Metabolic biomarker panels for depression diagnosis

Abstract

The present invention discloses a metabolic biomarker combination for depression diagnosis, including proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, and 2-hydroxybutyric acid. Based on the metabolic biomarker combination, the optimal diagnostic model is screened through a machine learning model algorithm, and a kit for depression diagnosis is constructed.

Description

Technical Field

[0001] The present invention relates to the field of metabolomics, and in particular, to a metabolic biomarker combination for the diagnosis of depression. Background Art

[0002] The low recognition rate of depression in the medical system is mainly because the diagnosis of depression in the clinic is mainly based on medical history, clinical symptoms, and course of the disease. At present, the diagnosis of depression mainly relies on the patient's self-reported symptoms and the results of clinical interviews. The diagnostic process is relatively subjective, which also leads to problems such as low treatment rate and high recurrence rate of depression. The internationally accepted diagnostic standards are ICD-10 and DSM-V. Moreover, there is a common phenomenon of low clinical consultation rate among patients with depression. Therefore, for the prevention and treatment of depression, an objective detection project is needed as a screening tool for depression management, which can not only advance the "defense line" of depression and actively discover a large number of potential patients with depression, but also effectively supplement the overly subjective judgment methods of existing clinical diagnosis of depression.

[0003] Studies have shown that patients with depression have poor medication compliance, with a relapse rate of about 33% within one year, which seriously affects the treatment effect and treatment outcome of patients with depression. After returning to society, patients usually relapse from time to time, making it difficult to determine the treatment endpoint of depression. Patients with depression at different stages require different treatment methods and medications. Most patients cannot comply with the full course of treatment strategy, which adds difficulties to clinical work. Therefore, it is necessary to monitor the course of depression patients, including treatment outcomes and relapse prediction, which is particularly important for providing timely and effective treatment for patients with depression.

[0004] In recent years, there has been an increasing number of studies on depression based on metabolomics. A study found that plasma hydroxylated sphingolipids were an important predictor of depression treatment outcomes after metabolomics analysis of 298 patients with depression (Joyce J B. Using Multi-Domain Data to Predict Remission of Unipolar Depressive Episodes Following Antidepressant Therapy [D]. College of Medicine-Mayo Clinic, 2020.). Another study showed that the levels of lactate, glucose, glutamine, creatinine, acetate, L-valine, L-alanine, and fatty acids were significantly reduced in serum, while the levels of acetone and choline were significantly increased (Song Z, Wang H, Yin X, et al. Application of NMR metabolomics to search for human disease biomarkers in blood [J]. Clinical Chemistry and Laboratory Medicine (CCLM), 2019, 57 (4): 417-441.). Many studies have shown that metabolites play an important role in the mechanism, medication, and prognosis of depression. The development of test kits for the screened molecular markers is of great significance for clinical applications.

[0005] Although many research institutions have studied diagnostic markers for depression, these existing studies have not provided a diagnostic method that is accurate, simple, reliable, and widely applicable in clinical practice.

[0006] In the Chinese patent applications with application numbers 201610105047.3 and 201080046087.6, the diagnosis of depression using serum metabolic markers is disclosed. However, serum samples are difficult to collect and require professional medical staff to conduct, and it is difficult for patients to collect them at home. In the Chinese patent application with application number 201480019392.4, the diagnosis using biomarkers of serum or urine is disclosed. The set of biomarkers used includes at least cGMP, cortisol, calprotectin, thromboxane, aldosterone, HVEM and substance P. Since the markers used include both small molecule metabolites and large molecule proteins, multiple technical platforms need to be used for detection, such as mass spectrometry and immunological methods, which increases the difficulty and cost of detection. In addition, the invention uses serum and urine samples, and the storage and transportation costs are very high. The Chinese patent application with application number 202010658243.X uses urine to make filter paper to collect samples. Although it effectively solves some problems existing in serum and plasma, its detection purpose is single, which is only to predict the diagnosis of depression. During the course of depression, graded diagnosis, differential targeting, treatment outcomes, and relapse also have a greater impact on patients. Traditional analytical methods such as immunoassays have always been the most widely used technology in clinical diagnosis. Among immunological methods, radioimmunoassays have been basically eliminated due to the existence of radioactive contamination. The ELISA method is manually operated, and the results are easily affected by human factors. Due to the needs for detection convenience and accuracy of test results, it is necessary to develop new detection markers, detection methods, and detection tools.

