July27Unitedkingdom  2021 

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Abstract Volume: 1 Issue: 3 ISSN:

Prevalence of Excessive Daytime Sleepiness and Risk Factors of Obstructive Sleep Apnea Among Type 2 Diabetes Mellitus at the Korle-Bu Teaching Hospital, Accra Ghana: A Pilot Study

Joseph Boateng Makae*

*Corresponding Author: Joseph Boateng Makae, Respiratory Therapist at Komfo Anokye Teaching Hospital Ashanti, Ghana.

Received Date:  October 20, 2020

Publication Date: November 01, 2020

 

Abstract

BACKGROUND:

Obstructive sleep apnea (OSA) is a breathing disorder of sleep that is gaining recognition in both developed and developing countries in recent years due to its associated morbidity and mortality worldwide. It contributes to the development of the cardiovascular disease, systemic hypertension and abnormalities in glucose metabolism. The relationship between OSA and Type 2 diabetes mellitus (T2DM) is bidirectional. The majority of studies on sleep-disordered breathing and T2DM have largely in developed countries hence, the need to explore the relationship between these conditions in developing countries like Ghana.

AIM:

This study aimed to determine the prevalence of excessive daytime sleepiness and the risk of obstructive sleep apnea among Type 2 diabetes mellitus patients attending the Korle-Bu Teaching Hospital (KBTH).

METHODS:

This study was a cross-sectional study. Telephone interviews were conducted on Type 2 diabetes mellitus patients attending the National Diabetic Management and Research Centre at the KBTH. These interviews were conducted to complete two validated questionnaires; the STOP-BANG questionnaire and the Epworth Sleepiness Scale (ESS) questionnaire which was used to assess the risk of OSA and the prevalence of excessive daytime sleepiness respectively. Patients’ demographic characteristics were also collected using a structured questionnaire and anthropometric measurement extracted from patients’ folders. The data was analyzed using SPSS version 22.0.

RESULTS:

The prevalence rate of excessive daytime sleepiness was high, 73.3% among the 60 Type 2 Diabetes patients who took part in the study. By STOP-BANG scores, patients who were at high and medium risk for obstructive sleep apnea were 15.0% and 65.0% respectively. However, a minority of the respondents had a low risk for OSA representing 20.0%. Combining patients with medium and high risk for OSA, the associated factors were found to be age > 55years, overweight, and obesity. Finally, correlation showed a significant linear relationship between STOP-BANG and ESS scores (r = 0.44; p < 0.01). This showed that there is a likelihood of T2DM patients having obstructive sleep apnea if they have excessive daytime sleepiness.

CONCLUSION:

The prevalence rate of excessive daytime sleepiness in T2DM patients was high as the compared lesser risk of obstructive sleep apnea. It can be concluded that there is a significant relationship between OSA and EDS in Type 2 Diabetes patients.

Prevalence of Excessive Daytime Sleepiness and Risk Factors of Obstructive Sleep Apnea Among Type 2 Diabetes Mellitus at the Korle-Bu Teaching Hospital, Accra Ghana: A Pilot Study

Introduction

1.1 BACKGROUND

Obstructive Sleep Apnea (OSA) refers to a form of sleep-disordered breathing which is characterized by recurring episodes of partial or complete obstruction of the upper airway during sleep resulting in repeated arousal and lack of restful sleep. OSA is associated with increased morbidity and mortality in the community. Notable clinical presentations of OSA include excessive daytime sleepiness, loud snoring, and observed pauses in a breath when asleep at night. Other symptoms include altered mental status, fatigue, loss of memory, restless sleep, gasping during sleep, and severe morning headaches. All these manifestations are a result of the frequent interruption of quality sleep during the night. There is the induction of nocturnal hypoxemia, hypercapnia, and sleep fragmentation due to recurrent episodes of airway obstruction of such patients (Kim et al., 2019). Evidence from previous studies suggests that obstructive sleep apnea influences the development of abnormalities in glucose metabolism (Punjabi & Polotsky, 2005), hypertension (Peppard et al., 2000), and cardiovascular disease (Peker et al., 2006). Both hospital and population-based investigation of OSA has revealed that about 50% of patients with OSA also have Type 2 Diabetes Mellitus (T2DM) and as much as 50% of patients with Type 2 Diabetes Mellitus have moderate-to-severe OSA (Resnick et al., 2003; Foster et al., 2009).

