Phenotypic Characterization of Sleep Apnea Using Clusters Derived from Subject-Based SpO₂ Weighted Correlation...

Area of Science:

• Sleep Medicine
• Cardiovascular Health
• Metabolic Disorders

Background:

• Obstructive Sleep Apnea (OSA) is common and linked to serious health issues.
• OSA patient groups are diverse, making one-size-fits-all treatments difficult.
• Existing phenotyping methods may not fully capture complex patient data.

Purpose of the Study:

• To develop a new method for identifying Obstructive Sleep Apnea (OSA) subgroups.
• To utilize SpO2 data and network analysis for improved phenotyping.
• To enhance personalized treatment strategies for OSA patients.

Main Methods:

• Developed subject-based SpO2 weighted correlation networks.
• Applied Blondel's modularity algorithm for cluster identification.
• Compared results with traditional k-means clustering using SHHS data.

Main Results:

• The correlation network method identified subgroups with more distinct characteristics (35 variables) than k-means (28 variables).
• This approach revealed hidden SpO2 patterns, indicating distinct OSA phenotypes.
• The method successfully identified clinically relevant subgroups without pre-specifying cluster numbers.

Conclusions:

• Subject-based correlation networks offer a precise way to phenotype Obstructive Sleep Apnea (OSA) using SpO2 data.
• This novel method can improve clinical decision-making and patient care through tailored interventions.
• Future work should validate findings and integrate multimodal data for refined OSA phenotyping.