In a study published in the journal eBioMedicine, researchers identified a gene expression signature that could predict the development of type 1 diabetes.
The study, led by Prof. Laura Ello and Prof. Rita Lahisma from the University of Turku in Finland, aimed to identify transcriptional changes associated with disease progression in patients with newly diagnosed type 1 diabetes.
The researchers analyzed blood samples collected through the INNODIA global partnership. The expression signature of a specific gene has been found to be associated with the rapid progression of the disease.
Researchers explain: “One of the benefits of this predictive signature is the ability to intervene early in the disease process. This could help slow the progression of the disease and potentially prevent or delay the onset of symptoms. Another benefit would be improved monitoring of disease progression, which would allow more personalized treatment plans and better outcomes.” treatment of patients.
Type 1 diabetes is a complex autoimmune disease that destroys the pancreatic beta cells that produce insulin. The progression of the disease varies from person to person, and there is currently no way to predict individual outcome.
INNODIA is a global partnership of 31 academic institutions, 6 industry partners, a small foundation, and two patient organizations with the common goal of fighting type 1 diabetes.
INNODIA researchers studied samples and data from patients with newly diagnosed type 1 diabetes and their healthy first-degree relatives in Europe.
Analyzing RNA sequencing data from the blood of 92 participants, the team found elevated B cells and reduced neutrophils in patients with rapidly developing disease. In addition, the study identified a gene expression signature that can predict the development of type 1 diabetes and found an association between changes in gene expression and a positive result for ZnT8A autoantibodies (an independent marker for diagnosing type 1 diabetes).
Researchers have identified the signature of 16 genes that can predict the progression of the disease. The identification of such a predictive genetic signature can help determine more personalized clinical and treatment interventions for each patient.
Source: Medical Express
