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Multi-perspective clustering of obstructive sleep apnea towards precision therapeutic decision including craniofacial intervention

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

Previous studies focusing on phenotyping obstructive sleep apnea (OSA) have outlined its heterogeneity in clinical symptoms, comorbidities, and polysomnographic features. However, the role of anatomical or pathophysiological causality including craniofacial skeletal deformity has not been studied. We aimed to identify and characterize phenotypes of OSA based on multi-perspective clustering by incorporating craniofacial risks with obesity, apnea severity, arousability, symptom, and comorbidity.

Methods

A total of 421 Korean patients with OSA (apnea-hypopnea index, AHI ≥ 5; age ≥ 20 years old) were recruited. A K-means cluster analysis was performed following principal component analysis with sagittal and vertical skeletal variables (ANB and mandibular plane angle), AHI, body mass index, and Epworth sleepiness scale. Inter-cluster comparison was conducted using demographic, cephalometric, and polysomnographic variables in addition to presence of diabetes and hypertension. Risk factors contributing to OSA severity were evaluated in each cluster using multivariable regression analysis with adjustment for age and gender.

Results

Three phenotypic clusters were identified and characterized as follows: Cluster-1 (noncraniofacial phenotype, 39%), non-obese moderate-to-severe OSA with no skeletal discrepancy representing low arousal threshold (ArTh), little sleepiness, and low comorbidity; Cluster-2 (craniofacial skeletal phenotype, 33%), non-obese moderate OSA with definite skeletal discrepancy showing low ArTh, mild sleepiness, and low comorbidity; and Cluster-3 (complicated phenotype, 28%), obese severe OSA with skeletal discrepancy exhibiting high ArTh, excessive daytime sleepiness, and high incidence of hypertension.

Conclusions

The three OSA phenotypes from multi-perspective clustering may provide a basis for precise therapeutic decision-making including craniofacial skeletal intervention beyond usual characterization of OSA subgroups.

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References

  1. Park JG, Ramar K, Olson EJ (2011) Updates on definition, consequences, and management of obstructive sleep apnea. In: Mayo Clinic Proceedings. vol 6. Elsevier, pp 549–555

  2. Eckert DJ (2018) Phenotypic approaches to obstructive sleep apnoea–new pathways for targeted therapy. Sleep Med Rev 37:45–59

    Article  Google Scholar 

  3. Bailly S, Destors M, Grillet Y, Richard P, Stach B, Vivodtzev I, Timsit J-F, Lévy P, Tamisier R, Pépin J-L (2016) Obstructive sleep apnea: a cluster analysis at time of diagnosis. PLoS One 11(6):e0157318

    Article  Google Scholar 

  4. Keenan BT, Kim J, Singh B, Bittencourt L, Chen N-H, Cistulli PA, Magalang UJ, McArdle N, Mindel JW, Benediktsdottir B (2018) Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis. Sleep 41(3):zsx214

    Article  Google Scholar 

  5. Wang Q, Zhang C, Jia P, Zhang J, Feng L, Wei S, Luo Y, Su L, Zhao C, Dong H (2014) The association between the phenotype of excessive daytime sleepiness and blood pressure in patients with obstructive sleep apnea-hypopnea syndrome. Int J Med Sci 11(7):713–720

    Article  Google Scholar 

  6. Kuang J (2016) Obstructive sleep apnea-hypopnea syndrome clinical in subtypes a principal component analysis-based cluster analysis. Chest 149(4):A566

    Article  Google Scholar 

  7. Ye L, Pien GW, Ratcliffe SJ, Björnsdottir 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(6):1600–1607

    Article  Google Scholar 

  8. Saaresranta T, Hedner J, Bonsignore MR, Riha RL, McNicholas WT, Penzel T, Anttalainen U, Kvamme JA, Pretl M, Sliwinski P (2016) Clinical phenotypes and comorbidity in European sleep apnoea patients. PLoS One 11(10):e0163439

    Article  Google Scholar 

  9. Vavougios GD, Natsios G, 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(1):31–38

    Article  Google Scholar 

  10. Gagnadoux F, Le Vaillant M, Paris A, Pigeanne T, Leclair-Visonneau L, Bizieux-Thaminy A, Alizon C, Humeau M-P, Nguyen X-L, Rouault B (2016) Relationship between OSA clinical phenotypes and CPAP treatment outcomes. Chest 149(1):288–290

    Article  Google Scholar 

  11. Burgel P-R, Paillasseur J-L, Janssens W, Piquet J, Ter Riet G, Garcia-Aymerich J, Cosio B, Bakke P, Puhan MA, Langhammer A (2017) A simple algorithm for the identification of clinical COPD phenotypes. Eur Respir J 50(5):1701034

    Article  Google Scholar 

  12. Nakayama H, Kobayashi M, Tsuiki S, Yanagihara M, Inoue Y (2019) Obstructive sleep apnea phenotypes in men based on characteristics of respiratory events during polysomnography. Sleep and Breathing 23(4):1087–1094 1–8

