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.

项目总结/文摘