Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

The Association between Sleep Duration and Coronary Artery Disease among American Adults in 2018

Jadthiel Oliva*, Marina Shehata, Asia Thomas, Fred Rudensky, Prince Adu Amoako

Department of Research, American University of Antigua College of Medicine, Osbourn, Antigua & Barbuda

Abstract: Context: Coronary artery disease (CAD) is the most prevalent form of heart disease in the United States. CAD has many modifiable risk factors, such as poor diet, smoking, and lack of exercise. Much like the factors above, short sleep duration is commonly reported by American adults. Objective: This study examines the association between sleep duration and CAD in American adults in 2018. Methods: We used data from adults 18 or older who participated in the 2018 Behavioral Risk Factor Surveillance System (BRFSS) cross-sectional study. The independent variable was sleep duration in hours (rounded up), and the categories were (≤5 hours, 6 hours, 7-8 hours, and ≥9 hours). The dependent variable was ever having MI, angina, or CAD as diagnosed by a healthcare professional. The frequency of CAD is presented as weighted sampling proportions. Uni- and multivariable logistic regression analysis were used to calculate unadjusted and adjusted odds ratio (OR) for CAD comparing selected characteristics and their corresponding 95% confidence interval (CI). STATA 16.1 was the statistical software we used. Results: Our sample included 421,983 participants. The reference value for sleep duration was between 7-8 hours. A higher percentage of participants who slept ≤5 hours (6.8% weighted) or ≥9 hours (7.5% weighted) experienced CAD as compared to those who slept 6 hours (4.1% weighted) or 7-8 hours (3.9% weighted). Logistic regression analysis indicates that individuals who slept ≤5 hours showed an increased odds of experiencing CAD based on OR 1.41 (95% CI 1.27-1.57) after adjusting for potential confounding variables. Furthermore, individuals who reported sleeping ≥9 hours also displayed increased odds of CAD (OR 1.21; 95% CI 1.03-1.42) after adjustment. The p-values for these categories were <0.001. Conclusion: Our study found that adults who slept 5 hours or less and those who slept 9 hours or more had an increased chance of experiencing coronary artery disease. This remained the case after adjusting for confounding variables such as age, sex, lifestyle, and comorbidities. These findings warrant further research regarding the association between sleep duration and coronary artery disease.

Keywords: acute myocardial infarction, sleep duration, chronic sleep deprivation

Received July 06, 2022; Revised August 09, 2022; Accepted August 16, 2022

Citation: Jadthiel Oliva, Marina Shehata, Asia Thomas, Fred Rudensky, Prince Adu Amoako, The Association between Sleep Duration and Coronary Artery Disease among American Adults in 2018. American Journal of Cardiovascular Disease Research, 8 (1). 37-48. January 2022. doi:10.12691/ajcdr-8-1-4

1. Introduction

The amount of sleep a person gets affects the general condition and well-being of patients in various ways. It is said that between 6 to 8 hours of sleep is considered healthy and beneficial for the body. On the other hand, studies have shown that lack of sleep alters judgment and may cause comorbidities. However, there is limited knowledge about its association with coronary heart disease, such as myocardial infarctions (MI). The risk factors for myocardial infarctions seem endless, but some theories suggest that sleep duration may play a role. This study explores the association between sleep deprivation and the risk of developing myocardial infarctions in adults. Establishing causality between sleep duration and MIs could have important implications for sleep-targeted interventions to reduce cardiovascular risk [1].

2. Backgrounds and Theoretical Framework (Literature Review)

Our search for peer-reviewed articles was completed via the American University of Antigua College of Medicine online library. The Pub sampled database was searched using terms deemed relevant to our focus. The terms used were: “Adults AND Sleep Deprivation AND Myocardial Infarction,” “Sleep Duration AND Myocardial infarction,” “Sleep AND Myocardial Infarction,” and “Sleep AND Coronary Artery Disease.” Slight variations were used to ensure a thorough search. The publication date window was limited to 1/1/2000-3/7/2021. Our search yielded 1,268 results, of which five articles were selected. Open access articles were located using Google Scholar.

