The relationship between lung function and obstructive sleep apnea: Finding from NHANES 2007-2008

The relationship between lung function and obstructive sleep apnea: Finding from NHANES 2007-2008 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The relationship between lung function and obstructive sleep apnea: Finding from NHANES 2007-2008 Gan Luo, Yali Peng, Dengjun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7886768/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The purpose of this research is to explore the correlation between pulmonary function and OSA. Methods This research analyzed the data of 3824 participants from 2007 to 2008. Based on lung function, they are divided into obstructive group, trestrictive group, normal group, FEV1 < 80%pre group and FVC < 80% pred group. Logistic regression was used to analyze the relationship between lung function and high risk factors of OSA, and patients with COPD were screened by bronchodilation test to further analyze the high risk factors of COPD complicated with OSA. Results This research reveals that participants with OSA were older, had a greater proportion of males and smokers, exhibited larger waist circumferences and higher BMI. In addition, a significantly elevated risk of cardiovascular and cerebrovascular diseases. Univariate Logistic regression analysis showed that obstructive pulmonary ventilation dysfunction, FVC < 80%pred and cough with phlegm symptoms of the incidence rate was significantly higher in the OSA group. However, in the multivariate adjustment model, only chronic cough with phlegm were identified as independent risk factors for OSA. Further analysis of COPD complicated with OSA shows that gender, smoking, waist circumference and arthritis are independent risk factors。With the increase of the severity of airflow restriction, the incidence of COPD complicated with OSA is gradually increasing. Conclusion There is a certain correlation between lung function indexes and OSA. Regardless of factors adjusted for, chronic cough with phlegm remained a significant risk factor. OSA COPD Lung function Cough with phlegm Health survey Figures Figure 1 Introduction Obstructive Sleep Apnea (OSA) is an apnea and hypopnea disease caused by repeated collapse and obstruction of the upper airway during sleep. OSA can lead to sleep structure disorder, and trigger a series of pathophysiological changes such as intermittent hypoxia, hypercapnia, significant fluctuation of pressure in chest cavity, autonomic nerve dysfunction and inflammatory activation [ 1 ] . As a common disease, OSA is especially common among people with clear risk factors and comorbidity. Its global incidence rate is about 45%, and the prevalence rate in Europe and America is between 14% and 49% [ 2 ] . The risk of OSA usually increases with age. A research on men over the age of 65 shows that the prevalence of OSA in people under the age of 72 is 23%, while that in people over the age of 80 rises to 30%. In contrast, the prevalence rate of men aged 30–40 is only 10% [ 3 ] . The research of sleep heart health further shows that there are racial differences in the prevalence rate of OSA, which is 30% among whites, 32% among blacks, 38% among Hispanics, and 39% among China people. Meanwhile, researches have shown that OSA is more prevalent among middle-aged populations with an increasing prevalence of obesity [ 3 , 4 ] . OSA has an important negative impact on quality of patients of life and multi-organ system function, which is characterized by excessive daytime sleepiness, impaired daily function and increased metabolic abnormalities, and significantly increases the risk of cardiopulmonary diseases and metabolic syndrome [ 2 , 4 ] . The severity of the disease is usually evaluated by the Apnea-Hypopnea Index, which is defined as the total number of times of apnea and hypopnea in sleep every hour [ 5 ] . Clinical screening of OSA include detailed collection of sleep history, systematic review of related symptoms and comprehensive physical examination.Polysomnography is an important auxiliary means to diagnose OSA and evaluate its severity [ 6 , 7 ] . COPD is a heterogeneous disease, which is characterized by chronic respiratory symptoms caused by airway and/or alveolar abnormalities, leads to continuously aggravated airflow restriction [ 8 ] . Sleep has a profound influence on breathing and gas exchange, especially in COPD patients. When COPD and OSA coexist, that is, overlap syndrome (OVS), the health risk of such patients will increase significantly [ 9 ] .There seems to be a correlation between the increase of COPD severity and the increase of OVS prevalence. According to the report of sleep heart health research [ 10 ] , compared with non-COPD patients, there is no significant difference in the incidence of OVS in mild COPD patients; However, in patients with moderate and severe COPD, the prevalence of OVS reached 39% and 47% respectively. The overall prevalence of OVS in the general population is reported to be 1% to 3.6% [ 11 ] . Among the patients with confirmed OSA, the prevalence rate of OVS is 7.6% to 55.7%. Similarly, the prevalence of OVS in COPD population is 2.9% to 65.9% [ 10 ] . It has been reported that OSA is related to the decline of lung function. People at high risk of OSA defined by multivariate apnea prediction (MAP) index usually show a significant decrease in forced expiratory volume (FEV1) and forced vital capacity (FVC) in the first second. Similarly, in a research matching body mass index (BMI), it was also found that there was a significant correlation between more severe OSA and lower lung function. Obesity affects expiratory reserve by reducing functional residual volume, and expiratory reserve decreases with the increase of residual volume, indicating that BMI is an important confounding factor between OSA risk and lung function [ 12 , 13 ] . The National Health and Nutrition examination (NHANES) database is a large-scale and well-designed clinical database. Based on this database, we conducted a research to explore the correlation between the risk of OSA and lung function. We put forward the hypothesis that the high risk of OSA is related to lung function, and this association still exists after adjusting for confounding factors. Materials and methods Data source The NHANES is a cross-sectional survey based on population, which aims to collect information about the health and nutrition status of American families. All the operating procedures of the survey were approved by the National Health Statistics Ethics Review Board, and the written informed consent of each participant was obtained at the time of registration. NHANES conducts an annual survey of a national representative sample with a sample size of about 5,000 people, including interviews and physical examinations. The interview part covers information about demography, socio-economic status, eating habits and health-related issues; The physical examination part includes basic medical information, blood pressure, hearing test, oral health status, grip strength measurement, as well as a large number of laboratory test data and some radiological examination data. Research design and population In this database, we found the participants who had complete lung function data and questionnaire data related to sleep apnea during the investigation period from 2007 to 2008. A total of 10149 respondents were included in the preliminary screening during this period. According to the research design criteria, 4215 individuals with incomplete physical data, missing OSA data or incomplete covariate information were excluded. Subsequently, 623 women under the age of 20 or in pregnancy were excluded; Finally, 1487 respondents who did not complete the lung function test or whose data quality was unacceptable were further excluded. After the above screening, a total of 3824 valid samples were finally determined to be included in the analysis, including 2003 cases in OSA group and 1821 cases in non-OSA group. The complete data collection process is shown in Fig. 1. The standard of OSA patients According to the following criteria: if the subject meets any of the following conditions, it will be classified as OSA; If the three conditions are met at the same time, it is judged as non-OSA. Specific conditions include: (1) snoring at least three nights a week; (2) Symptoms of apnea or conscious respiratory interruption during wheezing and snoring occur three nights a week or more frequently; (3) Sleeping for at least seven hours every night on weekdays or during work breaks, but still experiencing excessive drowsiness during the day, and the number of drowsiness episodes is between 16 and 30 times per day. Measurement data of lung function According to the results of pulmonary function measurement in NHANES database, this research selected the data whose first test status was completed and the quality attributes of FEV1 and FVC were Grade A or B for analysis. Among them, Grade A means that the data quality exceeds the data collection standard set by the American Thoracic Society (ATS), while Grade B means that it meets the standard. The values of FEV1%pred and FVC%pred are calculated based on the reference equation of lung function of American general population 2 . The lung function is divided into the following three categories: Normal is defined as FEV1/FVC ≥ 0.7, and FEV1 ≥ 80%pred and FVC ≥ 80%pred. Obstructive ventilation dysfunction is defined as FEV1/FVC < 0.7. Restrictive ventilation dysfunction is defined as FEV1/FVC ≥ 0.7 but FVC < 80%pred. For the data whose second test status is completed and the quality grade of FEV1 and FVC is A or B,.According to the global GOLD guidelines, FEV1/FVC < 70% after bronchodilator is adopted as the diagnostic standard.In the meanwhile, according to the GOLD classification, the severity is divided into: GOLD 1 (mild, FEV1%pred ≥ 80%) ;GOLD 2 (moderate, 50% ≤ FEV1%pred < 80%),;GOLD 3 (severe, FEV1%pred < 50%) ;GOLD 4 (extremely severe, FEV1%pred < 30%). Covariate The covariates selected in this research include age, gender, race (divided into mexican american, other Hispanics, non-Hispanic whites and non-Hispanic blacks), education level (below grade 9, grades 9 to 11, high school graduation or GED equivalent, some universities or AA degrees, junior college or above), waist circumference, height, weight, body mass index (BMI, calculated by dividing the weight by the square of height), Smoking status (current smokers: smoking more than 100 cigarettes in their lifetime and reporting current smoking; Former smoker: a person who smokes ≥ 100 cigarettes in his life and