Advertisement

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

  • Hideaki NakayamaEmail author
  • Mina Kobayashi
  • Satoru Tsuiki
  • Mariko Yanagihara
  • Yuichi Inoue
Sleep Breathing Physiology and Disorders • Original Article

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.

Keywords

Sleep apnea Apnea-hypopnea index Polysomnography Respiratory events Arousals 

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

Notes

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.

Author 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.

Compliance with ethical standards

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.

Supplementary material

11325_2019_1785_MOESM1_ESM.docx (54 kb)
ESM 1 (DOCX 53 kb)
11325_2019_1785_MOESM2_ESM.docx (18 kb)
ESM 2 (DOCX 17 kb)
11325_2019_1785_MOESM3_ESM.docx (54 kb)
ESM 3 (DOCX 54 kb)
11325_2019_1785_MOESM4_ESM.docx (57 kb)
ESM 4 (DOCX 57 kb)

References

  1. 1.
    American Academy of Sleep Medicine (2014) International classification of sleep disorders: diagnostic and coding manual, 3rd edn. American Academy of Sleep Medicine, DarienGoogle Scholar
  2. 2.
    Wellman A, Eckert DJ, Jordan AS, Edwards BA, Passaglia CL, Jackson AC, Gautam S, Owens RL, Malhotra A, White DP (2011) A method for measuring and modeling the physiological traits causing obstructive sleep apnea. J Appl Physiol (1985) 110:1627–1637CrossRefGoogle Scholar
  3. 3.
    Edwards BA, Wellman A, Owens RL (2013) PSGs: more than just the AHI. J Clin Sleep Med 9:527–528Google Scholar
  4. 4.
    Punjabi NM (2016) COUNTERPOINT: is the apnea-hypopnea index the best way to quantify the severity of sleep-disordered breathing? No. Chest 149:16–19CrossRefGoogle Scholar
  5. 5.
    Joosten SA, Hamza K, Sands S, Turton A, Berger P, Hamilton G (2012) Phenotypes of patients with mild to moderate obstructive sleep apnoea as confirmed by cluster analysis. Respirology 17:99–107CrossRefGoogle Scholar
  6. 6.
    Ye L, Pien GW, Ratcliffe SJ, Bjornsdottir E, Arnardottir ES, Pack AI, Benediktsdottir B, Gislason T (2014) The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J 44:1600–1607CrossRefGoogle Scholar
  7. 7.
    Bailly S, Destors M, Grillet Y, Richard P, Stach B, Vivodtzev I, Timsit JF, Levy P, Tamisier R, Pepin JL, registry(OSFP) sciotFnsa (2016) Obstructive sleep apnea: a cluster analysis at time of diagnosis. PLoS One 11:e0157318CrossRefGoogle Scholar
  8. 8.
    Vavougios GD, George DG, Pastaka C, Zarogiannis SG, Gourgoulianis KI (2016) Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis. J Sleep Res 25:31–38CrossRefGoogle Scholar
  9. 9.
    Lacedonia D, Carpagnano GE, Sabato R, Storto MM, Palmiotti GA, Capozzi V, Barbaro MP, Gallo C (2016) Characterization of obstructive sleep apnea-hypopnea syndrome (OSA) population by means of cluster analysis. J Sleep Res 25:724–730CrossRefGoogle Scholar
  10. 10.
    Zinchuk AV, Jeon S, Koo BB, Yan X, Bravata DM, Qin L, Selim BJ, Strohl KP, Redeker NS, Concato J, Yaggi HK (2018) Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea. Thorax 73:472–480CrossRefGoogle Scholar
  11. 11.
    Iber C, Ancoli-Israel S, Chesson AL, Quan SF (2007) The AASM Manual for the scoring of sleep and associated events: rules, terminology, and technical specifications. American Academy of Sleep Medicine, WestchesterGoogle Scholar
  12. 12.
    Edwards BA, Eckert DJ, McSharry DG, Sands SA, Desai A, Kehlmann G, Bakker JP, Genta PR, Owens RL, White DP, Wellman A, Malhotra A (2014) Clinical predictors of the respiratory arousal threshold in patients with obstructive sleep apnea. Am J Respir Crit Care Med 190:1293–1300CrossRefGoogle Scholar
  13. 13.
    Penzel T, Moller M, Becker HF, Knaack L, Peter JH (2001) Effect of sleep position and sleep stage on the collapsibility of the upper airways in patients with sleep apnea. Sleep 24:90–95CrossRefGoogle Scholar
  14. 14.
