Intestinal microbial markers of depression in adolescents accompanied by obesity, prediction system and applications thereof

Abstract

The application discloses an intestinal microorganism marker for adolescent depression accompanied by obesity, a prediction system and application thereof, and belongs to the field of biological medicine. The microorganism marker comprises one or more of the following: Buttlerella intestinalis, long-chain Dorea, fecal Prevotella and active Ruminococcus. The application provides a kit comprising detection reagents for detecting the relative abundance of the microorganism marker. The application also provides a product for diagnosing depression accompanied by obesity, a prediction system for evaluating the risk of a to-be-tested adolescent being a patient with depression accompanied by obesity, and the like. The product provided by the application has good feasibility and accuracy, can effectively evaluate the risk of depression accompanied by obesity, and 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, a predictive system, and its application for adolescent depression accompanied by obesity. Background Technology

[0002] Unlike adults with depression, adolescents with depression are in a critical period of physical and mental development, facing unique growth challenges and developmental pressures. Adolescents' cognitive and emotional regulation abilities are not yet fully developed, and they lack effective coping strategies when facing multiple pressures. Coupled with a weak awareness of seeking help and a strong sense of stigma, many depressed adolescents fail to receive timely professional identification and intervention.

[0003] Currently, among adolescents with depression and obesity, these patients often have metabolic abnormalities such as chronic inflammation and insulin resistance. Obesity-related symptoms (fatigue, reduced activity) overlap with depression, which can easily lead to misdiagnosis or overdiagnosis.

[0004] Therefore, distinguishing whether the adolescents being tested are patients with depression and obesity is a key prerequisite for achieving accurate diagnosis and individualized treatment. Current technology lacks targeted and objective assessment methods. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a gut microbiota biomarker and prediction system for depression accompanied by obesity, and its application, which can provide a targeted and objective assessment method for the accurate diagnosis of patients with depression and obesity.

[0006] The technical solution provided by this invention is as follows: In a first aspect, a reagent for quantitatively detecting microbial markers is provided for use in the preparation of products for diagnosing adolescent depression with obesity. The microbial markers include *Enterobacter bartlettii* and *Dorea longicatena*, which are significantly increased in adolescent patients with depression and obesity.

[0007] Secondly, a microbial biomarker for diagnosing whether a candidate adolescent is a patient with depression and obesity is provided, the microbial biomarker comprising *Intestinibacter bartlettii*, *Dorea longicatena*, *Prevotella copri* (fecal strain), and *Ruminococcus gnavus* (active rumenococcus).

[0008] Thirdly, a microbial biomarker is provided for diagnosing whether a candidate adolescent is a patient with depression and obesity, wherein the microbial biomarker is any one of the following combinations of biomarkers: a) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Dorea longicatena, and Intestinibacter bartlettii; b) The microbial markers are a combination of markers consisting of fecal Prevotella copri, active Ruminococcus gnavus, Dorea longicatena, and Intestinibacter bartlettii.

[0009] Fourthly, a kit is provided for diagnosing whether a candidate adolescent is a patient with depression and obesity, the kit containing reagents for quantitative detection of the microbial markers.

[0010] Fifthly, a product is provided for diagnosing whether a candidate adolescent is a patient with depression and obesity. The product includes one or more of reagents, primers, probes, antibodies, test strips, aptamers, and chips. The product is specific to the microbial biomarkers and is used for quantitative detection of the microbial biomarkers. The product is used to diagnose whether a candidate adolescent is a patient with depression and obesity.

[0011] Sixthly, a predictive system for assessing the risk of a tested adolescent having depression and obesity is provided, comprising: The detection module obtains quantitative detection results of the microbial markers in the fecal samples of the adolescents to be tested; The comparison module compares the detection results with a preset threshold and determines the risk of the adolescent being diagnosed with depression and obesity based on the comparison results.

[0012] Seventhly, a predictive system for adolescents with depression and obesity is provided, the predictive system being used to diagnose whether a candidate adolescent has depression and obesity, including: 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 Intestinibacter bartlettii, Dorea longicatena, Prevotella copri, and Ruminococcus gnavus. The calculation module is used to calculate the probability that the donor to be tested is a patient with depression and obesity based on the quantitative detection results, and denoted as the disease probability value; The comparison module is used to compare the disease probability value with a preset threshold, and determine whether the adolescent under test is a patient with depression and obesity based on the comparison result.

