Plasma Metabonomics as a Novel Diagnostic Approach for Major

Article pubs.acs.org/jpr

Plasma Metabonomics as a Novel Diagnostic Approach for Major Depressive Disorder Peng Zheng,⊥,†,§,∥ Hong C. Gao,⊥,‡ Qi Li,†,§,∥ Wei H. Shao,†,§,∥ Mei L. Zhang,‡ Ke Cheng,†,§,∥ De Y. Yang,†,§,∥ Song H. Fan,†,§,∥ Liang Chen,†,§,∥ Liang Fang,†,§,∥ and Peng Xie*,†,§,∥ †

Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China Department of Pharmacy, Wenzhou Medical College, Wenzhou, Zhejiang, China § Chongqing Key Laboratory of Neurobiology, Chongqing, China ∥ Institute of Neuroscience, Chongqing Medical University, Chongqing, China ‡

ABSTRACT: Major depressive disorder (MDD) is a socially detrimental psychiatric disorder, contributing to increased healthcare expenditures and suicide rates. However, no empirical laboratory-based tests are available to support the diagnosis of MDD. In this study, a NMR-based plasma metabonomic method for the diagnosis of MDD was tested. Proton nuclear magnetic resonance (1H NMR) spectra of plasma sampled from first-episode drug-naı̈ve depressed patients (n = 58) and healthy controls (n = 42) were recorded and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA score plots of the spectra demonstrated that the depressed patient group was significantly distinguishable from the healthy control group. Moreover, the method accurately diagnosed blinded samples (n = 26) in an independent replication cohort with a sensitivity and specificity of 92.8% and 83.3%, respectively. Taken together, NMR-based plasma metabonomics may offer an accurate empirical laboratory-based method applicable to the diagnosis of MDD. KEYWORDS: major depressive disorder, MDD, metabonomics, diagnosis, NMR

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symptom-based diagnosis of MDD to be a mere 47%.7 In light of these facts, the development of empirical laboratory-based diagnostic approaches for MDD is required. A fundamental prerequisite for an empirical laboratory-based diagnostic method is that the testing sample must be readily accessible at minimal risk and cost to the patient. In the case of MDD, brain biopsy samples are clearly not readily accessible for analysis as MDD is characterized by brain dysfunctions; Moreover, cerebrospinal fluid (CSF) sampling by lumbar puncture (LP) is not widely implementable due to the high risks associated with this invasive procedure.8 By contrast, plasma can be collected at minimal risk and cost to the patient through a simple venous blood stick. Thus, a plasma-based diagnostic test for MDD is more clinically practical. In addition, peripheral metabolic disturbances have been increasingly implicated in MDD, suggesting the characteristic metabolic

INTRODUCTION Major depressive disorder (MDD) is a serious psychiatric mood disorder with a lifetime prevalence ranging from 2% to 15%.1,2 MDD causes several detrimental socioeconomic effects including increased healthcare expenditures and suicide rates.3 Due to the lack of empirical laboratory-based tests, the diagnosis of MDD relies solely on the clinician’s subjective identification of symptomatic clusters and scales. The problematic issue with this current diagnostic methodology is the clinical presentation of MDD, which is highly heterogeneous.4 For example, depressed patients often present with more pronounced comorbid physical symptoms; as a result, their mental health is ignored.5 Moreover, in routine practice, clinicians are typically challenged by fitting their patients’ presentations, which lie along a continuous scale of depression severity, into strict DSM-IV-based diagnostic categories, so over one-third of diagnosed depressed patients are not appropriately diagnosed.6 Recently, a clinical meta-analysis of 50371 depressed patients from 41 studies found the accuracy of © 2012 American Chemical Society

