Depression marker combination, detection system and application

Abstract

The invention belongs to the technical field of depression metabolism markers, and particularly relates to a depression marker combination, a detection system and application, the marker combination comprises at least three of gamma-aminobutyric acid, glutamic acid, adenosine cyclophosphate, acyl carnitine (11: 1), glucuronic acid and 1-pyrrolidine-5-carboxylic acid (P5C), and the depression detection system comprises the marker combination; a liquid chromatograph, a mass spectrometer or a liquid chromatography-tandem mass spectrometer; the specific metabolic marker combination of a subject is detected, so that the depression is accurately detected, the sensitivity is high, convenience and high efficiency are realized, and the method can be applied to preparation of products for diagnosing, preventing or treating the depression; comprising a drug for preventing or treating depression, a depression diagnosis reagent, a depression diagnosis kit, a depression diagnosis chip and a depression auxiliary diagnosis system.

Description

Technical Field

[0001] The present invention belongs to the technical field of depression metabolic markers, and specifically relates to a depression marker combination, a detection system and applications. Background Art

[0002] Depression is a common, chronic, and severe mental illness. Its clinical manifestations include persistent low mood, low self-esteem, slowed thinking, attention deficit disorder, and altered social behavior. Its pathogenesis is complex and its clinical manifestations are diverse, severely impacting patients' physical, mental, and social well-being.

[0003] However, depression diagnosis is currently primarily performed through traditional interviews, including psychiatric interviews, psychological questionnaires, and inquiries about family history. This diagnostic approach faces two challenges: a shortage of trained medical personnel, and labor-intensive, clinical interview-based screening, which requires significant effort from medical staff; and a lack of objective, multi-criteria diagnostic accuracy, relying almost entirely on verbal patient-reported symptoms using questionnaires such as the clinician-administered Hamilton Depression Rating Scale (HAMD) and the self-reported Patient Health Questionnaire-9 (PHQ-9). Consequently, this diagnostic approach is highly susceptible to missed or misdiagnoses of depression.

[0004] At this stage, there is an urgent need for a screening technology that targets objectively existing diagnostic markers. This will allow for earlier and more accurate identification of depressive symptoms, shorten the diagnosis and treatment process, reduce misdiagnosis rates, enable timely intervention and treatment measures, and improve treatment success rates.

[0005] Mainstream hypotheses regarding the etiology of depression include the monoamine neurotransmitter hypothesis, the hypothalamic-pituitary-adrenal axis hypothesis, the neurotrophic hypothesis, and the glutamate hypothesis. Therefore, research on biomarkers for depression currently covers multiple areas, including monoamine neurotransmitters, inflammatory cytokines, neurotrophic factors, the hypothalamic-pituitary-adrenal axis (HPA axis), and brain-derived neurotrophic factor (BDNF).

[0006] Although some studies have shown that some metabolites are correlated with the development of depression, patent CN116482380A proposes a marker combination for auxiliary diagnosis of depression. The metabolite markers are: prostaglandin E2, leukotriene C4, arachidonic acid, lecithin, cephalin, creatine, oleamide, glycine, indolesulfonic acid, testosterone, lactic acid, 6-phospho-glucose, leucine and valine. The judgment method in this patent is that if the levels of prostaglandin E2, leukotriene C4, arachidonic acid, lecithin (16:0 / 16:0), hemolysolecithin (18:1(9Z) / 0:0), cephalin (14:0 / 16:0), creatine, oleamide and glycine are higher than normal levels, while the levels of indolesulfonic acid, testosterone, lactic acid, 6-phospho-glucose, leucine and valine are lower than normal levels, it can be determined that the subject has depression. For the above combination, if it is used in clinical practice, it is necessary to detect 15 metabolites at the same time, and if the test results of the 15 metabolites are not within the claimed range, the results cannot be judged. In addition, although there are many articles, reports, and patent results involving depression detection and diagnosis methods, there are very few solutions that are actually used in clinical practice. Therefore, exploring biomarkers closely related to depression and developing technologies that can be used for sensitive, convenient, efficient, and accurate detection of such biomarkers have become one of the key issues that need to be urgently addressed in the current field of early warning, screening, and objective diagnosis of depression. Summary of the Invention

[0007] In order to solve the problems in the prior art, the present invention provides a depression marker combination, a detection system and an application, which can achieve the purpose of accurate, sensitive, convenient and efficient detection of depression by detecting a specific metabolic marker combination of a subject.

