According to a story from Healio, chronic kidney disease and diabetes are closely related. Kidney disease is unfortunately a frequent complication of both type 1 and 2 diabetes. In the US, it is considered the most common cause of chronic kidney disease, about a quarter if diabetes patients are affected. A new study, utilizing biopsy samples and machine learning technologies, aims to learn more about the progression and development of chronic kidney disease that can be linked to diabetes.
About Chronic Kidney Disease
Chronic kidney disease is an illness in which the functionality of the kidneys is affected over time. This progressive disease can occur over a period of months or years and often results in eventual kidney failure. This disease often causes no symptoms at first. There are a number of risk factors for chronic kidney disease, such as diabetes, glomerulonephritis, family history, and high blood pressure. The cause is not known in all cases. As the disease progresses, symptoms such as leg swelling, confusion, fatigue, vomiting, loss of appetite, heart disease, bone disease, anemia, and high blood pressure may appear. Treatment may include dietary changes, certain medications, and, in later stages, dialysis or kidney transplant. The most common cause of death for people with chronic kidney disease is cardiovascular disease, which may appear before the kidneys completely shut down. To learn more about chronic kidney disease, click here.
Healio conducted an interview with Dr. Katalin Susztak, the principal investigator of the study, to learn more about its methods and goals.
What are some of the challenges in developing effective therapeutics for diabetic chronic kidney disease?
There is a lack of understanding about the genetics surrounding the disease; many of the drugs used to treat it were developed for other indications and happened to be effective; however, why they work isn’t entirely understood. Another limitation is that animal models generally do not work well for studying diabetic chronic kidney disease.
What led to the development of the TRIDENT study?
There was broad institutional agreement that the disease was not well understood. We chose to enroll patients who had their diagnosis confirmed with a biopsy, data that will play a central role in the study method going forward. The study will entail gathering genetic data through whole exome sequencing in an attempt to predict who will progress to kidney failure and identify contributing factors as well as which patients could respond to targeted treatments.
How will this trial help you and your colleagues gain a better understanding of the pathogenesis of DCKD?
We already understand that the rate of progression differs among patients, and some never experience organ failure. The study aims to identify specific drivers of the disease using machine learning algorithms. With this information we aim to identify or develop drugs that target these drivers or incorporate them into animal models. We aim to enroll at least 200 patients.
For more in-depth information, click here.
