Towards an etiological model of adolescent eating disorders through neuroimaging, genetics, and behavior

项目总结/摘要 进食障碍（ED）是由其严重的健康后果和往往棘手的过程结合在一起 这只能通过更好地了解艾德病因来改善。研究 通常集中于单独的诊断类别（例如，神经性厌食症/贪食症，暴食 疾病），尽管有证据表明遗传和症状在诊断中重叠。此外，有必要 在青春期高峰期之前和期间检查ED，考虑到动态神经发育 这一时期的变化。该项目采用跨诊断和多模式方法， 从青少年大脑认知发展（ABCD）研究中收集的大规模纵向数据， 前瞻性地识别可能预测艾德的遗传、神经影像学和行为指标， 青春期接受艾德治疗的青春期女孩样本也将纳入临床研究。 普遍性目标1（K99阶段）将确定行为和神经影像学衍生的ED相关性， ABCD（11-14岁）和临床（13-18岁）数据集，使用复杂的神经成像方法， 分析青少年ED的潜在形态学和微结构预测因素。目标2（R 00阶段） 将扩大其研究设计，包括基因组（ED和相关疾病的多基因风险）和纵向 行为和脑成像数据（9至17岁），以告知一个预测模型的出现艾德， 青春期，以及这些发现的预测因子在临床环境中（14-19岁）的影响。此项目的 ABCD研究数据的战略性利用为揭示以下疾病的病因提供了前所未有的机会： 使用前瞻性纵向多中心数据对男性和女性进行艾德分析，这项研究工作 否则从头开始将是极其困难和昂贵的。此外，包括临床 数据集允许对亚临床结果的普遍性进行罕见但急需的调查， 病情更严重的患者样本。该项目还将采用创新的方法，包括 在不同参与者中进行多基因风险评分，以及复杂的神经成像技术， 用于量化全脑微结构特征，其可以在检测中提供额外的灵敏度， 青少年ED的预测因素。Makowski博士提出的培训计划，包括艾德的培训 研究和机器学习方法，将提高她在精神病神经成像方面的现有技能， 基因组学选定的导师团队将增加必要的专业知识和支持，将促进博士。 Makowski过渡到独立研究职位，包括在艾德研究方面的额外培训 （导师：Wierenga博士;合作者：Bischoff-Grethe博士，Fennema-Notestine博士），神经影像学和基因组学 整合（共同导师：Dale博士），神经发育（合作者：Dr's Jernigan，Rhee）和机器学习 （顾问：邹博士）。该奖项将使Makowski博士成功完成该项目的目标 并致力于建立一个更完整的ED病因学模型，以帮助指导未来的治疗。

PROJECT SUMMARY/ABSTRACT Eating Disorders (EDs) are bound together by their severe health consequences and often intractable course for affected individuals, which can only be ameliorated through a better understanding of ED etiology. Studies have typically focused on separate diagnostic categories (e.g., anorexia/bulimia nervosa, binge eating disorder), despite evidence for genetic and symptom overlap across diagnoses. Further, there is a need to examine EDs before and during their peak onset in adolescence, given the dynamic neurodevelopmental changes characterizing this period. This project uses a transdiagnostic and multimodal approach, leveraging large-scale longitudinal data collection from the Adolescent Brain Cognitive Development (ABCD) Study to prospectively identify genetic, neuroimaging, and behavioral measures that may be predictive of an ED in adolescence. A sample of adolescent girls being treated for an ED will also be included for clinical generalizability. Aim 1 (K99 phase) will identify behavioral and neuroimaging-derived correlates of EDs across both ABCD (ages 11-14) and clinical (ages 13-18) datasets, using sophisticated neuroimaging methods to parse through potential morphological and microstructural predictors of adolescent EDs. Aim 2 (R00 phase) will expand its study design to include genomic (polygenic risk for EDs and related conditions) and longitudinal behavioral and brain imaging data (ages 9 to 17) to inform a predictive model of the emergence of an ED in adolescence, and the impact of these discovered predictors in a clinical setting (ages 14-19). This project’s strategic utilization of ABCD Study data holds an unparalleled opportunity to uncover the etiological factors of an ED across both males and females using prospective longitudinal multi-site data, a research endeavour that otherwise would be extremely difficult and costly to initiate from scratch. Moreover, the inclusion of a clinical dataset allows for a rare but much-needed investigation of the generalizability of results from a sub-clinical to more severely ill patient sample. This project will also apply innovative methodologies, including the integration of polygenic risk scoring across diverse participants, alongside sophisticated neuroimaging techniques allowing for quantification of whole-brain microstructural features that may provide additional sensitivity in detecting predictive factors of an adolescent ED. Dr. Makowski’s proposed training plan, including training in ED research and machine learning methods, will enhance her existing skillset in psychiatric neuroimaging and genomics. The chosen mentorship team will add the necessary expertise and support that will facilitate Dr. Makowski’s transition to an independent research position, including additional training in ED research (mentor: Dr. Wierenga; collaborators: Dr’s Bischoff-Grethe, Fennema-Notestine), neuroimaging and genomics integration (co-mentor: Dr. Dale), neurodevelopment (collaborators: Dr’s Jernigan, Rhee) and machine learning (consultant: Dr. Zou). This award will position Dr. Makowski to successfully complete the aims of this project and work towards a more complete etiological model of EDs that can help inform future treatment.