Daghlas et al. (2019) [1] conducted a prospective cohort study design. 461,347 participants of the United Kingdom Biobank (UKB) who were free of any cardiovascular diseases were included in this study. The independent variable was sleep duration. This was assessed via the question regarding how many hours they sleep (including naps) in 24 hours, in which <6 hours is short and >9 hours is long. The dependent variable was the risk of developing a myocardial infarction (MI). The confounding variables that may be present include age, sex, pre-disposed conditions, psychological sleep disturbances, comorbidities, and CAD genetic risk score. This study concluded that compared to sleeping 6-9 hours per night, which is the recommended sleep time, short sleepers had a 20% increased risk of incident MI (Hazard ratio [HR] 1.20, 95% CI 1.07-1.33, p-value 0.001) and long sleepers had a 34% higher risk of incident MI (HR 1.34, 95% CI 1.13-1.58, p-value 0.0006). Healthy sleep reduces the MI risk even among those with a high genetic predisposition (HR 0.82, 95% CI 0.68-0.998). The Mendelian randomization (MR) method was consistent with a causal effect of short sleep duration on MI risk (HR 1.19, 95% CI 1.09-1.29). Overall, there were strengths and limitations within this study. The strengths: (1) The study considers the various confounders such as some comorbidities using the MR analysis; (2) A large sample size was utilized, which is essential for the validity of the study; and (3) the incorporation of analyzing genetics along with social factors (sleep duration in this case) emphasizes that social behaviors can influence specific comorbidities. On the contrary, the weaknesses are: (1) Self- reported hours of sleep which can lead to recall bias; (2) Selection of only healthy individuals leading to “collider bias;” (3) the generalizability to European ancestry; and finally (4) there was no information regarding loss to follow up with the UKB, which may lead to the misclassification of cases to controls.

Chandola et al. (2010)[2] conducted a prospective cohort study. It used questions from the General Health Questionnaire-30 on the Whitehall II cohort. The study included 10,308 participants (3413 women and 6895 men) from 20 civil service departments in London, England. Participants were between the ages of 35 and 55 years at baseline (1985-1988) and were followed up for an average of 15 years. Independent variables included sleep duration and sleep disturbance. The question about sleep duration asked about the number of hours of sleep they would have on a given average weeknight. The question about sleep disturbance included two questions related to sleep: Have you recently “lost much sleep over worry?” and “been having restless, disturbed nights.” The dependent variables include coronary heart disease. Coronary heart diseases were verified by using primary care and hospital records. Some confounding variables that may be present include age, sex, smoking history, alcohol consumption, diet, exercise frequency, and BMI. However, the study controlled for confounding variables, which increased the study’s validity. This study concluded that in both men and women, there is an association that shows an increase in CHD in those with short sleep hours and sleep disturbances (Relative risk [RR]:1.69, 95% Confidence Interval 1.41-2.03 in men, and RR:1.33, 95% CI 1.04-1.70 in women). After controlling for confounding variables, only sleep disturbances showed an increase in CHD (RR 1.49, 95% CI 1.22-1.82 in sleep disturbances, and RR 1.20, 95% CI 0.95-1.52 in short sleep duration). Finally, the highest risk of CHD (RR: 1.55, 95% CI 1.33-1.81) was found in participants with short sleep duration and restless, disturbed nights. The study used Cox regression models to estimate both HR and CI. The strengths of this study include (1) the incorporation of more women, which previous studies had failed to do, and (2) after controlling a different number of risk factors, disturbed sleep continued to be a positive predictor for CHD. On the contrary, potential weaknesses include (1) the self-reported nature of the data about sleep hours and sleep disturbances, (2) the vague definition of sleep disturbance, and (3) the lack of information on whether participants had sleep apnea.

