Sidra Medicine contributes to breakthrough tool for early detection of Type 1 diabetes

Qatar Tribune

5 days ago

Tribune News Network
Doha
Sidra Medicine, a member of Qatar Foundation, has helped develop an artificial intelligence (AI) powered tool that predicts the risk of developing Type 1 Diabetes (T1D), enabling earlier diagnosis and more targeted care.
The tool uses microRNA signals in the blood to detect early biological changes linked to the disease.
Published in Nature Medicine, the study titled 'A microRNA-based dynamic risk score for type 1 diabetes' introduces a dynamic risk score based on microRNAs, which are tiny molecules in the blood that reflect changes in the body, including early stress in insulin-producing Beta cells. By analysing data from over 2,800 participants, the researchers found that these molecular signals could identify individuals at high risk of developing T1D long before symptoms appear.
Dr. Ammira Akil, principal investigator at Sidra Medicine and director of the Metabolic and Mendelian Disorders Translational Programme, who designed the study, said: 'This study marks a significant advancement in the way we understand and manage autoimmune diseases like Type 1 Diabetes. By combining microRNA profiling with artificial intelligence, we have developed a predictive risk score that can help identify individuals at highest risk, optimise treatment decisions and determine when to intervene. It is a powerful example of how AI and Machine Learning are transforming precision medicine into real-world clinical impact.'
Sidra Medicine's contribution to the study was led by the Mendelian and Metabolic Translational Research Programme under the Precision Genomics and Translational Omics Lab.
The team played a keyrole in the early laboratory work on primary human insulin-producing cells and conducted critical analyses that helped with the development and validation of the clinical risk score.
The study findings complement ongoing efforts at Sidra Medicine, including the DANNA1 cohort, a local foundational population-based registry of individuals with T1D in Qatar that underpin the upcoming national screening programme for early detection and prevention of Type 1 Diabetes.
Prof. Khalid Fakhro, chief research officer and chair of the Precision Medicine Programme at Sidra Medicine, said: 'The study findings demonstrate that the microRNA-based dynamic risk score can accurately differentiate between individuals with and without Type 1 Diabetes. It also predicts who may become insulin dependent after an islet cell transplant and identifies patients likely to respond to specific therapies - insights that current clinical markers cannot provide.
'Importantly, this assessment is achievable through a simple, minimally invasive blood test, making it highly promising for routine clinical use. The study also reflects our ongoing commitment to advancing the use of artificial intelligence as a powerful tool to drive earlier diagnosis, more precise treatments, and safer outcomes for patients.'
The next phase of the research will focus on validating the findings across broader and more diverse populations and assessing the integration of the dynamic risk score into clinical trials and early intervention strategies.
Sidra Medicine will continue to support these efforts through its research programmes and ongoing collaborations.
The groundbreaking study was made possible through a multinational collaboration involving experts from Sidra Medicine, Breakthrough T1D, the University of Western Sydney, and the PREDICT T1D Study Group.