Intestinal microbial markers for adolescent depression and uses thereof

Abstract

The application discloses a kind of adolescent depression intestinal microorganism marker and its application, belong to the field of biological medicine.The microorganism marker includes one or more of erysipelotrichaceae bacteria, sludge brouds' bacteria, fecal prevo's bacteria, active rumen coccus, hoffmann's true bacillus, pet big anaerobic rod-shaped bacteria and variant rare micrococcus.The application provides a kind of product (such as kit), including detection reagent for detecting the relative abundance of the microorganism marker.The product provided by the application has good feasibility and accuracy, can effectively evaluate whether adolescent is at risk of suffering from depression, provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to a gut microbiota biomarker for adolescent depression and its application. Background Technology

[0002] Adolescent depression has become a global public health focus. The World Health Organization (WHO) estimates that 5%-8% of adolescents suffer from depressive disorders, with the first onset often occurring during adolescence. The causes can be attributed to genetic susceptibility, childhood trauma, and gut-brain axis dysbiosis, which together trigger abnormal development of the prefrontal-limbic system. This can easily lead to persistent low mood, cognitive decline, and increased risk of suicide.

[0003] In response to the above-mentioned problems, the applicant of this application filed an invention patent application on March 4, 2025, entitled "Gut microbiota markers, products and applications related to adolescent depression", wherein the gut microbiota markers include any one or more of the prokaryotic streptococci, streptococci salivarius, albendibrio and spirochetes.

[0004] While the aforementioned gut microbiota markers can be used to effectively assess the risk of depression in adolescents, their sample size is small and the collection locations are relatively concentrated. As a result, the accuracy of these gut microbiota markers in diagnosing depression in adolescents needs to be improved. Therefore, it is necessary to improve them to lay a good foundation for subsequent treatment. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a gut microbiota biomarker for adolescent depression and its application, which can more accurately diagnose whether the adolescent being tested has depression.

[0006] The technical solution provided by this invention is as follows: In a first aspect, a reagent for the quantitative detection of microbial markers is provided for use in the preparation of products related to adolescent depression, wherein the microbial markers include Erysipelotrichaceaebacterium and Blautia luti.

[0007] In the above technical solution, the microbial markers also include fecal Prevotella copri and / or active Ruminococcus gnavus.

[0008] In the above technical solution, the microbial markers also include one or more of Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile.

[0009] Secondly, a microbial biomarker associated with adolescent depression is provided, the microbial biomarker including Erysipelotrichaceae bacterium, Blautia luti, Anaerostipes hadrus, and Subdoligranulum variabile.

[0010] In the above technical solution, the microbial markers also include active rumenococcus gnavus and / or Subdoligranulum variabile.

[0011] Thirdly, a microbial biomarker associated with adolescent depression is provided, the microbial biomarker including Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, and Ruminococcus gnavus.

[0012] In the above technical solution, the microbial markers also include Eubacterium hallii and / or Anaerostipes hadrus.

[0013] Fourthly, a microbial biomarker associated with adolescent depression is provided, wherein the microbial biomarker is a combination of biomarkers composed of Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile.

[0014] Fifthly, the product includes one or more of reagents, test strips, aptamers, and chips, wherein the microbial markers in the product are specific and used for quantitative detection of the microbial markers.

[0015] Sixthly, the kit contains reagents for the quantitative detection of the microbial markers.

[0016] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and adolescent depression, which can be used to diagnose whether a candidate adolescent suffers from depression. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the sample through relative abundance values, other methods for quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information), and can also be used to assist in the diagnosis of whether a candidate adolescent suffers from depression. Users can choose according to their own needs, and these will not be elaborated upon here.

[0017] The beneficial effects of this invention are as follows: 1. This invention newly discovers seven gut microbes associated with adolescent depression, including: Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile.

[0018] Research and verification have shown that one or more of the above seven gut microbiota can be used to diagnose whether a teenager with depression has depression.

[0019] 2. The present invention also provides a reagent and / or kit that can use one or more of the above 7 bacterial species as detection markers (i.e., microbial markers) to diagnose whether the adolescent under test has depression. It is completely non-invasive and highly accurate.

