Urinary proteomics for early diagnosis in diabetic nephropathy

Abstract

Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis-coupled mass spectrometry was used to profile the low-molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3-5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.

FIG. 1.

Dot-and-line plot of the comparison between early diagnoses of the CKD273 classifier and microalbuminuria in the longitudinal cohort. The y-axis depicts the years prior to onset of macroalbuminuria. The years prior to the onset of macroalbuminuria is the difference in examination date (Supplementary Table 1) of the first urine sample with UAER >200 µg/min, the examination date of the first sample with a classification factor >0.343 in the case of the CKD273 classifier, and the examination date of the first sample with UAER >20 μg/min in the case of microalbuminuria, respectively. The numbers next to the dots correspond with the number of patients in the longitudinal cohort.

FIG. 2.

Dot-and-line plots of the CKD273 classifier and UAER of progressors and nonprogressors of DN in the longitudinal data analysis. The x-axis shows the years prior to onset of macroalbuminuria. The blue line depicts the cutoff (0.343) of the CKD273 classifier and the cut off (20 μg/min) from normo- to microalbuminuria. The plots in the left panel show the progress of the CKD273 classifier and the UAER over the years prior to onset of macroalbuminuria. The right panel indicates the mean ± SD of the CKD273 classifier and UAER.

FIG. 3.

Comparison of ROC curves of the classification results from longitudinal collected urine samples. A: ROC analysis of all urine samples of diabetic patients at sampling date up to 5 years prior to onset of macroalbuminuria (DN). B: ROC analysis of all samples of diabetic patients who are normalbuminuric at sampling date up to 5 years prior to onset of DN. The black line shows the ROC curve from the CKD273 classifier and the dashed line from the UAER.

FIG. 4.

Box-and-Whisker plots of AUC values from biomarkers of the CKD273 classifier in type 1 (A) and type 2 (B) diabetic patients separated in collagen-derived and non–collagen-derived peptides. The black plot and dots depict the AUC values of the collagen fragments, and the gray plot and dots depict the AUC values of the noncollagen fragments.

FIG. 5.

Distribution of highest AUCs of CKD273 classifier biomarkers for type 1 diabetic patients at late stage (−5 to −3 years prior to onset on macroalbuminuria) (A) and early stage (−2 to 0 years prior to onset of macroalbuminuria) (B) and for type 2 diabetic patients at late stage (C) and early stage (D). For this figure, best AUCs (>0.7) were selected at late and at early stage in type 1 and 2 diabetic patients, respectively (Supplementary Table 2). In case subjects in whom more than two peptides of a protein were identified, only two peptides are depicted. The AUC values of late stage are depicted with black lines and of early stage with gray lines. The markers are characterized by protein ID and the SwissProt name.