Evaluation of Tubulointerstitial Lesions' Severity in Patients with Glomerulonephritides: An NMR-Based Metabonomic Study

Evaluation of Tubulointerstitial Lesions’ Severity in Patients with

Glomerulonephritides: An NMR-Based Metabonomic Study

Nikolaos G. Psihogios,† Rigas G. Kalaitzidis,‡ Sofia Dimou,§ Konstantin I. Seferiadis,†

Kostas C. Siamopoulos,‡ and Eleni T. Bairaktari*,†

Laboratory of Clinical Chemistry, Department of Nephrology, and Department of Histopathology,Medical School, University of Ioannina, GR-451 10, Ioannina, Greece

Received March 27, 2007

An 1H NMR-based metabonomic approach was used to investigate the correlation of histopathologicallyassessed tubulointerstitial lesions with the urinary metabolite profile in 77 patients with glomerulo-nephritides submitted to renal biopsy. The presence of renal damage was predicted with a sensitivityof 96% and a specificity of 99%. Patients with mild, moderate, and severe tubulointerstitial lesionswere progressively differentiated from the healthy individuals in the Orthogonal Signal Correction PartialLeast-Squares-Discriminant Analysis (OSC/PLS-DA) models with a statistically significant separationbetween those with mild and with severe lesions. The onset of the tubulointerstitial lesions ischaracterized by decreased excretion of citrate, hippurate, glycine, and creatinine, whereas furtherdeterioration is followed by glycosuria, selective aminoaciduria, total depletion of citrate and hippurate,and gradual increase in the excretion of lactate, acetate, and trimethylamine-N-oxide. NMR-basedmetabonomic urinalysis could contribute to the early evaluation of the severity of the renal damageand possibly to the monitoring of kidney function.

Keywords: glomerulonephritis • tubulointerstitial • urine • 1H NMR spectroscopy • metabonomics • kidney

Introduction

Glomerulonephritis (GN) is a group of disorders character-ized by inflammation in the filtering unit of the kidney, theglomerulus, which along with a long tubule comprises theanatomical and functional unit of the kidney, the nephron. Theunit is surrounded by a functionally important tissue calledinterstitial tissue. GN may be primary; secondary to drugs,infections, or tumors; or the presenting feature of systemicdisease.1 GN causes significant morbidity and mortality, andis a potentially preventable cause of renal failure and cardio-vascular risk. Although the glomerulus is the primary site ofdamage, subsequent injury to the tubulointerstitium plays amajor role in the overall outcome of glomerular disease.1,2

The diagnosis of GN can be suspected by clinical andlaboratory findings, such as proteinuria and abnormal urinemicroscopy.1 However, renal biopsy is considered as the maintool for the evaluation of the type and degree of renal injury inalmost all cases of glomerulonephritides.3 In addition, carefulpathological analysis reveals the extent of tubulointerstitialdamage and degree of renal tubular fibrosis, findings that in anumber of cases are correlated better with the deteriorationof renal function than the degree of glomerular damage itself.4

1H NMR spectroscopy of urine provides overall profiles oflow molecular weight (LMW) metabolites that alter character-istically in response to changes in physiological status, toxicinsult, or disease processes.5-8 In situations where renal damageis present in humans or experimental animals, the LMWmetabolite profile of urine is significantly altered, and this isclosely reflected in the 1H NMR spectral fingerprint. Further-more, in studies with experimental animals exposed to region-specific nephrotoxins, the NMR-generated metabolite profileswere characteristically changed according to the exact site andmechanism of the lesion (glomeruli, lower or upper regions ofthe proximal tubules, renal medulla).8,9 In the clinical field,NMR urinalysis has contributed to the assessment of renaltransplant dysfunction,10,11 to the early detection of latenttubulointerstitial distortions in glomerulonephritis,12,13 and tothe detection of renal dysfunction in several pathologicalstates.14-17

The exploitation of the NMR-generated metabolic data setscan be increased by the application of multivariate statisticalanalysis including pattern recognition (PR) methods that allowsample classification and effective interpretation.18-20 Thisrelatively new approach, known as metabonomics,21 has hadmajor applications in clinical and biomedical topics such asdrug toxicity assessment, identification of biomarkers of toxicityand disease, and the understanding of the mechanisms ofmetabolic responses.8,22-24

This prospective study investigates the correlation of tubu-lointerstitial lesions found in renal biopsies with the metabolite

* Author for correspondence. Eleni T. Bairaktari, Ph.D., EurClinChem,Assistant Professor, Laboratory of Clinical Chemistry, Medical SchoolUniversity of Ioannina, 451 10 Ioannina, Greece. Phone/fax, +30-2651097620/97871; e-mail, [email protected].

† Laboratory of Clinical Chemistry, Medical School, University of Ioannina.‡ Department of Nephrology, Medical School, University of Ioannina.§ Department of Histopathology, Medical School, University of Ioannina.

