Combination of 1H Nuclear Magnetic Resonance Spectroscopy and Liquid Chromatography/Mass Spectrometry with Pattern Recognition Techniques for Evaluation of Metabolic Profile Associated with Albuminuria Wai Siang Law,† Pei Yun Huang,† Eng Shi Ong,‡ Sunil Kumar Sethi,§,| Sharon Saw,| Choon Nam Ong,*,‡ and Sam Fong Yau Li*,† Department of Chemistry, National University of Singapore, 3 Science Drive 3, 117543, Republic of Singapore, Department of Community, Occupational, and Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, MD3, 16 Medical Drive, Republic of Singapore, Department of Pathology, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, and Department of Laboratory Medicine, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074 Received August 9, 2008
A method using 1H NMR and LC/MS with pattern recognition tools such as principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) was used to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population. The normalized peak intensities obtained from 1H NMR and LC/MS with nonparametric two-tailed Mann-Whitney analysis was used for the identification of network of potential biomarkers corresponding to the increase of albumin in urine. The specificity of detecting the stated metabolites by 1H NMR and LC/MS was demonstrated. Our preliminary data obtained demonstrated that LC/MS may produce more distinctive metabolic profiles. For the patient group, changes in alanine, kyneurnic acid, and xanthurenic acid might be associated with changes in the tryptophan metabolism. At the same time, other metabolites that were involved in citric acid cycle, amino acid metabolism, and cellular functions were affected in the patient group. The proposed approach provided a comprehensive picture of the metabolic changes induced by the increase of protein in urine and demonstrated the advantages of using multiple diagnostic biomarkers. At the same time, the current work was demonstrated as a potential cost-effective solution of high-throughput analysis with pattern recognition tools as applied here in a real clinical situation. Keywords: LC/MS • 1H NMR • pattern recognition • human urine • metabolic profile • albuminuria
Introduction Metabonomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response to pathophysiological stimuli or genetic modifications.1 It is a systemwide approach for studying in vivo metabolic profiles that will provide information on drug toxicity, disease processes, and gene function at several stages in the discovery and development process.2 Currently, high-resolution nuclear magnetic resonance (NMR) approach has been used extensively for metabolic profiling of mouse or rat urine for the monitoring * To whom correspondence should be addressed. Prof. Sam F.Y. Li, Department of Chemistry, National University of Singapore, 3 Science Drive 3, Republic of Singapore, 117543. E-mail:
[email protected]. Dr. Eng Shi Ong, Department of Community, Occupational, and Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, MD3, 16 Medical Drive, Republic of Singapore. E-mail:
[email protected]. † Department of Chemistry, National University of Singapore. ‡ Department of Community, Occupational, and Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore. § Department of Pathology, National University Hospital. | Department of Laboratory Medicine, National University Hospital.
1828 Journal of Proteome Research 2009, 8, 1828–1837 Published on Web 02/02/2009
of diet effects and evaluation of toxicity of chemical substances.1-7 It has been used for the investigation of the effects of gender, diurnal variation, and age in human urine8 and the susceptibility of human metabolic phenotypes to dietary modulation.9 Besides, Lenz et al. reported that 1H NMR could be used to collect consistent metabonomics data for clinical studies by analysis of urine and plasma samples.10 Later, the same research group investigated the metabonomics profiles of volunteers from 2 countries without dietary restriction. They concluded that the effect of cultural and severe dietary influences should be considered in the data interpretation.11 1H NMR has the advantages of being nondestructive, compatible to intact biomaterials, and rich in structural information. Another commonly used technique is the combination of liquid chromatography-tandem mass spectrometry (LC/MSMS) with multivariate statistical tools to compare metabolic signatures in biological samples.12-15 At the same time, a combination of 1 H NMR and LC/MS had been used for the investigation of metabolic profiles associated with the effects of drugs and chemical substances in rat urine samples.16-19 10.1021/pr800771f CCC: $40.75
2009 American Chemical Society
research articles