[0008] The present invention solves the technical problem by adopting the following technical solutions:

[0009] The present invention aims to provide a depression marker combination, which includes at least three of gamma-aminobutyric acid, glutamate, cyclic adenosine monophosphate, acylcarnitine (11:1), glucuronic acid and 1-pyrrolidine-5-carboxylic acid (P5C).

[0010] Furthermore, the marker combination includes gamma-aminobutyric acid, glutamate and cyclic adenosine monophosphate.

[0011] A depression detection system comprising the marker combination of claim 1 or 2.

[0012] Furthermore, it also includes a liquid chromatograph, a mass spectrometer or a liquid chromatography tandem mass spectrometer.

[0013] Furthermore, liquid chromatography tandem mass spectrometry was used to detect the concentration of each marker in the marker combination.

[0014] Furthermore, the liquid chromatography tandem mass spectrometry used an amide column with a column temperature of 30°C, an aqueous solution containing 25 mM ammonium hydroxide and ammonium acetate as mobile phase A, and acetonitrile as mobile phase B, with gradient elution at a flow rate of 0.3 mL / min and an injection volume of 5 μL.

[0015] Furthermore, the elution program was: 0-1 min, 5% mobile phase B; 1-3 min, 5-10% mobile phase B; 3-6 min, 10-65% mobile phase B; 6-16 min, 65-95% mobile phase B; 16-17.5 min, 95% mobile phase B; 17.5-18 min, 95-5% mobile phase B; 18-21 min, 5% mobile phase B.

[0016] The test sample is human serum or plasma. Sample preparation involves mixing the plasma sample with a methanol solution, freezing it, and removing the protein from the plasma through precipitation. The resulting supernatant is evaporated to dryness in a vacuum concentrator, and the dried extract is then reconstituted with a methanol solution.

[0017] The mass spectrometry parameters were as follows: operating in electrospray ionization source ESI+ and ESI- modes, temperature 550°C, sheath gas flow rate and auxiliary gas flow rate (both nitrogen) 35 L / min and 10 L / min respectively, and the scanning mass range was 50-1500 m / z.

[0018] Furthermore, an evaluation model is also included, which includes a risk index Logpit (P) and a model threshold; Logpit (P) = 2.371 + A1*C1 + A2*C2 + A3*C3 + ... + A n* C n , among which A1, A2, A3...A n Represents the fitting correction coefficient, C1, C2, C3...C n The detection concentration of the substances in the representative marker combination, the model threshold is 2.36, the risk index < the model threshold, is judged as a low risk of depression; the risk index > the model threshold, is judged as a high risk of depression.

[0019] Furthermore, Logpit(P)=2.371+1.923*C1-0.596*C2+1.358*C3, wherein C1, C2, and C3 represent the detection concentrations of γ-aminobutyric acid, glutamic acid, and cyclic adenosine monophosphate, respectively, and the unit is nmoL / mL.

[0020] The use of a depression marker combination or a depression detection system in the preparation of products for diagnosing, preventing or treating depression, including one or more of drugs for preventing or treating depression, depression diagnostic reagents, depression diagnostic kits, depression diagnostic chips, and depression auxiliary diagnostic systems.

[0021] Compared with the prior art, the beneficial technical effects of the present invention are:

[0022] 1. The present invention provides biomarkers closely related to depression. The combination of metabolic markers can be used for auxiliary screening of depression. It has high specificity, high sensitivity, and accurate detection. It can identify depressive symptoms earlier and more accurately, shorten the diagnosis and treatment process, reduce the misdiagnosis rate, take timely intervention and treatment measures, and improve the success rate of treatment.

[0023] 2. The detection system of the present invention is easy to use and efficient, providing a better technical solution for early warning, screening and objective diagnosis of depression.

[0024] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above contents of the present invention and its objectives, features and advantages more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the area under the curve (AUC) graph of marker combination 1 in the present invention. DETAILED DESCRIPTION

[0026] The technical solutions of the present invention are further described in detail below with reference to specific embodiments. It should be understood that the following embodiments are merely exemplary illustrations and explanations of the present invention and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above content of the present invention are encompassed within the scope of protection that the present invention is intended to protect.

