Urine metabolic marker combination, kit and application thereof for auxiliary diagnosis and syndrome differentiation of chronic atrophic gastritis with spleen-stomach dampness heat syndrome

Abstract

The present application relates to a urine metabolic marker combination, a kit and its application for the auxiliary diagnosis and syndrome differentiation of chronic atrophic gastritis with spleen-stomach dampness-heat syndrome. The metabolic marker combination comprises glucosaminic acid, acetylglycine and maleic acid in urine samples. Compared with healthy controls, the content of the above markers in urine of patients with chronic atrophic gastritis with spleen-stomach dampness-heat syndrome is reduced; compared with patients with chronic atrophic gastritis with spleen-stomach deficiency-cold syndrome, the content of glucosaminic acid is increased, while the content of acetylglycine and maleic acid is reduced. The present application also provides the application of reagents for detecting the above markers in the preparation of auxiliary diagnosis and syndrome differentiation products, kits containing the reagents, and methods for detecting the above metabolic markers in ex vivo urine samples. The present application realizes the non-invasive auxiliary diagnosis of chronic atrophic gastritis with spleen-stomach dampness-heat syndrome and the objective differentiation of traditional Chinese medicine syndromes.

Description

Technical Field

[0001] This invention relates to the field of disease diagnostic biomarker technology, specifically to a combination of urinary metabolic biomarkers, kits, and their applications for the auxiliary diagnosis and syndrome differentiation of chronic atrophic gastritis with spleen and stomach damp-heat syndrome. Background Technology

[0002] Chronic atrophic gastritis (CAG) is an important precancerous condition of the stomach, with approximately 2.09 cases (95% CI: 1.46-2.99) progressing to gastric cancer per 1000 people per year. A meta-analysis from 2010 to 2020 showed a global prevalence of approximately 25% for CAG. Early-stage CAG patients often present with no specific symptoms, mostly only upper abdominal distension, stomach pain, or nausea. Currently, the main clinical diagnostic method for CAG is gastroscopy, which is invasive and has poor patient compliance. Furthermore, according to the UK Endoscopy Database, three-quarters of gastroscopy patients have a cancer risk of less than 1%, indicating a significant waste of medical resources. Therefore, there is an urgent need for non-invasive diagnostic and risk stratification tools for CAG screening.

[0003] Traditional Chinese medicine (TCM) theory holds that chronic gastrointestinal tract atrophy (CAG) is closely related to spleen and stomach dysfunction, internal damp-heat, and obstruction of the stomach meridians. Spleen-stomach damp-heat syndrome (SSDHS) and spleen-stomach deficiency-cold syndrome (SSDCS) are both common TCM syndromes of CAG, and their differentiation is crucial for the diagnosis, treatment, and efficacy evaluation of CAG. If a non-invasive method can be used to differentiate the TCM syndromes simultaneously with the diagnosis of CAG, it will greatly aid in the diagnosis and efficacy evaluation of CAG treatment.

[0004] Metabolomics is the science that studies endogenous metabolites and their overall changes in organisms. Its results provide information on the metabolic state of biological systems, making the identification of disease biomarkers possible. Urine is an ideal sample for metabolomics testing due to its non-invasive and convenient collection, high patient compliance, and status as the site of excretion for most metabolic wastes, as well as its high tolerance to changes. However, current metabolomics research largely focuses on differentiating CAG patients from healthy individuals, or only explores single syndromes. There is a lack of urinary metabolic biomarker combinations that can simultaneously differentiate CAG with spleen and stomach damp-heat syndrome from healthy individuals, and also differentiate it from spleen and stomach deficiency-cold syndrome, failing to meet the clinical needs for objective diagnosis and treatment in traditional Chinese medicine. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a combination of urinary metabolic markers, a reagent kit, and their applications for the auxiliary diagnosis and syndrome differentiation of chronic atrophic gastritis with spleen and stomach damp-heat syndrome, so as to achieve non-invasive auxiliary diagnosis of chronic atrophic gastritis with spleen and stomach damp-heat syndrome and its differential diagnosis with spleen and stomach deficiency-cold syndrome.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of this invention relates to the application of a reagent for detecting the content of glucosamine, acetylglycine and maleic acid in a urine sample in the preparation of a product, said product being used to assist in the diagnosis of chronic atrophic gastritis with damp-heat syndrome of the spleen and stomach.

[0007] The second aspect of the present invention relates to the application of a reagent for detecting the content of glucosamine, acetylglycine and maleic acid in a urine sample in the preparation of a product, said product being used to differentiate between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome.

[0008] Furthermore, the product is tested on isolated urine samples.

[0009] Furthermore, the detection method for the product employs chromatography-mass spectrometry.

[0010] Preferably, the chromatography-mass spectrometry technique is gas chromatography-time-of-flight mass spectrometry (GC-TOF / MS).

