Intestinal microbe-based adolescent depression prediction system and model construction method

Abstract

This invention discloses a gut microbiota-based prediction system and model construction method for adolescent depression, belonging to the biomedical field. It includes a detection module and a comparison module. The detection module acquires quantitative detection results of preset microbial markers in fecal samples from the test donor, including Erysipelothrix rhusiopathiae and / or Brauthrix sphaeroides. The comparison module compares the quantitative detection results with preset thresholds to determine whether the adolescent in question suffers from depression. It can accurately diagnose whether the adolescent in question has depression and effectively assess the risk of depression in adolescents.

Description

Technical Field

[0001] This invention belongs to the field of combining biomedicine and artificial intelligence, and specifically relates to a prediction system and model construction method for adolescent depression based on gut microbiota. Background Technology

[0002] Adolescent depression has become a global public health concern. The World Health Organization (WHO) estimates that 5%-8% of adolescents suffer from depressive disorders, with the first onset often occurring during adolescence. However, current diagnosis of adolescent depression mainly relies on scale assessments, lacking objective indicators. In addition, adolescent symptoms are often atypical (often with irritability as the chief complaint), leading to significant misdiagnosis and missed diagnosis.

[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 applied to adolescent depression prediction systems and effectively assess the risk of adolescents developing depression, their sample size is small and the collection locations are relatively concentrated. Consequently, the diagnostic accuracy of these gut microbiota markers in diagnosing adolescent depression needs further improvement. Therefore, it is necessary to develop a prediction system specifically for adolescent depression that can more accurately diagnose whether an adolescent has depression by detecting fecal samples. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a gut microbiota-based adolescent depression prediction system and model construction method, which can accurately diagnose whether a adolescent under test has depression.

[0006] The technical solution provided by this invention is as follows: Firstly, a predictive system for adolescent depression is provided, comprising: a detection module for acquiring quantitative detection results of preset microbial markers in fecal samples of a test donor, said microbial markers including *Erysipelotrichaceae bacterium* and / or *Blautialuti*; and a comparison module for comparing the quantitative detection results with preset thresholds, and determining whether the adolescent to be tested suffers from depression based on the comparison results.

[0007] Secondly, a gut microbiota-based adolescent depression prediction system is provided, comprising: a detection module for acquiring quantitative detection results of preset microbial markers in fecal samples of a test donor, wherein the microbial markers include Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile; a calculation module for calculating the probability that the test donor is a patient with depression based on the quantitative detection results, denoted as a depression patient probability value; and a comparison module for comparing the depression patient probability value with a preset threshold, and determining whether the test adolescent suffers from depression based on the comparison result.

[0008] Thirdly, a method for constructing a predictive model for adolescent depression is provided. The method includes the following steps: collecting fecal samples from adolescent patients with depression and fecal samples from healthy adolescents according to preset inclusion and exclusion criteria, and designating the fecal samples from adolescent patients with depression as the depression group and the fecal samples from healthy adolescents as the healthy group; performing high-throughput sequencing on the depression group and the healthy group to identify differentially expressed microorganisms between the depression group and the healthy group, wherein the differentially expressed microorganisms include the microbial markers described in the predictive system; and using the differentially expressed microorganisms as a predictive model for adolescent depression to determine whether the adolescent to be tested suffers from depression.

[0009] 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.

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] 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

[0015] 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; Figure 4 This is a schematic diagram of the prediction system described in Example 6; Figure 5 This is a schematic diagram of the prediction system described in Example 7; Figure 6 This is a flowchart illustrating the method for constructing the prediction model described in Example 8. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] 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 chronicity and associated with self-harm risks. The causes can be attributed to genetic predisposition, childhood trauma, school bullying, 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%.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] This invention provides a gut microbiota-based predictive system and model construction method for adolescent depression. It not only has a larger sample size and stricter sample selection criteria, but also provides more accurate and representative results. These results can be used as predictive factors for adolescent depression, thereby diagnosing whether a candidate adolescent suffers from depression. Specifically, the technical approach of this invention is as follows: To address the clinical needs for the 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. After verification by ROC curve analysis, it is determined that the above seven biomarkers have high specificity and sensitivity as detection variables. These seven microbial species can be used as detection biomarkers for the prediction and diagnosis of adolescent depression patients.

