Study on the Characteristics of Traditional Chinese Medicine Syndromes in Patients with Erosive Gastritis Based on Metabolomics

According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.


Introduction
Erosive gastritis (EG) is one of the most common diseases in the world [1].Currently, the main diagnostic method for EG is electronic gastroscopy [2].However, this method is often too invasive for patients to tolerate [3].EG causes great trouble, although it generally does not endanger the life of patients.Terefore, it is worth exploring a simple and noninvasive diagnostic method to identify the characteristics of EG.
According to traditional Chinese medicine theory, tongue coatings refect changes in the body [4,5].Based on this theory, it has been found that tongue features can be used as a stable method for disease diagnosis [6].Tongue coating evaluation is sometimes even signifcantly superior to the use of traditional blood biomarkers [7].In accordance with this theory, EG can be classifed into two distinct syndromes: damp-heat syndrome (DHS) and liver depression and qi stagnation syndrome (LDQSS) [8].Based on the diferent EG syndromes, traditional Chinese medicine (TCM) can provide more efective personalized treatment according to the features of each syndrome [9].While previous studies have focused on bacteria from diferent tongue coatings in EG, variations in their metabolites remain unknown.
As a sensitive indicator for judging the condition of EG, tongue coatings play a pivotal role in the efective diagnosis and treatment [10].Due to the comprehensive, highly sensitive, specifc, and noninvasive nature of metabolomics, it has recently become a new research focus to promote disease diagnosis and is thought to be the most predictive phenotype [11].In recent years, metabolomics has widely been used in the research of tongue coatings, such as in gastric precancerous lesions, coronary heart disease, and chronic renal failure [12,13], with a particular focus on chronic gastritis [10].We have identifed changes in the salivary metabolome in patients with EG in our previous study [14].We carried out the present study to further explore the metabolomics changes in EG patients.Herein, we collected tongue coatings of patients with EG with DHS and LDQSS, the two most common conditions in TCM.Due to their prominence in clinical practice, these two conditions have been listed in the International Classifcation of Diseases by the World Health Organization [15].However, their biological characteristics have not yet been systematically studied.Terefore, in this study, we used nontargeted metabolomics methods to study the tongue coating samples of EG patients with DHS and LDQSS and to screen the metabolic products with characteristic signifcance.

Materials and Methods
2.1.Patients.We conducted a case control study to elucidate the composition of tongue coating-related metabolites in EG patients with DHS and LDQSS.A total of 64 patients with EG were selected from the Hebei Provincial Hospital of Traditional Chinese Medicine, including 32 cases with DHS and 32 cases with LDQSS.In addition, we selected 30 healthy volunteers, all of whom were either college students or hospital staf who did not have any digestive system symptoms and who passed the physical examination, tongue coating evaluation, and pulse condition assessment at the Hebei Province Hospital of Traditional Chinese Medicine.Te experimental procedure is shown in Figure 1.

Ethics Approval.
All of the subjects signed a written informed consent form before sample collection, and the study was conducted in accordance with the Declaration of Helsinki.In addition, the study was approved by the Ethics Committee of Hebei Province Hospital of Traditional Chinese Medicine (HBZY2021-KY-045-01).

Diagnostic Criteria in Gastroscopy.
According to the diagnostic criteria for EG formulated by the Digestive Branch of the Chinese Medical Association in 2017, the diagnosis of EG includes single or multiple erosive lesions in the gastric mucosa that range in size from the size of a needle tip to several centimeters, or else single or multiple verruciform, bulging folds or papuloid protrusions in the gastric mucosa with diameters of 5-10 mm, a mucosal defect or umbilical-like depression at the top, and erosion at the center [16].

Diagnostic Criteria in Pathology.
According to the pathological diagnostic criteria in the consensus on the Diagnosis and Treatment of Chronic Gastritis by Integrated Traditional Chinese and Western Medicine, mucosal infltration with monocytes or neutrophils is the main manifestation of EG [8].

