Clinical and Polysomnographic Differences in Obese and Nonobese Patients with Obstructive Sleep Apnea

Abstract

Background:

Obstructive sleep apnea (OSA) is characterized by repeated episodes of upper airway obstruction during sleep, resulting in fragmented sleep and associated health risks.

Objective:

This retrospective study investigated the clinical and polysomnographic differences between obese and nonobese patients diagnosed with OSA.

Methodology:

A total of 210 patients underwent a Level 3 sleep study. Based on the apnea-hypopnea index (AHI), 180 patients with OSA were included in the study. Patients were categorized as obese (body mass index [BMI] ≥27.5) or nonobese (BMI <27.5). Clinical parameters, AHI, oxygen desaturation index (ODI), Epworth Sleepiness Scale (ESS), neck circumference, age, and gender were compared.

Results:

Obese patients had significantly higher AHI, ODI, ESS scores, and neck circumference than nonobese patients (P < 0.05). Gender was associated with the severity of OSA only in the obese group. Age was not significantly associated with OSA severity in either group.

Discussion:

Obesity is strongly associated with more severe OSA and anatomical risk factors. Nonobese OSA patients also exhibited clinically relevant symptoms, suggesting distinct nonanatomical mechanisms.

Conclusion:

This study concludes that while obesity is linked to more severe OSA due to anatomical factors, non-obese patients also experience significant disease, highlighting the importance of individualized, phenotype-driven management strategies.

INTRODUCTION

Obstructive sleep apnea (OSA) is a global public health problem and is estimated to affect around one billion people.[1] OSA is associated with excessive daytime sleepiness, neurocognitive impairment, and reduced quality of life. It results in considerable morbidity and mortality by increasing the risk for cardiovascular, neurologic, and metabolic diseases. The development of OSA is influenced by certain modifiable and nonmodifiable risk factors.

The nonmodifiable risk factors include elderly age, male sex, race/ethnicity, family history of OSA, menopause, and craniofacial abnormalities. The modifiable risk factors for OSA include obesity, smoking, and alcohol use.[2,3]

Current estimates suggest that roughly 60%–70% of OSA patients are obese, leaving approximately 30%–40% who are not obese, which is a substantially large global burden of nonobese OSA. OSA is a complex disease which is associated with an interplay of anatomical and nonanatomical factors or traits.[4] The nonanatomical traits present in approximately 70% of patients with OSA include a low respiratory arousal threshold, unstable ventilatory control (high loop gain), and ineffective upper-airway dilator muscles during sleep.[5,6] Anatomical factors such as craniofacial abnormalities can lead to a crowded upper airway and increased upper airway collapsibility in certain nonobese OSA individuals.[7,8] This suggests that OSA in nonobese patients may be a unique disorder involving various domains. Future research into the clinical phenotypes and physiological endotypes of nonobese OSA, as well as the consequences of the disorder and its management, should be the focus for personalized or precision medicine. This retrospective study aimed to analyze the polysomnographic traits in the obese and nonobese OSA.

METHODOLOGY

This retrospective observational study was conducted using data collected from the sleep study register. Institutional ethics committee clearance was obtained for utilizing the patient data. The study included a total of 210 patients who underwent overnight polysomnography (level 3) for the evaluation of suspected OSA. These patients either presented with complaints of snoring, excessive daytime lethargy or were referred to the department for assessment of OSA. Patients were subjected to a Level III sleep study (ResMed Apnea-link plus™ [ResMed, San Diego, California, United States]) in the comforts of their home or in the hospital. This device used a noninvasive flow monitoring, pulse oximetry, thoracic and abdominal bands that defined airflow obstruction by the absence of flow despite continuing ventilatory effort. This is widely used as a screening test for OSA in the hospitals or home apnea sleep testing and has a sensitivity of at least 85% for any OSA and near 100% for moderate-to-severe OSA.[9,10,11]

Apnea and hypopnea were defined according to the AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications American Academy of Sleep Medicine Scoring Manual version 2.4 (American Academy of Sleep Medicine, Darien, Illinois, United States).[12] Based on apnea–hypopnea index (AHI), OSA was categorized as mild (5–14 events/h), moderate (15–29 events/h), and severe (≥30 events/h).

We classified OSA patients with a body mass index (BMI) <27.5 as nonobese and those with a BMI ≥27.5 as obese. Demographic profile and Epworth Sleepiness Scale (ESS) scores were retrieved from the register. Based on the sleep study findings, 30 patients were found to have normal AHI (<5), 60 were diagnosed with mild OSA (AHI 5–15), 59 with moderate OSA (AHI 15–30), and 61 with severe OSA (AHI >30). Patients with normal sleep study reports (n = 30) were excluded from the analysis. Consequently, data from 180 patients diagnosed with OSA (AHI >5) were included in the final analysis.