[0013] Eighthly, a method for constructing a predictive model for adolescent depression accompanied by obesity is provided, comprising the following steps: Fecal samples were collected from adolescents with depression and obesity and healthy adolescents according to the pre-set inclusion and exclusion criteria. The adolescents with depression and obesity were classified as the depression group, and the fecal samples from healthy adolescents were classified 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, and the differentially expressed microorganisms included the aforementioned microbial markers; The differentially expressed microorganisms were used as a predictive model for adolescent depression to determine whether the adolescents being tested had depression and obesity.

[0014] It should be noted that this invention has newly discovered and verified a strong correlation between the aforementioned gut microbiota and depression accompanied by obesity. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the test sample through relative abundance values, other methods that can achieve quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information, etc.) and can also be used to assist in the diagnosis of whether the test sample suffers from depression accompanied by obesity. People can choose according to their own needs, which will not be elaborated here.

[0015] The beneficial effects of this invention are as follows: 1. This invention newly discovers four gut microbiota associated with depression and obesity, including Intestinibacter bartlettii, Dorea longicatena, Prevotella copri, and Ruminococcus gnavus. After research and verification, it was found that one or more of the above four gut microbiota can serve as microbial biomarkers associated with depression and obesity, and can be used to diagnose whether the sample to be tested suffers from depression and obesity.

[0016] 2. This invention also provides a reagent and kit that can use one or more of the above four bacterial species as detection markers (i.e., microbial markers) to predict or diagnose whether a adolescent is suffering from depression accompanied by obesity. This method is completely non-invasive and highly accurate. This invention utilizes metagenomic sequencing to provide higher resolution, enabling the analysis of microbial communities to penetrate to the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. The four bacterial species can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0017] 3. This invention also provides a product and prediction system for diagnosing depression accompanied by obesity. This product and prediction system can calculate the probability of disease based on the relative abundance of each bacterial species, and then compare it with reference values ​​to predict or diagnose whether a patient has depression accompanied by obesity or is at risk of having depression accompanied by obesity. This product and prediction system have good feasibility and accuracy, and can effectively assess the risk of depression accompanied by obesity in test samples, providing a new tool for clinical diagnosis. Attached Figure Description

[0018] Figure 1 The graph shows the results of the linear discriminant analysis on the training set. Figure 2 Box plot of microbial biomarkers for the validation set; Figure 3 The ROC curve for the prediction score of the validation set. Detailed Implementation

[0019] 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.

[0020] Depression is a mental disorder characterized by persistent low mood, loss of interest, and cognitive impairment, and it has become one of the leading causes of disability worldwide. In recent years, mental health problems have shown a trend of affecting younger people.

[0021] Unlike adults with depression, adolescents are in a critical period of physical and mental development, facing unique growth challenges and developmental pressures. Even more serious is the fact that adolescents' cognitive and emotional regulation abilities are not yet fully developed, they lack effective coping strategies when facing multiple pressures, and coupled with a weak awareness of seeking help and a strong sense of stigma, many depressed adolescents fail to receive timely professional identification and intervention.

[0022] Among Chinese adolescents with depressive disorders, the obesity rate is 10.4% and the overweight rate is 17.9%, with obesity positively correlated with prolonged disease duration and non-suicidal self-harming behavior. When these two conditions coexist, their harmful effects are compounded, easily creating a vicious cycle and significantly increasing the difficulty of treatment.

[0023] Currently, the clinical diagnosis of adolescent depression still mainly relies on interviews and scale assessments. Commonly used tools include the Beck Depression Inventory (BDI), the Call for Self-Rating Depression Scale (CES-D), the Child Depression Inventory (CDI), the Kutcher Adolescent Depression Inventory (KADS), and the Patient Health Questionnaire-9 (PHQ-9). However, the application of these tools in adolescents has the following significant limitations: First, the scale has a significant adult bias: the BDI was originally designed for adults, and its abstract items pose a challenge to adolescents, especially younger students and individuals with limited cognitive abilities. They often struggle to understand items involving complex emotional introspection and metaphorical expression, affecting the accuracy of the assessment. Adolescents face significantly different social environments than adults—unique stressors such as academic pressure, peer relationships, and adolescent physical changes prevent the original scale items from fully capturing the depressive symptoms triggered by these adolescent-specific stressors.

