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Research ArticleClassification of Gan Dan Shi Re Pattern andGan Shen Yin Xu Pattern in Patients with Hepatitis BCirrhosis Using Metabonomics

Chao-qun Zhao 1 Long Chen 1 Hong Cai2 Wei-li Yao 1 Qun Zhou 1

Hui-ming Zhu 1 Yue Gao 1 Ping Liu 13 Xiao-jun Gou 4 and Hua Zhang 1

1 Institute of Liver Disease Shuguang Hospital Shanghai University of Traditional Chinese MedicineKey Laboratory of Liver and Kidney Diseases of Ministry of Education Key Laboratory of Clinical Chinese Medicine258 Zhangheng Road Pudong District Shanghai 201203 China

2Xiamen Hospital of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fujian 361000 China3E-Institute of Traditional Chinese Internal Medicine Shanghai Municipal Education CommissionShanghai University of Traditional Chinese Medicine 1200 Cailun Road Shanghai 201203 China

4Central Laboratory Baoshan District Hospital of Integrated Traditional Chinese and Western Medicine of ShanghaiShanghai 201999 China

Correspondence should be addressed to Xiao-jun Gou gouxiaojun1975163com and Hua Zhang lnutcmzh126com

Received 2 July 2018 Revised 24 September 2018 Accepted 6 November 2018 Published 21 November 2018

Academic Editor Caigan Du

Copyright copy 2018 Chao-qunZhao et alThis is an open access article distributed under theCreative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Objective This study aimed to analyze the differential metabolites and their metabolic pathways from the serum of patients withhepatitis B cirrhosis with two typical patterns of Gan Dan Shi Re (GDSR) and Gan Shen Yin Xu (GSYX) based on the theory oftraditional Chinese medicine (TCM) It also investigated the variation in the internal material basis for the two types of patternsand provided an objective basis for classifying TCM patterns using metabolomic techniquesMethods The serum samples takenfrom 111 qualified patients (40 GDSR cases 41 GSYX cases and 30 Latent Pattern (LP) cases with no obvious pattern characters)and 60 healthy volunteers were tested to identify the differential substances relevant to hepatitis B cirrhosis and the two typicalTCM patterns under the gas chromatographyndashtime-of-flight mass spectrometry platform The relevant metabolic pathways ofdifferential substanceswere analyzed usingmultidimensional statistical analysis Results After excluding the influence of LP groupssix common substances were found in GDSR andGSYX patterns whichwere mainly involved in themetabolic pathways of glycineserine threonine and phenylalanine Eight specific metabolites involved in the metabolic pathways of linoleic glycine threonineand serine existed in the two patterns Conclusions The data points on the metabolic spectrum were found to be well distributedamong the differential substances between the two typical TCM patterns of patients with hepatitis B cirrhosis using metabolomictechniques The differential expression of these substances between GDSR and GSYX patterns provided an important objectivebasis for the scientific nature of TCM pattern classification at the metabolic level

1 Introduction

Hepatitis B cirrhosis is one of the most fatal refractoryand progressive liver diseases worldwide according to recentqualified epidemiological studies [1] Based on its holisticand individualized diagnosis and treatment characteristicstraditional Chinese medicine (TCM) has unique advantagesin treating hepatitis B cirrhosis by improving the clinical

symptoms and liver function reversing liver fibrosis or evenpreventing the progression of cirrhosis

The pattern ldquoZhengrdquo according to the transliteration ofChinese is the core concept of TCM diagnosis treatmentand determination of curative effect Accurate pattern dis-cernment is the core link to improve the clinical efficacyof TCM Traditionally the pattern differentiation is basedmainly on practitioners own experience such as looking

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2018 Article ID 2697468 13 pageshttpsdoiorg10115520182697468

2 Evidence-Based Complementary and Alternative Medicine

smelling talking and feeling the pulse Limited objectiveevidence also restricts the development of the study aboutthe Chinese pattern With the advancement of science andtechnology many researchers tried to explain the patternclassification rules by modern scientific methods to make upfor the deficiency of objectivity and reproducibility in patterndifferentiation

Metabolomics examines mainly the dynamic changes inthe quality and quantity of metabolites produced in bio-logical systems in response to pathophysiological reactionsor genetic modification thus finding the relative relation-ship between metabolites and pathophysiological changesin organisms [2] It is consistent with the systematic andholistic view of TCM [3] Studying the exogenous molecularcompounds to understand the changing laws of the occur-rence and development of the disease is of great significancein discussing the pathogenesis diagnosis treatment andevaluation of the disease It also provides methodologicalsupport for the objective and accurate diagnosis of patternsin TCM As an effective method to study the physiolog-ical and pathological changes in body fluids and tissuesmetabolomics was widely applied to TCM in vitro or in vivosuch as efficacy in evaluating Chinese medicine and its bio-chemical mechanism of action [4 5] toxicity evaluation andtoxicological biomarker identification of natural products [6]active fraction identification of prescription [7] and researchon tongue coating [8]

Currently finding blood and urine biomarkers for thediagnosis treatment and prognosis of liver cirrhosis is ahot spot in the liver disease research [9] Guo et al [10]used GCMS methods to study the urine of healthy peopleand patients with hepatitis B cirrhosis They found that themetabolic spectrum could be clearly separated between thetwo groups providing the basis for diagnosing hepatitis Bcirrhosis from the perspective of metabonomics Yang et al[11] analyzed serummetabolic profiles in healthy controls andpatients with cirrhotic ascites and found potential biomarkersfor diagnosing and treating liver fibrosis and cirrhosis Tanget al [12] compared different metabolites in serum andurine of patients with primary biliary cirrhosis (PBC) andhealthy people using ultraperformance liquid chromatogra-phy coupled with quadrupole-time-of-flight mass spectrom-etry (UPLC-QTOFMS) The results showed that the bileacid level increased and the carnitine content decreased withthe development of PBC suggesting that the two substancesmay be the potential biomarkers of PBC These studies haveindicated the important role of metabonomics technologyin diagnosing liver cirrhosis Mcphail et al [13] examined80 patients with decompensated cirrhosis including 62 whosurvived and 18 who died using plasma metabonomicsanalysis The disorder of phosphatidylcholine and aminoacid metabolism is related to the increase in mortality andthe severity of the disease providing the objective basisfor accurately predicting the mortality of patients withdecompensated cirrhosis Based on GCMS and LCMSmetabolomic techniques the presence of specific compoundswas preliminarily confirmed in the urine of patients withhepatitis B cirrhosis that could reflect the patterns of GanDan Shi Re (GDSR) and Gan Shen Yin Xu (GSYX) [14] The

metabolomic study on the dampness-heat pattern of chronichepatitis B (CHB) nonalcoholic fatty liver disease (NAFLD)and chronic glomerulonephritis (CG) revealed five commonbiomarkers between the three diseases including inosineuridine aspartic acid oleic acid and lactate and their specificsubstances (27 substances in CHB 28 in NAFLD and 24 inCG) [15]

Based on previous findings this study examined thetypical GDSR and GSYX patterns of hepatitis B cirrhosisusing healthy persons as the control group Also the studyincluded an LP group (which means that the patients hadno obvious clinical manifestation of the patterns or had littleinformation making it difficult to distinguish these patternsfrom others) as the control The study was performed ontypical GDSR and GSYX patterns of hepatitis B cirrhosisa disease that could be treated using TCM Qualitative andquantitative analyses of the metabolites (serum samples)were performed on the basis of gas chromatographyndashtime-of-flight mass spectrometry (GC-TOFMS) multiple combi-nation techniques to determine the characteristic compoundspectrum of hepatitis B cirrhosis and the two typical patternsof GSYX and GSYXThe present study aimed to provide datasupport for classifying and identifying the TCM pattern ofrefractory hepatitis B cirrhosis

