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.