[0024] Secondly, the problems of homogeneity and heterogeneity in the scales are prominent: existing measurement scales are like "the blind men and the elephant"—no single symptom appears in all scales, and no single scale measures all symptoms. Each scale seems to describe a different aspect of depression, and none can encompass the whole picture. The heterogeneity of depression scales for children and adolescents in different developmental contexts has been largely ignored.

[0025] Third, poor cross-cultural applicability: Most existing scales are developed based on Western cultural backgrounds and have limitations in cross-cultural contexts, potentially failing to accurately capture the actual psychological state of patients from different cultural backgrounds. When using Western scales to diagnose adolescents from non-Western cultural backgrounds, misdiagnosis may occur if cross-cultural factors are not considered.

[0026] Fourth, insufficient screening accuracy: Studies have found that the sensitivity of PHQ-9 in Chinese children and adolescents is only 0.551, below acceptable levels. CES-D has a high false positive rate in adolescents. CDI also has a false positive rate as high as 25%. The insufficient sensitivity and specificity of these scales directly lead to missed diagnoses and misdiagnoses.

[0027] Therefore, developing an objective diagnostic method based on adolescent-specific gut microbiota markers that can simultaneously reflect the metabolic heterogeneity of depression and obesity, and establishing corresponding donor screening and efficacy prediction models, is of great significance for achieving precise diagnosis and treatment of adolescent depression accompanied by obesity.

[0028] To assess whether gut microbiota composition can serve as a predictor of depression-associated obesity, and addressing the clinical needs for the diagnosis and detection of depression-associated obesity, this invention collected fecal samples from adolescent patients with depression and obesity, as well as healthy adolescents. Through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-4), four microorganisms highly correlated with efficient donors were screened: *Intestinibacter bartlettii*, *Dorea longicatena*, *Prevotella copri* (fecal sample), and *Ruminococcus gnavus* (active rumenococcus).

[0029] ROC curve analysis showed that the above four biomarkers have high specificity and sensitivity as detection variables. These four bacterial species can be used as detection biomarkers for the prediction and diagnosis of patients with depression and obesity.

[0030] 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.

[0031] Example 1: Sample Collection The inclusion criteria for fecal samples from adolescents with depression and obesity are as follows: 1. Age 12-18 years; 2. Clinically diagnosed with depression and obesity; 3. Stable vital signs.

[0032] The exclusion criteria for stool samples from adolescents with depression and obesity were as follows: 1. Received treatment for depression or obesity within 1 month; 2. Took antibiotics, probiotics, or prebiotics within the past 3 months; 3. Have any chronic disease, including neurobehavioral disorders; 4. Received medications affecting gastrointestinal motility within 1 week; 5. Have a history of pain from functional dyspepsia, aerophagia, abdominal migraine, or functional constipation; 6. Showed growth retardation; 7. Have gastrointestinal obstruction or stenosis; 8. Have a history of abdominal surgery, peptic ulcer, or inflammatory bowel disease; 9. The researchers deemed the patient unsuitable for inclusion in this study.

[0033] The inclusion criteria for the control group (fecal samples from healthy adolescents) were as follows: 1. Age 12-18 years; 2. No depression or other neurological disorders; 3. No diabetes or other metabolic diseases; 4. No irritable bowel syndrome or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficient state; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study.

[0034] The exclusion criteria for the control group (fecal samples from healthy adolescents) were the same as those for the adolescent depression with obesity group.

[0035] The above data comes from fecal samples collected in Hubei Province. For both adolescents with depression and obesity, and healthy adolescents, written informed consent was obtained from both the individual and their legal guardian (if any). Based on the above inclusion and exclusion criteria, this invention collected fecal samples from a total of 66 adolescents with depression and obesity and 56 healthy adolescents. The statistical results are as follows: Table 1 Sample Information Table Example 2: DNA extraction, library construction, and sequencing 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0036] 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).

