Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea-hypopnea syndrome

被引:0
作者
Wang, Xiaoyi [1 ,5 ]
Zhao, Jinming [1 ]
Wang, Xiangdong [1 ,2 ,3 ]
Zhang, Luo [1 ,2 ,3 ,4 ,5 ]
机构
[1] Capital Med Univ, Beijing Tongren Hosp, Dept Otorhinolaryngol, Head & Neck Surg, 1 Dongjiaominxiang St, Beijing 100730, Peoples R China
[2] Beijing Inst Otolaryngol, Beijing Municipal Educ Commiss, Beijing Lab Allerg Dis, Beijing, Peoples R China
[3] Beijing Inst Otolaryngol, Beijing Key Lab Nasal Dis, Beijing, Peoples R China
[4] Capital Med Univ, Beijing Tongren Hosp, Dept Allergy, Beijing, Peoples R China
[5] Chinese Acad Med Sci, Res Unit Diag & Treatment Chron Nasal Dis, Beijing, Peoples R China
来源
JOURNAL OF CLINICAL SLEEP MEDICINE | 2024年 / 20卷 / 09期
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
obstructive sleep apnea-hypopnea syndrome; metabolomics; UHPLC-MS/MS; random forest model; OXIDATIVE STRESS; PALMITOYLETHANOLAMIDE; INHIBITION;
D O I
10.5664/jcsm.11160
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Study Objectives: Obstructive sleep apnea-hypopnea syndrome (OSAHS) poses a significant health hazard, intermittent hypoxia inflicts damage throughout the body and is considered a critical risk factor for metabolic disorders. The aim of this study was to establish a metabolic profile for patients with OSAHS using nontargeted metabolomics detection techniques, providing a basis for OSAHS diagnosis and novel biological marker identification. Methods: 45 patients with OSAHS composed the OSAHS group, and 44 healthy volunteers composed the control group. Nontargeted metabolomics technology was used to analyze participants' urinary metabolites. Differentially abundant metabolites were screened and correlated through hierarchical clustering analysis. We constructed a composite metabolite diagnostic model using a random forest model. Simultaneously, we analyzed the relationships between 20 metabolites involved in model construction and OSAHS severity. Results: The urinary metabolomics pattern of the OSAHS group exhibited significant changes, demonstrating noticeable differences in metabolic products. Urinary metabolite analysis revealed differences between the mild-to-moderate OSAHS and severe OSAHS groups. The composite metabolite model constructed in this study demonstrated excellent diagnostic performance not only in distinguishing healthy control participants from patients with mild-to-moderate OSAHS (area under the curve = 0.78) and patients with severe OSAHS (area under the curve = 0.78), but also in discriminating between patients with mild-to-moderate and severe OSAHS (area under the curve = 0.71). Conclusions: This study comprehensively analyzed the urinary metabolomic characteristics of patients with OSAHS. The established composite metabolite model provides robust support for OSAHS diagnosis and severity assessment. Twenty metabolites associated with OSAHS disease severity offer a new perspective for diagnosis.
引用
收藏
页码:1445 / 1453
页数:9
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