Meningioma intestinal microbial markers, products and applications

Abstract

The present invention discloses a meningioma intestinal microbial marker, product, and application thereof, belonging to the field of biomedicine. The intestinal microbial marker includes one or more of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius. The present invention provides a kit comprising a detection reagent for detecting the relative abundance of the intestinal microbial marker. The present invention provides a product for diagnosing meningioma and a computer program product related to meningioma. The product provided by the present invention has good feasibility and accuracy, can effectively assess the risk of meningioma, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to meningioma intestinal microbial markers, products and applications thereof. Background Art

[0002] In current clinical practice, the diagnosis of meningiomas primarily relies on imaging studies such as CT, MRI, and biopsy. Although these techniques can provide certain tumor localization and histological information, they still have significant limitations: 1) Imaging studies are insensitive to early or small lesions, which can easily lead to missed or delayed diagnosis; 2) The imaging features of different meningioma subtypes overlap significantly, making accurate classification difficult and potentially affecting the selection of subsequent treatment options; 3) Although invasive biopsy is the gold standard for diagnosis, it carries risks such as surgical trauma, neurological damage, and sampling errors.

[0003] In summary, the existing diagnostic model for meningioma urgently needs breakthroughs in accuracy, safety, and personalization. Currently, there is still a lack of a clear, safe, and non-invasive meningioma diagnostic product, which can provide a new idea and approach for the diagnosis of meningioma. Summary of the Invention

[0004] In response to the above technical problems, the present invention provides a meningioma intestinal microbial marker, product and application thereof, which is clear, safe and non-invasive, and can provide a new idea and approach for the diagnosis of meningioma.

[0005] The technical solutions provided by the present invention are as follows:

[0006] In a first aspect, a gut microbial marker associated with meningioma is provided, characterized in that the gut microbial marker includes Parabacteroides distasonis and Prevotella copri.

[0007] In the above technical solution, the intestinal microbial markers also include Bifidobacterium adolescentis and / or Streptococcus salivarius.

[0008] In the above technical solution, the intestinal microbial markers include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius.

[0009] In a second aspect, a reagent for detecting the intestinal microbial marker described in the first aspect is provided for use in preparing a product for diagnosing meningioma.

[0010] A third aspect provides a kit comprising a reagent for detecting the intestinal microbial markers described in the first aspect.

[0011] In a fourth aspect, a use of the kit described in the third aspect in preparing a product for detecting meningioma is provided.

[0012] In a fifth aspect, the invention provides the use of the intestinal microbial markers in the kit described in the fourth aspect in the non-diagnostic detection of meningioma.

[0013] In a sixth aspect, a product for diagnosing meningioma is provided, wherein the product comprises primers, probes, antibodies, aptamers or chips that are specific to the intestinal microbial markers described in the first aspect.

[0014] In a seventh aspect, a product for diagnosing meningioma is provided, wherein the product is used to detect the relative abundance value of each single bacterial species in the intestinal microbial marker described in the first aspect, and the product includes primers, probes, antibodies, aptamers or chips that are specific to the intestinal microbial marker.

[0015] In an eighth aspect, a computer program product related to meningioma is provided, wherein the computer program product is configured to execute a method for diagnosing whether a subject has a risk of meningioma, the method comprising:

[0016] Obtaining the relative abundance value of each single bacterial species in the intestine of the test subject;

[0017] Substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the logarithm y of the odds of the subject to be tested, the single bacterial species including Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius;

[0018] Calculate the probability Z of the subject being a meningioma patient based on y, Z = exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y;

[0019] Based on the comparison result of the probability Z and the reference value, it is diagnosed or predicted whether the subject suffers from meningioma or has the risk of suffering from meningioma.

[0020] In the above technical solution, the formula of the binary logistic regression equation is:

[0021]

[0022] Among them, A is the intercept term, B1-B4 are the regression coefficients of the independent variables, x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius.

[0023] In the above technical solution, A is -0.558, B1 is -528.9872, B2 is -6.0904, B3 is -103.3701, and B4 is 8.1074.

[0024] The beneficial effects of the present invention are as follows:

[0025] 1. The present invention newly discovered four intestinal microorganisms associated with meningioma, including Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius. After research and verification, it was found that one or more of the above four intestinal microorganisms can be used as intestinal microbial markers for meningioma to diagnose whether the test sample has meningioma.

