Taiwanese Journal of Psychiatry

ORIGINAL ARTICLE
Year
: 2019  |  Volume : 33  |  Issue : 1  |  Page : 27--32

Predictors of excessive daytime sleepiness in patients with obstructive sleep apnea syndrome


Hung- Ta Liao1, Tsung- Yi Tsai2, Dai- Yueh Lin1,  
1 Department of Psychiatry, Taichung Armed Forces General Hospital, Taichung; National Defense Medical Center, Taipei, Taiwan
2 Department of Psychiatry, Taichung Armed Forces General Hospital; Inservice Master Program in Life Sciences, College of Life Sciences, National Chung Hsing University, Taichung; National Defense Medical Center, Taipei, Taiwan

Correspondence Address:
Tsung- Yi Tsai
No. 348, Section 2, Zhongshan Road, Taiping District, Taichung 411
Taiwan

Abstract

Objective: Excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea syndrome (OSAS) is a known risk factor for various accidents and can cause poor quality of life. In this study, we intended to study the demographic and clinical characteristics of EDS in patients with OSAS. Methods: This retrospective study gathered data from 283 patients. We classified patients with Epworth Sleepiness Scale (ESS) scores ≥ 10 points as the EDS group and those with an ESS score <10 were the non-EDS group. We also compared the results of physiological and polysomnographic examinations to determine if EDS could be used to predict in patients with OSAS. A stratified analysis was also done to study subpopulation-related factors associated with EDS in OSAS patients. Results: Male OSAS patients with EDS had significantly shortened rapid eye movement latency (REML, p < 0.01), significantly greater sleep efficiency (p < 0.05), and significantly higher EES score (p < 0.001), compared to those without EDS. Female OSAS patients with EDS had significantly higher body mass index (BMI) (p < 0.05) and had significantly more EES score (p < 0.001). Female OSAS patients with EDS in childbearing age had significantly higher EES score (p < 0.001) and had significantly higher BMI (p < 0.05). OSAS patients with severe EDS had significantly shortened REML (p < 0.01) and significantly more EES score (p < 0.001). Conclusion: This study showed that there were differences in the characteristics of OSAS patients with and without EDS and they differed in their subgroups. All findings are mostly in line with those in existing literature.



How to cite this article:
Liao HT, Tsai TY, Lin DY. Predictors of excessive daytime sleepiness in patients with obstructive sleep apnea syndrome.Taiwan J Psychiatry 2019;33:27-32


How to cite this URL:
Liao HT, Tsai TY, Lin DY. Predictors of excessive daytime sleepiness in patients with obstructive sleep apnea syndrome. Taiwan J Psychiatry [serial online] 2019 [cited 2020 Nov 25 ];33:27-32
Available from: http://www.e-tjp.org/text.asp?2019/33/1/27/255144


Full Text



 Introduction



Obstructive sleep apnea syndrome (OSAS) is caused by airway stenosis, leading to repetitive obstruction of the upper respiratory tract during sleep. OSAS causes a lack of oxygen which influences the quality of sleep and causes excessive daytime sleepiness (EDS), headache, dry mouth, poor attention, memory impairment, and cardiovascular diseases (such as hypertension, myocardial infarction, and strokes). Urology-associated problems such as frequent urination and sexual dysfunction or even a higher risk for sudden death can also occur.

Higher prevalence of OSAS exists in Easterners than in Westerners due to the difference in craniofacial structures[1]. Male patients with OSAS have been found to have 2- to 3- fold higher prevalence of OSAS than female patients, but the prevalence of OSAS is increased in women after menopause[2].

The major symptoms of OSAS are EDS and poor attention, with the frequency of these symptoms ranging from 20% to 87%[3],[4],[5]. EDS is an important reason that OSAS patients suffer from higher accident rates in certain groups of the population: professional drivers and people working with machines,. The number of occupational accidents in patients with OSAS is 1.5–6-fold higher than those without[6],[7]. OSAS is even more dangerous than drunk driving[8],[9].

The mechanisms for EDS in OSAS patients are not clear[10]. Studies investigating a potential relation between EDS and several indices of polysomnography (PSG) have yielded controversial findings, some of which were limited by the small sample size. Other published papers using a large sample size supported a correlation between OSAS severity and EDS[11]. However, in clinical practice, patients are frequently examined with a high number of apneas, but without EDS[5].

In this study, we intended to analyze the correlation of sleep-related parameters with the degree of daytime sleepiness based on the results of polysomnographic examination and to do stratified analyses to study the relation between polysomnographic indices and EDS.