[0011] A third aspect of the present invention is a combination of metabolic markers for assisting in the diagnosis of damp-heat syndrome of spleen and stomach in chronic atrophic gastritis, and / or for differentiating damp-heat syndrome of spleen and stomach in chronic atrophic gastritis from cold-deficiency syndrome of spleen and stomach in chronic atrophic gastritis, including one or more of glucosamine, acetylglycine and maleic acid.

[0012] Furthermore, the combination of metabolic markers includes glucosamine, acetylglycine, and maleic acid.

[0013] Furthermore, in the combination of metabolic markers, glucosamine and acetylglycine are core markers that meet the preset difference screening criteria in two comparisons: one between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and healthy controls, and the other between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome; maleic acid is a combined discriminant marker used in conjunction with the core markers.

[0014] A fourth aspect of the present invention relates to a kit for assisting in the diagnosis of damp-heat syndrome of spleen and stomach in chronic atrophic gastritis, and / or for differentiating damp-heat syndrome of spleen and stomach in chronic atrophic gastritis from deficiency-cold syndrome of spleen and stomach in chronic atrophic gastritis, comprising: (1) Reagents for detecting the contents of glucosamine, acetylglycine and maleic acid in isolated urine samples, respectively; (2) Standards or calibrators of the metabolic markers.

[0015] Furthermore, in samples with chronic atrophic gastritis and spleen-stomach damp-heat syndrome, the levels of glucosamine, acetylglycine, and maleic acid were reduced compared to healthy samples; Compared to the samples with chronic atrophic gastritis and spleen-stomach deficiency syndrome, the glucosamine content was increased, while the acetylglycine and maleic acid content was decreased.

[0016] Among them, glucosamine and acetylglycine both met the preset screening criteria of P<0.05 and |log2FC|>0.5 in both comparisons. Maleic acid, when combined with the above two markers, further improved the discrimination ability.

[0017] Furthermore, the pretreatment reagents include urease, internal standard, and derivatization reagents, wherein the derivatization reagents include methoxyamine pyridine solution and N-trimethylsilyl-N-methyltrifluoroacetamide.

[0018] A fifth aspect of the present invention relates to a method for detecting metabolic markers in an isolated urine sample, comprising: determining the content of glucosamine, acetylglycine and maleic acid in the isolated urine sample, and comparing the content with a reference level to obtain a comparison result of the metabolite content of the sample.

[0019] The present invention adopts the above technical solution and has the following technical effects compared with the prior art: (1) The present invention found that the combination of glucosamine, acetylglycine and maleic acid can be used as a combination of urine metabolic markers for the spleen and stomach damp-heat syndrome of chronic atrophic gastritis, realizing non-invasive auxiliary diagnosis based on urine samples, with good patient compliance; (2) The present invention uses a double-control design between healthy people and patients with chronic atrophic gastritis and spleen and stomach deficiency-cold syndrome. Glucosamine and acetylglycine both met the preset difference screening criteria in both comparisons. Maleic acid, combined with both, further improved the discriminative ability. The combination of biomarkers can distinguish between chronic atrophic gastritis and spleen and stomach damp-heat syndrome and healthy people, and can also differentiate between chronic atrophic gastritis and spleen and stomach damp-heat syndrome and chronic atrophic gastritis and spleen and stomach deficiency-cold syndrome, providing an objective indicator for TCM diagnosis of CAG. (3) Based on the Bootstrap stratification of all 69 samples, the present invention performed 1000 repeated samplings with replacement. The average stability of acetylglycine, glucosamine and maleic acid were 0.6050, 0.5865 and 0.5850, respectively, ranking 2nd, 4th and 5th among all candidate metabolites, indicating that the marker has good internal stability. (4) Single-index ROC analysis showed that acetylglycine, glucosamine, and maleic acid had AUCs greater than 0.5 in both the discrimination tasks of chronic atrophic gastritis with spleen and stomach damp-heat syndrome and healthy controls, and chronic atrophic gastritis with spleen and stomach damp-heat syndrome and chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome, and the 95% confidence intervals did not include 0.5. The three metabolic markers in this invention showed different trends between groups. Their content can be detected and compared with the corresponding reference levels to provide intermediate detection information for auxiliary diagnosis and syndrome differentiation. (5) The combined model of the three biomarkers had an AUC of 0.7690 for distinguishing between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and healthy controls, with a sensitivity of 80.95% and a specificity of 74.07%; and an AUC of 0.8141 for distinguishing between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome, with a sensitivity of 80.95% and a specificity of 76.19%. The AUC of the combined model was higher than the AUC of the three biomarkers when used alone. (6) In the sample of this study, the correlation analysis between acetylglycine and stomach pain, and maleic acid and incomplete defecation, yielded a nominal P value of less than 0.05, providing clues for further verification of the relationship between metabolic markers and clinical symptoms. Attached Figure Description

