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来源 学习。 DSC 组建了一支高素质的研究人员团队，他们在以下领域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、不同组学数据的整合 和神经影像数据、数据管理、数据安全、机器学习/人工智能 (ML/AI) 以及 分析。 DSC 还将与其他地区的培训项目合作提供培训服务 这个 P01 的组件。除了支持 P01 中拟议的两项干预研究外，DSC 将利用现有数据资源来测试重要假设并建立预测模型 为酗酒者治疗艾滋病毒感染的个性化推荐工具。当 项目1和2的数据已可用，跨队列预测和个性化推荐工具将可用 采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 将提供研究设计、数据管理、数据共享、统计分析和研究方面的支持 传播，以确保这两个研究项目的适当和有效进行。与 行政核心和两个项目团队，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；产生 随机化方案；设计和实施数据收集/处理的质量控制程序；火车 现场工作人员使用数据收集和数据管理系统；提供数据脱敏、数据 协调和数据共享。根据三十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、神经影像学和微生物组生物标志物的变化以及神经认知的变化 功能。该目标将建立一个基线预测模型来预测干预后酒精使用的变化 机智基线信息。多尺度动态建模将用于集成体素级、组织级、 用于预测戒酒的区域级和脑叶级神经影像信息。我们还将确定 与酒精水平相关的多模式神经影像和微生物组生物标志物的关键变化 节制。基线特征对神经认知功能变化的直接影响及其间接影响 将评估酒精使用、神经影像学和微生物组生物标志物变化带来的影响，并 已测试。我们的目标 3 将使用两项新的随机临床试验的数据来验证和完善预测 Aim 2 中开发的模型并构建个性化干预推荐工具。跨队列验证 将使用既定方案和汇总数据在两项新临床试验中进行 两项试验旨在验证和完善预测饮酒减少的基线预测模型。纵向 将采用跨队列学习来创建跨三个研究项目的统一预测模型 构建个性化干预推荐工具。