Intestinal flora marker and application thereof in diagnosis and treatment of anxiety disorder

Abstract

The invention discloses an intestinal flora marker and application thereof in diagnosis and treatment of anxiety disorder. According to the present invention, the intestinal flora marker comprises a specific strain Lacnospiraceae bacterium.1 of the genus of Horsespirillum, Odorobacter splanchnicus, Coprococcus dexterous, and Clostridium aldenense, and the intestinal flora marker comprises a specific strain Lacnospiraceae bacterium.1 of the genus of Horsespirillum, Odorobacter splanchnicus, Coprococcus dexterous, and Clostridium aldenense, the ROC curve analysis of the intestinal flora marker as a detection variable of anxiety disorder has high specificity and sensitivity, so that the intestinal flora marker can be used as a detection marker to be applied to diagnosis of anxiety disorder patients. The intestinal flora marker disclosed by the invention is used as a detection marker of anxiety disorder, is completely noninvasive and is high in accuracy.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly relates to an intestinal flora biomarker and its application in the diagnosis and treatment of anxiety disorder. Background Art

[0002] Anxiety disorder is a common psychological disorder, manifested as excessive worry and fear in daily situations, interfering with daily activities and being difficult to control. Its common types include generalized anxiety disorder, panic disorder, social phobia, specific phobia, separation anxiety disorder, etc. The disease is caused by the combined effects of biological, psychological, social factors, etc. Therefore, the prediction, diagnosis, and treatment of anxiety disorder need to consider multiple factors comprehensively.

[0003] Currently, the diagnosis of anxiety disorder mainly relies on clinical interviews, self-report questionnaires, and psychological assessments. Although these methods can provide certain diagnostic bases, they also have defects such as strong subjectivity, high time cost, self-assessment bias, and tool limitations. In recent years, more and more studies have shown that there is a close connection between the intestinal flora and anxiety disorder. Therefore, the combined study of the intestinal flora and anxiety disorder is beneficial to the prediction, diagnosis, and treatment of anxiety disorder. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide an intestinal flora biomarker and its application in the diagnosis and treatment of anxiety disorder, so as to provide a new idea and approach for the diagnosis and treatment of anxiety disorder.

[0005] The present invention collects samples of anxiety disorder patients and healthy people, conducts metagenomic sequencing and uses bioinformatics to statistically analyze the sequencing data, discovers the intestinal flora related to the disease (specific strains of Lachnospiraceae bacterium.1 of the genus Lachnospira, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense), integrates the intestinal flora with the disease information, and maximally predicts anxiety disorder patients. The present invention provides a new idea and approach for the screening and diagnosis of anxiety disorder.

[0006] To achieve the above purpose, the technical solutions designed by the present invention are as follows:

[0007] The present invention provides an intestinal flora biomarker related to anxiety disorder, and the intestinal flora biomarker includes any one or more of a specific strain of Lachnospiraceae bacterium.1, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense.

[0008] Furthermore, the intestinal flora biomarker is any one of a1 to a5:

[0009] a1) A specific strain of Lachnospiraceae bacterium.1;

[0010] a2) Odoribacter splanchnicus;

[0011] a3) Coprococcus catus;

[0012] a4) Clostridium aldenense;

[0013] a5) A specific strain of Lachnospiraceae bacterium.1, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense.

[0014] The present invention also provides an application of the described intestinal flora biomarker in preparing a product for diagnosing or screening anxiety disorder or preparing a product for treating anxiety disorder.

[0015] The present invention also provides a kit, and the kit includes a reagent for detecting the relative abundance value of the described intestinal flora biomarker.

[0016] The present invention also provides an application of the described kit in non-diagnostically detecting anxiety disorder.

[0017] The present invention also provides a use of the detection reagent in the described kit in preparing a kit for diagnosing anxiety disorder.

[0018] The present invention also provides a product for diagnosing anxiety disorder, and the product includes a primer, a probe, an antibody, an aptamer, or a chip specific to the described intestinal flora biomarker.

