we first recruited a group of 65 type 2 diabetic patients with a steady decline in glomerular filtration rate ( gfr ) and various degrees of urine albumin excretion rate , with biopsy - proven diabetic nephropathy and without any histological evidence of concomitant nondiabetic glomerular or vascular disease .
next , we enrolled a second group of 10 patients with diabetes and ckd other than diabetic nephropathy ( 5 with membranous glomerular nephropathy , 2 with iga nephropathy , 2 with focal segmental glomerular sclerosis , and 1 with atheroembolic disease ) . of note ,
patients with diabetic nephropathy came from three independent divisions of nephrology : university of foggia ( n = 32 ) , sant'orsola malpighi hospital , university of bologna ( n = 5 ) , and university of modena and reggio emilia ( n = 28 ) .
next , we recruited 38 diabetic patients without any deterioration of estimated gfr ( egfr ) , 20 who were normoalbuminuric ( nad group ) and 18 who were microalbuminuric ( micro group )
. finally , we enrolled 57 nondiabetic patients with ckd ( nd - ckd ) , namely iga nephropathy
( n = 20 ) , membranous nephropathy ( n = 24 ) and benign nephroangiosclerosis ( n = 13 ) .
the clinical and laboratory features of all the subjects studied are reported in supplementary table a1 ( available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc10-0345/dc1 ) .
most patients examined showed fair blood pressure and glycemic control at the time of urine collection .
in addition , most of them , with the exception of patients in nad group , were taking ace inhibitors and/or angiotensin ii receptor antagonists as a part of their antihypertension treatment . the study was approved by the local ethics committee , and informed written consent was obtained from all participating subjects .
paraffin - embedded renal specimens were used for conventional histological staining ( hematoxylin - eosin , periodic acid - schiff , silver methenamine , and masson 's trichrome ) .
immunofluorescence microscopy was performed on cryostat sections with the use of antisera against igg , igm , iga , c3 , c4 , c1q , and fibrinogen .
diabetic nephropathy was diagnosed in the presence of nodular or diffuse glomerulosclerosis , glomerular hypertrophy , mesangial ( diffuse or nodular ) widening , glomerular capillary wall thickening , evidence of exudative lesions or fibrin caps ( i.e. , hyaline material heaped up on the inner side of the glomerular basement membrane ) , and the presence of microaneurysms of glomerular capillaries ( 13 ) .
patients with concomitant evidence of nondiabetic glomerular disease or vascular disease were excluded from the analysis .
urine samples were collected in the morning , after overnight fasting , and tested for standard parameters ( including ph , glucose , blood content , and specific gravity ) using multistix reactive stripes ( bayer diagnostics , munich , germany ) .
then they were centrifuged to remove cell debris , divided into aliquots with the addition of protease inhibitors , and stored at 80c until analysis .
urine samples were concentrated by 3-kda cutoff amicon filter devices ( millipore , billerica , ma ) , and 10 g of urine proteins , diluted ( 2:3 , v / v ) with a denaturing buffer solution ( 9 mol / l urea , 2% 3-[(3-cholamidopropyl)dimethylammonio]propanesulfonate [ chaps ] , and 100 mmol / l dithiothreitol ) , were analyzed in duplicate using a cm10 proteinchip ( bio - rad , hercules , ca ) array , whose chemical surface binds proteins by cationic exchange .
all experiments were performed according to the manufacturer 's instructions . at the end of each experiment ,
the chips were read by a proteinchip reader ( pcs-4000 enterprise version ; bio - rad ) and , after acquisition , the spectra were analyzed by proteinchip datamanager 3.5 software ( bio - rad ) . before each analysis , the software was externally calibrated by using a protein standard kit ( proteinchip oq kit ; bio - rad ) , and all of the spectra were normalized by means of total ion current .
the analysis was performed in a range of 3,000 to 30,000 m / z , considering as real peaks those having a signal - to - noise and valley depth ratio > 4 .
the reproducibility of the seldi analysis was assessed by running one urine sample in quadruplicate and measuring the percent coefficient of variation in the number and intensity of mass peaks . according to our previously published data ( 14 ) and the manufacturer 's instruction ,
percent coefficient of variation values for the number and intensity of peaks were about 8 and 23% , respectively ( data not shown ) .
urine samples were divided into a training set and a testing set to construct and validate the classification tree , respectively .
the intensity ( microamperes ) of all the mass peaks in the training set was transferred to biomarker pattern software ( bio - rad ) that identified a set of mass peaks whose intensity allowed us to classify each sample of the training set with the least error .
the independent testing set was then scored using the classification tree to evaluate the classification power on a blinded dataset .
the sensitivity was defined as the probability of predicting diabetic nephropathy , and the specificity was defined as the probability of predicting nd - ckd .
a logistic regression model was used to determine factors significantly related to the urinary proteomic signature .