 Methods



Study patients

The study protocol was approved by the Institutional Review Board of the Tri-service General Hospital. A total of 349 PSG reports of patients admitted to the BGA Center for Sleep Disorders (Taichung, Taiwan) due to snoring were collected from January 2013 to June 2013. The inclusion criteria were patients who (a) were diagnosed with OSAS according to the American Academy of Sleep Medicine diagnostic standard and (b) were adults aged 20–65 years. The exclusion criteria were patients with (a) central sleep apnea; (b) other sleep disorders such as narcolepsy and periodic limb movement disorder; and (c) serious physiological or psychological disorders such as liver cirrhosis, kidney failure, cancer, major depression, and schizophrenia.

Data from 283 patients satisfied the above criteria. Of these, 111 were categorized as the EDS group using a cutoff point being ≥ 10 on the Epworth Sleepiness Scale (ESS), whereas 172 were categorized as the non-EDS group with an ESS being < 10. The results of physiological and polysomnographic examinations were compared between the two groups to determine if OSAS patients showed characteristics of EDS.

Epidemiologic research has shown that the prevalence of OSAS differs among various subgroups; therefore, a stratified analysis was conducted in this study. There were 210 male patients and 73 female patients (42 female patients of childbearing age and 31 postmenopausal female patients) after sex stratification. Patients were also stratified on the basis of disease severity into three groups: 93 mild (5 ≦ apnea–hypopnea index [AHI] <15 events/h), 79 moderate (15 ≦ AHI <30 events/h), and 111 severe (AHI ≧ 30 events/h).

Polysomnographic study

All-night polysomnographic studies were done in the sleep laboratory. The PSG was made by Sandman Elite (Natus Medical Inc., Pleasanton, California, USA). The polysomnographic indices included index of disease severity (AHI), index of hypoxia (total mean SaO2, minimum SaO2, oxygen desaturation index, and length of time of SpO2< 90%), sleep time (total sleep time [TST] and rapid eye movement latency [REML]), the proportion of light sleep to TST (stage 1 and stage 1 + stage 2), sleep efficiency (SE), and arousal index. Each index was defined as follows:

AHI: The total number of complete cessations (apnea) and partial obstructions (hypopnea) of breathing occurring per hour of sleepTotal mean SaO2: The average level of oxygen saturation during TSTMinimum SaO2: The lowest level of oxygen saturation during TSTOxygen desaturation index: The number of hourly desaturation episodes during TSTLength of time of SaO2TST: The amount of actual sleep time in a sleep episode (not including stage wake)REML: The time from the beginning of sleep to the first REM stagePercentage of stage 1: The percentage of the time in stage 1 sleep during TSTPercentage of stage 2: The percentage of the time in stage 2 sleep during TSTSE: The percentage of TST over total time in bedArousal index: The number of hourly arousals during TST.

EDS Evaluation

The ESS was used to evaluate the degree of EDS. ESS is a simple and convenient questionnaire for patients to self-evaluate their degree of EDS. Based on eight assumed scenarios, scoring is as follows: 0 represents no chance of dozing, 1 a slight chance of dozing, 2 a moderate chance of dozing, and 3 a high chance of dozing. The maximum score is 24, and 10 points is generally set as a cutoff point for EDS[12]. The Chinese version of the ESS was used in this study[13].

Statistical analysis

The results were presented as mean ± standard deviation. The differences in polysomnographic indices were compared between the EDS and the non-EDS groups with independent t-tests to determine the factors which primarily affected the variables. Simple and multiple regression analyses were used to study the relationships between the polysomnographic indices and ESS scores to determine the independent predictors and the extent of their influence.

International Business Machine-Statistical Package for the Social Sciences software version 20.0 for Windows (IBM Corp., Armonk, New York, USA) was used to compute the study data. The differences between groups were considered statistically significant if p- values (two tailed) were < 0.05.

 Results



We collected data from 283 study patients. [Table 1] lists the basic characteristics of those study population. [Table 2] describes physiological and polysomnographic parameters of OSAS patients with and without EDS (n = 283). [Table 3] and [Table 4] show physiological and polysomnographic parameters of male and female OSAS patients with and without EDS (n = 210 and n = 73), respectively. [Table 5] lists physiological and polysomnographic parameters of childbearing female OSAS patients with and without EDS (n = 44). [Table 6] describes physiological and polysomnographic parameters of severe OSAS patients with and without EDS (n = 111).{Table 1}{Table 2}{Table 3}{Table 4}{Table 5}{Table 6}

 Discussion



In this study, the relations between the polysomnographic indices and daytime sleepiness were further studied in each stratified subpopulation. Shorten REML was a characteristic of patients with OSAS also having EDS. For male patients with OSAS, REML (p < 0.01) and SE (p < 0.05) were significantly different in characteristics of EDS [Table 3]. For female patients with OSAS, especially those of childbearing age, body mass index (BMI) (p < 0.05) was the significant predictor of EDS ([Table 4] and [Table 5]. For patients with severe OSAS, REML (p < 0.01) was significantly different in characteristic of EDS [Table 6].

