Validation of plasma biomarker candidates for the prediction of eGFR decline in patients with type 2 diabetes

Objective: The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable and early interventions would likely be cost effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors. Research Design and Methods: We studied participants in PROVALID, a prospective multinational cohort study of patients with type 2 diabetes and a follow up of more than 24 months (n = 2560; baseline median eGFR 84 mL/min/1.73m2, UACR 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex technology and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling. Results: In univariable analyses nine of the 17 markers showed significant differences in median concentration between the two groups. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62%. A panel of twelve biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% were due to five markers. Each biomarker’s individual contribution to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79%) and the contribution of each biomarker dropped below 1%. Conclusions: In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low

Integrative analysis of prognostic biomarkers derived from multiomics panels for the discrimination of chronic kidney disease trajectories in people with type 2 diabetes

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of; a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor