Dataset Information

Relationships between body fat distribution, epicardial fat and obstructive sleep apnea in obese patients with and without metabolic syndrome.

ABSTRACT:

Background

Obstructive sleep apnea (OSA) and metabolic syndrome, both closely related to obesity, often coexist in affected individuals; however, body mass index is not an accurate indicator of body fat and thus is not a good predictor of OSA and other comorbidities. The aim of this study was to investigate whether the occurrence of OSA could be associated with an altered body fat distribution and a more evident cardio metabolic risk independently from obesity and metabolic syndrome.

Methods and results

171 consecutive patients (58 men and 113 women) were included in the study and underwent overnight polysomnography. Anthropometric data, blood pressure, lipid profile, glycaemic parameters were recorded. Body composition by DXA, two-dimensional echocardiography and carotid intima/media thickness measurement were performed. 67 patients (39.2%) had no OSA and 104 (60.8%) had OSA. The percentage of patients with metabolic syndrome was significantly higher among OSA patients (65.4%) that were older, heavier and showed a bigger and fatter heart compared to the control group. Upper body fat deposition index , the ratio between upper body fat (head, arms and trunk fat in kilograms) and lower body fat (legs fat in kilograms), was significantly increased in the OSA patients and significantly related to epicardial fat thickness. In patients with metabolic syndrome, multivariate regression analyses showed that upper body fat deposition index and epicardial fat showed the best association with OSA.

Conclusion

The occurrence of OSA in obese people is more closely related to cardiac adiposity and to abnormal fat distribution rather than to the absolute amount of adipose tissue. In patients with metabolic syndrome the severity of OSA is associated with increase in left ventricular mass and carotid intima/media thickness.

SUBMITTER: Lubrano C

PROVIDER: S-EPMC3466221 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Relationships between body fat distribution, epicardial fat and obstructive sleep apnea in obese patients with and without metabolic syndrome.

Lubrano Carla C Saponara Maurizio M Barbaro Giuseppe G Specchia Palma P Addessi Eliana E Costantini Daniela D Tenuta Marta M Di Lorenzo Gabriella G Genovesi Giuseppe G Donini Lorenzo M LM Lenzi Andrea A Gnessi Lucio L

PloS one 20121008 10

<h4>Background</h4>Obstructive sleep apnea (OSA) and metabolic syndrome, both closely related to obesity, often coexist in affected individuals; however, body mass index is not an accurate indicator of body fat and thus is not a good predictor of OSA and other comorbidities. The aim of this study was to investigate whether the occurrence of OSA could be associated with an altered body fat distribution and a more evident cardio metabolic risk independently from obesity and metabolic syndrome.<h4> ...[more]

PMID: 23056581

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Project description:BackgroundObesity is prevalent among pregnant women in the United States; obstructive sleep apnea is highly comorbid with obesity and is associated with adverse pregnancy outcomes. Screening for obstructive sleep apnea in pregnant women has remained a challenge because of a lack of validated screening tools.ObjectiveThe purpose of this study was to evaluate established obstructive sleep apnea screening tools, a sleepiness scale, and individual component items in a cohort of pregnant women with extreme obesity in mid pregnancy with the use of objective testing to determine obstructive sleep apnea status and to describe the prevalence of obstructive sleep apnea among women with extreme obesity.Study designAdult pregnant subjects, between 24 and 35 weeks gestation, with a body mass index ≥40 kg/m2 at the time of enrollment completed obstructive sleep apnea screening tools (Berlin Questionnaire, American Society of Anesthesiologists checklist, and STOP-BANG questionnaire) and the Epworth Sleepiness Scale; they also underwent physical examination of the neck, mouth, and airway. The published obstructive sleep apnea in pregnancy prediction score was calculated for each subject. Obstructive sleep apnea status for each subject was determined by the results of an overnight, unattended type III home sleep apnea test.ResultsTwenty-four percent of pregnant women with extreme obesity had obstructive sleep apnea on home sleep apnea testing in mid pregnancy (Apnea-Hypopnea Index, ≥5 events per hour]. Established obstructive sleep apnea screening tools performed very poorly to screen for obstructive sleep apnea in this cohort. Age, body mass index, neck circumference, frequently witnessed apneas, and highly likely to fall asleep while driving were associated most strongly with obstructive sleep apnea status in this cohort.ConclusionWe found that 24% of pregnant women with body mass index ≥40 kg/m2 between 24 and 35 weeks gestation have obstructive sleep apnea, defined as Apnea-Hypopnea Index ≥5 events per hour on an overnight type III home sleep apnea test. We found the Berlin Questionnaire, American Society of Anesthesiologists checklist, STOP-BANG, obstructive sleep apnea in pregnancy score by Facco et al, and the Epworth Sleepiness Scale were not useful screening tools for obstructive sleep apnea in a cohort of obese pregnant women. However, age, body mass index, neck circumference, frequently witnessed apneas, and likely to fall asleep while driving were associated with obstructive sleep apnea in this cohort. Further studies are needed to adjust the criteria and thresholds within the available screening tools to better predict obstructive sleep apnea in pregnant women with obesity.

Dataset Information

Relationships between body fat distribution, epicardial fat and obstructive sleep apnea in obese patients with and without metabolic syndrome.

Background

Methods and results

Conclusion

Publications

Relationships between body fat distribution, epicardial fat and obstructive sleep apnea in obese patients with and without metabolic syndrome.

Similar Datasets

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