0.05 for simple logistic analysis was required for independent variables to be entered into a multivariate logistic model .
two proteins of about 11,700 and 8,589 m / z , chosen among the most prominent predictors included in the classification tree , were isolated , identified by tandem mass spectrometry , and validated by enzyme - linked immunosorbent assay ( elisa ) ( 2-microglobulin [ 2 mg ] ) and immunoprecipitation ( ubiquitin ) , respectively ( for details of methods , see supplementary data , available in an online appendix ) .
paraffin - embedded renal specimens were used for conventional histological staining ( hematoxylin - eosin , periodic acid - schiff , silver methenamine , and masson 's trichrome ) .
immunofluorescence microscopy was performed on cryostat sections with the use of antisera against igg , igm , iga , c3 , c4 , c1q , and fibrinogen .
diabetic nephropathy was diagnosed in the presence of nodular or diffuse glomerulosclerosis , glomerular hypertrophy , mesangial ( diffuse or nodular ) widening , glomerular capillary wall thickening , evidence of exudative lesions or fibrin caps ( i.e. , hyaline material heaped up on the inner side of the glomerular basement membrane ) , and the presence of microaneurysms of glomerular capillaries ( 13 ) .
patients with concomitant evidence of nondiabetic glomerular disease or vascular disease were excluded from the analysis .
urine samples were collected in the morning , after overnight fasting , and tested for standard parameters ( including ph , glucose , blood content , and specific gravity ) using multistix reactive stripes ( bayer diagnostics , munich , germany ) .
then they were centrifuged to remove cell debris , divided into aliquots with the addition of protease inhibitors , and stored at 80c until analysis .
urine samples were concentrated by 3-kda cutoff amicon filter devices ( millipore , billerica , ma ) , and 10 g of urine proteins , diluted ( 2:3 , v / v ) with a denaturing buffer solution ( 9 mol / l urea , 2% 3-[(3-cholamidopropyl)dimethylammonio]propanesulfonate [ chaps ] , and 100 mmol / l dithiothreitol ) , were analyzed in duplicate using a cm10 proteinchip ( bio - rad , hercules , ca ) array , whose chemical surface binds proteins by cationic exchange .
all experiments were performed according to the manufacturer 's instructions . at the end of each experiment , the chips were read by a proteinchip reader ( pcs-4000 enterprise version ; bio - rad ) and , after acquisition , the spectra were analyzed by proteinchip datamanager 3.5 software ( bio - rad ) . before each analysis ,
the software was externally calibrated by using a protein standard kit ( proteinchip oq kit ; bio - rad ) , and all of the spectra were normalized by means of total ion current .
the analysis was performed in a range of 3,000 to 30,000 m / z , considering as real peaks those having a signal - to - noise and valley depth ratio > 4 .
the reproducibility of the seldi analysis was assessed by running one urine sample in quadruplicate and measuring the percent coefficient of variation in the number and intensity of mass peaks . according to our previously published data ( 14 ) and the manufacturer 's instruction ,
percent coefficient of variation values for the number and intensity of peaks were about 8 and 23% , respectively ( data not shown ) .
urine samples were divided into a training set and a testing set to construct and validate the classification tree , respectively .
the intensity ( microamperes ) of all the mass peaks in the training set was transferred to biomarker pattern software ( bio - rad ) that identified a set of mass peaks whose intensity allowed us to classify each sample of the training set with the least error .
the independent testing set was then scored using the classification tree to evaluate the classification power on a blinded dataset .
the sensitivity was defined as the probability of predicting diabetic nephropathy , and the specificity was defined as the probability of predicting nd - ckd .
a logistic regression model was used to determine factors significantly related to the urinary proteomic signature . significance <
0.05 for simple logistic analysis was required for independent variables to be entered into a multivariate logistic model .
