for ADPKD, we next tested whether the urinary proteome of ADPKD patients might reflect disease severity and progression. Since the ADPKD_142 model was generated to distinguish ADPKD from healthy controls with optimal accuracy, the diagnostic score is not expected to correlate well with disease severity. Nevertheless, the ADPKD_142 score correlated positively with total kidney volume, height adjusted total kidney volume and absolute annual TKV growth and negatively with GFR, but these correlations were weak. No correlation was found with STA 9090 web proteinuria and albuminuria. Since proteomic markers that correlate highly with disease severity may have been excluded from the diagnostic model due to their large variability within ADPKD patients, we next tested the abundance of all 5352 urinary peptides detectable in ADPKD samples for correlation with htTKV, which has been shown to be predictive of future GFR decline and the development of CKD stage III. The analysis was done in a randomly chosen set of 134 patients and validated in a set of 158 patients derived from both ADPKD cohorts. 99 peptides showed a correlation of.0.25/,20.25 with htTKV. Aiming at a classifier that has superior value in comparison to a single biomarker, we combined all 99 peptides in a linear model. When examining this linear model, the correlation with htTKV was 0.590 in the dataset that was used to identify these biomarkers and 0.415 in the independent validation set of 158 patients. 43 of the 99 peptides could be identified by tandem MS sequencing. Clearly prominent is the negative correlation of urinary collagen fragments with htTKV. Discussion This to the best of our knowledge the largest clinical proteomic study reported so far. We analyzed urine samples from a total of 1,048 patients to characterize the urinary peptidomic pattern of patients with relatively early disease stages of ADPKD. Compared to our initial report, we have identified a large number of additional peptides altered specifically in ADPKD and now provide extensive validation in an independent, large and well characterized ADPKD cohort. Insights into the pathways of the proteomic patterns are now becoming clearer and specific proteomic markers appear to associate with disease severity. Sequencing of naturally occurring peptides still represents a major challenge that frequently cannot be solved successfully. Nevertheless, we were able to identify over 200 peptides associated with ADPKD in the training cohort. This vast number of potential biomarkers is certainly to some degree representative of the disease, enabling the generation of initial hypotheses linking these biomarkers to pathophysiology. Interestingly, the proteomic pattern of ADPKD showed some overlap with proteomic changes during AKI, supporting the hypothesis that some of the pathways driving cyst growth in ADPKD are mechanisms normally active during acute kidney injury repair.Protein name Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain 
Collagen alpha-1 chain Collagen alpha-1 22884612 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen 14871500 alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain Collagen alpha-1 chain CD99 antigen-like protein 2 Calsyntenin-2 Given are molecular mass, normalized migration time, adjusted
