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PSG-confirmed OSA in Koreans was reported to be 4.5% in men and 3.2% in women when
OSA was defined as an AHI 5 plus excessive daytime sleepiness [11], which is comparable to
that found in Caucasians [7,8]. Therefore, any discrepancies in the questionnaire-based preva-
lence among the study population might be attributed to correlates of OSA rather than the dis-
ease itself. Although the Berlin questionnaire is useful for screening subjects at high risk of
OSA [26], it should be kept in mind that the questionnaire survey cannot be interchangeable
with PSG for the diagnosis of OSA.
In this study, old age and BMI 25 kg/m
2
were independent factors associated with a high
risk OSA. This is in close agreement with previous observations that the prevalence of OSA
increases with age and excess body weight [8,9,34]. Notably, we found that at least three times
a week of regular physical activity significantly reduced the risk for OSA after adjusting for
BMI and other confounding covariates. Previous epidemiologic studies also demonstrated the
protective association of regular physical activity against sleep-disordered breathing [35,36].
Consistent with our finding, the protective effect of regular physical activity on OSA was
reported to be independent of body habitus [37]. Furthermore, a recent meta-analysis showed
that exercise training significantly improved sleep efficiency, cardiovascular fitness, and day-
time sleepiness as well as AHI although there was no significant reduction in BMI [38]. Given
the major contribution of comorbid hypertension to the high risk of OSA on the Berlin ques-
tionnaire, it is also possible that the beneficial effect of regular exercise was mediated by its
blood pressure lowering effect [39–41].
A higher prevalence of OSA in men compared with women has been established from pre-
vious epidemiologic studies [6,8]. In agreement with this, the unadjusted prevalence of high
risk for OSA in men was 1.83-fold higher than that in women in this study. However, male sex
was not found to be an independent factor for predicting high risk of OSA in the multivariable
analysis. As shown in Fig 1, the significant male predominance in high risk for OSA disap-
peared after age 50 years. Previous population-based studies demonstrated similar results that
sex differences in the prevalence of OSA in people older than 65 years were relatively small
compared with those in middle age [7,42,43]. This phenomenon might be partially accounted
for by the increase in the OSA risk in postmenopausal women [7,44]. It is also possible that
the higher mortality rate associated with OSA causes death in men more often than in women
[45,46], which relatively decreases the prevalence of OSA in men in older populations.
We found that hypertension, diabetes mellitus, and hyperlipidemia were comorbid condi-
tions independently associated with a high risk for OSA. It has been well-established that OSA
is implicated in cardiovascular diseases and notably hypertension [3,47]. Longitudinal data
from the Wisconsin Sleep Cohort Study indicated that moderate or severe OSA had a 3-fold
increased risk for the presence of hypertension at the 4-year follow-up [48]. Moreover, CPAP
treatment for 12 weeks significantly decreased 24 h mean blood pressure compared to the con-
trol [49]. In agreement with our observations, accumulating evidence has supported the associ-
ation of OSA with diabetes mellitus and insulin resistance [50–53]. Although clinical evidence
that OSA is associated with hyperlipidemia is relatively sparse [54,55], experimental data sug-
gested that intermittent hypoxia induces hyperlipidemia and atherosclerosis [56,57]. In terms
of psychiatric comorbidity, a high risk of OSA was significantly associated with anxiety. Our
observation supports previous studies showing that patients with sleep disordered breathing
had a higher prevalence of anxiety than controls [58,59]. Beneficial effects of positive airway
pressure (PAP) therapy on quality of life and anxiety in OSA patients also substantiate the
interaction between anxiety and OSA [60,61].
Perceived insufficient sleep is a sleep characteristic not only affected by quantitative sleep
deprivation but also reflecting the presence of underlying sleep disorders such as OSA [62].
Consistent with this, the association between perceived insufficient sleep and a high risk for
High risk for obstructive sleep apnea in South Korea
PLOS ONE | https://doi.org/10.1371/journal.pone.0193549 February 28, 2018 9 / 14
OSA was significant in our data, independent of average sleep duration. Considering sleep
fragmentation with repeated arousals in OSA [63], perceived insufficient sleep and excessive
daytime sleepiness would be inevitable consequences of OSA. In addition, it is noteworthy that
insomnia was independently associated with a high risk for OSA in this study. The dose-
response relationship between insomnia severity and ORs for high risk of OSA confirmed the
interaction between the two conditions. There has been accumulating evidence to support
comorbid insomnia in patients with OSA [64,65]. Previous studies reported that insomnia
coexists in 39%–55% of patients with OSA [66]. Although mechanisms of the comorbid rela-
tionship between the two sleep disorders are not fully understood, it is presumed that frequent
arousals with increased sympathetic and hypothalamic-pituitary-adrenal axis activity resulting
from OSA may precipitate or exacerbate insomnia symptoms [67].
It has been well established that OSA is significantly associated with an increased risk of
occupational accidents, particularly motor vehicle accidents [68,69]. A recent meta-analysis
showed that workers with suspected OSA have an approximately twofold increased odds of
work-related accidents compared to those without OSA [5]. However, the association between
a high risk for OSA and safety accidents at work was not found to be significant in our multi-
variate analysis. A possible explanation for this discrepancy is that our study did not include a
sufficient number of professional drivers. The effect size for non-driving accidents was signifi-
cantly smaller than that obtained for driving accidents [5]. Moreover, our study investigated a
variety of potential comorbidities of a high risk for OSA rather than focusing on the risk for
occupational accidents. Therefore, other covariates included in the multivariate analysis might
have contributed to the different result for risk of safety accidents. Our findings should not be
mistakenly interpreted that there is no possible association between a high risk for OSA and
occupational accidents. Further research will be required to address this issue, especially for
non-driving accidents at work.
There are several limitations in the current study. First, the response rate of the questionnaire
survey was relatively low, which might have caused sample selection bias. However, the fact that
the prevalence of high risk for OSA from this study was comparable to that from previous popu-
lation-based studies suggests the validity of the sampling method of our study. Furthermore,
because sleep habits were investigated based on self-report, quantitative data including sleep
duration and sleep-wake cycles might be less accurate than measured by objective testing. How-
ever, perceived insufficient sleep has its own clinical significance in health outcomes separately
from short sleep duration [24,70], which supports the importance of subjective sleep evaluation.
Finally, although we evaluated the associations of high risk for OSA with various factors, their
causal relationship cannot be determined from this cross-sectional study.
Supporting information
S1 File. Raw data on all subjects.
(XLSX)
Author Contributions
Conceptualization: Young Hwangbo, Won-Joo Kim, Min Kyung Chu, Chang-Ho Yun,
Kwang Ik Yang.
Data curation: Jun-Sang Sunwoo, Chang-Ho Yun, Kwang Ik Yang.
Formal analysis: Jun-Sang Sunwoo, Kwang Ik Yang.
Funding acquisition: Won-Joo Kim, Kwang Ik Yang.
High risk for obstructive sleep apnea in South Korea
PLOS ONE | https://doi.org/10.1371/journal.pone.0193549 February 28, 2018 10 / 14
Investigation: Jun-Sang Sunwoo, Min Kyung Chu, Kwang Ik Yang.
Methodology: Young Hwangbo, Won-Joo Kim, Min Kyung Chu, Chang-Ho Yun, Kwang Ik
Yang.
Resources: Won-Joo Kim.
Supervision: Won-Joo Kim, Min Kyung Chu, Chang-Ho Yun.
Writing – original draft: Jun-Sang Sunwoo.
Writing – review & editing: Jun-Sang Sunwoo, Won-Joo Kim, Kwang Ik Yang.
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