Late Breaking Abstract - Obstructive sleep apnoea phenotypes in women: data from the ESADA cohort
Bildiriler > Özet Bildiri
Yazarlar: Athanasia Pataka, Sebastien Bailly, Maria Bonsignore, Tarja Saaresranta, Sofia Schiza, Izolde Bouloukaki, Paschalis Steiropoulos, Georgia Trakada, Renata Riha, Zoran Dogas, Dries Testelmans, Özen KAÇMAZ BAŞOĞLU, Stefan Mihaicuta, Jan Hedner, Ludger Grote, Jean-Louis Pepin, On Behalf Of The Esada Group
Clinical and epidemiological respiratory sleep medicine, , , Uluslararası, Özet Bildiri, ss OA4837-OA4837
DOI: 10.1183/13993003.congress-2023.oa4837
Özet
Objectives/Introduction: Obstructive Sleep Apnoea (OSA) is a heterogeneous condition with different clinical presentation between genders. This study aimed to identify specific OSA phenotypes in women in the ESADA database. Methods: Latent class analysis was applied to data from 9,710 females among 32,700 ESADA participants. Clusters were built using: age, Body Mass Index (BMI), Epworth Sleepiness Scale (ESS), co-morbidities (cardiovascular, pulmonary, psychiatric, metabolic, other) and Apnea Hypopnea Index (AHI). Results: Four different clusters were revealed: Cluster 1 “Women with ischemic heart disease” (38.3%): middle aged women, overweight with moderate OSA[AHI: 22.9/h [17.4; 30]],non-sleepy(ESS: 9 [5; 12])characterized by ischemic heart disease (56%). Cluster 2 “Elderly women with co-morbidities” (23%): elderly (66 years[60-71]), obese (36 kg/m2, [31.6-40.4]) patients with severe OSA (AHI: 46 events/h, [30 -60.1]) but no sleepiness (ESS 9, [6-13] )and the highest degree of co-morbidities. Cluster 3: “Sleepy obese women” (16.2 %):the youngest (49 years [42- 55]), sleepiest (ESS 12, [8-16])patients with the highest BMI (43kg/m2[37.6 ; 48.9]), severe OSA (AHI 53.3 events /h, [32- 80.5]), psychiatric disease (22.7%) and asthma (13.9%).Cluster 4: “Women with mild OSA and low co-morbidities" (22.5 %): middle aged, overweight non-sleepy (ESS: 9 [5; 13]) women with mild OSA(AHI 8.6events/h [6.9 ; 10.4])with low co-morbidities. PAP adherence was higher in Clusters 2 and 3 but low in Cluster 4. Conclusion: Four distinct women phenotypes with different clinical presentation and co-morbidities were identified. Gender-based phenotypes may be relevant for patient’s risk stratification and personalized treatment.