CN119614730B - A Parkinson's syndrome-related intestinal flora marker and its application - Google Patents
A Parkinson's syndrome-related intestinal flora marker and its application Download PDFInfo
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Abstract
The invention discloses an intestinal flora marker related to parkinsonism and application thereof. The intestinal flora marker comprises one or more of bacteroides faecalis Bacteroidesstercoris, Bacteroides vulgatus, Bacteroides thetaiotaomicron, Ruminococcus and Lactobacillus salivarius. The intestinal flora marker is used as a detection marker to be applied to diagnosis of patients with parkinsonism, is completely noninvasive and has high accuracy.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to an intestinal flora marker related to parkinsonism and application thereof.
Background
Parkinsonism, also commonly referred to as "paralysis agitans", is a degenerative neurological disease with the most prominent symptoms of resting tremor, bradykinesia and myotonia, and with symptoms of posture balance in middle and late stage patients, followed by some non-motor symptoms including constipation, olfactory disorders, sleep disorders, autonomic nerve dysfunction and mental cognitive disorders. However, the etiology and pathogenesis are not well defined, and may be related to various factors such as heredity, environmental factors, and nervous system aging.
Currently diagnosis of parkinsonism relies mainly on medical history, clinical symptoms and signs. PET imaging with 18F-dopa as a tracer for dopa uptake shows reduced dopamine transmitter synthesis, and can be used to support diagnosis of Parkinson's disease in early and even sub-clinical stages, but this examination is expensive and has not been routinely performed. In recent years, more and more researches show that a close relation exists between intestinal flora and parkinsonism, so that the exploration of the intestinal flora as a biomarker for diagnosing parkinsonism is of great importance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intestinal flora marker related to parkinsonism and application thereof, so as to provide a new thought and approach for parkinsonism diagnosis and treatment.
According to the invention, samples of parkinsonism patients and healthy people are collected, metagenome sequencing is performed, sequencing data statistics is performed by using bioinformatics, intestinal flora related to diseases is found, the intestinal flora is integrated with disease information, and parkinsonism patients are predicted to the greatest extent. According to the invention, through metagenome sequencing, the correlation of the fecal bacteroides Bacteroidesstercoris, the bacteroides vulgaris Bacteroides vulgatus, the bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, the ruminococci Ruminococcus and the lactobacillus salivarius Lactobacillus salivarius with the parkinsonism patients is discovered for the first time, and the strains can be used as predictors of parkinsonism.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
The invention provides an intestinal flora marker related to parkinsonism, which is bacteroides faecalis Bacteroides stercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius.
The invention also provides application of a reagent for detecting the intestinal flora marker in preparing a product for diagnosing or screening the parkinsonism, wherein the intestinal flora marker comprises bacteroides faecalis Bacteroides stercoris, bacteroides vulgatus Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius.
The invention also provides a kit comprising reagents for detecting the intestinal flora marker.
The invention also provides application of the kit in preparation of products for detecting parkinsonism.
The invention also provides application of the detection reagent in the kit in preparing the kit for diagnosing parkinsonism.
The invention also provides a product for diagnosing parkinsonism, which comprises a primer, a probe, an antibody, an aptamer or a chip with specificity for the intestinal flora marker.
The present invention also provides a computer program product associated with parkinsonism for performing a diagnosis of a subject at risk for parkinsonism, comprising the steps of:
1) Obtaining the relative abundance value of each single strain in the excrement of the object to be detected, wherein the single strain comprises bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius;
2) Calculating a first probability value y of the object to be measured according to a binary logistic regression equation;
3) Substituting the first probability value y into the following formula to calculate the probability that the object to be detected is a healthy person, wherein P=exp (y)/{ 1+exp (y) };
wherein P is the probability value that the object to be detected is a healthy person, exp (y) is the natural exponential function of the first probability value y;
4) Diagnosing or predicting the risk of the subject for parkinsonism based on the comparison of the P value to a reference value.
