Intestinal flora marker related to depression of adults, product and application of intestinal flora marker

Abstract

The invention discloses an intestinal community marker combination for predicting or diagnosing depression, a product and application. The intestinal flora marker is prepared from any one or more of bacteroides vulgaris, bacteroides ovatus, parabacteroides desdistans and bacteroides thetaotamicrobe, and the intestinal flora marker is prepared from any one or more of bacteroides vulgaris, bacteroides ovatus, parabacteroides desdistans and bacteroides thetaotamicrobe, and the intestinal flora marker is prepared from any one or more of bacteroides vulgaris, bacteroides ovatus, parabacteroides desdistans, bacteroides thetaotamicrobe, bacteroides ovatus, bacteroides ovatus, parabacteroides desdistans and bacteroides thetaotamicrobe. The kit comprises a reagent for detecting the intestinal community marker. The products include computer program products including methods and binary logistic regression equations for performing diagnosis or prediction of the risk of a subject having depression. The regression equation has good feasibility and accuracy, the risk of depression can be effectively evaluated, and a new tool is provided for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to intestinal flora markers related to adult depression, products and their applications. Background Art

[0002] Depression is a chronic metabolic disease, but due to its complex etiology, involving multiple levels, including genetic, environmental, behavioral, psychological and physiological factors.

[0003] Depression, also known as depressive disorder, is a mental disorder with a high incidence, a high clinical cure rate but a low treatment acceptance rate and a high recurrence rate. Its main feature is significant and persistent low mood. Some patients may have self-harm or suicidal behaviors, and may even be accompanied by psychotic symptoms such as delusions and hallucinations. However, its etiology and pathogenesis are not very clear, and may be related to genetic factors, neurobiochemical factors and psychosocial factors. In recent years, more and more studies have shown that there is a close connection between the intestinal flora and depression. Therefore, the prediction, diagnosis and treatment of depression need to consider multiple factors comprehensively.

[0004] At present, no hypothesis can perfectly explain the etiology of depression fundamentally. Therefore, the prediction, diagnosis and treatment of depression need to consider multiple factors comprehensively.

[0005] At present, the methods for diagnosing depression are mainly divided into two categories: simple measurement methods and instrument measurement methods. The commonly used depression indicators in simple measurement methods are: Body Mass Index (BMI), waist-to-hip ratio, waist circumference, waist-to-height ratio, skinfold thickness; the main measurement methods in instrument measurement methods are: tomography, ultrasonic method, dual-energy X-ray absorptiometry, bioelectrical impedance analysis, etc. The simple measurement method is simple and easy to operate, so this method is mostly used. In epidemiological studies and medical institutions, the body mass index is mostly used to determine overweight and depression and analyze the relationship between body weight and diseases. However, due to the influence of different regions, the impact of the same BMI level on the human body is not completely the same. For a long time, the determination indicators and standards of overweight depression used by different countries and researchers are not unified, resulting in different prevalence rates. When taking public health measures according to the epidemic trend of depression, it is necessary to fully recognize the significant differences in detection rates due to different diagnostic methodologies.

[0006] Therefore, it is necessary to develop a clear and effective intestinal flora marker and related products to provide a new idea and approach for the diagnosis and treatment of depression. Summary of the Invention

[0007] In view of the above technical problems, the present invention provides intestinal flora markers related to adult depression, products and their applications, so as to provide a new idea and approach for the diagnosis and treatment of adult depression.

[0008] The technical solutions provided by the present invention are as follows:

[0009] In the first aspect, there is provided an intestinal flora biomarker related to adult depression, including Bacteroides ovatus.

[0010] In a possible implementation manner, the intestinal flora biomarker further includes one or several of Bacteroides vulgatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron.

[0011] Furthermore, the intestinal flora biomarker includes Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron.

[0012] In the second aspect, there is provided an application of a reagent for detecting an intestinal flora biomarker in the preparation or screening of adult depression products, where the intestinal flora biomarker includes one or several of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron.

[0013] In the third aspect, there is provided a kit, including a detection reagent for detecting the intestinal flora biomarker described in the first aspect.

[0014] In the fourth aspect, there is provided an application of the kit described in the third aspect in the preparation of products for detecting adult depression, and / or, the use of the detection reagent in the kit described in the third aspect in the preparation of a kit for diagnosing adult depression.

[0015] In the fifth aspect, there is provided a product for diagnosing adult depression, where the product includes primers, probes, antibodies, aptamers, or chips specific to the intestinal flora biomarker described in the first aspect.

