Data Science Core: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection

数据科学核心：改善 HIV 感染者肠脑轴酒精相关合并症的干预措施

• 批准号: 10682453
• 负责人: Zhigang Li
• 金额: $ 22.93万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682453
• 关键词: Alcohol consumption; Alcoholic beverage heavy drinker; Alcohols; Artificial Intelligence; Bacterial Translocation; Big Data; Biological Markers; Characteristics; Clinical Trials; Clinical Trials Design; Collaborations; Complement; Data; Data Analyses; Data Collection; Data Management Resources; Data Pooling; Data Science Core; Data Security; Databases; Development; Disease; Ensure; Equity; Ethics; Etiology; Faculty; Goals; HIV; HIV Infections; Health Policy; Heavy Drinking; Human Resources; Individual; Intervention; Intervention Studies; Intestinal permeability; Learning; Lobe; Machine Learning; Measurement; Mediation; Methodology; Methods; Modeling; Neurocognitive; Online Systems; Outcome; Pathogenicity; Pathway interactions; Patients; Persons; Prevention strategy; Procedures; Protocols documentation; Public Health; Public Policy; Publications; Qualifying; Quality Control; Randomized; Recommendation; Research; Research Design; Research Personnel; Research Project Grants; Resources; Sample Size; Scheme; Series; Services; Site; Source; Statistical Data Interpretation; System; Techniques; Testing; Thinness; Tissues; Training; Training Programs; Translational Research; Validation; Wit; Work; alcohol abstinence; alcohol effect; alcohol measurement; clinical practice; cohort; comorbidity; data harmonization; data management; data resource; data sharing; deep learning; design; electronic data capture system; experience; gut microbiome; gut-brain axis; improved; intervention effect; microbial; microbiome; multimodal neuroimaging; neuroimaging; novel; personalized intervention; population health; power analysis; predictive modeling; predictive tools; randomized, clinical trials; reduced alcohol use; research data dissemination; risk mitigation; statistical learning; success; systemic inflammatory response; tool; trial design

The Data Science Core (DSC) will provide critical support for the P01 project as a whole to ensure its success by offering a central source related to research design, data management, statistical analysis and machine learning. The DSC has assembled a team of highly qualified investigators with a broad range of expertise in HIV research including design of clinical trials, statistical inference methods, integration of diverse -omics data and neuroimaging data, data management, data security, machine learning/artificial intelligence (ML/AI), and analytics. The DSC will also provide training services in collaboration with the training programs in other components of this P01. In addition to supporting the proposed two intervention studies in the P01, the DSC will leverage existing data resources to test important hypotheses and build prediction models and personalized recommendation tools for treating HIV infections for patients who are heavy drinkers. When the data from Projects 1 and 2 are available, cross-cohort prediction and personalized recommendation tool will be constructed with state-of-the-art statistical learning and machine learning techniques. Specifically, our aim one will provide support in study design, data management, data sharing, statistical analysis, and research dissemination to ensure proper and efficient conduct of the two research projects. Working closely with the Administrative Core and two project teams, this aim will carry out a series of tasks including (but not limited to): development of centralized study database and web-based Electronic Data Capture (EDC) system; generate randomization schemes; design and implement quality control procedures for data collection/processing; train site staff in the use of data collection and data management system; provide support in data masking, data harmonization, and data sharing. Based on the existing data from the Thirty-Day Challenge Study, our aim 2 will perform causal analysis and AI modeling to explore causal relationships between baseline characteristics, changes in alcohol use, changes in neuroimaging and microbiome biomarkers, and changes in neurocognitive functions. This aim will build a baseline prediction model to predict change in alcohol use after the intervention wit baseline information. Multi-scale dynamic modeling will be used to integrate voxel-level, tissue-level, region-level, and lobe-level neuroimaging information for prediction of alcohol abstinence. We will also identify the key changes in multimodal neuroimaging and microbiome biomarkers associated with levels of alcohol abstinence. Direct effects of baseline characteristics on changes in neurocognitive functions, and their indirect effects through changes in alcohol use, neuroimaging and microbiome biomarkers will be estimated and tested. Our aim 3 will use the data from two new randomized clinical trials to validate and refine prediction models developed in Aim 2 and build a personalized intervention recommendation tool. Cross-cohort validation will be conducted in each of the two new clinical trials using established protocols and in the pooled data of the two trials to validate and refine the baseline prediction models for predicting alcohol use reduction. Longitudinal cross-cohort learning will be employed to create a uniform prediction model across three research projects and build a personalized intervention recommendation tool.

数据科学核心（DSC）将为整个P01项目提供关键支持，以确保其成功 通过提供与研究设计、数据管理、统计分析和机器相关的中央资源， 学习DSC已经组建了一支高素质的调查人员团队，他们拥有广泛的专业知识， HIV研究包括临床试验设计、统计推断方法、多样性组学数据整合 神经影像数据、数据管理、数据安全、机器学习/人工智能（ML/AI），以及 分析学DSC还将与其他培训计划合作提供培训服务。 P01的组成部分。除了支持P01中拟议的两项干预研究外，DSC 将利用现有的数据资源来测试重要的假设并建立预测模型， 为重度饮酒者提供治疗艾滋病毒感染的个性化推荐工具。当 项目1和2的数据可用，跨队列预测和个性化推荐工具将 用最先进的统计学习和机器学习技术构建。具体来说，我们的目标一 将在研究设计、数据管理、数据共享、统计分析和研究方面提供支持 传播，以确保适当和有效地进行这两个研究项目。密切配合 行政核心和两个项目小组，这一目标将执行一系列任务，包括（但不限于）： 开发集中研究数据库和基于网络的电子数据采集（EDC）系统;生成 随机化方案;设计和实施数据收集/处理的质量控制程序;培训 研究中心工作人员使用数据收集和数据管理系统;在数据屏蔽、数据 协调和数据共享。根据30天挑战研究的现有数据，我们的目标2 将进行因果分析和人工智能建模，以探索基线特征之间的因果关系， 酒精使用的变化，神经成像和微生物生物标志物的变化，以及神经认知功能的变化。 功能协调发展的该目标将建立一个基线预测模型，以预测干预后酒精使用的变化 wit基线信息。多尺度动态建模将用于集成体素级，组织级， 区域水平和脑叶水平的神经影像学信息用于预测酒精戒断。我们还将确定 与酒精水平相关的多模式神经成像和微生物生物标志物的关键变化 禁欲基线特征对神经认知功能变化的直接影响及其间接影响 将评估通过酒精使用、神经成像和微生物生物标志物变化产生的影响， 测试.我们的目标3将使用两个新的随机临床试验的数据来验证和完善预测 目标2中开发的模型，并建立个性化的干预建议工具。跨队列验证 将在两项新的临床试验中使用已建立的方案和 两项试验验证和完善用于预测酒精使用减少的基线预测模型。纵向 跨队列学习将被用来创建一个跨三个研究项目的统一预测模型， 建立个性化干预推荐工具。