Multidimensional assessment and cluster analysis for OSA phenotyping

Area of Science:

• Sleep Medicine
• Pulmonology
• Internal Medicine

Background:

• Obstructive sleep apnea (OSA) is a heterogeneous condition with varied clinical presentations.
• Current phenotyping may not fully capture the complexity of OSA.
• Refining OSA phenotypes is crucial for understanding disease mechanisms and optimizing treatment.

Purpose of the Study:

• To refine obstructive sleep apnea (OSA) phenotypes using cluster analysis.
• To integrate multidimensional patient characteristics for improved OSA classification.
• To identify clinically relevant OSA phenotypes with implications for management.

Main Methods:

• Cluster analysis of 291 patients with moderate to severe OSA.
• Inclusion of symptoms, anthropometry, polysomnography, craniofacial morphology, CPAP titration, and comorbidities.
• Assessment of excessive daytime sleepiness using the Epworth Sleepiness Scale.

Main Results:

• Three distinct OSA phenotypes were identified: normal weight/nonsleepy/moderate OSA; obese/nonsleepy/severe OSA; and obese/sleepy/very severe OSA with craniofacial limitation.
• Neck circumference and craniofacial limitations were associated with more severe phenotypes.
• Severe phenotypes exhibited higher prevalence of hypertension, metabolic syndrome, and poorer CPAP response.

Conclusions:

• Three distinct OSA phenotypes were identified based on multidimensional clinical features.
• Phenotypes differed in comorbidities, diurnal blood gas parameters, and CPAP titration response.
• Integrating diverse disease characteristics is essential for comprehensive OSA phenotyping and understanding clinical variability.