Study of the urinary metabolite profile associated with osteoarthritis and the correlation with its TCM syndromes Li-Xi Chu1, Song-Bin Yang1, Wei Jia2, Yun-Ping Qiu2, Ming-Ming Su2 1

College of Acupuncture and Manipulation, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; 2. school of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract — To compare the distinction of endogenous small molecular metabolites in urine samples from the patients suffering from osteoarthritis(OA) and the healthy people. To contrast the variance of metabolic phenotypes belong to the two different TCM syndromes of OA. Methods: Urine samples of 85 participants (43 non-OA controls and 42 individuals with the disturbance of OA which include two different classes in TCM theory) were selected .Utilizing the combined gas chromatography and mass spectrometry (GC-MS) method for the detection of such urine samples. Pattern recognition was applied to analyze the raw data. Some potential biomarkers of OA were identified by means of retrieval of mass spectral database. By the same means, comparative study was carried out between the two groups of TCM syndromes. Results: Obvious differences were found in the total ion chromatograms (TIC) of urine samples between the OA and control subjects. When compared the insufficiency of liver and kidney with tendon and vessel stagnation syndrome group with the control, the plot of PCA scores showed that satisfactory separation was made. Better division was realized when the more sophisticated model PLSDA and OPLSDA were utilized. Well-pleasing separation was achieved when the comparison between of the asthenia of both spleen and kidney with dampness invading joints syndrome group and the control one as well as between of the two TCM syndromes. Conclusion: There are differences in the urine metabolite profiles among different types of syndrome of OA and the control group. The metabolic regulatory network in organism probably is perturbed in the progress of OA. The nature of types of syndrome of TCM maybe base on the variation of metabolites in vivo and the technique of metabonomics could facilitate the research of the nature of TCM theory. Keyword — Osteoarthritis(OA); metabonomics; gas chromatography and mass spectrometry (GC-MS); urinary metabolite profile; pattern recognition; study of the nature of TCM syndromes

Osteoarthritis (OA), a chronic joint disease common to the middle-aged and senile people, may result in pain and functional disability in the joints, or even cause one disabled, hence affecting considerably the life quality of patients. Via such metabonomics techniques as gas chromatography-mass spectrometry (GC-MS) and data mining, our study aims to observe the influence of OA on endogenous metabolites of the body by detection and analysis of urine

from knee OA patients with different syndromes in TCM, and by comparison with the normal population; through comparison of metabolic phenotypes from different TCM syndromes, we aim to provide grounds for exploring the essence of “syndrome”in TCM from systems biology level. I. MATERIALS AND METHODS 1.1 Selection of cases 1.1.1 Diagnostic criteria in western medicine: please refer to the diagnostic criteria of knee osteoarthritis revised by American Rheumatology Association (ASA) in 1995. 1.1.2 Diagnostic criteria in TCM differentiation: please refer to the diagnostic criteria of TCM symptoms and signs of knee osteoarthritis in Clinical Research Guidelines for New Traditional Chinese Medicines (trial) revised by Drug Administration Bureau of the People's Republic of China in 2002. 1.1.3 Including criteria: (1) comply with clinical criteria of knee osteoarthritis in western medicine; (2)comply with the clinical criteria of knee osteoarthritis with the syndrome of deficiency in the liver and kidney and stagnation in the tendons and vessels and the syndrome of deficiency in the spleen and kidney and dampness in the joints in TCM; (3) informed consent was signed by patients or their families. 1.1.4 Excluding criteria: (1) complicated with severe diseases in heart, lung, liver, kidney, hematopoietic and endocrine systems (2) complications affecting the joints, such as psoriasis, syphilitic neurosis, metabolic bone disease, rheumatoid arthritis and acute trauma; (3) patients who still use glucocorticoids within half a month during this study, or without stopping application of non-steroidal anti-inflammatory analgesic drugs within a week during the study; (4) Patients with such metabolic or endocrine diseases as thyropathy, diabetes and hyperlipemia or other diseases significantly affecting the biochemical metabolism of the body. 1.2 Source of study subjects Disease group 2 cases of patients with final diagnosis as knee osteoarthritis and treated in outpatient department of fracture and wounds in Yueyang Integrated Chinese and

Yi Peng, Xiaohong Weng (Eds.): APCMBE 2008, IFMBE Proceedings 19, pp. 691–696, 2008 www.springerlink.com © Springer-Verlag Berlin Heidelberg 2008

692

Li-Xi Chu, Song-Bin Yang, Wei Jia, Yun-Ping Qiu, Ming-Ming Su

Western Medicine Hospital affiliated to Shanghai University of TCM from September, 2006∼November 2006; Normal group: 43 cases of healthy people received medical examination in medical examination center of Yueyang Integrated Chinese and Western Medicine Hospital affiliated to Shanghai University of TCM in December 2006.

zation was performed prior to multivariate analyses. Then principal components analysis (PCA) was utilized with Simca-P11.0 Software package. Additionally, the majority of the metabolites detected were identified by Turbomass 4.1.1 software (PerkinElmerInc.,USA) coupled NIST mass spectra database.