[0026] 2. The present invention also provides a reagent and kit that can predict or diagnose meningioma using one or more of the four bacterial species mentioned above as detection markers, completely non-invasively and with high accuracy. Metagenomic sequencing provides higher resolution, enabling analysis of microbial communities down to the species or even strain level, thereby improving diagnostic accuracy and reliability. The four bacterial species can also serve as target microorganisms for the development of these systems, filling a gap in this field.

[0027] 3. The present invention also provides a product for diagnosing meningioma. This product can calculate the probability of disease based on the relative abundance of each bacterial species, and then compare it with a reference value to predict or diagnose whether a patient has meningioma or is at risk of developing meningioma. This product has good feasibility and accuracy, can effectively assess the risk of meningioma, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1This is the result graph of linear discriminant analysis;

[0029] Figure 2 is a box scatter plot of intestinal microbial markers;

[0030] Figure 3 is the ROC curve. DETAILED DESCRIPTION

[0031] To evaluate whether the composition of intestinal symbiotic flora can serve as a predictive factor for meningioma, the present invention collected samples from meningioma patients and healthy people, performed metagenomic sequencing, and used bioinformatics to perform statistics on the sequencing data. The present invention discovered the intestinal flora associated with the disease, integrated the intestinal flora with disease information, and predicted meningioma patients to the greatest extent possible.

[0032] Through metagenomic sequencing, the present invention discovered the correlation between Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius and meningioma patients.

[0033] It has been verified that there is a significant association between the above four bacteria and meningioma. Specifically, the staff screened out one marker that was significantly increased in the meningioma group, including Parabacteroides distasonis; and three markers that were significantly decreased in the meningioma group, including Bifidobacterium adolescentis, Prevotella copri, and Streptococcus salivarius.

[0034] ROC curve analysis showed that the above four markers had high specificity and sensitivity as detection variables. Therefore, these four bacterial species can be used as detection markers for the prediction and diagnosis of meningioma patients.

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. Experimental methods not specifying specific conditions in the examples are generally based on conventional conditions.

[0036] Example 1: Sample Collection

[0037] Intestinal samples from 60 meningiomas and 80 healthy individuals were collected:

[0038] The sample sources and inclusion criteria for the meningioma group are as follows: patients were recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria were: 1. Age older than 18 years; 2. Diagnosis of meningioma (radiological or histopathological diagnosis); 3. No antibiotic or immunosuppressant treatment within 1 month before sampling; 4. Patients or their guardians agreed to participate in the study and signed an informed consent form.

[0039] Exclusion criteria for the meningioma group: 1. Patients with other malignant tumors; 2. Patients with severe liver disease and renal damage or receiving continuous renal replacement therapy, hemodialysis, or peritoneal dialysis; 3. Patients unable to cooperate with stool sample collection.

[0040] Inclusion criteria for the control group: 1. Age over 18 years old; 2. No history of neurological disease and no abnormalities in physical examinations in the past year; 3. Good eating habits and lifestyle.

[0041] Exclusion criteria for the control group: 1. Suffering from severe infection or chronic gastrointestinal inflammatory disease in the past three months; 2. Unable to cooperate with the collection of stool samples.

[0042] Example 2: DNA extraction, library construction and sequencing

[0043] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0044] 2. After extraction, the DNA concentration was detected using Qubit and the integrity of the extracted genomic DNA was detected by 1.5% agarose gel electrophoresis. The extracted genomic DNA was quality tested to screen out qualified genomic DNA samples (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng).

[0045] 3. For qualified DNA samples, random shearing, end repair, ligation of A bases, addition of adapters and indexes, and purification and library amplification after adapter ligation are performed. After amplification, the DNA concentration is tested (DNA concentration ≥ 40 ng / μL).

[0046] 4. After the libraries pass the test, the different libraries are pooled according to the effective concentration and target data volume before sequencing. The metagenomic sequencing platform used is the BGI T7, and the sequencing strategy is PE150.

