Excessive Daytime Sleepiness Is Associated With Depression Scores, But Not With Sleep-Disordered Breathing in Patients With Cardiovascular Diseases

Abstract

Background: Excessive daytime sleepiness (EDS) is a significant public health concern, with sleep-disordered breathing (SDB) being a common cause. However, their precise relationship in patients with cardiovascular disease (CVD) is unclear. Furthermore, whether comorbid psychological disorders could contribute to EDS remains unknown. We aimed to assess the prevalence of EDS and its related factors, including SDB and depression, in patients with CVD.

Methods and Results: We analyzed data from 1,571 patients admitted for various CVDs in a single university hospital (median age, 67 [56–76] years; 29.6% women). We assessed EDS using the Japanese version of the Epworth Sleepiness Scale (ESS; median 6.0 [4.0–9.0]). The presence of EDS (ESS >10, n=297 [18.9%]) did not differ between patients with and without SDB, which was screened with nocturnal pulse oximetry. In contrast, the patients with EDS had higher depression scores (Hospital Anxiety and Depression Scale subscore for depression [HADS-D] and Patient Healthcare Questionnaire [PHQ]-9). The depression scores, measured by HADS-D (odds ratio [OR] 1.14; 95% confidence interval [CI], 1.07–1.22) and PHQ-9 (OR, 1.14; 95% CI, 1.07–1.20) were independent determinants of EDS. These relationships among EDS, SDB, and depression were consistent among the subgroups with cardiovascular comorbidities.

Conclusions: The presence of EDS is associated with depressive symptoms, but not with SDB, in patients with CVD, suggesting that these patients should be thoroughly assessed for psychological disturbances.

Excessive daytime sleepiness (EDS) is a significant public concern because of its adverse consequences; it is associated with poor quality of life and also increases the risk of driving accidents and work-related injuries.^1–5 EDS is also known to increase the risk of cardiovascular disease (CVD), as well as the mortality rate, even after adjusting for confounding factors.^6–11 In the Cardiovascular Health Study, EDS was independently associated with total mortality as well as cardiovascular morbidity and mortality in community-dwelling older adults.⁸ A recent study in a multiethnic prospective cohort also demonstrated that EDS was independently associated with an increased risk for myocardial infarction and stroke.¹⁰ Despite its clinical significance, EDS remains unrecognized in patients with CVD and its prevalence and related factors are not well known. Although the etiology and pathophysiology of EDS are still poorly understood, sleep and psychological disorders are associated with EDS in the general population, particularly inadequate sleep duration, sleep-disordered breathing (SDB), and depression.^12,13 SDB is characterized by repetitive periods of suspension or reduction of breathing, leading to intermittent oxygen desaturation.^14,15 Although there is widespread evidence that SDB independently predicts cardiovascular morbidity and mortality,^14,15 SDB also remains under-recognized and undertreated in these patients. Therefore, it is plausible that examining for the presence of EDS might be important in the management of CVD, through the detection of SDB. However, the link between EDS and SDB is validated only in the general population, and whether this relation is consistent in patients with CVD has not been elucidated. Depression, another under-recognized disorder, has a negative effect on quality of life, and could also shorten life expectancy; it is associated with an increased risk of CVD in healthy participants, as well as increased mortality in patients with existing CVD.^16–18 Previous community cohort studies indicated that depression is related to complaints of EDS.^13,19 Despite their linkage in the general population, knowledge regarding the association between EDS and depression in patients with CVD is also currently lacking. If EDS is prevalent and associated with SDB or depression among patients with CVD, cardiologists should undertake greater efforts for their identification and treatment. In the present study, we examined the prevalence of EDS and its related factors, including SDB and depression, in patients hospitalized for CVD.

