Redefining Chiari Type I Malformation through Genetically, Radiologically, and Clinically-Derived Endophenotypes that are Predictive of Long-Term Neurological Outcome

项目总结 项目4：使用预测长期结果的多维内表型重新定义CM Chiari 1型畸形(CM)是一种常见的、可能使人衰弱的疾病，通常由尾部定义 枕大孔下方的小脑扁桃体移位。而X线片上的CM可能有1% 或者更多的种群，这个种群是非常不同的。具有经典定义的CM的患者可能 没有临床症状或出现严重症状，如衰弱的头痛或脑干症状 或者是脊髓功能障碍。此外，临床表现相似的患者可能有不同的基因 表明不同的病因导致表面上相似的疾病的概况和并存的诊断。 对这种异构性的日益认识强调了数据驱动的方法的必要性 对传统定义的“CM”进行重新分类。此外，当代成像方法的进展， 基因组学和以患者为中心的临床措施大大扩大了表型鉴定的机会 复杂的疾病，如CM。利用华盛顿大学在这些领域的独特优势，总体 项目4的目标是在传统的- 定义了“CM”，以便为临床管理提供信息并优化患者的长期结果。特别是，我们预计 这些多维内表型不仅有助于结果预测，还将提供新的生物学 关于疾病发病机制的见解，从而支持个性化治疗策略。我们打算 通过三个具体目标实现这一目标。在目标1中，我们将采用成熟的无监督和半监督 多维临床和放射学数据的有监督聚类方法以获得新的聚类 大型多中心数据集。然后我们将在三个方面对这些多维内表型进行外部验证 外部、多中心数据集。在目标2中，我们将比较不同集群和 也使用最先进的遗传学和先进的成像技术来获得对生物学的新见解 CM异质性和发病机制的差异。在目标3中，我们将研究两者之间的关联 这些集群和重要的CM结果(例如，生活质量的变化、术后并发症)。我们会 然后使用预测建模来检查集群身份和其他特征如何影响这些 结果。最后，我们将使用改进的Delphi方法在此基础上创建一个新的治疗框架 项目的发现以及来自CM研究和临床管理专家的结构化反馈。 综上所述，该项目的结果将为CM分类提供一种全新的数据驱动方法。我们 希望这些发现将对临床管理实践产生立竿见影的影响，并指导未来 在这一领域的研究。

PROJECT SUMMARY Project 4: Redefining CM Using Multidimensional Endophenotypes Predictive of Long-Term Outcome Chiari type 1 malformation (CM) is a common and potentially debilitating disease typically defined by caudal displacement of the cerebellar tonsils below the foramen magnum. While radiographic CM may be found in 1% or more of the population, this population is extremely heterogeneous. Patients with classically defined CM may be clinically asymptomatic or present with severe symptoms such as debilitating headaches or signs of brainstem or spinal cord malfunction. Furthermore, patients with similar clinical presentations may have distinct genetic profiles and comorbid diagnoses that suggest differing etiologies contributing to superficially similar disease. Increasing recognition of this heterogeneity has emphasized the need for a data-driven approach to reclassifying traditionally-defined “CM.” Furthermore, advances in contemporary methods in imaging, genomics, and patient-centered clinical measures have substantially expanded opportunities for phenotyping complex diseases, such as CM. Leveraging Washington University’s unique strengths in these areas, the overall objective of Project 4 is to derive clinically relevant, multidimensional endophenotypes within traditionally- defined “CM” to inform clinical management and optimize long-term patient outcomes. In particular, we expect these multidimensional endophenotypes will both aid in outcome prediction and also provide new biological insights regarding disease pathogenesis, thereby supporting personalized treatment strategies. We intend to pursue this objective through three specific aims. In Aim 1, we will apply well-established unsupervised and semi- supervised clustering methods to multidimensional clinical and radiological data to derive novel clusters in a large, multicenter dataset. We will then externally validate those multidimensional endophenotypes in three external, multicenter datasets. In Aim 2 we will compare differences in disease presentation across clusters and also use state-of-the-art genetics and advanced imaging techniques to obtain new insights into biological differences underlying CM heterogeneity and pathogenesis. In Aim 3 we will examine the association between these clusters and important CM outcomes (e.g., change in quality of life, postoperative complications). We will then use prediction modeling to examine how both cluster identity and other characteristics impact these outcomes. Finally, we will use a Modified Delphi Approach to create a novel treatment framework based on this Project’s findings along with structured feedback from experts in the research and clinical management of CM. Taken together, this Project’s results will offer a fundamentally new data-driven approach for classifying CM. We expect these findings will have an immediate impact on clinical management practices and will guide future research in this field.