Received: October 8, 2011 Published: January 13, 2012 1741

dx.doi.org/10.1021/pr2010082 | J. Proteome Res. 2012, 11, 1741−1748

Journal of Proteome Research

Article

Table 1. Demographic Details of Participantsa primary cohort sample size sex (M/F) age (year)b BMIb HDRS scoresb

independent replication cohort

HC

FEDNDP

P

HC

FEDNDP

P

42 16/26 31.5 ± 5.4 22.8 ± 2.3 0.5 ± 0.7

58 24/34 34.4 ± 11.6 23.6 ± 1.9 22.4 ± 3.8

0.74 0.36 0.09 0.00

12 4/8 32.0 ± 4.5 23.2 ± 3.2 0.4 ± 0.6

14 6/8 30.3 ± 9.6 21.9 ± 3.5 21.2 ± 3.5

0.70 0.59 0.32 0.00

a

Abbreviations: HC, healthy controls; FEDFDP, first-episode drug-naı̈ve depressed patients; M, male; F, female; BMI, body mass index; HDRS, Hamilton depression rating scale. bValues expressed as the mean ± SD (range).

disorders, medication, and/or illicit drug use, were excluded from the depressed group. During the same time period, healthy control subjects were recruited from the medical examination center of First Affiliated Hospital of Chongqing Medical University. Candidates with any previous lifetime history of neurological and/or DSM-IV Axis I or Axis II illness or systemic medical illness were excluded from the healthy control group. In total, 58 first-episode drug-naı̈ve depressed patients and 42 demographically matched controls were recruited to serve as the primary cohort (Table 1). In addition, an independent replication cohort of 14 first-episode drug-naı̈ve depressed patients and 12 demographically matched controls were also enrolled by the exact same procedures as the primary cohort during the final period of sample collection (Table 1). The sample size of subjects used to independently validate this diagnostic method was based on previous literature.22

alterations associated with the pathophysiologic mechanisms of MDD in the blood may generate a detectable molecular phenotype for diagnosis.9 Metabonomics enables simultaneous quantitative measurement of numerous low molecular weight molecules within a particular sample.10 Metabolic profiling techniques, such as gas/ liquid chromatography−mass spectrometry (GC−MS) and nuclear magnetic resonance (NMR) coupled with multivariate statistical modeling, offer the ability to capture considerable biochemical changes and thus provide novel platforms for diagnostics.11,12 A NMR-based metabonomic approach instituting a sensitive high-throughput molecular screening has already demonstrated promising results in diagnosing a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and autism.13−15 In the case of MDD, early studies employing a metabonomic approach focused on characterizing metabolic alterations in animal models. These studies have identified a panel of metabolites associated with depression-like behavior, which has facilitated a deeper understanding of the etiology of depression.16−19 In humans, metabolic perturbation of lipids and neurotransmitters have been reported to be associated with geriatric depression in elderly patients.20 However, thus far, no study has attempted to assess the feasibility of metabonomic techniques in supporting the diagnosis of MDD. In this study, the applicability of a plasma metabonomic method in the diagnosis of MDD was evaluated. NMR-based plasma metabonomic techniques were applied to distinguish 58 first-episode drug-naı̈ve depressed patients and 42 healthy controls. In addition, an independent replication cohort of 14 first-episode drug-naı̈ve depressed patients and 12 healthy controls were used to further validate the performance of a metabonomic diagnosis of MDD.

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Sample Preparation and NMR Acquisition