Methods

Study Population

A total of 1,571 patients admitted to the Cardiology department of Keio University Hospital between July 2013 and December 2015 were included in this cross-sectional study. Patients whose primary diagnosis on admission was coronary artery disease (CAD: n=483), arrhythmia (n=606), heart failure (HF: n=113), valvular heart disease (VHD: n=222), and other CVDs (n=147) were evaluated. HF as a principal diagnosis was defined as rapid-onset HF or a change in the signs and symptoms of HF requiring urgent therapy and hospitalization, based on the Framingham criteria.²⁰ A previous hospital admission with the principal diagnosis of HF was considered evidence of HF history based on the medical records. We excluded patients with mental diseases or significant cognitive impairment, as well as those who refused to participate. The study protocol was approved by the Keio University School of Medicine Ethics Committee, and informed consent was given by all patients.

Parameters for Analysis

Patients’ background data included age, sex, body mass index (BMI), history of CVD, hypertension, diabetes mellitus, dyslipidemia, smoking, alcohol use, and living conditions. Laboratory data included the levels of C-reactive protein, albumin, B-type natriuretic peptide, uric acid, and hemoglobin A1c, and estimated glomerular filtration rate (eGFR).²¹ We assessed EDS using the Japanese version of the Epworth Sleepiness Scale (ESS), a self-rated questionnaire that can help people identify their own level of daytime sleepiness.^22,23 The sum of the scores for these components rate the likelihood of falling asleep while reading something while sitting in a chair, watching television, sitting at a meeting or a theater without actively speaking, being in a car for an hour as a passenger, lying down and taking a rest in the afternoon, sitting and talking with someone, sitting quietly after taking lunch, and sitting and writing by hand. Eight components are weighted equally on a 0–3 scale, for a total scale ranging from 0 to 24. EDS was defined as ESS >10.²⁴

We evaluated the severity of SDB in all patients using nocturnal pulse oximetry, as reported previously.^25,26 Pulse oximetry is widely used to record arterial oxyhemoglobin saturation using a finger probe (PULSOX-Me300, Teijin Pharma, Tokyo, Japan). The sampling efficiency of pulse oximetry was 1 Hz during the memory interval for an average time of 3 s each. The resolution for SpO₂ was 0.1%. Arterial oxyhemoglobin saturation was recorded using specialized software (DS-Me, Teijin Pharma). Because the measurement time of pulse oximetry is often longer than the true total sleep time, we used a single-night sleep log to exclude waking time from the analysis and minimize the potential for overestimating total sleep time. We used an oxygen desaturation index (ODI) ≥3% (3% ODI), that is, the frequency of 3% desaturated episodes per hour, as a surrogate marker of SDB. Pulse oximetry has been previously validated on the basis of simultaneous 1-night recordings of both polysomnography and pulse oximetry, and its sensitivity and specificity were 85% and 100%, respectively, for detecting an apnea-hypopnea index ≥20 determined by polysomnography using a cutoff threshold of 3% ODI=15.^25,27,28

Depression was assessed using the Hospital Anxiety and Depression Scale (HADS) subscore for depression (HADS-D).²⁹ It consists of 7-component self-rated subscales designed to evaluate depressive symptoms. Each component is scored from 0 (minimally present) to 3 (maximally present), and its sum score ranges from 0 to 21. Depressive symptoms were also evaluated with the Patient Health Questionnaire (PHQ)-9.^30–32 Each item of the PHQ-9 corresponds to one of the DSM-IV diagnostic criteria for the symptoms of major depressive disorder. The patients were asked how often, over the past 2 weeks, they had been bothered by each of the depressive symptoms. Each item is scored from 0 (not at all) to 3 (nearly every day), and its sum score ranges from 0 to 27.

Sleep duration was assessed with the corresponding item of the Pittsburgh Sleep Quality Index (PSQI).^33,34 “During the past month, how many hours of actual sleep did you get at night? This may be different from the number of hours you spend in bed.”, and self-reported short sleep duration was defined as total sleep time ≤6 h/night. Use of sleep medication was also similarly assessed: “During the past month, how often have you taken medicine (prescribed or “over the counter”) to help you sleep?” Not during the past month (=0), less than once a week (=1), once or twice a week (=2), three or more times a week (=3). Use of sleep medication was defined as a score ≥1. All patients completed these questionnaires during their index hospitalization period. Most patients were administered the questionnaires within a few days after hospital admission, except those who initially needed intensive treatment and completed the questionnaires before discharge.

