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

> **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2024 · $225,531

## Abstract

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 r...

## Key facts

- **NIH application ID:** 10910897
- **Project number:** 5P01AA029543-04
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Zhigang Li
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $225,531
- **Award type:** 5
- **Project period:** 2021-09-10 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10910897

## Citation

> US National Institutes of Health, RePORTER application 10910897, Data Science Core: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection (5P01AA029543-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10910897. Licensed CC0.

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