[0037] 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).

[0038] 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.

[0039] Example 3: LEfSe analysis for screening microbial biomarkers 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 count 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 adolescents with depression and obesity and healthy adolescents) 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.

[0040] The results are as follows Figure 1 As shown, this application is the first to discover four bacterial species associated with depression and obesity. Specifically, researchers screened out one biomarker that was significantly reduced in the adolescent depression and obesity group, including fecal Prevotella copri; and three biomarkers that were significantly increased in the adolescent depression and obesity group, including active Ruminococcus gnavus, Dorea longicatena, and Intestinibacter bartlettii.

[0041] Example 4: Verifying the reliability of the above four microbial biomarkers 1. First, the remaining 20% ​​of the test subjects in Example 1 (including the group with depression and obesity 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).

[0042] 2. Use IBM SPSS Statistics (v27) 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.

[0043] 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 four single bacterial species) and individual bacteria are as follows: Table 2 ROC diagnostic curve results .

[0044] As shown in Table 2, for single bacterial species, Prevotella copri had the highest AUC value (approximately 0.937), while Intestinibacter bartlettii had the lowest AUC value (approximately 0.808). The AUC value of the mimicry marker (a marker formed by the combination of four single bacterial species) (approximately 0.965) was higher than that of the single bacterial species.

[0045] refer to Figure 3 As described in Table 2, the mimicry markers are combinations of the four single bacterial species described in this application. Composition 1 is a marker combination composed of Prevotella copri (fecal), Ruminococcus gnavus (active), and Dorea longicatena (long-chain Dorsella). Composition 2 is a marker combination composed of Ruminococcus gnavus (active), Dorea longicatena (long-chain Dorsella), and Intestinibacter bartlettii (bartlettii). Composition 3 is a marker combination composed of Prevotella copri (fecal) and Ruminococcus gnavus (active). Composition 4 is a marker combination composed of Dorea longicatena (long-chain Dorsella) and Intestinibacter bartlettii (bartlettii).

[0046] 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 individuals with depression and obesity in the training set, the relative abundance values ​​of the four detected bacterial species were further used as single variables. The linear relationship between the relative abundance values ​​of the four single 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 adolescents under testing. y=A+B1×x1+B2×x2+B3×x3+B4×x4; 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 Ruminococcus gnavus, x3 is the relative abundance value of Dorea longicatena, and x4 is the relative abundance value of Intestinibacter bartlettii.

[0047] b. Determine the values ​​of A and B1 to B4 above. After statistical analysis of the sample data, A was -1.3872, B1 was -1.6598, B2 was 409.2981, B3 was 95.7922, and B4 was 76.6179. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=-1.3872-1.6598×x1+409.2981×x2+95.7922×x3+76.6179×x4; c. Calculate the probability Z of disease in the adolescents to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the adolescent to be tested is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the adolescent to be tested is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0048] After processing, the formula for calculating the probability Z of disease is: .

[0049] 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. Here, "patient" refers to adolescent patients with depression and obesity.

[0050] Table 4 shows the mean and standard deviation of the relative abundance of each bacterial species. The mean relative abundance determines the central location of the data distribution, while the standard deviation reflects the degree of dispersion of the data relative to the mean. The p-value is a statistic calculated using the formula of the rank-sum test. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0051] Table 3. Relevant data on validation set markers Table 4. Statistical data on the abundance of biomarkers in the validation set. Note: E represents 10 to the power of 10. For example, 9.76514818612e-05 means 9.76514818612 × 10⁻⁵. -5 The mean refers to the relative abundance mean, and the standard deviation is similar.

[0052] e. Results and Analysis The results from Examples 4 and 5 show that mimicry biomarkers can be used as diagnostic markers in patients with depression and obesity, exhibiting better efficacy and higher accuracy. Using one or more of these four bacterial species as diagnostic markers is completely non-invasive and highly accurate.

[0053] 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 health probability in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal samples 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.

[0054] Example 6: Products for detecting gut microbiota Based on the description of Examples 1 to 4 above, it can be seen that the microbial biomarkers selected in this application have good predictive effects. Medical personnel can use single or multiple bacterial species from Examples 1 to 4 as biomarkers to detect and diagnose the test samples individually in order to assess whether the adolescents under test suffer from depression.

