Abstract
To study Tibetan and Han adult male patients with Obstructive Sleep Apnea Syndrome (OSAS) in two hospitals in Tibet Plateau and Beijing Plain, and to explore the clinical characteristics of adult male OSAS patients in Tibet Plateau. 100 and 86 adult male OSAS patients diagnosed by Polysomnography (PSG) in Tibet Autonomous Region People’s Hospital and Peking University Third Hospital from April 2017 to October 2021 were retrospectively analyzed, and the following data were collected: Age, neck circumference, Body Mass Index (BMI), blood pressure, Apnea Hypopnea Index (AHI), proportion of N2 stage and REM stage, lowest oxygen saturation at night (LSpO2), etc. The differences between the two groups were analyzed. There were no statistical differences in age distribution (non-obese patients, BMI < 28 kg/m2), BMI, blood pressure, AHI, N2 stage, and AHI of mild and moderate OSAS patients between the two places. The age of obese patients (BMI ≥ 28 kg/m2) in plateau area (47 ± 12.7, n = 54) was higher than that in plain area (40.6 ± 8.2, n = 42, P < 0.05). The neck circumference (39.6 ± 4.4, n = 100) of patients from Tibet Plateau was significantly lower than that of patients from Beijing area (42 ± 2.7, n = 86, P < 0.05), the proportion of patients with history of hypertension (52.5%) was significantly higher than that of patients from Beijing area (37.2%), and the REM period (18.1 ± 7.3, n = 100) was significantly higher than that in patients treated in Beijing area (11.7 ± 6.2, n = 86, P < 0.05), and LSpO2 at night [66% (50-72%)] was significantly lower than that in patients treated in Beijing area [79% (65-85%), P < 0.05]. Among patients with severe OSAS in two places, the disease severity of patients in Tibet Plateau area [AHI = 62 (45.5–90.3)] was significantly higher than that in Beijing plain area [AHI = 53.8 (38.4–67), P < 0.05]. 96% of patients treated in the Tibetan plateau had LSpO2 below 80%, while 51% of patients treated in the Beijing Plain had LSpO2 below 80%. In plateau patients, BMI (OR = 1.242, 95% CI1.051-1.468) was an independent risk factor for AHI ≥ 15 times/h. There are differences in many clinical features between male Tibetan OSAS patients in Tibetan plateau hospitals and male Han OSAS patients in Beijing Plain hospitals. Compared with plain patients, plateau patients had smaller neck circumference, lower LaSO2, higher REM period and higher history of hypertension. Overall, the severity of the disease was higher in plateau patients than in plain patients. For Tibetan plateau men, BMI and neck circumference were independent risk factors for moderate to severe OSAS and could be used as predictors of OSAS severity.
Keywords: Obstructive sleep apnea syndrome, A high plateau, The plain, Male
Subject terms: Signs and symptoms, Hypoxia, Respiratory signs and symptoms, Respiratory tract diseases
Introduction
Obstructive Sleep Apnea Syndrome (OSAS) is a clinical condition caused by repeated airway obstruction during sleep, leading to low ventilation and/or breathing interruptions. This results in intermittent hypoxia, hypercapnia, and disruptions in sleep structure, triggering a series of pathophysiological changes1. Recent studies show that the prevalence of OSAS among adults aged 54–93 exceeds 60%2, with some studies reporting an incidence as high as 55%3. Moreover, OSAS is a systemic disease that poses significant health risks, potentially leading to cardiovascular and cerebrovascular diseases4, cognitive dysfunction5, type 2 diabetes6, and other multi-organ and multi-system damage.
As China’s society and economy rapidly develop, people have higher demands for quality of life. Simultaneously, the incidence, consultation rate, and medical burden associated with OSAS are increasing worldwide. It is estimated that approximately 936 million individuals aged 30–69 suffer from OSAS globally (AASM 2012 standard)7. OSAS has become a major research focus across various medical fields, including otorhinolaryngology, respiratory medicine, oral and maxillofacial surgery, cardiovascular medicine, neurology, psychiatry, bariatric surgery, and more.
The etiology and risk factors for OSAS include common factors such as age, gender8, obesity9, family history10, and lifestyle factors such as tobacco use, alcohol consumption, and drug use11. Additionally, conditions like cerebrovascular diseases, congestive heart failure, hypothyroidism, acromegaly, vocal cord paralysis, laryngeal obstruction, brain tumors, neuromuscular diseases, laryngeal reflux, gastroesophageal reflux, and upper mediastinal masses pressing on the airway are rare but significant contributors.
