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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.

Lung cancer (LC) is the most fatal complication of idiopathic pulmonary fibrosis (IPF). However, the molecular pathogenesis of the development of LC from IPF is still unclear. Here, we report a case of IPF‐associated LC for which we investigated the genetic alterations between IPF and LC. We extracted formalin‐fixed paraffin‐embedded DNA from each part of the surgical lung tissue using a laser‐assisted microdissection technique. The mutations in each part were detected by next‐generation sequencing (NGS) using 72 lung cancer‐related mutation panels. Five mutations were found in IPF and four in LC. Almost all somatic mutations did not overlap between the IPF and LC regions. These findings suggest that IPF‐associated LC may not be a result of the accumulation of somatic mutations in the regenerated epithelium of the honeycomb lung in the IPF region.

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