Application of tongue coating biomarker in preparation of product for predicting chronic atrophic gastritis

Abstract

The invention provides application of a biomarker in preparation of a product for predicting damp-heat syndrome of chronic atrophic gastritis, and the biomarker comprises TM7x bacteria, and further comprises at least one of kiwifruit bacteria, actinomycetes, twin coccus and phenylacetic acid. Compared with traditional gastroscopy, the tongue coating biomarker obtained by screening through a bioinformatics method provides a non-invasive diagnosis method, so that the compliance of a patient is improved. According to the method, the medical cost is reduced, the diagnosis feasibility and accuracy are improved, and the method not only can be used for diagnosing the chronic atrophic gastritis damp-heat syndrome, but also can be popularized to early screening and prevention.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a product application of tongue coating biomarker preparation for predicting chronic atrophic gastritis. Background Art

[0002] Chronic atrophic gastritis (CAG) is a chronic digestive disease characterized by atrophy of the gastric mucosal epithelium and glands. It is primarily caused by Helicobacter pylori infection or autoimmune disease, and is considered a precursor to gastric cancer (GC). Gastric cancer is the fifth most common cancer worldwide and the third most common cause of mortality. Studies have shown that the incidence of CAG progressing to GC within approximately 10 years is 4.41%. Therefore, intervention and treatment during the CAG stage are crucial for preventing GC.

[0003] Tongue diagnosis is a unique and important diagnostic method in Traditional Chinese Medicine (TCM). According to TCM theory, the formation of the tongue coating is closely related to the function of human organs, particularly the spleen and stomach. However, there are reports that during the development of chronic gastritis, changes in the tongue are slower than those in the tongue coating, suggesting that the tongue coating can quickly and clearly reflect the severity of the disease. The tongue coating microbiome, as an important diagnostic factor, can reflect the host's physiological function, immune function, energy metabolism, and nutritional status. Studies have suggested that the tongue coating microbiome has the potential to infer gastrointestinal diseases in clinical diagnosis. In addition, the tongue coating microbiome can communicate with distal organs of the host through metabolites. Reports have found that abnormal tongue coating metabolites in chronic gastritis are associated with chronic gastritis, and revealed that changes in sugar metabolism may be the basis for the formation of the tongue coating in chronic gastritis.

[0004] Endoscopic biopsy is the gold standard for diagnosing atrophic gastritis. However, as an invasive diagnostic procedure, it may cause patient discomfort and potential risks. Yellow and greasy tongue coating (YGC) is a typical tongue appearance of chronic atrophic gastritis of damp-heat syndrome, reflecting the biological characteristics of damp-heat syndrome. Therefore, it would be of great significance to identify noninvasive biomarkers for the development and progression of YGC-CAG (yellow and greasy tongue coating type chronic atrophic gastritis). Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies in the prior art and provide an application of tongue coating biomarkers in the preparation of products for predicting chronic atrophic gastritis.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present application provides a tongue coating biomarker comprising TM7x genus for use in the manufacture of a product for predicting chronic atrophic gastritis.

[0008] Optionally, the tongue coating biomarker can be further selected from at least one of the genus of Megasphaera, Actinomyces, Gemella and phenylacetic acid.

[0009] Preferably, the tongue coating biomarker is a combination of TM7x genus, Megasphaera, Actinomyces, Gemella and phenylacetic acid.

[0010] In some embodiments, the chronic atrophic gastritis is chronic atrophic gastritis of damp-heat syndrome (yellow greasy coating type).

[0011] In some embodiments, the product for predicting chronic atrophic gastritis comprises a reagent, test paper, kit or system.

[0012] In some embodiments, the reagent is a reagent for detecting the tongue coating biomarker in a sample by one or more of 16S rRNA gene sequencing, nuclear magnetic resonance method, chromatography or mass spectrometry.

[0013] In some embodiments, the reagent is used to detect the presence or absence, abundance or concentration of tongue coating biomarkers in a sample.

[0014] In some embodiments, the sample is a tongue coating sample of a patient to be tested.