Evaluation of Metabolic Profile Associated with Albuminuria For the analysis of complex metabolic profiles, computer aided pattern recognition multivariate techniques such as principal component analysis (PCA) are commonly used to establish any related clustering that may exist in a data set. Without prior knowledge of the samples, PCA offers a reduced dimensional model that summarizes the major variations in the data from the different groups into two or three components. To derive a set of components that is biased toward describing variation related to a particular feature such as time or dose, supervised methods, such as orthogonal projection to latent structure discriminant analysis (O-PLS-DA), are required. Microalbuminuria could be defined as a a very small increase in urinary albumin, corresponding to excretion of between 20 and 200 µg/min or 30 and 300 mg in overnight and 24 h urine collections, respectively. It has been recognized as a cardiovascular and renal risk factor in patients with diabetes, obesity, dislipidemia or hypertension and the general population.20-24 Microalbuminuria could be used to predict the clinical nephropathy and death in patients with type 1 diabetes.25 On the basis of the studies conducted by Mongensen et al., a patient with type II diabetes who had microalbuminuria doubles the risks of having cardiovascular disease.26 This observation was supported by several subsequent cross-sectional studies with type II diabetes patients.27 Measurement of microalbuminuria could be performed by the following methods: (1) measurement of albumin-creatinine ratio (ACR) in a first-morning spot collection, (2) measurement of creatinine via a 24 h urine collection, and (3) 4-h or overnight urine collection.21-23 However, these urine dipstick assessments were reported to produce false positive results and the specificity also varied from 36 to 97%. This might be due to the variations in urine concentrations caused by hydration levels.21,28 High-Performance Liquid Chromatography (HPLC) has been employed in the detection of the urinary albumin.29-31 However, the accuracy of this method has been questioned as the amount of albumin detected in urine by HPLC compared to conventional assays was significantly greater.32 Investigation by US National Institute of Health revealed that this might be due to the coelution of the 4 proteins of comparable size.33 Hence, there is currently no gold standard method to measure urinary albumin concentrations. Metabolic profiling approach has been performed to investigate and characterize the metabolic changes in diabetes patients. LC/MS was applied to study the phospholipid metabolic profiling in diabetes patients.34 To obtain the metabolic profiles of type 1 diabetes, 1H NMR was used to measure the serum samples from 613 patients. The study revealed the complex interaction between diabetic kidney, insulin resistance, and the metabolic syndrome. Besides, this work also illustrated the potential of nondestructive NMR technique that could be applied in the real clinical situation.35 A similar approach has been applied to examine the changes of urinary metabolic profiles in type 2 diabetes mouse, rat, and human patients.36 However, the investigation of urinary metabolic profile associated with an increase in urinary excretion of protein albumin with 1H NMR and LC/MS was limited. Hence, the aim of the current work is to demonstrate the use of a combination of 1H NMR and LC/MS with pattern recognition tools such as PCA and O-PLS-DA to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population.
Table 1. Demographic Information of Control and Patient Group category
albumin-to-creatinine ratio (ACR)
Set 1 30 mg/g, less than 300 mg/g creatinine (Set 2)
Control group (6 males, 6 females) Patient group (14 males, 8 females)
30 mg/g, less than 300 mg/g creatinine
Experimental Procedures Chemicals. Methanol and acetonitrile of HPLC grade were purchased from APS (NSW, Australia). Pure water was obtained from Millipore Alpha-Q water system (Bedford, MA). Formic acid was purchased from Merck (Darmstadt, Germany). Valeric acid, L-leucine, D-serine, homocysteine, lysine, creatinine, phenylalanine, and hippuric acid were purchased from Sigma (St. Louis, MO). Sample Preparation. Two sets of samples were obtained. A smaller set of urine samples was collected from 9 healthy adults (control samples, 9 Chinese) and 7 patients with high urinary albumin (set 1, 7 Chinese). The larger set of urine samples was collected from 12 healthy adults (11 Chinese and 1 Indian) and 22 patients (14 Chinese, 5 Malay, 3 Indian) with high urinary albumin (set 2). Overall, there were 21 healthy subjects and 29 patients. The body mass index (BMI) for the control and patient groups was 24.7 (3.9) and 25.1 (3.4), respectively. All urine samples were collected at the same time point and upon recommendations for the determination of albumin. The demographic information on the two sets of samples can be seen in Table 1 and the age range of the healthy adults and subjects in the patient group was between 32 and 65 years old. All patients were from National University Hospital, Singapore. All samples were anonymized urines for disposal after requested testing was complete and were stored at -20 °C until analysis. The concentration of microalbumin