Microbial marker for thyroid-associated ophthalmopathy, product and application of microbial marker

Abstract

The invention discloses a microbial marker for thyroid-associated ophthalmopathy, a product and application of the microbial marker, and belongs to the field of biological medicine. The microbial marker comprises one or more of the following components: Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistilla finegili, and Bacteroides uniformiformis, and the microbial marker comprises one or more of the following components: the Faecalibacterium prausnitzii, the Parabacteroides sp.CT06, the Escherichia coli, the Alistilla finegili, and the Bacteroides uniformii. The invention provides a kit which comprises a detection reagent for detecting the relative abundance of the microbial marker. The product provided by the invention has good feasibility and accuracy, can effectively evaluate the risk of thyroid-related eye diseases, 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 microbial markers, products and applications of thyroid-related eye diseases. Background Art

[0002] Thyroid-related eye disease (exophthalmos) is one of the most common orbital diseases in adults and is an autoimmune disease. Treatment should be tailored to disease activity, severity, and individual needs: 1) For mild cases, lifestyle interventions (such as smoking cessation and eye care) and thyroid function regulation are the primary treatment, supplemented by artificial tears to alleviate dry eye symptoms. 2) For patients with moderate to severe disease activity, glucocorticoids (such as intravenous methylprednisolone) are preferred, combined with immunosuppressants (cyclosporine) or new targeted drugs (tetucumab) to suppress inflammation. 3) For patients resistant to steroids, orbital radiotherapy may be considered, but the risk of radiation damage should be considered. 4) For stable patients with proptosis, optic nerve compression, or eyelid deformities, surgical procedures such as orbital decompression and ocular muscle surgery may be necessary to improve function and appearance.

[0003] Although existing diagnostic and treatment methods have formed a system, they still have significant shortcomings. For example: 1) In terms of drug treatment, glucocorticoids are the first-line option. Long-term use can easily cause side effects such as metabolic abnormalities and osteoporosis, and about 30% of patients respond poorly to hormone treatment; 2) New targeted drugs (such as tetanusinib) have significant efficacy, but they are expensive and lack long-term safety data; 3) Radiotherapy is effective for active inflammation, but may aggravate dry eye or induce radiation cataracts and is not suitable for adolescent patients; 4) Although surgical treatment can improve late-stage deformities, there is a risk of complications such as postoperative diplopia and bleeding, and it cannot reverse damaged optic nerve function. Summary of the Invention

[0004] In response to the above technical problems, the present invention provides a microbial marker, product and application of thyroid-related eye disease, which can provide a new idea and approach for the diagnosis of thyroid-related eye disease.

[0005] The technical solutions provided by the present invention are as follows: In a first aspect, a microbial marker for thyroid-related eye disease is provided, wherein the microbial marker includes Faecalibacterium prausnitzii, Parabacteroides sp. CT06, and Escherichia coli.

[0006] In the above technical solution, the microbial markers also include Alistipes finegoldii and / or Bacteroides uniformis.

[0007] In the above technical solution, the microbial markers include Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

[0008] In a second aspect, a reagent for detecting the microbial marker described in the first aspect is provided for use in preparing a product for diagnosing thyroid-related eye diseases.

[0009] A third aspect provides a kit comprising a reagent for detecting the microbial marker described in the first aspect.

[0010] In a fourth aspect, a method is provided for using the kit described in the third aspect in preparing a product for detecting thyroid-related eye diseases.

[0011] In a fifth aspect, a product for diagnosing thyroid-related eye diseases is provided, wherein the product comprises primers, probes, antibodies, aptamers or chips that are specific to the microbial markers described in the first aspect.

[0012] In a sixth aspect, a computer program product related to thyroid-related eye disease is provided, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of thyroid-related eye disease, 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 Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipesfinegoldii and Bacteroides uniformis; Calculate the probability Z of the subject being a patient with thyroid-related eye disease 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 to be tested suffering from thyroid-related eye disease 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+B5×x5; Among them, A is the intercept term, B1–B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alistipes finegoldii, x2 is the relative abundance value of Escherichia coli, x3 is the relative abundance value of Faecalibacterium prausnitzii, x4 is the relative abundance value of Parabacteroides sp CT06, and x5 is the relative abundance value of Bacteroides uniformis.

[0014] In the above technical solution, A is -1.61, B1 is 5026.24, B2 is -12.37, B3 is 13.75, B4 is 2377.38, and B5 is 16.98.