Type 2 Diabetes Mellitus is a condition characterized by an elevated concentration of glucose in the bloodstream (Cho et al., 2018). This is due to a deficiency in the production of insulin by the islet of Langerhans of the pancreas (WHO, 2018) or the destruction of insulin produced. T2DM is a complex disease that can be inherited or acquired through genetic mutation and also through environmental factors (Bais, 2005). T2DM poses macrovascular complications such as coronary artery disease and stroke (Yen, 2017) and also microvascular consequences that can affect the nervous system, kidney, and retina of the eye (Cho et al., 2018). Statistics show that there is a prevalence rate of 8.4% of diabetes mellitus globally and 3.8% in Ghana (IDF, 2017). Past studies investigating the relationship between OSA and T2DM have revealed a higher prevalence of OSA among T2DM patients even after adjusting for confounding variables like BMI and age.

 

1.2 PROBLEM STATEMENT

The association between Obstructive Sleep Apnea (OSA) and Diabetes Mellitus (DM) has raised public health concerns worldwide. Notwithstanding, the relationship between these two conditions has not been well understood in developing countries. According to the International Diabetes Federation (IDF), the estimated number of diabetes cases at the outpatient care setting in Ghana was 518,400 in the year 2017 (Primary Care Diabetes Europe: Colophon, 2017). Most of these patients mostly go undiagnosed for OSA and hence management of their condition is problematic. The presence of OSA in DM worsens

glycemic control and further contributes to DM-related cardiovascular complications. Despite the outstanding technological advancement to understand the bidirectional relationship between OSA and type 2 diabetes mellitus, few data are addressing the severity of the effect each condition has on the other (Moon et al., 2015).

A study conducted by Arosohn and colleagues in 2010 among 60 diabetes mellitus patients revealed that increasing severity of OSA was associated with poor glycemic control after adjusting for age, BMI, sex, race, number of diabetes medications, years of diabetes, total sleep, and physical exercise (Aronsohn et al., 2010). OSA and diabetes mellitus share common risk factors of age and obesity, which are also risk factors for cardiovascular disease. Predominantly, obesity is a prevalent risk factor. Studies have shown that a 10% increase in weight increases the risk of getting OSA by six-fold (Peppard, 2000). Hypoxaemia, evident in OSA has been shown to elevate inflammatory mediators in DM patients and this further worsens the condition of such patients. Even though OSA affects 24% of men and 9% of women, it is estimated that about 80 – 90% of patients are undiagnosed. (Young et al., 1997; Hussain et al., 2009). The public health burden of undiagnosed OSA cannot be underestimated due to its relationship with diabetes and cardiovascular diseases. Though, the implications OSA has on the management of T2DM has been elucidated in several studies (Hermans et al., 2009; Pillai et al., 2011) notwithstanding, OSA remains underdiagnosed and under-treated among individual populations with T2DM (West et al., 2006; Hermans et al., 2009; Pillai et al., 2011).

Additionally, the cost of management of DM is very high because of the comorbidities associated with it (Cho et al., 2018). It was therefore needful to investigate the risk of OSA and the prevalence of excessive daytime sleepiness in type 2 diabetes mellitus patients at the KorleBu Teaching Hospital using a questionnaire based approach.

 

1.3 SIGNIFICANCE OF STUDY

Given the comorbidities and complications associated with diabetes mellitus, patients are advised to adhere to management protocols. Early identification of modifiable risk factors of DM is very relevant in the prevention of long-term cardiovascular risks associated with DM (Go et al., 2017). Information from this study will help factor the treatment of OSA as part of the general management of T2DM. OSA is treatable using weight control and non-invasive ventilation with Continous Positive Airway Pressure (CPAP) device in T2DM patients. The knowledge obtained from this research will also allow relevant stakeholders of health to put preventive measures in place to curb the burden T2DM poses considering its association with sleep-disordered breathing. In effect, there would be a conservation of resources in terms of healthcare delivery. Information from this study will also serve as a reference for further studies for researchers investigating similar research questions.


