OSAHS, a prevalent sleep-disordered breathing condition, is identified by airway constriction causing interrupted breathing and hypopnea during sleep. The disorder poses numerous risks, such as: (1) Respiratory Issues: Individuals with OSAHS encounter recurrent breathing interruptions and reduced airflow during sleep, leading to oxygen deprivation and carbon dioxide buildup. This can adversely affect different tissues and organs in the body. (2) Impaired Sleep Quality: OSAHS patients face interrupted sleep due to frequent breathing pauses, leading to inadequate rest (Bener et al., 2020). This can result in issues like daytime drowsiness, fatigue, memory impairment, and reduced concentration, impacting daily activities and work performance (Zhang et al., 2022) and raising the likelihood of accidents. (3) Cardiovascular Complications: OSAHS exhibits a strong correlation with cardiovascular conditions such as hypertension, cardiac ailments, and strokes (Geer et al., 2021; Pengo et al., 2021; Affas et al., 2022; Jorquera et al., 2023). During apneic episodes, oxygen levels in the blood decrease while carbon dioxide levels rise, placing additional strain on the cardiovascular system and heightening the risk of heart attacks and strokes.(4) Metabolic Abnormalities: OSAHS is closely linked with metabolic conditions like diabetes and obesity (Silva et al., 2021; Zeng et al., 2022; Sangchan et al., 2023). Disturbed sleep patterns can contribute to insulin resistance, irregular blood sugar control, and heightened appetite, exacerbating metabolic issues. (5) Respiratory Infections: Oral breathing tendencies in OSAHS patients can desiccate the mouth and throat regions, elevating the likelihood of bacterial proliferation. This situation can result in recurrent respiratory infections such as pneumonia, laryngitis, and tonsillitis (Kar et al., 2021). Additional Concerns: OSAHS can also lead to complications including gastroesophageal reflux disease, mood disorders, sexual dysfunction, and accidents.Hence, timely identification and management of obstructive sleep apnea syndrome are crucial. Failure to address OSAHS can result in severe health implications such as compromised sleep quality, daytime drowsiness, metabolic irregularities, heightened susceptibility to cardiovascular and cerebrovascular conditions, among others.
The aim of this research was to conduct PSG analysis on individuals with OSAHS living in the Qingpu area of Shanghai, China. The study aimed to assess variations in mean parameter values across different age groups, genders, and OSAHS severity levels. It sought to investigate the features of AHI, ODI, SI, AGE, and BMI under various OSAHS severities, along with exploring the correlations between these parameters. Furthermore, the study aimed to elucidate snoring patterns among OSAHS patients in Qingpu District, offering insights for clinicians to develop tailored treatment strategies.
In our research, we utilized a 22-channel polysomnographic sleep monitoring device manufactured in Beijing, China. PSG is an extensive examination that tracks and documents various physiological markers to evaluate an individual's sleep quality and associated issues. This includes EEG, EOG, EMG, respiratory airflow and oxygen saturation, pulse rate, pulse waveform, thoracic and abdominal respiration, airflow pressure, snoring pressure, physical movements, body posture, pulse transit time, and thoracoabdominal phase angle.
To analyze the gathered data statistically, we employed the mean bar graph, correlation coefficient matrix, and grouped box plot. The mean bar graph enables a visual comparison of parameter variations across varying degrees of OSAHS. Through this chart, changes in parameters among different levels of severity can be easily interpreted. Correlation coefficient matrices were utilized to assess the strength of linear associations among variables, depicting interactions within the dataset. These diagrams aid in identifying correlations between different parameters, leading to a deeper comprehension of their relationships.The grouped box plot serves to illustrate parameter distributions, facilitating the identification of outliers and the extent of data spread. By using a grouped box chart, one can observe the median, upper and lower quartiles of parameters, as well as identify outliers. This aids in enhancing comprehension regarding parameter discrepancies across distinct groups.
Through the utilization of these statistical tools, we can delve deeper into the relationships between parameters in sleep analysis outcomes, unveiling significant correlations. This approach allows for a more comprehensive understanding of OSAHS traits and patterns, contributing valuable insights for tailoring personalized treatment strategies.