3760 Journal of Proteome Research 2007, 6, 3760-3770 10.1021/pr070172w CCC: $37.00 2007 American Chemical SocietyPublished on Web 08/18/2007

profile of urine analyzed by NMR-based metabonomics inpatients with glomerulonephritides.

Materials and Methods

Subjects. The study initially included 80 consequentlyadmitted patients to the Department of Nephrology of theUniversity Hospital of Ioannina to be submitted to renal biopsydue to renal function abnormalities such as increased serumcreatinine and/or proteinuria and chronic renal disease stages1-3.25 Three patients were excluded during the analysis of thespectroscopic data, as described in the Results. The inclusioncriteria were moderate proteinuria (<2 g/24 h) and serumcreatinine <3 mg/dL. Eighty-five sex- and age-matched healthyindividuals who did not require regular medication other thanoral contraception or over-the-counter drugs constituted thecontrol group. All study participants gave informed consent forthe investigation, which was approved by the Ethical Commit-tee of the University Hospital of Ioannina.

Histopathology. Patients were submitted to renal biopsy,and the renal tissues were examined by light microscopy andin certain cases with immunofluorescence and/or electronmicroscopy. Immunostainings were performed on formalin-fixed, paraffin-embedded tissue sections by the labeled strepta-vidin biotin (LSAB) method. All biopsies were reviewed by onepathologist who was blind to the NMR data. The renal biopsydiagnoses included focal segmental glomerulosclerosis in 23patients, membranous nephropathy in 13 patients, IgA neph-ropathy in 8 patients, mesangioproliferative glomerulopathy in5 patients, systemic lupus erythematosus in 8 patients, vascu-litis in 5 patients, diabetic nephropathy in 9 patients, minimalchange disease in 4 patients, and other causes in 5 patients.Tubulointerstitial lesions included tubular atrophy, interstitialfibrosis, and mononuclear cell infiltration. The extent of theselesions was graded as follows: mild (n ) 25), moderate (n )27), and severe (n ) 25).

Samples. All subjects were requested to fast overnight andabstain from any medication (including over-the-counterdrugs), alcohol, and fish consumption, known to significantlyaffect the urinary metabolite profile,26 24 h before sampling.Blood and urine samples were obtained before patients weresubmitted to renal biopsy. Serum was separated by centrifuga-tion at 1500g for 15 min. First void urine samples werecentrifuged at 1000g for 10 min, and an aliquot was taken forclinical chemistry tests. Sodium azide (1 g/L, 100 µL) was addedto the remaining urine sample to prevent bacterial contamina-tion and stored at -80 °C until NMR analysis.

Clinical Chemistry. Analysis of clinical chemistry parametersof serum and urine was carried out on an Olympus AU600Clinical Chemistry analyzer (Olympus Diagnostica, Hamburg,Germany) by standard procedures. GFR was calculated by theMDRD equation.27

1H NMR Spectroscopy. Four hundred microliters of urinewas mixed with 200 µL of phosphate buffer (0.2 M Na2HPO4/0.2 M NaH2PO4, pH 7.4) in order to minimize pH variations,and then a solution of 0.075% sodium 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate (TSP) in D2O was added.

1H NMR spectra were measured at 300 K on a 500 MHzBruker DRX NMR instrument operating at 500.13 MHz andrunning on XWINNMR V.2.6 software. For the suppression ofthe water signal, the standard 1D pulse sequence NOESYPRE-SAT (RD-90°-t1-90°-tm-90°-FID acquisition) was used.28 RD wasa 3 s relaxation delay to ensure T1-relaxation between succes-sive scans and during which the water peak was selectively

irradiated; t1 represented the first increment in the NOESYexperiment and was set to 3 µs; tm was the mixing time of 150ms, during which the water resonance was again selectivelyirradiated. For each spectrum, 128 scans were collected into64K computer data points with a spectral width of 6009.6 Hz.The FIDs were multiplied with an exponential line broadeningfunction of 0.3 Hz prior to Fourier transformation. The acquiredNMR spectra were manually corrected for phase and baselinedistortions by applying a simple polynomial curve fit withTopSpin 1.2 (Bruker Biospin Ltd.) and referenced to TSP (δ1H0.0). The metabolites were assigned according to publishedliterature and 2D experiments (Supplementary Figure 1 inSupporting Information).

Two-dimensional (2D) NMR spectra were carried out onselected samples for identification of urine metabolites. 1H-1HTOCSY spectra were acquired using the MLEV17 spin-lockscheme (mlevesgpph). Fifty-six transients per increment for 800increments were collected into 2048 data points in the F2dimension using a spectral width of 12.02 ppm in bothfrequency axes and a relaxation delay of 1.2 s. A sine-bellsquared function was applied to the data prior to Fouriertransformation.

Statistical Analysis. Statistical analysis was performed withStatistica Ver. 6.0 (StatSoft, Inc., Tulsa, OK). Values wereexpressed as mean value ( standard deviation (SD) andcompared by using t test. Significance levels were set at 0.05.

