Plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents

Abstract

The invention relates to the technical field of diagnosis markers and diagnosis methods for mental diseases of children and adolescents, in particular to a plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents. The plasma metabolism diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenyl carnitine, histidine, prolyl hydroxyproline, tryptophan and glucuronic acid. According to the scheme, non-target metabonomics data is analyzed, and seven candidate diagnostic markers for depression of children and adolescents are screened and determined from common differential metabolites. Then, through targeted metabonomics research, diagnostic markers and combinations thereof with ideal diagnostic effects are determined, and then a diagnostic model is constructed. And a sex factor is also incorporated into the diagnosis model, so that the diagnosis effect is further improved. According to the technical scheme, the technical problem that in the prior art, reliability and popularization potential of depression plasma metabolism biomarkers of children and adolescents are not ideal can be solved, and the depression plasma metabolism biomarkers have ideal popularization and application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of diagnostic markers and diagnostic methods for mental illnesses in children and adolescents, and in particular to plasma metabolic diagnostic markers and diagnostic systems for depression in children and adolescents. Background Art

[0002] According to the World Health Organization (WHO), about 320 million people suffer from depression worldwide, and there are about 95 million patients with depression in China. Depression has become the second largest disease burden in the world. It is worth noting that children and adolescents have become a high-risk group for depression. The first epidemiological report on mental disorders in children and adolescents in China in 2021 shows that the prevalence of mental disorders in children and adolescents in my country is as high as 17.5%, of which the prevalence of depression in children and adolescents is about 3%. It is estimated that about 15 million minors in my country are suffering from depression. Compared with adult depression, children and adolescents with depression have the characteristics of difficult diagnosis and poor treatment effect (only 22% stable remission rate), which leads to 62% of children and adolescents with depression accompanied by strong suicidal ideas and / or suicidal behaviors. It not only seriously affects the growth and development and physical and mental health of minors, but also imposes a serious burden on patients' families and the whole society. In addition, compared with adult depression patients, children and adolescents with depression are significantly different in clinical manifestations, treatment effects, disease prognosis and other aspects.

[0003] In order to cope with the above difficulties, a large number of studies have focused on the diagnosis, treatment and biological mechanisms of depression in children and adolescents. Among these aspects, improving diagnostic effectiveness is the most urgent clinical need and the basis for more efficient research in other areas. In order to improve the diagnostic effectiveness of depression in children and adolescents, some studies have tried multiple methods to determine biomarkers, including genomics, proteomics, metabolomics and microbiome. Among the various omics technologies, metabolomics can be used to determine the metabolic spectrum of life activities and is one of the best tools for identifying biomarkers and molecular mechanisms of specific diseases.

[0004] A meta-analysis of the peripheral blood metabolome of patients with depression, including 46 articles, suggests that the metabolome in the peripheral blood of patients with depression is disordered, but most of the included articles focus on adults, and only 3 focus on children and adolescents. Two of the articles on adolescent depression only include 14 cases of depression / 7 healthy controls and 11 cases of depression / 13 healthy controls. Another non-targeted metabolomics study involving 84 cases of depression / 50 healthy controls was published by the inventor's team in 2018. However, the sample size of our study was relatively small, including only 134 subjects including 84 cases of depression and 50 healthy controls. The accuracy of the biomarkers revealed needs further study, which limits the clinical promotion of the research results.

[0005] Therefore, there is an urgent need to further improve the credibility and promotion potential of plasma metabolic biomarkers for depression in children and adolescents, discover new biomarkers or biomarker combinations with better accuracy, and better achieve effective diagnosis of depression in children and adolescents. Summary of the invention

[0006] The present invention is intended to provide the application of plasma metabolic diagnostic markers in constructing a diagnostic system for depression in children and adolescents, so as to solve the technical problems of the existing technology of the credibility and promotion potential of plasma metabolic biomarkers for depression in children and adolescents.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme:

[0008] Application of plasma metabolic diagnostic markers in constructing a diagnostic system for depression in children and adolescents, wherein the plasma metabolic diagnostic markers include at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecenoylcarnitine, histidinol, prolylhydroxyproline, tryptophan and glucuronic acid; the structural formula of the unsaturated undecenoylcarnitine is:

[0009]

[0010] Furthermore, the plasma metabolic diagnostic marker is cyclic adenosine monophosphate.

[0011] Furthermore, the plasma metabolic diagnostic marker consists of cyclic adenosine monophosphate and glucuronic acid.

[0012] Furthermore, the plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, cyclic adenosine monophosphate, unsaturated undecylenylcarnitine, histidinol, prolylhydroxyproline, tryptophan and glucuronic acid.

[0013] Furthermore, the plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid and unsaturated undecenoylcarnitine.

[0014] Furthermore, the plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, cyclic adenosine monophosphate, unsaturated undecylenylcarnitine, histidinol, prolylhydroxyproline, tryptophan and glucuronic acid.

[0015] The technical solution also provides a child and adolescent depression diagnosis system, characterized in that it includes a data acquisition unit and a diagnosis unit;

[0016] The data acquisition unit includes a content detection subunit, or includes a content detection subunit and a gender information acquisition subunit; the content detection subunit is used to detect the content of plasma metabolic diagnostic markers in plasma; the plasma metabolic diagnostic markers include at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecenoylcarnitine, histidinol, prolyl hydroxyproline, tryptophan and glucuronic acid; the structural formula of the unsaturated undecenoylcarnitine is:

[0017]

[0018] The diagnostic unit is used to obtain the test variable value P for diagnosing depression risk through a logistic regression model:

[0019] The logistic regression model is: Logit(P)=a1×M1+a2×M2+a3×M3+a4×M4+a5×M5+a6×M6+a7×M7+a8×M8+b;

[0020] Among them, M1-M7 are independent variables, which are used to input the numerical values ​​of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecenoylcarnitine, methylpyridine, histidyl alcohol, prolylhydroxyproline and tryptophan in plasma in sequence; among the independent variables of M1-M7, at least one of the independent variables inputs the numerical value of the content of the corresponding metabolite, and the remaining independent variables are set to 0;

[0021] M8 is the independent variable. When gender is considered, M8 is used to input gender information, with the value of male being 1 and the value of female being 2. When gender is not considered, M8 is set to 0.

[0022] a1-a8 are the coefficients of M1-M8 respectively, and b is a constant;

[0023] 0≤P≤1, compare the P value with the critical value to achieve diagnosis.

[0024] Furthermore, the logistic regression model is constructed by the following method: collecting the plasma content information of the plasma metabolic diagnostic markers in several samples, or collecting the plasma content information and gender information of the plasma metabolic diagnostic markers in several samples at the same time, thereby obtaining a data set; using a logistic regression algorithm to perform model training on the data set to obtain a logistic regression model;

[0025] The critical value is obtained by the following method: in the data set, a ROC curve is fitted using a logistic regression model, and the value of the test variable corresponding to the maximum value of the Youden index of the ROC curve is the critical value for diagnosis.

[0026] Furthermore, the plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid, and unsaturated undecylenylcarnitine:

[0027] Regardless of gender, the test variable value P for diagnosing depression risk is calculated by the following formula:

[0028] Logit(P)=-0.2102011×M1+0.0024516×M2-0.0087530×M3+2.5230796.

[0029] Alternatively, considering gender factors, the test variable value P for diagnosing depression risk is calculated by the following formula:

[0030] Logit(P)=-0.219851×M1+0.0024516×M2-0.011070×M3-0.671532M8+4.026209.

[0031] Furthermore, the plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecylenylcarnitine, methylpyridine, histidinol, prolylhydroxyproline and tryptophan:

[0032] Regardless of gender, the test variable value P for diagnosing depression risk is calculated by the following formula:

[0033] Logit(P)=-0.2722×M1+0.002628×M2-0.01476×M3+0.002021×M4+0.0005301×M5+0.000555×M

[0034] 6+0.000008763×M7-0.8001;

[0035] Alternatively, considering gender factors, the test variable value P for diagnosing depression risk is calculated by the following formula:

[0036] Logit(P)=-0.2756×M1+0.002628×M2-0.001359×M3+0.001737×M4+0.0006020×M5+0.0007109

[0037] ×M6+0.000009975×M7+0.5543×M8-2.336.

[0038] Furthermore, the content detection subunit includes a high performance liquid chromatography-mass spectrometry device.

[0039] Further, the HPLC conditions in the HPLC-MS / MS equipment are:

[0040] Chromatographic column: UPLC BEH Amide column; mobile phase A: aqueous solution of CH3COONH4 and NH4OH; mobile phase B: acetonitrile; flow rate: 0.5mL / min; sample volume: 4μL; gradient elution mode was used.

[0041] Furthermore, the gradient elution program was: 0-0.5 min, 95% mobile phase B; 0.5-7 min, 95%-65% mobile phase B; 7-8 min, 65%-40% mobile phase B; 8-9 min, 40% mobile phase B; 9-9.1 min, 40%-95% mobile phase B; 9.1-12 min, 95% mobile phase B.

[0042] Furthermore, the mass spectrometry conditions in the HPLC-MS / MS equipment were: ESI positive and negative ion modes; the ESI source conditions were set as follows: sheath gas temperature 350°C; sheath gas flow rate 12L / min; capillary voltage: +3000V, -2500V.