[0055] Therefore, this embodiment also provides a product for detecting microbial biomarkers. The product may include one or more of reagents, test strips, aptamers, and chips. The reagents may contain primers, probes, antibodies, etc. The product is specific to the microbial biomarkers described in Examples 1 to 4 and can be used for quantitative detection of the microbial biomarkers described in Examples 1 to 4.

[0056] In practical applications, the product described in this embodiment can be used in the preparation of products for diagnosing adolescent depression to assess whether the adolescent being tested has depression accompanied by obesity. Simultaneously, the microbial markers may include the four single bacterial species found in this application that are associated with adolescent depression. That is, the microbial markers in this test reagent, used to detect whether the adolescent being tested has depression accompanied by obesity, may include *Enterobacter bartlettii*, *Dorea longicatena*, *Prevotella copri* (fecal predilection), and *Ruminococcus gnavus* (active rumenococcus).

[0057] Example 7: Kit for detecting gut microbiota This embodiment also provides a first reagent kit, which may contain the detection reagents described in the above embodiments. Since the detection reagents can be used to distinguish whether the adolescent to be tested has depression accompanied by obesity, the reagent kit can also be used to distinguish whether the adolescent to be tested has depression accompanied by obesity. The limitations of the reagent kit and the technical solutions in this application can be referred to the description 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 either.

[0058] Example 8: Use of the reagent in products for predicting / diagnosing adolescent depression As described in the above embodiments, the detection reagent containing any one or more of the four single bacterial species has sensitivity and specificity, and can be used to distinguish whether a tested adolescent is a patient with depression and obesity. This detection reagent may contain one or more of the four single bacterial species. Similarly, products containing the above-mentioned detection reagent can also be used to predict / diagnose whether a tested adolescent is a patient with depression and obesity. These products can be kits and prediction systems, etc., which will not be elaborated upon here.

[0059] Example 9: 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 tested adolescent being a patient with depression and obesity. 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 adolescent to be tested; wherein, the single bacterial species may include one or more of the following: Prevotella copri, Ruminococcus gnavus, Dorea longicatena, and Intestinibacter bartlettii. The comparison module is used to compare the quantitative detection results with a preset threshold, and to determine the risk of the adolescent being diagnosed with depression and obesity based on the comparison results.

[0060] 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.

[0061] In practical application, to illustrate how the prediction system assesses the risk of the tested adolescent being a patient with depression and obesity, this invention uses relative abundance values ​​as an example. Specifically: The detection module obtains the relative abundance value of Enterobacter bartlettii in the fecal sample of the adolescent to be tested, 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 *Intestinibacter bartlettii* in Table 4 to assess the risk of the adolescent being diagnosed with depression and obesity. For example, if the first abundance value is within the range defined by the mean and standard deviation of the patients with depression and obesity, the adolescent being diagnosed with depression and obesity has a higher risk; conversely, if the first abundance value is outside the range defined by the mean and standard deviation, the adolescent being diagnosed with depression and obesity has a lower risk.

[0062] Similarly, referring to the detection and evaluation method of *Intestinibacter bartlettii* mentioned above, the prediction system can also perform similar detection and evaluation on microorganisms such as *Dorea longicatena*, *Prevotella copri* (fecal), and *Ruminococcus gnavus*, thereby assessing the risk of the adolescent being diagnosed with depression and obesity.

[0063] Due to factors such as genetics and environment, it is normal that not all four newly discovered bacterial species can be detected in fecal samples. One or more of the four newly discovered bacterial species can be used as detection markers to assess the risk of the adolescent being diagnosed with depression and obesity.

[0064] 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 adolescent being a patient with depression and obesity, one can refer to the description above, which will not be repeated here.

[0065] Example 10: Adolescent Depression Diagnosis / Prediction System Based on the above embodiments, this embodiment provides a diagnostic / predictive system for adolescent depression to detect whether an adolescent is a patient with depression and obesity, 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 *Intestinibacter bartlettii*, *Dorea longicatena*, *Prevotella copri* (fecal sample), and *Ruminococcus gnavus* (active rumen coccus).