2 Materials and Methods

21 Study Participants All the 111 patients in this study onhepatitis B cirrhosis were aged 18ndash65 years and admitted atShuguang Hospital Affiliated to Shanghai University of TCMXiamen TCM Hospital and Shanghai Putuo District CentralHospital between March 2016 and March 2017 A total of 60healthy sex- and age-matched volunteers were selected fromthe Shuguang Hospital Health Examination Center All theparticipants in the study signed informed consent

211 Inclusion Criteria Participants were selected accordingto the standards of the Ishak Liver Fibrosis Grading Criteriaand the Guidelines for the Prevention and Treatment ofChronic Hepatitis B (2015 Edition) [16] which was revisedjointly by the Chinese Medical Association HepatologyBranch and the Infectious Diseases Branch and in accor-dance with GDSR GSYX [17] and LP [18] diagnostic criteria

212 Exclusion Criteria The exclusion criteria were as fol-lows (1) age less than 18 years or more than 65 years (2) acombination with other types of hepatitis such as hepatitisA C D and E or severe hepatitis (3) a combination withheart liver hematopoietic and neurological disorders ordrug allergy (4) a combination with malignant tumors orconnective tissue diseases (5) pregnant or lactating women(6) patients with anaphylaxis and other severe diseases and(7) patients with mental disorders who could not cooperatewith investigators

22 Instruments and Reagents High-purity methoxyaminehydrochloride fatty acid methyl ester (C7ndashC30 FAME)anhydrous pyridine (995) and anhydrous sodium sulfate

Evidence-Based Complementary and Alternative Medicine 3

were obtained from SigmandashAldrich (MO USA) Derivati-zation reagents MSTFA (containing 1 TMCS) methanol(Optima LC-MS) and n-hexane were purchased fromThermo Fisher (NJ USA) Dichloromethane (995) chlo-roform (99) and acetone (995) were purchased fromChina National Pharmaceutical Group Corporation (BeijingChina) Ultrapure water was prepared from a Millipore Ref-erence ultrapure water system (MA USA) equipped with anLC-MS filter GC-TOFMS (LECO Corp MI USA) based onsilanization-derived GC-TOFMS was used as an analyticalplatform for untargeted metabolomics

23 Clinical Information Collection and Pattern IdentificationUsing the ldquoLiver and Kidney Disease and Pattern ClinicalInformation Collection Form of the Key Laboratory ofMinistry of Educationrdquo the clinical information of the par-ticipants was collected including basic information genderage and ethnicity physical examination body temperatureheart rate breathing and blood pressure biochemical exam-ination liver function fibrosis index and blood routine andfour examinations of TCM Using the posthepatitis cirrhosispattern rating scale the clinical symptoms of TCM werequantified by five levels with no symptoms rated as 0 points(1ndash4 points representing different degrees of severity) [19] Allinformation was input into the database Three experts in thefield gave the pattern differentiation using four examinations(including tongue photo) on the basis of inclusion criteria

24 Collection of Blood Samples BD Vacutainer vacuumblood collection tubes were used to collect 7 mL of wholeblood from the fasting patients in the morning and keptundisturbed at 4∘C for 2 h The blood was separated andcentrifuged (3000g 15 min 4∘C) to obtain the serum Theserum was packed in tubes (400 120583L per tube) and storedat ndash80∘C to avoid repeated freezing and thawing so that thesample remained stable

25 GC-TOFMS Detection

251 Sample Pretreatment Serum samples from the patientswere prepared and stored for the detection The detailedprocedure of serum pretreatment is provided in the supple-mentary file (available here)

252 Analysis Conditions

(1) Chromatography Column Rxi-5MS (Restek CorporationPA USA) column parameters 30 m (length) times 025 mm(internal diameter) 025 120583m (film thickness) oven tempera-ture program 80∘C (2min) 80ndash300∘C (12∘Cmin) 300∘C (57min) injection volume (120583L) 1 inlet temperature 270∘C injec-tion mode no split carrier gas helium (999999) carriergas flow rate (mLmin) 10 constant current transmissionline temperature 270∘C

(2) Ion Source Type EI detection parameters electron energyndash70 V detector voltage ndash1400 V ion source temperature220∘C acquisition rate 25 spectras scan range 50ndash500mz

26 Metabolic Pathway Analysis The specific metabolitesof different patterns by screening were introduced into theonline system MetaboAnalyst for analyzing the metabolicpathways It is generally believed that changes in key locationsin the network have a serious impact on the occurrenceof events Therefore the threshold was set to 010 [20]in this study and the pathways above this threshold wereclassified as potential metabolic pathways The metabolicpathways in this study were all generated using KEGG(httpwwwgenomejpkegg)

27 Data Processing and Statistical Analysis The raw datawere automatically exported using the ChromaTOF software(v45160 CA USA) to the self-developed metabolomicmacrometabolism software XploreMET (v20 Metabo-Profile Shanghai China) for baseline smoothing andcorrection deconvolution signal extraction from originalchromatographic peaks and alignment retention indexcorrection metabolite identification data preprocessing(normalization and standardization) statistical analysismetabolic network analysis and reporting The analysiswas performed using SPSS 210 statistical software (IBMNY USA) in which the measurement data conforming tothe normal distribution and the homogeneity of variancewere described as mean plusmn standard deviation (119909 plusmn 119904) Thecomparison between multisample groups was analyzedby variance for nonconformity with normal distributionor variance The measurement data were described as themedian M (Q1 Q3) The Wilcoxon rank-sum test was usedfor comparison between the two groups The KruskalndashWallisH test was used for the multisample comparison of the groupdesign the count data were described as the relative number() The groups were compared using the 1205942 test Setting 120572= 005 a P value less than 005 suggested that the differencewas statistically significant

3 Results

31 Demographic Characteristics The study comprised 171participants including 30 patients having an LP pattern40 patients having a GDSR pattern 41 patients having aGSYX pattern and 60 healthy volunteers Table 1 shows theirdemographic characteristics

32 Physical and Chemical Examination No significantdifference in alanine aminotransferase (ALT) was foundbetween the groups (P gt 005) The level of albumin (ALB)in the LP group the levels of total bilirubin (TBil) DirectBilirubin (DBil) aspartate transaminase (AST) alkalinephosphatase (ALP) gamma-glutamyl transpeptidase (GGT)total biliary acid (TBA) and ALB in the GDSR group andthe levels of TBil ALP GGT TBA and ALB in the GSYXgroup were statistically significantly different compared withthe healthy group (P lt 005) Significant differences in thelevels of TBil DBil AST ALP TBA and ALB in the GDSRgroup and the levels of TBA and ALB in the GSYX groupwere found compared with the LP group (P lt 005) Astatistically significant difference in the levels of TBil andDBil


Table 1 Distribution of demographic characteristics in patients with hepatitis B cirrhosis and healthy volunteers

HG WZ GDSR GSYX X2FGender(MF) 4020 246 3010 2615 308

Age(year) 3405 plusmn 820 4637 plusmn 1021lowast 4698 plusmn 1020lowast 5132 plusmn 916lowast998771amp 3320

BMI(kgm2)

2204(2163 2301)

2286(2211 2391)

2367(2282 2568)

2243(2200 2392) 507

lowastP lt 005 compared with the normal group 998771P lt005 compared with the LP group amp P lt005 compared with the GDSR group

Table 2 Physical and chemical examination results between different groups [M (Q1 Q3)]

Index GroupHG LP GDSR GSYX

TBil(120583molL)

1138(1008 1412)

2050(1124 2718)

2805lowast998771(2105 4940)

