Meningioma enteric microbial marker, product and application thereof

Abstract

The invention discloses a meningioma enteric microbial marker, a product and application of the meningioma enteric microbial marker, and belongs to the field of biological medicine. The invention relates to an enteric microbial marker, which is characterized by being prepared from one or more of the following components: parabacteroides dissoni, prevotella copri, bifidobacterium adolescentis and streptococcus salivarius, and is characterized in that the enteric microbial marker comprises one or more of the following components: parabacteroides dissoni, prevotella copri, bifidobacterium adolescentis and streptococcus salivarius, and the enteric microbial marker comprises one or more of parabacteroides dissoni, prevotella copri, bifidobacterium adolescentis and streptococcus salivarius. The invention provides a kit which comprises a detection reagent for detecting the relative abundance of the intestinal microbial marker. The invention provides a product for diagnosing meningioma and a computer program product related to meningioma. The product provided by the invention has good feasibility and accuracy, can effectively evaluate 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 particularly relates to meningioma intestinal microbial markers, products and their applications. Background Art

[0002] In current clinical practice, the diagnosis of meningioma mainly relies on imaging examinations such as CT, MRI and pathological biopsy. Although these techniques can provide certain tumor localization and histological information, there are still significant limitations: 1) Imaging examinations have insufficient sensitivity to early or small lesions, which is prone to missed diagnosis or delayed diagnosis; 2) The imaging features of different subtypes of meningioma overlap highly, making it difficult to accurately classify, which may affect the selection of subsequent treatment plans; 3) Although invasive biopsy is the gold standard for diagnosis, there are risks such as surgical trauma, nerve function damage and sampling error.

[0003] In summary, the existing diagnosis mode of meningioma urgently needs to be broken through in terms of accuracy, safety and personalization. At present, there is still a lack of a clear, safe and non-invasive meningioma diagnosis product, thus providing a new idea and approach for the diagnosis of meningioma. Summary of the Invention

[0004] In view of the above technical problems, the present invention provides a meningioma intestinal microbial marker, a product and their applications, which are 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: In the first aspect, there is provided an intestinal microbial marker related to meningioma, characterized in that the intestinal microbial marker includes Parabacteroides distasonis and Prevotella copri.

[0006] In the above technical solution, the intestinal microbial marker further includes Bifidobacterium adolescentis and / or Streptococcus salivarius.

[0007] In the above technical solution, the intestinal microbial marker includes Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius.

[0008] Second aspect, there is provided an application of a reagent for detecting the intestinal microbial marker described in the first aspect in the preparation of a product for diagnosing meningioma.

[0009] Third aspect, a kit, which contains a reagent for detecting the intestinal microbial marker described in the first aspect.

[0010] Fourth aspect, there is provided an application of the kit described in the third aspect in the preparation of a product for detecting meningioma.

[0011] Fifth aspect, there is provided an application of the intestinal microbial marker in the kit described in the fourth aspect in the non-diagnostic detection of meningioma.

[0012] Sixth aspect, there is provided a product for diagnosing meningioma, which includes primers, probes, antibodies, aptamers or chips specific to the intestinal microbial marker described in the first aspect.

[0013] Seventh aspect, there is provided a product for diagnosing meningioma, which is used to detect the relative abundance value of each single strain in the intestinal microbial marker described in the first aspect, and the product includes primers, probes, antibodies, aptamers or chips specific to the intestinal microbial marker.

[0014] Eighth aspect, there is provided a computer program product related to meningioma, which is used to execute a method for diagnosing the risk of whether a subject to be tested has meningioma, and the method includes: Obtaining the relative abundance value of each single strain in the intestine of the subject to be tested; Substituting the relative abundance value of each single strain into a binary logistic regression equation to calculate the logarithm of the odds y of the subject to be tested, and the single strains include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis and Streptococcus salivarius; Calculating the probability Z of the subject to be tested being a meningioma patient according to y, Z = exp(y) / {1 + exp(y)}, where exp(y) is the exponential function of y; Diagnosing or predicting whether the subject to be tested has meningioma or is at risk of having meningioma according to the comparison result of the probability Z with the reference value.

[0015] In the above technical solution, the formula of the binary logistic regression equation is:

[0016] Among them, A is the intercept term, B1 to B4 are the regression coefficients of independent variables, x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Prevotella copri in feces, x3 is the relative abundance value of Parabacteroides distasonis, and x4 is the relative abundance value of Streptococcus salivarius in the intestine.

[0017] 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.

