Gut microbiota markers, products and their applications associated with type 2 diabetes

Abstract

This invention discloses a gut microbiome biomarker, product, and its application related to type 2 diabetes, belonging to the biomedical field. The microbiome biomarker includes one or more of *Streptococcus pharyngitis*, *Macrococcus escherichiae*, *Bifidobacterium longum*, *Eubacterium tumefaciens*, and *Bifidobacterium bifidum*. 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 type 2 diabetes, a predictive system for assessing the risk of a subject having type 2 diabetes, and a computer program product related to type 2 diabetes. The product provided by this invention has good feasibility and accuracy, can effectively assess the risk of type 2 diabetes, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to a gut microbiota marker, product, and application related to type 2 diabetes. Background Technology

[0002] Currently, the most common clinical methods for diagnosing and monitoring type 2 diabetes rely on the detection of blood glucose and glycated hemoglobin, including fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT), etc.

[0003] While the above-mentioned testing methods constitute the current gold standard for type 2 diabetes detection, they still have the following significant limitations: both fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT) require the collection of venous blood to ensure the accuracy of the results; at the same time, the oral glucose tolerance test (OGTT) is cumbersome, requiring subjects to have their blood drawn multiple times and remain fasting, resulting in a poor experience and low subject compliance.

[0004] Therefore, there is a need in the field to develop a new product for detecting whether a subject has type 2 diabetes, which does not require the collection of venous blood from the subject or the subject to fasting. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a microbial biomarker for type 2 diabetes, a product thereof, and its application, which can offer a new approach and method for the diagnosis of type 2 diabetes.

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

[0007] In a first aspect, a microbial marker for type 2 diabetes is provided, said microbial marker comprising Streptococcus anginosus and / or Megasphaera elsdenii.

[0008] In the above technical solution, the microbial markers also include one or more of Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

[0009] In the above technical solution, the microbial markers also include a combination of markers composed of Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

[0010] In a second aspect, the application of a reagent for quantitatively detecting the microbial markers described in the first aspect in the preparation of products for diagnosing type 2 diabetes is provided.

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

[0012] Fourthly, a product for diagnosing type 2 diabetes, 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 type 2 diabetes 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 type 2 diabetes patient based on the comparison results.

[0016] Sixthly, a computer program product related to type 2 diabetes, the computer program product being used to perform steps for diagnosing the risk of a subject having type 2 diabetes, including:

[0017] The relative abundance value of each single bacterial species in the feces of the test subject was obtained, and the single bacterial species included Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum;

[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 the subject being diagnosed with type 2 diabetes is calculated based on y, Z = exp(y) / {1 + exp(y)}, where 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 type 2 diabetes 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+B5×x5;

[0023] Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Megasphaeraelsdenii, x2 is the relative abundance value of Streptococcus anginosus, x3 is the relative abundance value of Eubacterium eligens, x4 is the relative abundance value of Bifidobacterium bifidum, and x5 is the relative abundance value of Bifidobacterium longum.

[0024] In the above technical solution, A is -0.671, B1 is 69880, B2 is 84390, B3 is -1731, B4 is -195.6, and B5 is 9.884.

[0025] It should be noted that this invention has newly discovered and verified that the above-mentioned gut microbiota are strongly correlated with type 2 diabetes. 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 type 2 diabetes. 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 five gut microbiota associated with type 2 diabetes, including Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum;

[0028] After research and verification, it was found that one or more of the above five gut microbiota can serve as microbial biomarkers associated with type 2 diabetes and can be used to diagnose whether the sample being tested has type 2 diabetes.

[0029] 2. This invention also provides a reagent and kit that can use one or more of the above five bacterial species as detection markers (i.e., microbial markers) to predict or diagnose type 2 diabetes 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 five 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 prediction system for diagnosing type 2 diabetes. This product and prediction 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 type 2 diabetes or is at risk of developing type 2 diabetes. This product and prediction system have good feasibility and accuracy, and can effectively assess the risk of type 2 diabetes 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] Type 2 diabetes is a complex metabolic disease caused by insulin resistance and progressive decline in pancreatic β-cell function, and has become one of the most serious public health problems worldwide. Its harm lies not only in its persistently high prevalence, but also in the serious systemic complications it causes, placing a heavy burden on patients, their families, and society.