Further, the formula of the binary logistic regression equation is:
y=A+B1*x1+B2*x2+B3*x3+B4*x4+B5*x5;
A is an intercept term, B 1～B5 is a regression coefficient of an independent variable, x1 is a relative abundance value of Bacteroides vulgare Bacteroides vulgatus, x2 is a relative abundance value of Bacteroides faecalis Bacteroidesstercoris, x3 is a relative abundance value of Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, x4 is a relative abundance value of Lactobacillus salivarius Lactobacillus salivarius, and x5 is a relative abundance value of Ruminococcus Ruminococcus.
Still further, the A is-0.2137, B 1 is 7.6305, B 2 is 8.7249, B 3 is 8.1012, B 4 is-4939.5908, and B 5 is-487.0028.
The invention has the beneficial effects that:
1. The invention discovers the remarkable association between bacteroides faecalis (Bacteroidesstercoris), bacteroides vulgare (Bacteroides vulgatus), bacteroides thetaiotaomicron (Bacteroides thetaiotaomicron), ruminococcus (Ruminococcus) and lactobacillus salivarius (Lactobacillus salivarius) and parkinsonism for the first time. Specifically, the common bacteroides (Bacteroides vulgatus) have the highest single-bacterium prediction effect on parkinsonism, and the single-bacterium prediction effect on fecal bacteroides, bacteroides thetaiotaomicron, lactobacillus salivarius and ruminococcus is inferior. The 5 markers are used as detection variables through ROC curve analysis and have higher specificity and sensitivity, so that the 5 strains can be used as detection markers for the prediction and diagnosis of patients with parkinsonism.
2. The 5 strains are used as detection markers, so that the method is completely noninvasive and has high accuracy. By means of metagenomic sequencing, higher resolution is provided, so that analysis of microbial communities can go deep into the level of strains, even strains, and the accuracy and reliability of diagnosis are improved. We used a larger sample size to verify that the parkinsonism prediction effect was excellent.
3. At present, no mature parkinsonism prediction system or early screening kit exists, and the 5 strains provided by the invention can be used as target microorganisms for developing the systems, so that the blank in the field is filled.
4. The invention not only provides an intestinal flora detection means for patients with parkinsonism, but also judges parkinsonism and intestinal flora disorder of the patients, provides a mycogenic basis, provides scientific support for later intestinal fungus transplantation, and provides an individualized treatment scheme for the patients more accurately.
Drawings
FIG. 1 is a technical scheme;
FIG. 2 is a flow chart of an experimental scheme;
FIG. 3 is a LEfSe score of Parkinson's disease and healthy people;
FIG. 4 is a box scatter plot of relative abundance of Parkinson's disease and healthy people;
Fig. 5 is a ROC diagnostic graph.
Detailed Description
The present invention is described in further detail below in conjunction with specific embodiments for understanding by those skilled in the art.
The technical scheme of the embodiment is shown in fig. 1, and a specific experimental analysis flow is shown in fig. 2.
Example 1 screening of intestinal flora markers related to parkinsonism
1. Sample collection
1. The criteria for parkinsonism group sample inclusion were as follows:
(1) Age distribution is 30-90 years old;
(2) The vital signs are stable;
(3) Is diagnosed with parkinson's disease. Among them, the parkinsonism group exclusion criteria were as follows:
a. Refusing to sign an informed consent form;
b. experimental drug regimens have been enrolled over the past 12 weeks;
c. there is a history of bariatric surgery, total colectomy, ileo-rectal anastomosis or rectocele;
d. antibiotics or probiotic supplements are currently being taken;
e. there is a history of stroke, rheumatoid arthritis, type 1 diabetes and IBD;
f. there is a known history of diseases such as autoimmune diseases and heart diseases.
2. The inclusion criteria for the control group (healthy group) were as follows:
(1) Age distribution is 30-90 years old;
(2) No patients with diabetes or other metabolic diseases;
(3) No depression or other neurological disease;
(4) No suffering from irritable bowel syndrome or gastrointestinal disease;
(5) No other immune system diseases or no immunodeficiency state;
(6) No antibiotics (e.g., neomycin, rifaximin) or probiotic prebiotics, etc. were taken prior to and during the study.
