A Parkinson's syndrome-related intestinal flora marker and its application

Abstract

The present invention discloses an intestinal flora marker related to Parkinson's syndrome and its application. The intestinal flora marker includes fecal Bacteroides Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgaris Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivary One or more combinations of. The intestinal flora marker is used as a detection marker in the diagnosis of Parkinson's syndrome patients, which is completely non-invasive and highly accurate.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to an intestinal flora marker associated with Parkinson's syndrome and an application thereof. Background Art

[0002] Parkinson's syndrome, also often called "tremor paralysis", is a degenerative disease of the nervous system. The most notable symptoms are resting tremor, bradykinesia and muscle rigidity. In the middle and late stages, patients experience postural balance disorders. Before and after the onset of the disease, there are also some non-motor symptoms, including constipation, olfactory disorders, sleep disorders, autonomic dysfunction and mental cognitive disorders. However, its etiology and pathogenesis are not very clear, and may be related to multiple factors such as genetics, environmental factors and aging of the nervous system.

[0003] At present, the diagnosis of Parkinson's syndrome mainly relies on medical history, clinical symptoms and signs. PET imaging of dopa uptake function using 18F-dopa as a tracer can show a decrease in dopamine neurotransmitter synthesis, which can be shown in the early stage of the disease or even in the subclinical stage, and can be used to support the diagnosis of Parkinson's syndrome. However, this examination is expensive and has not yet been routinely performed. In recent years, more and more studies have shown that there is a close connection between intestinal flora and Parkinson's syndrome. Therefore, it is of great significance to explore the use of intestinal flora as a biomarker for the diagnosis of Parkinson's syndrome. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide an intestinal flora marker related to Parkinson's syndrome and its application, so as to provide a new idea and approach for the diagnosis and treatment of Parkinson's syndrome.

[0005] The present invention collects samples from Parkinson's patients and healthy people, performs metagenomic sequencing, and uses bioinformatics to perform statistics on sequencing data, discovers intestinal flora associated with the disease, integrates intestinal flora with disease information, and predicts Parkinson's patients to the greatest extent. The present invention discovered for the first time through metagenomic sequencing that Bacteroides faecalis Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgaris Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius Correlation with patients with Parkinson's syndrome, these species may serve as predictors of Parkinson's syndrome.

[0006] To achieve the above purpose, the technical solution designed by the present invention is as follows:

[0007] The present invention provides an intestinal flora marker associated with Parkinson's syndrome, wherein the intestinal flora marker is Bacteroides faecalis Bacteroides suffocatus , Bacteroides vulgaris Bacteroides vulgaris Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivary .

[0008] The present invention also provides a reagent for detecting intestinal flora markers for use in preparing a product for diagnosing or screening Parkinson's syndrome, wherein the intestinal flora markers include fecal Bacteroides Bacteroides suffocatus , Bacteroides vulgaris Bacteroides vulgaris Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius .

[0009] The present invention also provides a kit, which comprises a reagent for detecting the intestinal flora marker.

[0010] The present invention also provides an application of the kit in preparing a product for detecting Parkinson's syndrome.

[0011] The present invention also provides a use of the detection reagent in the kit in preparing a kit for diagnosing Parkinson's syndrome.

[0012] The present invention also provides a product for diagnosing Parkinson's syndrome, which comprises primers, probes, antibodies, aptamers or chips that are specific to the intestinal flora markers.

[0013] The present invention also provides a computer program product related to Parkinson's syndrome, wherein the computer program product is used to diagnose the risk of a subject suffering from Parkinson's syndrome, comprising the following steps:

[0014] 1) Obtain the relative abundance value of each single bacterial species in the feces of the test subject; single bacterial species include fecal Bacteroides Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgaris Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius ;

[0015] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0016] 3) Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp (y) / {1 + exp (y)};

[0017] Wherein, P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0018] 4) Diagnosing or predicting the risk of the subject suffering from Parkinson's syndrome based on the comparison of the P value with the reference value.