[0036] To address the aforementioned issues and in order to evaluate whether the composition of gut microbiota can serve as a predictor of type 2 diabetes, and to meet the clinical needs for the diagnosis and detection of type 2 diabetes, this invention collected samples from type 2 diabetes patients and healthy individuals. Through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-3), five microorganisms highly associated with efficient donors were screened, including: Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

[0037] ROC curve analysis showed that the above five biomarkers have high specificity and sensitivity as detection variables, and these five bacterial species can be used as detection biomarkers for the prediction and diagnosis of type 2 diabetes patients.

[0038] 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.

[0039] Example 1: Sample Collection

[0040] Stool samples were collected from 120 patients with type 2 diabetes and 105 healthy individuals:

[0041] The inclusion criteria for the type 2 diabetes group are as follows: 1. Age > 18 years; 2. Clinical diagnosis of type 2 diabetes; 3. Ability to understand and sign the informed consent form.

[0042] The exclusion criteria for type 2 diabetes are as follows: 1. Exclusion of type 1 diabetes, gestational diabetes, special types of diabetes, and other special types of diabetes; 2. Exclusion of patients with severe heart, liver, or kidney dysfunction (such as abnormal serum creatinine levels), or active liver disease, severe infection, hypoxia, or who have undergone major surgery; 3. Recent (usually within 1 month) use of antibiotics, prebiotics, or probiotic products; or use of drugs that may significantly affect glucose metabolism or gut microbiota (such as certain antipsychotics or statins); 4. History of gastrointestinal diseases such as diarrhea, constipation, or dysentery within the past month; or acute or chronic hepatobiliary diseases and other digestive tract diseases; or history of gastrointestinal surgery (such as gastrectomy or intestinal resection); 5. Inability to provide stool samples as required, or insufficient sample volume or improper processing.

[0043] The inclusion criteria for the healthy group 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 not in an immunodeficient state; 5. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics in the three months prior to and during the study; 6. Normal glucose tolerance confirmed by standard medical evaluation. The exclusion criteria for the healthy group were as follows: 1. Use of any oral or intravenous antibiotics within at least one month prior to sampling; 2. History of any acute or chronic gastrointestinal disease, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), chronic diarrhea, chronic constipation, peptic ulcers, colonic polyposis, or gastrointestinal malignancies; 3. Pregnancy or lactation; 4. Glucose tolerance exceeding the normal range confirmed by standard medical evaluation; 5. Consumption of probiotic-containing yogurt, supplements, or prebiotic products within at least two weeks prior to sampling. The above data were collected from stool samples in Hubei Province.

[0044] Example 2: DNA extraction, library construction, and sequencing

[0045] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0046] 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).

[0047] 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).

[0048] 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.

[0049] Example 3: LEfSe analysis for screening microbial biomarkers

[0050] 1. Split the dataset

[0051] 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.

[0052]

[0053] The results are as follows Figure 1 As shown, this application is the first to discover five bacterial species associated with type 2 diabetes. Specifically, researchers screened out two biomarkers that were significantly increased in the type 2 diabetes group, including *Streptococcus anginosus* and *Megasphaera elsdenii*; and three biomarkers that were significantly decreased in the type 2 diabetes group, including *Bifidobacterium longum*, *Eubacterium eligens*, and *Bifidobacterium bifidum*.

[0054] Example 4: Verifying the reliability of the above 5 microbial biomarkers

[0055] 1. First, the remaining 20% ​​of the participants in Example 1 (including those in the type 2 diabetes 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 characteristic (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0056] 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.

[0057] Specificity (true negative rate) = TN / (TN + FP)

[0058] Sensitivity (true positive rate) = TP / (TP + FN)

[0059] 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.

[0060] 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 5 single bacterial species) and individual bacteria are shown in Table 2.

[0061]

[0062] As shown in Table 2, for single bacterial species, Megasphaera elsdenii had the highest AUC value (approximately 0.771), while Bifidobacterium longum had the lowest AUC value (approximately 0.638). The AUC value of the mimicry marker (a marker formed by the combination of 5 single bacterial species) (approximately 1) was significantly higher than that of the single bacterial species.