The percentage of patients with OSAS and EDS was 39.2% [Table 1]. This finding is similar to research results from other researchers in Asia. Zhong et al. (Chengdu, China) suggested that about 42% of 410 OSAS patients also had EDS[14]. Another study done by Sun et al. (Guangzhou, China) reported that around 40% of 182 OSAS patients also had EDS[15]. Lee et al. also found that 38.5% of 96 OSAS patients also had EDS based on the analysis of their PSG reports[16].

We found in this study [Table 2] that a characteristic of the sleep of our total 283 patients with OSAS with EDS compared with those with OSAS without EDS (112.40 ± 59.77 min compared with 129.29 ± 65.59 min) was significantly shortened REML (p < 0.01). The same finding has been reported in previous studies[14],[15]. Another study showed that a correlation exists between shortened REML and the increased driving force of daytime sleepiness[17]. However, a causal relation between shortened REML and EDS in OSAS patients has not yet to be determined. However, Le Bon et al. showed that the length of REML is found to be negatively correlated with the number of nighttime sleep episodes, but positively correlated with the average length of sleep cycles[18]. Shortened REML represents an increased number of sleep episodes and decreased sleep time and implies changes in sleep structure such as sleep fragmentation and poor quality of sleep. Shortened REML is probably one of the reasons for EDS in patients with OSAS.

In a further study of our 210 male patients [Table 3], we found that REML of male patients with OSAS with EDS compared with those with OSAS without EDS (105.53 ± 53.86 vs. 131.92 ± 67.19 min) was significantly decreased (p < 0.01). However, in our 73 female patients [Table 4], we found that REML of the OSAS patients with and without EDS (127.90 ± 69.77 and 120.33 ± 58.29 min) did not have any significant difference. For those findings, we offer two possible explanations. First, gender differences may influence disease severity. Data not shown, AHI was significantly higher in the male group than in the female group (33.58 ± 25.12/h vs. 22.78 ± 18.78/h, p < 0.001). In this study on 111 severe OSAS patients [Table 6], we observed that REML of OSAS patients with EDS compared to those without EDS (114.02 ± 61.46 compared to 149.86 ± 70.73) was significantly decreased in severe OSAS group (p < 0.01), but not in mild or moderate groups. Second, gender differences possibly exist in sleep. Manber and Armitage in 1999 strongly suggested that reproductive hormones have an impact on sleep[19]. Those investigators found that progesterone primarily affects non-REM sleep whereas estrogen primarily affects REM sleep, and that gender differences in REM sleep are influenced by androgens, i.e., testosterone[19]. Taken all evidence together, we suggested that the divergence of characteristics of EDS in different sex may be due to disease severity and impact of reproductive hormones.

A greater SE (by 2.1% points, 89.12 ± 8.96 and 87.07 ± 9.11 min, nonsignificantly different) was found in OSAS patients with and without EDS, respectively [Table 2]. Sun et al. used ESS and multiple sleep latency test (MSLT) in conjunction to determine EDS among patients with OSAS. They found that, compared to non-EDS patients, EDS patients have shortened REML, increased TST, and have greater SE. Those characteristics showed that EDS patients constantly maintain a higher drive to sleep during the day and at night[15]. The mechanism for this is not clear, but may be a mechanism to compensate for poor quality of sleep and sleep fragmentation. In our study, data not shown, stratified analyses revealed that significantly greater SE is a characteristic of EDS in male OSAS patients, but not in the female group. For one possible reason for the discrepancy, we suggest that SE is affected by several factors such as insomnia and fragmented sleep. In Taiwan, the prevalence of insomnia is higher in women than in men[20]. It may be due to the fact that these confounding factors lead to no difference of SE in the female group.