two proteins of about 11,700 and 8,589 m / z , chosen among the most prominent predictors included in the classification tree , were isolated , identified by tandem mass spectrometry , and validated by enzyme - linked immunosorbent assay ( elisa ) ( 2-microglobulin [ 2 mg ] ) and immunoprecipitation ( ubiquitin ) , respectively ( for details of methods , see supplementary data , available in an online appendix ) .
we first tested the ability of biomarker pattern software to differentiate diabetic nephropathy from nd - ckd ( fig .
initially , urine samples from 54 patients with diabetic nephropathy and 57 nondiabetic patients with nd - ckd were distributed into the training set ( 31 diabetic nephropathy and 41 nd - ckd ) and the testing set ( 23 diabetic nephropathy and 16 nd - ckd ) . to minimize the influence of preanalytical biases on the classification approach , urine samples of patients with diabetic nephropathy collected in three distinct nephrology units were evenly distributed in both the training and the testing set .
the best classification tree created in the training set was then applied to an independent blinded set ( testing set ) of urine samples to validate its discriminatory power .
diabetic nephropathy and nd - ckd were correctly classified with 78.2 and 87.5% sensitivity and specificity , respectively ( fig .
1b ) . eighteen mass peaks ( 8,586 , 13,593 , 13,687 , 8,515 , 8,665 , 11,724 , 8,423 , 13,902 , 13,780 , 19,335 , 6,320 , 13,422 , 4,115 , 4,049 , 4,024 , 4,308 , 4,370 , and 3,086
m / z ) were identified as the best predictors for the diagnosis of diabetic nephropathy .
moreover , receiver operator characteristic ( roc ) analysis showed 80% accuracy in the diagnosis of diabetic nephropathy ( fig .
next , we were interested in examining the discriminatory power of the classification tree toward diabetic patients with ckd other than diabetic nephropathy . to this purpose ,
the diabetic nephropathy urine signature was scored on a second independent testing set including 11 newly recruited patients with diabetic nephropathy and 10 diabetic patients with nd - ckd .
the diabetic nephropathy classification tree correctly distinguished diabetic nephropathy from nd - ckd with 80% specificity , as further confirmed by roc analysis ( fig .
the accuracy of the proteomic signature to discriminate diabetic nephropathy from nd - ckd was thus largely independent from the presence or the absence of diabetes in patients with nd - ckd .
classification and regression tree analysis of diabetic nephropathy and nd - ckd . a : histological picture of one patient with diabetic nephropathy and one patient with nd - ckd and their respective seldi urine protein profiles .
b : prediction success of cart analysis on the training set ( upper table ) and on the testing set with nondiabetic ( intermediate table ) and diabetic ( lower table ) patients with nd - ckd .
c : roc analysis of the ability of the proteomic signature to identify diabetic nephropathy .
dn , biopsy - proven diabetic nephropathy ; nd - ckd1 , nondiabetic patients with nondiabetic chronic kidney disease ; nd - ckd2 , diabetic patients with nondiabetic chronic kidney disease .
( a high - quality digital representation of this figure is available in the online issue . ) to further explore the relationship between proteomic signature and histological diagnosis , we applied logistic regression analysis , selecting a wide range of clinical , laboratory , and demographic variables ( age , sex , duration of diabetes , blood pressure levels , egfr , daily proteinuria , use of renin - angiotensin system [ ras ] blockers , and smoking ) and setting the proteomic signature as the dependent nominal variable ( absent / present ) .
simple logistic analysis showed that only smoking was significantly associated with the diabetic nephropathy signature . when smoking and histological diagnosis were entered into a multiple logistic model ,
only the latter independent variable retained the same coefficient , indicating the lack of a significant confounding effect of smoking ( not shown ) .
we examined the ability of cart analysis to discriminate among diabetic patients with normoalbuminuria , microalbuminuria , and diabetic nephropathy .
thirty - eight patients from all classes ( 16 with diabetic nephropathy , 10 with microalbuminuria , and 12 with normoalbuminuria ) were used to constitute the training sample group , whereas the remaining 8 samples of each group were used as a testing set .
we chose to limit the analysis to 24 randomly selected patients with diabetic nephropathy of 65 patients because a higher prevalence of diabetic nephropathy in the training set and mainly in the testing set could influence the results of the analysis .