2530lowastamp(1887 3865)

DBil(120583molL)

222(180 249)

392(265 67)

858lowast998771(460 1977)

556amp(435 1149)

ALT(IUL)

1800(1200 25)

2500(1650 2950)

3095(1975 4325)

27(1950 4325)

AST(UL)

2000(1700 2300)

2600(2125 3325)

4050lowast998771(2900 5625)

3700(2600 5850)

ALP(UL)

7700(6600 9200)

7700(5650 9150)

10000lowast998771(7700 14850)

9100lowast(7150 11150)

GGT(UL)

1828(1404 2701)

3125(2150 5497)

4000lowast(2800 5267)

3462lowast(2135 6579)

TBA(120583molL)

335(210 674)

1200(675 1988)

4465lowast998771(1456 10461)

4319lowast998771(1001 8221)

ALB(gL)

4731(4536 4889)

4495lowast(3998 4776)

36093lowast998771(3097 4070)

3534lowast998771(2683 4265)

lowastP lt 005 compared with the normal group 998771P lt 005 compared with the LP group ampP lt 005 compared with the GDSR group

was observed between theGDSR andGSYX groups (P lt 005)(Table 2)

33 Metabolites

331 Multivariate Data Analysis Theunsupervised principalcomponents analysis (PCA) method was used to observe thenatural distribution of each group Separation trends werefound in the LP GDSR and GSYX compared with HG usingthree-dimensional PCA analysis (see Figure 1) For furthervalidation partial least square discriminant analysis (PLS-DA)was used to verify a good separation between disease andhealthy groups and among disease groups (see Figure 2)Theorthogonal partial least square discriminant analysis (OPLS-DA) method was used for further analysis The metabolicspectrum between the LP and GDSR LP and GSYX and LPand HG could be completely distinguished on the principalcomponent t [1] (each point in the figure represents theinformation of the sample metabolite the farther it was fromthe original point the greater the contribution to distin-guish the two groups is leading to metabolites with largerdifferences) The metabolic spectrum of serum in GDSRand GSYX also showed a tendency of separation suggesting

differences in the endogenous metabolites between the twogroups (Figure 3)

332 Identification of Different Metabolites The differentialvariables were screened with variable importance in projec-tion (VIP) gt15 (P lt 005) based on the OPLS-DA analysisresults for each group using endogenous metabolite databaseJIALIB and standard substances aiming to look for metabo-lites highly related to liver cirrhosis Compared with thehealthy group the results showed that 22 differentmetaboliteswere obtained in the LP group (Table 3) Compared with theLP group 20 differential metabolites were obtained in theGDSR group (Table 4) and 18 in the GSYX group (Table 5)

333 Analysis of Common Metabolites of Different PatternsThe GDSR and GSYX groups were compared with theLP group to find out the specific metabolites represent-ing only the two typical patterns Eight specific metabo-lites including citrulline urea myristic acid palmitic acidpalmitoleic acid linoleic acid glycolic acid and petroselinicacid were found in GDSR Also eight specific metabolitesincluding L-threonine pyroglutamic acid L-arabitol 15-anhydrosorbitol glyceric acid L-pipecolic acid glutaric acid


Table 3 Different metabolites between LP and HG

No Differential metabolites VIP P FC Tendency Class1 Beta-alanine 26 le0001 032 darr Amino acid2 Creatinine 18 le0001 068 darr Amino acid3 L-Asparagine 16 le0001 133 uarr Amino acid4 L-Tyrosine 17 0001 144 uarr Amino acid5 D-Threitol 18 le0001 170 uarr Carbohydrate6 Rhamnose 20 le0001 172 uarr Carbohydrate7 L-Sorbose 17 le0001 053 darr Carbohydrate8 D-Fructose 17 le0001 053 darr Carbohydrate9 Gluconolactone 18 le0001 186 uarr Carbohydrate10 Sucrose 16 le0001 201 uarr Carbohydrate11 D-Maltose 17 le0001 7959 uarr Carbohydrate12 Pelargonic acid 20 le0001 190 uarr Fatty acid13 Tetracosanoic acid 18 le0001 132 uarr Fatty acid14 Normetanephrine 17 le0001 072 darr Hormone15 Tryptamine 16 le0001 062 darr Indoles16 Uracil 16 0001 197 uarr Nucleotide17 Inosine 20 le0001 515 uarr Nucleotide18 Malic acid 16 0002 178 uarr Organic acids19 Oxoglutaric acid 18 le0001 322 uarr Organic acids20 Taurine 18 le0001 045 darr Organic acids21 Citric acid 18 le0001 251 uarr Organic acids22 Isocitric acid 21 le0001 276 uarr Organic acidsFC fold change VIP variable importance in projection uarr the content of this metabolite was higher in the group of latent pattern (LP) than in the healthygroup darr the content of this metabolite was lower

Table 4 Different metabolites between LP and GDSR

No Differential metabolites VIP P FC Tendency Class1 2-Hydroxybutyric acid 172 0004 071 darr Amino acid2 Urea 179 0027 046 darr Amino acid3 L-Serine 167 0002 078 darr Amino acid4 D-2-Hydroxyglutaric acid 175 0002 070 darr Amino acid5 L-Phenylalanine 193 le0001 058 darr Amino acid6 L-Asparagine 150 0007 082 darr Amino acid7 Citrulline 15 0026 079 darr Amino acid8 L-Tyrosine 212 le0001 068 darr Amino acid9 L-Arabinose 184 0002 079 darr Carbohydrate10 L-Sorbose 173 0001 054 darr Carbohydrate11 D-Fructose 173 0001 054 darr Carbohydrate12 Pelargonic acid 224 le0001 171 uarr Fatty acid13 Myristic acid 186 0001 049 darr Fatty acid14 Palmitoleic acid 178 0001 036 darr Fatty acid15 Palmitic acid 177 le0001 059 darr Fatty acid16 Linoleic acid 165 0002 075 darr Fatty acid17 Tryptamine 210 0001 064 darr Indoles18 Glycolic acid 172 0001 068 darr Organic acid19 Quinic acid 163 0004 077 darr Organic acid20 Petroselinic acid 194 0001 050 darr Organic acid


Table 5 Different metabolites between LP and GSYX

No Differential metabolites VIP P FC Tendency Class1 2-Hydroxybutyric acid 188 le0001 056 darr Amino acid2 L-Serine 166 0011 083 darr Amino acid3 L-Threonine 157 0017 077 darr Amino acid4 Pyroglutamic acid 171 0014 077 darr Amino acid5 D-2-Hydroxyglutaric acid 191 0001 072 darr Amino acid6 L-Phenylalanine 257 le0001 059 darr Amino acid7 L-Asparagine 168 0008 081 darr Amino acid8 L-Tyrosine 154 0006 067 darr Amino acid9 L-Arabinose 237 le0001 079 darr Carbohydrate10 L-Arabitol 200 0001 069 darr Carbohydrate11 15-Anhydrosorbitol 173 0005 198 uarr Carbohydrate12 D-Fructose 272 le0001 202 uarr Carbohydrate13 Pelargonic acid 178 le0001 062 darr Fatty acid14 Glyceric acid 185 0015 065 darr Organic acid15 L-Pipecolic acid 154 0003 075 darr Organic acid16 Glutaric acid 170 0001 016 darr Organic acid17 Quinic acid 162 021 084 darr Organic acid18 Alpha-Tocopherol 188 le0001 056 darr Vitamin

Figure 1 Unsupervised PCA analysis of the four groups (ldquoblackcirclerdquo LP ldquored plusrdquo GDSR ldquopink asteriskrdquo GSYX ldquoblue squarerdquoHG)

and alpha-tocopherol were found in GSYX Further sixcommon substances including D-2-hydroxyglutaric acid 2-hydroxybutyric acid L-phenylalanine L-arabinose L-serineand quinic acid were found in both the two patterns (Table 6and Figure 4)

