Microbial markers of thyroid-associated ophthalmopathy, products and uses thereof

Abstract

The application discloses a microbial marker of thyroid-associated ophthalmopathy, a product and application thereof, and belongs to the field of biological medicine. The microbial marker comprises one or more of Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis. The application provides a kit comprising detection reagents for detecting the relative abundance of the microbial marker. The product provided by the application has good feasibility and accuracy, can effectively evaluate the risk of thyroid-associated ophthalmopathy, and provides a new tool for clinical diagnosis.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to a microbial marker of thyroid-associated ophthalmopathy, a product and application thereof. BACKGROUND

[0002] Thyroid-associated ophthalmopathy (exophthalmos) is one of the most common orbital diseases in adults, and is an autoimmune disease. In terms of treatment, it is necessary to base on disease activity, severity and individual needs: 1) for patients with mild symptoms, lifestyle intervention (such as smoking cessation, eye care) and thyroid function regulation are mainly used, supplemented by artificial tears to relieve dry eye symptoms; 2) for patients with moderate to severe active period, glucocorticoids (such as intravenous methylprednisolone) are preferred, combined with immunosuppressive agents (cyclosporine) or new targeted drugs (teprotumumab) to inhibit inflammatory response; 3) for hormone-resistant patients, orbital radiotherapy can be considered, but the risk of radiation damage needs to be warned; 4) for patients in stable period, if there are exophthalmos, optic nerve compression or eyelid deformity, surgical means such as orbital decompression and eye muscle surgery are needed to improve function and appearance.

[0003] Although the existing diagnosis and treatment methods have formed a system, there are still significant deficiencies, for example: 1) in terms of drug treatment, glucocorticoids as a first-line solution can easily cause metabolic abnormalities, osteoporosis and other side effects during long-term use, and about 30% of patients have poor response to hormone therapy; 2) new targeted drugs (such as teprotumumab) have significant efficacy, but are expensive and lack long-term safety data; 3) radiotherapy is effective for active inflammation, but can aggravate dry eye or induce radiation cataract, and is not suitable for adolescent patients; 4) surgical treatment can improve late-stage deformity, but has the risk of postoperative complications such as diplopia and hemorrhage, and cannot reverse the damaged optic nerve function. SUMMARY

[0004] In view of the above technical problems, the present application provides a microbial marker of thyroid-associated ophthalmopathy, a product and application thereof, which can provide a new idea and approach for the diagnosis of thyroid-associated ophthalmopathy.

[0005] The technical solution provided by the present application is as follows:

[0006] In a first aspect, a microbial marker of thyroid-associated ophthalmopathy is provided, and the microbial marker comprises Faecalibacterium prausnitzii, Parabacteroides sp.CT06 and Escherichia coli.

[0007] In the technical solution, the microbial markers further include Alistipes finegoldii and / or Bacteroides uniformis.

[0008] In the technical solution, the microbial markers include Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

[0009] In a second aspect, there is provided a use of a reagent for detecting the microbial markers in the first aspect in the preparation of a product for diagnosing thyroid-associated ophthalmopathy.

[0010] In a third aspect, there is provided a kit containing a reagent for detecting the microbial markers in the first aspect.

[0011] In a fourth aspect, there is provided a use of the kit in the third aspect in the preparation of a product for detecting thyroid-associated ophthalmopathy.

[0012] In a fifth aspect, there is provided a product for diagnosing thyroid-associated ophthalmopathy, the product comprising primers, probes, antibodies, aptamers or chips specific to the microbial markers in the first aspect.

[0013] In a sixth aspect, there is provided a computer program product associated with thyroid-associated ophthalmopathy, the computer program product being used to perform a method of diagnosing a risk of a subject suffering from thyroid-associated ophthalmopathy, the method comprising:

[0014] obtaining a relative abundance value of each single species in the feces of the subject;

[0015] substituting the relative abundance value of each single species into a binary logistic regression equation to calculate the log odds y of the subject, the single species including Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis;

[0016] According to y, a disease probability Z of the to-be-tested object as a patient with thyroid-associated ophthalmopathy is calculated, Z = exp(y) / {1 + exp(y)}, wherein exp(y) is an exponential function of y;

[0017] According to a comparison result of the disease probability Z and a reference value, a risk of the to-be-tested object suffering from thyroid-associated ophthalmopathy is diagnosed or predicted.