reports that he has given up smoking; Never smoker: a person who has never smoked more than 100 cigarettes in his life), drinking situation, and the history of complicated diseases (including hypertension, cholesterol, diabetes, asthma, arthritis, gout, stroke, thyroid disease, tumor, heart failure, coronary heart disease and angina pectoris) obtained through questionnaire survey. Statistical methods In this research, the data of NHANES database was collected, classified, screened and sorted by R language and was statistically analyzed by SPSS Statistics 27.0.1. Firstly, the distribution type of continuous variables is judged by Kruskal-Wallis H test. Those that conform to normal distribution are represented by mean standard deviation, while those that are not normal distribution are represented by median (interquartile interval). T test or Mann-Whitney U test was used for comparison between the two groups according to the data distribution. Classification variables were described by frequency or percentage, and chi-square test was used for comparison between groups. The relationship between OSA and risk factors was analyzed by univariate and multivariate logistic regression analysis. The results were expressed by odds ratio (OR) and 95% confidence interval (CI). In multivariate analysis, Model 1 adjusted age, gender, race and body mass index; Model2 further incorporates smoking and drinking conditions on the basis of Model 1; Model 3 adjusts complications such as hypertension, hyperlipidemia, asthma, arthritis, gout, diabetes, stroke, thyroid diseases, tumors and cardiovascular diseases on the basis of Model 2. Result The baseline demographic data Compared with the non-OSA group, the participants in OSA group are older, with a higher proportion of men, a larger waist circumference, a higher body mass index (BMI), and a higher proportion of current smokers and former smokers. In addition, in patients with hypertension, hypercholesterolemia, diabetes, arthritis, stroke and coronary heart disease, the diagnosed rate of OSA is significantly higher (Table 1 ). Table 1 Comparison of baseline demographic data between OSA group and non-OSA group Variable OSA Non-OSA P value Count 2003 1821 Age 50(38–61) 44(31–60) < 0.001 Gender < 0.001 Male 1137(56.80%) 797(43.80) Female 866(43.20%) 1024(56.20) Race 0.319 El Chicano 378(18.9%) 329(18.10%) Other Hispanics 249(12.40%) 199(10.90%) Non-Hispanic whites 980(48.90%) 903(49.60%) Non-Hispanic blacks 396(19.8%) 390(21.40%) Education 0.21 Below grade 9 215(10.70%) 189(10.40%) Grade 9–11 372(18.60%) 300(16.5%) Graduated from high school 524(26.20%) 431(23.7%) Graduated from university 542(27.1%) 514(28.2%) Postgraduate or above 350(17.5%) 386(21.3%) waistline 102.1(92.8-112.5) 93.1(83.6-103.50) < 0.001 Height cm 168.9(162–176) 166.(159.65–175.10) < 0.001 Weight kg 84.3(72.8–98.8) 75.00(63.65–87.15) < 0.001 BMI kg/m 2 29.37(26.15–33.93) 26.51(23.35–30.69) < 0.001 Smoking status < 0.001 Never smoke 941(47.00%) 1018(55.90%) Smoking in the past 549(27.40%) 381(20.90%) Current smoking 512(25.60%) 41(23.10%) Drinking 1459(72.80%) 1282(70.40%) 0.046 hypertension 765(38.20%) 466(25.60) < 0.001 cholesterol 718(35.84%) 481(25.57%) < 0.001 diabetes 767(13.30%) 145(8.00%) < 0.001 asthma 272(13.60%) 203(11.10%) < 0.001 arthritis 574(28.7%) 47(2.6%) 0.076 gout 91(4.5%) 47(2.6%) < 0.001 stroke 62(3.1%) 27(1.5%) < 0.001 thyroid 175(8.7%) 155(8.5%) 0.952 tumour 173(8.6%) 137(7.5%) 0.139 cardiac failure 43(2.1%) 26(1.4%) 0.116 coronary heart disease 69(3.4%) 36(2.0%) 0.019 angina pectoris 51(2.5%) 28(1.5%) 0.068 Lung function and respiratory symptoms Compared with the non-OSA group, the measured values of FEV1%pred, FVC% pred and FEV1/FVC of participants in OSA group were lower, but the actual value of FVC is not much different. In the classification of lung function, the prevalence of FVC < 80%pred and restrictive pulmonary ventilation dysfunction in OSA group was significantly higher than that in non-OSA group. In addition, in terms of respiratory symptoms, the duration of cough with phlegm in OSA group was significantly longer (Table 2 ). Table 2 Comparison of lung function and respiratory symptoms between OSA group and non-OSA group Variable OSA group Non-OSA group P value Count 2003 1821 Lung function parameters FEV1 actual value 3.04 ± 0.89 3.09 ± 0.91 0.105 FEV1 estimated value 3.21 ± 0.77 3.21 ± 0.82 0.966 FEV1%pred 0.94 ± 0.15 0.97 ± 0.18 < 0.01 FVC actual value 3.95 ± 1.09 3.95 ± 1.08 0.884 FVC estimated value 4.18 ± 0.92 4.02 ± 0.99 < 0.001 FVC%pred 0.94 ± 0.15 0.98 ± 0.14 < 0.001 FEV1/FVC 0.77 ± 0.08 0.78 ± 0.083 < 0.001 Type of lung function Normal 1385 1309 0.143 Obstructive 321 265 0.021 Limit 260 123 < 0.001 FEV1 < 80%pred 310 303 0.777 FVC < 80%pred 322 178 < 0.001 Symptoms of respiratory Cough is greater than 3 months 184 90 < 0.001 Coughing up phlegm for more than 3 months 150 71 < 0.001 Values are expressed as weighted percentages (%) and mean ± standard deviation (mean ± SD). FEV1:forced expiratory volume in one second FVC:forced vital capacity FEV1/FVC: one second rate FEV1%pred:FEV1 Proportion of estimated value FVC%pred:FVC Proportion of estimated value Normal: FEV1/FVC > = 0.7,FEV1 > = 80%pred, FVC > = 80%pred Block: FEV1/FVC = 0.7 FVC < 80%pred Correlation between OSA incidence and lung function Taking patients with normal lung function as the reference category, in the unadjusted model, the incidence of patients with obstructive pulmonary ventilation dysfunction, FVC < 80%pred, cough with phlegm as the main symptoms is significantly higher in OSA group (Table 3 ). Table 3 univariate and multivariate logistic regression analysis of high-risk incidence in OSA group Tape of lung function Univariate OR(95%CI) P value Modle 1 OR(95%CI) P value Modle 2 OR(95%CI) P value Modle 3 OR(95%CI) P value normal reference value reference value reference value reference value FEV1 < 80%pred 1.034(0.868–1.23) 0.707 1.244(1.019–1.528) 0.032 1.808(1.339–2.441) < 0.001 1.944 (1.289–2.930) 0.002 FVC < 80%pred 0.585 (0.480–0.713) < 0.001 0.802 (0.639–1.006) 0.056 0.992 (0.706–1.314) 0.963 1.349 (0.842–2.165) 0.215 Obstructive 0.501 (0.399–0.628) < 0.001 0.699 (0.54–0.901) 0.006 0.718 (0.483–1.068) 0.102 1.158 (0.664–2.018) 0.605 cough 1.605 (1.231–2.094) < 0.001 1.427 (1.030–1.978) 0.033 1.324 (0.829–2.115) 0.241 1.785 (0.962–3.311) 0.066 expectoration 1.645 (1.225–2.208) < 0.001 1.454 (1.017–2.079) 0.040 1.579 (0.912–2.732) 0.103 2.174 (1.059–4.403) 0.034 Values are presented as median (interquartile range, IQR) In Model 1, age, gender, race and BMI are adjusted. In Model 2, smoking and drinking are adjusted again on the basis of Model 1. In Model 3, based on Model 2, hypertension, blood lipid, asthma, arthritis, gout, diabetes, stroke, thyroid, tumor and cardiovascular diseases (congestive heart failure, coronary heart disease and angina pectoris) were adjusted again. In Model 1, obstructive pulmonary ventilation dysfunction is still the main influencing factor of OSA, FEV1 < 80% ,cough with phlegm symptoms are also significant risk factors. In Model 2, FEV1 < 80%pred is still an independent risk factor for OSA. In Model 3, FEV1 < 80%pred continues to be a high risk factor for OSA, and expectoration symptoms are also identified as relative risk factors. Secondary logistic regression analysis in COPD patients with comorbid OSA Among 713 patients who completed the second pulmonary function test and were diagnosed as COPD according to FEV1/FVC < 70% after bronchodilation, 380 patients were complicated with OSA. The variables with statistical significance in univariate analysis (including gender, body mass index, waist circumference, smoking status, diabetes, hyperlipidemia and arthritis), drinking history, hypertension, asthma, tumor, stroke, gout, thyroid disease, congestive heart failure, coronary heart disease and angina pectoris) were included in binary logistic regression analysis. The results showed that gender, smoking, waist circumference and arthritis were independent risk factors for COPD complicated with OSA (Table 4 ). Table 4 Univariate and multivariate logistic regression analyses of risk factors for COPD with comorbid OSA Parameter Univariate OR(95%CI) P value Multivariate OR(95%CI) P value Gender 2.292(1.695-3.100) < 0.001 4.319(2.311–8.073) < 0.001 BMI 0.977(0.962–0.992) 0.003 1.026(0.996–1.058) 0.095 Waistline 0.968(0.958–0.978) < 0.001 0.961(0.946–0.971) < 0.001 Smoke 1.595(1.162–2.188) 0.004 1.359(0.863–2.140) 0.086 Drink alcohol 1.227(0.863–1.744) 0.255 0.924(0.542–1.574) 0.771 Diabetes 1.375(1.015–1.863) 0.04 0.897(0.431–1.865) 0.770 Hypertension 1.414(0.894–2.237) 0.139 0.672(0.414–1.092) 0.109 Blood fat 0.997(0.837–1.189) 0.015 1.333(0.855–2.079) 0.204 Asthma 1.318(0.888–1.958) 0.170 1.292(0.701–2.201) 0.457 Tumour 0.877(0.577–1.331) 0.537 1.155(0.619–2.155) 0.650 Stroke 1.235(0.541–2.819) 0.616 1.156(0.288–4.637) 0.838 Arthritis 13.878(7.672–25.104) < 0.001 27.281(11.028–67.485) < 0.01 Gout 1.369(0.669–2.464) 0.390 0.277(0.086–0.897) 0.277 Thyroid 1.450(0.875–2.404) 0.150 0.277(0.086–0.897) 0.032 Congestive heart failure 0.875(0.359–2.128) 0.768 0.461(0.118–1.801) 0.265 Coronary heart disease 2.054(0.995–4.241) 0.052 2.048(0.583–7.194) 0.264 Angina pectoris 1.211(0.981–3.046) 0.685 0.410(0.093–1.807) 0.239 Values are presented as median (interquartile range, IQR) Comparison of COPD complications with different airflow levels With the increase of the severity of airflow restriction, the incidence of COPD complicated with OSA shows a gradual upward trend; Similarly, the incidence of OSA increased with the severity of the disease in patients with asthma and arthritis, but the other variables did not show statistical significance (Table 5). Table.5-Comparison of COPD complications with different airflow levels Comorbidity Mild Moderate Severe or extremely severe X 2 P value OSA 223(44.6%) 140(74.1%) 17(70.8%) 50.931 <0.001 Diabetes 54(10.8%) 30(15.9%) 3(12.5%) 4.090 0.664 Hypertension 180(36.00%) 87(46.0%) 10(41.7%) 5.893 0.053 Hyperlipemia 158(46.8%) 94(59.1%) 10(50.0%) 7.318 0.120 Asthma 73(14.6%) 40(21.2%) 9(37.5%) 11.816 0.019 Tumour 70(14.0%) 30(15.9%) 3(12.5%) 0.465 0.792 Stroke 16(3.2%) 7(3.7%) 1(4.2%) 0.760 0.944 Arthritis 80(16.0%) 61(32.3%) 9(37.5%) 25.921 <0.001 Gout 19(3.8%) 14(7.4%) 0 5.249 0.072 Thyroid 48(9.6%) 18(9.5%) 4(16.7%) 1.461 0.834 Congestive heart failure 12(2.4%) 7(3.7%) 1(4.2%) 8.501 0.075 Coronary heart disease 22(4.4%) 12(6.3%) 2(8.3%) 6.882 0.142 Angina pectoris 11(2.2%) 6(3.2%) 2(8.3%) 9.161 0.057 Values are presented as numbers (weighted percentages). Discussion This research explored the complex relationship between OSA and lung function based