INTRODUCTION
Obstructive sleep apnea (OSA) is a common yet underdiagnosed sleep disorder characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to intermittent hypoxia, sleep fragmentation, and significant disruption of sleep architecture [1]. Clinically, OSA manifests as excessive daytime sleepiness (EDS), loud snoring, and witnessed apneas, which collectively impair cognitive function, cardiovascular health, and overall quality of life. The pathophysiology of OSA is multifactorial, involving anatomical abnormalities, neuromuscular dysfunction, and systemic factors such as obesity and hormonal changes [2,3].
Diagnosis of OSA relies on advanced techniques, including polysomnography (PSG) and home sleep apnea testing, whereas management strategies include continuous positive airway pressure therapy [4], oral appliances [5], and surgical interventions [6]. Effective treatment requires a multidisciplinary approach that integrates the expertise of pulmonologists, sleep specialists, otorhinolaryngologists, and other healthcare professionals to optimize patient outcomes [7].
However, this collaborative OSA management approach differs significantly among sleep centers. In Europe, the diversity of diagnostic approaches for OSA and treatment decisions is significant [8]. Nevertheless, the primary criterion for initiating treatment is the apnea-hypopnea index (AHI). Other centers may also consider patient age, symptoms, associated comorbidities, oxygen desaturation, and hypoxic burden when making treatment decisions [9]. The variability in diagnostic algorithms and treatment options may reflect insufficient clinical guidance and limited outcome data of OSA across different sleep centers. A data registry can be useful for identifying different clinical approaches to the diagnosis and management of OSA and for tracking the progress of treatment effectiveness and clinical outcomes [10].
The primary aim of this study was to comprehensively characterize the clinical, demographic, and polysomnographic profiles of Egyptian patients diagnosed with OSA between 2022 and 2024, as documented in the Sleep Breathing Disorders Cairo University Registry (SBDCUR). Specifically, this study sought to identify the independent predictors of OSA severity, with a focus on distinguishing mild from moderateto-severe cases using binary logistic regression. Associations between key clinical variables (e.g., body mass index [BMI] and Epworth Sleepiness Scale [ESS]) and polysomnographic parameters (e.g., AHI and oxygen desaturation index [ODI]) were explored. Moreover, sex-specific differences in OSA presentation, comorbidities, and severity were investigated to provide insights into the unique characteristics of the Egyptian population. This registry represents a single-center, retrospective, cross-sectional study that provides foundational data to inform tailored diagnostic and therapeutic strategies, improve patient outcomes, and contribute to a broader understanding of OSA in diverse populations.
METHODS
Ethical approval
The SBDCUR protocol was approved by the Ethics and Scientific Committee of Cairo University Hospital (N-20-2025). Patients voluntarily participated in the study after receiving both oral and written information about the SBDCUR. However, according to the Ethics and Scientific Committee of Cairo University Hospital, no signed informed consent was obtained for participation in the registry. All patients had the right to withdraw at any time without affecting their management or follow-up care. The confidentiality of the patient information and collected data was maintained in accordance with the ethical principles of the Declaration of Helsinki.
Study design and population
The SBDCUR is a single-center, retrospective, cross-sectional study designed to collect and analyze clinical, demodemographic, and polysomnographic data from Egyptian patients diagnosed with OSA between 2022 and 2024. Patients were recruited from the Sleep Breathing Disorders Unit, Department of Pulmonology, Cairo University, Cairo, Egypt. Only clinical data from confirmed cases of OSA based on AHI were included in the SBDCUR as shown in Fig. 1. The registry included adult patients aged 18 years with a confirmed OSA diagnosis based on full-night PSG. The exclusion criteria included bronchopulmonary infection, communication difficulties, and pregnancy.
A total of 403 patients were enrolled in the study period, of whom 324 (80.4%) were confirmed to have OSA. Diagnosis was established using the American Academy of Sleep Medicine (AASM) Clinical Practice Guidelines (2024) [1], and respiratory events were scored according to the AASM Scoring Manual, Version 2.6.