[0018] In the technical solution, a formula of the binary logistic regression equation is:

[0019] y = A + B1 * x1 + B2 * x2 + B3 * x3 + B4 * x4 + B5 * x5;

[0020] wherein A is an intercept term, B1-B5 are regression coefficients of independent variables; x1 is a relative abundance value of Alistipes finegoldii, x2 is a relative abundance value of Escherichia coli, x3 is a relative abundance value of Faecalibacterium prausnitzii, x4 is a relative abundance value of Parabacteroides sp CT06, and x5 is a relative abundance value of Bacteroides uniformis.

[0021] In the 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.

[0022] The present application has the following advantages:

[0023] 1. The present application discovers five intestinal microorganisms related to thyroid-associated ophthalmopathy, including Faecalibacterium prausnitzii, Parabacteroides sp CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

[0024] 2. The application also provides a reagent and a kit, which can realize prediction or diagnosis of thyroid-associated ophthalmopathy by taking one or more of the above-mentioned five strains as a detection marker, is completely non-invasive, and has high accuracy. By means of metagenomic sequencing, higher resolution is provided, so that the analysis of microbial community can be deepened to the strain level, thereby improving the accuracy and reliability of diagnosis. The five strains can also be used as target microorganisms for developing these systems, filling the gap in this field.

[0025] 3. The application also provides a product for diagnosing thyroid-associated ophthalmopathy. The product can realize calculation of the probability of disease based on the relative abundance of each strain, and can predict or diagnose whether a patient has thyroid-associated ophthalmopathy or has the risk of thyroid-associated ophthalmopathy by comparing with a reference value. The product has good feasibility and accuracy, and can effectively evaluate the risk of thyroid-associated ophthalmopathy, thereby providing a new tool for clinical diagnosis. BRIEF DESCRIPTION OF DRAWINGS

[0026] Fig. 1 is a linear discriminant analysis result graph;

[0027] Fig. 2 is a box scatter plot of the microbial marker;

[0028] Fig. 3 is an ROC curve. DETAILED DESCRIPTION

[0029] In order to evaluate whether the composition of intestinal symbiotic flora can be used as a predictor of thyroid-associated ophthalmopathy, the application collects fecal samples of patients with thyroid-associated ophthalmopathy and healthy people, performs metagenomic sequencing, and uses bioinformatics to statistically analyze the sequencing data, finds disease-related intestinal flora, integrates the intestinal flora with disease information, and maximally predicts patients with thyroid-associated ophthalmopathy.

[0030] The application finds that Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis are related to patients with thyroid-associated ophthalmopathy by metagenomic sequencing.

[0031] It is verified that the above-mentioned five strains are significantly associated with thyroid-associated ophthalmopathy, specifically: Fig. 1 and Fig. 2It can be seen that the workers screened out 5 markers that were significantly increased in the thyroid-associated ophthalmopathy patient group, including Alistipes finegoldii, Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Bacteroides uniformis, and 1 marker that was significantly reduced in the thyroid-associated ophthalmopathy patient group, including Escherichia coli.

[0032] Through ROC curve analysis, the above-mentioned 5 markers have high specificity and sensitivity as detection variables, so the 5 species can be used as detection markers and applied to the prediction and diagnosis of thyroid-associated ophthalmopathy patients.

[0033] The application will be further described in detail below in combination with the drawings and examples. The following examples are only used to illustrate the application and are not used to limit the scope of the application. The experimental methods not specified in the examples are generally carried out under conventional conditions.

[0034] Example 1: Sample collection

[0035] Fifty-five samples of feces of patients with thyroid-associated ophthalmopathy and fifty-five samples of feces of healthy people were collected:

[0036] The inclusion criteria of the thyroid-associated ophthalmopathy group are as follows: 1. Age > 18 years old; 2. Clinically diagnosed as thyroid-associated ophthalmopathy.

[0037] The exclusion criteria of the thyroid-associated ophthalmopathy group are as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnant or lactating.