[0019] Furthermore, the formula of the binary logistic regression equation is:

[0020] y=A+B1* x 1 + B2* x 2 + B3* x 3 + B4* x 4 + B5* x 5;

[0021] A is the intercept term, and B1 to B5 are the regression coefficients of the independent variables; x 1 is Bacteroides vulgaris Bacteroides vulgaris The relative abundance value of x 2 is Bacteroides faecalis Bacteroides stercoris The relative abundance value of x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of x 4 Lactobacillus salivarius Lactobacillus salivary The relative abundance value of x5 Ruminococcus Ruminococcus The relative abundance value of .

[0022] Furthermore, A is -0.2137, B1 is 7.6305, B2 is 8.7249, B3 is 8.1012, B4 is -4939.5908, and B5 is -487.0028.

[0023] Beneficial effects of the present invention:

[0024] 1. The present invention discovered for the first time Bacteroides faecalis ( Bacteroides stercoris )、Bacteroides vulgaris( Bacteroides vulgaris ), Bacteroides thetaiotaomicron ( Bacteroides thetaiotaomicron )、Ruminococcus( Ruminococcus ) and Lactobacillus salivarius ( Lactobacillus salivarius ) and Parkinson's disease. Specifically: Bacteroides vulgaris ( Bacteroides vulgaris ) had the highest single-bacterial prediction effect for Parkinson's syndrome, followed by Bacteroides faecalis, Bacteroides thetaiotaomicron, Lactobacillus salivarius and Ruminococcus. ROC curve analysis showed that the above five markers had high specificity and sensitivity as detection variables, so these five bacterial species can be used as detection markers for the prediction and diagnosis of patients with Parkinson's syndrome.

[0025] 2. Using these five bacterial species as detection markers is completely non-invasive and highly accurate. Through metagenomic sequencing, higher resolution is provided, allowing the analysis of microbial communities to go deep into the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. We use a larger sample size for verification to ensure excellent prediction of Parkinson's syndrome.

[0026] 3. Currently, there is no mature Parkinson's syndrome prediction system or early screening kit. The five strains provided by the present invention can be used as target microorganisms for developing these systems, filling the gap in this field.

[0027] 4. The present invention not only provides a means of intestinal flora detection for patients with Parkinson's syndrome, but also uses it to judge the patient's Parkinson's syndrome and intestinal flora disorder, provides a bacterial basis, provides scientific support for later intestinal flora transplantation, and provides patients with more accurate individualized treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a technical scheme diagram;

[0029] Figure 2 It is a flow chart of the experimental scheme;

[0030] Figure 3 LEfSe score diagram for Parkinson's disease and healthy people;

[0031] Figure 4 Box scatter plot of relative abundance between Parkinson's syndrome and healthy people;

[0032] Figure 5 It is the ROC diagnostic curve diagram. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below in conjunction with specific embodiments so that those skilled in the art can understand.

[0034] The technical solution of the embodiment is as follows Figure 1 The specific experimental analysis process is as shown in Figure 2 shown.

[0035] Example 1 Screening of intestinal flora markers associated with Parkinson's syndrome

[0036] 1. Sample Collection

[0037] 1. The inclusion criteria for Parkinson's syndrome group samples are as follows:

[0038] (1) Age distribution: 30 to 90 years old;

[0039] (2) Stable vital signs;

[0040] (3) Diagnosed with Parkinson's disease. The exclusion criteria for the Parkinson's syndrome group are as follows:

[0041] a. Refusing to sign the informed consent form;

[0042] b. Participated in an experimental drug program within the past 12 weeks;

[0043] c. History of bariatric surgery, total colectomy with ileorectal anastomosis, or proctocolectomy;

[0044] d. Currently taking antibiotics or probiotic supplements;

[0045] e. History of stroke, rheumatoid arthritis, type 1 diabetes, and IBD;

[0046] f. Have a known history of disease, such as autoimmune disease, heart disease.