was determined using an immunoturbidimetric method assayed on ADVIA 2400 (Siemens Medical Diagnostics Solutions). For an increase in albumin in human urine samples, it was defined that the albumin/creatinine ratio for spot (morning) sample range is greater than 30 mg/g as seen in the patient group.24,37 Reversed-Phased LC/MS/MS. For LC/MS assay, a 30 µL aliquot of urine was diluted to 70 µL with distilled water. An Agilent 1200 RRLC system (Waldbronn, Germany) equipped with a binary gradient pump, autosampler, column oven, and diode array detector was coupled with an Agilent 6410 triple quadrupole mass spectrometer. The gradient elution involved a mobile phase consisting of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. The initial condition was set at 5% of B, gradient up to 100% in 10 min and returning to initial condition for 5 min. Oven temperature was set at 50 °C and flow rate was set at 200 µL/min. For all experiments, 5 µL of samples was injected. The column used for the separation was a reversed-phase Zorbax SB18, 50 × 2.0 mm, 1.8 µm (Agilent Technologies, Santa Clara, CA). The ESI-MS was acquired in the positive ion mode. The product ions of m/z range from 100 to 800 were collected. The heated capillary temperature was maintained at 350 °C; the drying gas and nebulizer nitrogen gas flow rates were 10 L/min and 50 psi, respectively. Journal of Proteome Research • Vol. 8, No. 4, 2009 1829
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Figure 1. (A) 1H NMR spectra of human urine from healthy volunteer (chemical shift from 6.50 to 8.50 ppm); (B) 1H NMR spectra of human urine from albuminuria patient (chemical shift from 6.50 to 8.50 ppm); (C) 1H NMR spectra of human urine from healthy volunteer (chemical shift from 0.80 to 4.20 ppm); and (D) 1H NMR spectra of human urine from albuminuria patient (chemical shift from 0.80 to 4.20 ppm). (1) Leucine/isoleucine, (2) valine, (3) isobutyrate, (4) lactate, (5) alanine, (6) acetate, (7) glutamate, (8) pyruvate, (9) succinate, (10) citrate, (11) trimethylamine, (12) creatinine/creatine, (13) choline, (14) TMAO/betaine, (15) taurine, (16) glycine, (17) creatine, (18) creatinine, (19) tyrosine, (20) phenylacetylglutamine, (21) hippurate, and (22) formate. 1830
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Evaluation of Metabolic Profile Associated with Albuminuria
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Figure 2. (A) PCA score plot for the first two components from set 1 on Day 1 (color code: red, healthy volunteer group (n ) 9); black, patient group (n ) 7), R2X[1] ) 0.42, R2X[2] ) 0.13). (B) PCA score plot for the first two components from set 1 on Day 2 (color code: red, healthy volunteer group (n ) 9); black, patient group (n ) 7), R2X[1] ) 0.36, R2X[2] ) 0.12). (C) O-PLS-DA score plot derived from 1 H NMR spectra (Set 2) (color code: red, healthy volunteer group (n ) 12); black, patient group (n ) 23), R2X[1] ) 0.11, R2X[2] ) 0.27).
Analysis of Urine Samples by 1H NMR. A total of 300 µL of urine samples was buffered with 300 µL of 0.2 M phosphate buffer/D2O (pH 7.4) prior to analysis by 1H NMR. The solutions were pipetted into NMR tubes (5 mm o.d, 7 in. length, SigmaAldrich) and one-dimensional 1H NMR spectra were obtained on a Bruker DRX500 operating at 500.15 MHz observation frequency. Solvent suppression was achieved by applying the standard
Noesypresat pulse sequence (Bruker Biospin Rheinstetten, Germany) with secondary irradiation of the dominant water signal during the mixing time of 150 ms and the relaxation delay of 2 s. Spectra were referenced to the internal reference standard TSP dissolve in D2O to provide a field-frequency lock. Chemometric Analysis. Fourier transformed 1H NMR spectra were manually phased and baseline corrected using XWINNMR Journal of Proteome Research • Vol. 8, No. 4, 2009 1831
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a
Table 2. Metabolites of Control and Patient Groups’ Urine Samples Measured by H NMR
chemical shift (ppm)
fmultiplicity
0.96 2.55 2.72 3.04 3.94 3.77 3.04 4.05 6.87 7.16 7.41 7.42 7.44 7.73 7.64 7.55
m d d s s t s s d d m
d t t
aqueous components assignment
Leucine/Isoleucine Citrate* Creatine Lysine* Creatinine** Tyrosine Phenylacetylglutamine
Hippurate*
relative peak intensity control group
patient group
0.19 ( 0.04 2.55 ( 0.58 1.79 ( 0.33 26.73 ( 5.82 0.42 ( 0.25 0.46 ( 0.07 26.73 ( 5.82 1.96 ( 0.49 0.10 ( 0.07 0.15 ( 0.08 0.40 ( 0.25 0.40 ( 0.27 0.18 ( 0.12 0.11 ( 0.11 0.31 ( 0.21 0.70 ( 0.44
0.16 ( 0.10 1.11 ( 1.23 1.09 ( 0.76 14.20 ( 10.55 0.22 ( 0.11 0.76 ( 0.33 14.20 ( 10.55 0.91 ( 0.60 0.08 ( 0.06 0.14 ( 0.10 0.37 ( 0.26 0.37 ( 0.25 0.15 ( 0.10 0.067 ( 0.11 0.15 ( 0.11 0.33 ( 0.24
a The relative intensity of metabolites in the healthy and patient is expressed with their relative peak height. Values are represented as mean ( SD; statistics: significant difference between healthy (n ) 9) and patient group (n ) 7) is based on nonparametric, two-tailed Mann-Whitney analysis (*p < 0.05, **p < 0.01).