[0020] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to reach the species and even strain level, thereby improving the accuracy and reliability of diagnosis. The seven species mentioned can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0021] 3. This invention also provides a product and / or prediction system for diagnosing adolescent depression. This product and / or prediction system can diagnose the risk of depression in adolescents based on the relative abundance values ​​of various bacterial species. The entire diagnostic process is safe, non-invasive, and efficient, demonstrating good feasibility and accuracy. It can effectively assess the risk of depression in adolescents (from fecal samples), with relatively accurate assessment results. Attached Figure Description

[0022] Figure 1 This is a graph showing the results of the linear discriminant analysis; Figure 2 Box plots showing the relative abundance of depression in patients with depression and healthy individuals; Figure 3 ROC curve for predicting scores. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0024] Adolescent depression is a complex metabolic disorder caused by insulin resistance and progressive decline in pancreatic β-cell function, and has become one of the most serious public health problems worldwide. Its harm lies not only in its persistently high prevalence, but also in the serious systemic complications it causes, placing a heavy burden on patients, their families, and society.

[0025] Adolescent depression has become a global public health focus: the World Health Organization (WHO) estimates that 5%-8% of adolescents suffer from depressive disorders, with the first episode often occurring during adolescence. Compared to adults, the adolescent brain is still maturing, and the interplay of hormones, neurotransmitters, and social stress makes the condition more prone to becoming chronic and associated with self-harm risks. Its causes can be attributed to genetic predisposition, childhood trauma, and gut-brain axis dysbiosis, all of which trigger abnormalities in the development of the prefrontal-limbic system, leading to persistent low mood, cognitive decline, and increased suicide risk. Without systematic intervention, the relapse rate within two years can exceed 60%.

[0026] For adolescents with depression, the main treatment options currently include psychotherapy and medication. Psychotherapy includes cognitive behavioral therapy (CBT) and interpersonal therapy (IPT), which can correct negative cognitions and interpersonal conflicts; however, its effectiveness is limited by therapist resources, family support, and patient motivation, with about 40% of adolescents responding poorly. As for medication, the FDA has only approved fluoxetine and escitalopram for adolescents; however, it takes 4 to 8 weeks to take effect, and 30% to 50% of patients do not respond to multiple antidepressants. In addition, the black box warning indicates that early treatment may increase suicidal ideation, so clinical medication use is generally conservative.

[0027] In response to the above-mentioned problems, the applicant of this application filed an invention patent on March 4, 2025, entitled "Gut microbiota markers, products and their applications related to adolescent depression". The gut microbiota markers include any one or more of the following: Streptococcus parasanguinis, Streptococcus salivarius, Alistipes sp., and Lachnospiraceae bacterium.

[0028] While the aforementioned gut microbiota markers can be used to assess the risk of depression in adolescents, their sample sizes are small and concentrated, resulting in regional limitations in their use in diagnosing depression in adolescents. Their universality and accuracy need to be improved. Therefore, it is necessary to improve them to lay a good foundation for subsequent treatment.

[0029] This invention provides a microbial biomarker associated with adolescent depression and its application. It not only has a larger sample size and stricter sample selection criteria, but also provides more accurate and representative results. It can be used as a predictor of adolescent depression, thereby diagnosing whether a candidate adolescent suffers from depression. Specifically, the technical concept of this invention is as follows: To address the clinical need for diagnosis and detection of adolescent depression, this invention collects samples from adolescent depression patients and healthy individuals, and through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-3), screens out seven microorganisms associated with adolescent depression, including: Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile.

[0030] ROC curve analysis showed that the above seven biomarkers have high specificity and sensitivity as detection variables. These seven bacterial species can be used as detection biomarkers for the prediction and diagnosis of adolescent depression.

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0032] Example 1: Sample Collection

[0033] The sample sources and inclusion criteria for adolescent depression are as follows: a) Age 12-18 years old; b) Clinical diagnosis of depression by two or more physicians; c) Total score of Hamilton Depression Rating Scale (HAMD-17) ≥17 points; d) Exclusion of subjects who do not meet the criteria, such as those with bipolar disorder, other mental illnesses, substance abuse, serious physical illness, or who are pregnant or lactating.