[0027] In addition, unless otherwise specified, various raw materials, reagents, instruments and equipment used in the present invention can be purchased from the market or prepared by existing methods.

[0028] Example 1: Screening of metabolic markers

[0029] (1) Subject recruitment: A total of 300 subjects were recruited, ranging in age from 12 to 65 years, including 181 patients with major depressive disorder (MDD) and 119 healthy controls (HC). Detailed information about the subjects is shown in Table 1.

[0030] Table 1 Subject information

[0031] HC (n=119) MDD (n=181) p-value Gender (male / female) 54 / 65 65 / 116 2.26E-01 Age (years) 38±10.23 32±12.31 1.02E-03 BMI (kg / m2) 22.98±3.22 23.54±2.65 2.57E-02 HAMD-17 2.38±1.57 21.80±5.10 0.00 HAMA 3.59±1.61 21.20±5.22 0.00 Course of disease (months) NA 48(0.5-240) / .

[0032] The abbreviations in the above table have the following meanings:

[0033] BMI: body mass index;

[0034] HAMD-17: Hamilton Depression Rating Scale;

[0035] HAMA: Hamilton Anxiety Rating Scale;

[0036] NA: Not applicable.

[0037] (2) Sample collection

[0038] On the day following enrollment, fasting blood samples were collected from participants in the morning using EDTA-anticoagulated vacuum blood collection tubes. Gently invert the tubes several times to thoroughly mix, and the tubes were centrifuged in a low-temperature centrifuge (centrifugation conditions: 8000 rpm, 4°C, 10 min). After centrifugation, the supernatant was aliquoted and stored at -80°C.

[0039] (3) Sample pretreatment

[0040] Take 50 μL of plasma in a 1.5 mL centrifuge tube and add 800 μL of internal standard 13 C methanol solution, shake for 10 minutes, precipitate the sample at -20℃ for 1 hour, and then centrifuge at 18000 rpm for 10 minutes (at 4℃), aspirate 700 μL of the supernatant into a new 1.5 mL centrifuge tube, evaporate to dryness in a vacuum concentrator, and store at -80℃ until use.

[0041] Before injection, add 150 μL of ice methanol to the above centrifuge tube, shake for 15 minutes, centrifuge at 18000 rpm for 10 minutes (at 4°C), draw 100 μL of supernatant into a new 1.5 mL centrifuge tube, centrifuge again at 18000 rpm for 10 minutes (at 4°C), draw 50 μL of supernatant into an HPLC vial and wait for injection.

[0042] (4) Liquid chromatography tandem mass spectrometry detection system parameter settings

[0043] An amide column was used for elution at 30°C, with a gradient elution using 25 mM ammonium hydroxide and ammonium acetate in water as mobile phase A and acetonitrile as mobile phase B. The flow rate was 0.3 mL / min, and the injection volume was 5 μL. The elution program was as follows: 0-1 min, 5% mobile phase B; 1-3 min, 5-10% mobile phase B; 3-6 min, 10-65% mobile phase B; 6-16 min, 65-95% mobile phase B; 16-17.5 min, 95% mobile phase B; 17.5-18 min, 95-5% mobile phase B; 18-21 min, 5% mobile phase B. Electrospray ionization was performed in ESI+ and ESI- modes at 550°C, with sheath and auxiliary gas flows (both nitrogen) of 35 L / min and 10 L / min, respectively, and the mass range scanned was 50-1500 m / z.

[0044] (5) Data processing and analysis

[0045] The raw mass spectrometry data were preprocessed using the instrument's own software for peak extraction, peak alignment, and normalization to reduce errors introduced during the experiment. MetaboAnalyst 5.0 was used to perform statistical analysis on the preprocessed data. Metaboanalyst 5.0 is a web-based platform with a rich database of compounds and pathways. It has been widely used for comprehensive metabolomics data analysis, interpretation, and multi-omics data integration of various species. The data of the present invention were mainly classified by partial least squares discriminant analysis (PLS-DA), and differential metabolites were screened by combining P value and fold change (FC).

[0046] Example 2: Comparison of diagnostic efficacy

[0047] In order to further optimize the effect of auxiliary diagnosis, the present invention attempts to use the detection content of different metabolite marker combinations to compare the diagnostic effects.

