Obstructive sleep apnea syndrome (OSAS) and other forms of sleep-related breathing disorders (SRBD) occur at high prevalence rates in obese children. The pool of subjects with obesity- related SRDB display a large variation of polysomnographic and clinical characteristics. These may be classified broadly into the following 4 phenotypic categories: (a) primary snorers with no abnormalities of gas exchange, respiratory pattern or sleep disruption;(b) obstructive hypoventilation with hypercapnia or hypoxemia with near-normal respiratory or sleep patterns;(c) high arousal frequency without prominent gas exchange abnormality, obstructive apneas or hypopneas (includes upper airway resistance syndrome);(d) traditional OSAS with recurrent episodes of obstructive hypopnea and apnea. We hypothesize that this diversity in phenotypic behavior results from the existence of different underlying physiological mechanisms, and that a quantitative dynamic model would allow us to better delineate the mechanistic differences. To test this hypothesis, we propose to: (1) establish a database of information pertinent to upper airway and ventilatory control dynamics, derived from dynamic magnetic resonance imaging (MRI) and noninvasive physiological measurements obtained during wakefulness and sleep;(2) estimate from these data the key parameters of a closed-loop minimal computational model of SRBD and determine how these parameters differ across phenotypic categories;and (3) extend the existing computational model of SRBD using the novel information about upper airway dynamics derived from dynamic MRI. The extended computational model will be used to simulate ventilatory control dynamics during sleep, and metrics derived from these simulations will be compared against the corresponding indices derived from the observed characteristics of SRBD in the 4 phenotypic categories. In parallel, technological improvements will be made to the existing real-time MRI methodology in order to increase spatio-temporal resolution and decrease acoustic noise during imaging. The knowledge derived from this study may lead to a better understanding of the mechanisms through which the different phenotypes of SRBD occur in obese children, and could be useful in providing better guidelines for customizing therapeutic strategies to individual patients.

Public Health Relevance

The prevalence of overweight and obesity in children has increased dramatically over the past two decades. A significant fraction of these obese children also have sleep-related breathing disorders. The proposed study combines computational modeling and state-of-the-art dynamic magnetic resonance imaging (MRI) of the upper airway and with physiological measurements to investigate why this subject population exhibits large variations in sleep and clinical characteristics. The knowledge derived from this study may provide improved guidelines for customizing therapeutic strategies to individual patients. As well, the technological improvements in real-time MRI methodology developed in this project will help to significantly advance dynamic upper airway imaging in general.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL105210-03
Application #
8318682
Study Section
Special Emphasis Panel (ZHL1-CSR-G (S1))
Program Officer
Blaisdell, Carol J
Project Start
2010-09-17
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
3
Fiscal Year
2012
Total Cost
$851,620
Indirect Cost
$210,318
Name
University of Southern California
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
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Wu, Ziyue; Kim, Yoon-Chul; Khoo, Michael C K et al. (2014) Evaluation of an independent linear model for acoustic noise on a conventional MRI scanner and implications for acoustic noise reduction. Magn Reson Med 71:1613-20
Khoo, Michael C K; Oliveira, Flavia M G S; Cheng, Limei (2013) Understanding the metabolic syndrome: a modeling perspective. IEEE Rev Biomed Eng 6:143-55