[0019] The present invention also provides a computer program product related to anxiety disorder, which is used to execute the risk of diagnosing an object to be tested with anxiety disorder, including the following steps:

[0020] 1) Obtain the relative abundance value of each single bacterial species in the feces of the object to be tested; the single bacterial species is any one of the specific species Lachnospiraceae bacterium.1 of the genus Lachnospira, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense;

[0021] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0022] 3) Substitute the above first probability value y into the following formula to calculate the probability that the object to be tested is a healthy person: P = exp(y) / {1 + exp(y)};

[0023] where P is the probability value that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0024] 4) According to the comparison between the P value and the reference value, diagnose or predict the risk that the object to be tested has anxiety disorder.

[0025] Further, the formula of the binary logistic regression equation is:

[0026] y = A + B1 * x1 + B2 * x2 + B3 * x3 + B4 * x4;

[0027] A is the intercept term, and B1 to B4 are the regression coefficients of the independent variables;

[0028] x1 is the relative abundance value of Coprococcus catus; x2 is the relative abundance value of Odoribacter splanchnicus; x3 is the relative abundance value of the specific species Lachnospiraceae bacterium.1 of the genus Lachnospira; x4 is the relative abundance value of Clostridium aldenense.

[0029] Furthermore, A is -0.1103, B1 is 136.3856, B2 is 246.5264, B3 is 14.8687, and B4 is -342.8902.

[0030] Advantages of the present invention:

[0031] 1. The present invention first discovered that specific species of the genus Lachnospiraceae (Lachnospiraceae bacterium.1), Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense are associated with anxiety disorder. Among them, Clostridium aldenense shows a significant increase in patients with anxiety disorder, while specific species of the genus Lachnospiraceae (Lachnospiraceae bacterium.1), Odoribacter splanchnicus, and Coprococcus catus show a significant decrease in patients with anxiety disorder. ROC curve analysis shows that they have high specificity and sensitivity as detection variables. Therefore, these four species can be used as detection markers for the diagnosis of patients with anxiety disorder. Using these four species as detection markers is completely non-invasive and highly accurate.

[0032] 2. The present invention provides a non-invasive and highly accurate detection method. Using these four species as detection markers is completely non-invasive and highly accurate. Through metagenomic sequencing, higher resolution is provided, enabling the analysis of microbial communities to reach the species or even strain level, thereby improving the accuracy and reliability of diagnosis. The present invention uses a larger sample size for verification to ensure excellent prediction effects for anxiety disorder.

[0033] 3. Develop target microorganisms for an anxiety disorder prediction system and early screening kit. Currently, there is no mature anxiety disorder prediction system or early screening kit. The four species provided by the present invention can be used as target microorganisms for developing these systems, filling the gap in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a technical solution diagram;

[0035] Figure 2 It is an experimental analysis flow chart;

[0036] Figure 3 It is a LEfSe score diagram of patients with anxiety disorder and healthy people;

[0037] Figure 4 It is a box scatter plot of relative abundances of patients with anxiety disorder and healthy people;

[0038] Figure 5 It is an ROC diagnostic curve diagram. DETAILED DESCRIPTION OF THE INVENTION

[0039] The following further describes the present invention in detail with specific embodiments for those skilled in the art to understand.

[0040] The technical solutions of the embodiments are as Figure 1 shown, and the specific experimental analysis process is as Figure 2 shown.

