Predicting kidney disease trajectories with a simple blood test

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited condition that can lead to kidney failure. Being able to accurately predict how the disease will progress is very important for selecting the right treatments and providing effective patient counseling. However, the currently available prediction tools aren’t very accurate and require MRI images or genetic exams, which are not always available.

A University of Cologne research team has developed a new method to identify biomarkers involved in ADPKD progression. The study, “Developing serum proteomics based prediction models of disease progression in ADPKD,” was published in Nature Communications.

In this study, researchers looked at proteins in the blood to see if they could do a better job of predicting disease progression. The team consists of scientists from Translational Nephrology (CECAD Cluster of Excellence for Aging Research) and the Center for Rare and Genetic Kidney Diseases Cologne (University Hospital Cologne) led by Professor Dr. Roman-Ulrich Müller, in collaboration with the Computational Biology of Aging group at the Center for Molecular Medicine Cologne (CMMC) led by Dr. Philipp Antczak. The work is the result of close collaboration between a clinician scientist, Dr. Sita Arjune, and a data scientist, Hande Aydogan Balaban.

Using mass spectrometry, the team obtained a report of proteins present—the proteome—in blood samples from patients of one of the largest well-characterized ADPKD cohorts worldwide. By integrating a novel dedicated robotic pipeline into this process, they were able to analyze more than 1,000 samples and build a proteome-based prediction model. They identified 29 proteins which are involved in the immune system, fat transport, and metabolism that are linked to how quickly kidney function declines each year.

“Our study shows that blood proteins can offer powerful clues about how fast a patient’s kidney function is likely to decline, potentially allowing for more personalized care in the most common genetic cause of kidney failure, ADPKD,” said Professor Dr. Müller. The proteomics data provide not only biomarkers, but also important information about the mechanisms driving ADPKD.

“By identifying specific proteins linked to disease progression, we’ve taken a meaningful step towards more accurate and earlier prediction, beyond what current clinical tools can provide,” Professor Dr. Müller adds.

The researchers now plan to evaluate how current therapeutic interventions influence the proteome patterns of patients and to develop novel proteome-based markers with the potential to enter and revolutionize routine clinical care.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited condition that can lead to kidney failure. Being able to accurately predict how the disease will progress is very important for selecting the right treatments and providing effective patient counseling. However, the currently available prediction tools aren’t very accurate and require MRI images or genetic exams, which are not always available.

A University of Cologne research team has developed a new method to identify biomarkers involved in ADPKD progression. The study, “Developing serum proteomics based prediction models of disease progression in ADPKD,” was published in Nature Communications.

In this study, researchers looked at proteins in the blood to see if they could do a better job of predicting disease progression. The team consists of scientists from Translational Nephrology (CECAD Cluster of Excellence for Aging Research) and the Center for Rare and Genetic Kidney Diseases Cologne (University Hospital Cologne) led by Professor Dr. Roman-Ulrich Müller, in collaboration with the Computational Biology of Aging group at the Center for Molecular Medicine Cologne (CMMC) led by Dr. Philipp Antczak. The work is the result of close collaboration between a clinician scientist, Dr. Sita Arjune, and a data scientist, Hande Aydogan Balaban.

Using mass spectrometry, the team obtained a report of proteins present—the proteome—in blood samples from patients of one of the largest well-characterized ADPKD cohorts worldwide. By integrating a novel dedicated robotic pipeline into this process, they were able to analyze more than 1,000 samples and build a proteome-based prediction model. They identified 29 proteins which are involved in the immune system, fat transport, and metabolism that are linked to how quickly kidney function declines each year.

“Our study shows that blood proteins can offer powerful clues about how fast a patient’s kidney function is likely to decline, potentially allowing for more personalized care in the most common genetic cause of kidney failure, ADPKD,” said Professor Dr. Müller. The proteomics data provide not only biomarkers, but also important information about the mechanisms driving ADPKD.

“By identifying specific proteins linked to disease progression, we’ve taken a meaningful step towards more accurate and earlier prediction, beyond what current clinical tools can provide,” Professor Dr. Müller adds.

The researchers now plan to evaluate how current therapeutic interventions influence the proteome patterns of patients and to develop novel proteome-based markers with the potential to enter and revolutionize routine clinical care.