Gut microbial biomarkers, products and their applications associated with non-alcoholic fatty liver disease

Abstract

This invention discloses a gut microbial biomarker, product, and application related to non-alcoholic fatty liver disease (NAFLD), belonging to the biomedical field. The microbial biomarker includes one or more of *Streptococcus pharyngis*, *Bacteroides foetida*, *Bifidobacterium adolescentis*, and *Trichophyton* bacteria. This invention provides a kit including detection reagents for detecting the relative abundance of the aforementioned microbial biomarkers. This invention also provides a product for diagnosing NAFLD, a predictive system for assessing the risk of NAFLD in a test subject, and a computer program product related to NAFLD. The product provided by this invention has good feasibility and accuracy, can effectively assess the risk of NAFLD, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to gut microbiota markers, products and their applications related to non-alcoholic fatty liver disease. Background Technology

[0002] Non-alcoholic fatty liver disease (NAFLD) is a metabolic stress-related liver disease closely associated with insulin resistance and genetic susceptibility. Its pathological feature is excessive fat accumulation in hepatocytes (mainly macrovesicular steatosis).

[0003] Currently, routine diagnostic methods for non-alcoholic fatty liver disease include ultrasound examination, serum liver enzyme tests, and liver biopsy. However, these diagnostic methods still have the following significant limitations: 1) Ultrasound examination remains the preferred screening tool for hepatic steatosis, but it is not sensitive to mild lesions and is highly dependent on operation; 2) Serum liver enzyme tests are the most commonly used biochemical indicators, but their sensitivity and specificity are insufficient; 3) Although liver biopsy is the gold standard for diagnosis, it is not suitable for large-scale screening and routine monitoring due to its invasiveness, sampling errors, and observer variability.

[0004] Therefore, although there are currently various diagnostic methods for non-alcoholic fatty liver disease, 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 non-alcoholic fatty liver disease. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a microbial biomarker, product, and application for non-alcoholic fatty liver disease, which can offer a new approach and method for the diagnosis of non-alcoholic fatty liver disease.

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

[0007] In a first aspect, a reagent for quantitatively detecting the microbial markers described in the first aspect is provided for use in the preparation of products for diagnosing non-alcoholic fatty liver disease, wherein the microbial markers include Streptococcus anginosus.

[0008] In the above technical solution, the microbial markers also include one or more of Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0009] In the above technical solution, the microbial markers also include a combination of markers composed of Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0010] In a second aspect, a kit containing reagents for the quantitative detection of the microbial markers described in the first aspect.

[0011] Thirdly, a product for diagnosing non-alcoholic fatty liver disease, 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.

[0012] Fourthly, a predictive system for assessing the risk of a subject having non-alcoholic fatty liver disease includes:

[0013] 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;

[0014] The comparison module compares the detection results with a preset threshold and determines the risk of the subject being a non-alcoholic fatty liver disease patient based on the comparison results.

[0015] Fifthly, a computer program product related to non-alcoholic fatty liver disease (NAFLD), the computer program product being used to perform steps for diagnosing whether a subject has a risk of having NAFLD, including:

[0016] The relative abundance value of each single bacterial species in the feces of the subject to be tested is obtained. The single bacterial species include Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0017] 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;

[0018] The probability Z of a subject being diagnosed with non-alcoholic fatty liver disease is calculated based on y, where Z = exp(y) / {1 + exp(y)}, and exp(y) is an exponential function of y.

[0019] Based on the comparison of the probability of disease Z with the reference value, the risk of the subject having non-alcoholic fatty liver disease can be diagnosed or predicted.

[0020] In the above technical solution, the formula for the binary logistic regression equation is:

[0021] y=A+B1×x1+B2×x2+B3×x3+B4×x4;

[0022] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Lachnospiraceaebacterium, x3 is the relative abundance value of Bacteroides coprophilus, and x4 is the relative abundance value of Streptococcus anginosus.

