Obstructive sleep apnea phenotypes in men based on characteristics of respiratory events during polysomnography

Abstract

Purpose

The upper airway (UA) anatomical collapsibility, UA muscle responsiveness, breathing control, and/or arousability are important contributing factors for obstructive sleep apnea (OSA). Differences in clinical manifestations of OSA are believed to reflect interactions among these factors. We aimed to classify OSA patients into subgroups based on polysomnographic (PSG) variables using cluster analysis and assess each subgroup’s characteristics.

Methods

Men with moderate or severe OSA and without any concomitant heart or psychosomatic disease were recruited. A hierarchical cluster analysis was performed using variables including fraction of apnea, respiratory event duration, minimum oxygen saturation, arousal rate before termination, and frequency of respiratory events in the supine position. The impact of sleep stages or body position on PSG variables was also evaluated in each cluster.

Results

A total of 210 men (mean age, 50.0 years, mean body mass index, 27.4 kg/m2) were studied. The three subgroups that emerged from the analysis were defined as follows: cluster 1 (high fraction of apnea and severe desaturation (20%)), cluster 2 (high fraction of apnea and long event duration (31%)), and cluster 3 (low fraction of apnea (49%)). There were differences in the body mass index and apnea type between the three clusters. Sleep stages and/or body position affected PSG variables in each cluster.

Conclusions

Patients with OSA could be divided into three distinct subgroups based on PSG variables. This clustering may be used for assessing the pathophysiology of OSA to tailor individual treatment other than continuous positive airway pressure therapy.

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Abbreviations

AHI:

Apnea-hypopnea index

BMI:

Body mass index

CA:

Central apnea

EMG:

Electromyogram

Fapnea :

Fraction of apnea

OSA:

Obstructive sleep apnea

PSG:

Polysomnography

Rar :

Ratio of arousal

SDB:

Sleep-disordered breathing

SE:

Standard error

SN:

Distance from sella to nasion

SpO2 :

Saturation of oxygen

UA:

Upper airway

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Acknowledgments

The authors thank Youichiro Takei, RPSGT, for his help with data collection and Editage (www.editage.jp) for English language editing.

Financial disclosures

This study is partly supported by the Japan Society for the Promotion of Science (grant numbers 15K11463, 26507011, and 15H05301). The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

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Affiliations

Authors

Contributions

HN, MK, ST, and YI contributed to the study design; HN, MK, and MY contributed in data acquisition; HN, ST, and YI analyzed data; and HN, MK, ST, and YI prepared the manuscript.

Corresponding author

Correspondence to Hideaki Nakayama.

Ethics declarations

Conflict of interest

YI consults for Takeda Pharmaceutical Co., MSD and Eisai Co. and has received grant/research support from Astellas Pharma, Otsuka Pharmaceutical Co., Pacific Medico, and Philips Respironics. All other authors have no conflicts of interest to disclose.

Additional information

Comments

Subphenotyping of sleep-disordered breathing is a first step into the refinement of the spectrum of sleep disorders. Given the spate of recent publications demonstrating poor efficacy of gold standard therapies and previous research revealing different aspects of sleep-disordered breathing affecting outcomes (e.g., Dr. Punjabi’s work within the SHHS showing 4% desaturations associating with cardiovascular risks), new means of delineating the obvious differences we see in patients will help us to not only move away from the coarse estimate of sleep-disordered breathing encompassed by the AHI, but will also enable more personalized therapeutic selections and accurate monitoring of successes and failures. While other experts in the field have demonstrated similar successes at subphenotyping with clustering on symptoms and/or comorbidities, objective data derived from the polysomnogram holds the promise of more valid and reliable sleep metrics that can be explored in the full host of sleep disorders contained within the ICSD-3/DSM-V.

Logan Schneider

CA, USA

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Nakayama, H., Kobayashi, M., Tsuiki, S. et al. Obstructive sleep apnea phenotypes in men based on characteristics of respiratory events during polysomnography. Sleep Breath 23, 1087–1094 (2019). https://doi.org/10.1007/s11325-019-01785-8

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Keywords

  • Sleep apnea
  • Apnea-hypopnea index
  • Polysomnography
  • Respiratory events
  • Arousals