4.1 Mean Age Comparative Bar Graph
In Fig. 1, the average values of key parameters across various age groups are showcased to elucidate the distribution characteristics within each age bracket. The bar chart illustrates the fluctuations in parameter distribution among different age ranges.Observing the mean values depicted in the age group bar chart, it's evident that the average values of AHI, ODI, SI, and BMI tend to be relatively high across all age groups. Certain age brackets exhibit slightly higher or notably elevated values compared to others.These data suggest that while age may influence the distribution of AHI, ODI, SI, and BMI, this influence is not directly linked to the severity of Obstructive Sleep Apnea and Hypopnea Syndrome (OSAHS). Unique trends are noticeable in the 61 to 80 and 21 to 40 age groups. Notably, in the 0 to 20 age group, although BMI shows a slight increase, its association with age appears insignificant.Hence, tailored interventions targeting specific age groups are essential for the prevention and management of OSAHS and its related complications.
4.2 Bar Graph Comparing Mean Values Across Genders
In Fig. 2, the mean differences between men and women across various measurement parameters are depicted to highlight distribution discrepancies based on gender. The bar chart presenting sex distributions conveys specific observations:
Notably, the AHI and ODI displayed significantly higher average values for males, almost double that of females. This stark contrast underscores substantial disparities in apnea and hypoxemia severity between men and women in the study group, emphasizing gender-specific distinctions.
Conversely, gender variations were negligible for the Snoring Index, age of onset, and BMI. This suggests that while apnea and hypoxemia show distinct gender differences, no significant disparities exist between men and women in these specific parameters.
The results suggest that men experienced more severe apnea and hypoxemia compared to women, revealing gender-related characteristics within the sample. However, gender did not seem to impact the mean values associated with sleep index, age, and BMI. These findings offer insights into how gender may influence the manifestations of OSAHS and the interplay among diverse metrics.
4.3 Visualization of Correlation Coefficient Matrix
The examination of the correlation coefficient matrix, as depicted in Fig. 3, aids in unraveling internal associations and patterns within the data. Particularly suited for analyzing linear relationships across multiple variables, this matrix facilitates understanding the extent of correlations among various factors and extracting insights regarding data interrelations.To enhance comprehension, color coding or thermal maps can be applied to represent the correlation matrix visually. This visual representation allows for a swift identification of highly correlated or inversely correlated variables. Notably, the Pearson correlation coefficient operates within a range from − 1 to 1: A correlation coefficient of 1 signifies perfect positive correlation between two variables. A correlation coefficient of -1 indicates complete negative correlation between two variables. A correlation coefficient of 0 suggests the absence of a linear relationship between the paired variables.
Based on our analysis outcomes detailed in Fig. 3 of the correlation coefficient matrix, the key findings are as follows:
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The most robust correlations (R-values 0.30, 0.55, 0.77, 0.84) were observed between the AHI and the ODI, with these correlations intensifying alongside OSAHS severity. This underscores a close link between apnea events and oxygen desaturation among OSAHS patients.
(2) A moderate relationship was noted between AHI and BMI (R-values 0.47, 0.02, 0.44, 0.49), particularly prominent in cases of severe OSAHS but lacking in mild instances. This signifies a partial association between AHI and patient weight, predominantly evident in heavier OSAHS conditions.
(3) The connection between AHI and SI exhibited weaker associations (R-values − 0.21, 0.30, -0.47, -0.26), evolving into a more notable link within moderate OSAHS scenarios. This implies a potential interplay between apnea occurrences and sleep efficiency in moderate OSAHS cases.
(4) The correlation between AHI and Age displayed minimal strength, diminishing or becoming insignificant with escalating OSAHS severity levels. This suggests that age exerts minimal impact on apnea severity within our dataset. Notably, while Age exhibited strong correlations with certain parameters at various levels, these connections were nearly nonexistent under different circumstances.
In summary, the AHI and ODI exhibited notable correlations across varying degrees of OSAHS, with these correlations strengthening as the condition worsened. Notably, in cases of mild OSAHS, a linear correlation between AHI and BMI was absent, while the weakest linear association was observed between AHI and Age. Likewise, the relationship between ODI and the parameters followed a similar pattern, although the specific values and extents of correlation might differ.
4.4 Subgroup Box Plot Examination
We employ a grouped box plot diagram (Fig. 4) to illustrate parameter distributions across varying degrees of OSAHS. Through our analysis of the subgroup box plot, observations reveal a consistent rise in both the AHI and ODI with the progression of OSAHS severity levels.Additionally, the SI and BMI exhibit elevated values across all OSAHS case groups, with notably heightened readings observed in individuals with severe OSAHS. These two indicators demonstrate substantial significance in severe OSAHS cases. Notably, we found no significant correlation between age and OSAHS severity.