Current treatments for OSAS include general therapies (e.g., weight loss, diet, regular exercise, positional sleep changes, smoking cessation, limiting alcohol intake, avoiding excessive work), etiological treatments, non-invasive positive airway pressure (CPAP) therapy, non-invasive positive pressure ventilation (NPPV), oral appliance (OA) therapy, and surgical options12.
The population of Tibet is predominantly Tibetan, while Beijing’s population is primarily Han. Tibet, with an average altitude exceeding 4000 m, is a high-altitude, low-pressure region where the air density is 60-70% of that at sea level, and oxygen content is low. Long-term exposure to this hypoxic environment may lead to high-grade pulmonary hypertension and related conditions13. For patients with OSAS, living in a low-oxygen environment further exacerbates the risks associated with hypoxia. Although numerous studies have been conducted on OSAS in plains, particularly in Beijing, research on sleep characteristics in Tibet, a high-risk area for OSAS, remains limited. The aim of this study was to compare the clinical characteristics of OSAS patients treated in hospitals in Tibet and Beijing, exploring the impact of regional and ethnic factors on sleep-disordered breathing in OSAS patients, to inform the diagnosis and treatment of Tibetan OSAS patients in the hypoxic high-altitude environment.
Materials and methods
Research object
A total of 120 patients with suspected sleep disordered breathing admitted to Tibet Autonomous Region People’s Hospital from April 2017 to October 2021 were retrospectively included. Overnight poly sleep monitoring (PSG) was performed, and 100 patients meeting the criteria were finally selected. During the same period, 174 patients with suspected sleep apnea disorder were treated in Peking University Third Hospital, and the same overnight PSG was performed. Finally, 86 patients meeting the criteria were selected, and all the data were statistically analyzed.
As for the determination of the sample size of this study, since there is no pre-research basis related to this study in the world, it is impossible to obtain sample parameters, so this study directly adopts the inclusion and exclusion criteria of income research objects, without the calculation and determination of the sample size.
Inclusion and exclusion criteria
Inclusion criteria: 1. OSAS with AHI ≥ 5 times/hour; 2, plateau Tibetan, plain Han; 3, age ≥ 18 years old; 4. Male (A total of 3 female patients were treated in the Tibetan Plateau area, which was difficult to conduct statistical analysis). Exclusion criteria: 1, overnight sleep monitoring time is less than 4 h; 2, with severe - very severe chronic obstructive pulmonary disease (GOLD3 ~ 4) (COPD), epilepsy, mental disease, neuromuscular disease, malignant tumor.
Observation index
Age, neck circumference, Body Mass Index (BMI), blood pressure, sleep apnea and Apnea-Hypopnea Index (AHI), proportion of N2 and REM periods, and lowest oxygen saturation at night (LSpO2). Currently, it is recommended in China to use BMI ≥ 24.0 kg/m2 and ≥ 28.0 kg/m2 as the diagnosis of overweight (24.0 kg/m2 ≤ BMI < 28.0 kg/m2) and obesity (BMI ≥ 28.0 kg/m2) in adults, respectively14. The severity of OSAS was classified according to AHI (times/hour). 5 ≤ AHI < 15 was mild, 15 ≤ AHI < 30 was moderate, and AHI ≥ 30 was severe12. Hypertension is defined by a 3-day measurement of systolic blood pressure greater than 140 mmHg or diastolic blood pressure greater than 90 mmHg. Statistical analysis SPSS20.0 software was used for statistical analysis of data. The measurement data satisfying the normal distribution were expressed as mean ± standard deviation (x ± s), and T-test was used for comparison between the two groups. The measurement data of non-normal distribution were expressed as the median (interquartile distance) [M (QR)], and Mann-Whitney rank sum test was used to compare the two groups. Counting data were expressed as percentage (%), and the comparison between the two groups was performed by X2 test. P < 0.05 was considered statistically significant, and P > 0.05 was not statistically significant. The process of this research is presented in Fig. 1.
Fig. 1.
Flow chart of this study.
Results
Among the 101 male Tibetan OSAS patients admitted to the People’s Hospital of Tibet Autonomous Region, 54 (53.5%) were obese, and among the 86 male Han OSAS patients admitted to the Third Hospital of Peking University, 42 (48.8%) were obese. The comparison of various indicators between patients in plateau areas and patients in plain areas is shown in Table 1. For obese patients, see Table 2; for non-obese patients, see Table 3.
Table 1.
