Evidence-Based Complementary and Alternative Medicine
Volume 2021 (2021), Article ID 5515325, 16 pages
https://doi.org/10.1155/2021/5515325

Research Article

Metabolite Characteristics in Tongue Coating from Damp Phlegm Pattern in Patients with Gastric Precancerous Lesion

Yifeng Xu,¹ Renling Zhang,² Robert Morris,³ Feng Cheng,³ Yiqin Wang,¹ Zhujing Zhu,² and Yiming Hao¹

¹Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
²Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
³Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL 33612, USA

Correspondence should be addressed to Yiming Hao; hymjj888@163.com

Received 23 February 2021; Revised 12 April 2021; Accepted 21 May 2021; Published 2 June 2021

Academic Editor: Yu CAI

Copyright © 2021 Yifeng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Objective. In this study, we analyzed the metabolite profile of the tongue coating of patients having gastric precancerous lesion (GPL) with damp phlegm pattern and proposed a mechanism of pathological transition. Methods. The changes in tongue-coating metabolites in patients with GPL damp phlegm pattern were analyzed using GC-TOF-MS and UHPLC-QE-MS metabolomics methods. Results. When compared with 20 patients who did not exhibit a nondamp phlegm pattern, 12 metabolites were highly expressed and 10 metabolites were under expressed in 40 cases of damp phlegm pattern, of which involved 9 metabolic pathways. Compared with 15 healthy people, 134 metabolites were upregulated and 3 metabolites were downregulated in 40 cases exhibiting a damp phlegm pattern, of which involved 17 metabolic pathways. The patients with damp phlegm pattern were compared with nondamp phlegm pattern patients and healthy people, the main differential metabolites were primarily lipids and lipid-like molecules, and the main differential metabolic pathways were related to glycerophospholipid metabolism. In the glycerophospholipid metabolism, the metabolites with changes were phosphatidylethanolamine and lysoPC(18 : 1 (9z)). Among them, phosphatidylethanolamine exists in the synthesis stage of glycerophospholipid metabolism.Conclusions. Abnormal expression of lipids and lipid-like molecules, as the major metabolic change, was involved in the formation of GPL patients with damp phlegm pattern.

1. Introduction

Gastric cancer (GC) has grown in frequency and has become the third leading cause of cancer-related deaths in the world [1]. China is a region in which gastric cancer has a high prevalence with studies estimating that the number of gastric cancer cases in China alone is approximately 697,000, accounting for 70% of the total world incidence [2]. Gastric precancerous lesions (GPLs) involve a process of evolution before the onset of gastric cancer and include intestinal metaplasia and dysplasia, which are primarily related to chronic atrophic gastritis [3]. GPL has the characteristics of bidirectional transformation, and early intervention can effectively reverse the malignant development of cells [4]. Therefore, early diagnosis and early treatment of GPL play a crucial role in reducing the incidence of gastric cancer [5].

Traditional Chinese medicine (TCM) theory is the theoretical basis of damp phlegm pattern. In TCM, damp phlegm pattern is caused by dysfunction of an internal organ, resulting in disrupted body water movement and water stagnation that forms dampness and phlegm. Fullness and heaviness of the whole body as well as a greasy tongue coating are the main manifestations of damp phlegm pattern in patients. Currently, the World Health Organization has listed damp phlegm pattern in the International Classification of Diseases [6]. Similarly, phlegm dampness pattern is also one of the main patterns of GPL [7].

In TCM, tongue diagnosis as a noninvasive diagnostic method, which is very important in the identification of disease states. Tongue diagnosis is designed to observe the changes of the tongue body and tongue coating over time in order to understand the physiological state of the human body. Inspection of tongue coating is the main component of tongue diagnosis. TCM holds that the tongue coating can react very sensitively to abnormal changes in the spleen and stomach. Compared with the thin coating of physiologically healthy people, the tongue coating of chronic gastritis patients that exhibit a damp phlegm pattern is greasy. Studies have also shown that the shape of the tongue coating in gastric cancer and chronic gastritis patients differed significantly from the tongue-coating shape of patients without gastric complications [8, 9]. Tongue coating adheres to the tongue body and consists of desquamated epithelial cells, blood cells, metabolites, nutrients, and bacteria [10]. In our previous study, we found that there were differential metabolites in the tongue coating of patients with coronary heart disease and chronic renal failure with damp phlegm pattern [11]; other researchers also found differential metabolites in the tongue coating of hepatitis B patients compared with healthy people [12]. As a result, the tongue-coating metabolites obtained by the noninvasive method can be used as adjunctive diagnostic tools for some diseases [13]. However, current studies on the changes of GPL patients’ tongue coating in damp phlegm pattern are still insufficient.

Metabolomics is a scientific discipline that reveals the nature of life metabolism by investigating the alterations of metabolite profiles in a biological system in response to stimulation or disturbance [14, 15]. The combination of mass spectrometry (MS) and nuclear magnetic resonance (NMR) and other separation methods have become important analytical tools in metabolomics [16].

Some researchers have used ultra-performance liquid chromatography and mass spectrometry (UPLC-MS) while others have utilized liquid chromatography in tandem with mass spectrometry (LC-MS) technology exclusively to detect and analyze the substances in chronic gastritis patients’ tongue coating. In these studies, some metabolites were differentially expressed between healthy people and patients with chronic gastritis [17, 18]. These differential metabolites may be used as potential markers to monitor the occurrence and development of chronic gastritis. However, these studies only used LC-MS technology. The results produced by using only one detection method may have limitations and cannot fully reflect the metabolites in tongue coating. And, at present, we have not found any reports on metabolomics study of tongue-coating metabolites in patients with GPL that exhibit the damp phlegm pattern.

Therefore, in this study, the ultra-high performance liquid chromatography-Q exactive orbitrap-mass spectroscopy (UHPLC-QE-MS) and gas chromatography-time-of-flight-mass spectroscopy (GC-TOF-MS) were combined in order to detect and analyze the metabolites in the tongue coating of GPL patients with damp phlegm pattern. As a result, the abnormal fluctuation of these metabolites will more clearly show the occurrence and development of GPL damp phlegm pattern.

2. Materials and Methods

2.1. Samples

In this paper, we used a case-control study to elucidate the composition of tongue-coating-related metabolites in GPL patients with damp phlegm pattern. Sixty patients with GPL were selected from Longhua Hospital, an affiliate of the Shanghai University of TCM for Gastroscopy and pathological examination of gastric mucosa, including 40 cases of damp phlegm pattern and 20 cases of nondamp phlegm pattern. Fifteen teachers and students from the Shanghai University of Traditional Chinese Medicine were selected as a healthy control group. Selected controls did not have a history of stomach disease and were not currently experiencing any degree of stomach discomfort. Their blood cell analysis, blood lipid levels, blood pressure, blood glucose, tumor indicators, renal function, color Doppler ultrasound of neck and abdomen, liver function, chest computed tomography, and barium metal fluoroscopy were all normal. All participants were of Chinese ancestry (self-reported) and were enrolled in the study from December 2018 to October 2019.