the intensity of all the protein peaks and the presence or absence of diabetic retinopathy were used to construct the classificatory model . among the differently expressed mass peaks , those of 10,533 , 7,919 , 8,185 , 9,072 , 9,135 , 3,396 , 8,982 , 22,735 , 8,847 , 22,245 , 17,084 , and 16,710
m / z were identified as the best predictors to set up the discriminatory tree .
the cross - validation of the model on the training set showed 100% correct classification for all groups . when the classification tree was applied to the blinded testing set ,
seven of eight patients ( 87.5% ) of the micro and diabetic nephropathy groups and six of eight patients ( 75% ) of the nad group were correctly classified ( fig .
the roc curve for diabetic nephropathy showed a diagnostic power of 84% for the current analysis ( fig .
a : prediction success of the cart analysis for the training set ( upper table ) , after 10-fold cross - validation and the independent testing set ( lower table ) .
b : roc analysis of the ability of the proteomic signature to identify diabetic nephropathy .
dn , biopsy - proven diabetic nephropathy ; micro , microalbuminuric diabetic patients ; nad , normoalbuminuric diabetic patients . because many variables ( sex , duration of diabetes , blood pressure levels , egfr , daily proteinuria , use of ras blockers , and smoking ) differed among groups , we examined their impact on proteomic profile , set as a dependent polytomous variable , by logistic analysis .
nonetheless , none of the independent variables reached a significance < 0.05 at simple logistic analysis .
we examined and identified two mass peaks ( 11,700 and 8,589 m / z ) among the most prominent predictors included in the classification tree for diabetic nephropathy .
the 11,700 m / z peak was identified as 2-microglobulin by matrix - assisted laser desorption ionization - tof / tandem mass spectrometry ( supplementary figure a1 , available in an online appendix ) . to explore the relationship between 2 mg urinary excretion and diabetic nephropathy
, we compared 2 mg levels in the urine of diabetic patients with and without diabetic nephropathy ( fig .
3a , top panel ) by seldi profiling and measured 2 mg urinary excretion in patients with diabetic nephropathy and nd - ckd by elisa ( fig .
2 mg urine excretion was considerably higher in diabetic patients with diabetic nephropathy compared with that for the nad or micro groups or for patients with nd - ckd .
a , top : representative seldi spectra ( gel view ) showing 2 mg excretion in patients with diabetic nephropathy compared with that in healthy subjects and patients with normoalbuminuria , microalbuminuria ( left ) , and nd - ckd ( right ) .
bottom : 2-mg urine ( u ) excretion as measured by elisa ( mean  sem ) in patients with diabetic nephropathy compared with nd - ckd .
b , top : ubiquitin urine excretion as measured by seldi analysis on the whole urine profile ( mean  sem ) in patients with diabetic nephropathy compared with nd - ckd . bottom : seldi profiling of urine ubiquitin immunoprecipitated by a specific monoclonal antibody ( ubiquitin ip ) and run on a cm10 proteinchip array .
representative seldi spectra ( gel view ) from six patients with diabetic nephropathy and eight patients with nd - ckd are shown .
dn , biopsy - proven diabetic nephropathy ; micro , microalbuminuric diabetic patients ; hs , healthy subjects ; nad , normoalbuminuric diabetic patients .
the 8,589 m / z peak was provisionally identified as ubiquitin on the basis of its molecular weight and of the available literature on putative biomarkers of diabetic nephropathy . to confirm the identification ,
a recombinant ubiquitin standard was loaded on a blank spot of the cm10 proteinchip array , and its mass and shape were compared with those of the 8,589 m / z peak in the protein profiles of the patients ( supplementary figure a2 , available in an online appendix ) .
further , we immunoprecipitated ubiquitin from the urine of six patients with diabetic nephropathy and eight patients with nd - ckd , and each immunoprecipitate was then analyzed by a cm10 proteinchip array to confirm the increased excretion of ubiquitin in urine samples from patients with diabetic nephropathy ( fig .
we first tested the ability of biomarker pattern software to differentiate diabetic nephropathy from nd - ckd ( fig .
initially , urine samples from 54 patients with diabetic nephropathy and 57 nondiabetic patients with nd - ckd were distributed into the training set ( 31 diabetic nephropathy and 41 nd - ckd ) and the testing set ( 23 diabetic nephropathy and 16 nd - ckd ) . to minimize the influence of preanalytical biases on the classification approach , urine samples of patients with diabetic nephropathy collected in three distinct nephrology units were evenly distributed in both the training and the testing set .
the best classification tree created in the training set was then applied to an independent blinded set ( testing set ) of urine samples to validate its discriminatory power .
diabetic nephropathy and nd - ckd were correctly classified with 78.2 and 87.5% sensitivity and specificity , respectively ( fig .