[0020] Figure 1 This is a quality control and distribution trend chart of urine samples to be tested in one embodiment of the present invention. A is a multivariate quality control chart of urine samples (black dots represent test samples, yellow dots represent quality control samples. The horizontal axis represents the testing order of all samples. The letters K, S and H before the sample number represent the healthy control group, the spleen and stomach damp-heat syndrome CAG group and the spleen and stomach deficiency-cold syndrome CAG group, respectively). B is a correlation heatmap among quality control samples. C is the PCA score chart and principal component box plot of metabolites of the three groups of samples. D is the PLS-DA score chart and principal component box plot of metabolites of the three groups of samples. The three groups are the spleen and stomach damp-heat syndrome CAG group (SSDHS-CAG), the spleen and stomach deficiency-cold syndrome CAG group (SSDCS-CAG), and the healthy control group (HC).

[0021] Figure 2 This is a classification diagram of urinary metabolites in one embodiment of the present invention, where A is the relative abundance of each metabolite category in the healthy control group, the spleen and stomach damp-heat syndrome CAG group, and the spleen and stomach deficiency-cold syndrome CAG group, and B is a stacked bar chart of the relative abundance of each type of metabolite in each sample. The three groups are the spleen and stomach damp-heat syndrome CAG group (SSDHS-CAG), the spleen and stomach deficiency-cold syndrome CAG group (SSDCS-CAG), and the healthy control group (HC).

[0022] Figure 3This is a graph showing changes in urine metabolic profiles based on preliminary univariate analysis in one embodiment of the present invention. A is a heatmap of differential metabolites between the healthy control group and the spleen-stomach damp-heat syndrome CAG group; B is a univariate statistical volcano plot between the spleen-stomach damp-heat syndrome CAG group and the spleen-stomach deficiency-cold syndrome CAG group; C, D, and E are box plots of potential biomarkers glucosamine (C), maleic acid (D), and acetylglycine (E) among the three groups, respectively; the three groups are the spleen-stomach damp-heat syndrome CAG group (SSDHS-CAG), the spleen-stomach deficiency-cold syndrome CAG group (SSDCS-CAG), and the healthy control group (HC); the preliminary screening criteria are P<0.05 and |log2FC|>0.

[0023] Figure 4 This is the ROC curve of the top 5 single biomarkers with Bootstrap stability in one embodiment of the present invention, distinguishing between patients with chronic atrophic gastritis and spleen-stomach damp-heat syndrome and healthy controls (S vs K) in all samples.

[0024] Figure 5 This is the ROC curve of the top 5 single markers with Bootstrap stability in one embodiment of the present invention, which distinguishes between the spleen and stomach damp-heat syndrome and the spleen and stomach deficiency-cold syndrome (S vs H) in all samples.

[0025] Figure 6 This is the ROC curve of the triple combination model of glucosamine, acetylglycine and maleic acid in one embodiment of the present invention, which distinguishes between patients with chronic atrophic gastritis and spleen-stomach damp-heat syndrome and healthy controls (S vs K) in all samples. The AUC is 0.7690 (95% CI: 0.6310-0.9069).

[0026] Figure 7 This is the ROC curve of the triple combination model of glucosamine, acetylglycine and maleic acid in one embodiment of the present invention, which distinguishes between the spleen and stomach damp-heat syndrome and the spleen and stomach deficiency-cold syndrome (S vs H) in all samples of chronic atrophic gastritis. The AUC is 0.8141 (95% CI: 0.6812-0.9470). Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Experimental methods in the following embodiments that do not specify specific conditions are generally determined according to national standards. Experimental materials in the following embodiments that do not specify their source are all commercially available raw materials. The equipment used in each step of the following embodiments is conventional equipment. If there is no corresponding national standard, it is carried out according to general international standards, conventional conditions, or conditions recommended by the manufacturer. Unless otherwise stated, all parts are parts by weight, and all percentages are percentages by mass. Unless otherwise defined or stated, all professional and scientific terms used in the present invention have the same meaning as those skilled in the art. In addition, any methods and materials similar or equivalent to those described can be applied to the methods of the present invention.