[0023] Based on the above-mentioned gut microbiota, this application provides a gut microbiota-based prediction system and model construction method for adolescent depression. The prediction system can diagnose the risk of depression in adolescents based on the relative abundance values ​​of each bacterial species. The entire diagnostic process is safe, non-invasive, and efficient, with good feasibility and accuracy. It can effectively assess the risk of depression in adolescents (fecal samples) and the assessment results are relatively accurate.

[0024] 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.

[0025] Example 1: Sample Collection

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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: .

[0035] Example 2: DNA extraction, library construction, and sequencing

[0036] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0037] 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).

[0038] 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).

[0039] 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.

[0040] Example 3: LEfSe analysis for screening microbial biomarkers

[0041] 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.

[0042] Example 4: Verifying the reliability of the above 7 microbial biomarkers

[0043] 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).

[0044] 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.

[0045] 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.

[0046]

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] Note: In Table 3, E represents powers of 10. For example, 6.78E-05 means 6.78 * 10^6. -5 .

[0067]

[0068] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] Example 6: Predictive system associated with adolescent depression Based on the above embodiments, this embodiment provides a predictive system related to adolescent depression, a predictive system for assessing the risk of a test subject being a patient with depression. The predictive system includes a detection module and a comparison module, specifically: The detection module is used to obtain quantitative detection results of a single bacterial species in the fecal sample of the test subject; wherein, the single bacterial species may include one or more of the following: Erysipelothrix family bacteria, Blautella sludgeii, Prevotella feces, active rumenococci, Eubacterium hominis, Corynebacterium petroleum, and rare variant cocci. The comparison module is used to compare the quantitative detection results with a preset threshold, and to determine the risk of the subject being a patient with depression based on the comparison results.

[0073] Preferably, the quantitative detection results include one or more of relative abundance, absolute abundance, and total microbial load information, and the preset threshold is a range of values ​​determined by the mean and standard deviation of the quantitative detection results.

[0074] In practical application, to illustrate how the prediction system assesses the risk of a adolescent being diagnosed with depression, this invention uses relative abundance values ​​as an example. Specifically: The detection module obtains the relative abundance value of Erysipelothrix bacteria in the fecal sample of the test subject, which is recorded as the first abundance value. This first abundance value is also the quantitative detection result obtained by the detection module. The comparison module compares the first abundance value with the mean and standard deviation of Erysipelothrix bacteria in Table 4 to assess the risk that the subject is a patient with depression. For example, if the first abundance value is within the range determined by the mean and standard deviation of patients with depression, the risk that the subject is a patient with depression is high; otherwise, the risk that the subject is a patient with depression is low.

[0075] Similarly, referring to the detection and evaluation methods for Erysipelothrix bacteria mentioned above, the prediction system can also perform similar detection and evaluation on microorganisms such as Brutus sludge, Prevotella feces, active Ruminococcus, Eubacterium hominis, Corynebacterium pulmonale, and Micrococcus variants, thereby assessing the risk of the adolescent being tested having depression.

[0076] Due to factors such as genetics and environment, it is normal that not all seven newly discovered bacterial species can be detected in fecal samples. People can use one or more of the seven newly discovered bacterial species as detection markers to assess the risk of the subject being a patient with depression.

[0077] Given that obtaining relative abundance values ​​and other quantitative detection data (such as absolute abundance or total microbial load information) are routine methods in this field, if one wants to use other quantitative detection methods to assess the risk of the subject being a patient with depression, one can refer to the description above, which will not be repeated here.

[0078] Example 7: Adolescent Depression Diagnosis / Prediction System Based on the above embodiments, this embodiment provides a diagnostic / predictive system for adolescent depression, including a detection module, a calculation module, and a comparison module, wherein: The detection module is used to obtain quantitative detection results of preset microbial markers in the fecal samples of the donor to be tested. The microbial markers include Erysipelothrix bacteria, Brauts sludge, Prevotella feces, active Ruminococcus, Eubacterium hominis, Corynebacterium petroleum, and rare variant cocci.

