1 INTRODUCTION

Obstructive sleep apnoea-hypopnoea syndrome (OSAHS) is the most common sleep-related breathing disorder, which is characterized by periodic, partial, or complete collapse of the upper airway during sleep, resulting in brief sleep arousals to restore airway patency.¹ In the general population, the prevalence of OSAHS ranges from 9% to 38% and increases as age and body mass index, especially in males.² A recent study found that obesity and OSAS adversely affect many organs and systems. Besides the factors affecting the diagnosis of the OSAS-obesity relationship, mutual organ interactions among the respiratory system, adipose tissue and intestines should not be ignored for prevention and treatment of OSAS and obesity.³ Untreated OSAHS is usually associated with adverse long-term health outcomes, such as systemic hypertension, pulmonary hypertension, heart failure, cardiac arrhythmias, diabetes mellitus, and stroke.^4-6 OSAHS is also associated with poorer health-related quality of life and higher cost of healthcare resources.^7-9 Recently, a special issue of Frontiers in Medicine for ‘Obstructive sleep apnoea syndrome (OSAS). What's new?’ also collected many recent researches about the OSAS, which is linked with a high risk of hypertension, cardiovascular diseases, daytime sleepiness, home and work-related accidents, and a consequent worsening of life quality. Numerous studies stress the importance of praecox diagnosis and a multidisciplinary approach in addressing all these situations.¹⁰

Currently, public awareness of OSAHS is increasing, and more and more high-risk patients are willing to evaluate the disease and seek treatment opportunities, but OSAHS remains severely underdiagnosed,¹¹ so that a large proportion of patients do not receive any appropriate treatment, even though have no idea of this disease. At present, the gold standard for diagnosing OSAHS is polysomnography (PSG).¹² However, the measurement of PSG requires the patient to stay overnight in the sleep monitoring centre. The PSG is time-consuming and expensive, additionally not all medical institutions have sleep monitoring centers and well-trained and professional sleep physicians. With the rising incidence of OSAHS due to rising obesity rates and an aging population, there is an urgent need to develop a simple and effective diagnostic tool that could allow for a wider screening and early diagnosis of OSAHS.

A lot of scales have been designed and developed to screen for OSAHS patients, including Espworth sleepiness scale (ESS), STOP-Bang and Berlin score. For example, a recent study found that a non-invasive assessment of low ArTH could help to identify an endotype with a lower predicted response to oral appliances in a clinical setting.¹³ However, previous studies shown that the specificity and negative predictive value (NPV) of these scales were not high, less than 60%,¹⁴ especially in the Chinese population.¹⁵ These scales cannot effectively discriminate the non-OSAHS patients and lead to a large proportion of unnecessary PSG screening. Therefore, we conduct a cross-sectional study in a Chinese population with high-risk to develop and validate a practical clinical model to screen patients of OSAHS.

2 MATERIALS AND METHODS

3 RESULTS

A total of 782 patients aged 46 years (37–55 year) with high-risk of OSAHS were met the inclusion criteria and enrolled into this study (Table 1). Among them, 23.02% (n = 180) were women. Most of the patients' characteristics between training and validation datasets were comparable, only several laboratory tests, such as high-density lipoprotein, low-density lipoprotein, prothrombin, albumin, and platelet count, Berlin score, and age were slightly different between two groups of patients (all P-values > 0.05). Compared with non-OSAHS patients, those finally diagnosed OSAHS patients were more frequently to be men (79.24% vs. 64.36%), with older age (47 vs. 39.5 years), current drinkers (36.13% vs. 23.23%), with high blood pressures, with the symptom of morning dry mouth/mouth breathing and nocturia, higher AHI, ESS, and Berlin score, higher levels of fasting glucose, triglycerides, alanine aminotransferase, gamma-glutamyl transferase, and alkaline phosphatase; and lower levels of total bilirubin (all P-values < 0.05) (Table 2).

The univariate logistic regression model selected 15 factors from totally 34 factors were significantly related with OSAHS with all P-values < 0.05 (Table 3). Then, using the stepwise multivariate logistic regression model, we obtained the final model, including age (1.032 [95% CI: 1.010, 1.056], P = 0.005), BMI (1.221 [1.109, 1.345], P < 0.001), symptom of morning dry mouth/mouth breathing (3.033 [1.381, 6.663], P = 0.0057), Berlin score (2.154 [1.165, 3.983], P = 0.0144), and total bilirubin (0.939 [0.891, 0.989], P = 0.0179). Considering the previous evidence, we finally selected three models: Model 1: age, BMI, total bilirubin, symptom of morning dry mouth/mouth breathing, and Belin scores, Model 2: age, gender, BMI, total bilirubin, and symptom of morning dry mouth/mouth breathing, and Model 3: age, gender, BMI, total bilirubin, symptom of morning dry mouth/mouth breathing, and Berlin score.

In both training dataset and validation dataset, the above three models presented similar discriminant ability; however, the best one is Model 1 with an AUC of 0.780 (0.711, 0.848), sensitivity of 0.848 (0.811, 0.885), specificity of 0.629 (0.509, 0.749), accuracy of 0.816 (0.779, 0.853), positive predictive value of 0.930 (0.903, 0.958), negative predictive value of 0.415 (0.315, 0.515), positive likelihood ratio of 2.286 (1.538, 3.034), and negative likelihood ratio of 0.242 (0.167, 0.316) in training dataset (Table 4). Even though, compared with other two models, the ROC curve and AUC were not statistically significant with P-value = 0.5433 for Model 2 versus Model 1 and P-value = 0.9518 for Model 3 versus Model 1 in training dataset (Figure 1). Similarly, in validation dataset, the comparisons were also non-significant (all P-value > 0.05).