[0047] 2. The inclusion criteria for the control group (healthy group) are as follows:

[0048] (1) Age distribution: 30 to 90 years old;

[0049] (2) Not suffering from diabetes or other metabolic diseases;

[0050] (3) Not suffering from depression or other neurological diseases;

[0051] (4) No irritable bowel syndrome or gastrointestinal disease;

[0052] (5) Not suffering from other immune system diseases or being in an immunodeficient state;

[0053] (6) No antibiotics (such as neomycin, rifaximin) or probiotics or prebiotics were taken before and during the study.

[0054] The exclusion criteria were the same as those of the Parkinson's syndrome group.

[0055] 3. Stool samples from 43 patients with Parkinson's syndrome and 69 healthy subjects were collected according to the above standards.

[0056] The above data comes from stool samples collected by Wuhan hospitals.

[0057] 2. DNA extraction, library construction and sequencing

[0058] 1. Select the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected stool samples.

[0059] 2. After extraction, use Qubit to detect DNA concentration, and use 1.5% agarose gel electrophoresis to detect the integrity of the extracted genomic DNA. Perform quality inspection on the extracted genomic DNA to screen out genomic DNA samples with qualified quality (DNA concentration ≥20 ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0060] 3. For DNA samples with qualified quality, they are randomly sheared, end-repaired, connected to A bases, and connected with adapters and indexes. After the adapters are connected, they are purified and the library is amplified. After amplification, the DNA concentration is detected (DNA concentration ≥ 40 ng / μL).

[0061] 4. After the library is qualified, different libraries are pooled and sequenced according to the effective concentration and target data volume. The metagene sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0062] LEfSe analysis and screening of biomarkers

[0063] KneadData software was used to perform quality control (based on Trimmomatic) and host removal (based on Bowtie2) on the raw data. Kraken2 was used to calculate the number of sequences of species contained in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the subjects to be tested (including those in the Parkinson's syndrome group and the healthy group) were randomly selected as the training set, and the remaining 20% ​​of the samples were used as the validation set. The abundance data of each sample in the training set was then analyzed using LEfSe software, with the default setting of the LDA Score screening value of 2.5. The sample information table is shown in Table 1.

[0064] Table 1 Sample information table

[0065]

[0066] The results are as follows Figure 3 As shown, Bacteroides faecalis Bacteroides stercoris , Bacteroides vulgaris Bacteroides popular Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius Associated with Parkinson's disease, among which Bacteroides faecalis Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgaris and Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron In patients with Parkinson's disease, Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivariusIn patients with Parkinson's syndrome, there was a significant increase. Therefore, three biomarkers that were significantly reduced in the Parkinson's syndrome group were screened, including fecal Bacteroides Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgaris and Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron ; Two biomarkers that were significantly increased in the Parkinson's syndrome group were screened out, namely Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius .

[0067] like Figure 4 As shown, the present invention found that Bacteroides faecalis Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius Bacteroides vulgaris was associated with Parkinson's disease as a biomarker, and when a single strain was used for prediction Bacteroides vulgatus The single bacteria prediction effect for Parkinson's syndrome is the highest, followed by the single bacteria prediction effects of Bacteroides faecalis, Bacteroides thetaiotaomicron, Lactobacillus salivarius and Ruminococcus. When the strains are predicted jointly, the combined prediction effect of the five bacteria is the best.

[0068] Example 2 Verification of the reliability of the above five biomarkers

[0069] 1. First, the remaining 20% ​​of the subjects to be tested in Example 1 (including the subjects in the Parkinson's 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 operation, and then the receiver operating characteristic curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value).

[0070] 2. Use IBM SPSS Statistics (v27) statistical software to complete the calculation of specificity and sensitivity and draw the ROC curve. The software first calculates the threshold of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold;

[0071] Specificity (true negative rate) = TN / (TN+FP),

[0072] Sensitivity (true positive rate) = TP / (TP+FN),

[0073] 3. The ROC curve can be constructed by 1-specificity and sensitivity, and the integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of an indicator, the Youden coefficient (Youden index = sensitivity + specificity - 1) is calculated first. The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the indicator.