[0023] In the above technical solution, A is -0.6621, B1 is 0.2329, B2 is -24.11, B3 is 69710, and B4 is 80090.

[0024] It should be noted that this invention newly discovered and verified that the above-mentioned gut microbiota are strongly correlated with non-alcoholic fatty liver disease. 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 non-alcoholic fatty liver disease. People can choose according to their own needs, which will not be elaborated here.

[0025] The beneficial effects of this invention are as follows:

[0026] 1. This invention newly discovers four intestinal microorganisms associated with non-alcoholic fatty liver disease, including Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0027] After research and verification, it was found that one or more of the above four gut microbiota can serve as microbial markers associated with non-alcoholic fatty liver disease (NAFLD) and can be used to diagnose whether a sample to be tested has NAFLD.

[0028] 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 non-alcoholic fatty liver disease. This method is completely non-invasive and highly accurate. Metagenomic sequencing provides higher resolution, enabling the analysis of the microbial community to penetrate to the level of bacterial species or even strains, 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.

[0029] 3. This invention also provides a product and a prediction system for diagnosing non-alcoholic fatty liver disease (NAFLD). 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 NAFLD or is at risk of having it. This product and prediction system have good feasibility and accuracy, and can effectively assess the risk of NAFLD in test samples, providing a new tool for clinical diagnosis. Attached Figure Description

[0030] Figure 1 This is a graph showing the results of the linear discriminant analysis;

[0031] Figure 2 Box plot of microbial biomarkers;

[0032] Figure 3 This is the ROC curve. Detailed Implementation

[0033] 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.

[0034] To assess whether gut microbiota composition can serve as a predictor of non-alcoholic fatty liver disease (NAFLD), and addressing the clinical needs for the diagnosis and detection of NAFLD, this invention collected samples from NAFLD patients and healthy individuals. Through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-3), four microorganisms highly correlated with high-efficiency donors were screened, including: Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0035] 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 non-alcoholic fatty liver disease.

[0036] 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.

[0037] Example 1: Sample Collection

[0038] Stool samples were collected from 115 patients with non-alcoholic fatty liver disease and 115 healthy individuals:

[0039] The sample sources and inclusion criteria for the non-alcoholic fatty liver disease group are as follows: 1. Age distribution greater than 18 years; 2. Patients clinically diagnosed with non-alcoholic fatty liver disease; 3. Stable vital signs.

[0040] Exclusion criteria for non-alcoholic fatty liver disease (NAFLD): 1. Received NAFLD treatment within 1 month prior; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Have any chronic illness, 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. Have 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, or breastfeeding women of childbearing age; 10. Patients deemed unsuitable for inclusion in this study by the researchers.

[0041] The healthy individuals sample and inclusion criteria are as follows: 1. Age greater than 18 years; 2. No non-alcoholic fatty liver disease 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.

[0042] The exclusion criteria for healthy individuals were the same as those for the non-alcoholic fatty liver disease group. The above data are from stool samples collected in Hubei Province.

[0043] Example 2: DNA extraction, library construction, and sequencing

[0044] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0045] 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).

[0046] 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).

[0047] 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.

[0048] Example 3: LEfSe analysis for screening microbial biomarkers

[0049] 1. Split the dataset

[0050] 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 anxiety 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.

[0051]

[0052] The results are as follows Figure 1 As shown, this application is the first to discover four bacterial species associated with non-alcoholic fatty liver disease (NAFLD). Specifically, researchers screened out two biomarkers that were significantly increased in the NAFLD group, including Bacteroides coprophilus and Streptococcus anginosus; researchers also screened out two biomarkers that were significantly decreased in the NAFLD group, including Bifidobacterium adolescentis and Lachnospiraceae bacterium.

[0053] Example 4: Verifying the reliability of the above four microbial biomarkers

[0054] 1. First, the remaining 20% ​​of the participants in Example 1 (including those in the non-alcoholic fatty liver 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).

[0055] 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.

