STUDY OBJECTIVES: In an attempt to better understand the heterogeneity of individuals with obstructive sleep apnea (OSA), unbiased analytic approaches such as cluster analysis have been used worldwide; however, only a few such studies have been conducted for Asian populations alone, despite the potential racial/ethnic differences. We thus applied this approach to a Japanese population with OSA. METHODS: In this single-center, retrospective, observational study, our nocturnal polysomnography dataset included the findings for 1,020 patients between May 2016 and December 2020. Of these, 712 patients met the study criteria: aged > 20 years, fully completed questionnaire, no missing data on all-night full polysomnography, and confirmed OSA diagnosis with an apnea-hypopnea index (AHI) > 15 events/h. We employed hierarchical cluster analysis using demographic data, self-reported symptoms, and polysomnographic data. RESULTS: We identified 5 distinct clinical clusters within the OSA patient population, which were labeled as "classic OSA" (20--67 years, obese, high AHI, high Epworth Sleepiness Scale [ESS]), "milder classic OSA" (22--77 years, obese, high AHI, low ESS), "nonobese and minimally symptomatic" (20--88 years, moderate AHI, low ESS), "excessive sleepiness without severe OSA" (26--79 years, moderate AHI, high ESS), and "older adult and severe OSA" 55--92 years, (high AHI, low ESS). Of these, the last 3 clusters were characterized as nonobese. Notably, we identified the cluster with excessive sleepiness despite less severe OSA. We did not identify any clusters with predominant upper-airway obstruction symptoms because the symptoms were prevalent and equally distributed in all clusters. CONCLUSIONS: We found some unique clinical phenotypes in nonobese patients with OSA in a Japanese population. CITATION: Ida H, Suga T, Nishimura M, Aoki Y. Unique clinical phenotypes of patients with obstructive sleep apnea in a Japanese population: a cluster analysis. J Clin Sleep Med. 2022;18(3):895-902.
STUDY OBJECTIVES: In an attempt to better understand the heterogeneity of individuals with obstructive sleep apnea (OSA), unbiased analytic approaches such as cluster analysis have been used worldwide; however, only a few such studies have been conducted for Asian populations alone, despite the potential racial/ethnic differences. We thus applied this approach to a Japanese population with OSA. METHODS: In this single-center, retrospective, observational study, our nocturnal polysomnography dataset included the findings for 1,020 patients between May 2016 and December 2020. Of these, 712 patients met the study criteria: aged > 20 years, fully completed questionnaire, no missing data on all-night full polysomnography, and confirmed OSA diagnosis with an apnea-hypopnea index (AHI) > 15 events/h. We employed hierarchical cluster analysis using demographic data, self-reported symptoms, and polysomnographic data. RESULTS: We identified 5 distinct clinical clusters within the OSA patient population, which were labeled as "classic OSA" (20--67 years, obese, high AHI, high Epworth Sleepiness Scale [ESS]), "milder classic OSA" (22--77 years, obese, high AHI, low ESS), "nonobese and minimally symptomatic" (20--88 years, moderate AHI, low ESS), "excessive sleepiness without severe OSA" (26--79 years, moderate AHI, high ESS), and "older adult and severe OSA" 55--92 years, (high AHI, low ESS). Of these, the last 3 clusters were characterized as nonobese. Notably, we identified the cluster with excessive sleepiness despite less severe OSA. We did not identify any clusters with predominant upper-airway obstruction symptoms because the symptoms were prevalent and equally distributed in all clusters. CONCLUSIONS: We found some unique clinical phenotypes in nonobese patients with OSA in a Japanese population. CITATION: Ida H, Suga T, Nishimura M, Aoki Y. Unique clinical phenotypes of patients with obstructive sleep apnea in a Japanese population: a cluster analysis. J Clin Sleep Med. 2022;18(3):895-902.
Authors: Andrey V Zinchuk; Sangchoon Jeon; Brian B Koo; Xiting Yan; Dawn M Bravata; Li Qin; Bernardo J Selim; Kingman P Strohl; Nancy S Redeker; John Concato; Henry K Yaggi Journal: Thorax Date: 2017-09-21 Impact factor: 9.139
Authors: Xiao Lei Zhang; Li Zhang; Yi Ming Li; Bo Yun Xiang; Teng Han; Yan Wang; Chen Wang Journal: J Clin Sleep Med Date: 2022-07-01 Impact factor: 4.324