[0018] The beneficial effects of the present invention are as follows: 1. The present invention newly discovers 4 kinds of gut microbes related to meningioma, including Parabacteroides distasonis, Prevotella copri in feces, Bifidobacterium adolescentis, and Streptococcus salivarius; after research and verification, it is found that one or several of the above 4 kinds of gut microbes can be used as gut microbial markers for meningioma to diagnose whether a test sample has meningioma.

[0019] 2. The present invention also provides a reagent and a kit, which can use one or several of the above 4 strains as detection markers to predict or diagnose meningioma, which is completely non-invasive and has high accuracy. By means of metagenomic sequencing, higher resolution is provided, enabling the analysis of microbial communities to reach the level of species or even strains, thereby improving the accuracy and reliability of diagnosis. The above 4 strains can also be used as target microbes for developing these systems, filling the blank in this field.

[0020] 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 strain, and then compare it with the reference value to predict or diagnose whether a patient has meningioma or is at risk of having meningioma. This product has good feasibility and accuracy, can effectively evaluate the risk of meningioma, and provides a new tool for clinical diagnosis. Description of the Drawings

[0021] Figure 1 It is a result graph of linear discriminant analysis; Figure 2 It is a box plot of gut microbial markers; Figure 3 is the ROC curve. Specific implementation manners

[0022] In order to evaluate whether the composition of the intestinal commensal flora can be used as a predictor of meningioma, the present invention collected samples from meningioma patients and healthy individuals, performed metagenomic sequencing, and used bioinformatics for statistical analysis of the sequencing data, discovered intestinal flora related to the disease, integrated the intestinal flora and the disease information, and maximally predicted meningioma patients.

[0023] Through metagenomic sequencing, the present invention discovered the correlations of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius with meningioma patients.

[0024] After verification, the significant associations between the above four bacteria and meningioma are as follows: Specifically, the staff screened out 1 biomarker that was significantly increased in the meningioma group, including Parabacteroides distasonis; and 3 biomarkers that were significantly decreased in the meningioma group, including Bifidobacterium adolescentis, Prevotella copri, and Streptococcus salivarius.

[0025] Through ROC curve analysis, the above four biomarkers have high specificity and sensitivity as detection variables. Therefore, these four bacterial species can be used as detection biomarkers for the prediction and diagnosis of meningioma patients.

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. For the experimental methods without specific conditions noted in the embodiments, they are generally in accordance with conventional conditions.

[0027] Example 1: Sample collection Collect intestinal samples from 60 meningioma patients and 80 healthy individuals: The sources and inclusion criteria of the samples in the meningioma group are as follows: Recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria: 1. Age greater than 18 years old; 2. Diagnosed with meningioma (imaging or histopathological diagnosis); 3. Not received antibiotic or immunosuppressive treatment within 1 month before sampling; 4. The patient or his / her guardian agrees to participate in the study and signs the informed consent form.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Example 2: DNA extraction, library construction and sequencing 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0032] 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).

[0033] 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).

[0034] 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.

[0035] Example 3: LEfSe analysis and screening of intestinal microbial markers 1. Divide the dataset 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. Table 1 Sample information table

[0036] 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.

[0037] 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.

[0038] Example 4: Verification of the reliability of the above four intestinal microbial markers 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).

[0039] 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. Specificity (true negative rate) = TN / (TN+FP), Sensitivity (true positive rate) = TP / (TP+FN), 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.

[0040] 4. The relative abundance values of the gut microbial biomarkers of a single strain were directly subjected to 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 3 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the predicted mimic biomarkers (biomarkers formed by a combination of 4 single strains) and single bacteria are shown in Table 2.

[0041] Table 2 Results of ROC diagnostic curve

[0042] As can be seen from the above: The ROC curve analysis shows that the 4 gut microbial biomarkers as detection variables have high specificity and sensitivity, and the AUC of the 4 gut microbial biomarkers are all greater than 75%. Therefore, one or more of the 4 gut microbial biomarkers can be used as detection biomarkers for the diagnosis of meningioma patients.

[0043] Example 5: Establishment of logistic regression model Based on the above-screened gut microbial biomarkers and obtaining the relative abundance values of each metabolic biomarker, the binary logistic regression algorithm in SPSS software was used to calculate the first disease probability of each sample (i.e., the logarithm of the odds y 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 situation (such as severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establish a model Through the above-mined biomarkers, based on the proportion of the meningioma population and the patient population in the training set, further, the relative abundance values of these 4 detected strains were used as single variables. On this basis, the linear relationship between the relative abundance values of the 4 single bacteria and the disease probability of the sample was discussed, and the logarithm of the odds y (also called the first probability value y) of the object to be tested was calculated through the binary logistic regression equation:

[0044] where A is the intercept term, and B1 to 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.