Exclusion criteria were identical to parkinsonism group.
3. Stool samples were collected for 43 cases of parkinsonism and 69 healthy persons according to the above criteria.
The above data were derived from stool samples collected by the martial arts hospitals.
2. DNA extraction, construction of libraries and sequencing
1. The HiPure Stool DNA MINI KIT kit was selected for DNA extraction experiments on the collected Stool samples.
2. After the extraction is finished, the concentration of the DNA is detected by using Qubit, the integrity of the extracted genomic DNA is detected by using 1.5% agarose gel electrophoresis, the quality of the extracted genomic DNA is checked, and a genomic DNA sample with qualified quality (the DNA concentration is more than or equal to 20 ng/mu L, the volume is more than or equal to 20 mu L, and the total amount is more than or equal to 400 ng) is screened out.
3. DNA samples with qualified quality are subjected to random breaking, terminal repair and connection of A base, joint and index, purification and library amplification are carried out after joint connection, and the concentration of DNA (the concentration of DNA is more than or equal to 40 ng/. Mu.L) is detected after amplification.
4. After the library is detected to be qualified, different libraries pooling are sequenced after being put on machine according to the requirements of effective concentration and target data amount. The macro gene sequencing platform is Huada T7, and the sequencing strategy is PE150.
3. LEfSe analysis screening biomarkers
The original data of the machine is subjected to quality control (based on Trimmomatic) and host removal (based on Bowtie 2) by adopting KNEADDATA software. The number of sequences of the species contained in the sample was calculated using Kraken a2 alignments, and the actual abundance of the species in the sample was estimated using Bracken. 80% of the individuals to be tested (including those of parkinsonism and healthy groups) were randomly selected as the training set, leaving 20% of the samples as the validation set. The abundance data for each sample in the training set was then analyzed using LEfSe software, setting the LDA Score screening value to 2.5 by default, and the sample information table is shown in table 1.
Table 1 sample information table
As a result, as shown in FIG. 3, bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius were associated with Parkinson's syndrome, wherein Bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus and Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron exhibited a significant decrease in patients with Parkinson's syndrome and Ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius exhibited a significant increase in patients with Parkinson's syndrome. Thus, 3 biomarkers were screened for significant reduction in the parkinsonism group, including bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, and bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, and 2 biomarkers were screened for significant increase in the parkinsonism group, namely ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius.
As shown in FIG. 4, the present invention found that the strains of Bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius are related to Parkinson's syndrome as biomarkers, and that the single strain prediction effect of Bacteroides faecalis Bacteroides vulgatus on single bacteria of Parkinson's syndrome is highest when single strains are predicted, the single bacteria prediction effect of Bacteroides faecalis, bacteroides thetaiotaomicron, lactobacillus salivarius and Ruminococcus is inferior, and the five bacteria combined prediction effect is the best when strains are combined predicted.
Example 2 validation of the reliability of the five biomarkers described above
1. Firstly, taking the rest 20% of the personnel to be tested (including the personnel of the parkinsonism group and the healthy group) in the embodiment 1 as a verification set, performing binary logistic regression operation on the abundance data of each sample in the verification set, and then performing test of a subject working curve (ROC curve) analysis to obtain a cutoff value (optimal cut-off value).
2. The method comprises the steps of completing specificity and sensitivity calculation and ROC curve drawing by utilizing IBM SPSS STATISTICS (v 27) statistical software, calculating a threshold value of an actual measurement value in the software, and then calculating the number of true positive cases (TP), the number of false positive cases (FP), the number of true negative cases (TN) and the number of false negative cases (FN) corresponding to the threshold value;
specificity (true negative rate) =tn/(tn+fp),
Sensitivity (true positive rate) =tp/(tp+fn),
3. And constructing an ROC curve through 1-specificity and sensitivity, wherein the integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of a certain index, a about sign coefficient (about sign index=sensitivity+specificity-1) is calculated first, and the specificity and sensitivity corresponding to the maximum value of the about sign coefficient is the specificity and sensitivity of the certain index.