[0015] In some embodiments, the system for predicting chronic atrophic gastritis of damp-heat syndrome comprises the following modules:

[0016] a data input module for inputting the concentration data of the tongue coating biomarkers obtained in the sample of the subject;

[0017] a database storage module for storing the abundance or concentration data of the tongue coating biomarker combination in the biological samples of the healthy population;

[0018] a disease prediction module connected to the data input module and the database storage module, respectively, based on the concentration data of the tongue coating biomarker combination of the subject obtained from the data input module, and the data given by the database storage module, according to whether the abundance of TM7x, Megasphaera, Actinomyces and the concentration of phenylacetic acid are significantly up-regulated relative to the healthy population, and whether the abundance of Gemella is significantly reduced relative to the healthy population, to diagnose whether the subject has chronic atrophic gastritis of damp-heat syndrome or to predict whether the subject has the risk of developing chronic atrophic gastritis of damp-heat syndrome.

[0019] In a second aspect, the present application provides a kit for assessing the risk and infection type of chronic atrophic gastritis in a subject, the kit comprising reagents for detecting TM7x, Deferribacteres, Actinomyces, and Gemella, and phenylacetic acid.

[0020] Compared with the prior art, the present application has the following technical effects:

[0021] The random forest algorithm is a supervised learning algorithm that allows feature filtering of raw data to reduce the dimensionality of the data. The present application performed random forest and AUC analysis to determine specific microbiota and metabolites associated with YGC-CAG. Through comparative comparison, it was found that there were 4 tongue moss diagnostic markers in the YGC-CAG group, which were TM7x, Deferribacteres, Actinomyces, and Gemella; and there was 1 tongue metabolite diagnostic marker, which was phenylacetic acid.

[0022] The present application provides a non-invasive diagnostic method indirectly compared with traditional gastroscopy, thereby improving patient compliance. The method of the present application reduces medical costs and improves the feasibility and accuracy of diagnosis, and can not only be used for the diagnosis of chronic atrophic gastritis, but also be popularized to early screening and prevention. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is the AUC plot of the A sample set when TM7x is used as the characteristic genus, the left figure is to distinguish the YGC-CAG group from the HC group, and the right figure is to distinguish the YGC-CAG group from the NYGC-CAG group.

[0024] Figure 2 is the AUC plot of the A sample set when TM7x, Deferribacteres, Gemella, and Actinomyces are used as characteristic genera, the left figure is to distinguish the YGC-CAG group from the HC group, and the right figure is to distinguish the YGC-CAG group from the NYGC-CAG group.

[0025] Figure 3 is the AUC plot of the A sample set when 5 metabolites are used as characteristic metabolites, the left figure is to distinguish the YGC-CAG group from the HC group, and the right figure is to distinguish the YGC-CAG group from the NYGC-CAG group.

[0026] Figure 4 is the AUC plot of the A sample set when TM7x, Deferribacteres, Actinomyces, Gemella, and phenylacetic acid are used as characteristic biomarkers, the left figure is to distinguish the YGC-CAG group from the HC group, and the right figure is to distinguish the YGC-CAG group from the NYGC-CAG group.

[0027] Figure 5AUC plot of B sample set when TM7x is the feature genus, left panel is to distinguish YGC-CAG group from HC group, right panel is to distinguish YGC-CAG group from NYGC-CAG group.

[0028] Figure 6 AUC plot of B sample set when TM7x, Deferribacteres, Gemella, Actinomyces are the feature genera, left panel is to distinguish YGC-CAG group from HC group, right panel is to distinguish YGC-CAG group from NYGC-CAG group.

[0029] Figure 7 AUC plot of B sample set when 5 metabolites are combined as the feature biomarkers, left panel is to distinguish YGC-CAG group from HC group, right panel is to distinguish YGC-CAG group from NYGC-CAG group.

[0030] Figure 8 AUC plot of B sample set when TM7x genus, Deferribacteres, Actinomyces, Gemella and phenylacetic acid are the feature biomarkers, left panel is to distinguish YGC-CAG group from HC group, right panel is to distinguish YGC-CAG group from NYGC-CAG group. DETAILED DESCRIPTION

[0031] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application is described and explained below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. Based on the examples provided in the present application, all other examples obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present application.

