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Incorporating polysomnography into obstructive sleep apnoea phenotyping: moving towards personalised medicine for OSA
  1. Jean Louis Pépin1,2,
  2. Sebastien Bailly1,2,
  3. Renaud Tamisier1,2
  1. 1Institut National de la Santé et de la Recherche Médicale (INSERM), U 1042, HP2 Laboratory (Hypoxia: Pathophysiology), University of Grenoble-Alpes, Grenoble, France
  2. 2Thorax and Vessels Division, Grenoble University Hospital, Grenoble, France
  1. Correspondence to Professor Jean Louis Pépin, Laboratoire HP2, Inserm 1042, Universite Grenoble Alpes, Grenoble 38400, France; jpepin{at}

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Obstructive sleep apnoea (OSA) is a major health concern worldwide with multiorgan consequences and the resulting increased economic and social burden.1 OSA is associated with obesity in more than 60% of cases and often with comorbidities including hypertension, arrhythmia, stroke, coronary heart disease and metabolic dysfunction. The comorbidities are of major importance because they have a significant impact on healthcare use and mortality in patients with OSA. Only half of the patients with OSA are symptomatic and a significant percentage of subjects are referred and treated with the goal of limiting their cardiometabolic risk. This heterogeneity in a highly prevalent chronic disease with millions of patients on home-based long-term treatment requires the complete reshaping of patient characterisation and therapeutic strategies.

Detailed phenotyping is the prerequisite for the development of precision and personalised medicine in chronic disease and particularly in OSA.3 The phenotyping of patients with OSA has been tackled in several different ways. One strategy has been to address the anatomical and physiological traits underlying OSA occurrence and the range of severity.4 The pathophysiological causes of OSA vary considerably between patients. Determining the respective contributions of upper airway anatomy and collapsibility, arousal threshold and loop gain (ventilator drive) will help to predict which therapeutic modality would be beneficial to the appropriately targeted patient subgroups.4 Another topic has been to define distinct OSA clinical phenotypes. Several recent studies exploiting large data sets have used unsupervised cluster analysis to identify distinct phenotypes.5 6 Subgroups have been recognised that varied considerably in age, gender, symptoms, obesity, comorbidities and environmental risk factors.5 6 The most significant differences between clusters were minimally symptomatic versus sleepy patients with OSA, lean versus obese, and among obese patients …

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