Biomarker panels associated with progression of renal disease in type 1 diabetes

AIMS/HYPOTHESIS: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. METHODS: ) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone. RESULTS: was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well. CONCLUSIONS/INTERPRETATION: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.