BACKGROUND: Risk stratification tools for Autosomal Dominant Polycystic Kidney Disease (ADPKD) predict kidney outcomes on a group level but lack precision in individuals. METHODS: We assessed the value of adding 13 prognostic biomarkers to established risk factors (sex, age, estimated glomerular filtration rate (eGFR), systolic blood pressure, Mayo Imaging Class (MIC), and mutation type) for predicting disease progression. We included 596 patients from the DIPAK (Developing Intervention strategies to halt Progression of Autosomal Dominant Polycystic Kidney Disease) cohorts with ≥2 eGFR measurements and ≥1 year follow-up. RESULTS: During a mean±SD follow-up of 5.0±2.3 years, the mean±SD eGFR slope was -3.46±2.5 mL/min/1.73m2/year. Rapid disease progression (eGFR loss ≥3 mL/min/1.73m2/year) occurred in 303 patients (50.8%), and 279 patients (46.8%) reached the combined endpoint of kidney failure or 30% eGFR decline. Urinary albumin/creatinine, urinary monocyte chemotactic protein-1 (MCP-1)/creatinine, and serum copeptin consistently and independently predicted eGFR slope (all P <
0.001), rapid disease progression (AUC increasing from 0.79 [95% CI: 0.76, 0.85] for a baseline model to 0.83 [95% CI: 0.81, 0.88] when MCP-1/creatinine and copeptin were included, P=0.006) and reaching the combined kidney endpoint (C-index improving from 0.806 [95% CI: 0.78, 0.84] for a baseline model to 0.82 [95% CI: 0.79, 0.85] for a model also containing albumin/creatinine and copeptin, P <
0.001). These results were confirmed in an independent external validation cohort (N=144), and were robust in early disease stages and in patients with moderately increased kidney volumes (MIC 1C). CONCLUSION: Our findings suggest that incorporating these biomarkers into ADPKD risk stratification tools will improve risk prediction, even in subgroups where prognostication is most challenging and relevant.