[0034] Exclusion criteria for the depression group: a) received treatment for depression within 1 month prior; b) took antibiotics, probiotics, or prebiotics within the past 3 months; c) had any chronic illness, including neurobehavioral disorders; d) received medications affecting gastrointestinal motility within 1 week; e) had a history of functional dyspepsia, aerophagia, or abdominal migraine pain; f) exhibited growth retardation; g) had gastrointestinal obstruction or stricture; h) had a history of abdominal surgery, peptic ulcer, or a family history of inflammatory bowel disease; i) the researchers deemed the patient unsuitable for inclusion in this study.

[0035] The sample sources and inclusion criteria for healthy adolescents are as follows: a. Source: The screening targets are from non-urbanized areas with stable living environments and no record of major infectious disease outbreaks in the past 5 years; the content of heavy metals and organic pollutants in soil and water must meet the risk control requirements; areas with high-risk environmental factors such as industrial pollution sources and excessive use of agricultural chemicals are excluded.

[0036] b. Information Collection: 2.1. Age 12-18 years old. 2.2. Family medical history investigation: no family history of disease, with particular attention to hereditary diseases, digestive system diseases, infectious diseases, any family history of mental illness or tumors; confirm the average lifespan of immediate family members of the candidate's gut microbiota. 2.3. Personal medical history investigation: no gastrointestinal diseases (inflammatory bowel disease, irritable bowel syndrome, chronic constipation or diarrhea, malignant tumors or known polyposis, celiac disease, congenital or chronic liver disease, rectal bleeding, major surgery), autoimmune diseases, atopic diseases (asthma, atopic dermatitis, eczema, gastrointestinal eosinophilic diseases), cardiovascular or metabolic diseases (such as diabetes, hypertension, heart disease, etc.), neurological diseases (anxiety, multiple sclerosis, Parkinson's disease, etc.), immunosuppression, chronic pain, infectious diseases, community-acquired pneumonia, etc. 2.4. Candidates should have good on-site verbal communication skills, be in good mental condition, and have no tattoos, puncture wounds, or history of blood transfusions or other high-risk behaviors.

[0037] c. Scale Assessment: 3.1 Interviews with psychiatrists or counselors indicate that the selected subjects are in good mental condition. 3.2 Scores on the Self-Rating Mental Health Scale (SCL-90), Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) are all normal.

[0038] d. Health Check-up: 4.1. A comprehensive health check-up conducted by a professional medical institution, with satisfactory results. Blood routine tests, liver and kidney function tests, hepatitis A / E tests, cytomegalovirus tests, EBV (IgM + IgG), hepatitis B tests (HBsAg + anti-Hbcore), HCV hepatitis C tests, HIV tests (anti-HIV), syphilis tests, C13 breath tests, and endocrine indicator tests all meet relevant requirements. 4.2. Screening for hereditary diseases by detecting exons relevant to the selected candidates, screening for single-gene hereditary diseases, and excluding pathogenic and suspected pathogenic mutations. 4.3. Sensitivity testing for common allergens. Selected candidates have no obvious history of allergic reactions, and test results show no positive reaction to common allergens.

[0039] e. Stool Testing: 5.1 The stool characteristics of the selected candidates should conform to Bristol Stool Classification Type III and IV. 5.2 The selected candidates should be excluded from carrying pathogenic bacteria, drug-resistant bacteria, and potential pathogenic microorganisms. This includes Clostridium difficile, Salmonella (which easily causes bacterial gastroenteritis), Campylobacter, Yersinia, and Shiga toxin-producing Escherichia coli; antibiotic-resistant bacteria such as vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL), and methicillin-resistant Staphylococcus aureus (MRSA); and viral pathogens such as norovirus (types I and II), enteroviruses, and hepatitis E virus. 5.3 The stool of the selected candidates should be free of parasitic infections. Exclude parasites such as Clonorchis sinensis, Clonorchis sinensis, Balantidium coli, Hookworm, Giardia lamblia, Cyclospora cayetta, Trichodina, Strongyloides stercoralis, Intestinal nematodes, Mesozoa natans, Taenia spp., Cryptosporidium spp., Ascaris spp., Entamoeba histolytica, and Entamoeba histolytica.

