patients, particularly those with a PKD2 genotype. We therefore wondered whether urinary proteomics might be useful for ADPKD diagnosis in this patient group. However, similar to the accuracy of ultrasound diagnostic criteria the diagnostic biomarker model exhibited a reduced sensitivity in young patients and in patients with PKD2 genotype and a slightly reduced specificity in older patients. Since for all patients in the validation cohort ADPKD diagnosis was based on ultrasound imaging, the sensitivity of our proteomic biomarker model might be somewhat lower when applied to an at-risk population, including 25833960 patients very early in the course with genetically proven disease but negative imaging results. Hence, despite the very high overall accuracy of our diagnostic biomarker model, it will need further refinement before providing benefit over ultrasound based diagnosis in clinical practice. Urine proteome analysis of very young, mutation positive ADPKD patients with no detectable cysts yet might allow the identification of very early and subtle proteomic alterations that may have gone undetected in our study. A major challenge in the management of patients with ADPKD is to predict prognosis. Even within a family the disease course exhibits a high variability. Disease prediction will gain further importance with the development of specific treatment options. Such treatments will most likely need to be started early during disease course to affect outcome, before the majority of functioning kidney tissue has been replaced by cysts. One focus of our studies was therefore the evaluation of urine proteome utility in predicting NVP BGJ398 web severity and progression of ADPKD. We anticipated that the diagnostic biomarker score would not exhibit strong associations with disease severity and progression, since it was designed to discriminate ADPKD patients from controls with high accuracy, but not to detect differences among ADPKD patients. Urinary peptides with highly variable excretion among Diagnosis FSGS IgAN MN MCD DNP AKI Fanconi Renal diseases, others DM type 1 without DNP DM type 2 without DNP SLE Vasculitis Bladder cancer Liver transplantation Stem cell transplantation All diseased controls combined N 31 70 46 29 83 16 11 10 42 
12 45 12 22 6 46 481 Number of false positive results Age 2 9 2 2 8 0 0 0 7 0 6 1 1 0 9 47 38.8611.6 36.7612.8 44.667.9 35.6612.3 48.666.7 61.7613.3 13.469.4 48.967.7 40.9610.3 49.469.1 38.768.8 42.6615.2 51.166.2 45.7613.2 50.6613.5 42.7611.7 Sex 35.4 32.9 19.6 41.4 26.5 50.0 36.4 40.0 45.2 25.0 71.1 41.7 9.1 0 37.0 35.6 FSGS, focal and segmental glomerulosclerosis; IgAN, IgA nephropathy; MN, membranous nephropathy; MCD, minimal change disease; 14530216 DNP, diabetic nephropathy; AKI, acute kidney injury; DM, diabetes mellitus; SLE, systemic lupus erythematosus. doi:10.1371/journal.pone.0053016.t003 7 Urine Proteomics in ADPKD all patients cut off 20.169 20.250 20.400 sens 0.844 0.875 0.906 spec 0.942 0.907 0.895 age,30 sens 0.720 0.768 0.817 spec 0.975 0.950 0.925 age.30 sens 0.920 0.937 0.958 spec 0.913 0.870 0.870 PKD1 sens 0.863 0.891 0.914 spec 0.942 0.907 0.895 PKD2 sens 0.774 0.806 0.903 spec 0.942 0.907 0.895 doi:10.1371/journal.pone.0053016.t004 ADPKD patients that might correlate with disease severity may have been excluded from the diagnostic model since they are less useful to differentiate ADPKD versus controls. Nevertheless, ADPKD_142 correlated with several measures of disease severity and progression, including the ann