1.3 Laboratory apparatuses and reagents Main apparatuses: gas chromatography- mass spectrometry (American Perkin Elmer company AutoSystem XL Gas Chromatogram/ TurboMass Mass Spectrometer). Required reagents: ethyl chlorformate (ECF), chloroform, absolute alcohol, pyridine, sodium hydroxide and natrii sulfas exsiccatus, all of which are analytical reagents, and purchased from Shanghai Runjie Chemical Agent Limited Company. Internal label: L-2-chlorophenylalanine, purchased from American SIGMA Company. 1.4 The collection and pretreatment of urine samples About 10∼15ml of urina sanguinis was collected on an empty stomach from patients with knee OA and healthy people in the control group included in this study. After antiseptic treatment by 0.5ml of toluene, the sample was put in -20°C deep freezer for cryopreservation. 30min prior to the experiment, the urine sample was taken out to thaw in common temperature.. Derivatization by ethyl chlorformate (ECF) was performed before GC-MS analysis. 1.5 GC-MS analysis A 1-μL extract aliquot of the extracts was injected into a DB-5MS capillary column coated with 5% diphenyl crosslinked 95% dimethylpolysiloxane (30m×250m i.d.,0.25μm thickness) in the split mode (3:1). Either the injection temperature or the interface temperature was set to 260°C; and the ion source temperature was adjusted to 200°C. Initial GC oven temperature was 80°C. 2 min after injection, the GC oven temperature was raised to 140°C with 10°C min-1 ,to 240°C at a rate of 4°C min-1,to 280°C with 10°Cmin-1 again,and finally held at 280°C for 3min. Helium was the carrier gas with a flow rate set at 1mL min-1 The measurements were made with electron impact ionization (70eV)in the full scan mode(m/z30-550). 1.6 Data analysis All the GC-MS raw fills were converted to CDF format via DataBridge (PerkinElmer Inc.,USA),subsequently processed by the XCMS toolbox. The resulting table(TSV file)was exported into Matlab software 7.0,where normali-

II. RESULTS 2.1 Comparison of general conditions There were 42 cases included into the disease group in all (including 20 cases of deficiency in the liver and kidney and stagnation in the tendons and vessels and 22 cases of deficiency in the spleen and kidney and dampness in the joints), and among them, there 9 males and 33 females, with the youngest as 42 years old, the oldest as 74 years old, the average age as 56.8±7.1 years old, and the average body weight index (BMI) as 25.13±3.25; There were 43 cases in the normal group, and among them, there were 12 males, 31 females, with the youngest as 40, the oldest as 73, the average age as 56.0±7.4 years old, and average BMI as 4.06±2.11. There was no statistical significance between the two groups in the differences of age distribution, sex constitution and body weight index. 2.2 The acquisition of GC-MS data and the expression of chromatographic peak After GC-MS analysis of urine sample in each group, the corresponding chromatograms were obtained, which were showed in graph 1. From the graph, it was observed that there were obvious differences in the expression of chromatographic peak between the two varied syndrome groups regarding OA and the normal group, indicating that endogenous metabolites level in the OA patient had changed. A: Normal control B :OA patients with deficiency in the liver and kidney and stagnation in the tendons and vessels C: OA patients with deficiency in the spleen and kidney and dampness in the joints A. 2.3 The comparison between metabolite spectra from the group of OA patients with deficiency in the liver and kidney and stagnation in the tendons and vessels and the group of normal people Principal component analysis (PCA) was performed on the two groups of data, and a principal component integrogram with PC2 and PC3 as coordinate axes was drawn. As indicated in graph 2, the black square marks represent samples from the normal (control) group, and the red round

_________________________________________ IFMBE Proceedings Vol. 19 ___________________________________________

Study of the urinary metabolite profile associated with osteoarthritis and the correlation with its TCM syndromes