[0040] f. Exclusion criteria for healthy adolescents: a. Received treatment for depression within 1 month; b. Taken antibiotics, probiotics, or prebiotics within the past 3 months; c. Have any chronic illness, including neurobehavioral disorders; d. Received medications affecting gastrointestinal motility within 1 week; e. Have a history of functional dyspepsia, aerophagia, or abdominal migraine pain; f. Showing signs of growth retardation; g. Have gastrointestinal obstruction or stenosis; h. Have a history of abdominal surgery, peptic ulcer, or a family history of inflammatory bowel disease; i. Patients deemed unsuitable for inclusion in this study by the researchers.

[0041] The fecal samples used in this invention were collected from Hubei Province. Written informed consent was obtained from both the adolescents with depression and healthy adolescents, along with their legal guardians. Based on the aforementioned inclusion and exclusion criteria, this invention collected fecal samples from a total of 430 adolescents with depression and 345 healthy adolescents. The statistical results are as follows: .

[0042] Example 2: DNA extraction, library construction, and sequencing

[0043] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0044] 2. After extraction, the DNA concentration was detected using Qubit, and the integrity of the extracted genomic DNA was detected by 1.5% agarose gel electrophoresis. The extracted genomic DNA was then subjected to quality control, and qualified genomic DNA samples were selected (DNA concentration ≥20ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0045] 3. For qualified DNA samples, random fragmentation, end repair, A base ligation, adapter and index are added. After adapter ligation, purification and library amplification are performed. After amplification, the DNA concentration is detected (DNA concentration ≥40 ng / μL).

[0046] 4. After the libraries pass the testing, different libraries are pooled according to the effective concentration and target data volume requirements before sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0047] Example 3: LEfSe analysis for screening microbial biomarkers

[0048] 1. Split the dataset KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the participants (including those in the disease and healthy groups) were randomly selected as the training set, and the remaining 20% ​​of the samples were used as the validation set. The abundance data of each sample in the training set were then analyzed using LEfSe software, with the default LDA Score filter value set to 2. The sample information is shown in Table 1. The results are as follows Figure 1As shown, the researchers identified seven biomarkers that were significantly increased in the adolescent depression group, including: Erysipelotrichaceae bacterium, Eubacterium hallii, Ruminococcus gnavus, Anaerostipes hadrus, Blautia luti, and Subdoligranulum variabile; and one biomarker that was significantly increased in the adolescent health group, including: Prevotella copri.

[0049] Example 4: Verifying the reliability of the above 7 microbial biomarkers

[0050] 1. First, the remaining 20% ​​of the test subjects in Example 1 (including the adolescent depression group and the healthy group) were used as the validation set. The abundance data of each sample in the validation set were first subjected to binary logistic regression, and then the receiver operating function (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0051] 2. Use R language statistical software to calculate specificity and sensitivity and plot ROC curves. The software first calculates the threshold of the actual measurement value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN + FP) Sensitivity (true positive rate) = TP / (TP + FN) 3. The ROC curve can be constructed using 1 minus specificity and sensitivity. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the certain indicator.

[0052] 4. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 2, the AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of 7 single bacterial species) and individual bacteria are shown in Table 2.

[0053]

[0054] As shown in Table 2, for single bacterial species, Prevotella feces had the highest AUC value (approximately 0.943), while Micrococcus variegata had the lowest AUC value (approximately 0.786). The AUC value of the mimicry marker (a marker formed by a combination of 7 single bacterial species) (approximately 1) was higher than that of the single bacterial species.

[0055] As can be seen from the above, one or more of the seven newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the seven microbial markers is greater than 60%. Therefore, one or more of the seven microbial markers can be used as detection markers for the diagnosis of adolescent patients with depression.

