Scientists have developed a conceptual approach to predict whether a person with type 2 diabetes will develop kidney disease. The study was published in the journal Nature Communications. The approach can help doctors prevent or better manage kidney disease in people with type 2 diabetes.
Co-senior author Kevin Yip, Ph.D., professor and director of Bioinformatics at Sanford Burnham Prebys, said, “This study provides a glimpse into the powerful future of predictive diagnostics. Our team has demonstrated that by combining clinical data with cutting-edge technology, it’s possible to develop computational models to help clinicians optimize the treatment of type 2 diabetes to prevent kidney disease.”
"There has been significant progress developing treatments for kidney disease in people with diabetes," says co-senior author Ronald Ma, MB BChir, FRCP, a professor in the Department of Medicine and Therapeutics at the Chinese University of Hong Kong. "However, it can be difficult to assess an individual patient's risk for developing kidney disease based on clinical factors alone, so determining who is at greatest risk of developing diabetic kidney disease is an important clinical need.”
The new algorithm depends on measurements of 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 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," says 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.
The researchers are currently working to further refine their model. They are also expanding the application of their approach to looking at other questions about human health and disease--such as determining why some people with cancer don't respond well to certain treatments.