[0032] Sample source

[0033] A total of 172 tongue coating samples were collected from Shanghai, and 48 cases of gastric polyps, gastric bleeding, gastric tumors, gastrointestinal resection or special gastritis patients were excluded, and finally a total of 124 samples were included. They were randomly divided into sample set (A sample set) and validation set (B sample set). The design and implementation of this study were approved by the ethical vote of the medical ethics committee, and the written informed consent of all participants has been obtained.

[0034] Sample analysis

[0035] 1) Microbiota determination method

[0036] Use Total bacterial DNA was extracted from tongue coating samples using a centrifugal kit (MP Biomedicals), and the DNA concentration and purity were detected by 1.0% agarose gel electrophoresis, and the qualified samples were stored at -80°C for standby.

[0037] Bacterial universal primers (ACTCCTACGGCAGCAG and GGACTACHVGGGTWTCTAAT) were used to amplify the V3-V4 region of bacterial 16S rRNA gene on an ABI 9700 PCR machine (ABI, CA, USA).

[0038] The PCR amplification program was as follows: pre-denaturation at 95 °C for 3 min; 27 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s; and final extension at 72 °C for 10 min.

[0039] The PCR reaction system was as follows: a total volume of 20 μL, containing 2 μL x 2.5 mM dNTPs, 0.8 μL forward primer (5 μM), 0.8 μL reverse primer (5 μM), 0.4 μL TransStart FastPfu DNA polymerase, 10 ng template DNA, and the rest was ddH2O.

[0040] Each sample was set up with 3 repeated PCR reactions, and after verifying the size of the amplified fragments by agarose gel electrophoresis, the PCR products were purified using an AxyPrep DNA gel recovery kit (Axygen, China). The purified amplicons were mixed in equimolar amounts, and Majorbio Biomedical Technology Co., Ltd. (Shanghai) was commissioned to perform double-end sequencing on the Illumina MiSeq PE300 platform / NovaSeq PE250 platform (Illumina, San Diego, USA). The raw sequencing data has been submitted to the NCBI Sequence Read Archive (SRA) with the accession number PRJNA764325-SRP337893.

[0041] 2) Metabolite testing method

[0042] After each tongue sample was extracted with a swab and mixed with 1 mL of 90% methanol, it was ultrasonically extracted for 5 minutes and centrifuged at 18,000 g for 20 minutes. 800 μL of supernatant was transferred to a new centrifuge tube, completely dried by nitrogen blowing, and 50 μL of 50% methanol was added to each tube to re-dissolve the sample. After the sample was vortexed and mixed for 20 minutes, 100 μL of internal standard methanol solution was added. Then 40 μL of freshly prepared derivatization reagent was added to each sample, and the derivatization reaction was carried out at 30 °C for 60 minutes, and then 130 μL of pre-cooled 50% methanol solution was added to terminate the reaction. After termination of the reaction, the sample was centrifuged at 18,000 g for 20 minutes, and 140 μL of supernatant was transferred to an autosampler vial, and 10 μL of internal standard mixed solution was added, and then subjected to LC-MS analysis.

[0043] All standards were purchased from Sigma-Aldrich (St. Louis, MO, USA), Stelllaids (Newport, RI, USA) and TRC Chemicals (Toronto, ON, Canada). Each standard was accurately weighed and dissolved in water, methanol, sodium hydroxide solution or hydrochloric acid solution to prepare a stock solution with a concentration of 5.0 mg / mL. Metabolite quantitative analysis was performed using an ultra-high performance liquid chromatography-tandem mass spectrometry system (ACQUITY UPLC-Xevo TQ-S, Waters, USA). The detection of all target metabolites in this study was completed by Metabonutrition Biotechnology (Shanghai) Co., Ltd.

[0044] 3) Sample analysis results

[0045] First, the basic characteristics of the A sample set and the B sample set were statistically analyzed, and the specific results are shown in Table 1. The HC group is the healthy group, the YGC-CAG group is the yellow greasy fur type chronic atrophic gastritis group, and the NYGC-CAG group is the non-yellow greasy fur type chronic atrophic gastritis group.

[0046] Table 1 Pathological characteristics of samples

[0047]

[0048]

[0049] The results of the microbiota determination are as follows, and are shown in detail in Table 2:

[0050] A total of 1281 OTUs were identified in the A sample set, which were divided into 24 different bacterial phyla, 52 different bacterial classes, 125 different bacterial orders, 200 different bacterial families and 342 different bacterial genera. Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria and Patescibacteria were the six most common phyla in the three groups. The relative abundance of these six phyla was 99.68% in the YGC-CAG group, 99.66% in the NYGC-CAG group and 99.27% in the HC group.

