Model-based Phenotyping of OSAS in Pediatric Obesity using Dynamic MR Imaging

描述（由申请人提供）：阻塞性睡眠呼吸暂停综合征（OSAS）和其他形式的睡眠相关呼吸障碍（SRBD）在肥胖儿童中的患病率很高。肥胖相关性睡眠呼吸障碍患者的多导睡眠图和临床特征差异很大.这些可以大致分为以下4种表型类别：（a）没有气体交换、呼吸模式或睡眠中断异常的原发性打鼾者;（B）具有接近正常呼吸或睡眠模式的高碳酸血症或低氧血症的阻塞性通气不足;（c）没有显著气体交换异常、阻塞性呼吸暂停或呼吸不足的高唤醒频率（包括上呼吸道阻力综合征）;（d）传统OSAS伴阻塞性低通气和呼吸暂停的反复发作。我们假设，这种多样性的表型行为的结果，从存在不同的潜在生理机制，定量动态模型将使我们能够更好地描绘的机制差异。为了验证这一假设，我们建议：（1）建立一个与上气道和呼吸控制动力学相关的信息数据库，这些信息来自于清醒和睡眠期间获得的动态磁共振成像（MRI）和无创生理测量;（2）根据这些数据估计SRBD闭环最小计算模型的关键参数，并确定这些参数在表型类别中的差异;（3）利用动态MRI获得的上呼吸道动力学新信息扩展了SRBD的现有计算模型。扩展的计算模型将用于模拟睡眠期间的解释性控制动态，并将从这些模拟中获得的度量与从4个表型类别中观察到的SRBD特征中获得的相应指数进行比较。与此同时，将对现有的实时MRI方法进行技术改进，以提高时空分辨率并减少成像期间的声学噪声。从这项研究中获得的知识可能会导致更好地了解SRBD的不同表型在肥胖儿童中发生的机制，并可能有助于为个体患者定制治疗策略提供更好的指导。公共卫生相关性：在过去的二十年里，儿童超重和肥胖的流行率急剧增加。这些肥胖儿童中有很大一部分还患有与睡眠有关的呼吸障碍。该研究结合了上气道的计算建模和最先进的动态磁共振成像（MRI）以及生理测量，以研究为什么该受试者群体在睡眠和临床特征方面表现出较大的变化。从这项研究中获得的知识可以为个体患者定制治疗策略提供改进的指南。同时，本项目中开发的实时MRI方法的技术改进将有助于显着推进动态上气道成像。(End摘要）

DESCRIPTION (provided by applicant): 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. (End of Abstract)