[0038] The inclusion criteria of the healthy person group are as follows: 1. Age > 18 years old; 2. No diabetes or other metabolic diseases; 3. No other gastrointestinal diseases or other nervous system diseases; 4. No other immune system diseases or immune deficiency; 5. No use of antibiotics (such as neomycin, rifaximin) or probiotics and prebiotics three months before the study and during the study. The exclusion criteria of the healthy person group are as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnant or lactating; 4. Thyroid function and its antibodies beyond the normal range. The above data are derived from the fecal samples collected by Huazhong University of Science and Technology Affiliated Union Hospital.

[0039] Example 2: DNA extraction, library construction and sequencing

[0040] 1. Select Hi Pure Stool DNA Mini Kit kit to extract DNA from the collected fecal samples.

[0041] 2. After extraction, use Qubit to detect DNA concentration, use 1.5% agarose gel electrophoresis to detect the integrity of the extracted genomic DNA, and perform quality inspection on the extracted genomic DNA to screen out qualified genomic DNA samples (DNA concentration ≥ 20 ng / μL, volume ≥ 20 μL, total amount ≥ 400 ng).

[0042] 3. For the qualified DNA samples, after random interruption, end repair, A base connection, add adapters and indexes, after adapter connection, purification and library amplification, after amplification, detect the DNA concentration (DNA concentration ≥ 40 ng / μL).

[0043] 4. After the library detection is qualified, different libraries are pooled according to the effective concentration and the target data amount required for the next machine, and then sequenced. The macrogenomic sequencing platform is Huada T7, and the sequencing strategy is PE150.

[0044] Example 3: LEfSe analysis to screen microbial markers

[0045] 1. Divide the data set

[0046] Use KneadData software to perform quality control (based on Trimmomatic) and dehosting (based on Bowtie2) on the raw data. Use Kraken2 alignment to calculate the number of sequences of species contained in the sample, and then use Bracken to estimate the actual abundance of species in the sample. Randomly select 80% of the test subjects (including thyroid-related eye disease group and healthy group) as the training set, and the remaining 20% of the samples as the validation set. Then use LEfSe software to analyze the abundance data of each sample in the training set, with the default setting of LDA Score filter value of 2.5, and the sample information table is shown in Table 1, as shown in Table 1:

[0047] Table 1 Sample information table

[0048]

[0049] The results are as follows: Fig. 1As shown, the present application first discovered 5 species related to thyroid-associated ophthalmopathy, specifically: the staff screened out 4 markers significantly increased in the thyroid-associated ophthalmopathy patient group, including Alistipes finegoldii, Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Bacteroides uniformis, and 1 marker significantly reduced in the thyroid-associated ophthalmopathy patient group, including Escherichia coli.

[0050] Example 4: Verification of the reliability of the above 5 microbial markers

[0051] 1. First, the remaining 20% of the subjects in Example 1 (including those in the thyroid-associated ophthalmopathy group and the healthy group) were used as a validation set. The abundance data of each sample in the validation set was first subjected to binary logistic regression operation, and then subjected to receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value).

[0052] 2. IBM SPSS Statistics (v27) statistical software was used to calculate specificity and sensitivity and to draw ROC curves. The software first calculates the threshold value of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold value.

[0053] Specificity (true negative rate) = TN / (TN + FP),

[0054] Sensitivity (true positive rate) = TP / (TP + FN),

[0055] 3. The ROC curve can be constructed by 1-specificity and sensitivity, and 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 certain indicator.

[0056] 4. The relative abundance value of the microbial marker of a single strain was directly subjected to receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is shown in Fig. 3 . The AUC, optimal cutoff value, sensitivity, and specificity of the prediction quasispecies marker (marker formed by combination of 5 single species) and single bacteria are shown in Table 2.

[0057] Table 2 ROC diagnostic curve results

[0058]

[0059] According to Table 2, for single species, the AUC value of Faecalibacterium prausnitzii is the highest (about 0.8512), and the AUC value of Bacteroides uniformis is the lowest (about 0.6198); the AUC value of the mimic marker (a marker formed by the combination of 5 single species) is obviously higher than the AUC value of single bacteria (about 1).

[0060] From the above, it can be seen that the 5 species newly discovered by the present application can be used as detection variables, and they all have high specificity and sensitivity, and the AUC of the 5 microbial markers is greater than 60%, therefore, one or more of the 5 microbial markers can be used as a detection marker for the diagnosis of patients with thyroid-associated ophthalmopathy.

