
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-06722
00061
10.1097/MD.0000000000039698
3
3400
Research Article
Observational Study
Sleep disorders are closely associated with coronary heart disease in US adults (≥20 years): A cross-sectional study
Gan Tian-Ming BS a
Yang Ying-Jie BS a
Mo Guan-Lian MS a
Wang Shi-Rong MS 915692420@qq.com
a
Li Shu-Hu MS 17834968@qq.com
a
https://orcid.org/0000-0002-8414-4866
Li Jin-Yi MD a*
a Department of Cardiology, Affiliated Hospital of Guilin Medical University, Guilin, China.
* Correspondence: Jin-Yi Li, Department of Cardiology, Affiliated Hospital of Guilin Medical University, Guilin, 541001, China (e-mail: 17834968@qq.com).
13 9 2024
13 9 2024
103 37 e3969817 6 2024
15 8 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

The purpose of this research was to assess the association between sleep disorders and coronary heart disease (CHD) using data from the National Health and Nutrition Examination Survey (NHANES) database. This cross-sectional study included 9886 eligible participants with valid data on sleep disorders and CHD from the NHANES from 2011 to 2014. The complex NHANES sampling led to use of sample weights in analyses. Various statistical methods and covariates were utilized. Significance was set at P < .05. Receiver operating characteristic curves were used to assess the diagnostic efficacy of sleep disorders in relation to CHD. Sleep disorders were significantly associated with CHD (P < .001). In the model corrected for age, sex, race, hypertension, diabetes, and uric acid as covariates, sleep disorders and CHD remained significantly associated (P < .001, odds ratio = 1.83 [95% confidence interval: 1.31–2.58]). The correlation between sleep disorders and CHD varies by age and gender. Sleep disorders have some predictive value for CHD (0.5 < area under curve ≤ 0.7). Sleep disorders were associated with and predictive of CHD risk, warranting consideration in clinical assessments.

cardiovascular disease
coronary heart disease
sleep disorders
National Natural Science Foundation of China 10.13039/501100001809 82160077 Jinyi LiSelf-Funded Scientific Research Project of Guangxi Health DepartmentZ20211177 Jinyi LiGeneral Program of Natural Science Foundation of Guangxi Province of China2017GXNSFAA198129 Jinyi LiKey Project of Scientific Research and Technology Development of Qingxiu District of Nanning, Guangxi government2017027 Jinyi LiOPEN-ACCESSTRUE
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pmc1. Introduction

Sleep is integral in maintaining human health and plays a critical role in various physiological functions, including cardiovascular health preservation.[1] Sleep disorders have emerged as a significant risk factor for cardiovascular disease (CVD) in recent times, sparking interest in investigating their connection with conditions like coronary heart disease (CHD) and other CVDs.[2]

Classified under 7 main categories in the International Classification of Sleep Disorders, Third Edition, sleep disorders range from insomnia and sleep-related breathing disorders to circadian rhythm disruptions and sleep-related movement issues.[3] Of these, insomnia, the most prevalent sleep disorder, is closely linked to heightened risks of CVD and mortality.[4,5] Typically characterized by difficulties initiating or maintaining sleep, insomnia often presents symptoms like irritability and fatigue upon waking. Its increasing prevalence raises concerns given the adverse effects on health and performance,[6] highlighting the urgent need for further research and intervention strategies to address this widespread health issue.

Insomnia is a common issue among outpatients with CHD, often associated with psychological, lifestyle factors, and underlying inflammation. Identifying and intervening in patients with insomnia is crucial due to its connections to various cardiovascular risk factors, warranting further investigation into its impact on cardiovascular outcomes in individuals with coronary artery disease (CAD).[7] Recognizing and addressing insomnia in patients with heart conditions is essential.[8] Understanding the interplay between sleep disorders and CVD, especially in the context of CHD, is vital for susceptible populations. While research on sleep disorders remains limited, this study presents findings from a cross-sectional analysis that explores the association between sleep disorders and CHD using National Health and Nutrition Examination Survey (NHANES) data.