[0056] Specificity (true negative rate) = TN / (TN + FP)

[0057] Sensitivity (true positive rate) = TP / (TP + FN)

[0058] 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.

[0059] 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.

[0060]

[0061] As shown in Table 2, for single bacterial species, Lachnospiraceae bacterium had the highest AUC value (approximately 0.943), while Bacteroides coprophilus had the lowest AUC value (approximately 0.630). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 1) was significantly higher than that of the single bacterial species.

[0062] 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.

[0063] 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 the combination of Trichophyton spp., Streptococcus pharyngis, and Bifidobacterium adolescentis (approximately 0.992); the AUC value of the combination of Trichophyton spp., Streptococcus pharyngis, and Bifidobacterium adolescentis (approximately 0.992) is not lower than the AUC value of the combination of Trichophyton spp. and Streptococcus pharyngis (approximately 0.992); the AUC value of the combination of Trichophyton spp. and Streptococcus pharyngis (approximately 0.992) is not lower than the AUC value of Trichophyton spp. alone (approximately 0.943) and the AUC value of Streptococcus pharyngis alone (approximately 0.891).

[0064] 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 60%. Therefore, one or more of the four microbial markers can be used as detection markers for the diagnosis of patients with non-alcoholic fatty liver disease.

[0065] Example 5: Establishing a Logistic Regression Model

[0066] 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:

[0067] a. Establishing a model

[0068] Based on the biomarkers identified above and the proportion of non-alcoholic fatty liver disease (NAFLD) 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 disease probability of 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.

[0069] y=A+B1×x1+B2×x2+B3×x3+B4×x4

[0070] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Lachnospiraceaebacterium, x3 is the relative abundance value of Bacteroides coprophilus, and x4 is the relative abundance value of Streptococcus anginosus.

[0071] b. Determine the values ​​of A and B1 to B4 above.

[0072] After statistical analysis of the sample data, A was -0.6621, B1 was 0.2329, B2 was -24.11, B3 was 69710, and B4 was 80090.

[0073] At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is:

[0074] ;

[0075] c. Calculate the disease probability Z of the subjects to be tested.

[0076] 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.

[0077] After processing, the formula for calculating the probability Z of disease is:

[0078]

[0079] d. Validation set data calculation and statistical analysis

[0080] 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 non-alcoholic fatty liver disease.

[0081] Table 3 shows the relevant data for the validation set markers, and Table 4 shows the mean and standard deviation of the relative abundance of each bacterial species. The mean relative abundance determines the central location of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The p-value is a statistic calculated using the formula of the rank-sum test. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0082] Table 3. Relevant data on validation set markers

[0083]

[0084]

[0085]

[0086] Note: E represents 10 to the power of 10. For example, 9.76514818612e-05 means 9.76514818612 × 10⁻⁵. -5 .

[0087]

[0088] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0089] e. Results and Analysis

[0090] 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 0.668, the sensitivity is approximately 1, and the specificity is 1. Therefore, using the mimicry biomarker as a detection marker in the diagnosis of non-alcoholic fatty liver disease has better efficacy and higher accuracy. Using one or more of these four bacterial species as detection markers is completely non-invasive and highly accurate.

[0091] 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.

[0092] Example 6: Examining and verifying the prevalence of fatty liver disease in a sample of patients and non-alcoholic fatty liver disease.

[0093] Based on the product and method of Example 5, the probability of developing fatty liver disease in a group of healthy individuals and patients with non-alcoholic fatty liver disease was examined and verified. The specific steps are as follows:

[0094] 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, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0095] S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation;

[0096] ;

[0097] Where x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Lachnospiraceae bacterium, x3 is the relative abundance value of Bacteroides coprophilus, and x4 is the relative abundance value of Streptococcus anginosus.

[0098] S3. Calculate the probability Z of the subject being a non-alcoholic fatty liver disease patient based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0099] The probability of disease Z can also be expressed as:

[0100]

[0101] 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 non-alcoholic fatty liver disease.