Statistical Analysis

Significant differences were determined using Student’s t-test, Mann-Whitney U test, Kruskal Wallis tests, or chi-squared test, as appropriate. Multiple logistic regression analyses were performed to clarify the association between EDS and the depression scores (HADS-D or PHQ-9). Variables submitted to the model included age, sex, obesity (BMI ≥25 kg/m²), SDB (3% ODI >15), short sleep duration (≤6 h/night), use of sleep medications, and cardiovascular comorbidities (CAD, HF, atrial fibrillation [AF], and VHD), hypertension, diabetes mellitus, dyslipidemia, smoking, living alone, employment status, and laboratory data (C-reactive protein, albumin, eGFR, and uric acid). Before the multiple logistic regression analyses were performed, multicollinearity was assessed, and factors indicating serious multicollinearity were accordingly eliminated from the model. Effect of the interaction between depression and other confounding factors on EDS was examined by adding an interaction term to the statistical model. All statistical analyses were performed using IBM SPSS software (version 24; SPSS Inc., Chicago, IL, USA).

Results

Baseline Characteristics

The median ESS score in this study population was 6.0 (interquartile range [IQR] 4.0–9.0), and 297 patients (18.9%) were classified as having EDS (ESS >10) (Figure 1). Table 1 shows the patients’ demographic characteristics. EDS was associated with younger age, smoking, alcohol use, employment status, shorter sleep duration, and lower prevalence of hypertension and dyslipidemia, higher eGFR, and lower plasma B-type natriuretic peptide level. The prescription rate of lipid-soluble β-blockers did not differ between patients with EDS and those without (43.2% vs. 46.9%, P=0.25).

Figure 1.

Distribution of Epworth Sleepiness Scale (ESS) scores in a cohort of patients with cardiovascular disease. EDS, excessive daytime sleepiness.

Table 1. Study Patients’ Clinical Characteristics

	Total (n=1,571)	ESS ≤10 (n=1,274)	ESS >10 (n=297)	P value
Female (%)	29.6	29.3	30.7	0.62
Age (years)	67.0 (56.0–76.0)	68.0 (58.0–77.0)	60.0 (49.0–70.0)	<0.001
BMI (kg/m2)	23.5 (21.2–25.9)	23.5 (21.1–25.8)	23.9 (21.6–26.5)	0.071
Principal diagnosis (%)
CAD	30.7	31.1	29.3	0.55
ACS	5.6	5.3	6.7	0.35
Stable CAD	25.1	25.7	22.6	0.25
Arrhythmia	38.6	38.4	39.4	0.75
HF	7.2	7.4	6.4	0.56
VHD	14.1	14.8	11.4	0.14
Miscellaneous	9.4	8.4	13.5	0.007
Medical history (%)
CAD	40.7	41.4	37.4	0.20
HF	18.4	18.4	18.2	0.92
AF	37.1	37.5	35.8	0.60
Hypertension	54.1	55.7	47.1	0.008
Diabetes mellitus	22.5	23.5	18.2	0.050
Dyslipidemia	45.2	46.8	38.4	0.009
Lifestyle (%)
Smoking	57.2	55.9	62.6	0.037
Alcohol	53.0	51.5	59.0	0.021
Living alone	16.8	16.9	16.1	0.75
Employed	54.1	51.0	66.7	<0.001
Questionnaire
ESS	6.0 (4.0–9.0)	5.0 (3.0–7.0)	13.0 (11.0–15.0)	<0.001
Sleep duration (min)	390 (360–420)	390 (360–450)	360 (328–420)	<0.001
Short sleep duration (%)	43.7	40.5	57.5	<0.001
Use of sleep medications (%)	20.7	21.9	15.8	0.023
Laboratory data
CRP (mg/dL)	0.06 (0.02–0.21)	0.06 (0.02–0.21)	0.07 (0.03–0.24)	0.29
Albumin (g/dL)	4.1 (3.8–4.4)	4.1 (3.8–4.4)	4.2 (3.8–4.4)	0.19
BNP (pg/mL)	60.1 (24.0–160.3)	63.9 (25.4–167.4)	48.9 (18.9–142.0)	0.006
eGFR (mL/min/1.73 m2)	62.0 (51.0–72.8)	62.0 (50.3–72.0)	63.5 (54.0–77.8)	0.004
HbA1c (%)	5.8 (5.5–6.2)	5.8 (5.5–6.2)	5.7 (5.4–6.2)	0.16
Uric acid (mg/dL)	6.0 (4.9–7.1)	6.0 (4.9–7.0)	6.0 (4.9–7.1)	0.90

