Intestinal microbial marker related to irritable bowel syndrome, product and application of intestinal microbial marker
By using specific intestinal microbial markers and logistic regression models, the problem of insufficient accuracy in the diagnosis of irritable bowel syndrome is solved, an efficient and non-invasive diagnostic tool is provided, and accurate detection of irritable bowel syndrome is achieved.
Patent Information
- Application Number
- CN202510842917.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-09-30
AI Technical Summary
The lack of specific biomarkers in existing technologies leads to insufficient diagnostic accuracy of irritable bowel syndrome. The Rome IV criteria rely on subjective symptoms and are difficult to distinguish from other intestinal diseases. There is a lack of non-invasive and efficient diagnostic methods.
Using intestinal microbial markers such as Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius, combined with metagenomic sequencing and binary logistic regression models, the probability of disease is calculated through stool sample testing, providing a non-invasive diagnostic tool.
It improves the diagnostic accuracy and reliability of irritable bowel syndrome, provides a non-invasive detection method with high specificity and sensitivity, fills the gap in biomarkers, and realizes the efficient diagnosis of irritable bowel syndrome.
Smart Images
Figure CN120719010A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of biomedicine, and specifically relates to intestinal microbial markers, products and applications thereof related to irritable bowel syndrome. Background Art
[0002] Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder characterized by chronic abdominal pain, bloating, and altered bowel habits (diarrhea, constipation, or mixed), often without a clear underlying organic lesion. Its global prevalence is approximately 5%–15%, significantly impairing patients' quality of life and imposing a significant medical and economic burden.
[0003] The pathophysiology of IBS is complex, involving the interplay of multiple factors, including visceral hypersensitivity, gut-brain axis dysregulation, intestinal flora disturbances, mucosal immune activation, and impaired intestinal barrier function. Due to the lack of specific biomarkers, its diagnosis currently relies primarily on the Rome IV criteria. While the Rome IV criteria categorize IBS into diarrhea-type (IBS-D), constipation-type (IBS-C), and mixed (IBS-M) types, they still have the following limitations: 1) The symptoms underlying its diagnosis, such as abdominal pain and bloating, vary widely in subjective perception, and patient descriptions may be inconsistent, impacting diagnostic accuracy; 2) IBS symptoms may overlap with other intestinal diseases (such as inflammatory bowel disease and colon cancer), making it difficult to completely rule out these conditions using the Rome IV criteria alone; 3) The lack of specific biomarkers or test results directly associated with IBS leads to diagnostic uncertainty. Summary of the Invention
[0004] In response to the above technical problems, the present invention provides an intestinal microbial marker, product and application thereof related to irritable bowel syndrome, which can provide a new idea and approach for the diagnosis of irritable bowel syndrome.
[0005] The technical solutions provided by the present invention are as follows: In a first aspect, a gut microbial marker associated with irritable bowel syndrome is provided, wherein the gut microbial marker includes Bacteroides thetaiotaomicron and Clostridium celatum.
[0006] In the above technical solution, the intestinal microbial markers further include Bacteroides ovatus and / or Streptococcus salivarius.
[0007] In the above technical solution, the intestinal microbial markers include Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius.
[0008] 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 irritable bowel syndrome.
[0009] A third aspect provides a kit comprising a reagent for detecting the intestinal microbial markers described in the first aspect.
[0010] In a fourth aspect, a method for preparing a product for detecting irritable bowel syndrome is provided.
[0011] In a fifth aspect, a product for diagnosing irritable bowel syndrome is provided, wherein the product comprises primers, probes, antibodies, aptamers or chips specific to the intestinal microbial markers described in the first aspect.
[0012] In a sixth aspect, a computer program product related to irritable bowel syndrome is provided, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of irritable bowel syndrome, 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 object to be tested, the single bacterial species including Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius; Calculate the probability Z of the subject being a patient with irritable bowel syndrome based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y; According to the comparison result of the disease probability Z and the reference value, the risk of the subject suffering from irritable bowel syndrome is diagnosed or predicted.
[0013] In the above technical solution, 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 Bacteroides ovatus, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Streptococcus salivarius, and x4 is the relative abundance value of Clostridium celatum.
[0014] In the above technical solution, A is -0.17, B1 is 71.40, B2 is 24.64, B3 is -24.52, and B4 is -25.06.
[0015] The beneficial effects of the present invention are as follows: 1. The present invention newly discovered four intestinal microorganisms associated with irritable bowel syndrome, including Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus, 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 irritable bowel syndrome.