[0043] Further, for cAMP, the parent ion is 330.06Da, and the daughter ions are 136.00Da and 119.00Da; for unsaturated undecenoylcarnitine, the parent ion is 328.25Da, and the daughter ions are 85.00Da and 57.10Da; for glucuronic acid, the parent ion is 193.03Da, and the daughter ions are 113.10Da and 131.10Da; for histidinol, the parent ion is 124.09Da, and the daughter ions are 81.10Da and 95.00Da; for tryptophan, the parent ion is 205.10Da, and the daughter ions are 146.00Da and 188.00Da; for prolylhydroxyproline, the parent ion is 229.12Da, and the daughter ions are 70.10Da and 131.90Da; for methylpyridine, the parent ion is 94.07Da, and the daughter ions are 50.10Da and 52.10Da.

[0044] In summary, the principle and beneficial effects of this technical solution are:

[0045] This technical solution combines non-targeted and targeted metabolomics to explore the changes in plasma metabolites of depression in children and adolescents, and identifies plasma biomarkers that can be used for the diagnosis of depression in children and adolescents and verifies them in both non-targeted and targeted cohorts. The non-target cohort includes three sets: untargeted training set, untargeted validation set, and untargeted test set, and the targeted cohort includes two sets: targeted training set and targeted predication set.

[0046] In this study, we firstly performed batch correction on the non-target metabolomics data of three batches of 1073 samples, namely, non-target training set, non-target validation set, and non-target test set. Then, we performed joint univariate metabolomics analysis in the non-target training set and non-target validation set. We identified 100 differential metabolites. Then, based on the non-target discovery set (70% of the non-target training set) and the non-target internal validation set (30% of the non-target training set) divided in the non-target training set, we performed feature screening by genetic algorithm and cross-validation, taking into account collinearity and substitutability. Among the common differential metabolites, we further identified 7 plasma metabolic candidate diagnostic markers of depression in children and adolescents, namely, cyclic adenosine monophosphate (cAMP), picoline, unsaturated undecenoylcarnitine (Car(11:1), undecenoylcarnitine), histidinol, prolylhydroxyproline, tryptophan, and glucuronic acid. These seven metabolites were then detected in a new targeted cohort consisting of 400 multicenter samples using targeted metabolomics. Finally, three metabolites, cyclic adenosine monophosphate (cAMP), unsaturated undecenoylcarnitine (Car(11:1), undecenoylcarnitine) and glucuronic acid, passed targeted validation and were used as the final diagnostic markers.

[0047] A diagnostic model equation was constructed based on the three final biomarkers, and the diagnostic efficacy was evaluated in the targeted cohort and the non-target cohort. It was found that the combined diagnostic model constructed by the three diagnostic markers had good diagnostic efficacy. Then, the optimal Cut-off value calculated by YoudenIndex had good diagnostic performance. Subsequently, the correlation between the three final diagnostic markers and clinical indicators was evaluated, and it was finally found that there was a stable correlation between gender and unsaturated undecenoylcarnitine (Car(11:1)). In the subgroup analysis, it was found that the enrolled population may also affect the carnitine concentration of patients with depression. Therefore, we fitted specific Cut-off values ​​for different gender subgroups and enrolled population subgroups, and found that the subgroup-specific Cut-off values ​​based on the same diagnostic model can better adapt to the diagnostic needs of different populations than the global Cut-off values. It should be noted that the results of our subgroup-specific cut-off value research should be correctly understood as follows: Our results suggest that each region and laboratory should fit their own normal reference levels (specific cut-off values) for men and women based on their own test subjects, and should not be understood as simply copying our specific cut-off values ​​based on limited sample subgroups. If necessary, our cut-off values ​​can be used temporarily at the beginning of the project, and the normal reference values ​​for the region can be fitted after the local test database is established.

[0048] Among them, Car(11:1) is an unsaturated acylcarnitine and an intermediate product of unsaturated fatty acid β-oxidation metabolism. The results of this study suggest that carnitine (Car(11:1)) is affected by gender. The plasma carnitine (Car(11:1)) concentration of normal female children and adolescents is lower than that of normal males, and the concentration of carnitine (Car(11:1)) is also downregulated in patients with depression compared with normal people. This suggests that the mechanism of the high incidence of depression in women may involve the naturally lower plasma carnitine (Car(11:1)) concentration in women. This gender difference leads to less protection of women from carnitine in depression.

[0049] Plasma metabolic diagnostic marker glucuronic acid is a substance of the uronic acid pathway of sugar metabolism bypass and is widely expressed in the body. On the one hand, its product UDP-glucuronic acid has a detoxifying effect and can also be used for mucopolysaccharide synthesis. On the other hand, the uronic acid pathway is connected to the pentose phosphate pathway and is the bypass part of the sugar metabolism network. At present, there are few studies on glucuronic acid in depression. We observed that the possible reason for the upregulation of glucuronic acid in children and adolescents’ plasma is that there is an energy barrier in depression, and the aerobic oxidation of glucose is impaired (the metabolites of the tricarboxylic acid cycle are downregulated in the pathway analysis), followed by compensatory upregulation of bypasses such as glycolytic nodes and pentose phosphate. In addition, antidepressant treatment will also secondarily inhibit the activity of pentose phosphate-related enzymes. Glucuronic acid related to the pentose phosphate pathway is also upregulated.

[0050] cAMP exists in trace amounts in cells and is a second messenger in cells. It is stimulated by first messenger hormones or other molecular signals to activate adenylate cyclase (AC) to catalyze the cyclization of ATP. cAMP is eventually hydrolyzed into 5'-AMP by phosphodiesterase (PDE) and inactivated. AC and PDE can regulate the concentration of intracellular cAMP from two different aspects, thereby affecting the functions of cells, tissues, and organs. When the activity of AC increases, the concentration of cAMP increases, and when the concentration of PDE increases, the concentration of cAMP decreases. cAMP participates in the synaptic transmission of nerve cells. After the receptors on the postsynaptic membrane are activated by transmitters, AC is activated to form cAMP and then activate PKA, which phosphorylates membrane proteins to regulate ion channels and gene expression.

[0051] In addition, in the differential metabolite and pathway analysis, we found that there were obvious abnormalities in amino acid metabolism in the plasma diagnostic markers. Although these differences are unstable in multicenter studies, amino acids are not as stable as carnitine (Car(11:1)), glucuronic acid and cAMP as diagnostic markers. The possible reason is that multicenters bring more heterogeneity. In previous literature, changes in amino acid metabolic pathways in children, adolescents and adults with depression have been widely reported, but the consistency between previous literature reports is poor.

[0052] In summary, the advantages of this technical solution are: for markers of depression in children and adolescents, the existing technology generally uses non-targeted metabolomics detection, so the diagnostic markers obtained are not verified between batches, and there is no targeted verification of absolute quantification. There are strong batch differences in non-target metabolomics, but this technical solution creatively solves this problem by unifying the detection method and correcting the algorithm between batches, making the three batches of non-target metabolism comparable. On the basis of the batch correction of three batches of non-target metabolism, this technical solution uses genetic algorithms to perform feature screening, and also combines collinearity and model performance to select metabolites. Finally, targeted metabolomics verification was also carried out. Targeted verification can clarify the objective absolute concentration of metabolites, making the diagnostic formula more reliable and able to be directly promoted clinically. The combination of carnitine (Car (11: 1)), glucuronic acid and cAMP diagnostic markers used in this technical solution is the best marker for diagnosis, and the diagnostic model composed of it can more accurately realize the diagnosis of depression. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Flow chart of the research process.

[0054] Figure 2 Screening of differentially expressed metabolites for depression and normal in the non-target validation set of Example 2 (A: metabolite volcano plot of the MDD group and HC group in the training set; B: metabolite volcano plot of the MDD group and HC group in the validation set; C: Venn diagram showing the union of differentially expressed metabolites from the training set and the validation set).

[0055] Figure 3 The metabolic characteristics of the depression and normal groups in the non-target cohort of Example 2 (down-regulated metabolites are blue, and up-regulated metabolites are red).

[0056] Figure 4 This is a simplified schematic diagram of the differential metabolic pathways between the depression and normal groups in the non-target cohort of Example 2 (down-regulated metabolites are blue, and up-regulated metabolites are red).

[0057] Figure 5 : is the differential distribution of the diagnostic markers of Example 2 in the non-target training set, non-target validation set and non-target test set.

[0058] Figure 6This is a non-target metabolic diagnostic evaluation of the 7 candidate diagnostic markers between depression and normal in Example 2 (A: combined diagnosis of 7 metabolites, in the targeted training set, the AUC value of the ROC curve for MDD-HC was 0.881; in the targeted training set, the AUC value of the ROC curve for MDD-HC was 0.838; B: combined diagnosis of 7 metabolites, in the discovery set of the non-targeted training set, the AUC value of the ROC curve for MDD-HC was 0.941; in the internal validation set of the non-targeted training set, the AUC value of the ROC curve for MDD-HC was 0.919; C: combined diagnosis of 7 metabolites, in the non-targeted validation set, the AUC value of the ROC curve for MDD-HC was 0.810; in the non-targeted test set, the AUC value of the ROC curve for MDD-HC was 0.883).