[0048] Based on these six metabolite markers, a diagnostic model was established using logistic regression, with the characteristic abundance of the metabolite markers as the independent variable and the dependent variable being MDD patients and healthy controls. Receiver operating characteristic (ROC) curve analysis was then performed to quantify the diagnostic performance.

[0049] The ROC curve is a very important and common statistical analysis method used to evaluate the quality of classification and test results. It is a coordinate graph with the false positive rate (1-specificity) on the horizontal axis and the true positive rate (sensitivity) on the vertical axis. The curve is drawn by testing samples and then using different judgment criteria (thresholds) to obtain different results. The area under the curve (AUC) is used to indicate accuracy. Higher AUC values ​​indicate higher accuracy.

[0050] Combination 1:

[0051] γ-Aminobutyric acid, glutamate and cyclic adenosine monophosphate were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.916, with a 95% confidence interval of 0.858-0.961.

[0052] Combination 2:

[0053] Glutamate, cyclic adenosine monophosphate and acylcarnitine (11:1) were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.814, with a 95% confidence interval of 0.742-0.877.

[0054] Combination three:

[0055] Cyclic adenosine monophosphate, acylcarnitine (11:1) and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.723, with a 95% confidence interval of 0.655-0.799.

[0056] Combination 4:

[0057] Acylcarnitine (11:1), glucuronic acid, and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.756, with a 95% confidence interval of 0.685-0.802.

[0058] Combination 5:

[0059] γ-Aminobutyric acid, glutamate and acylcarnitine (11:1) were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.714, with a 95% confidence interval of 0.642-0.782.

[0060] Combination six:

[0061] γ-Aminobutyric acid, glutamate, and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.745, with a 95% confidence interval of 0.683-0.797.

[0062] Combination Seven:

[0063] γ-Aminobutyric acid, glutamate and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.678, with a 95% confidence interval of 0.597-0.735.

[0064] Combination Eight:

[0065] γ-Aminobutyric acid, cyclic adenosine monophosphate and acylcarnitine were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.737, with a 95% confidence interval of 0.684-0.785.

[0066] Combination Nine:

[0067] γ-Aminobutyric acid, cyclic adenosine monophosphate, and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.785, with a 95% confidence interval of 0.711-0.851.

[0068] Combination 10:

[0069] γ-Aminobutyric acid, cyclic adenosine monophosphate and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.812, with a 95% confidence interval of 0.722-0.901.

[0070] Group 11:

[0071] γ-Aminobutyric acid, acylcarnitine and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.842, with a 95% confidence interval of 0.756-0.922.

[0072] Combination 12:

[0073] γ-Aminobutyric acid, acylcarnitine and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.489, with a 95% confidence interval of 0.402-0.526.

[0074] Combination Thirteen:

[0075] γ-Aminobutyric acid, glucuronic acid and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.376, with a 95% confidence interval of 0.299-0.436.

[0076] Combination 14:

[0077] Glutamate, cyclic adenosine monophosphate, and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.654, with a 95% confidence interval of 0.563-0.721.

[0078] Combination 15:

[0079] Glutamate, cyclic adenosine monophosphate and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers. After logistic regression and ROC analysis, the AUC value of the diagnostic model was 0.582, with a 95% confidence interval of 0.501-0.654.

[0080] Combination 16:

[0081] Glutamate, acylcarnitine and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.657, with a 95% confidence interval of 0.584-0.628.

[0082] Combination 17:

[0083] Glutamate, acylcarnitine and glucuronic acid were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.528, with a 95% confidence interval of 0.452-0.604.

[0084] Combination 18:

[0085] Glutamic acid, acylcarnitine and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the AUC value of the diagnostic model was 0.684, with a 95% confidence interval of 0.609-0.768.

[0086] Combination 19:

[0087] Cyclic adenosine monophosphate, acylcarnitine and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the diagnostic model AUC value was 0.857, with a 95% confidence interval of 0.779-0.915.

[0088] Combination 20:

[0089] Cyclic adenosine monophosphate, glucuronic acid and 1-pyrrolidine-5-carboxylic acid (P5C) were selected as metabolic markers for depression screening. After logistic regression ROC analysis, the diagnostic model AUC value was 0.712, with a 95% confidence interval of 0.629-0.797.

