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24-213-1
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Development and Validation of a Non-Invasive Microbiome-Based Diagnostic Tool for Early Detection of Gestational Diabetes Mellitus
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Dr. Andrea Shields
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Gestational Diabetes Mellitus (GDM) is the development of glucose intolerance during pregnancy and is a serious complication with an incidence of 18% among pregnant people in the US. Recent evidence suggests that the gut microbiome undergoes changes parallel to GDM development early in pregnancy. This creates an important opportunity to establish a microbiome-based early screening tool to predict and potentially prevent GDM. Hypothesis/Question: Can a report containing risk scores for GDM based on proprietary stool collection from pregnant patients during their first trimester be developed to send to healthcare professionals promptly? Aims / objectives: We aim to develop a screening tool for the prediction of GDM as early as trimester 1 (T1) before the recommended administration of Oral Glucose Tolerance Test. Through non-invasive gut microbiome testing, we will determine T1 gut microbiome signatures associated with GDM and, based on these findings, develop an Machine Learning-based prediction model for GDM. This would allow for preventative treatment strategies before the disease becomes symptomatic and its side effects on the pregnant person and child become irreversible. The project aim is to demonstrate that the T1 maternal gut microbiome is a valid early indicator of GDM development, and an ML predictive model based on the microbiome';s characteristics permits accurate prediction of GDM development.
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Diabetes
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Check with study contact
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Kayceety Mullaj. Telephone: 860-679-3837. Email: kmullaj@uchc.edu or Kristen Annis. Telephone: 860-679-3812. Email: annis@uchc.edu or Makayla Murphy. Telephone: 860-679-3331. Email: makmurphy@uchc.edu
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Enrolling/recruiting. For current recruitment status, please check with study contact.
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