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0029] Example 1: Analysis of Differential Metabolites and Identification of Biomarkers in Samples This embodiment involves the screening and determination of characteristic urinary metabolic biomarkers for chronic atrophic gastritis (CAG) with spleen and stomach damp-heat syndrome. Urine samples were collected from healthy controls, subjects with CAG of spleen and stomach deficiency-cold syndrome, and subjects with CAG of spleen and stomach damp-heat syndrome. GC-TOF / MS non-targeted metabolomics analysis, combined with PCA, PLS-DA multivariate analysis, and univariate statistical analysis, were used to screen and determine a combination of characteristic urinary metabolic biomarkers for CAG of spleen and stomach damp-heat syndrome. The results are as follows: Figure 1-3 As shown, the details are as follows: (I) Research Subjects and Sample Collection 1. Study Subjects: The study population consisted of participants from the endoscopy room, gastroenterology outpatient department, and physical examination center of Shuguang Hospital affiliated with Shanghai University of Traditional Chinese Medicine. Urine samples and syndrome information were collected from the subjects. All subjects signed informed consent forms, and the study was approved by the Ethics Committee of Shuguang Hospital affiliated with Shanghai University of Traditional Chinese Medicine (Approval No.: 2020-834-41-01). A total of 69 subjects were included in this study, including 27 healthy controls (HC), 21 patients with chronic atrophic gastritis with spleen and stomach damp-heat syndrome (SSDHS-CAG), and 21 patients with chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome (SSDCS-CAG). The purpose of setting up the SSDCS-CAG group as a control was to eliminate the interference of other common syndromes of CAG on urinary metabolites, thereby identifying the characteristic urinary metabolites of spleen and stomach damp-heat syndrome CAG.

[0030] 2. Diagnostic Criteria: The diagnosis of CAG is based on the "Chinese consensus opinion on chronic gastritis (2017)". Patients diagnosed by gastroscopy and gastric mucosal biopsy with pathological reports showing atrophy of intrinsic glands are included in the CAG group. Specific diagnostic points are as follows: Endoscopic diagnosis: ① Flattening or disappearance of gastric mucosal folds; ② Gastric mucosa is red and white, with white as the main color; ③ Some blood vessels are exposed; ④ Gastric mucosa may be accompanied by granules, nodules, etc.

[0031] Pathological and histological diagnosis: ① Five histological changes require grading, including Helicobacter pylori (H. pylori) infection, activity, inflammatory response, atrophy, and intestinal metaplasia. A pathological report showing atrophy of intrinsic glands is sufficient for a diagnosis of atrophic gastritis. ② Grading is based on severity, divided into grades I to III (+, ++, +++): mild, moderate, and severe.

[0032] 3. Diagnostic Criteria for Traditional Chinese Medicine Syndromes: The diagnostic criteria for spleen and stomach damp-heat syndrome and spleen and stomach deficiency-cold syndrome are based on the "Consensus on diagnosis and treatment of chronic atrophic gastritis with integrated Chinese and western medicine (2017)". A diagnosis can be made based on the presence of the primary symptom, typical tongue appearance, and two or more secondary symptoms, along with consideration of pulse characteristics. Specific diagnostic points are as follows: Diagnostic criteria for spleen and stomach damp-heat syndrome: ① Primary symptoms: abdominal distension or pain; ② Secondary symptoms: bitter taste in the mouth; nausea and vomiting; burning sensation in the stomach; sticky or loose stools; ③ Tongue and pulse characteristics: red tongue, yellow and greasy coating, and slippery pulse.

[0033] Diagnostic criteria for spleen and stomach deficiency-cold syndrome: ① Primary symptoms: abdominal distension or dull pain; preference for pressure or warmth in the stomach; ② Secondary symptoms: loss of appetite, nausea and vomiting; loose stools; fatigue and weakness; shortness of breath and reluctance to speak; abdominal distension after meals; ③ Tongue and pulse characteristics: pale tongue and weak pulse.

[0034] 4. Inclusion and exclusion criteria for cases: The inclusion criteria for CAG patients are: confirmed by gastroscopy and pathological examination within 6 months for chronic atrophic gastritis, diagnosed by traditional Chinese medicine syndrome as spleen and stomach damp-heat syndrome or spleen and stomach deficiency-cold syndrome, aged 35-75 years, and signed informed consent form.

[0035] Exclusion criteria included individuals with gastric polyps, gastric bleeding, gastric tumors, a history of gastrointestinal resection, or other digestive system diseases; those who had taken antibiotics, chemotherapy drugs, probiotic preparations, or traditional Chinese medicine preparations within 4 weeks prior to sampling; those with severe systemic diseases of the heart, lungs, liver, brain, or kidneys, or mental illnesses; long-term smokers and drinkers, or vegetarians; pregnant or lactating women; and those with moderate to severe anemia. The healthy control group was confirmed by physical examination to have no digestive system diseases or organic lesions in any system, and no special lifestyle habits such as smoking or drinking.

[0036] 5. The clinical characteristics of the three groups of subjects are shown in Table 1. There were no significant differences in gender, age, and body mass index (BMI) among the three groups. In the spleen and stomach damp-heat syndrome CAG group, the frequency of symptoms from high to low was bitter taste in the mouth, yellow and greasy tongue coating, bloating, red tongue, stomach pain, incomplete defecation, yellow urine, heaviness of the body, and acid reflux. In the spleen and stomach deficiency-cold syndrome CAG group, only four symptoms were observed: stomach pain, bloating, acid reflux, and yellow urine.