DHS and LDQSS Diagnostic Criteria.
According to the TCM syndrome standard in the consensus on the Diagnosis and Treatment of Chronic Gastritis by Integrated Traditional Chinese and Western Medicine [8]: DHS is defned as follows: having two main symptoms + one secondary disease, or one main symptom + two secondary diseases, combined with an evaluation based on gastroscopic fndings.
Te main symptoms are ① bloating or stomachache and ② red tongue with yellow, greasy, or thick fur.
Minor symptoms include ① heartburn in the stomach; ② bitter mouth and bad breath; ③ nausea and vomiting; ④ sticky stool; and ⑤ a slippery or wet pulse.
Relevant gastric results include ① thick and turbid mucus and ② obviously congested, edematous, and erosive gastric mucosa.
LDQSS is defned as follows: having two main symptoms + one secondary symptom, or the frst main symptom + two secondary symptoms, combined with an evaluation based on gastroscopic fndings.
Te main symptoms include ① epigastric distension or pain in both fanks; ② pain due to emotional factors; and ③ pulse string.
Minor symptoms include ① frequent belching; ② chest tightness or excessive breathing; ③ lack of appetite; ④ mental depression; and ⑤ a light red tongue with thin white fur; relevant gastric results include ① active or slow peristalsis; ② erythema of the gastric mucosa, in the form of dots, patches, or strips; and ③ bile refux.
Typical photographs taken from the patients who participated in the research are presented in Figure 2.

Sample Collection
2.4.1.Tongue Collection.First, we asked the subjects to clean their oral cavity for three times with distilled water free of any impurities.Ten, we gently scraped an appropriate amount of tongue coating on the surface of the participant's tongue.Finally, the scraped samples were put into a centrifuge tube and immediately stored at −80 °C until further analysis [17].

Sample Extraction.
Te samples were thawed without any damage.Ten, 20 mg of the samples were taken, and 50 μl of water and 200 μl of methanol/acetonitrile solution (1 : 1, v/v) were added before vortex mixing.After that, the samples were sonicated at low temperature for 30 min.Ten, they were centrifuged for 20 minutes at 14000 g at 4 °C.Te supernatant was collected, and 5 μl was injected into the instrument for fnal detection [18].International Journal of Analytical Chemistry AB Triple TOF 6600 mass spectrometer was used to collect the primary and secondary spectra of the samples [19][20][21].
For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 µm column (waters, Ireland).In both ESI positive and negative modes, the mobile phase contained A � 25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B � acetonitrile.Te gradient was 85% B for 1 min and was linearly reduced to 65% in 11 min and then was reduced to 40% in 0.1 min and kept for 4 min and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed.
For RPLC separation, a 2.1 mm × 100 mm ACQUIY UPLC HSS T3 1.8 µm column (waters, Ireland) was used.In ESI positive mode, the mobile phase contained A (water with 0.1% formic acid) and B (acetonitrile with 0.1% formic acid); and in ESI negative mode, the mobile phase contained A (0.5 mM ammonium fuoride in water) and B (acetonitrile).
Te gradient was 1% B for 1.5 min and was linearly increased to 99% in 11.5 min and kept for 3.5 min.Ten, it was reduced to 1% in 0.1 min, and 3.4 min of the re-equilibration period was employed.Te gradients were at a fow rate of 0.3 mL/ min, and the column temperatures were kept constant at 25 °C.
Te ESI source conditions were set as follows: Ion Source Gas1 (Gas1) was 60 psi, Ion Source Gas2 (Gas2) was 60 psi, curtain gas (CUR) was 30 psi, source temperature was 600 °C, and IonSpray Voltage Floating (ISVF) was ±5500 V.In MS only acquisition, the instrument was set to acquire over the m/z range 60-1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra.In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25-1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra.Te product ion scan was acquired using information dependent acquisition (IDA) with a high International Journal of Analytical Chemistry sensitivity mode selected.Te collision energy (CE) was fxed at 35 V with ±15 eV; declustering potential (DP) was 60 V (+) and −60 V (−); exclude isotopes were set within 4 Da, and candidate ions to monitor per cycle were 10.