Values are % or median (interquartile range). ACS, acute coronary syndrome; AF, atrial fibrillation; BMI, body mass index; BNP, B-type natriuretic peptide; CAD, coronary artery disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; ESS, Epworth Sleepiness Scale; HbA1c, hemoglobin A1c; HF, heart failure; VHD, valvular heart disease.

We analyzed the relationship between EDS and SDB using nocturnal pulse oximetry (Table 2). The prevalence of SDB (3% ODI >15) did not significantly differ between patients with and without EDS. The patients with EDS had lower 3% ODI than those without EDS. There were no significant differences in mean SpO₂ (95.7 [IQR 95.0–96.6] vs. 95.7 [IQR 94.7–96.5], P=0.26) and minimum SpO₂ (82.0 [IQR 75.0–87.0] vs. 83.0 [IQR 79.0–87.0], P=0.21) between patients with and without EDS. No relationship between EDS and SDB was consistent among the subgroups with medical history of CAD, HF, AF, and VHD (Table 2). We stratified patients into 3 groups according to age (age <50 [n=238], 50≤ age ≤70 [n=707], and age >70 [n=626]), and there was no relationship between SDB and EDS in any of the stratified groups (Table 2). Furthermore, we also divided the patients into 3 groups according to 3% ODI (none-mild SDB [3% ODI <15, n=1,119], moderate SDB [15≤ 3% ODI <30, n=318], and severe SDB [3% ODI ≥30, n=134] groups). Among these groups, ESS (none-mild SDB; 6.0 [IQR 4.0–9.0], moderate SDB; 6.0 [IQR 3.0–9.0], severe SDB; 5.0 [IQR 3.0–9.0], P=0.11) and the prevalence of EDS (none-mild SDB; 18.9%, moderate SDB; 18.9%, severe SDB; 18.7%, P=0.997) did not differ among the 3 groups.

Table 2. Relationship Between EDS and SDB in the Overall Cohort and Subgroups

	Total	ESS ≤10	ESS >10	P value
3% ODI (times/h)
Overall cohort	8.8 (5.1–16.2)	9.0 (5.3–16.2)	7.9 (4.2–16.2)	0.038
Subgroup: CV comorbidity
CAD	10.1 (6.2–18.0)	10.2 (6.2–18.2)	10.0 (5.6–17.9)	0.46
HF	12.9 (6.7–23.8)	13.3 (7.0–23.5)	10.4 (6.0–24.9)	0.47
AF	9.0 (5.3–16.4)	9.2 (5.5–16.3)	8.3 (4.1–17.1)	0.17
VHD	10.1 (5.9–19.1)	10.2 (5.9–19.1)	8.5 (5.5–19.2)	0.62
Subgroup: age (years)
<50	5.8 (3.2–11.0)	5.9 (3.3–10.3)	5.6 (2.9–11.3)	0.97
50–70	8.6 (5.0–15.2)	8.7 (5.3–15.1)	7.7 (4.0–15.7)	0.20
>70	10.8 (6.1–19.9)	10.8 (6.0–19.9)	11.7 (6.6–22.3)	0.41
SDB: 3% ODI >15 (%)
Overall cohort	26.8	26.8	26.6	0.93
Subgroup: CV comorbidity
CAD	31.2	31.6	29.7	0.71
HF	43.8	44.0	42.6	0.85
AF	27.4	27.2	28.3	0.81
VHD	33.1	32.9	34.0	0.88
Subgroup: age (years)
<50	14.7	12.6	19.0	0.19
50–70	24.3	23.8	26.5	0.48
>70	34.2	34.1	35.2	0.85

Values are % or median (interquartile range). CV, cardiovascular; EDS, excessive daytime sleepiness; ODI, oxygen desaturation index; SDB, sleep-disordered breathing. Other abbreviations as in Table 1.