34 Metabolic Pathways

341 GDSR Eight screened substances including citrullineurea myristic acid palmitic acid palmitoleic acid linoleicacid glycolic acid and petroselinic acid of GDSR wereused for analyzing the metabolic pathways The results

showed one important metabolic pathway of linoleic acidmetabolism Metabolic pathways were generated usingKEGG (httpwwwgenomejpkegg) (Figure 5)

342 GSYX The metabolic pathways of the screened eightsubstances including L-threonine pyroglutamic acid L-arabitol 15-anhydrosorbitol glyceric acid L-pipecolic acidglutaric acid and alpha-tocopherol in GSYX were analyzedAn important metabolic pathway of glycine serine andthreonine was found as shown in Figure 6

343 Common Metabolic Pathways of GDSR and GSYXSix common metabolites in GDSR and GSYX includ-ing D-2-hydroxyglutaric acid 2-hydroxybutyric acid L-phenylalanine L-arabinose L-serine and quinic acid wereused to analyze the metabolic pathways Two importantmetabolic pathways of glycine serine and threonine andphenylalanine metabolism were found in two patterns (Fig-ure 7)

4 Discussion

Metabolomics has been applied to explore the nature ofTCM pattern in recent years Gou et al [21] used GCMSmetabolomics to analyze the urine of children with asthmahaving lung spleen qi deficiency those having qi and yindeficiency pattern and normal children and finally founddifferential markers that distinguished childrens asthmaCheng et al [22] performed a comprehensive metabolomicanalysis of coronary heart disease phlegm pattern and qideficiency pattern and found that the endogenous metaboliteserine which distinguished the two patterns was charac-terized mainly by abnormal amino acid metabolism Su etal [23] investigated the differential metabolites of patients


(a) (b)

Figure 2 (a) PLS-DA score plot of disease and health groups (b) PLS-DA score plot of disease groups (ldquoblack circlerdquo LP ldquored asteriskrdquoGDSRldquopink plusrdquo GSYX ldquoblue squarerdquo HG)

70

60

50

40

30

20

10

0

minus10

minus6 minus4 minus2 0 2 4

t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10

minus6 minus4 minus2 0 2 4 6

t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)

Figure 3 (a) OPLS-DA metabolic map of LP (the black circle) and GDSR (the red triangle) (b) OPLS-DA metabolic map of LP and GSYX(the pink diamond) (c) OPLS-DA metabolic map of LP and HG (the blue square) (d) OPLS-DA metabolic map of GDSR and GSYX


Table 6 Common and specific metabolites between GDSR and GSYX

GDSR Common metabolites GSYX

Common with disease Specificmetabolites

Specificmetabolites

Common withdisease

L-SorboseTryptamine

Palmitoleic acid D-2-Hydroxyglutaric acid 15-Anhydrosorbitol

No

Myristic acid L-Asparagine L-ArabitolCitrulline 2-Hydroxybutyric acid L-Threonine

Palmitic acid L-phenylalanine L-Pipecolic acidPetroselinic acid L-arabinose Pyroglutamic acidLinoleic acid L-Serine Alpha-tocopherolGlycolic acid L-Tyrosine Glyceric acid

Urea D-Fructose Glutaric acidQuinic acid

Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04

Figure 4 Venn diagram of differential substances in three patternsWith the removal of disease information (LP) eight specificmetabo-lites were found in GDSR eight specific metabolites were foundin GSYX and six common substances were found in two typicalpatterns

with type 2 diabetes mellitus having qi and yin deficiency Qiand yin deficiency pattern was related to protein and glucosemetabolism disorders insulin resistance and intestinal floradisorder compared with non-qi and yin deficiency patternIn addition Sun et al explored the changes in metabolitesin patients with hepatitis B cirrhosis (31 cases) havingfour different patterns (spleen deficiency with dampnessencumbrance pattern (SDDE) GSYX GDSR and bloodstasis pattern (BS)) before and after using fuzhenghuayutablet (FZHY) (24 weeks) They dynamically observed thetherapeutic effect of FZHY on four different patterns Aftertreatment for 12 and 24 weeks the metabolites of GSYX andSDDE were found to be significantly reversed but not toGDSR and BS This provided preliminary evidence for ldquofangzheng xiang yingrdquo [24]

Because of the complexity of internal metabolites andthe pattern of TCM attempts were made to minimize theinterference of accompanied symptoms and seek the materialbasis that could truly reflect the nature of pattern Thisstudy used hepatitis B cirrhosis a disease that could be

treated using TCM and two typical patterns of deficiency(GSYX pattern) and excess (GDSR pattern) for metabolomicanalysis Healthy persons were used as the control and thenLP as the other control to eliminate the information notrelated to the pattern so as to find out the specific substancesthat could reflect the nature of the disease and the patternof TCM and hence identify the material basis of patterns ofdeficiency and excess in hepatitis B cirrhosis

The results showed eight specific metabolites includingcitrulline urea myristic acid palmitic acid palmitoleic acidlinoleic acid glycolic acid and petroselinic acid in theGDSR pattern removing information on metabolites fromthe healthy and disease groups (LP) These specific small-molecule compounds may be the intrinsic material basisof GDSR pattern The metabolic pathway was linoleic acidmetabolism which was related to the biosynthesis of fattyacids The synthesis of fatty acids uses Coenzyme A (CoA)as a carbon source and nicotinamide adenine dinucleotidephosphate (NADPH reduced coenzyme II) as a hydrogendonor They are synthesized in the cytosol of liver brainand fat The liver is the most important site for the synthe-sis and ATP provides the needed energy The metabolitesinvolved in fatty acid biosynthesis in this study includedmyristic acid palmitoleic acid and palmitic acid All theseare free fatty acids and important intermediate products oflipid metabolism which are hydrolyzed by triglycerides insubcutaneous and visceral organs and then ingested andoxidized by the liver to supply energy Palmitoleic acid isa monounsaturated fatty acid Myristic acid and palmiticacid are saturated fatty acids which can activate the NK-120581B signaling pathway in the cell by activating Toll-likereceptors [25] The NK-120581B signaling pathway is involved ina variety of physiological and pathological activities suchas inflammation and cell survival [26] Recent studies haveshown that lipid metabolism disorders are closely relatedto inflammation [27] and inflammation is an importantmaterial basis for the damp-heat pattern [28] A clinicalinvestigation was conducted on the pattern characteristicsand biological indicators of 223 and 440 patients with pos-thepatitis B cirrhosis The results showed that AST and ALT


8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)

Figure 5 (a) Summary of pathway analysis (b) Linoleic acid metabolism

45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)

Figure 6 (a) Summary of pathway analysis (b) Glycine serine and threonine metabolism

were involved in hepatocyte inflammation in patients withGDSR (SRNY) pattern The values of GGT TBil and DBilwere significantly high suggesting that hepatic inflammationin posthepatitis (hepatitis B) cirrhosis is the pathological basisof damp-heat pattern [29 30]This provided the clinical basisof the theory that inflammation is a material basis for thedamp-heat pattern