3.5 (Bruker Biospin, Rheinstetten, Germany). Each spectrum was integrated between 0.5-4.5 and 5.1-10 ppm. The spectral region containing the water resonance was removed from all data sets prior to normalization and multivariate data analysis in order to eliminate variation due to water suppression efficiency. The resulting two-dimensional data, 1H chemical shift, and peak heights were generated.
Statistical Analysis. From the normalized data obtained from LC/MS and 1H NMR, indication of significance was based on a nonparametric, two-tailed Mann-Whitney analysis performed with SPSS 14.0 for Windows (SPSS, Chicago, IL). For theidentificationofpotentialbiomarkers,two-tailedMann-Whitney analysis (P < 0.05 and P < 0.01) was used.
The resulting LC-MS data served as raw data for PCA analysis. The LC-MS data were peak-detected and noisereduced such that only true analytical peaks were further processed by the PCA software. A list of the peak areas of the peaks detected was then generated manually and tabulated into Microsoft Excel for each sample run, using the retention tine (RT) and m/z data pairs as the identifier for each peak. The peak areas for each peak detected were then normalized within each sample, to the sum of the peak area in that sample. Normalization was required to remove concentration differences between dilute and concentrated urine samples. To account for any difference in concentration between samples, all 1H NMR and LC/MS data were normalized to a total value of 100. The resulting three-dimensional data for 1H NMR and LC/MS were analyzed by PCA and O-PLS-DA. The resulting data were then exported to Simca-P+ Software package (Umetrics, Umea, Sweden) for subsequent processing by unsupervised and supervised method.
Results
For PCA, the data were reduced to 2 latent variables (or principal components, PCs) that would describe maximum variation within the data. The PCs which were obtained from the scores would highlight clustering, trends, and outliers in the observation direction in the data set. At the same time, O-PLS-DA was used on the data set obtained to find the relationship between the descriptors in X and class identity of the samples that was described by a dummy matrix representing the class information.38 With the assistance of Simca-P+, plotting weight (w × 1, weights that combine the X variables in the first dimension or the residuals of X variables in the subsequent dimensions to form scores t) from the loadings would allow identification of potential biomarkers. The O-PLS coefficients (or loadings) relating to the relative influence of a particular RT-m/z pair could then be determined. 1832
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Typical 1H NMR spectra of the control and patient group’s urine samples acquired using the standard 1D pulse sequence for water suppression are shown in Figure 1, panels A and B, respectively. The expanded spectra for human urine from the healthy (control) group for chemical shifts from 0.80 to 8.50 ppm, and from 0.80 to 4.20 ppm are shown in 1, panels C and D, respectively. For the validation of the proposed approach, 1 H NMR spectra from a smaller set of human urine samples of controls and patients were obtained on two different days. From the PCA scores plot in Figure 2A,B, it was clear that data obtained by 1H NMR within 2 separate days were highly reproducible. The current data was also consistent with other reports.5,6,8,9,36,39,40 Subsequently, 1H NMR spectra were obtained for 21 healty subjects and 29 patients. To increase the class separation, simplify interpretation, and identify potential biomarkers, a supervised model, O-PLS-DA, was used and the score plot is shown in Figure 2C. This model indicated that the patient group had a different urinary excretion of endogenous metabolites profile from that of the control group. Representative metabolites detected by 1H NMR that were also detected by the LC/MS method in this study were listed in Table 2. Most of the resonances had been assigned previously and the compounds detected included amino acids, tricarboxylic acids, trimethylamines, and others.4-9,16-19,36,41 Visual inspection of these spectra revealed that dominant chances were observed in regions of δ: 0.5-1.5, 3.5-4.0 and 7.0-8.0. A similar pattern changes occurred in all the patients’ urines with relative decreases in valine, 3-D-hydroxybutyrate, isobutyrate, alanine, citrate, hypotaurine, dimethylglycine, and creatinine and with relative increases in glucose, glycine, lysine, and betaine (Table 3). The regions δ 4.50-6.5 were deleted to remove any spurious