We then analyzed the relationship between EDS and the depression scores using the HADS-D and PHQ-9 (Figure 2): the respective median scores were 4.0 [IQR 2.0–7.0] and 3.0 [IQR 1.0–6.0]. The patients with EDS had higher HADS-D (5.0 [IQR 2.0–8.0] vs. 3.0 [IQR 1.0–6.0], P<0.001; Figure 2A) and PHQ-9 (5.0 [IQR 2.0–7.3] vs. 2.0 [IQR 1.0–5.0], P<0.001; Figure 2B) than those without EDS. The ESS score correlated positively with the depression scores (HADS-D; r=0.181, P<0.001, PHQ-9; r=0.246, P<0.001).

Figure 2.

Comparison of subscores of depression using HADS (A) and PHQ-9 (B) between patients with and without excessive daytime sleepiness. Bars denote medians, boxes denote interquartile ranges, whiskers denote ranges excluding statistical outliers (open circles) (>1.5 box lengths from either the 25th or 75th percentile). *P<0.001. ESS, Epworth Sleepiness Scale; HADS, Hospital Anxiety and Depression Scale; PHQ, Patient Health Questionnaire.

Multiple Regression Analysis

The depression scores were associated with the presence of EDS after adjustment for age and sex (HADS-D; odds ratio [OR]: 1.14, 95% confidence interval [CI]: 1.09–1.18, P<0.001, PHQ-9; OR: 1.15, 95% CI: 1.11–1.19, P<0.001). The significant association of the depression scores with EDS persisted after adjustment for coronary risk factors, lifestyle, employment status, SDB, short sleep duration, use of sleep medications, cardiovascular comorbidities, and laboratory data (Table 3; HADS-D; OR: 1.14, 95% CI: 1.07–1.22, P<0.001, PHQ-9; OR: 1.14, 95% CI: 1.07–1.20, P<0.001). Younger age and short sleep duration were also independent determinants of the presence of EDS (Table 3).

Table 3. Multiple Regression Analysis of Determinants of EDS

Variables	Model 1		Model 2
	OR (95% CI)	P value	OR (95% CI)	P value
HADS depression score: per each score increase	1.14 (1.07–1.22)	<0.001
PHQ-9 score: per each score increase			1.14 (1.07–1.20)	<0.001
Age: per each year increase	0.98 (0.95–0.998)	0.033	0.97 (0.95–0.997)	0.025
Female sex	1.41 (0.77–2.56)	0.26	1.31 (0.71–2.43)	0.39
Obesity	0.69 (0.40–1.18)	0.18	0.72 (0.42–1.25)	0.24
SDB	1.64 (0.97–2.76)	0.06	1.51 (0.88–2.58)	0.14
Short sleep duration	1.98 (1.23–3.18)	0.005	1.64 (1.01–2.66)	0.044
Use of sleep medications	0.69 (0.37–1.29)	0.25	0.62 (0.33–1.17)	0.14
CAD	1.16 (0.67–2.02)	0.59	1.14 (0.65–2.01)	0.64
AF	0.84 (0.50–1.40)	0.50	0.92 (0.54–1.55)	0.75
HF	0.80 (0.43–1.49)	0.48	0.65 (0.34–1.26)	0.20
VHD	1.33 (0.71–2.48)	0.37	1.39 (0.73–2.63)	0.32
Hypertension	1.29 (0.78–2.14)	0.32	1.25 (0.75–2.07)	0.40
Diabetes mellitus	0.84 (0.46–1.53)	0.58	0.996 (0.55–1.81)	0.99
Dyslipidemia	0.66 (0.39–1.11)	0.11	0.74 (0.43–1.26)	0.27
Smoking	1.55 (0.93–2.59)	0.09	1.53 (0.90–2.59)	0.11
Living alone	0.55 (0.29–1.07)	0.08	0.61 (0.32–1.19)	0.15
Employed	1.75 (0.98–3.11)	0.057	1.48 (0.83–2.65)	0.18
Albumin: per each g/dL increase	0.72 (0.42–1.24)	0.23	0.75 (0.43–1.31)	0.32
CRP: per each mg/dL increase	0.95 (0.79–1.13)	0.54	0.95 (0.81–1.12)	0.55
Uric acid: per each mg/dL increase	1.07 (0.92–1.23)	0.39	1.04 (0.90–1.20)	0.64
eGFR: per each L/min/1.73 m2	1.001 (0.99–1.02)	0.94	0.999 (0.98–1.01)	0.87

