Relationship of Psychiatric Disorders and Sleep Quality to Physical Symptoms in Coronary Artery Disease.

ABSTRACT: Psychiatric disorders (depression/anxiety) and sleep quality are linked to coronary artery disease (CAD). CAD patients often complain of various physical symptoms (PSs), which are not only organic changes of biological origin, but also somatization of mental illness. PSs also affect CAD progression. However, the relationship between psychiatric disorders/sleep quality and PS in CAD is less studied, which this study aims to clarify. This cross-sectional observational study was conducted on the first 305 CAD patients diagnosed with PS by Patient Health Questionnaire-15. They were compared with 521 other CAD patients showing no PS. On multivariate analysis, sex, age, family history, psychiatric disorders, and sleep quality were the significant independent predictors of PS. A significant correlation was found between PS severity and the severity of both psychiatric disorders and sleep quality. Further studies are warranted to explore the impact of psychological and sleep intervention on PS and long-term outcomes.

Psychiatric disorders (depression/anxiety) and sleep quality are linked to coronary artery disease (CAD). Anxiety, depression, and other emotional problems are not only independent prognostic risk factors for various types of CADs but may aggravate other risk factors for cardiovascular diseases (Penninx, 2017). Previously, researchers conducted a survey on emotional problems in CAD patients and found that 21%, 46.6%, and 38% of patients had comorbid anxiety, depression, and combined depression and anxiety, respectively (Daniel et al., 2018; De Smedt et al., 2019; Greden, 2003). Epidemiological data show that the risk of death is doubled in acute myocardial infarction with comorbid depression (Feng et al., 2019). At the same time, studies show that sleep disorders and CAD have a close relationship, and sleep duration is one of the prognostic markers for cardiovascular diseases (Cappuccio et al., 2011). Interestingly, sleep disorders may induce CAD, and CAD will also exacerbate sleep disorders. A national health and nutrition survey for clinical research of US citizens found a significant correlation between sleep duration and the prevalence of stroke, myocardial infarction, congestive heart failure, CAD, and angina (Aggarwal et al., 2013). Another study shows that the risk of cardiovascular disease and CAD are increased by 63% and 79%, respectively, in subjects with short sleep duration and poor sleep quality compared with subjects with normal sleep patterns and good sleep quality (Hoevenaar-Blom et al., 2011). In conclusion, psychiatric disorders (depression/anxiety) and sleep quality were strongly correlated with the condition of CAD patients.

Physical symptoms (PSs) are defined as unpleasant subjective feelings associated with tissue injury or latent injury. CAD patients often complain of various PS, such as chest pain, dizziness, and shortness of breath (Ketterer et al., 2008). More and more studies are showing that these PS are not only organic changes of biological origin but may also be physical manifestations of psychiatric disorders (Bekhuis et al., 2015). However, cardiologists lack sufficient understanding of PS in patients at present. Symptomatic treatment is usually biomedical treatment based on coronary angiography results, and the roles of psychiatric and psychological factors tend to be ignored. This results in poor efficacy in the treatment of PS and, consequently, repeated follow-ups (Barsky et al., 2005). Studies conducted to better understand the relationship between PS and CAD found that cerebrocardiovascular disease patients, such as those with hypertension (Wiener et al., 2020) or coronary arteriosclerosis (Sokolov et al., 1984), not only experience physiological changes but also significant emotional changes. Anxiety, depression, and sleep disorders occur with disease progression and may lead to PS (Dijkstra-Kersten et al., 2017; Hansotia, 1996). The clinical presentations of PSs are diverse, and these may involve different systems, including a series of PS, such as pain (gastric pain, headache, and neuropathy), muscle tension, shivering, dyspnea, heart palpitations, fatigue, and gastrointestinal symptoms (Dorner et al., 2010). Patients with PS may also develop CAD. Severe PS may lead to autonomic dysfunction, increased plasma catecholamine levels, and increased heart rate. These in turn lead to vascular endothelial injury and increased incidence of CAD (Peters et al., 2018). Therefore, CAD patients develop more intense PS, and PS worsens the disease in CAD patients.

However, to our knowledge, very few data are available on the relationship between psychiatric disorders/sleep quality and PS in CAD. Prompt identification of the effects of psychiatric disorders/sleep quality on PS and timely intervention will not only improve the treatment protocol and prevent excessive diagnosis and treatment but also the prognosis and efficacy of treatment in cardiovascular disease patients. Thus, this study aims to elucidate the relationship between psychiatric disorders/sleep quality and PS in CAD.