4. Relative abundance values of biomarkers for individual strains were analyzed directly in the subject work curve test (ROC curve) to yield cutoff values (optimal cut-off values). The ROC curve of the predictive score is shown in fig. 5. The predicted mimicry markers (markers formed by the combination of five biomarkers) and the AUC, optimal cut-off, sensitivity, and specificity of the single bacteria are shown in table 2.
The ROC curve analysis shows that 5 biomarkers have higher specificity and sensitivity as detection variables, and the AUC of the 5 biomarkers is more than 74%, so that the 5 biomarkers can be used as detection markers for diagnosing Parkinson's disease patients;
The predicted score for the mimicry marker had an AUC of 98.8%, an optimal cutoff of 0.468, a sensitivity of 1, and a specificity of 0.889. Therefore, the mimicry marker is used as a detection marker for diagnosing the Parkinson's disease patient, has better effect and high accuracy. The 5 strains are used as detection markers, so that the method is completely noninvasive and has high accuracy.
As is clear from the above results, these 5 biomarkers were found to be associated with Parkinson's syndrome for the first time, and the single bacterial prediction effect of Bacteroides vulgare Bacteroides vulgatus on Parkinson's syndrome was highest, and the single bacterial prediction effect of Bacteroides faecalis, bacteroides thetaiotaomicron, lactobacillus salivarius and ruminococcus was second.
TABLE 2 ROC diagnostic curve results
Example 3 logistic regression model building
A. modeling
Through the above-mentioned biomarker of excavation, based on the proportion of parkinsonism crowd or healthy crowd in the training set, further, regard 5 strains that this detects as mimicry marker, discuss the relative abundance value of 5 single bacteria and with sample healthy (or sick) probability between the linear relation on this basis, calculate the first probability value y of the object to be measured through the binary logistic regression equation:
y=-0.2137+7.6305*x1+8.7249*x2+8.1012*x3+(-4939.5908)*x4+(-487.0028)*x5;
x1 is the relative abundance value of bacteroides vulgare Bacteroides vulgatus;
x2 is the relative abundance value of bacteroides faecalis Bacteroidesstercoris;
x3 is the relative abundance value of bacteroides thetaiotaomicron Bacteroides thetaiotaomicron;
x4 is the relative abundance value of lactobacillus salivarius Lactobacillus salivarius;
x5 is the relative abundance value of ruminococcus Ruminococcus.
B. Substituting the first probability value y into the following formula to calculate the probability that the object to be detected is a healthy person, wherein P=exp (y)/{ 1+exp (y) }, P is the probability value that the object to be detected is a healthy person, exp (y) is a natural exponential function of the first probability value y;
P can also be written as:
c. verification model
Based on the proportion of parkinsonism population or healthy population in the verification set, statistics of relevant abundance of markers in the verification set are counted and verified, wherein the mean value determines the central position of data distribution, the standard deviation reflects the discrete degree of the data relative to the mean value, the Q value is calculated statistic by using a formula of rank sum test, and the smaller the Q value is, the larger the difference between a disease group and a healthy group is, specifically as follows:
table 3 validation set of related abundance statistics for markers
| Bacterial strain | Parkinson's group mean | Health group mean | Standard deviation of parkinsonism | Standard deviation of health group | Q value |
| Bacteroides vulgaris | 0.00509344590443489 | 0.106565986879878 | 0.00768020293485829 | 0.0877884969800801 | 0.00106 |
| Bacteroides for fecal disease | 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 sp | 0.000858817078567556 | 6.60628521975556E-05 | 0.00102297745349853 | 0.000198188556592667 | 0.0401 |
Note that in table 3E is used to represent the power of 10, e.g. 6.60628521975556E-05 represents 6.60628521975556 x 10 -05.