Statistical Analysis.
A chi-square test was used for intergroup comparisons of gender, and the Mann-Whitney U test was used for intergroup comparison of age.Tis study relied on MetaboAnalyst 5.0 (https://www.metaboanalyst.ca) to fnd signifcant diferences between the groups.Data analysis was mainly based on fold-change analysis (FC Analysis), variable importance for the projection (VIP), and orthogonal partial least squares discrimination analysis (OPLS-DA) [22].

Basic Information.
A total of 32 EG patients with DHS (15 males and 17 females) and 30 healthy volunteers (15 males and 15 females) were included.Tere were no intergroup diferences in gender distribution (P > 0.05), and age did not difer signifcantly between the DHS group with average 49.63 ± 9.76 years and the control group with average 50.13 ± 8.79 years (P > 0.05).We also included 32 EG patients with LDQSS (15 males and 17 females) and the 30 healthy volunteers mentioned above.Tere were no intergroup diferences in gender distribution (P > 0.05), and age did not difer signifcantly between the LDQSS group with average 49.81 ± 9.56 years and the control group with average 50.13 ± 8.79 years (P > 0.05).Te above data can be found in the supplementary material (available here).

OPLS-DA.
Tere were 481 metabolites by identifcation, 313 for positive and 229 for negative; they were used to generate the OPLS-DA models.In this study, OPLS-DA was performed between the DHS group and the control group based on the positive and negative ion modes.To avoid overftting of the supervised model in the modeling process, we used the permutation test to check the model.Te results showed that the two groups were well distinguished (Figure 3), and the model had never been ftted.OPLS-DA was also performed between the LDQSS group and the control group.Te results showed that the two groups were also well distinguished (Figure 4), and the model had never been ftted.

Volcano Plot.
Based on univariate analysis, the diferential metabolites between the DHS group and the control group were determined.We analyzed all metabolites detected in positive and negative ion modes.Te diferential metabolites with a FC > 2 or FC < 0.5 and a P value <0.05 were visualized in the form of a volcano plot.Te diferential metabolites between the LDQSS group and the control group were also analyzed.Te results are shown in Figure 5.

Diferential Metabolites.
In this study, strict thresholds of VIP > 1 and P value <0.05 were used as the screening criteria for metabolites with signifcant diferences between the DHS group and the control group.We demonstrated diferential metabolites with FC > 2 or FC < 0.5.Te results are shown in Table 1.Te metabolites with signifcant differences between the LDQSS group and the control group were determined.Te results are shown in Table 2.

Cluster Analysis.
To more comprehensively and intuitively display the diferences, we conducted cluster analysis on the above results.Metabolites in the same cluster have similar expression patterns and may have similar functions or participate in the same metabolic process or cellular pathway together.Heat maps were generated to visualize the altered pattern of the signifcantly diferent metabolites (Figure 6).