Gastroscopy and pathological examination of gastric mucosa were performed immediately after tongue-coating samples were collected. Clinical information of the participants and summary of demographics are provided in Table 1. As shown in Table 1, the highest number of subjects presented with only mild intestinal metaplasia, including 31 damp phlegm pattern patients and 13 nondamp phlegm pattern patients. The number of damp phlegm pattern patients that exhibited either moderate intestinal metaplasia or severe intestinal metaplasia were 7 and 1, respectively. The number of nondamp phlegm pattern patients that exhibited either moderate intestinal metaplasia or severe intestinal metaplasia were 3 and 4, respectively. Finally, only one patient with damp phlegm pattern had mild intestinal metaplasia and mild dysplasia concurrently. In terms of treatment, 10 people did not take the treatment regimen as prescribed due to discomfort (8 cases with damp phlegm pattern and 2 cases with nondamp phlegm pattern), while the other 50 subjects were treated with western medicine or TCM. Among the treated cases, 10 patients with damp phlegm pattern and 5 patients with a nondamp phlegm pattern were treated with western medicine such as proton pump inhibitors alone while 17 patients with damp phlegm pattern and 5 patients with nondamp phlegm pattern were treated with traditional Chinese medicine exclusively. In addition, 5 patients with damp phlegm pattern and 8 patients that exhibited a nondamp phlegm pattern were treated with a combination of traditional Chinese medicine and western medicine (proton pump inhibitors).

Table 1: Summary of demographics and clinical information of the participants.

Demographics and clinical information
Damp phlegm pattern group
Nondamp phlegm pattern group
Healthy control group

Sample number
40
20
15

Ratio of male to female
1 : 0.82
1 : 1.22
1 : 1.14

Average age (year)
43.28 ± 14.73
42.9 ± 16.1
34.53 ± 11.40

Number (percentage) of samples diagnosed for less than 10 years
30 (75.00%)
15 (75.00%)
N/A

Number (percentage) of samples diagnosed for 10–20 years
6 (15.00%)
2 (10.00%)
N/A

Number (percentage) of samples diagnosed for 20–30 years
2 (5.00%)
1 (5.00%)
N/A

Number (percentage) of samples diagnosed for 30–40 years
2 (5.00%)
2 (10.00%)
N/A

Number (percentage) of samples only with intestinal metaplasia (mild)
31 (77.50%)
13 (65.00%)
N/A

Number (percentage) of samples only with intestinal metaplasia (moderate)
7 (17.50%)
3 (15.00%)
N/A

Number (percentage) of samples only with intestinal metaplasia (severe)
1 (2.50%)
4 (20.00%)
N/A

Number (percentage) of samples with intestinal metaplasia (mild) and dysplasia (mild)
1 (2.50%)
0 (0.00%)
N/A

Number (percentage) of samples with Helicobacter pylori infection
9 (22.50%)
1 (5.00%)
N/A

Number (percentage) of samples untreated
8 (20.00%)
2 (10.00%)
N/A

Number (percentage) of samples only taking western medicine (proton pump inhibitors)
10 (25.00%)
5 (25.00%)
N/A

Number (percentage) of samples only taking traditional Chinese medicine
17 (42.50%)
5 (25.00%)
N/A

Number (percentage) of samples taking western medicine (proton pump inhibitors) and traditional Chinese medicine
5 (12.50%)
8 (40.00%)
N/A

2.2. Ethics Approval

In this study, all subjects gave written informed consent before collecting samples, and the study was conducted in accordance with the Declaration of Helsinki. In addition, this study was approved by the Ethics Committee of Shanghai University of TCM in December 2018.

2.3. Criteria

2.3.1. Diagnostic Criteria

(1)Patients were endoscopically examined and biopsies were taken from suspicious lesion sites such as gastric antrum, angle, body, and cardia(2)The histopathological assessment was performed by two experienced pathologists in accordance with the clinical guidelines of “the updated Sydney System” [19](3)The diagnosis of GPL chronic atrophic gastritis was accompanied by intestinal metaplasia and/or dysplasia [20]

The diagnostic criteria of the damp phlegm pattern in GPL were set according to the “Diagnostics of Traditional Chinese Medicine” [21].

The diagnostic criteria of the damp phlegm pattern in GPL were as follows:

(1)Patient felt epigastric fullness or distending pain(2)Patient’s feces were not shaped(3)Patients were nauseous and/or vomiting(4)Patient had a bad appetite(5)Patient’s tongue had a greasy coating

2.3.2. Inclusion Criteria

The patients met the diagnostic criteria of GPL and the healthy control group had no evidence of systemic organic lesions. In addition, the participants’ age range was from 20 to 70 years.

2.3.3. Exclusion Criteria

Patients were not eligible if they had gastric hemorrhaging, a duodenal ulcer, gastroesophageal reflux disease, gastric cancer, gastric ulcers, or other intestinal diseases identified through endoscopic examination as well as any additional systemic diseases. Patients with diagnosed mental illness, pregnant or lactating female subjects, or participants that had lesions of the tongue, mouth, nose, or pharynx within one month prior to the collection of samples were not included in this study. Finally, candidates that had received antibiotics or probiotics within one month prior to sample collection, used tobacco or consumed alcohol, or had a body mass index (BMI) greater than 28 were also excluded [22].

2.4. Tongue-Coating Samples Collection

Tongue-coating samples were collected before the participants ate in the morning. Before collection, in order to remove the residues in the mouth, the participants rinsed the mouth with stroke-physiological saline solution 3 times. Then, the collector scraped the tongue-coating samples with a sterile specimen collection swab (CY-98000, iClean, Huachenyang Technology Co., Ltd, CN) at the thick part of the tongue coating 5 times, and put the head of swab with tongue-coating sample into a sterile centrifuge tube. All tongue-coating samples were collected by the same person. Finally, the tongue-coating samples were stored at −80°C.

2.5. GC-TOF-MS Metabolomics Processing

The reagents used in GC-TOF-MS experiment are listed in Supplementary Table S1a. The experimental procedures were as follows.

The head of the swab with the tongue-coating sample was moved to a sterile Eppendorf (EP) tube 5 mL and weighed, and 1500 μL of pre-cold extraction mixture with 15 μL of internal standard were added. The sample underwent ultrasonication in the ice water for 30 min. Subsequently, the head of the swab was removed. After centrifugation for 15 minutes at 4°C, the 500 μL supernatant was moved to a fresh tube. Quality control (QC) samples were prepared by combining 100 μL from each sample. T 40 μL of Methoxyamination hydrochloride was added after evaporation in a vacuum concentrator, and the samples were incubated for a half hour at 80°C. After that, 60 μL bis-(trimethylsilyl)-trifluoroacetamide reagent was derivatized for 1.5 hours at 70°C. When the sample was cooled to room temperature, 5 μL of fatty acid methyl esters was added.

GC-TOF-MS detection was performed using a TOF mass spectrometer coupled to an Agilent 7890 gas chromatograph. The system utilized capillary column, 1 μL aliquots of the samples, and helium as carrier gas. The initial temperature was maintained at 50°C, before subsequently was increased to 310°C and held for 6 minutes. The injection temperature was 280°C, the transmission line temperature was 280°C, and the ion source temperature was 250°C. The energy in electron impact mode was −70 ev. After a solvent delay of 6.30 minutes, the mass spectrometry data were obtained in full scan mode, with m/z range of 50–500. Chroma TOF (V 4.3×, LECO) [23] software was used in order to analyze the original data while the LECO-Fiehn Rtx5 database was utilized to match the mass spectrum and retention index of metabolites. Finally, the peaks with RSD > 30% in QC samples were removed. [24].