2. Methods

2.1. Data collection

We investigated the correlation between sleep disorders and CHD by utilizing data from the NHANES. This research dataset is intended to evaluate the health and nutritional status of the U.S. population and is gathered using a multifaceted, multi-stage probability sampling design. On average, the NHANES surveys 5000 individuals biennially. After data cleaning and removal of missing samples, NHANES (2011–2014) was chosen as the preliminary dataset for analysis. Written informed consent was obtained from all study participants. No ethical review was necessary owing to the public availability of NHANES. Our analysis focused on the 2 survey cycles of NHANES, conducted from 2011 to 2014. We utilized demographic and laboratory data, which encompasses uric acid files, along with questionnaire data consisting of blood pressure, cholesterol, diabetes mellitus, medical conditions, and sleep disorder files published in a 2-year cycle. The research involved 2 interview cycles spanning the years 2011–2012 and 2013–2014, and a total of 19,931 individuals participated in NHANES 2011–2014. The exclusion criteria excluded individuals under the age of 20, and participants with incomplete or unreliable data. Participants under the age of 20 were excluded from the study (n = 8602). Those with incomplete or unreliable records of sleep disorders, CHD, hypertension, diabetes, and uric acid were also excluded (n = 1443). In the end, a total of 9886 eligible participants were included in the study.

2.2. Variables

We explored the relationship between sleep disorders and CHD. We identified these conditions from participants’ questionnaire data. The dependent variable CHD was defined according to NHANES survey questions. Medical condition documentation was used to identify CHD. The question was, “Ever told you had coronary heart disease?.” We categorized this variable as a dichotomous variable, i.e., 1 = yes, 0 = no.

Our primary independent variable was sleep disturbance. The independent variable sleep disorder was defined based on the NHANES survey question. Question, “Ever told by doctor have sleep disorder?”. We categorized this variable as a dichotomous variable, i.e., 1 = yes, 0 = no.

The covariates used were age, gender, race, hypertension, diabetes, and uric acid. Age, gender, and race were available from the demographic file. Using the blood pressure and cholesterol files from the questionnaire data for determining hypertension and using the diabetes file for determining diabetes, the questions “Ever told you had high blood pressure?” and “Doctor told you have diabetes?” categorized these 2 variables as dichotomous, i.e., 1 = yes, 0 = no. Use the standard biochemistry files from the laboratory data to determine the uric acid laboratory results. Uric acid is a continuous variable.

2.3. Data analysis

Percentages (%) were used to express categorical variables. For continuous variables, those conforming to a normal distribution were expressed as mean and standard deviation, and skewed distribution variables were expressed as median and interquartile spacing. Because of the complex sampling characteristics of NHANES, we used NHANES sample weights for Wilcoxon rank-sum tests for complex survey samples, chi-square tests with Rao & Scott second-order correction, and logistic regression analyses to obtain nationally representative estimates. Covariates (age, sex, race, hypertension, diabetes, uric acid) were also included in the model. Odds ratios (ORs) and 95% confidence intervals (CIs) and P values were calculated, and P < .05 was considered statistically significant. In addition, we constructed receiver operating characteristic curves to further evaluate the diagnostic value of sleep disorders in CHD.

3. Results

3.1. Sample selection results

A total of 19,931 individuals participated in NHANES 2011–2014. The screening adhered to the exclusion criteria, and ultimately, 9886 qualified participants were included in this study. The screening process of eligible participants of NHANES 2011–2014 is presented in Figure 1.

Figure 1. Diagram showing the screening process of eligible participants for NHANES 2011–2014. NHANES = National Health and Nutrition Examination Survey.

3.2. Characteristics of study participants at the baseline

At the baseline level of the study, 9886 participants were enrolled, 4805 males and 5081 females. Differences in baseline demographics and clinical data were observed between the CHD and non-CHD groups (Table 1). The CHD group had a greater proportion of older adults (78% vs 26%, P < .001), males (61% vs 48%, P = .001), individuals with sleep disorders (23% vs 9%, P < .001), hypertension (74% vs 31%, P < .001), and diabetes (37% vs 8.9%, P < .001) compared to the non-CHD group. Furthermore, uric acid levels were significantly higher in the coronary group compared to the non-coronary group (5.90 vs 5.30, P < .001).

Table 1 Baseline characteristics of NHANES-eligible participants, 2011–2014.