[0074] 4. The relative abundance values ​​of biomarkers of a single strain are directly analyzed by receiver operating characteristic curve test (ROC curve) to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is as follows: Figure 5 The AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of five biomarkers) and single bacteria are shown in Table 2.

[0075] From the above, we can see that the ROC curve analysis of the five biomarkers as detection variables has high specificity and sensitivity, and the AUC of the five biomarkers is greater than 74%. Therefore, the five biomarkers can be used as detection markers for the diagnosis of Parkinson's syndrome patients;

[0076] The AUC of the prediction score of the mimicry marker was 98.8%, the optimal cutoff value was 0.468, the sensitivity was 1, and the specificity was 0.889. Therefore, the application of mimicry markers as detection markers in the diagnosis of Parkinson's syndrome patients has better effects and high accuracy. Using these five bacterial species as detection markers is completely non-invasive and highly accurate.

[0077] The above results show that these five biomarkers are the first to be found to be associated with Parkinson's syndrome. Bacteroides vulgatus The single bacteria prediction effect for Parkinson's syndrome is the highest, followed by Bacteroides faecalis, Bacteroides thetaiotaomicron, Lactobacillus salivarius and Ruminococcus aureus.

[0078] Table 2 ROC diagnostic curve results

[0079]

[0080] Example 3 Logistic regression model establishment

[0081] a. Model building

[0082] Through the above-mentioned mined biomarkers, based on the proportion of Parkinson's syndrome people or healthy people in the training set, the five detected bacterial species are further used as mimicry markers. On this basis, the linear relationship between the relative abundance values ​​of the five single bacteria and the probability of the sample being healthy (or sick) is discussed, and the first probability value y of the object to be tested is calculated through the binary logistic regression equation:

[0083] y=-0.2137+7.6305* x 1 + 8.7249* x 2 + 8.1012* x 3 + (-4939.5908)* x 4+ (-487.0028)* x 5;

[0084] x 1 is Bacteroides vulgaris Bacteroides vulgatus The relative abundance value of

[0085] x 2 is Bacteroides faecalis Bacteroidesstercoris The relative abundance value of

[0086] x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of

[0087] x 4 Lactobacillus salivarius Lactobacillus salivarius The relative abundance value of

[0088] x5 Ruminococcus Ruminococcus The relative abundance value of .

[0089] b. Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp (y) / {1 + exp (y)}; wherein P is the probability value of the subject to be tested being a healthy person, and exp (y) is the natural exponential function of the first probability value y;

[0090] P can also be written as:

[0091]

[0092] c. Validation Model

[0093] Based on the proportion of Parkinson's syndrome population or healthy population in the validation set, the relevant abundance statistics of the validation set markers were counted and verified. The mean determines the center position of the data distribution, the standard deviation reflects the degree of dispersion of the data relative to the mean, and the Q value is a statistic calculated using the formula of the rank sum test. The smaller the Q value, the greater the difference between the disease group and the healthy group, as follows:

[0094] Table 3. Statistical data of the relative abundance of the validation set markers

[0095] Bacteria Parkinson's disease group mean The mean of the healthy group Parkinson's disease group standard deviation Standard deviation of healthy group Q Value Bacteroides vulgaris 0.00509344590443489 0.106565986879878 0.00768020293485829 0.0877884969800801 0.00106 Bacteroides faecalis 0.00295961577845656 0.0167072953207667 0.00800335845067963 0.0131324671282021 0.0172 Bacteroides thetaiotaomicron 0.00200170442158153 0.00207437355900367 0.00573925282136419 0.0022515999468543 0.0385 Lactobacillus salivarius 0.00105039934994067 0 0.00176376680827376 0 0.0139 Ruminococcus 0.000858817078567556 6.60628521975556E-05 0.00102297745349853 0.000198188556592667 0.0401

[0096] Note: In Table 3, E is used to represent the power of 10. For example, 6.60628521975556e-05 represents 6.60628521975556*10 -05 .