[0059] Figure 7 Targeted metabolic assessment of normal-disease diagnostic markers for depression according to Example 3.

[0060] Figure 8 Targeted and non-target metabolic diagnostic evaluation of the three final diagnostic markers between depression and normal in Example 3 (A: combined diagnosis of the three diagnostic metabolites, in the targeted training set, the AUC value of the ROC curve for MDD-HC was 0.849; in the targeted prediction set, the AUC value of the ROC curve for MDD-HC was 0.858; B: combined diagnosis of the three diagnostic metabolites, in the discovery set of the non-targeted training set, the AUC value of the ROC curve for MDD-HC was 0.878; in the internal validation set of the non-targeted training set, the AUC value of the ROC curve for MDD-HC was 0.863; C: combined diagnosis of the three diagnostic metabolites, in the non-targeted validation set, the AUC value of the ROC curve for MDD-HC was 0.793; in the non-targeted test set, the AUC value of the ROC curve for MDD-HC was 0.833).

[0061] Fig. 9 This is the correlation analysis between the three final diagnostic markers in Example 3 and clinical information (A: correlation analysis of non-target training set; B: correlation analysis of non-target validation set; C: correlation analysis of non-target test set; D: correlation analysis of target test set).

[0062] Fig.10 This is the gender subgroup analysis of carnitine Car (11:1) of Example 3 (A: correlation analysis of non-target training set; B: correlation analysis of non-target validation set; C: correlation analysis of non-target test set; D: correlation analysis of target test set).

[0063] Fig.11This is the gender subgroup analysis of carnitine Car (11:1) in different centers of Example 3 (A: correlation analysis of non-target set of Chong Qing center; B: correlation analysis of target set of Chong Qing center; C: correlation analysis of target set of Changsha center; D: correlation analysis of target set of Nan Jing center). DETAILED DESCRIPTION

[0064] The present invention is further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto. Unless otherwise specified, the technical means used in the following examples and experimental examples are conventional means well known to those skilled in the art, and the materials, reagents, etc. used can be obtained from commercial sources.

[0065] In order to further improve the credibility and promotion potential of plasma metabolic biomarkers for depression in children and adolescents, the changes in plasma metabolites of depression in children and adolescents were explored by combining non-targeted and targeted metabolomics, and plasma biomarkers that can be used for the diagnosis of depression in children and adolescents were identified and verified in both non-targeted and targeted cohorts. The non-target cohort includes three sets: untargeted training set, untargeted validation set, and untargeted test set, and the targeted cohort includes two sets: targeted training set and targeted predication set. For details of the research flow chart, please see Figure 1 .

[0066] Embodiment 1:

[0067] (1) Patient enrollment and sample collection

[0068] The research flow chart is as follows Figure 1 As shown in A, all children and adolescents with MDD who underwent non-targeted metabolomics were recruited from the Department of Psychiatry of the First Affiliated Hospital of Chongqing Medical University, and healthy controls matching the patient demographic information were recruited from the First Affiliated Hospital of Chongqing Medical University through advertisements. At the same time, healthy controls were also recruited in two communities in the main urban area of ​​Chongqing and Dianjiang.

[0069] Patients with MDD in children and adolescents who underwent targeted metabolomics were recruited from the Department of Psychiatry of the First Affiliated Hospital of Chongqing Medical University, the Department of Psychiatry of Xiangya Hospital in Changsha, and Nanjing Brain Hospital, and healthy controls matched with the patients' demographic information were recruited from the First Affiliated Hospital of Chongqing Medical University and Xiangya Hospital in Changsha through advertisements. At the same time, healthy controls were also recruited in two communities in Dianjiang and Bishan, Chongqing.

[0070] The diagnosis of all children and adolescents with depression was confirmed by two experienced psychiatrists according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V). Participants were included in the study only if they met the following criteria: 1) aged between 12 and 18 years old, 2) diagnosed with MDD or healthy individuals matched with the depression group, and 3) both the individual and his / her legal guardian provided informed consent. Individuals were excluded if they met any of the following criteria: 1) a history of head injury resulting in persistent loss of consciousness or cognitive consequences; 2) a current or lifetime diagnosis of a serious neurological or other psychiatric disorder, such as epilepsy, encephalitis, autism, bipolar disorder, schizophrenia, attention-deficit / hyperactivity disorder, or obsessive-compulsive disorder; 3) the presence of a chronic physical illness that could significantly affect peripheral metabolic or neurological function, such as hepatitis, chronic nephrosis, or chronic enteritis; or 4) a history of substance abuse or dependence.

[0071] Then, general demographic and clinical characteristics, including sex, age, body mass index (BMI), and symptom scales were collected. HCs and MDD patients were clinically assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24) and the 14-item Hamilton Anxiety Rating Scale (HAMA-14). All subjects in the non-target cohort were divided into untargeted training set (Tr: untargeted training set, n = 563), untargeted validation set (Va: untargeted validation set, n = 397), and untargeted test set (Te: untargeted test set, n = 113), where the untargeted training set was randomly divided into discovery set (Di: discovery set, n = 394) and internal validation set (IV: inter validation set, n = 179) at 70% and 30% respectively. All subjects in the targeted cohort (Ta: targeted set, n = 400) were divided into a targeted training set (TT: targeted training set, n = 200) and a targeted prediction set (TP: targeted prediction set, n = 200). Participants and their legal guardians read and signed the informed consent form before participation. This study protocol was approved by the First Affiliated Ethics Committee of Chongqing Medical University.

[0072] All blood samples were collected from participants between 8:00 am and 12:00 pm by venipuncture and placed in tubes coated with EDTA (anticoagulant). The tubes were then centrifuged at 3000 rpm and 4°C for 10 min. After centrifugation, plasma was separated and stored at -80°C until transported on dry ice to the metabolomics laboratory for metabolomics analysis.

[0073] (2) Demographic characteristics and clinical information of the subjects

[0074] Detailed information on the demographic and clinical characteristics of the recruited non-target cohort participants is shown in Table 1 . A total of 1073 child and adolescent participants were recruited, including 509 MDD patients and 564 healthy controls. The non-target discovery set included 256 depressed patients and 307 healthy controls; the non-target validation set included 197 depressed patients and 200 healthy controls; and the non-target test set included 56 depressed patients and 57 healthy controls. The age range of the participants was between 10 and 18 years old. The mean ± SD of depression and normal in the non-target discovery set was 15.4 ± 1.7 and 15.4 ± 1.7, respectively; the mean ± SD of depression and normal in the non-target validation set was 15.0 ± 1.8 and 14.6 ± 1.7, respectively; and the mean ± SD of depression and normal in the non-target test set was 15.2 ± 1.4 and 14.7 ± 1.5, respectively. In the three cohorts, there were no significant differences between the depression group and the normal control group in age (non-target discovery set P = 0.178, non-target validation set P = 0.178, non-target test set P = 0.178), sex (non-target discovery set P = 0.178, non-target validation set P = 0.178, non-target test set P = 0.178), or BMI (non-target discovery set P = 0.178, non-target validation set P = 0.178, non-target test set P = 0.178).

[0075] Table 1: Demographic characteristics of the non-target cohort of depressed and healthy controls

[0076]

[0077] In the above table, continuous variables are expressed as mean ± SD, sex was tested by chi-square test, age, BMI, HAMA, and HAMD were tested by Kruskal-Wallis test. Abbreviations: MDD, major depressive disorder; HC, health control; BMI, body mass index; HAMD, 24-item Hamilton Depression Rating Scale; HAMA, 14-item Hamilton Anxiety Rating Scale. The superscript a indicates chi-square test, and the superscript b indicates Kruskal-Wallis test.

[0078] Detailed information on the demographic and clinical characteristics of the recruited targeted cohort participants is provided in Table 2 . A total of 400 child and adolescent participants were recruited, including 200 MDD patients and 200 healthy controls. The ChongQing center included 84 depressed patients and 138 healthy controls; the ChangSha center included 66 depressed patients and 62 healthy controls; and the NanJing center included 50 depressed patients. The age range of the participants was between 11 and 18 years old. In the three cases, there were no significant differences between the depressed and normal control groups in age (P = 0.261 for the targeted training set, P = 0.727 for the targeted prediction set, and P = 0.307 for all subjects), sex (P = 0.884 for the targeted training set, P = 0.767 for the targeted prediction set, and P = 0.917 for all subjects), or BMI (P = 0.906 for the targeted training set, P = 0.263 for the targeted prediction set, and P = 0.422 for all subjects).

[0079] Table 2: Demographic characteristics of the targeted cohort of depressed patients and healthy controls

[0080]

[0081] Example 2: Non-targeted metabolomics study

[0082] (1) Non-targeted metabolomics detection and raw data collation

[0083] (1.1) Main reagents and sample preparation for LC-MS analysis

[0084] Non-target metabolism experimental process Figure 1 B. LC-MS (liquid chromatography-mass spectrometry) grade water (H2O) and methanol (MeOH) were purchased from Honeywell (Muskegon, USA). Ammonium hydroxide (NH4OH) and ammonium acetate (NH4OAc) were purchased from Sigma-Aldrich (St. Louis, USA). Metabolite chemical standards were purchased from J&K (Beijing, China), Sigma (St. Louis, USA), Carbosynth (Berkshire, UK), TCI (Tokyo, Japan), and Energy Chemical (Shanghai, China).