Standard covariates=age, sex, obesity (BMI ≥25 kg/m²), SDB (3% ODI >15), short sleep duration (≤6 h/night), use of sleep medications, CV comorbidities (CAD, HF, AF, and VHD), hypertension, diabetes mellitus, dyslipidemia, smoking, living alone, employment status, and laboratory data (CRP, albumin, uric acid, and eGFR). Model 1=standard covariates+HADS depression score. Model 2=standard covariates+PHQ-9 score. CI, confidence interval; HADS, Hospital Anxiety and Depression Scale; OR, odds ratio; PHQ, Patient Health Questionnaire. Other abbreviations as in Tables 1,2.

We further assessed the association between the depression and EDS in patients stratified by sex, obesity, age, SDB, and medical history of cardiovascular comorbidities. The relationship between EDS and the depression scores was not modified by age, obesity, and cardiovascular comorbidities (Tables 4,5). Sex tended to affect the association between EDS and depression assessed by HADS-D (P value for interaction=0.092). EDS tended to be associated with higher HADS-D among female patients (OR: 1.20, 95% CI: 1.11–1.28, P<0.001) than male patients (OR: 1.11, 95% CI: 1.06–1.16, P<0.001). However, the association between EDS and depression assessed by the PHQ-9 was not modified by sex (P value for interaction=0.30).

Table 4. Adjusted ORs of HADS Depression Score for EDS in the Various Subgroups of Patients With CV Disease

	P value	OR	95% CI	P value for interaction
Overall	<0.001	1.13	1.09–1.17
Subgroups
Sex
Female	<0.001	1.20	1.11–1.28	0.092
Male	<0.001	1.11	1.06–1.16
Obesity (BMI ≥25 kg/m2)
Yes	<0.001	1.14	1.07–1.21	0.84
No	<0.001	1.13	1.08–1.18
Age (years)
≤67	<0.001	1.12	1.07–1.17	0.50
>67	<0.001	1.15	1.08–1.23
SDB (3% ODI >15)
Yes	<0.001	1.14	1.06–1.22	0.84
No	<0.001	1.13	1.08–1.18
Medical history
HF
Yes	<0.001	1.19	1.09–1.30	0.14
No	<0.001	1.12	1.07–1.16
AF
Yes	0.001	1.11	1.04–1.19	0.54
No	<0.001	1.15	1.09–1.20
CAD
Yes	0.004	1.09	1.03–1.16	0.15
No	<0.001	1.16	1.11–1.22

Models adjusted for older age (age ≥median value), sex, obesity (BMI ≥25 kg/m²), SDB, and CV comorbidities (CAD, HF, and AF). Abbreviations as in Tables 1–3.

