Intestinal microbial markers associated with polycystic ovary syndrome, products and uses thereof

Abstract

This invention discloses a gut microbiome biomarker, product, and its application associated with polycystic ovary syndrome (PCOS), belonging to the biomedical field. The microbiome biomarker includes one or more of *Dolphobicella longiformis*, *Brutobacterium sludgeii*, *Ruminococcus viridans*, and *Bacteroides polymorpha*. This invention provides a kit including detection reagents for detecting the relative abundance of the aforementioned microbiome biomarkers. This invention also provides a product for diagnosing PCOS, a predictive system for assessing the risk of a candidate having PCOS, and a computer program product related to PCOS. The product provided by this invention has good feasibility and accuracy, can effectively assess the risk of PCOS, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to an intestinal microbial marker, product, and application related to polycystic ovary syndrome. Background Technology

[0002] Polycystic ovary syndrome (PCOS) is a common reproductive endocrine and metabolic disorder closely related to insulin resistance and genetic susceptibility. Its core pathological features are hyperandrogenemia, ovulation dysfunction, and polycystic ovarian changes.

[0003] Currently, the routine diagnostic methods for polycystic ovary syndrome (PCOS) include clinical assessment, serum hormone testing, and ultrasound imaging. However, these methods still have the following significant limitations: 1) Clinical assessment (such as hirsutism scoring) is highly subjective and lacks specificity; 2) Serum hormone testing (such as testosterone) has low sensitivity in some patients and is greatly affected by the timing of testing; 3) Although ultrasound imaging is the main tool for assessing ovarian morphology, its results are easily affected by equipment resolution and operator subjectivity, resulting in low standardization.

[0004] Therefore, although there are currently various diagnostic methods for polycystic ovary syndrome (PCOS), these methods still have certain limitations. Hence, there is a need in this field to develop a new product to detect whether a subject has PCOS. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a microbial biomarker, product, and application for polycystic ovary syndrome (PCOS), which can offer a new approach and method for the diagnosis of PCOS.

[0006] The technical solution provided by this invention is as follows:

[0007] In a first aspect, a microbial marker for polycystic ovary syndrome is provided, said microbial marker comprising *Dorea longicatena* and / or *Blautia luti*.

[0008] In the above technical solution, the microbial markers also include active Ruminococcus gnavus and / or Bacteroides thetaiotaomicron.

[0009] In the above technical solution, the microbial markers also include a combination of markers composed of Dorealongicatena, Blautia luti, Ruminococcus gnavus, and Bacteroides thetaiotaomicron.

[0010] Secondly, the application of a reagent for quantitatively detecting the microbial markers described in the first aspect in the preparation of products for diagnosing polycystic ovary syndrome is provided.

[0011] Thirdly, a kit containing reagents for the quantitative detection of the microbial markers described in the first aspect.

[0012] Fourthly, a product for diagnosing polycystic ovary syndrome, the product comprising one or more of reagents, primers, probes, antibodies, test strips, aptamers, and chips, the product being used for quantitative detection of the microbial biomarkers described in the first aspect and having specificity for the microbial biomarkers.

[0013] Fifthly, a predictive system for assessing the risk of a subject having polycystic ovary syndrome includes:

[0014] The detection module obtains quantitative detection results of microbial markers as described in the first aspect in the fecal sample of the subject to be tested;

[0015] The comparison module compares the detection results with a preset threshold and determines the risk of the subject being a patient with polycystic ovary syndrome based on the comparison results.

[0016] Sixthly, a computer program product related to polycystic ovary syndrome (PCOS), the computer program product being used to perform steps for diagnosing the risk of a subject having PCOS, including:

[0017] The relative abundance value of each single bacterial species in the feces of the test subject is obtained, wherein the single bacterial species include Dorea longicatena, Blautia luti, Ruminococcus gnavus, and Bacteroides thetaiotaomicron.

[0018] Substitute the relative abundance value of the single bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object;

[0019] The probability Z of a subject being diagnosed with polycystic ovary syndrome is calculated based on y, where Z = exp(y) / {1 + exp(y)}, and exp(y) is an exponential function of y.

[0020] Based on the comparison of the probability of disease Z with the reference value, the risk of the subject having polycystic ovary syndrome can be diagnosed or predicted.