Example 4
Based on the above embodiments, the present embodiment provides a computer program product related to parkinsonism for performing diagnosis of a risk of parkinsonism in a subject, comprising the steps of:
1) Obtaining the relative abundance value of each single strain in the excrement of the object to be detected, wherein the single strain comprises any one of bacteroides faecalis Bacteroidesstercoris, bacteroides vulgatus Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius;
2) Calculating a first probability value y of the object to be measured according to a binary logistic regression equation;
y=-0.2137+7.6305*x1+8.7249*x2+8.1012*x3+(-4939.5908)*x4+(-487.0028)*x5;
Wherein, x1 is the relative abundance value of bacteroides vulgatus Bacteroides vulgatus, x2 comprises the relative abundance value of bacteroides vulgatus Bacteroidesstercoris, x3 is the relative abundance value of bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, x4 is the relative abundance value of lactobacillus salivarius Lactobacillus salivarius, and x5 is the relative abundance value of ruminococcus Ruminococcus;
3) Substituting the first probability value y into the following formula to calculate the probability of the object to be detected being a healthy person, wherein P=exp (y)/{ 1+exp (y) };
Wherein P is the probability value of the object to be detected as a healthy person, exp (y) is a natural exponential function of the first probability value y;
4) Diagnosing or predicting the risk of the subject suffering from parkinsonism according to the comparison of the probability P value of a healthy person with a reference value.
In actual operation, when the P value is larger than 0.5, the probability that the object to be tested has parkinsonism is smaller, when the P value is smaller than 0.5, the probability that the object to be tested has parkinsonism is larger, and when the P value is 0.5, other means are needed to be further used for detection, and the other means are blood convention and physical sign judgment. The closer the P value is to 0.5, the more detection by other means is required.
Example 5
Based on the product and method of example 4, the health probability of healthy persons and parkinsonism patients in the centralized collection was checked and verified as follows:
1) Collecting fecal samples of the person to be detected, and detecting the relative abundance of single strains in the feces, wherein the single strains respectively comprise Bacteroides fecal Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and Lactobacillus salivarius Lactobacillus salivarius, as shown in Table 4.
2) Calculating a first probability value y of the object to be measured according to a binary logistic regression equation;
y=-0.2137+7.6305*x1+8.7249*x2+8.1012*x3+(-4939.5908)*x4+(-487.0028)*x5;
Wherein x1 is the relative abundance of Bacteroides vulgatus Bacteroides vulgatus, x2 is the relative abundance of Bacteroides vulgatus Bacteroidesstercoris, x3 is the relative abundance of Bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, x4 is the relative abundance of Lactobacillus salivarius Lactobacillus salivarius, and x5 is the relative abundance of Ruminococcus Ruminococcus.
3) Substituting the first probability value y into the following formula to calculate the probability of the object to be detected being a healthy person, wherein P=exp (y)/{ 1+exp (y) };
wherein P is the probability value of the object to be detected as a healthy person, exp (y) is the natural exponential function of the first probability value y.
4) Diagnosing or predicting the risk of the subject suffering from parkinsonism according to the comparison of the probability P value of a healthy person with a reference value.
In actual operation, when the P value is larger than 0.5, the probability that the object to be tested has parkinsonism is smaller, when the P value is smaller than 0.5, the probability that the object to be tested has parkinsonism is larger, and when the P value is 0.5, other means are needed to be further used for detection, and the other means are blood convention and physical sign judgment. The closer the P value is to 0.5, the more detection by other means is required.
In practical situations, the condition that the fecal sample of the person to be detected does not completely meet the judgment standard exists, because the fecal sample of the person to be detected may have false positive results or false negative results, and other means are needed to be further used for detection, and the other means are blood routine and physical sign judgment.
Table 4 data on the relative abundance values of validation set markers
Note that in table 4, ①, x1 are relative abundance values of bacteroides vulgatus Bacteroides vulgatus, x2 is relative abundance value of bacteroides vulgatus Bacteroidesstercoris, x3 is relative abundance value of bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, x4 is relative abundance value of lactobacillus salivarius Lactobacillus salivarius, and x5 is relative abundance value of ruminococcus Ruminococcus;
② . E is used to represent the power of 10, e.g. 5.95E-05 represents 5.95 x 10 -05.