Data collection
The registry collected comprehensive clinical and demographic data, including age, sex, anthropometric measures (height, weight, and BMI), smoking history, and self-reported medical comorbidities, such as diabetes mellitus, systemic hypertension, ischemic heart disease, pulmonary hypertension, atrial fibrillation, chronic kidney disease, and hypothyroidism. Symptoms suggestive of OSA, including snoring and EDS, were assessed using the ESS.
Clinical data
Inclusion and exclusion criteria
The study included participants who met the following criteria: 1) adults aged ≥18 years, both male and female patients; 2) patients with a confirmed diagnosis of OSA based on fullnight PSG, in accordance with the American Academy of Sleep Medicine (AASM) Clinical Practice Guidelines (2024); and 3) patients with stable mental health, to ensure accurate self-reported data and compliance with study protocols.
The following individuals were excluded from the study: 1) patients with bronchopulmonary infections, as these conditions can confound the interpretation of respiratory parameters during PSG; 2) patients with communication difficulties, which could hinder the collection of accurate self-reported data and compromise the reliability of symptom assessment; and 3) pregnant women, as physiological changes during pregnancy may alter sleep patterns and respiratory function, potentially confounding the study results.
Rationale for criteria
The inclusion criteria were designed to ensure the recruitment of a representative sample of adult Egyptian patients with confirmed OSA, while minimizing confounding factors that could affect data accuracy. By focusing on patients with stable mental health, the aim of this study was to ensure the reliability of self-reported symptoms and medical histories. The exclusion criteria were established to eliminate potential confounders, such as acute respiratory infections, communication barriers, and pregnancy-related physiological changes, which could affect the interpretation of polysomnographic data and clinical findings. These criteria are essential for maintaining the scientific integrity of the study and ensuring the validity of the results.
Polysomnography data
Full-night attended laboratory PSG was used as the diagnostic gold standard in this registry to ensure uniform event scoring, capture oxygenation and sleep architecture, and minimize misclassification. Home sleep apnea testing (HSAT) was not routinely adopted during the registry period because of limited local availability and validation, as well as the need for standardized, registry-quality measurements.
PSG data included the AHI, ODI, oxygen saturation below 90% (T90), nadir oxygen saturation (SpO2), snoring index, periodic limb movements (PLMs), and arrhythmia index.
Diagnosis of sleep apnea
Diagnosis of OSA was made through history taking and clinical examination and confirmed by a full-night PSG. All PSG recordings were scored in accordance with the AASM Clinical Practice Guidelines (2024) [1]. The studies were initially scored by trained sleep technologists and subsequently reviewed by a senior sleep physician and a pulmonology consultant to ensure internal consistency. Discrepant epochs and events were resolved through consensus review [1]. Respiratory events included apnea and hypopnea. Apnea was defined as a 90% reduction in flow for at least 10 s. Hypopnea was defined as a 30% reduction in airflow lasting at least 10 s combined with either ≥3% oxygen desaturation or arousal (recommended) or ≥4% oxygen desaturation (acceptable). The AASM classifies OSA severity based on the following AHI cutoff limits: normal (AHI <5 events/h), mild OSA (AHI 5 to <15 events/h), moderate OSA (AHI 15 to <30 events/h), and severe OSA (AHI ≥30 events/h) [1].
The oxygen desaturation index (ODI) was defined as the number of times per hour of sleep that the blood oxygen saturation decreased by a specific percentage (typically ≥3% or ≥4%) from baseline, often lasting at least 10 s. It was calculated using pulse oximetry during PSG. T90 was defined as the percentage of total sleep time (TST) during which the arterial SpO2 was below 90%. The nadir SpO2 was defined as the minimum arterial oxygen saturation reached during sleep, representing a key indicator of nocturnal hypoxemia severity. The nadir SpO2 was classified as follows: 1) normal: 95%–100%; 2) mild hypoxia/desaturation: 90%–94%; 3) moderate hypoxia/desaturation: 80%–89%; and 4) severe hypoxia/desaturation: below 80% [1].
Statistical data
Statistical analysis of the SBDCUR was performed using SPSS version 26 (IBM Corp.). Data are summarized as frequencies and percentages for categorical variables and as means± standard deviations for continuous variables. Normality was assessed using the Shapiro–Wilk test. A p-value ≤0.05 was considered statistically significant.
