Unique clinical phenotypes of patients with obstructive sleep apnea in a Japanese population: a cluster analysis

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

Obstructive sleep apnea is a highly prevalent sleep-related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter-individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea-Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea-Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea-Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.

Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

INTRODUCTION

Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization.

METHODS

Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range).

RESULTS

981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m2 (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3.

CONCLUSIONS

The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.

Multidimensional assessment and cluster analysis for OSA phenotyping

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a heterogeneous disease with varying phenotype. A cluster analysis based on multidimensional disease characteristics, including symptom, anthropometry, polysomnography (PSG), and craniofacial morphology, in combination with auto-continuous positive airway pressure (CPAP) titration response and comorbidity profiles was performed within a well-characterized cohort of patients with OSA, with the aim to refine current phenotypic expressions of OSA with clinical implications.

METHODS

Two hundred and ninety-one subjects with a new diagnosis of moderate to severe OSA, referred for auto-CPAP titration to the sleep center were included for analysis. In-laboratory PSG and craniofacial computed tomography (CT) scanning was performed, followed by an auto-CPAP titration. The symptom of excessive daytime sleepiness (EDS) was assessed by Epworth sleepiness scale (ESS).

RESULTS

Three patient phenotypes, corresponding to the "normal weight, non-sleepy and moderate OSA", the "obese, non-sleepy and severe OSA" and "obese, sleepy, very severe OSA with craniofacial limitation" were identified. Among the PSG parameters, only N3% and mean pulse oxygen saturation (SPO₂) were found to be associated with ESS, and they only explain small fraction of the variation (R²=0.136). Neck circumference and craniofacial limitation were associated the more severe phenotype, which had higher prevalence of hypertension, metabolic syndrome, greater diurnal blood gas abnormalities and worse PAP titration response.

CONCLUSIONS

Three OSA phenotypes were identified according to multiple aspect of clinical features in patients with moderate to severe OSA, which differed in prevalence of hypertension, metabolic syndrome, diurnal blood gas parameters and CPAP titration response. Self-reported EDS was not related with the severity of sleep breathing disturbance, and craniofacial limitation was associated the more severe phenotype. These findings highlight the necessity of integrate multiple disease characters into phenotyping to achieve better understanding of the clinical pictures of OSA.