CN115711954A - Urine biomarker combination for diagnosing depression of children and teenagers and application thereof - Google Patents

Abstract

The invention relates to the technical field of neuropsychiatric disease diagnosis markers and diagnosis methods, in particular to a urine biomarker combination for diagnosing children and teenager depression and application thereof. The biomarker combination consists of carnitine compounds, nicotine, guanidinoacetic acid, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid, and can realize the diagnosis of the depression of children and teenagers by detecting the 7 metabolites in urine. The inventor selects the common metabolic difference foreign matter of the medicine group without depression and the medicine group with depression through metabonomics research on a large number of children and teenagers with depression and normal individuals, can be used as a biomarker combination to realize accurate diagnosis of depression, and the AUC value can reach about 0.8 through analysis. The technical scheme solves the technical problem of lack of easy-sampling and-processing biomarkers for the children and teenager depression, and has wide application prospect and ideal application value.

Description

Urine biomarker combination for diagnosing depression of children and teenagers and application thereof

Technical Field

The invention relates to the technical field of neuropsychiatric disease diagnosis markers and diagnosis methods, in particular to a urine biomarker combination for diagnosing children and teenager depression and application thereof.

Background

Depression (Major Depressive Disorder) is an affective Disorder with marked and persistent mood swings, thought retardation and decreased mental activity as the main symptoms. Epidemiological investigations have shown that the prevalence of depression in childhood (under 13 years) is 4.3% and in adolescents (13-18 years) 5.0%. WHO reports in 2014 that depression is the leading cause of juvenile disease and disability, more than half of suicide victims meet the diagnostic criteria for depression at death, increasing the disease burden at this age. In addition, childhood and adolescent depression may be associated with anxiety disorder, sleep disorder, attention deficit and hyperactivity disorder, which may lead to missed diagnosis and misdiagnosis.

Biomarker (Biomarker) refers to a biochemical marker that can mark structural or functional changes or changes that may occur in systems, organs, tissues, cells and subcellular structures, and has a very wide range of uses. Biomarkers can be used for disease diagnosis, to determine disease stage, or to evaluate the safety and effectiveness of new drugs or therapies in a target population. Urine is a good biomarker carrier not only because it is collected non-invasively, but also because it is large in volume, less pre-treated, and convenient to collect. Studies based on adult urine have found that metabolites are changed mainly in glucose metabolism, kynurenine metabolism, tyrosine-phenylalanine metabolism, etc. in patients with adult depression, and thus various markers for adult depression have been found. The applicant reports in the literature (A novel urea synthesis signature for diagnosis of major expression vector expression recorder, J Proteome Res.2013Dec 6 (12): 5904-11.Doi: through GC-MS studies, sorbitol (sorbitol), uric acid (uric acid), azelaic acid (azelaic acid), quinolinic acid (quinolinic acid), hippuric acid (tyrosine), and tyrosine (tyrosine) were found in adult urine as biomarkers for diagnosing adult depression. However, the metabolic pathways reported in the prior art are not exactly the same in juvenile and adult depression. According to the research results, the urine metabolic characteristics of the children and teenager depression are different from those of adults, the related biomarkers reflecting the children and teenager depression are different from various markers of the adult depression, and no biomarker taking urine as a carrier and capable of accurately reflecting the children and teenager depression is found in the prior art.

Disclosure of Invention

The invention aims to provide a urine biomarker combination for diagnosing children and teenager depression, and solves the technical problem that a biomarker which is easy to sample and process and aims at the children and the teenager depression is absent in the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme:

a biomarker combination consisting of carnitine-like compounds, nicotine, guanidinoacetic acid, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid; the molecular structure of the SMILES of the carnitine compound is [ O- ] C (CC (OC (/ C = C/CCCCCCCCCCCCCC (O) = O) = O) C [ N + ] (C) (C) C) = O.

The invention also provides the application of the biomarker combination in the preparation of a diagnostic system, wherein the diagnostic system is used for detecting the contents of carnitine compounds, nicotine, glycocyamine, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid in urine; the molecular structure of the SMILES of the carnitine compound is [ O- ] C (CC (OC (/ C = C/CCCCCCCCCCCCCC (O) = O) = O) C [ N + ] (C) (C) C) = O.