on data from the NHANES database. The results showed that the occurrence of OSA was closely related to high body mass index, long smoking history and the existence of many related diseases (including hypertension, high cholesterol, diabetes, arthritis, stroke and coronary heart disease). Patients with OSA showed a downward trend of lung function characterized by the decrease of FEV1%pred, FVC%pred and FEV1/FVC ratio, and the incidence of respiratory symptoms increased significantly. In univariate and multivariate analysis, cough with phlegm symptoms were confirmed as independent high risk factors of OSA. In multivariate models (Model1, Model2 and Model3), only FEV1 < 80%pred shows significant differences related to OSA risk. Through logistic regression analysis, this research found that chronic cough with phlegm is always an independent high-risk factor for OSA, no matter how the independent variables are adjusted. These findings are consistent with those reported by Kim et al [ 14 ] , who previously identified OSA as a risk factor for chronic cough through questionnaire-based high-risk screening and subsequent regression analyses. OSA may cause persistent symptoms of cough with phlegm by interacting with common causes of chronic cough (such as CVA/GERD/UACS). Chronic cough with phlegm are partly caused by airway inflammation, and the characteristics of OSA (repeated airway collapse during sleep and airway trauma and inflammation caused by it) may further prolong the course of chronic cough [ 15 ] . In the multivariate adjustment analysis, this research also found that body mass index and smoking were significantly related to the decrease of FEV1/FVC and FEV1 < 80%pred, and the duration of cough with phlegm still showed stronger statistical differences in the multivariate model. A longitudinal study in Canada shows that when FEV1%pred is lower than 50%, the risk of chronic cough increases five times and the incidence of OSA is higher [ 16 ] . Its mechanism may be related to the changes of anatomical structure of the upper airway in obese patients (especially the stenosis of the upper airway caused by neck fat accumulation), the increase of abdominal fat (causing the decrease of vital capacity and functional residual capacity) and the chronic inflammatory state related to obesity, which make the upper airway more prone to collapse. According to statistics, about 70% of patients with severe OSA are obese, and the risk of apnea increases by 6 times for every 10% increase in weight [ 9 ] . At the same time, the chemicals in tobacco can stimulate the nasopharynx, larynx and airway mucosa, causing chronic airway inflammation and lumen stenosis, thus increasing the risk of OSA [20]. In this research, it was also observed that compared with the non-OSA group, patients in OSA group smoked longer, had higher body mass index, and FEV1%pred, FVC%pred and FEV1/FVC were significantly decreased, and the proportion of cough with phlegm symptoms was higher. When COPD and OSA coexist, it is called COPD-OSA overlap syndrome. This research found that in univariate and multivariate analysis, gender, waist circumference and arthritis are all high-risk factors for this overlap syndrome; The prevalence of adult OSA has obvious gender differences (the ratio of male to female is about 2:1 to 3:1) [ 17 ] . We classify individuals with waistlines exceeding the standard thresholds (90 cm for men and 85 cm for women) as having abdominal obesity, also termed apple-type obesity. This is based on the fact that excessive adipose tissue secretes various inflammatory cytokines, which can aggravate airway edema and heighten the respiratory center's sensitivity to hypoxia [ 18 ] . Metabolic syndrome (MS) which has been widely concerned in recent years, is a kind of clinical syndrome including abdominal obesity, hypertension, abnormal blood sugar and dyslipidemia. Alejandra et al. elaborated in detail how MS affects the risk of OSA through an evidence-based research [ 19 ] . In the analysis of COPD complicated with diseases, it is found that with the aggravation of airflow restriction, the incidence of OSA increases gradually, and the prevalence of asthma and arthritis also increases accordingly. This phenomenon may be related to chronic hypoxia caused by persistent airflow limitation of COPD, which further leads to neuroregulatory dysfunction and hemodynamic changes, thus promoting complications [ 20 ] . For asthma patients, inhaled corticosteroids (ICS) can delay the decline of lung function, but asthma patients with OSA may not respond well to ICS treatment, which leads to the accelerated decline of FEV1, and then pushes up the prevalence of comorbidity [ 21 ] . Rheumatoid arthritis can directly shorten the sleep time and increase the frequency of awakening at night due to symptoms such as pain and morning stiffness in the active stage of the disease. If the disease involves the neck bone or temporomandibular joint, it can also cause structural stenosis of the upper airway, affect the ventilation function and induce sleep-disordered breathing [ 22 ] . Therefore, in the early treatment of COPD, we should not only pay attention to the lung function itself, but also pay attention to the screening and intervention of its complications as soon as possible, especially to actively screen whether it is complicated with OSA. And through lifestyle adjustment, physical therapy and sleep ventilator and other early intervention measures to improve sleep disorders of patients. This research has the following limitations: firstly, the judgment of OSA population is only based on questionnaire survey for screening, and professional instruments such as polysomnography are not used for diagnosis, which may have certain classification deviation, but we try to reduce the influence of this deviation by setting three coincidence indicators; Secondly, in the judgment of complications (such as hypertension, hyperlipidemia and diabetes), it is not entirely based on the laboratory test data in NHANES, but depends on the self-reported information of the questionnaire, which may lead to the omission of some complications; Thirdly, the lung function index is calculated according to the formula based on the basic lung function data, and it is rounded off in the process of processing, which may introduce some measurement errors.In addition, in the diagnosis of COPD, the data in line with the completion of pulmonary function after diastolic is relatively limited, which may also have a certain impact on the accuracy of the results. Nevertheless, the data of this research comes from a large sample with national representation and covers a variety of variables, which provides an important basis for exploring the relationship between OSA risk and lung function. To sum up, the results of this research provide some valuable evidence for the relationship between high-risk factors of OSA and lung function. Early identification of OSA and systematic screening of complications should be strengthened, and the risk of OSA should be reduced by actively controlling weight and improving lifestyle. For patients with decreased lung function, it is necessary to actively promote the secondary prevention strategy for COPD complicated with OSA. In daily life, we should raise public awareness of OSA, and should not simply regard it as the performance of "sleeping soundly". If you have symptoms such as severe snoring at night, excessive drowsiness and listlessness during the day, you should go to a professional medical institution for polysomnography as soon as possible to make a clear diagnosis. Declarations Ethics approval and consent to participate Consent for publication The informed consent was provided by all NHANES survey participants before health examination. The study protocols were approved by the National Center for Health Statistics Research Ethics Review Committee. Data sharing framework All data sources for this article can be found in the public database NHANES. The names of the repository/repositories and accession number(s) can be found below: https://www.cdc.gov/nchs/nhanes/index.htm . Competing interests There is no conflict of interest in this article. Funding This research received no extrenal funding. Author Contribution GL participated in the data collection, analysis, and writing of this article as the first autho and the corresponding author, is responsible for the accuracy of the entire data collection and analysis. LYP participated in some data collection and analysis. DJL performed some of the data analyses. Acknowledgments We appreciate the reviewers of this journal for their comments, which have been of great help and contribution to our articles. References YEGHIAZARIANS Y, JNEID H. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association [J]. Circulation. 2021;144(3):e56–67. KORITALA B S C, CONROY Z, SMITH DF. Circadian Biology in Obstructive Sleep Apnea [J]. Diagnostics (Basel, Switzerland), 2021, 11(6). COZOWICZ C, MEMTSOUDIS S G. Perioperative Management of the Patient With Obstructive Sleep Apnea: A Narrative Review [J]. Anesth Analg. 2021;132(5):1231–43. LYONS M M, BHATT N Y, PACK A I, et al. Global burden of sleep-disordered breathing and its implications [J]. Respirol (Carlton Vic). 2020;25(7):690–702. RUNDO J V. Obstructive sleep apnea basics [J]. Cleve Clin J Med. 2019;86(9 Suppl 1):2–9. RUEDA JR, MUGUETA-AGUINAGA I, VILARó J, et al. Myofunctional therapy (oropharyngeal exercises) for obstructive sleep apnoea [J]. Cochrane Database Syst Rev. 2020;11(11):Cd013449. AUCKLEY D H KAPURVK, CHOWDHURI S, et al. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline [J]. J Clin sleep medicine: JCSM : official publication Am Acad Sleep Med. 2017;13(3):479–504. LOCKE B W, LEE J J, SUNDAR KM. OSA and Chronic Respiratory Disease: Mechanisms and Epidemiology [J]. Int J Environ Res Public Health, 2022, 19(9). MCNICHOLAS W T, HANSSON D. SCHIZA S, Sleep in chronic respiratory disease: COPD and hypoventilation disorders [J]. Eur respiratory review: official J Eur Respiratory Soc, 2019, 28(153). VAN ZELLER M, MCNICHOLAS W T. Sleep disordered breathing: OSA-COPD overlap [J]. Expert Rev Respir Med. 2024;18(6):369–79. SHAWON MS, PERRET J L, SENARATNA C V, et al. Current evidence on prevalence and clinical outcomes of co-morbid obstructive sleep apnea and chronic obstructive pulmonary disease: A systematic review [J]. Sleep Med Rev. 2017;32:58–68. EMILSSON Ö I, SUNDBOM F, LJUNGGREN M, et al. Association between lung function decline and obstructive sleep apnoea: the ALEC study [J]. Volume 25. Sleep & breathing = Schlaf & Atmung; 2021. pp. 587–96. 