[0079] The calculation module is used to calculate the probability that the donor to be tested is a patient with depression based on the quantitative detection results, and is denoted as the probability value of the patient with depression.

[0080] In actual operation, the calculation module also includes a first probability module and a second probability module, wherein: the first probability module is used to substitute the relative abundance value of each single species in the microbial biomarker into the binary logistic regression equation to calculate the logarithm y of the dominance of the test subject; the second probability module is used to calculate the probability Z of the test subject being a type 2 diabetic patient based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is an exponential function of y.

[0081] The comparison module is used to compare the probability value of the patient with a preset threshold, and to determine whether the adolescent under test has depression based on the comparison result.

[0082] Based on the descriptions in Examples 1-7, the present invention provides a method for constructing a predictive model for adolescent depression, comprising the following steps: S1. Sample collection: Collect fecal samples from adolescent patients with depression and fecal samples from healthy adolescents according to the preset inclusion and exclusion criteria. In this step, given that different groups of people have different definitions of "healthy person," such as some people considering family members of long-lived families as healthy people (passing physical examinations), some people considering teenagers aged 20-30 as healthy people (passing physical examinations), some people considering college students of sports colleges as healthy people (passing physical examinations), etc., people can use the healthy person screening method listed in Example 1 of this invention to screen for healthy people, or they can use other standards to screen for healthy people. This embodiment does not impose any restrictions here, as long as the collected fecal samples conform to people's general understanding.

[0083] S2. Sample grouping: DNA extraction, library construction and sequencing were performed on the fecal samples of the above-mentioned adolescent patients with depression and the fecal samples of healthy adolescents, respectively. The fecal samples of adolescent patients with depression were assigned to the depression group, and the fecal samples of healthy adolescents were assigned to the healthy group. In this step, after selecting specific fecal samples from healthy individuals, people should be able to extract DNA from the fecal samples, construct libraries, and sequence them according to the preset steps to distinguish the differential microorganisms between the depression group and the healthy group. The specific experimental steps can be referred to the descriptions of Examples 1 to 4 above, and will not be repeated here.

[0084] S3. Identify differentially expressed microorganisms: Perform high-throughput sequencing on the depression group and the healthy group to identify differentially expressed microorganisms between the depression group and the healthy group. The differentially expressed microorganisms may include one or more of the following: Erysipelothrix family bacteria, Brautella sludgeii, Prevotella feces, active rumen cocci, Eubacterium hominis, Corynebacterium pulmonale, and rare variant cocci. The steps for performing high-throughput sequencing and identifying differentially expressed microorganisms can be found in the descriptions of Examples 1-4 above, and will not be repeated here.

[0085] S4. Use the differentially expressed microorganisms as a predictive model for adolescent depression to distinguish whether the adolescent to be tested is a patient with depression.

[0086] In practical work, in conjunction with Examples 6 and 7, this step provides two methods for using the differentially expressed microorganisms as predictive models, specifically including: Referring to Example 6, the first method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the adolescent to be tested; S42, compare the quantitative detection results with preset thresholds, and determine whether the adolescent to be tested is a patient with depression based on the comparison results.

[0087] Referring to Example 7, the second method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the adolescent to be tested; S42, calculate the probability that the adolescent to be tested is a patient with depression based on the quantitative detection results, and use the probability value of the patient with depression as the quantitative detection result; S43, compare the quantitative detection results with a preset threshold, and determine whether the adolescent to be tested is a patient with depression based on the comparison result.

[0088] In actual work, the above steps S41 to S43 can also be the following steps: K41. Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Erysipelothrix family bacteria, Blautella sludgeii, Prevotella feces, active Ruminococcus, Eubacterium hominis, Corynebacterium petroleum, and rare variant cocci. K42. Substitute the relative abundance value of each single bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object (i.e., the first probability value y). 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 Klebsiella pneumoniae, x2 is the relative abundance value of Clostridium aldenense, x3 is the relative abundance value of Bacteroides plebeius, x4 is the relative abundance value of Bifidobacterium adolescentis, x5 is the relative abundance value of Eubacterium eligens, x6 is the relative abundance value of Coprococcus catus, and x7 is the relative abundance value of Prevotella copri.