METHODS

Study Population

This is a retrospective clinical study wherein we collected the previous data of the target patients from January 2021 to May 2021 through outpatient follow-up and telephone follow-up. A total of 833 patients hospitalized for coronary heart disease between July 2018 and December 2019 were included in this study. On the day of admission, basic information was collected, and the Patient Health Questionnaire-15 (PHQ15), Pittsburgh Sleep Quality Index (PSQI), General Anxiety Disorder-7 (GAD7), and Patient Health Questionnaire-9 (PHQ9) scales were completed. For elderly patients who were unable to fill in the forms, staff described the scale contents and assisted in completion. The exclusion criteria were patients younger than 25 years and uncooperative patients. Authors had access to information that could identify individual participants during data collection.

Clinical Assessment

All patients were assessed for CAD risk factors and body mass index (BMI), which was calculated by dividing the body weight (in kilograms) over the square of the height (in meters). Blood was drawn on the day of admission for random blood glucose testing. Smoking status was documented as current tobacco use or ex-smoker (if stopped >2 years).

Psychiatric Assessment

Psychiatric interviews were performed to assess the severity of PS, anxiety, depression, and sleep quality using the following scales:

PHQ-15: This 15-item scale assesses PS severity. Item scores were summed (score: 0–4, normal; >4, presence of PS).

PSQI: This 18-item scale assesses sleep quality. Item scores were summed (score: 0–4, normal; 5–9, mild sleeplessness; 10–15, moderate sleeplessness; >15, severe sleeplessness).

PHQ-9: This 9-item scale assesses depression severity. Item scores were summed (score: 0–4, normal; 5–9, mild anxiety; 10–15, moderate anxiety; >15, severe anxiety).

GAD-7: This 7-item scale assesses depression severity. Item scores were summed (score: 0–4, normal; 5–9, mild anxiety; 10–15, moderate anxiety; >15, severe anxiety).

Statistical Methods

SPSS 23.0 (IBM, Armonk, NY) was used for data analysis. Qualitative data were described as frequency (percentage). The chi-square test (categorical data) and nonparametric test (ranked data) were used to analyze qualitative data. Quantitative data that were normally distributed or approximately normally distributed were described using mean ± standard deviation, and the independent sample t-test was used for analysis. Nonnormally distributed quantitative data were described using median (lower quartile, upper quartile), and the nonparametric U test was used for analysis. Quantitative data were analyzed using Spearman's rank correlation to establish correlations among the four scales. Finally, PHQ15 (control = 0; case = 1) was used as the dependent variable; PSQI, GAD7, and PHQ9 were used as main effect variables, and variables that were statistically significant in the aforementioned univariate analysis were used as control variables in the regression formula to construct a multivariate logistic regression model. The odds ratio (OR) was used as the parameter for risk assessment. Subsequently, sex and age were used as subgroup variables for analysis to test differences in PSQI, GAD7, and PHQ9 between the two PHQ15 (control = 0; case = 1) groups. Furthermore, p < 0.05 was considered statistically significant, and a two-tailed test was used.

RESULTS

Descriptive Analysis

A total of 826 subjects were enrolled. Among these, 471 were male (57.02%) and 355 were female (42.98%). The oldest patient was 98 years old, the youngest was 25 years old, and the median age was 66 years. Seventy-five patients (9.09%) were aged 25–44 years, 353 (42.87%) were aged 45–64 years, and 397 patients (48.12%) were aged older than 65 years. Five patients were unmarried (0.61%), 790 patients were married (95.64%), and 31 patients were widowed (3.75%). A total of 288 patients (34.86%) had a history of smoking, whereas 240 patients (29.05%) had a history of alcohol drinking. Lastly, 520 patients (62.95%) had previous surgical histories (Table 1).