In this study the contents of myristic acid palmitoleicacid and palmitic acid decreased in the GDSR groupreferring to the fatty acid metabolism disorder comparedwith the LP groupThe metabolomic analysis was performedon serum samples of patients with CHB NAFLD and CGFive common substances including inosine and uridine

were involved in fatty acid metabolism disorders [15] Inaddition according to the theory of correspondence of theprescription and the pattern several classical prescriptionswere used to treat dimethyl nitrosamine- (DMN-) inducedfibrotic model rats Yinchenhaotang a decoction that clearsheat and promotes diuresis could effectively inhibit liverfibrosis in DMN model rats Also differential genes wereinvolved in the regulation of fatty acidmetabolism indicatingthat rats with DMN-induced liver fibrosis had characteristicsof SR pattern and an abnormal fatty acid metabolism mightbe one of the biological basis of this pattern [31] consistentwith the results of the present study Xu et al [32] and Zhao etal [33] also found abnormalities in fatty acid metabolism and


40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)

Figure 7 (a) Summary of pathway analysis (b) Glycine serine and threonine metabolism (c) Phenylalanine metabolism

lipid metabolism disorders in patients with type 2 diabetesmandatory spondylitis and gouty arthritis

Similarly eight specific metabolites including L-threonine pyroglutamic acid L-arabitol 15-anhydrosorbitolglyceric acid L-pipecolic acid glutaric acid and alpha-tocopherol were found inGSYX and themetabolic pathwayswere those of glycine serine and threonine Glycine is anonessential amino acid that protects multiple organs (liverkidney and so on) from ischemia and reperfusion [34]Huang et al [35] found that glycine injection through the tailvein of rats with severe pancreatitis exerted protective effectson hepatocytes Recent studies showed immunomodulatory[36] and anti-inflammatory effects of glycine [37] Glycineproduces heme with nephrotoxicity and its abnormalmetabolic pathways may cause abnormal immune regulationand kidney damage Previous studies showed that glycine

inhibited liver fibrosis [38] and prevented alcoholic liverdisease [39] When liver tissue is damaged to a certainextent glycine is consumed in large quantities for protectingagainst damage and the content is significantly reducedTherefore the decrease in the glycine content in patientswith GSYX suggests severe liver tissue damage and liverfibrosis Additionally glycine is an important componentof glutathione in the metabolism of threonine and serineand has an antioxidant effect A previous study showedthe abnormal oxidative metabolism of GSYX in hepatitis Bcirrhosis [40]

Threonine is an essential amino acid in the human bodywith four isomers and only the L-type has an effect onthe body It is the only amino acid that can transforminto other substances without deamination and transami-nation L-threonine binding to oligosaccharide chains has


a protective effect on cell membranes and can promotephospholipid synthesis and fatty acid beta-oxidation It hasbeen used to treat fatty liver [41] Glyceric acid is formed bythe oxidation of glycerol which is phosphorylated to formglycerate-3-phosphate an intermediate product of serinedegradation and can further metabolize sugar or participatein glycolysisThedecrease in content reflects abnormal aminoacid metabolism and carbohydrate metabolism

GSYX of hepatitis B cirrhosis is more common inthe later stages of the disease along with longer durationand weakness A previous study confirmed that GSYX wasreflected mainly in the decrease in the number and declinein the function of liver parenchyma cells and damage ofthe hepatic sinus wall [42 43] Clinical studies also foundthat decreased liver synthesis and liver parenchymal damagewere the pathological basis of GXYX of hepatitis B cirrhosis[28 29] The level of amino acids in the blood decreasedwith the increase in protein consumption by the body andless ability of the liver to synthesize proteins Wei et al [44]found the common metabolites as glycine tryptophan andalanine in the metabolomic analysis of patients having GSYXafter colorectal cancer and liver cancer surgery Lu et al[45] performed serummetabolomic analysis on patients withchronic kidney failure chronic nephritis chronic urinarytract infection and diabetic nephropathy having GSYXTheyfound metabolic abnormalities of amino acids in the fourdiseases with GSYX Song et al conducted a metabolomicanalysis on patients with cirrhosis having GSYX by TCMpattern differentiation and found abnormal amino acidmetabolism in GSYX [38] These results were all consistentwith the finding of the present study in terms of amino acidmetabolic pathway

Six common substances including D-2-hydroxyglutaricacid 2-hydroxybutyric acid L-phenylalanine L-arabinose L-serine and quinic acid were found by further comparingthe two typical patterns These substances reflecting thecommon characteristics of the two patterns are involvedin the common metabolic pathways of glycine serineand threonine metabolism and phenylalanine metabolismmainly manifesting as impairment of amino acid metabolicnetwork pathway Another study found commonmetabolitesassociated with amino acid metabolic pathways in patientswith hepatitis B cirrhosis having GSYX [46]

In summary either the GDSR pattern or the GSYX pat-tern has imbalances in homeostasis and the metabolites andtheir characteristics are the results of internal environmentalimbalances Six common substances were found because thetwo patterns belonged to the same disease and had the samedisease location and common symptoms in the clinic (drymouth fatigue yellow urine multiple dreams irritabilityand red tongue) The performance was also different afterstimulation by the external environment because of thedifferent internal environment of different patterns in thebody Hence eight specific substances found in each of thetwo patterns represented the difference between the twogroups of patterns The present study only referred to typicalpatterns Hence further studies are needed to address theissue of concurrent patterns Also the sample size needs tobe expanded to perform target validation and hence increase

the reliability of the results Later drug interventions shouldbe carried out to provide a more reliable basis for theaccurate identification of the typical patterns of hepatitis Bcirrhosis

5 Conclusions

The 22 differential metabolites obtained in the LP group inthis study may serve as potential markers for hepatitis B cir-rhosis Eight potential biomarkers including citrulline ureamyristic acid palmitic acid palmitoleic acid linoleic acidglycolic acid and petroselinic acid were found in the GDSRpattern removing disease factors and common substancesand they all were involved in abnormalities of the linoleicacid metabolic pathway Another eight potential biomarkersincluding L-threonine pyroglutamic acid L-arabitol 15-anhydrosorbitol glyceric acid L-pipecolic acid glutaric acidand alpha-tocopherol were found in the GSYX pattern andthey all were involved in abnormalities of glycine serineand threonine metabolic pathways These small-molecularcompounds may serve as potential markers for distin-guishing between GDSR and GSYX patterns Six commonsubstances including 2-hydroxybutyric acid L-serine D-2-hydroxyglutaric acid L-phenylalanine L-arabinose andquinic acid were found in GDSR and GSYX patterns Theyall were involved in the common metabolic pathways ofglycine serine threonine and phenylalaninemanifesting thecommonality of the metabolic level of the two patterns

Data Availability

The data were detected in March 2017 The data used tosupport the findings of this study are available from thecorresponding author upon request

Conflicts of Interest

The authors declare that there are no conflicts of interest

Acknowledgments

This work is financially supported by National Natural Sci-ence Foundation of China (81673849 81473475) the ResearchInnovation Project form Shanghai Municipal Commission ofEducation (14ZZ119) andProject of ShanghaiNatural ScienceFoundation (12ZR1432300) The authors thank physiciansnurses and research staff at Shuguang Hospital XiamenHos-pital of Traditional Chinese Medicine Putuo District CentralHospital and Metabo-Profile Biotechnology (Shanghai) fortheir assistance and support for this project

Supplementary Materials

In order to analyze metabolites in serum samples neededto be pretreated Detailed procedure was provided in thesupplementary file (Supplementary Materials)


References

[1] X Wang S-X Lin J Tao et al ldquoStudy of liver cirrhosis overten consecutive years in Southern Chinardquo World Journal ofGastroenterology vol 20 no 37 pp 13546ndash13555 2014

[2] J K Nicholson J C Lindon and E Holmes ldquoldquoMetabonomicsrdquounderstanding the metabolic responses of living systems topathophysiological stimuli viamultivariate statistical analysis ofbiological NMR spectroscopic datardquo Xenobiotica vol 29 no 11pp 1181ndash1189 1999