[0089] K43. Calculate the probability Z of the subject being a patient 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: .

[0090] K44. Based on the comparison of the patient's probability Z-score with the reference value, diagnose or predict the risk of the subject having adolescent depression.

[0091] 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 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.

[0092] 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.

[0093] (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.

[0094] (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.

[0095] Example 8: Method for constructing a prediction model

[0096] Based on the descriptions in Examples 1-7, the present invention provides a method for constructing a predictive model for adolescent depression, comprising the following steps: S1. Sample collection: Collect fecal samples from adolescent patients with depression and fecal samples from healthy adolescents according to the preset inclusion and exclusion criteria. In this step, given that different groups of people have different definitions of "healthy person," such as some people considering family members of long-lived families as healthy people (passing physical examinations), some people considering teenagers aged 20-30 as healthy people (passing physical examinations), some people considering college students of sports colleges as healthy people (passing physical examinations), etc., people can use the healthy person screening method listed in Example 1 of this invention to screen for healthy people, or they can use other standards to screen for healthy people. This embodiment does not impose any restrictions here, as long as the collected fecal samples conform to people's general understanding.

[0097] S2. Sample grouping: DNA extraction, library construction and sequencing were performed on the fecal samples of the above-mentioned adolescent patients with depression and the fecal samples of healthy adolescents, respectively. The fecal samples of adolescent patients with depression were assigned to the depression group, and the fecal samples of healthy adolescents were assigned to the healthy group. In this step, after selecting specific fecal samples from healthy individuals, people should be able to extract DNA from the fecal samples, construct libraries, and sequence them according to the preset steps to distinguish the differential microorganisms between the depression group and the healthy group. The specific experimental steps can be referred to the descriptions of Examples 1 to 4 above, and will not be repeated here.

[0098] S3. Identify differentially expressed microorganisms: Perform high-throughput sequencing on the depression group and the healthy group to identify differentially expressed microorganisms between the depression group and the healthy group. The differentially expressed microorganisms may include one or more of the following: Erysipelothrix family bacteria, Brautella sludgeii, Prevotella feces, active rumen cocci, Eubacterium hominis, Corynebacterium pulmonale, and rare variant cocci. The steps for performing high-throughput sequencing and identifying differentially expressed microorganisms can be found in the descriptions of Examples 1-4 above, and will not be repeated here.

[0099] S4. Use the differentially expressed microorganisms as a predictive model for adolescent depression to distinguish whether the adolescent to be tested is a patient with depression.

[0100] In practical work, in conjunction with Examples 6 and 7, this step provides two methods for using the differentially expressed microorganisms as predictive models, specifically including: Referring to Example 6, the first method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the adolescent to be tested; S42, compare the quantitative detection results with preset thresholds, and determine whether the adolescent to be tested is a patient with depression based on the comparison results.

[0101] Referring to Example 7, the second method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the adolescent to be tested; S42, calculate the probability that the adolescent to be tested is a patient with depression based on the quantitative detection results, and use the probability value of the patient with depression as the quantitative detection result; S43, compare the quantitative detection results with a preset threshold, and determine whether the adolescent to be tested is a patient with depression based on the comparison result.

[0102] In actual work, the above steps S41 to S43 can also be the following steps: K41. Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Erysipelothrix family bacteria, Blautella sludgeii, Prevotella feces, active Ruminococcus, Eubacterium hominis, Corynebacterium petroleum, and rare variant cocci. K42. Substitute the relative abundance value of each single bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object (i.e., the first probability value y). 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 Klebsiella pneumoniae, x2 is the relative abundance value of Clostridium aldenense, x3 is the relative abundance value of Bacteroides plebeius, x4 is the relative abundance value of Bifidobacterium adolescentis, x5 is the relative abundance value of Eubacterium eligens, x6 is the relative abundance value of Coprococcus catus, and x7 is the relative abundance value of Prevotella copri.