[0056] Example 5: Establishing a Logistic Regression Model Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker, the binary logistic regression algorithm in RStudio software was used to calculate the first disease probability (also known as the logarithm of dominance y) for each sample. Then, the disease probability optimization formula Z=exp(y) / {1+exp(y)} was used to calculate the disease probability Z of the sample under test. Finally, this disease probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establishing a model Based on the biomarkers identified above, and considering the proportion of adolescents with depression in the training set, the relative abundance of the seven detected bacterial species was further used as a single variable. The linear relationship between the relative abundance of these seven bacteria and the probability of disease in the samples was then discussed. A binary logistic regression equation was used to calculate the logarithm y (also known as the first probability value y) of the test subjects. y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6+B7×x7 Where A is the intercept term, B1 to B7 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotella copri in feces, x2 is the relative abundance value of Erysipelotrichaceae bacterium, x3 is the relative abundance value of Eubacterium hallii, x4 is the relative abundance value of Ruminococcus gnavus, x5 is the relative abundance value of Anaerostipes hadrus, x6 is the relative abundance value of Blautia luti, and x7 is the relative abundance value of Subdoligranulum variabile.

[0057] b. Determine the values ​​of A and B1 to B7 above. After statistical analysis of the sample data, the values ​​are: A = -0.1822, B1 = -1.7862, B2 = 10.5212, B3 = 25.3088, B4 = 24.8349, B5 = 24.5535, B6 = 36.9036, and B7 = 4.6762. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=-0.1822-1.7862×X1+10.5212×X2+25.3088×X3+24.8349×X4+24.5535×X5+36.9036×X6+4.6762×X7; c. Calculate the disease probability Z of the subjects to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the subject is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the subject is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0058] After processing, the formula for calculating the probability Z of disease is: ; d. Validation set data calculation and statistical analysis Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group for each sample was obtained. Then, the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z of the test sample being a patient was then calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a patient with irritable bowel syndrome.

[0059] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Note: In Table 3, E represents powers of 10. For example, 6.78E-05 means 6.78 * 10^6. -5 .

[0074]

[0075] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0076] e. Results and Analysis Based on the results of Examples 4 and 5, it can be seen that, for single bacterial species, Prevotella feces has the highest AUC value (approximately 0.943), while Micrococcus variegata has the lowest AUC value (approximately 0.786). For mimicry markers, the AUC value of the mimicry markers is approximately 0.983, the optimal cutoff value is approximately 0.6008, the sensitivity is approximately 0.942, and the specificity is 0.965.

[0077] The AUC of the seven microbial biomarkers identified in this application is greater than 75%. One or more of the seven microbial biomarkers can be used as detection biomarkers for the diagnosis of adolescent patients with depression. At the same time, since this invention only requires the collection of stool samples from the test subjects, this invention is not only completely non-invasive, but also highly accurate, and can be used to diagnose whether the test adolescents (stool samples) have depression.

[0078] Based on the results obtained from the description in Table 3 above and the calculation formula for the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease in the sample to be tested, which is summarized in this application, is basically correct and can be used to diagnose the risk and probability of disease in the sample to be tested. The probability of disease in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal sample of the person to be tested may produce false positive or false negative results. Further testing using other methods is required, including blood routine tests, diagnostic physical signs, etc.

[0079] Example 6: Examining the prevalence of depression in patients in a validation set and adolescent patients.

[0080] Based on the product and method of Example 5, the probability of developing depression in a group of healthy individuals and adolescent patients was examined and verified. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each single strain in the intestine; among which, the single strains include Erysipelothrix family bacteria, Brauts sludge, Prevotella feces, active Ruminococcus, Eubacterium hominis, Corynebacterium petroleum, and Subdoligranulum variabile. S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6+B7×x7 Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Prevotella copri in feces, x2 is the relative abundance value of Erysipelotrichaceae bacterium, x3 is the relative abundance value of Eubacterium hallii, x4 is the relative abundance value of Ruminococcus gnavus, x5 is the relative abundance value of Anaerostipes hadrus, x6 is the relative abundance value of Blautia luti, and x7 is the relative abundance value of Subdoligranulum variabile. S3. Calculate the probability Z of the subject being a adolescent with depression based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y; The probability of disease Z can also be expressed as: ; S4. Based on the comparison between the patient's probability Z-score and the reference value, diagnose or predict the risk of the subject having adolescent depression.