[0047] Example 3: LEfSe analysis and screening of intestinal microbial markers

[0048] 1. Divide the dataset

[0049] KneadData software was used to perform quality control (based on Trimmomatic) and host removal (based on Bowtie2) on the raw data. Kraken2 was used to calculate the number of species sequences contained in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the subjects to be tested (including those in the anxiety group and the healthy group) were randomly selected as the training set, and the remaining 20% ​​of the samples were used as the validation set. The abundance data of each sample in the training set was then analyzed using LEfSe software, with the default LDA Score screening value set to 2.5. The sample information table is shown in Table 1.

[0050] Table 1 Sample information table

[0051]

[0052] The results are as follows Figure 1 As shown, this application discovered for the first time four bacterial species related to meningioma. Specifically: the staff screened out four markers that were significantly increased in the meningioma group, including Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius.

[0053] The relevant data showed that the abundance of Parabacteroides distasonis was significantly increased in meningioma patients, while the abundance of Bifidobacterium adolescentis, Prevotella copri and Streptococcus salivarius was significantly lower than that in healthy people.

[0054] Example 4: Verification of the reliability of the above four intestinal microbial markers

[0055] 1. First, the remaining 20% ​​of the subjects to be tested in Example 1 (including those in the meningioma group and the healthy group) were used as a validation set. A binary logistic regression operation was first performed on the abundance data of each sample in the validation set, and then a receiver operating characteristic curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value).

[0056] 2. Specificity and sensitivity were calculated and ROC curves were drawn using IBM SPSS Statistics (v27) statistical software. The software first calculated the threshold of the actual measurement value, and then calculated the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold.

[0057] Specificity (true negative rate) = TN / (TN+FP),

[0058] Sensitivity (true positive rate) = TP / (TP+FN),

[0059] 3. The ROC curve can be constructed by dividing specificity and sensitivity by 1. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of the indicator.

[0060] 4. The relative abundance values ​​of intestinal microbial markers of a single strain were directly analyzed by receiver operating characteristic (ROC) curve to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is as follows: Figure 3 The AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of four single bacterial species) and individual bacteria are shown in Table 2.

[0061] Table 2 ROC diagnostic curve results

[0062]

[0063] From the above, we can see that the ROC curve analysis of the four intestinal microbial markers as detection variables has high specificity and sensitivity, and the AUCs of the four intestinal microbial markers are all greater than 75%. Therefore, one or more of the four intestinal microbial markers can be used as detection markers for the diagnosis of meningioma patients.

[0064] Example 5: Logistic regression model establishment

[0065] Based on the intestinal microbial markers screened above and the relative abundance values ​​of each metabolite marker obtained, the binary logistic regression algorithm in SPSS software was used to calculate the first probability of disease for each sample (that is, the logarithm y of the odds of the object to be tested). On this basis, the disease probability optimization formula Z = exp (y) / {1 + exp (y)} was used to calculate the disease probability Z of the sample to be tested. Finally, the disease probability Z was compared with the actual disease condition (such as severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically:

[0066] a. Build a model

[0067] Through the biomarkers mined above, based on the ratio of meningioma population and patient population in the training set, we further used the relative abundance values ​​of the four detected bacterial species as a single variable. On this basis, we discussed the linear relationship between the relative abundance values ​​of the four single bacteria and the probability of disease in the sample, and calculated the logarithm y of the odds of the object to be tested (also known as the first probability value y) through the binary logistic regression equation:

[0068]

[0069] Among them, A is the intercept term, B1-B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius.

[0070] b. Determine the values ​​of A and B1 to B4 above

[0071] After statistical analysis of the sample data, the values ​​of A and B1 to B4 are: A is -0.558, B1 is -528.9872, B2 is -6.0904, B3 is -103.3701, and B4 is 8.1074;

[0072] At this point, after sorting, the calculation formula for the logarithm y of the odds is:

[0073] ;

[0074] c. Calculate the probability Z of the subject being tested suffering from the disease

[0075] Substitute the above-mentioned first probability value y into the following formula to calculate the probability Z that the object to be tested is a patient: Z=exp(y) / {1+exp(y)}; wherein Z is the probability value of the object to be tested being a patient, and exp(y) is the natural exponential function of the first probability value y.