[0007] The kit provided by the Chinese patent application with application number 201610105047.3 can measure the content of 14 metabolic markers screened in the blood: methionine, niacin, phosphoserine, lactic acid, glutamic acid, hypoxanthine, choline, tryptophan, 5-hydroxytryptamine, propionylcarnitine, glutarylcarnitine, estradiol, cortisol and estriol. The specificity of the kit for early diagnosis of depression is 86.8%, the sensitivity is 96.8% and the accuracy is 93.0%. The Chinese patent application with application number 201080046087.6 only involves molecular markers for diagnosing depression and does not indicate the diagnostic efficacy of the metabolite combination. The literature (PhD dissertation of Chongqing Medical University, author: Zheng Peng, screening of diagnostic markers for depression in plasma and urine based on metabolomics strategy, 2013 and Zheng P, Wang Y, Chen L, et al. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Mol Cell Proteomics. 2013; 12 (1): 207-214) disclosed that 126 urine samples of depression patients and 134 normal controls were measured by nuclear magnetic resonance, and a total of 23 differential metabolites were identified. Logistic regression analysis found that the biomarker group composed of five metabolites, namely malonate, formate, N-methylnicotinamide, M-hydroxyphenylacetate and alanine, has potential clinical diagnostic value. The AUC value of this biomarker panel for distinguishing 82 depression patients from 82 normal controls in the training set was 0.812 (95% confidence interval: 0.829-0.961); the AUC value for distinguishing 44 depression patients from 52 normal controls in the test set was 0.895 (95% confidence interval: 0.829-0.961).

[0008] Based on the above research status, there is still a need to develop a better combination of metabolic biomarkers for the diagnosis of depression, so that they have excellent characteristics such as higher specificity, sensitivity, accuracy and easy detection when used for the diagnosis of depression. Summary of the invention

[0009] One of the purposes of the present invention is to provide a metabolic biomarker combination for the diagnosis of depression. Based on the metabolic biomarker combination, the optimal diagnostic model can be screened through a machine learning model algorithm, and a depression diagnostic kit can be constructed.

[0010] One of the further objects of the present invention is to provide a use of the metabolic biomarker combination in preparing a kit for diagnosing depression in a subject.

[0011] One of the further objects of the present invention is to provide a method for detecting the types and contents of metabolic biomarkers in a test recipient dried blood spot sample.

[0012] Based on the above invention purpose, the present invention provides a metabolic biomarker combination for depression diagnosis, including: proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid. Preferably, the present invention provides a metabolic biomarker combination for depression diagnosis, the metabolic biomarker combination consisting of proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid.

[0013] The present invention further provides a metabolic biomarker combination for diagnosing depression, comprising: methionine, phenylalanine, guanine, hypoxanthine, γ-aminobutyric acid, allantoin, uric acid, histidine, aspartic acid, threonine, androstenedione, cytosine, xanthine, bilirubin, pyruvate, linoleic acid, glutamine, serine, adipic acid, pseudouridine, tyrosine, proline, valine, ornithine, betaine, alanine, tryptophan, leucine, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, 2-hydroxybutyrate, glutamate, glucose, lysine, and arginine.

[0014] The present invention further provides a kit for diagnosing depression, which comprises the metabolic biomarker combination. Furthermore, the present invention provides a kit for detecting the metabolic markers as described above, including a standard substance, a quality control substance, an isotope internal standard, a diluent, formic acid, and an ammonium acetate solution of the metabolic marker. The metabolic marker extraction solvent is selected from a mixed solution of one or more of water, methanol, ethanol, acetonitrile, and isopropanol, which is required to be chromatographically pure; the water, methanol, acetonitrile, isopropanol, ethanol, formic acid, and ammonium acetate used in the mobile phase must all reach chromatographic grade. The diluent is composed of one or more of methanol, water, and DMSO, and needs to be chromatographic grade.