Comparison of various indexes of patients in plateau and plain areas. It can be observed that there are significant differences between patients in the two locations regarding age, neck circumference, REM proportion, and blood oxygen saturation. (AHI unit: number per hour)
| Plateau(n = 101) | Plain(n = 86) | T | P | |
|---|---|---|---|---|
| Age(year) | 46.7 ± 12.4 | 43.3 ± 9.6 | 2.1 | 0.034* |
| Neck Circumference(cm) | 39.6 ± 4.4 | 42.0 ± 2.7 | 4.4 | 0.000* |
| BMI(kg/m2) | 28.1(25.6–31.8) | 27.8(25.9–30.3) | 0.46 | 0.64 |
| Bedtime DBP(mmHg) | 88.9 ± 12.3 | 90.0 ± 12.3 | 0.63 | 0.53 |
| Bedtime SBP(mmHg) | 131.8 ± 19.4 | 132.7 ± 15.8 | 0.34 | 0.74 |
| Morning DBP(mmHg) | 91.6 ± 13.2 | 90.0 ± 11.5 | 0.89 | 0.38 |
| Morning SBP(mmHg) | 132.5 ± 18.1 | 131.2 ± 13.5 | 0.56 | 0.58 |
| N2(%) | 48.7 ± 10.9 | 48.7 ± 18.2 | 0.002 | 0.99 |
| REM(%) | 18.1 ± 7.3 | 11.7 ± 6.2 | 6.39 | 0* |
| AHI(n/h) | 30.7(14.3–64.8) | 34.4(16.5–55.6) | 0.65 | 0.52 |
| LSpO2(%) | 66(50–72) | 79(65–85) | 6.73 | 0.000* |
| mSpO2 < 80% | 95(96%) | 44(51%) | 49.43 | 0.000* |
Table 2.
Comparison of various indexes of obese patients in plateau and plain areas. (AHI unit: number per hour)
| Plateau(n = 54) | Plain(n = 42) | T | P | |
|---|---|---|---|---|
| Age(year) | 47 ± 12.7 | 40.6 ± 8.2 | 3.016 | 0.003* |
| Neck Circumference(cm) | 41.7 ± 4.2 | 43.6 ± 2.5 | 2.78 | 0.007* |
| BMI(kg/m2) | 135 ± 18.8 | 138.6 ± 16.6 | 0.94 | 0.35 |
| Bedtime DBP(mmHg) | 90.2 ± 11.4 | 93.3 ± 13.2 | 1.22 | 0.22 |
| Bedtime SBP(mmHg) | 135.6 ± 17.8 | 135.4 ± 12.8 | 0.95 | 0.57 |
| Morning DBP(mmHg) | 94 ± 12.9 | 93.6 ± 11.7 | 0.15 | 0.88 |
| Morning SBP(mmHg) | 135.6 ± 17.8 | 135.4 ± 12.8 | 0.95 | 0.57 |
| N2(%) | 49.7 ± 10.9 | 50.9 ± 18.7 | 0.36 | 0.72 |
| REM(%) | 18.0 ± 7.3 | 10.5 ± 6.7 | 5.19 | 0.000* |
| AHI(n/h) | 45.4(23.9–80.1) | 49.3(26.9–66.4) | 0.49 | 0.62 |
| BMI(kg/m2) | 31.2(29.5–33.4) | 30.4(29.3–32.9) | 1.07 | 0.28 |
| LSpO2(%) | 60(47–70) | 72.5(62-81.3) | 3.96 | 0.000* |
Table 3.
Comparison of various indexes of non-obese patients in plateau and plain areas. There is a difference in age comparison between this table and the previous two tables, suggesting that there is no significant age difference in non-obese patients among these patients. (AHI unit: number per hour)
| Plateau(n = 47) | Plain(n = 44) | T | P | |
|---|---|---|---|---|
| Age(year) | 46.3 ± 12.2 | 45.8 ± 10.2 | 0.22 | 0.83 |
| Neckband(cm) | 37.2 ± 3.4 | 40.5 ± 2.0 | 5.51 | 0.000* |
| BMI(kg/m2) | 128.2 ± 19.7 | 127.3 ± 13 | 0.23 | 0.82 |
| Bedtime DBP(mmHg) | 87.4 ± 13.4 | 87.0 ± 10.6 | 0.15 | 0.88 |
| Bedtime SBP(mmHg) | 128.9 ± 18.1 | 127.4 ± 13.1 | 0.47 | 0.64 |
| Morning DBP(mmHg) | 88.9 ± 13.2 | 86.7 ± 10.5 | 0.88 | 0.38 |
| Morning SBP(mmHg) | 47.5 ± 11.1 | 46.5 ± 17.8 | 0.31 | 0.76 |
| N2(%) | 18.3 ± 7.4 | 12.9 ± 5.4 | 3.9 | 0.000* |
| REM(%) | 21.2(12.1–48.2) | 23.5(13.8–36.3) | 0.07 | 0.94 |
| AHI(n/h) | 25.3(23.2–26.8) | 26(24.8–26.7) | 1.39 | 0.16 |
| LSpO2(%) | 69.5(54.7–75) | 80.5(75-85.7) | 5.66 | 0.000* |
Age distribution of obese patients
The age of obese OSAS patients in plateau area (47 ± 12.7, n = 54) was higher than that in plain area (40.6 ± 8.2, n = 42, P < 0.05).