The principle and the advantages of the scheme are as follows:

the inventor finds that the contents of carnitine compounds, nicotine, glycocyamine, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid in urine can objectively reflect whether the depression is affected or not through a large amount of metabonomics research and comparative research on children and teenagers depression patients and normal children and teenagers. Moreover, the seven metabolites are used for diagnosis and evaluation, so that the depression can be reflected more accurately. Based on the relative content of the seven metabolites, the inventor establishes a diagnostic model by Binary logistic regression (Binary logistic regression analysis), and then performs receiver operating characteristic curve analysis (ROC) on the diagnostic efficacy of the diagnostic model, so that the diagnostic model established by the seven independent variables has good diagnostic effect, and the AUC value of the area under the curve can reach nearly 0.8. Therefore, the seven metabolites can be used as biomarkers for diagnosing the depression of children and teenagers and combined with other clinical indexes for diagnosis. In subsequent applications, we can study the results of the present technical scheme (seven biomarkers) in more biological samples. Through the detection of seven biomarkers of more people, a diagnosis model which can more accurately reflect the disease condition is obtained through fitting, and then a diagnosis reference value is obtained to be applied to the practice of practical clinical diagnosis.

At present, more researches are carried out on adult depression, and a diagnosis method for children and teenagers depression is still lacked in the prior art. The pathogenesis and treatment means of adult depression and children and teenager depression are different, and the research result of adult depression has poor guidance on children and teenager depression. According to the technical scheme, the medicines with depression in children and teenagers for non-medication and depression and the health control are researched in groups for the first time, common difference metabolites of the medicines with depression and the medicines with depression are screened out, and the biomarker capable of accurately reflecting the depression of the children and the teenagers is obtained. Different from the research result of combining the medicines without depression and the medicines with depression in the prior art.

In addition, the seven biomarkers for diagnosing the depression of the children and the teenagers are from urine in clinical application, are simple in material taking and noninvasive, and have a certain clinical application value.

Further, a biomarker combination for use in the diagnosis of childhood depression. The biomarker of the technical scheme can be used for diagnosing the depression of children and teenagers. Metabolic pathways of the children and teenager depression are not completely the same as metabolic pathways of the adult depression, urine metabolic characteristics of the children and teenager depression are different from those of adults, related biomarkers reflecting the children and teenager depression are different from various markers of the adult depression, and no biomarker which can accurately reflect the children and teenager depression and takes urine as a carrier is found in the prior art.

Further, a biomarker combination extracted from the urine of an organism. Urine is a good carrier for metabolite biomarkers, which can be collected non-invasively, in large quantities and with little pretreatment.

Further, the diagnostic system comprises a pre-processing unit and a detection unit, the detection unit comprising a liquid chromatography device and a mass spectrometry device.

The detection of the seven different metabolites can be realized by means of conventional gas chromatography, liquid chromatography-mass spectrometry/mass spectrometry and the like. According to the technical scheme, liquid chromatography equipment and mass spectrometry equipment are selected for detection, so that the screening of markers and the characterization of relative contents of substances can be realized, and the purpose of evaluating or diagnosing diseases is finally achieved.

Further, the liquid chromatography apparatus uses a chromatography column including an amide column and a C18 column; when an amide column is used, mobile phase A is an aqueous solution containing 25mM ammonium hydroxide and 25mM ammonium acetate, and mobile phase B is acetonitrile; when a C18 column is used, the mobile phase A is an aqueous solution containing 0.01% of acetic acid, and the mobile phase B is a mixture of 1:1 of isopropanol and acetonitrile.

Further, the parameters of the liquid chromatography apparatus are set to: the column temperature is 25 ℃, the sample injection amount is 4 mu L, and the flow rate is 0.5mL/min;

for the amide column, the gradient was set as follows: 95% mobile phase B in 0-0.5 min; from 95% mobile phase B to 65% mobile phase B for 0.5-7 minutes; from 65% mobile phase B to 40% mobile phase B for 7-8 minutes; 40% flow B for 8-9 minutes; from 40% mobile phase B to 95% mobile phase B for 9-9.1 minutes; and 95% mobile phase B for 9.1-12 minutes;

for the C18 column, the gradient was set as follows: 1% mobile phase B for 0-1 min; 1-8 minutes from 1% mobile phase B to 99% mobile phase B; 99% mobile phase B for 8-9 minutes; 9-9.1 minutes from 99% mobile B to 1% mobile phase B; 1% mobile phase B for 9.1-12 minutes.