[0015] The beneficial effects of the present invention are as follows: 1. The present invention newly discovered five intestinal microorganisms associated with thyroid-related eye diseases, including Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii, and Bacteroides uniformis. After research and verification, it was found that one or more of the above five intestinal microorganisms can be used as detection markers for thyroid-related eye diseases to diagnose whether the test sample has thyroid-related eye diseases.

[0016] 2. The present invention also provides a reagent and kit that can predict or diagnose thyroid-related eye diseases non-invasively and with high accuracy using one or more of the five bacterial species mentioned above as detection markers. Using metagenomic sequencing, higher resolution is achieved, enabling analysis of microbial communities down to the species or even strain level, thereby improving diagnostic accuracy and reliability. The five 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 thyroid-related eye diseases. 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 thyroid-related eye disease or is at risk of developing thyroid-related eye disease. This product has good feasibility and accuracy, can effectively assess the risk of thyroid-related eye disease, 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 microbial markers; Figure 3 is the ROC curve. DETAILED DESCRIPTION

[0019] To evaluate whether the composition of intestinal symbiotic flora can serve as a predictive factor for thyroid-related eye disease, the present invention collected fecal samples from patients with thyroid-related eye disease 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 thyroid-related eye disease.

[0020] Through metagenomic sequencing, the present invention discovered the correlation between Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis and patients with thyroid-related eye disease.

[0021] It has been verified that there is a significant association between the above five bacteria and thyroid-related eye disease. Specifically: Figure 1 and Figure 2 It can be seen that the staff screened out 5 markers that were significantly increased in the group of patients with thyroid-related eye disease, including Alistipes finegoldii, Faecalibacterium prausnitzii, Parabacteroides sp.CT06, and Bacteroides uniformis, and 1 marker that was significantly decreased in the group of patients with thyroid-related eye disease, including Escherichia coli.

[0022] ROC curve analysis showed that the above five markers had high specificity and sensitivity as detection variables. Therefore, these five bacterial species can be used as detection markers for the prediction and diagnosis of patients with thyroid-related eye diseases.

[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. Example 1: Sample Collection Fecal samples were collected from 55 patients with thyroid-associated eye disease and 55 healthy subjects: The inclusion criteria for the thyroid-related eye disease group were as follows: 1. Age > 18 years; 2. Clinical diagnosis of thyroid-related eye disease.

[0024] The exclusion criteria for the thyroid-related eye disease group were as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnancy or lactation.

[0025] Inclusion criteria for the healthy subjects were as follows: 1. Age > 18 years; 2. No diabetes or other metabolic diseases; 3. No other gastrointestinal or neurological diseases; 4. No other immune system diseases or immunodeficiency; 5. No antibiotics (e.g., neomycin, rifaximin) or probiotics or prebiotics taken in the three months prior to and during the study. Exclusion criteria for the healthy subjects were as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnancy or lactation; 4. Thyroid function and thyroid antibodies exceeding normal ranges. The above data were obtained from stool samples collected at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

[0026] 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.

[0027] 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).

[0028] 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).

[0029] 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.

[0030] Example 3: LEfSe analysis and screening of 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 thyroid-related eye disease 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

[0031] The results are as follows Figure 1 As shown, this application discovered for the first time five bacterial species related to thyroid-related eye diseases. Specifically: the staff screened out four markers that were significantly increased in the thyroid-related eye disease patient group, including Alistipes finegoldii, Faecalibacterium prausnitzii, Parabacteroides sp.CT06, and Bacteroides uniformis, and one marker that was significantly reduced in the thyroid-related eye disease patient group, including Escherichia coli.

[0032] Example 4: Verification of the reliability of the above five microbial markers 1. First, the remaining 20% ​​of the subjects to be tested in Example 1 (including the thyroid-related eye disease 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).

[0033] 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.

[0034] 4. The relative abundance values ​​of microbial markers of a single strain are 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 five single bacterial species) and individual bacteria are shown in Table 2.

[0035] Table 2 ROC diagnostic curve results

[0036] According to Table 2, for single bacterial species, Faecalibacterium prausnitzii had the highest AUC value (approximately 0.8512), and Bacteroides monomorpha had the lowest AUC value (approximately 0.6198); the AUC value of the mimicry marker (a marker formed by the combination of 5 single bacterial species) (approximately 1) was significantly higher than the AUC value of a single bacteria.

