Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women

Chloé Van Overstraeten , Fabio Andreozzi , Sidali Ben Youssef , Ionela Bold , Sarah Carlier , Alexia Gruwez , Anne-Violette Bruyneel , Marie Bruyneel

Current Medical Science ›› 2021, Vol. 41 ›› Issue (4) : 729 -736.

PDF
Current Medical Science ›› 2021, Vol. 41 ›› Issue (4) : 729 -736. DOI: 10.1007/s11596-021-2388-0
Article

Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women

Author information +
History +
PDF

Abstract

Objective

Several clinical obstructive sleep apnea syndrome (OSAS) phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified. The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients.

Methods

This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period. Demographic, symptom, comorbidity, polysomnographic, and continuous positive airway pressure (CPAP) adherence data were collected. Statistical analyses were performed to identify clusters of patients.

Results

A total of 567 patients were included (67% men, 54±13 years, body mass index: 32±7 kg/m2, 65% Caucasian, 32% European African). Five clusters were identified: less severe OSAS (n=172); healthier severe OSAS (n=160); poorly sleeping OSAS patients with cardiometabolic comorbidities (n=87); younger obese men with sleepiness at the wheel (n=94); sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities (n=54). Patients in clusters 3 and 5 were older than those in clusters 2 and 4 (P=0.034). Patients in clusters 4 and 5 were significantly more obese than those in the other clusters (P=0.04). No significant differences were detected in terms of symptoms and comorbidities. Polysomnographic profiles were very discriminating between clusters. CPAP adherence was similar in all clusters but, among adherent patients, daily usage was more important in cluster 1 (less severe patients) than in cluster 5.

Conclusion

This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33% females.

Keywords

obstructive sleep apnea syndrome / polysomnography / continuous positive airway pressure / cluster analysis / sleep disturbance / cardiovascular risk

Cite this article

Download citation ▾
Chloé Van Overstraeten, Fabio Andreozzi, Sidali Ben Youssef, Ionela Bold, Sarah Carlier, Alexia Gruwez, Anne-Violette Bruyneel, Marie Bruyneel. Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women. Current Medical Science, 2021, 41(4): 729-736 DOI:10.1007/s11596-021-2388-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

MarinJM, CarrizoSJ, VicenteE, et al.. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet, 2005, 365: 1046-1053

[2]

ArztM, OldenburgO, GramlA, et al.. Phenotyping of Sleep-Disordered Breathing in Patients With Chronic Heart Failure With Reduced Ejection Fraction-the SchlaHF Registry. J Am Heart Assoc, 2017, 6(12): e005899

[3]

MehraR, StoneKL, VarosyPD, et al.. Nocturnal Arrhythmias across a spectrum of obstructive and central sleep-disordered breathing in older men: outcomes of sleep disorders in older men (MrOS sleep) study. Arch Intern Med, 2009, 169(12): 1147-1155

[4]

Lam JCM, Lui MMS, Ip MSM. Diabetes and metabolic aspects of OSA. Eur Respi Monogr, 2010,189–215

[5]

WuH, ZhanX, ZhaoM, et al.. Mean apnea-hypopnea duration (but not apnea-hypopnea index) is associated with worse hypertension in patients with obstructive sleep apnea. Medicine (Baltimore), 2016, 95(48): e5493

[6]

AzarbarzinA, SandsSA, Taranto-MontemurroL, et al.. Hypoxic burden captures sleep apnoea-specific nocturnal hypoxaemia. Eur Heart J, 2019, 40(35): 2989-2990

[7]

YeL, PienGW, RatcliffeSJ, et al.. The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J, 2014, 44(6): 1600-1607

[8]

CostaLE, UchoaCH, HarmonRR, et al.. Potential underdiagnosis of obstructive sleep apnoea in the cardiology outpatient setting. Heart, 2015, 101(16): 1288-1292

[9]

ZinchukAV, JeonS, KooBB, et al.. Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea. Thorax, 2018, 73(5): 472-480

[10]

GagnadouxF, Le VaillantM, ParisA, et al.. Relationship Between OSA Clinical Phenotypes and CPAP Treatment Outcomes. Chest, 2016, 149(1): 288-290

[11]

Chen WC, Maitra R. EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution. URLhttps://CRAN.R-project.org/package=EMCluster,2015,R package version 0.2–5. p289

[12]

GovaertG, NadifM. Clustering of contingency table and mixture model. Eur J Operat Res, 2007, 183(3): 1055-1066

[13]

Pagès J. Analyse factorielle de donnees mixtes. Revue Statistique Appliquee LII (4),2004, pp 93–111

[14]

Maechler M, Rousseeuw P, Struyf A, et al. Cluster: Cluster Analysis Basics and Extensions, 2016, R package version 2.0.5.