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Evaluation of Metabolic Profile Associated with Albuminuria 1
a
Table 3. Metabolites of Control and Patient Groups’ Urine Samples Measured by H NMR
relative peak intensity chemical shift (ppm)
multiplicity
0.96 0.98 0.99 1.04 1.05 1.11 1.20 1.33 1.47 1.48 1.92 2.29 2.34 2.40 2.42 2.44 2.55 2.72 2.66 2.88 2.93 3.04 3.94 3.25 3.53 3.84 3.20 3.27 3.55 3.77 3.04 4.05 3.43 3.89 6.87 7.16 7.18 7.29 7.53 7.41 7.42 7.64 7.55 8.47
m d
aqueous components assignment
Leucine/Isoleucine Valine*
d d d d d
Isobutyrate* 3-D-hydroxybutyrate* Lactate Alanine*
s s m s s m d d t s s s s m m m s s s t s s t s d d d d
Acetate Acetoacetate Glutamate Pyruvate Succinate Glutamine Citrate** Hypotaurine* Trimethylamine Dimethylglycine** Creatine Glucose Glucose* Glucose** Choline Trimethylamine-N-oxide/betaine Glycine* Lysine** Creatinine* Taurine Betaine** Tyrosine Tryptophan
m
Phenylacetylglutamine
t t s
Hippurate Formate
control group
patient group
0.17 ( 0.05 0.15 ( 0.05 0.13 ( 0.04 0.11 ( 0.05 0.13 ( 0.06 0.31 ( 0.25 1.21 ( 1.17 1.14 ( 1.18 0.66 ( 0.30 0.81 ( 0.21 0.57 ( 0.20 0.51 ( 0.27 0.97 ( 0.47 0.62 ( 0.18 0.33 ( 0.05 0.41 ( 0.09 2.16 ( 0.93 1.70 ( 0.38 2.84 ( 1.35 0.34 ( 0.19 0.43 ( 0.19 26.81 ( 7.04 0.35 ( 0.25 0.59 ( 0.21 0.26 ( 0.08 0.27 ( 0.07 0.53 ( 0.15 4.68 ( 2.62 0.27 ( 0.15 0.44 ( 0.08 26.81 ( 7.04 1.88 ( 0.47 0.47 ( 0.28 0.18 ( 0.06 0.09 ( 0.06 0.13 ( 0.08 0.16 ( 0.05 0.26 ( 0.14 0.40 ( 0.24 0.33 ( 0.16 0.37 ( 0.23 0.38 ( 0.25 0.90 ( 0.56 0.08 ( 0.04
0.13 ( 0.08 0.10 ( 0.06 0.08 ( 0.07 0.06 ( 0.07 0.07 ( 0.08 0.14 ( 0.10 0.46 ( 0.25 0.73 ( 0.40 0.49 ( 0.17 0.38 ( 0.20 0.52 ( 0.25 0.52 ( 0.26 0.78 ( 0.71 0.70 ( 0.58 0.30 ( 0.14 0.47 ( 0.62 1.33 ( 1.20 1.40 ( 0.51 1.78 ( 1.65 0.18 ( 0.16 0.22 ( 0.18 23.68 ( 9.94 0.26 ( 0.11 1.29 ( 1.67 0.57 ( 0.49 0.55 ( 0.44 0.57 ( 0.20 5.52 ( 3.90 0.63 ( 0.58 0.63 ( 0.24 23.68 ( 9.94 1.40 ( 0.52 1.25 ( 1.59 0.46 ( 0.45 0.14 ( 0.12 0.17 ( 0.15 0.17 ( 0.09 0.27 ( 0.14 0.36 ( 0.19 0.42 ( 0.41 0.44 ( 0.25 0.31 ( 0.19 0.71 ( 0.42 0.11 ( 0.13
The relative intensity of metabolites in the healthy and patient is expressed with their relative peak height. Values are represented as mean ( SD; statistics: significant difference between healthy (n ) 12) and patient group (n ) 22) is based on nonparametric, two-tailed Mann-Whitney analysis (*p < 0.05, **p < 0.01). a b
effects of variability in the suppression of the water resonance and any cross relaxation effects on the urea signal. The observed changes in endogenous metabolites in urine were summarized in Table 3. Although the back projections of statistical data from O-PLS-DA back into the spectra was proposed as the best way to identify biomarkers,38 a simpler method based on two-tailed Mann-Whitney analysis (P < 0.05 and P < 0.01) was used to identify metabolites that were perturbed in the patient group. The first set of sample which consisted of control (n ) 9) and patient (n ) 7) group was used for preliminary LC-MS analysis. A comparison of the total ion chromatogram (TIC) for the positive ESI/MS of the control and patient samples demonstrated quantitative and qualitative differences (Figure 3). Visual inspection of the data suggested that the control and
patient group had unique metabolic profiles, although this observation needs to be verified with additional samples since a very small sample set has been examined. There were several peaks in the patients’ urine samples that showed significant changes when compared to the control urine samples. These peaks eluted in the 5.5-9.5 min time region of the chromatogram. Several compounds (listed in Table 4) had been characterized in human urine using this approach. They belong to various chemical families, namely, amino acids (L-leucine, valine, homocysteine, lysine, and tyrosine), vitamins (riboflavin and pantothenic acid), organic acids (valeric acid, kyneurnic acid, citric acid, and hippuric acids), phenylacetylglutamine (a gut microbial metabolite),42 and sulfoconjugates of phenolic compounds (phenol sulfates). The identities of these species Journal of Proteome Research • Vol. 8, No. 4, 2009 1833
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Figure 3. Total ion chromatogram of human urine from (A) healthy volunteer and (B) albuminuria patient. (1) Valeric acid, (2) leucine, (3) homocysteine, (4) lysine, (5) tyrosine, (6) creatinine, (7) phenylalanine, (8) hippuric acid, (9) xanthuric acid, and (10) kynurenic acid.