2.6. UHPLC-QE-MS Metabolomics Processing

The reagents used in UHPLC-QE-MS experiment are listed in Table S1b. The experimental procedures were as follows:

1500 μL of extract solution containing isotopically labeled internal standard mixture was added to the samples. After being vortexed for 30 seconds, the solution was sonicated on ice for 30 minutes. Next, samples were incubated at −40°C for 1 hour and then centrifuged at 4°C for 15 minutes. The supernatant was then isolated and moved to a fresh glass vial for analysis. All the supernatants were mixed to prepare QC samples. The UHPC system using a UPLC BEH Amide column coupled to Q Exactive HFX mass spectrometer was used to detect LC-MS. Twenty-five mmol/L ammonium acetate and 25 ammonia hydroxides in water and acetonitrile were composed in the mobile phase with the elution gradient being used for analysis. The injection volume was 3 μL, the column temperature was 25°C, and the autosampler temperature was 4°C.

The QE HFX mass spectrometer was successful in generating MS/MS spectra under the acquisition software. In this mode, full scan MS spectrum was continuously evaluated by the acquisition software. ProteoWizard was used to convert raw data to mzXML format and peak detection, extraction, alignment, and integration data were obtained through the use of internal programs. Metabolites were annotated used MS2 database (BiotreeDB V2.1), and the cutoff value of annotation was set to 0.3 [25].

2.7. Statistical Analysis

The number of peaks, sample names, and standardized peak areas were entered into Simca-p+ 13.0 software for orthogonal projection of principal component analysis (PCA) and orthogonal projection of latent structures-discriminant analysis (OPLS-DA). To further verify the model, displacement experiments were carried out. The -value adjusted with the false discovery rate (FDR) of rank sum test (), variable importance in the projection (VIP) of first principal component in OPLS-DA model (VIP > 1), similarity value (SV) of GC-TOF-MS detection (SV > 700) [26], and the significant differences of metabolites between the two groups were determined by MS2 score (MS2 score > 0.6) [27] using UHPLC-QE-MS. The log fold change (FC) values were calculated by comparing the means of metabolites peak areas of the two groups. KEGG pathway analysis was used to search and determine the significant metabolic pathways that were differentially expressed between the experimental and control groups.

3. Results

3.1. Metabolic Spectrums

Metabolomics of tongue coating in 60 patients with GPL (including 40 cases of damp phlegm pattern and 20 cases of nondamp phlegm pattern) and 15 cases of healthy control group were analyzed by GC-TOF-MS and UHPLC-QE-MS. After quality control by GC-TOF-MS analysis, 533 peaks were obtained in the tongue-coating samples. A total of 9,168 peaks and 4,322 peaks were obtained in tongue-coating samples after quality control of UHPLC-QE-MS positive ion mode and negative ion mode analysis, respectively.

Examples of GC-TOF-MS and UHPLC-QE-MS spectra of the same person are shown in Figure 1 fv. As shown, there were some distinct mass spectrum peaks between the GPL damp phlegm pattern group and the GPL nondamp phlegm pattern group as well as with the healthy control group.

(a)

(b)

(c)

Figure 1: Mass spectrum peaks of GPL damp phlegm pattern group, GPL nondamp phlegm pattern group, and healthy control group. (a) GC-TOF-MS, (b) UHPLC-QE-MS positive ion mode, and (c) UHPLC-QE-MS negative ion mode. As the examples of the GC-TOF-MS and UHPLC-QE-MS spectra, there were some different mass spectrum peaks among the GPL damp phlegm pattern patients, GPL nondamp phlegm pattern patients, and healthy control people.

3.2. OPLS-DA Score Plots

The OPLS-DA score plot of damp phlegm pattern group and nondamp phlegm pattern group showed that the two groups could be well distinguished by GC-TOF-MS and UHPLC-QE-MS negative ion mode. UHPLC-QE-MS positive ion mode detection showed a tendency to distinguish the two groups of samples, but there was a small amount of overlap (Figure 2). The permutation tests of two groups of OPLS-DA models show that all the models of GC-TOF-MS (R²Y = 0.671, Q² = 0.125), UHPLC-QE-MS positive ion mode (R²Y = 0.617, Q² = -0.0357), and UHPLC-QE-MS negative ion mode (R²Y = 0.775, Q² = 0.0659) had good robustness and no overfitting was observed (Figure 3).

(a)

(b)

(c)

Figure 2: OPLS-DA analysis of metabolomic data of GPL damp phlegm pattern group and nondamp phlegm pattern group. (a) OPLS-DA analysis by GC-TOF-MS, (b) OPLS-DA analysis by UHPLC-QE-MS positive ion mode, and (c) OPLS-DA analysis by UHPLC-QE-MS negative ion mode. The figures of OPLS-DA illustrated that there were significant differences between GPL damp phlegm pattern group and nondamp phlegm pattern group.

(a)

(b)

(c)

Figure 3: Validation plots of OPLS-DA scores of GPL damp phlegm pattern group and GPL nondamp phlegm pattern group. (a) Validation plots of OPLS-DA scores by GC-TOF-MS, (b) validation plots of OPLS-DA scores by UHPLC-QE-MS positive ion mode, and (c) validation plots of OPLS-DA scores by UHPLC-QE-MS negative ion mode. The figures of validation plots of OPLS-DA scores showed that the models of GPL damp phlegm pattern group and nondamp phlegm pattern group had good robustness as well as had no over fitting phenomenon.

Similarly, as shown in the OPLS-DA score plot, there was a significant difference between the damp phlegm pattern group and the healthy control group (Figure 4). Permutation test of OPLS-DA models for the two groups showed that all the models of GC-TOF-MS (R²Y = 0.953, Q² = 0.879), UHPLC-QE-MS positive ion mode (R²Y = 0.951, Q² = 0.895), and UHPLC-QE-MS negative ion mode (R²Y = 0.968, Q² = 0.901) had good robustness and no overfitting was observed (Figure 5).

(a)

(b)

(c)

Figure 4: OPLS-DA analysis of metabolomic data of GPL damp phlegm pattern group and healthy control group. (a) OPLS-DA analysis by GC-TOF-MS, (b) OPLS-DA analysis by UHPLC-QE-MS positive ion mode, and (c) OPLS-DA analysis by UHPLC-QE-MS negative ion mode. The figures of OPLS-DA illustrated that there were significant differences between GPL damp phlegm pattern group and healthy control group.

(a)

(b)

(c)

Figure 5: Validation plots of OPLS-DA scores of GPL damp phlegm pattern group and healthy control group. (a) Validation plots of OPLS-DA scores by GC-TOF-MS, (b) validation plots of OPLS-DA scores by UHPLC-QE-MS positive ion mode, and (c) validation plots of OPLS-DA scores by UHPLC-QE-MS negative ion mode. The figures of validation plots of OPLS-DA scores showed that the models of GPL damp phlegm pattern group and healthy control group had good robustness as well as had no over fitting phenomenon.