2. BIKOV A, LOSONCZY G. Role of lung volume and airway inflammation in obstructive sleep apnea [J]. Respiratory Invest. 2017;55(6):326–33. KIM TH, HEO I R, KIM HC. Impact of high-risk of obstructive sleep apnea on chronic cough: data from the Korea National Health and Nutrition Examination Survey [J]. BMC Pulm Med. 2022;22(1):419. SUNDAR K M, DALY S E. Chronic cough and OSA: a new association? [J]. J Clin sleep medicine: JCSM : official publication Am Acad Sleep Med. 2011;7(6):669–77. SATIA I, MAYHEW A J, SOHEL N, et al. Prevalence, incidence and characteristics of chronic cough among adults from the Canadian Longitudinal Study on Aging [J]. Volume 7. ERJ open research; 2021. 2. PEPPARD P E, YOUNG T, BARNET JH, et al. Increased prevalence of sleep-disordered breathing in adults [J]. Am J Epidemiol. 2013;177(9):1006–14. CASTANEDA A, JAUREGUI-MALDONADO E, RATNANI I, et al. Correlation between metabolic syndrome and sleep apnea [J]. World J diabetes. 2018;9(4):66–71. DRAGER L F, TOGEIRO S M, POLOTSKY V Y, et al. Obstructive sleep apnea: a cardiometabolic risk in obesity and the metabolic syndrome [J]. J Am Coll Cardiol. 2013;62(7):569–76. PATAKA A, KOTOULAS S, KALAMARAS G et al. Does Smoking Affect OSA? What about Smoking Cessation? [J]. J Clin Med, 2022, 11(17). WANG T Y, LO Y L, LIN SM, et al. Obstructive sleep apnoea accelerates FEV(1) decline in asthmatic patients [J]. BMC Pulm Med. 2017;17(1):55. KUMAR GEORGERJ, ACHENBACH R. Sleep disorders in rheumatoid arthritis: Incidence, risk factors and association with dementia [J]. Semin Arthritis Rheum. 2025;73:152722. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7886768","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536021249,"identity":"38fc4a1d-f22d-4177-8efe-5d47ea676f9d","order_by":0,"name":"Gan Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCcYGCIOZ/8OBDxUScvLEa2FvMHw444yFsWEDQS0wBs8BY2PetopEhgMEdBjcbm7+XFBxz25+REKa5Mx5EgmMDcwPH93Ap+XOwTbpGWeKkzfeSDgm8XGbRB47A5uxcQ4eLWY3EtuYedsSkg1nJLZJztwmUczYwMMmTUBL82fefyAtyWzSvHMkEhsOENbSIM3bkGAnz3OM2Zi3gQgt9kCHSc84lpBgwN7D+HDGMQljw2YCfpGckf74c0FNgr18Mw/DgQ81dXLy7M0PH+PTAgLMQJy44QAylxAAqbGXbyBC5SgYBaNgFIxMAADullE6S5XDNgAAAABJRU5ErkJggg==","orcid":"","institution":"Renhe Hospital Affiliated to Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"Gan","middleName":"","lastName":"Luo","suffix":""},{"id":536021250,"identity":"a9c021c5-6b71-478f-aaee-8fe17ba1ebdb","order_by":1,"name":"Yali Peng","email":"","orcid":"","institution":"Renhe Hospital Affiliated to Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Yali","middleName":"","lastName":"Peng","suffix":""},{"id":536021251,"identity":"1dfb3d5a-8653-46a6-8c44-4e4bc2bd57bc","order_by":2,"name":"Dengjun Li","email":"","orcid":"","institution":"Renhe Hospital Affiliated to Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Dengjun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-10-17 12:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7886768/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7886768/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94639260,"identity":"597071a6-65e0-42c0-b0bf-1fa39768692a","added_by":"auto","created_at":"2025-10-29 07:36:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92043,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886768/v1/4b122aa7415c6bf50ab45bb2.jpg"},{"id":94672007,"identity":"00fb5dc8-4e1b-41de-bf3b-b72c9e847e7e","added_by":"auto","created_at":"2025-10-29 13:32:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7886768/v1/0911d857-0f4e-409c-8bc8-f6301859d54b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe relationship between lung function and obstructive sleep apnea: Finding from NHANES 2007-2008\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObstructive Sleep Apnea (OSA) is an apnea and hypopnea disease caused by repeated collapse and obstruction of the upper airway during sleep. OSA can lead to sleep structure disorder, and trigger a series of pathophysiological changes such as intermittent hypoxia, hypercapnia, significant fluctuation of pressure in chest cavity, autonomic nerve dysfunction and inflammatory activation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As a common disease, OSA is especially common among people with clear risk factors and comorbidity. Its global incidence rate is about 45%, and the prevalence rate in Europe and America is between 14% and 49%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The risk of OSA usually increases with age. A research on men over the age of 65 shows that the prevalence of OSA in people under the age of 72 is 23%, while that in people over the age of 80 rises to 30%. In contrast, the prevalence rate of men aged 30\u0026ndash;40 is only 10%\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The research of sleep heart health further shows that there are racial differences in the prevalence rate of OSA, which is 30% among whites, 32% among blacks, 38% among Hispanics, and 39% among China people. Meanwhile, researches have shown that OSA is more prevalent among middle-aged populations with an increasing prevalence of obesity\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. OSA has an important negative impact on quality of patients of life and multi-organ system function, which is characterized by excessive daytime sleepiness, impaired daily function and increased metabolic abnormalities, and significantly increases the risk of cardiopulmonary diseases and metabolic syndrome\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The severity of the disease is usually evaluated by the Apnea-Hypopnea Index, which is defined as the total number of times of apnea and hypopnea in sleep every hour\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Clinical screening of OSA include detailed collection of sleep history, systematic review of related symptoms and comprehensive physical examination.Polysomnography is an important auxiliary means to diagnose OSA and evaluate its severity \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCOPD is a heterogeneous disease, which is characterized by chronic respiratory symptoms caused by airway and/or alveolar abnormalities, leads to continuously aggravated airflow restriction\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Sleep has a profound influence on breathing and gas exchange, especially in COPD patients. When COPD and OSA coexist, that is, overlap syndrome (OVS), the health risk of such patients will increase significantly\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.There seems to be a correlation between the increase of COPD severity and the increase of OVS prevalence. According to the report of sleep heart health research \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, compared with non-COPD patients, there is no significant difference in the incidence of OVS in mild COPD patients; However, in patients with moderate and severe COPD, the prevalence of OVS reached 39% and 47% respectively. The overall prevalence of OVS in the general population is reported to be 1% to 3.6%\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Among the patients with confirmed OSA, the prevalence rate of OVS is 7.6% to 55.7%. Similarly, the prevalence of OVS in COPD population is 2.9% to 65.9%\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt has been reported that OSA is related to the decline of lung function. People at high risk of OSA defined by multivariate apnea prediction (MAP) index usually show a significant decrease in forced expiratory volume (FEV1) and forced vital capacity (FVC) in the first second. Similarly, in a research matching body mass index (BMI), it was also found that there was a significant correlation between more severe OSA and lower lung function. Obesity affects expiratory reserve by reducing functional residual volume, and expiratory reserve decreases with the increase of residual volume, indicating that BMI is an important confounding factor between OSA risk and lung function\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The National Health and Nutrition examination (NHANES) database is a large-scale and well-designed clinical database. Based on this database, we conducted a research to explore the correlation between the risk of OSA and lung function. We put forward the hypothesis that the high risk of OSA is related to lung function, and this association still exists after adjusting for confounding factors.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eThe NHANES is a cross-sectional survey based on population, which aims to collect information about the health and nutrition status of American families. All the operating procedures of the survey were approved by the National Health Statistics Ethics Review Board, and the written informed consent of each participant was obtained at the time of registration. NHANES conducts an annual survey of a national representative sample with a sample size of about 5,000 people, including interviews and physical examinations. The interview part covers information about demography, socio-economic status, eating habits and health-related issues; The physical examination part includes basic medical information, blood pressure, hearing test, oral health status, grip strength measurement, as well as a large number of laboratory test data and some radiological examination data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch design and population\u003c/h3\u003e\n\u003cp\u003eIn this database, we found the participants who had complete lung function data and questionnaire data related to sleep apnea during the investigation period from 2007 to 2008. A total of 10149 respondents were included in the preliminary screening during this period. According to the research design criteria, 4215 individuals with incomplete physical data, missing OSA data or incomplete covariate information were excluded. Subsequently, 623 women under the age of 20 or in pregnancy were excluded; Finally, 1487 respondents who did not complete the lung function test or whose data quality was unacceptable were further excluded. After the above screening, a total of 3824 valid samples were finally determined to be included in the analysis, including 2003 cases in OSA group and 1821 cases in non-OSA group. The complete data collection process is shown in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eThe standard of OSA patients\u003c/h3\u003e\n\u003cp\u003eAccording to the following criteria: if the subject meets any of the following conditions, it will be classified as OSA; If the three conditions are met at the same time, it is judged as non-OSA. Specific conditions include: (1) snoring at least three nights a week; (2) Symptoms of apnea or conscious respiratory interruption during wheezing and snoring occur three nights a week or more frequently; (3) Sleeping for at least seven hours every night on weekdays or during work breaks, but still experiencing excessive drowsiness during the day, and the number of drowsiness episodes is between 16 and 30 times per day.