Statistical analysis

Data were analyzed using descriptive and inferential statistical methods. Continuous variables were expressed as means with standard deviations. To assess differences between obese and nonobese groups, independent samples t-tests were performed for normally distributed variables, including ESS, AHI, oxygen desaturation index (ODI), and neck circumference. A P < 0.05 was considered statistically significant. All analyses were conducted using standard statistical software. All analysis were done using SPSS version 25 (IBM Corporation, Armonk, New York, United States).

RESULTS

A comparison of OSA severity across obese (BMI >27.5) and nonobese (BMI ≤27.5) individuals revealed a significant association between obesity and the severity of OSA. Among nonobese patients, mild, moderate, and severe OSA were observed in 40.8%, 35.5%, and 23.7% respectively, whereas in the obese group, the distribution shifted markedly to 27.9% mild, 30.8% moderate, and 41.3% severe OSA. A Chi-square test of independence confirmed this difference to be statistically significant (P = 0.038), indicating that obesity is strongly correlated with increased severity of OSA [Table 1].

Table 1.

Severity of obstructive sleep apnea in obese and nonobese groups

Group	Mild OSA	Moderate OSA	Severe OSA	Total
Nonobese	31 (40.8)	27 (35.5)	18 (23.7)	76
Obese	29 (27.9)	32 (30.8)	43 (41.3)	104
Total	60 (33.33)	59 (32.78)	61 (33.89)	180

OSA=Obstructive sleep apnea

The ESS scores were compared between obese individuals (BMI > 27.5) and nonobese individuals (BMI ≤27.5) to assess the impact of obesity on daytime sleepiness. The mean ESS score was significantly higher in the obese group (14.78) compared to the nonobese group (13.42). An independent samples t-test revealed this difference to be statistically significant (P = 0.026), indicating a positive association between obesity and increased daytime sleepiness [Figure 1]. These findings suggest that obesity may contribute to greater subjective sleepiness among individuals, reinforcing the importance of weight management in patients presenting with symptoms of OSA.

Figure 1.

Comparison of Epworth Sleepiness Scale score in obese and nonobese groups

The AHI, a key measure of sleep apnea severity, was compared between obese (BMI >27.5) and nonobese (BMI ≤27.5) individuals. The mean AHI was substantially higher in the obese group (33.59) compared to the nonobese group (23.68). Statistical analysis using an independent samples t-test confirmed that this difference was significant (P = 0.003), underscoring the strong association between obesity and increased severity of OSA [Figure 2]. These results highlight the need to consider body weight in the clinical evaluation and management of patients at risk for OSA.

Figure 2.

Comparison of apnea-hypopnea index in obese and nonobese groups

The ODI, an indicator of nocturnal hypoxemia severity, was analyzed in obese (BMI >27.5) and nonobese (BMI ≤27.5) subjects. The mean ODI was significantly elevated in the obese group (23.29) compared to the nonobese group (11.99). This difference was statistically significant as determined by an independent samples t-test (P = 0.0001) [Figure 3]. These findings suggest that obesity contributes to a greater frequency of oxygen desaturation events during sleep, supporting the role of adiposity in exacerbating the physiological burden of OSA.

Figure 3.

Age and obstructive sleep apnea severity in obese and nonobese groups

Neck circumference, a known anatomical risk factor for OSA, was compared between obese (BMI > 27.5) and nonobese (BMI ≤ 27.5) individuals. The mean neck circumference was significantly greater in the obese group (41.66 cm) compared to the nonobese group (36.67 cm). This difference was highly statistically significant (P < 0.0001) based on an independent samples t-test [Figure 4]. These findings underscore the anatomical predisposition of obese individuals to upper airway obstruction during sleep, reinforcing the relevance of neck circumference as a clinical indicator in the evaluation of OSA.

Figure 4.

Comparison of neck circumference in obese and nonobese groups

The distribution of gender across obese and nonobese groups revealed 54 males and 50 females in the obese group, and 39 males and 37 females in the nonobese group [Table 2]. A Chi-square test was conducted to evaluate the relationship between gender and OSA severity (mild, moderate, and severe) within each group. In the obese group, a statistically significant association was observed between gender and severity of OSA (χ² = 17.26, P = 0.00007), with males being more likely to present with severe OSA compared to females. Conversely, in the nonobese group, no significant association was found between gender and OSA severity (χ² = 1.20, P = 0.548), suggesting a more uniform distribution of severity across both genders.