[0015] Furthermore, the kit can be prepared according to the following method:

[0016] (1) Preparation of standard pure lyophilized powder of metabolic markers;

[0017] (2) Preparation of lyophilized powder of isotope internal standard;

[0018] (3) preparing the solvent for extracting metabolic markers;

[0019] (4) Dissolving agents for preparing standards and isotope internal standards;

[0020] (5) Assembling the above reagents into the kit.

[0021] Furthermore, the present invention provides a method for using the above kit, comprising:

[0022] (1) Collecting a blood sample from a subject, preparing a dried blood spot paper, and using the dried blood spot sample as a test sample;

[0023] (2) extracting the dried blood spot sample using a metabolite extraction solvent and an isotope internal standard solution to obtain an extract;

[0024] (3) Using liquid chromatography-tandem mass spectrometry (LC-MS / MS) targeted metabolomics analysis to measure the concentration of metabolites in dried blood spot extracts;

[0025] (4) Compare the measured metabolite concentrations with the normal values ​​of each metabolite concentration. If the concentrations of proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, and 2-hydroxybutyrate are significantly reduced (less than 0.8 times the normal value), it is diagnosed as depression.

[0026] In addition, the test sample of the kit provided by the present invention may also be serum and / or plasma.

[0027] The present invention can prepare a dried blood spot sample by the following method, which comprises the following steps: (1) collecting dried blood spot cards: the cards should be clean and free of contamination and mold, and the cards should be sealed before use. Each card should have independent identification information (name, age, gender, date code), and the card is a special card with a dedicated blood collection area. (2) The dried blood spot card is divided into two parts, one part is the blood collection area, which has an inner circle with a radius of 3 mm and an outer circle with a radius of 5 mm, and this area is prohibited from contact; the other part is the holding area, and clean medical gloves must be worn when holding this area. (3) The dried blood spot card contains an antioxidant (VC) and an enzyme inactivator 1-aminobenzotriazole (ABT). (4) Capillary blood and venous blood can be used for blood sampling. When collecting capillaries such as fingertips, they should be cleaned in advance, and non-volatile disinfectants such as iodine tincture should not be used. A drop of blood is dripped into each inner circle of the dried blood spot card to ensure that the entire circle is covered and the filter paper is completely penetrated, while avoiding blood from penetrating the outer circle. The collection process should be protected from light. If conditions permit, strong light should be avoided to avoid affecting photosensitive substances. (5) After the blood spot is collected, avoid long-term exposure to the air. Nitrogen blowing can be used to quickly dry it. Or use a vacuum instrument to quickly dry it. The collected dried blood spots should be quickly soaked in liquid nitrogen to inactivate the enzyme, and then dried. The cards should not be stacked. After being divided into separate sealed bags, they should be placed in a -80℃ refrigerator for freezing.Figure 7 An illustrative example of a dried blood spot card is provided.

[0028] The processing method of the dried blood spot sample in the present invention is: use a puncher to cut a small disc with a diameter of 10 mm along the outer circle line in the dried blood spot sample area, put it in a 1.5 mL centrifuge tube, and then add 500 mL of eluent / precipitant containing an internal standard. Vortex for about 5 minutes. Centrifuge at 15000g for 10 minutes, take the supernatant, perform secondary centrifugation (or filter with a filter membrane), and take the supernatant for sampling. During the punching process of the puncher, the punching must be carried out strictly according to the outer circle of the cardboard to prevent mutual contamination between samples during the punching process and affect the test results.

[0029] The metabolic markers of the present invention can be used for the diagnosis of depression or affective disorder in a subject.

[0030] In the present invention, the depression is selected from mild depression, moderate to severe depression or bipolar depression. For the sake of simplicity, the other parts of the present invention are expressed as "depression", and those skilled in the art can understand that the principles and purposes of the technical solutions described are also applicable to other types of affective disorders.

[0031] In the present invention, "metabolic marker" refers to a unique biological or biologically derived indicator of a process, event or condition. Biomarkers can be used for the diagnosis of depression. The biomarkers provided by the present invention have high specificity, sensitivity and accuracy for the diagnosis of depression. The present invention makes the diagnosis of depression no longer completely dependent on the subjective judgment of the clinician's experience and questionnaire scale, and greatly improves the accuracy of diagnosis and treatment.

[0032] In the present invention, the "subject" is a human being.