Variables	Overall
N = 9886 (100%)	Coronary heart disease
N = 360 (3%)	Non-coronary heart disease
N = 9526 (97%)	P value	
Age, n (%)				<.001	
 ≥60 yr	3185 (28)	289 (78)	2896 (26)		
 20–59 yr	6701 (72)	71 (22)	6630 (74)		
Sex, n (%)				.001	
 Female	5081 (52)	135 (39)	4946 (52)		
 Male	4805 (48)	225 (61)	4580 (48)		
Race, n (%)				<.001	
 Mexican American	1161 (8.5)	23 (3.5)	1138 (8.7)		
 Other Hispanic	940 (6.1)	25 (3.6)	915 (6.1)		
 Non-Hispanic White	4026 (67)	222 (81)	3804 (66)		
 Non-Hispanic Black	2217 (11)	57 (7.0)	2160 (11)		
 Other	1542 (7.8)	33 (4.8)	1509 (7.9)		
High blood pressure, n (%)				<.001	
 Yes	3513 (33)	283 (74)	3230 (31)		
 No	6373 (67)	77 (26)	6296 (69)		
Diabetes, n (%)				<.001	
 Yes	1265 (9.8)	145 (37)	1120 (8.9)		
 No	8621 (90)	215 (63)	8406 (91)		
Sleep disorders, n (%)				<.001	
 Yes	892 (9.5)	74 (23)	818 (9.0)		
 No	8994 (91)	286 (77)	8708 (91)		
Uric acid, mg/dL	5.30 (4.40, 6.30)	5.90 (5.00, 6.90)	5.30 (4.40, 6.20)	<.001	
Median (interquartile range [IQR]); n (unweighted) (%). Wilcoxon rank-sum test for complex survey samples; chi-squared test with Rao & Scott’s second-order correction. For categorical variables, data are expressed as counts (percentages); for continuous variables, mean and standard deviation if normally distributed; median and IQR if not normally distributed.

3.3. The association between sleep disorders and CHD

Figure 2 shows the results of logistic regression analysis of sleep disorders and CHD before and after adjusting for covariates. In Model 1, sleep disorders were significantly associated with CHD (P < .001, OR = 3.09 [95% CI: 2.31–4.12]). In Model 2, the correlation remained significant after adjusting for age, sex, and race (P < .001, OR = 2.60 [95% CI: 1.82–3.70]). In Model 3, after adjusting for age, sex, race, and hypertension, the correlation remained significant (P < .001, OR = 2.28 [95% CI: 1.64–3.19]). In Model 4, after adjusting for age, sex, race, hypertension, and diabetes, the correlation remained significant (P < .001, OR = 1.90 [95% CI: 1.34–2.70]). In Model 5, after adjusting for age, sex, race, hypertension, diabetes, and uric acid, the correlation remained significant (P < .001, OR = 1.86 [95% CI: 1.31–2.62]). When stratified by age, this association was not significant in age ≥60 years (OR = 1.56, 95% CI: 0.93–2.61, Model 4) and (OR = 1.50, 95% CI: 0.88–2.54, Model 5). Although not always statistically significant, sleep disorders were consistently more strongly associated with CHD in participants aged 20 to 59 years compared with those aged ≥60 years in all 5 models. When stratified by gender, the correlation of sleep disorders to CHD was consistently stronger in female participants compared with male participants in the remaining 4 models, except for Model 1. The correlation between sleep disorders and CHD varied by age and sex (Table 2). Receiver operating characteristic curves were constructed to further evaluate the diagnostic value of sleep disorders on CHD. Figure 3 shows that sleep disorders have some predictive value for CHD compared with other covariates (0.5 < area under curve [AUC] ≤ 0.7, P < .001). The AUC for sleep disorders falls below that of age, hypertension, diabetes, and uric acid, likely stemming from the diverse spectrum of sleep disorders, each potentially contributing unique influences to the predictive model. Notwithstanding the modest AUC value observed for sleep disorders in this study, its pivotal role as a predictive factor remains undeniable and deserving of attention.

Table 2 Association between sleep disorders and CHD by age and gender.

	Model 1	Model 2	Model 3	Model 4	Model 5	
Stratified by age				
 20–59 yr	5.69 (2.67–12.1), P < .001	5.57 (2.58–12.0), P < .001	4.05 (1.76–9.30), P = .002	3.20 (1.41–7.24), P = .007	3.31 (1.45–7.57), P = .006	
 ≥60 yr	2.10 (1.31–3.36), P = .003	1.99 (1.20, 3.28), P = .009	1.85 (1.13–3.02), P = .016	1.56 (0.93–2.61), P = .092	1.50 (0.88–2.54), P = .13	
Stratified by gender				
 Male	3.08 (1.99–4.76), P < .001	2.54 (1.54–4.17), P < .001	2.23 (1.38–3.62), P = .002	1.89 (1.15–3.11), P = .014	1.89 (1.16–3.08), P = .013	
 Female	2.95 (1.89–4.61), P < .001	2.70 (1.66–4.41), P < .001	2.37 (1.50–3.73), P < .001	2.06 (1.29–3.27), P = .004	2.00 (1.24–3.23), P = .006	
In the subgroup analysis, the model was not adjusted for the stratification variable itself. Figures are expressed as odds ratio (95% confidence interval) and P value. Model 1: unadjusted. Model 2: Adjusted for age, gender, and race. Model 3: Further adjusted for high blood pressure. Model 4: Further adjusted for diabetes. Model 5: Further adjusted for uric acid. P values that are bolded signify the corresponding results are not statistically significant.