3.3. Different Peaks by GC-TOF-MS Analysis

Using the criteria that -value <0.05 and VIP > 1, GC-TOF-MS analysis showed that compared with the nondamp phlegm pattern group, there were 9 different peaks in the damp phlegm pattern group (all the metabolites were downregulated); compared with the healthy control group, there were 33 different peaks in the damp phlegm pattern group, of which 31 peaks increased and 2 decreased.

Using the criteria that similarity SV > 700, GC-TOF-MS analysis showed that compared with the nondamp phlegm pattern group, the phlegm pattern group had 5 different peaks (all metabolites decreased in concentration); compared with the healthy control group, the damp phlegm pattern group had 13 different peaks (all metabolites showed an increase in expression).

3.4. Different Peaks by UHPLC-QE-MS Analysis

Using the criteria that -value <0.05 and VIP > 1, UHPLC-QE-MS positive ion mode analysis showed that compared with the nondamp phlegm pattern group, there were 11 different peaks in the damp phlegm pattern group, of which 7 peaks increased and 4 peaks decreased; compared with healthy control group, there were 146 different peaks in the damp phlegm pattern group, of which 142 peaks increased and 4 peaks decreased. UHPLC-QE-MS negative ion mode analysis showed that compared with the nondamp phlegm pattern group, there were 9 different peaks in the damp phlegm pattern group, among which 8 peaks increased and 1 decreased; compared with the healthy control group, there were 23 different peaks in the damp phlegm pattern group (all metabolites increased in concentration).

Using the criteria that MS2 score > 0.6, we align molecular mass data (m/z) of the significantly different peaks with online database KEGG. By UHPLC-QE-MS positive ion mode analysis, when compared with the nondamp phlegm pattern group, the damp phlegm pattern group had 9 different peaks (5 increased, 4 decreased); compared with the healthy control group, the damp phlegm pattern group had 106 different peaks (103 increased, 3 decreased). By UHPLC-QE-MS negative ion mode analysis, compared with the nondamp phlegm pattern group, the damp phlegm pattern group had 8 different peaks (7 increased, 1 decreased); compared with the healthy control group, the damp phlegm pattern group had 106 different peaks (all metabolites increased).

3.5. Differential Metabolites Analysis

Among the 156 matched metabolites, the metabolites in the damp phlegm pattern group were mainly divided into 8 categories compared with the nondamp phlegm pattern group. The most abundant metabolites were lipids and lipid-like molecules as well as organic acids and derivatives (each containing 5 metabolites), followed by organic oxygen compounds, organic nitrogen compounds, benzenoids (each containing two metabolites), organoheterocyclic compounds, phenylpropanoids and polyketides, nucleosides, nucleosides, and analogues (each containing one metabolite) (Table 2).

Table 2: Identification of significant differential metabolites in tongue coating by comparison of the damp phlegm pattern group and nondamp phlegm pattern group.

Metabolite name
RT (min)
m/z
Mean damp phlegm pattern
Mean nondamp phlegm pattern

VIP
Log fold change
Detection method

Lipids and lipid-like molecules

Cholesterol
25.385
369
0.137
0.081
0.028
1.172
0.749
LC+

(3b,4b,11b,14b)-11-ethoxy-3,4-epoxy-14-hydroxy-12-cyathen-15-al 14-xyloside
148.586
495
0.343
0.470
0.040
3.021
−0.453
LC+

PG(18 : 1(11Z)/22 : 5(4Z,7Z,10Z,13Z,16Z))
230.237
824
0.707
0.521
0.048
2.571
0.440
LC+

3a,7b,12a-Trihydroxyoxocholanyl-Glycine
243.238
466
0.648
0.370
0.028
3.020
0.810
LC+

Pantothenol
65.302
204
0.033
0.019
0.015
1.043
0.811
LC−

Organic acids and derivatives

Glycolic acid
5.611
147
0.043
1.045
<0.01
1.104
−4.598
GC

Ustiloxin D
287.091
495
0.041
0.048
0.045
2.236
−0.238
LC+

Leucyl-Valine
398.503
231
0.174
0.095
<0.01
1.230
0.875
LC+

Sarcosine
369.003
88
0.937
0.592
0.045
2.108
2.108
LC−

L-Proline
334.791
114
0.607
0.275
<0.01
1.175
1.175
LC−

Organic oxygen compounds

D-xylitol
9.799
147
0.001
0.051
0.016
1.363
−5.218
GC

L-Iditol
317.933
181
0.023
0.014
<0.01
2.021
0.740
LC−

Organic nitrogen compounds

L-Carnitine
375.368
162
2.626
3.708
0.028
1.077
−0.498
LC+

Dimethyl dialkyl ammonium chloride
65.873
304
0.008
0.002
<0.01
1.158
1.680
LC+

Benzenoids

N-Undecylbenzenesulfonic acid
103.010
313
0.187
0.238
0.031
1.501
−0.342
LC+

Terephthalic acid
372.085
165
1.273
1.524
<0.01
2.378
−0.260
LC−

Organoheterocyclic compounds

Xanthine
233.387
151
4.516
3.253
0.042
1.854
0.473
LC+

Phenylpropanoids and polyketides

Curcumin
98.995
367
0.019
0.013
0.022
1.749
0.493
LC−

Nucleosides, nucleotides, and analogues

Nelarabine
217.309
296
0.034
0.015
<0.01
2.257
1.140
LC−

Others

Ethanol phosphate
6.715
211
0.000
0.007
0.042
1.166
−4.719
GC

6-deoxyglucitol
10.173
117
0.006
0.277
<0.01
1.747
−5.418
GC

Capric acid
8.355
117
0.006
0.236
<0.01
2.048
−5.262
GC

GC, GC-TOF-MS; LC+, UHPLC-QE-MS positive ion; LC−, UHPLC-QE-MS negative ion.

Compared with the healthy controls, the matched metabolites in the damp phlegm pattern group were mainly divided into 10 categories, and the largest metabolite group were lipids and lipid-like molecules (containing 69 metabolites), followed by organoheterocyclic compounds (containing 13 metabolites), organic acids and derivatives (containing 12 metabolites), benzenoids (containing 7 metabolites), organic oxygen compounds, organic nitrogen compounds (each containing 5 metabolites), phenylpropanoids and polyketides (each containing 4 metabolites), homogeneous nonmetal compounds, organosulfon compounds, hydrocarbons (each containing 1 metabolite) (Table 3).

Table 3: Identification of significant differential metabolites in tongue coating by comparison of the damp phlegm pattern group and healthy control group.