History of hypertension
The rate of OSAS patients with hypertension in plateau areas (52.5%, 95%CI: 42.6-62.4%) was significantly higher than that in plain areas (37.2%, 95%CI: 26.8-47.6%, P = 0.037).
Neck circumference of patients
The neck circumference of patients in plateau areas is smaller. It was (39.6 ± 4.4, n = 100) in plateau and (42 ± 2.7, n = 86, P < 0.05) in plain. For the obese patients in the two places, the plateau was (41.7 ± 4.2, n = 54) and the plain was (43.6 ± 2.5, n = 42, P < 0.05). For the non-obese patients in the two places, the plateau was (37.2 ± 3.4, n = 47) and the plain was (40.5 ± 2.0, n = 44, P < 0.05).
Sleep REM period of patients
The REM period of patients in plateau areas is longer than that in plain areas. It was (18.1 ± 7.3, n = 100) in plateau and (11.7 ± 6.2, n = 86, P < 0.05) in plain. For obese patients, the plateau was (18.0 ± 7.3, n = 54) and the plain was (10.5 ± 6.7, n = 42, P < 0.05). For non-obese patients, the plateau was (18.3 ± 7.4, n = 47) and the plain was (12.9 ± 5.4, n = 44, P < 0.05).
The minimum blood oxygen saturation (LSpO2) of patients at night
The LSpO2 of patients in plateau area is lower than that of patients in plain area. 66% (50-72%) in plateau and 79% (65-85%) in plain, P < 0.05. For obese patients, 60% (47-70%) were in plateau and 72.5% (62-81.3%) were in plain, P < 0.05. For non-obese patients, the incidence was 69.5% (54.7%75%) in plateau and 80.5% (75-85.7%, P < 0.05) in plain. For the distribution of LSpO2 at night, the blood oxygen level of patients in plateau area was lower than that in plain area. As shown in Figs. 2 and 96% of patients with LSpO2 < 80% and 4% of patients with 80-85% in plateau areas were treated. As shown in Fig. 3, patients in plain areas with LSpO2 < 80% accounted for 51.2%, 80-85% accounted for 23.3%, 85–90% accounted for 19.8%, and ≥ 90% accounted for 5.8%.
Fig. 2.
Nighttime LSpO2 distribution(%) in Plateau indicates a relatively high degree of low nocturnal oxygen minimums in the majority of plateau patients.
Fig. 3.
Nighttime LSpO2 distribution(%) in Plain shows that there are different distributions of nocturnal minimum oxygen levels in patients from the plains, with higher values and a smaller proportion of fractions less than 80 than in the highlands.
Severity of OSAS
In this study, AHI was used to mark the severity of OSAS, and we will explore the different distribution of AHI in patients in both places. In Table 4 ,it was told that the disease severity of patients in plateau areas is higher than that in plain areas in patients with severe OSAS (AHI ≥ 30), the plateau AHI was 62 (45.5–90.3) and the plain AHI was 53.8 (38.4–67.0, P = 0.013). Furthermore, We analyzed obese and non-obese patients separately in Tables 5 and 6. Visualized evidence was shown in Fig. 4.
Table 4.
Comparison of AHI(n/h) in patients with different severity in two places. Significant differences in patients with high-grade OSAS are found in the three tables below, representing differences in the likely severity of disease between the two patients.
| Plateau(n = 101) | Plain(n = 86) | T | P | |
|---|---|---|---|---|
| 5 ≤ AHI < 15 | 12.1(6.0-13.1) | 8.7(6.2–13.7) | 0.2 | 0.84 |
| 15 ≤ AHI < 30 | 23.7(19.2–27.5) | 22.9(17.9–26.9) | 0.27 | 0.79 |
| AHI ≥ 30 | 62(45.5–90.3) | 53.8(38.4–67.0) | 2.48 | 0.013* |
Table 5.
Comparison of AHI(n/h) in patients with different severity of obesity in two places.
| Plateau(n = 54) | Plain(n = 42) | T | P | |
|---|---|---|---|---|
| 5 ≤ AHI < 15 | 12.7(10.9–14.1) | 6.6(5.2-13.45) | 1.32 | 0.19 |
| 15 ≤ AHI < 30 | 24.1(20.5–27.5) | 21.5(16.5–24.7) | 1.01 | 0.32 |
| AHI ≥ 30 | 73.0(46.2–91.1) | 58.3(44.7–71.8) | 1.88 | 0.06* |
Table 6.