[0037] Table 1. Clinical characteristics of subjects in each group (II) GC-TOF / MS detection and data analysis 1. Pretreatment of urine samples for GC-TOF / MS detection: After thawing on ice, urine samples were centrifuged at 3000g for 5 minutes at 4℃ to separate debris or lipid layers. 50 μL of sample was added to 10 μL of urease (30 U / 10 μL), and the mixture was shaken at 37℃ for 15 minutes at 650 rpm. Then, 10 μL of internal standard (chlorophenylalanine) and 170 μL of pre-cooled methanol were added, and the mixture was centrifuged at 4℃ for 20 minutes at 14000g. 100 μL of the supernatant was transferred to an autosampler (Agilent Technologies, Foster City, CA, USA). The methanol in the autosampler was removed by vacuum concentration. The evaporated sample was freeze-dried to obtain a lyophilized sample. Derivatization and injection were performed using a dual-head multi-functional syringe (Gerstel, Muehlheim, Germany): The dried sample was derivatized with 50 μL of methoxyamine (20 mg / mL, dissolved in pyridine) at 30℃ for 2 hours, and then 50 μL of methoxyamine was added to the sample. μL of N-trimethylsilyl-N-methyltrifluoroacetamide (MSTFA, containing FAMEs retention index standard) containing 1% trimethylchlorosilane (TMCS) was reacted at 37.5℃ for 1 hour to obtain the sample to be analyzed for GC-TOF / MS.

[0038] 2. Instruments and conditions for GC-TOF / MS analysis: An Agilent 7890B gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) coupled with a time-of-flight mass spectrometer (GC-TOF / MS) system (PegasusHT, Leco Corp., St. Joseph, MO, USA) was used. 1 μL of derivatized sample was injected into an Rxi-5 ms capillary column (30 m × 250 μm inner diameter, 0.25 μm film thickness), with helium as the carrier gas and a constant flow rate of 1.0 mL / min. The ion source temperature was 220℃, and the injection port and transfer line temperatures were both 270℃. Electron impact ionization (70 eV) was used, with a scan range of m / z 50–500. The instrument was optimized every 24 hours.

[0039] 3. Data Processing: Raw data underwent baseline denoising and smoothing, peak extraction, deconvolution, and peak alignment using ChromaTOF software (v4.71). Provisional compounds were identified by comparing their mass spectrometry similarity and FAMEs retention index distance with standards in the JiaLib database. Among them, glucosamine, acetylglycine, and maleic acid were candidate differential metabolites screened based on database searches, and their final confirmation required comparison with control standards or high-resolution mass spectrometry verification. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and univariate analysis were performed on the iMAP platform (v1.0).

[0040] (III) Test Results Quality control results as follows Figure 1 As shown: Most quality control (QC) samples remained stably distributed near the zero axis during metabolic profile detection. Figure 1 In Part A), the Pearson correlation coefficient between any two QC samples is between 0.992 and 1.0. Figure 1 Part B of the PCA score plot indicates that the detection data has good stability and reproducibility. Figure 1 Part C of the graph showed that most CAG group samples were distinct from the healthy control group in the first principal component direction (P<0.05); PLS-DA score plot ( Figure 1 Part D of the study showed a separation trend among the three groups: the healthy control group, the spleen and stomach damp-heat syndrome CAG group, and the spleen and stomach deficiency-cold syndrome CAG group.

[0041] like Figure 2As shown, a total of 212 metabolites were identified in the healthy control group, the spleen and stomach damp-heat syndrome CAG group, and the spleen and stomach deficiency-cold syndrome CAG group, belonging to 20 categories. These included 52 carbohydrates, 43 amino acids, 32 organic acids, 17 nucleotides, 15 phenols, 12 fatty acids, 6 alcohols, 5 benzoic acids, 5 indoles, 5 phenylpropionic acids, 4 amines, 4 pyridines, 3 vitamins, 2 lipids, 2 peptides, 1 amide, 1 imidazole, 1 inorganic oxide, 1 phenylpropanoid, and 1 pyrrolidone. Figure 2 Parts A and B).

[0042] Univariate analysis was used for preliminary screening of differentially expressed metabolites, with P < 0.05 and |log2FC| > 0 as the screening criteria. This criterion did not pre-set an effect size threshold, aiming to avoid missing potential candidates and was considered a preliminary screening step. Compared with the healthy control group, the spleen and stomach damp-heat syndrome CAG group showed 83 differentially expressed urinary metabolites, among which maltose and sucrose levels were increased, while the levels of other metabolites such as leucine and ornithine were decreased. Figure 3 Part A). Compared with the spleen and stomach deficiency-cold syndrome CAG group, the spleen and stomach damp-heat syndrome CAG group showed decreased levels of acetylglycine, digalacturonicacid, and maleic acid, and increased levels of palatinitol and glucosamine. Figure 3 Part B).