[0063] Combine Table 2 and Figure 3 It 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.

[0064] For example, the AUC value (approximately 1) of the mimicry marker (a marker formed by a combination of 4 single bacterial species) is not lower than the AUC value (approximately 1) of the combination of Streptococcus pharyngitis, Megacoccus ehrlich, Eubacterium tumefaciens, and Bifidobacterium bifidum; the AUC value (approximately 1) of the combination of Streptococcus pharyngitis, Megacoccus ehrlich, and Eubacterium tumefaciens is not lower than the AUC value (approximately 1) of the combination of Streptococcus pharyngitis and Megacoccus ehrlich; and the AUC value (approximately 0.977) of the combination of Streptococcus pharyngitis and Megacoccus ehrlich is not lower than the AUC value (approximately 0.750) of Streptococcus pharyngitis and the AUC value (approximately 0.771) of Megacoccus ehrlich.

[0065] As can be seen from the above, all five 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 all five microbial markers is greater than 60%. Therefore, one or more of the five microbial markers can be used as detection markers for the diagnosis of patients with type 2 diabetes.

[0066] Example 5: Establishing a Logistic Regression Model

[0067] 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:

[0068] a. Establishing a model

[0069] Based on the biomarkers identified above and the proportion of type 2 diabetes patients in the training set, the relative abundance values ​​of the five detected bacterial species were further used as single variables. The linear relationship between the relative abundance values ​​of the five single bacteria and the probability of disease in the samples was then discussed. The logarithm y (also known as the first probability value y) of the subject's dominance was calculated using a binary logistic regression equation.

[0070] y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5

[0071] Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Megasphaeraelsdenii, x2 is the relative abundance value of Streptococcus anginosus, x3 is the relative abundance value of Eubacterium eligens, x4 is the relative abundance value of Bifidobacterium bifidum, and x5 is the relative abundance value of Bifidobacterium longum.

[0072] b. Determine the values ​​of A and B1 to B5 above.

[0073] After statistical analysis of the sample data, the values ​​of A and B1 to B5 are as follows: A is -0.671, B1 is 69880, B2 is 84390, B3 is -1731, B4 is -195.6, and B5 is 9.884.

[0074] At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is:

[0075] ;

[0076] c. Calculate the disease probability Z of the subjects to be tested.

[0077] 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.

[0078] After processing, the formula for calculating the probability Z of disease is:

[0079]

[0080] d. Validation set data calculation and statistical analysis

[0081] 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. Finally, the probability Z of the test sample being a patient was calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a type 2 diabetic patient.

[0082] 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.

[0083] Table 3. Relevant data on validation set markers

[0084]

[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 0.655, the sensitivity is approximately 1, and the specificity is 1. Therefore, using the mimicry biomarker as a detection biomarker in the diagnosis of type 2 diabetes patients has better efficacy and higher accuracy. Using these five 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 health probability in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal samples 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 prevalence of diabetes in patients in a validation set and patients with type 2 diabetes.

[0095] Based on the product and method of Example 5, the probability of disease in healthy individuals and patients with type 2 diabetes 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 individual bacterial strain in the intestine; among which, the individual bacterial strains include Streptococcus anginosus, Megasphaeraelsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum;

[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 Megasphaera elsdenii, x2 is the relative abundance value of Streptococcus anginosus, x3 is the relative abundance value of Eubacterium eligens, x4 is the relative abundance value of Bifidobacterium bifidum, and x5 is the relative abundance value of Bifidobacterium longum.

[0100] S3. Calculate the probability Z of the subject being a type 2 diabetes patient 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] 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 type 2 diabetes.

[0103] In practice, a Z-score greater than 0.5 indicates a low probability that the subject has type 2 diabetes; a Z-score less than 0.5 indicates a higher probability; and a Z-score of 0.5 suggests the subject may be a patient or have type 2 diabetes, requiring further testing using methods such as complete blood count and physical examination. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0104] 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 value, other means of quantitative detection of microorganisms are also feasible for the present 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 type 2 diabetes. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0105] Example 7: A predictive system for assessing the risk of patients with type 2 diabetes.