[0051] The results of the B sample set were similar to those of the A sample set. A total of 1181 OTUs were identified, which were divided into 24 different bacterial phyla, 53 different bacterial classes, 124 different bacterial orders, 194 different bacterial families and 340 different bacterial genera. Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria and Patescibacteria were the six most common phyla in the three groups. The relative abundance of these six phyla was 99.36% in the YGC-CAG group, 99.65% in the NYGC-CAG group and 99.83% in the HC group.

[0052] Table 2 Sample tongue coating microbial community distribution characteristics

[0053]

[0054] The metabolite test results are as follows, and are shown in detail in Table 3:

[0055]

[0056]

[0057] In summary, from Tables 1-3, it can be seen that the characteristics of the A sample set and the B sample set set by the experiment are basically similar.

[0058] A sample set analysis

[0059] 1. Microbial community analysis

[0060] First, Kruskal-Wallis H test was used to analyze the differences between YGC-CAG, NYGC-CAG and HC groups in the A sample set, and the conclusions are as follows:

[0061] 1) At the door level, there were 2 doors (Patibacteriales, Unclassified bacteria) between YGC-CAG, NYGC-CAG and HC groups.

[0062] 2) At the genus level, there were 11 genera (Haemophilus, Actinomyces, Porphyromonas, TM7x, Alistipes, Gemella, Unclassified Lachnospiraceae, Sutterellaceae, Unclassified bacteria, Bifidobacterium, Unclassified Firmicutes) between YGC-CAG, NYGC-CAG and HC groups.

[0063] Then, the importance of the 11 kinds of microbial genera in the above conclusion to the YGC group was calculated using the random forest model (RF) (number of decision trees: 500; with replacement sampling: yes; number of candidate features per node: 10), and the importance of tongue coating microbial community in descending order was TM7x (9.49), Alistipes (7.41), Gemella (7.21), Actinomyces (6.65).

[0064] Finally, the classification ability of the model was evaluated using the ROC curve. And the area under the curve (AUC) was used as the evaluation index. AUC greater than 0.7 indicates that these biomarkers have predictive value. The conclusions are as follows:

[0065] 1) TM7x can distinguish YGC-CAG, NYGC-CAG and HC groups. When distinguishing YGC-CAG group from HC group, the AUC value is 0.868( Figure 1Left, SP = 0.931, SE = 0.828, 95% CI [0.853-0.995]); AUC = 0.916 (95% CI [0.858-0.972], P < 0.001) when distinguishing YGC-CAG from NYGC-CAG; Figure 1 Right, SP = 0.793, SE = 0.862, 95% CI [0.763-0.962]);

[0066] 2) When the feature genera were TM7x, Alistipes, Gemmiger, and Clostridium, the AUC values were all greater than 0.9. Among them, AUC = 0.924 (95% CI [0.872-0.972], P < 0.001) when distinguishing YGC-CAG from HC; Figure 2 Left, SP = 0.931, SE = 0.828, 95% CI [0.853-0.995]); AUC = 0.916 (95% CI [0.858-0.972], P < 0.001) when distinguishing YGC-CAG from NYGC-CAG; Figure 2 Right, SP = 0.793, SE = 0.931, 95% CI [0.848-0.984]).

[0067] 3) Compared with the NYGC-CAG and HC groups, the abundance of TM7x, Alistipes, Clostridium was significantly increased in the YGC-CAG group, while the abundance of Gemmiger was significantly decreased.

