Western Sydney University researchers have led a global team to pioneer a new AI-powered tool to assess the risk of developing Type 1 Diabetes (T1D) and predict treatment responses, potentially changing how the disease is diagnosed and managed.
This innovative risk score, based on microRNAs – small RNA molecules measured from blood – could help accurately capture the changing risk of T1D. The same microRNA markers used in the study were able to accurately predict early response to certain treatments, such as a cell therapy (islet transplantation), as well as a drug therapy (imatinib) for T1D.
Published in Nature Medicine , the research analysed molecular data in 5,983 study samples from participants across Australia, Canada, Denmark, Hong Kong SAR China, India, New Zealand, and USA, to develop a Dynamic Risk Score (DRS4C) that can classify people as having or not having T1D.
By leveraging artificial intelligence, the researchers enhanced the risk score, which was validated in 662 other participants. Just an hour after therapy, the risk score predicted which individuals with T1D would remain insulin-free. The same set of microRNAs also identified responders and non-responders to a T1D drug therapy, before their treatment began.
In addition to T1D risk and drug efficacy prediction, another strength of this risk score is its potential to discriminate T1D from T2D.
Professor Anand Hardikar, lead investigator from the University's School of Medicine and Translational Health Research Institute, emphasised that current approaches to testing for Type 1 Diabetes (T1D) have remained largely unchanged for decades.
"For decades, the way we test for T1D has remained largely unchanged for the last several decades, relying on symptoms and biomarkers that often only appear at the start of the disease – meaning early warning signs can be missed," said Professor Hardikar.
According to the 2025 IDF Atlas, there are over 1.7 million Australians living with diabetes, including more than 135,000 with T1D.
"T1D risk prediction is timely, with therapies that can delay T1D progression becoming recognised and available. Since early-onset T1D before the age of 10 years is particularly aggressive and linked to up to 16 years of reduced life expectancy, accurately predicting progression gives doctors a powerful tool to intervene sooner," he added.
Professor Hardikar also acknowledged community concerns around genetic testing for Type 1 diabetes.
"Speaking with the T1D community and their families, we realised that many are hesitant to genetic risk assessments due to feelings of guilt. However, 80 per cent of T1D cases occur without a family history of T1D, highlighting a significant role of the environment."
Dr Mugdha Joglekar, lead researcher also from the School of Medicine and Translational Health Research Institute at the University, explained the difference between genetic and dynamic risk markers, adding that genetic testing offered a static view of risk.
"Genetic markers identify lifelong risk, it's like knowing you live in a flood zone, but dynamic risk scores offer a real-time check on the rising water levels; it reflects current risk rather than a lifelong sentence, allowing for timely and adaptive monitoring without stigma," said Dr Joglekar.
Beyond T1D, the risk score and modelling approach could have potential applications in other areas. A sub-analysis also demonstrated the potential to stratify Type 2 Diabetes individuals from those with T1D. This is an area that the team is looking forward to assessing, as many adults with T1D can be incorrectly diagnosed as T2D.
This landmark study, led by Western Sydney University, brought together insights from tissue and plasma, a wide range of ethnic and geographic backgrounds, and state-of-the-art AI technologies. It involved 79 researchers across 33 institutions in seven countries from four continents.
This research was funded through Breakthrough T1D (formerly JDRF Australia), Australian Research Council, National Health and Medical Research Council and The Leona M. and Harry B. Helmsley Charitable Trust, with support through Danish Diabetes and Endocrine Academy and Western Sydney University.