4.4.1 AHI: The Apnea-Hypopnea Index (AHI) is identified as a crucial predictor in the diagnosis of OSAHS and serves as one of the key criteria, as stated by Marriott et al. (2022). The rationale behind employing AHI in diagnosing OSAHS is as follows:
(1) Reflecting the extent of respiratory dysfunction: AHI provides an objective measure of the frequency of apnea and hypopnea episodes that occur during sleep in patients, serving as a diagnostic tool and a common metric for grading OSAHS. Elevated AHI values signify a greater occurrence of apnea and hypopnea events during sleep, indicating the seriousness of the condition.
(2) Association with clinical manifestations: Elevated AHI values are closely linked to symptoms characteristic of OSAHS (e.g., snoring, frequent nighttime disruptions, lethargy, fatigue, etc.) due to the impact of apnea and reduced ventilation on sleep quality and oxygen delivery. (3) Treatment strategy formulation: AHI measurements aid in assessing the severity of OSAHS and devising tailored treatment plans, which may involve interventions like CPAP therapy or oral appliances. Higher AHI scores signal a heightened requirement for immediate therapeutic intervention. (4) Validation through research: Numerous studies validate the significance of AHI in OSAHS diagnosis, bolstered by extensive research endeavors and epidemiological inquiries as elucidated by Marriott et al. (2022). (5) Global applicability: AHI stands as a universal benchmark for diagnosing OSAHS, embraced across various nations and territories. This consistency enables healthcare providers to assess patients' conditions utilizing a uniform criterion and devise treatment strategies accordingly.
While the AHI holds significance, it is imperative to incorporate additional elements like symptoms, physical assessments, and other sleep monitoring parameters in the diagnosis and treatment of OSAHS. This holistic approach ensures a thorough and precise evaluation, leading to the formulation of optimal treatment strategies. Hence, a comprehensive assessment serves as the cornerstone in guaranteeing an accurate diagnosis and crafting effective treatment plans for individuals with OSAHS.
4.4.2 ODI: Assessment of respiratory events primarily relies on the AHI and ODI to gauge the frequency and severity of apnea and hypopnea episodes during sleep. To comprehensively evaluate the severity of OSAHS, factors beyond AHI, such as decreased oxygen saturation, need consideration. Hypoxia has been associated with endothelial dysfunction, primarily impacting mechanisms like oxidative stress, endothelial barrier impairment, release of pro-inflammatory cytokines, and reduced synthesis of nitric oxide (Duchna et al., 2006; Deol et al., 2020). These alterations impact blood vessel functionality and circulation, fostering the progression of hypertension and cardiovascular diseases (Pengo et al., 2021). Throughout sleep, individuals with OSAHS encounter repetitive apnea and hypopnea episodes, leading to decreased blood oxygen levels. This drop triggers a stress response, involving sympathetic excitation and elevated blood pressure. Consistent variations in blood pressure and hypoxia over time strain the cardiovascular system, heightening the likelihood of hypertension development in patients. Hence, a strong correlation exists between OSAHS and high blood pressure, with a notable prevalence of hypertension observed in OSAHS patients. OSAHS serves as a significant contributor to secondary hypertension. Furthermore, OSAHS may intertwine with other hypertension risk factors like overweight, obesity, metabolic syndrome, and diabetes, further escalating the risk of hypertension.
Hence, individuals with OSAHS experiencing severe hypoxia should promptly consult a healthcare professional for timely intervention to mitigate the likelihood of hypertension and cardiovascular or cerebrovascular incidents. Typical treatments encompass the utilization of Continuous Positive Airway Pressure (CPAP) devices, lifestyle modifications such as weight management, smoking cessation, limited alcohol intake and improving the environment(Wang et al. 2020; Fuhrimann et al. 2023).
4.4.3 AGE: According to our findings, consistent age distribution was observed across all OSAHS severity groups, suggesting that age may not be a key factor in determining OSAHS severity (Olaithe et al. 2023). Consistent with the results presented in Fig. 3, the association between AHI and SI was weak (R-values − 0.21, 0.30, -0.47, -0.26). In particular, among patients with moderate OSAHS, the mean age was slightly higher, about 52.80 ± 15.03 years, while moderate OSAHS occurred relatively infrequently in those under 40 years of age. This finding suggests that age 40 May be a critical age threshold. Therefore, awareness and education efforts should be strengthened for people under 40 years old to reduce the risk of OSAHS and its potential complications.