Comparison of AHI(n/h) in non-obese patients with different severity in two places.
| Plateau(n = 47) | Plain(n = 44) | T | P | |
|---|---|---|---|---|
| 5 ≤ AHI < 15 | 11.9(5.8–12.7) | 9.1(6.2–13.8) | 0.71 | 0.48 |
| 15 ≤ AHI < 30 | 22.9(18.3–27.6) | 23.5(18.2–27.5) | 0.22 | 0.82 |
| AHI ≥ 30 | 53.2(45.2–85.7) | 39.5(36.3–56.0) | 1.79 | 0.074* |
Fig. 4.
Comparison of disease severity between the two groups show that patients at altitude are more likely to have OSAS with severe symptoms(AHI ≥ 30 n/h).
Correlation analysis of N2, REM, LSpO2 and AHI
In order to explore the possible interconnections between the various factors mentioned above, we conducted a Correlation analysis of N2, REM, LSpO2 and AHI. Regarding to Table 7, the analysis showed a significant negative correlation between LSpO2 and AHI, with a correlation coefficient of -0.239 (p = 0.018) in plateau patients and − 0.718 (p < 0.001) in plain patients. N2 and REM had no significant correlation with AHI.
Table 7.
Correlation analysis between different indexes and AHI in plateau and plain patients. It can be seen that oxygen saturation and AHI were significantly correlated in both patients, indicating that this index May have some significance in the differentiation of patients with plateau.
| Index | Plateau | Plain | ||
|---|---|---|---|---|
| r | P | r | P | |
| N2 | -0.140 | 0.168 | 0.097 | 0.375 |
| REM | -0.136 | 0.179 | -0.196 | 0.071 |
| LSpO2 | -0.239 | 0.018* | -0.718 | < 0.001* |
Multivariate logistic regression analysis
Since OSAS is a complex syndrome with multifactorial effects, multivariate Logistic regression analysis was performed on all patients according to AHI ≥ 15 times /h to explore the predictive value of patients’ origin (plateau/plain), age, neck circumference and BMI for moderate to severe OSAS (AHI ≥ 15 times /h). The results in Table 8 showed that BMI (OR = 1.200, 95%CI1.057-1.363) was an independent risk factor for AHI ≥ 15 times /h, that is, the risk of moderate to severe OSAS increased by 20.0% for every 1 kg/m2 increase in BMI. Patient origin, age and neck circumference were not independent risk factors for AHI ≥ 15 times /h.
Table 8.
Shows the results of multivariate logistic regression analysis according to AHI ≥ 15 times /h.
| β | S.E. | P | OR | 95% CI | |
|---|---|---|---|---|---|
| Plateau | 0.205 | 0.414 | 0.620 | 1.228 | 0.546 ~ 2.763 |
| Age | -0.009 | 0.016 | 0.567 | 0.991 | 0.960 ~ 1.022 |
| BMI | 0.182 | 0.065 | 0.005* | 1.200 | 1.057 ~ 1.363 |
| Neck Circumference | 0.036 | 0.067 | 0.591 | 1.037 | 0.909 ~ 1.182 |
As is shown in Table 9, multivariate Logistic regression analysis was performed for plateau and plain patients according to AHI ≥ 15 times /h. In plateau patients, BMI (OR = 1.242, 95%CI1.051-1.468) was still an independent risk factor for AHI ≥ 15 times /h, that is, the risk of moderate to severe OSAS increased by 24.2% for every 1 kg/m2 increase in BMI. In plain patients, age, BMI, and neck circumference were not independent risk factors for AHI ≥ 15 times /h.
Table 9.
The results of multivariate logistic regression analysis were performed according to whether AHI was ≥ 15 times/h in plateau and plain patients, respectively. BMI has a significant effect on AHI in patients with altitude and May be an indicator that should be taken into account.