NMR Data Reduction and Pattern Recognition (PR). The1H NMR spectra were automatically reduced using AMIX(Analysis of MIXtures) software package (version 3.2.4, BrukerAnalytik, Rheinstetten, Germany) to 244 continuous integralsegments (variables or bins) of equal width of 0.04 ppmcorresponding to the chemical shift range δ1H, 0.2-10.0. Thearea between 4.38 and 6.30 ppm was excluded to remove anyeffect of variation from the suppression of the water resonanceand from any cross-relaxation effect on the urea signal viasolvent exchanging protons. The integral regions of the citrate(2.50-2.58 and 2.66-2.74) and the creatinine (3.02-3.06 and4.02-4.06) resonances were merged to take into account thepH-dependent peak shifts and formed the “superbins” 2.54 and2.7 for citrate and 3.04 and 4.04 for creatinine, respectively. Alldata was normalized by dividing each integral segment by thetotal area of the spectrum in order to compensate for thedifferences in overall concentration between individual urinesamples. The resulting data matrix, consisting of 194 NMRintegral segments, was exported to the SIMCA-P softwarepackage (version 10.5, UMETRICS AB, Box 7960, SE 90719,Umeå, Sweden) for the PR analysis. Prior to the analysis, theNMR data were centered and Pareto scaled (scaling factor1xSD).

PCA was used for the overview of the metabonomic data setand the spotting of outliers, and then for the detection of anygrouping or separation trend.20 The PCA scores plot was usedto reveal observations lying outside the 0.95 Hotelling’s T2ellipse (strong outliers) and the loadings plot to interpret thepatterns seen in the scores plot. The model residuals plot,DModX, was used to detect observations that exceeded thecritical distance of significance <0.05 (moderate outliers).20

With Partial Least-Squares Discriminant Analysis (PLS-DA)a relationship was sought between the matrix of variables X(NMR spectral bins) and a matrix of dependent variables Y(dummy variables encoding the class membership, i.e., patientor control). The method was used to find the best possiblediscriminant function (model) that separates renal patients

Tubulointerstitial Lesions’ Severity in Patients with GN research articles

Journal of Proteome Research • Vol. 6, No. 9, 2007 3761

from controls as well as the three defined histopathology groupson the basis of their X variables.20 For the interpretation of thescores plot, the regression coefficients plot was used, whichshows all spectral regions that contribute to the separationbetween the studied groups.

The technique of Orthogonal Signal Correction (OSC) wasapplied to remove linear combinations of variables X that wereorthogonal to the Y vector of the dependent variables, toeliminate the intersubject variability and to describe maximumseparation based on class.29

The default method of 7-fold internal cross-validation (CV)of SIMCA-P software was applied, and the extracted parameterQ2 was used to provide an estimation of the predictivecapability of the PLS-DA models with Q2 > 0.5 considered‘good’ and Q2 > 0.9 ‘excellent’.20 The parameter R2 describesthe explained variation and how well the data can be math-ematically reproduced by the training model.

In addition, validation was performed using both held-backand external data procedures. In held-back data validation, astest set serves a portion of the training set used for theconstruction of the model, whereas in external data validation,as test set (prediction set) serves a new set of data not usedwhen the model was built.

Held-back data validation for the patients-controls modelwas performed using 81% of the data as the training set andthe remaining 19% as the test set, whereas for the modelbetween patient groups, 68% of the data defined the trainingset and the remaining 32% the test set. External validation forthe patients-controls model was performed using 70% of thedata as the training set and the remaining 30% as the predictionset, whereas for the model between patient groups, 68% of thedata defined the training set and the remaining 32% theprediction set. All observations were assigned with a class-specific numerical value to form a response Y matrix. Correctclassification was based on a predicted Y cutoff of 0.5 with a95% confidence level.

Correct and incorrect assignments were used to defineTrue Positives (TP), True Negatives (TN), False Positives(FP), and False Negatives (FN) classification rates and then toestimate as percent sensitivity [TP/(TP + FN) × 100] andspecificity [TN/(TN + FP) × 100].

Results

Clinical Chemistry. In Table 1, the main demographic andclinical chemistry parameters of the populations studied areshown. Patients with GN presented statistically significanthigher levels of serum creatinine and urine total proteins andlower levels of serum albumin and GFR than the controlpopulation. There were also statistically significant differences

among the three groups of patients defined by the severity ofthe tubulointerstitial lesions in renal biopsy estimation. Patientswith moderate and severe lesions had significantly differentlevels of creatinine, GFR, and urine protein from those withmild lesions. However, no significant differences were observedbetween those with moderate and severe lesions.