Metabolite name
RT (min)
m/z
Mean damp phlegm pattern
Mean healthy control

VIP
Log fold change
Detection method

Lipids and lipid-like molecules

Foeniculoside VII
71.454
349
0.516
0.057
<0.01
1.411
3.166
LC+

Glycerol tripropanoate
61.345
261
1.152
0.109
<0.01
1.417
3.400
LC+

Solavetivone
115.309
219
0.198
0.014
<0.01
1.081
3.785
LC+

Patchoulenone
61.062
219
3.206
0.278
<0.01
1.238
3.528
LC+

LysoPE(14 : 1(9Z)/0 : 0)
194.563
424
1.679
0.000
<0.01
1.340
14.686
LC+

Gibberellin A79
71.856
365
0.059
0.010
<0.01
1.350
2.615
LC+

Oleic acid
33.290
283
0.246
0.086
<0.01
1.196
1.518
LC+

Dexamethasone
77.202
393
0.241
0.030
<0.01
1.343
3.000
LC+

Leukotriene D4
70.925
497
3.867
1.431
<0.01
1.138
1.434
LC+

Gynosaponin S
73.680
948
0.083
0.013
<0.01
1.251
2.655
LC+

3-O-methylniveusin A
77.445
409
0.040
0.004
<0.01
1.287
3.346
LC+

Hoduloside VII
248.651
932
0.917
0.103
<0.01
1.407
3.153
LC+

L-Acetylcarnitine
328.145
204
0.039
0.025
<0.01
1.092
0.629
LC+

Fucoxanthin
259.139
659
0.081
0.008
<0.01
1.408
3.303
LC+

Citronellyl beta-sophoroside
91.099
481
0.081
0.009
<0.01
1.334
3.169
LC+

N-Cyclopropyl-trans-2-cis-6-nonadienamide
46.068
194
0.175
0.013
<0.01
1.306
3.748
LC+

3-Hydroxyisovalerylcarnitine
57.688
262
0.107
0.010
<0.01
1.405
3.358
LC+

Lyciumoside III
142.803
649
0.026
0.002
<0.01
1.359
3.643
LC+

Fencamfamine
34.439
216
0.275
0.113
<0.01
1.099
1.291
LC+

(3beta,5alpha,9alpha,22E,24R)-5,9-epidioxy-3-hydroxyergosta-7,22-dien-6-one
246.064
443
0.703
0.060
<0.01
1.374
3.547
LC+

Herculin
194.301
252
0.753
0.416
<0.01
1.175
0.858
LC+

Eicosadienoic acid
33.311
309
0.801
0.094
<0.01
1.413
3.096
LC+

3beta-acetoxy-11alpha-methoxy-12-ursen-28-oic acid
193.697
543
0.079
0.009
<0.01
1.217
3.211
LC+

Antibiotic X 14889C
257.759
615
0.086
0.009
<0.01
1.409
3.311
LC+

2-Ethyl-2-hydroxybutyric acid
58.656
133
0.710
0.056
<0.01
1.360
3.670
LC+

Alliospiroside C
120.642
725
0.799
0.113
<0.01
1.386
2.821
LC+

(22E, 24x)-ergosta-4,6,8,22-tetraen-3-one
38.056
393
0.588
0.196
<0.01
1.273
1.585
LC+

3-Methoxy-4-hydroxyphenylglycol glucuronide
293.193
419
0.404
0.045
<0.01
1.411
3.159
LC+

Physalin O
314.512
529
0.128
0.013
<0.01
1.340
3.291
LC+

Fevicordin B 2-gentiobioside
185.565
869
0.274
0.018
<0.01
1.353
3.943
LC+

Desglucocoroloside
220.265
505
0.031
0.008
<0.01
1.074
2.006
LC+

3-Methylglutarylcarnitine
84.851
290
0.270
0.023
<0.01
1.410
3.552
LC+

Smilanippin A
113.780
725
0.602
0.047
<0.01
1.418
3.682
LC+

PS(16 : 1(9Z)/18 : 4(6Z,9Z,12Z,15Z))
207.222
754
0.075
0.007
<0.01
1.407
3.514
LC+

CPA(18 : 0/0 : 0)
278.918
421
0.208
0.020
<0.01
1.416
3.379
LC+

Hericenone E
243.862
595
0.352
0.032
<0.01
1.415
3.474
LC+

Glycerol tributanoate
125.367
303
0.179
0.009
<0.01
1.423
4.287
LC+

Fasciculic acid C
116.186
710
0.444
0.044
<0.01
1.354
3.324
LC+

Hebevinoside I
38.382
809
0.100
0.008
<0.01
1.399
3.557
LC+

Phosphatidylethanolamine
165.695
784
0.121
0.008
<0.01
1.340
3.899
LC+

Lycoperoside D
154.407
740
0.548
0.043
<0.01
1.419
3.659
LC+

Chondrillasterol 3-[glucosyl-(1->4)-glucoside]
97.620
737
0.674
0.131
<0.01
1.338
2.358
LC+

PA(18 : 4(6Z,9Z,12Z,15Z)/14 : 1(9z))
246.799
639
0.178
0.029
<0.01
1.326
2.599
LC+

(24E)-3alpha,15alpha-diacetoxy-23-oxo-7,9(11),24-lanostatrien-26-oic acid
188.646
569
0.359
0.058
<0.01
1.236
2.639
LC+

Physapruin B
227.103
603
0.095
0.021
<0.01
1.146
2.200
LC+

PG(18 : 1(11Z)/22 : 5(4Z,7Z,10Z,13Z,16Z))
230.237
824
0.707
0.015
<0.01
1.430
5.573
LC+

Trihexosylceramide (d18 : 1/12 : 0)
257.583
969
0.362
0.090
<0.01
1.309
2.008
LC+

Traumatic acid
281.345
229
0.507
0.047
<0.01
1.364
3.440
LC+

Lactosylceramide
31.388
1003
0.004
4.397
<0.01
1.316
-10.250
LC+

Sphinganine 1-phosphate
51.580
382
0.149
0.008
<0.01
1.429
4.202
LC+

Budesonide
167.555
431
0.880
0.012
<0.01
1.228
6.183
LC+

3-Benzoyloxy-11-oxo-12-ursen-28-oic acid
34.377
575
2.136
0.156
<0.01
1.422
3.776
LC+

3a,7b,12a-Trihydroxyoxocholanyl-Glycine
243.238
466
0.648
0.047
<0.01
1.051
3.788
LC+

Momordicoside K
69.766
649
0.691
0.161
<0.01
1.340
2.100
LC+

PI(22 : 5(4Z,7Z,10Z,13Z,16Z)/16 : 0)
244.507
886
0.141
0.010
<0.01
1.338
3.842
LC+

Vinaginsenoside R3
84.853
932
0.486
0.035
<0.01
1.418
3.811
LC+

PA(18 : 4(6Z,9Z,12Z,15Z)/18 : 4(6Z,9Z,12Z,15Z))
144.619
689
0.018
0.001
<0.01
1.185
3.617
LC+

Dodecanoic acid
49.522
199
10.480
5.041
<0.01
1.467
1.056
LC−

Hydroxyisocaproic acid
251.772
131
3.168
0.297
<0.01
1.428
3.414
LC−

5,15-DiHETE
108.697
335
0.119
0.002
<0.01
1.741
5.644
LC−

Adipic acid
394.038
145
7.034
1.213
<0.01
1.584
2.536
LC−

13S-hydroxyoctadecadienoic acid
50.007
295
0.642
0.230
<0.01
1.585
1.481
LC−

Prostaglandin D2
46.555
351
1.268
0.105
<0.01
1.190
3.601
LC−

9,10-DHOME
81.276
313
0.120
0.008
<0.01
1.607
3.946
LC−

Azelaic acid
359.713
187
0.188
0.010
<0.01
1.787
4.170
LC−

(10E,12Z)-(9S)-9-hydroperoxyoctadeca-10,12-dienoic acid
60.715
311
0.120
0.005
<0.01
1.703
4.569
LC−-