[0068] 2. Metabolite analysis

[0069] Single factor analysis was used to determine the differences in metabolites among the YGC-CAG, NYGC-CAG, and HC groups. There were 33 metabolites that were significantly different between the YGC-CAG group and the NYGC-CAG group and the HC group. These metabolites were divided into the following 8 categories in total:

[0070] 1) 16 amino acids (histidine, glutamine, glutamic acid, alanine, gamma-aminobutyric acid, serine, threonine, homoserine, tyrosine, aspartic acid, citrulline, pyroglutamic acid, methionine, isoleucine, leucine, tryptophan);

[0071] 2) 1 aromatic compound (phenylacetic acid);

[0072] 3) 2 carbohydrates (glyceric acid, glucose);

[0073] 4) 3 fatty acids (citramalic acid, alpha-linolenic acid, linoleic acid);

[0074] 5) 3 indoles (indole-3-acetic acid methyl ester, indole-3-carboxylic acid, indoleacetic acid);

[0075] 6) 5 organic acids (malic acid, alpha-ketoisovaleric acid, ketindoxin, 3-methyl-2-oxoglutaric acid);

[0076] 7) 2 phenols (4-hydroxyphenylpyruvic acid, p-hydroxyphenylacetic acid);

[0077] 8) 1 pyridine (nicotinic acid).

[0078] The importance of the above 33 metabolites for YGC-CAG group was calculated using RF. Then the classification ability of the model was evaluated using ROC curve. The conclusions are as follows:

[0079] 1) 5 metabolites can distinguish YGC-CAG group, HC group, NYGC-CAG group, and the importance of the 5 metabolites from high to low is benzoic acid (4.01), indoleacetic acid (3.29), gamma-aminobutyric acid (2.98), indole-3-acetic acid methyl ester (2.96), and p-hydroxyphenylacetic acid (2.56). As shown by the area under the ROC curve (AUC), the value is as high as 0.908 when distinguishing YGC-CAG group from HC group (left, SP = 0.862, SE = 0.931, 95% CI [0.827-0.99]); the value is 0.832 when distinguishing YGC-CAG group from NYGC-CAG group (right, SP = 0.862, SE = 0.828, 95% CI [0.722-0.943]). Figure 3 Figure 3 2) Compared with NYGC-CAG and HC groups, the relative concentration of the 5 metabolites in YGC-CAG group is significantly up-regulated.

[0080] 3. Comprehensive discussion

[0081] 3. Comprehensive discussion

[0082] Finally, we used RF to calculate the importance of the above 4 tongue microbial groups and 5 metabolites for YGC group. The importance of the 4 tongue microbial groups and 5 metabolites in descending order is TM7x (8.62), Deinococcus (6.96), Actinomyces (6.93), benzoic acid (6.86), Gemella (6.14), gamma-aminobutyric acid (5.69), indoleacetic acid (5.68), indole-3-acetic acid methyl ester (5.46), and p-hydroxyphenylacetic acid (5.00).

[0083] Then the classification ability of the model was evaluated using ROC curve. The results show that 4 microbial groups (TM7x, Deinococcus, Actinomyces, and Gemella) and 1 metabolite (benzoic acid) can distinguish YGC group, HC group, and NYGC group, as shown by the area under the ROC curve (AUC), the value is as high as 0.941 when distinguishing YGC-CAG group from HC group (left, SP = 0.793, SE = 0.966, 95% CI [0.879-1]); the value is 0.924 when distinguishing YGC-CAG group from NYGC-CAG group (right, SP = 0.793, SE = 0.828, 95% CI [0.722-0.943]). Figure 4 Figure 4 ​​Right, SP = 0.862, SE = 0.862, 95% CI [0.86 - 0.987]).

[0084] B sample set validation

[0085] Based on the results of the analysis in the A sample set described above, the B sample set was validated, with the following conclusions:

[0086] 1) Similarly, in the B sample set, TM7x could distinguish between YGC-CAG, NYGC-CAG, and HC groups. When distinguishing between YGC-CAG and HC groups, the AUC value was 0.91 ( Figure 5 Left, SP = 1, SE = 0.769, 95% CI [0.769 - 1]); when distinguishing between YGC-CAG and NYGC-CAG groups, the AUC value was 0.849 ( Figure 5 Right, SP = 0.692, SE = 0.917, 95% CI [0.696 - 1]).

[0087] 2) When the feature was 4 tongue microbiota, the AUC value was as high as 0.936 ( Figure 6 Left, SP = 1, SE = 0.846, 95% CI [0.84 - 1]); when distinguishing between YGC-CAG and NYGC-CAG groups, the AUC value was 0.885 ( Figure 6 Right, SP = 0.923, SE = 0.833, 95% CI [0.744 - 1]).

