Urinary proteomics in diabetes and CKD

Abstract

Urinary biomarkers for diabetes, diabetic nephropathy, and nondiabetic proteinuric renal diseases were sought. For 305 individuals, biomarkers were defined and validated in blinded data sets using high-resolution capillary electrophoresis coupled with electrospray-ionization mass spectrometry. A panel of 40 biomarkers distinguished patients with diabetes from healthy individuals with 89% sensitivity and 91% specificity. Among patients with diabetes, 102 urinary biomarkers differed significantly between patients with normoalbuminuria and nephropathy, and a model that included 65 of these correctly identified diabetic nephropathy with 97% sensitivity and specificity. Furthermore, this panel of biomarkers identified patients who had microalbuminuria and diabetes and progressed toward overt diabetic nephropathy over 3 yr. Differentiation between diabetic nephropathy and other chronic renal diseases reached 81% sensitivity and 91% specificity. Many of the biomarkers were fragments of collagen type I, and quantities were reduced in patients with diabetes or diabetic nephropathy. In conclusion, this study shows that analysis of the urinary proteome may allow early detection of diabetic nephropathy and may provide prognostic information.

Figure 1.

(A) Protein patterns of the patients with diabetes and control subjects examined in this study. Shown are compiled patterns consisting of all samples from each of the four groups. The molecular mass (0.7 to 25 kD, on a logarithmic scale) is plotted against normalized migration time (17 to 47 min). Signal intensity is encoded by peak height and color. (B) Distribution of potential differential-diagnostic biomarkers for diabetes in the patients with normoalbuminuria and healthy control subjects. All statistically significant biomarkers from Supplemental Table 1 are shown. (C) Distribution of potential differential-diagnostic biomarkers for diabetic nephropathy in the different groups of patients and healthy control subjects. Shown are all statistically significant biomarkers listed in Supplemental Table 2.

Figure 2.

(A) Protein patterns of the patients with chronic renal disease (CRD). Shown are compiled patterns consisting of samples from patients with FSGS (n = 35) IgA nephropathy (IgAN; n = 57), minimal-change disease (MCD; n = 25), and membranous glomerulonephritis (MNGN; n = 29). In comparison with Figure 1A, these compiled data show a much higher degree of similarity to the patients with macroalbuminuria than to any other group with diabetes. The molecular mass (0.7 to 25 kD, on a logarithmic scale) is plotted against normalized migration time (17 to 47 min). Signal intensity is encoded by peak height and color. (B) Distribution of potential differential-diagnostic biomarkers for diabetic nephropathy(DN) in all patients with macroalbuminuria (DN group) and all control subjects with (CRD group) used in the study. All 37 statistically significant biomarkers from Table 2 are shown. (C) Receiver operating characteristic analysis of the performance of the differential diagnostic biomarker pattern for DN. (Left) Data from the training set of 44 case patients and 104 control subjects (sensitivity of 95.5% and specificity of 94.2%; area under the curve was 0.971). (Right) Data from validating the masked test set consisting of 64 samples (81.0% sensitivity and 90.7% specificity; area under the curve was 0.856).