[0021] In the above technical solution, the formula for the binary logistic regression equation is:

[0022] y=A+B1×x1+B2×x2+B3×x3+B4×x4;

[0023] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Dorea longicatena, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Blautia luti, and x4 is the relative abundance value of Ruminococcus gnavus.

[0024] In the above technical solution, A is -1.1382, B1 is 257.9758, B2 is -132.2734, B3 is 33.3171, and B4 is 16.0783.

[0025] It should be noted that this invention newly discovered and verified that the above-mentioned gut microbiota are strongly correlated with polycystic ovary syndrome. Based on this, although the present invention only lists how to achieve quantitative detection of the test sample through relative abundance value in the embodiments, other means to achieve quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information, etc.) and can also be used to assist in the diagnosis of whether the test sample has polycystic ovary syndrome. People can choose according to their own needs, which will not be elaborated here.

[0026] The beneficial effects of this invention are as follows:

[0027] 1. This invention newly discovers four intestinal microorganisms associated with polycystic ovary syndrome, including Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron.

[0028] After research and verification, it was found that one or more of the above four gut microbiota can serve as microbial markers associated with polycystic ovary syndrome (PCOS) and can be used to diagnose whether a sample to be tested has PCOS.

[0029] 2. This invention also provides a reagent and kit that can use one or more of the above four bacterial species as detection markers (i.e., microbial markers) to predict or diagnose polycystic ovary syndrome (PCOS) in a completely non-invasive and highly accurate manner. Metagenomic sequencing provides higher resolution, enabling the analysis of the microbial community to penetrate to the species or even strain level, thereby improving the accuracy and reliability of diagnosis. The four bacterial species can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0030] 3. This invention also provides a product and a prediction system for diagnosing polycystic ovary syndrome (PCOS). This product and system can calculate the probability of disease based on the relative abundance of each bacterial species, and then compare it with reference values ​​to predict or diagnose whether a patient has PCOS or is at risk of having PCOS. This product and system have good feasibility and accuracy, and can effectively assess the risk of PCOS in test samples, providing a new tool for clinical diagnosis. Attached Figure Description

[0031] Figure 1 This is a graph showing the results of the linear discriminant analysis.

[0032] Figure 2 Box plot of microbial biomarkers;

[0033] Figure 3 This is the ROC curve. Detailed Implementation

[0034] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0035] To evaluate whether gut microbiota composition can serve as a predictor of polycystic ovary syndrome (PCOS), and addressing the clinical needs for PCOS diagnosis and detection, this invention collected samples from PCOS patients and healthy individuals. Through standardized experimental testing procedures (specific experimental methods are described in Examples 1-3), four microorganisms highly associated with efficient donors were screened: Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron.

[0036] ROC curve analysis showed that the above four biomarkers have high specificity and sensitivity as detection variables, and these four bacterial species can be used as detection biomarkers for the prediction and diagnosis of polycystic ovary syndrome.

[0037] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0038] Example 1: Sample Collection

[0039] Stool samples were collected from 120 patients with polycystic ovary syndrome and 105 healthy individuals:

[0040] The sample sources and inclusion criteria for polycystic ovary syndrome (PCOS) are as follows: 1. Age distribution greater than 18 years; 2. Patients clinically diagnosed with PCOS; 3. Stable vital signs.

[0041] Exclusion criteria for the Polycystic Ovary Syndrome (PCOS) group: 1. Received PCOS treatment within 1 month prior; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Had any chronic disease, including neurobehavioral disorders; 4. Received medications affecting gastrointestinal motility within 1 week; 5. History of functional dyspepsia, aerophagia, abdominal migraine, or functional constipation with pain; 6. Demonstrated growth retardation; 7. Had gastrointestinal obstruction or stricture; 8. History of abdominal surgery, family history of peptic ulcers, or inflammatory bowel disease; 9. Pregnant, planning to become pregnant during the study period, or currently breastfeeding women of childbearing age; 10. Patients deemed unsuitable for inclusion in this study by the researchers.

[0042] The healthy population sample and inclusion criteria are as follows: 1. Female, aged 18 years or older; 2. No polycystic ovary syndrome or other metabolic diseases; 3. No functional abdominal pain syndrome or other neurological diseases; 4. No irritable bowel syndrome or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficient state; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study.

[0043] The exclusion criteria for the healthy population group were the same as those for the polycystic ovary syndrome group.

[0044] The above data comes from fecal samples collected in Hubei Province.

