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. 2016 Jun 17;11(6):e0157318.
doi: 10.1371/journal.pone.0157318. eCollection 2016.

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

Sébastien Bailly  1   2 , Marie Destors  1   3 , Yves Grillet  4 , Philippe Richard  5 , Bruno Stach  6 , Isabelle Vivodtzev  1 , Jean-Francois Timsit  2   7 , Patrick Lévy  1   3 , Renaud Tamisier  1   3 , Jean-Louis Pépin  1   3 ; scientific council and investigators of the French national sleep apnea registry (OSFP)
Affiliations

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

Sébastien Bailly et al. PLoS One. .

Abstract

Background: The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies.

Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea.

Methods: An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression.

Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors.

Conclusions: Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Representation of six clusters after ascending hierarchical clustering analysis.
Axes correspond to individual coordinates for the two main dimensions of the multiple correspondence analysis. Cluster 1: the young symptomatic. Cluster 2: the old obese. Cluster 3: the multi-disease (MD) old obese. Cluster 4: the young snorers. Cluster 5: the drowsy obese. Cluster 6: the MD obese symptomatic.
Fig 2
Fig 2. Conditional probabilities of BMI, age and risk factors to highlight the major differences among clusters.
Cluster 1: the young symptomatic. Cluster 2: the old obese. Cluster 3: the multi-disease (MD) old obese. Cluster 4: the young snorers. Cluster 5: the drowsy obese. Cluster 6: the MD obese symptomatic
Fig 3
Fig 3. Conditional probabilities of symptoms to highlight the major differences among clusters.
Cluster 1: the young symptomatic. Cluster 2: the old obese. Cluster 3: the multi-disease (MD) old obese. Cluster 4: the young snorers. Cluster 5: the drowsy obese. Cluster 6: the MD obese symptomatic
Fig 4
Fig 4. Conditional probabilities of co-morbidities to highlight the major differences among clusters.
Cluster 4 was not represented in this figure because the probability to have cardiovascular and metabolic co-morbidities was near 0. Cluster 1: the young symptomatic. Cluster 2: the old obese. Cluster 3: the multi-disease (MD) old obese. Cluster 5: the drowsy obese. Cluster 6: the MD obese symptomatic
See this image and copyright information in PMC

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