[0103] K43. Calculate the probability Z of the subject being a patient 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: .

[0104] K44. Based on the comparison of the patient's probability Z-score with the reference value, diagnose or predict the risk of the subject having adolescent depression.

[0105] 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 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.

[0106] Example 9: Diagnosing whether the test sample is a adolescent with depression

[0107] 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.

[0108] (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.

[0109] (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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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. A prediction system related to adolescent depression, characterized by, include: The detection module is used to obtain the quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested, wherein the microbial markers include Erysipelotrichaceae bacterium and / or Blautia luti. The comparison module is used to compare the quantitative detection results with a preset threshold, and determine whether the adolescent under test has depression based on the comparison results.

2. The prediction system of claim 1, wherein, The microbial markers also include Klebsiella pneumoniae and / or Bifidobacterium adolescentis.

3. The prediction system of claim 1 or 2, wherein, The microbial markers also include one or more of Bacteroides plebeius, Eubacterium eligens, and Prevotella copri.

4. A gut microbiota-based system for predicting adolescent depression, characterized by: include: The detection module is used to obtain quantitative detection results of preset microbial markers in the fecal samples of the donor to be tested. The microbial markers include Erysipelotrichaceae bacterium, Blautia luti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile. The calculation module is used to calculate the probability that the donor to be tested is a patient with depression based on the quantitative detection results, and denoted as the probability value of the patient with depression. The comparison module is used to compare the probability value of the patient with a preset threshold, and determine whether the adolescent under test has depression based on the comparison result.

5. The adolescent depression prediction system of claim 4, wherein: The calculation module further includes a first probability module and a second probability module, wherein: The first probability module is used to substitute the relative abundance value of each single species in the microbial biomarker into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object; The second probability module is used to calculate the probability Z of the subject being a type 2 diabetes patient based on y, Z = exp(y) / {1 + exp(y)}, where exp(y) is an exponential function of y.

6. The adolescent depression prediction system of claim 5, wherein: The formula for the binary logistic regression equation is: 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.

7. The adolescent depression prediction system of claim 6, wherein: 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. 8.A method for constructing a prediction model for adolescent depression, the method comprising: collecting a plurality of pieces of data on a plurality of subjects; and constructing a prediction model for adolescent depression by using the collected data. The method for constructing the prediction model includes the following steps: Fecal samples were collected from adolescent patients with depression and healthy adolescents according to the pre-set inclusion and exclusion criteria. Fecal samples from adolescent patients with depression were designated as the depression group, and fecal samples from healthy adolescents were designated as the healthy group. High-throughput sequencing was performed on the depression group and the healthy group to identify differentially expressed microorganisms between the depression group and the healthy group, wherein the differentially expressed microorganisms include the microbial biomarkers described in the prediction system of any one of claims 1 to 3; The differentially expressed microorganisms were used as a predictive model for adolescent depression to determine whether the adolescents under test had depression.

9. The construction method of claim 8, wherein, The use of the differentially expressed microorganisms as a predictive model for adolescent depression includes: Obtain quantitative detection results of pre-defined microbial markers in fecal samples from the donor to be tested; The quantitative detection results are compared with a preset threshold, and the donor to be tested is determined to be either a patient with depression or an inefficient donor based on the comparison results.

10. The construction method of claim 8, wherein, The use of the differentially expressed microorganisms as a predictive model for adolescent depression includes: Quantitative detection results of preset microbial markers in fecal samples from the donor to be tested are obtained. The microbial markers include Erysipelotrichaceae bacterium, Blautialuti, Prevotella copri, Ruminococcus gnavus, Eubacterium hallii, Anaerostipes hadrus, and Subdoligranulum variabile. According to the quantitative detection result, the probability of the to-be-tested donor being a depression patient is calculated, and the probability value of the depression patient is taken as the quantitative detection result; The quantitative detection result is compared with a preset threshold value, and according to the comparison result, it is judged whether the to-be-tested teenager has depression.

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