6beta-hydroxyasiatic acid
51.604
503
0.135
0.012
<0.01
1.762
3.463
LC−

Fasciculol C
257.943
553
0.006
0.001
<0.01
1.354
3.406
LC−

Traumatic acid
139.932
227
0.230
0.022
<0.01
1.608
3.369
LC−

Organic acids and derivatives

Pantothenic acid
11.358
103
0.014
0.002
<0.01
1.118
2.536
GC

Bortezomib
285.056
407
0.077
0.006
<0.01
1.414
3.773
LC+

Ustiloxin D
287.091
495
0.041
0.003
<0.01
1.412
3.737
LC+

N,N-Dimethylformamide
300.102
74
0.143
0.085
<0.01
1.035
0.743
LC+

Oseltamivir
314.576
313
0.143
0.015
<0.01
1.322
3.249
LC+

Arginyl-arginine
296.631
331
2.122
0.170
<0.01
1.421
3.643
LC+

Arginyl-histidine
166.723
312
0.412
0.030
<0.01
1.419
3.784
LC+

Dihydro-3-(2-octenyl)-2,5-furandione
281.391
211
0.131
0.017
<0.01
1.243
2.963
LC+

N-Acetylleucine
265.067
174
0.140
0.010
<0.01
1.352
3.829
LC+

L-cis-3-amino-2-pyrrolidinecarboxylic acid
75.782
131
0.027
0.002
<0.01
1.340
3.693
LC+

L-Homocysteic acid
124.120
182
0.127
0.008
<0.01
1.786
3.970
LC−

3-Hydroxycapric acid
83.306
187
0.241
0.088
<0.01
1.450
1.448
LC−

Organic oxygen compounds

Leonuriside A
372.037
333
0.012
0.001
<0.01
1.429
4.365
LC+

Kanamycin
205.447
485
0.036
0.004
<0.01
1.372
3.253
LC+

Guanadrel sulfate
79.840
214
3.912
0.331
<0.01
1.418
3.562
LC+

Trimethylaminoacetone
206.235
116
0.113
0.007
<0.01
1.228
4.102
LC+

4-Ipomeanol
72.941
167
0.311
0.024
<0.01
1.004
3.699
LC−

Benzenoids

Dictagymnin
60.998
203
0.068
0.005
<0.01
1.081
3.698
LC+

4’-hydroxyfenoprofen glucuronide
281.454
435
0.089
0.007
<0.01
1.401
3.626
LC+

Methoxamine
410.726
212
0.037
0.004
<0.01
1.302
3.038
LC+

N-undecylbenzenesulfonic acid
103.010
313
0.187
0.092
<0.01
1.133
1.031
LC+

Salmeterol
57.091
416
0.468
0.044
<0.01
1.419
3.415
LC+

Esmolol
175.513
296
0.168
0.015
<0.01
1.345
3.498
LC+

(Â±)-2-(1-methylpropyl)-4,6-dinitrophenol
31.378
239
2.850
1.286
<0.01
1.073
1.148
LC−

Organoheterocyclic compounds

Pirenzepine
132.148
352
0.433
0.047
<0.01
1.403
3.206
LC+

Neferine
264.617
625
0.845
0.118
<0.01
1.390
2.841
LC+

Pyrimidine
112.505
81
0.097
0.008
<0.01
1.416
3.566
LC+

5,6-Dihydroxyindole
45.066
150
0.256
0.031
<0.01
1.226
3.029
LC+

2-Pyrrolidineacetic acid
64.058
130
0.684
0.340
<0.01
1.165
1.009
LC+

Alpha-carboxy-delta-decalactone
125.959
215
0.085
0.004
<0.01
1.413
4.275
LC+

I-Urobilin
174.113
591
0.102
0.006
<0.01
1.413
4.117
LC+

4-Ethyl-5-methyl-2-(1-methylethyl)oxazole
244.532
154
0.657
0.081
<0.01
1.249
3.023
LC+

Garcimangosone C
282.076
413
0.893
0.071
<0.01
1.405
3.654
LC+

8-Hydroxycarteolol
116.908
309
0.164
0.018
<0.01
1.415
3.203
LC+

3-Methyl-1-hydroxybutyl-ThPP
281.408
511
0.040
0.003
<0.01
1.394
3.675
LC+

Azaspiracid 3
109.997
828
0.089
0.034
<0.01
1.000
1.385
LC+

3’-N-acetyl-4'-O-(9-octadecenoyl)fusarochromanone
328.728
599
0.027
0.004
<0.01
1.080
2.884
LC+

Organic nitrogen compounds

1-Butylamine
268.847
74
11.933
0.834
<0.01
1.360
3.839
LC+

Nervonyl carnitine
237.324
102
659.917
66.315
<0.01
1.342
3.315
LC+

Isobutylpropylamine
231.650
116
0.444
0.041
<0.01
1.417
3.420
LC+

2-Diethylaminoethanol
300.009
118
195.976
13.198
<0.01
1.424
3.892
LC+

Dipyridamole
47.761
253
0.298
0.016
<0.01
1.426
4.263
LC+

Phenylpropanoids and polyketides

Kanzonol V
244.573
377
0.005
0.457
<0.01
1.096
−6.613
LC+

Sofalcone
285.882
451
0.063
0.005
<0.01
1.415
3.788
LC+

Kanzonol F
247.943
421
0.003
0.360
<0.01
1.135
-7.002
LC+

Kanzonol I
83.271
437
0.060
0.000
<0.01
1.051
9.876
LC+

Homogeneous nonmetal compounds

Phosphoric acid
32.551
99
0.643
0.052
<0.01
1.246
3.632
LC+

Organosulfur compounds

(Â±)-2-pentanethiol
32.769
105
0.397
0.080
<0.01
1.089
2.302
LC+

Hydrocarbons

1-Methyl-1,3-cyclohexadiene
32.571
95
0.477
0.093
<0.01
1.182
2.356
LC+

Others

6-deoxyglucitol
10.173
117
0.006
0.001
<0.01
2.220
2.432
GC

1,2,4-benzenetriol
9.344
124
0.213
0.015
<0.01
2.240
3.856
GC

Isoxanthopterin
12.091
100
0.002
0.001
<0.01
1.598
1.130
GC

Lactobionic acid 2
14.932
147
0.032
0.013
<0.01
1.342
1.289
GC

2,4-dichloro-1-(2-chloroethenyl)-benzene
7.925
170
0.476
0.034
<0.01
2.341
3.823
GC

Asparagine dehydrated
8.628
110
0.011
0.002
<0.01
2.212
2.434
GC

4-Hydroxybenzoate
9.423
110
2.458
0.161
<0.01
2.343
3.933
GC

Conduritol-beta-expoxide
11.541
103
0.174
0.019
<0.01
2.231
3.190
GC

Methylmaleic acid
7.923
170
0.477
0.034
<0.01
2.341
3.826
GC

4-Methylcatechol
7.928
170
0.452
0.034
<0.01
2.148
3.749
GC

Beta-mannosylglycerate
12.271
204
0.000
0.000
<0.01
1.350
0.739
GC

Uridine minor
13.354
145
0.066
0.007
<0.01
2.318
3.263
GC

(10S,11R)-pterosin C 4-glucoside
282.232
397
16.094
1.266
<0.01
1.331
3.669
LC+

PC(P-18 : 1(9Z)/16 : 0)
36.173
745
0.693
0.070
<0.01
1.416
3.299
LC+

LysoPC(18:1(9Z))
210.778
544
0.019
0.001
<0.01
1.004
4.867
LC+

PC(P-16 : 0/18 : 4(6Z,9Z,12Z,15Z))
153.493
739
0.048
0.005
<0.01
1.200
3.387
LC+

GC: GC-TOF-MS; LC+: UHPLC-QE-MS positive ion; LC−: UHPLC-QE-MS negative ion.