[0066] The calculation module is used to calculate the probability that the donor to be tested is a patient with depression and obesity based on the quantitative detection results, and denoted as the disease probability value.

[0067] In actual operation, referring to Embodiment 5, 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 marker into the binary logistic regression equation to calculate the logarithm y of the dominance of the adolescent to be tested; the second probability module is used to calculate the probability Z of the adolescent to be tested being a patient with depression and obesity based on y, Z=exp(y) / {1+exp(y)}, where exp(y) is an exponential function of y.

[0068] The comparison module is used to compare the disease probability value with a preset threshold, and determine whether the adolescent under test is a patient with depression and obesity based on the comparison result.

[0069] Example 11: Method for constructing a prediction model Based on the descriptions in Examples 1-7, the present invention provides a method for constructing a predictive model for adolescent depression, which distinguishes whether the adolescent to be tested is a patient with depression and obesity, including the following steps: S1. Sample collection: Collect fecal samples from adolescent patients with depression and obesity and 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.

[0070] S2. Sample grouping: DNA extraction, library construction and sequencing were performed on the fecal samples of the above-mentioned adolescent patients with depression and obesity and the fecal samples of healthy adolescents. The fecal samples of adolescent patients with depression and obesity were assigned to the depression group, and the fecal samples of healthy adolescents were assigned to the healthy group. In this step, after the specific fecal sample has been selected, people should be able to extract DNA from the fecal sample, construct a library and sequence it according to the preset steps in order to distinguish the differential microorganisms between the depression group and the healthy group. The specific experimental steps can be referred to the description of Examples 1 to 4 above, and will not be repeated here.

[0071] 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: Streptococcus salivarius, Bifidobacterium longum, active Ruminococcus, Bacteroides dystrophus, Dolceae longum, and Bosnia ileum. 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.

[0072] S4. Use the differentially expressed microorganisms as a predictive model for adolescent depression to distinguish whether the adolescents being tested are patients with depression and obesity.

[0073] In practical work, in conjunction with Examples 9 and 10, this embodiment provides two methods for using the differentially expressed microorganisms as predictive models, specifically including: Referring to Example 9, 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 and obesity based on the comparison results.

[0074] Referring to Example 10, 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 and obesity based on the quantitative detection results, and use the probability value of the patient with depression and obesity as the quantitative detection result; S43, compare the quantitative detection result with a preset threshold, and determine whether the adolescent to be tested is a patient with depression and obesity based on the comparison result.

[0075] In practice, a Z-score greater than 0.5 indicates a higher probability that the tested adolescent has adolescent depression; a Z-score less than 0.5 indicates a lower probability; a Z-score of 0.5 suggests that the individual may be a patient or may have depression accompanied by obesity. In this case, further testing using other methods is necessary, 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.

[0076] Example 12: Diagnosing whether the test sample is a patient with depression and obesity If it is necessary to diagnose whether a person under test has depression accompanied by obesity, 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 active Ruminococcus gnavus, Intestinibacter bartlettii and Dorea longicatena of the subject is found to be high (compared to the mean and standard deviation of the adolescent depression with obesity group in Table 4), or shows a significant increasing trend, then the adolescent subject is more likely to be a patient with depression and obesity.

[0077] (2) If, after observation over multiple consecutive periods, the content of Prevotella copri 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 adolescent to be tested is more likely to be a healthy person.

[0078] (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 And Table 4, to make a preliminary judgment on whether the subject of the test is a patient with depression and obesity; (4) If medical staff want to more accurately determine whether the person to be tested is a patient with depression and obesity, they can calculate which type of meningioma or glioma the patient to be tested is based on the relative abundance values ​​of the four single bacterial species and with reference to the technical solutions described in Example 5 and Example 11.

[0079] Conclusion and explanation: 1. By Figures 1-3 As shown in Table 2, any one of the four newly discovered single bacterial species in this application can serve as a microbial biomarker for depression accompanied by obesity. Each single bacterial species exhibits both sensitivity and specificity for depression accompanied by obesity. Therefore, the microbial biomarker for depression accompanied by obesity can be selected from any one or more of the four newly discovered single bacterial species in this application. Specifically: Microbial markers of depression associated with obesity may include one or more of the following: *Intestinibacter bartlettii*, *Dorea longicatena*, *Prevotella copri*, and *Ruminococcus gnavus*.