[0106] Based on the above embodiments, this embodiment provides a predictive system for assessing the risk of a subject being a patient with type 2 diabetes. The predictive system includes a detection module and a comparison module, specifically:

[0107] 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 the following: Streptococcus anginosus, Megasphaeraelsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum;

[0108] 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 type 2 diabetes patient based on the comparison results.

[0109] In practical application, to illustrate how the prediction system assesses the risk of a subject having type 2 diabetes, this invention uses abundance values ​​as an example. Specifically:

[0110] The detection module obtains the relative abundance value of Streptococcus anginosus in the fecal sample of the subject to be tested, which is recorded as the first abundance value. This first abundance value is also the quantitative detection result obtained by the detection module.

[0111] 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 type 2 diabetes. For example, if the first abundance value is within the range determined by the mean and standard deviation of patients with type 2 diabetes, the risk of the subject being a patient with type 2 diabetes is high; otherwise, the risk of the subject being a patient with type 2 diabetes is low.

[0112] Similarly, referring to the detection and assessment method for Streptococcus anginosus mentioned above, the prediction system can also perform similar detection and assessment for microorganisms such as Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum, thereby assessing the risk that the subject is a patient with type 2 diabetes.

[0113] Due to factors such as genetics, region, and environment, it is normal that not all five newly discovered bacterial species can be detected in fecal samples. People can use one or more of the five newly discovered bacterial species as detection markers to assess the risk of the subject being a type 2 diabetic patient.

[0114] Given that abundance values ​​and other quantitative detection data (such as absolute abundance or total microbial load information) are conventional methods in this field, if one wants to use other quantitative detection methods to assess the risk of a subject being a patient with type 2 diabetes, one can refer to the description above, which will not be repeated here.

[0115] Example 8: Computer program products related to type 2 diabetes

[0116] Based on the above embodiments, this embodiment provides a computer program product related to type 2 diabetes. The computer program product is used to perform a method for diagnosing the risk of a subject having type 2 diabetes, including the following steps:

[0117] S1. Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum;

[0118] 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).

[0119] y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5

[0120] Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Megasphaeraelsdenii, x2 is the relative abundance value of Streptococcus anginosus, x3 is the relative abundance value of Eubacterium eligens, x4 is the relative abundance value of Bifidobacterium bifidum, and x5 is the relative abundance value of Bifidobacterium longum.

[0121] S3. Calculate the probability Z of the subject being a type 2 diabetes patient based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0122] The probability of disease Z can also be expressed as:

[0123]

[0124] 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 type 2 diabetes.

[0125] In practice, a Z-score greater than 0.5 indicates a low probability that the subject has type 2 diabetes; a Z-score less than 0.5 indicates a higher probability; and a Z-score of 0.5 suggests the subject may be a patient or have type 2 diabetes, requiring further testing using methods such as complete blood count and physical examination. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0126] Example 9: Detection Reagent

[0127] 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 type 2 diabetes. 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 type 2 diabetes.

[0128] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing type 2 diabetes to diagnose whether a sample to be tested has type 2 diabetes; at the same time, the microbial markers can be selected from the five single bacterial species related to type 2 diabetes discovered in this application, that is, the microbial markers in the detection reagent can include one or more of Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

[0129] Example 10: Reagent Kit

[0130] 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 type 2 diabetes. 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 type 2 diabetes, and will not be repeated here.

[0131] Example 11: Products for diagnosing type 2 diabetes

[0132] This application also provides a product for diagnosing type 2 diabetes, to diagnose whether a test sample has type 2 diabetes; the product is specific to one or more of the five single bacterial species found in this application that are associated with type 2 diabetes, and the product includes primers, probes, antibodies, aptamers or chips.

[0133] 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.

[0134] Example 12: Diagnosing whether a test sample belongs to a patient with type 2 diabetes.

[0135] If it is necessary to diagnose whether a person being tested has type 2 diabetes, in addition to using conventional testing methods, medical staff can also use the methods or products described in Examples 1-11 above to diagnose the person being tested, in order to assist medical staff in making a more accurate judgment:

[0136] (1) If, after continuous observation over multiple time periods, the content of Streptococcus anginosus and Megasphaera elsdenii in the subject is found to be high (compared to the mean and standard deviation of the type 2 diabetes group in Table 4), or even shows a significant increasing trend, then the subject is more likely to be a type 2 diabetes patient.

