New algorithm can predict diabetic kidney disease
A simple blood sample could help doctors catch kidney disease earlier in type 2 diabetes patients
Researchers from The Chinese University of Hong Kong (CUHK) and Sanford Burnham Prebys have developed a computational approach to predict whether a person with type 2 diabetes will develop kidney disease, a frequent and dangerous complication of the condition. Their results, published in Nature Communications, could help doctors prevent or better manage kidney disease in people with type 2 diabetes.
Diabetes is the leading cause of kidney failure worldwide. In Asia, about 50% of cases of end stage kidney disease and dialysis are due to diabetes.
“There has been significant progress developing treatments for kidney disease in people with diabetes,” said co-senior author Professor Ronald Ma, S.H. Ho Professor of Diabetes and Head (Academic Affairs), Division of Endocrinology and Diabetes, Department of Medicine and Therapeutics at CUHK’s Faculty of Medicine (CU Medicine). “However, it can be difficult to assess an individual patient’s risk of 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.”
“This study provides a glimpse into the powerful future of predictive diagnostics,” said co-senior author Professor Kevin Yip, Professor and Director of Bioinformatics at Sanford Burnham Prebys, and Professor (by Courtesy) at CUHK’s Hong Kong Institute of Diabetes and Obesity. “Our team has demonstrated that by combining clinical data with cutting-edge technology, it’s possible to develop computational models that help clinicians optimise the treatment of type 2 diabetes and prevent kidney disease.”
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 of kidney disease,” said Professor 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 that improve our understanding of diabetes and its complications,” said study co-author Professor Juliana Chan, Chair Professor of Medicine and Therapeutics at CU Medicine, who established the Hong Kong Diabetes Register more than two decades ago.
“The Hong Kong Diabetes Register is a scientific treasure,” added first author Dr Kelly Li Yichen, a postdoctoral scientist at Sanford Burnham Prebys. “It follows up with patients for many years, giving us a full picture of how human health can change over decades in people with diabetes.”
The researchers are currently working to further refine their model. They are also expanding the application of their approach to incorporate other data that can further enhance their ability to predict other diabetes-related outcomes.
“Our collaboration with experts in clinical diabetes, computational science and bioinformatics started in Hong Kong,” added Professor Ma. “We are delighted that the findings of this study could improve future care and make it easier to determine who will benefit most from these new treatments to prevent kidney damage from diabetes. The science is still evolving, but we are working on incorporating additional information into our model to further empower precision diabetes medicine.”
The study was supported by grants from The Hong Kong Research Grants Council Theme-based Research Scheme and Research Impact Fund, with additional support from the Research Grants Council, National Institutes of Health, the Croucher Foundation and CUHK. The project team have already filed a patent related to their invention.
The study’s DOI is 10.1038/s41467-023-37837-7