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

通过神经影像学、遗传学和行为建立青少年饮食失调的病因学模型

• 批准号: 10644429
• 负责人: Carolina Makowski
• 金额: $ 9.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644429
• 关键词: 16 year old; 18 year old; Adolescence; Adolescent; Adult; Affect; Age; Age of Onset; Anorexia; Anorexia Nervosa; Area; Award; Behavior; Behavioral; Binding; Binge eating disorder; Brain; Brain imaging; Bulimia; Categories; Clinical; Clinical Data; Clinical assessments; Corpus striatum structure; Data; Data Collection; Data Discovery; Data Set; Development; Diagnosis; Diagnostic; Diffusion; Disease; Eating Disorders; Etiology; Exhibits; Female; Female Adolescents; Future; Genetic; Genetic Risk; Genetic study; Genomics; Health; Heritability; Heterogeneity; Image; Imaging Techniques; Individual; Insula of Reil; Investigation; Lead; Machine Learning; Magnetic Resonance Imaging; Measures; Mediating; Mentors; Mentorship; Methodology; Methods; Modeling; Morphology; Neuroanatomy; Neurobiology; Onset of illness; Outcome; Participant; Patient-Focused Outcomes; Patients; Personal Satisfaction; Phase; Phenotype; Positioning Attribute; Predictive Factor; Prevalence; Property; Prospective Studies; Publishing; Reporting; Research; Research Design; Restriction Spectrum Imaging; Risk; Risk Factors; Sampling; Severity of illness; Site; Structure; Surface; Symptoms; Techniques; Thick; Training; Translating; Treatment outcome; Work; associated symptom; behavior measurement; brain tissue; case control; cognitive development; cost; diagnostic criteria; disorder control; imaging modality; improved; innovation; longitudinal dataset; longitudinal, prospective study; machine learning method; male; mortality; multimodality; neurodevelopment; neuroimaging; neuroimaging marker; patient prognosis; physical conditioning; polygenic risk score; predict clinical outcome; predictive modeling; programs; prospective; skills; statistics; support vector machine; trait; treatment planning; white matter

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.

项目概要/摘要 饮食失调 (ED) 因其严重的健康后果和往往棘手的病程而紧密相连 对于受影响的个体来说，只有通过更好地了解 ED 病因研究才能改善这种情况。 通常专注于单独的诊断类别（例如，厌食症/神经性贪食症、暴食症 疾病），尽管有证据表明诊断之间存在遗传和症状重叠。 鉴于神经发育的动态变化，在青春期高峰期之前和期间检查 ED 该项目采用了跨诊断和多模式方法，利用了这一时期的特征。 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 ED 的遗传、神经影像和行为测量 接受 ED 治疗的青春期女孩样本也将被纳入临床。 目标 1（K99 阶段）将识别 ED 的行为和神经影像相关性。 ABCD（11-14 岁）和临床（13-18 岁）数据集，使用复杂的神经影像方法 解析青少年 ED 的潜在形态和微观结构预测因子。 将扩大其研究设计，包括基因组（ED 和相关病症的多基因风险）和纵向研究 行为和脑成像数据（9 至 17 岁）为 ED 出现的预测模型提供信息 青春期，以及这些发现的预测因素对临床环境（14-19 岁）的影响。 ABCD 研究数据的战略利用为揭示疾病的病因学因素提供了无与伦比的机会 使用前瞻性纵向多站点数据对男性和女性进行 ED，这是一项研究工作 否则从头开始将极其困难且成本高昂，而且纳入临床。 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 该项目还将应用创新方法，包括整合。 跨不同的多基因风险评分，以及复杂的神经影像技术允许 用于量化全脑微观结构特征，这可能会在检测中提供额外的灵敏度 Makowski 博士提出的青少年 ED 培训计划的预测因素，包括 ED 培训。 研究和机器学习方法，将增强她在精神病学神经影像学和 选定的导师团队将增加必要的专业知识和支持，以促进博士。 Makowski 过渡到独立研究职位，包括 ED 研究方面的额外培训 （导师：Wierenga 博士；合作者：Bischoff-Grethe 博士、Fennema-Notestine）、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：Jernigan、Rhee 博士）和机器学习 （顾问：Zou 博士）。该奖项将使 Makowski 博士能够成功完成该项目的目标。 并致力于建立更完整的 ED 病因学模型，以帮助为未来的治疗提供信息。