[0081] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has adolescent depression; a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient or have adolescent depression, requiring further investigation using other methods such as mental health testing and medication testing. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0082] It should be noted that although this embodiment only lists the methods and means of quantitative detection of the sample to be tested through relative abundance value, other means of quantitative detection of microorganisms are also feasible for the present invention (such as absolute abundance or total microbial load information, etc.), and can also be used to assist in the diagnosis of whether the sample to be tested suffers from adolescent depression. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0083] Example 7: Detection Reagent

[0084] Based on the description of embodiments 1 to 5 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical staff can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has adolescent depression. Medical staff can also combine multiple single bacterial species together as biomarkers to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has adolescent depression.

[0085] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing adolescent depression to diagnose whether the sample to be tested suffers from adolescent depression; at the same time, the microbial markers can be selected from the seven single bacterial species found in this application that are associated with adolescent depression, that is, the microbial markers in the detection reagent can include one or more of Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile.

[0086] Example 8: Reagent Kit This embodiment also provides a reagent kit, which may contain the detection reagent described in Embodiment 7, to diagnose whether a sample to be tested suffers from adolescent depression; the limitations and technical solutions of this application can be referred to the description of Embodiment 7 above, and will not be repeated here. Similarly, the above reagent kit can also be used in the preparation of products for detecting adolescent depression, and will not be repeated here.

[0087] Example 11: Products for diagnosing adolescent depression This application also provides a product for diagnosing adolescent depression, to diagnose whether a sample to be tested suffers from adolescent depression; the product is specific to one or more of the seven single bacterial species found in this application that are associated with adolescent depression, and the product includes primers, probes, antibodies, test strips, aptamers or chips, wherein primers, probes and antibodies can be used to make reagents and kits, which are conventional methods in the field and will not be described in detail here.

[0088] As can be seen from the descriptions of Examples 1-5 and Example 9, when one or more of the seven bacterial species described in this application are used as detection markers (i.e., microbial markers) to diagnose whether a adolescent under test has depression, it is a technical solution that those skilled in the art can implement to produce corresponding reagents, test strips, aptamers, and chips. That is, reagents, test strips, aptamers, and chips can also be used to assist in the diagnosis of whether an adolescent under test has depression. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0089] The method for diagnosing depression in adolescents is completely non-invasive and highly accurate. Using the seven newly discovered single bacterial species as microbial biomarkers, the creation of corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) should be feasible for those skilled in the art and will not be elaborated upon here.

[0090] Example 9: Diagnosing whether the test sample is a adolescent with depression

[0091] If it is necessary to diagnose whether a person under test is a adolescent with depression, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme test, and liver tissue biopsy, medical staff can also use the methods or products described in Examples 1 to 11 above to diagnose the person under test, in order to assist medical staff in making a more accurate judgment: (1) If, after continuous observation over multiple time periods, the content of Erysipelothrix family bacteria, Brauts sludge, active rumen cocci, Eubacterium hominis, Corynebacterium maxima and rare variant cocci is found to be high (compared to the mean and standard deviation of the depression group in Table 4), or even shows a significant increasing trend, then the test subject is more likely to be a adolescent with depression.

[0092] (2) If, after observation over multiple consecutive periods, the content of one or more Prevotella bacteria in the feces of the person to be tested is found to be high (compared to the mean and standard deviation of the healthy group in Table 4), then the person to be tested is more likely to be a healthy person.

[0093] (3) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figure 1 According to Table 4, a preliminary judgment is made on whether the person being tested is a adolescent with depression. (4) If medical staff want to more accurately determine whether the person to be tested is a adolescent with depression, they can calculate the probability that the person to be tested is an adolescent with depression based on the quantitative detection results of 7 single bacterial species (such as relative abundance values) and the technical solutions described in Examples 5 to 7.