[0137] (2) If, after continuous observation over multiple time periods, the test subject is found to have a high level of one or more of Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum (compared to the mean and standard deviation of the type 2 diabetes group in Table 4), then the test subject is more likely to be a healthy person.

[0138] (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 type 2 diabetes patient.

[0139] (4) If medical staff want to determine more accurately and intuitively whether the person to be tested is a type 2 diabetic patient, they can calculate whether the person to be tested is a type 2 diabetic patient 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.

[0140] Conclusion and explanation:

[0141] 1. By Figures 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 type 2 diabetes. Each single bacterial species exhibits both sensitivity and specificity for type 2 diabetes. Therefore, the microbial biomarker for type 2 diabetes can be selected from any one or more of the five newly discovered single bacterial species in this application. Specifically:

[0142] Microbial markers for type 2 diabetes can be selected from one or more of the following: Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

[0143] 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 disease probability Z of 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 type 2 diabetes.

[0144] 3. Predictive Results: For single bacterial species, Megasphaera elsdenii had the highest AUC value (approximately 0.771), while Bifidobacterium longum had the lowest (approximately 0.638). The AUC value of the mimicry marker (a marker formed by a combination of 5 single bacterial species) (approximately 1) was significantly higher than that of the single bacterial species. When other microorganisms were added to one or more single bacterial species for testing, the detection result (AUC value) did not decrease; that is, the detection result with the addition of other microorganisms was not lower than the detection result (AUC value) without the addition of microorganisms.

[0145] Meanwhile, the five newly discovered bacterial species in this application can all be used as detection variables, all of which have high specificity and sensitivity, and the AUC of all five microbial markers is greater than 60%. One or more of the five microbial markers can be used as detection markers for the diagnosis of type 2 diabetes patients.

[0146] 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 application of a reagent for quantitative detection of microbial markers in the preparation of products for diagnosing type 2 diabetes, characterized in that, The microbial markers include Streptococcus anginosus, Megasphaera elsdenii, Eubacterium eligens, and Bifidobacterium bifidum.

2. The application according to claim 1, characterized in that, The microbial markers also include Bifidobacterium longum.

3. The application according to claim 2, characterized in that, The microbial markers are a combination of markers consisting of Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum.

4. A reagent kit, characterized in that: The kit contains products for the quantitative detection of microbial markers according to any one of claims 1 to 3.

5. A product for diagnosing type 2 diabetes, characterized in that: The product comprises one or more of reagents, primers, probes, antibodies, test strips, aptamers, and chips, and the product is specific to the microbial biomarkers of any one of claims 1 to 3 and is used for the quantitative detection of the microbial biomarkers of any one of claims 1 to 3.

6. A predictive system for assessing the risk of a subject being a patient with type 2 diabetes, 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 type 2 diabetes patient based on the comparison results.

7. A computer program product related to type 2 diabetes, characterized in that: The computer program product is used to perform steps for diagnosing whether a subject has a risk of having type 2 diabetes, including: The relative abundance value of each single bacterial species in the feces of the test subject was obtained, and the single bacterial species included Streptococcus anginosus, Megasphaera elsdenii, Bifidobacterium longum, Eubacterium eligens, and Bifidobacterium bifidum; The relative abundance values ​​of the single bacterial species are simultaneously substituted into the same binary logistic regression equation to calculate the logarithm y of the dominance of the test object. The probability Z of the subject being diagnosed with type 2 diabetes is calculated based on y, Z = exp(y) / {1 + exp(y)}, where 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 type 2 diabetes 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+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 Megasphaeraelsdenii, x2 is the relative abundance value of Streptococcus anginosus, x3 is the relative abundance value of Eubacterium eligens, x4 is the relative abundance value of Bifidobacterium bifidum, and x5 is the relative abundance value of Bifidobacterium longum.

9. The product according to claim 8, characterized in that: The values ​​are: A = -0.671, B1 = 69880, B2 = 84390, B3 = -1731, B4 = -195.6, and B5 = 9.884.

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