[0045] Example 2: DNA extraction, library construction, and sequencing

[0046] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0047] 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 then subjected to quality control, and qualified genomic DNA samples were selected (DNA concentration ≥20ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0048] 3. For qualified DNA samples, random fragmentation, end repair, A base ligation, adapter and index are added. After adapter ligation, purification and library amplification are performed. After amplification, the DNA concentration is detected (DNA concentration ≥40 ng / μL).

[0049] 4. After the libraries pass the testing, different libraries are pooled according to the effective concentration and target data volume requirements before sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0050] Example 3: LEfSe analysis for screening microbial biomarkers

[0051] 1. Split the dataset

[0052] KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the participants (including those in the disease and healthy groups) 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 were then analyzed using LEfSe software, with the default LDA Score filter value set to 2. The sample information is shown in Table 1.

[0053]

[0054] The results are as follows Figure 1 As shown, this application is the first to discover four bacterial species associated with polycystic ovary syndrome (PCOS). Specifically, researchers screened out three biomarkers that were significantly increased in the PCOS group, including *Dorea longicatena*, *Blautia luti*, and *Ruminococcus gnavus*; researchers screened out one biomarker that was significantly decreased in the PCOS group, including *Bacteroides thetaiotaomicron*.

[0055] Example 4: Verifying the reliability of the above four microbial biomarkers

[0056] 1. First, the remaining 20% ​​of the participants in Example 1 (including those in the polycystic ovary 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, and then the receiver operating function (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0057] 2. Use IBM SPSS Statistics (v27) statistical software to calculate specificity and sensitivity and plot ROC curves. The software first calculates the threshold of the actual measurement value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold.

[0058] Specificity (true negative rate) = TN / (TN + FP)

[0059] Sensitivity (true positive rate) = TP / (TP + FN)

[0060] 3. The ROC curve can be constructed using 1 minus specificity and sensitivity. 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 value of the Youden coefficient are the specificity and sensitivity of the certain indicator.

[0061] 4. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 2, 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.

[0062]

[0063] As shown in Table 2, for single bacterial species, Dorea longicatena has the highest AUC value (approximately 1), while Ruminococcus gnavus has the lowest AUC value (approximately 0.819). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 1) is significantly higher than that of the single bacterial species.

[0064] Combine Table 2 and Figure 3It can be seen that when other microorganisms are added to a single bacterial species for testing, the test result (AUC value) will not decrease. In other words, the test result with the addition of other microorganisms is not lower than the test result (AUC value) without the addition of microorganisms.

[0065] For example, the AUC value of the mimicry marker (a marker formed by a combination of 4 single bacterial species) (approximately 1) is not lower than the AUC value of Bacteroides polymorpha, Dolceae longiformis, and Brontë sludgei combined (approximately 1); the AUC value of Bacteroides polymorpha, Dolceae longiformis, and Brontë sludgei combined (approximately 1) is not lower than the AUC value of Bacteroides polymorpha and Dolceae longiformis combined (approximately 1); and the AUC value of Bacteroides polymorpha and Brontë sludgei combined (approximately 0.977) is not lower than the AUC value of Bacteroides polymorpha (approximately 0.958) and the AUC value of Brontë sludgei (approximately 0.956).

[0066] As can be seen from the above, one or more of the four newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the four microbial markers is greater than 80%. Therefore, one or more of the four microbial markers can be used as detection markers for the diagnosis of patients with polycystic ovary syndrome.

[0067] Example 5: Establishing a Logistic Regression Model

[0068] Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker obtained, the first disease probability (i.e., the logarithm y of the prevalence of the test subject) of each sample was calculated using the binary logistic regression algorithm in SPSS software. Then, the disease probability Z of the test sample was calculated using the disease probability optimization formula Z = exp(y) / {1 + exp(y)}. Finally, this disease probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically:

[0069] a. Establishing a model

[0070] Based on the biomarkers identified above and the proportion of polycystic ovary syndrome (PCOS) individuals and patients in the training set, the relative abundance of the four detected bacterial species was further used as a single variable. The linear relationship between the relative abundance of the four single bacteria and the probability of disease in the samples was then discussed. A binary logistic regression equation was used to calculate the logarithm y (also known as the first probability value y) of the subject's dominance.