1H NMR Spectroscopy. Four typical 1H NMR 500 MHzspectra of urine from a healthy individual and patients withmild, moderate, and severe renal damage indicating thedifferent excretive profile of the LMW metabolites in each caseare shown in Figure 1. The main constituents of the urinespectrum from the healthy individual are creatinine, hippurate,citrate, glycine, trimethylamine-N-oxide (TMAO), dimethy-lamine (DMA), and small amounts of lactate, 3-hydroxybutyrate(3-HB), N-acetyl groups from glycoproteins (N-Acs), and aminoacids such as alanine, phenylalanine, and valine.30 The spec-trum from the patient with mild renal damage indicates partialinhibition in the excretion of hippurate, citrate, and glycine,whereas the spectrum from the patient with moderate renaldamage reflects further decrease in the excretion of hippurate,citrate, and glycine followed by an increase in the levels oflactate, alanine, and phenylalanine. The spectrum from thepatient with severe renal damage indicates significant tocomplete inhibition in the excretion of hippurate, citrate, andglycine; increased levels of glucose, lactate, alanine, phenyla-lanine, and histidine; and a slight elevation of the spectrum’sbaseline in the region (1.8-0.5 ppm) due to the resonance ofthe aliphatic moieties of proteins excreted in urine. A significantnumber of patients, apart from elevated TMAO levels, excretedone or more choline headgroup containing metabolites (be-tween 3.20 and 3.30 ppm), but not in accordance to the severityof the renal damage.

The metabonomic data set initially consisted of 165 urineNMR spectra: 80 from patients that underwent renal biopsyand 85 from healthy individuals. The urine spectra were visuallyinspected, and 3 from the patient group were excluded: thefirst one showed 2 intense unidentified peaks at 2.16 and 2.18ppm (probably metabolites from paracetamol ingestion) andthe other two showed intense peaks within the region 3.5-3.8ppm probably due to metabolites from drugs that the patientshad received before sampling.

Pattern Recognition Analysis. In this data set (from 77patients and 85 healthy individuals), PCA was applied, and thescores plot (Figure 2a) showed a separation trend between thetwo groups with healthy individuals clustering to the rightsection and patients spreading mainly to the left side of theplot. The PCA plot also revealed 2 spectra with significantalterations from patients mainly characterized by high peaksof glucose and decreased excretion of creatinine. In the relative

Table 1. Demographic and Clinical Chemistry Characteristics of the Populations Studied

Subgrouping of GN patientsa

controls patients p mild moderate severe

Number 85 77b 25 27 25Age 52.0 ( 8.7 55.5 ( 15.4 NS 49.2 ( 17.8 56.9 ( 9.7 55.4 ( 16.8Sex (males/females) 42/43 45/32 12/13 19/8 15/10S Creatinine (mg/dL) 0.9 ( 0.2 2.0 ( 1.8 <0.001 1.1 ( 0.4 1.5 ( 0.4c,## 1.6 ( 0.5c,###

S Albumin (g/dL) 4.4 ( 0.3 3.5 ( 0.7 <0.001 3.6 ( 0.7 3.6 ( 0.7 3.2 ( 0.7U Total Proteins (g/24 h) 0.015 ( 0.012 0.370 ( 0.540 <0.001 0.150 ( 0.209 0.427 ( 0.595c,# 0.622 ( 0.710c,##

GFR (mL/min/1.73 m2) 85.2 ( 11.1 48.6 ( 25.2 <0.001 68.8 ( 23.9 49.4 ( 15.5c,## 40.4 ( 14.4c,###

a Severity of the tubulointerstitial lesions in renal biopsy estimation. b Three out of the 80 patients were excluded (see Results). c #, p < 0.05; ##, p < 0.01;###, p < 0.001 t test compared to mild.

research articles Psihogios et al.

3762 Journal of Proteome Research • Vol. 6, No. 9, 2007

loadings plot (Figure 2b), variables (i.e., bins) contributingsimilar information are grouped together, and hence, metabo-lites located at the left part (glucose) are positively correlatedwith patients, whereas those located at the right part (hippu-rate, citrate, and creatinine) are positively correlated withhealthy subjects.

With PLS-DA, an improved separation was achieved still con-taining, however, a degree of overlapping between the two clas-ses (Figure 2c). The model parameters for the explained varia-tion R2 and the predictive capability Q2 were significantly high,0.67 and 0.61, respectively (Table 2). The relative regressioncoefficients plot (Figure 2d) showed that, in addition to the

Figure 1. 1H NMR 500 MHz spectra of urine (δ 0.3-4.6 and 6.8-8.7 ppm) from one healthy subject and patients with mild, moderate,and severe renal damage. Abbreviations: 3-HB, 3-hydroxybutyrate; Ac, acetate; Ala, alanine; Chl, choline headgroup containingmetabolites; Cit, citrate; Crn, creatinine; DMA, dimethylamine; Fm, formate; Gly, glycine; Glc, glucose; Hip, hippurate; His, histidine;Lac, lactate; N-Acs, N-acetyl groups from glycoproteins; Phe, phenylalanine; TMAO, trimethylamine-N-oxide; Val, valine.



above-mentioned metabolites, glycine was found in relativelyhigher levels in controls (positive coefficients), whereas lactate,

acetate, TMAO, and aliphatic moieties of proteins were foundin relatively higher levels in patients (negative coefficients).