1b ) . eighteen mass peaks ( 8,586 , 13,593 , 13,687 , 8,515 , 8,665 , 11,724 , 8,423 , 13,902 , 13,780 , 19,335 , 6,320 , 13,422 , 4,115 , 4,049 , 4,024 , 4,308 , 4,370 , and 3,086
m / z ) were identified as the best predictors for the diagnosis of diabetic nephropathy .
moreover , receiver operator characteristic ( roc ) analysis showed 80% accuracy in the diagnosis of diabetic nephropathy ( fig .
next , we were interested in examining the discriminatory power of the classification tree toward diabetic patients with ckd other than diabetic nephropathy . to this purpose ,
the diabetic nephropathy urine signature was scored on a second independent testing set including 11 newly recruited patients with diabetic nephropathy and 10 diabetic patients with nd - ckd .
the diabetic nephropathy classification tree correctly distinguished diabetic nephropathy from nd - ckd with 80% specificity , as further confirmed by roc analysis ( fig .
the accuracy of the proteomic signature to discriminate diabetic nephropathy from nd - ckd was thus largely independent from the presence or the absence of diabetes in patients with nd - ckd .
classification and regression tree analysis of diabetic nephropathy and nd - ckd . a : histological picture of one patient with diabetic nephropathy and one patient with nd - ckd and their respective seldi urine protein profiles .
b : prediction success of cart analysis on the training set ( upper table ) and on the testing set with nondiabetic ( intermediate table ) and diabetic ( lower table ) patients with nd - ckd .
c : roc analysis of the ability of the proteomic signature to identify diabetic nephropathy .
dn , biopsy - proven diabetic nephropathy ; nd - ckd1 , nondiabetic patients with nondiabetic chronic kidney disease ; nd - ckd2 , diabetic patients with nondiabetic chronic kidney disease .
( a high - quality digital representation of this figure is available in the online issue . ) to further explore the relationship between proteomic signature and histological diagnosis , we applied logistic regression analysis , selecting a wide range of clinical , laboratory , and demographic variables ( age , sex , duration of diabetes , blood pressure levels , egfr , daily proteinuria , use of renin - angiotensin system [ ras ] blockers , and smoking ) and setting the proteomic signature as the dependent nominal variable ( absent / present ) .
simple logistic analysis showed that only smoking was significantly associated with the diabetic nephropathy signature . when smoking and histological diagnosis were entered into a multiple logistic model ,
only the latter independent variable retained the same coefficient , indicating the lack of a significant confounding effect of smoking ( not shown ) .
we examined the ability of cart analysis to discriminate among diabetic patients with normoalbuminuria , microalbuminuria , and diabetic nephropathy .
thirty - eight patients from all classes ( 16 with diabetic nephropathy , 10 with microalbuminuria , and 12 with normoalbuminuria ) were used to constitute the training sample group , whereas the remaining 8 samples of each group were used as a testing set .
we chose to limit the analysis to 24 randomly selected patients with diabetic nephropathy of 65 patients because a higher prevalence of diabetic nephropathy in the training set and mainly in the testing set could influence the results of the analysis .
the intensity of all the protein peaks and the presence or absence of diabetic retinopathy were used to construct the classificatory model . among the differently expressed mass peaks , those of 10,533 , 7,919 , 8,185 , 9,072 , 9,135 , 3,396 , 8,982 , 22,735 , 8,847 , 22,245 , 17,084 , and 16,710
m / z were identified as the best predictors to set up the discriminatory tree .
the cross - validation of the model on the training set showed 100% correct classification for all groups . when the classification tree was applied to the blinded testing set ,
seven of eight patients ( 87.5% ) of the micro and diabetic nephropathy groups and six of eight patients ( 75% ) of the nad group were correctly classified ( fig .
the roc curve for diabetic nephropathy showed a diagnostic power of 84% for the current analysis ( fig .
