# eMOM: enhanced Monitoring to Optimize Maternal Diabetes detection

> **NIH NIH U01** · YALE UNIVERSITY · 2023 · $323,305

## Abstract

Project Summary/Abstract
Over 250,000 women develop gestational diabetes mellitus (GDM) each year in the U.S,
affecting 6.4% of all pregnancies. Current methods for the diagnosis of GDM are not conducted
until the end of the second or early in the third trimester; however, it is possible dysglycemia
leading to both adverse maternal and neonatal outcomes are present well before this gold
standard screening. With the newest generation of continuous glucose monitors (CGM), the
ability to collect accurate data that does not require fingerstick calibration and can be
comfortably worn long enough to capture glucose dynamics is now feasible. The present
proposal seeks to longitudinally assess glycemia in healthy, non-diabetic pregnant women by
using blinded CGM data collection in 4-week intervals beginning between 6-12 weeks’
gestation. These data will allow for determination if a difference in mean sensor glucose levels
exists between women who develop GDM vs. those who do not using standard of care oral
glucose tolerance test (OGTT) screening. Furthermore, with the frequency of CGM data
collection, the optimal time to first detect the difference in mean sensor glucose levels can be
explored. Finally, more rigorous OGTTs will be conducted allowing for assessment of the
metabolic abnormalities that underlie the diagnosis of GDM including whether the dysglycemia
is due to insulin resistance or β-cell dysfunction. With the use of a consortium to conduct the
present study, a large cohort is feasible which will allow for assessment of factors that may
contribute to risk of dysglycemia and GDM, including race/ethnicity, pre-pregnancy BMI,
maternal age, parity and weight gain during pregnancy. The findings of this study may provide
a paradigm shift in how we diagnose GDM if blinded CGM can be used in place of an OGTT
and understanding of the metabolic alterations that lead to diagnosis of GDM.

## Key facts

- **NIH application ID:** 10701660
- **Project number:** 5U01DK123799-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Audrey Merriam
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $323,305
- **Award type:** 5
- **Project period:** 2019-09-20 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10701660

## Citation

> US National Institutes of Health, RePORTER application 10701660, eMOM: enhanced Monitoring to Optimize Maternal Diabetes detection (5U01DK123799-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10701660. Licensed CC0.

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