    Dolnicar S (2002) A review of unquestioned standards in using cluster analysis for data-driven market segmentation. In: the Australian and New Zealand Marketing Academy Conference 2002 (ANZMAC 2002); 2002 2–4 December 2002; Deakin University, MelbourneGoogle Scholar
  15. 15.
    Watson PF, Petrie A (2010) Method agreement analysis: a review of correct methodology. Theriogenology 73:1167–1179CrossRefGoogle Scholar
  16. 16.
    Jordan AS, Wellman A, Edwards JK, Schory K, Dover L, MacDonald M, Patel SR, Fogel RB, Malhotra A, White DP (2005) Respiratory control stability and upper airway collapsibility in men and women with obstructive sleep apnea. J Appl Physiol (1985) 99:2020–2027CrossRefGoogle Scholar
  17. 17.
    Patil SP, Schneider H, Marx JJ, Gladmon E, Schwartz AR, Smith PL (2007) Neuromechanical control of upper airway patency during sleep. J Appl Physiol (1985) 102:547–556CrossRefGoogle Scholar
  18. 18.
    Schwartz AR, O’Donnell CP, Baron J, Schubert N, Alam D, Samadi SD, Smith PL (1998) The hypotonic upper airway in obstructive sleep apnea: role of structures and neuromuscular activity. Am J Respir Crit Care Med 157:1051–1057CrossRefGoogle Scholar
  19. 19.
    Joosten SA, Edwards BA, Wellman A, Turton A, Skuza EM, Berger PJ, Hamilton GS (2015) The effect of body position on physiological factors that contribute to obstructive sleep apnea. Sleep 38:1469–1478CrossRefGoogle Scholar
  20. 20.
    Oksenberg A, Arons E, Radwan H, Silverberg DS (1997) Positional vs nonpositional obstructive sleep apnea patients. Chest 112:629–639CrossRefGoogle Scholar
  21. 21.
    Berry RB, Gleeson K (1997) Respiratory arousal from sleep: mechanisms and significance. Sleep 20:654–675CrossRefGoogle Scholar
  22. 22.
    Eckert DJ, Younes MK (2014) Arousal from sleep: implications for obstructive sleep apnea pathogenesis and treatment. J Appl Physiol (1985) 116:302–313CrossRefGoogle Scholar
  23. 23.
    Sarstedt M, Mooi E (2014) Cluster analysis. In: Sarstedt M, Mooi E (eds) A concise guide to market research. Springer, Verlag Berlin, pp 273–324Google Scholar
  24. 24.
    Peppard PE, Ward NR, Morrell MJ (2009) The impact of obesity on oxygen desaturation during sleep-disordered breathing. Am J Respir Crit Care Med 180:788–793CrossRefGoogle Scholar
  25. 25.
    Dempsey JA, Xie A, Patz DS, Wang D (2014) Physiology in medicine: obstructive sleep apnea pathogenesis and treatment--considerations beyond airway anatomy. J Appl Physiol (1985) 116:3–12CrossRefGoogle Scholar
  26. 26.
    Terrill PI, Edwards BA, Nemati S, Butler JP, Owens RL, Eckert DJ, White DP, Malhotra A, Wellman A, Sands SA (2015) Quantifying the ventilatory control contribution to sleep apnoea using polysomnography. Eur Respir J 45:408–418CrossRefGoogle Scholar
  27. 27.
    Kapsimalis F, Kryger MH (2002) Gender and obstructive sleep apnea syndrome, part 1: clinical features. Sleep 25:412–419Google Scholar
  28. 28.
    Ohdaira F, Nakamura K, Nakayama H, Satoh M, Ohdaira T, Nakamata M, Kohno M, Iwashima A, Onda A, Kobayashi Y, Fujimori K, Kiguchi T, Izumi S, Kobayashi T, Shinoda H, Takahashi S, Gejyo F, Yamamoto M (2007) Demographic characteristics of 3,659 Japanese patients with obstructive sleep apnea-hypopnea syndrome diagnosed by full polysomnography: associations with apnea-hypopnea index. Sleep Breath 11:93–101CrossRefGoogle Scholar
  29. 29.
    Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 177:1006–1014CrossRefGoogle Scholar
  30. 30.
    Duce B, Milosavljevic J, Hukins C (2015) The 2012 AASM respiratory event criteria increase the incidence of hypopneas in an adult sleep center population. J Clin Sleep Med 11:1425–1431CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Respiratory MedicineTokyo Medical UniversityTokyoJapan
  2. 2.Japan Somnology CenterInstitute of NeuropsychiatryTokyoJapan
  3. 3.Foundation of Sleep and Health ScienceTokyoJapan
  4. 4.Department of SomnologyTokyo Medical UniversityTokyoJapan

Personalised recommendations