| β | S.E. | P | OR | 95% CI | ||
|---|---|---|---|---|---|---|
| Plateau | Age | -0.005 | 0.020 | 0.807 | 0.995 | 0.956 ~ 1.036 |
| BMI | 0.217 | 0.085 | 0.011* | 1.242 | 1.051 ~ 1.468 | |
| Neck Circumference | 0.032 | 0.078 | 0.678 | 1.033 | 0.887 ~ 1.203 | |
| Plain | Age | -0.021 | 0.027 | 0.450 | 0.980 | 0.929 ~ 1.033 |
| BMI | 0.132 | 0.110 | 0.231 | 1.141 | 0.919 ~ 1.417 | |
| Neck Circumference | 0.025 | 0.143 | 0.859 | 1.026 | 0.776 ~ 1.356 | |
Discussion
Obstructive sleep apnea syndrome (OSAS) is characterized by snoring and daytime sleepiness, significantly affecting sleep quality and potentially leading to sudden death. Tibet and Beijing differ notably in geological and demographic factors, such as lower oxygen concentration in Tibet’s plateau compared to Beijing’s plain, and the predominance of Tibetan ethnicity in Tibet versus Han ethnicity in Beijing. Patients with OSAS in Tibet may face higher hypoxia risks due to the low-oxygen environment, but limited research exists on this. In our study, we analyzed 186 OSAS patients (AHI ≥ 5 events/hour), including 100 from Tibet Autonomous Region People’s Hospital and 86 from Peking University Third Hospital. Due to the extensive inclusion of all patients meeting the inclusion criteria from both institutions in this study, it possesses a certain degree of regional representativeness. Our analysis shows that there are many differences in the clinical characteristics between male Tibetan OSAS patients in Tibet plateau area and male Han OSAS patients in Beijing plain area: compared with the patients in the plain area, the patients in the plateau area have a smaller neck circumference, a longer REM phase, and lower nocturnal LSpO2, but a higher proportion of hypertension history, and among the patients with severe OSAS in the two regions, the disease severity of the patients in the plateau area is higher than that in the plain area.
Age distribution difference of obese patients with OSAS in two places and the suggestive significance of BMI on the severity of OSAS
Obesity is a significant contributing factor to Obstructive Sleep Apnea Syndrome (OSAS), with several mechanisms linking the two conditions. Firstly, parapharyngeal fat deposits and increased neck fat contribute to narrowing of the airway during sleep, leading to upper airway obstruction. Secondly, increased abdominal circumference and the supine position reduce lung capacity, exacerbating hypoxia by decreasing upper respiratory tract traction. Thirdly, obese individuals experience a greater degree of upper respiratory tract collapse compared to non-obese individuals. Finally, obesity is thought to influence the neuromuscular control of the upper respiratory tract15. Additionally, OSAS itself adversely affects sleep quality, leading to daytime fatigue and sleepiness, which in turn increases the appetite for high-calorie foods. This disruption of endocrine function results in hormonal imbalances, further contributing to the vicious cycle of obesity and OSAS, where each condition exacerbates the other.
In this study, the age of obese patients in plateau region was older (P < 0.05). In the existing studies16, the negative correlation between obesity and altitude has been proved. In this study, the age of obese patients in the plateau area is higher. On the one hand, it may be because the obesity rate in the high-altitude area is lower than that in the low-altitude area at the same age. With the increase of age, the obesity rate of patients increases, and finally, the phenomenon of plateau obesity patients being older is higher. On the other hand, it may also be that with the increase of age, the incidence of complications will increase, and young obese patients are less willing to see a doctor.
In this study, BMI in plain-region patients was significantly correlated with moderate to severe OSAS, while neck circumference was not. This may be due to differences in fat distribution and airway morphology. Lower altitudes and higher temperatures in the plains may lead to a different fat distribution compared to plateau residents, where fat around the neck provides more protection. Since BMI doesn’t fully capture these variations, this could explain the observed discrepancies.
BMI has important implications for the severity of OSAS. For plain patients, BMI demonstrates high specificity in indicating OSAS. Therefore it can easily identify those who do not have OSAS in plain. For plateau, multivariate Logistic regression analysis showed that BMI (OR = 1.242, 95% CI1.051-1.468) was an independent risk factor for moderate to severe OSAS (AHI ≥ 15 times /h) in patients with plateau.
Different history of hypertension in OSAS patients
Hypertension, a chronic condition defined by sustained elevation in blood pressure, manifests as systemic vascular dysfunction with multi-organ complications. Its etiology involves genetic predisposition and environmental triggers, classified as primary (idiopathic) or secondary (disease-driven).
This study reveals elevated hypertension prevalence among plateau-dwelling OSAS patients. High-altitude hypoxia synergizes with modifiable risks (high-fat diets, alcohol intake) to disrupt glucose/lipid metabolism: suppressed lipolysis and upregulated lipid synthesis under chronic hypoxia promote dyslipidemia, atherosclerosis, and hypertension (Tibetan prevalence: 31.4% vs. national 27.5%)17,18. OSAS itself independently exacerbates hypertension risk4, with altitude-related hypoxia potentially amplifying this interaction through enhanced oxidative stress and sympathetic activation.