[0016] In the sixth aspect, there is provided a computer program product related to adult depression, where the computer program product is used to execute a method for diagnosing the risk of whether a test subject has depression, and the method includes:

[0017] Obtain the relative abundance value of each single bacterial species in the biomarker of the object to be tested as described in the first aspect;

[0018] Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation;

[0019] Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}, where exp() represents the natural exponential function;

[0020] Diagnose or predict whether the object to be tested has depression or is at risk of having depression according to the comparison between the probability P and the reference value.

[0021] In a possible implementation manner, the formula of the binary logistic regression equation is:

[0022] y = A + B1 * x1 + B2 * x2 + B3 * x3 + B4 * x4

[0023] Wherein, A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1, x2, x3, and x4 are the relative abundance values of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron in sequence.

[0024] Furthermore, A is -0.3923, B1 is 4.8942, B2 is 27.2713, B3 is 91.3815, and B4 is -10.1168.

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

[0026] 1. The intestinal flora biomarker combination of the present invention includes Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron. It has been verified that there is a significant association between these four bacteria and depression. Specifically: Bacteroides ovatus shows a significant increase in patients with depression, while Bacteroides vulgatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron show different degrees of decrease or change. Through ROC curve analysis, the above four biomarkers have high specificity and sensitivity as detection variables, so these four bacterial species can be used as detection biomarkers for the prediction and diagnosis of depression in adults.

[0027] 2. The kit of the present invention can use these four bacterial species as detection markers to predict or diagnose depression, which is completely non-invasive and highly accurate. By means of metagenomic sequencing, higher resolution is provided, enabling the analysis of microbial communities to reach the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. These four bacterial species can also be used as target microorganisms for developing these systems, filling the gap in this field.

[0028] 3. The present invention also provides a product for diagnosing depression in adults. This product can calculate the health probability based on the relative abundances of each bacterial species and then compare it with a reference value to predict or diagnose whether a patient has depression or is at risk of having depression. This product has good feasibility and accuracy, can effectively evaluate the risk of depression, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a result graph of linear discriminant analysis;

[0030] Figure 2 It is a result graph of the rank sum test of biomarkers;

[0031] Figure 3 It is an ROC curve. DETAILED DESCRIPTION OF THE INVENTION

[0032] To evaluate whether the composition of gut commensal flora can be used as a predictor of depression, the present invention collected samples from adult depression patients and healthy individuals, performed metagenomic sequencing and used bioinformatics for statistical analysis of the sequencing data, discovered gut flora related to the disease, and integrated the gut flora with disease information to predict depression patients to the greatest extent.

[0033] Through metagenomic sequencing, the present invention first discovered the correlations between Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron and adult depression patients. The abundances of these four bacterial species in the depression group were significantly lower than those in the control group, indicating that these bacterial species can be used as reference factors for adult depression.

[0034] The present invention will be further described in detail below with reference to the drawings and embodiments. The following embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. The experimental methods without specific conditions noted in the embodiments are usually in accordance with conventional conditions.

[0035] Example 1: Sample Collection

[0036] Stool samples were collected from 71 adult patients with depression and 74 adult healthy individuals.

[0037] The sample sources and inclusion criteria for the depression group were as follows: from 71 individuals. Inclusion criteria: 1. Age distribution from 18 to 85 years old; 2. Diagnosis of depression based on the DSM-V criteria; 3. Stable vital signs.

[0038] Exclusion criteria for the adult depression group: 1. History of bariatric surgery, total colectomy with ileorectal anastomosis, or proctocolectomy; 2. Taking antibiotics, probiotics, or prebiotics within the past three months; 3. Participating in any experimental drug regimen within the past 12 weeks; 4. Receiving total parenteral nutrition therapy; 5. Reproductive-age women who are pregnant, planning to become pregnant during the study, or are breastfeeding; 6. The researchers considered the patient unsuitable for inclusion in this study.

[0039] Control group: from 74 individuals. Inclusion criteria: 1. Age distribution from 18 to 85 years old; 2. No diabetes or other metabolic diseases; 3. No depression or other neurological diseases; 4. No irritable bowel syndrome or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficiency state; 6. Not taking antibiotics (such as neomycin, rifaximin) or probiotic prebiotics, etc. before and during the study. Exclusion criteria were the same as those for the depression group.

[0040] The above data were from stool samples collected by Jingmen Central Hospital.

[0041] Example 2: DNA Extraction, Sequencing, and Analysis

[0042] The CTAB (cetyltrimethylammonium bromide) method was used to extract the microbial genomic DNA of the samples, and the extracted genomic DNA was quality inspected to screen out genomic DNA samples with qualified quality. For these screened DNA samples, after random fragmentation, end repair, addition of A bases, ligation of adapters and indexes (index), and fragment selection, a library of about 300 bp was obtained. Finally, the paired-end method was used to sequence the inserted fragments on the BGI platform.