Fasting blood samples were collected in 5 mL Vacutainer tubes containing the chelating agent ethylene diamine tetraacetic acid (EDTA). All blood samples were then centrifuged at 1500g for 15 min. Each plasma sample was divided into equal aliquots and stored at −80 °C. No plasma sample underwent more than two freeze−thaw cycles prior to undergoing NMR analysis. For NMR analysis, the plasma samples were thawed and 200 μL aliquots were mixed with 400 μL of phosphate buffer (0.2 M Na2HPO4/0.2 M NaH2PO4, pH 7.4) to minimize pH variations. The plasma samples were centrifuged at 12000g for 10 min. 500 μL samples of supernatant were transferred into the 5 mm NMR tubes containing 50 μL of D2O for a field frequency lock. The proton spectra were collected at 25 °C on a Bruker AVANCE III 600 spectrometer operating at 600.13 MHz 1H frequency and equipped with a triple resonance probe. One-dimensional spin−echo spectra were recorded using the CPMG (Carr−Purcell−Meiboom−Gill) sequence D−[−90°− (τ−180°−τ)n−ACQ], 23 where a fixed total spin−spin relaxation delay 2nτ of 96 ms was applied to attenuate the broad NMR signals from slowly tumbling molecules (such as proteins) and retain those from low-molecular weight compounds and some lipid components. Typically, 256 transients and 64K data points were collected with a spectral width of 12000 Hz, an acquisition time of 2.65 s, and a relaxation delay of 6.00 s. The free induction decay (FID) was zero-filled, and an exponential line-broadening function of 0.3 Hz was applied to the FID prior to Fourier transformation. Blood plasma metabolites were assigned by comparison with chemical shift, which is detailed in both our previous work24 and published literature.23 To validate the assignments made from 1D 1H NMR spectra, some plasma samples were further examined by 2D 1H−1H correlation spectroscopy (COSY) and

MATERIALS AND METHODS

Clinical Samples

Written informed consents were acquired from all participants recruited in the study. The Ethical Committee of Chongqing Medical University reviewed and approved the protocol of this study and the procedures employed for sample collection. Depressed patients were enrolled in the psychiatric center of the First Affiliated Hospital of Chongqing Medical University. A structured clinical interview employing DSM-IV criteria was used to assess patients with a single depressed episode,21 and the 17-item version of the observer-rated Hamilton Depression Rating Scale (HDRS) was applied to rate the severity of MDD.21 Only depressed subjects with HDRS scores greater than 17 were recruited. Depressed patients with one or more confounding factors, such as pre-existing physical or mental 1742



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Figure 1. NMR spectra of depressed patients and healthy controls. Representative 600 MHz one-dimensional Carr−Purcell−Meiboom−Gill (1DCPMG) 1H NMR spectra of blood plasma samples from a first-episode drug-naı̈ve depressed patient (A) and a healthy control subject (B) indicating key metabolites.

total correlation spectroscopy (TOCSY) spectra with solvent suppression. Both spectra were acquired with a 2.0 s relaxation delay, 1.5 s water signal suppression, and 8000 Hz spectral widths for 1H dimensions. For each 2D spectrum, 512 increments with 128 transients per increment were collected and extended to 4K data points using linear prediction and zero-filling techniques. The TOCSY experiments employed a MLEV-17 spin-lock scheme for 1H−1H transfers with a mixing time of 80 ms at the spin-lock strength of 8 kHz.

The quality of the OPLS-DA models was described by three parameters (R2X, R2Y, and Q2Y), which were calculated by the default leave-one-out procedure. R2X and R2Y were used to quantify the goodness of fit; Q2Y was employed to assess the predictability of the model.29 To assess the nonrandomness of separation of the two groups, a 100-iteration permutation test was performed in a PLS-DA model with the same number of components as the OPLS-DA model.30 To validate the robustness of the OPLS-DA model, an independent replication cohort of 14 depressed patients and 12 controls was used. Combining the foregoing analysis, the sensitivity and specificity of the OPLS-DA model were calculated.

Data Processing of NMR Spectra and Multivariate Pattern Recognition

All spectra were manually phased and baseline referenced to TSP resonance at δ0.0. In order to exploit all metabolic information embedded in the spectra, all NMR spectra (0.2− 10.0 ppm) were segmented into equal widths of both 0.01 ppm and 0.0015 ppm using the AMIX package (Bruker Biospin, Germany). Spectral regions of δ4.70−5.10, δ3.65−3.57, δ3.22− 3.06, δ2.72−2.66, and δ2.60−2.53 were removed in order to eliminate variations caused by imperfect water suppression, EDTA, and EDTA metal complexes.25 The remaining spectral segments in each NMR spectrum were normalized to the total sum of the spectral intensity to partially compensate for differences in concentration among the numerous metabolites. The normalized integral values were imported into SIMCA-P+ 12.0 software (Umetrics, Umeå, Sweden) as variables and then mean-centered and pareto-scaled prior to multivariate data analysis. A supervised multivariate approach, termed orthogonal partial least-squares discriminant analysis (OPLS-DA), was used to visually discriminate between MDD and controls.26,27 The corresponding loading plots, where differential metabolite peaks are displayed as positive and negative signals to indicate the relative changes of metabolites, were used to identify the spectral variables responsible for sample differentiation on the scores plot.28