[0045] b. Determine the values of A and B1 - B4 above After statistical analysis of the sample data, the values of A and B1 - B4 above are: A is -0.558, B1 is -528.9872, B2 is -6.0904, B3 is -103.3701, and B4 is 8.1074; At this time, after arrangement, the calculation formula for the logarithm of the odds y is: ; c. Calculate the disease probability Z of the object to be tested Substitute the above 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)}; where Z is the probability value that the object to be tested is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0046] After arrangement, the calculation formula for the disease probability Z is:

[0047] d. Calculation and statistical analysis of the validation set data Based on the data of the validation set, obtain the relative abundance of each single strain in the disease group and the healthy group for each sample, then use the aforementioned binary logistic regression method to obtain the first probability value, and then calculate the probability that the sample to be tested is a patient through the formula. The results are shown in Table 3 and Table 4.

[0048] Table 3 shows the mean and standard deviation of the relative abundance of each strain. The mean of the relative abundance determines the central 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 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.

[0049] Table 3 Related data of the validation set markers

[0050] Note: E is the N - th power of 10. For example, 9.88115E - 05 is 9.88115×10 -05 Table 4 Statistical data of the related abundances of the validation set markers

[0051] Note: In Table 3, the mean refers to the mean of the relative abundance, and the same applies to the standard deviation.

[0052] e. Results and analysis Combining the results of Example 4 and Example 5, it can be seen that the AUC of the predicted score of the mimetic biomarker is 1, the optimal cut-off value is 0.272017572, the sensitivity is 1, and the specificity is 1. Therefore, using the mimetic biomarker as a detection biomarker for the diagnosis of meningioma patients has better effects and a higher accuracy rate. Using these 4 strains as detection biomarkers is completely non-invasive and highly accurate.

[0053] Meanwhile, these 4 gut microbiota biomarkers are discovered to be related to meningioma for the first time, and the single-strain prediction effect of Prevotella copri on meningioma is the highest, reaching 93.75%.

[0054] Combining the records in Table 3 above and the results obtained from the calculation formula of the disease probability Z, it can be seen that the calculation formula for summarizing the disease probability of the sample to be tested in this application is basically correct and can be adopted. There are situations in Table 3 above that do not fully meet the judgment criteria. The reason is that false positive results or false negative results may occur in the intestinal samples of the personnel to be tested, and other means need to be further used for detection. Other means include blood routine, judging physical signs, etc.

[0055] Example 6: Computer program product related to meningioma Based on the above embodiments, this embodiment provides a computer program product related to meningioma. The computer program product is used to execute a method for diagnosing the risk of whether a to-be-tested object has meningioma, including the following steps: S1. Obtain the relative abundance value of each single strain in the intestine of the to-be-tested object; the single strains include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius; S2. Substitute the relative abundance value of each single strain into the binary logistic regression equation to calculate the logarithm of the odds y (i.e., the first probability value y) of the to-be-tested object; ; where 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 in the intestine; S3. Calculate the probability Z of the subject to be a meningioma patient based on y, where Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y. S4. Based on the comparison between the probability Z value of the patient and the reference value, diagnose or predict the risk of the subject having meningioma.

[0056] In actual work, when the Z value is greater than 0.5, it indicates that the probability of the subject having meningioma is relatively small; when the Z value is less than 0.5, it indicates that the probability of the subject having meningioma is relatively large; when the Z value is 0.5, it indicates that the person to be tested may be a patient or a meningioma patient. In this case, other means need to be further used for detection, and the other means are blood routine and judging physical signs. Further, the closer the Z value is to 0.5, the more other means are needed for detection.

[0057] Example 7: Check the disease probabilities of patients in the validation set and meningioma patients Based on the product and method of Example 5, check the disease probabilities of healthy people and meningioma patients in the validation set. The specific steps are as follows: 1. Collect the intestinal samples of the persons to be tested, and detect the relative abundance values of each single strain in the intestine; among them, the single strains include including Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius; 2. Calculate the logarithm of the odds y of the subject according to the binary logistic regression equation; ; In the formula, 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 in the intestine; 3. Calculate the probability Z of the subject to be a meningioma patient based on y, where Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y. 4. Based on the comparison between the probability P value of the patient and the reference value, diagnose or predict the risk of the subject having meningioma.

[0058] During actual operation, when the P value is greater than 0.5, it indicates that the probability of the object to be tested having meningioma is relatively small; when the P value is less than 0.5, it indicates that the probability of the object to be tested having meningioma is relatively large; when the P value is 0.5, it indicates that the object to be tested may be a patient or a meningioma patient. At this time, other means need to be further used for detection, and the other means are blood routine and judging physical signs. Further, when the P value is closer to 0.5, more other means are needed for detection.