CHD = coronary heart disease.

Figure 2. Logistic regression analysis of sleep disorders and CHD. CHD = coronary heart disease, CI = confidence interval, OR = odds ratio.

Figure 3. ROC curve of sleep disorders and CHD. AUC = area under curve, CHD = coronary heart disease, CI = confidence interval, ROC = receiver operating characteristic curve.

In conclusion, there is a notable correlation between sleep disorders and CHD, with sleep disorders serving as a risk factor for the latter. This correlation was confirmed even after adjusting for covariates such as age, gender, race, hypertension, diabetes, and uric acid. The correlation between sleep disorders and CHD varies by age and gender. Furthermore, sleep disorders demonstrate some predictive value for CHD.

4. Discussion

Numerous studies have established the correlation between sleep disorders and CVDs, particularly CHD. Sofi et al[2] demonstrated that sleep disorders can contribute to increased morbidity and mortality associated with CVD. Li et al[4] also highlighted that insomnia significantly raises the risk of adverse cardiovascular events and mortality. Additionally, Bertisch et al[9] identified a connection between insomnia and a heightened occurrence of CVD. Several studies, including those by Zhuang et al[10] and Laugsand et al,[11] have further reinforced the association between sleep disorders and increased risk of CVDs such as CHD, acute myocardial infarction (MI), and stroke.[10–12] Frøjd et al[8] documented that insomnia is linked to a greater risk of recurrence of major adverse cardiovascular events. Moreover, Madsen et al[13] observed that patients with CAD often experience sleep disorders, particularly during acute coronary events. While sleep disorders may normalize post-acute coronary syndrome, the long-term trajectory remains uncertain due to limitations in current research. Notably, individuals with more severe ischemic disorders may have a higher prevalence of sleep disorders.[13] Collectively, these studies emphasize the significance of recognizing and addressing sleep disorders as a crucial risk factor for CVDs like CHD.

The duration of sleep is recognized to influence the occurrence of cardiovascular events. Adequate sleep duration has been associated with a reduced risk of CVD.[14] Daghlas et al[15] found that short sleep duration may increase the risk of MI and revealed that maintaining a healthy sleep duration can mitigate this risk, particularly in individuals with a high genetic predisposition to MI. There is evidence suggesting that sleep duration can be a predictor or indicator of adverse cardiovascular outcomes. Individuals who consistently sleep less than 5 hours nightly are at a higher risk of CVD morbidity and mortality. Short sleep duration has been associated with increased risks of CHD and stroke compared to individuals sleeping 7 to 8 hours per night. Conversely, individuals with prolonged sleep duration also show an elevated risk of these events, indicating a U-shaped relationship between sleep duration and adverse cardiovascular outcomes.[16] Similarly, Kwok et al[17] reported that mortality and adverse cardiovascular events are more prevalent when sleep duration deviates from the recommended 7 to 8 hours, showing a J-shaped correlation with mortality in adverse cardiovascular events. Individuals with longer sleep duration may be at a higher risk of adverse outcomes compared to those with shorter sleep duration, emphasizing the importance of considering both sleep duration and quality in patient counseling.[17] According to Fan et al,[18] adhering to a healthy sleep pattern, including early bedtime, achieving 7 to 8 hours of nightly sleep, rare or no occurrences of insomnia, absence of snoring, and infrequent daytime hypersomnolence, is linked to a reduced risk of CVDs, CHD, and stroke.

Sleep disorders pose a significant risk for CVD across all age groups. Zheng et al[19] found that insomnia symptoms independently contribute to the development of CVD, ischemic heart disease, and ischemic stroke, with potential influences from hypertension and age. Early identification and treatment of insomnia symptoms could reduce CVD risk, particularly among young individuals and adults without hypertension.[19] Grandner et al[1] discovered a link between sleep disorders in children and increased CVD risk, emphasizing the importance of addressing modifiable factors such as short sleep duration and sleep breathing disorders to mitigate CVD risk. Laugsand et al[11] highlighted insomnia as a prevalent and treatable condition that, when evaluated, can provide valuable data for clinical risk assessment to prevent CVD. Further research is needed to explore the associated risks and possible underlying mechanisms of insomnia.[11]