Figure 2. (a) PCA scores plot (PC1 vs PC2) of the 1H NMR urinary spectroscopic data from 162 subjects. The samples are colored forthe 77 patients (red squares) and for the 85 controls (blue triangles). (b) The corresponding loadings plot. (c) PLS-DA scores plot (PC1vs PC2). (d) The corresponding regression coefficients for PC1. (e) OSC/PLS-DA scores plot (t1 vs t2): 85 healthy subjects (blue triangles),25 patients with mild (red squares), 27 patients with moderate (green diamonds), and 25 patients with severe renal damage (purplecircles). The ellipses surrounding the samples denote each group. (f) The corresponding regression coefficients plot for t1. Abbreviationsas in Figure 1; Prot: proteins.



To determine the ability of the 1H NMR-based metabonomicanalysis to distinguish the severity of the tubulointerstitialinjury, PLS-DA was also applied to compare the healthysubjects with each one of the 3 patient groups characterizedby mild, moderate, and severe damage (Table 2). From thecorresponding R2 and Q2 parameters, it can be seen that themore severe the renal damage is assessed, the more the valuesand therefore the predictive capability of the model increase.

To minimize the possible intrinsic contribution of intersub-ject variability, the method of OSC filtering was applied fromwhich two orthogonal components were removed and PLS-DA was repeated. Inspection of the two excluded orthogonalcomponents revealed the metabolites possessing the greatestintersubject variability, that is, hippurate, creatinine, citrate,and TMAO, that are known to exhibit a large biologicalvariation.26,31 The scores plot of the first two PCs from theresulting OSC/PLS-DA model revealed a clear separationbetween patients and controls, which was more distinct as therenal damage deteriorated from mild to severe (Figure 2e). Theregression coefficients plot for the first component of the OSC/PLS-DA model indicated that the spectroscopic regions con-tributing to the clustering were almost the same as in theunfiltered data set, but with enhanced importance of thecoefficients, mainly for creatinine, hippurate, citrate, andglucose (Figure 2f). The values of the corresponding R2 and Q2

parameters were improved to 0.80 and 0.73, respectively, aswell as those between each patient group and the controls(Table 2). In Supplementary Figure 2 (Supporting Information),the scores plots between each patient group and the controlsshow clearly that the spectra from patients are placed awayfrom the control region following the severity of the disease.On the basis of the values of the OSC/PLS-DA regressioncoefficients, controls mainly excreted higher levels of citrate,hippurate, and creatinine, whereas patients mainly excretedhigher levels of glucose and a group of unidentified metabolites(3.70-3.74 ppm), choline headgroup containing metabolites,proteins, and acetate (Table 3).

Additional models were developed to compare the threepatient groups together as well as pairwise (Table 4, Figure 3).As it is indicated by the R2 and Q2 parameters of the PLS-DAmodels in Table 4, the separation between the three patientgroups was of low significance. In the pairwise comparison,moderate damage group was partially separated from both mildand severe groups, whereas a more distinct separation wasobserved between those of mild and severe damage (R2 ) 0.73and Q2 ) 0.46). The OSC/PLS-DA models were of similardiscriminating power (Table 4), except for the mild-severemodel, in which a higher and significant predictive capability(0.46 vs 0.55) was noted, seen also in the corresponding scoresplot (Figure 3d). On the basis of the values of the regressioncoefficients, the metabolites that predominantly contributedto the separation of the moderate from the mild damage groupwere citrate, creatine, phenylalanine, glucose, and a group ofunidentified metabolites (3.70-3.74 ppm) and from the severedamage group were creatinine, acetate, hippurate, glucose, a

group of unidentified metabolites (3.70-3.74 ppm), and thealiphatic moieties of proteins (Table 5). The metabolites thatmainly contributed to the separation of the mild from thesevere damage group were citrate, creatinine, hippurate, cre-atine, glucose, a group of unidentified metabolites (3.70-3.74ppm), the aliphatic moieties of proteins, and phenylalanine(Table 5).

Exclusion of the 0.2-1.82 ppm Region. Since proteinuriaoften characterizes renal patients,25 the spectral region contain-ing bulk signals from the aliphatic moieties of proteins (0.2-1.82 ppm) was excluded, and OSC/PLS-DA was repeated withthe residual spectral data. The new models were still able todistinguish the patient groups with a similar predictive capabil-ity (Table 4), whereas the spectral region attributed to pro-teinuria was not able on its own to distinguish the three groups(Table 4 and Supplementary Figures 3 and 4 in SupportingInformation).

Prediction of Class Membership. To test the reliability ofthe OSC/PLS-DA models between patients and controls andbetween patients with mild and severe renal damage, validationwith both held-back and external data was carried out.

1. Held-Back Data Validation. For each model, the corre-sponding training and test sets were randomly selected, andvalidation was repeated 3 times with a new random selectionof equally numbered sets each time.