[0043] Preliminary screening results showed that glucosamine, maleic acid, and acetylglycine were statistically different between the spleen and stomach damp-heat syndrome CAG group and the healthy control group, and between the spleen and stomach damp-heat syndrome CAG group and the spleen and stomach deficiency-cold syndrome CAG group. Figure 3 (C~E sections). Among them, the contents of the three metabolites in the spleen and stomach damp-heat syndrome CAG group were all lower than those in the healthy control group; the contents of glucosamine in the spleen and stomach damp-heat syndrome CAG group were higher than those in the spleen and stomach deficiency-cold syndrome CAG group, while the contents of acetylglycine and maleic acid in the spleen and stomach damp-heat syndrome CAG group were lower than those in the spleen and stomach deficiency-cold syndrome CAG group.

[0044] Based on all 69 samples, a rigorous threshold verification was performed using the Wilcoxon rank-sum test. The screening criteria were uncorrected P < 0.05 and |log2FC| > 0.5. The S vs K comparison identified 82 differentially regulated metabolites, with 1 upregulated and 81 downregulated. The S vs H comparison identified 4 differentially regulated metabolites, with 2 upregulated and 2 downregulated. The metabolites that met the rigorous screening criteria in both comparisons were acetylglycine and glucosamine. Specifically, the log2FC of acetylglycine in the S vs K comparison was -1.0879 (P = 0.01339), and in the S vs H comparison was -1.2118 (P = 0.04417). Similarly, the log2FC of glucosamine in the S vs K comparison was -0.6153 (P = 0.03402), and in the S vs H comparison was +2.3275 (P = 0.02207).

[0045] Based on preliminary differential screening, rigorous threshold verification, subsequent Bootstrap stability evaluation, single-index ROC analysis, and joint model results, glucosamine, acetylglycine, and maleic acid were identified as a triad of urinary metabolic biomarkers. Glucosamine and acetylglycine were the core biomarkers that met the rigorous screening criteria in both comparisons. Although maleic acid did not simultaneously enter the rigorous screening intersection, its direction of change was statistically supported in the preliminary analysis and showed supplementary value in subsequent stability evaluation and joint discriminant modeling.

[0046] The above results indicate that the combination of glucosamine, acetylglycine, and maleic acid can distinguish patients with spleen and stomach damp-heat syndrome (CAG) from healthy individuals, and can also differentiate CAG patients with spleen and stomach damp-heat syndrome from those with spleen and stomach deficiency-cold syndrome. It can be used as a urinary metabolic marker for the auxiliary diagnosis and syndrome differentiation of spleen and stomach damp-heat syndrome in chronic atrophic gastritis.

[0047] Example 2: Correlation analysis between biomarkers and symptoms of damp-heat syndrome in the spleen and stomach This embodiment involves an exploratory association analysis between the biomarkers identified in Example 1 and the typical clinical symptoms of spleen and stomach damp-heat syndrome (CAG). Spearman rank correlation analysis was performed on the levels of three biomarkers in the urine of subjects in the spleen and stomach damp-heat syndrome CAG group and their corresponding TCM syndrome symptoms to examine the possible association between biomarker expression levels and clinical phenotype. The results are shown in Table 2, as detailed below: Spearman rank correlation analysis was used to investigate the relationship between typical symptoms of CAG with spleen-stomach damp-heat syndrome and three markers, with P<0.05 as the screening criterion. The results are shown in Table 2. In the analysis without multiple comparison correction, the nominal P values of the association tests between acetylglycine and stomach pain, and between maleic acid and unsmooth defecation were 0.038 and 0.040, respectively; the above results suggest potential correlations, which still need further verification in combination with correlation coefficients, multiple comparison correction and independent samples.

[0048] Table 2 Spearman Correlation Analysis (P Values) Between CAG Symptoms of Spleen-Stomach Damp-Heat Syndrome and Three Markers Note: * indicates 0.01<P<0.05; the bitter taste in the mouth symptom occurred in all CAG cases of the spleen-stomach damp-heat syndrome group, with no intra-group variation, so the correlation coefficient was not calculated.

[0049] Example 3: Evaluation of Marker Stability Based on Bootstrap Resampling This example relates to the internal stability evaluation of the marker screening results under different sample combinations. Since a single full-cohort difference analysis may be affected by the specific sample composition, this example performs stratified Bootstrap resampling with replacement based on all 69 samples, to evaluate the stability degree of candidate metabolites that repeatedly meet the same difference screening criteria. The results are shown in Table 3, which are specifically as follows: As a further verification of the above full-cohort screening results in Example 1, in order to evaluate the stability and reproducibility of the marker screening results under the condition of full-cohort resampling, stratified Bootstrap resampling with replacement was repeated 1000 times, with the random seed fixed as seed=12345; the Bootstrap sampling of the CAG group with spleen-stomach damp-heat syndrome in S vs K and S vs H was independent of each other. For each Bootstrap iteration, the intra-group median, log2FC and Wilcoxon rank-sum test results were recalculated for all 212 metabolites, and the exact same screening criteria were adopted: P<0.05 and |log2FC|>0.5. The frequency of each metabolite meeting the screening criteria in 1000 repeated samplings was recorded, and the equal-weighted average of the S vs K frequency and the S vs H frequency was calculated as the average stability indicator. The results are shown in Table 3.