Calculation of Significance of Altered Metabolites

The main metabolites responsible for class discrimination were manually calculated by peak integration. The nonparametric Mann−Whitney U test was used to detect significant differences in selected signals between the two groups. A pvalue of less than 0.05 was considered to be statistically significant. The false discovery rate was controlled according to the Bonferroni step-down procedure.31

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RESULTS

Clinical Characteristics of Subjects

The detailed characteristics of clinical data and plasma samples acquired from the depressed and control groups are shown in Table 1. All depressed patients scored higher on the Hamilton Depression Rating Scale than healthy controls. In addition, both groups were not differentiable by any key demographic characteristic, such as age, gender, or BMI. Therefore, the findings cannot be attributed to demographic factors. 1

H NMR Spectra of Plasma Samples

Representative plasma 600 MHz one-dimensional Carr− Purcell−Meiboom−Gill (1D-CPMG) 1 H NMR spectra obtained from depressed and control groups are shown in 1743



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Figure 1. In total, 26 metabolites have been identified including lipid/protein complex (low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), N-acetylglycoprotein, cholesterol, and unsaturated lipid), amino acids (isoleucine, leucine, valine, alanine, glutamine, taurine, glycine, tyrosine, histidine), and some other metabolites (3-hydroxybutyrate, lactate, acetate, acetone, pyruvate, citrate, creatine, creatinine, choline, glucose, histamine, formate). The spectrum resonances assigned to these key metabolites are noted in Figure 1B. Metabonomics Analysis of Plasma Samples of MDD and Controls

The aim of this study was to assess the potential of plasma metabonomics in the diagnosis of MDD. Therefore, the OPLSDA classification model using one predictive component and five orthogonal components was established to discriminate between 58 first-episode drug-naı̈ve depressed patients and 42 healthy controls. The OPLS-DA score plots revealed that the depressed patients were statistically distinguishable from healthy controls (R2X = 0.821, R2Y = 0.842, Q2 = 0.556; Figure 2). The parameters (R2X, R2Y, and Q2Y) for describing

Figure 3. OPLS-DA loadings plot of key metabolites. OPLS-DA loadings plot demonstrating discrimination of key metabolite levels between first-episode drug-naı̈ve depressed patients and healthy controls.

Statistical Validation of the OPLS-DA Model

To validate the performance of the OPLS-DA model, a permutation test was performed. The validation plot demonstrated that the original OPLS-DA model was not random and overfitted as both permutated Q2 and R2 values were significantly lower than the corresponding original values (Figure 4). Independent Validation of the OPLS-DA Model

To independently validate the diagnostic performance of the OPLS-DA model, this model was used to predict class membership in an independent replication cohort of 14 depressed patients and 12 healthy controls. Thirteen of 14 depressed patients and 10 of 12 healthy controls were correctly predicted by the OPLS-DA model, demonstrating the ability of the OPLS-DA model to diagnose blinded samples with a sensitivity of 92.8% and a specificity of 83.3% (Figure 5).

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DISCUSSION MDD is a serious psychiatric mood disorder, resulting in several detrimental socioeconomic effects, including increased healthcare expenditures and suicide rates.3 Nevertheless, the diagnosis of MDD remains primarily symptom-based and, therefore, highly subject to human error. The aim of this study was to examine the feasibility of an empirical laboratory-based method to diagnose MDD. This NMR-based plasma metabonomic method was able to distinguish first-episode drug-naı̈ve depressed patients from healthy controls. Moreover, this approach was shown to accurately diagnose blinded samples with both high sensitivity and high specificity. A fundamental prerequisite for assessing the diagnostic performance of any metabonomic approach is to “capture” the metabolic alterations that accurately represent the disease state in question. Considering that plasma metabonomic profiling reflects a collective “snapshot” of metabolic perturbations inducible by various confounding factors such as acute illness, chronic impairment, and drug intake,32 the importance of exclusively employing samples from first-episode drug-naı̈ve depressed patients cannot be overestimated. As the pathophysiological processes of MDD remain largely unknown, one cannot be absolutely certain that the observed metabolic alterations are solely attributable to major depression and not related to confounding factors. However, it should be noted