\u003c/p\u003e\n\u003ch3\u003eMeasurement data of lung function\u003c/h3\u003e\n\u003cp\u003eAccording to the results of pulmonary function measurement in NHANES database, this research selected the data whose first test status was completed and the quality attributes of FEV1 and FVC were Grade A or B for analysis. Among them, Grade A means that the data quality exceeds the data collection standard set by the American Thoracic Society (ATS), while Grade B means that it meets the standard. The values of FEV1%pred and FVC%pred are calculated based on the reference equation of lung function of American general population\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe lung function is divided into the following three categories:\u003c/p\u003e\u003cp\u003eNormal is defined as FEV1/FVC\u0026thinsp;\u0026ge;\u0026thinsp;0.7, and FEV1\u0026thinsp;\u0026ge;\u0026thinsp;80%pred and FVC\u0026thinsp;\u0026ge;\u0026thinsp;80%pred.\u003c/p\u003e\u003cp\u003eObstructive ventilation dysfunction is defined as FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7.\u003c/p\u003e\u003cp\u003eRestrictive ventilation dysfunction is defined as FEV1/FVC\u0026thinsp;\u0026ge;\u0026thinsp;0.7 but FVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred.\u003c/p\u003e\u003cp\u003eFor the data whose second test status is completed and the quality grade of FEV1 and FVC is A or B,.According to the global GOLD guidelines, FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% after bronchodilator is adopted as the diagnostic standard.In the meanwhile, according to the GOLD classification, the severity is divided into: GOLD 1 (mild, FEV1%pred\u0026thinsp;\u0026ge;\u0026thinsp;80%) ;GOLD 2 (moderate, 50% \u0026le; FEV1%pred\u0026thinsp;\u0026lt;\u0026thinsp;80%),;GOLD 3 (severe, FEV1%pred\u0026thinsp;\u0026lt;\u0026thinsp;50%) ;GOLD 4 (extremely severe, FEV1%pred\u0026thinsp;\u0026lt;\u0026thinsp;30%).\u003c/p\u003e\n\u003ch3\u003eCovariate\u003c/h3\u003e\n\u003cp\u003eThe covariates selected in this research include age, gender, race (divided into mexican american, other Hispanics, non-Hispanic whites and non-Hispanic blacks), education level (below grade 9, grades 9 to 11, high school graduation or GED equivalent, some universities or AA degrees, junior college or above), waist circumference, height, weight, body mass index (BMI, calculated by dividing the weight by the square of height), Smoking status (current smokers: smoking more than 100 cigarettes in their lifetime and reporting current smoking; Former smoker: a person who smokes\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes in his life and reports that he has given up smoking; Never smoker: a person who has never smoked more than 100 cigarettes in his life), drinking situation, and the history of complicated diseases (including hypertension, cholesterol, diabetes, asthma, arthritis, gout, stroke, thyroid disease, tumor, heart failure, coronary heart disease and angina pectoris) obtained through questionnaire survey.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical methods\u003c/h2\u003e\u003cp\u003eIn this research, the data of NHANES database was collected, classified, screened and sorted by R language and was statistically analyzed by SPSS Statistics 27.0.1. Firstly, the distribution type of continuous variables is judged by Kruskal-Wallis H test. Those that conform to normal distribution are represented by mean standard deviation, while those that are not normal distribution are represented by median (interquartile interval). T test or Mann-Whitney U test was used for comparison between the two groups according to the data distribution. Classification variables were described by frequency or percentage, and chi-square test was used for comparison between groups. The relationship between OSA and risk factors was analyzed by univariate and multivariate logistic regression analysis. The results were expressed by odds ratio (OR) and 95% confidence interval (CI). In multivariate analysis, Model 1 adjusted age, gender, race and body mass index; Model2 further incorporates smoking and drinking conditions on the basis of Model 1; Model 3 adjusts complications such as hypertension, hyperlipidemia, asthma, arthritis, gout, diabetes, stroke, thyroid diseases, tumors and cardiovascular diseases on the basis of Model 2.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eThe baseline demographic data\u003c/h2\u003e\u003cp\u003eCompared with the non-OSA group, the participants in OSA group are older, with a higher proportion of men, a larger waist circumference, a higher body mass index (BMI), and a higher proportion of current smokers and former smokers. In addition, in patients with hypertension, hypercholesterolemia, diabetes, arthritis, stroke and coronary heart disease, the diagnosed rate of OSA is significantly higher (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of baseline demographic data between OSA group and non-OSA group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-OSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50(38\u0026ndash;61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44(31\u0026ndash;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1137(56.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e797(43.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e866(43.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1024(56.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEl Chicano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e378(18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e329(18.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249(12.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e199(10.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic whites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e980(48.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e903(49.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic blacks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e396(19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390(21.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow grade 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215(10.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189(10.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 9\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e372(18.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300(16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduated from high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524(26.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e431(23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduated from university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e542(27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e514(28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostgraduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e350(17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e386(21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewaistline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.1(92.8-112.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.1(83.6-103.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168.9(162\u0026ndash;176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.(159.65\u0026ndash;175.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.3(72.8\u0026ndash;98.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.00(63.65\u0026ndash;87.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.37(26.15\u0026ndash;33.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.51(23.35\u0026ndash;30.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e941(47.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1018(55.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking in the past\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e549(27.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e381(20.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e512(25.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(23.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1459(72.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1282(70.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e765(38.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e466(25.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003echolesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e718(35.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e481(25.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ediabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e767(13.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145(8.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003easthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272(13.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203(11.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003earthritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e574(28.