cart analysis of diabetic patients with normoalbuminuria , microalbuminuria , and diabetic nephropathy . a : prediction success of the cart analysis for the training set ( upper table ) , after 10-fold cross - validation and the independent testing set ( lower table ) .
b : roc analysis of the ability of the proteomic signature to identify diabetic nephropathy .
dn , biopsy - proven diabetic nephropathy ; micro , microalbuminuric diabetic patients ; nad , normoalbuminuric diabetic patients .
because many variables ( sex , duration of diabetes , blood pressure levels , egfr , daily proteinuria , use of ras blockers , and smoking ) differed among groups , we examined their impact on proteomic profile , set as a dependent polytomous variable , by logistic analysis .
nonetheless , none of the independent variables reached a significance < 0.05 at simple logistic analysis .
we examined and identified two mass peaks ( 11,700 and 8,589 m / z ) among the most prominent predictors included in the classification tree for diabetic nephropathy .
the 11,700 m / z peak was identified as 2-microglobulin by matrix - assisted laser desorption ionization - tof / tandem mass spectrometry ( supplementary figure a1 , available in an online appendix ) . to explore the relationship between 2 mg urinary excretion and diabetic nephropathy
, we compared 2 mg levels in the urine of diabetic patients with and without diabetic nephropathy ( fig .
3a , top panel ) by seldi profiling and measured 2 mg urinary excretion in patients with diabetic nephropathy and nd - ckd by elisa ( fig .
2 mg urine excretion was considerably higher in diabetic patients with diabetic nephropathy compared with that for the nad or micro groups or for patients with nd - ckd .
a , top : representative seldi spectra ( gel view ) showing 2 mg excretion in patients with diabetic nephropathy compared with that in healthy subjects and patients with normoalbuminuria , microalbuminuria ( left ) , and nd - ckd ( right ) .
bottom : 2-mg urine ( u ) excretion as measured by elisa ( mean  sem ) in patients with diabetic nephropathy compared with nd - ckd .
b , top : ubiquitin urine excretion as measured by seldi analysis on the whole urine profile ( mean  sem ) in patients with diabetic nephropathy compared with nd - ckd .
bottom : seldi profiling of urine ubiquitin immunoprecipitated by a specific monoclonal antibody ( ubiquitin ip ) and run on a cm10 proteinchip array .
representative seldi spectra ( gel view ) from six patients with diabetic nephropathy and eight patients with nd - ckd are shown .
dn , biopsy - proven diabetic nephropathy ; micro , microalbuminuric diabetic patients ; hs , healthy subjects ; nad , normoalbuminuric diabetic patients .
the 8,589 m / z peak was provisionally identified as ubiquitin on the basis of its molecular weight and of the available literature on putative biomarkers of diabetic nephropathy . to confirm the identification ,
a recombinant ubiquitin standard was loaded on a blank spot of the cm10 proteinchip array , and its mass and shape were compared with those of the 8,589 m / z peak in the protein profiles of the patients ( supplementary figure a2 , available in an online appendix ) .
further , we immunoprecipitated ubiquitin from the urine of six patients with diabetic nephropathy and eight patients with nd - ckd , and each immunoprecipitate was then analyzed by a cm10 proteinchip array to confirm the increased excretion of ubiquitin in urine samples from patients with diabetic nephropathy ( fig .
the data reported in the present work suggest that the analysis of urine protein profiles of type 2 diabetic patients , as generated by seldi - tof / ms with a cm10 proteinchip , can be considered a reliable method to identify patients with diabetic nephropathy among diabetic patients and , more importantly , to distinguish biopsy - proven diabetic nephropathy from nd - ckd in both diabetic and nondiabetic patients . currently , microalbuminuria is the only noninvasive marker of early diabetic nephropathy .
however , microalbuminuria may instead reflect the existence of endothelial damage , in the absence of specific renal lesions , or , on the contrary , be associated with advanced renal pathological changes .
furthermore , clinically overt nephropathy and/or albuminuria in diabetic patients does not necessarily imply the presence of diabetic glomerulosclerosis . in the largest histological study ( 393 renal biopsies )
performed to date , other glomerulonephritides superimposed on diabetic glomerulosclerosis or glomerulonephritides without the presence of diabetic nephropathy occurred in up to 57% of patients ( 3 ) .