[0097] Example 4

[0098]

[0099] Based on the above embodiments, this embodiment provides a computer program product related to Parkinson's syndrome, which is used to diagnose the risk of a subject suffering from Parkinson's syndrome, including the following steps:

[0100] 1) Obtain the relative abundance value of each single bacterial species in the feces of the test subject; single bacterial species include fecal Bacteroides Bacteroidesstercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius Any one of;

[0101] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0102] y=-0.2137+7.6305* x 1 + 8.7249* x 2 + 8.1012* x 3 + (-4939.5908)* x 4 + (-487.0028)* x 5;

[0103] In the formula, x 1 is Bacteroides vulgaris Bacteroides vulgatus The relative abundance value of x 2Including Bacteroides faecalis Bacteroidesstercoris The relative abundance value of x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of x 4 Lactobacillus salivarius Lactobacillus salivarius The relative abundance value of x5 Ruminococcus Ruminococcus The relative abundance value of

[0104] 3) Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp (y) / {1 + exp (y)};

[0105] Where P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y;

[0106] 4) Diagnosing or predicting the risk of the subject suffering from Parkinson's syndrome based on the comparison of the probability P value of a healthy person with a reference value.

[0107] In actual work, when the P value is greater than 0.5, it means that the probability of the subject suffering from Parkinson's syndrome is low. When the P value is less than 0.5, it means that the probability of the subject suffering from Parkinson's syndrome is high. When the P value is 0.5, it is necessary to further use other means for testing, such as blood routine and physical signs. The closer the P value is to 0.5, the more it is necessary to use other means for testing.

[0108] Example 5

[0109] Based on the product and method of Example 4, the health probability of healthy people and Parkinson's patients in the verification set is checked, and the specific steps are as follows:

[0110] 1) Collect stool samples from the people to be tested and detect the relative abundance of single strains in the stool; the single strains include Bacteroides faecalis Bacteroidesstercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius ; See Table 4.

[0111] 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation;

[0112] y=-0.2137+7.6305* x 1 + 8.7249* x 2 + 8.1012* x 3 + (-4939.5908)* x 4 + (-487.0028)* x 5;

[0113] In the formula, x 1 is Bacteroides vulgaris Bacteroides vulgatus The relative abundance value of x 2 is Bacteroides faecalis Bacteroidesstercoris The relative abundance value of x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of x 4 Lactobacillus salivarius Lactobacillus salivarius The relative abundance value of x5 Ruminococcus Ruminococcus The relative abundance value of .

[0114] 3) Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp (y) / {1 + exp (y)};

[0115] Where P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y.

[0116] 4) Diagnosing or predicting the risk of the subject suffering from Parkinson's syndrome based on the comparison of the probability P value of a healthy person with a reference value.

[0117] In actual work, when the P value is greater than 0.5, it means that the probability of the subject suffering from Parkinson's syndrome is low. When the P value is less than 0.5, it means that the probability of the subject suffering from Parkinson's syndrome is high. When the P value is 0.5, it is necessary to further use other means for testing, such as blood routine and physical signs. The closer the P value is to 0.5, the more it is necessary to use other means for testing.

[0118] In actual situations, there are situations where the judgment criteria are not fully met. The reason is that the stool samples of the persons to be tested may show false positive or false negative results, and further testing is required using other means, such as routine blood tests and assessment of physical signs.

[0119] Table 4 Relevant abundance data of validation set markers

[0120]

[0121]

[0122] Note: In Table 4, ①, x 1 is Bacteroides vulgaris Bacteroides vulgatus The relative abundance value of x 2 is Bacteroides faecalis Bacteroidesstercoris The relative abundance value of x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of x 4 Lactobacillus salivarius Lactobacillus salivarius The relative abundance value of x5 Ruminococcus Ruminococcus The relative abundance value of

[0123] ②, E is used to represent the power of 10, for example, 5.95E-05 means 5.95*10 -05 .