[0071] y=A+B1×x1+B2×x2+B3×x3+B4×x4

[0072] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Dorea longicatena, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Blautia luti, and x4 is the relative abundance value of Ruminococcus gnavus.

[0073] b. Determine the values ​​of A and B1 to B4 above.

[0074] After statistical analysis of the sample data, A was -1.1382, B1 was 257.9758, B2 was -132.2734, B3 was 33.3171, and B4 was 16.0783.

[0075] At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is:

[0076] ;

[0077] c. Calculate the disease probability Z of the subjects to be tested.

[0078] Substitute the first probability value y into the following formula to calculate the probability Z that the subject is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the subject is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0079] After processing, the formula for calculating the probability Z of disease is:

[0080]

[0081] d. Validation set data calculation and statistical analysis

[0082] Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group for each sample was obtained. Then, the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z of the test sample being a patient was then calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a patient with irritable bowel syndrome.

[0083] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0084] Table 3. Relevance of Validation Set Markers

[0085]

[0086]

[0087]

[0088] Note: E represents 10 to the power of 10. For example, 9.76514818612e-05 means 9.76514818612 × 10⁻⁵. -5 .

[0089]

[0090] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0091] e. Results and Analysis

[0092] Based on the results of Examples 4 and 5, the AUC of the mimicry biomarker's predictive score is approximately 1, the optimal cutoff value is approximately 0.576, the sensitivity is approximately 1, and the specificity is 1. Therefore, using the mimicry biomarker as a detection biomarker in the diagnosis of polycystic ovary syndrome (PCOS) patients has better efficacy and higher accuracy. Using one or more of these four bacterial species as detection biomarkers is completely non-invasive and highly accurate.

[0093] Based on the results obtained from the description in Table 3 above and the calculation formula for the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease in the sample to be tested, which is summarized in this application, is basically correct and can be used to diagnose the risk and probability of disease in the sample to be tested. The probability of disease in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal sample of the person to be tested may produce false positive or false negative results. Further testing using other methods is required, including blood routine tests, diagnostic physical signs, etc.

[0094] Example 6: Examining the probability of disease in patients in a validation set and patients with polycystic ovary syndrome.

[0095] Based on the product and method of Example 5, the probability of disease in healthy individuals and patients with polycystic ovary syndrome was examined and verified. The specific steps are as follows:

[0096] S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each single strain in the intestine; among which, the single strains include Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron.

[0097] S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation;

[0098] ;

[0099] Where x1 is the relative abundance value of Dorea logicatena, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Blautia luti, and x4 is the relative abundance value of Ruminococcus gnavus.

[0100] S3. Calculate the probability Z of the subject being a patient with polycystic ovary syndrome based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0101] The probability of disease Z can also be expressed as:

[0102]

[0103] S4. Based on the comparison between the patient's probability Z-score and the reference value, diagnose or predict the risk of the subject having polycystic ovary syndrome.

[0104] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has polycystic ovary syndrome (PCOS); a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient with PCOS, or may be a patient with PCOS. In this case, further testing using other methods is necessary, such as complete blood counts and physical examinations. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to use additional testing methods.

[0105] It should be noted that although this embodiment only lists the methods and means of quantitative detection of the sample to be tested through relative abundance values, other means of quantitative detection of microorganisms are also feasible for this invention (such as absolute abundance or total microbial load information, etc.), and can also be used to assist in the diagnosis of whether the sample to be tested has polycystic ovary syndrome. People can choose the appropriate quantitative detection method of microorganisms according to their own needs, which will not be elaborated here.

[0106] Example 7: A predictive system for assessing the risk of a subject having polycystic ovary syndrome (PCOS).

[0107] Based on the above embodiments, this embodiment provides a predictive system for assessing the risk of a subject being a patient with polycystic ovary syndrome. The predictive system includes a detection module and a comparison module, specifically:

[0108] The detection module is used to obtain quantitative detection results of a single bacterial species in the fecal sample of the subject to be tested; wherein, the single bacterial species may include one or more of Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron;

[0109] The comparison module is used to compare the quantitative detection results with a preset threshold, and to determine the risk of the subject being a patient with polycystic ovary syndrome based on the comparison results.

[0110] In practical application, to illustrate how the prediction system assesses the risk of a subject having polycystic ovary syndrome (PCOS), this invention uses relative abundance values ​​as an example. Specifically:

[0111] The detection module obtains the relative abundance value of Ruminococcus gnavus in the fecal sample of the test subject, which is recorded as the first abundance value. This first abundance value is also the quantitative detection result obtained by the detection module.