[0085] Human plasma samples (50 μL) were extracted with 150 μL MeOH and internal standards (d3-leucine and d6-phenylalanine). The samples were then shaken for 30 seconds and sonicated for 15 minutes. To precipitate proteins, the samples were incubated at -20°C for 1 hour and then centrifuged at 13,500 rpm and 4°C for 15 minutes. The resulting supernatant was transferred to a high performance liquid chromatography (HPLC) bottle and stored at -80°C for LC-MS / MS analysis.

[0086] (1.2) LC-MS detection

[0087] The LC-MS analysis method is described in the literature (WANG H, JIA H. Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy inpatients with rectal cancer [J]. 2022, 13 (1): 7802.). Data acquisition was performed using a ThermoScientific Vanquish UHPLC system coupled to a Thermo Scientific Orbitrap Exploris 480. LC separation was performed using a Waters ACQUITY UPLC BEH Amide column (particle size 1.7 μm; length 100 mm × inner diameter 2.1 mm) and a Kinetex C18 column (2.6 μm, 2.1 × 100 mm), and the column temperature was maintained at 25 ° C. For HILIC analysis, the mobile phase A in the positive (ESI+) and negative (ESI-) modes was 25 mM ammonium hydroxide (NH4OH) + 25 mM ammonium acetate (NH4OAc) in water, and B was acetonitrile (ACN). The flow rate was 0.5 mL / min, and the gradient was set as follows: 0-0.5 min, 95% B; 0.5-7 min, 95% B to 65% B; 7-8 min, 65% B to 40% B; 8-9 min, 40% B; 9-9.1 min, 40% B to 95% B; 9.1-12 min, 95% B. The injection volume was 2 μL. For reverse phase liquid chromatography (RPLC) analysis, mobile phase A was 0.01% acetic acid in water, B was a mixture of IPA and ACN (1:1), and both positive (ESI+) and negative (ESI-) modes were used. The flow rate was 0.3 mL / min, and the gradient was set as follows: 0-1 min: 1% B; 1-8 min: 99% B; 8-9 min: 99% B; 9.0-9.1 min, 99% B to 1% B; 9.1-12 min: 1% B. The injection volume was 2 μL. All samples were randomly injected during data acquisition. Data acquisition was performed in full MS scan mode in positive / negative ion polarity switching mode. Information dependent acquisition (IDA) mode was used for quality control (QC) samples to obtain MS / MS spectra. The source parameters were set as follows: spray voltage was 3,000 V in positive ion mode and -3,000 V in negative ion mode. Auxiliary gas heater temperature was set to 350 °C. Substrate gas was set to 50 arb. Auxiliary gas was set to 15 arb. Capillary temperature was set to 400 °C. Full MS resolution was set to 60,000 and AGC target was set to 1e6 in positive ion mode. Maximum IT was set to 100 ms. Mass range was set to 70–1,200 Da.For the dd-MS2 settings, the MS resolution was set to 30,000 and the AGC target was set to 1e5. The maximum IT was set to 60 ms. The Top N was set to 6. The isolation width was set to 1.0 m / z Da. The MS / MS spectra of the QC samples were acquired at SNCE 20-30-40% of the QC samples. The dynamic exclusion was set to 3.0 s and the isotope exclusion was turned on.

[0088] (1.3) Metabolomics data processing

[0089] The data processing method can be found in the literature (SHEN X, WANG R, XIONG X, et al. Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics [J]. 2019, 10 (1): 1516.; ZHOU Z, LUO M, ZHANG H, et al. Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking [J]. 2022, 13 (1): 6656.). In brief, the raw MS data (.raw) file was first converted into mzXML format using ProteoWizard (version 3.0.20360). Then, the R software package “xcms” (version 3.2; https: / / bioconductor.org / packages / release / bioc / html / xcms.html) was used to perform peak detection, retention time correction, and peak alignment on the mzXML data file of the sample. The key parameters were set as follows: method, “centWave”; ppm, 10; snthr, 3; peakwidth, c(5,30); minfrac, 0.5. Then, MetFlow software (http: / / metflow.zhulab.cn / ) was used for missing value interpolation and data normalization. In brief, the missing values ​​were interpolated by 10 nearest neighbors using the k-nearest neighbor algorithm, and then the data were normalized using the support vector algorithm based on QC samples to eliminate unwelcome systematic errors occurring in the batch. Metabolite peaks with RSD less than 30% in the QC samples were used for subsequent analysis. Metabolite annotation was performed using MetDNA (version 1.2.2; http: / / metdna.zhulab.cn / ). The metabolite annotation parameters were set to “HILIC” or “RP” according to the liquid chromatography mode, and the collision energy was “30” or “SNCE_20_30_40%”. We performed metabolite annotation separately in both positive and negative modes. According to the definition of the Metabolomics Standards Initiative (MSI), we annotated metabolites by matching MS1, RT, and MS / MS spectra with our own metabolite library, and performed MS1, RT, and MS / MS annotations by matching MS1 and MS / MS2 spectra with public metabolite libraries (mainly from NIST 2017).

[0090] (1.4) Processing batch effects of non-targeted metabolomics data

[0091] Non-target metabolism experimental process Figure 1 As shown in B. We only retained the metabolites that were detected in both the training set and the validation set. Subsequently, we aligned the two batches of metabolites based on the liquid chromatographic mode and ionization polarity of the metabolites. Then, the combat algorithm was used to correct the intensity between the two data sets to reduce batch effects. After correction, metabolites with a difference of more than 40% were removed, resulting in an integrated metabolomics dataset containing 516 metabolites. Finally, in the test set, we also aligned the metabolites with the training set based on the liquid chromatographic mode and ionization polarity of the metabolites, and used the combat algorithm to correct the intensity between the two data sets to obtain an integrated metabolomics dataset containing 470 metabolites.

[0092] (2) Identification of differential metabolites and metabolic pathways between depression and normal groups

[0093] First, we used the Wilcoxon-Mann-Whitney test in both the training and validation sets to determine the differential levels of all metabolites in the comparison between depression and HC. In addition, the fold change (FC) of the metabolites was calculated based on the ratio of the mean ratio of each metabolite. Metabolites with the same trend of change in the Wilcoxon-Mann-Whitney test P < 0.05 in both the training and validation sets were selected as plasma differential metabolites in children and adolescents with depression. Finally, in order to further study the specific metabolic pathways of depression in children and adolescents, we annotated the expressed metabolites according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https: / / www.kegg.jp / ) and drew a simplified pathway diagram of the differential metabolites.

[0094] (3) Screening and evaluation of normal diagnostic criteria for depression

[0095] (3.1) Screening of normal diagnostic criteria for depression

[0096] In order to further screen diagnostic markers from the differential metabolites in plasma of children and adolescents with depression, we randomly divided the training set into a discovery set containing 394 samples (70% of the training set) and an internal validation set containing 169 samples (30% of the training set) for cross-validation. First, we removed metabolites with low signal intensity in both the training set and validation set (<1×10 6). Then, a feature selection process was performed using a genetic algorithm based on the “deap” package in Python. We performed selection, crossover, and mutation operations on the initial feature combinations randomly generated using the genetic algorithm to generate new feature combinations. We retained combinations with high fitness and deleted combinations with low fitness. Fitness values ​​were calculated using logistic regression, and 3-fold cross validation was performed on the discovery set to obtain robust fitness values. We repeated the selection process 40 times (number of features = 10), and then obtained 13 candidates based on the criterion of being selected more than 10 times. In addition, glucuronic acid was also retained due to its good discrimination performance, and cAMP was also screened and retained from low-abundance substances due to its good discrimination performance and potential neurobiological functions.

[0097] Among the 15 candidates, we selected 7 metabolites, namely cyclic adenosine monophosphate (cAMP), picoline, undecenoylcarnitine (Car(11:1), undecenoylcarnitine), histidinol, prolylhydroxyproline, tryptophan, and glucuronic acid, as alternative diagnostic markers based on the discriminatory performance of the model and the colinearity and substitutability of the metabolites (Fisher's exact ptest).

[0098] (3.2) Non-target evaluation of seven normal candidate diagnostic markers for depression

[0099] We also evaluated the degree of difference of the seven diagnostic biomarkers between children and adolescents with depression and HC in the test set by the Wilcoxon-Mann-Whitney test. The relative concentrations of these seven metabolites in the training set, validation set, and test set were visualized by box plots using the "ggplot2" function in R software. Subsequently, we constructed a logistic regression model based on the seven potential biomarkers using the discovery set based on the "glm" function in R software. Subsequently, in the discovery set (70% of the training set), internal validation set (70% of the training set), validation set, and test set, we evaluated the diagnostic efficacy of the logistic regression model based on the seven candidate biomarkers according to the receiver operating characteristic (ROC) curve.