[3] P Li L-P Yang and Y-W Gong ldquoApplication of systems biol-ogy technology in research of traditional Chinese medicinerdquoJournal of Traditional Chinese Medicine vol 29 no 2 pp 153ndash157 2009

[4] M Wang L Chen D Liu H Chen D-D Tang and Y-YZhao ldquoMetabolomics highlights pharmacological bioactivityand biochemical mechanism of traditional Chinese medicinerdquoChemico-Biological Interactions vol 273 pp 133ndash141 2017

[5] Z-H Zhang N D Vaziri F Wei X-L Cheng X Bai andY-Y Zhao ldquoAn integrated lipidomics and metabolomics revealnephroprotective effect and biochemical mechanism of Rheumofficinale in chronic renal failurerdquo Scientific Reports vol 6 no3 p 22151 2016

[6] D-Q Chen H Chen L Chen D-D Tang H Miao andY-Y Zhao ldquoMetabolomic application in toxicity evaluationand toxicological biomarker identification of natural productrdquoChemico-Biological Interactions vol 252 pp 114ndash130 2016

[7] L-L Lu Y-Z Zhou Z-J Ma B Wu and X-M QinldquoAntidepressant-like active fraction of Xiaoyaosan by metabo-nomics based [1 H] NMR spectroscopyrdquo Chinese Journal ofPharmacology and Toxicology vol 26 no 2 pp 225ndash230 2012

[8] Y Zhao X-j Gou J-y Dai et al ldquoDifferences in Metabolites ofDifferent Tongue Coatings in Patients with Chronic HepatitisBrdquo Evidence-Based Complementary and Alternative Medicinevol 2013 Article ID 204908 12 pages 2013

[9] H-Z Cui L-M Wang X Zhao et al ldquoMetabonomics-Based Study of Clinical Urine Samples in Suboptimal HealthwithDifferent SyndromesrdquoEvidence-BasedComplementary andAlternative Medicine vol 2013 Article ID 509134 7 pages 2013

[10] Z GouW YWang J Y Dai Z Q Fan H J Cao S J Sun et alldquoInvestigation on Urinary Metabolomic in Syndrome Types ofCirrhosis Caused byHepatitis BrdquoWorld Science andTechnology-Modernization of Traditional Chinese Medicine vol 14 no 1 pp1282ndash1287 2012

[11] T Yang X Zheng F Xing H Zhuo and C Liu ldquoSerumMetabolomic Characteristics of Patients with Liver CirrhoticAscitesrdquo Integrative Medicine International vol 1 no 3 pp 136ndash143 2015

[12] Y-M Tang J-P Wang W-M Bao et al ldquoUrine and serummetabolomic profiling reveals that bile acids and carnitinemay be potential biomarkers of primary biliary cirrhosisrdquoInternational Journal of Molecular Medicine vol 36 no 2 pp377ndash385 2015

[13] M J McPhail D L Shawcross M R Lewis et al ldquoMultivariatemetabotyping of plasma predicts survival in patients withdecompensated cirrhosisrdquo Journal of Hepatology vol 64 no 5pp 1058ndash1067 2016

[14] X Wang X Wang G Xie et al ldquoUrinary metabolite variationis associated with pathological progression of the post-hepatitisB cirrhosis patientsrdquo Journal of Proteome Research vol 11 no 7pp 3838ndash3847 2012

[15] C Liu X J Gou D Huang C Q Zhao Y P Mu P Liuet al ldquoDifferent Diseases of the Same Syndromeldquo Damp-heat Syndrome based on Metabonomicsrdquo World Science andTechnology-Modernization of Traditional Chinese Medicine vol19 no 3 pp 392ndash407 2017

[16] GQWang F SWang J Cai et al ldquoThe guideline of preventionand treatment for chronic hepatitis Brdquo Journal of ClinicalHepatology vol 9 no 5 pp 570ndash589 2015

[17] X Y Zheng e Guideline for Clinical Trials of New PatentChineseMedicines ChinaMedical Science Press Beijing China2002

[18] Y L Yang Y Li H C Li J X Chen T F Wang F Li et alldquoClinical Study on the Yin (not-yet-manifested) Symptoms ofTraditional Chinese Medicinerdquo Journal of Traditional ChineseMedicine vol 48 no 10 pp 917ndash919 2007

[19] X C Hu H Zhang Y Zhou and P Liu ldquoEstablishmentand screening on entries of rating acale for patient reportedoutcomes of posthepatitic B cirrhosisrdquo Chinese Journal ofTraditional Chinese Medicine and Pharmacy vol 27 no 6 pp1526ndash1530 2012

[20] XWang B Yang A Zhang H Sun andG Yan ldquoPotential drugtargets on insomnia and intervention effects of Jujuboside Athrough metabolic pathway analysis as revealed by UPLCESI-SYNAPT-HDMS coupled with pattern recognition approachrdquoJournal of Proteomics vol 75 no 4 pp 1411ndash1427 2012

[21] X J Gou S L Wang L W Kong et al ldquoMetabolomicsResearch on TCM Syndrome of Childhood Asthmardquo Journal ofTraditional Chinese Medicine vol 35 no 1 pp 36ndash40 2017

[22] P Chen Z Q Chen and D S Wang ldquoMetabonomics Researchon Coronary Heart Disease Patients of Phlegm TurbiditySyndrome and Qi Deficiency Syndromerdquo Chinese Journal ofIntegrated Traditional andWestern Medicine vol 35 no 02 pp193ndash197 2015

[23] J M Su G Y Xu and W H Ge ldquoUrinary Metabolomic Studyon Syndrome of Both Qi - Yin Deficiency of Type 2 DiabetesMellitusrdquo Yin Deficiency of Type vol 33 no 6 pp 1323ndash13262015

[24] S Sun J Dai W Wang et al ldquoMetabonomic Evaluationof ZHENG Differentiation and Treatment by FuzhenghuayuTablet in Hepatitis-B-Caused Cirrhosisrdquo Evidence-Based Com-plementary and Alternative Medicine vol 2012 Article ID453503 8 pages 2012

[25] H Shi M V Kokoeva K Inouye I Tzameli H Yin and JS Flier ldquoTLR4 links innate immunity and fatty acid-inducedinsulin resistancerdquoe Journal of Clinical Investigation vol 116no 11 pp 3015ndash3025 2006

[26] R B Marienfeld L Palkowitsch and S Ghosh ldquoDimerizationof the I B Kinase-Binding Domain of NEMO Is Requiredfor Tumor Necrosis Factor Alpha-Induced NF- B ActivityrdquoMolecular and Cellular Biology vol 26 no 24 pp 9209ndash92192006

[27] T AWynn A Chawla and J W Pollard ldquoMacrophage biologyin development homeostasis and diseaserdquoNature vol 496 no7446 pp 445ndash455 2013

[28] M Zhu Q H He and J N Xun ldquoResearch progress on diseaseand syndrome combined animal models with TCM damp-heatsyndromerdquo China Journal of Traditional Chinese Medicine andPharmacy vol 32 no 2 pp 656ndash658 2017

[29] Q Zhang P Liu H F Cheng et al ldquoClinical investigationon characteristics of traditional Chinese medical syndrome ofhepatocirrhosisrdquo Journal of Chinese Integrative Medicine vol 1no 2 pp 108ndash112 2003


[30] J-L Yuan H Zhang L Wang et al ldquoBiochemical charac-teristics of traditional Chinese medicine syndromes and theirelements in patients with hepatitis B cirrhosisrdquo Journal ofChinese Integrative Medicine vol 9 no 4 pp 374ndash381 2011

[31] YQ Bian P Liu andMY Sun ldquoThePathogenesis ofDampnessHeat Accumulation Syndrome of Liver Cirrhosis Based on theTheories of Chinese Formula and SyndromerdquoWorld Science andTechnology-Modernization of Traditional Chinese Medicine vol18 no 9 pp 1477ndash1482 2016

