Identification of oropharyngeal and craniofacial risk factors in obstructive sleep apnea.

识别阻塞性睡眠呼吸暂停的口咽和颅面危险因素。

• 批准号: 414051770
• 负责人: Dr. Amro Daboul, Ph.D.
• 金额: --
• 依托单位: Poliklinik für Zahnärztliche Prothetik, Alterszahnheilkunde und medizinische Werkstoffkunde
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/414051770?language=en
• 关键词: Identification; oropharyngeal; craniofacial; risk; factors

Among the many sleep disorders affecting human health, obstructive sleep apnea syndrome (OSAS) has one of the highest prevalence rates in the general population and is a major cause of morbidity, mortality and impaired quality of life. Despite the increase in the number of diagnostic modalities, the majority of those affected remains undiagnosed and there is an increased need to understand OSAS pathogenesis in the general population.The current research points out that obstructuve sleep apnea is the result of structural and functional abnormalities that promote repetitive collapse of the upper airway during sleep, and that factors, such as obesity, oropharyngeal structures anatomy and craniofacial morphology are key determinants to the presence and severity of the disorder. In this project, we will investigate the anatomical and morphological aspects that constitute important risk factors to obstructive sleep apnea development. We will utilize magnetic resonance images, state-of-the-art methods in combination with polysomnographic findings from a large sample of subjects to provide reliable quantifications of oropharyngeal structures and anthropometric measurements of the craniofacial complex, which we will use to establish an integrated predictive model of obstructive sleep apnea severity.

在影响人类健康的许多睡眠障碍中，阻塞性睡眠呼吸暂停综合征（OSAS）是普通人群中患病率最高的疾病之一，是发病率、死亡率和生活质量受损的主要原因。尽管诊断方式的数量有所增加，但大多数受影响的患者仍未得到诊断，因此越来越需要了解OSAS在普通人群中的发病机制。目前的研究指出，阻塞性睡眠呼吸暂停是结构和功能异常导致睡眠时上呼吸道反复塌陷的结果，肥胖、口咽结构解剖和颅面形态等因素是障碍存在和严重程度的关键决定因素。在这个项目中，我们将研究构成阻塞性睡眠呼吸暂停发展的重要危险因素的解剖学和形态学方面。我们将利用磁共振图像、最先进的方法结合来自大量受试者样本的多导睡眠图结果，提供可靠的口咽结构量化和颅面复合体的人体测量，我们将利用这些数据建立阻塞性睡眠呼吸暂停严重程度的综合预测模型。