TABLE 1

Demographic Data, Risk Factors, and Psychiatric Test Results in Study Groups

		Frequency	%
Sex	Male	471	57.00
	Female	355	43.00
Age	25–44 y	75	9.10
	45–64 y	353	42.70
	>65 y	397	48.10
Surgical history	No	310	37.50
	Yes	514	62.20
Culture	Primary school or below	306	37.00
	Junior high school	389	47.10
	Senior high school	94	11.40
	University	37	4.50
Marital history	Unmarried	5	0.60
	Married	790	95.60
	Widowed	31	3.80
Smoking history	No	538	65.10
	Yes	288	34.90
Drinking history	No	586	70.90
	Yes	240	29.10
Previous history	No	170	20.60
	Yes	656	79.40
Family history	No	707	85.60
	Yes	118	14.30
Psychiatric history	No	776	93.90
	Yes	49	5.90
PHQ15	No	521	63.10
	Yes	305	36.90
PSQI	No	298	36.10
	Light	264	32.00
	Medium/heavy	263	31.80
GAD7	No	689	83.40
	Light	109	13.20
	Medium/heavy	27	3.30
PHQ9	No	693	83.90
	Light	110	13.30
	Medium/heavy	22	2.70

Baseline Characteristics

Demographic data and risk factors are summarized in Table 1. Sex, age, family history, and psychiatric history were higher in the case group. BMI, blood glucose, culture, marital status, smoking, drinking, and surgical history have no significant correlation with PS (Table 2).

TABLE 2

Correlation Between the Baseline Characteristics and Physical Symptoms

Atherosclerotic Risk Factors	PHQ15		t/Z/χ2	p
	Control (n = 521)	Case (n = 305)
Sex
Male	333 (63.9)	138 (45.2)	27.362	0.001
Female	188 (36.1)	167 (54.8)
Age
25–44	57 (10.9)	18 (5.9)	12.198	0.002
45–64	235 (45.1)	118 (38.8)
More than 65	229 (44.0)	168 (55.3)
BMI, kg/m2	23.94 ± 3.57	23.55 ± 3.44	1.63	0.103
Blood glucose	9.66 ± 2.82	9.66 ± 2.63	−0.017	0.986
Culture
Primary school or below	183 (35.1)	123 (40.3)	2.398	0.494
Junior high school	253 (48.6)	136 (44.6)
Senior high school	62 (11.9)	32 (10.5)
University	23 (4.4)	14 (4.6)
Marriage
Unmarried	2 (0.4)	3 (1.0)	1.201	0.554
Married	500 (96.0)	290 (95.1)
Widowed	19 (3.6)	12 (3.9)
Smoking history
No	329 (63.1)	209 (68.5)	2.449	0.118
Yes	192 (36.9)	96 (31.5)
Drinking history
No	361 (69.3)	225 (73.8)	1.874	0.171
Yes	160 (30.7)	80 (26.2)

Main Effect Analysis

PS patients had significantly higher PSQI, GAD7, and PHQ9I scores. Most of the patients had moderate to severe sleep disorders (n = 171, 56.3%) (Table 3).

TABLE 3

Correlation Between the Key Factors and Physical Symptoms

Atherosclerotic Risk Factors	PHQ15		t/Z/χ2	p
	Control (n = 521)	Case (n = 305)
Previous history
No	110 (21.1)	60 (19.7)	0.244	0.621
Yes	411 (78.9)	245 (80.3)
Family history
No	465 (89.3)	242 (79.6)	14.574	0.001
Yes	56 (10.7)	62 (20.4)
Psychiatric history
No	503 (96.5)	273 (89.8)	15.622	0.001
Yes	18 (3.5)	31 (10.2)
Surgical history
No	118 (36.2)	122 (40.0)	1.167	0.280
Yes	331 (63.8)	183 (60.0)
PSQI
No (0–4)	244 (46.8)	54 (17.8)	−11.316	0.001
Light (5–9)	185 (35.5)	79 (26.0)
Medium/heavy (≥10)	92 (17.7)	171 (56.3)
GAD7
No (0–4)	479 (91.9)	210 (69.1)	−8.584	0.001
Light (5–9)	36 (6.9)	73 (24.0)
Medium/heavy (≥10)	6 (1.2)	21 (6.9)
PHQ9
No (0–4)	490 (94.0)	203 (66.8)	−10.348	0.001
Light (5–9)	28 (5.4)	82 (27.0)
Medium/heavy (≥10)	3 (0.6)	19 (6.3)

Multivariate Analysis

A multivariate regression model using significant univariate variables (sex, age, family history, psychiatric history, PSQI, GAD7, and PHQ9) revealed that sex, age, family history, PSQI, GAD7, and PHQ9 were significant independent determinants of PS (χ2 = 221.102; p = 0.001). The “>65 years group” was an independent determinant of PS, whereas the “45–64 age group” was significantly associated but not an independent factor of PS (Table 4).