[32] W Xu L Zhang Y Huang Q Yang H Xiao and D ZhangldquoDiscrimination of type 2 diabetes mellitus corresponding todifferent traditional Chinese medicine syndromes based onplasma fatty acid profiles and chemometric methodsrdquo Journalof Ethnopharmacology vol 143 no 2 pp 463ndash468 2012

[33] T Zhao T T Yin Y Z Zhang et al ldquoStudy on CommonFeatures of Damp-Heat Syndrome in Rheumatism Based onMetabolomicsrdquo Journal of Traditional Chinese Medicine vol 54no 07 pp 592ndash596 2013

[34] V Eulenburg W Armsen H Betz and J Gomeza ldquoGlycinetransporters Essential regulators of neurotransmissionrdquoTrendsin Biochemical Sciences vol 30 no 6 pp 325ndash333 2005

[35] Q Y Huang and Q F Tan ldquoThe Effect of Glycocine of LiverInjury of the Severe Acute Pancreatitis in Ratsrdquo Journal of HubeiUniversity for Nationalities (Medical Edition) vol 30 no 1 pp21ndash24 2013

[36] R P Du X F Zhang M Gao and C J Ao ldquoImmunomodula-tory Effects of Glycine and Its Molecular Mechanismrdquo ChineseJournal of Animal Nutrition vol 27 no 3 pp 663ndash670 2015

[37] I Tsune K Ikejima M Hirose et al ldquoDietary glycine preventschemical-induced experimental colitis in the ratrdquo Gastroen-terology vol 125 no 3 pp 775ndash785 2003

[38] CA Rivera BU Bradford K J Hunt et al ldquoAttenuation ofCCl(4)-induced hepatic fibrosis by GdCl (3) treatment or dietaryglycinerdquo American Journal of Physiology-Gastrointestinal andLiver Physiology vol 281 no 1 pp G200ndashG207 2001

[39] A Zeb and S U Rahman ldquoProtective effects of dietary glycineand glutamic acid toward the toxic effects of oxidized mustardoil in rabbitsrdquo Food amp Function vol 8 no 1 pp 429ndash436 2017

[40] Y Song J Chen F Cai et al ldquoA metabolic mechanism analysisof Fuzheng-Huayu formula for improving liver cirrhosis withtraditional Chinesemedicine syndromesrdquoActa PharmacologicaSinica vol 39 no 6 pp 942ndash951 2017

[41] J Huang Q Y Xu and N Chen ldquoTheMethods and Study Evo-lution of L-threonine Productionrdquo Journal of Henan Universityof Technology (Natural Science Edition) no 05 pp 88ndash92 2007

[42] Y P Mu P Liu G L Du et al ldquoThe action mechanism oftraditional Chinese medicine prescription of removing bloodstasis and nourishing Yin in CCl4 induced liver cirrhosis inratsrdquo Progress in Natural Science vol 16 no 9 pp 1101ndash11082006

[43] L Wang P Liu Y P Mu F H Li A H Long and H T GuldquoStudy onTCMRecipe and Syndrome ofDimethylnitrosamine-inducedHepatic Fibrosis in Ratsrdquo Journal of Traditional ChineseMedicine vol 47 no 12 pp 929ndash932 2006

[44] B Wei X Q Hu Y N Song et al ldquoA Metabolomic Researchon rdquoThe Same TCM Syndromerdquo in the Postoperative Patientswith Liver Cancer and Colorectal Cancerrdquo World Science andTechnology-Modernization of Traditional Chinese Medicine vol18 no 9 pp 1500ndash1506 2016

[45] Y Lu Z L Su L Yang and Y Wang ldquoExploration on metabo-nomics of the kidney diseases of yin deficiency liver-kidney

patternrdquo Shanghai Journal of Traditional Chinese Medicine no3 pp 10ndash13 2015

[46] Y Y Lu Y N Song G B Zhang et al ldquoApplication of SystemsBiology in TCM Syndrome Classification of Chronic HepatitisB and Posthepatitic Cirrhosisrdquo World Science and Technology-Modernization of Traditional Chinese Medicine vol 15 no 6 pp1281ndash1287 2013

Stem Cells International

Hindawiwwwhindawicom Volume 2018


MEDIATORSINFLAMMATION

of

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Immunology ResearchHindawiwwwhindawicom Volume 2018

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine


Behavioural Neurology

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary andAlternative Medicine

Volume 2018Hindawiwwwhindawicom

Submit your manuscripts atwwwhindawicom


smelling talking and feeling the pulse Limited objectiveevidence also restricts the development of the study aboutthe Chinese pattern With the advancement of science andtechnology many researchers tried to explain the patternclassification rules by modern scientific methods to make upfor the deficiency of objectivity and reproducibility in patterndifferentiation

Metabolomics examines mainly the dynamic changes inthe quality and quantity of metabolites produced in bio-logical systems in response to pathophysiological reactionsor genetic modification thus finding the relative relation-ship between metabolites and pathophysiological changesin organisms [2] It is consistent with the systematic andholistic view of TCM [3] Studying the exogenous molecularcompounds to understand the changing laws of the occur-rence and development of the disease is of great significancein discussing the pathogenesis diagnosis treatment andevaluation of the disease It also provides methodologicalsupport for the objective and accurate diagnosis of patternsin TCM As an effective method to study the physiolog-ical and pathological changes in body fluids and tissuesmetabolomics was widely applied to TCM in vitro or in vivosuch as efficacy in evaluating Chinese medicine and its bio-chemical mechanism of action [4 5] toxicity evaluation andtoxicological biomarker identification of natural products [6]active fraction identification of prescription [7] and researchon tongue coating [8]

Currently finding blood and urine biomarkers for thediagnosis treatment and prognosis of liver cirrhosis is ahot spot in the liver disease research [9] Guo et al [10]used GCMS methods to study the urine of healthy peopleand patients with hepatitis B cirrhosis They found that themetabolic spectrum could be clearly separated between thetwo groups providing the basis for diagnosing hepatitis Bcirrhosis from the perspective of metabonomics Yang et al[11] analyzed serummetabolic profiles in healthy controls andpatients with cirrhotic ascites and found potential biomarkersfor diagnosing and treating liver fibrosis and cirrhosis Tanget al [12] compared different metabolites in serum andurine of patients with primary biliary cirrhosis (PBC) andhealthy people using ultraperformance liquid chromatogra-phy coupled with quadrupole-time-of-flight mass spectrom-etry (UPLC-QTOFMS) The results showed that the bileacid level increased and the carnitine content decreased withthe development of PBC suggesting that the two substancesmay be the potential biomarkers of PBC These studies haveindicated the important role of metabonomics technologyin diagnosing liver cirrhosis Mcphail et al [13] examined80 patients with decompensated cirrhosis including 62 whosurvived and 18 who died using plasma metabonomicsanalysis The disorder of phosphatidylcholine and aminoacid metabolism is related to the increase in mortality andthe severity of the disease providing the objective basisfor accurately predicting the mortality of patients withdecompensated cirrhosis Based on GCMS and LCMSmetabolomic techniques the presence of specific compoundswas preliminarily confirmed in the urine of patients withhepatitis B cirrhosis that could reflect the patterns of GanDan Shi Re (GDSR) and Gan Shen Yin Xu (GSYX) [14] The

metabolomic study on the dampness-heat pattern of chronichepatitis B (CHB) nonalcoholic fatty liver disease (NAFLD)and chronic glomerulonephritis (CG) revealed five commonbiomarkers between the three diseases including inosineuridine aspartic acid oleic acid and lactate and their specificsubstances (27 substances in CHB 28 in NAFLD and 24 inCG) [15]

