# Development and Validation of a Non-Invasive Microbiome-Based Diagnostic Tool for Early Detection of Gestational Diabetes Mellitus

> **NIH NIH R43** · BROOKLYN INNOSEQ INC. · 2024 · $296,351

## Abstract

PROJECT SUMMARY
Brooklyn InnoSeq (doing business as MaMome) is developing a microbial screening test for the early detection
of Gestational Diabetes Mellitus (GDM) to reduce the risk of adverse pregnancy outcomes. GDM is defined as
diabetes diagnosed within the mother in the second or third trimester of pregnancy provided that overt diabetes
early in pregnancy has been excluded [1]. GDM is the most common medical complication and metabolic
disorder of pregnancy with a reported incidence of 18% among pregnant women worldwide [1-4]. The risk of
gestational diabetes increases with each pregnancy. The current diagnosis standard for GDM is a 2-step Oral
Glucose Tolerance Test (OGTT) administered between weeks 24-28 of gestation [5]. However, the OGTT
methodology is a diagnostic tool for GDM and not a tool for the prevention of this disease. Currently, there are
no effective clinical tools to accurately predict GDM or to detect latent Type II diabetes in early pregnancy as
many women do not receive screenings before they are pregnant [6]. One of our primary innovations has been
in the identification of microbial biomarkers associated with GDM in earlier pregnancies through microbiome
profiling and machine learning technologies. These biomarkers will provide the basis for developing a clinically
viable non-invasive microbiome screening test to predict GDM development significantly earlier than the
current OGTT method. The sampling procedure of our screening test does not require any significantly
invasive measures, extending its utilization not just in hospitals with healthcare providers, but also to the home
via a user-friendly sampling kit. With this approach, we can initiate the appropriate interventions and
therapeutics to prevent GDM and subsequent associated complications. Our previous study, consisting of 66
pregnant women, has identified 5 bacteria in the first trimester that are predictive of GDM. Our Phase I project
aims to validate these biomarkers with an independent cohort and to refine machine learning approaches
based on a newly enrolled cohort. In aim 1, we will prospectively enroll 250 pregnant women, collecting stool
samples at the first trimester and at the OGTT diagnosis window. This will be followed by metagenomic whole
genome shotgun sequencing (mWGS) to identify microbiome species and (or) microbiome genes that are
associated with GDM. In aim 2, we will develop a predictive model for GDM using machine learning
approaches. Our ultimate goal is to create a predictive microbiome screening test to detect GDM within
Trimester 1. With approximately 4.6 million annual births in the United States [7], patients and physicians
would benefit greatly from more accurate, non-invasive tests at home before GDM is established. We will first
target OB/GYNs to use our screening test, focusing on developing relationships in the East Coast US markets
as well as partnering with global distributors to enter a larger market.

## Key facts

- **NIH application ID:** 10921940
- **Project number:** 1R43HD115494-01
- **Recipient organization:** BROOKLYN INNOSEQ INC.
- **Principal Investigator:** Nini Fan
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $296,351
- **Award type:** 1
- **Project period:** 2024-07-22 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10921940

## Citation

> US National Institutes of Health, RePORTER application 10921940, Development and Validation of a Non-Invasive Microbiome-Based Diagnostic Tool for Early Detection of Gestational Diabetes Mellitus (1R43HD115494-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10921940. Licensed CC0.

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