Insomnia is prevalent among patients in CHD clinics and is associated with psychological, lifestyle factors, and subclinical inflammation. Due to its close connection with cardiovascular risk factors, investigating the relationship between insomnia and cardiovascular outcomes in individuals with CAD is critical for early identification and intervention. Recognizing the importance of identifying individuals with insomnia and providing appropriate interventions to improve sleep quality is crucial.[7] Utilizing a comprehensive approach encompassing medical, psychological, and behavioral interventions may prove effective in enhancing quality of life and reducing the risk of CVD in patients with insomnia.[12] Research by Sarkar et al[20] highlights the high prevalence of obstructive sleep apnea among patients with CVD, in addition to insomnia, as a common form of sleep-disordered breathing (SDB). Untreated obstructive sleep apnea is associated with the development of hypertension, stroke, CAD, and heart failure.[20] Li et al[21] revealed associations between SDB, insomnia, and hypertension development. The findings emphasize the significance of SDB as a disease prevention target.[21]

Shift work sleep disorder and jet lag sleep disorder are categorized as specific types of sleep disorders. Individuals, notably healthcare professionals, who engage in night shifts or experience jet lag are at an increased risk of developing these sleep disorders. The presence of such disorders can heighten the susceptibility to CVD.[22] Jankowiak et al[23] observed harmful changes in the atherosclerotic process associated with night work, with the duration of night shifts playing a significant role, potentially influenced by lifestyle and individual characteristics. Gu et al[24] reported a modest increase in all-cause mortality and CVD mortality among women working consecutive night shifts for 5 years, underscoring the adverse health effects of night shift work. Torquati et al[25] found a progressively elevated risk of morbidity and mortality from CVD in shift workers, emphasizing the need for protective measures to mitigate CVD risks among this population. Teixeira et al[26] identified reduced antioxidant defenses and higher oxidative stress levels in night shift workers, suggesting the importance of implementing changes in work conditions and lifestyle practices to enhance worker health and well-being. Further research by Wang et al[27] demonstrated the correlation between nighttime employment and increased risks of atrial fibrillation and CHD, highlighting the need to consider reducing the frequency and duration of night shifts to protect heart health.

Sleep disorders have been associated with compromised bodily functions and an increased susceptibility to CVD, underscoring the importance of recognizing sleep as a multidimensional concept for advancing the prevention and treatment of cardiovascular conditions.[28] However, this study has certain limitations that merit acknowledgment. The cross-sectional nature of the NHANES survey in the United States presents challenges in tracking changes over time and establishing causality due to the lack of longitudinal follow-up data. Further research is necessary to determine a causal relationship between sleep disorders and CHD, given the retrospective design of the study. Additionally, the presence of recall bias, inherent in data obtained through questionnaires, highlights the need for cautious interpretation and opens avenues for future studies to address these limitations and strengthen the evidence base in this field.

5. Conclusions

In conclusion, there is a notable correlation between sleep disorders and CHD, with sleep disorders serving as a risk factor for the latter. The study highlights a strong connection between sleep disorders and CHD, emphasizing the significance of sleep disorders as a risk factor for CHD. The correlation between sleep disorders and CHD varies by age and gender, emphasizing the importance of a detailed understanding of these associations among healthcare providers for effectively managing the heightened risk of CHD linked to sleep disorders.

Author contributions

Conceptualization: Tian-Ming Gan.

Data curation: Tian-Ming Gan, Guan-Lian Mo, Shu-Hu Li.

Investigation: Tian-Ming Gan, Ying-Jie Yang, Shi-Rong Wang, Jin-Yi Li.

Writing—original draft: Tian-Ming Gan.

Formal analysis: Ying-Jie Yang, Shi-Rong Wang, Shu-Hu Li, Jin-Yi Li.

Writing—review & editing: Ying-Jie Yang, Jin-Yi Li.

Methodology: Guan-Lian Mo, Shi-Rong Wang.

Validation: Guan-Lian Mo.

Funding acquisition: Jin-Yi Li.

Supervision: Jin-Yi Li.

Abbreviations:

CAD coronary artery disease

CHD coronary heart disease

CVD cardiovascular disease

MI myocardial infarction

NHANES National Health and Nutrition Examination Survey

SDB sleep-disordered breathing

This study was supported by the National Natural Science Foundation of China (Grant No. 82160077), the Self-Funded Scientific Research Project of Guangxi Health Department (Grant No. Z20211177), the General Program of Natural Science Foundation of Guangxi Province of China (Grant No. 2017GXNSFAA198129), and the Key Project of Scientific Research and Technology Development of Qingxiu District of Nanning, Guangxi government (Grant No. 2017027).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Gan T-M, Yang Y-J, Mo G-L, Wang S-R, Li S-H, Li J-Y. Sleep disorders are closely associated with coronary heart disease in US adults (≥20 years): A cross-sectional study. Medicine 2024;103:37(e39698).

T-MG and Y-JY contributed equally to this work.
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