Based on previous findings this study examined thetypical GDSR and GSYX patterns of hepatitis B cirrhosisusing healthy persons as the control group Also the studyincluded an LP group (which means that the patients hadno obvious clinical manifestation of the patterns or had littleinformation making it difficult to distinguish these patternsfrom others) as the control The study was performed ontypical GDSR and GSYX patterns of hepatitis B cirrhosisa disease that could be treated using TCM Qualitative andquantitative analyses of the metabolites (serum samples)were performed on the basis of gas chromatographyndashtime-of-flight mass spectrometry (GC-TOFMS) multiple combi-nation techniques to determine the characteristic compoundspectrum of hepatitis B cirrhosis and the two typical patternsof GSYX and GSYXThe present study aimed to provide datasupport for classifying and identifying the TCM pattern ofrefractory hepatitis B cirrhosis

2 Materials and Methods

21 Study Participants All the 111 patients in this study onhepatitis B cirrhosis were aged 18ndash65 years and admitted atShuguang Hospital Affiliated to Shanghai University of TCMXiamen TCM Hospital and Shanghai Putuo District CentralHospital between March 2016 and March 2017 A total of 60healthy sex- and age-matched volunteers were selected fromthe Shuguang Hospital Health Examination Center All theparticipants in the study signed informed consent

211 Inclusion Criteria Participants were selected accordingto the standards of the Ishak Liver Fibrosis Grading Criteriaand the Guidelines for the Prevention and Treatment ofChronic Hepatitis B (2015 Edition) [16] which was revisedjointly by the Chinese Medical Association HepatologyBranch and the Infectious Diseases Branch and in accor-dance with GDSR GSYX [17] and LP [18] diagnostic criteria

212 Exclusion Criteria The exclusion criteria were as fol-lows (1) age less than 18 years or more than 65 years (2) acombination with other types of hepatitis such as hepatitisA C D and E or severe hepatitis (3) a combination withheart liver hematopoietic and neurological disorders ordrug allergy (4) a combination with malignant tumors orconnective tissue diseases (5) pregnant or lactating women(6) patients with anaphylaxis and other severe diseases and(7) patients with mental disorders who could not cooperatewith investigators

22 Instruments and Reagents High-purity methoxyaminehydrochloride fatty acid methyl ester (C7ndashC30 FAME)anhydrous pyridine (995) and anhydrous sodium sulfate












3 Results







BMI(kgm2)

2204(2163 2301)

2286(2211 2391)

2367(2282 2568)

2243(2200 2392) 507




TBil(120583molL)

1138(1008 1412)

2050(1124 2718)

2805lowast998771(2105 4940)

2530lowastamp(1887 3865)

DBil(120583molL)

222(180 249)

392(265 67)

858lowast998771(460 1977)

556amp(435 1149)

ALT(IUL)

1800(1200 25)

2500(1650 2950)

3095(1975 4325)

27(1950 4325)

AST(UL)

2000(1700 2300)

2600(2125 3325)

4050lowast998771(2900 5625)

3700(2600 5850)

ALP(UL)

7700(6600 9200)

7700(5650 9150)

10000lowast998771(7700 14850)

9100lowast(7150 11150)

GGT(UL)

1828(1404 2701)

3125(2150 5497)

4000lowast(2800 5267)

3462lowast(2135 6579)

TBA(120583molL)

335(210 674)

1200(675 1988)

4465lowast998771(1456 10461)

4319lowast998771(1001 8221)

ALB(gL)

4731(4536 4889)

4495lowast(3998 4776)

36093lowast998771(3097 4070)

3534lowast998771(2683 4265)



33 Metabolites




















4 Discussion



(a) (b)


70

60

50

40

30

20

10

0

minus10


t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10


t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)






Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of






























3 Results







BMI(kgm2)

2204(2163 2301)

2286(2211 2391)

2367(2282 2568)

2243(2200 2392) 507




TBil(120583molL)

1138(1008 1412)

2050(1124 2718)

2805lowast998771(2105 4940)

2530lowastamp(1887 3865)

DBil(120583molL)

222(180 249)

392(265 67)

858lowast998771(460 1977)

556amp(435 1149)

ALT(IUL)

1800(1200 25)

2500(1650 2950)

3095(1975 4325)

27(1950 4325)

AST(UL)

2000(1700 2300)

2600(2125 3325)

4050lowast998771(2900 5625)

3700(2600 5850)

ALP(UL)

7700(6600 9200)

7700(5650 9150)

10000lowast998771(7700 14850)

9100lowast(7150 11150)

GGT(UL)

1828(1404 2701)

3125(2150 5497)

4000lowast(2800 5267)

3462lowast(2135 6579)

TBA(120583molL)

335(210 674)

1200(675 1988)

4465lowast998771(1456 10461)

4319lowast998771(1001 8221)

ALB(gL)

4731(4536 4889)

4495lowast(3998 4776)

36093lowast998771(3097 4070)

3534lowast998771(2683 4265)



33 Metabolites




















4 Discussion



(a) (b)


70

60

50

40

30

20

10

0

minus10


t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10


t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)






Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of























BMI(kgm2)

2204(2163 2301)

2286(2211 2391)

2367(2282 2568)

2243(2200 2392) 507




TBil(120583molL)

1138(1008 1412)

2050(1124 2718)

2805lowast998771(2105 4940)

2530lowastamp(1887 3865)

DBil(120583molL)

222(180 249)

392(265 67)

858lowast998771(460 1977)

556amp(435 1149)

ALT(IUL)

1800(1200 25)

2500(1650 2950)

3095(1975 4325)

27(1950 4325)

AST(UL)

2000(1700 2300)

2600(2125 3325)

4050lowast998771(2900 5625)

3700(2600 5850)

ALP(UL)

7700(6600 9200)

7700(5650 9150)

10000lowast998771(7700 14850)

9100lowast(7150 11150)

GGT(UL)

1828(1404 2701)

3125(2150 5497)

4000lowast(2800 5267)

3462lowast(2135 6579)

TBA(120583molL)

335(210 674)

1200(675 1988)

4465lowast998771(1456 10461)

4319lowast998771(1001 8221)

ALB(gL)

4731(4536 4889)

4495lowast(3998 4776)

36093lowast998771(3097 4070)

3534lowast998771(2683 4265)



33 Metabolites




















4 Discussion



(a) (b)


70

60

50

40

30

20

10

0

minus10


t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10


t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)






Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of


































4 Discussion



(a) (b)


70

60

50

40

30

20

10

0

minus10


t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10


t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)






Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















(a) (b)


70

60

50

40

30

20

10

0

minus10


t[1]

(a)

70

60

50

40

30

20

10

0

minus10


t[1]

(b)

70

60

50

40

30

20

10

0

minus10


t[1]

90

80

(c)


t[1]

70

60

50

40

30

20

10

0

minus10

80

minus8

(d)






Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of























Specificmetabolites

Common withdisease

L-SorboseTryptamine


No




Pelargonic acid

Latent Pattern

(disease)

16

Gan Dan

Shi Re

Pattern

8

Gan Shen

Yin Xu

Pattern

8

2

6

04







8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















8

7

6

5

4

3

2

-log(

p)

00 01 02 03 04 05 06

Pathway Impact

a

(a) (b)


45

40

35

30

25

20

15

-log(

p)

000 002 004 006 008 010

Pathway Impact

b

(a) (b)






40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















40

35

30

25

20

15

-log(

p)

Pathway Impact014012010008006004002000

d c

(a)c

(b)

d

(c)












5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of

























5 Conclusions


Data Availability




Acknowledgments





References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















References





















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of










































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of



















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