Catherine M. Heinzinger DO, MS , Brittany Lapin PhD, MPH , Nicolas R. Thompson MS , Yadi Li MEd , Alex Milinovich BA , Anna M. May MD, MS , Cinthya Pena Orbea MD , Michael Faulx MD , David R. Van Wagoner PhD , Mina K. Chung MD , Nancy Foldvary-Schaefer DO, MS , Reena Mehra MD, MS
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Sleep-based phenotypes can account for this complexity and translate to actionable approaches to identify at-risk patients and therapeutic interventions.</div></div><div><h3>Objectives</h3><div>This study hypothesized discrete phenotypes of symptoms and polysomnography (PSG)-based data differ in relation to incident AF.</div></div><div><h3>Methods</h3><div>Data from the STARLIT (sleep Signals, Testing, And Reports LInked to patient Traits) registry on Cleveland Clinic patients (≥18 years of age) who underwent PSG from November 27, 2004, to December 30,2015, were retrospectively examined. Phenotypes were identified using latent class analysis<span> of symptoms and PSG-based measures of sleep-disordered breathing and sleep architecture. Phenotypes were included as the primary predictor in a multivariable-adjusted Cox proportional hazard models for incident AF.</span></div></div><div><h3>Results</h3><div>In our cohort (N = 43,433, age 51.8 ± 14.5 years, 51.9% male, 74.9% White), 7.3% (n = 3,166) had baseline AF. Over a 7.6- ± 3.4-year follow-up period, 8.9% (n = 3,595) developed incident AF. Five phenotypes were identified. The hypoxia<span> subtype (n = 3,245) had 48% increased incident AF (HR: 1.48; 95% CI: 1.34-1.64), the apneas + arousals subtype (n = 4,592) had 22% increased incident AF (HR: 1.22; 95% CI: 1.10-1.35), and the short sleep + nonrapid eye movement<span> subtype (n = 6,126) had 11% increased incident AF (HR: 1.11; 95% CI: 1.01-1.22) compared with long sleep + rapid eye movement (n = 26,809), the reference group. The hypopneas subtype (n = 2,661) did not differ from reference (HR: 0.89; 95% CI: 0.77-1.03).</span></span></div></div><div><h3>Conclusions</h3><div><span>Consistent with prior evidence supporting hypoxia as an AF driver and cardiac risk of the sleepy phenotype, this constellation of symptoms and physiologic alterations illustrates vulnerability for AF development, providing potential value in enhancing our understanding of integrated sleep-specific symptoms and physiologic risk of atrial </span>arrhythmogenesis.</div></div>","PeriodicalId":14573,"journal":{"name":"JACC. Clinical electrophysiology","volume":"10 9","pages":"Pages 2074-2084"},"PeriodicalIF":8.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel Sleep Phenotypic Profiles Associated With Incident Atrial Fibrillation in a Large Clinical Cohort\",\"authors\":\"Catherine M. Heinzinger DO, MS , Brittany Lapin PhD, MPH , Nicolas R. Thompson MS , Yadi Li MEd , Alex Milinovich BA , Anna M. May MD, MS , Cinthya Pena Orbea MD , Michael Faulx MD , David R. Van Wagoner PhD , Mina K. Chung MD , Nancy Foldvary-Schaefer DO, MS , Reena Mehra MD, MS\",\"doi\":\"10.1016/j.jacep.2024.05.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>While sleep disorders are implicated in atrial fibrillation (AF), the interplay of physiologic alterations and symptoms remains unclear. Sleep-based phenotypes can account for this complexity and translate to actionable approaches to identify at-risk patients and therapeutic interventions.</div></div><div><h3>Objectives</h3><div>This study hypothesized discrete phenotypes of symptoms and polysomnography (PSG)-based data differ in relation to incident AF.</div></div><div><h3>Methods</h3><div>Data from the STARLIT (sleep Signals, Testing, And Reports LInked to patient Traits) registry on Cleveland Clinic patients (≥18 years of age) who underwent PSG from November 27, 2004, to December 30,2015, were retrospectively examined. Phenotypes were identified using latent class analysis<span> of symptoms and PSG-based measures of sleep-disordered breathing and sleep architecture. Phenotypes were included as the primary predictor in a multivariable-adjusted Cox proportional hazard models for incident AF.