Characterization of obstructive sleep apnea–hypopnea syndrome (OSA) population by means of cluster analysis

Abstract

Obstructive sleep apnea-hypopnea syndrome (OSA) is being identified increasingly as an important health issue. It is typified by repeated episodes of upper airway collapse during sleep leading to occasional hypoxaemia, sleep fragmentation and poor sleep quality. OSA is also being considered as an independent risk factor for hypertension, diabetes and cardiovascular diseases, leading to increased multi-morbidity and mortality. Cluster analysis, a powerful statistical set of techniques, may help in investigating and classifying homogeneous groups of patients with similar OSA characteristics. This study aims to investigate the (possible) different groups of patients in an OSA population, and to analyse the relationships among the main clinical variables in each group to better understand the impact of OSA on patients. Starting from a well-characterized OSA population of 198 subjects afferent to our sleep centre, we identified three different communities of OSA patients. The first has a very severe disease [apnea-hypopnea index (AHI) = 65.91 ± 22.47] and sleep disorder has a strong impact on daily life: a low level of diurnal partial pressure of oxygen (PaO ) (77.39 ± 11.64 mmHg) and a high prevalence of hypertension (64%); the second, with less severe disease (AHI = 28.88 ± 17.13), in which sleep disorders seem to be less important for diurnal PaO and have a minimum impact on comorbidity; and the last with very severe OSA (AHI = 57.26 ± 15.09) but with a low risk of nocturnal hypoxaemia (T90 = 11.58 ± 8.54) and less sleepy (Epworth Sleepiness Scale 10.00 ± 4.77).

Prevalence and association analysis of obstructive sleep apnea with gender and age differences – Results of SHIP‐Trend

“…Some studies stated that there may not be a causal relationship between OSA and hypertension (Cano-Pumarega et al, 2011), while other studies found no influence of the BMI on these associations (Carlson, Hedner, Ejnell, & Peterson, 1994). Recent studies introduced the cluster analysis approach, forming subtypes based on clinical symptoms to account for OSA heterogeneity (Lacedonia et al, 2016;Ye et al, 2014). They found that the association to CVDs depended on clusters (Ye et al, 2014).…”

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

“…In a small single-centre study, LACEDONIA et al [91] stratified the patients with moderate-severe OSA according to the level of nocturnal hypoxaemia and identified three clusters. Severe nocturnal hypoxaemia was found in relatively young, severely obese patients who accounted for about half of the total sample, whereas elderly patients with mild nocturnal hypoxaemia and few symptoms accounted for ∼8% of the total sample.…”

Interleukin-6 and vitamin D serum levels in patients with obstructive sleep apnea syndrome before and after long-term continuous positive airway pressure treatment

“…Obesity has been reported to pose a substantial risk for the development of OSAS. Adipose tissues interact with the respiratory system physiologically and pathologically by producing adipocytokines and regulating metabolic and in ammatory processes [34,35]. IL-6 seems to contribute to weight gain in patients with OSAS and can modify the risk of obesity-related metabolic disorders [36].…”