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Article: Metabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers

TitleMetabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers
Authors
Issue Date2016
Citation
Scientific Reports, 2016, v. 6, article no. 30958 How to Cite?
AbstractFew clinical studies have explored altered urinary metabolite levels in patients with obstructive sleep apnea (OSA). Thus, we applied a metabolomics approach to analyze urinary metabolites in three groups of participants: patients with polysomnography (PSG)-confirmed OSA, simple snorers (SS), and normal subjects. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry were used. A total of 21 and 31 metabolites were differentially expressed in the SS and OSA groups, respectively. Patients with OSA had 18 metabolites different from those with SS. Of the 56 metabolites detected among the 3 groups, 24 were consistently higher or lower. A receiver operator curve analysis revealed that the combination of 4-hydroxypentenoic acid, arabinose, glycochenodeoxycholate-3-sulfate, isoleucine, serine, and xanthine produced a moderate diagnostic score with a sensitivity (specificity) of 75% (78%) for distinguishing OSA from those without OSA. The combination of 4-hydroxypentenoic acid, 5-dihydrotestosterone sulfate, serine, spermine, and xanthine distinguished OSA from SS with a sensitivity of 85% and specificity of 80%. Multiple metabolites and metabolic pathways associated with SS and OSA were identified using the metabolomics approach, and the altered metabolite signatures could potentially serve as an alternative diagnostic method to PSG.
Persistent Identifierhttp://hdl.handle.net/10722/342526
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, Huajun-
dc.contributor.authorZheng, Xiaojiao-
dc.contributor.authorQian, Yingjun-
dc.contributor.authorGuan, Jian-
dc.contributor.authorYi, Hongliang-
dc.contributor.authorZou, Jianyin-
dc.contributor.authorWang, Yuyu-
dc.contributor.authorMeng, Lili-
dc.contributor.authorZhao, Aihua-
dc.contributor.authorYin, Shankai-
dc.contributor.authorJia, Wei-
dc.date.accessioned2024-04-17T07:04:26Z-
dc.date.available2024-04-17T07:04:26Z-
dc.date.issued2016-
dc.identifier.citationScientific Reports, 2016, v. 6, article no. 30958-
dc.identifier.urihttp://hdl.handle.net/10722/342526-
dc.description.abstractFew clinical studies have explored altered urinary metabolite levels in patients with obstructive sleep apnea (OSA). Thus, we applied a metabolomics approach to analyze urinary metabolites in three groups of participants: patients with polysomnography (PSG)-confirmed OSA, simple snorers (SS), and normal subjects. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry were used. A total of 21 and 31 metabolites were differentially expressed in the SS and OSA groups, respectively. Patients with OSA had 18 metabolites different from those with SS. Of the 56 metabolites detected among the 3 groups, 24 were consistently higher or lower. A receiver operator curve analysis revealed that the combination of 4-hydroxypentenoic acid, arabinose, glycochenodeoxycholate-3-sulfate, isoleucine, serine, and xanthine produced a moderate diagnostic score with a sensitivity (specificity) of 75% (78%) for distinguishing OSA from those without OSA. The combination of 4-hydroxypentenoic acid, 5-dihydrotestosterone sulfate, serine, spermine, and xanthine distinguished OSA from SS with a sensitivity of 85% and specificity of 80%. Multiple metabolites and metabolic pathways associated with SS and OSA were identified using the metabolomics approach, and the altered metabolite signatures could potentially serve as an alternative diagnostic method to PSG.-
dc.languageeng-
dc.relation.ispartofScientific Reports-
dc.titleMetabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1038/srep30958-
dc.identifier.pmid27480913-
dc.identifier.scopuseid_2-s2.0-84982706164-
dc.identifier.volume6-
dc.identifier.spagearticle no. 30958-
dc.identifier.epagearticle no. 30958-
dc.identifier.eissn2045-2322-
dc.identifier.isiWOS:000380640400001-

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