Background: Nephropathy associated with diabetes is a severe complication that cause slow kidneys deterioration, leading to end-stage renal disease. Renal involvement during diabetes mellitus may affect all the structural components of the kidneys, causing functional and organic alterations frequently associated with inflammatory processes, that give rise to multiple clinical manifestations. Currently, despite rapid research progress, predictors able to assess prospectively and with high precision the risk to develop diabetic nephropathy (DN) are still lacking. Methods: The aim of this project was to identify differences in urinary protein excretion, both in type 1 (T1D) and type 2 diabetic (T2D) patients, in comparison with healthy control subjects. Ninety diabetic patients were recruited and divided in 3 groups (for each diabetes type), according to the level of albuminuria: normoalbuminuric, with microalbuminuria (MA) and with overt proteinuria. Second void morning urine samples were collected and centrifuged to remove cell debris and contaminations. Urinary proteins were separated by twodimensional electrophoresis (2-DE) and identified by mass spectrometry analysis (MS). Results: Comparing the patients proteomic profiles with those of normal subjects, firstly we noted a significant increase of alpha-1-antitrypsin and albumin, also in the form of numerous fragments, in urine of diabetic subjects. Particularly, statistical analysis and spot quantification by PDQuest image software revealed several proteins differentially expressed in diabetes condition. Some proteins resulted increased in urine of both T1D and T2D patients with MA, such as transthyretin, apolipoprotein-A1 and transferrin, while the majority of the overexcreted proteins were found in T2D patients with proteinuria, e.g. vitamin-D-binding protein, protein AMBP, zinc-alpha-2- glycoprotein, fetuin-A and ganglioside GM2 activator. Conclusions: This protein pattern might represent a potential tool for a better understanding of DN and could help to identify patients at increased risk of renal disease progression. Therefore, in diagnostic field, 2-DE and MS proteomic analysis could be a suitable approach to discover early and predictive biomarkers of DN in urine of diabetic patients.
Detection of predictive urinary biomarkers of nephropathy in type 1 and type 2 diabetes by proteomic analysis / Bellei, Elisa; Cuoghi, Aurora; Monari, Emanuela; Bergamini, Stefania; Ligabue, Giulia; Cappelli, Gianni; Ozben, Tomis. - In: BIOCHIMICA CLINICA. - ISSN 0393-0564. - STAMPA. - 37(SS):(2013), pp. 605-605. (Intervento presentato al convegno 20th IFCC-EFLM European Congress of clinical Chemistry and laboratory medicine (EuroMedLab) tenutosi a Milan nel 19-23 May 2013).
Detection of predictive urinary biomarkers of nephropathy in type 1 and type 2 diabetes by proteomic analysis
BELLEI, Elisa;CUOGHI, Aurora;MONARI, Emanuela;BERGAMINI, Stefania;LIGABUE, Giulia;CAPPELLI, Gianni;
2013
Abstract
Background: Nephropathy associated with diabetes is a severe complication that cause slow kidneys deterioration, leading to end-stage renal disease. Renal involvement during diabetes mellitus may affect all the structural components of the kidneys, causing functional and organic alterations frequently associated with inflammatory processes, that give rise to multiple clinical manifestations. Currently, despite rapid research progress, predictors able to assess prospectively and with high precision the risk to develop diabetic nephropathy (DN) are still lacking. Methods: The aim of this project was to identify differences in urinary protein excretion, both in type 1 (T1D) and type 2 diabetic (T2D) patients, in comparison with healthy control subjects. Ninety diabetic patients were recruited and divided in 3 groups (for each diabetes type), according to the level of albuminuria: normoalbuminuric, with microalbuminuria (MA) and with overt proteinuria. Second void morning urine samples were collected and centrifuged to remove cell debris and contaminations. Urinary proteins were separated by twodimensional electrophoresis (2-DE) and identified by mass spectrometry analysis (MS). Results: Comparing the patients proteomic profiles with those of normal subjects, firstly we noted a significant increase of alpha-1-antitrypsin and albumin, also in the form of numerous fragments, in urine of diabetic subjects. Particularly, statistical analysis and spot quantification by PDQuest image software revealed several proteins differentially expressed in diabetes condition. Some proteins resulted increased in urine of both T1D and T2D patients with MA, such as transthyretin, apolipoprotein-A1 and transferrin, while the majority of the overexcreted proteins were found in T2D patients with proteinuria, e.g. vitamin-D-binding protein, protein AMBP, zinc-alpha-2- glycoprotein, fetuin-A and ganglioside GM2 activator. Conclusions: This protein pattern might represent a potential tool for a better understanding of DN and could help to identify patients at increased risk of renal disease progression. Therefore, in diagnostic field, 2-DE and MS proteomic analysis could be a suitable approach to discover early and predictive biomarkers of DN in urine of diabetic patients.
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