[0102] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has non-alcoholic fatty liver disease (NAFLD); 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 NAFLD or a patient with NAFLD, 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.

[0103] 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 non-alcoholic fatty liver disease. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0104] Example 7: A predictive system for assessing the risk of non-alcoholic fatty liver disease in test subjects.

[0105] Based on the above embodiments, this embodiment provides a predictive system for assessing the risk of a patient with non-alcoholic fatty liver disease. The predictive system includes a detection module and a comparison module, specifically:

[0106] 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 Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium;

[0107] 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 non-alcoholic fatty liver disease patient based on the comparison results.

[0108] In practical application, to illustrate how the prediction system assesses the risk of a subject having non-alcoholic fatty liver disease, this invention uses relative abundance values ​​as an example. Specifically:

[0109] 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.

[0110] The comparison module compares the first abundance value with the data of Streptococcus longicornis in Table 4 to assess the risk of the test subject being a non-alcoholic fatty liver disease patient. For example, if the first abundance value is within the range determined by the mean and standard deviation of non-alcoholic fatty liver disease patients, the risk of the test subject being a non-alcoholic fatty liver disease patient is high; conversely, the risk of the test subject being a non-alcoholic fatty liver disease patient is low.

[0111] Similarly, referring to the detection and assessment method of Streptococcus anginosus mentioned above, the prediction system can also perform similar detection and assessment of microorganisms such as Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium, thereby assessing the risk of the test subject being a patient with non-alcoholic fatty liver disease.

[0112] Due to factors such as genetics, region, 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 type 2 diabetic patient.

[0113] 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.

[0114] Example 8: Computer program products associated with non-alcoholic fatty liver disease

[0115] Based on the above embodiments, this embodiment provides a computer program product related to non-alcoholic fatty liver disease (NAFLD). The computer program product is used to perform a method for diagnosing whether a subject is at risk of having NAFLD, including the following steps:

[0116] 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, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0117] 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).

[0118] y=A+B1×x1+B2×x2+B3×x3+B4×x4

[0119] Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Lachnospiraceaebacterium, x3 is the relative abundance value of Bacteroides coprophilus, and x4 is the relative abundance value of Streptococcus anginosus.

[0120] S3. Calculate the probability Z of the subject being a non-alcoholic fatty liver disease patient based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0121] The probability of disease Z can also be expressed as:

[0122]

[0123] 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 non-alcoholic fatty liver disease.

[0124] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has non-alcoholic fatty liver disease (NAFLD); a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests that the subject may be a patient with NAFLD or a patient with NAFLD, 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 it is to utilize additional testing methods.

[0125] Example 9: Detection Reagent

[0126] 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 non-alcoholic fatty liver disease. 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 non-alcoholic fatty liver disease.

[0127] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing non-alcoholic fatty liver disease to diagnose whether a test sample has non-alcoholic fatty liver disease; at the same time, the microbial markers can be selected from the four single bacterial species related to non-alcoholic fatty liver disease discovered in this application, that is, the microbial markers in the test reagent can include one or more of Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0128] Example 10: Reagent Kit

[0129] 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 non-alcoholic fatty liver disease (NAFLD). 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 NAFLD, and will not be repeated here.

[0130] Example 11: Products for diagnosing non-alcoholic fatty liver disease

[0131] This application also provides a product for diagnosing non-alcoholic fatty liver disease (NAFLD) to determine whether a sample to be tested has NAFLD; the product is specific to one or more of the four single bacterial species found in this application that are associated with NAFLD, and the product includes primers, probes, antibodies, aptamers, or chips.

[0132] 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.

[0133] Example 12: Diagnosing whether a test sample belongs to a patient with non-alcoholic fatty liver disease

[0134] If it is necessary to diagnose whether a person being tested has non-alcoholic fatty liver disease, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme tests, and liver tissue biopsy, 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:

[0135] (1) If, after continuous observation over multiple time periods, the content of Bacteroides coprophilus and Streptococcus anginosus in the subject is found to be high (compared with the mean and standard deviation of the non-alcoholic fatty liver group in Table 4), or even shows a significant increasing trend, then the subject is more likely to be a non-alcoholic fatty liver patient.