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91(4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62(3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethyroid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175(8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155(8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etumour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173(8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137(7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecardiac failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69(3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eangina pectoris\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLung function and respiratory symptoms\u003c/h2\u003e\u003cp\u003eCompared with the non-OSA group, the measured values of FEV1%pred, FVC% pred and FEV1/FVC of participants in OSA group were lower, but the actual value of FVC is not much different. In the classification of lung function, the prevalence of FVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred and restrictive pulmonary ventilation dysfunction in OSA group was significantly higher than that in non-OSA group. In addition, in terms of respiratory symptoms, the duration of cough with phlegm in OSA group was significantly longer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of lung function and respiratory symptoms between OSA group and non-OSA group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOSA group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-OSA group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung function parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1 actual value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.04\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.09\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1 estimated value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.21\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC actual value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.95\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC estimated value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.18\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.02\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of lung function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstructive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptoms of respiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCough is greater than 3 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoughing up phlegm for more than 3 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as weighted percentages (%) and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFEV1:forced expiratory volume in one second\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFVC:forced vital capacity\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFEV1/FVC: one second rate\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFEV1%pred:FEV1 Proportion of estimated value\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eFVC%pred:FVC Proportion of estimated value\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNormal: FEV1/FVC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.7,FEV1\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;80%pred, FVC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;80%pred\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBlock: FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eLimit: FEV1/FVC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.7 FVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between OSA incidence and lung function\u003c/h2\u003e\u003cp\u003eTaking patients with normal lung function as the reference category, in the unadjusted model, the incidence of patients with obstructive pulmonary ventilation dysfunction, FVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred, cough with phlegm as the main symptoms is significantly higher in OSA group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eunivariate and multivariate logistic regression analysis of high-risk incidence in OSA group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTape of lung function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate OR(95%CI)\u003c/p\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModle 1\u003c/p\u003e\u003cp\u003eOR(95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModle 2\u003c/p\u003e\u003cp\u003eOR(95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModle 3\u003c/p\u003e\u003cp\u003eOR(95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ereference value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ereference value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003ereference value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003ereference value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.034(0.868\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.244(1.019\u0026ndash;1.528)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.808(1.339\u0026ndash;2.441)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.944\u003c/p\u003e\u003cp\u003e(1.289\u0026ndash;2.930)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003cp\u003e(0.480\u0026ndash;0.713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003cp\u003e(0.639\u0026ndash;1.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003cp\u003e(0.706\u0026ndash;1.314)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.349\u003c/p\u003e\u003cp\u003e(0.842\u0026ndash;2.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstructive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003cp\u003e(0.399\u0026ndash;0.628)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003cp\u003e(0.54\u0026ndash;0.901)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003cp\u003e(0.483\u0026ndash;1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.158\u003c/p\u003e\u003cp\u003e(0.664\u0026ndash;2.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecough\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.605\u003c/p\u003e\u003cp\u003e(1.231\u0026ndash;2.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.427\u003c/p\u003e\u003cp\u003e(1.030\u0026ndash;1.978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.324\u003c/p\u003e\u003cp\u003e(0.829\u0026ndash;2.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.785\u003c/p\u003e\u003cp\u003e(0.962\u0026ndash;3.311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eexpectoration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.645\u003c/p\u003e\u003cp\u003e(1.225\u0026ndash;2.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.454\u003c/p\u003e\u003cp\u003e(1.017\u0026ndash;2.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.579\u003c/p\u003e\u003cp\u003e(0.912\u0026ndash;2.732)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.174\u003c/p\u003e\u003cp\u003e(1.059\u0026ndash;4.403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eValues are presented as median (interquartile range, IQR)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eIn Model 1, age, gender, race and BMI are adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eIn Model 2, smoking and drinking are adjusted again on the basis of Model 1.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eIn Model 3, based on Model 2, hypertension, blood lipid, asthma, arthritis, gout, diabetes, stroke, thyroid, tumor and cardiovascular diseases (congestive heart failure, coronary heart disease and angina pectoris) were adjusted again.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Model 1, obstructive pulmonary ventilation dysfunction is still the main influencing factor of OSA, FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80% ,cough with phlegm symptoms are also significant risk factors.\u003c/p\u003e\u003cp\u003eIn Model 2, FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred is still an independent risk factor for OSA.\u003c/p\u003e\u003cp\u003eIn Model 3, FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred continues to be a high risk factor for OSA, and expectoration symptoms are also identified as relative risk factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSecondary logistic regression analysis in COPD patients with comorbid OSA\u003c/h2\u003e\u003cp\u003eAmong 713 patients who completed the second pulmonary function test and were diagnosed as COPD according to FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% after bronchodilation, 380 patients were complicated with OSA. The variables with statistical significance in univariate analysis (including gender, body mass index, waist circumference, smoking status, diabetes, hyperlipidemia and arthritis), drinking history, hypertension, asthma, tumor, stroke, gout, thyroid disease, congestive heart failure, coronary heart disease and angina pectoris) were included in binary logistic regression analysis. The results showed that gender, smoking, waist circumference and arthritis were independent risk factors for COPD complicated with OSA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate logistic regression analyses of risk factors for COPD with comorbid OSA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate OR(95%CI)\u003c/p\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate OR(95%CI)\u003c/p\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.292(1.695-3.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.319(2.311\u0026ndash;8.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.977(0.962\u0026ndash;0.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.026(0.996\u0026ndash;1.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaistline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.968(0.958\u0026ndash;0.978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.961(0.946\u0026ndash;0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.595(1.162\u0026ndash;2.188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.359(0.863\u0026ndash;2.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.227(0.863\u0026ndash;1.744)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.924(0.542\u0026ndash;1.574)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.375(1.015\u0026ndash;1.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.897(0.431\u0026ndash;1.865)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.414(0.894\u0026ndash;2.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.672(0.414\u0026ndash;1.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood fat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997(0.837\u0026ndash;1.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.333(0.855\u0026ndash;2.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.318(0.888\u0026ndash;1.958)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.292(0.701\u0026ndash;2.201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.877(0.577\u0026ndash;1.331)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.155(0.619\u0026ndash;2.155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.235(0.541\u0026ndash;2.819)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.156(0.288\u0026ndash;4.637)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.878(7.672\u0026ndash;25.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.281(11.028\u0026ndash;67.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.369(0.669\u0026ndash;2.464)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.277(0.086\u0026ndash;0.897)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThyroid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.450(0.875\u0026ndash;2.404)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.277(0.086\u0026ndash;0.897)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.875(0.359\u0026ndash;2.128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.461(0.118\u0026ndash;1.801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.054(0.995\u0026ndash;4.241)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.048(0.583\u0026ndash;7.194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina pectoris\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.211(0.981\u0026ndash;3.