[Multidimensional clinical phenotypes of obstructive sleep apnea hypopnea syndrome based on unsupervised clustering algorithm].

Objective:To advance the precision diagnosis and treatment of obstructive sleep apnea-hypopnea syndrome（OSAHS）, this study aimed to identify phenotypes with distinct pathophysiological and clinical features based on multidimensional clinical data using unsupervised clustering methods. Methods:Polysomnography indicators （apnea-hypopnea index[AHI], lowest oxygen saturation, oxygen desaturation index, arousal index） and clinical symptom scale data（Epworth Sleepiness Scale[ESS], Insomnia Severity Index[ISI], Self-Rating Anxiety Scale[SAS], Self-Rating Depression Scale[SDS10]） were collected from 6 633 OSAHS patients who attended the Departments of Otorhinolaryngology-Head and Neck Surgery at the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an Central Hospital, and the Stomatological Hospital of Xi'an Jiaotong University. After Z-score normalization, K-means clustering was applied. The optimal number of clusters was determined using the elbow method and silhouette coefficient, and the results were visualized using box plots and radar charts. Results:Following data cleaning, 1 331 OSAHS patients were included in the final analysis and classified into four clinical phenotypes: ①Classic sleepy type（n=227）: characterized by concurrent objective sleep disturbance and subjective sleepiness; ②Asymptomatic hypoxic type（n=383）: characterized by severe physiological abnormalities but mild subjective symptoms; ③High symptom-mild physiological type（n=413）: characterized by high subjective symptom burden but relatively mild objective abnormalities; ④Symptom perception-dominant type（n=308）: characterized by prominent emotional and insomnia symptoms with the mildest physiological abnormalities. Significant differences were observed among phenotypes in sex distribution（P<0.05）, whereas no statistically significant differences were found in age, body mass index, or AHI. Significant differences were observed among phenotypes in BMI（P=0.023） and SDS（P=0.016）, whereas no statistically significant differences were found in age or AHI. Conclusion:OSAHS patients exhibit multidimensional clinical phenotypes. Classification based on the integration of objective AHI with multidimensional subjective and objective information should be adopted to formulate the individualized treatment strategies.