3.6. Differential Metabolic Pathways Analysis

According to the annotations of database KEGG on the significantly differential metabolites, following the enrichment analysis and topology analysis, the key pathways with the highest correlation between metabolite differences were found on the basis of both raw and impact values.

In the comparison of damp phlegm pattern group and nondamp phlegm pattern group, GC-TOF-MS analysis generated two pathways (glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions), UHPLC-QE-MS positive ion model analysis generated two pathways (primary bile acid biosynthesis and steroid hormone biosynthesis), and UHPLC-QE-MS negative ion mode analysis generated four pathways (arginine and proline metabolism; caffeine metabolism; glycine, serine, and threonine metabolism; purine metabolism). Six key metabolites were involved, including glycolic acid, D-xylitol, cholesterol, L-proline, sarcosine, and xanthine (Table 4).

Table 4: Key metabolic pathways identified from the significant differential metabolites in tongue coating between the damp phlegm pattern group and nondamp phlegm pattern group.

Pathway
Raw
Impact
Significantly different metabolites

GC-TOF-MS metabolomics analysis

Glyoxylate and dicarboxylate metabolism
0.041
0.007
Glycolic acid

Pentose and glucuronate interconversions
0.044
0.032
D-Xylitol

UHPLC-QE-MS positive ion mode metabolomics analysis

Primary bile acid biosynthesis
0.057
0.055
Cholesterol

Steroid hormone biosynthesis
0.118
0.004
Cholesterol

UHPLC-QE-MS negative ion mode metabolomics analysis

Arginine and proline metabolism
0.025
0.109
L-Proline; sarcosine

Caffeine metabolism
0.068
0.031
Xanthine

Glycine, serine and threonine metabolism
0.149
0.050
Sarcosine

Purine metabolism
0.268
0.036
Xanthine

In the comparison of damp phlegm pattern group and healthy control group, GC-TOF-MS analysis had one pathway named pantothenate and CoA biosynthesis, UHPLC-QE-MS positive ion mode analysis had eight pathways (glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, arachidonic acid metabolism, valine, leucine and isoleucine degradation, starch and sucrose metabolism, pentose and glucuronate interconversions, and tyrosine metabolism), UHPLC-QE-MS negative ion mode analysis had one pathway named arachidonic acid metabolism. Ten key metabolites were involved, including pantothenic acid, phosphatidylethanolamine, lysoPC(18 : 1(9Z)), sphinganine 1-phosphate, lactosylceramide, leukotriene D4, 3-methyl-1-hydroxybutyl-ThPP, 3-methoxy-4-hydroxyphenylglycol glucuronide, 5,6-dihydroxyindole, and prostaglandin D2 (Table 5).

Table 5: Key metabolic pathways identified from the significant differential metabolites in tongue coating between the damp phlegm pattern group and healthy control group.

Pathway
Raw
Impact
Significantly different metabolites

GC-TOF-MS metabolomics analysis

Pantothenate and CoA biosynthesis
0.044
0.180
Pantothenic acid

UHPLC-QE-MS positive ion mode metabolomics analysis

Glycerophospholipid metabolism
0.018
0.231
Phosphatidylethanolamine; LysoPC(18:1(9Z))

Sphingolipid metabolism
0.050
0.017
Sphinganine 1-phosphate; lactosylceramide

Glycosylphosphatidylinositol(GPI)-anchor biosynthesis
0.186
0.044
Phosphatidylethanolamine

Arachidonic acid metabolism
0.227
0.026
Leukotriene D4

Valine, leucine and isoleucine degradation
0.446
0.035
3-Methyl-1-hydroxybutyl-ThPP

Starch and sucrose metabolism
0.523
0.013
3-Methoxy-4-hydroxyphenylglycol glucuronide

Pentose and glucuronate interconversions
0.544
0.009
3-Methoxy-4-hydroxyphenylglycol glucuronide

Tyrosine metabolism
0.677
0.007
5,6-Dihydroxyindole

UHPLC-QE-MS negative ion mode metabolomics analysis

Arachidonic acid metabolism
0.042
0.034
Prostaglandin D2

4. Discussion

GC-TOF-MS and UHPLC-QE-MS have different advantages in the analysis of metabolites. GC-TOF-MS has higher separation ability than UHPLC-QE-MS in the analysis of polar metabolites. Moreover, GC-TOF-MS system has highly repeatable mass spectra, which makes GC-TOF-MS libraries more comprehensive than UHPLC-QE-MS. However, GC-TOF-MS can detect approximately 100 metabolites while UHPLC-QE-MS can detect thousands of metabolites, including semipolar metabolites [28]. Because the composition of metabolites on tongue coating is complex, the combined use of the two detection methods can more comprehensively detect the characteristics of tongue-coating metabolites.

In the metabolomics analysis of damp phlegm pattern tongue-coating samples, lipids and lipid-like molecules were the largest group of differentially expressed metabolites (74 types) when comparing nondamp phlegm pattern patients and healthy controls. It can be seen from Tables 1 and 2 that 72 kinds of lipids and lipid-like molecules were significantly upregulated in the damp phlegm pattern group, and only (3b, 4b, 11b, 14b)-11-ethoxy-3,4-epoxy-14-hydroxy-12-cyathen-15-al 14-xyloside and lactosylceramide (d18 : 1/26 : 0) were significantly downregulated. This result indicates that lipids and lipid-like molecules are the main metabolic disorder differential metabolites of the GPL damp phlegm pattern patient in tongue-coating metabolomics detection. Some studies have found that lipids participate in the development of chronic atrophic gastritis [29]. Chronic gastritis may be related to the global increase in the inflammation state of the body, which was influenced by poor lipid status such as decreased serum high-density lipoprotein (HDL), and the decrease of HDL was closely related to the increased risk of gastric cancer [30, 31]. Some scholars also found that elevated serum-free fatty acids may increase the risk of gastric cancer [32]. In addition, it was also found in experiments on mice that high cholesterol and high-fat diets may increase gastritis incidence in mice [33]. It can be seen that these findings indicate that lipid is related to the occurrence of GPL, but we did not find reports on the relationship between GPL damp phlegm pattern and lipid metabolism. It is worth noting that we found that there is a correlation between damp phlegm pattern and dyslipidemia in other diseases. For example, studies have shown that most patients with hyperlipidemia and atherosclerosis belong to the damp phlegm pattern group, and the level of total cholesterol in hyperlipidemic rats was significantly elevated [34]. Total cholesterol and low-density lipoprotein levels were higher in patients with hypertension-complicated diabetes and damp phlegm pattern [35]. In our previous study, we found that histidine, tryptophan, lysine, and other metabolites exist in chronic renal failure (CRF) and coronary heart disease (CHD) of patients’ tongue coating with damp phlegm pattern [11], but these metabolites were not found in the GPL damp phlegm pattern patients’ tongue coating, which may be due to the following two reasons. One is that although they are all damp phlegm syndrome, they belong to different diseases, so the metabolites of tongue coating are also different. The second reason is that we only use GC-MS to detect the metabolites of tongue coating in CHD and CRF, which leads to the failure of comprehensive retrieval of the different metabolites of damp phlegm pattern between the two diseases.

