الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Diabetes Mellitus (DM) is considered one of the most frequent chronic diseases worldwide, being a great challenge in health issues in the 21st century due to increased disease incidence and the higher frequency of chronic complications. Type 2 diabetes mellitus (T2DM), representing 90% to 95% of cases, is considered the most prevalent form of the disease.
Diabetic kidney disease (DKD), being one of the major microvascular complications of diabetes, is the most common cause of end-stage renal disease (ESRD) worldwide. This raises the importance of early identification, treatment and prevention of DKD.
Detection of albuminuria has been used for a long time for screening of DKD. Moderately increased urinary albumin excretion (UAE), formerly called microalbuminuria (30–300 mg/g), is the most widely used early clinical indicator of DKD and has been considered a predictor of progression to ESRD in both type 1 and T2DM.
However, using moderately increased UAE as a diagnostic tool for the onset of DKD has a limited predictive power which makes a research priority to identify more sensitive and specific biomarkers for the early detection of DKD, and prediction of progression to ESRD.
Proteomics refers to the systematic analysis of proteins for their identity, quantity and function. Proteomics has played a significant role in the study of DKD since 2002. Consequently, a large number of biomarker candidates for diagnosis and prediction of DKD have been identified; however, the information obtained from these studies is currently not closed to clinical application.
Very recently, proteomic-based strategies to discover urine and serum biomarkers of DKD have been extensively reviewed. Profiling method, one of the available proteomic approaches, is a promising successful tool for the identification of new reliable biomarkers of DKD.
Numerous studies have been conducted to outline urinary proteomic profile in patients with DKD. However, there is paucity of data on the serum proteomics in this field. That invited us to conduct the current research.
The aim of the present study was to identify serum proteomic profile of patients with T2DM and early stages of DKD as we compared patients with T2DM with and without albuminuria with a healthy control group.
This study was conducted on 120 subjects divided into three groups: group I: 40 patients with T2DM with normal UAE (<30mg/g)(the normoalbuminuric group), group II: 40 patients with T2DM with moderately increased UAE (30-300mg/g)(the albuminuric group) and group III : 40 healthy control subjects matched for age, sex and socioeconomic status. Each group was divided equally into training set and test set. Cases were recruited from the diabetes outpatient clinic in Alexandria Main University Hospital. Specimens were purified with magnetic beads-based weak cation exchange chromatography and analyzed in a MALDI-TOF MS.
All serum samples were subjected to solid-phase extraction (SPE). We compared the basic parameters of the total peak number, peak area, peak height and the ability to analyze the differences in peaks using ClinProTool software. The use of proteomic pattern was determined using Genetic Algorithm (GA), Quick classifier (QC) and Supervised Neural Network (SNN) analysis.
A Supervised Neural Network (SNN) gave the best results to serum proteomic pattern. Seventeen peaks represented the proteomic profile that differentiates between the normoalbuminuric and the albuminuric groups with 9 up-regulated and 8 down-regulated peaks with sensitivity 81% and specificity 75%. Twenty four peaks differentiate between the normoalbuminuric and the control groups with 17 up-regulated and 7 down-regulated peaks with sensitivity 97.1% and specificity 67.9%.