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Study Uncovers Potential Urine Marker for Chronic Kidney Disease Progression

NEW YORK (GenomeWeb) – A University of Michigan-led team has tracked down a urine biomarker that appears to coincide with progression to serious, end-stage disease in those with chronic kidney disease.

Starting with kidney biopsy samples from more than 150 individuals, the researchers used array-based expression profiling to narrow in on transcripts whose expression coincided with kidney function, as measured by each individual's baseline estimated glomerular filtration rate (eGFR), validating three of these markers through targeted testing in two groups of patients with chronic kidney disease.

From there, the team looked at whether protein products of these transcripts in the urine also coincided with kidney injury or baseline eGFR. Indeed, it found a set of urine markers — most notably epidermal growth factor protein — successfully predicted end-stage kidney disease and/or a significant decline in baseline eGFR levels in three different groups of patients with chronic kidney disease. Results of the study appeared online today in Science Translational Medicine.

"Our approach identified [epidermal growth factor protein in urine] as an independent risk predictor of [chronic kidney disease] progression," senior author Matthias Kretzler, an internal medicine, computational medicine, and bioinformatics researcher at the University of Michigan, and his co-authors wrote.

"Addition of uEGF to standard clinical parameters improved the prediction of disease events in diverse [chronic kidney disease] populations with a wide spectrum of causes and stages," they added.

As ever more chronic kidney disease-affected individuals go on to develop more serious complications such as heart disease, end-stage kidney disease, and death, the team reasoned that there would be a benefit to being able to proactively find those at risk of such progression.

With that in mind, the researchers began by using Affymetrix GeneChip arrays to measure transcript levels in renal biopsy samples from 164 European individuals with known kidney function, as measured by baseline eGFR.

The search yielded candidate transcripts coinciding with 72 genes, which the team tested by qRT-PCR in samples from 55 chronic kidney disease patients from Europe.

From that first validation cohort, the researchers whittled the candidate marker set down to half a dozen transcripts showing potential ties to baseline eGFR.

When they tested those transcripts in samples from 42 North American chronic kidney disease patients, the most pronounced associations involved transcripts for the EGF, NNMT, and TSMB10 genes.

After prioritizing potential kidney function markers based on their biology and other features, the team focused on EGF, which codes for an epidermal growth factor protein that's expressed in the kidney tubule and involved in cell differentiation and regeneration.

In matched urine and kidney biopsy samples from more than 100 individuals with chronic kidney disease from the European and North American cohorts, the team found that urine levels of the EGF protein coincided with the EGF gene's transcript levels in kidney tissue.

Moreover, the researchers saw a significant correlation between kidney function and levels of the EGF protein in urine when they tested urine samples alone for almost 350 chronic kidney disease patients from North America, 141 individuals enrolled through a cohort called the Nephrotic Syndrome Study Network, and 452 individuals enrolled through a prospective kidney study in China.

And their subsequent analyses hinted that accounting for urine levels of EGF protein may increase the accuracy of models for predicting poor renal function and chronic kidney disease progression.

"Addition of [epidermal growth factor protein in urine] into a [chronic kidney disease] biomarker panel will likely improve risk stratification of [chronic kidney disease] patients," the authors concluded, "and thereby enhance the ability to target clinical care and limited healthcare resources to those in most need, as well as to optimize clinical trial design."