Sabanayagam (2010) [3] conducted a cross-sectional study. The sample was 30,397 National Health Interview Survey 2005 participants ≥18 years of age (57.1% women). Independent variables included sleep duration categorized as ≤5 hours, 6 hours, 7 hours, 8 hours, and ≥9 hours. Dependent variables were cardiovascular diseases (angina, myocardial infarction, stroke). Confounding variables may include age, sex, smoking history, alcohol consumption, diet, exercise frequency, diabetes history, and BMI. This study concluded that there was a positive association between shorter and longer sleep hours and any CVD (Odds ratio [OR] 2.20, 95% CI 1.78-2.71 in shorter sleep hours and OR1.57, 95% CI 1.31-1.89 in longer). It also concludes that MI was positively associated with shorter and longer sleep hours in the multivariable-adjusted model. This was supported by OR 1.70, 95% CI 1.31-2.22 in shorter hours, and OR 1.30, 95% CI 1.02-1.60 in longer sleep hours. The strengths of this study include that (1) it provides similar findings to previous studies, in which there was a positive association between sleep duration and CVD; (2) a large sample size was used; and (3) information was provided regarding potential confounding variables. On the other hand, there were a few weaknesses: (1) The study being a cross-sectional study is a limitation itself, as it only provides data from a specific point in time, and (2) sleep duration was reported by the participants, in which this can lead to bias. Some side notes to consider: a multivariable logistic regression model was used to assess sleep duration and the risk of any CVD. Two more multivariable models were created. The first adjusted for age and sex, and the second adjusted for other confounding variables such as smoking, diabetes, and alcohol consumption. Finally, 7 hours of sleep duration was selected as the reference value, as previous studies have shown this to be the optimal sleep duration.

Nagai et al. (2010) [4] concluded that (1) there was a positive correlation between an increased risk for hypertension and decreased sleep duration. (2) The Nurses’ Health Study (2003) showed a U-shaped phenomenon between individuals having an increased risk of CHD by sleeping less than 5 hours nightly and more than 9 hours nightly. Finally (3), Liu et al. found an increased odds ratio of subjects with 5 or less hours of sleep compared to those who received 6-8 hours of sleep.

Laugsand et al. (2011) [6] concluded that the multi-adjusted HR for AMI was 1.45 (95% CI 1.18–1.80) for people with difficulties initiating sleep almost every night, 1.30 (95% CI 1.01–1.68) for those with problems maintaining sleep nearly every night, and 1.27 (95% CI 1.03–1.57) for those with a feeling of nonrestorative sleep more than once a week compared with people who never experienced these sleep difficulties.

After doing the literature review on the five articles, we could appreciate a positive direct association between chronic sleep deprivation and an increased risk of myocardial infarction. All the studies showed that short sleep duration is associated with an increased risk not only of myocardial infarction but in other cardiovascular diseases such as angina, stroke, etc. In the same way, throughout the reviewed literature, we could also appreciate a positive association between long sleep hours and an increase in myocardial infarction risk; the same was true for other cardiovascular diseases. Most of the studies could control confounding variables by stratifying for different confounding variables, which further improved the validity of the results.

3. Research Objectives and Hypothesis

3.1. Research Objective

This study examines the association between chronic sleep deprivation and CAD in American adults aged 18-65 in 2018.

3.2. Research Hypothesis

We hypothesize that American adults between 18-65 with chronic sleep deprivation will report increased odds of CAD.

3.3. Justification

The guidelines for the proposed amount of sleep for adults is seven or more hours per night [7]. However, many are unable to achieve this recommendation. Adults in Western countries often must work long hours, thus leading to a decreased amount of nightly sleep. Buell et al. cited “a prospective study in the United States showed that the standardized mortality ratio of CHD was highest among those who worked 67 hours or more a week”. Sleep deprivation can have adverse effects on cardiovascular health. Researchers have concluded that sleep deprivation may cause “increased sympathetic nervous system activity, heart rate, and vasoconstriction, as well as salt retention” [4]. Insufficient sleep may play a role in hypertension, thus leading to coronary heart disease. Lack of sleep has been associated with insulin resistance, weight gain, high blood pressure, and depression. Current public health policy recommends that there should be stricter regulation of work hours and schedules. However, this recommendation only applies to specific industries, such as aviation and medicine. There is a strong need for working hours to be more tightly regulated nationally because sleep deprivation is a public health issue that can lead to many deleterious effects on the human body, most specifically coronary heart disease.

4. Methods

4.1. Design

This study will utilize a cross-sectional design and consist of a secondary analysis of data obtained from the 2018 Behavioral Risk Factor Surveillance System (BRFSS).

