# Data Integration Core

> **NIH NIH U54** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2020 · $44,092

## Abstract

The scientific goal of the Medical University of South Carolina Transdisciplinary Collaborative Center (MUSC
TCC) is to conduct translational research to understand the dynamic interaction between biological, social,
psychological, behavioral, and clinical factors and health care and disease outcomes to determine the most
effective ways to integrate these data into precision medicine approaches to promote health equity using
allostatic load (AL) as a framework. To do this, it is essential to develop robust strategies and methods to
harmonize diverse types of data on key biologic factors as well as data obtained by patient self-report and
electronic health records. The Data Integration Core will create new knowledge about allostasis and
estimation of allostatic load by integrating data from projects within the MUSC TCC with supplemental
information about individuals and populations. Specifically, the Data Integration Core will create
databases/registries that bring together the diverse types of data generated in bench experiments with clinical
measurements derived from the electronic health record (EHR) and EHR data on evolution of diseases over
time. This resource will be generated as part of the following specific aims: 1) Create a standards-based
resource using nationally-recognized tools including REDCap and Informatics for Integrating Bench to
Bedside(i2b2) for integration of data on the tumor micro environments derived from proteoglycan analyses of
prostate tissue and clinical studies of impacts of glucocorticoids on pathways for vitamin D effects; 2) Integrate
clinical and experimental data into longitudinal patient records to expand data sets to represent the
chronological order of significant clinical and social events surrounding the timing of a critical cancer diagnosis;
3) Develop natural language processing-based tools to extract discrete details on social stressors from
clinicians’ notes and merge these data with clinical data within the i2b2 environment; and 4) Use data mining
strategies to determine the temporal links between AL and disease risk and outcomes.

## Key facts

- **NIH application ID:** 9900596
- **Project number:** 5U54MD010706-05
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** LESLIE A. LENERT
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $44,092
- **Award type:** 5
- **Project period:** — → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9900596

## Citation

> US National Institutes of Health, RePORTER application 9900596, Data Integration Core (5U54MD010706-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9900596. Licensed CC0.

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