Our study [Table 4] showed that obesity as determined by BMI was an independent significant predictor of EDS in female patients with OSAS versus those without (29.37 ± 6.82 vs. 26.37 ± 4.32 kg/cm3, p < 0.05), especially in childbearing age women (26.34 ± 5.30 vs. 30.90 ± 8.16 kg/cm3, p < 0.05) [Table 5]. Previous research showed that OSAS occurs most commonly in middle-aged men whereas the incidence of OSAS is lower in women of childbearing age due to high amounts of progesterone, which is a potent respiratory stimulant[21],[22]. Progesterone increases chemoreceptor responses to hypercapnia, and hypoxia as well as increases the depth of breathing and the number of breaths[21],[22]. Obesity is a primary risk factor for OSAS[23]. When BMI is increased by 6 kg/m2, the risk of OSAS is increased by 4-fold or more[23]. Waist circumference and waist-to-height ratio are also associated with the severity of OSAS[24]. Over 50% of OSAS is caused by obesity. Obesity causes increased visceral fat and neck circumference, thus leading to respiratory tract stenosis which changes the structure and function of the upper respiratory tract[25],[26],[27],[28]. For patients over 60 years of age, the influence of BMI is declined[29]. Women of childbearing age, although protected by progesterone, still suffer from the risk of OSAS if their BMI is excessively high. For example, the average BMI of female patients in this study was 27.7 kg/m2 [not shown in the presented data, but can be calculated from [Table 4] which has been considered “obese.”

In this study [Table 5], the BMI of childbearing age OSAS women with EDS reached up to 30.9 ± 8.16 versus 26.34 ± 5.30 kg/m2 in those OSAS women without EDS, showing a significant difference between those two groups (p < 0.05). Increased BMI increases the risk of suffering from metabolic syndrome. Vgontzas suggested that metabolic factors, such as hyperlipidemia and glycemic control, play important roles in causing daytime sleepiness[5],[30]. We found [Table 5] that BMI was a significant predictor in childbearing age OSAS women with and without EDS (p < 0.05). To note, inflammatory cytokines such as interleukin-6 and tumor necrosis factor may be important factors linking obesity to EDS[31]. However, we do not have any data to discuss this issue in this study.

Study limitations

The readers are cautioned not to overinterpret the study results because this study has four limitations:

ESS is not the best tool to evaluate sleepiness because it is a subjective measure. Patients may not accurately estimate their sleepiness severity. MSLT is the standard objective measure for patients with a tendency to fall asleep. However, it discriminates poorly between patients with EDS-linked sleep disorders, such as those with OSAS, and normal individuals[10]. Although the ESS is the most widely used validated measure, and maybe more discriminative for sleepiness than the MSLT[32], other tool requires to be developed for better determining the presence or severity of EDS. The validity of current instruments are among the primary challenges to understand sleepiness.Like any other observational studies, our current study cannot establish any causal relationship.All the study patients were from only one site. We are not sure that study findings can be extrapolated to the results from those in different sleep laboratories.The issue of inadequate sample size still existed in this study. Although this study had a total 283 study participants, the number in study participants of OSAS women patients with and without EDS was only 73, and that in childbearing age only 44. Statistical power is limited or compromised in those subgroup comparisons.

Summary

Our study findings indicated that differences in the characteristics of OSAS patients with and without EDS existed among distinct subgroups such as sex and disease severity. Almost all the findings in this study are in line with those of published papers in the literature. We suggest that we need to do further analyses on currently unknown factors associated with EDS in OSAS patients and that we need to develop therapies for known factors, to enhance job performance, and reduce the number of accidents by patients with OSAS in the future.

 Acknowledgment



The funding sources have no further rôle in the study design, collection, management, analysis, interpretation of the data, preparation, review, approval of manuscript, and decision to submit the manuscript for publication.

 Financial Support and Sponsorship



Funding for this study was from the Civilian Administration Division of Taichung Armed Forces General Hospital.

 Conflicts of Interest



There are no conflicts of interest.