According to the metabolic pathway analysis of lipids and lipid-like molecules, six metabolic pathways were involved, including primary bile acid biosynthesis, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and arachidonic acid metabolism. The six metabolic pathways contained seven metabolites: cholesterol, phosphatidylethanolamine, lysoPC(18:1(9Z)), sphinganine 1-phosphate, lactosylceramide, leukotriene D4, and prostaglandin D2.

Among them, sphinganine 1-phosphate (S1P) is involved in sphingolipid metabolism. Sphingosine kinase (Sphk) exists in chronic gastritis and gastric cancer cells. Sphk can produce S1P, and many inflammatory reactions are affected by the circulating S1P. The Sphk/S1P axis is an inflammatory mediator in tumor microenvironment and has been identified as a therapeutic target for gastric diseases [38]. Leukotriene D4 and prostaglandin D2 have participated in arachidonic acid metabolism. Arachidonic acid metabolic pathway has an impact on the occurrence and development of gastric cancer [39]. Prostaglandin D2 can inhibit the development of gastric cancer [40]. Leukotriene D4 can aggravate gastric contraction and promote gastric acid secretion [41, 42]. However, gastric pH increases during GPL and gastric cancer [43]. Therefore, leukotriene D4 and prostaglandin D2 as metabolites are considered as new targets for the treatment and prevention of GC [39, 44]. Besides the levels of leukotriene D4 and prostaglandin D2 increased in GPL patients with damp phlegm pattern, which indicates that human body may have certain self-healing function in the process of chronic gastritis developing into gastric cancer. Thus, it can be seen, Sphk/S1P axis, as a target for the treatment of gastric diseases, could be explained from the side that S1P aggravated the development of gastric diseases. The increased expression of leukotriene D4 and prostaglandin D2 indicated that GPL patients had the ability of self-healing in the process of disease.

Among the metabolites of GPL damp phlegm pattern patients, organic acids and derivatives (17 types) and organoheterocyclic compounds (14 types) were the second and third major metabolites. Studies have found that organic acids may be used as a potential metabolic marker of gastric adenocarcinoma, which can be used for the detection, diagnosis, and treatment of gastric adenocarcinoma in the coming years [45]. According to the analysis of metabolic pathways, four metabolites were involved: glycolic acid, pantothenic acid, L-proline, and sarcosine. Among them, pantothenic acid and proline may be potential biomarkers of gastric cancer [46]. Elevated levels of sarcosine may also be associated with the formation of gastric cancer [47]. These studies found that pantothenic acid, proline, and sarcosine all play a role in the development of gastric cancer.

The kinds of other metabolites were fewer; the content of terephthalic acid, L-carnitine, N-undecylbenzenesulfonic acid, kanzonol V, and kanzonol F decreased, while the content of other metabolites increased. Among them the increase of isoxanthopterin may be related to the existence of gastric cancer [48]. Some studies have found that 4-methylcatechol is carcinogenic and can promote gastric cancer and adenocarcinoma [49]. In the experiment of rats, 1,2,4-benzenetriol can increase the thickness of gastric mucosa [50]. 5-ﬂuorouracil (5-Fu), one of the first-line antitumor drugs, can effectively induce the apoptosis of cancer cells while dipyridamole can enhance the cytotoxicity of 5-Fu [51, 52].

According to the annotations of database KEGG on the significantly different metabolites, following the enrichment analysis and topology analysis, on the basis of both raw p and impact values, it was found that glycerophospholipid metabolism was the most important metabolic pathway. The level of phosphatidylethanolamine involved in the synthesis of glycerophospholipid metabolism was significantly increased in group of GPL patients with damp phlegm pattern, and another study has shown that the concentration of phosphatidylethanolamine in the plasma of patients with chronic gastritis increased significantly [52].

However, our research results are still insufficient. Nine GPL patients with damp phlegm pattern were infected with Helicobacter pylori (Hp). Although studies have found that Hp may affect the content of some metabolites in tongue coating of patients with chronic gastritis [53], we previously analyzed the differential metabolites in the tongue coating of 60 patients with GPL compared with healthy people, of which 10 patients were infected with Hp, and the inclusion of these Hp-infected patients did not affect our final screening of differential metabolites [54]. Even so, in future studies, we will still carefully consider the factors of Hp infection.

5. Conclusions

In this study, GC-TOF-MS and UHPLC-QE-MS metabolomics technologies were used to detect the metabolites in the tongue coating of GPL patients with damp phlegm pattern for the first time. The results illustrated that there were significant differences in metabolites between GPL patients with damp phlegm pattern and GPL patients with nondamp phlegm pattern as well as healthy people. The types of lipids and lipid-like molecules were the most prominent, involving primary bile acid biosynthesis, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and arachidonic acid metabolism. Among them, the most important metabolic pathway was glycerophospholipid metabolism, and the metabolites involved were phosphatidylethanolamine and lysoPC (18:1(9Z)). In addition, organoheterocyclic compounds as well as organic acids and derivatives also contained more kinds of metabolites, which were involved in glyoxylate and dicarboxylate metabolism and pantothenate and CoA biosynthesis as well as arginine and proline metabolism. With further studies, we hope that these different metabolites may be potential diagnostic markers which can be obtained noninvasively for patients with GPL damp phlegm pattern.

Data Availability

The Ethics Committee of Shanghai University of Traditional Chinese Medicine limited the measurement data used to support the results of this study in order to protect the privacy of patients. For researchers who meet the criteria for obtaining confidential data, the data of this study can be obtained from Yiming Hao (e-mail: hymjj888@163.com).

Disclosure

Yifeng Xu and Renling Zhang share the first authorship.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Yiming Hao was responsible for the study. Yifeng Xu wrote the manuscript. Renling Zhang helped collect clinical samples. Robert Morris and Feng Cheng helped revise the grammar of the manuscript. Yiqin Wang helped with the ideas of the study. Zhujing Zhu helped with the experimentation. All authors of this study agreed to be accountable for all aspects of the work. For this study, Yifeng Xu and Renling Zhang made an equal contribution.

Acknowledgments

This work was funded by the National Natural Science Foundation of China (Grant No. 81703982). This work was also supported by Shanghai Biotree Biotech Co. Ltd.

Supplementary Materials

Table S1 shows the reagents used in GC-TOF-MS and UHPLC-QE-MS experiments. (Supplementary Materials)

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