[0088] 3) Similarly, in the B sample set, 5 metabolites could distinguish between YGC-CAG, NYGC-CAG, and HC groups. When distinguishing between YGC-CAG and HC groups, the AUC value was 1 ( Figure 7 Left, SP = 1, SE = 1, 95% CI [1 - 1]); when distinguishing between YGC-CAG and NYGC-CAG groups, the AUC value was 0.878 ( Figure 7 Right, SP = 0.846, SE = 0.917, 95% CI [0.736 - 1]).

[0089] 4) Similarly, in the B sample set, 4 tongue microbiota and 1 tongue metabolite could distinguish between YGC-CAG, NYGC-CAG, and HC groups. When distinguishing between YGC-CAG and HC groups, the AUC value was as high as 0.987 ( Figure 8 Left, SP = 0.917, SE = 1, 95% CI [0.957 - 1]); when distinguishing between YGC-CAG and NYGC-CAG groups, the AUC value was 0.917 ( Figure 8 Right, SP = 0.923, SE = 0.833, 95% CI [0.802 - 1]).

[0090] 5) Similarly, in the B sample group, the abundance of TM7x, Deferribacteres, Actinomyces and the concentration of phenylacetic acid were significantly up-regulated in the YGC-CAG group compared to the NYGC-CAG and HC groups, while the abundance of Gemella was significantly down-regulated.

[0091] It can be seen that four tongue microbes (TM7x, Deferribacteres, Actinomyces and Gemella) and one tongue metabolite (phenylacetic acid) are non-invasive biomarkers of patients with yellowish fur type chronic atrophic gastritis (YGC-CAG).

[0092] The above merely describes preferred embodiments of the present application, but does not limit the embodiments and protection scope of the present application. It should be understood by those skilled in the art that equivalent replacements and obvious changes made according to the content of the present application and drawings should be included in the protection scope of the present application.

Claims

1. Application of tongue coating biomarkers in the preparation of a product for predicting chronic atrophic gastritis, characterized in that: The tongue coating biomarkers include TM7x bacteria.

2. The use according to claim 1, characterized in that The tongue coating biomarker can also be selected from at least one of the genus Parabacterium, Actinomyces, Geminis and phenylacetic acid.

3. The use according to claim 1 or 2, characterized in that The tongue coating biomarker is a combination of TM7x, Parabacterium, Actinomyces, Geminis and phenylacetic acid.

4. The use according to claim 1, characterized in that The chronic atrophic gastritis is a chronic atrophic gastritis with damp-heat syndrome.

5. The use according to claim 1, characterized in that The product for predicting chronic atrophic gastritis includes a reagent, a test paper, a kit or a system.

6. The use according to claim 5, characterized in that The reagent is a reagent for detecting the tongue coating biomarker in a sample by one or more of 16S rRNA gene sequencing, nuclear magnetic resonance, chromatography or mass spectrometry.

7. The use according to claim 6, characterized in that The reagent is used to detect the presence, abundance or concentration of tongue coating biomarkers in a sample.

8. The use according to claim 6, characterized in that The sample is a tongue coating sample of a patient to be tested.

9. The use according to claim 4 or 5, characterized in that The system for predicting damp-heat syndrome of chronic atrophic gastritis includes the following modules: A data input module, used to input the concentration data of tongue coating biomarkers in the obtained subject sample; A database storage module, configured to store the abundance or concentration data of the tongue coating biomarker combination in biological samples of a healthy population; A disease prediction module is connected to the data input module and the database storage module, respectively. Based on the concentration data of the tongue coating biomarker combination of the subject obtained from the data input module, the data is compared with the data provided by the database storage module. According to whether the abundance of TM7x, Trichoderma, and Actinomyces and the concentration of phenylacetic acid are significantly increased relative to the healthy group, and whether the abundance of Geminis is significantly decreased relative to the healthy group, whether the subject suffers from chronic atrophic gastritis with damp-heat syndrome or whether the subject is at risk of suffering from chronic atrophic gastritis with damp-heat syndrome is diagnosed.

10. A kit for assessing the risk and infection type of chronic atrophic gastritis in a subject, characterized in that: The kit contains reagents for detecting TM7x, Parabacterium, Actinomyces, and Geminis, and phenylacetic acid.

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