Scientists from Columbia University found a new algorithm that can analyze thousands of variants across the genome and estimate a person’s risk of developing chronic kidney disease.
The research is published in Nature Medicine and was conducted by Krzysztof Kiryluk et al.
Diabetes, high blood pressure, obesity, and certain medications, such as NSAIDs, are known to increase the risk of kidney disease.
Early detection of kidney disease could prevent many cases of kidney failure and reduce the need for transplant or dialysis, but the disease is often silent until it has caused significant kidney damage.
Genetic testing could offer a way to predict a person’s risk of kidney disease well before symptoms appear, but thousands of inherited variants are likely involved and most have only small effects.
In the study, the team described their method and tested it on 15 different groups of people, including those of European, African, Asian, and LatinX descent.
The algorithm analyzes variants of a gene called APOL1—known to be a common cause of kidney disease in people of African descent—and thousands of other kidney disease variants found in people of all ancestries.
Across all ancestries, people with the highest scores (in the top 2%) had triple the risk of kidney disease as the general population, equivalent to having a family history of kidney disease.
The study also confirmed that APOL1 was an important risk factor in people of African descent.
But even when APOL1 is present in an individual, other genes can increase or decrease the risk of developing chronic kidney disease.
This information may be important when new drugs being developed specifically for people with APOL1 become available.
More testing of the new prediction method is needed before it can be used in clinical settings.
For more information about kidney disease, please see recent studies about common eating habits that may harm your kidney health, and results showing why processed foods trigger chronic kidney disease.
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