Analyzing the phenotypic and endotypic characteristics of Sleep Apnea, along with DISE obstruction situations, is crucial for precise diagnosis and treatment. In this study, we aim to construct and apply a multidimensional predictive model based on four aspects: basic physiological characteristics of OSA, clinical phenotypes, mechanistic endotypes, and DISE obstruction levels. The study will begin by categorizing the clinical phenotypes; subsequently, it will quantify endotypic indicators based on PSG signal information and construct the PALM scale for Chinese individuals. Following this, a comprehensive clinical profile and a treatment efficacy prediction model for OSA patients will be built based on the results from the aforementioned multidimensional data.

Obstructive Sleep Apnea (OSA) is characterized by repeated episodes of upper airway obstruction and apneas during sleep, resulting in chronic intermittent hypoxemia, autonomic fluctuations, and sleep fragmentation. OSA is a heterogeneous disease influenced by multifactorial elements. The effectiveness of treatments and prognoses may vary due to differences in etiological factors, pathophysiological mechanisms, and clinical subtypes. Zinchuk et al. identified four clinical symptom and comorbidity-based subtypes and two subtypes based on polysomnography (PSG) indicators, which are useful for guiding treatment. However, relying solely on external phenotypes does not allow for analysis of intrinsic mechanisms, often leading to large treatment outcome disparities within the same phenotype due to different underlying mechanisms. Thus, the concept of OSA endotypes, which can elucidate pathophysiological mechanisms, has been introduced. OSA phenotypes are broadly defined as a classification of OSA patients related to clinically significant attributes such as symptoms, treatment response, underlying diseases, and quality of life; whereas endotypes refer to disease subtypes with distinct functional or pathophysiological mechanisms. There are at least four key pathophysiological endotypes in OSA, including 1) high upper airway closing pressure (Pcrit), 2) low arousal threshold (ArThr), 3) high loop gain (LG), and 4) impaired pharyngeal dilator muscle responsiveness. Each endotype represents a target or "treatable trait" from a mechanistic perspective. The advantages of OSA endotype quantification based on PSG signal information are evident. Eckert et al. proposed a potential classification of OSA patients into three subgroups based on the impairment of upper airway anatomy and the non-anatomical phenotypes (loop gain, arousal threshold, and muscle responsiveness) - the PALM scale. This phenotyping introduces different possible therapeutic strategies.

The same PSG outcomes may be caused by different endotypic mechanisms, and different endotypic mechanisms may lead to varying PSG outcomes, resulting in inconsistent treatment effects. To accurately align endotypes with PSG outcomes, a standard for obstruction anchoring is essential. Drug-induced sleep endoscopy (DISE) offers a bridge between the two by providing an assessment of the severity and plane of upper airway obstruction, which is related to both the severity of apneas and the upper airway closing pressure in the PALM model. In our preliminary research, the measurement of upper airway closing pressure and muscle responsiveness was achievable through DISE-PAP. Given the importance of distinguishing OSA patient phenotypic characteristics, quantifying endotypes, developing new indices, and assessing DISE obstruction planes, this study aims to construct and apply a multidimensional predictive model that integrates basic physiological characteristics of OSA, clinical phenotypes, mechanistic endotypes, and DISE obstruction planes. The study will start with the classification of clinical phenotypes, followed by the quantification of endotypic indicators based on PSG signal information and the construction of a PALM scale suitable for Chinese individuals. Subsequently, based on the results from the aforementioned multidimensional data, a comprehensive clinical portrait and predictive model of treatment outcomes for OSA patients will be built.

Inclusion Criteria:

1. Patients aged between 18 and 80 years.
2. Diagnosed with Obstructive Sleep Apnea (OSA)(apnea-hypopnea index≥5/h).
3. First-time diagnosis, with no previous surgical interventions or CPAP treatment for OSA.
4. Ability and willingness to provide informed consent for participation in the study.

Exclusion Criteria:

1. History of severe stroke or cerebral hemorrhage, or presence of neurological or psychiatric conditions that could affect study results.
2. Presence of active malignancies or other severe underlying diseases, such as severe liver or kidney dysfunction. Diagnosed with diabetes or other significant vascular diseases.
3. Presence of severe chronic obstructive pulmonary disease (COPD), severe asthma, severe pulmonary hypertension, or heart failure caused by any condition.
4. Pregnancy or having other conditions that make participation in this study unsuitable.
5. Extremely debilitated patients or those with severe underlying conditions.

This study will involve participants diagnosed with OSA who are not currently undergoing any active treatment for the condition. The study aims to explore the detailed phenotypic and endotypic characteristics of these patients to better understand OSA dynamics and develop predictive models for individualized treatment approaches.

The individual participant data will not be shared. The informed consent will be ansigned before enrolled in the study and ensured to keep personal information confidential.