Figure 2. OPLS-DA score plots of depressed patients and healthy controls. OPLS-DA score plots displaying discrimination between the first-episode drug-naı̈ve depressed patients (red diamonds) and healthy controls (black box).

the OPLS-DA model were significantly elevated (R2, Q2 > 0.5), suggesting that the OPLS-DA model was robust. Analysis of the OPLS-DA loading plot indicated that 17 metabolites of high variable importance (projection value > 1.0) were responsible for the discrimination in the score plot (Figure 3). As compared to healthy controls, depressed patients were characterized by higher lipid/protein complex levels (lowdensity lipoprotein (LDL), very low-density lipoprotein (VLDL), N-acetylglycoprotein and unsaturated lipid associated with lower individual amino acid levels (glycine, taurine, glutamine, alanine, valine, and leucine), and lower levels of other metabolites (glucose, myo-inositol, creatinine, creatine, acetate, lactate, and pyruvate). The Mann−Whitney U test was then applied to test the significance of these differential metabolites, showing that the majority of these metabolites were also significant after controlling for the false discovery rate (Table 2). 1744



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Table 2. Key Metabolites Differentiating Depressed Patients and Healthy Controls in the OPLS-DA Modela metabolite

chemical shift (ppm)

HCb

FEDNDPb

fold changec

P-valued

VIPe

leucine valine LDL VLDL lactate alanine acetate NAC pyruvate glutamine creatine creatinine taurine myo-inositol glycine glucose unsaturated lipid

0.99−1.02 1.02−1.05 1.22−1.29 1.29−1.31 1.31−1.34 1.46−1.49 1.91−1.92 2.02−2.05 2.36−2.37 2.42−2.47 3.02−3.03 3.03−3.04 3.38−3.44 3.52−3.55 3.55−3.56 3.88−3.91 5.26−5.35

0.344 ± 0.048 0.383 ± 0.058 13.872 ± 3.508 6.916 ± 2.689 4.260 ± 1.042 0.816 ± 0.156 0.097 ± 0.022 1.407 ± 0.147 0.245 ± 0.055 1.077 ± 0.182 0.102 ± 0.023 0.149 ± 0.021 4.783 ± 0.623 3.572 ± 0.312 0.630 ± 0.137 5.326 ± 0.708 1.135 ± 0.421

0.312 ± 0.056 0.345 ± 0.063 18.525 ± 4.513 10.035 ± 3.562 3.882 ± 0.570 0.712 ± 0.151 0.086 ± 0.019 1.497 ± 0.176 0.185 ± 0.043 0.953 ± 0.173 0.097 ± 0.025 0.128 ± 0.025 4.522 ± 0.786 3.321 ± 0.268 0.471 ± 0.125 5.145 ± 0.811 1.655 ± 0.545

0.91 0.91 1.34 1.45 0.91 0.87 0.88 1.06 0.75 0.88 0.95 0.86 0.94 0.93 0.74 0.96 1.45

0.035 0.011 0.000 0.000 0.094 0.001 0.084 0.07 0.000 0.018 0.328 0.000 0.080 0.016 0.000 0.262 0.000

1.06 1.31 8.13 6.86 3.59 2.36 1.06 1.91 2.63 1.63 1.42 1.73 2.55 2.41 4.06 1.51 2.35

a

Abbreviations: HC, healthy controls; FEDNDP, first-episode drug-naı̈ve depressed patients; LDL, low-density lipoprotein; VLDL, very low-density lipoprotein; NAC, N-acetylglycoprotein. bValues expressed as the mean ± SD (range). cValues greater than 1 indicate higher levels in depressed patients relative to healthy controls; values less than 1 indicate lower levels in depressed patients relative to healthy controls. dP values were calculated from the Mann−Whitney U test. eVariable importance in the projection (VIP) was acquired from the OPLS-DA model with a threshold of 1.0.

