New method developed by researchers from Aarhus University could be a significant step in the treatment of chronic kidney disease. The method can predict the progression of the disease, which could ensure better and more targeted treatment and reduce the need for frequent hospital visits.
Researchers from Aarhus University have developed a new method to predict which patients with chronic kidney failure are at risk of losing kidney function over time.
The method is based on an analysis of acid-base balances in urine samples, which can reveal early signs of acid buildup - a condition that can be harmful to kidney function.
"We discovered that the balance between different acid-base elements in urine samples from patients with chronic kidney disease differs significantly from healthy individuals. This led us to develop a calculation model where the relationship between several of the urine's acid-base elements could be associated with kidney function and disease progression over time," explains Mads Vaarby Sørensen, PhD and researcher at the Department of Biomedicine, Aarhus University.
Reveals imbalances early
The new method allows doctors to detect acid buildup earlier than what is possible with current blood tests.
According to Mads Vaarby Sørensen, existing biomarkers can only measure acid buildup when it is severe enough to affect the blood's acid-base balance.
The new acid-base score can reveal imbalances in the urine much earlier in the process.
Another advantage of the new method is its precision.
"Our method has been tested in several independent cohorts and has proven to be very accurate, even when we analyze urine samples from the same patients over a longer period," explains Peder Berg, MD and postdoc at the Department of Biomedicine, Aarhus University.
Reduces need for hospital visits
The method has the potential to change how patients with chronic kidney disease are monitored and treated.
It can distinguish between patients with stable kidney function and those who rapidly lose kidney function.
Chronic kidney disease affects more than ten percent of the adult population and places significant demands on healthcare resources.
"The new method could potentially reduce the need for frequent check-ups for stable patients and free up resources for those with more aggressive disease progression," says Samuel Svendsen, medical resident at the Department of Nephrology at Aarhus University Hospital.
Individualized treatment
The research group is already in dialogue with several international partners to expand their research in the field.
In the short term, the researchers hope to validate the method in up to 4,000 patients in collaboration with major European and American research centers.
In the long term, the researchers hope that the new method can help individualize the treatment of kidney disease.
"If we can predict acid buildup earlier, we can intervene with acid-reducing treatment earlier, which could potentially extend the time patients can avoid dialysis," explains Samuel Svendsen.
A valuable tool in the future?
The method is not yet part of established clinical practice, but researchers have developed a device that can automatically measure the relevant markers in urine.
The device is designed to fit into the workflow of nephrology departments and is cost-effective to operate.
"If the method proves valuable for both patients and the healthcare system, it could become an important tool in the future treatment of chronic kidney disease," says Mads Vaarby Sørensen.
Behind the research findings:
Study type: Retrospective cohort study
Collaborators: Department of Nephrology, Aarhus University Hospital, Department of Nephrology, Rigshospitalet
External funding: Augustinus Foundation and Beta.Health
Conflict of interest: Mads Vaarby Sørensen, Peder Matzen Berg, Jens Leipziger, Henrik Birn, Niels Henrik Buus, and Samuel Levi Svendsen are inventors on a patent application from Aarhus University.
Link to scientific article: https://journals.lww.com/jasn/fulltext/9900/a_urine_ph_ammonium_acid_base_score_and_ckd.372.aspx