    Article  Google Scholar 

  13. An H-J, Baek S-H, Kim S-W, Kim S-J, Park Y-G (2019) Clustering-based characterization of clinical phenotypes in obstructive sleep apnoea using severity, obesity, and craniofacial pattern. Eur J Orthod 42(1):93–100

    Google Scholar 

  14. Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV (2012) The AASM manual for the scoring of sleep and associated events. Rules, terminology and technical specifications. American Academy of Sleep Medicine, Darien, p 176

    Google Scholar 

  15. Joosten SA, Leong P, Landry SA, Sands SA, Terrill PI, Mann D, Turton A, Rangaswamy J, Andara C, Burgess G (2017) Loop gain predicts the response to upper airway surgery in patients with obstructive sleep apnea. Sleep 40(7):zsx094

    Article  Google Scholar 

  16. Cho YW, Lee JH, Son HK, Lee SH, Shin C, Johns MW (2011) The reliability and validity of the Korean version of the Epworth sleepiness scale. Sleep and Breathing 15(3):377–384

    Article  Google Scholar 

  17. Dahlberg G (1940) Statistical methods for medical and biological students. Br Med J 2(4158):358–359

    Google Scholar 

  18. Kim J, Keenan BT, Lim DC, Lee SK, Pack AI, Shin C (2018) Symptom-based subgroups of Koreans with obstructive sleep apnea. J Clin Sleep Med 14(03):437–443

    Article  Google Scholar 

  19. Eckert DJ, White DP, Jordan AS, Malhotra A, Wellman A (2013) Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. Am J Respir Crit Care Med 188(8):996–1004

    Article  Google Scholar 

  20. Zinchuk A, Edwards BA, Jeon S, Koo BB, Concato J, Sands S, Wellman A, Yaggi HK (2018) Prevalence, associated clinical features, and impact on continuous positive airway pressure use of a low respiratory arousal threshold among male United States veterans with obstructive sleep apnea. J Clin Sleep Med 14(05):809–817

    Article  Google Scholar 

  21. Sands SA, Edwards BA, Terrill PI, Taranto-Montemurro L, Azarbarzin A, Marques M, Hess LB, White DP, Wellman AJ (2018) Phenotyping pharyngeal pathophysiology using polysomnography in patients with obstructive sleep apnea. Am J Respir Crit Care Med 197(9):1187–1197

    Article  Google Scholar 

  22. Sforza E, Petiau C, Weiss T, Thibault A, Krieger JJ (1999) Pharyngeal critical pressure in patients with obstructive sleep apnea syndrome: clinical implications. Am J Respir Crit Care Med 159(1):149–157

    Article  CAS  Google Scholar 

  23. Lee RW, Vasudavan S, Hui DS, Prvan T, Petocz P, Darendeliler MA, Cistulli PA (2010) Differences in craniofacial structures and obesity in Caucasian and Chinese patients with obstructive sleep apnea. Sleep 33(8):1075–1080

    Article  Google Scholar 

  24. Sutherland K, Keenan BT, Bittencourt L, Chen N-H, Gislason T, Leinwand S, Magalang UJ, Maislin G, Mazzotti DR, McArdle N (2019) A global comparison of anatomic risk factors and their relationship to obstructive sleep apnea severity in clinical samples. J Clin Sleep Med 15(04):629–639

    Article  Google Scholar 

  25. Edwards BA, Wellman A, Sands SA, Owens RL, Eckert DJ, White DP, Malhotra A (2014) Obstructive sleep apnea in older adults is a distinctly different physiological phenotype. Sleep 37(7):1227–1236A

    Article  Google Scholar 

  26. Ferguson KA, Ono T, Lowe AA, Ryan CF, Fleetham JA (1995) The relationship between obesity and craniofacial structure in obstructive sleep apnea. Chest 108(2):375–381

    Article  CAS  Google Scholar 

  27. Chi L, Comyn F-L, Mitra N, Reilly MP, Wan F, Maislin G, Chmiewski L, Thorne-FitzGerald MD, Victor UN, Pack AI (2011) Identification of craniofacial risk factors for obstructive sleep apnoea using three-dimensional MRI. Eur Respir J 38(2):348–358

    Article  CAS  Google Scholar 

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Acknowledgments

The authors have no commercial interests related to the subject of the study, and the study did not receive any commercial financial or material support.

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Correspondence to Su-Jung Kim.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the Institutional Review Board of Kyung Hee University Dental Hospital (KHD IRB 1811-2) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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This was a retrospective study based on records, and formal consent was not required.

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Kim, SJ., Alnakhli, W.M., Alfaraj, A.S. et al. Multi-perspective clustering of obstructive sleep apnea towards precision therapeutic decision including craniofacial intervention. Sleep Breath 25, 85–94 (2021). https://doi.org/10.1007/s11325-020-02062-9

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  • DOI: https://doi.org/10.1007/s11325-020-02062-9

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