Further, the parameters of the mass spectrometry equipment are set as: the spraying voltage is 3500V or-2800V in positive and negative modes; the temperature of the ion transfer tube is 350 ℃; sheath gas 50arb; an assist gas 15arb; the evaporator temperature was 400 ℃.

Further, data acquisition is carried out in full MS scanning mode and dd-MS2 scanning mode; the resolution of full MS scan mode is set to 60000; the AGC target for positive and negative mode is set to 1e6; the maximum IT is set to 100ms; the mass range is set to be 70-1200Da; for dd-MS2 scan mode, resolution is set to 30000, AGC target for positive and negative mode is set to 1e5; maximum IT is set to 60ms; top N is set to 6; the isolation width was set to 1.0; the collision energy is set to be SNCE 20-30-40%; the kinetic exclusion was set to 3.0 seconds and isotope exclusion was turned on.

Further, the pretreatment unit comprises a centrifugal device, an ultrasonic device, a freezing device, a vacuum concentration device, and an aqueous solution of methanol and acetonitrile.

Before the measurement on the machine, the urine sample is centrifuged, the obtained supernatant is mixed with the sample by using methanol, the mixture is further frozen, and the protein in the urine is removed by the precipitation operation. The resulting supernatant was evaporated to dryness in a vacuum concentrator and the dried extract was redissolved with an aqueous solution of acetonitrile and subjected to LC-MS/MS analysis. The operation can fully remove impurities, and ensure the smooth proceeding of subsequent liquid phase and mass spectrometry.

Drawings

FIG. 1 shows the OPLS-DA analysis of AN-MDD and HC subjects from example 1.

FIG. 2 shows the results of OPLS-DA analysis of AT-MDD and HC subjects from example 1.

FIG. 3 is a statistical result of the relative amounts of seven common differential metabolites of example 1.

FIG. 4 is a graph showing the results of MetabioAnalyst metabolic pathway analysis of the AN-MDD group of example 1.

FIG. 5 is a graph showing the results of MetabioAnalyst metabolic pathway analysis of the AT-MDD group of example 1.

FIG. 6 is a ROC curve for the training set samples of example 1.

FIG. 7 is a ROC curve for a sample of the test set of example 1.

Detailed Description

The present invention will be described in further detail with reference to 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 and the like used therein are commercially available.

Example 1:

(1) Test object

192 subjects aged 10-18 years were enrolled in the experiment, with 80 persons suffering from depression medication (AN-MDD), 37 persons suffering from depression medication (AT-MDD), and 75 Healthy Controls (HC). There were no statistical differences in age, gender, BMI between the three groups. See table 1 for details of the subjects.

Table 1: subject information

The abbreviations and superscripts in the above tables are specifically defined as follows:

HC, health controls, healthy controls;

AN-MDD,

major depressive disorder, not to be used in case of depression;

AT-MDD, antidepressant-treated major-depression disorder, medication for depression;

BMI, body mass index;

HAMD-17,17-item Hamilton Depression Rating Scale, item 17 of Hamilton Depression Scale;

HAMA, hamilton Anxiety Scale;

NA, not applicable;

P-value＜0.05versus HC；

P-value＜0.05versus AN-MDD；

^a Chi-square test；

^b Kruskal-Wallis H test；

^c Mann-Whitney U test。

(2) Collection and pretreatment of samples

Subject mid-morning urine was collected in a sterile tube. The urine sample was then centrifuged for 10 minutes (3,000rpm, room temperature). The resulting supernatant was aliquoted and stored at-80 ℃. After completion of the enrollment, 50 μ L of the sample was mixed with 150 μ L of methanol and then vortexed for 30 seconds. To precipitate the proteins, the samples were pelleted at-20 ℃ for 1 hour, then centrifuged for 15 minutes (13000rpm, 4 ℃). The resulting supernatant was evaporated to dryness in a vacuum concentrator. The dried extract was re-dissolved in 100 μ L of reconstitution solution (acetonitrile: water =1, 1,v/v), sonicated for 10 minutes, centrifuged for 15 minutes (13000rpm, 4 ℃). The supernatant was transferred to an HPLC vial and stored at-80 ℃ before LC-MS/MS analysis. Samples (50. Mu.L) were extracted with 150. Mu.L methanol and internal controls (d 3-leucine and d 8-phenylalanine).

