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Urinary Metabolomic Signatures in Obstructive Sleep Apnea through Targeted Metabolomic Analysis: A Pilot Study
Authors:Kumsun Cho,Dae Wui Yoon,Mingyu Lee,Daeho So,Il-Hee Hong,Chae-Seo Rhee,Jong-Wan Park,Hyun-Woo Shin
Affiliation:1.Metabolomics Medical Research Center (MMRC),Seoul National University College of Medicine,Seoul,South Korea;2.Department of Clinical Pharmacology and Therapeutics,Seoul National University College of Medicine and Seoul National University Hospital,Seoul,South Korea;3.Obstructive Upper airway Research (OUaR) Laboratory, Department of Pharmacology,Seoul National University College of Medicine,Seoul,South Korea;4.Department of Biomedical Science,Seoul National University Graduate School,Seoul,South Korea;5.Ischemic/Hypoxic Disease Institute,Seoul National University College of Medicine,Seoul,South Korea;6.Cancer Research Institute,Seoul National University College of Medicine,Seoul,South Korea;7.Seoul Sleep Clinic,Seoul,South Korea;8.Department of Otorhinolaryngology-Head and Neck Surgery,Seoul National University Hospital,Seoul,South Korea
Abstract:

Introduction

Obstructive sleep apnea (OSA) is very common sleep problem, and it is associated with serious morbidities such as cardiovascular diseases and metabolic diseases. Overnight polysomnography (PSG) is the gold standard test for OSA, but it is expensive and requires specific facilities and equipment. Thus, novel screening methods are needed for effective diagnosis and follow-up in OSA.

Objectives

The aims of the study were to investigate the urinary metabolic signatures and identify potential urine markers for OSA using a mass spectrometry (MS)-based assay for targeted metabolomics.

Methods

Urine samples were collected from 48 male subjects who visited a sleep clinic for suspicious OSA. All underwent overnight in-laboratory polysomnography. The Biocrates AbsoluteIDQ p180 kit was used for targeted metabolomics.

Results

Among the 86 metabolites quantified, three acylcarnitines, one biogenic amine, two glycerophospholipids, and two sphingomyelins were differently expressed in OSA patients [apnea-hypopnea index (AHI) ≥5] compared with control groups (AHI <5 and/or simple snoring with no other sleep disorders). Additional partial correlation and multivariate logistic regression analysis revealed that long-chain acylcarnitine C14:1, symmetric dimethylarginine, and sphingomyelin C18:1 might be potential biomarkers for OSA. Receiver operating characteristic analysis showed favorable predictive properties of these metabolites. Furthermore, a combination of the metabolites exceeding cutoff values yielded further improved sensitivity or specificity.

Conclusions

MS-based targeted metabolomics identified specific classes of urinary metabolites that were up-regulated in OSA patients. Further assessments in large populations are required to clarify the screening values of these metabolite markers.
Keywords:
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