[0037] From the above, it can be seen that the five newly discovered bacterial species in this application can all be used as detection variables, they all have high specificity and sensitivity, and the AUCs of the five microbial markers are all greater than 60%. Therefore, one or more of the five microbial markers can be used as detection markers for the diagnosis of patients with thyroid-related eye diseases.

[0038] Example 5: Logistic regression model establishment Based on the above-screened microbial markers and the relative abundance values ​​of each metabolite marker, the binary logistic regression algorithm in SPSS software was used to calculate the first disease probability of 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 Through the biomarkers mined above, based on the proportion of thyroid-related eye disease population and patient population in the training set, we further used the relative abundance values ​​of the five detected bacterial species as a single variable. On this basis, we discussed the linear relationship between the relative abundance values ​​of the five 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+B5×x5 Among them, A is the intercept term, B1–B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alistipes finegoldii, x2 is the relative abundance value of Escherichia coli, x3 is the relative abundance value of Faecalibacterium prausnitzii, x4 is the relative abundance value of Parabacteroides sp CT06, and x5 is the relative abundance value of Bacteroides uniformis.

[0039] b. Determine the values ​​of A and B1 to B5 above After statistical analysis of the sample data, the values ​​of A and B1 to B5 are: A is -1.61, B1 is 5026.24, B2 is -12.37, B3 is 13.75, B4 is 2377.38, and B5 is 16.98; At this point, after sorting, the calculation formula for the logarithm y of the odds is:

[0040] 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

[0042] 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 patients refer to patients with thyroid-related eye diseases.

[0043] 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.

[0044] Table 3 Data related to the validation set markers

[0045] Note: E represents the power of 10, for example, 9.76514818612e-05 represents 9.76514818612×10 -5 .

[0046] Table 4. Statistical data of the relative abundance of markers in the validation set

[0047] Note: In Table 4, the mean refers to the relative abundance mean, and the same applies to the standard deviation.

[0048] e. Results and Analysis Combining the results of Examples 4 and 5, it can be seen that the predictive score of the mimicry marker has an AUC of approximately 1, an optimal cutoff value of approximately 0.5659, a sensitivity of approximately 1, and a specificity of 1. Therefore, the application of mimicry markers as detection markers in the diagnosis of patients with thyroid-related eye diseases has better results and higher accuracy. Using these five bacterial species as detection markers is completely non-invasive and highly accurate.

[0049] 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; there are cases in Table 3 above 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 using other means, including blood routine, diagnostic physical signs, etc.

[0050] Example 6: Computer program product related to thyroid-related eye disease Based on the above embodiments, this embodiment provides a computer program product related to thyroid-related eye disease, wherein the computer program product is used to execute a method for diagnosing whether a subject has a risk of thyroid-related eye disease, 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 Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipes finegoldii, and Bacteroides uniformis; 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+B5×x5 Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alistipes finegoldii, x2 is the relative abundance value of Escherichia coli, x3 is the relative abundance value of Faecalibacterium prausnitzii, x4 is the relative abundance value of Parabacteroides sp CT06, and x5 is the relative abundance value of Bacteroides uniformis; S3. Calculate the probability Z that the subject is a patient with thyroid-related eye disease 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:

[0051] S4. Diagnose or predict the risk of the subject suffering from thyroid-related eye disease based on the comparison of the patient's probability Z value with the reference value.

[0052] In practice, when the Z value is greater than 0.5, it indicates that the subject is more likely to have thyroid-related eye disease; when the Z value is less than 0.5, it indicates that the subject is less likely to have thyroid-related eye disease; and when the Z value is 0.5, it indicates that the subject may be a patient or a thyroid-related eye disease patient. 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.

[0053] Example 7: Checking the probability of thyroid-related eye disease in patients in the validation set Based on the product and method of Example 5, the probability of disease among healthy individuals and patients with thyroid-related eye disease 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 stool samples; the single bacterial strains include Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipes finegoldii, and Bacteroides uniformis; S2. Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation;

[0054] Among them, x1 is the relative abundance value of Alistipes finegoldii, x2 is the relative abundance value of Escherichia coli, x3 is the relative abundance value of Faecalibacterium prausnitzii, x4 is the relative abundance value of Parabacteroides sp CT06, and x5 is the relative abundance value of Bacteroides uniformis; S3. Calculate the probability Z that the subject is a patient with thyroid-related eye disease 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:

[0055] S4. Diagnose or predict the risk of the subject suffering from thyroid-related eye disease based on the comparison of the patient's probability Z value with the reference value.