[0094] Conclusion and explanation: 1. By Figures 1-3 As shown in Table 2, any one of the seven newly discovered single bacterial species in this application can serve as a microbial biomarker for adolescent depression. Each single bacterial species exhibits both sensitivity and specificity for adolescent depression. Therefore, the microbial biomarker for adolescent depression can be selected from any one or more of the seven newly discovered single bacterial species in this application. Specifically: Microbial markers for adolescent depression can be selected from one or more of the following: Erysipelothrix family bacteria, Brauts sludge, Prevotella feces, active rumen cocci, Eubacterium hominis, Corynebacterium petroleum, and rare variant cocci.

[0095] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing intestinal samples. This is because there are individual differences. The probability of disease Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has adolescent depression.

[0096] 3. Predictive effect: The AUC value of the mimicry marker (a marker formed by a combination of 7 single bacterial species) (approximately 0.983) is higher than that of a single bacterial species. When other microorganisms are added to one or several single bacterial species for testing, the test result (AUC value) will not be lower than the lowest AUC value of multiple single bacterial species.

[0097] 4. All seven newly discovered bacterial species in this application can be used as detection variables. They all have high specificity and sensitivity, and the AUC of all seven microbial markers is greater than 75%. One or more of the seven microbial markers can be used as detection markers for the diagnosis of adolescent patients with depression.

[0098] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. The application of a reagent for quantitative detection of microbial markers in the preparation of a product for diagnosing whether a adolescent is suffering from depression, characterized in that, The microbial biomarker is any one of the following combinations: a) The microbial markers are a combination of markers consisting of Blautia luti, Anaerostipes hadrus, Erysipelotrichaceae bacterium, and Subdoligranulum variabile. b) The microbial markers are a combination of markers consisting of *Eubacterium hallii*, *Blautia luti*, *Anaerostipes hadrus*, *Erysipelotrichaceae bacterium*, and *Subdoligranulum variabile*. c) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Eubacterium hallii, Blautia luti, Anaerostipeshadrus, Erysipelotrichaceae bacterium, and Subdoligranulum variabile; d) The microbial markers are a combination of markers consisting of fecal Prevotella copri, active Ruminococcus gnavus, Eubacterium hallii, Blautialuti, Anaerostipes hadrus, and Erysipelotrichaceaebacterium. e) The microbial markers described are a combination of markers consisting of Prevotella copri, Erysipelotrichaceae bacterium, Blautia luti, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile; Among them, the bacteria of the Erysipelotrichaceae family, Blautia luti, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile were significantly increased in patients with depression, while the bacteria of Prevotella copri were significantly reduced in patients with depression.

2. A microbial biomarker associated with adolescent depression, characterized in that, The microbial biomarker is any one of the following combinations: a) The microbial markers are a combination of markers consisting of Erysipelotrichaceae bacterium, Blautia luti, Anaerostipes hadrus, and Subdoligranulum variabile. b) The microbial markers are a combination of markers consisting of *Eubacterium hallii*, *Blautia luti*, *Anaerostipes hadrus*, *Erysipelotrichaceae bacterium*, and *Subdoligranulum variabile*. c) The microbial markers are a combination of markers consisting of fecal Prevotella copri, active Ruminococcus gnavus, Eubacterium hallii, Blautialuti, and Anaerostipes hadrus. d) The microbial markers are a combination of markers consisting of fecal Prevotella copri, active Ruminococcus gnavus, Eubacterium hallii, Blautialuti, Anaerostipes hadrus, and Erysipelotrichaceaebacterium. e) The microbial markers described are a combination of markers consisting of active Ruminococcus gnavus, Eubacterium hallii, Blautia luti, Anaerostipeshadrus, Erysipelotrichaceae bacterium, and Subdoligranulum variabile; f) The microbial markers described are a combination of markers consisting of Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile. Among them, the bacteria of the Erysipelotrichaceae family, Blautia luti, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile were significantly increased in patients with depression, while the bacteria of Prevotella copri were significantly reduced in patients with depression.

3. A product for diagnosing or predicting adolescent depression, characterized in that: The product includes one or more of reagents, test strips, aptamers, and chips, and the product is specific to the microbial biomarkers of claim 2 and is used for quantitative detection of the microbial biomarkers of claim 2.

4. A reagent kit, characterized in that: The kit contains reagents for the quantitative detection of the microbial markers of claim 2.

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