[0050] Table 3 Bootstrap Stability Evaluation Results of Candidate Metabolites (Top 20 Sorted by Average Stability) The results showed that acetylglycine, glucosamine, and maleic acid all had high recurrence frequencies in 1000 bootstrap replicates, with average stability values ​​of 0.6050, 0.5865, and 0.5850, respectively, ranking 2nd, 4th, and 5th among all candidate metabolites. Specifically, glucosamine's bootstrap frequency in the S vs H comparison was 0.661, higher than its S vs K frequency, suggesting a relatively stable contribution to the differentiation of spleen and stomach damp-heat syndrome and spleen and stomach deficiency-cold syndrome. Acetylglycine and maleic acid both showed certain replicate screening stability in both comparisons. These results support the use of acetylglycine, glucosamine, and maleic acid as candidate biomarkers for subsequent ROC evaluation and joint model construction.

[0051] Example 4: Single-index ROC analysis This embodiment involves an exploratory evaluation of the distinguishing efficacy of core metabolic biomarkers across all samples. Using the spleen-stomach damp-heat syndrome (CAG) group as the positive category, single-index ROC analyses were performed on the top 5 metabolites in terms of bootstrap stability (S vs K and S vs H). AUC, AUC 95% confidence interval, Youden index, optimal cut-off value, sensitivity, specificity, and discriminant direction were calculated. Core biomarkers with both high stability and good classification ability were screened. The results are shown in Tables 4 and 5. Figure 4 , Figure 5 As shown, the details are as follows: For the candidate metabolites with high Bootstrap stability in Example 3, single-index ROC curves were established for S vs K and S vs H in all 69 samples to evaluate the ability of each metabolite to distinguish different groups individually; the results of the S vs K single-index ROC analysis are shown in Table 4, and the corresponding ROC curves are shown in Table 4. Figure 4 As shown.

[0052] Table 4. Single-index ROC analysis results of candidate metabolites in differentiating CAG from healthy controls in patients with spleen and stomach damp-heat syndrome. The results of the S vs H single-index ROC analysis are shown in Table 5, and the corresponding ROC curves are shown in Table 5. Figure 5 As shown.

[0053] Table 5. Single-index ROC analysis results of candidate metabolites distinguishing CAG from CAG in spleen and stomach damp-heat syndrome and spleen and stomach deficiency-cold syndrome. When multiple optimal cut-off values ​​with the same maximum Youden index exist, the cut-off value with the smaller absolute value of the difference between sensitivity and specificity is selected. Therefore, in the S vs H comparison, the cut-off value for lysine was determined to be 39172.2660, meaning that when lysine ≤ 39172.2660, it was diagnosed as spleen and stomach damp-heat syndrome (CAG); the cut-off value for glucosamine was determined to be 650.0048, meaning that when glucosamine ≥ 650.0048, it was diagnosed as spleen and stomach damp-heat syndrome (CAG).

[0054] The results showed that acetylglycine, glucosamine, and maleic acid all had AUCs greater than 0.5 in both the S vs K and S vs H discrimination tasks, and none of them included 0.5 in their 95% confidence intervals. In contrast, lysine, which ranked first in average Bootstrap stability, and glyceric acid, which ranked third, both had AUCs of 0.5 in their 95% confidence intervals in the S vs H comparison, suggesting that their syndrome differentiation efficacy is not yet stable. Therefore, based on differential screening, Bootstrap stability, and single-index ROC results, acetylglycine, glucosamine, and maleic acid were selected to form a triple urinary metabolic biomarker combination.

[0055] Example 5: Construction and Evaluation of a Triad Biomarker Joint Model This embodiment involves the construction and evaluation of a joint discriminant model using acetylglycine, glucosamine, and maleic acid as urinary metabolic markers. Based on single-indicator ROC analysis, binary logistic regression was used to establish S vs K and S vs H joint models to evaluate the discriminant ability of the three markers combined. The results are shown in Tables 6 and 7. Figure 6 , Figure 7 As shown, the details are as follows: Using glucosamine, acetylglycine, and maleic acid as X1, X2, and X3 respectively, the spleen and stomach damp-heat syndrome CAG group was set as the positive category, i.e., S=1; in the S vs K model, the healthy control group K=0, with a sample size of 48 cases, including 21 cases in the S group and 27 cases in the K group; in the S vs H model, the spleen and stomach deficiency-cold syndrome CAG group H=0, with a sample size of 42 cases, including 21 cases in the S group and 21 cases in the H group.