For the patients-controls model, 132 samples (62 patients/70 controls) from the data set were selected as the training set,and the remaining 30 samples (15 patients/15 controls) servedas the test set (Supplementary Figure 5 in Supporting Informa-tion). As seen in Table 6, the R2 value of the models for thethree repeats was 0.80 in all cases, and the Q2 values rangedfrom 0.71 to 0.72. The classification rate for both patients andcontrols was 100% (15 out of 15) in all cases. Sensitivity andspecificity that were calculated on the basis of a predicted Ycutoff of 0.5 were both 100%

Table 2. The PLS-DA and OSC/PLS-DA Parameters for the Patients-Controls Models

All subjects: 85 Controls - 77 Patients (25 mild - 27 moderate - 25 severe)

Groups Total (Patients-Controls) Mild-Control Moderate-Control Severe-Control

parameters R2 Q2 R2 Q2 R2 Q2 R2 Q2

PLS-DA 0.67 0.61 0.63 0.39 0.73 0.62 0.85 0.79OSC/PLS-DA 0.80 0.73 0.75 0.58 0.81 0.70 0.89 0.84

Table 3. The OSC/PLS-DA Regression Coefficients for thePatients-Controls Model

Patients-Controls

loadings metabolites Ca Pa Coefficients

0.94-0.98 Proteins V v 0.221.34 Lactate V v 0.161.94 Acetate V v 0.202.54, 2.70 Citrate v V 0.673.04, 4.04 Creatinine v V 0.443.22-3.30 Choline metabolites V v 0.303.58 Glycine v V 0.163.34-3.903.70-3.74

Glucose-Unidentifiedb

V v 0.31

3.94 Creatine v V 0.063.98, 7.82 Hippurate v V 0.557.38, 7.46 Phenylalanine V v 0.1

a Abbreviations: C, controls; P, patients. b A group of unidentified me-tabolites (3.70-3.74 ppm) probably from overlapping resonances fromglucose, other sugars, and R-protons of amino acids.42



The training set for the mild-severe model consisted of 34patients (17 with mild and 17 with severe renal damage) andthe test set of 16 patients (8 with mild and 8 with severe renaldamage) (Supplementary Figure 6 in Supporting Information).As seen in Table 6, R2 ranged from 0.81 to 0.84 and Q2 from0.41 to 0.43, whereas the classification rate was 100% for themild renal damage sets (8 out of 8) and 88% for the severe renaldamage sets (7 out of 8) in all cases.

2. External Validation. For each model, the correspondingtraining and prediction sets were randomly selected, and

validation was repeated 3 times with equally numbered setsof different samples each time, which were not used when themodels were built.

For the patients-controls model, 114 samples (53 patients/61 controls) from the data set defined the training set, and 48samples (24 patients/24 controls) served as the prediction set.In Figure 4a, the Y-predicted scatter plot of the second repeatis shown with the Y cutoff of 0.5 for classification, whereas theother two plots are shown in Supplementary Figure 7 (Sup-porting Information). As seen in Table 6, R2 ranged from 0.81

Figure 3. (a-d) OSC/PLS-DA scores plots (PC1 vs PC2) of the urinary spectroscopic data from the 77 patients: (a) 25 with mild (redsquares), 27 with moderate (green diamonds), and 25 with severe renal damage (purple circles); (b) 25 with mild (red squares) and 27with moderate renal damage (green diamonds); (c) 27 with moderate (green diamonds) and 25 with severe renal damage (purplecircles); (d) 25 with mild (red squares) and 25 with severe renal damage (purple circles).

Table 4. Parameters of the PLS-DA and OSC/PLS-DA Models for the Three Patient Groups Together and Pairwise for the FullSpectrum, after the Exclusion of the Bulk Signals from the Aliphatic Moieties of Proteins (0.2-1.82 ppm) and for the AliphaticRegion Alone

All patients: 25 mild - 27 moderate - 25 severe

Groups Mild-Moderate-Severe Mild-Moderate Moderate-Severe Mild-Severe

parameters R2 Q2 R2 Q2 R2 Q2 R2 Q2

PLS-DAFull spectrum 0.39 0.12 0.66 0.24 0.59 0.17 0.73 0.46

OSC/PLS-DAFull spectrum 0.43 0.16 0.72 0.31 0.60 0.23 0.79 0.550.2-1.82 ppm excluded 0.36 0.10 0.64 0.28 0.59 0.14 0.80 0.61Region 0.2-1.82 ppm 0.19 0.04 0.37 < 0 0.39 0.15 0.45 0.16



to 0.90 and Q2 from 0.73 to 0.88. The average classification ratewas 100% for controls (24 out of 24) and 85% for patients (20.33out of 24). The calculated sensitivity and specificity were 96%and 99%, respectively.

Similarly for the mild-severe patient group, the training setcomprised 34 patients (17 with mild and 17 with severe renaldamage), and the prediction set comprised 16 patients (8 withmild and 8 with severe renal damage). In Figure 4b, theY-predicted scatter plot of the second repeat is shown with theY cutoff of 0.5 for classification, whereas the other two plotsare shown in Supplementary Figure 8 (Supporting Information).As seen in Table 6, R2 ranged from 0.84 to 0.87 and Q2 from0.29 to 0.44. The average classification rate was 83% for themild renal damage sets (6.67 out of 8) and 79% for the severerenal damage set (6.33 out of 8).