[0061] Example 5: Establishment of a logistic regression model

[0062] Based on the above-mentioned microbial markers screened and the relative abundance value of each metabolic marker, the binary logistic regression algorithm in the SPSS software is used to calculate the first probability of each sample (i.e. the logarithm of the advantage of the object to be tested y), and on this basis, the disease probability optimization formula Z=exp(y) / {1+exp(y)} is used to calculate the disease probability Z of the sample to be tested, and finally the disease probability Z is compared with the actual disease condition (such as severity) of each sample, so as to verify the accuracy of the disease probability calculation equation, specifically:

[0063] a. Model establishment

[0064] Through the above-mentioned biomarkers, based on the proportion of thyroid-associated ophthalmopathy patients and patients in the training set, further, the relative abundance values of the 5 species detected are used as single variables, and on this basis, the linear relationship between the relative abundance values of the 5 single bacteria and the disease probability of the sample is discussed, and the binary logistic regression equation is used to calculate the logarithm of the advantage of the object to be tested y (also referred to as the first probability value y):

[0065] y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5

[0066] Wherein, A is the intercept term, B1-B5 are the regression coefficients of 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.

[0067] b. Determine the values of A and B1-B5

[0068] After statistical analysis of the sample data, the values of A and B1-B5 are as follows: 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.

[0069] At this time, after sorting, the calculation formula of the logarithm y of the advantage is:

[0070]

[0071] c. Calculate the probability Z of the subject being a patient

[0072] Substitute the first probability value y into the following formula to calculate the probability Z of the subject being a patient: Z = exp(y) / {1 + exp(y)}; wherein, Z is the probability value of the subject being a patient, and exp(y) is the natural exponential function of the first probability value y.

[0073] After sorting, the calculation formula of the probability Z of being a patient is

[0074]

[0075] d. Verification set data calculation and statistical analysis

[0076] Based on the data of the verification set, the relative abundance of each single species in the disease group and the health group is obtained for each sample, and then the first probability value y is obtained by using the aforementioned binary logistic regression method, and the probability Z of the test sample being a patient is calculated by the formula, and the results are shown in Tables 3 and 4, wherein the patient refers to a patient with thyroid-related eye disease.

[0077] Table 4 is the relative abundance mean and standard deviation of each species, the relative abundance mean determines the center position of the data distribution, and the standard deviation reflects the dispersion degree of the data relative to the mean, the P value is the statistical quantity calculated by using the formula of rank sum test, and the lower the P value, the greater the difference between the disease group and the health group.

[0078] Table 3 Correlation data of validation set markers

[0079]

[0080] Note: E represents the power of 10, for example, 9.76514818612e-05 represents 9.76514818612 x 10 -5 .

[0081] Table 4 Correlation abundance statistical data of validation set markers

[0082]

[0083] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is the same.

[0084] e. Results and analysis

[0085] According to the results of Example 4 and Example 5, the AUC of the prediction score of the mimic marker is about 1, the optimal cutoff value is about 0.5659, the sensitivity is about 1, and the specificity is 1. Therefore, the mimic marker is used as a detection marker for the diagnosis of thyroid-related eye disease patients, which has better effect and higher accuracy. The five species are used as detection markers, which are completely non-invasive and have high accuracy.

[0086] According to the results obtained by combining the description of Table 3 above and the calculation formula of the disease probability Z, the calculation formula of the disease probability of the test sample summarized in the present application is basically correct, and can be used to diagnose the risk and probability of the test sample; there are some cases in Table 3 above that do not fully meet the diagnostic criteria, which is because the fecal sample of the test person may have false positive results or false negative results, and further detection is required using other means, including blood routine, diagnosis of physical signs, etc.