4.2. Study Population

The BRFSS samples households and other non-institutional living facilities using a complex survey design that follows a multistage area probability sampling. The BRFSS is a state-based telephone survey coordinated by the Centers for Disease Control and Prevention (CDC). Self-reported information regarding chronic conditions and health risk behaviors is collected annually using telephone surveys in all 50 states, Washington DC, Guam, Puerto Rico, and the US Virgin Islands [8]. More than 400,000 adults complete the survey annually, making the BRFSS the largest telephone survey in the world [9]. Our study population will consist of U.S. adults because the BRFSS collects self-reported information from one adult (aged 18 years or over) from each randomly sampled household.

The main inclusion criteria are 18 years or older and participation in the 2018 BRFSS.

The main exclusion criteria for participants will be if they have missing data in any BRFSS items measuring the independent variable, dependent variables, and covariates relevant to this study. If participants gave one of the following responses to any of the relevant BRFSS items: “Don’t know/Not sure” or “Refused,” they will also be excluded.

4.3. Study Variables

4.3.1. Sleep Duration (Primary Independent Variable)

Sleep duration will be measured from the following question of the BRFSS: “On average, how many hours of sleep do you get in 24 hours?” Participants of the BRFSS survey were asked to provide a numerical value of the hours of sleep in 24 hours. According to Hirshkowitz et al., 7 to 9 hours for young adults and adults and 7 to 8 hours of sleep for older adults are considered appropriate [10]. The continuous variable sleep duration will be divided into four categories ≤5 hours, 6 hours, 7-8 hours, and ≥9 hours.

4.3.2. Outcome

The primary outcome of interest is a healthcare professional diagnosed with MI, angina, or coronary artery disease (CAD). The information will be obtained by asking: “Have you been told by a doctor, nurse, or another healthcare professional that you have had a heart attack, also called myocardial infarction, angina, or coronary artery disease?”

4.3.3. Possible Confounding Variables

Other covariates that will be included in the study are age indicated by the question “What is your age?”, sex indicated by “Indicate sex of respondent,” race/ethnicity asked by the following questions: “Are you Hispanic, Latino/a, or Spanish origin?” and “Which one or more of the following would you say is your race?”, health care access indicated by the following question: “Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?”, marital status indicated by “Are you married, divorced/separated, widowed, never married, member of an unmarried couple?”, employment status indicated by “Are you currently employed for wages or self-employed, out of work, student/homemaker/retired, unable to work?” smoking status asked by “Do you now smoke cigarettes every day, some days, or not at all?”, BMI question as Four-categories of Body Mass Index (BMI): Underweight (<18.5), Normal weight (18.5-25), Overweight (25-30), Obese (>30). Physical activity participation indicated by “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?” and comorbid chronic conditions as indicated by Has a doctor, nurse, or another health professional EVER told you that you had any of the following: Chronic Obstructive Pulmonary Disease or COPD, emphysema or chronic bronchitis, Diabetes?”

4.3.4. Statistical Analysis

We will go through four steps, and they are as follows: (1) Descriptive analysis (checking for missing data, checking frequency distributions, re-categorizing variables), (2) Bi-variate analysis: frequency distributions according to the primary exposure of sleep duration and the main outcome variable of CAD; chi-square tests will be used to check whether the variables are equally distributed or not according to main exposure/outcome variables), (3) Collinearity diagnostics (correlation analysis to check for collinearity), and (4) Unadjusted and adjusted logistic regression analysis will be used to calculate odds ratios and their corresponding 95% confidence interval. The statistical software we will use is STATA version 16.1.

4.4. Study Ethics

The study protocol will be presented to the Internal Review Board (IRB) of the Florida International University Herbert Wertheim College of Medicine for approval. This study is not considered a human subject study as it uses secondary de-identified data. No live persons will be involved in this study, with minimal risk of harm.

Table 1. Distribution of baseline characteristics of population by sleep duration

4.4.1. Sleep Duration and Age

Each sample was statistically significant, with a p-value of <0.001. Participants in the 18-39 age group had an increase in sleep duration with a weighted percentage of 40.8, in the 6-hours category, compared to those in this age group in the remaining sleep duration categories. Individuals in the 40-64 age group had an increase in sleep duration with a weighted percentage of 44 in the <5-hours category. The 65-79 age group had an increase in sleep duration with a weighted percent of 23.7 in the ≥9-hours category, contrasting with those in the other sleep duration categories, which had decreased amounts. The 80+ age group, similarly to the 65-79 age group, had an increase in sleep duration with a weighted percentage of 10.2 in the ≥9-hours category.