Discussion

The application of pattern recognition techniques in NMR-based urinalysis for the evaluation of renal damage has been

predominantly focused on experimental studies.9 In the currentstudy, an 1H NMR-based metabonomic approach was used forthe first time to investigate the correlation of histopathologicallyassessed tubulointerstitial lesions with the urinary metaboliteprofile in patients with glomerulonephritides.

The urinary metabolite profiles of the patients with glom-erulonephritis at any disease stage presented distinct alterationsfrom those recorded from healthy individuals. These alterationswere more obvious in patients with severe renal disease andreflected the extent of the damage in the proximal tubules and/or the tubulointerstitial tissue as it was assessed by thehistopathological analysis. Similar alterations have been seenin previously published experimental and human studies.9,12,13,32

The onset of the tubulointerstitial lesions is characterized bydecreased excretion of citrate, hippurate, glycine, and creati-nine, whereas further deterioration is followed by glycosuria,selective aminoaciduria, total depletion of citrate and hippu-rate, and gradual increase in the excretion of lactate, acetate,and TMAO.

Table 5. The OSC/PLS-DA Regression Coefficients for the Three Patient Models Shown in Pairwise Comparison

Patient models

Mild-Moderate Moderate-Severe Mild-Severe

loadings metabolites Mia Moa coefficients Mo Sea coefficients Mi Se coefficients

0.94-0.98 Proteins V v 0.26 V v 0.32 V v 0.341.34 Lactate V v 0.29 v V 0.16 V v 0.031.94 Acetate V v 0.20 v V 0.42 v V 0.242.54, 2.70 Citrate v V 0.46 v V 0.27 v V 0.493.04, 4.04 Creatinine V v 0.29 v V 0.51 v V 0.363.22-3.30 Choline metabolites V v 0.33 v V 0.23 v V 0.233.58 Glycine v V 0.33 v V 0.07 v V 0.243.34-3.903.70-3.74

Glucose-Unidentifiedb

V v 0.34 V v 0.33 V v 0.34

3.94 Creatine v V 0.35 v V 0.28 v V 0.343.98, 7.82 Hippurate v V 0.09 v V 0.35 v V 0.357.38, 7.46 Phenylalanine v V 0.34 v V 0.06 V v 0.25

a Abbreviations: Mi, mild; Mo, moderate; Se, severe. b A group of unidentified metabolites (3.70-3.74 ppm) probably from overlapping resonances fromglucose, other sugars, and R-protons of amino acids.42

Figure 4. Y-predicted scatter plots of the OSC/PLS-DA models validated with external data. (a) Second repeat of the patients-controlsmodel. The samples are colored for the training set: 61 controls (blue triangles) and 53 patients (red squares); for the prediction set:24 controls ([C) and 24 patients ([P). (b) Second repeat of the mild-severe model. The samples are colored for the training set: 17patients with mild (red squares) and 17 with severe renal damage (purple circles); for the prediction set: 8 with mild ([g1) and 8 withsevere renal damage ([g3).



Depletion of urinary citrate has been attributed to either animpairment of the tricarboxylic acid cycle or to renal tubularacidosis, which typically appears as part of a generalizedproximal tubule dysfunction.9,33,34 A significant decrease ofhippurate in urine may be indicative of a metabolic alterationand, even more importantly, of the efficacy of tubular secre-tion,35 whereas increased renal hippurate synthesis wouldrequire over-utilization of glycine that could account for thelow levels of glycine detected in patients with moderate andsevere renal damage.26 The urinary levels of acetate can beaffected by the metabolic status of the organism,36 and in-creased excretion has been reported in proximal tubularnecrosis after exposure to HgCl2.9

Lactic aciduria has been related to increased activity ofanaerobic metabolic pathways, to decreased proximal tubularreabsorption, and also appears to be a general marker of renalcortical necrosis.9,37 The pattern of selective aminoaciduria,lactic aciduria, and glycosuria that were detected in the presentstudy indicate impairment of the reabsorption mechanisms inthe proximal tubular epithelial cells.38

Leakage of methylamines in urine, mainly TMAO and DMA,39

has been reported in medullary damage9 and in acute graftrejection following renal transplantation.11 In the present study,elevated excretion of TMAO was detected mainly in themoderate and severe damage groups, but it was not followedby increased excretion of DMA indicating tubulointerstitialdistortions rather than papillary necrosis.13

The application of PR methods allowed the extraction of themost discriminate information from the multivariate NMR dataand an effective sample classification. PCA along with visualinspection of the raw data was important for the detection ofoutliers in order to assess a consistent metabonomic approach.Through PLS-DA, a strong separation trend between patientand control groups was detected, whereas the application ofOSC-filtering led to the elimination of the intersubject variationand enabled a clear separation in the resulting models. Thesemodels were able to predict the presence of renal damage witha sensitivity of 96% and a specificity of 99% based on a 95%confidence limit for class membership. Patients with mild,moderate, and severe tubulointerstitial lesions were progres-sively differentiated from the healthy individuals. The com-parison between groups showed a statistically significantseparation between patients with mild and severe lesions anda high predictive ability of the corresponding OSC/PLS-DAmodels. Concerning the comparisons between moderate and

mild and between moderate and severe damage groups, theseparation was not statistically significant, but quite evidentin the OSC/PLS-DA models.

