Towards Personalized Medicine: Unraveling Biomarkers in Gestational Diabetes for Predicting Diabetes Subtypes and Prevention

Gestational diabetes mellitus is transient hyperglycemia during pregnancy that can have negative consequences for the mother and the child in both the short and long term. GDM shares many similarities with type 2 diabetes (T2D). Gestational diabetes mellitus (GDM), like type 2 diabetes (T2D), does not look the same for everyone. Different people can experience it in different ways because it can result from a mix of factors like genetics, lifestyle, or hormonal changes during pregnancy. This variation in how it manifests is called "heterogeneous presentation." It means that the symptoms, severity, and underlying causes can vary widely from person to person. The specific goals of the project focus on understanding the genetic basis of GDM as well as carefully analyzing the heterogeneity of the disease.

We will investigate whether a previous occurrence of GDM can predict predisposition to diabetes and risk of complications. Furthermore, we will identify gene expression networks for the subtypes, which will provide valuable clues to the underlying pathophysiology, as well as identify biomarkers specific to the subtypes in question. With this, we aim to be able to create a web resource for the prediction of GDM to facilitate individual-based treatments.

It has been shown that women with GDM as well as their offspring have a high risk of developing T2D later in life. GDM creates an "overnutrition environment" in the womb, which can alter fetal programming and make them more vulnerable to cardiovascular disease and diabetes later in life. We will therefore also investigate signs of fetal programming in the offspring of mothers with GDM. The overall goal is to identify women at high risk of developing T2D, in order to tailor "personal" treatment and individualized care.