References

1Mirrakhimov AE, Sooronbaev T, Mirrakhimov EM: Prevalence of obstructive sleep apnea in Asian adults: a systematic review of the literature. BMC Pulm Med 2013; 13: 10.
2Pack AI: Advances in sleep-disordered breathing. Am J Respir Crit Care Med 2006; 173: 7-15.
3Bixler EO, Kales A, Soldatos CR, et al.: Prevalence of sleep disorders in the Los Angeles metropolitan area. Am J Psychiatry 1979; 136: 1257-62.
4Seneviratne U, Puvanendran K: Excessive daytime sleepiness in obstructive sleep apnea: prevalence, severity, and predictors. Sleep Med 2004; 5: 339-43.
5Vgontzas AN: Excessive daytime sleepiness in sleep apnea: it is not just apnea hypopnea index. Sleep Med 2008; 9: 712-4.
6Bilyukov RG, Nikolov MS, Pencheva VP, et al.: Cognitive impairment and affective disorders in patients with obstructive sleep apnea syndrome. Front Psychiatry 2018; 9: 357.
7Ulfberg J, Carter N, Edling C: Sleep-disordered breathing and occupational accidents. Scand J Work Environ Health 2000; 26: 237-42.
8George CF, Boudreau AC, Smiley A: Simulated driving performance in patients with obstructive sleep apnea. Am J Respir Crit Care Med 1996; 154: 175-81.
9George CF, Boudreau AC, Smiley A: Comparison of simulated driving performance in narcolepsy and sleep apnea patients. Sleep 1996; 19: 711-7.
10Iranzo A: Excessive daytime sleepiness in OSA: Eur Respir Mon 2010; 50: 17-30.
11Roure N, Gomez S, Mediano O, et al.: Daytime sleepiness and polysomnography in obstructive sleep apnea patients. Sleep Med 2008; 9: 727-31.
12Johns MW: A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991; 14: 540-5.
13Chen NH, Johns MW, Li HY, et al.: Validation of a Chinese version of the Epworth sleepiness scale. Qual Life Res 2002; 11: 817-21.
14Zhong ZQ, Tao Y, Zhang ZP, et al.: Relationship between excessive daytime sleepiness and oxygen saturation in obstructive sleep apnea-hypopnea syndrome. Zhonghua Yi Xue Za Zhi 2011; 91: 40-3.
15Sun Y, Ning Y, Huang L, et al.: Polysomnographic characteristics of daytime sleepiness in obstructive sleep apnea syndrome. Sleep Breath 2012; 16: 375-81.
16Lee SJ, Kang HW, Lee LH: The relationship between the Epworth sleepiness scale and polysomnographic parameters in obstructive sleep apnea patients. Eur Arch Otorhinolaryngol 2012; 269: 1143-7.
17Mignot E, Young T, Lin L, et al.: Reduction of REM sleep latency associated with HLA-DQB1*0602 in normal adults. Lancet 1998; 351: 727.
18Le Bon O, Staner L, Hoffmann G, et al.: Shorter REM latency associated with more sleep cycles of a shorter duration in healthy humans. Psychiatry Res 2001; 104: 75-83.
19Manber R, Armitage R: Sex, steroids, and sleep: a review. Sleep 1999; 22: 540-55.
20Kao CC, Huang CJ, Wang MY, et al.: Insomnia: prevalence and its impact on excessive daytime sleepiness and psychological well-being in the adult Taiwanese population. Qual Life Res 2008; 17: 1073-80.
21Andersen ML, Bittencourt LR, Antunes IB, et al.: Effects of progesterone on sleep: a possible pharmacological treatment for sleep-breathing disorders? Curr Med Chem 2006; 13: 3575-82.
22Wimms A, Woehrle H, Ketheeswaran S, et al.: Obstructive sleep apnea in women: specific issues and interventions. Biomed Res Int 2016; 2016: 1764837.
23Young T, Shahar E, Nieto FJ, et al.: Predictors of sleep-disordered breathing in community-dwelling adults: the sleep heart health study. Arch Intern Med 2002; 162: 893-900.
24Unal Y, Ozturk DA, Tosun K, et al.: Association between obstructive sleep apnea syndrome and waist-to-height ratio. Sleep Breath, In press.
25Hoffstein V, Mateika S: Differences in abdominal and neck circumferences in patients with and without obstructive sleep apnoea. Eur Respir J 1992; 5: 377-81.
26Martins AB, Tufik S, Moura SM: Physiopathology of obstructive sleep apnea-hypopnea syndrome. J Bras Pneumol 2007; 33: 93-100.
27Romero-Corral A, Caples SM, Lopez-Jimenez F, Somers VK: Interactions between obesity and obstructive sleep apnea: implications for treatment. Chest 2010; 137 :711-9.
28Canapari CA, Hoppin AG, Kinane TB, et al.: Relationship between sleep apnea, fat distribution, and insulin resistance in obese children. J Clin Sleep Med 2011; 7: 268-73.
29Lee W, Nagubadi S, Kryger MH, et al.: Epidemiology of obstructive sleep apnea: a population-based perspective. Expert Rev Respir Med 2008; 2: 349-64.
30Ye L: Factors influencing daytime sleepiness in Chinese patients with obstructive sleep apnea. Behav Sleep Med 2011; 9: 117-27.
31Vgontzas AN: Does obesity play a major role in the pathogenesis of sleep apnoea and its associated manifestations via inflammation, visceral adiposity, and insulin resistance? Arch Physiol Biochem 2008; 114: 211-23.
32Johns MW: Sensitivity and specificity of the multiple sleep latency test (MSLT), the maintenance of wakefulness test and the Epworth Sleepiness Scale: failure of the MSLT as a gold standard. J Sleep Res 2000; 9: 5-11.