4.4.2. Sleep Duration and Sex

Males increased in the 6-hour sleep duration category, with a weighted percent of 50.2. Females increased in the ≥9-hours category with a weighted percentage of 54.6.

4.4.3. Sleep Duration and Race

The White Non-Hispanics had an increase in sleep duration, with a weighted percentage of 65.7 in the 7-8-hours category. In the Black Non-Hispanic group, there was an increase in sleep duration in the less than or equal to ≤5-hours category with a weighted percentage of 17.6. In the Hispanic race, there was an increase in sleep duration in the greater than or equal to ≥9-hours category, with a weighted percentage of 17.7. In the other Non-Hispanic group, there was an increase in sleep duration in the less than or equal to ≤5-hours category with a weighted percentage of 9.3.

Figure 1. Sex(percentage) by Sleep Duration category

Figure 2. Race(percentage) by Sleep Duration category

4.4.4. Sleep Duration and Employment Status

Employment status was also measured. In the employed group, the sleep duration was highest in the category of 6 hours, with a weighted percentage of 63.6. The out-of-work category had an elevated sleep duration in the ≥9-hours category, with a weighted percentage of 6.92. Students or persons who were retired followed the same trend as the out-of-work group, with high sleep duration in the ≥9-hours category, with a weighted percent of 45.1. Those who reported they could not work had an increase in sleep duration in the ≤5- hours category, with a weighted percentage of 16.1.

4.4.5. Sleep Duration and Medical Costs

In the group that could afford Medical Costs, the sleep duration was elevated in the ≤5-hours category, with a weighted percentage of 23.2. On the other hand, the group that could not afford Medical Costs had an increased sleep duration in the 7-8-hours category, with a weighted percentage of 90.1.

4.4.6. Sleep Duration and Marital Status

Five marital statuses were measured: Married, Divorced, Widowed, Never married, and Unmarried. The married group had an increased sleep duration in the 7-8-hours category, with a weighted percentage of 53.8. The divorced group had an elevated sleep duration in the ≤5-hours category, with a weighted percentage of 19.2. The widowed group had an increased sleep duration in the ≥9-hours category, with a weighted percentage of 12.2. The never-married participants had a high sleep duration in the ≤5-hours category, with a weighted percentage of 27.7. The Unmarried group had an increased sleep duration in the 6-hours category, with a weighted percentage of 5.0.

4.4.7. Sleep Duration and Smoking

Current everyday smokers had an elevated sleep duration in the ≤5-hours category, with a 19.3 weighted percentage. Current Some-day smokers followed the same trend as current every day smokers, having a higher sleep duration in the ≤5-hours category, with a weighted percentage of 7.3. Former smokers had an increased sleep duration in the ≥9-hours category, with a weighted percentage of 26.8. Never smokers had an increased sleep duration in the 7-8-hours category, with a weighted percentage of 63.4.

4.4.8. Sleep Duration and Exercise

Persons who exercise had an increased sleep duration in the 7-8-hours category, with a weighted percentage of 79. Participants who did not exercise had an elevated sleep duration in the ≥9-hours category, with a weighted percentage of 33.7.

4.4.9. Sleep Duration and Stroke

Participants who had strokes had a heightened sleep duration in the ≥9-hours category, with a weighted percentage of 5.97. Participants who did not have strokes had an elevated sleep duration in the 6-hour category, with a weighted percentage of 97.0.

4.4.10. Sleep Duration and COPD

Individuals with COPD had an increased sleep duration in the ≤5-hours category, with a weighted percent of 12.6. Persons who did not have COPD had an elevated sleep duration in the 7-8-hours category with a weighted percentage of 95.

4.4.11. Sleep Duration and Diabetes

Diabetic participants had a high sleep duration in the ≥9-hours category, with a weighted percentage of 16. Pre-diabetics had an increased sleep duration in the ≤5-hours category, with a weighted percentage of 2.4. Non-diabetics had an elevated sleep duration in the 7-8-hours category, with a weighted percent of 88.3.