The difference of neck circumference between two places in OSAS patients and the suggestive significance of neck circumference on the severity of OSAS
Neck circumference serves as a critical morphometric marker in OSAS pathogenesis, reflecting upper airway adipose deposition and structural compromise19. Recognized as a robust clinical indicator for obesity-related OSAS, it demonstrates diagnostic equivalence to conventional anthropometric measures. Gender-specific thresholds (male ≥ 43 cm, female ≥ 40 cm) are widely validated as high-risk OSAS criteria20. Mechanistically, neck circumference shows positive correlations with both BMI and AHI (r > 0.6 in most cohorts), while increased BMI further drives hypertrophic remodeling of oropharyngeal structures21, synergistically exacerbating airway collapsibility.
In this study, we found that patients in plateau areas had smaller neck circumference (P < 0.05), which means that patients in plateau areas may have less fat accumulation in the neck and upper airway than those in plain areas, which may be related to ethnic differences including local diet and lifestyle habits, air density, pressure, oxygen content and other factors.
As noted, male patients in the plateau area have smaller neck circumferences, suggesting a lower body fat percentage. It is hypothesized that obesity and fat accumulation may have a more significant impact on neck circumference and AHI in the plateau population than in the plain population. Therefore, changes in neck circumference may better indicate OSAS severity in high-altitude areas and should be considered in diagnosis.
Effects of altitude, hypoxia and other factors on sleep structure and severity of OSAS
Lowlanders transitioning to high altitudes commonly exhibit sleep disturbances22, characterized by prolonged N1, reduced N3 and REM sleep, and frequent arousals23. While partial acclimatization (3 months at 1800 m prior to 3800 m exposure) yields sleep architecture comparable to Tibetan natives, acclimatized lowlanders show lower mean SpO₂ (p = 0.018) and shorter NREM duration (p = 0.03)24, indicating residual hypoxia-driven sleep disruption. Current research predominantly focuses on acute altitude exposure effects, with insufficient exploration of sleep patterns in long-term high-altitude residents.
OSAS patients demonstrate altitude-like sleep fragmentation, marked by increased N1/N2 dominance and progressive N3 suppression with disease severity25. Apnea-hypopnea events predominantly occur during N1/N2 phases, with significantly fewer events in N3/REM stages26,27. Notably, N3 deficiency correlates strongly with daytime dysfunction28, while prolonged N1 exacerbates nocturnal hypoxemia and sleep discontinuity29. Reduced total sleep time (TST) associates with elevated N1 and diminished REM proportions30, though severe OSAS may paradoxically increase REM allocation relative to N331.
A recent study has shown that OSAS may lead to a lowering of the arousal threshold, which in turn leads to a reduction in deep sleep (N3) and a higher percentage of others, and that this also affects the body’s ability to recruit the dilator muscles of the upper airway, leading to more severe obstructive respiratory events32. The result is that if OSAS patients in the plateau region show corresponding changes in sleep structure can also explain to some extent the differences in their clinical characteristics.
High-altitude populations exhibit enhanced physiological adaptation to hypoxia, leading to attenuated obstructive sleep apnea syndrome (OSAS)-induced hypoxic effects compared to lowland counterparts at equivalent severity. Although nocturnal hypoxic-driven oxygen desaturation disrupts sleep architecture (reduced total sleep time [TST] and N3 stage duration), these individuals demonstrate resilience to REM sleep suppression, ultimately manifesting higher REM proportions. Notably, this contrasts with prior studies suggesting hypoxia-induced sleep fragmentation diminishes REM sleep in high-altitude environments.
This study found that nighttime LSpO2 in OSAS patients from the plateau was significantly lower than in those from the plain. In the plateau group, 96% had LSpO2 below 80%, compared to 51.2% in the plain group, indicating more severe hypoxia during sleep in the plateau population. This is closely linked to the low oxygen levels at high altitudes, as intermittent hypoxia is a key factor in OSAS. Upper airway obstruction leads to apnea and hypopnea, resulting in hypoxemia and hypercapnia. At plateaus, hypoxia can cause central sleep apnea (CSA), manifested by periodic breathing at night, that is, one breathing after every 2–4 breaths33,34, further increasing AHI. A study conducted in Peru by Luu V Pham et al.35 found that the AHI of plateau (3,825 m elevation) residents was twice as elevated as that of plain (sea level) residents, due to an increase in central rather than obstructive apnea; The blood oxygen saturation of plateau residents was lower than that of plain residents when awake, and further decreased during sleep. It can be seen that there is a bidirectional effect between apnea and hypoxia.
LSpO₂ demonstrated an inverse correlation with AHI, with a notably stronger association in lowland populations than in high-altitude residents. While elevated AHI universally reduced nocturnal oxygen intake and lowered SpO₂ minima, plateau populations exhibited attenuated sensitivity to hypoventilation-induced desaturation. This disparity stems from adaptive physiological mechanisms developed in response to chronic hypobaric hypoxia, which partially buffer blood oxygen fluctuations in high-altitude dwellers.