We finally built the nomogram based on the Model 2 (Figure 2). The points assigned to each factor was on the score line and the total probability and score were on the bottom. For example, a patient aged 40 years with a BMI of 25 kg/m², high Berlin score, with total bilirubin of 21.52 mg/dL, with a symptom of mouth breathing has an approximately 80% chance to be OSAHS.

4 DISCUSSION

Using the cross-sectional data of 782 patients, we developed and validated a practical nomogram model to help us screen the OSAHS patient in a high-risk Chinese population with a well discriminant ability. Our nomogram included five factors provides an efficient, convenient, and simple tool for clinical diagnosis of OSAHS.

Previous evidence suggested that the males have a high risk of OSA, the rate of males versus females in OSA patient populations was 8:1.¹⁶ However, in our study, after stepwise multivariable selection, the gender was not included into the final diagnostic model. The potential reason may be that the patients in our study were those high-risk patients of OSAHS. Although the males were more likely to develop OSA than females, among those high-risk patients, no matter males or females develop OSA in the similar risk. Additionally, in undiagnosed OSA populations, the ratio of males versus females was 2:1, which indicated that women with OSA are less likely to be evaluated and diagnosed.⁶ Moreover, evidence suggested that OSA in women may be diagnosed late in the course of the disease or may not be aggressively treated.⁶ Considering the gender difference in OSAHS, we additionally included gender into other models, but non-significant differences from our final model (without gender) were detected in both training and validation datasets.

Age^17-19 and BMI^{16, 20} have been confirmed as the critical risk factors of OSAHS, and all these two factors were included in our final model as a diagnostic factor. Our nomogram model indicated that BMI indicated a high contribution to OSAHS diagnosis. Previous evidence not only suggested that BMI increase results in OSAHS, nearly a 10% increase in weight was associated with a sixfold greater risk of developing OSA among persons initially free of OSA,²⁰ but also the weight loss according to surgery or diet are significantly decrease in the severity of OSAHS.¹⁶ Besides that, the OSA patients with obesity would have a high risk of cardiovascular diseases in further.²¹ The underlying mechanism of obesity and OSAHS might be caused by the effects of intermittent hypoxia on adipose tissue.^{22, 23} Also, insufficient sleep may counteract with dysmetabolic changes.²⁴ Moreover, both OSAHA and obesity induced the systemic pro-inflammatory changes and finally enhanced the risk of cardiovascular diseases.^{25, 26} Older age adults (age ≥65) have a twofold to threefold higher prevalence of OSA than those aged less than 65 years.^17-19 However, the under mechanism of the influence of age on OSAHS is still uncertain.

Bilirubin, an endogenous antioxidant in human vascular endothelial cells, provides an additional mechanism for hypoxic adaptation mediated through feedback inhibition of the heme biosynthetic pathway.^{27, 28} A meta-analysis of 11 studies found an inverse relationship between bilirubin concentrations and atherosclerosis severity in men with a 6.5% decrease in cardiovascular events for every 1 mol/L increase in bilirubin.²⁹ Among patients with OSAHS, the hyoxaemia would introduce a series of chain reactions in metabolism and changes in metabolism. Thus, the metabolism biomarkers might be the sensitivity response factors of OSAHS. This hypothesis has been supported by previous evidence,³⁰ which found an increase in bilirubin levels of patients with OSA in the morning. Consistently, after the competition of multiple variables, total bilirubin levels were selected as a critical indicator for OSAHS in Chinese adults.

Morning dry mouth or mouth breathing also has been considered as an screening factors for sleep apnoea, because the dry mouth symptom significantly correlated with snoring and sleep apnoea.³¹ The specificity and diagnostic accuracy of the STOP-Bang questionnaire have been improved by integrating dry mouth. Besides of other influence factors, such as hypertension and diabetes, due to the open-mouth breathing during night sleep, the patients with OSA more frequently report a more typical dry mouth symptom in the morning. Our study reconfirmed that the morning dry mouth or mouth breathing might be a valuable and typical predictor for OSA.³²

Using the data from a cross-sectional study in Chinese adults, we develop a convenient, simple, and efficient diagnostic nomogram model, which could efficiently help clinicians to quickly discriminant the OSAH patients from those high-risk population. All the risk factors were easily obtained, which might be have a wider applicability in practice. However, this study has several limitations. This is single centre study, which included the patients from one hospital and might include selection bias. Further need an external validation in other population, although we validated the model efficient and utility in a special training population form the same centre. We included many risk factors; however, there might be other diagnostic factors still not included, for example other saliva biomarkers³³ and gene assessments.^{34, 35}

Our study provided an efficient, convenient, and simple nomogram scale with five factors to help clinical screening OSAHS patients in those high-risk population in China, which has a wider applicability in practice. However, due to the limits of cross-sectional design and single centre study, the utility and feasibility need further reconfirmed in other population and verified by prospective studies.

AUTHOR CONTRIBUTIONS

Jie Liu contributed to conceptualization. Feng Pang contributed to methodology. Xiaofeng Huang and Minmin Lin contributed to data collection and supervision. Xiangmin Zhang and Tianrun Liu contributed to project administration. Wenmin Deng contributed to data curation. Zhen Long contributed to reviewing and editing of the manuscript. All authors have read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

All authors declare that they have no conflict of interest.

ETHICS STATEMENT

The study was approved by the Ethics Committee of The Sixth Affiliated Hospital Sun Yat-sen University (Approval number: 2023ZSLYEC-001). Consent to participate is waived due to it was an observational study.