were confirmed by comparison with authentic standards and interpretation of the MS/MS spectra. After data normalization, multivariate statistical methods such as PCA and O-PLS-DA were carried out. The PCA scores plot showed that the two groups were scattered into two different regions (Figure 4A) and all control samples were clearly separated from the patient group samples. To improve the classifications of the albuminuria patients and the control group, O-PLS-DA was used. The plot of the first two O-PLSDA scores is shown in Figure 4B; it was noted that the control and patients groups were scattered into 2 different regions. The experiments on the same set of samples were repeated 4 weeks later and a similar trend was observed. Ions which contributed to the O-PLS-DA separation included m/z 114 (0.89 min), m/z 359 (5.33 min), m/z 190 (2.39 min), m/z 180 (3.79 min) and m/z 377 (7.02 min) in the patients’ samples. Riboflavin was present in trace amount or not detected in the human urine samples in the control group. It was noted that riboflavin which was absent in the control group was consistently detected in the urine samples of the patient group (Table 4 and Figure S1, Supporting Information).
Discussion In this study, both 1H NMR and LC/MS were used to investigate the urinary metabolic profile associated with an increase in urinary excretion of protein albumin. The results obtained for both 1H NMR and LC/MS revealed the variations of certain metabolites in urinary composition. Hence, it allowed us to obtain information about the mechanisms involved in metabolite excretion, particularly in microalbuminuria patients. The reliability of the current method was consistent with our earlier works.43,44 1834
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However, while the changes in metabolite profiles observed by H NMR and LC/MS with pattern recognition tools were similar, the markers which indicated the separations of patient group from control group observed were different. The most obvious examples were betaine, taurine, acetate, and glutamate which were noticeable markers for 1H NMR, but due to either poor ionization abilities in ESI or poor retention by LC, these compounds were not identified from the chromatograms obtained from LC/MS. On the other hand, markers such as pantothetic acids, riboflavin, and kynurenic acid that were prominent compounds in LC/MS were not obvious for 1H NMR. 1
Albuminuria is associated with metabolic syndrome, which includes insulin resistance, low HDL cholesterol resistance, high triglyceride levels, and truncal obesity.22,37 However, a normal albumin level in human urine samples does not preclude the cardiovascular risk factors as a significant portion of patients with dyslipidemia and insulin resistance had normal or intermediate albumin excretion as opposed to macroalbuminuria.35 At the same time, the biological heterogeneity of diabetic complications makes the borderline between health and disease ambiguous. The metabolic profiles of individuals with microalbuminuria and diabetic kidney diseases measured using 1 H NMR from the serum samples were rather different.35 For the patient group, an increase in glucose and a similar level of lactate and pyruvate in the urine samples as compared to the control group suggests that the carbohydrate metabolism was not affected. The decrease in the level of citrate in Table 3 suggested a modulation of the Krebs cycle in the patient group. Our current data showed that albuminuria resulted in no significant changes in leucine, acetate, and acetoacetate, with decreases in n-butyrate and 3-D-hydroxybutyrate in the urine samples that may be linked to changes in the partial β-oxida-
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Evaluation of Metabolic Profile Associated with Albuminuria Table 4. Metabolites of Control and Patient Groups’ Urine Samples Measured by LC/MS
normalized peak intensitya retention time (min)
m/z
identified compounds
control group (%)
patient group (%)
0.65 0.66 0.66 0.70 0.70 0.89 1.27 2.77 1.92 1.28 7.02 6.68
103 132 136 146 182 114 166 190 206 180 377 220
Positive Mode Valeric acid L-leucine Homocysteine Lysine Tyrosine Creatinine** Phenylalanine Kynurenic acid* Xanthuric acid* Hippuric acid Riboflavin* Pantothenic acid
0.0052 ( 0.0034 0.38 ( 0.29 0.41 ( 0.16 0.088 ( 0.067 0.13 ( 0.03 3.04 ( 0.88 0.72 ( 0.22 0.42 ( 0.11 0.64 ( 0.46 0.85 ( 0.58 ND 1.02 ( 0.36
0.0052 ( 0.0035 0.44 ( 0.22 0.46 ( 0.25 0.16 ( 0.09 0.18 ( 0.06 1.77 ( 0.39 0.67 ( 0.31 0.29 ( 0.07 0.17 ( 0.22 0.53 ( 0.24 0.003 ( 0.0154 0.89 ( 0.77
0.90 2.25
191 173
Negative Mode Citric acid* Phenol sulfate*
0.62 ( 0.32 0.29 ( 0.15
0.42 ( 0.18 ND
a Values are represented as mean ( SD. Statistics: *Significant difference between the healthy group (n ) 9) and the patient group (n ) 7) is based on a nonparametric, two-tailed Mann-Whitney analysis (*p < 0.05, ** p < 0.01).