Comparisons between groups were conducted using the chi-square test or Fisher’s exact test, as appropriate, for qualitative variables and the independent samples t-test for normally distributed quantitative variables. For comparisons between mild and moderate-to-severe OSA, binary logistic regression analysis was performed. The purpose of the regression analysis was to identify independent predictors of moderate-to-severe OSA compared with mild OSA. The dependent variable was OSA severity (mild as the reference category; moderateto-severe as the outcome). Independent variables included age, sex, BMI, smoking status, diabetes mellitus (DM), systemic hypertension, pulmonary hypertension, EDS, and ESS score. This approach was chosen for its clinical relevance, as it enhances statistical power and the interpretability of key risk factors. Binary regression simplifies the analysis by categorizing OSA severity into two clinically meaningful groups, distinguishing patients at higher risk (moderate-or-severe OSA) from those with milder disease.
Effect sizes are reported as odds ratios (ORs) with 95% confidence intervals (CIs) and exact p-values. Statistical significance was set at p<0.05. Pearson’s correlation analysis was performed to assess the relationship between variables (e.g., BMI vs. AHI and ESS vs. ODI). The analysis of BMI versus AHI was conducted to investigate the established role of obesity in OSA pathophysiology, while analysis of ESS versus ODI aimed to assess the impact of nocturnal hypoxia on subjective daytime sleepiness. A complete-case approach was used for inferential analyses, meaning that only cases with complete data for the variables of interest were included. Missing data were assessed for each variable, and no missing data were identified (0%). Variables with significant missing data (>10%) have been excluded from the analysis to minimize bias. The analytical denominator for each variable is reported in the corresponding table, where applicable.
RESULTS
Demographic and clinical characteristics
The SBDCUR included 403 patients enrolled between 2022 and 2024, of whom 324 (80.4%) were confirmed to have OSA based on full-night PSG. The cohort consisted of 183 females (56.5%, 95% CI: 51.1%–61.9%) and 141 males (43.5%, 95% CI: 38.1%–48.9%), yielding a male-to-female ratio of 1:1.3. The mean age of the study population was 56.1±13.0 years. The demographic and polysomnographic data of the SBDCUR patients are summarized in Table 1.
Associated comorbidities
Cardiometabolic comorbidities were prevalent in the study population. Systemic hypertension was observed in 41% of the patients (95% CI: 35.7%–46.4%; p=0.043), DM in 32.7% (95% CI: 27.6%–37.8%; p=0.004), and ischemic heart disease in 9.0% (95% CI: 6.0%–12.9%; p=0.588), and pulmonary hypertension in 5.6% (95% CI: 3.1%–8.1%; p=0.004). Smoking was reported in 35.5% of patients (95% CI: 30.3%–40.7%), with males accounting for 76.6% of smokers (95% CI: 68.9%– 84.3%; p<0.001). Some sex-specific differences in comorbidities were evident, with female patients exhibiting higher rates of systemic hypertension (45.9%), diabetes mellitus (39.3%), and pulmonary hypertension (8.7%) compared to males, who showed higher rates of ischemic heart disease (9.9%) and smoking prevalence (Fig. 2). Other comorbidities, including atrial fibrillation (2.5%, 95% CI: 1.1%–4.9%), chronic kidney disease (2.2%, 95% CI: 0.9%–4.5%), and hypothyroidism (2.2%, 95% CI: 0.9%–4.5%), did not show statistically significant associations (Table 2). The arrhythmia index was significantly higher among female patients compared to males (p=0.016) (Table 1).
OSA symptoms and severity
EDS, assessed using the ESS score, was statistically significant among male patients with OSA compared with female patients (p=0.010). The snoring index showed no significant difference between sexes (Table 1). The severity of OSA was categorized into three levels: mild, moderate, and severe, based on the AHI. Among the 324 confirmed patients with OSA, 110 (34%) had mild OSA, 99 (30.6%) had moderate OSA, and 115 (35.5%) had severe OSA (Table 3). A higher proportion of mild OSA severity was more prevalent among male patients (p<0.001) (Table 2).
