Predicting AUD development, risk and resilience phenotypes through integration of multi-modal COGA data

通过整合多模式 COGA 数据预测 AUD 发展、风险和复原力表型

基本信息

批准号：
10665027
负责人：
Sivan Kinreich
金额：
$ 47.96万
依托单位：
SUNY DOWNSTATE MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-15 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10665027
关键词：
18 year old Address Adult Affect African American African American population Age Alcohol dependence Alcohols American Attention deficit hyperactivity disorder Biological Biological Factors Biological Markers Brain Cause of Death Cessation of life Characteristics Classification Clinical Complex DSM-IV Data Demographic Factors Development Diagnosis Dimensions Disease Drug usage Drunk driving Early Diagnosis Electroencephalography Electrophysiology (science)Environment Environmental Risk Factor European Evaluation Family Study Female Functional disorder Gender Genetic Genetic Markers Genetic Models Genetic study Goals Health Home Individual Learning Literature Machine Learning Major Depressive Disorder Measures Methods Modality Modeling Participant Performance Phenotype Population Post-Traumatic Stress Disorders Predisposition Prevention Psychiatric Diagnosis Psychiatry Public Health Questionnaires Research Research Personnel Risk Sampling Side Single Nucleotide Polymorphism Subgroup Surveys Symptoms Techniques Testing Training Trees Underrepresented Populations United States Validation Woman Work addiction age stratification aged alcohol use disorder base cohort deep learning demographics disorder risk genetics of alcoholism gradient boosting improved innovation interest machine learning algorithm machine learning method men model building multidisciplinary multimodal data multimodality neurogenetics neurophysiology peer personalized diagnostics personalized medicine polygenic risk score population stratification precision medicine prediction algorithm predictive modeling psychosocial recruit resilience risk prediction model secondary analysis sex sociodemographics software development

项目摘要

Project Summary Alcohol use disorder (AUD) is a major public health challenge in the USA and the world. In the National Survey on Drug Use and Health (2018), 14.4 million adults aged 18 and older had AUD. This included 9.2 million men and 5.3 million women. Furthermore, in 2014, alcohol-impaired driving fatalities accounted for 9,967 deaths in the USA. Despite its importance, not much research has been done to identify the predisposing biological factors that may lead to the development of AUD. While predictive models have been successful in distinguishing between individuals with AUD and healthy controls, models identifying in advance if an individual is prone to develop AUD, as well as the biomarkers indicating a predisposition for AUD, are still unclear. To address this, the Collaborative Study of the Genetics of Alcoholism (COGA) of European American (EA) and African American (AA) has recruited subjects aged 8-68, who are longitudinally followed and evaluated for AUD over 30 years. The subjects were also assessed in terms of electrophysiology (EEG), single-nucleotide polymorphisms (SNP), psychosocial and psychiatry evaluation and demographic questionnaires. The goal of our proposed study is to conduct secondary analyses of COGA’s rich multimodal longitudinal data to develop predictive models that can accurately predict vulnerability to AUD before an individual actually develops the disorder. Machine learning (ML) methods hold particular promise to address this problem. Over the last decade, ML methods applied to complex biomedical data have generally outperformed classical regression approaches, suggesting that multi-dimensional modeling of genetic, biological and psychosocial data may best reflect the underlying pathophysiology of AUD. Thus, in this project, we will leverage innovative ML methods, especially those based on deep and ensemble learning, and the rich COGA data to develop multi-modal predictive models of vulnerability to the disorder. Furthermore, the majority of the AUD predictive modeling work has been conducted in EA populations, necessitating increased studies among underrepresented groups, including AA and females, so that the benefits of precision medicine can reach all populations. Therefore, we will conduct our predictive modeling analyses in subgroups stratified by age, sex, and ancestry (AA, EA). We will also rigorously evaluate the developed predictive models in an independent validation set, stratified based on the same criteria. Finally, we will employ systematic interpretation strategies for the models to identify EEG, genetic (SNP, polygenic risk scores), psychosocial, psychiatric and demographic features that contribute most strongly to accurate AUD prediction. At the conclusion of the secondary analysis-oriented work outlined in this proposal, we expect to have identified an accurate, generalizable multi-modal predictive model of vulnerability to AUD, as well as identified features that are associated with this serious disorder. Our work is likely to contribute to a deeper understanding of this major public health challenge, as well as its personalized diagnosis and treatment.

项目概要根据国家调查，酒精使用障碍（AUD）是美国和世界范围内的一项重大公共卫生挑战。根据《药物使用与健康》报告（2018 年），1440 万 18 岁及以上的成年人拥有澳元，其中包括 920 万男性。此外，2014 年，酒精驾驶导致 9,967 人死亡。尽管它很重要，但尚未进行太多研究来确定易感生物因素。可能导致 AUD 发展的因素，而预测模型已取得成功。区分 AUD 个体和健康对照者，模型提前识别个体是否是否容易发生 AUD，以及指示 AUD 易感性的生物标志物仍不清楚。为了解决这个问题，欧洲美国人（EA）和酒精中毒遗传学合作研究（COGA）非裔美国人 (AA) 招募了 8-68 岁的受试者，对他们进行纵向跟踪和评估 AUD 还对受试者进行了 30 多年的电生理学 (EEG)、单核苷酸方面的评估。多态性（SNP）、社会心理和精神病学评估以及人口调查问卷的目标。我们提出的研究是对 COGA 丰富的多模式纵向数据进行二次分析，以开发预测模型可以在个人实际发展之前准确预测澳元的脆弱性机器学习 (ML) 方法特别有希望解决这个问题。十年来，应用于复杂生物医学数据的机器学习方法普遍优于经典回归方法，表明遗传、生物和心理社会数据的多维建模可能是最好的反映 AUD 的潜在病理生理学，因此，在这个项目中，我们将利用创新的 ML 方法，尤其是基于深度学习和集成学习，以及丰富的COGA数据来开发多模态此外，大多数 AUD 预测模型。工作已在 EA 人群中进行，因此需要加强对代表性不足群体的研究，包括AA和女性，让精准医疗的好处惠及所有人群。因此，我们。我们将按年龄、性别和血统（AA、EA）进行亚组的预测模型分析。还将在独立的验证集中严格评估开发的预测模型，基于分层最后，我们将采用系统的模型解释策略来识别脑电图，遗传（SNP、多基因风险评分）、社会心理、精神病学和人口特征影响最大强烈要求准确的澳元预测。提案中，我们希望能够确定一个准确的、可推广的多模态脆弱性预测模型澳元，以及与这种严重疾病相关的已识别特征。有助于更深入地了解这一重大公共卫生挑战及其个性化诊断和治疗。