[0136] (2) If, after continuous observation over multiple time periods, the person being tested is found to have a high level of one or more of Bifidobacterium adolescentis and Lachnospiraceae bacterium (compared to the mean and standard deviation of the healthy population in Table 4), then the person being tested is more likely to be a healthy person.

[0137] (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 And Table 4, to make a probabilistic judgment on whether the person being tested is a patient with non-alcoholic fatty liver disease;

[0138] (4) If medical staff want to more accurately determine whether the person to be tested is a patient with non-alcoholic fatty liver disease, they can calculate the risk of the person to be tested having non-alcoholic fatty liver disease 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.

[0139] Conclusion and explanation:

[0140] 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 non-alcoholic fatty liver disease (NAFLD). Each single bacterial species exhibits both sensitivity and specificity for NAFLD. Therefore, the microbial biomarker for NAFLD can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically:

[0141] Microbial markers for non-alcoholic fatty liver disease can be selected from one or more of the following: Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis, and Lachnospiraceae bacterium.

[0142] 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 differences between individuals (samples). 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 non-alcoholic fatty liver disease.

[0143] 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.

[0144] 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 60%. One or more of the four microbial markers can be used as detection markers for the diagnosis of patients with non-alcoholic fatty liver disease.

[0145] 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. Use of a reagent for quantitatively detecting microbial markers comprising Streptococcus anginosus and Lachnospiraceae bacterium in the preparation of a product for diagnosing non-alcoholic fatty liver.

2. Use according to claim 1, characterized in that, The microbial markers further comprise Bacteroides coprophilus and Bifidobacterium adolescentis.

3. Use according to claim 2, characterized in that, The microbial markers are a marker combination consisting of Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis and Lachnospiraceae bacterium.

4. A prediction system for assessing the risk of a subject to be measured as a non-alcoholic fatty liver patient, characterized by, The prediction system is used to diagnose or predict the risk of the subject suffering from non-alcoholic fatty liver, comprising: a detection module for obtaining the quantitative detection results of the microbial markers in the fecal sample of the subject as claimed in any one of claims 1 to 3; a comparison module for comparing the quantitative detection results with a preset threshold value, and determining the risk of the subject being a non-alcoholic fatty liver patient according to the comparison result.

5. A computer program product related to non-alcoholic fatty liver, characterized by: The computer program product is used to perform the steps of diagnosing the risk of the subject suffering from non-alcoholic fatty liver, comprising: obtaining the relative abundance value of each single bacterial species in the feces of the subject, the single bacterial species comprising Streptococcus anginosus, Bacteroides coprophilus, Bifidobacterium adolescentis and Lachnospiraceae bacterium; substituting the relative abundance value of the single bacterial species into a binary logistic regression equation to calculate the log of the advantage of the subject y; calculating the probability Z of the subject being a non-alcoholic fatty liver patient according to y, Z = exp (y) / {1 + exp (y)}, wherein exp (y) is the exponential function of y; diagnosing or predicting the risk of the subject suffering from non-alcoholic fatty liver according to the comparison result of the probability Z and the reference value; The formula of the binary logistic regression equation is: y = A + B1 x x1 + B2 x x2 + B3 x x3 + B4 x x4; Wherein, A is the intercept term, B1-B4 are the regression coefficients of independent variables; x1 is the relative abundance value of Bifidobacterium adolescentis, x2 is the relative abundance value of Lachnospiraceae bacterium, x3 is the relative abundance value of Bacteroides coprophilus, and x4 is the relative abundance value of Streptococcus anginosus; The A is -0.6621, the B1 is 0.2329, the B2 is -24.11, the B3 is 69710, and the B4 is 80090.

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