It is of interest that similar findings were also observed inconventional clinical chemistry analysis. However, it is well-known that the degree of proteinuria is not always correlatedto the severity of the interstitial damage. Proteinuria usuallyreflects an increase in glomerular permeability that allows thefiltration of normally nonfiltered macromolecules such asalbumin, and thus, tubular proteinuria may be masked by anovert glomerular hyper-filtration.40 On the other hand, serumcreatinine and GFR estimation are affected by factors such asblood pressure levels and hydration of the patients.41 Therefore,NMR findings further support the existence of the tubuloint-erstitial lesions and could be a useful tool to the globalassessment of kidney damage and contribute to the attenuationof the confounding factors mentioned above.

In conclusion, since the coexistence of tubular and interstitiallesions in glomerulonephritides is of crucial prognostic impor-tance for the progress of renal glomerular function, NMR-basedmetabonomic urinalysis, as a rapid and noninvasive technique,could contribute to the early evaluation of the severity of therenal damage and possibly to the monitoring of the kidneyfunction. This last indication is currently being evaluated inour laboratory.

Abbreviations: LMW, low molecular weight; 1H NMR, protonnuclear magnetic resonance; PR, pattern recognition; MDRD,modification of diet in renal disease; TSP, 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate; FID, free induction decay; PCA,principal component analysis; PC, principal component; PLS-DA, partial least-squares discriminant analysis; OSC, orthogonalsignal correction; 3-HB, 3-hydroxybutyrate; TMAO, trimethyl-amine-N-oxide; DMA, dimethylamine.

Acknowledgment. The research Project is co-funded bythe European Union - European Social Fund (ESF) & NationalSources, in the framework of the program “HRAKLEITOS” ofthe “Operational Program for Education and Initial VocationalTraining” of the 3rd Community Support Framework of theHellenic Ministry of Education. The NMR spectra were recordedon a 500 MHz Bruker DRX NMR instrument, at the NMRLaboratory of NCSR “Demokritos”, Agia Paraskevi, Greece.

Supporting Information Available: Figures showingthe 1H NMR 2D TOCSY spectra of urine from a patient with

Table 6. OSC/PLS-DA Parameters and Classification Scores of the Patients-Controls and the Mild-Severe Models Validated withHeld-Back and External Dataa

OSC/PLS-DA Patients-Controls Mild-Severe renal damage

Training set Test set Parameters Classification Training set Test set Parameters Classification

held-back data 132 (62P/70C) 30 (15P/15C) R2 Q2 controls patients 34 (17Mi/17Se) 16 (8Mi/8Se) R2 Q2 mild severe

First model 0.80 0.72 15/15b 15/15 0.81 0.41 8/8 7/8Second model 0.80 0.71 15/15 15/15 0.82 0.43 8/8 7/8Third model 0.80 0.71 15/15 15/15 0.84 0.42 8/8 7/8

Training set Prediction set Parameters Classification Training set Prediction set Parameters Classification

external data 114 (53P/61C) 48 (24P/24C) R2 Q2 controls patients 34 (17Mi/17Se) 16 (8Mi/8Se) R2 Q2 mild severe

First model 0.81 0.73 24/24 19/24 0.84 0.29 6/8 6/8Second model 0.90 0.88 24/24 20/24 0.87 0.44 7/8 7/8Third model 0.90 0.87 24/24 22/24 0.84 0.42 7/8 6/8

a Abbreviations: C, controls; P, patients; Mi, mild; Se, severe. b Fifteen out of 15 correctly classified.



severe renal damage; OSC/PLS-DA scores plots of the urinaryspectroscopic data from controls and patients with mild,moderate, and severe renal damage; OSC/PLS-DA scores plotsof the urinary spectroscopic data, after the removal of thealiphatic moieties of proteins from patients with mild, moder-ate, and severe renal damage; OSC/PLS-DA scores plots of theurinary spectroscopic data of the aliphatic moieties of proteinsfrom patients with mild, moderate, and severe renal damage;Y-predicted scatter plots of the 3 repeats of the OSC/PLS-DApatients-controls and mild-severe models validated with held-back data; and Y-predicted scatter plots of the other 2 OSC/PLS-DA patients-controls and mild-severe models validatedwith external data. This material is available free of charge viathe Internet at http://pubs.acs.org.

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Evaluation of Tubulointerstitial Lesions' Severity in Patients with Glomerulonephritides: An NMR-Based Metabonomic Study