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.410(0.093\u0026ndash;1.807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are presented as median (interquartile range, IQR)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eComparison of COPD complications with different airflow levels\u003c/h2\u003e\u003cp\u003eWith the increase of the severity of airflow restriction, the incidence of COPD complicated with OSA shows a gradual upward trend; Similarly, the incidence of OSA increased with the severity of the disease in patients with asthma and arthritis, but the other variables did not show statistical significance (Table\u0026nbsp;5).\u003c/p\u003e\n\u003cp\u003eTable.5-Comparison of COPD complications with different airflow levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003eSevere or extremely severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e223(44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e140(74.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e17(70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e50.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e54(10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e30(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e3(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e4.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e180(36.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e87(46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e10(41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e5.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eHyperlipemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e158(46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e94(59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e10(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e7.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e73(14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e40(21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e9(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e11.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eTumour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e70(14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e30(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e3(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e16(3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e7(3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e1(4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e80(16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e61(32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e9(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e25.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eGout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e19(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e14(7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e5.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eThyroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e48(9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e18(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e4(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e1.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e12(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e7(3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e1(4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e8.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e22(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e12(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e2(8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e6.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003eAngina pectoris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9982%;\"\u003e\n \u003cp\u003e11(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8174%;\"\u003e\n \u003cp\u003e6(3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6365%;\"\u003e\n \u003cp\u003e2(8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e9.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as numbers (weighted percentages).\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis research explored the complex relationship between OSA and lung function based on data from the NHANES database. The results showed that the occurrence of OSA was closely related to high body mass index, long smoking history and the existence of many related diseases (including hypertension, high cholesterol, diabetes, arthritis, stroke and coronary heart disease). Patients with OSA showed a downward trend of lung function characterized by the decrease of FEV1%pred, FVC%pred and FEV1/FVC ratio, and the incidence of respiratory symptoms increased significantly. In univariate and multivariate analysis, cough with phlegm symptoms were confirmed as independent high risk factors of OSA. In multivariate models (Model1, Model2 and Model3), only FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred shows significant differences related to OSA risk.\u003c/p\u003e\u003cp\u003eThrough logistic regression analysis, this research found that chronic cough with phlegm is always an independent high-risk factor for OSA, no matter how the independent variables are adjusted. These findings are consistent with those reported by Kim et al\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, who previously identified OSA as a risk factor for chronic cough through questionnaire-based high-risk screening and subsequent regression analyses. OSA may cause persistent symptoms of cough with phlegm by interacting with common causes of chronic cough (such as CVA/GERD/UACS). Chronic cough with phlegm are partly caused by airway inflammation, and the characteristics of OSA (repeated airway collapse during sleep and airway trauma and inflammation caused by it) may further prolong the course of chronic cough \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In the multivariate adjustment analysis, this research also found that body mass index and smoking were significantly related to the decrease of FEV1/FVC and FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pred, and the duration of cough with phlegm still showed stronger statistical differences in the multivariate model. A longitudinal study in Canada shows that when FEV1%pred is lower than 50%, the risk of chronic cough increases five times and the incidence of OSA is higher\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Its mechanism may be related to the changes of anatomical structure of the upper airway in obese patients (especially the stenosis of the upper airway caused by neck fat accumulation), the increase of abdominal fat (causing the decrease of vital capacity and functional residual capacity) and the chronic inflammatory state related to obesity, which make the upper airway more prone to collapse. According to statistics, about 70% of patients with severe OSA are obese, and the risk of apnea increases by 6 times for every 10% increase in weight \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. At the same time, the chemicals in tobacco can stimulate the nasopharynx, larynx and airway mucosa, causing chronic airway inflammation and lumen stenosis, thus increasing the risk of OSA [20]. In this research, it was also observed that compared with the non-OSA group, patients in OSA group smoked longer, had higher body mass index, and FEV1%pred, FVC%pred and FEV1/FVC were significantly decreased, and the proportion of cough with phlegm symptoms was higher.\u003c/p\u003e\u003cp\u003eWhen COPD and OSA coexist, it is called COPD-OSA overlap syndrome. This research found that in univariate and multivariate analysis, gender, waist circumference and arthritis are all high-risk factors for this overlap syndrome; The prevalence of adult OSA has obvious gender differences (the ratio of male to female is about 2:1 to 3:1)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. We classify individuals with waistlines exceeding the standard thresholds (90 cm for men and 85 cm for women) as having abdominal obesity, also termed apple-type obesity. This is based on the fact that excessive adipose tissue secretes various inflammatory cytokines, which can aggravate airway edema and heighten the respiratory center's sensitivity to hypoxia\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Metabolic syndrome (MS) which has been widely concerned in recent years, is a kind of clinical syndrome including abdominal obesity, hypertension, abnormal blood sugar and dyslipidemia. Alejandra et al. elaborated in detail how MS affects the risk of OSA through an evidence-based research\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the analysis of COPD complicated with diseases, it is found that with the aggravation of airflow restriction, the incidence of OSA increases gradually, and the prevalence of asthma and arthritis also increases accordingly. This phenomenon may be related to chronic hypoxia caused by persistent airflow limitation of COPD, which further leads to neuroregulatory dysfunction and hemodynamic changes, thus promoting complications\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. For asthma patients, inhaled corticosteroids (ICS) can delay the decline of lung function, but asthma patients with OSA may not respond well to ICS treatment, which leads to the accelerated decline of FEV1, and then pushes up the prevalence of comorbidity\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Rheumatoid arthritis can directly shorten the sleep time and increase the frequency of awakening at night due to symptoms such as pain and morning stiffness in the active stage of the disease. If the disease involves the neck bone or temporomandibular joint, it can also cause structural stenosis of the upper airway, affect the ventilation function and induce sleep-disordered breathing\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Therefore, in the early treatment of COPD, we should not only pay attention to the lung function itself, but also pay attention to the screening and intervention of its complications as soon as possible, especially to actively screen whether it is complicated with OSA. And through lifestyle adjustment, physical therapy and sleep ventilator and other early intervention measures to improve sleep disorders of patients.