Treatment options for
autosomal dominant polycystic kidney disease (
ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic
biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in
ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41
ADPKD patients to 189 healthy controls and identified 657
peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic
biomarker model based on the 142 most consistent
peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251
ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in
ADPKD included, but were not limited to markers previously associated with
acute kidney injury (AKI). The diagnostic
biomarker model was highly specific for
ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify
biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134
ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158
ADPKD patients. In conclusion, the performance of peptidomic
biomarker scores is superior to any other
biochemical markers of
ADPKD and the proteomic
biomarker patterns are a promising tool for prognostic evaluation of
ADPKD.