[15]

LeS, JosseJ, HussonF. FactoMineR: An R Package for Multivariate Analysis. J Statist Software, 2008, 25(1): 1-18

[16]

TsuchiyaM, LoweAA, PaeEK, et al.. Obstructive sleep apnea subtypes by cluster analysis. Am J Orthod Dentofacial Orthop, 1992, 101(6): 533-542

[17]

PevernagieDA, Gnidovec-StrazisarB, GroteL, et al.. On the rise and fall of the apnea-hypopnea index: A historical review and critical appraisal. J Sleep Res, 2020, 29(4): e13066

[18]

BaillyS, DestorsM, GrilletY, et al.. Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis. PLoS One, 2016, 11(6): e0157318

[19]

ZinchukAV, GentryMJ, ConcatoJ, et al.. Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches. Sleep Med Rev, 2017, 35: 113-23

[20]

BasogluOK, TasbakanMS. Gender differences in clinical and polysomnographic features of obstructive sleep apnea: a clinical study of 2827 patients. Sleep Breath, 2018, 22(1): 241-249

[21]

RodgersJL, JonesJ, BolledduSI, et al.. Cardiovascular Risks Associated with Gender and Aging. J Cardiov Dev Dis, 2016, 6: E19

[22]

PrasadB, SteffenAD, Van DongenHPA, et al.. Determinants of sleepiness in obstructive sleep apnea. Sleep, 2018, 41(2): zsx199

[23]

BaldwinCM, ErvinAM, MaysMZ, et al.. Sleep disturbances, quality of life, and ethnicity: the Sleep Heart Health Study. J Clin Sleep Med, 2010, 6(2): 176-183

[24]

ChenX, WangR, ZeeP, et al.. Racial/Ethnic Differences in Sleep Disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep, 2015, 38(6): 877-888

[25]

KeenanBT, KimJ, SinghB, et al.. Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis. Sleep, 2018, 41(3): zsx214

[26]

ZinchukA, YaggiHK. Phenotypic sub-types of OSA: a challenge and opportunity for precision medicine. Chest, 2020, 157(2): 403-420

[27]

MazzottiDR, KeenanBT, LimDC, et al.. Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes. Am J Respir Crit Care Med, 2019, 200(4): 493-506

[28]

CasselW, KesperK, BauerA, et al.. Significant association between systolic and diastolic blood pressure elevations and periodic limb movements in patients with idiopathic restless legs syndrome. Sleep Med, 2016, 17: 109-120

[29]

KingshottRN, SimePJ, EngelmanHM, et al.. Self assessment of daytime sleepiness: patient versus partner. Thorax, 1995, 50: 994-995

[30]

KingshottRN, EngelmanHM, DearyIJ, et al.. Does arousal frequency predict daytime function?. Eur Respir J, 1998, 12: 1264-70

[31]

DündarY, SaylamG, TatarE, et al.. Does AHI Value Enough for Evaluating the Obstructive Sleep Apnea Severity?. Indian J Otolaryngol Head Neck Surg, 2015, 67(Suppl 1): 16-20

[32]

MedianoO, BarceloA, de la PenaM, et al.. Daytime sleepiness and polysomnographic variables in sleep apnoea patients. Eur Respir J, 2007, 30(1): 110-113

[33]

PienGW, YeL, KeenanBT, et al.. Changing Faces of Obstructive Sleep Apnea: Treatment Effects by Cluster Designation in the Icelandic Sleep Apnea Cohort. Sleep, 2018, 41(3): zsx201

[34]

KapurVK, AuckleyDH, ChowdhuriS, et al.. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med, 2017, 13: 479-504

AI Summary AI Mindmap
PDF

92

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/