Advanced topic modeling of electronic health records from 36,426 obstructive sleep apnea patients revealed 19 distinct age-dependent comorbidity patterns, each significantly linked to OSA diagnosis. The analysis identified recognizable disease clusters including metabolic, neuropsychiatric, and immune-mediated conditions, with some distinguished by timing—such as early versus late-onset asthma. Seventeen of these patterns correlated with specific sleep study measurements, including connections between cardiometabolic topics and apnea severity indices. This computational approach challenges the traditional view of OSA as a single disorder, instead revealing it as a heterogeneous condition with predictable age-related comorbidity trajectories. The findings could revolutionize personalized sleep medicine by enabling clinicians to anticipate disease progression and tailor interventions based on individual comorbidity profiles. However, this preprint awaits peer review, and the machine learning approach requires validation across diverse populations before clinical implementation. The work represents a significant step toward precision medicine in sleep disorders, potentially improving risk stratification and treatment targeting for millions affected by sleep apnea.