Objective: To identify novel biomarkers in addition to HbA1c to aid prediction and risk management for diabetic kidney disease (DKD) in patients with type 1 diabetes. Research Design and Methods: A novel multi-component mass spectrometry adductomics approach was used to measure urinary MG-AGEs from patients with type 1 diabetes enrolled in the Diabetes Complications and Control Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications (EDIC) studies. MG-AGEs were measured in samples collected during DCCT to determine their ability to predict the risk of developing DKD during the EDIC observation phase of the trial, focusing on DCCT years 1-3 to study the timepoint closest to diabetes diagnosis and furthest from DKD diagnosis. The association of MG-AGEs with DKD risk was determined using univariate and multivariate analysis with calculation of odds ratios (OR) and 95% confidence intervals (CIs). Results: MG-AGEs N2-carboxyethyl-guanosine (CEG), N2-carboxyethyl-2'-deoxyguanosine (CEdG), and carboxyethyllysine (CEL) univariately associated with increased risk of developing DKD at least 16.3 ± 4.6 years before diagnosis (5.3 OR, p<0.001, 95% CI 3.0-10.0), (1.8 OR, p<0.001, 95% CI 1.3-2.4), and (1.5 OR, p<0.001, 95% CI 1.2-1.9) respectively. MG-AGEs remained associated with DKD in adjusted multiple logistic regression models including HbA1c, age, sex, albumin excretion rate, and other clinical variables. Conclusions: DNA, RNA, and protein MG-AGEs are predictive of DKD risk independent of HbA1c. We also provide the first description of a novel class of biomarkers, DNA and RNA MG-AGEs, as predictors of DKD.