[0043] The specific steps are as follows:

[0044] S1: Extract sample DNA; perform quality inspection on the extracted genomic DNA to screen out genomic DNA samples with qualified quality.

[0045] S2: Library construction and on-machine sequencing: For these screened DNA samples, they are randomly fragmented, end-repaired, adenine bases are added at the ends, adapters and indexes are added, and a library of about 300 bp is obtained through fragment selection. The paired-end method is used to sequence the inserted fragments.

[0046] S3: The raw data is preprocessed: The PCR products in the raw reads are removed using the FastUniq tool, the adapters are excised and quality filtering is performed using the software Trimmomatic, and the contaminant sequences from humans and the host are removed using the software KneadData v0.10.0. After processing, clean data (Clean Reads) is obtained.

[0047] S4: The software MEGAHIT is used to assemble the Clean Reads to generate small fragments (Contig). The Contig with a length less than 500 bp is discarded.

[0048] S5: Gene prediction: The software Prodigal is used to identify the coding regions (CDS) existing on the Contig, and the CDS with a length less than 100 bp is filtered.

[0049] S6: Construction of a non-redundant gene set and abundance statistics: The tool CD-HIT is used to cluster the genes obtained in each sample and remove redundancy. The thresholds are coverage > 90% and identity > 95%. The bioinformatics tool Salmon is used to calculate the normalized expression level (TPM) value of each gene in each sample. Using the TPM value as the relative abundance, the subsequent species abundance and functional abundance are both obtained by summing up the TPM values.

[0050] Example 3: Data segmentation and model training

[0051] 3.1. Dataset division

[0052] In the samples of Example 1, 80% of the samples are randomly selected as the training set, and the remaining 20% of the samples are used as the validation set. See Table 1 for details.

[0053] Table 1 Sample information table

[0054]

[0055] 3.2. Screening of biomarkers

[0056] Based on the abundance data of the training set, the LEfSe software is used for analysis, and the default screening value of the LDA Score (Linear Discriminant Analysis Score) is set to 2.5.

[0057] See Figure 1 In the figure, the linear discriminant analysis scores of the four bacteria are > 2.5, and the linear discriminant analysis scores. The linear discriminant analysis score is an analytical method that can mine and interpret biological markers from high-dimensional data, with significant statistical significance and biological relevance. Therefore, four markers that changed significantly (decreased) in the depression group were screened out, including Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron.

[0058] 3.3, Wilcoxon rank-sum test

[0059] The biomarkers mined by LEFse analysis were further subjected to Wilcoxon rank-sum test, and it was found that there were significant differences in Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron (q < 0.001). Note: * represents q < 0.05; ** represents q < 0.01; *** represents q < 0.001. As Figure 2 shown.

[0060] 3.4, Binary logistic regression to determine the regression equation

[0061] 3.41. Based on the above-screened biomarkers, the binary logistic regression algorithm in SPSS software was used to calculate the probability of each sample's four bacteria predicting disease or health, facilitating the subsequent individual evaluation of the performance of each bacteria's binary regression model.

[0062] 3.42. Construct a binary logistic regression equation for the relative abundance of the mimetic biomarker (combined four bacteria)

[0063] The formula for the binary logistic regression equation is:

[0064] y = A + B1*x1 + B2*x2 + B3*x3 + B4*x4

[0065] Among them, A is the intercept term, and B1 to B4 are the regression coefficients of independent variables; x1, x2, x3, and x4 are the relative abundance values of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron in sequence.

[0066] By fitting the training set data, the values of each variable are obtained, and then the following formula is obtained:

[0067] y = -0.3923 + 4.8942 * x1 + 27.2713 * x2 + 91.3815 * x3 - 10.1168 * x4

[0068]

[0069] Among them, P is the probability that the object to be tested is a healthy person.

[0070] On this basis, the probability that the object to be tested is healthy can be calculated, and it can be diagnosed or predicted whether the object to be tested has depression or is at risk of having depression. The specific method is as follows:

[0071] Obtain the relative abundance value of each single strain among the 4 gut microbiota markers of the object to be tested;

[0072] Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation;

[0073] Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}, where exp() represents the natural exponential function;

[0074] Diagnose or predict whether the object to be tested has depression or is at risk of having depression according to the comparison between the probability P and the reference value.