Figure 4. Statistical validation of the OPLS-DA model by PLS-DA. A permutation test performed with 100 random permutations in a PLSDA model showing R2 (green triangles) and Q2 (blue triangles) values from the permuted analysis (bottom left) as significantly lower than corresponding original values (top right). Figure 5. T-predicted scatter plot from the OPLS-DA model. Firstepisode drug-naı̈ve depressed patients (red triangles), healthy controls (black box), first-episode drug-naı̈ve depressed patient prediction set (green triangles), and control prediction set (blue triangles). Class membership of blinded samples from an independent replication cohort predicted using the OPLS-DA model.

that the first-episode drug-naı̈ve depressed subjects participating in this study may not reflect the general population of MDD patients. Moreover, the diagnostic method was only validated by discriminating MDD subjects from healthy controls. Whether this method can be used to differentiate MDD patients from those with other neuropsychiatric disorders (e.g., bipolar disorder (BP), schizophrenia (SCZ)) remains unknown. Further studies with larger cohorts including other mental disorders are required to validate this method.

high sensitivity test is more clinically relevant than a high specificity test in underdiagnosed disorders such as MDD.33 Thus, this metabonomic approach may prove useful in improving the underdiagnosis of MDD. Moreover, this metabonomic method achieves a stronger diagnostic performance than equivalent genetic methods. For example, a recent investigation by Spijker et al.34 showed that a panel of differential genes could discriminate depressed patients from controls with a sensitivity of 76.9% and a specificity of 71.8%. The higher diagnostic sensitivity and specificity observed in our study can be explained by the fact that, as opposed to genetic profiling, metabolic profiling more closely reflects the disease phenotype at a functional level. Despite these methodological

High Sensitivity and Specificity

The key features of evaluating the efficacy of a diagnostic method are the sensitivity and specificity of detection. Here, the NMR-based plasma metabonomic approach diagnosed blinded samples with a sensitivity of 92.8% and a specificity of 83.3%. The diagnostic sensitivity of this method (92.8%) is significantly higher than that of the current symptom-based diagnostic method (47.3−50.1%).7 The specificity (83.3%), on the other hand, is similar to that of available tests (83.1%).7 A 1745



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differences, both studies demonstrate that analyzing peripheral alterations has value in diagnosing MDD.

also support a preliminary hypothesis of lipid metabolic dysregulation associated with MDD.

Key Metabolites

Lipid Metabolism-Related Molecules

Apart from assessing the diagnostic performance of a plasma metabonomic method, the key metabolites responsible for discrimination between MDD patients and healthy controls were identified. These metabolites include amino acids (glycine, taurine, glutamine, alanine, valine, leucine), lipid/protein complexes (LDL, VLDL, N-acetylglycoprotein, and unsaturated lipid), lipid metabolism-related molecules (acetate, myoinositol), and energy metabolism-related molecules (creatine, creatinine).

Levels of lipid metabolism-related molecules were also altered in depressed patients. Acetate is produced in vivo via lipid oxidation. The decreased level of acetate found in depressed patients can be linked to decreased lipolysis, which correlates with the elevated circulating lipid levels observed in MDD. The plasma level of myo-inositol (a byproduct of membrane-bound phospholipid metabolism) was decreased in depressed patients as compared to healthy controls. A previous study based on proton magnetic resonance spectroscopy (1H-MRS) has shown a reduction of myo-inositol in specific cerebral regions of MDD,44 which supports the above finding. Contrastingly, compared to healthy controls, the serum level of myo-inositol was significantly increased in BP and SCZ patients, suggesting that myo-inositol may be used to distinguish MDD from BP and SCZ.14,38