[0076] After sorting, the calculation formula for the disease probability Z is:

[0077]

[0078] d. Validation set data calculation and statistical analysis

[0079] Based on the validation set data, we obtained the relative abundance of each bacterial species in each sample in the disease and healthy groups. We then used the aforementioned binary logistic regression method to obtain a first probability value, which was then calculated using a formula to determine the probability that the sample was a patient. The results are shown in Tables 3 and 4.

[0080] Table 3 shows the relative abundance mean and standard deviation of each bacterial species. The relative abundance mean determines the center position of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The P value is a statistic calculated using the formula of the rank sum test. The lower the P value, the greater the difference between the disease group and the healthy group.

[0081] Table 3 Data related to the validation set markers

[0082]

[0083] Note: E is 10 to the power of N, for example, 9.88115E-05 is 9.88115×10 -05

[0084] Table 4. Statistical data on the abundance of markers in the validation set

[0085]

[0086] Note: In Table 3, the mean refers to the relative abundance mean, and the same applies to the standard deviation.

[0087] e. Results and Analysis

[0088] Combining the results of Examples 4 and 5, we can see that the predictive score of the mimicry marker has an AUC of 1, an optimal cutoff value of 0.272017572, a sensitivity of 1, and a specificity of 1. Therefore, using mimicry markers as detection markers in the diagnosis of meningioma patients has better results and higher accuracy. Using these four bacterial species as detection markers is completely non-invasive and highly accurate.

[0089] At the same time, these four intestinal microbial markers were found to be associated with meningioma for the first time, and fecal Prevotella had the highest single-bacteria prediction effect for meningioma, at 93.75%.

[0090] Combining the records in Table 3 above and the results obtained by the calculation formula of the disease probability Z, it can be seen that the calculation formula for calculating the disease probability of the sample to be tested summarized in this application is basically correct and can be used. The above Table 3 has the following situations that do not fully meet the judgment criteria. The reason is that the intestinal samples of the person to be tested may have false positive results or false negative results, and further testing is required by other means, including blood routine, judgment of physical signs, etc.

[0091] Example 6: Computer program product related to meningioma

[0092] Based on the above embodiments, this embodiment provides a computer program product related to meningioma, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of meningioma, including the following steps:

[0093] S1. Obtain the relative abundance value of each single bacterial species in the intestinal tract of the test subject; the single bacterial species include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius;

[0094] S2. Substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the logarithm y of the odds of the object to be tested (i.e., the first probability value y);

[0095] Wherein, x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius;

[0096] S3. Calculate the probability Z that the subject is a patient with meningioma based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y.

[0097] S4. Diagnose or predict the risk of the subject suffering from meningioma based on the comparison of the patient's probability Z value with the reference value.

[0098] In practice, when the Z value is greater than 0.5, it indicates that the probability of the subject having meningioma is low; when the Z value is less than 0.5, it indicates that the probability of the subject having meningioma is high; when the Z value is 0.5, it means that the subject may be a patient or a patient with meningioma. In this case, further testing is required using other means, such as blood tests and physical signs. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other means for testing.

[0099] Example 7: Checking the probability of disease in patients in the validation set and patients with meningioma

[0100] Based on the product and method of Example 5, the probability of disease occurrence among healthy individuals and patients with meningioma in the validation set was checked. The specific steps were as follows:

[0101] 1. Collect intestinal samples from the people to be tested and detect the relative abundance of each single strain in the intestine; include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius;

[0102] 2. Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation;

[0103] ;

[0104] Wherein, x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius;

[0105] 3. Calculate the probability Z that the subject is a meningioma patient based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y.

[0106] 4. Diagnose or predict the risk of the subject suffering from meningioma based on the comparison of the patient's probability P value with the reference value.

[0107] In actual practice, when the P value is greater than 0.5, it means the subject has a low probability of meningioma; when the P value is less than 0.5, it means the subject has a high probability of meningioma; when the P value is 0.5, it means the subject may be a patient or may have meningioma. In this case, further testing is required using other means, such as blood tests and physical signs. Furthermore, the closer the P value is to 0.5, the more necessary it is to use other means for testing.