[0041] Example 1 Screening of Intestinal Microbiota Biomarkers Related to Anxiety Disorder

[0042] I. Sample Collection

[0043] 1. The sample sources and inclusion criteria for the anxiety disorder group are as follows: from 100 individuals. Inclusion criteria:

[0044] (1) Age distribution is 10 - 80 years old;

[0045] (2) Diagnosis of anxiety disorder based on the DSM-V criteria;

[0046] (3) Stable vital signs. Among them, the exclusion criteria for the anxiety disorder group are as follows:

[0047] a. Having a history of bariatric surgery, total colectomy with ileorectal anastomosis, or proctocolectomy;

[0048] b. Having taken antibiotics, probiotics, or prebiotics within the past three months;

[0049] c. Participating in any experimental drug regimen within the past 12 weeks;

[0050] d. Having received total parenteral nutrition therapy;

[0051] e. Reproductive-age women who are pregnant, planning to become pregnant during the study, or are breastfeeding;

[0052] f. The researchers believe that the patient is not suitable for inclusion in this study.

[0053] 2. Control group (healthy group): from 108 individuals. Inclusion criteria:

[0054] (1) Age distribution is 10 - 80 years old;

[0055] (2) Without diabetes or other metabolic diseases;

[0056] (3) Without anxiety disorder or other neurological diseases;

[0057] (4) Without irritable bowel syndrome or gastrointestinal diseases;

[0058] (5) Without other immune system diseases or not in an immunodeficiency state;

[0059] (6) Without taking antibiotics (such as neomycin, rifaximin) or probiotics, prebiotics, etc. before and during the study.

[0060] The exclusion criteria were the same as those for the anxiety disorder group.

[0061] 3. According to the above criteria, fecal samples from 100 patients with anxiety disorder and 108 healthy individuals were collected.

[0062] The above data were from fecal samples collected by Jingmen Central Hospital.

[0063] II. DNA Extraction, Library Construction and Sequencing

[0064] 1. Select the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected fecal samples.

[0065] 2. After extraction, use Qubit to detect the DNA concentration, and use 1.5% agarose gel electrophoresis to detect the integrity of the extracted genomic DNA. Perform quality control on the extracted genomic DNA, and screen out genomic DNA samples with qualified quality (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng).

[0066] 3. For DNA samples with qualified quality, after random fragmentation, end repair, addition of A bases, addition of adapters and indexes, perform purification and library amplification after adapter ligation, and detect the DNA concentration after amplification (DNA concentration ≥ 40 ng / μL).

[0067] 4. After the library is detected to be qualified, pool different libraries according to the requirements of the effective concentration and the target output data volume and then perform on-machine sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0068] III. Screening Biomarkers by LEfSe Analysis

[0069] Use the KneadData software to perform quality control (based on Trimmomatic) and dehost (based on Bowtie2) on the raw data downloaded from the machine. Use Kraken2 alignment to calculate the number of sequences of the species contained in the samples, and then use Bracken to estimate the actual abundance of the species in the samples. Randomly select 80% of the tested individuals (including those in the anxiety group and the healthy group) as the training set, and the remaining 20% of the samples as the validation set. Then use the LEfSe software to analyze the abundance data of each sample in this training set, and the default screening value of the LDA Score is set to 2.5. The sample information table is shown in Table 1.

[0070] Table 1 Sample Information Table

[0071]

[0072] The results are as Figure 3As shown in the figure, specific species of Lachnospiraceae bacterium.1, Odoribactersplanchnicus, Coprococcus catus and Clostridium aldenense are associated with anxiety disorder, among which Clostridium aldenense is significantly increased in patients with anxiety disorder, while specific species of Lachnospiraceae bacterium.1, Odoribactersplanchnicus and Coprococcus catus are significantly reduced in patients with anxiety disorder; therefore, three biomarkers that are significantly reduced in the anxiety disorder group were screened out, namely specific species of Lachnospiraceae bacterium.1, Coprococcus catus and Clostridium aldenense. catus, Odoribacters planchnicus, and one biomarker that was significantly increased in the anxiety group was found to be Clostridium aldenense.

[0073] like Figure 4 As shown: The present invention found that specific species of Lachnospiraceae bacterium.1, Coprococcus catus, Odoribactersplanchnicus and Clostridium aldenense strains are associated with anxiety as biomarkers, and when a single strain is predicted, the predictive ability of Coprococcus catus for anxiety is much higher than that of the other three biomarkers. When the strains are predicted jointly, the effect of the combined prediction of the four bacteria is the best.

