A recent study led by MedUni Vienna has identified significant variations in the progression of gestational diabetes (GDM), highlighting the need for personalized treatment strategies. Published in “Diabetologia,” the research conducted by an international team including experts from MedUni Vienna, Charité – Universitätsmedizin Berlin, and Consiglio Nazionale delle Ricerche Padua analyzed data from 2682 women with GDM spanning from 2015 to 2022.
Employing cluster analysis, a machine learning technique, researchers categorized patients into three distinct subgroups based on parameters such as age, pre-pregnancy body mass index (BMI), and blood glucose levels from oral glucose tolerance tests (OGTT). According to study leader Christian Göbl from MedUni Vienna’s Department of Obstetrics and Gynecology, these subgroups exhibited varied disease presentations and differing rates of pregnancy complications.
The first subgroup identified comprised women with elevated blood glucose levels, high rates of obesity, and a pronounced need for glucose-lowering medications. The second subgroup included women with moderate BMIs and elevated fasting blood glucose levels. The third subgroup consisted of women with normal BMIs but elevated blood glucose levels following OGTT. These distinctions underscored the necessity for tailored treatment approaches, including varying intensities of glucose-lowering medications and specific insulin regimens tailored to each subgroup’s needs.
“This study’s findings carry significant clinical implications,” noted Göbl, emphasizing the potential for personalized treatment strategies informed by machine learning models. The research marks a critical step towards enhancing our understanding of GDM and advancing targeted therapeutic interventions.
The study’s approach, rooted in machine learning, represents a pivotal application of artificial intelligence in healthcare, paving the way for further investigations aimed at optimizing treatment outcomes for GDM patients across different clinical profiles.
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