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Comorbid conditions analyzed from MGB\u2019s medical record system included 9 representative cardiometabolic and respiratory diseases, as well as 408 prevalent diseases. Associations were assessed using modified Poisson and least absolute shrinkage and selection operator (LASSO) regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The sample included 62% females, aged 52.9\u2009\u00b1\u200916.8\u2009years, and an apnea-hypopnea index (AHI) of 21.4\u2009\u00b1\u200915.9 events\/h. Associations between endotypes and demographics\/obesity-related factors were consistent with prior observational studies (median difference in \u03b2\u2009=\u20090.03 SD). After adjusting for AHI, older age was associated with lower heart rate (\u22120.40 SD) and arousal burdens (\u22120.23 SD), while higher body mass index was associated with increased hypoxic burden (0.25 SD). Having demonstrated reasonable concordance with published data, our subsequent analysis identified distinct and clinically meaningful associations between advanced PSG metrics and comorbid conditions. Specifically, elevated loop gain, ventilatory burden, and hypoxic burden were associated with hypertension, diabetes, and renal failure; increased ventilatory instability was associated with cardiovascular disease; and reduced collapsibility and ventilatory instability were associated with chronic airway obstruction. Even after LASSO-based selection, no single PSG metric consistently predicted risk across all comorbidities; ventilatory instability showed the most associations among endotypic traits and heart rate burden among physiological burdens, underscoring the heterogeneity of obstructive sleep apnea pathophysiology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Phenome-wide analyses of a large clinical cohort demonstrate the real-world feasibility and clinical relevance of extracting advanced PSG metrics, supporting their potential to identify personalized, mechanism-specific intervention targets for sleep apnea.<\/jats:p>\n                  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