[0087] Example 6: Computer program product related to thyroid-related eye disease

[0088] Based on the above examples, the present example provides a computer program product related to thyroid-related eye disease, which is used to execute a method for diagnosing whether a test object has a risk of thyroid-related eye disease, comprising the following steps:

[0089] S1, obtaining the relative abundance value of each single bacterial species in the feces of the subject to be tested; the single bacterial species includes Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis;

[0090] S2, substituting the relative abundance value of each single bacterial species into a binary logistic regression equation to calculate the log of the advantage of the subject to be tested y (i.e. the first probability value y);

[0091] y = A + B1x1 + B2x2 + B3x3 + B4x4 + B5x5

[0092] Wherein, 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;

[0093] S3, calculating the probability Z of the subject to be tested as a patient with thyroid-associated ophthalmopathy according to y, Z = exp (y) / {1 + exp (y)}; exp (y) is the natural exponential function of y;

[0094] The probability of disease Z can also be expressed as:

[0095]

[0096] S4, according to the comparison of the probability Z value of the patient with the reference value, diagnosing or predicting the risk of the subject to be tested with thyroid-associated ophthalmopathy.

[0097] In actual work, when the Z value is greater than 0.5, it indicates that the probability of the subject to be tested with thyroid-associated ophthalmopathy is relatively large; when the Z value is less than 0.5, it indicates that the probability of the subject to be tested with thyroid-associated ophthalmopathy is relatively small; when the Z value is 0.5, it indicates that the subject to be tested may be a patient or a patient with thyroid-associated ophthalmopathy, at this time, further detection is needed by using other means, and the other means are blood routine and diagnosis of physical signs. Further, the closer the Z value is to 0.5, the more detection is needed by using other means.

[0098] Example 7: Checking the prevalence of patients and patients with thyroid-associated ophthalmopathy in the validation set

[0099] Based on the product and method of Example 5, the prevalence of healthy people and patients with thyroid-associated ophthalmopathy in the validation set is checked, and the specific steps are as follows:

[0100] S1, collect the fecal sample of the person to be detected, and detect the relative abundance value of each single strain in the fecal sample; wherein the single strain includes Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis;

[0101] S2, calculate the logarithm of the advantage y of the object to be measured according to the binary logistic regression equation;

[0102]

[0103] Wherein, 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;

[0104] S3, calculate the prevalence Z of the object to be measured as a patient with thyroid-associated ophthalmopathy according to y, Z=exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y;

[0105] The prevalence Z can also be expressed as:

[0106]

[0107] S4, according to the comparison of the probability Z value of the patient with the reference value, diagnose or predict the risk of the object to be measured suffering from thyroid-associated ophthalmopathy.

[0108] In actual work, when the Z value is greater than 0.5, it indicates that the probability of the to-be-tested object suffering from thyroid-related eye disease is relatively large; when the Z value is less than 0.5, it indicates that the probability of the to-be-tested object suffering from thyroid-related eye disease is relatively small; when the Z value is 0.5, it indicates that the to-be-tested object may be a patient or a thyroid-related eye disease patient, at this time, further detection needs to be performed by using other means, and the other means are blood routine and diagnosis of physical signs. Further, the closer the Z value is to 0.5, the more detection needs to be performed by using other means.

[0109] Example 8: detection reagent

[0110] Based on the description of the above examples 1-5, it can be known that the prediction effect of the marker selected in the application is good, and medical personnel can use a single strain as a marker to detect and diagnose the to-be-tested sample alone to diagnose whether the to-be-tested sample suffers from thyroid-related eye disease; medical personnel can also combine multiple single strains together as a marker to detect and diagnose the to-be-tested sample to diagnose whether the to-be-tested sample suffers from thyroid-related eye disease.

[0111] Therefore, the embodiment also provides a reagent for detecting a microbial marker, which can be applied in the preparation of a product for diagnosing thyroid-related eye disease to diagnose whether the to-be-tested sample suffers from thyroid-related eye disease; meanwhile, the microbial marker can be selected from the five single strains related to thyroid-related eye disease discovered in the application, that is, the microbial marker in the detection reagent can be selected from one or more of Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii and Bacteroides uniformis.

[0112] Example 9: kit

[0113] The embodiment also provides a kit, which can contain the detection reagent described in example 7 to diagnose whether the to-be-tested sample suffers from thyroid-related eye disease; the definition and technical solution of the kit can be referred to the description of the above example 7, which is not repeated here. Similarly, the above kit can also be applied in the preparation of a product for detecting thyroid-related eye disease, which is not repeated here.

[0114] Example 10: product for diagnosing thyroid-related eye disease

[0115] This application also provides a product for diagnosing thyroid-associated eye disease, to diagnose whether a sample to be tested has thyroid-associated eye disease; the product is specific to one or more of the five single bacterial species found in this application that are associated with thyroid-associated eye disease, and the product includes primers, probes, antibodies, aptamers or chips.