[0016] 2. The present invention also provides a reagent and kit that can predict or diagnose irritable bowel syndrome non-invasively and with high accuracy using one or more of the four bacterial species mentioned above as detection markers. 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.
[0017] 3. The present invention also provides a product for diagnosing irritable bowel syndrome. This product can calculate the probability of illness based on the relative abundance of various bacterial species, and then compare it with reference values to predict or diagnose whether a patient has irritable bowel syndrome or is at risk of developing irritable bowel syndrome. This product has good feasibility and accuracy, can effectively assess the risk of irritable bowel syndrome, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 This is the result graph of linear discriminant analysis; Figure 2 is a box scatter plot of intestinal microbial markers; Figure 3 is the ROC curve. DETAILED DESCRIPTION
[0019] To evaluate whether the composition of intestinal symbiotic flora can serve as a predictor of irritable bowel syndrome, the present invention collected samples from patients with irritable bowel syndrome and healthy people, performed metagenomic sequencing, and used bioinformatics to perform statistics on the sequencing data. The intestinal flora associated with the disease was discovered, and the intestinal flora was integrated with disease information to maximize the prediction of patients with irritable bowel syndrome.
[0020] The present invention discovered the correlation between Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius and patients with irritable bowel syndrome through metagenomic sequencing.
[0021] It has been verified that there is a significant association between the above four bacteria and irritable bowel syndrome. Specifically: Figure 2 It can be seen that the staff screened out two markers that were significantly increased in the irritable bowel syndrome group, including Bacteroides ovatus and Bacteroides thetaiotaomicron; and two markers that were significantly decreased in the irritable bowel syndrome group, including Streptococcus salivarius and Clostridium celatum.
[0022] 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 patients with irritable bowel syndrome.
[0023] 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.
[0024] Example 1: Sample Collection Fecal samples were collected from 166 patients with irritable bowel syndrome and 189 healthy subjects. The irritable bowel syndrome (IBS) group included 166 families. Inclusion criteria included: 1. Age range 18 to 50 years; 2. Clinical diagnosis of IBS meeting the Rome IV IBS criteria; 3. Stable vital signs.
[0025] Exclusion criteria for irritable bowel syndrome: 1. Systemic or local treatment for irritable bowel syndrome one month ago; 2. Taking antibiotics or probiotics or prebiotics in the past three months; 3. Suffering from any chronic disease, including neurobehavioral disorders; 4. Received medication that affects gastrointestinal motility within one week; 5. Has a history of pain such as functional dyspepsia, aerophagia, abdominal migraine, or functional constipation; 6. Shows growth disorders; 7. Suffering from gastrointestinal obstruction or stenosis; 8. Has a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. Women of childbearing age who are pregnant, plan to become pregnant during the study, or are breastfeeding; 10. The researchers believe that the patient is not suitable for inclusion in this study.
[0026] The control group consisted of 189 families. Inclusion criteria included: 1. Age range: 18-50 years; 2. No clinical diagnosis of IBS symptoms; 3. No diabetes or other metabolic diseases; 4. No other gastrointestinal or neurological diseases; 5. No other immune system diseases or immunodeficiency; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics or prebiotics before or during the study.
[0027] The exclusion criteria were the same as those of the irritable bowel syndrome group.
[0028] The above data come from stool samples collected by Jingmen Central Hospital.
[0029] Example 2: DNA extraction, library construction and sequencing 1. Select the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected stool samples.
[0030] 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).
[0031] 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).
[0032] 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.
[0033] 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 The results are as follows Figure 1 As shown, this application discovered for the first time four bacterial species related to irritable bowel syndrome. Specifically: the staff screened out three markers that were significantly increased in the irritable bowel syndrome group, including Ottowia sp., Campylobacter concisus, and Megasphaera micronuciformis; and two markers that were significantly decreased in the irritable bowel syndrome group, including Streptococcus cristatus and Veillonella rogosae.
[0034] 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 the irritable bowel syndrome 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).
[0035] 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.
[0036] 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.
[0037] Table 2 ROC diagnostic curve results According to Table 2, for single bacterial species, the AUC value of Bacteroides ovatus was the highest (about 0.9629), and the AUC value of Clostridium crypticum was the lowest (about 0.7149); the AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (0.9984) was significantly higher than the AUC value of a single bacteria.
[0038] From the above, it can be seen that the four newly discovered bacterial species in this application can all be used as detection variables, all of which have high specificity and sensitivity, and the AUCs of the four intestinal microbial markers are all greater than 70%. Therefore, one or more of the four intestinal microbial markers can be used as detection markers for the diagnosis of meningioma patients.