\u003c/p\u003e\u003cp\u003eThis research has the following limitations: firstly, the judgment of OSA population is only based on questionnaire survey for screening, and professional instruments such as polysomnography are not used for diagnosis, which may have certain classification deviation, but we try to reduce the influence of this deviation by setting three coincidence indicators; Secondly, in the judgment of complications (such as hypertension, hyperlipidemia and diabetes), it is not entirely based on the laboratory test data in NHANES, but depends on the self-reported information of the questionnaire, which may lead to the omission of some complications; Thirdly, the lung function index is calculated according to the formula based on the basic lung function data, and it is rounded off in the process of processing, which may introduce some measurement errors.In addition, in the diagnosis of COPD, the data in line with the completion of pulmonary function after diastolic is relatively limited, which may also have a certain impact on the accuracy of the results. Nevertheless, the data of this research comes from a large sample with national representation and covers a variety of variables, which provides an important basis for exploring the relationship between OSA risk and lung function.\u003c/p\u003e\u003cp\u003eTo sum up, the results of this research provide some valuable evidence for the relationship between high-risk factors of OSA and lung function. Early identification of OSA and systematic screening of complications should be strengthened, and the risk of OSA should be reduced by actively controlling weight and improving lifestyle. For patients with decreased lung function, it is necessary to actively promote the secondary prevention strategy for COPD complicated with OSA. In daily life, we should raise public awareness of OSA, and should not simply regard it as the performance of \"sleeping soundly\". If you have symptoms such as severe snoring at night, excessive drowsiness and listlessness during the day, you should go to a professional medical institution for polysomnography as soon as possible to make a clear diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate Consent for publication\u003c/h2\u003e\n\u003cp\u003eThe informed consent was provided by all NHANES survey participants before health examination. The study protocols were approved by the National Center for Health Statistics Research Ethics Review Committee.\u003c/p\u003e\n\u003ch2\u003eData sharing framework\u003c/h2\u003e\n\u003cp\u003eAll data sources for this article can be found in the public database NHANES. The names of the repository/repositories and accession number(s) can be found below: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThere is no conflict of interest in this article.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no extrenal funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eGL participated in the data collection, analysis, and writing of this article as the first autho and the corresponding author, is responsible for the accuracy of the entire data collection and analysis. LYP participated in some data collection and analysis. DJL performed some of the data analyses.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe appreciate the reviewers of this journal for their comments, which have been of great help and contribution to our articles.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYEGHIAZARIANS Y, JNEID H. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association [J]. Circulation. 2021;144(3):e56\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKORITALA B S C, CONROY Z, SMITH DF. Circadian Biology in Obstructive Sleep Apnea [J]. Diagnostics (Basel, Switzerland), 2021, 11(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOZOWICZ C, MEMTSOUDIS S G. Perioperative Management of the Patient With Obstructive Sleep Apnea: A Narrative Review [J]. Anesth Analg. 2021;132(5):1231\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLYONS M M, BHATT N Y, PACK A I, et al. Global burden of sleep-disordered breathing and its implications [J]. Respirol (Carlton Vic). 2020;25(7):690\u0026ndash;702.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRUNDO J V. Obstructive sleep apnea basics [J]. Cleve Clin J Med. 2019;86(9 Suppl 1):2\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRUEDA JR, MUGUETA-AGUINAGA I, VILAR\u0026oacute; J, et al. Myofunctional therapy (oropharyngeal exercises) for obstructive sleep apnoea [J]. Cochrane Database Syst Rev. 2020;11(11):Cd013449.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAUCKLEY D H KAPURVK, CHOWDHURI S, et al. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline [J]. J Clin sleep medicine: JCSM : official publication Am Acad Sleep Med. 2017;13(3):479\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLOCKE B W, LEE J J, SUNDAR KM. OSA and Chronic Respiratory Disease: Mechanisms and Epidemiology [J]. Int J Environ Res Public Health, 2022, 19(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMCNICHOLAS W T, HANSSON D. SCHIZA S, Sleep in chronic respiratory disease: COPD and hypoventilation disorders [J]. Eur respiratory review: official J Eur Respiratory Soc, 2019, 28(153).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVAN ZELLER M, MCNICHOLAS W T. Sleep disordered breathing: OSA-COPD overlap [J]. Expert Rev Respir Med. 2024;18(6):369\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSHAWON MS, PERRET J L, SENARATNA C V, et al. Current evidence on prevalence and clinical outcomes of co-morbid obstructive sleep apnea and chronic obstructive pulmonary disease: A systematic review [J]. Sleep Med Rev. 2017;32:58\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEMILSSON \u0026Ouml; I, SUNDBOM F, LJUNGGREN M, et al. Association between lung function decline and obstructive sleep apnoea: the ALEC study [J]. Volume 25. Sleep \u0026amp; breathing\u0026thinsp;=\u0026thinsp;Schlaf \u0026amp; Atmung; 2021. pp. 587\u0026ndash;96. 2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBIKOV A, LOSONCZY G. Role of lung volume and airway inflammation in obstructive sleep apnea [J]. Respiratory Invest. 2017;55(6):326\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKIM TH, HEO I R, KIM HC. Impact of high-risk of obstructive sleep apnea on chronic cough: data from the Korea National Health and Nutrition Examination Survey [J]. BMC Pulm Med. 2022;22(1):419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSUNDAR K M, DALY S E. Chronic cough and OSA: a new association? [J]. J Clin sleep medicine: JCSM : official publication Am Acad Sleep Med. 2011;7(6):669\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSATIA I, MAYHEW A J, SOHEL N, et al. Prevalence, incidence and characteristics of chronic cough among adults from the Canadian Longitudinal Study on Aging [J]. Volume 7. ERJ open research; 2021. 2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePEPPARD P E, YOUNG T, BARNET JH, et al. Increased prevalence of sleep-disordered breathing in adults [J]. Am J Epidemiol. 2013;177(9):1006\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCASTANEDA A, JAUREGUI-MALDONADO E, RATNANI I, et al. Correlation between metabolic syndrome and sleep apnea [J]. World J diabetes. 2018;9(4):66\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDRAGER L F, TOGEIRO S M, POLOTSKY V Y, et al. Obstructive sleep apnea: a cardiometabolic risk in obesity and the metabolic syndrome [J]. J Am Coll Cardiol. 2013;62(7):569\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePATAKA A, KOTOULAS S, KALAMARAS G et al. Does Smoking Affect OSA? What about Smoking Cessation? [J]. J Clin Med, 2022, 11(17).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWANG T Y, LO Y L, LIN SM, et al. Obstructive sleep apnoea accelerates FEV(1) decline in asthmatic patients [J]. BMC Pulm Med. 2017;17(1):55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKUMAR GEORGERJ, ACHENBACH R. Sleep disorders in rheumatoid arthritis: Incidence, risk factors and association with dementia [J]. Semin Arthritis Rheum. 2025;73:152722.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"OSA, COPD, Lung function, Cough with phlegm, Health survey","lastPublishedDoi":"10.21203/rs.3.rs-7886768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7886768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThe purpose of this research is to explore the correlation between pulmonary function and OSA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis research analyzed the data of 3824 participants from 2007 to 2008. Based on lung function, they are divided into obstructive group, trestrictive group, normal group, FEV1\u0026thinsp;\u0026lt;\u0026thinsp;80%pre group and FVC\u0026thinsp;\u0026lt;\u0026thinsp;80% pred group. Logistic regression was used to analyze the relationship between lung function and high risk factors of OSA, and patients with COPD were screened by bronchodilation test to further analyze the high risk factors of COPD complicated with OSA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis research reveals that participants with OSA were older, had a greater proportion of males and smokers, exhibited larger waist circumferences and higher BMI. In addition, a significantly elevated risk of cardiovascular and cerebrovascular diseases. Univariate Logistic regression analysis showed that obstructive pulmonary ventilation dysfunction, FVC\u0026thinsp;\u0026lt;\u0026thinsp;80%pred and cough with phlegm symptoms of the incidence rate was significantly higher in the OSA group. However, in the multivariate adjustment model, only chronic cough with phlegm were identified as independent risk factors for OSA. Further analysis of COPD complicated with OSA shows that gender, smoking, waist circumference and arthritis are independent risk factors。With the increase of the severity of airflow restriction, the incidence of COPD complicated with OSA is gradually increasing.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThere is a certain correlation between lung function indexes and OSA. Regardless of factors adjusted for, chronic cough with phlegm remained a significant risk factor.\u003c/p\u003e","manuscriptTitle":"The relationship between lung function and obstructive sleep apnea: Finding from NHANES 2007-2008","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 07:36:49","doi":"10.21203/rs.3.rs-7886768/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46eb7451-4942-4a12-8675-21ebb690f1dc","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T07:36:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 07:36:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7886768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7886768","identity":"rs-7886768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}