Table 2.

Gender distribution and relationship with obstructive sleep apnea severity in obese and nonobese group

Obesity group	Gender	Mild OSA	Moderate OSA	Severe OSA
Obese group	Female	17	23	10
	Male	12	9	33
Non-obese group	Female	15	15	7
	Male	16	12	11

OSA=Obstructive sleep apnea

To evaluate the potential relationship between age and the severity of OSA, a one-way ANOVA was performed within the obese and nonobese groups. In the obese group, no statistically significant difference in age was observed across the mild, moderate, and severe OSA subgroups (P = 0.606). Similarly, in the nonobese group, age did not significantly vary with increasing severity of OSA (P = 0.605) [Figure 5]. These findings suggest that age distribution is relatively uniform across different levels of OSA severity, irrespective of obesity status.

Figure 5.

Age and obstructive sleep apnea severity in obese and nonobese groups

DISCUSSION

This study aimed to evaluate the differences in clinical and polysomnographic traits between obese and nonobese patients diagnosed with OSA. The findings confirm the strong association between obesity and the severity of OSA, as demonstrated by significantly high AHI, ODI, and ESS scores in obese patients. Studies consistently demonstrate a strong relationship between obesity and OSA. The prevalence of OSA is significantly higher in obese individuals compared to normal-weight individuals, and the severity of OSA tends to increase with BMI. Obesity and OSA share common risk factors, including genetic predisposition, sex, age, and lifestyle factors such as diet and physical inactivity.[13,14,15] In addition, there is a bidirectional causal relationship between these two conditions. The Wisconsin Sleep Cohort Study showed that a 10% weight gain predicted a corresponding 32% increase in the AHI and a 6-fold increase in the risk of developing moderate to severe OSA. Conversely, a 10% weight loss predicted a 26% decrease in the AHI.[16]

Additionally, increased neck circumference–a known anatomical risk factor–was observed in obese participants, reinforcing the notion that anatomical contributors to airway collapse are more pronounced in this group. In a study by Hoffstein and Mateika on 670 patients, obese subjects suspected of having OSA had a higher neck circumference compared to equally obese nonapneic snorers.[17]

The greater severity of OSA among obese individuals aligns with the existing literature, which indicates that excess adipose tissue in the neck and upper airway regions may increase airway collapsibility during sleep. Conversely, the nonobese group, though presenting with milder forms of OSA, still exhibited significant symptoms and AHI values. This underlines the importance of recognizing nonobese OSA as a distinct clinical entity potentially driven more by nonanatomical factors such as altered ventilatory control, arousal threshold, or upper airway neuromuscular tone.

Interestingly, while gender was significantly associated with OSA severity in the obese group–males being more affected–this association was not observed in the nonobese group, suggesting that sex-related risk factors may be more prominent when compounded by obesity. In addition, age did not show any significant variation with OSA severity in either group, indicating that while age is a risk factor for OSA, its impact on disease severity may be modulated by other variables such as BMI and anatomical traits.

The study is limited by its retrospective design and reliance on Level 3 sleep studies, which, while practical and cost-effective, do not provide data on sleep architecture or arousals. We used the portable ApneaLink device for our study, whose performance is comparable to a Level 1 sleep study, particularly in people with moderate to severe OSA. The device cannot distinguish obstructive and central respiratory events, but this was unlikely to have affected our results, as the population prevalence of central sleep apnea is negligible in men and more so in women. Our study probably underestimated the prevalence of clinically relevant OSA by not scoring electroencephalography-based arousals. Comorbidities, medication use, and lifestyle habits, which could have shed a lot of insight were not extensively analyzed in this study as this was a retrospective study.

CONCLUSION

This study demonstrates that obesity significantly influences the severity and physiological burden of OSA. Obese patients exhibit higher AHI, ODI, and ESS scores along with larger neck circumferences indicating greater anatomical predisposition and more severe disease. However, nonobese individuals with OSA also represent a substantial proportion of cases and may exhibit distinct nonanatomical pathophysiologic mechanisms.

These findings highlight the need for a personalized approach to the diagnosis and management of OSA that considers both BMI and the underlying endotypes. Clinicians should be vigilant in screening for OSA in nonobese individuals who may otherwise be underdiagnosed. Future research should explore targeted therapies based on individual pathophysiological traits to enhance treatment outcomes in both obese and nonobese populations.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.