693

10.66

100

16.35

%

3.41 8.83 4.16

5.86 6.87

8.27

19.27

15.59

10.31 9.68

11.16

19.75 13.24

14.20

23.09 24.28

25.27

20.59 21.78 23.02 24.26

25.26

17.81

27.36

28.83 30.06

31.29 32.52

34.30

0 16.41

100

15.61

%

19.27

3.41

8.80

4.30

5.86

6.86

8.26

10.57

8.98

19.75 12.67

14.08

17.79

27.31

28.82

30.07 30.88

32.51

34.46

0 16.32

100

30.15

19.26 8.82

3.42

15.58

%

28.89

19.75 4.17 4.56 5.86

6.87

8.99 7.75

12.39 13.26 14.19

9.67

17.80

20.59 21.49

23.05 24.26

25.24

32.54 34.47

27.38

31.25

33.28

0 5.00

7.50

10.00

12.50

15.00

17.50

20.00

22.50

25.00

27.50

30.00

32.50

35.00

Time

Fig. 1 Chromatogram of urine sample

marks represent the samples from the group of OA patients with deficiency in the liver and kidney and stagnation in the tendons and vessels (OA1). From this graph, it can be seen that the two groups of samples were well differentiated in PC2 dimension. In order to make the maximum reflection of the separation trend of the two groups, the partial least-squares discriminant analysis (PLSDA) and orthogonal partial leastsquares-discriminant analysis (OPLSDA) were employed to construct the model and explore the mode relationship between the high-dimensional variable data concentration samples. The integrated model of PLSDA was shown in graph 3. In the three-dimensional space constructed by PC1, PC2 and PC3, the samples from the OA1 group and the normal group were clearly differentiated. To further evaluate the construction effect of the model, the SIMCA-P 11.0 software was utilized to calculate the values of R2Y (to evaluate the reliability of the model) and Q2Y (to evaluated the predictability of the model). The calculation showed that R2Y=0.986 and Q2Y＝0.679, indicating that the model has good differentiation and predictability. Graph 4 is the integrated diagrammatic figure of OPLSDA, and further calculation showed that R2Y=0.871 and Q2Y＝0.498. On the basis of model construction, variable importance projection (VIP) analysis was used to determine the variables (chromatographic peaks) accountable for the differential expressions of metabolite spectra in the two groups, and the NIST database was also employed to qualitatively analyze the chromatographic peaks ( part of them could not be determined due to the limited resources in the chromatographic database). Many metabolite levels changed in different degrees in the urine of OA1 patients, as compared to those in the urine of normal people. The metabolites with increased levels were mainly glycine, indoleacetic acid, tyrosine, aconitate, acetyl-citrate, citrate, hippurate, isocitrate, benzeneacetate, 4-hydroxy and tyrosine, etc. The metabolites with decreased levels were mainly alanine, threonine, glutamine, dopamine and histidine, etc.

Fig. 2 the PCA integrogram of the OA1 group and the normal group

Fig. 3 the PLSDA diagrammatic figure of the OA1 group and the normal group

Fig. 4 the OPLSDA diagrammatic figure of the OA1 group and the normal group

_________________________________________ IFMBE Proceedings Vol. 19 ___________________________________________

694

Li-Xi Chu, Song-Bin Yang, Wei Jia, Yun-Ping Qiu, Ming-Ming Su

2.4 The comparison between metabolite spectra from the group of OA patients with deficiency in the spleen and kidney and dampness in the joints and the group of normal people PCA result was shown in graph 5, the back square marks represent samples from the normal (control) group, and the red round marks represent the samples from the group of OA patients with deficiency in the spleen and kidney and dampness in the joints (OA2). From this graph, it can be seen that the two groups of samples were well differentiated in PC2 dimension, and obvious intra-group clustering trend existed. The model indicated in graph 6 was constructed by means of PLSDA. In the three-dimensional space constructed by PC1, PC2 and PC3, the samples from the OA2 group and the normal group were clearly differentiated, and there was clear clustering trend and no inter-group confounding in the samples. The calculation showed that R2Y=0.872 and Q2Y＝0.537, indicating that the model has good differentiation and predictability. The integrated diagrammatic figure of OPLSDA is shown in graph 7. The two groups were well differentiated in PC1 dimension. The calculation showed that R2Y=0.872 and Q2Y＝0.576.

Fig. 7 the OPLSDA diagrammatic figure of the OA2 group and the normal group

2.5 The comparison between metabolite spectra from the OA1group and the OA2 group PCA result was shown in graph 8. From this graph, it can be seen that the two groups of samples were well differentiated in the PC2 dimension. The model constructing result of PLSDA was shown in graph 9. In the three-dimensional space constructed by PC1, PC2 and PC3, there was clear intra-group clustering trend and inter-group mutual separation in the samples from the OA1 group and OA2 group, indicating that there were different expression phenotypes in the two groups. The calculation showed that R2Y=0.863 and Q2Y＝0.705, indicating that the model had been well constructed. The model constructed by means of OPLSDA is shown in graph 10. The two groups were well differentiated in PC1 (t1) dimension. The calculation showed that R2Y=0.782 and Q2Y＝0.637, indicating good reliability and predictability of the model.

