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

描述(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紊乱(SRBD)在肥胖儿童中发病率很高。与肥胖相关的SRDB受试者在多导睡眠图和临床特征上表现出很大的差异。这些患者可大致分为以下4个表型：(A)没有气体交换异常、呼吸模式或睡眠障碍的原发鼾症；(B)阻塞性低通气伴高碳酸血症或低氧血症，并有接近正常的呼吸或睡眠模式；(C)高觉醒频率，但没有明显的气体交换异常、阻塞性呼吸暂停或低呼吸综合征(包括上呼吸道阻力综合征)；(D)传统的OSAS，反复发作阻塞性低呼吸暂停和呼吸暂停。我们假设，表型行为的这种多样性是由于存在不同的潜在生理机制，而定量的动态模型将使我们能够更好地描述机制差异。为了验证这一假设，我们建议：(1)建立一个与上呼吸道和呼吸控制动力学相关的信息数据库，来自动态磁共振成像(MRI)和在清醒和睡眠期间获得的非侵入性生理测量；(2)从这些数据估计SRBD闭环最小计算模型的关键参数，并确定这些参数在不同表型类别之间的差异；以及(3)使用来自动态MRI的关于上呼吸道动力学的新信息扩展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呼吸控制动力学，并将从这些模拟得出的指标与从4个表型类别中观察到的SRBD特征得出的相应指数进行比较。同时，将对现有的实时磁共振成像方法进行技术改进，以提高时空分辨率，降低成像过程中的声学噪声。从这项研究中获得的知识可能有助于更好地理解肥胖儿童发生不同SRBD表型的机制，并可能有助于为个别患者提供更好的定制治疗策略的指导方针。与公共卫生相关：在过去的二十年里，儿童超重和肥胖的流行率急剧上升。这些肥胖儿童中有相当一部分还患有与睡眠相关的呼吸障碍。这项拟议的研究结合了计算建模和最先进的上呼吸道动态磁共振成像(MRI)，并结合生理测量来研究为什么这一受试者群体在睡眠和临床特征方面表现出很大的差异。从这项研究获得的知识可能会为为个别患者定制治疗策略提供改进的指导方针。此外，在该项目中开发的实时MRI方法学的技术改进将有助于在总体上显著推进动态上呼吸道成像。(摘要结束)

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)