Original article
Identification of urinary protein pattern in Type 1 diabetic adolescents with early diabetic nephropathy by a novel combined proteome analysis

https://doi.org/10.1016/j.jdiacomp.2004.10.002Get rights and content

Abstract

Diabetic nephropathy is the main cause of morbidity and mortality in patients with Type 1 diabetes mellitus. Microalbuminuria has been established as a risk factor for the development and the progression of diabetic renal disease. A strong demand exists for better technologies to provide accurate diabetic nephropathy risk estimates before renal functional or structural disturbances already become established. Here, we present the application of a novel proteomics technology identifying urinary polypeptides and proteins. In this pilot study, we investigated 44 Type 1 diabetic patients with more than 5 years of diabetes duration compared with an age-matched control group. Random spot urine samples were examined utilizing high-resolution capillary electrophoresis (CE), coupled to mass spectrometry (MS). More than 1000 different polypeptides, characterized by their separation time and mass, were found between 800 Da and 66.5 kDa. Mathematical analysis revealed specific clusters of 54 polypeptides only found in Type 1 diabetic patients and an additional 88 polypeptides present or absent in patients with beginning nephropathy defined by the albumin-to-creatinine ratio (ACR; >35 mg/mmol). We observed polypeptide patterns characteristic for healthy controls and diabetic patients and subdivision of patients according to the excretion of polypeptides typical for diabetic nephropathy. Our study revealed that the urinary proteome contains a much greater variety of polypeptides than previously recognized and demonstrated the successful application of a novel high-throughput technology towards the human urinary proteome. Future prospective studies with the application of this technique may enable the earlier and more accurate detection of individuals at high risk to develop diabetic nephropathy.

Introduction

Diabetic nephropathy is the most important cause of morbidity and mortality in Type 1 diabetic patients, about 25% to 40% eventually develop end-stage renal failure (ESRF; Andersen, Christiansen, Andersen, Kreiner, & Deckert, 1983). Microalbuminuria (MA) is a predictive marker of progressive renal disease in diabetes mellitus and also an independent risk factor for cardiovascular disease (Gaede et al., 1999, Gorman et al., 1999, Heart Outcomes Prevention Evaluation Study Investigators, 2000, Mogensen, 1984, The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group, 2000, The EUCLID Study Group, 1997). Multicenter intervention studies using MA as a surrogate marker indicated that intensified insulin therapy and tight blood pressure control in diabetic patients may delay or even prevent the development of progressive renal disease, but the exact mechanisms behind remain elusive (Gaede et al., 1999, Heart Outcomes Prevention Evaluation Study Investigators, 2000, The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group, 2000, The EUCLID Study Group, 1997).

In both Types 1 and 2 diabetes mellitus, the evolution of the renal disease from the first clinical sign of MA to overt proteinuria represents a continuum of progressively damaged glomerular capillary wall function and breakdown of the filtration barrier. However, (micro-)albuminuria/proteinuria already represents a clinical stadium of diabetic kidney disease when advanced structural renal damage has occurred and may further progress independently from metabolic control (Mogensen, Christensen, & Vittinghus, 1983). Although the assessment of albumin excretion rate (AER) is currently the best available noninvasive method for the early recognition of pending renal disease in nonproteinuric diabetic patients, a controversy debate exists about its sensitivity and specificity in the current literature (Caramori et al., 2000, Parving et al., 2002, Perkins et al., 2003, Ritz, 2003). Notably, some data suggested an improved prognosis in Type 1 diabetes mellitus within the last few decades due to advances in therapy (Bojestig et al., 1984, Borch-Johnsen, 1999), and, more recently, a regression of MA has been shown to frequently occur in Type 1 diabetic patients (Perkins et al., 2003). Furthermore, evidence from epidemiologic and family studies suggest genetic susceptibility to diabetic nephropathy (Araki et al., 2003, Krolewski et al., 2001). Therefore, parameters for the earlier detection of individuals at high risk to develop renal disease, leading to organ failure, is an important goal to achieve. Such strategy would be superior to current aggressive intervention therapies in patients with already deteriorated renal function (Caramori et al., 2000). Hence, a strong demand is evolving for better technologies to provide accurate risk estimates for the development of diabetic nephropathy before renal structural disturbances are established (Caramori et al., 2000, Conquering Diabetes: A Report of the Congressionally-Established Diabetes Research Working Group, 1999).

Proteomics has now become an emerging field in the postgenomic area and offers the opportunity of large-scale protein analysis (Hochstrasser, Sanchez, & Appel, 2002). Two-dimensional polyacrylamide gel electrophoresis (PAGE) has commonly been used for protein separation and combined with mass spectrometry (MS) to determine the molecular weight of polypeptides and proteins (Mann, Hendrickson, & Pandey, 2001). Surface-enhanced laser desorption and ionization (SELDI) MS is an alternative technology employed for high-throughput analysis of biological samples, which is, however, limited by protein loss due to matrix binding for selected polypeptides and proteins (Hampel et al., 2001). We have recently established a novel technology by use of an online combination of capillary electrophoresis (CE) coupled to MS (ESI–TOF–MS), allowing the fast and sensitive evaluation of several hundred polypeptides found in body fluids (Kaiser et al., 2003, Weissinger et al., 2004, Wittke et al., 2003). In this pilot study, we validated the application of this novel proteomics-based technology in Type 1 diabetic adolescents to identify urinary protein patterns characteristic for the early recognition and the development of diabetic nephropathy.

Section snippets

Patients

Forty-four patients with Type 1 diabetes mellitus and a disease duration of more than 5 years have been included in this pilot study and compared with 9 healthy, age-matched controls (14.1±2.2 years in controls vs. 14.8±2.8 years in the diabetic group). The local ethical committee approved the protocol, and all patients and their legal representatives were informed about the purpose of the investigation and agreed by written consent. All patients were consecutively recruited in an outpatient

Results

CE–MS was applied to samples of 44 patients and 9 healthy volunteers, subsequently analyzed utilizing the MosaiquesVisu software. An average of 767 different polypeptides in an individual sample could be detected. Overall, 5076 different polypeptides in the mass range 800 Da–66.5 kDa were detected, each one defined by its mass and standardized CE retention time. Data processing is shown in Fig. 1. This graph shows the generation of the data in two samples, one from a healthy control (K0; left

Discussion

The aim of this study was to identify urinary polypeptide patterns from Type 1 diabetic adolescents that may serve as diagnostic tool (pattern) for early diabetic nephropathy. CE–ESl–TOF–MS revealed that the urinary proteome contains a much greater variety of polypeptides than previously recognized and allowed the establishment of a “normal polypeptide pattern” from healthy controls. Subsequent comparative data analysis discovered distinct polypeptide patterns characteristic for Type 1 diabetic

Acknowledgments

The authors kindly acknowledge Prof. Hans-Ulrich Häring and Dr. Eva M. Weissinger for critically reading the manuscript.

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