Predicting Kidney Disease in People with Diabetes
By Becca Kranz
Discover how a new predictive tool may forecast the risk of chronic kidney disease in type 2 diabetes patients, as well as its benefits, limitations, and potential for early diagnosis.
Type 2 diabetes raises the risk for chronic kidney disease (CKD), which affects roughly 40% of people with diabetes. In many cases, CKD symptoms go unnoticed until the disease has progressed to a more severe stage, which is why it’s so important to catch and treat it early on.
A recent study validated a new tool that can predict changes in kidney function over time. Using data obtained during primary care visits, the prediction tool could help healthcare professionals guide and plan treatment for patients.
Given the prevalence and potential severity of CKD, a tool that would assist in better predicting the likelihood of developing CKD would benefit many people with diabetes and help them better manage their kidney care.
Why do we need predictive models for chronic kidney disease?
Kidney disease can occur in anyone, however, people with diabetes, high blood pressure, or a family history of kidney issues are at an increased risk. CKD is a progressive disease, meaning it worsens over time. Without treatment or in its most advanced stages, CKD can eventually result in the need for dialysis or a kidney transplant.
It’s important for clinicians to monitor kidney function in people with diabetes because CKD often presents without symptoms until the disease becomes advanced. In fact, approximately 90% of people in the U.S. with CKD don’t even know they have it, according to the National Kidney Foundation.
Healthcare professionals learn about how your kidneys are functioning by testing levels of common substances in the blood and urine. Higher-than-normal levels of certain substances can indicate kidney problems.
With this new predictive model, researchers from the Biomarker Enterprise to Attack Diabetic Kidney Disease Consortium assessed trends in kidney function over time by measuring what’s called the estimated glomerular filtration rate (eGFR). In addition to other tests, healthcare professionals use eGFR to monitor changes in kidney function over time, which may indicate CKD.
What are the limitations of this model?
The researchers who developed this model recognize several limitations and encourage further research to improve its accuracy. One limitation is that participants came from six European countries and were all white. Researchers recommended that future studies include participants of diverse backgrounds and from different countries.
Another limitation is that participants were recruited in 2010 and CKD treatments have improved dramatically since then. This specific predictive model also does not account for whether patients take medication for CKD, which is an important parameter that should be included in future versions.
“The model has been developed correctly, and the variables are reasonable and relevant,” said Dr. Susanne Stolpe, a research associate at Essen University Hospital in Germany who specializes in CKD and heart disease. “However, unless all the information is available, I’m not sure how beneficial it can be.”
What does this mean for people with diabetes?
While not a substitute for medical consultation, this research model is now available as an online tool where anyone (including people with diabetes and healthcare professionals) can enter variables and see predictions of future eGFR for up to five years.
That said, it’s important to remember that this model is still a work in progress. If you want to know more about trends in your kidney function, it’s best to talk to a healthcare professional about what screening tools and tests they recommend, plus what the results mean for you.
People who receive an early diagnosis of CKD can often slow its progression with lifestyle changes such as diet, exercise, and medication.
In this way, predictive models can be seen as tools to empower people with diabetes and CKD to learn more about their condition and take control of their health.
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