[0100] (4) Differential metabolites and metabolic pathways between depression and normal groups

[0101] First, based on the fold change (FC) of metabolites and the P value of the Wilcoxon-Mann-Whitney test, we identified 333 differentially expressed metabolites in child and adolescent depression from the non-target training set, of which 51 were upregulated and 282 were downregulated ( Figure 2 A). Subsequently, using the same approach, we identified 241 differentially expressed metabolites in the non-target validation set, of which 67 were upregulated and 174 were downregulated ( Figure 2 B). Finally, it was found that there were 160 metabolites that were different in both sets, and 100 of them had the same change trend, of which 5 were upregulated and 95 were downregulated ( Figure 2 C).

[0102] We then used heat maps to visualize the expression of the 100 screened metabolites in the non-target discovery set and non-target validation set, and annotated the major pathways to which the metabolites belonged on the heat maps. Among them, there were 52 metabolites enriched in the lipid metabolism pathway, followed by 25 metabolites in the amino acid metabolism pathway, 4 metabolites in the nucleotide metabolism pathway, 4 metabolites in the cofactor and vitamin metabolism pathway, 3 metabolites in the sugar metabolism pathway, and 12 metabolites in other metabolic pathways ( Figure 3 ). In addition, the simplified pathway diagrams we drew based on the differential metabolites also showed that the vast majority of metabolites in the lipid metabolism pathway, amino acid metabolism pathway, nucleotide metabolism pathway, cofactor and vitamin metabolism pathway, sugar metabolism pathway and other metabolic pathways were downregulated. Interestingly, the main tricarboxylic acid cycle pathway in the sugar metabolism pathway was downregulated, while the bypass pentose pathway was upregulated ( Figure 4 ).

[0103] (5) Identification of candidate diagnostic markers

[0104] Based on genetic algorithms and cross-validation, we considered both collinearity and substitutability. We identified cyclic adenosine monophosphate (cAMP, chemical formula C 10 H 12 N5O6P), picoline (chemical formula C6H7N), undecenoylcarnitine (Car (11:1), chemical formula C 18 H 33 NO4), histidinol (chemical formula C6H 11 N3O), prolylhydroxyproline (prolylhydroxyproline, chemical formula C 10 H 16 N2O4), tryptophan (chemical formula C 11 H 12N2O2), glucuronic acid (chemical formula C6H 10 O7) has a total of 7 candidate diagnostic markers. Among them, glucuronic acid is upregulated in depression; cyclic adenosine monophosphate, methylpyridine, unsaturated undecenoylcarnitine (Car (11:1), histidinol, prolyl hydroxyproline, and tryptophan are downregulated. Except for prolyl hydroxyproline, which had no difference in the non-target test set (P = 0.663), other metabolites were significantly different in the three cohorts ( Figure 5 ). Among them, histidinol, prolyl hydroxyproline, and tryptophan come from the amino acid metabolic pathway; unsaturated undecenoylcarnitine (Car (11:1) comes from the fatty acid metabolic pathway; glucuronic acid comes from the sugar metabolic pathway; cyclic adenosine monophosphate comes from the nucleotide metabolic pathway; methylpyridine comes from other pathways ( Figure 3 , Figure 4 ). Detailed information of these diagnostic markers can be found in Table 3. Among them, the structural formula of unsaturated undecenoylcarnitine is:

[0105]

[0106] Table 3: Metabolite information for depression diagnosis by non-target screening

[0107]

[0108] In the above table, TPvalue refers to the P value of the training set passing the Kruskal-Wallis test; T.FC is the fold change of the training set; VPvalue refers to the P value of the validation set passing the Kruskal-Wallis test; V.FC is the fold change of the validation set.

[0109] (6) Non-target metabolic assessment of seven candidate diagnostic biomarkers

[0110] The logistic regression model containing 7 potential biomarkers constructed based on the non-target discovery set achieved good diagnostic efficacy in the non-target discovery set (70% of the non-target training set), the internal non-target validation set (30% of the non-target training set), the non-target validation set, and the non-target test set. The AUC of the ROC of the non-target discovery set was 0.941, the AUC of the ROC of the internal non-target validation set was 0.919, the AUC of the ROC of the non-target validation set was 0.810, and the AUC of the ROC of the non-target test set was 0.883 ( Figure 6 AB).

[0111] Example 3: Targeted metabolomics study

[0112] (1) Main reagents and sample preparation for targeted metabolic testing

[0113] Targeted metabolic experiment process Figure 1C. The reagents required for the experiment, methanol, ultrapure water, and ammonia water were purchased from HoneyWell, LC-MS grade; acetonitrile was purchased from Merck, LC-MS grade; ammonia water and ammonium acetate were LC-MS grade. The standards used included Picoline, 3-Picoline, (D7, 98%), [13C6]-L-Histidine, Histidinol, Tryptophan, L-Tryptophan, (13C11, 99%), Proylhydroxyproline, L-Proline ((2,5,5-D3), Glucuronic acid, [U-13C6]-D-glucuronic acid, L-Carnitine (D9) (Car (12:1)), Undecenoylcarnitine (Car (11:1)), Car (10:1), cAMP, cAMP_13C5.

[0114] Take 50 μL of plasma sample, place it in a 1.5 mL EP tube, add 150 μL of ice methanol containing internal standard, vortex for 30 seconds; store at -20 ° C for 1 hour to precipitate protein, centrifuge at 17000g, 4 ° C for 15 minutes; take no less than 150 μL of supernatant into the LC-MS injection bottle. The internal standard concentrations in the sample are 3-Picoline, (D7, 98%) 2 ng / mL, L-TRYPTOPHAN, (13C11) 5000 ng / mL, L-Picoline ((2,5,5-D3) 10 ng / mL, [U-13C6]-D-glucuronic acid 50 ng / mL, cAMP_13C5 2 ng / mL, [13C6]-L-Histidine 200 ng / mL, L-Carnitine (D9) (Car (12:1)) 2 ng / mL.

[0115] (2) Targeted metabolomics testing

[0116] LC-MS analysis was performed using an HPLC system (1290 series, Agilent Technologies) and a triple quadrupole mass spectrometer (Agilent 6495QqQ, Agilent Technologies). A Waters, UPLC BEH Amide column (1.7 μm, 2.1×100 mm) was selected. The mobile phase A = 100% H2O + 25 mM CH3COONH4 + 25 mM NH4OH, B = 100% acetonitrile, for ESI positive and negative ion modes. The liquid phase linear gradient was 95% B (0-0.5 min), 95%-65% B (0.5-7 min), 65%-40% B (7-8 min), 40% B (8-9 min), 40%-95% B (9-9.1 min), 95% B (9.1-12 min). The flow rate was 0.5 mL / min, and the sample injection volume was 4 μL. The ESI source conditions were set as follows: sheath gas temp, 350°C; sheath gas flow, 12 L / min; capillary voltage positive 3000 V, negative 2500 V. Table 4 shows the various metabolite information of the MRM method.

[0117] Table 4: Information of various metabolites by MRM method

[0118]

[0119] (3) Targeted metabolomics data processing

[0120] Skyline (version 22.2.0.351) was used to manually integrate the peak areas of metabolites and internal standards. First, the raw data file (.d) and the procursor m / z, product m / z, precursor charge, product charge, and RT of the target metabolite were imported into Skyline, and the target metabolites were manually checked and the integration interval was adjusted. The peak areas of the internal and external standards were exported, the peak area ratio of the external and internal standards was calculated, and the standard curve was established. For the data collected each day, two sets of standard curves were collected at the beginning and end of each day to establish the standard curve of the day and calculate the content in the quantitative sample. Finally, the laboratory's self-compiled R package metflowR (version 0.99.01) was used to perform data standardization on the sample quantification results.

[0121] (4) Multicenter targeted metabolomics differential analysis and final diagnostic model evaluation

[0122] (4.1) Targeted metabolomics differential analysis of candidate diagnostic standards

[0123] Targeted metabolic analysis workflow Figure 1 C. We evaluated the difference between 7 diagnostic biomarkers in children and adolescents with depression and HC in the multicenter targeted cohort by Wilcoxon-Mann-Whitney test. The absolute concentrations of these 7 metabolites were visualized by box plots using the "ggplot2" function in R software. Subsequently, we selected metabolites with Wilcoxon-Mann-Whitney test P < 0.05 in the targeted cohort and consistent change direction in the non-target cohort as the final diagnostic markers. Finally, 3 candidate diagnostic markers were determined as the final diagnostic markers.

[0124] (4.2) Evaluation of diagnostic models for final diagnostic standards

[0125] First, we matched the multicenter targeted cohort into a targeted training set and a targeted prediction set according to demographic information. Based on the "glm" function in R software, we used the targeted training set to construct multiple logistic regression models based on the three final diagnostic markers. Then, in the targeted training set and the targeted prediction set, we evaluated the diagnostic efficacy of the logistic regression model based on the three final diagnostic markers according to the receiver operating characteristic (ROC) curve.

[0126] And in the non-target cohort data, we also built a logistic regression model based on the final three diagnostic markers using the discovery set based on the "glm" function in R software. Then in the discovery set (70% of the training set), internal validation set (30% of the training set), validation set and test set, we evaluated the diagnostic efficacy of the logistic regression model based on the final three diagnostic markers according to the receiver operating characteristic (ROC) curve. Finally, we built a diagnostic model based on the targeted training set. Based on the "glm" function in R software, the best global cur-off value was fitted by Youden Index in the targeted training set.

