# Leveraging electronic medical records to perform large-scale diabetes pharmacogenomics among ancestrally diverse patient populations

> **NIH NIH R01** · HENRY FORD HEALTH SYSTEM · 2020 · $635,940

## Abstract

ABSTRACT
Diabetes mellitus is a modern day scourge, affecting an ever increasing proportion of individuals worldwide,
including 26 million Americans currently. Moreover, type-2 diabetes (T2D) disproportionately affects
historically disadvantaged U.S. minority groups, as evidenced by the much higher rates of disease and more
severe complications among African American individuals. Although there are multiple therapeutic classes of
oral medication available for treating T2D, metformin is currently recommended as the first-line therapy.
Metformin lowers blood glucose levels by reducing hepatic gluconeogenesis, improving skeletal muscle insulin
sensitivity, and limiting intestinal glucose uptake. It has also been shown to be an effective therapy for
preventing incident diabetes. Despite being one of the most frequently prescribed drugs worldwide, very little
is known about the biologic mechanism(s) through which metformin mediates its effect. This knowledge would
be of value therapeutically to better understand and predict treatment response. By extension, even less is
known about the activity of metformin among African American individuals, as few studies have included
substantial numbers of non-European population groups. This application will help rectify existing knowledge
gaps by studying a large and diverse patient population with T2D. Specifically, we will utilize electronic
medical record (EMR) data for large-scale diabetes pharmacogenomics. These data have the advantage of
being able to account for medication use and drug exposure over time; to provide substantial numbers of
individuals for combined and population group specific analyses; and to assess clinical end-points both
retrospectively and prospectively. In this application, we propose the following study aims: 1) To assess
whether there are differences in metformin treatment response by self-reported race-ethnicity and genetic
ancestry; 2) To use novel, gene-based association approaches to identify both shared and population group
specific genetic variants influencing metformin's effect on blood glycemia (i.e., HbA1c levels); and 3) To
replicate our findings in a separate group of patients and to include additional exploratory analyses to assess
whether the identified genetic variants influence diabetes-related microvascular events, macrovascular events,
and adverse drug reactions. The knowledge gained through this study will directly address the goals of Health
People 2020 – “achieve health equity, eliminate disparities, and improve the health of all groups.”

## Key facts

- **NIH application ID:** 9895775
- **Project number:** 5R01DK113003-04
- **Recipient organization:** HENRY FORD HEALTH SYSTEM
- **Principal Investigator:** Keoki Williams
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $635,940
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895775

## Citation

> US National Institutes of Health, RePORTER application 9895775, Leveraging electronic medical records to perform large-scale diabetes pharmacogenomics among ancestrally diverse patient populations (5R01DK113003-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9895775. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*