[0039] Example 5: Logistic regression model establishment 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: a. Build a model Based on the biomarkers mined above and the proportion of IBS patients and patients 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: 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 Bacteroides ovatus, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Streptococcus salivarius, and x4 is the relative abundance value of Clostridium celatum.
[0040] b. Determine the values of A and B1 to B4 above After statistical analysis of the sample data, the values of A and B1 to B4 are: A is -0.17, B1 is 71.40, B2 is 24.64, B3 is -24.52, and B4 is -25.06; At this point, after sorting, the calculation formula for the logarithm y of the odds is: 4; c. Calculate the probability Z of the subject being tested suffering from the disease 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.
[0041] After sorting, the calculation formula for the disease probability Z is d. Validation set data calculation and statistical analysis Based on the data of the validation set, the relative abundance of each single bacterial species in each sample in the disease group and the healthy group was obtained, and then the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z that the sample to be tested was a patient was calculated by the formula. The results are shown in Tables 3 and 4, where the patient refers to a patient with irritable bowel syndrome.
[0042] Table 4 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.
[0043] Table 3 Data related to the validation set markers Note: E represents the power of 10, for example, 9.76514818612e-05 represents 9.76514818612×10 -5 .
[0044] Table 4. Statistical data of the relative abundance of markers in the validation set Note: In Table 3, the mean refers to the relative abundance mean, and the same applies to the standard deviation.
[0045] e. Results and Analysis Combining the results of Examples 4 and 5, it can be seen that the AUC for the prediction score of the mimicry marker is approximately 99.84%, the optimal cutoff value is approximately 0.4787, the sensitivity is approximately 0.9697, and the specificity is 1. Therefore, the application of mimicry markers as detection markers in the diagnosis of patients with irritable bowel syndrome has better results and higher accuracy. Using these four bacterial species as detection markers is completely non-invasive and highly accurate.
[0046] 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 to diagnose the disease risk and disease probability of the sample to be tested; the above Table 3 has situations that do not fully meet the diagnostic criteria. The reason is that the stool samples of the persons to be tested may have false positive results or false negative results, and further testing is required by other means, including blood routine, diagnostic physical signs, etc.
[0047] Example 6: Computer Program Product Related to Irritable Bowel Syndrome Based on the above embodiments, this embodiment provides a computer program product related to irritable bowel syndrome, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of irritable bowel syndrome, including the following steps: S1. Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus, and Streptococcus salivarius; 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); 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 Bacteroides ovatus, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Streptococcus salivarius, and x4 is the relative abundance value of Clostridium celatum; S3. Calculate the probability Z that the subject is suffering from irritable bowel syndrome based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y; The probability of disease Z can also be expressed as: S4. Diagnose or predict the risk of the subject suffering from irritable bowel syndrome based on the comparison of the patient's probability Z value with the reference value.
[0048] In practice, when the Z value is greater than 0.5, it indicates a high probability that the subject has irritable bowel syndrome; when the Z value is less than 0.5, it indicates a low probability that the subject has irritable bowel syndrome; and when the Z value is 0.5, it indicates that the subject may be a patient or may have irritable bowel syndrome. In this case, further testing is necessary using other means, such as blood tests and physical examinations. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other means for testing.
[0049] Example 7: Checking the probability of illness in patients in the validation set and patients with irritable bowel syndrome Based on the product and method of Example 5, the probability of irritable bowel syndrome in healthy individuals and patients in the validation set was checked. The specific steps were as follows: S1. Collect stool samples from the individuals to be tested and measure the relative abundance of each single bacterial strain in the intestine; the single bacterial strains include Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus, and Streptococcus salivarius; S2. Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation; 4 x1 is the relative abundance value of Bacteroides ovatus, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Streptococcus salivarius, and x4 is the relative abundance value of Clostridium celatum; S3. Calculate the probability Z that the subject is suffering from irritable bowel syndrome based on y, where Z = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y; The probability of disease Z can also be expressed as: S4. Diagnose or predict the risk of the subject suffering from irritable bowel syndrome based on the comparison of the patient's probability P value with the reference value.
[0050] In practice, when the P value is greater than 0.5, it indicates that the subject has a high probability of suffering from irritable bowel syndrome; when the P value is less than 0.5, it indicates that the subject has a low probability of suffering from irritable bowel syndrome; when the P value is 0.5, it means that the subject may be a patient or may also have irritable bowel syndrome. In this case, further testing is required using other means, such as blood tests and physical examinations. Furthermore, the closer the P value is to 0.5, the more necessary it is to use other means for testing.