[0074] Example 2 Verification of the reliability of the above four biomarkers

[0075] 1. First, the remaining 20% ​​of the subjects to be tested in Example 1 (including the subjects in the anxiety group and the healthy group) were used as the validation set. The abundance data of each sample in the validation set were first subjected to binary logistic regression operation, and then the receiver operating characteristic curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value).

[0076] 2. Use IBM SPSS Statistics (v27) statistical software to complete the calculation of specificity and sensitivity and the drawing of the ROC curve. The software first calculates the threshold of the actual measured value internally, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold;

[0077] Specificity (true negative rate) = TN / (TN + FP),

[0078] Sensitivity (true positive rate) = TP / (TP + FN).

[0079] 3. The ROC curve can be constructed through 1 - specificity and sensitivity, and the integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain index, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of a certain index.

[0080] 4. The relative abundance values of the biomarkers of a single strain are directly subjected to the receiver operating characteristic curve test (ROC curve) analysis to obtain the cutoff value (optimal cut-off value). The ROC curve of the prediction score is as Figure 5 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the mimetic biomarker (biomarker formed by the combination of four biomarkers) and single bacteria are shown in Table 2.

[0081] As can be seen from the above: The ROC curve analysis shows that the four biomarkers as detection variables have high specificity and sensitivity, and the AUC of the four biomarkers is greater than 65%. Therefore, the four biomarkers can all be used as detection biomarkers for the diagnosis of patients with anxiety disorder;

[0082] The AUC of the prediction score of the mimetic biomarker is 87.3%, the optimal cut-off value is 0.5032, the sensitivity is 0.864, and the specificity is 0.8. Therefore, using the mimetic biomarker as a detection biomarker for the diagnosis of patients with anxiety disorder has better effects and high accuracy.

[0083] Using these four strains as detection biomarkers is completely non-invasive and has high accuracy.

[0084] Table 2 Results of the ROC diagnostic curve

[0085] Genus name AUC Cut-off value Sensitivity Specificity Mimic biomarker 87.3％ 0.5032 0.864 0.8 Coprococcus eutactus 76.8％ 0.0009 0.818 0.75 Odoribacter splanchnicus 70.5％ 0.0001 0.591 0.85 Specific species of Lachnospira 69.7％ 0.0013 0.545 0.85 Clostridium ardleyi 68.9％ 0.0002 0.5 0.864

[0086] Example 3 Establishment of a logistic regression model

[0087] a. Establish a model

[0088] Based on the proportion of people with anxiety disorder or healthy people in the training set for the biomarkers mined above, further, take the four detected bacterial species as mimic biomarkers, and on this basis, discuss the linear relationship between the relative abundance values of the four single bacteria and the probability of the sample being healthy (or diseased). Calculate the first probability value y of the object to be tested through a binary logistic regression equation:

[0089] y = -0.1103 + 136.3856 * x1 + 246.5264 * x2 + 14.8687 * x3 + (-342.8902) * x4;

[0090] In the formula, x1 is the relative abundance value of Coprococcus catus;

[0091] x2 is the relative abundance value of Odoribacters planchnicus;

[0092] x3 is the relative abundance value of a specific species of Lachnospiraceae bacterium.1;

[0093] x4 is the relative abundance value of Clostridium aldenense.

[0094] b. Substitute the above first probability value y into the following formula to calculate the probability that the object to be tested is a healthy person: P = exp(y) / {1 + exp(y)}; where P is the probability value that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y.

[0095] P can also be written as:

[0096]

[0097] c. Verify the model

[0098] Based on the proportion of people with anxiety disorder or healthy people in the validation set, statistically verify the relevant abundance statistical data of the validation set biomarkers. Among them, the mean determines the central position of the data distribution, the standard deviation reflects the degree of dispersion of the data relative to the mean, and the q value is calculated using the formula of the rank sum test. The smaller the q value, the more significant the difference between the disease group and the healthy group. Specifically as follows:

[0099] Table 3 Statistical data of the relevant abundances of the validation set biomarkers

[0100]

[0101] Note: In Table 3, E is used to represent the power of 10. For example, 7.93E-05 represents 7.93 * 10 -05 .