[0112] The comparison module compares the first abundance value with the data of Streptococcus longicornis in Table 4 to assess the risk that the subject is a patient with polycystic ovary syndrome (PCOS). For example, if the first abundance value is within the range determined by the mean and standard deviation of PCOS patients, the risk of the subject being a patient with PCOS is high; otherwise, the risk of the subject being a patient with PCOS is low.

[0113] Similarly, referring to the detection and evaluation method of Ruminococcus gnavus mentioned above, the prediction system can also perform similar detection and evaluation of microorganisms such as Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron, thereby assessing the risk that the subject is a patient with polycystic ovary syndrome.

[0114] Due to factors such as genetics and environment, it is normal that not all four newly discovered bacterial species can be detected in fecal samples. People can use one or more of the four newly discovered bacterial species as detection markers to assess the risk of the subject being a patient with polycystic ovary syndrome.

[0115] Given that abundance values ​​and other quantitative detection data (such as absolute abundance or total microbial load information) are standard methods in this field, if one wishes to use other quantitative detection methods to assess the risk of a subject having polycystic ovary syndrome, one can refer to the descriptions above, which will not be repeated here.

[0116] Example 8: Computer program products related to polycystic ovary syndrome

[0117] Based on the above embodiments, this embodiment provides a computer program product related to polycystic ovary syndrome (PCOS). The computer program product is used to perform a method for diagnosing the risk of a subject having PCOS, including the following steps:

[0118] S1. Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Ruminococcus gnavus, Blautia luti, Dorealongicatena, and Bacteroides thetaiotaomicron.

[0119] S2. Substitute the relative abundance value of each single bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object (i.e., the first probability value y).

[0120] y=A+B1×x1+B2×x2+B3×x3+B4×x4

[0121] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Dorea longicatena, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Blautia luti, and x4 is the relative abundance value of Ruminococcus gnavus.

[0122] S3. Calculate the probability Z of the subject being a patient with polycystic ovary syndrome based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0123] The probability of disease Z can also be expressed as:

[0124]

[0125] S4. Based on the comparison between the patient's probability Z-score and the reference value, diagnose or predict the risk of the subject having polycystic ovary syndrome.

[0126] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has polycystic ovary syndrome (PCOS); a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient or have PCOS, requiring further testing using methods such as complete blood counts and physical examinations. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0127] Example 9: Detection Reagent

[0128] Based on the description of embodiments 1 to 5 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical staff can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has polycystic ovary syndrome. Medical staff can also combine multiple single bacterial species together as biomarkers to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has polycystic ovary syndrome.

[0129] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing polycystic ovary syndrome (PCOS) to diagnose whether a sample to be tested has PCOS. Simultaneously, the microbial markers can be selected from the four single bacterial species found in this application that are associated with PCOS. That is, the microbial markers in the detection reagent can include one or more of Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron.

[0130] Example 10: Reagent Kit

[0131] This embodiment also provides a kit that may contain the detection reagent described in Example 7 to diagnose whether a sample to be tested has polycystic ovary syndrome (PCOS). The limitations and technical solutions of the kit in this application can be found in the description of Example 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting PCOS, and will not be repeated here.

[0132] Example 11: Products for diagnosing polycystic ovary syndrome

[0133] This application also provides a product for diagnosing polycystic ovary syndrome (PCOS) to diagnose whether a sample to be tested has PCOS; the product is specific to one or more of the four single bacterial species found in this application that are associated with PCOS, and the product includes primers, probes, antibodies, aptamers, or chips.

[0134] 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 four newly discovered single bacterial species as microbial markers, and will not be elaborated here.

[0135] Example 12: Diagnosing whether a sample to be tested is from a patient with polycystic ovary syndrome

[0136] If it is necessary to diagnose whether a person under test has polycystic ovary syndrome, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme test, and liver tissue biopsy, medical staff can also use the methods or products described in Examples 1-11 above to diagnose the person under test, in order to assist medical staff in making a more accurate judgment:

[0137] (1) If, after continuous observation over multiple time periods, the content of Blautialuti and Ruminococcus gnavus in the sludge of the test subject is found to be high (compared with the mean and standard deviation of the glioma group in Table 4), or even shows a significant increasing trend, then the test subject is more likely to be a patient with polycystic ovary syndrome.