[0127] (4.3) Correlation analysis between clinical information and three diagnostic markers and subgroup analysis

[0128] The diagnostic marker correlation and subgroup analysis process are as follows Figure 1.1D. We used the “rcorr” function in R software to perform Spearman correlation analysis on the correlation between clinical indicators (grouping, age, BMI, gender, HAMD, and HAMA) and the three final diagnostic markers in three non-target cohorts and one targeted cohort. We finally found that gender and unsaturated undecylenylcarnitine (Car(11:1)) had a stable correlation. Therefore, we further used the Kruskal-Wallis test to perform statistical analysis on the gender subgroups of the subjects in the non-target training set, non-target validation set, non-target test set, and targeted cohort. Finally, we also tried to use the Kruskal-Wallis test to compare the differences between the gender subgroups of depression and normal subjects in subjects from different centers.

[0129] (4.4) Analysis of optimal subgroup-specific cut-off values ​​of the diagnostic model based on Youden Index

[0130] Based on the "glm" function in R software, we tested the sensitivity and 1-specificity of the optimal global (overall) cut-off value in different grouping types, including: the entire targeted cohort, targeted training set, targeted prediction set, different gender groups (male, female) and different enrollment center groups (Chong Qing, Chang Sha and NanJing). Finally, we used Youden Index to fit subgroup-specific cut-off values ​​in different subgroups (different gender groups and different enrollment center groups), and compared the sensitivity and 1-specificity of the model in each subgroup under the global and subgroup-specific cut-off values.

[0131] (4.5) Data Statistical Analysis

[0132] The demographic and clinical characteristics of the participants were analyzed using SPSS. Gender differences were analyzed using the chi-square test, while differences in age, BMI, HAMA score, and HAMD score were analyzed using the Wilcoxon-Mann-Whitney test. All statistical tests in this study were two-sided.

[0133] (5) Targeted metabolic differential validation of seven candidate diagnostic markers

[0134] Through screening of the non-target cohort, we identified seven plasma metabolites, including cyclic AMP, methylpyridine, unsaturated undecenoylcarnitine Car (11:1), histidinol, prolylhydroxyproline, tryptophan and glucuronic acid, as candidate diagnostic markers. Subsequently, we performed targeted metabolomics validation of these seven metabolites using isotope-labeled standards with absolute quantification. Based on the metabolite's P < 0.05 value of the Wilcoxon-Mann-Whitney test in the targeted data, and the fold change (FC) direction was consistent with that in the non-target cohort. We screened three metabolites: cyclic AMP (Down, P < 0.001), unsaturated undecenoylcarnitine (Car (11:1) (Down, P < 0.001) and glucuronic acid (Up, P < 0.001) as the final diagnostic markers ( Figure 7 AC). Among the other four alternative metabolites, methylpyridine (P = 0.125), histidinol (P = 0.503) and tryptophan (P = 0.078) showed no significant difference. Although prolylhydroxyproline (P = 0.006) showed a difference, the change trend was opposite to that in the non-target cohort ( Figure 7 DG). These experimental data also illustrate the accuracy and reliability of targeted metabolomics absolute quantitative research compared to non-targeted metabolomics research. This technical solution is the first targeted metabolomics study on plasma depression markers in children and adolescents. It is the first time that cyclic adenosine monophosphate, unsaturated undecenoylcarnitine (Car (11:1) and glucuronic acid are proposed as joint diagnostic markers. There is no existing technology to refer to. Targeted verification can clarify the objective absolute concentration of metabolites, making the diagnostic formula more reliable and directly clinically promoted. In addition, proline and hydroxyproline have been reported in some literature to change in the plasma of adult patients with depression, and histidine metabolism and tryptophan metabolism are related to adult depression. However, in the population of children and adolescents with depression, the change pattern of amino acid metabolites is different from that of adults, and there is no difference in these substances between children and adolescents with depression and normal people. This is also the special feature of children and adolescents with depression. The situation of this solution cannot be inferred from the changes in metabolites of adult depression.

[0135] The literature records that in non-adolescent populations (e.g., adults over 30 years old), there is no significant difference in the cAMP content of platelets between MDD patients and healthy people (original text: The mean basal cAMP activity was 24.3 ± 13.6nM cAMP / well in the MDD subjects and 27.1 ± 14.15nM cAMP / well in the healthy controls (t = -0.87; p = ns)). The literature also records that when platelets in the blood are stimulated by prostaglandins, the cAMP level of depressed patients does not increase as much as that of normal people. The response of platelets in the blood to prostaglandin stimulation (the ability to release cAMP) can diagnose depression and normality (the difference in cAMP content in adults will only appear under the condition of applying prostaglandins) and can predict the efficacy of antidepressant treatment. It can be seen that in the absence of prostaglandins, there is no difference in cAMP in platelets between MDD patients and healthy people, and the content of cAMP in plasma and or platelets cannot be directly used as a diagnostic marker to distinguish adult MDD patients from healthy people. However, the present technical solution found that in the absence of release stimulation, in the special population of children and adolescents, there is a significant difference in the cAMP content in the plasma of MDD patients and healthy people, which can be directly used as a diagnostic marker, which is unpredictable by existing reports. Among them, the prior art document mentioned in this paragraph is: Steven D. Targum, A novel peripheral biomarker for depression and antidepressant response, Molecular Psychiatry volume 27, pages 1640–1646 (2022). Our research found that cAMP in the plasma of children and adolescents with depression decreased significantly, but previous adult metabolomics studies did not find that cAMP changed in depression plasma. The inventor analyzed that the reason for this phenomenon may be that children and adolescents are in a critical period of rapid growth and development. Growth and development are accompanied by the reconstruction of organs and the body, which requires a large amount of cell decomposition and proliferation. Among them, cell decomposition aggravates the metabolism of purine into uric acid and excretion from the body, and cell proliferation requires a large amount of purine for gene amplification and transcription, which aggravates the body's purine consumption and weakens the protective effect of cAMP on the nervous system. Ultimately, the additional purine metabolism demands of growth and development and adverse mental stimulation work together to lead to the occurrence of depression accompanied by downregulation of purine metabolism.

[0136] In addition, in the inventor's prior patent, the biomarkers of various mental illnesses were studied using a non-targeted metabolomics method. The results showed that cAMP is a member of the biomarker combination used to distinguish adolescent schizophrenia patients from healthy people, and there is no correlation between the plasma content of cAMP and the diagnosis of depression. This shows that the reports of the prior art have formed a negative technical inspiration for this technical solution. This solution overcomes the bias of the prior art and reveals that cAMP is an ideal biomarker for diagnosing depression in adolescent children. The inventor's prior patent application is: CN118010881A A plasma biomarker combination and diagnostic system for diagnosing three mental illnesses in children and adolescents.

[0137] In the same prior patent (CN118010881A), the inventor found that glucuronic acid is a member of the biomarker combination used to distinguish adolescent bipolar disorder patients from healthy people, and the plasma content of glucuronic acid is not correlated with the diagnosis of depression. This also shows that the reports of the prior art have formed a negative technical inspiration for this technical solution. This solution overcomes the bias of the prior art and reveals that glucuronic acid is an ideal biomarker for diagnosing depression in adolescents and children.

[0138] Although it is recorded in the prior patent (CN118010881A) that unsaturated undecenoylcarnitine Car (11: 1) is a marker for diagnosing depression, in this study, unsaturated undecenoylcarnitine Car (11: 1) needs to be combined with 5-aminoacetylacetone acid, ethanol ketone, capric acid, salicylic acid, pyruvic acid, etc., as a biomarker combination, in order to achieve the diagnosis of depression. Whether unsaturated undecenoylcarnitine Car (11: 1) is used as a diagnostic marker alone, whether there is still diagnostic efficacy, can not get technical inspiration from the prior patent. In fact, unsaturated undecenoylcarnitine Car (11: 1) is used alone as a diagnostic marker in the technical solution, and it can not effectively achieve the distinction between depression patients and healthy people. The independent diagnostic efficacy of unsaturated undecenoylcarnitine Car (11: 1) is not even as good as cAMP or glucuronic acid. Unsaturated undecenoylcarnitine Car (11: 1) needs to be used in combination with cAMP and glucuronic acid as a diagnostic marker to effectively achieve the diagnosis of depression.

[0139] (6) Evaluation of the diagnostic efficacy of three diagnostic markers

[0140] Based on the three final biomarkers, we used the logistic regression model to construct the equation (Logit(P) = a1×M cAMP +a2×M Glucuronic +a3×M Car(11:1)+b), specifically: Logit(P) = -0.2102011×M cAMP +0.0024516×M Glucuronic -0.0087530×M Car(11:1) +2.5230796 (b is the intercept (constant), a n is the coefficient of each diagnostic marker; M represents the content value of the metabolite obtained by quantitative detection; P is the risk value of depression (test variable), 0≤P≤1; P is compared with the cut-off value (threshold) to diagnose whether the sample is depressed). The ROC curves of the targeted training set and the targeted prediction set were fitted using this equation. It was found that the absolute quantitative equation had good diagnostic efficacy for both the targeted training set (AUC=0.849) and the targeted prediction set (AUC=0.858) ( Figure 8 A). In the targeted training set, the best cut-off value fitted by Youden Index was 0.534, which had good diagnostic performance in both the targeted training set (1-Specificity=0.800, Sensitivity=0.730) and the targeted prediction set (1-Specificity=0.840, Sensitivity=0.690) ( Figure 8 A).