4.4.12. Sleep Duration and BMI

Participants in the Underweight category had an increased sleep duration in the ≥9- hours category, with a weighted percentage of 2.73. Normal weight participants had an elevated sleep duration in the 7-8-hours category, with a weighted percentage of 34. Overweight participants had an elevated sleep duration in the 7-8-hour category, with a weighted percentage of 36.2. Obese participants had an increased sleep duration in the ≤5-hour category, with a weighted percentage of 37.9.

Table 2. Distribution of coronary artery disease according to exposure of interest and other covariates

Figure 3. CAD Percentage by Sleep Duration Category

4.4.13. CAD and Sleep Duration

A higher percentage of participants who slept ≤5 hours (6.8% (weighted)) or ≤9 hours (7.5% (weighted)) experienced CAD as compared to those who slept 6 hours (4.1%(weighted)) or 7-8 hours (3.9% (weighted)).

4.4.14. CAD and Age

The percentage of participants who experienced CAD was also seen within each subsequent age group from 0.855%(weighted) for participants in the 18-39 group to 14%(weighted) in the 80 above age group.

4.4.15. CAD and Sex

Males represented a more significant percentage of participants experiencing CAD (5.77% (weighted) vs. 3.42% (weighted) in females).

4.4.16. CAD and Race

The highest percentage of participants with CAD were in the White, non-Hispanic group (5%(weighted)) compared to other race groups.

4.4.17. CAD and Employment Status

Participants who reported being unable to work had the highest percentage of CAD (13.7% (weighted)), followed by students, homemakers, and retired participants (7.36% (weighted)).

4.4.18. CAD and Medical Cost

Participants who were not able to seek medical care due to cost also represented a higher percentage of participants with CAD as opposed to those who were able to seek medical care (5.73% (weighted) vs. 4.39% (weighted)).

4.4.19. CAD and Marital Status

Widowed participants (11.5% (weighted)) had the highest percentage of CAD.

4.4.20. CAD and Smoking

Former smokers (7.76% (weighted)) had the highest percentage of CAD.

4.4.21. CAD and Exercise

Those who didn’t report exercising had approximately twice as high a percentage of CAD as those who did report exercising (7.42% (weighted) vs. 3.65% (weighted)).

4.4.22. CAD and BMI

Obese, overweight, and underweight participants all had a higher percentage of CAD than participants with normal BMI, with obese patients having the highest rate (5.9%(weighted)).

4.4.23. CAD and Comorbidities

Regarding comorbidities, participants with a history of stroke had the highest percentage of CAD (28.3% (weighted)), followed by COPD (16.2% (weighted)), and finally, diabetes (14.3% (weighted)). The p-value for all categories was <0.001.

4.4.24. CAD and Sleep Duration

Individuals who reported a sleep duration of ≤5 hours had increased odds of coronary artery disease (CAD) based on the OR (95% CI), 1.81 (1.66-1.96) unadjusted, and 1.41 (1.27-1.57) after adjusting for potential confounding variables. One surprising result was seen in participants who reported 6 hours of sleep duration unadjusted. There was no significant association between the number of hours they slept and coronary artery disease; this was reflected by 1.06 (0.98-1.15). On the other hand, after adjusting for confounding variables these same participants showed increased odds of CAD because of 1.12 (1.02-1.23). Furthermore, individuals who reported a sleep duration of ≥9 hours showed high odds of CAD in both unadjusted and adjusted, 2.00 (1.78-2.24) and 1.21 (1.03-1.42), respectively. In this study, we could appreciate that more than 7-8 hours of sleep leads to increased odds of CAD.

Table 3. Association between coronary artery disease and sleep duration, and confounders

Figure 4. Odds of CAD by Sleep Duration Category. Reference value: 7-8 hours

4.4.25. CAD and Age

Compared to the reference age of 40-64, individuals aged 65-79 and 80+ had increased odds of CAD with 1.88 (1.69-2.10) and 2.71 (2.35-3.13), respectively.

4.4.26. CAD and Sex

Findings showed that women had decreased odds of CAD compared to men, with a 95% CI of 0.46 (0.43-0.50).

4.4.27. CAD and Comorbidities

Furthermore, participants who reported comorbid conditions such as stroke, COPD, and diabetes had increased odds of CAD.