[0102] Example 4

[0103] Based on the above embodiments, this embodiment provides a computer program product related to anxiety disorder, which is used to execute the risk of diagnosing an object to be tested with anxiety disorder, and includes the following steps:

[0104] 1) Obtain the relative abundance value of each single strain in the feces of the object to be tested; the single strain is any one of the specific strain Lachnospiraceae bacterium.1 of the genus Lachnospira, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense;

[0105] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0106] y = -0.1103 + 136.3856 * x1 + 246.5264 * x2 + 14.8687 * x3 + (-342.8902) * x4;

[0107] In the formula, x1 is the relative abundance value of Coprococcus catus;

[0108] x2 is the relative abundance value of Odoribacter splanchnicus;

[0109] x3 is the relative abundance value of the specific strain Lachnospiraceae bacterium.1 of the genus Lachnospira;

[0110] x4 is the relative abundance value of Clostridium aldenense;

[0111] 3) Substitute the above first probability value y into the following formula to calculate the probability that the object to be tested is a healthy person: P = exp(y) / {1 + exp(y)};

[0112] In the formula, P is the probability value that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0113] 4) According to the comparison between the probability P value of a healthy person and the reference value, diagnose or predict the risk that the object to be tested has anxiety disorder;

[0114] During actual operation, when the P value is greater than 0.5, it indicates that the probability of the object to be tested having anxiety disorder is relatively small. When the P value is less than 0.5, it indicates that the probability of the object to be tested having anxiety disorder is relatively large. When the P value is 0.5, other means need to be further used for detection, and the other means are blood routine and psychological examination. The closer the P value is to 0.5, the more other means are needed for detection.

[0115] Example 5

[0116] Based on the products and methods of Example 4, check and verify the health probabilities of healthy people and anxiety disorder patients in the validation set. The specific steps are as follows:

[0117] 1) Collect fecal samples of the people to be tested, and detect the relative abundance values of single strains in the feces; among them, the single strains are specific strains of Lachnospiraceae bacterium.1 of the genus Lachnospira, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense; see Table 4.

[0118] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0119] y = -0.1103 + 136.3856 * x1 + 246.5264 * x2 + 14.8687 * x3 + (-342.8902) * x4;

[0120] In the formula, x1 is the relative abundance value of Coprococcus catus;

[0121] x2 is the relative abundance value of Odoribacter splanchnicus;

[0122] x3 is the relative abundance value of the specific strain Lachnospiraceae bacterium.1 of the genus Lachnospira;

[0123] x4 is the relative abundance value of Clostridium aldenense.

[0124] 3) Substitute the above first probability value y into the following formula to calculate the probability that the object to be tested is a healthy person: P = exp(y) / {1 + exp(y)};

[0125] In the formula, P is the probability value that the object to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y.

[0126] 4) Based on the comparison of the P-value of a healthy person with the reference value, diagnose or predict the risk that the object to be tested has anxiety disorder.

[0127] In actual work, when the P-value is greater than 0.5, it indicates that the probability of the object to be tested having anxiety disorder is relatively small. When the P-value is less than 0.5, it indicates that the probability of the object to be tested having anxiety disorder is relatively large. When the P-value is 0.5, other means need to be further used for detection, and the other means are blood routine and psychological assessment. The closer the P-value is to 0.5, the more other means are needed for detection.

[0128] In actual situations, there are cases that do not fully meet the judgment criteria. The reason is that the fecal samples of the personnel to be tested may show false positive results or false negative results, and other means need to be further used for detection, and the other means are blood routine and psychological assessment.

