Abstract

AIMS:

Microalbuminuria is the earliest clinical sign of diabetic nephropathy (DN). However, the multifactorial nature of DN supports the application of combined markers as a diagnostic tool. Thus, another screening approach, such as protein profiling, is required for accurate diagnosis. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a novel method for biomarker discovery. We aimed to use SELDI and bioinformatics to define and validate a DN-specific protein pattern in serum.

METHODS:

SELDI was used to obtain protein or polypeptide patterns from serum samples of 65 patients with DN and 65 non-DN subjects. From signatures of protein/polypeptide mass, a decision tree model was established for diagnosing the presence of DN. We estimated the proportion of correct classifications from the model by applying it to a masked group of 22 patients with DN and 28 non-DN subjects. The weak cationic exchange (CM10) ProteinChip arrays were performed on a ProteinChip PBS IIC reader.

RESULTS:

The intensities of 22 detected peaks appeared up-regulated, whereas 24 peaks were down-regulated more than twofold (P < 0.01) in the DN group compared with the non-DN groups. The algorithm identified a diagnostic DN pattern of six protein/polypeptide masses. On masked assessment, prediction models based on these protein/polypeptides achieved a sensitivity of 90.9% and specificity of 89.3%.

CONCLUSION:

These observations suggest that DN patients have a unique cluster of molecular components in serum, which are present in their SELDI profile. Identification and characterization of these molecular components will help in the understanding of the pathogenesis of DN. The serum protein signature, combined with a tree analysis pattern, may provide a novel clinical diagnostic approach for DN.