[0056] In practice, when the Z value is greater than 0.5, it indicates that the subject is more likely to have thyroid-related eye disease; when the Z value is less than 0.5, it indicates that the subject is less likely to have thyroid-related eye disease; when the Z value is 0.5, it indicates that the subject may be a patient or may also have thyroid-related eye disease. In this case, further testing is required 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.

[0057] 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 has thyroid-related eye disease; 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 has thyroid-related eye disease.

[0058] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of a product for diagnosing thyroid-related eye diseases to diagnose whether the sample to be tested suffers from thyroid-related eye diseases; at the same time, the microbial marker can be selected from the five single bacterial species discovered in this application and associated with thyroid-related eye diseases, that is, the microbial marker in the detection reagent can be selected from one or more of Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipesfinegoldii and Bacteroides uniformis.

[0059] 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 thyroid-related eye disease. 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 products for detecting thyroid-related eye diseases, which is not further described here.

[0060] Example 10: Product for diagnosing thyroid-related eye diseases The present application also provides a product for diagnosing thyroid-related eye diseases to diagnose whether the sample to be tested suffers from thyroid-related eye diseases; the product is specific to one or more of the five single bacterial species discovered in this application and associated with thyroid-related eye diseases, and the product includes primers, probes, antibodies, aptamers or chips.

[0061] It can be seen from the description of Examples 1 to 5 and the conventional means in the art that, when the newly discovered five single bacterial species are used as 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.

[0062] Conclusion and explanation: 1. By Figures 1 to 3 As shown in Table 2, any one of the five newly discovered single bacterial species in this application can be used as a microbial marker for thyroid-related eye disease. Each single bacterial species has sensitivity and specificity for thyroid-related eye disease. Therefore, the microbial marker for thyroid-related eye disease can be selected from any one or more of the five newly discovered single bacterial species in this application. Specifically: The microbial markers of thyroid-related eye disease can be selected from one or more of Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

[0063] 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 thyroid-related eye disease.

[0064] 3. Predictive Effect: For single bacterial species, Faecalibacterium prausnitzii had the highest AUC value (approximately 0.8512), while Bacteroides monomorpha had the lowest AUC value (approximately 0.6198). The AUC value of the mimicry marker (a marker formed by the combination of five single bacterial species) (approximately 1) was significantly higher than the AUC value of a single bacteria. Furthermore, all five newly discovered bacterial species can be used as detection variables, with high specificity and sensitivity. The AUCs of all five microbial markers were greater than 60%. One or more of these five microbial markers can be used as detection markers for the diagnosis of patients with thyroid-related eye diseases.

[0065] 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 microbial marker for thyroid-related eye disease, characterized in that: The microbial markers include Faecalibacterium prausnitzii, Parabacteroides sp. CT06 and Escherichia coli.

2. The microbial marker according to claim 1, characterized in that The microbial markers also include Alistipes finegoldii and / or Bacteroides uniformis.

3. The microbial marker according to claim 2, characterized in that The microbial markers include Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

4. Use of a reagent for detecting the microbial marker according to any one of claims 1 to 3 in the preparation of a product for diagnosing thyroid-related eye diseases.

5. A kit, characterized in that: The kit contains a reagent for detecting the 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 thyroid-related eye diseases.

7. A product for diagnosing thyroid-related eye diseases, characterized by: The product comprises a primer, a probe, an antibody, an aptamer or a chip that is specific to the microbial marker according to any one of claims 1 to 3.

8. A computer program product related to thyroid-related eye disease, characterized in that: The computer program product is used to execute a method for diagnosing whether a subject has a risk of thyroid-related eye disease, 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 Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipesfinegoldii and Bacteroides uniformis; Calculate the probability Z of the subject being a patient with thyroid-related eye disease 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 to be tested suffering from thyroid-related eye disease 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+B5×x5; Among them, A is the intercept term, B1-B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Alistipes finegoldii, x2 is the relative abundance value of Escherichia coli, x3 is the relative abundance value of Faecalibacterium prausnitzii, x4 is the relative abundance value of Parabacteroides sp CT06, and x5 is the relative abundance value of Bacteroides uniformis.

10. The product according to claim 9, characterized in that: A is -1.61, B1 is 5026.24, B2 is -12.37, B3 is 13.75, B4 is 2377.38, and B5 is 16.98.

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