1.4 AIM

This study aimed to determine the prevalence of excessive daytime sleepiness and the risk of obstructive sleep apnea among Type 2 Diabetes Mellitus patients attending the KorleBu Teaching Hospital (KBTH).


1.5 OBJECTIVES

The objectives of these studies were to:

1.Determine the presence of risk factors for OSA among T2DM patients at the KBTH.

2. Determine the prevalence of EDS among T2DM patients at the KBTH.

3.To determine the relationship between obstructive sleep apnea, excessive daytime sleepiness, and T2DM.

 

CHAPTER TWO

LITERATURE REVIEW

2.1 OBSTRUCTIVE SLEEP APNEA (OSA)

2.2 OBSTRUCTIVE SLEEP APNEA AND CARDIOVASCULAR DISEASE

2.3 CLASSIFICATION OF OBSTRUCTIVE SLEEP APNEA

2.4 RISK FACTORS FOR OSA

2.5 DIABETES MELLITUS (DM)

2.6 TYPE 2 DM

2.7 OBSTRUCTIVE SLEEP APNEA AND TYPE 2 DIABETES MELLITUS

2.8 ASSESSMENT AND DIAGNOSIS OF OBSTRUCTIVE SLEEP APNEA

2.9 MANAGEMENT AND TREATMENT OF OBSTRUCTIVE SLEEP APNEA
 

CHAPTER THREE

METHODOLOGY

3.1 STUDY DESIGN

3.2 STUDY SITE

3.3 PARTICIPANTS

3.4 SAMPLING PROCEDURE

3.5 SAMPLE SIZE DETERMINATION

3.6 INCLUSION CRITERIA

3.7 EXCLUSION CRITERIA

3.8 SAMPLE COLLECTION PROCEDURE

3.9 DATA MANAGEMENT PLAN

3.10 STATISTICAL ANALYSIS

3.11 ETHICAL APPROVAL

        3.12 DISSEMINATION OF RESULTS
 

CHAPTER  FOUR

RESULTS.

4.1 Introduction

4.2. Socio-Demographic Data

4.3. Observation of Patients’ Characteristics

4.4. Risk of OSA by STOP-BANG score

4.5. Assessment of Excessive Daytime Sleepiness (EDS)

4.6 Relationship Between OSA And EDS


CHAPTER FIVE

DISCUSSION

5.1 INTRODUCTION

      5.2.  PREVALENCE  OF  ESS  AND  RISK  FACTORS  FOR  OSA  AMONG   T2DM PATIENTS

5.3 CONCLUSION.

5.4 LIMITATIONS

5.5 RECOMMENDATIONS.


REFERENCES.

 

LIST OF FIGURES

FIGURE 1. A FLOW CHART OF RELATIONSHIP BETWEEN OSA AND DM

FIGURE 2. GENDER OF RESPONDENTS

FIGURE 3.CORRELATION BETWEEN STOP-BANG SCORES AND ESS SCORES


LIST OF TABLES

TABLE 1: COMPARISON BETWEEN OSA AND DM

TABLE 2: AGE GROUP OF PATIENTS

TABLE 3: EDUCATIONAL LEVEL OF PATIENTS

TABLE 4: BMI OF PATIENTS

TABLE 5: STOP-BANG SCORE OF PATIENTS

TABLE 6: BMI AND ABNORMAL STOP-BANG

TABLE 7: STOP RISK FACTORS IN STOP-BANG

TABLE 8: ESS SCORE OF PATIENTS

TABLE 9: SNORING AND ABNORMAL ESS SCORE

TABLE 10: ESS SCORE WITH NORMAL AND ABNORMAL STOP-BANG

TABLE 11: NORMAL VERSUS ABNORMAL STOP-BANG SCORES

TABLE 12: NORMAL VERSUS ABNORMAL ESS SCORES
 

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Volume 1 Issue 3 November 2020

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