# Diabetes Prediction During Pregnancy and In Utero Using Pancreas MRI

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $613,253

## Abstract

PROJECT SUMMARY
The pancreas is smaller in individuals with diabetes and individuals at increased risk for developing diabetes,
suggesting that small pancreas size may convey risk for developing the disease. However, it is not known
whether individuals at risk for developing diabetes are born with a smaller pancreas or whether their pancreas
shrinks as part of the pathogenesis of the disease. Establishing the link between pancreas size and
development of diabetes is difficult, as the time course of diabetes progression is not well established and time
to progression can be long. However, pregnancy is a period of physiological beta cell proliferation that presents
a diabetogenic state with known and rapid onset. MRI can safely and noninvasively assay multiple aspects of
the maternal and fetal pancreas, including pancreas size as well as other markers of pancreas structure and
composition. Image acquisition and analysis will leverage our expertise assessing human pancreas size,
shape, fat content, and inflammation using multimodal quantitative MRI. We propose to perform longitudinal
MRI of the maternal pancreas over the course of pregnancy and postpartum and correlate imaging metrics with
diabetes development and metabolic phenotyping. We will also assess the capability of MRI to measure
pancreas growth in the fetus. Study participants will include mothers who not develop diabetes, mothers with
pregestational type 2 diabetes, and mothers who develop gestational diabetes during pregnancy. These
studies will establish the first model of maternal pancreas growth and multimodal imaging signature and their
interactions with diabetes. Our central hypothesis is that maternal pancreas growth will be altered by diabetes
and can be used to predict diabetes incidence in the mother. While the focus of this study is on the pancreas,
the images generated will encompass the maternal abdomen and entire fetus. Thus, the data generated will be
valuable datasets for secondary analysis of the interaction of diabetes with fetal development and maternal
liver, fat, and placenta dynamics over the course of pregnancy.

## Key facts

- **NIH application ID:** 10939572
- **Project number:** 1R01HD115565-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** JOHN MICHAEL VIROSTKO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $613,253
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10939572

## Citation

> US National Institutes of Health, RePORTER application 10939572, Diabetes Prediction During Pregnancy and In Utero Using Pancreas MRI (1R01HD115565-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10939572. Licensed CC0.

---

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