Kavitha Venkatnarayan1, Uma Maheswari Krishnaswamy2, Nithin Kumar Reddy Rajamuri3, Sumithra Selvam4, Chitra Veluthat1, Uma Devaraj1, Priya Ramachandran1, George D'Souza1. 1. Department of Pulmonary Medicine, St. John's National Academy of Health Sciences, Bengaluru, 560034, India. 2. Department of Pulmonary Medicine, St. John's National Academy of Health Sciences, Bengaluru, 560034, India. umamohan99@gmail.com. 3. Department of Pulmonary Medicine, SVS Medical College, Mahbubnagar, Telangana, India. 4. Division of Epidemiology, Biostatistics and Population Health, St. John's National Academy of Health Sciences, Bengaluru, India.
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.
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.
Authors: Diego R Mazzotti; Brendan T Keenan; Diane C Lim; Daniel J Gottlieb; Jinyoung Kim; Allan I Pack Journal: Am J Respir Crit Care Med Date: 2019-08-15 Impact factor: 21.405
Authors: Simon A Joosten; Kais Hamza; Scott Sands; Anthony Turton; Philip Berger; Garun Hamilton Journal: Respirology Date: 2012-01 Impact factor: 6.424
Authors: R Doug McEvoy; Nick A Antic; Emma Heeley; Yuanming Luo; Qiong Ou; Xilong Zhang; Olga Mediano; Rui Chen; Luciano F Drager; Zhihong Liu; Guofang Chen; Baoliang Du; Nigel McArdle; Sutapa Mukherjee; Manjari Tripathi; Laurent Billot; Qiang Li; Geraldo Lorenzi-Filho; Ferran Barbe; Susan Redline; Jiguang Wang; Hisatomi Arima; Bruce Neal; David P White; Ron R Grunstein; Nanshan Zhong; Craig S Anderson Journal: N Engl J Med Date: 2016-08-28 Impact factor: 91.245
Authors: Clete A Kushida; Deborah A Nichols; Tyson H Holmes; Stuart F Quan; James K Walsh; Daniel J Gottlieb; Richard D Simon; Christian Guilleminault; David P White; James L Goodwin; Paula K Schweitzer; Eileen B Leary; Pamela R Hyde; Max Hirshkowitz; Sylvan Green; Linda K McEvoy; Cynthia Chan; Alan Gevins; Gary G Kay; Daniel A Bloch; Tami Crabtree; William C Dement Journal: Sleep Date: 2012-12-01 Impact factor: 5.849