[0124] Conclusion and explanation:

[0125] 1. Single bacteria prediction effect: Bacteroides vulgaris has the highest single bacteria prediction effect on Parkinson's syndrome, followed by Bacteroides faecalis, Bacteroides thetaiotaomicron, Lactobacillus salivarius and Ruminococcus;

[0126] 2. Prediction effect of mimicry markers: mimicry markers, fecal Bacteroides ( Bacteroidesstercoris)、Bacteroides vulgaris( Bacteroides vulgatus ), Bacteroides thetaiotaomicron ( Bacteroides thetaiotaomicron )、Ruminococcus( Ruminococcus ) and Lactobacillus salivarius ( Lactobacillus salivarius ) can all be used as biomarkers for predicting Parkinson's syndrome, and the prediction accuracy rate is over 74%. Among them, the accuracy rate of mimetic markers (markers formed by the combination of multiple biomarkers) is the highest, at 98.8%, with the best prediction effect, and can provide more accurate prediction of Parkinson's syndrome.

[0127] Although the above embodiments have been described in detail, they are only a part of the embodiments of the present invention, not all of them. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all belong to the protection scope of the present invention.

Claims

1. A Parkinson's disease-related intestinal flora marker, characterized in that: The intestinal flora marker is Bacteroides faecalis Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius .

2. Use of a reagent for detecting intestinal flora markers in the preparation of a product for diagnosing or screening Parkinson's syndrome, wherein the intestinal flora markers include fecal Bacteroides Bacteroides stercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius .

3. A kit, characterized in that: The kit comprises a reagent for detecting the intestinal flora marker according to claim 1.

4. Use of the kit according to claim 3 in preparing a product for detecting Parkinson's syndrome.

5. Use of the detection reagent in the kit according to claim 3 in preparing a kit for diagnosing Parkinson's syndrome.

6. A product for diagnosing Parkinson's syndrome, characterized in that: The product includes primers, probes, antibodies, aptamers or chips that are specific to the intestinal flora markers described in claim 1.

7. A computer program product related to Parkinson's syndrome, characterized in that: The computer program product is used to diagnose the risk of a subject suffering from Parkinson's syndrome, comprising the following steps: 1) Obtain the relative abundance value of each single bacterial species in the feces of the test subject; single bacterial species include fecal Bacteroides Bacteroidesstercoris , Bacteroides vulgaris Bacteroides vulgatus Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron , Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius ; 2) Calculate the first probability value y of the object to be tested according to the binary logistic regression equation; 3) Substituting the first probability value y into the following formula to calculate the probability that the subject to be tested is a healthy person: P = exp (y) / {1 + exp (y)}; Wherein, P is the probability value that the subject to be tested is a healthy person, and exp(y) is the natural exponential function of the first probability value y; 4) Diagnosing or predicting the risk of the subject suffering from Parkinson's syndrome based on the comparison of the P value with the reference value.

8. The product according to claim 7, characterized in that: The formula for the binary logistic regression equation is: <h2 style=";text-align:left;direction:ltr">y=A+B1*<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> 1<h2 style=";text-align:left;direction:ltr"> + <h2 style=";text-align:left;direction:ltr"> B2*<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> 2<h2 style=";text-align:left;direction:ltr"> + <h2 style=";text-align:left;direction:ltr"> B3*<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> 3<h2 style=";text-align:left;direction:ltr"> + <h2 style=";text-align:left;direction:ltr"> B4*<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> 4<h2 style=";text-align:left;direction:ltr"> + <h2 style=";text-align:left;direction:ltr"> B5*<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> 5; A is the intercept term, and B1 to B5 are the regression coefficients of the independent variables; x 1 is Bacteroides vulgaris Bacteroides vulgatus The relative abundance value of x 2 is Bacteroides faecalis Bacteroidesstercoris The relative abundance value of x 3 is Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron The relative abundance value of x 4 Lactobacillus salivarius Lactobacillus salivarius The relative abundance value of x5 Ruminococcus Ruminococcus The relative abundance value of .

9. The product according to claim 8, characterized in that: A is -0.2137, B1 is 7.6305, B2 is 8.7249, B3 is 8.1012, B4 is -4939.5908, and B5 is -487.0028.

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