TABLE 4

Independent Predictors of Physical Symptoms

		β	OR	95% CI for OR	p
Sex	Male	0.000	1.000
	Female	0.350	1.419	1.011–1.991	0.043
Age	25–44	0.000	1.000
	45–64	0.591	1.805	0.892–3.654	0.101
	More than 65	0.954	2.597	1.291–5.222	0.007
Family history	No	0.000	1.000
	Yes	0.784	2.191	1.393–3.445	0.001
Psychiatric history	No	0.000	1.000
	No	−0.092	0.912	0.456–1.826	0.796
PSQI	No	0.000	1.000
	Mild	0.298	1.347	0.885–2.053	0.165
	Moderate/severe	1.510	4.528	2.931–6.996	0.001
GAD7	No	0.000	1.000		0.001
	Mild	0.877	2.405	1.433–4.036	0.001
	Moderate/severe	1.168	3.217	1.113–9.293	0.031
PHQ9	No	0.000	1.000
	Mild	1.100	3.005	1.762–5.126	0.001
	Moderate/severe	1.576	4.836	1.261–18.545	0.022

Subgroup Analysis

Sex Stratification

Sex was an independent determinant of PS. However, psychiatric disorders and sleep quality both significantly increased the PS positivity rate in both male and female patients (Table 5).

TABLE 5

Sex Stratification

	Male		Z	p	Female		Z	p
	PHQ15 = Control	PHQ15 = Case			PHQ15 = Control	PHQ15 = Case
PSQI	4.00 (2.00, 7.00)	10.00 (6.00, 13.00)	−9.083	0.001	6.00 (4.00, 10.00)	11.00 (7.00, 13.00)	−6.65	0.001
GAD7	0.00 (0.00, 1.00)	2.00 (0.00.5.00)	−7.637	0.001	1.00 (0.00, 2.75)	3.00 (1.00, 6.00)	−6.698	0.001
PHQ9	0.00 (0.00, 2.00)	3.00 (1.00.5.00)	−9.439	0.001	1.00 (0.00, 2.00)	4.00 (1.00, 6.00)	−8.766	0.001

Age Stratification

Although the “>65 years group” was the only independent determinant of PS, psychiatric disorders and sleep quality significantly increased the PS positivity rate in all age groups (Table 6).

TABLE 6

Age Stratification

	25–44 y		Z	p	45–64 y		Z	p	>65 y		Z	p
	PHQ15 = Control	PHQ15 = Case			PHQ15 = Control	PHQ15 = Case			PHQ15 = Control	PHQ15 = Case
PSQI	4.00 (2.00, 6.50)	9.50 (4.75, 12.25)	−3.571	0.001	5.00 (3.00, 8.00)	10.00 (6.00, 14.00)	−7.827	0.001	5.00 (3.00, 9.00)	11.00 (6.00, 13.00)	−8.049	0.001
GAD7	1.00 (0.00, 2.50)	6.50 (4.00, 11.25)	−4.843	0.001	0.00 (0.00, 2.00)	4.00 (1.00, 5.25)	−7.61	0.001	0.00 (0.00, 1.00)	2.00 (0.00, 4.00)	−6.986	0.001
PHQ9	1.00 (0.00, 2.00)	5.50 (4.00, 9.00)	−4.774	0.001	0.00 (0.00, 2.00)	3.00 (1.00, 7.00)	−9.425	0.001	0.00 (0.00, 2.00)	3.00 (1.00, 5.00)	−8.662	0.001

DISCUSSION

The present study showed that PS was significantly and independently associated with sex, age, and family history. Psychiatric history was significantly associated with but not independent risk factor of PS. The “>65 years group” (OR, 2.597; 95% confidence interval [CI], 1.291–5.222; p = 0.07) and “family history” (OR, 2.191; 95% CI, 1.393–3.445; p = 0.01) are the most important independent predictors of PS. In addition, female sex is also an important independent predictor of PS (OR, 1.419; 95% CI, 1.011–1.991; p = 0.043). Old age and family history are both traditional risk factors for CAD and both lead to risk of CAD exacerbation (Arad, 2002). In addition, severe disease often leads to a higher PS positivity rate. Most female CAD patients are menopausal women. Due to hormone fluctuations, they experience a higher incidence of psychiatric disorder compared with men (Gonda et al., 2008). At the same time, women are more sensitive to physical discomfort than men. This therefore leads to a higher PS positivity rate in female patients. These findings underscored the need for psychiatrists to intervene or treat patients with traditional high-risk factors more aggressively. These would be of great benefit to the improvement of CAD patients' condition.

