The study, published in Nature Medicine, analyzed molecular data from 5,983 participants across India, Australia, Canada, Denmark, Hong Kong, New Zealand, and the United States to develop the DRS4C tool
Australian researchers have developed an innovative artificial intelligence (AI)-powered tool to transform the diagnosis and treatment of type 1 diabetes (T1D) by accurately predicting disease risk and treatment response. The breakthrough comes from Western Sydney University, where scientists created the Dynamic Risk Score (DRS4C), an AI-based risk assessment tool that classifies individuals as having or not having T1D. This dynamic tool leverages microRNAs—small RNA molecules measured from blood samples—to capture the fluctuating risk of developing T1D in real time.
“T1D risk prediction is timely, as therapies that can delay disease progression are becoming more available,” said Professor Anand Hardikar, lead investigator from the School of Medicine and Translational Health Research Institute at Western Sydney University. “Early-onset T1D, especially before age 10, is aggressive and linked to up to 16 years of reduced life expectancy. Accurately predicting progression provides clinicians a powerful tool to intervene sooner.”
The study, published in Nature Medicine, analyzed molecular data from 5,983 participants across India, Australia, Canada, Denmark, Hong Kong, New Zealand, and the United States to develop the DRS4C tool. The AI-enhanced risk score was then validated on an additional 662 participants, showing its robustness and predictive power.
Notably, the DRS4C score predicted which individuals with T1D would remain insulin-free just one hour after initiating therapy. Beyond assessing risk, the score also helps predict drug efficacy, allowing for more personalized treatment plans. Additionally, the tool shows promise in distinguishing T1D from Type 2 diabetes, a critical diagnostic challenge.
Dr. Mugdha Joglekar, the study’s lead researcher, highlighted the importance of dynamic risk markers over genetic testing. “Genetic markers provide a static view, similar to knowing you live in a flood zone. But dynamic risk scores offer a real-time assessment, reflecting current risk levels and allowing for adaptive, timely monitoring without stigma,” she explained.
This advancement addresses a crucial gap in diabetes care, where early and precise risk prediction can significantly improve patient outcomes. The DRS4C tool, by providing real-time risk updates, empowers healthcare providers to initiate interventions earlier, potentially delaying disease progression and improving quality of life.
The researchers emphasized the global nature of their study, which incorporated diverse populations, enhancing the tool’s applicability worldwide. As T1D affects millions globally, innovations like the AI-powered DRS4C mark a significant step toward personalized medicine in diabetes management. Professor Hardikar added, “Our goal is to equip clinicians with reliable tools that not only predict who is at risk but also guide treatment decisions based on individual responses. This technology has the potential to change how T1D is diagnosed, monitored, and treated, improving long-term outcomes.”
The integration of AI and molecular biology in this study exemplifies the future of medical diagnostics, where continuous monitoring and precision risk assessment become standard practice.