Predictors of moderate-to-severe OSA
Binary logistic regression revealed that patients with DM had significantly higher odds of moderate-to-severe OSA compared with mild OSA (adjusted OR=3.426, 95% CI: 1.414– 8.301, p=0.006). Each 1-point increase in ESS score was associated with a 13.6% increase in the odds of moderate-to-severe OSA (adjusted OR=1.136, 95% CI: 1.065–1.212, p=0.001). The covariates included in the model were age, sex, BMI, smoking status, systemic hypertension, pulmonary hypertension, EDS, and ESS score. These findings highlight the importance of DM and ESS scores as independent predictors of OSA severity. These results emphasize the need for targeted screening and management strategies for patients with these risk factors and align with the study’s objective of identifying actionable predictors to improve clinical outcomes for patients with OSA (Table 4).
Correlation analysis
A significant positive correlation was observed between the ESS score and the oxygen desaturation index (ODI) (r=0.272, p=0.001), indicating that a higher hypoxic burden is associated with increased daytime sleepiness. However, no significant correlation was observed between BMI and AHI (r=0.063, p=0.260). This finding may reflect the high average BMI in the cohort, where prolonged hypoventilation without apnea may have obscured the relationship (Table 5).
DISCUSSION
Recognizing the need for robust epidemiological data, the SBDCUR was established to systematically collect and analyze clinical, demographic, and polysomnographic data from Egyptian patients diagnosed with OSA. The SBDCUR is a single-center, retrospective, cross-sectional cohort that provides critical insights into the clinical characteristics and comorbidities of Egyptian patients diagnosed with OSA. This registry serves as a foundational framework for generating valuable data to support future research and clinical practice in sleeprelated breathing disorders in Egypt.
The SBDCUR revealed a higher proportion of female patients with OSA, with a male-to-female ratio of 1:1.3. This finding contrasts with global trends, in which OSA is typically more prevalent among males [10]. The higher frequency among females in this registry may be attributed to the nature of the cohort, which was derived from a clinic-based registry rather than a population-based sample. Moreover, older age (i.e., postmenopausal hormonal changes) and obesity rates were observed among female patients with OSA in this cohort. Postmenopausal women experience a loss of the protective effects of estrogen and progesterone on upper airway patency, which may contribute to increased OSA severity [11,12].
The SBDCUR findings are consistent with data from other international registries, such as the European Sleep Apnea Database (ESADA) and Moroccan cross-sectional studies [10,13]. The ESADA reported a younger average age (51.9±13.1 years) compared with the SBDCUR (56.1±13.0 years), while the Moroccan study also reported a higher proportion of female patients with OSA, with a male-to-female ratio of 0.61.13 These findings are consistent with a previous study that reported similar patterns in a Moroccan population [14]. These comparisons highlight the importance of referral bias, cultural factors influencing healthcare access, and the role of regional registries in capturing population-specific trends and providing tailored interventions.
Binary logistic regression analysis identified DM and the ESS score as significant independent predictors of OSA severity. These results align with existing literature and underscore the multifactorial nature of OSA. Patients with DM had more than threefold higher odds of moderate-to-severe OSA compared to those with mild OSA (adjusted OR=3.426, 95% CI: 1.414–8.301, p=0.006). This association highlights the bidirectional relationship between OSA and metabolic dysfunction, in which intermittent hypoxia and sleep fragmentation exacerbate insulin resistance and glycemic control. Each 1-point increase in ESS score was associated with a 13.6% increase in the odds of moderate-to-severe OSA (adjusted OR=1.136, 95% CI: 1.065–1.212, p=0.001). This finding emphasizes the clinical relevance of excessive daytime sleepiness as a marker of nocturnal hypoxia and OSA severity [15]. The identification of DM and ESS as independent predictors underscores the im-portance of integrating metabolic and sleep assessments into OSA management. Targeted screening for OSA in patients with diabetes and those with high ESS scores may facilitate early diagnosis and intervention, potentially mitigating the long-term cardiometabolic consequences of untreated OSA.