moreover , patients with type 2 diabetes can progress to a significant degree of renal impairment even if they remain normoalbuminuric , and this occurrence may reflect renal parenchymal diseases other than classic diabetic glomerulosclerosis ( 15 ) .
diabetic patients with nd - ckd have a different rate of gfr decline ( 16,17 ) and , more importantly , may be amenable to specific treatments . at present , however , there is not a strong predictor to differentiate diabetic nephropathy from nd - ckd by clinical or biochemical data . given this background , novel biomarkers for earlier diagnosis of diabetes - related renal damage as well as for the proper identification of diabetic glomerulosclerosis are crucially required . over the last several years , only a few studies have adopted proteomic strategies focused on identifying one or more urine biomarkers that would allow either the early detection of diabetic nephropathy or its discrimination from other nd - ckd or the identification of normoalbuminuric type 2 diabetic patients prone to develop diabetic nephropathy ( 1012,18,19 ) . all of them , however , identified diabetic nephropathy exclusively on a clinical basis ( i.e. , the presence of macroalbuminuria with or without a decline of egfr ) , which may potentially lead to a misclassification in more than one - third of patients in the absence of histological verification ( 3 ) . moreover , most studies failed to apply supervised learning algorithms to validate the proposed signatures .
the main aim of the present study was to identify biopsy - proven diabetic nephropathy by urine proteomic fingerprint .
we therefore started by comparing the urine proteome of 54 biopsy - proven diabetic nephropathy with that of 57 nondiabetic patients with proteinuria and ckd .
cart analysis of the blinded testing set revealed 78.2 and 87.5% sensitivity and specificity , respectively , thus indicating that the urine proteome contains a set of key information useful to accurately distinguish biopsy - proven diabetic nephropathy from nd - ckd .
moreover , multivariate analysis allowed us to rule out the confounding effect of a number of potentially relevant covariates ( see results ) . to corroborate the discriminatory power of the classification tree , we tested the urinary fingerprint of biopsy - proven diabetic nephropathy in a further group of 21 diabetic patients , 11 with diabetic nephropathy and 10 with nd - ckd .
cart analysis identified patients with diabetic nephropathy with 80% accuracy , thus demonstrating the ability of the urine proteomic fingerprint to discriminate diabetic nephropathy from nd - ckd , both in the presence and in the absence of diabetes .
however , we are aware that the small size of the dataset we explored limits the possibility of drawing any final conclusions .
we then applied seldi protein profiling to the study of the urine proteome of the whole population of diabetic patients and analyzed urine profiles by both unsupervised and supervised learning methods .
when seldi spectra were analyzed by unsupervised methods , a list of mass peaks showed a significantly different expression among groups ( data not shown ) , but none of these putative biomarkers was , by itself , sensitive and specific enough to allow a reliable discrimination among classes . to overcome the limitations of the univariate analysis ,
we analyzed seldi spectra by supervised learning methods , which screened all mass peaks to build up an optimal classification tree .
the classification tree correctly identified 87.5% of patients with microalbuminuria and biopsy - proven diabetic nephropathy and 75% of patients with normoalbuminuria in the blinded test set .
the inclusion of a retinopathy score ( present / absent ) in the analysis did not improve the accuracy of the classification model ( data not shown ) .
finally , by logistic analysis we ruled out the possibility that the urinary proteomic signature would be significantly influenced by a number of variables ( sex , blood pressure levels , degree of deterioration of renal function , smoking , and ras blocker therapy ) differently distributed among groups .
we must recognize that albuminuria per se could not properly reflect the type and the degree of renal damage , and this fact might help explain some apparent misclassification within the groups lacking histological examination .
if so , proteomic  signature  might unmask the existence of a clinically silent renal injury . indeed , all patients with normoalbuminuria were checked again after a median of 1.8 years , but none of them had progressed toward microalbuminuria nor
, we were unable to correlate the initial misclassification of the two patients with normoalbuminuria with the progression of renal disease .
we may infer that a longer follow - up is required to ascertain whether some of the protein peaks in the normoalbuminuria signature depicted here would serve as early predictive biomarkers of diabetic glomerulosclerosis in normoalbuminuric diabetes ( 11 ) .