(3) Ultra-high performance liquid chromatography-mass spectrometry combined detection parameter setting

Data collection was performed on an ultra-high performance liquid chromatography system (Vanqish UHPLC, thermoFisher Scientific, united States) and used with a mass spectrometer (Orbitrap applications 480, thermoFisher Scientific, united States).

Separation was carried out using a Waters ACQUITY UPLC BEH Amide column (1.7 μm particle size, 100 mm. Times.2.1 mm) at a column temperature of 25 ℃ and a sample injection volume of 4. Mu.L. For the amide column, mobile phase A was an aqueous solution containing 25mM ammonium hydroxide and 25mM ammonium acetate, mobile phase B was acetonitrile, the flow rate was 0.5mL/min, and the gradient was set as follows: 95% (volume percent) mobile phase B in 0-0.5 min; 95% mobile phase B to 65% mobile phase B in 0.5-7 minutes; from 65% mobile phase B to 40% mobile phase B for 7-8 minutes; 40% flow B for 8-9 minutes; from 40% mobile phase B to 95% mobile phase B for 9-9.1 minutes; and 95% mobile phase B for 9.1-12 minutes. And separated using Phenomenex Kinetex C18 column (2.6 μm particle size, 100 mm. Times.2.1 mm), column temperature 25 ℃, sample injection amount 4 μ L. For the C18 column, mobile phase a was an aqueous solution containing 0.01% acetic acid, B was isopropanol and acetonitrile (v: v = 1), the flow rate was 0.5mL/min, and the gradient was set as follows: 1% mobile phase B for 0-1 min; 1-8 minutes from 1% mobile phase B to 99% mobile phase B; 99% mobile phase B for 8-9 minutes; 9-9.1 minutes from 99% mobile B to 1% mobile phase B; 1% mobile phase B for 9.1-12 minutes. During data acquisition, all samples were injected randomly.

The power supply parameters are set as follows: the spraying voltage is 3500V or-2800V in positive and negative modes; the temperature of the ion transfer tube is 350 ℃; sheath gas, 50arb; auxiliary gas, 15arb; evaporator temperature, 400 ℃. Data acquisition is carried out in a full MS scanning mode and a dd-MS2 scanning mode, and the resolution of the full MS scanning mode is set to 60000; the AGC target for positive and negative mode is set to 1e6; the maximum IT is set to 100ms; the mass range was set to 70-1200Da. For dd-MS2 scan mode, resolution is set to 30000, AGC target for positive and negative mode is set to 1e5; the maximum IT is set to 60ms. Top N is set to 6. The isolation width was set to 1.0. The collision energy was set at SNCE 20-30-40%. The kinetic exclusion was set to 3.0 seconds and isotope exclusion was turned on.

(4) Data analysis

The raw mass spectral data (. Raw) obtained in the previous step was converted into mzXML format by ProteWizard (version 3.06150). The R software package "XCMS" (version 3.12) is used for peak detection, retention time correction and peak calibration. The XCMS processing parameters are set as follows: mass accuracy of peak detection =10ppm; peak width c = (5, 30); snthresh =3; mzwid =0.015; minfrac =0.5. The peak tables were uploaded to MetFlow (http:// MetFlow. Zhula. Cn /) for normalization to eliminate unnecessary systematic errors occurring within and between batches. Metabolite annotation was performed using MetDNA. In short, we used internal metabolite spectral libraries for metabolite annotation by matching exact mass, retention time and MS/MS similarity. According to MSI, matched metabolites were considered as a level 1 identification. Metabolite identification at MSI level 2 confidence was achieved by matching exact masses and MS/MS similarities. An external public metabolite pool and a lipid profile pool were used. Other metabolite identifications annotated by MetDNA were considered to be MSI3 grade. MS/MS spectral similarity was calculated using a dot product algorithm and the cutoff was set to 0.8. The number of unique metabolites was calculated using Kyoto Encyclopedia of Genes and Genomes ID. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to visualize the discrimination between comparable groups after log conversion. To exclude non-randomness between groups, 200 permutation tests were performed. Differential metabolites responsible for the discrimination between the two groups were determined by analysis of the OPLS-DA load (predicted Variable Importance (VIP) > 1 and P value < 0.05). MetabioAnalyst (www. Metabioanalysis. Ca) and Ingenity Pathway Analysis (IPA, http:// www. Inguity. Com) were used to further explore the biological functions of the differential metabolites. The common differential metabolite between AN-MDD and AT-MDD was identified as a potential diagnostic biomarker. Receive Operating Characteristic (ROC) analysis was performed to quantify the ability of the metabolite biomarker panel to distinguish MDDs from HCs. The data processing process is a conventional data processing method for metabonomics research.