Table 5. Adjusted ORs of PHQ-9 Score for EDS in the Various Subgroups of Patients With CV Disease

	P value	OR	95% CI	P value for interaction
Overall	<0.001	1.15	1.12–1.19
Subgroups
Sex
Female	<0.001	1.19	1.12–1.26	0.30
Male	<0.001	1.14	1.09–1.19
Obesity (BMI ≥25 kg/m2)
Yes	<0.001	1.19	1.12–1.27	0.31
No	<0.001	1.14	1.10–1.19
Age (years)
≤67	<0.001	1.15	1.10–1.20	0.80
>67	<0.001	1.16	1.10–1.23
SDB (3% ODI >15)
Yes	<0.001	1.18	1.10–1.26	0.84
No	<0.001	1.15	1.11–1.20
Medical history
HF
Yes	<0.001	1.17	1.09–1.26	0.61
No	<0.001	1.15	1.11–1.20
AF
Yes	<0.001	1.13	1.06–1.20	0.48
No	<0.001	1.17	1.12–1.22
CAD
Yes	<0.001	1.17	1.10–1.24	0.77
No	<0.001	1.15	1.11–1.20

Models adjusted for older age (age ≥ median value), sex, obesity (BMI ≥25 kg/m²), SDB, and CV comorbidities (CAD, HF, and AF). Abbreviations as in Tables 1–3.

Discussion

We demonstrated that approximately one-fifth of patients with CVD had EDS, and EDS was not associated with SDB, but was independently associated with high depression scores. These findings suggest that EDS was common and independently associated with the depression, but not SDB, in patients with CVD.

Prevalence of EDS in Patients With CVD

EDS has been defined as the inability to maintain wakefulness and alertness during the major waking episodes of the day, with sleep occurring unintentionally or at inappropriate times almost daily. In addition to its significant link with CVD,^6–11 EDS has been associated with medication nonadherence in patients with CVD.³⁵ However, EDS is not routinely evaluated in patients with CVD in clinical practice. To our knowledge, this is the first study to focus on EDS in patients with CVD. Evidence regarding EDS is heterogeneous because of the various definitions used previously; thus it is difficult to compare the prevalence and patients’ characteristics of EDS among different cohorts.^8–11,19 The ESS is the most commonly used and validated questionnaire for assessing a person’s average level of EDS.^22,24 Two community-based studies conducted in Japan revealed that the prevalence of men and women with EDS based on ESS >10 was reportedly 7.2–9.6% and 8.4–13.3%, respectively.^36,37 In our study, the prevalence of EDS was 18.9%, so its prevalence may be higher in patients with CVD than in the general population. According to the previous studies in general populations or patients with SDB or hypertension, the ESS score is high in patients who have coronary risk factors or cardiovascular comorbidities.^7,10,11 Bixler et al demonstrated that the presence of EDS was strongly associated with obesity and diabetes in a population-based cohort in the USA (random sample of 16,583 men and women).³⁸ Interestingly, in our study population, there was no difference in the prevalence of obesity or each cardiovascular comorbidity between patients with EDS and those without. Furthermore, the prevalence of coronary risk factors, such as hypertension and diabetes mellitus, was rather lower in patients with EDS, which is inconsistent with the data from that North American population-based cohort.³⁸ These findings suggest that factors other than cardiovascular comorbidities or coronary risk factors could be associated with EDS in patients with CVD, in contrast to the general population.

Relationship Between SDB and EDS

EDS has been recognized as an important sign because it can alert undiagnosed SDB in the general population.^12,22,24 Therefore, the ESS is administered to patients to estimate their risk of SDB and guide decisions regarding overnight polysomnography. However, the ESS was validated against in-laboratory polysomnography in community individuals.^22,24 Interestingly, previous studies have demonstrated that patients with SDB and comorbid HF or AF do not have EDS and report lower ESS scores for any apnea-hypopnea index in comparison with the general population.^39,40 A notable strength of our study was screening by nocturnal pulse oximetry for SDB in the overall study population. In accordance with previous reports,^39,40 we found a nearly universal consistency of no association between SDB and EDS in the overall cohort with CVD or in the cardiovascular comorbidities subgroups. These findings suggested that screening for undiagnosed SDB by examining for the presence of EDS might not be efficient in patients with CVD. Because the current diagnostic process using polysomnography can only identify a limited number of patients with SDB from a large cohort of patients with CVD, more patients could benefit from pulse oximetry, a readily available and inexpensive screening tool for SDB. Portable polygraphy that monitors chest and abdominal respiratory effort, nasal airflow through a pressure transducer, and arterial oxyhemoglobin saturation through pulse oximetry, also could be an ideal screening for the diagnosis of SDB because of its ability to distinguish between obstructive and central types of SDB.

