ArticleThe clinical respiratory journal2025
Body Composition Analysis in Obstructive Sleep Apnea: A Cross-Sectional Study Using Bioelectrical Impedance Analysis.
Article in The clinical respiratory journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MFR, SVR and ASVR as promising biomarkers in overweight and obese population with obstructive sleep apnea syndrome: insights from an observational study.Annals of medicine · 2026Observational
- Sleep-Disordered Breathing Symptoms and Central Adiposity in Children with Adenotonsillar Hypertrophy.Journal of clinical medicine · 2026Article
- Body Composition Analysis in Obstructive Sleep Apnea: A Cross-Sectional Study Using Bioelectrical Impedance Analysis.The clinical respiratory journal · 2025Article
- Associations between body composition, hydration status, and sleep architecture in obstructive sleep apnea.Frontiers in endocrinology · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionObstructive sleep apnea (OSA) is a prevalent disorder characterized by recurrent upper airway collapse, resulting in intermittent hypoxia and sleep fragmentation. While obesity is a major risk factor, traditional markers such as body mass index (BMI) inadequately reflect the complex interplay of body composition in OSA pathogenesis. This study aimed to investigate the predictive value of body composition parameters assessed by bioelectrical impedance analysis (BIA) for OSA.
methodsIn this cross-sectional single-center study, 78 patients diagnosed with OSA by polysomnography (PSG) and 78 age-, gender-, and BMI-matched controls without OSA were analyzed. BIA was used to assess fat distribution, muscle mass, and body water composition. Logistic regression analyses were performed to identify independent predictors of OSA.
resultsCompared to controls, the OSA group had significantly higher lean mass, trunk fat percentage, and total body water. Multivariable logistic regression identified body fat mass (OR = 1.06), visceral fat area (OR = 0.83), and total body water (OR = 1.10) as independent predictors of OSA. Notably, total body water had the strongest association with OSA risk, independent of traditional obesity metrics.
conclusionBIA-derived body composition analysis provides nuanced insights beyond BMI, highlighting the roles of central fat distribution and fluid balance in OSA pathophysiology. These findings underscore the clinical utility of incorporating detailed body composition assessment into the routine evaluation of patients at risk for OSA.
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