(5) Analysis of results

(5.1) comparison of Metabolic events between AN-MDD and HC groups

695 and 846 metabolites were identified in positive and negative modes (ESI +/-) respectively by non-target metabolomic analysis. The score plot of the OPLS-DA model showed a clear distinction between AN-MDD and HC subjects (FIG. 1A). Furthermore, permutation tests showed that the established OPLS-DA model was valid and not overfit (fig. 1B). Next, we screened 140 metabolites according to the standard (P value < 0.05 and VIP value > 1), with the differential metabolites exemplified in Table 2. In Table 3, the retention time of characteristic peaks and the characteristic peak area of the differential metabolites between the AN-MDD group and the HC group were counted to determine the change of the content of substances in urine between the diseased and normal persons. In table 2, the peak area ratio is the average of the characteristic peak areas of the specific substances in the AN-MDD group divided by the average of the characteristic peak areas of the specific substances in the HC group, which reflects the variation of the substance content of a specific substance in urine. The P value in Table 2 is a u test performed on the characteristic peak area of a specific substance in the AN-MDD group and the characteristic peak area of a specific substance in the HC group, and P < 0.05 indicates that there is a significant difference; VIP > 1, namely, the differential metabolite is considered to be meaningful, and the value is credible. VIP (Variable immobilized in project) is a Variable weight value of an OPLS-DA model Variable, and can be used for measuring the influence strength and the interpretation capability of accumulation difference of various metabolites on classification and judgment of various groups of samples, and VIP is more than or equal to 1 and is a common differential metabolite screening standard.

Table 2: examples of differential metabolites between the AN-MDD group and the HC group

(5.2) comparison of Metabolic events between AT-MDD and HC groups

The analysis process was kept consistent with the above analysis. The score plot of the OPLS-DA model showed a clear separation between AT-MDD and HC subjects (FIG. 2A). Furthermore, the results of the displacement test showed that the OPLS-DA model was also valid (FIG. 2B). Then, we screened 68 differential metabolites according to the standard (P value < 0.05 and VIP value > 1), and representative differential metabolites are detailed in Table 3.

Table 3: examples of differential metabolites between the AT-MDD group and the HC group

(5.3) screening biomarkers for identifying childhood depression

To investigate urinary biomarkers associated with childhood MDD diagnosis, rather than disease severity, and to avoid the possible effects of antidepressant therapy, common differential metabolites between AN-MDD and AT-MDD were further evaluated. Among 140 differential metabolites of the AN-MDD group and 68 differential metabolites of the AT-MDD group, there were 7 common differential metabolites with a confidence of 1 based on the MetDNA standard (fig. 3, table 4, table 5), and fig. 3 shows that 2 of the peak area of the sample within each group is a log2 (peak area of the target metabolite in the sample), ordinate, and there is no significant difference in the levels of these seven metabolites between the AN-MDD and AT-MDD groups.