EDS and Psychological Disturbances

Although previous studies of population-based cohorts have shown that EDS is related to psychological disturbances,^13,19,41 the prevalence of CVD was less in those cohorts, so it remains unknown whether the relationship is consistent in patients with CVD. Here, we demonstrated that EDS was independently associated with depressive symptoms, even after adjusting for sleep duration, use of sleep medications, and maladaptive lifestyle, such as obesity, smoking, and alcohol use, which are all reportedly associated with EDS in the general population.^{10,11,19,41,42} Although it was difficult to determine the cause-effect relationship between EDS and depression because of the nature of our study’s design, it may be bidirectional. Previous longitudinal studies have demonstrated that depression is an important factor in predicting incident EDS,⁴² and EDS also increases the risk of subsequent depression.¹⁹ The precise mechanism of this bidirectional relationship remains unclear, but there are several possibilities. Patients with depression have poor sleep quality and quantity, which could contribute to the subjective experience of a state of sleepiness.^43,44 Conversely, patients with EDS may be perceived by others as lazy, leading to problems with family life and interpersonal relationships.⁴⁵ Furthermore, EDS may even interfere with the enjoyment of recreational activities.⁴⁵ These sequelae could be associated with psychological disturbances. To conclude the cause-effect relationship, future clinical studies should investigate whether EDS responds better to treatment targeting depression and whether enhancing alertness through pharmacological agents or lifestyle modification would prevent EDS and depression. From a clinical perspective, EDS could signal undiagnosed psychological disturbance that should be evaluated and managed. Although screening for depression is recommended in the field of CVD because of its high prevalence and relationship with worse clinical outcome,^16,46 its screening has not become a widely implemented part of routine medical care. One of the barriers that has to be overcome by cardiologists is hesitation in asking their patients directly about psychological issues. Our findings suggested that cardiologists need to keep in mind that CVD patients with a complaint of EDS, which is commonly encountered in clinical practice, should be thoroughly assessed for depression to ensure early diagnosis of psychological disturbances.

Study Limitations

There are several to mention. First, several methodological issues should be mentioned such as the lack of a polysomnography study and the use of a questionnaire vs. objective sleepiness testing. Polysomnography is preferred over pulse oximetry in terms of accurately determining the severity and type of SDB (obstructive or central type) as well as sleep stages. In terms of EDS, the subjective measure of the ESS and objective tool of multiple sleep latency or maintenance of wakefulness testing could evaluate different but complimentary aspects of sleepiness.⁴⁷ Thus, the use of polysomnography as well as objective assessment of EDS will need to be performed in future to convincingly confirm our conclusion. Second, because of the limitations of the study design, it is difficult to comment on the effects of EDS on subsequent cardiovascular clinical outcomes. Although it is plausible that EDS could be associated with cardiovascular mortality, at least in part through its relationship with depression, it remains unknown whether EDS has a direct role, through an undefined mechanism, in increasing mortality. We have to assess the consequences of screening and treatment of EDS and its underlying causes on subsequent clinical outcomes in future study. Such investigations may not just improve EDS management, but also justify initiatives to identify EDS routinely in patients with CVD.

Conclusions

The presence of EDS was not associated with SDB, but was independently associated with depressive symptoms in patients with CVD. Clinicians need to keep in mind that EDS, which is a known symptom of SDB, could be a quite useful marker of psychological disturbances in patients with CVD.

Acknowledgments

No financial support was received for this study. The authors declare there are no conflicts of interest including related consultancies, shareholdings, and funding grants.

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