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    Body Composition Analysis in Obstructive Sleep Apnea: A Cross-Sectional Study Using Bioelectrical Impedance Analysis
    (WILEY, 2025) Yetim, M; Kalçık, M; Bekar, L; Karavelioğlu, Y; Yılmaz, Y
    Introduction Obstructive 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.Methods In 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.Results Compared 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.Conclusion BIA-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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    Diagnosis of Systolic Heart Failure Disease with an Electronic Nose
    (MDPI, 2025) Yetim, M; Karavelioğlu, Y; Şimşek, C; Aydemir, Ö; Tozlu, BH
    Electronic nose technology is attracting attention with its diagnostic applications in the healthcare field. In this study, respiratory samples of individuals with systolic heart failure (HFrEF) were analyzed using an electronic nose device to investigate the diagnostic feasibility for this disease. A total of 275 breath samples were collected from 29 patients and 31 healthy volunteers followed in a cardiology clinic. Classification using support vector machines (SVM) yielded an average accuracy rate of 85.21%. The simplicity of the statistical features used in the classification, combined with the low computational complexity, increases the method's practicality. This study demonstrates that, unlike existing imaging and laboratory techniques, electronic nose technology can be considered a non-invasive, rapid, and cost-effective alternative for diagnosing heart failure, particularly notable for its potential to contribute to early diagnosis.
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    Reconsidering Electrocardiographic Predictors of Culprit Coronary Artery Occlusion in NSTEMI Patients
    (WILEY, 2025) Yetim, M; Çelik, ÖB; Kalçık, M
    This letter provides a critical appraisal of the study by Wei et al. on clinical and electrocardiographic predictors of left circumflex artery occlusion in NSTEMI patients. While the authors identified STV5 + STV6 >= 2.5 mm and T-wave imbalance as potential markers, concerns remain regarding the single-center, retrospective design, limited sensitivity of ECG findings, and the lack of significant differences in clinical outcomes. Prior meta-analyses suggest a higher risk in patients with occluded culprit arteries, highlighting inconsistencies with the present study. Future research should employ multicenter prospective designs and advanced diagnostic modalities, including posterior ECG leads and artificial intelligence-based analysis, to improve detection and risk stratification of culprit LCX occlusion in NSTEMI.