In the present study, PS was also significantly associated with depression and anxiety, irrespective of sex and age. Moderate/severe depression (OR, 4.836; 95% CI, 1.261–18.545; p = 0.022) is the most important independent predictor for PS; every unit increase in the PHQ9 score is associated with a 4.836-fold increase in PS probability (p = 0.022). Mild anxiety (OR, 2.405; 95% CI, 1.433–4.036; p = 0.01) and moderate/severe anxiety (OR, 3.217; 95% CI, 1.113–9.293; p = 0.031) are also significantly associated with PS. These findings are concordant with the previous study: Many CAD patients experience anxiety, depression, and other psychiatric disorders. Anxiety and depression in patients will increase the PS positivity rate. In-patients usually worry about high medical fees and may develop anxiety, depression, and other emotional problems due to disease occurrence, progression, uncertain prognosis, and other factors. Previous studies also proved that PS are closely associated with anxiety and depression and are in fact common in anxious and depressed patients. In addition, the risk of developing anxiety and depression is increased by at least twofold in patients with PS (Zhang et al., 2018). Furthermore, patients with anxiety and depression tend to develop habits such as smoking, alcoholism, insomnia, and binge drinking and eating and exercise less due to distorted and erroneous thinking. This increases visceral fat and activates the hypothalamus-pituitary-adrenal axis that promotes the secretion of proinflammatory cytokines, thereby promoting a vicious cycle of atherosclerosis and ischemia (Brown et al., 2005; Capuron et al., 2008; Skilton et al., 2007). Studies also showed that persistent negative emotions may overstimulate the sympathetic nervous system, increase blood catecholamine and norepinephrine concentrations, and release many procoagulation factors and vasoconstrictors such as angiotensin II (Peters et al., 2018). These in turn exacerbate myocardial ischemia and cause myocardial infarction or even sudden death. Therefore, cardiologists need to focus not only on early psychological problems and the prompt identification of patients with atypical psychological problems but also on secondary psychological problems caused by cardiovascular disease factors.

Sleep quality is the second most important independent predictor for PS; in the “moderate/severe group,” every unit increase in the PSQI score is associated with a 4.5282-fold increase in PS probability (p = 0.001). In addition, sleep quality significantly increases PS probability in different sex and age groups. Sleep possibly affects cardiovascular pathophysiology through different methods, but the relationship between sleep duration and cardiovascular disease is still unclear. Endothelial cell dysfunction may be a marker of the causal relationship between sleep duration and cardiovascular risk. Endothelial dysfunction is usually the clinical presentation that precedes cardiovascular disease and indicates a potential mechanism in the relationship between cardiovascular disease and sleep quality. Sleep disorders may be a highly suggestive marker of endothelial cell dysfunction and is an important marker of cardiovascular disease risk (Hall et al., 2017). A study also showed that insufficient sleep or lack of sleep induces the spontaneous activation of innate immunity, signal transduction, and transcription factor activation, which are involved in the molecular signaling pathway of dynamic inflammation regulation and other immune responses ensuing from lack of sleep. Taken together, this cascade of reactions promotes a proinflammatory microenvironment that eventually leads to cardiovascular diseases (Irwin et al., 2015). Therefore, poor sleep in CAD patients will aggravate CAD, leading to an increase in the positive rate of related symptoms including PS. Similarly, poor sleep will also exacerbate anxiety and depression in patients and overlap with somatization symptoms in psychiatric disorders to further increase the positivity rate of PS in patients.

CONCLUSIONS

Psychiatric disorders, sleep quality, female sex, age, and family history are highly associated with PS in CAD patients. A significant correlation was found between PS severity and the severity of both psychiatric disorders and sleep quality. This guided psychiatrists to participate more actively in the treatment of CAD patients for specific patients. As the statistical results showed, psychiatrists should focus on anxiety, depression, and sleep therapy. Early detection and early intervention could significantly improve the clinical symptoms of CAD patients, which was conducive to the rehabilitation of patients. Further studies are warranted to explore the impact of psychological and sleep intervention on PS, so as to provide a theoretical basis for the relevant treatment.