Amino Acids

The altered amino acid profile is noteworthy in light of previous studies that suggest the synthesis of brain neurotransmitters related to MDD pathogenesis can be influenced by circulating amino acid levels.35,36 In this study, plasma amino acid levels were consistently decreased in the depressed group. These results contrast with those of a previous study that reported higher plasma levels of glutamine, glycine, and taurine in depressed patients receiving antidepressant medication.37 These discrepancies may be explained by confounding antidepressant therapeutic effects. Another group, using a gas chromatography−mass spectrometry (GC-MS)-based metabonomic approach, found that CNS amino acid levels were significantly perturbed in various brain regions of a depressed rat model.17 These findings suggest that some depressionassociated metabolic alterations detected in the CNS are reflected peripherally in the bloodstream. Interestingly, amino acid metabolic profiling has also shown promise in distinguishing MDD from other mental disorders. Altered plasma amino acid levels (proline, glutamine, valine, asparagine, arginine, and lysine) have been observed in a previous NMR-based metabonomic study of bipolar patients.14 It is noteworthy that the altered plasma amino acid profile in MDD was strikingly different from that observed in bipolar disorder. In addition, contrary to the increased level of glycine found in our study, using a GC-MS based metabolomic method, a decreased level of glycine was found in SCZ patients as compared to healthy controls. 38 These differences demonstrate the potential of plasma metabolomics in discriminating MDD from BP and SCZ.

Energy Metabolism Molecules

Creatinine production stems from creatine and phosphocreatine metabolism; in turn, the creatine−phosphocreatine system plays a major role in cellular energy transport. Zheng et al.18 employed an ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to analyze urine in a depressed model and discovered that the creatinine level was significantly reduced. In this study, decreased levels of creatinine and creatine were found in depressed patients. Although creatinine’s and creatine’s role in MDD is not clear, the foregoing results imply that creatinine and creatine may be involved in energy deficiencies associated with MDD. As fatigue, lethargy, psychomotor retardation, and somnolence are psychosomatic symptoms commonly found in MDD,45 it is logically consistent that these energy metabolism-related molecules are down-regulated in the plasma of depressed patients.

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CONCLUSION In this study, a NMR-based metabonomic approach was shown to independently distinguish MDD patients from healthy controls with high reliability. These findings indicate this approach’s potential as a dependable laboratory-based diagnostic tool for MDD. Given the functional and transient nature of the metabonome, further studies should examine whether this approach can be applied to assess disease progression as well as differentiate drug-responsive subgroups within depressed populations.

Lipoproteins and Lipids

Apolipoproteins (LDL and VLDL) were the most prominent factors differentiating depressed patients from healthy controls. In concurrence with our results, previous proteomic analysis of serum samples from a depression model also found significant alterations in apolipoprotein levels.39 The changes in apolipoprotein levels suggest that MDD may be associated with lipid metabolic dysregulation, as apolipoproteins play an important role in lipid metabolism. Consistent with this speculation, the apolipoprotein E gene has been reported to be involved in MDD,40 and another study has demonstrated an association between lipid regulation and MDD.41 These results may account for the high comorbidity between MDD and metabolic syndrome.42 In the current study, the unsaturated lipid level in the plasma of the depressed group was also elevated. Consistent with this finding, Chilton et al.43 reported increased plasma levels of unsaturated fatty acids in depressed monkeys. These results

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +86-23-68485490. Fax: +86-23-68485111. E-mail: [email protected]. Author Contributions ⊥

P.Z. and H.C.G. contributed equally to this work.

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ACKNOWLEDGMENTS Our sincere gratitude is extended to Professors Huaqing Meng, Delan Yang, and Hua Hu for their efforts in sample collection. This work was supported by the National Basic Research Program of China (973 Program) (Grant No. 2009CB918300), the National Natural Science Foundation of China (Grant No. 1746



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30900456 and 21175099), and the Natural Science Foundation Project of Chongqing (CSTC, 2008BB5238 and 2010BB5393).

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Plasma Metabonomics as a Novel Diagnostic Approach for Major

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