Literature DB >> 26711651

Anthropometric and physiologic assessment in sleep apnoea patients regarding body fat distribution.

J Wysocki, A Charuta1, K Kowalcze, I Ptaszyńska-Sarosiek.   

Abstract

BACKGROUND: Obstructive sleep apnoea (OSA) is characterised by repeated episodes of pauses in breathing during sleep due to obstruction of the upper airway that result in transient hypoxaemia, sleep fragmentation and long-term cardiovascular disease. The most common risk factors for OSA include: obesity, age over 50 and neck circumference of more than 41 cm for females and more than 43 cm in males. Sleep apnoea is more common in men than in women. The aim of the conducted research was to evaluate relations between the anthropometric features connected with adipose tissue distribution and the severity of OSA.
MATERIALS AND METHODS: The study was carried out on 180 patients (144 males and 36 females) diagnosed with OSA syndrome. The standard sleep parameters obtained from night polysomnography as well as skin-fat fold thickness and neck circumference and waist-to-hip ratio were analysed. Statistical analysis was performed using STATISTICA 10.
RESULTS: It was stated that anthropometric parameters connected with the accu-mulation of adipose tissue in upper body were significantly related to severity of OSA in males (p ≤ 0.05). Body mass index (BMI) was significantly correlated with severity of OSA in females (p ≤ 0.05).
CONCLUSIONS: In males, there is a connection between the severity of OSA, BMI and a higher accumulation of adipose tissue in upper part of the body measured by neck circumference and shoulder thickness of skin-fat folds, whereas in females only by BMI.

Entities:  

Keywords:  SO2nadir; anthropometric assessment; apnoea-hypopnoea index; obesity; obstructive sleep apnoea

Mesh:

Year:  2015        PMID: 26711651     DOI: 10.5603/FM.a2015.0127

Source DB:  PubMed          Journal:  Folia Morphol (Warsz)        ISSN: 0015-5659            Impact factor:   1.183


  2 in total

1.  Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.

Authors:  Wen-Te Liu; Hau-Tieng Wu; Jer-Nan Juang; Adam Wisniewski; Hsin-Chien Lee; Dean Wu; Yu-Lun Lo
Journal:  PLoS One       Date:  2017-05-04       Impact factor: 3.240

2.  The prediction of obstructive sleep apnea severity based on anthropometric and Mallampati indices.

Authors:  Babak Amra; Mohsen Pirpiran; Forogh Soltaninejad; Thomas Penzel; Ingo Fietze; Christoph Schoebel
Journal:  J Res Med Sci       Date:  2019-07-24       Impact factor: 1.852

  2 in total

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