Discussion
Due to the comprehensive, highly sensitive, specifc, and noninvasive nature of metabolomics, it has become a new research focus in disease diagnosis research in recent years.As one of the main methods for fnding potential diagnostic biomarkers, metabolomics has extensively been used in the research on tongue coating [23].Tere is mounting evidence that metabolic changes are associated with the initiation and International Journal of Analytical Chemistry development of tongue coatings [10,12,13].Previous studies have demonstrated that gastric precancerous lesions, coronary heart disease, and chronic renal failure [12,13], but especially chronic gastritis [10], have unique metabolic characteristics.However, although EG is a common type of chronic gastritis, the tongue metabolic profle of EG has not been thoroughly studied, and the underlying mechanism of diferent syndromes of EG remains unknown.Terefore, we carried out the present study based on metabolomics to discriminate between diferent features to improve diagnosis.In this study, the baseline data did not signifcantly deviate between the DHS group and the control group.Te results showed that the diferences between the two were not afected by factors such as age and gender.In the comparison between the DHS group and the control group, the OPLS-DA results showed a signifcant diference between the two groups.Te results proved that there was clear diferentiation between the two groups.Te volcano plot analyses also showed alterations in various metabolites; some were elevated, while others were decreased.In order to obtain more comprehensive and accurate results, characteristic metabolites were detected simultaneously in both positive and negative ion modes.In both modes, diferent metabolites are detected, but sometimes, there may be duplication.In the positive mode, there  were 10 metabolites with an increasing trend.Among them, L-anserine, a bioactive dipeptide found in muscles and brains of vertebrates, was the most elevated metabolite [24].Tere were seven metabolites with a downward trend.Among them, 3-hydroxycapric acid was the most signifcantly reduced metabolite.Along with L-anserine, they were enriched as potential spoilage biomarkers [25], consistent with erosion in the EG.In the negative mode, there were eight metabolites with an increasing trend.Of them, cytidine was the most signifcantly changed.Tere were two metabolites with a downward trend.Among them, pantetheine acid was the most signifcantly changed.Both of them have been used as potential biomarkers for unclassifed patients with pediatriconset multiple sclerosis [26].Te discovery of them may serve as an important potential marker for DHS.
Te common goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of TCM syndromes in EG with high sensitivity (true positive rate) and specifcity (true negative rate).So, we identifed metabolites with signifcant diferences that could act as biomarkers for distinguishing between the two groups by ROC curve analysis.Te results showed that these were phenylacetic acid, Cer (d18 : 1/18 : 1(9Z)), Ile-Ser, triethanolamine, albuterol, Pro-Glu, 3-hydroxycapric acid,       10 International Journal of Analytical Chemistry D-mannitol, Leu-Tr, palmitoyl ethanolamide in the positive mode, and cytidine, guanosine, deoxyguanosine, phosphoenolpyruvate, and deoxycytidine in the negative mode.We also conducted cluster analysis to enhance the reliability of the results.Based on the enrichment analysis, it was found that pyrimidine metabolism, pantothenate and CoA biosynthesis, TCA cycle, pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism played an important role in the comparison between the DHS group and the control group.Pyrimidine metabolism, glycolysis/ gluconeogenesis, and purine metabolism are closely linked to infammation and oxidative stress, the commonly accepted mechanistic pathway associated with marked susceptibility to infection [27][28][29].CoA in pathway of pantothenate and CoA biosynthesis is mainly involved in the metabolism of pyruvate, which can stimulate TCA cycle and provide 90% of the energy requirements for the body.Tis result indicated that there were diferences in energy changes in the DHS group [30].
Similarly, in the comparison between the LDQSS group and the control group, the same operation was performed and satisfactory results were also obtained.OPLS-DA results also showed a signifcant diference between the two groups, proving that the two groups have signifcant heterogeneity.Te volcano plot results showed up or downregulated diferential metabolites.Interestingly, the diferent metabolites between LDQSS and DHS were the same in the negative mode, while they were diferent in the positive mode; they were Arg-Tr and 1-palmitoylglycol, respectively.Next, we identifed the metabolites with signifcant diferences, which can act as biomarkers for distinguishing between the two groups.Te results of ROC curve analysis showed that they were Ile-Ser, D-mannitol, phenylacetic acid, albuterol, 2-phenylbutyric acid, Met-Val, 3-hydroxycapric acid, 2-methylbutyroylcarnitine, Pro-Glu, and 3-butynoic acid in the positive mode, and guanosine, L-tryptophan, alloxan, deoxyguanosine, dodecanoic acid, L-leucine, citrate, lumichrome, 3-methoxy-4-hydroxyphenylglycol sulfate, adrenic acid, L-aspartate, gamma-glutamyl-L-methionine, pantetheine, succinate, and L-alanine in the negative mode.We also conducted cluster analysis to enhance the reliability of the results.Based on the enrichment analysis, it was found that the LDQSS group involved more metabolic pathways, and the top fve were TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and pentose phosphate pathway.Among them, the TCA cycle, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and pentose phosphate pathways were largely similar to the DHS group.Moreover, the function of sphingolipid metabolism is immense and touches almost all major aspects of cell biology, including roles in cell growth, cell cycle, cell death, cell senescence, infammation, immune responses, cell adhesion and migration, angiogenesis, nutrient uptake, metabolism, responses to stress stimuli, and autophagy [31].
Te abovementioned results fully demonstrated the differences in metabolic characteristics between the LDQSS group and the DHS group.Tese fndings not only provide technical support for the tongue for EG diagnosis but also refect that diferent signs of EG have diferent characteristics, thereby providing support for further precision treatment and research basis for further exploration of the potential mechanisms of diferent signs based on metabolomics.In the future, the metabolites identifed in this study may be used as noninvasive and convenient biomarkers to distinguish DHS and LDQSS of EG patients.