[0116] As can be seen from the descriptions of Examples 1-5 and from conventional methods in the art, it should be feasible for those skilled in the art to produce corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) when using the five newly discovered single bacterial species as microbial markers, and will not be elaborated here.

[0117] Conclusion and explanation:

[0118] 1. By Figs. 1-3 As shown in Table 2, any one of the five newly discovered single bacterial species in this application can serve as a microbial biomarker for thyroid-associated eye disease. Each single bacterial species exhibits both sensitivity and specificity for thyroid-associated eye disease. Therefore, the microbial biomarker for thyroid-associated eye disease can be selected from any one or more of the five newly discovered single bacterial species in this application. Specifically:

[0119] Microbial markers for thyroid-associated ophthalmopathy can be selected from one or more of the following: Faecalibacterium prausnitzii, Parabacteroides sp. CT06, Escherichia coli, Alistipes finegoldii, and Bacteroides uniformis.

[0120] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing fecal samples. This is because there are individual differences. The probability of disease Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has thyroid-associated ophthalmopathy.

[0121] 3. Predictive Results: For single bacterial species, *Bacillus prenanti* had the highest AUC value (approximately 0.8512), while *Bacteroides monomorpha* had the lowest (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 that of the single bacterial species. Furthermore, all five newly discovered bacterial species in this application can be used as detection variables, exhibiting high specificity and sensitivity. The AUC of all five microbial markers is greater than 60%, and one or more of these five microbial markers can be used as detection markers for the diagnosis of patients with thyroid-associated ophthalmopathy.

[0122] The above description is only preferred specific embodiments of the present application, but the scope of protection of the present application is not limited thereto, any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the present application shall be included in the scope of protection of the present application.

Claims

1. A microbial marker for thyroid-associated ophthalmopathy, characterized in that, The microbial markers are a combination of markers consisting of *Faecalibacterium prausnitzii*, *Parabacteroides* sp. CT06, *Escherichia coli*, *Alistipes finegoldii*, and *Bacteroides uniformis*.

2. The application of a reagent for detecting microbial markers in the preparation of products for diagnosing thyroid-associated eye diseases, characterized in that, The microbial markers include *Faecalibacterium prausnitzii*, *Parabacteroides* sp. CT06, *Escherichia coli*, *Alistipes finegoldii*, and *Bacteroides uniformis*.

3. The application according to claim 2, characterized in that, The microbial markers are a combination of markers consisting of *Faecalibacterium prausnitzii*, *Parabacteroides* sp. CT06, *Escherichia coli*, *Alistipes finegoldii*, and *Bacteroides uniformis*.

4. A reagent kit, characterized in that: The kit contains reagents for detecting the microbial markers of claim 1.

5. The use of the kit according to claim 4 in the preparation of a product for detecting thyroid-associated ophthalmopathy.

6. A product for diagnosing thyroid-associated ophthalmopathy, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips that are specific to the microbial markers of claim 1.

7. A computer program product related to thyroid-associated eye disease, characterized in that: The computer program product is used to perform a method for diagnosing the risk of a subject having thyroid-associated ophthalmopathy, the method comprising: Obtain the relative abundance value of each individual bacterial species in the feces of the test subject; The relative abundance value of each individual bacterial species is substituted into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object. The individual bacterial species include Faecalibacterium prausnitzii, Parabacteroides sp.CT06, Escherichia coli, Alistipes finegoldii, and Bacteroides uniformis. The probability Z of a subject being diagnosed with thyroid-associated ophthalmopathy is calculated based on y, where Z = exp(y) / {1 + exp(y)}, and exp(y) is an exponential function of y. Based on the comparison of the probability of disease Z with the reference value, the risk of the subject having thyroid-associated ophthalmopathy is diagnosed or predicted.

8. The computer program product according to claim 7, characterized in that: The formula for the binary logistic regression equation is: 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.

9. The product according to claim 8, characterized in that: The values ​​are: A = -1.61, B1 = 5026.24, B2 = -12.37, B3 = 13.75, B4 = 2377.38, and B5 = 16.98.

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