[0056] The Logistic regression model first calculates: β 0: Logistic regression intercept; β 1- β 3: Regression coefficients for the three metabolites; Then convert it into S-group predicted probabilities: The larger the p-value, the more likely the sample is to be classified as the CAG group of spleen and stomach damp-heat syndrome. Using the predicted probability P generated by the logistic model as the ROC predictor variable, AUC, AUC 95% confidence interval, best probability cut-off value, sensitivity, specificity, and Youden index were calculated. The ROC analysis results of the S vs K joint model are shown in Table 6, and the corresponding ROC curves are shown in... Figure 6 As shown.

[0057] Table 6. ROC analysis results of the triple biomarker combined model in differentiating CAG syndrome from healthy controls in spleen and stomach damp-heat syndrome. When P ≥ 0.4539834, the sample was classified as the CAG group with spleen and stomach damp-heat syndrome; when P < 0.4539834, the sample was classified as the healthy control group. The AUC of the triple combination model was higher than the AUC of glucosamine, acetylglycine, and maleic acid used individually.

[0058] The ROC analysis results of the S vs H joint model are shown in Table 7, and the corresponding ROC curves are shown in Table 7. Figure 7 As shown.

[0059] Table 7. ROC analysis results of the triple biomarker combined model in differentiating CAG from CAG in spleen and stomach damp-heat syndrome and spleen and stomach deficiency-cold syndrome. When P ≥ 0.5653016, the sample was classified as the spleen and stomach damp-heat syndrome (CAG group); when P < 0.5653016, the sample was classified as the spleen and stomach deficiency-cold syndrome (CAG group). The AUC of the triplet combined model was higher than the AUC of the three biomarkers used individually.

[0060] The above results indicate that the combined use of glucosamine, acetylglycine, and maleic acid demonstrates superior discriminative ability compared to single indicators in differentiating between spleen and stomach damp-heat syndrome CAG and healthy controls, as well as in differentiating between spleen and stomach damp-heat syndrome and spleen and stomach deficiency-cold syndrome CAG. Since the combined model in this embodiment was constructed and evaluated based on all 69 samples, its discriminative efficacy still needs further validation in independent cohorts with larger sample sizes. The aforementioned probability cut-off values ​​are specific implementation methods in this embodiment and do not constitute a limitation on the scope of protection of this invention.

[0061] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. The application of a reagent for detecting the content of glucosamine, acetylglycine, and maleic acid in urine samples in the preparation of products, characterized in that, The product is used to assist in the diagnosis of chronic atrophic gastritis with damp-heat syndrome of the spleen and stomach.

2. The application of reagents for detecting the content of glucosamine, acetylglycine, and maleic acid in urine samples in product preparation, characterized in that... The product is used to differentiate between chronic atrophic gastritis with spleen and stomach damp-heat syndrome and chronic atrophic gastritis with spleen and stomach deficiency-cold syndrome.

3. The application according to claim 1 or 2, characterized in that, The product is tested on isolated urine samples.

4. The application according to claim 1 or 2, characterized in that, The product is suitable for detecting the metabolic biomarkers using chromatography-mass spectrometry (GC-MS); preferably, the GC-MS technique is gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS).

5. A combination of metabolic markers for assisting in the diagnosis of damp-heat syndrome of spleen and stomach in chronic atrophic gastritis, and / or for differentiating damp-heat syndrome of spleen and stomach in chronic atrophic gastritis from deficiency-cold syndrome of spleen and stomach in chronic atrophic gastritis, characterized in that, It includes one or more of glucosamine, acetylglycine, and maleic acid.

6. The metabolic biomarker combination according to claim 5, characterized in that, The combination of metabolic markers includes glucosamine, acetylglycine, and maleic acid.

7. A kit for assisting in the diagnosis of damp-heat syndrome of spleen and stomach in chronic atrophic gastritis, and / or for differentiating damp-heat syndrome of spleen and stomach in chronic atrophic gastritis from deficiency-cold syndrome of spleen and stomach in chronic atrophic gastritis, characterized in that, include: (1) Reagents for detecting the contents of glucosamine, acetylglycine and maleic acid in isolated urine samples, respectively; (2) Standards or calibrators of the metabolic markers.

8. The reagent kit according to claim 7, characterized in that, In samples with chronic atrophic gastritis and spleen-stomach damp-heat syndrome, the levels of glucosamine, acetylglycine, and maleic acid were reduced compared to healthy samples. Compared to the samples with chronic atrophic gastritis and spleen-stomach deficiency syndrome, the glucosamine content was increased, while the acetylglycine and maleic acid content was decreased.

9. The reagent kit according to claim 7, characterized in that, It also includes sample pretreatment reagents.

10. A method for detecting metabolic markers in isolated urine samples, characterized in that, include: The levels of glucosamine, acetylglycine, and maleic acid in isolated urine samples were determined, and these levels were compared with reference levels to obtain a comparison of the metabolite content of the samples.

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