Children who develop type 1 diabetes have shown epigenetic changes in their immune system already before the antibodies of the disease are detected in their blood. Two new analyses offer new opportunities to identify the youngsters with a genetic risk for developing diabetes very early on.
Environmental factors, such as viral infections, can cause epigenetic changes.
Two new experiments led by Turku Bioscience scientists at the University of Turku, Finland, demonstrated the findings in the genetic structure of diabetes.
A group leader in the InFLAMES research flagship initiative has discovered previously unknown, early-onset epigenetic changes. These are a way to further develop techniques to identify children who have a risk of developing type 1 diabetes even before they get sick.
Certain antibodies found in childrens blood samples demonstrate an increase risk of developing type 1 diabetes in the near future. Hence, medical professionals may intervene in the disease even sooner, with proper disease indicators than the antibodies are required to detect the danger. This involves searching for biomarkers that indicate type 1 diabetes, and epigenetic modifications may be beneficial.
ProfessorLaura Elo, the director of Turku Bioscience''s medical bioinformatics facility, and a group leader in the InFLAMES research flagship, believes that our efforts to develop methods and tools to prevent type 1 diabetes.
Children in Finland are at an increased risk of developing Type 1 diabetes.
Children''s risk of developing type 1 diabetes is at an all-time high in Finland. Environmental factors have a huge role in developing the disease, including for example, an excessive level of hygiene, biodiversity loss, and environmental toxins.
The newly published findings are based on a long-term interdisciplinary research collaboration with international partners. The project includes physicians who are in charge of the patients and also conduct clinical research, researchers in molecular medicine and immunology, and experts in computational science. In the interviews, researchers analysed longitudinal samples with deep sequencing covering the entire genome as well as with computational methods and artificial intelligence.