[0051] Example 8: Detection Reagents Based on the description of Examples 1 to 5 above, 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 diagnose whether the sample to be tested suffers from irritable bowel syndrome; medical staff can also combine multiple single bacterial species together as markers to detect and diagnose the sample to be tested to diagnose whether the sample to be tested suffers from irritable bowel syndrome.
[0052] Therefore, this embodiment also provides a reagent for detecting intestinal microbial markers, which can be used in the preparation of a product for diagnosing irritable bowel syndrome to diagnose whether the sample to be tested suffers from irritable bowel syndrome; at the same time, the intestinal microbial marker can be selected from the four single bacterial species discovered in this application and associated with irritable bowel syndrome, that is, the intestinal microbial marker in the detection reagent can be selected from one or more of Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius.
[0053] Example 9: Kit This embodiment also provides a kit, which may include the detection reagent described in Example 7, for diagnosing whether a sample to be tested has irritable bowel syndrome. The limitations and technical solutions of this application regarding the kit can be found in the description of Example 7 above and are not further described here. Similarly, the kit can also be used in the preparation of a product for detecting irritable bowel syndrome, which is not further described here.
[0054] Example 10: Product for diagnosing irritable bowel syndrome The present application also provides a product for diagnosing irritable bowel syndrome to diagnose whether the sample to be tested suffers from irritable bowel syndrome; the product is specific to one or more of the four single bacterial species discovered in the present application and associated with irritable bowel syndrome, and the product includes primers, probes, antibodies, aptamers or chips.
[0055] As can be seen from the description of Examples 1 to 5 and conventional means in the art, 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.
[0056] Conclusion and explanation: 1. By Figures 1 to 3 As shown in Table 2, any one of the four newly discovered single bacterial species in this application can be used as an intestinal microbial marker for irritable bowel syndrome. Each single bacterial species is sensitive and specific to irritable bowel syndrome. Therefore, the intestinal microbial marker associated with irritable bowel syndrome can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically: The intestinal microbial marker associated with irritable bowel syndrome can be selected from one or more of Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius.
[0057] 2. As can be seen from Table 3, when testing fecal samples, 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 irritable bowel syndrome.
[0058] 3. Predictive Effect: For single bacterial species, Bacteroides ovatus had the highest AUC value (approximately 0.9629), while Clostridium crypticum had the lowest AUC value (0.7149). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) was significantly higher than that of a single bacterial species. Furthermore, the four newly discovered bacterial species can all be used as detection variables, with high specificity and sensitivity. The AUCs of the four gut microbial markers were all greater than 70%. One or more of the four gut microbial markers can be used as detection markers for the diagnosis of meningioma patients.
[0059] 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 irritable bowel syndrome, characterized in that: The intestinal microbial markers include Bacteroides thetaiotaomicron and Clostridium celatum.
2. The intestinal microbial marker according to claim 1, characterized in that The intestinal microbial markers also include Bacteroides ovatus and / or Streptococcus salivarius.
3. The intestinal microbial marker according to claim 2, characterized in that The intestinal microbial markers include Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius.
4. Use of a reagent for detecting an intestinal microbial marker according to any one of claims 1 to 3 in the preparation of a product for diagnosing irritable bowel syndrome.
5. A kit, characterized in that: The kit contains a reagent for detecting the intestinal microbial marker according to any one of claims 1 to 3.
6. Use of the kit according to claim 5 in preparing a product for detecting irritable bowel syndrome.
7. A product for diagnosing irritable bowel syndrome, characterized in that: The product comprises a primer, a probe, an antibody, an aptamer or a chip that is specific to the intestinal microbial marker according to any one of claims 1 to 3.
8. A computer program product related to irritable bowel syndrome, characterized in that: The computer program product is used to execute a method for diagnosing whether a subject has a risk of irritable bowel syndrome, 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 object to be tested, the single bacterial species including Bacteroides thetaiotaomicron, Clostridium celatum, Bacteroides ovatus and Streptococcus salivarius; Calculate the probability Z of the subject being a patient with irritable bowel syndrome based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is the exponential function of y; According to the comparison result of the disease probability Z and the reference value, the risk of the subject suffering from irritable bowel syndrome is diagnosed or predicted.
9. The computer program 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 Among them, A is the intercept term, B1–B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bacteroides ovatus, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Streptococcus salivarius, and x4 is the relative abundance value of Clostridium celatum.
10. The product according to claim 9, characterized in that: A is -0.17, B1 is 71.40, B2 is 24.64, B3 is -24.52, and B4 is -25.06.