[0108] Example 8: Detection Reagents

[0109] Based on the description of the above embodiments 1 to 5, it can be seen that the markers selected in this application have a good predictive effect. Medical staff can use a single bacterial species as a marker to detect and diagnose the sample to be tested alone to determine whether the sample to be tested has meningioma; medical staff can also combine multiple single bacterial species together as markers to detect and diagnose the sample to be tested to determine whether the sample to be tested has meningioma.

[0110] Therefore, this embodiment also provides a reagent for detecting intestinal microbial markers, which can be used in the preparation of diagnostic meningioma products to determine whether the sample to be tested has meningioma; at the same time, the intestinal microbial marker can be selected from the four single bacterial species discovered in this application and related to meningioma, that is, the intestinal microbial marker in the detection reagent can be one or more of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius.

[0111] Example 9: Kit

[0112] This application also provides a kit that can include the detection reagent described in Example 7 to determine whether a sample to be tested has meningioma. The limitations and technical solutions of this application regarding the kit can be found in the description of Example 7 above and are not described in detail here. Similarly, the kit can also be used in the preparation of a product for detecting meningioma, which is not described in detail here.

[0113] Example 10: Product for diagnosing meningioma

[0114] The present application also provides a product for diagnosing meningioma, which is specific to one or more of the four single bacterial species associated with meningioma discovered in the present application, and includes primers, probes, antibodies, aptamers or chips.

[0115] Combining the description of Examples 1 to 5 and conventional means in the art, it can be seen that when the newly discovered four single bacterial species are used as intestinal microbial markers, it should be achievable for those skilled in the art to produce corresponding specific products (primers, probes, antibodies, aptamers or chips, etc.), and no further details are given here.

[0116] Conclusion and explanation:

[0117] 1. By Figures 1 to 3As shown in Table 2, any one of the four newly discovered single bacterial species can be used as a gut microbial marker for meningioma. Each single bacterial species is sensitive and specific for meningioma. Therefore, the gut microbial marker associated with meningioma can be selected from any one or more of the four newly discovered single bacterial species. Specifically:

[0118] The intestinal microbial marker associated with meningioma can be selected from one or more of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius.

[0119] 2. As can be seen from Table 3, when intestinal samples are tested, only one or several bacterial species are detected, which is a normal phenomenon. This is because individuals are different. The disease probability Z of this application is obtained through calculation. Therefore, even if a sample only contains a single bacterial species, this application can calculate the probability that the sample to be tested has meningioma.

[0120] 3. Prediction effect: Fecal Prevotella had the highest single-bacterial prediction effect for meningioma, and the prediction effect of the above three single bacterial species was lower than that of mimicry markers (the four bacterial species combined); the prediction effect of mimicry markers (the four bacterial species combined) was relatively the best.

[0121] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent replacements and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention should be included in the scope of protection of the invention.

Claims

1. A gut microbial marker associated with meningioma, comprising Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius.

2. Use of a reagent for detecting the relative abundance value of the intestinal microbial marker according to claim 1 in the preparation of a product for diagnosing meningioma.

3. A kit, characterized in that: The kit contains a reagent for detecting the relative abundance value of the intestinal microbial marker according to claim 1.

4. Use of the kit according to claim 3 in preparing a product for detecting meningioma.

5. A product for diagnosing meningioma, characterized in that: The product comprises primers, probes, antibodies, aptamers or chips that are specific to the intestinal microbial markers according to claim 1, and the product is used to detect the relative abundance value of the intestinal microbial markers.

6. A computer program product related to meningioma, characterized in that: The computer program product is used to execute a method for diagnosing whether a subject has a risk of meningioma, the method comprising: Obtaining the relative abundance value of each single bacterial species in the feces of the test subject; Substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the logarithm y of the odds of the subject to be tested, the single bacterial species including Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius; Calculate the probability Z of the subject being a meningioma patient based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y; The risk of the subject suffering from meningioma is diagnosed or predicted based on the comparison result of the disease probability Z with the reference value.

7. The computer program product according to claim 6, wherein: The formula of the binary logistic regression equation is: y=A+B1×x1+B2×x2+B3×x3+B4×x4 Among them, A is the intercept term, B1-B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius.

8. The product according to claim 7, characterized in that: A is -0.558, B1 is -528.9872, B2 is -6.0904, B3 is -103.3701, and B4 is 8.1074.

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