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dc.contributor.authorPinilla, Lucía
dc.contributor.authorBenitez, Ivan
dc.contributor.authorSantamaría Martos, Fernando
dc.contributor.authorTarga, Adriano
dc.contributor.authorMoncusí Moix, Anna
dc.contributor.authorDalmases, Mireia
dc.contributor.authorMinguez Roure, Olga
dc.contributor.authorAguilà, Maria
dc.contributor.authorJové Font, Mariona
dc.contributor.authorSol, Joaquim
dc.contributor.authorPamplona Gras, Reinald
dc.contributor.authorBarbé Illa, Ferran
dc.contributor.authorSánchez de la Torre, Manuel
dc.description.abstractIntroduction: Obstructive sleep apnea (OSA) is a chronic, heterogeneous and multicomponent disorder with associated cardiovascular and metabolic alterations. Despite being the most common sleep-disordered breathing, it remains a significantly undiagnosed condition. Objective: We examined the plasma metabolome and lipidome of patients with suspected OSA, aiming to identify potential diagnosis biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Additionally, we evaluated the impact of continuous positive airway pressure (CPAP) treatment on the circulating metabolomic and lipidomic profile. Material and methods: Observational-prospective-longitudinal study including 206 consecutive subjects referred to the sleep unit. OSA was defined as an apnea-hypopnoea index ≥ 15 events/h after polysomnography (PSG). Patients treated with CPAP were followed-up for 6 months. Untargeted plasma metabolomic and lipidomic profiling was performed using liquid chromatography coulpled to massspectrometry. Results: A plasma profile composed of 33 metabolites (mainly glycerophospholipids and bile acids) was identified in OSA vs. non-OSA patients. This profile correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses disclosed a 4-metabolites-signature that provided an accuracy (95% CI) of 0.98 (0.95-0.99) for OSA detection. CPAP treatment was associated with changes in 5 plasma metabolites previously altered by OSA. Conclusions: This analysis of the circulating metabolome and lipidome reveals a molecular fingerprint of OSA, which was modulated after effective CPAP treatment. Our results suggest blood-based biomarker candidates with potential application in the personalized management of OSA and suggest the activation of adaptive mechanisms in response to OSA-derived hypoxia.ca_ES
dc.description.sponsorshipWe thank the individuals who participated in this trial, their families, and the clinical and research team in the sleep department. LP is the recipient of a predoctoral fellowship from the Ministry of Universities of Spain (FPU19/01555). MJ is a “Serra Húnter” Fellow. MSdT has received financial support from the “Ramon ´ y Cajal” grant (RYC2019- 027831-I) from the “Ministerio de Ciencia e Innovacion ´ - Agencia Estatal de Investigacion´ ” cofunded by the European Social Fund (ESF) / “Investing in your future”ca_ES
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofBiomedicine & Pharmacotherapy, january 2022, vol. 145, 112425ca_ES
dc.rightscc-by-nc-nd (c) authors, 2021ca_ES
dc.subjectObstructive sleep apneaca_ES
dc.titlePlasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apneaca_ES

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cc-by-nc-nd (c)  authors, 2021
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) authors, 2021