Fig. 5 the PCA integrogram of the OA2 group and the normal group

Fig. 8 the PCA integrogram of the OA1 group and the OA2 group

Fig. 6 the PLSDA diagrammatic figure of the OA2 group and the normal group

_________________________________________ IFMBE Proceedings Vol. 19 ___________________________________________

Study of the urinary metabolite profile associated with osteoarthritis and the correlation with its TCM syndromes

Fig. 9 the PLSDA diagrammatic figure of the OA1 group and the OA2 group

Fig. 10 the OPLSDA diagrammatic figure of the OA1 group and the OA2 group

III. DISCUSSION In recent years, with the rapid development of various spectroscopy and bioinformatics techniques, metabonomics has displayed huge potential in disease prediction and diagnosis. Life is an integral system, in which the biological molecular tissues and its manipulation level are interrelated and interdependent, meanwhile, they are also influenced by various physiological and environmental factors. All chemical reactions directly caused by physio-pathological disorder, or disturbance in terms of the proportion, concentration, metabolic flux of endogenous biochemical material by binding with enzyme or nucleic aicd which control the metabolism can be reflected in the components of metabolites[2]. At present, studies showed that compared with normal person, the neutral hormone of body fluid, amino acid, cytokine, as well as the metabolite of collagen of articular cartilage and proteoglycan in OA patients witnessed changes.

695

OA is mainly caused by the degeneration of articular cartilage, featuring the damaged and degenerative of articular cartilage. Besides the change of cartilage, its metabolites also flow into the body fluid through the lymph system, causing a series of meaningful changes [5] and our early study also proved this point [6].In this study, GC-MS and pattern recognition were applied to test and analyze endogenous metabolites as well as their concentration in the urine of testees. Findings show that there is a desirable recognition between OA patient and controlled testees according to those minute changes. It also showed that OA can disturb the metabolic system, causing the change of internal metabolic phenotype.The techniques of metabonimics can be a novel diagnostic method of OA, and need further development. The research made a comparison and analysis over the metabolites in urinary samples of OA patient with different TCM syndromes. The findings showed that there is significant difference between the two OA groups in terms of urinary metabolites. By applying PCA, and model construction of PLSDA, OPLSDA, we finely differentiated these two groups of patients, indicating a substantial material basis of the “syndrome differentiation ”, and the change of disease disturbed metabolic phenotype is closely related with the nature of “syndrome”. “syndrome ”in TCM is the pathological generalization of a disease at certain developmental stage, featured by holism and dynamics. By using microscopic way, metabonomics makes a systematic research over the rule of change of the metabolite during the dynamic process of the metabolism. There is similarity between the metabonomics and TCM syndrome in terms of way of thinking and research method. A disease covers different syndromes, on the other hand, a syndrome may be shared by different diseases, and the application of metabonomics techniques &methods to make qualitative and quantitative analysis over their metabolite is of vital importance to grasp the nature of TCM syndrome and to standardize the clinical syndrome differentiation.

_________________________________________ IFMBE Proceedings Vol. 19 ___________________________________________

696

Li-Xi Chu, Song-Bin Yang, Wei Jia, Yun-Ping Qiu, Ming-Ming Su

REFERENCES 1.

2. 3. 4.

Brindle J T, Nicholson J K，Schofield P M，et a1．Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension[J]. Analyst. 2003, 128(1):32-36． Yan Xianzhong, Zhao Jianyu, Pengshuanqing.The role of metabonomics in post-gene era, Journal of spectroscopy, 2004, 21 (6):267. Garnero P, Conrozier T, Chrislgau S, et al. Urinary type IIcollagen Ctelopeptide levels are increased in patients with rapidly destructive hip osteoarthritis. Ann Rheum Dis,2003;62(10):939-943. Wei Xiaoen, Yang Qingmin, Deng Lianfu,etc. Experimental study on metabolic change of proteoglycan in body fluid of osteoarthritis [J]. Chinese Journal of rheumatology,2002, 6 (6):445－446.

5.

6.

Ishiguro N, Ito T, Ito H, et a1.Relationship of matrix metalloproteinases and their inhibitors to cartilage proteoglycan and collagen tunover: analyses of synovial fluid from patients with osteoarthritis. Arthritis Rheum,1999,42:129-136． Zhu Lixi, Xie Dianhong. Correlation of biochemical metabolism of knee osteoarthritis fingerprint spectrum and sign & symptom. Shanghai:Shanghai University of TCM, 2005. Author: CHU Li-Xi Institute: College of Acumox and Tuina, Shanghai University of TCM Street: 1200 Cailun Road City: Shanghai Country: China Email: [email protected]

_________________________________________ IFMBE Proceedings Vol. 19 ___________________________________________