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Identifying phenotypes of obstructive sleep apnea using cluster analysis

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

Over the last decade, advances in understanding the pathophysiology, clinical presentation, systemic consequences and treatment responses in obstructive sleep apnea (OSA) have made individualised OSA management plausible. As the first step in this direction, this study was undertaken to identify OSA phenotypes.

Methods

Patients diagnosed with OSA on level 1 polysomnography (PSG) were included. Clinical and co-morbidity profile, anthropometry and sleepiness scores were compiled. On PSG, apnea–hypopnea index, positional indices, sleep stages and desaturation indices (T90) were tabulated. Cluster analysis was performed to identify distinct phenotypes among included patients with OSA.

Results

One hundred patients (66 males) with a mean age of 49.5 ± 13.3 years were included. Snoring was reported by 94% subjects, and 50% were excessively sleepy. Two-thirds of subjects had co-morbidities, the most frequent being hypertension (55%) and dyslipidemia (53%). Severe OSA was diagnosed on PSG in 42%, while 29% each had mild and moderate OSA, respectively. On cluster analysis, 3 distinct clusters emerged. Cluster 1 consisted of older, obese subjects with no gender predilection, higher neck circumference, severe OSA with more co-morbidities and higher T90. Cluster 2 comprised of younger, less obese males with snoring, witnessed apnea, moderate and supine predominant OSA. Cluster 3 consisted of middle-aged, obese males with lesser co-morbidities, mild OSA and lower T90.

Conclusions

This study revealed three OSA clusters with distinct demographic, anthropometric and PSG features. Further research with bigger sample size and additional parameters may pave the way for characterising distinct phenotypes and individualising OSA management.

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Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Authors and Affiliations

  1. Department of Pulmonary Medicine, St. John’s National Academy of Health Sciences, Bengaluru, 560034, India

    Kavitha Venkatnarayan, Uma Maheswari Krishnaswamy, Chitra Veluthat, Uma Devaraj, Priya Ramachandran & George D’Souza

  2. Department of Pulmonary Medicine, SVS Medical College, Mahbubnagar, Telangana, India

    Nithin Kumar Reddy Rajamuri

  3. Division of Epidemiology, Biostatistics and Population Health, St. John’s National Academy of Health Sciences, Bengaluru, India

    Sumithra Selvam

Authors
  1. Kavitha Venkatnarayan
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  2. Uma Maheswari Krishnaswamy
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Corresponding author

Correspondence to Uma Maheswari Krishnaswamy.

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Ethics approval and consent to participate

The present study has been approved by the Institutional Ethics Committee of St. John’s Medical College and Hospital, Bangalore, India (Protocol no. 138/2019). The study has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All patients involved in the study have given informed consent prior to their inclusion in the study.

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The authors declare no competing interests.

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Venkatnarayan, K., Krishnaswamy, U.M., Rajamuri, N.K.R. et al. Identifying phenotypes of obstructive sleep apnea using cluster analysis. Sleep Breath 27, 879–886 (2023). https://doi.org/10.1007/s11325-022-02683-2

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  • DOI: https://doi.org/10.1007/s11325-022-02683-2

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