Conclusions
Taken together, this study revealed that EG with DHS and EG with LDQSS have diferent characteristics.In summary, 14 potential biomarkers related to the DHS group were identifed and six metabolic pathways were enriched.Te diferential metabolites were enriched in pyrimidine metabolism, pantothenate and CoA biosynthesis, TCA cycle, pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism, pathways related to infammation, oxidative stress, and energy change.Similarly, in the LDQSS group, 25 potential biomarkers were identifed and 18 metabolic pathways were enriched.Te top fve pathways were TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and pentose phosphate pathway.Te results showed that the LDQSS group involved more metabolic pathways than the DHS group.Tere was consistency in the metabolic pathways involved between the two groups, but there were also signifcantly diferent pathways.Among them, the function of sphingolipid metabolism is immense and touches almost all major aspects of cell biology.Based on the above results, we hope that this research can provide reference and guidance for follow-up research and even lay the foundation for its application in TCM clinical diagnosis and treatment.However, there are still many shortcomings in this research.
We hope that related research can be carried out in the future.

Figure 3 :
Figure 3: OPLS-DA analysis of metabolomics data between the DHS group and the control group.(a) Comparison between the two groups in the positive ion mode.(b) Permutation test results in the positive ion mode.(c) Comparison between the two groups in the negative ion mode.(d) Permutation test results in the negative ion mode.Te DHS group is represented by green dots, and the control group is represented by blue dots.

Figure 4 :Figure 5 :
Figure 4: OPLS-DA analysis of metabolomics data between the LDQSS group and the control group.(a) Comparison between the two groups in the positive ion mode.(b) Permutation test results in the positive ion mode.(c) Comparison between the two groups in the negative ion mode.(d) Permutation test results in the negative ion mode.Te LDQSS group is represented by green dots, and the control group is represented by blue dots.

Figure 6 :
Figure 6: Results of cluster analysis.(a) Between the DHS group and the control group in the positive ion mode.(b) Between the DHS group and the control group in the negative ion mode.(c) Between the LDQSS group and the control group in the positive ion mode.(d) Between the LDQSS group and the control group in the positive ion mode.

Figure 8 :
Figure 8: Results of KEGG analysis.(a) Between the DHS group and the control group.(b) Between the LDQSS group and the control group.
Of note, the LDQSS group involved more pathways, and the top fve were TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and pentose phosphate pathway (Figure8(b)).
3.6.Biomarker Analysis.Biomarker discovery was achieved through building predictive models of multiple metabolites to classify the patients into diferent categories.In this study, we chose random forest (RF) as the multivariate algorithm for ROC curve analysis.ROC curves for biomarkers between the DHS group and the control group were plotted based on the average performance runs (Figures7(a)-7(c)), and the signifcant features of the biomarker model were ranked by importance (Figures 7(b)-7(d)).Likewise, ROC curves for biomarkers between the LDQSS group and the control group were plotted based on the average performance runs (Figures 7(e)-7(g)), and the signifcant features of the biomarker model were ranked by importance (Figures 7(f)-7(h)).3.7.KEGG Analysis.On the basis of the previous work, we conducted enrichment analysis on the selected signifcantly diferent metabolites.Te results showed that pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism played an important role in the comparison between the DHS group and the control group (Figure8(a)).

Table 1 :
Diferential metabolites in the positive ion mode.

Table 2 :
Diferential metabolites in the negative ion mode.