New Delhi, May 15 A team of researchers has developed an AI-based approach to predict whether a person with Type 2 diabetes will develop kidney disease, a frequent and dangerous complication of diabetes.
The study by researchers from Sanford Burnham Prebys in the US and the Chinese University of Hong Kong, published in Nature Communications, could help doctors prevent or better manage kidney disease in people with type 2 diabetes.
"Our team has demonstrated that by combining clinical data with cutting-edge technology, it's possible to develop computational models to help clinic optimise the treatment of Type 2 diabetes to prevent kidney disease," said Kevin Yip, a professor and director of Bioinformatics at Sanford Burnham Prebys.
The new algorithm depends on measurements of a process called DNA methylation, which occurs when subtle changes accumulate in our DNA.
DNA methylation can encode important information about which genes are being turned on and off, and it can be easily measured through blood tests.
"Our computational model can use methylation markers from a blood sample to predict both current kidney function and how the kidneys will function years in the future, which means it could be easily implemented alongside current methods for evaluating a patient's risk for kidney disease," said Yip.
The researchers developed their model using detailed data from more than 1,200 patients with Type 2 diabetes in the Hong Kong Diabetes Register.
They also tested their model on a separate group of 326 Native Americans with Type 2 diabetes, which helped ensure that their approach could predict kidney disease in different populations.
"This study highlights the unique strength of the Hong Kong Diabetes Register and its huge potential to fuel further discoveries to improve our understanding of diabetes and its complications," says study co-author Juliana Chan, M.D., FRCP, a professor in the Department of Medicine and Therapeutics at the Chinese University of Hong Kong, who established the Hong Kong Diabetes Register more than two decades ago.
The researchers are currently working to further refine their model. They are also expanding the application of their approach to look at other questions about human health and diseasea"such as determining why some people with cancer don't respond well to certain treatments.
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