Analysis of metabolic pathways in the AN-MDD group using MetabioAnalyst identified six altered pathways: niacin and nicotinamide metabolism, histidine metabolism, taurine and hypotaurine metabolism, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism and arginine biosynthesis (fig. 4), IPA-defined molecular interaction networks are significant alterations of the network of "developmental disorders, genetic disorders, metabolic diseases" and "free radical scavenging, amino acid metabolism, intercellular signaling and interactions". Analysis of metabolic pathways using MetabioAnalyst in the AT-MDD group identified two altered pathways: glyoxylate and dicarboxylic acid metabolism and fatty acid biosynthesis (figure 5), the molecular interaction network defined by IPA is "molecular trafficking, cell signaling, vitamin and mineral metabolism". From the results of functional analysis, the metabolic pathways of patients were significantly changed due to the administration of antidepressant drugs, so that the two groups, AN-MDD and AT-MDD, were greatly different from those of normal persons. According to the routine analytical logic of those skilled in the art, human metabolism is very sensitive to drugs, and taking a drug results in a very large change in metabolic pathways. The results of the analysis of fig. 4 and 5 also fully illustrate the above considerations. If we use metabolites as markers to diagnose the depression of teenagers and children, based on the above knowledge, the detection markers used by patients at the first diagnosis (not taking medicine) should be different from the detection markers used after taking medicine, so as to ensure the accuracy of diagnosis. The inventors compared the differential metabolites of the AN-MDD group (relative to the normal group) and the AT-MDD group (relative to the normal group) after metabonomic analysis, and unexpectedly found the existence of the common differential metabolite, which indicates that 7 common differential metabolites still exist after the medicine taking although the whole metabolic pathway is changed in a skyrocken way, and the prior art does not report any. In order to further verify whether the 7 different metabolites can effectively diagnose the juvenile and child depression, the inventor further applies the 7 common metabolites to a training set and a test set to carry out a diagnosis effect test, finds that the combined diagnosis effect of the 7 common metabolites is very ideal, and can effectively distinguish juvenile and child depression patients from normal people. Therefore, for the reason of taking medicine, the two groups (AN-MDD group and AT-MDD group) have different metabolic pathways compared with normal people, but have common different metabolites between the two groups, and the overall VIP value of the common different foreign matters is ideal, and the combination of 7 common metabolites can realize the diagnosis of the juvenile depression, and the discovery breaks through the conventional cognition in the field.

In addition, in the study, the HAMD-17 values of AN-MDD group and AT-MDD group are similar in the selected sample, which indicates that the overall metabolic pathway is changed by taking the medicine, but the depression is not effectively relieved. The present study found 7 co-metabolites that are essentially linked to the symptoms of depression. The 7 co-metabolites did not form a significant association with the absence of medication, but they could directly reflect a cure for depression. The accuracy of the marker in diagnosing the depression is not influenced by the fact that whether the medicine is taken or not, and if the medicine is taken to promote the relief of depression symptoms, the diagnosis can be reflected in the detection of the marker. The discovery of the above markers has great practical significance. Often, the first visit (patient is not taking medicine) is carried out to confirm whether the children and teenager depression exists, a treatment scheme is established, and then follow-up visits or regular examinations are carried out to determine the recovery condition of the patients. First and follow-up visits typically require the use of different markers or means. However, due to the fact that 7 common metabolites of the technical scheme have essential relation with depression conditions, the common metabolites can be jointly applied to the first diagnosis of depression of children and teenagers and follow-up visits after treatment measures are taken.

Table 4: basic case of 7 common differential metabolites

Table 5: functional description of biomarkers

After completing the above marker screen, we measured 192 subjects as 7: the 3-point was randomized to a training set consisting of 82 MDD patients and 53 HC and a test set consisting of 35 MDD patients and 22 HC. Based on the seven biomarkers, a diagnosis model is established by utilizing binary logistic regression (diagnosis variables (independent variables) are characteristic peak areas of the seven biomarkers, dependent variables are binary variables of MDD and non-patient HC, namely whether the MDD is suffered or not), and then Receiving Operation Characteristic (ROC) analysis is carried out to quantify the diagnosis performance of the biomarker combination containing the seven metabolites. Both the binary logistic regression and ROC analysis were performed using the conventional statistical software of the prior art, SPSS 21.0 (IBM corp., armonk, NY, USA). An ROC curve is used to judge whether a classification result is good or bad, and is a very important and common statistical analysis method, and is a curve drawn by using a coordinate graph composed of a false positive rate (1-specificity) as a horizontal axis and a true positive rate (sensitivity) as a vertical axis, and different results obtained by testing a sample under different judgment standards (thresholds). The area under the curve, AUC, is used to represent accuracy, with higher AUC values indicating higher accuracy. The area under the ROC curve (AUC) values in the training set were 0.820 (95% Confidence Interval (CI): 0.750-0.891; sensitivity: 69.9%; specificity: 88.7%, FIG. 6), and the area under the ROC curve (AUC) values in the testing set were 0.796 (95% Confidence Interval (CI): 0.681-0.911; sensitivity: 68.6%; specificity: 86.4%, FIG. 7).