[0141] In addition, the inventor also used cAMP as a marker alone to form ROC curves for targeted training sets and targeted prediction sets, with AUC=0.776 for the targeted training set and AUC=0.867 for the targeted prediction set. On this basis, cAMP and glucuronic acid were combined as diagnostic markers, and the logistic regression model was used to construct equations to form ROC curves for targeted training sets and targeted prediction sets, with AUC=0.838 for the targeted training set and AUC=0.856 for the targeted prediction set. On this basis, cAMP, glucuronic acid and unsaturated undecenoylcarnitine Car (11: 1) were combined as diagnostic markers to construct logistic regression model construction equations to form ROC curves for targeted training sets and targeted prediction sets, with AUC=0.849 for the targeted training set and AUC=0.858 for the targeted prediction set.

[0142] In addition, for glucuronic acid alone as a marker, ROC curves for the targeted training set and the targeted prediction set were formed, with AUC = 0.698 for the targeted training set and AUC = 0.580 for the targeted prediction set. For unsaturated undecenoylcarnitine Car (11: 1) alone as a marker, ROC curves for the targeted training set and the targeted prediction set were formed, with AUC = 0.669 for the targeted training set and AUC = 0.716 for the targeted prediction set.

[0143] It can be seen from the above experimental results that when cAMP, glucuronic acid or unsaturated undecenoylcarnitine Car (11: 1) are used alone as diagnostic markers, cAMP has the best diagnostic effect. In the targeted training set and the targeted prediction set, cAMP can achieve an AUC value of >0.7 or even >0.8, which meets the requirements of the diagnostic model. However, in the targeted training set, the AUC value of the ROC curve of glucuronic acid or unsaturated undecenoylcarnitine Car (11: 1) is difficult to reach 0.7, and the effect of using it alone as a diagnostic marker is not very ideal. In order to further improve the diagnostic effect, the inventor used a logistic regression model to construct an equation and used two or three diagnostic markers in combination. It was found that the combined use of cAMP and glucuronic acid can effectively increase the AUC value to above 0.8, and the combined use of the three markers can further increase the AUC value to obtain a more ideal diagnostic model.

[0144] Among them, the ROC curve (receiver operating characteristic curve) is used to judge the quality of classification and test results. It is a very important and common statistical analysis method. It is a coordinate graph composed of the false positive rate (Falsepositive rate, 1-specificity) as the horizontal axis and the true positive rate (Truepositive rate, sensitivity) as the vertical axis. The curve is drawn by different results obtained by the test samples under different judgment criteria (thresholds). The area under the curve AUC is used to indicate accuracy. The higher the AUC value, the higher the accuracy. AUC value > 0.7 indicates that the diagnostic model can diagnose the disease more accurately. The value is close to 1, and the effect of the diagnostic model is more ideal.

[0145] In addition, we constructed an equation using a logistic regression model in the non-target discovery set based on the three final diagnostic markers, and used this equation to fit the ROC curves of the non-target discovery set (70% of the non-target training set) (AUC = 0.878), the internal non-target validation set (30% of the non-target training set) (AUC = 0.863), the non-target validation set (AUC = 0.793), and the non-target test set (AUC = 0.833), all of which achieved good diagnostic efficacy ( Figure 8 BC).

[0146] In addition to the combined diagnosis of 3 markers, a combined diagnosis of 7 markers was also performed, and the equation was constructed using a logistic regression model in the targeted training set:

[0147] Logit(P)=-0.2722×M1+0.002628×M2-0.01476×M3+0.002021×M4+0.0005301×M5+0.000555×M

[0148] 6+0.000008763×M7-0.8001. M1-M7 are variables, which are used to input the values ​​of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecylenylcarnitine, methylpyridine, histidyl alcohol, prolylhydroxyproline and tryptophan in plasma. It was found that the absolute quantitative equation has good diagnostic efficacy for both the targeted training set (AUC=0.8807) and the targeted prediction set (AUC=0.8375).

[0149] (7) Correlation analysis between three diagnostic markers and clinical information

[0150] Then, based on the three final diagnostic markers, we used Spearman correlation analysis in three non-target cohorts and one targeted cohort to explore the correlation between clinical indicators (grouping, age, BMI, gender, HAMD and HAMA) and the three final biomarkers. Finally, it was found that gender and unsaturated undecylenylcarnitine (Car (11:1)) had a stable correlation: in the non-target training set, non-target validation set, non-target test set and the entire population (MDD & HC), depressed patients (MDD) and normal controls (HC) in the targeted cohort, among a total of 12 combinations, only the depressed patients in the targeted cohort had no significant correlation, and the other 11 combinations had significant correlations ( Fig. 9 AD). In addition, we found that the three metabolites were correlated with the group information and clinical symptom scale scores (HAMD and HAMA) at the level of the entire population (MDD & HC), but there was no significant difference in the correlation between the three metabolites and the clinical symptom scale scores (HAMD and HAMA) in the individual depressed patients (MDD) and normal controls (HC) ( Fig. 9 AD). This suggests that the correlation between the three metabolites and the clinical symptom scale scores (HAMD and HAMA) is due to the great differences in clinical symptoms in different groups.

[0151] (8) Gender subgroup analysis of carnitine Car(11:1)

[0152] Because in the correlation analysis we found that carnitine Car (11:1) and gender are stably correlated. Therefore, we performed a statistical analysis of the Kruskal-Wallis test on the depression and normal subgroups of different genders in the non-target training set, non-target validation set, non-target test set, and targeted cohort. It was found that in all four populations of non-target training set, non-target validation set, non-target test set, and targeted cohort, the carnitine Car (11:1) of normal men was significantly higher than that of normal women, and the men in the depression group were significantly lower than those in the normal group, and the women in the depression group were also significantly lower than those in the normal group ( Fig.10AC). However, the carnitine Car(11:1) of depressed men was significantly higher than that of depressed women only in the three non-target cohorts. In the targeted cohort, the carnitine Car(11:1) of depressed men tended to be higher than that of depressed women, but it was not significant ( Fig.10 D).

[0153] There are differences in carnitine in patients with depression of different genders between the non-target population and the targeted population. It is not clear whether it is related to the fact that the targeted cohort comes from three different centers, while the non-target cohort is all from Chong Qing. Therefore, it is necessary to further clarify the differences in carnitine changes in depressed populations from different sources. Therefore, we used the Kruskal-Wallis test to compare the gender subgroups in the depression and normal groups of the populations from different centers. It was found that the carnitine Car (11:1) in male depressed patients in the non-target population from Chong Qing was significantly higher than that in females, but there was no difference in the targeted population ( Fig.11 AB). There was no difference in carnitine between genders in the targeted population of depressed patients from Chang Sha ( Fig.11 C). However, among the targeted population of depression patients from Nan Jing, male carnitine Car (11:1) was significantly higher than female ( Fig.11 D).

[0154] (9) Specific cut-off values ​​for gender and enrollment center subgroups

[0155] Through correlation and subgroup analysis, we found that different genders and population sources may affect the extent of downregulation of diagnostic biomarkers (carnitine Car (11:1)) in patients with depression. Therefore, we tried to find subgroup-specific Cut-off values ​​in our absolute quantitative diagnostic model based on gender subgroups and enrollment center source subgroups. We found that the diagnostic effect of the Cut-off value based on the simple male fitting was better than the global Cut-off value directly applied to men, and the diagnostic effect of the Cut-off value based on the simple Chang Sha source sample fitting was better than the direct application of the global Cut-off value. The diagnostic effects of the Cut-off values ​​fitted by other subgroups and the global Cut-off value were not much different (Table 5).

[0156] Table 5: Specific cut-off values ​​for gender subgroups and enrollment center subgroups

[0157]

[0158] Example 4: Evaluation of the combined diagnostic effect of gender and three diagnostic markers

[0159] Based on the three final biomarkers and gender variables, we constructed the equation (Logit(P) = a1×McAMP +a2×M Glucuronic +a3×M Car(11:1) +a4×Sex+b), specifically: Logit(P)=-0.219851×M cAMP +0.002460×M Glucuronic -0.011070×M Car(11:1) -0.671532×Sex+4.026209(b is the intercept, a is the n is the coefficient of each diagnostic marker; M represents the content value of the metabolite obtained by quantitative detection, and Sex is taken as male = 1, female = 2). The ROC curves of the targeted training set and the targeted prediction set were fitted using this equation. It was found that the absolute quantitative equation had good diagnostic efficacy for both the targeted training set (AUC = 0.855) and the targeted prediction set (AUC = 0.852). In the targeted training set, the best Cut-off value fitted by Youden Index was 0.490, and this Cut-off value had good diagnostic performance in both the targeted training set (1-Specificity = 0.800, Sensitivity = 0.790) and the targeted prediction set (1-Specificity = 0.810, Sensitivity = 0.770). After adding the sex variable, the diagnostic effects of the Cut-off value fitted by the subgroup and the global Cut-off value were not much different (Table 6).