Figure 4. (A) PCA score plot for the first two components (set 1), R2X[1] ) 0.17, R2X[2] ) 0.15); (B) O-PLS-DA score plot derived from LC-MS spectra (set 1), R2X[1] ) 0.15, R2X[2] ) 0.10) (color code: red, healthy volunteer group (n ) 9); black, patient group (n ) 7)).
tion of fatty acids liver and skeletal muscle producing short chain fatty acids and ketone bodies.36,45 Although the change in choline was not significant, it is proposed that albuminuria affected the formation of glycine
from choline as a result of changes in metabolites such as glycine, betaine, and dimethylglycine. Hence, microalbuminuria may be associated with a partial change in amino acid metabolism that is linked to lysine, glycine, and alanine and Journal of Proteome Research • Vol. 8, No. 4, 2009 1835
research articles will not affect amino acids such as glutamine, glutamate, leucine/isoleucine, tryptophan, and tyrosine. At the same time, microalbuminuria is observed to affect valine and alanine, without affecting pyruvate, succinate, acetate, acetoacetate, and formate that are involved in ATP production. We also observed that concentration trends of common renal functions indicators such as creatinin and taurine varied in our study. Depletion of creatinine, a byproduct of muscle metabolism and produced from phosphocreatine and creatine, could be observed in type 2 diabetes patients.20,36 In our study, decreases of creatinine and creatine were observed. This may be due to changes in muscle mass, creatinine reabsorption, cell leakage, and changes in caloric content. Moreover, the decreases of both creatinine and creatine and increase in glucose served as positive control for the urine samples collected. It is noted that urinary taurine level was regulated by both the renal absorption and taurine availability.36 Hence, the increase in taurine (Table 4) in the patient group may arise from altered renal reabsorption of taurine as a result of reduced glomerular filtration rate or possibly as a general stress response as seen in heptotoxicity and nephrotoxicity in rat model.5,6,46 At the same time, the level of homocysteine did not differ in the control and patient group with renal failure.46-48 Hence, microalbuminuria did not affect the normalized level of homocysteine in the control and patient groups. Although we observed no significant changes of tryptophan in patients’ urines, our small data set suggested that microalbuminuria might affect metabolites such as alanine, kynurenic acid, and xanthurenic acid which are involved in the tryptophan metabolism.49 The disruption of metabolites such as kynurenic acid, xanthurenic acid, riboflavin, and phenol sulfate were observed in urine samples after the administration of model nephrotoxin in the rat model.16-18 For tryptophan metabolism, the kidneys are a major pathway of its derivatives elimination, mostly in the forms of kynurenic acid, xanthurenic acid, and others.49-51 The accumulation of L-kynurenine and its degradation products in liver and kidneys in rats were proportional to the severity of renal failure and correlated with the concentration of renal insufficiency marker, creatinine. Although we observed no significant changes of tryptophan in patients’ urines, our small data set suggested that microalbuminuria might affect tryptophan metabolism. Concurrently, the vitamin Bs are required as coenzymes for enzymes essential for cell function and mitochondria energy metabolism. Riboflavin is required for the flavoenzymes of the respiratory chain.52
Conclusions A method based on combination of LC/MS and 1H NMR with PCA/O-PLS-DA had been developed for the metabolic profiling of human urine samples associated with microalbuminuria. Results obtained show that metabolites such as citric acid, amino acid metabolism, and cellular functions were affected. This allowed the identification of potential biomarkers corresponding to microalbuminuria. The complementary nature of the LC/MS and NMR results was confirmed by this study. Our preliminary data showed that LC/MS may produce more distinctive metabolic profiles associated with microalbuminuria. However, due to the small number of subjects in this study, our results serve to demonstrate the methodology rather than to provide definitive conclusions about the disease. Our results show that combination of high resolution analytical tools (e.g., 1H Nuclear Magnetic Resonance Spectroscopy and 1836
Journal of Proteome Research • Vol. 8, No. 4, 2009
Law et al. Liquid Chromatography/Mass Spectrometry) with pattern recognition tools such as principal component analysis and orthogonal projection to latent structure discriminant analysis provided a firm platform for future metabolic profiling.