[0129] Table 4 Relative abundance value data of validation set markers

[0130]

[0131]

[0132]

[0133]

[0134] Note: In Table 4, ①, x1 is the relative abundance value of Coprococcus catus; x2 is the relative abundance value of Odoribacter splanchnicus; x3 is the relative abundance value of a specific strain of Lachnospiraceaebacterium.1; x4 is the relative abundance value of Clostridium aldenense;

[0135] ②, E is used to represent the power of 10. For example, 5.47E-05 represents 5.47 * 10 -05 .

[0136] Conclusions and explanations:

[0137] 1. Single-bacterium prediction effect: The single-bacterium prediction effect of Coprococcus catus on anxiety disorder is the highest, and the single-bacterium prediction effects of Odoribacter splanchnicus, a specific strain of Lachnospiraceaebacterium, and Clostridium aldenense are the next;

[0138] 2. Prediction effect of mimetic biomarkers: The combination of four bacteria, Coprococcus eutactus, Odoribacter splanchnicus, specific species of Lachnospiraceae, and Clostridium ardantii can all be used as biomarkers for predicting anxiety disorder, and the prediction accuracy rates all reach over 68%. Among them, the combination of four bacteria has the highest accuracy rate of 87.3% and the best prediction effect, capable of providing a more accurate prediction of anxiety disorder.

[0139] Other parts not detailed are all prior arts. Although the above embodiments have described the present invention in detail, they are only a part of the embodiments of the present invention, rather than all embodiments. People can also obtain other embodiments based on these embodiments without creative efforts, and these embodiments all fall within the protection scope of the present invention.

Claims

1. An intestinal flora biomarker related to anxiety disorder, characterized in that: The intestinal flora markers include any one or more of a specific strain of Lachnospiraceae bacterium.1, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense.

2. The intestinal flora biomarker according to claim 1, characterized in that: The intestinal flora marker is any one of a1 to a5: a1) A specific strain of Lachnospiraceae bacterium.1; a2) Odoribacter splanchnicus; a3) Coprococcus catus; a4) Clostridium aldenense; a5) A specific strain of Lachnospiraceae bacterium.1, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense.

3. Use of the intestinal flora marker according to claim 1 in the preparation of a product for diagnosing or screening anxiety disorder or in the preparation of a product for treating anxiety disorder.

4. A kit, characterized in that: The kit includes reagents for detecting the relative abundance value of the intestinal flora marker according to claim 1.

5. Use of the kit according to claim 4 in the non-diagnostic detection of anxiety disorder.

6. Use of the detection reagent in the kit according to claim 4 in the preparation of a kit for diagnosing anxiety disorder.

7. A product for diagnosing anxiety disorders, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips specific to the intestinal flora marker according to claim 1.

8. A computer program product related to anxiety disorder, characterized in that: The computer program product is used to execute the risk of diagnosing a subject to be tested with anxiety disorder, including the following steps: 1) Obtaining the relative abundance value of each single strain in the feces of the subject to be tested; the single strain is any one of a specific strain of Lachnospiraceae bacterium.1, Odoribacter splanchnicus, Coprococcus catus, and Clostridium aldenense; 2) Calculating the first probability value y of the subject to be tested according to the binary logistic regression equation; 3) Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp(y) / {1 + exp(y)}; where P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y; 4) According to the comparison of the P value with the reference value, diagnosing or predicting the risk of the subject to be tested having anxiety disorder.

9. The product according to claim 8, wherein: The formula of the binary logistic regression equation is: y = A + B1*x1 + B2*x2 + B3*x3 + B4*x4; A is the intercept term, and B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Coprococcus catus; x2 is the relative abundance value of Odoribacter splanchnicus; x3 is the relative abundance value of a specific strain of Lachnospiraceae bacterium.1; x4 is the relative abundance value of Clostridium aldenense.

10. The product according to claim 9, characterized in that: The said A is -0.1103, B1 is 136.3856, B2 is 246.5264, B3 is 14.8687, and B4 is -342.8902.

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