ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2024
Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea-hypopnea syndrome.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- TGF‑β1 in obstructive sleep apnea: linking intermittent hypoxia, upper‑airway remodeling, and systemic comorbidities.Sleep & breathing = Schlaf & Atmung · 2026Review
- Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.Frontiers in big data · 2026Article
- Metabolomic Biomarkers for Non-Invasive Diagnosis of Coronary Artery Disease and Obstructive Sleep Apnea Co-Occurrence.Nature and science of sleep · 2026Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesObstructive 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.
methods45 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.
resultsThe 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).
conclusionsThis 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. CITATION: Wang X, Zhao J, Wang X, Zhang L. Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea-hypopnea syndrome.
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