[0075] Example 4, Validation Set Experimental Method and Analysis Results

[0076] 1. Calculation and Statistical Analysis of Validation Set Data

[0077] Based on the data of the validation set, calculate the relative abundance of the mimic markers of each sample in the disease group and the healthy group, then use the binary logistic regression method described in Example 3 to obtain the health probability, and then calculate the mean and standard deviation results of the relative abundance for predicting or diagnosing depression. The results are shown in Tables 2 and 3. The q-value is calculated using the formula of the rank sum test. The lower the q-value, the greater the difference between the disease group and the healthy group.

[0078] Table 3 shows the mean and standard deviation of the relative abundances of each bacterial species. The mean relative abundance determines the central position of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean.

[0079] Table 2: Related data of the validation set markers

[0080]

[0081]

[0082] Note: In Table 2, 9.88E-05 means 9.88 * 10 -5 .

[0083] Table 3: Statistical data of the related abundances of the validation set markers

[0084]

[0085] Note: In Table 3, the mean refers to the mean relative abundance, and the same applies to the standard deviation.

[0086] 2. Validation results

[0087] Based on the data in Table 2 above, a Receiver Operating Characteristic curve (ROC curve) test was performed to obtain the cutoff value (optimal cut-off value).

[0088] Using IBM SPSS Statistics (v27) statistical software to complete the calculation of specificity and sensitivity and the drawing of the ROC curve. Specifically: first calculate the threshold of the actual measurement value, and then calculate the true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN + FP), sensitivity (true positive rate) = TP / (TP + FN). The ROC curve can be constructed through 1 - specificity and sensitivity, and the integral of the ROC curve is the AUC (Area Under the Curve, the area covered under the ROC curve).

[0089] To calculate the specificity and sensitivity of a certain index, first calculate the Youden index (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden index are the specificity and sensitivity of a certain index.

[0090] The relative abundance values of individual microbial markers were directly subjected to a Receiver Operating Characteristic curve (ROC curve) test to obtain the cutoff value (optimal cut-off value). The ROC curve of the prediction score is as Figure 3 shown. The AUC, optimal cut-off value, sensitivity, and specificity of the mimic marker and the prediction scoring methods of each single bacterium are shown in Table 4.

[0091] According to the relevant data, Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron were significantly decreased in patients with depression, while significantly increased in healthy individuals. Further, through the validation set data, it was found that using the ROC curve to analyze them as detection variables had high specificity and sensitivity. Therefore, they can be used as detection markers for the diagnosis of adult depression patients. Using these four bacterial species as detection markers is completely non-invasive and highly accurate.

[0092] Table 4 Results of the ROC Diagnostic Curve

[0093]

[0094] From the above results, it can be seen that the above 4 biomarkers were first discovered to be related to depression. Among them, Bacteroides ovatus had the highest single-bacterium prediction effect on depression, followed by Parabacteroides distasonis, Bacteroides vulgatus, and Bacteroides thetaiotaomicron.

[0095] As can be seen from Table 4, the AUC of the mimic biomarker for predicting depression was 82.6%, which was higher than that of other biomarkers, that is, it was confirmed that the performance of the model combining the four bacteria was higher than that of other models, and it had good feasibility and accuracy. Through this binary logistic regression equation, the risk of suffering from adult depression can be effectively evaluated, providing a new tool for clinical diagnosis.

[0096] Example 5

[0097] Based on the above embodiments, this embodiment provides a computer program product related to adult depression, which is used to execute the risk of diagnosing a test subject with depression, including the following steps:

[0098] 1) Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species is any one of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron;

[0099] 2) Calculate the logarithm of the odds y of the test subject according to the binary logistic regression equation;

[0100] y = -0.3923 + 4.8942 * x1 + 27.2713 * x2 + 91.3815 * x3 - 10.1168 * x4

[0101] Among them, x1, x2, x3, and x4 are the relative abundance values of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron in sequence;

[0102] 3) Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0103] 4) According to the comparison between the probability P and the reference value, diagnose or predict whether the object to be tested has depression or is at risk of having depression.

[0104] In actual work, when the P value is greater than 0.5, it means that the probability that the person to be tested has depression is small; when the P value is less than 0.5, it means that the probability that the person to be tested has depression is large; when the P value is 0.5, it means that the person to be tested may be a healthy person or a depression patient. At this time, other means need to be further used for detection, and the other means are blood routine and judging physical signs. Further, the closer the P value is to 0.5, the more other means are needed for detection.

