Predicting Diabetic Kidney Disease with the Use of a Novel Algorithm

Scientists from Sanford Burnham Prebys and the Chinese University of Hong Kong say they have developed a computational approach to predict whether a person with type 2 diabetes will develop kidney disease. Their results, “DNA methylation markers for kidney function and progression of diabetic kidney disease,” published in Nature Communications, could help doctors prevent or better manage kidney disease in people with type 2 diabetes.

“This study provides a glimpse into the powerful future of predictive diagnostics,” notes co-senior author Kevin Yip, PhD, a professor and director of bioinformatics at Sanford Burnham Prebys. “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.”

Diabetes is the leading cause of kidney failure worldwide. In the U.S., 44% of cases of end-stage kidney disease and dialysis are due to diabetes. In Asia, this number is 50%.

“There has been significant progress developing treatments for kidney disease in people with diabetes,” adds co-senior author Ronald Ma, 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.”

Epigenetic biomarkers

“Epigenetic markers are potential biomarkers for diabetes and related complications. Using a prospective cohort from the Hong Kong Diabetes Register, we perform two independent epigenome-wide association studies to identify methylation markers associated with baseline estimated glomerular filtration rate (eGFR) and subsequent decline in kidney function (eGFR slope), respectively, in 1,271 type 2 diabetes subjects,” write the investigators.

“Here we show 40 (30 previously unidentified) and eight (all previously unidentified) CpG sites individually reach epigenome-wide significance for baseline eGFR and eGFR slope, respectively. We also developed a multisite analysis method, which selects 64 and 37 CpG sites for baseline eGFR and eGFR slope, respectively. These models are validated in an independent cohort of Native Americans with type 2 diabetes. Our identified CpG sites are near genes enriched for functional roles in kidney diseases, and some show association with renal damage.

This study highlights the potential of methylation markers in risk stratification of kidney disease among type 2 diabetes individuals.”

The new algorithm depends on 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,” says Yip.

Model based on detailed data

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,” points out says study co-author Julianna Chan, MD, 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 Hong Kong Diabetes Register is a scientific treasure,” adds first author Kelly Yichen Li, PhD, a postdoctoral scientist at Sanford Burnham Prebys. “They follow up with patients for many years, which gives 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 look at other questions about human health and disease—such as determining why some people with cancer don’t respond well to certain treatments.

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bioinformatics
diagnostics
therapeutics
biomarkers