[0160] Table 6: Evaluation of diagnostic model for three metabolites combined with gender

[0161]

[0162]

[0163] For the diagnosis of 7 markers combined with gender, the logistic regression model was used to construct the equation in the targeted training set: Logit (P) = -0.2756 × M1 + 0.002628 × M2 - 0.001359 × M3 + 0.001737 × M4 + 0.0006020 × M5 + 0.0007109 × M6 + 0.000009975 × M7 + 0.5543 × M8 - 2.336. M1-M7 are variables, which are used to input the values ​​of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecylenylcarnitine, methylpyridine, histidyl alcohol, prolylhydroxyproline and tryptophan in plasma in turn; M8 is a variable. When considering gender factors, M8 is used to input gender information, with a value of 1 for males and 2 for females. It was found that the absolute quantification equation had good diagnostic efficacy for both the targeted training set (AUC = 0.8806) and the targeted prediction set (AUC = 0.8376).

[0164] Example 5: Other available diagnostic markers and diagnostic marker combinations

[0165] In this study, in addition to the discovery that cyclic adenosine monophosphate, a combination of cyclic adenosine monophosphate and glucuronic acid, a combination of cyclic adenosine monophosphate, glucuronic acid and unsaturated undecylenylcarnitine, and a combination of cyclic adenosine monophosphate, cyclic adenosine monophosphate, unsaturated undecylenylcarnitine, histidinol, prolylhydroxyproline, tryptophan and glucuronic acid can be used as markers for the diagnosis of depression in adolescents and children, other combinations of these seven substances can also be used for the diagnosis of depression in adolescents and children, and achieve good diagnostic results. In this field, it is generally believed that if the AUC value of the ROC curve of the diagnostic model is greater than 0.7, the diagnostic model has the potential for further application.

[0166] In this technical solution, a logistic regression model is constructed using the data set obtained by the targeted metabolome and the non-targeted metabolome. Among them, the data set includes several records (samples), which are from children and adolescents with depression or normal children and adolescents. Each record includes the name of each corresponding diagnostic marker and its content information. If it is necessary to include gender factors to build a diagnostic model, each record also includes gender information. According to the conventional method of the prior art, based on the "glm" function in R software, a logistic regression model based on different markers (combinations) (or based on different markers (combinations) and gender information) is trained and constructed, and the diagnostic efficacy of the logistic regression model based on different markers (combinations) is evaluated according to the receiver operating characteristic (ROC) curve (the evaluation results are shown in Tables 7 and 8). The key point of this study is the establishment of seven biomarkers (and their combinations) studied by metabolomics, as well as the establishment of gender influencing factors, which is also the key technical point for achieving diagnostic model establishment and effective diagnosis. The process of training, fitting, modeling and efficacy evaluation based on the above-mentioned markers and gender information is carried out by conventional means of the prior art.

[0167] More specifically, the logistic regression model is:

[0168] Logit(P)=a1×M1+a2×M2+a3×M3+a4×M4+a5×M5+a6×M6+a7×M7+a8×M8+b;

[0169] Among them, M1-M7 are independent variables, which are used to input the numerical values ​​of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecenoylcarnitine, methylpyridine, histidyl alcohol, prolylhydroxyproline and tryptophan in plasma in sequence; among the independent variables of M1-M7, at least one of the independent variables inputs the numerical value of the content of the corresponding metabolite, and the remaining independent variables are set to 0;

[0170] M8 is the independent variable. When gender is considered, M8 is used to input gender information, with the value of male being 1 and the value of female being 2. When gender is not considered, M8 is set to 0.

[0171] a1-a8 are the coefficients of M1-M8 respectively, and b is a constant; a1-a8 and b can obtain specific values ​​through the specific process of constructing the logistic regression model mentioned above.

[0172] 0≤P≤1, compare the P value with the critical value to achieve diagnosis.

[0173] Table 7: AUC values ​​of logistic regression models of different diagnostic markers (combinations) with or without considering gender factors in targeted metabolomics data

[0174]

[0175]

[0176]

[0177] Table 8: AUC values ​​of logistic regression models of different diagnostic markers (combinations) with or without considering gender factors in non-targeted metabolomics data

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. The application of plasma metabolic diagnostic markers in the construction of a diagnostic system for depression in children and adolescents, characterized in that: The plasma metabolic diagnostic marker includes at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecenoylcarnitine, histidinol, prolyl hydroxyproline, tryptophan and glucuronic acid; the structural formula of the unsaturated undecenoylcarnitine is:

2. The use of the plasma metabolic diagnostic marker according to claim 1 in constructing a diagnostic system for depression in children and adolescents, characterized in that: The plasma metabolic diagnostic marker is cyclic adenosine monophosphate.

3. The use of the plasma metabolic diagnostic marker according to claim 1 in constructing a diagnostic system for depression in children and adolescents, characterized in that: The plasma metabolic diagnostic marker consists of cyclic adenosine monophosphate and glucuronic acid.

4. The use of the plasma metabolic diagnostic marker according to claim 1 in constructing a diagnostic system for depression in children and adolescents, characterized in that: The plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid and unsaturated undecylenylcarnitine.

5. The use of the plasma metabolic diagnostic marker according to claim 1 in constructing a diagnostic system for depression in children and adolescents, characterized in that: The plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, cyclic adenosine monophosphate, unsaturated undecylenylcarnitine, histidinol, prolylhydroxyproline, tryptophan and glucuronic acid.

6. A child and adolescent depression diagnosis system, characterized in that: It includes a data acquisition unit and a diagnostic unit; The data acquisition unit includes a content detection subunit, or includes a content detection subunit and a gender information acquisition subunit; the content detection subunit is used to detect the content of plasma metabolic diagnostic markers in plasma; the plasma metabolic diagnostic markers include at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecenoylcarnitine, histidinol, prolyl hydroxyproline, tryptophan and glucuronic acid; the structural formula of the unsaturated undecenoylcarnitine is: The diagnostic unit is used to obtain the test variable value P for diagnosing depression risk through a logistic regression model: The logistic regression model is: Logit(P)=a1×M1+a2×M2+a3×M3+a4×M4+a5×M5+a6×M6+a7×M7+a8×M8+b; Among them, M1-M7 are independent variables, which are used to input the numerical values ​​of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecenoylcarnitine, methylpyridine, histidyl alcohol, prolylhydroxyproline and tryptophan in plasma in sequence; among the independent variables of M1-M7, at least one of the independent variables inputs the numerical value of the content of the corresponding metabolite, and the remaining independent variables are set to 0; M8 is the independent variable. When gender is considered, M8 is used to input gender information, with the value of male being 1 and the value of female being 2. When gender is not considered, M8 is set to 0. a1-a8 are the coefficients of M1-M8 respectively, and b is a constant; 0≤P≤1, compare the P value with the critical value to achieve diagnosis.

7. A child and adolescent depression diagnosis system according to claim 6, characterized in that: The logistic regression model is constructed by the following method: collecting the plasma content information of the plasma metabolic diagnostic markers in several samples, or collecting the plasma content information and gender information of the plasma metabolic diagnostic markers in several samples at the same time, thereby obtaining a data set; using a logistic regression algorithm to perform model training on the data set to obtain a logistic regression model; The critical value is obtained by the following method: in the data set, a ROC curve is fitted using a logistic regression model, and the value of the test variable corresponding to the maximum value of the Youden index of the ROC curve is the critical value for diagnosis.

8. A child and adolescent depression diagnosis system according to claim 6, characterized in that: Plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid, and unsaturated undecylenylcarnitine: Regardless of gender, the test variable value P for diagnosing depression risk is calculated by the following formula: Logit(P)=-0.2102011×M1+0.0024516×M2-0.0087530×M3+2.5230796. Alternatively, considering gender factors, the test variable value P for diagnosing depression risk is calculated by the following formula: Logit(P)=-0.219851×M1+0.0024516×M2-0.011070×M3-0.671532M8+4.026209.

9. A child and adolescent depression diagnosis system according to claim 6, characterized in that: Plasma metabolic diagnostic markers consist of cyclic adenosine monophosphate, glucuronic acid, unsaturated undecylenylcarnitine, methylpyridine, histidinol, prolylhydroxyproline and tryptophan composition: Regardless of gender, the test variable value P for diagnosing depression risk is calculated by the following formula: Logit(P)=-0.2722×M1+0.002628×M2-0.01476×M3+0.002021×M4+0.0005301×M5+0.00 0555×M6+0.000008763×M7-0.8001; Alternatively, considering gender factors, the test variable value P for diagnosing depression risk is calculated by the following formula: Logit(P)=-0.2756×M1+0.002628×M2-0.001359×M3+0.001737×M4+0.0006020×M5+0.0 007109×M6+0.000009975×M7+0.5543×M8-2.

336.

10. A child and adolescent depression diagnosis system according to any one of claims 6-9, characterized in that: The content detection subunit includes a high performance liquid chromatography-mass spectrometry device.

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