</span></div></div><div><h3>Results</h3><div>In our cohort (N = 43,433, age 51.8 ± 14.5 years, 51.9% male, 74.9% White), 7.3% (n = 3,166) had baseline AF. Over a 7.6- ± 3.4-year follow-up period, 8.9% (n = 3,595) developed incident AF. Five phenotypes were identified. The hypoxia<span> subtype (n = 3,245) had 48% increased incident AF (HR: 1.48; 95% CI: 1.34-1.64), the apneas + arousals subtype (n = 4,592) had 22% increased incident AF (HR: 1.22; 95% CI: 1.10-1.35), and the short sleep + nonrapid eye movement<span> subtype (n = 6,126) had 11% increased incident AF (HR: 1.11; 95% CI: 1.01-1.22) compared with long sleep + rapid eye movement (n = 26,809), the reference group. The hypopneas subtype (n = 2,661) did not differ from reference (HR: 0.89; 95% CI: 0.77-1.03).</span></span></div></div><div><h3>Conclusions</h3><div><span>Consistent with prior evidence supporting hypoxia as an AF driver and cardiac risk of the sleepy phenotype, this constellation of symptoms and physiologic alterations illustrates vulnerability for AF development, providing potential value in enhancing our understanding of integrated sleep-specific symptoms and physiologic risk of atrial </span>arrhythmogenesis.</div></div>\",\"PeriodicalId\":14573,\"journal\":{\"name\":\"JACC. Clinical electrophysiology\",\"volume\":\"10 9\",\"pages\":\"Pages 2074-2084\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JACC. 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Novel Sleep Phenotypic Profiles Associated With Incident Atrial Fibrillation in a Large Clinical Cohort
Background
While sleep disorders are implicated in atrial fibrillation (AF), the interplay of physiologic alterations and symptoms remains unclear. Sleep-based phenotypes can account for this complexity and translate to actionable approaches to identify at-risk patients and therapeutic interventions.
Objectives
This study hypothesized discrete phenotypes of symptoms and polysomnography (PSG)-based data differ in relation to incident AF.
Methods
Data from the STARLIT (sleep Signals, Testing, And Reports LInked to patient Traits) registry on Cleveland Clinic patients (≥18 years of age) who underwent PSG from November 27, 2004, to December 30,2015, were retrospectively examined. Phenotypes were identified using latent class analysis of symptoms and PSG-based measures of sleep-disordered breathing and sleep architecture. Phenotypes were included as the primary predictor in a multivariable-adjusted Cox proportional hazard models for incident AF.
Results
In our cohort (N = 43,433, age 51.8 ± 14.5 years, 51.9% male, 74.9% White), 7.3% (n = 3,166) had baseline AF. Over a 7.6- ± 3.4-year follow-up period, 8.9% (n = 3,595) developed incident AF. Five phenotypes were identified. The hypoxia subtype (n = 3,245) had 48% increased incident AF (HR: 1.48; 95% CI: 1.34-1.64), the apneas + arousals subtype (n = 4,592) had 22% increased incident AF (HR: 1.22; 95% CI: 1.10-1.35), and the short sleep + nonrapid eye movement subtype (n = 6,126) had 11% increased incident AF (HR: 1.11; 95% CI: 1.01-1.22) compared with long sleep + rapid eye movement (n = 26,809), the reference group. The hypopneas subtype (n = 2,661) did not differ from reference (HR: 0.89; 95% CI: 0.77-1.03).
Conclusions
Consistent with prior evidence supporting hypoxia as an AF driver and cardiac risk of the sleepy phenotype, this constellation of symptoms and physiologic alterations illustrates vulnerability for AF development, providing potential value in enhancing our understanding of integrated sleep-specific symptoms and physiologic risk of atrial arrhythmogenesis.
期刊介绍：
JACC: Clinical Electrophysiology is one of a family of specialist journals launched by the renowned Journal of the American College of Cardiology (JACC). It encompasses all aspects of the epidemiology, pathogenesis, diagnosis and treatment of cardiac arrhythmias. Submissions of original research and state-of-the-art reviews from cardiology, cardiovascular surgery, neurology, outcomes research, and related fields are encouraged. Experimental and preclinical work that directly relates to diagnostic or therapeutic interventions are also encouraged. In general, case reports will not be considered for publication.
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