interestingly , all biopsy - proven diabetic nephropathy showed a conserved proteomic pattern , independent from the individual rate of urine albumin excretion , which supports the assumption that the albumin excretion rate does not necessarily reflect the type and degree of renal damage .
the identification of some of the proteins in the proteomic signature of diabetic nephropathy might provide insight into the mechanisms underlying the disease , besides serving as candidate biomarkers to diagnose diabetic nephropathy .
therefore , two proteins 8,589 and 11,700 m / z , chosen among the most prominent predictors included in the classification tree , were isolated and identified as ubiquitin and 2 mg , respectively .
their increased excretion in the urine of patients with diabetic nephropathy compared with that in diabetic patients without diabetic nephropathy and with nd - ckd was confirmed by immunoprecipitation ( ubiquitin ) or elisa ( 2 mg ) and was found to be independent of the severity of daily proteinuria .
our findings are consistent , at least in part , with those reported by dihazi et al .
these authors found a significant excretion of 2 mg in proteinuric diabetic patients and a selective excretion of ubiquitin ribosomal fusion protein in micro- and macroalbuminuric diabetic patients , who instead released very low amounts of the truncated form of ubiquitin .
these partial discrepancies may be ascribed to the use of protease inhibitors in our study , which possibly prevented ubiquitin - specific degradation ( 12 ) .
regardless , deranged excretion of ubiquitin is a novel and potentially interesting biomarker of diabetic nephropathy . at variance ,
increased levels of urine 2 mg , a recognized marker of renal tubular damage , have long been described in diabetic patients with micro- and macrovascular complications and mainly in those with associated hypertension ( 20 ) , as well as in diabetic patients with micro- or macroalbuminuria ( 12 ) , but studies comparing 2 mg release in patients with diabetic nephropathy versus those with nd - ckd are currently lacking . in summary , we report that the standardization of urine analysis by seldi - tof / ms ( 14,21 ) and the elaboration of its complex datasets by means of supervised statistical methods allowed us to generate a robust multiparametric panel of mass peaks that was able to reliably discriminate biopsy - proven diabetic nephropathy from nd - ckd in both diabetic and nondiabetic patients . these findings ,
if confirmed in larger cohorts of diabetic patients , encourage the use of supervised learning approaches for the analysis of urine proteomic profiles to achieve a noninvasive differential diagnosis of renal lesions in diabetic patients , whereas the appraisal of their possible predictive power demands longitudinal studies . finally , the selective release of high amounts of ubiquitin and 2 mg in the urine of patients with diabetic nephropathy may suggest a role as candidate biomarkers and possible involvement in the pathophysiology of the disease .
 objectivechronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases ( ckd ) other than classic diabetic nephropathy in more than one - third of patients . 
 we interrogated urine proteomic profiles generated by surface - enhanced laser desorption / ionization - time of flight / mass spectrometry with the aim of isolating a set of biomarkers able to reliably identify biopsy - proven diabetic nephropathy and to establish a stringent correlation with the different patterns of renal injury.research design and methodsten micrograms of urine proteins from 190 subjects ( 20 healthy subjects , 20 normoalbuminuric , and 18 microalbuminuric diabetic patients and 132 patients with biopsy - proven nephropathy : 65 diabetic nephropathy , 10 diabetic with nondiabetic ckd [ nd - ckd ] , and 57 nondiabetic with ckd ) were run using a cm10 proteinchip array and analyzed by supervised learning methods ( classification and regression tree analysis).resultsthe classification model correctly identified 75% of patients with normoalbuminuria , 87.5% of those with microalbuminuria , and 87.5% of those with diabetic nephropathy when applied to a blinded testing set . 
 most importantly , it was able to reliably differentiate diabetic nephropathy from nd - ckd in both diabetic and nondiabetic patients . among the best predictors of the classification model 
 , we identified and validated two proteins , ubiquitin and 2-microglobulin.conclusionsour data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis . 
RESEARCH DESIGN AND METHODS
Renal biopsy examination
Urine collection and management
SELDI profiling
CART analysis
Multivariate analysis
Protein identification and validation
RESULTS
Differential diagnosis between diabetic nephropathy and nd-CKD
Setup of a classification tree to discriminate normoalbuminuria, microalbuminuria, and biopsy-proven diabetic nephropathy
Protein identification
2MG
Ubiquitin
CONCLUSIONS
Supplementary Material