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.
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Acknowledgements
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Authors contributions
KC, J-WP, C-SR, and H-WS participated in study conception and design. I-HH, C-SR, and H-WS supported the clinical study and sample collection. ML and DS supported sample management and clinical data acquisition. KC, J-YC, DWY, and H-WS participated in the acquisition of data and interpretation of results from the metabolomic analysis. KC and DWY drafted the article, and all authors reviewed and revised the manuscript.
Funding
This work was supported by a grant from the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI15C2310) and by a National Research Foundation of Korea grant funded by the Korea government (MEST) (NRF-2014R1A2A2A01005541). D.W.Y. received a scholarship from the BK21-plus education program provided by the National Research Foundation of Korea.
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The authors declare that they have no conflict of interest with the contents of this article.
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration. The Institutional Review Board at Seoul National University Hospital reviewed and approved the study protocol.
Informed Consent
Informed consent for the use of specimens was obtained from all individual participants included in the study.
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Kumsun Cho and Dae Wui Yoon have contributed equally to this work.
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11306_2017_1216_MOESM1_ESM.tif
Supplementary Figure 1—Comparison of hexoses levels according to OSA severity. All levels were corrected with corresponding urinary creatinine concentrations. Data are represented as mean ± SD (TIF 242 KB)
11306_2017_1216_MOESM2_ESM.tif
Supplementary Figure 2—Multivariate analysis between control and OSA groups. A, principal component analysis (PCA) plot; B, partial least squares discriminant analysis (PLS-DA) plot highlight the separation between controls (green) and OSA patients (red); C, variable importance in projection (VIP) plot. The most discriminating metabolites are shown in descending order of importance. The color boxes indicate whether metabolite concentration is increased (red) or decreased (green) in OSA vs. controls (TIF 766 KB)
11306_2017_1216_MOESM3_ESM.tif
Supplementary Figure 3—Identification of metabolites with significant changes in expression by significance analysis of microarray (SAM). A, the relation between delta and false discovery rate (FDR) (left), and delta and the number of significant metabolites (right). B, resultant metabolites from SAM with a delta value of 0.8. The significant variables are highlighted in green (TIF 643 KB)
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Cho, K., Yoon, D.W., Lee, M. et al. Urinary Metabolomic Signatures in Obstructive Sleep Apnea through Targeted Metabolomic Analysis: A Pilot Study. Metabolomics 13, 88 (2017). https://doi.org/10.1007/s11306-017-1216-9
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DOI: https://doi.org/10.1007/s11306-017-1216-9