[0059] Example 8: Detection reagent Based on the descriptions of the above Examples 1 to 5, it can be seen that the prediction effect of the selected biomarker of the present application is good. Medical staff can use a single strain as a biomarker to separately detect and diagnose the sample to be tested to determine whether the sample to be tested has meningioma; medical staff can also combine multiple single strains together as a biomarker to detect and diagnose the sample to be tested to determine whether the sample to be tested has meningioma.

[0060] Therefore, this embodiment also provides a reagent for detecting intestinal microbial biomarkers, which can be used in the preparation of products for diagnosing meningioma to determine whether the sample to be tested has meningioma; at the same time, the intestinal microbial biomarker can be selected from 4 single strains found in the present application and related to meningioma, that is, the intestinal microbial biomarker in the detection reagent can be one or more of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius.

[0061] Example 9: Kit The present application also provides a kit, which can include the detection reagent described in Example 7 to determine whether the sample to be tested has meningioma; the definition and technical solution of the kit in the present application can refer to the description of Example 7 above, and will not be elaborated here. Similarly, the above kit can also be used in the preparation of products for detecting meningioma, and will not be elaborated here.

[0062] Example 10: Product for diagnosing meningioma The present application also provides a product for diagnosing meningioma, which is specific to one or more of the 4 single strains found in the present application and related to meningioma, and includes primers, probes, antibodies, aptamers or chips.

[0063] Combined with the descriptions of Examples 1 to 5 and conventional means in the art, it can be known to those skilled in the art that when using the newly discovered 4 single strains as intestinal microbial markers, it should be achievable to produce corresponding specific products (primers, probes, antibodies, aptamers, chips, etc.), which will not be elaborated here.

[0064] Conclusions and explanations: 1. From Figures 1 to 3 and Table 2, it can be seen that any one of the newly discovered 4 single strains in this application can be used as an intestinal microbial marker for meningioma. Each single strain has sensitivity and specificity for meningioma. Therefore, the intestinal microbial markers related to meningioma can be selected from any one or several of the newly discovered 4 single strains in this application. Specifically: The intestinal microbial markers related to meningioma can be selected from one or several of Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius.

[0065] 2. From Table 3, it can be seen that when detecting intestinal samples, it is normal to only detect a certain strain or several strains because of individual differences. The disease probability Z of this application is calculated. Therefore, even if a certain sample only contains a certain single strain, this application can still calculate the probability that the sample to be tested has meningioma.

[0066] 3. Prediction effect: Prevotella copri has the highest single-strain prediction effect on meningioma, and the prediction effects of the above three single strains are lower than that of the mimic marker (4 strains combined); the prediction effect of the mimic marker (4 strains combined) is relatively the best.

[0067] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope disclosed by the present invention shall be included in the protection scope of the invention.

Claims

1. An intestinal microbial biomarker related to meningioma, characterized in that, The intestinal microbial markers include Parabacteroides distasonis and Prevotella copri.

2. The intestinal microbial marker according to claim 1, wherein The intestinal microbial markers further include one or more of Bifidobacterium adolescentis and / or Streptococcus salivarius.

3. The intestinal microbial marker according to claim 2, wherein The intestinal microbial markers include Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius.

4. Use of a reagent for detecting the intestinal microbial markers according to any one of claims 1 to 3 in the preparation of a product for diagnosing meningioma.

5. A kit, characterized in that: The kit contains a reagent for detecting the intestinal microbial markers according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in the preparation of a product for detecting meningioma.

7. A product for diagnosing meningioma, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips specific to the intestinal microbial markers according to any one of claims 1 to 3.

8. A computer program product related to meningioma, characterized in that: The computer program product is used to execute a method for diagnosing the risk of a subject to be tested having meningioma, and the method includes: Obtaining the relative abundance value of each single bacterial species in the feces of the subject to be tested; Substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the logarithm of the odds y of the subject to be tested, and the single bacterial species includes Parabacteroides distasonis, Prevotella copri, Bifidobacterium adolescentis, and Streptococcus salivarius; Calculate the probability Z of the object to be measured being a meningioma patient according to y, , where exp(y) is the exponential function of y; Diagnosing or predicting the risk of the subject to be tested having meningioma according to the comparison result of the disease probability Z with a reference value.

9. The computer program product according to claim 8, characterized in that: The formula of the binary logistic regression equation is as follows: Wherein, A is the intercept term, and B1 to 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.

10. The product according to claim 9, characterized in that: The 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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