4.4.28. Conclusion

This study demonstrated that adults who slept less or more than the recommended duration of 7-8 hours had increased odds of experiencing CAD. Individuals who slept ≤5 hours daily had 41% higher odds of having CAD. In the same way, individuals sleeping 6 hours a day had 12% higher odds of having CAD. Furthermore, those sleeping ≥9 hours daily had 21% higher odds of having CAD. We appreciated the increased odds of CAD with an increase in age. Compared to the reference age of 40-64, individuals aged 65-79 and 80+ had increased odds of 2 to 3 times more CAD. Also, women had about 40-50% decreased odds of CAD compared to men. Furthermore, participants with comorbid conditions such as stroke, COPD, and diabetes had increased odds of 2 to 3 times more CAD, with the highest among adults with a history of stroke. This was expected because those with pre-existing vascular conditions are at higher risk for more vascular events, such as CAD.

This study further validates the theory in previous studies that both short and long sleep duration increased the odds of CAD. One key difference between this study and previous ones is that we used 7-8 hours as the reference value for “proper sleep,” and other studies used 6-9 hours. Strengths in this study included (1) using 7-8 hours as the reference value because it allowed for the use of four sleep duration categories, giving our results a more in-depth evaluation of how sleep duration is associated with CAD, (2) Large sample size; (3) Analysis completed with and without age, race, employment, med-cost, marital status, smoking, exercise, BMI, stroke, COPD, and diabetes. Our study was limited by the self-reporting of sleep duration by the study participants. One unexpected finding was that current smokers who do not smoke daily had higher odds of CAD odds than current everyday smokers. In addition, another surprising result was that overweight adults had a 4% decrease in odds of CAD.

Our hypothesis was supported by the results obtained. It was shown that in American adults aged 18-65, there are increased odds of CAD found in those who sleep less or more than the recommended amount. This study demonstrates how important getting adequate sleep is. If an individual can achieve the optimal 7-8 hours of sleep daily, they will lead a lifestyle that will help them prevent CAD in the future. Further research, which would involve monitoring sleep quality, patterns, and duration under clinical supervision, or utilizing polysomnography, would assist in understanding the association between sleep duration and coronary artery disease.

Abbreviations

BRFSS: Behavioral Risk Factor Surveillance System

CAD: Coronary Artery Disease.

References

Appendix

Table A1. Inclusion and exclusion Criteria

Table A2. Variables in statistical analysis

[1]   | Daghlas I, Dashti HS, Lane J, Aragam KG, Rutter MK, Saxena R, Vetter C. Sleep Duration and Myocardial Infarction. J Am Coll Cardiol. 2019 Sep 10; 74(10): 1304-1314.
[2]   | Chandola T, Ferrie JE, Perski A, Akbaraly T, Marmot MG. The effect of short sleep duration on coronary heart disease risk is mostificant among those with sleep disturbance: a prospective study from the Whitehall II cohort. Sleep. 2010 Jun; 33(6): 739-44.
[3]   | Sabanayagam C, Shankar A. Sleep duration and cardiovascular disease: results from the National Health Interview Survey. Sleep. 2010 Aug; 33(8): 1037-42.
[4]   | Nagai M, Hoshide S, Kario K. Sleep duration as a risk factor for cardiovascular disease- a review of the recent literature. Curr Cardiol Rev. 2010; 6(1): 54-61.
[5]   | Buell P, Breslow L. Mortality from coronary heart disease in California men who work long hours. J Chronic Dis. 1960 Jun; 11: 615-26.
[6]   | Laugsand, L., Vatten, L., Platou, C. and Janszky, I., 2011. Insomnia and the Risk of Acute Myocardial Infarction. Circulation, 124(19), pp.2073-2081.

[7]  
Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, Malhotra RK, Martin JL, Patel SR, Quan SF, Tasali E. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med 2015; 11(6): 591-592.

[8]   | Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004-2011. BMC Med Res Methodol. 2013 Mar 24; 13: 49.
[9]   | Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. http://www.cdc.gov/brfss.

[10]  
Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N, Herman J, Katz ES, Kheirandish-Gozal L, Neubauer DN, O'Donnell AE, Ohayon M, Peever J, Rawding R, Sachdeva RC, Setters B, Vitiello MV, Ware JC, Adams Hillard PJ. National Sleep Foundation's sleep time duration recommendations: methodology and results summary.