Acknowledgment. The authors would like to acknowledge the financial support from the National University of Singapore and Ministry of Education (R-143-000-325-272 and R-143-000-281-305,), A-STAR (0521010044) and MEWR (MEWR C651/06/144). Supporting Information Available: MS/MS spectra of riboflavin in human urine sample, and EICs of riboflavin from albuminuria patient and healthy volunteer. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181–1189. (2) Nicholson, J. K. C. J.; Lindon, J. C. E. H. Nat. Rev. Drug Discovery 2003, 1, 153–161. (3) Keun, H. C. Pharmacol. Ther. 2006, 109, 92–106. (4) Williams, R. E. J. M.; Lock, E. A. Chem. Res. Toxicol. 2003, 16, 1207– 1216. (5) Bollard, M. E. K. H. C.; Beckonert, O.; Ebbels, T. M. D.; Antti, H.; Nicholls, A. W.; Shockcor, J. P.; Cantor, G. H.; Stevens, G. L. J. C.; Holmes, E.; Nicholson, J. K. Toxicol. Appl. Pharmacol. 2005, 204, 135–151. (6) Waters, N. J. W. C. J.; Farrant, R. D.; Holmes, E.; Nicholson, J. K. J. Proteome Res. 2006, 5, 1448–1459. (7) Dieterle, F. S. G. T.; Ross, A.; Niederhauser, U.; Senn, H. Chem. Res. Toxicol. 2006, 19, 1175–1181. (8) Slupsky, C. M. R. K. N.; Wagner, J.; Fu, H.; Chang, D.; Weljie, A. M.; Saude, E. J.; Lix, B.; Adamko, D. J.; Shah, S.; Greiner, R. S., B. D.; Marrie, T. J. Anal. Chem. 2007, 79, 6995–7004. (9) Stella, C. B. H. B.; Cloarec, O.; Holmes, E.; Lindon, J. C.; Powell, J.; van der Ouderaa, F.; Bingham, S.; Cross, A J.; Nicholson, J. K. J. Proteome Res. 2006, 5, 2780–2788. (10) Lenz, E. M. B. J.; Wilson, I. D.; Morgan, S. R.; Nash, A. F. P. J. Pharm. Biomed. Anal. 2003, 33, 1103–1115. (11) Lenz, E. M. B. J.; Wilson, I. D.; Hughes, A.; Morrisson, J.; Lindberg, H.; Lockton, A. J. Pharm. Biomed. Anal. 2004, 36, 841–849. (12) Plumb, R. S. S. C. L.; Gorenstein, M. V.; Castro-Perez, J. M.; Dear, G. J.; Anthony, M.; Sweatman, B. C.; Conner, S. C.; Haselden, J. N. Rapid Commun. Mass Spectrom. 2002, 16, 1991–1996. (13) Lafaye, A. J. C.; Gall, B. R.; Fritsch, P.; Tabet, J. C.; Ezan, E. Rapid Commun. Mass Spectrom. 2003, 17, 2541–2549. (14) Jonsson, P. B. S. J.; Moritz, T.; Trygg, J.; Sjo¨stro¨m, M.; Plumb, R.; Granger, J.; Maibaum, E.; Nicholson, J. K.; Holmes, E.; Antti, H. Analyst 2005, 130, 701–707. (15) Wagner, S. S. K.; Sieber, M.; Kellert, M.; Voelkel, W. Anal. Chem. 2007, 79, 2918–2926. (16) Lenz, E. M. B. J.; Knight, R.; Westwood, F. R.; Davies, D.; Major, H.; Wilson, I. D. Biomarkers 2005, 10, 173–187. (17) Lenz, E. M. B. J.; Knight, R.; Wilson, I. D.; Major, H. J. Pharm. Biomed. Anal. 2004, 35, 599–608. (18) Lenz, E. M. B. J.; Knight, R.; Wilson, I. D.; Major, H. Analyst 2004, 129, 636–641. (19) Lenz, E. M. W. R. E.; Sidaway, J.; Smith, B. W.; Plumb, R. S.; Johnson, K. A.; Rainville, P.; Shockcor, J.; Stumpf, C. L.; Granger, J. H. W. I. D. J. Pharm. Biomed. Anal. 2007, 44, 845–852. (20) Brunzel, N. A. Fundamentals of Urine and Body Fluid Analysis, 2nd ed.; Saunders: Philadelphia, PA, 2004. (21) Basi, S.; Lewis, J. B. Am. J. Kidney Dis. 2006, 47, 927–946. (22) Ruggenenti, P. R. G. Kidney Int. 2006, 70, 1214–1222. (23) Polkinghorne, K. R. Curr. Opin. Nephrol. Hypertens. 2006, 15, 625– 630. (24) Lambers Heerspink, H. J. B. J. W.; Bakker, S. J.; Gansevoort, R. T.; de Zeeuw, D. Curr. Opin. Nephrol. Hypertens. 2006, 15, 631–636. (25) Viberti, G. C.; Hill, R. D.; Jarett, R. J.; Argyropoulos, A. U. M. Lancet 1982, 1, 1430–1432. (26) Mogensen, C. N. Eng. J. Med. 1984, 310, 356–360. (27) Dinneen, S. F. H. C. G. Arch. Intern. Med. 1997, 157, 1413–1418. (28) American Diabetes Association. Clinical practice recommendations. Diabetes Care 2005, 28, S1.
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