[0105] Example 6

[0106] Based on the products and methods of Example 5, check and verify the health probabilities of healthy people and depression patients in the validation set. The specific steps are as follows:

[0107] 1) Collect the fecal samples of the people to be tested and detect the relative abundance values of single strains in the feces; among them, the single strains are any one of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron;

[0108] 2) Calculate the logarithm of the odds y of the object to be tested according to the binary logistic regression equation;

[0109] y = -0.3923 + 4.8942*x1 + 27.2713*x2 + 91.3815*x3 - 10.1168*x4

[0110] Among them, x1, x2, x3, and x4 are the relative abundance values of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron in sequence;

[0111] 3) Calculate the probability P that the object to be tested is a healthy person according to y, P = exp(y) / {1 + exp(y)}, where exp(y) is the natural exponential function of y;

[0112] 4) According to the comparison between the probability P and the reference value, diagnose or predict whether the object to be tested has depression or is at risk of having depression.

[0113] In actual work, when the P value is greater than 0.5, it means that the probability that the person to be tested has depression is relatively small; when the P value is less than 0.5, it means that the probability that the person to be tested has depression is relatively large; when the P value is 0.5, it means that the person to be tested may be a healthy person or may be a depression patient.

[0114] Conclusion and explanation:

[0115] 1. From Figures 1 to 3 and Tables 2 to 4, it can be seen that Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides distasonis, and Bacteroides thetaiotaomicron can be used as relevant markers for depression.

[0116] 2. From Table 2, it can be seen that when detecting fecal samples, it is normal to only detect a certain strain or several strains, because individuals have differences. The detection probability values of the 4 strains are calculated. That is, even if a certain sample only contains a single strain, the present application can calculate the probability that the sample to be tested has depression.

[0117] 3. Single-bacterium prediction effect: The single-bacterium prediction effect of Bacteroides ovatus on depression is the highest, and the single-bacterium prediction effects of Parabacteroides distasonis, Bacteroides vulgatus, and Bacteroides thetaiotaomicron are the second.

[0118] 4. Predictive effect of mimetic biomarkers: The combination of four bacteria, *Bacteroides vulgatus*, *Bacteroides ovatus*, *Parabacteroides distasonis* and *Bacteroides thetaiotaomicron* can all be used as biomarkers for predicting depression. The AUC of the prediction scoring method for the combination of four bacteria is 82.6%, the optimal cut-off value is 0.471, the sensitivity is 66.7%, and the specificity is 85.7%. The prediction effect is the best and can provide a more accurate prediction of depression.

[0119] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent replacements and improvements made by those skilled in the art within the technical scope disclosed by the present invention shall be included in the scope of protection of the invention.

Claims

1. A gut flora marker associated with depression in adults, characterized by: The intestinal flora marker includes Bacteroides ovatus.

2. The intestinal flora marker according to claim 1, characterized in that: The intestinal flora markers also include one or more of Bacteroides vulgatus, Parabacteroides desdistasonis and Bacteroides thetaiotaomicron.

3. The intestinal flora marker according to claim 2, characterized in that: The intestinal flora markers include Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides desdistasonis and Bacteroides thetaiotaomicron.

4. Use of a reagent for detecting intestinal flora markers in the preparation or screening of products for adult depression, wherein the intestinal flora markers include one or more of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides desdistasonis and Bacteroides thetaiotaomicron.

5. A kit, characterized in that: The kit comprises a detection reagent for detecting the intestinal flora marker according to any one of claims 1 to 3.

6. Use of the kit according to claim 5 in the preparation of a product for detecting depression in adults, and / or use of the detection reagent in the kit according to claim 5 in the preparation of a kit for diagnosing depression in adults.

7. A product for diagnosing depression in adults, characterized in that: The product comprises primers, probes, antibodies, aptamers or chips that are specific to the intestinal flora markers described in any one of claims 1 to 3.

8. A computer program product related to depression in adults, characterized in that: The computer program product is used to execute a method for diagnosing whether a subject to be tested has a risk of depression, the method comprising: Obtaining the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species includes any one of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides desdistasonis, and Bacteroides thetaiotaomicron; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; According to y, the probability P of the tested object being a healthy person is calculated, P = exp(y) / {1+exp(y)}, where exp() represents a natural exponential function; Based on the comparison between the probability P and the reference value, it is diagnosed or predicted whether the subject suffers from depression or has the risk of suffering from depression.

9. The computer program product according to claim 8, characterized in that: The formula for the binary logistic regression equation is: y=A+B1*x1+B2*x2+B3*x3+B4*x4 Among them, A is the intercept term, B1~B4 are the regression coefficients of the independent variables; x1, x2, x3 and x4 are the relative abundance values ​​of Bacteroides vulgatus, Bacteroides ovatus, Parabacteroides desdistasonis and Bacteroides thetaiotaomicron respectively.

10. The computer program product according to claim 9, characterized in that: A is -0.3923, B1 is 4.8942, B2 is 27.2713, B3 is 91.3815, and B4 is -10.1168.

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