Cardiometabolic comorbidities and their association with OSA
This study identified a high frequency of cardiometabolic comorbidities in patients with OSA, including DM (32.7%, p=0.004), systemic hypertension (41%, p=0.043), and pulmonary hypertension (5.6%, p=0.004). These findings align with previous studies that have established strong associations between OSA and metabolic disorders, particularly insulin resistance and type 2 DM [15,16]. Fragmented sleep and intermittent hypoxia are known to exacerbate these conditions, highlighting the need for integrated care models that address both sleep disorders and chronic diseases.
Interestingly, pulmonary hypertension was more frequent among female patients (8.7%) than among male patients (1.4%). This difference may be attributed to the higher BMI and postmenopausal status of female patients, which may contribute to an increased hypoxic burden and vascular remodeling [11]. These findings emphasize the importance of targeted interventions to mitigate the long-term health effects of OSA, particularly in women.
Complex interplay between OSA and clinical parameters
A significant positive correlation was observed between ESS and ODI (r=0.272, p=0.001), indicating that a higher nocturnal hypoxic burden is associated with increased daytime sleepiness. This finding underscores the impact of oxygen desaturation events on subjective symptoms of EDS in patients with OSA.
No significant correlation was observed between BMI and AHI (r=0.063, p=0.260). This lack of association may be attributed to the high average BMI of the study population, where prolonged hypoventilation periods without apnea may obscure the relationship between obesity and OSA severity.
The significant correlation between ESS and ODI highlights the importance of addressing nocturnal hypoxia to alleviate daytime symptoms in patients with OSA. The absence of a significant correlation between BMI and AHI suggests that factors other than obesity, such as anatomical variations or neuromuscular control, may play a more prominent role in determining OSA severity in this cohort. These findings are consistent with previous studies, suggesting that daytime sleepiness is closely linked to nocturnal hypoxia in patients with OSA, whereas BMI remains an important risk factor for OSA severity [17].
Strengths and limitations
The SBDCUR represents the first national registry of patients with OSA in Egypt, offering a robust dataset of 324 confirmed cases diagnosed using full-night PSG. The registry’s comprehensive collection of demographic, clinical, and polysomnographic data provides high-quality information for both research and clinical practice. Furthermore, the inclusion of sex-specific analyses offers critical insights into the differential impact of OSA on male and female patients in populations where obesity is more prevalent among females. The use of standardized diagnostic criteria, including the AASM guidelines, enhances the reliability and comparability of these findings with those of international studies [1].
Despite its strengths, the SBDCUR has certain limitations. First, as a single-center study, the findings may not be generalizable to a broader Egyptian population, particularly in rural or underserved areas. Second, the registry focuses exclusively on confirmed OSA cases, which may introduce selection bias and limit its ability to identify population-level trends. Third, the absence of data on non-OSA patients and the lack of longitudinal follow-up restrict the ability to assess the natural history of OSA and its long-term impact on comorbidities. Finally, the study did not account for potential confounding factors, such as socioeconomic status, menopausal status, and lifestyle factors, which may have influenced the observed sex differences and comorbidity patterns. Addressing these limitations in future research, such as expanding the registry to include multiple centers and broader demographic groups, may enhance the generalizability of the findings and improve the diagnosis, management, and outcomes of patients with OSA in Egypt.
Conclusion
The SBDCUR provides critical insights into the clinical and demographic characteristics of patients with OSA in Egypt. This study highlights a notable female predominance and a high prevalence of cardiometabolic comorbidities, including DM and systemic hypertension. Binary logistic regression identified DM and ESS score as significant independent predictors of moderate-to-severe OSA, emphasizing the interplay between metabolic dysfunction and sleep-disordered breathing. These findings underscore the importance of early screening and multidisciplinary management of OSA to mitigate its long-term health impacts. The SBDCUR registry serves as a foundational resource for advancing research and clinical practice in sleep medicine, with implications for improving diagnostic strategies, tailoring interventions, and addressing sex-specific differences in OSA presentations and outcomes. Future research should focus on expanding the registry to include broader populations, exploring additional risk factors, and assessing the effects of treatment adherence on long-term outcomes.
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