Plasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apnea

https://doi.org/10.1016/j.biopha.2021.112425Get rights and content
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Highlights

  • Plasma metabolomic profiling represents a feasible approach for biomarker discovery.

  • Sleep apnoea is associated with a specific circulating metabolic fingerprint.

  • A blood-based metabolite signature is a potential tool for detecting sleep apnoea.

  • Sleep apnoea is related to impaired glycerophospholipid and bile acid metabolism.

  • Treatment of sleep apnea is associated with changes in the plasma metabolic content.

Abstract

Introduction

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.

List of abbreviations

AHI
apnea-hypopnea index
BMI
body mass index
CI
confidence interval
CL
cardiolipin
CPAP
continuous positive airway pressure
ESI-Q-TOF
electrospray-ionization quadrupole time of flight
ESS
Epworth sleepiness scale
FC
fold change
HIF-1-alpha
hypoxia inducible factor-1-alpha
HMDB
human metabolome database
LC-MS/MS
liquid chromatography/tandem mass spectrometry
MS/MS
tandem mass spectrometry
OSA
obstructive sleep apnea
PC
phosphatidylcholine
PCA
principal component analysis
PE
phosphatidylethanolamine
PSG
polysomnography
SaO2
oxygen saturation
TSat90
time with SaO2 < 90%
UHPLC
ultra-high-performance liquid chromatography

Keywords

Obstructive sleep apnea
Metabolomics
Lipidomics
Biomarker
Diagnosis
CPAP

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