[0090] In summary, combination 1, i.e., γ-aminobutyric acid, glutamate, and cyclic adenosine monophosphate, as a combination of metabolic markers for depression screening has better diagnostic efficacy.

[0091] Example 3: Validation

[0092] 500 subjects, aged 12-65 years, with different marker screening criteria, were enrolled, including 281 patients with major depressive disorder (MDD) and 219 healthy controls (HC). Sample processing was the same as in Example 1, with marker combinations 1-4 from Example 2 selected as the marker combinations. Receiver operating characteristic (ROC) curves were plotted to analyze the AUC values, sensitivity, and specificity of the marker diagnostic indicators to determine the diagnostic efficacy of the marker combinations, as shown in Table 2.

[0093] Table 2 Diagnostic efficacy of metabolic marker combinations

[0094]

[0095]

[0096] As shown in Table 2, the area under the curve (AUC) of the metabolic marker combination 1 (γ-aminobutyric acid, glutamate and cyclic adenosine monophosphate) ( Figure 1 ) was 95.2%, specificity was 96.7%, and sensitivity was 91.9%, all of which were superior to other marker combinations. The metabolic marker combination of the present invention can be used for auxiliary screening of depression, with high specificity, high sensitivity, and accurate detection. The detection system is convenient and efficient to use, providing a better solution for early warning, screening and objective diagnosis of depression.

[0097] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0098] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A combination of depression markers, characterized in that: The marker combination includes at least three of gamma-aminobutyric acid, glutamate, cyclic adenosine monophosphate, acylcarnitine, glucuronic acid and 1-pyrrolidine-5-carboxylic acid.

2. A depression marker combination according to claim 1, characterized in that: The marker panel included gamma-aminobutyric acid, glutamate, and cyclic adenosine monophosphate.

3. A depression detection system, characterized by: Comprising the marker combination of claim 1 or 2.

4. A depression detection system according to claim 3, characterized in that: Also included is a liquid chromatograph, a mass spectrometer, or a liquid chromatography-tandem mass spectrometer.

5. A depression detection system according to claim 4, characterized in that: The concentration of the marker combination was detected by liquid chromatography-tandem mass spectrometry.

6. A depression detection system according to claim 5, characterized in that: Liquid chromatography tandem mass spectrometry used an amide column with a column temperature of 30°C, an aqueous solution containing 25 mM ammonium hydroxide and ammonium acetate as mobile phase A, and acetonitrile as mobile phase B. Gradient elution was performed at a flow rate of 0.3 mL / min and an injection volume of 5 μL.

7. A depression detection system according to claim 6, characterized in that: The elution program was: 0-1 min, 5% mobile phase B; 1-3 min, 5-10% mobile phase B; 3-6 min, 10-65% mobile phase B; 6-16 min, 65-95% mobile phase B; 16-17.5 min, 95% mobile phase B; 17.5-18 min, 95-5% mobile phase B; 18-21 min, 5% mobile phase B.

8. A depression detection system as claimed in claim 3, characterized in that: The evaluation model is also included, and the evaluation model includes the risk index Logpit (P) and the model threshold; Logpit (P) = 2.371 + A1*C1 + A2*C2 + A3*C3 + ... + A n* C n , among which A1, A2, A3...A n Represents the fitting correction coefficient, C1, C2, C3...C n The detection concentration of the substances in the representative marker combination, the model threshold is 2.36, the risk index < model threshold, is judged as a low risk of depression; the risk index > model threshold, is judged as a high risk of depression.

9. A depression detection system according to claim 8, characterized in that: Logpit(P)=2.371+1.923*C1-0.596*C2+1.358*C3, wherein C1, C2, and C3 represent the detection concentrations of γ-aminobutyric acid, glutamic acid, and cyclic adenosine monophosphate, respectively.

10. Use of a depression marker combination according to claim 1 or 2 or a depression detection system according to any one of claims 3 to 9 in the preparation of a product for diagnosing, preventing or treating depression, characterized in that: It includes one or more of drugs for preventing or treating depression, depression diagnostic reagents, depression diagnostic kits, depression diagnostic chips, and depression auxiliary diagnostic systems.

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