New algorithm can detect patients at risk of kidney disease: Research

A new algorithm, according to a recent research, can analyse hundreds of mutations throughout the genome and determine a person’s risk of getting chronic kidney disease.

The findings were reported in the journal ‘Nature Medicine. “With this polygenic method, we can identify individuals at risk decades before the onset of kidney disease, and those at high risk may adopt protective lifestyle changes to reduce that risk,” said Krzysztof Kiryluk, MD, an associate professor of medicine and a physician-scientist in the division of nephrology at Columbia University’s Vagelos College of Physicians and Surgeons.

Early identification of kidney illness might avoid many cases of renal failure and lessen the need for transplantation or dialysis, but the disease is typically quiet until considerable kidney damage has occurred.

Genetic testing may be able to identify a person’s risk of kidney disease long before symptoms occur, but hundreds of hereditary variations are likely to be involved, and the majority have extremely minor impacts. To further complicate matters, certain genetic variations are more frequent in particular ethnicities than others.

“We can’t just look at one or two genetic variations in most populations and tell you what your risk is,” Kiryluk explained. “Thousands of varieties are most likely involved,” she continued.

Early identification of kidney illness might avoid many cases of renal failure and lessen the need for transplantation or dialysis, but the disease is typically quiet until considerable kidney damage has occurred.

Genetic testing may be able to identify a person’s risk of kidney disease long before symptoms occur, but hundreds of hereditary variations are likely to be involved, and the majority have extremely minor impacts. To further complicate matters, certain genetic variations are more frequent in particular ethnicities than others.

The study also indicated that APOL1 was a significant risk factor among African Americans. Even if an individual has APOL1, other genes can increase or reduce the likelihood of getting chronic kidney disease.

“APOL1 is an essential component of the picture for persons of African descent, but it is not the sole element,” Kiryluk explained. This information might be useful when new medications designed exclusively for persons with APOL1 become available.

“Individuals with APOL1 but low polygenic risk may not require particular therapies,” Kiryluk added, “since their risk may be equivalent to that of the general population.”

“In contrast, persons with the highest genetic risk—those with APOL1 and a high polygenic risk—may benefit the most from lifestyle adjustments or pharmacological therapy,” says the study’s lead author.

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Kiryluk adds that more testing of the novel prediction system is required before it can be employed in clinical settings.

The approach is being explored in eMERGE-IV, a big nationwide trial that will screen participants and provide further follow-up and lab testing for persons at high genetic risk. The project will look at how genetic testing for the new risk score influences clinical outcomes, such as lifestyle modifications and the rate of new kidney disease diagnoses.

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