Comparative example 1

To further optimize the diagnostic effect, the inventors tried to use the relative amounts of the different biomarkers for diagnostic effect analysis, as in the examples, before identifying the 7 common metabolites.

Testing one: nicotine, glycocyamine, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid are selected as markers for diagnosing the juvenile depression, and the AUC value of a diagnostic model is as follows through binary logistic regression and ROC analysis: training set 0.802, test set 0.805.

And (2) testing: CAR 14; the AUC value of a diagnosis model is as follows by binary logistic regression and ROC analysis, wherein the AUC value of the diagnosis model is as follows: training set 0.804, test set 0.787. The markers selected for this test were nicotine deficient compared to the 7 common markers, and the VIP values and ranking of nicotine in table 2 and table 3 were lower, and one skilled in the art would prefer to exclude nicotine from the biomarker combinations. Tests show that the AUC value of a diagnostic model is reduced after nicotine is eliminated, the diagnostic effect is not ideal, and the fact that nicotine is included in the diagnostic marker combination is very necessary.

And (3) testing: CAR 14; the AUC value of the diagnosis model is as follows by binary logistic regression and ROC analysis, wherein the O2, nicotine, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid are used as markers for diagnosing the juvenile depression: training set 0.805, test set 0.743. The markers selected for this test lack guanidinoacetic acid, which is lower in VIP values and ranking in table 2 than the 7 common markers, and the skilled person will tend to exclude guanidinoacetic acid from the biomarker combinations. Tests show that the AUC value of a diagnostic model is reduced after guanidinoacetic acid is eliminated, the diagnostic effect is not ideal, and the guanidinoacetic acid is proved to be very necessary to be included in a diagnostic marker combination.

And (4) testing: CAR 14; the AUC value of a diagnosis model is as follows by binary logistic regression and ROC analysis, wherein the AUC value of the diagnosis model is as follows by taking O2, nicotine, glycocyamine, acrylic acid, xanthurenic acid and dodecanoic acid as markers for diagnosing the juvenile depression: training set 0.772, test set 0.756. The markers selected for this test lack hexaylglycine compared to the 7 common markers, which is lower in VIP values and ranking in table 2, and one skilled in the art would prefer to exclude hexaylglycine from the biomarker combinations. Tests show that the AUC value of a diagnostic model is reduced after hexaacyl glycine is eliminated, the diagnostic effect is not ideal, and the hexaacyl glycine is very necessary to be included in a diagnostic marker combination.

And testing: CAR 14; the AUC value of a diagnosis model is determined by using O2, nicotine, guanidinoacetic acid, hexaacyl glycine, xanthurenic acid and dodecanoic acid as markers for diagnosing the juvenile depression through binary logistic regression and ROC analysis: training set 0.789, test set 0.777. The marker selected for this test lacks acrylic acid compared to the 7 common markers, acrylic acid has a lower VIP value and rank in table 3, and the skilled person would prefer to exclude acrylic acid from the biomarker combination. Tests show that the AUC value of a diagnostic model is reduced after acrylic acid is eliminated, the diagnostic effect is not ideal, and the incorporation of acrylic acid into a diagnostic marker combination is very necessary.

And (6) testing: CAR 14; the AUC values of the diagnosis model by binary logistic regression and ROC analysis are as follows by taking O2, nicotine, guanidinoacetic acid, hexaacyl glycine, acrylic acid and dodecanoic acid as markers for diagnosing the juvenile depression: training set 0.811, test set 0.800.

And test seven: CAR 14; the AUC values of the diagnosis model are as follows by binary logistic regression and ROC analysis, wherein the O2, nicotine, guanidinoacetic acid, hexaacyl glycine, acrylic acid and xanthurenic acid are used as markers for diagnosing the juvenile depression: training set 0.817, test set 0.770. Compared with 7 common markers, the marker selected by the test lacks dodecanoic acid, and the AUC value of a diagnostic model is reduced after acrylic acid is eliminated through the test, so that the diagnostic effect is not ideal, and the fact that acrylic acid is included in the diagnostic marker combination is proved to be very necessary.

In conclusion, the combination of only and only seven biomarkers can have more excellent diagnostic efficacy.