Conclusion and description:
1. The single bacteria prediction effect is that the common bacteroides has the highest single bacteria prediction effect on parkinsonism, and the single bacteria prediction effect of fecal bacteroides, bacteroides thetaiotaomicron, lactobacillus salivarius and ruminococcus is secondary;
2. The mimicry marker has the effect of predicting the mimicry marker, the fecal bacteroides (Bacteroidesstercoris), the bacteroides vulgare (Bacteroides vulgatus), the bacteroides thetaiotaomicron (Bacteroides thetaiotaomicron), the ruminococcus (Ruminococcus) and the lactobacillus salivarius (Lactobacillus salivarius) can be used as biomarkers for predicting the parkinsonism, and the accuracy of the prediction is more than 74%. Wherein the accuracy of the mimicry marker (the marker formed by combining multiple biomarkers) is highest and is 98.8%, the prediction effect is best, and more accurate parkinsonism prediction can be provided.
Other parts not described in detail are prior art. Although the foregoing embodiments have been described in some, but not all, embodiments of the invention, it should be understood that other embodiments may be devised in accordance with the present embodiments without departing from the spirit and scope of the invention.
Claims (9)
1. The intestinal flora marker related to parkinsonism is characterized by comprising bacteroides faecalis Bacteroides stercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius.
2. Use of a reagent for detecting intestinal flora markers including bacteroides faecalis Bacteroides stercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococci Ruminococcus and lactobacillus salivarius Lactobacillus salivarius in the preparation of a product for diagnosing or screening parkinsonism.
3. A kit comprising a reagent for detecting the marker of intestinal flora according to claim 1.
4. Use of a kit according to claim 3 for the preparation of a product for the detection of parkinson's disease.
5. Use of the detection reagent in the kit of claim 3 for preparing a kit for diagnosing parkinson's disease.
6. A product for diagnosing Parkinson's disease, characterized in that it comprises a primer, probe, antibody, aptamer or chip specific for the marker of intestinal flora according to claim 1.
7. A computer program product associated with parkinsonism, characterized in that said computer program product is adapted to perform a diagnosis of a risk of parkinsonism in a subject, comprising the steps of:
1) Obtaining the relative abundance value of each single strain in the excrement of the object to be detected, wherein the single strain comprises bacteroides faecalis Bacteroidesstercoris, bacteroides vulgare Bacteroides vulgatus, bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, ruminococcus Ruminococcus and lactobacillus salivarius Lactobacillus salivarius;
2) Calculating a first probability value y of the object to be measured according to a binary logistic regression equation;
3) Substituting the first probability value y into the following formula to calculate the probability that the object to be detected is a healthy person, wherein P=exp (y)/{ 1+exp (y) };
wherein P is the probability value that the object to be detected is a healthy person, exp (y) is the natural exponential function of the first probability value y;
4) Diagnosing or predicting the risk of the subject for parkinsonism based on the comparison of the P value to a reference value.
8. The product of claim 7, wherein the binary logistic regression equation is formulated as:
y=A+B1*x1+B2*x2+B3*x3+B4*x4+B5*x5;
a is an intercept term, and B 1～B5 is a regression coefficient of an independent variable;
x1 is the relative abundance of bacteroides vulgatus Bacteroides vulgatus, x2 is the relative abundance of bacteroides vulgatus Bacteroidesstercoris, x3 is the relative abundance of bacteroides thetaiotaomicron Bacteroides thetaiotaomicron, x4 is the relative abundance of lactobacillus salivarius Lactobacillus salivarius, and x5 is the relative abundance of ruminococcus Ruminococcus.
9. The product of claim 8, wherein A is-0.2137, B 1 is 7.6305, B 2 is 8.7249, B 3 is 8.1012, B 4 is-4939.5908, and B 5 is-487.0028.
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