4.4.4 SI: OSAHS, a prevalent sleep disorder, is distinguished by recurring apnea and hypopnea incidents during sleep. Snoring can stem from various factors such as nasal issues, relaxation of soft tissues in the throat, pharyngeal constriction, enlarged tonsils, neck fat accumulation, or jaw abnormalities, all of which narrow or obstruct the upper airway, causing vibrations and noise during airflow, leading to snoring (Turnbull & Stradling, 2023). Our research on OSAHS patients in Qingpu reveals from Fig. 4 that the SI generally remains elevated across patients with varying severities of OSAHS, displaying no significant alteration based on disease severity. While severe OSAHS patients exhibit the highest SI average, differences among other severity groups are minimal. This aligns with the findings showing a weak correlation between AHI and SI, as illustrated in Fig. 3 (R-values − 0.21, 0.30, -0.47, -0.26). Consequently, we deduce that snoring impairs the environment, where whether it's mere snoring or falls within mild, moderate, or severe categories, it can profoundly impact the surrounding setting.
Research has highlighted that snoring can disrupt individuals' sleep patterns and overall quality of life, leading to various challenges:(1) Noise Disruption: Snoring disturbances can impact sleep quality, making it difficult for family members or roommates to get adequate rest. This noise disturbance may strain relationships. (2) Increased Energy Usage: Using measures like earplugs, air conditioners, or fans to mitigate snoring effects can increase energy consumption, resulting in wastage.(3) Social Strain: Persistent snoring disruptions can affect the sleep and daily performance of those nearby, raise psychological stress levels, strain social connections, and even cause friction within families. Addressing these issues involves seeking medical help, adjusting sleeping positions, weight management, maintaining a consistent schedule, and adopting a healthy lifestyle. Early identification and management of snoring through PSG testing can improve environmental conditions, enhance individuals' and communities' quality of life, and reduce the negative impacts of snoring.
4.4.5 BMI: OSAHS is a respiratory disorder characterized by the partial or complete obstruction of the upper airway during sleep, resulting in breathing difficulties. Research indicates that obesity stands as a primary risk factor for OSAHS due to the accumulation of excess fat in the neck (Ernst et al., 2023) and surrounding the throat, elevating upper airway resistance and leading to breathing challenges (Ernst et al., 2023). As per the World Health Organization's (WHO) International BMI classification from 2000, a BMI below 18.5 categorizes individuals as underweight, while 18.5 to 24.9 falls within the normal weight range, 25 to 29.9 denotes overweight status, and 30 and above signifies obesity.
Based on our investigation, patients with severe OSAHS exhibited the highest mean BMI at 30.11 ± 3.56 kg/m², indicating a prevalence of obesity or overweight status. Moderate OSAHS individuals showed a mean BMI of 26.29 ± 2.39 kg/m², suggesting tendencies toward being overweight. On the other hand, normal and mild OSAHS patients had average BMIs ranging from 22.5 to 27.5, implying they were either within the normal weight range or categorized as overweight without being underweight.
Obesity sets off interrelated mechanisms that function together, including heightened blood volume, increased vascular resistance, hormonal imbalances, inflammatory reactions, disrupted nervous system activity, and insulin resistance. These mechanisms are intricately associated with excess body weight, hypertension, and heart disease (Dalmar et al., 2021; Kar et al., 2021; Zhang et al., 2022). Consequently, weight loss can assist in reducing the weight of severe OSAHS patients to normal levels, reducing neck fat accumulation, and improving upper airway patency, thereby enhancing respiratory conditions.
In the context of managing hypertension and OSAHS, reducing body weight to normal levels among severe OSAHS patients can reduce neck fat accumulation, decrease upper airway resistance, and improve respiratory conditions. This improvement may significantly lower the AHI and occasionally even normalize it (Kaar et al., 2021; Hnatiak et al., 2023). This finding is pivotal for OSAHS patients, as it reduces apnea events and diminishes the cardiovascular and cerebrovascular risks associated with the disorder (Hnatiak et al., 2023). Nonetheless, it is vital to recognize that OSAHS is a complex condition influenced by multiple factors. Managing weight alone is insufficient for comprehensive OSAHS treatment. Alongside weight management, a balanced diet, active lifestyle, and individual considerations should be integrated with other therapeutic approaches for a holistic approach to treating OSAHS (Boulos et al., 2021; Guimaraes et al., 2023; Hnatiak et al., 2023; Ortiz & Rosselli, 2023).