Future perspectives of this study
Obstructive sleep apnea syndrome (OSAS) remains a critical research focus, with recent advances in diagnostic and prognostic evaluation. Emerging evidence indicates that apnea-hypopnea duration (AHD) outperforms the apnea-hypopnea index (AHI) as a biomarker for blood oxygenation dynamics in OSAS, offering enhanced correlations with disease severity and complication risks36. Future investigations will prioritize AHD’s clinical utility to strengthen methodological frameworks.
Pharmacotherapeutic innovations show promise for OSAS management. Agents targeting dopamine/noradrenaline pathways significantly improve daytime alertness and clinical outcomes, as evidenced by CGI-C scale assessments37. While CPAP remains the gold-standard therapy for symptom control, persistent challenges in long-term patient adherence underscore the need for alternative strategies.
These advancements will inform tailored diagnostic and therapeutic strategies for OSAS patients in the Tibetan Plateau region, ultimately addressing region-specific healthcare disparities.
Bias in this study
We have made many efforts to avoid bias in this study, including but not limited to using machines of the same model from the same manufacturer, excluding some cases with incomplete data, and repeatedly confirming the inclusion and exclusion criteria of samples. However, there are still some biases.
Hospital Admission Bias: This study only included patients from Peking University Third Hospital and Tibet Autonomous Region People’s Hospital, which may not fully represent the overall patient population. If certain patients, due to factors such as symptom severity or regional differences, tend to seek treatment earlier or more frequently, the sample may not accurately reflect the general patient characteristics, affecting the external validity (generalizability) of the findings.
Measurement Bias: Despite efforts to standardize equipment, measurement bias may still arise due to factors like equipment malfunction, operator differences, or varying levels of training. For example, equipment errors or inconsistencies in measurement standards across healthcare providers could introduce systematic errors, affecting the accuracy of the results.
Limitation of this study
This study included only male OSAS patients from the People’s Hospital of Tibet Autonomous Region and Peking University Third Hospital due to the small number of female patients. The sample size could not be statistically calculated as there was no prior data, and patients were included based on screening criteria. The low number of female patients may be due to factors such as health literacy, medical awareness, religious beliefs, and a lower incidence of OSAS in women in the region.
This retrospective analysis had a small sample size and was limited by inconsistent monitoring and data analysis across the two centers, as well as missing or inaccurate clinical data. For example, hypertension history was self-reported by patients without objective verification, potentially leading to bias. Additionally, factors like hypertension duration and severity, which could affect OSAS severity, were not considered due to limited data.
Conclusion
In conclusion, this study found significant clinical differences between male Tibetan OSAS patients in the Tibet Plateau and male Han OSAS patients in Beijing. Plateau patients had smaller neck circumference, lower LpSO2, longer REM sleep, and a higher history of hypertension compared to plain patients. Overall, the severity of OSAS was greater in plateau patients. For Tibetan men, BMI and neck circumference were independent risk factors for moderate to severe OSAS and could predict disease severity. Based on these findings, we recommend considering low BMI and neck circumference when screening for high-risk OSAS groups in plateau regions.
Abbreviations
- OSAS
Obstructive Sleep Apnea Syndrome
- AHI
Apnea hypopnea index
- BMI
Body mass index
- LSpO2
Low saturation of peripheral oxygen
- REM
Rapid eye movement
- NREM
Non-rapid eye movement
- DBP
Diastolic blood pressure
- SBP
Systolic blood pressure
Author contributions
Li Tao, Lian Zihe and Lin Yuzhi wrote the main manuscript text; Lian Zihe and Lin Yuzhi prepared figures and tables; Li Tao, Yan Yan and Fan Rui provided datas of patients of Beijing; Gesang Quzhen, Liu Pan, Meilang Qucuo provided datas of patients of Tibet; Tao Liyuan provided advice on data analysis；All authors reviewed the manuscript.
Funding
This work was sponsored by Project 81860024 supported by the National Natural Science Foundation of China, Project HDCXZHKC2023202 supported by PUTH-Haidian Innovation & Transformation Fund and Project XZ2021ZR-ZY05(Z) supported by Tibet Autonomous Region Natural Science Fund Group Assistance Medical Project.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
Declarations
Competing interests
The authors declare no competing interests.
Informed consent statements
All subjects or their legal guardians have provided their own informed consent.
Research method statements
Research methods of this study were carried out in accordance with relevant guidelines and regulations.
Experimental protocols statements
Experimental protocols of this study were approved by Peking University Third Hospital and People’s Hospital of Tibet Autonomous Region.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Qucuo Meilang, Email: meilang2008@126.com.
Yan Yan, Email: yanyan_ent@bjmu.edu.cn.
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Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.