The foregoing is merely an example of the present invention and common general knowledge in the art of designing and/or characterizing particular aspects and/or features is not described in any greater detail herein. It should be noted that, for those skilled in the art, without departing from the technical solution of the present invention, several variations and modifications can be made, and these should also be considered as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.

Claims (10)

1. A biomarker combination, which is characterized by consisting of carnitine compounds, nicotine, glycocyamine, hexaacyl glycine, acrylic acid, xanthurenic acid and dodecanoic acid; the molecular structure of the SMILES of the carnitine compound is [ O- ] C (CC (OC (/ C = C/CCCCCCCCCCCCCC (O) = O) = O) C [ N + ] (C) (C) C) = O.

2. A biomarker combination according to claim 1, for use in the diagnosis of depression in children or adolescents.

3. A biomarker combination according to claim 2, which is extracted from urine.

4. Use of a biomarker combination according to any of claims 1 to 3 in the preparation of a diagnostic system for the detection of the content of carnitine compounds, nicotine, guanidinoacetic acid, hexa-acylglycine, acrylic acid, xanthurenic acid and dodecanoic acid in urine; the molecular structure of the SMILES of the carnitine compound is [ O- ] C (CC (OC (/ C = C/CCCCCCCCCCCCCC (O) = O) = O) C [ N + ] (C) (C) C) = O.

5. Use of a biomarker combination according to claim 4 in the preparation of a diagnostic system comprising a pre-processing unit and a detection unit comprising a liquid chromatography device and a mass spectrometry device.

6. Use of a biomarker combination according to claim 5, in the manufacture of a diagnostic system, wherein the liquid chromatography apparatus uses chromatography columns comprising an amide column and a C18 column; when an amide column is used, mobile phase A is an aqueous solution containing 25mM ammonium hydroxide and 25mM ammonium acetate, and mobile phase B is acetonitrile; when a C18 column is used, the mobile phase A is an aqueous solution containing 0.01% of acetic acid, and the mobile phase B is a mixture of 1:1 of isopropanol and acetonitrile.

7. Use of a biomarker combination according to claim 6, wherein the parameters of the liquid chromatography device are set to: the column temperature is 25 ℃, the sample injection amount is 4 mu L, and the flow rate is 0.5mL/min;

for the amide column, the gradient was set as follows: 95% mobile phase B in 0-0.5 min; from 95% mobile phase B to 65% mobile phase B for 0.5-7 minutes; from 65% mobile phase B to 40% mobile phase B for 7-8 minutes; 40% flow B for 8-9 minutes; from 40% mobile phase B to 95% mobile phase B for 9-9.1 minutes; and 95% mobile phase B for 9.1-12 minutes;

for the C18 column, the gradient settings were as follows: 1% mobile phase B for 0-1 min; 1-8 minutes from 1% mobile phase B to 99% mobile phase B; 99% mobile phase B for 8-9 minutes; 9-9.1 minutes from 99% mobile B to 1% mobile phase B; 1% mobile phase B for 9.1-12 minutes.

8. Use of a biomarker panel according to claim 7 in the manufacture of a diagnostic system, wherein the parameters of the mass spectrometry equipment are set as: the spraying voltage is 3500V or-2800V in positive and negative modes; the temperature of the ion transfer tube is 350 ℃; sheath gas 50arb; an assist gas 15arb; the evaporator temperature was 400 ℃.

9. Use of a biomarker combination according to claim 5 in the manufacture of a diagnostic system, wherein data acquisition is performed in a fullMS scan mode and a dd-MS2 scan mode; the resolution of the fullMS scan mode is set to 60000; the AGC target for positive and negative mode is set to 1e6; the maximum IT is set to 100ms; the mass range is set to be 70-1200Da; for dd-MS2 scan mode, resolution is set to 30000, AGC target for positive and negative mode is set to 1e5; maximum IT is set to 60ms; top N is set to 6; the isolation width was set to 1.0; the collision energy is set to be SNCE 20-30-40%; the kinetic exclusion was set to 3.0 seconds and isotope exclusion was turned on.

10. Use of a biomarker combination according to claim 5, in the manufacture of a diagnostic system, wherein the pre-treatment unit comprises a centrifuge, ultrasound, refrigeration, vacuum concentration, an aqueous solution of methanol and acetonitrile.

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