[0138] (2) If, after continuous observation over multiple time periods, the test subject is found to have a high level of one or more of Dorea longicatena and Bacteroides thetaiotaomicron (compared to the mean and standard deviation of the meningioma group in Table 4), then the test subject is more likely to be a healthy person.

[0139] (3) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figure 1 Table 4 provides a preliminary assessment of whether the individual being tested is a patient with polycystic ovary syndrome.

[0140] (4) If medical staff want to more accurately determine whether the person to be tested is a patient with polycystic ovary syndrome, they can calculate whether the patient to be tested has meningioma or glioma based on the relative abundance values ​​of the four single bacterial species and with reference to the technical solutions described in Examples 5 to 7.

[0141] Conclusion and explanation:

[0142] 1. By Figures 1-3 As shown in Table 2, any one of the four newly discovered single bacterial species in this application can serve as a microbial biomarker for polycystic ovary syndrome (PCOS). Each single bacterial species exhibits both sensitivity and specificity for PCOS. Therefore, the microbial biomarker for PCOS can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically:

[0143] Microbial markers for polycystic ovary syndrome can be selected from one or more of Ruminococcus gnavus, Blautia luti, Dorea longicatena, and Bacteroides thetaiotaomicron.

[0144] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing intestinal 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 polycystic ovary syndrome.

[0145] 3. Predictive effect: The AUC value (approximately 1) of the mimicry marker (a marker formed by the combination of 4 single bacterial species) is significantly higher than that of a single bacterial species. When other microorganisms are added to one or several single bacterial species for testing, the test result (AUC value) will not decrease. That is, the test result with the addition of other microorganisms is not lower than the test result (AUC value) without the addition of microorganisms.

[0146] 4. The four newly discovered bacterial species in this application can all be used as detection variables. They all have high specificity and sensitivity, and the AUC of the four microbial markers is greater than 80%. One or more of the four microbial markers can be used as detection markers for the diagnosis of patients with polycystic ovary syndrome.

[0147] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. The use of a reagent for quantitative detection of microbial markers in the preparation of products for diagnosing polycystic ovary syndrome, wherein the microbial markers include *Dorea longicatena* and *Blautialuti*.

2. The application according to claim 1, characterized in that, The microbial markers also include active Ruminococcus gnavus and / or Bacteroides thetaiotaomicron.

3. The application according to claim 2, characterized in that, The microbial markers are a combination of markers consisting of Dorea longicatena, Blautia luti, Ruminococcus gnavus, and Bacteroides thetaiotaomicron.

4. A reagent kit, characterized in that: The kit contains reagents for the quantitative detection of the microbial markers described in claim 3.

5. A product for diagnosing polycystic ovary syndrome, characterized in that: The product includes one or more of reagents, primers, probes, antibodies, test strips, aptamers, and chips. The product is specific to the microbial biomarkers of claim 3 and is used for the quantitative detection of the microbial biomarkers of claim 3.

6. A predictive system for assessing the risk of a subject being a patient with polycystic ovary syndrome, characterized in that, include: The detection module is used to obtain the quantitative detection results of the microbial markers as described in any one of claims 1 to 3 in the fecal sample of the subject to be tested; The comparison module is used to compare the quantitative detection results with a preset threshold, and to determine the risk of the subject being a patient with polycystic ovary syndrome based on the comparison results.

7. A computer program product related to polycystic ovary syndrome, characterized in that: The computer program product is used to perform steps for diagnosing the risk of a subject having polycystic ovary syndrome, including: The relative abundance value of each single bacterial species in the feces of the test subject is obtained, wherein the single bacterial species include Dorea longicatena, Blautia luti, Ruminococcus gnavus, and Bacteroides thetaiotaomicron. Substitute the relative abundance value of the single bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object; The probability Z of a subject being diagnosed with polycystic ovary syndrome 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 polycystic ovary syndrome can be 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; Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Dorea longicatena, x2 is the relative abundance value of Bacteroides thetaiotaomicron, x3 is the relative abundance value of Blautia luti, and x4 is the relative abundance value of Ruminococcus gnavus.

9. The product according to claim 8, characterized in that: The values ​​are: A = -1.1382, B1 = 257.9758, B2 = -132.2734, B3 = 33.3171, and B4 = 16.0783.

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