Cluster analysis identifies a pathophysiologically distinct subpopulation with increased serum leptin levels and severe obstructive sleep apnea

Abstract

Purpose To investigate the different pathophysiologies of obstructive sleep apnea (OSA) phenotypes using cluster analysis. Differences between leptin/adiponectin levels in the resulting OSA phenotypes were also examined. Methods In total, 1057 OSA patients were selected, and a retrospective survey of clinical records, polysomnography results, and blood gas data was conducted. Patients were grouped into four clusters by their OSA severity, PaCO2, body mass index (BMI), and sleepiness. A k-means cluster analysis was performed, resulting in a division into four subpopulations. The Tukey or Games-Howell tests were used for intergroup comparisons. Results Among the 20 clinical OSA items, four common factors (Epworth Sleepiness Scale [ESS], BMI, Apnea-Hypopnea Index [AHI], and PaCO2) were extracted by principal component analysis, and a cluster analysis was performed using the k-means method, resulting in four distinct phenotypes. The Clusters 1 (middle age, symptomatic severe OSA) and 4 (young, obese, symptomatic very severe OSA) exhibited high leptin levels. C-reactive protein levels were also elevated in Cluster 4, indicating a different pathophysiological background. No apparent differences between clusters were observed regarding adiponectin/leptin ratios and adiponectin levels. Classification into groups based on phenotype showed that Epworth Sleepiness Scale [ESS] score and disease severity were not correlated, suggesting that sleepiness is affected by multiple elements. Conclusions The existence of multiple clinical phenotypes suggests that different pathophysiological backgrounds exist such as systemic inflammation and metabolic disorder. This classification may be used to determine the efficacy of continuous positive airway pressure treatment that cannot be determined by the AHI.

Application of Multimedia Technology in Online Piano Teaching

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Clinical phenotypes of obstructive sleep apnea: a cluster analysis based on sleep perception and sleep quality

“…They found that the probabilities of having hypertension, diabetes, and cardiovascular disease were the highest in the minimally symptomatic group [ 4 ]. Kozu et al divided patients with OSA into four clusters according to OSA severity, PaCO 2 , body mass index, and sleepiness, and found different serum C-reactive protein and leptin levels among clusters, which revealed different pathophysiological backgrounds of OSA [ 5 ]. A recent study identified nine clusters according to 10 clinically relevant and objectively identified comorbidities in patients with moderate-to-severe OSA, which found different cardiovascular risk, adherence to positive airway pressure, and long-term survival among clusters [ 6 ].…”