[0080] 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 depression accompanied by obesity.

[0081] 3. Predictive effect: The AUC value (approximately 1) of the mimicry marker (a marker formed by the combination of 4 single bacterial species) is significantly 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 decrease. That is, the test result with the addition of other microorganisms is not lower than the test result (AUC value) without the addition of microorganisms.

[0082] 4. The four newly discovered bacterial species in this application can all be used as detection variables. They all have high specificity and sensitivity, and the AUC of the four microbial markers is greater than 80%. One or more of the four microbial markers can be used as detection markers for the diagnosis of patients with depression and obesity.

[0083] 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 products for diagnosing adolescent depression accompanied by obesity, characterized in that, The microbial markers include *Intestinibacter bartlettii* and *Dorea longicatena*, which are significantly increased in adolescent patients with depression and obesity.

2. The application according to claim 1, characterized in that, The microbial markers also include fecal Prevotella copri and / or active Ruminococcus gnavus, wherein fecal Prevotella copri is significantly reduced in adolescent patients with depression and obesity, and active Ruminococcus gnavus is significantly increased in adolescent patients with depression and obesity.

3. The application according to claim 2, characterized in that, The microbial markers are a combination of Intestinibacter bartlettii, Dorea longicatena, Prevotella copri (fecal), and Ruminococcus gnavus (active). The Prevotella copri (fecal) markers are significantly reduced in adolescents with depression and obesity, while the Ruminococcus gnavus, Intestinibacter bartlettii, and Dorea longicatena (active) markers are significantly increased in adolescents with depression and obesity.

4. A microbial biomarker for diagnosing whether a candidate adolescent has depression accompanied by obesity, characterized in that, The microbial markers include *Intestinibacter bartlettii*, *Dorea longicatena*, *Prevotella copri* (fecal strain), and *Ruminococcus gnavus* (active rumenococcus).

5. A microbial biomarker for diagnosing whether a candidate adolescent has depression accompanied by obesity, characterized in that, The microbial biomarker is any one of the following combinations of biomarkers: a) The microbial markers are a combination of markers consisting of active Ruminococcus gnavus, Dorealongicatena, and Intestinibacter bartlettii; b) The microbial markers are a combination of markers consisting of fecal Prevotella copri, active Ruminococcus gnavus, Dorea longicatena, and Intestinibacter bartlettii.

6. A kit for diagnosing whether a test-taker is a patient with depression and obesity, characterized in that: The kit contains reagents for the quantitative detection of microbial markers according to any one of claims 1 to 5.

7. A product for diagnosing whether a adolescent is suffering from depression accompanied by obesity, characterized in that: The product includes one or more of reagents, test strips, aptamers, and chips. The product is specific to the microbial biomarkers of any one of claims 1 to 5 and is used for quantitative detection of the microbial biomarkers of any one of claims 1 to 5. The product is used to diagnose whether the adolescent to be tested is a patient with depression and obesity.

8. A predictive system for assessing the risk of a tested adolescent being a patient with depression and obesity, characterized in that, The prediction system is used to diagnose whether the adolescent being tested has depression accompanied by obesity, including: The detection module is used to obtain quantitative detection results of microbial markers as described in any one of claims 1 to 5 in the fecal samples of the adolescents to be tested; The comparison module is used to compare the quantitative detection results with a preset threshold, and to determine the risk of the adolescent being diagnosed with depression and obesity based on the comparison results.

9. A predictive system for adolescents with depression and obesity, characterized in that: The prediction system is used to diagnose whether the adolescent being tested has depression accompanied by obesity, including: 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 Intestinibacter bartlettii, Dorea longicatena, Prevotella copri, and Ruminococcus gnavus. The calculation module is used to calculate the probability that the donor to be tested is a patient with depression and obesity based on the quantitative detection results, and denoted as the disease probability value; The comparison module is used to compare the disease probability value with a preset threshold, and determine whether the adolescent under test is a patient with depression and obesity based on the comparison result.

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