[0033] The inventors of the present invention have developed a targeted metabolomics analysis technique based on liquid chromatography-tandem mass spectrometry (LC-MS / MS) and obtained a variety of biomarkers that can be used for the diagnosis of depression by testing healthy people and patients with depression, which can achieve high specificity, sensitivity and accuracy.

[0034] The present invention detects metabolites in dried blood spot paper, which can not only effectively avoid reliance on doctors' subjective judgment, but also assist in the diagnosis of depression, which is of great clinical significance; the samples are stored on dried blood spot paper, which has the advantages of simple preparation and collection, convenient transportation, and room temperature storage. The present invention adopts liquid chromatography-tandem mass spectrometry (LC-MS / MS) method to target and quantify the biomarkers to be tested, and the sample detection is accurate and convenient.

[0035] The present invention uses a generalized linear model (GLM) when diagnosing depression, with an area under the curve (AUC) value of 1, an accuracy of 99.30%, a sensitivity of 100%, and a specificity of 98.96%. This indicates that the metabolic biomarker combination provided by the present invention and the model constructed have excellent diagnostic value, significantly better than similar previous reports. The metabolic biomarker combination provided by the present invention is innovative, especially since no studies have shown that 2-hydroxybutyrate is associated with the diagnosis of depression. 2-Hydroxybutyrate is associated with insufficient energy metabolism and impaired glucose regulation. Studies have shown that 2-hydroxybutyrate is significantly increased in patients with bipolar disorder ( , , ,et al.Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients[J].Metabolites,2023,13(5):607.). However, no study has shown that 2-hydroxybutyric acid can be used as a molecular marker for the diagnosis of depression. The present invention adds 2-hydroxybutyric acid as a diagnostic molecular marker, which is innovative. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Represents the metabolic marker chromatogram.

[0037] Figure 2 A PCA analysis of two groups of samples, healthy people and patients with depression, is shown.

[0038] Figure 3 Another PCA analysis of two groups of samples, healthy people and patients with depression, is shown.

[0039] Figure 4 An OPLS-DA analysis of two sample groups, healthy people and patients with depression, is shown.

[0040] Figure 5 Another OPLS-DA analysis of two sample groups, healthy people and depression patients, is shown.

[0041] Figure 6 ROC curve showing the ability of the generalized linear model (GLM) to distinguish healthy people from patients with depression.

[0042] Figure 7 An exemplary schematic diagram of a dried blood spot card. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The present invention provides a group of metabolic markers that can be used for depression diagnosis. The metabolic markers include: proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid. Alternatively, the metabolic markers include: methionine, phenylalanine, guanine, hypoxanthine, γ-aminobutyric acid, allantoin, uric acid, histidine, aspartic acid, threonine, androstenedione, cytosine, xanthine, bilirubin, pyruvic acid, linoleic acid, glutamine, serine, adipic acid, pseudouridine, tyrosine, proline, valine, ornithine, betaine, alanine, tryptophan, leucine, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid, glutamic acid, glucose, lysine, arginine. The present invention preferably uses targeted mass spectrometry technology, and other measurement techniques such as immunoassay technology, chemiluminescence, etc. can also be used for the measurement of metabolic markers of the present invention.

[0045] The metabolic markers of the present invention are screened by the following method:

[0046] (1) Depressive patients and healthy controls were selected, and plasma samples were taken from them to make dried blood spots, from which metabolites were extracted;

[0047] (2) Using metabolomics methods, liquid chromatography-tandem mass spectrometry (LC-MS / MS) was used to measure the content of major metabolites in dried blood spots;

[0048] (3) The obtained data were analyzed by orthogonal partial least squares-discriminant analysis (OPLS-DA) and variable projection importance (VIP) to identify metabolites with significant differences between the two groups as candidate metabolic markers;

[0049] (4) Through the receiver operator characteristic curve (ROC curve), we further screened and determined the metabolic marker combination with high accuracy and specificity.

[0050] These metabolic markers can be detected in the blood sample of the subject. The present invention preferably uses the plasma sample of the subject and prepares a dried blood spot as the detection sample.

[0051] During the determination, the metabolites in the dried blood spots are first extracted. The present invention uses but is not limited to water, methanol, ethanol, acetonitrile, isopropanol, and a mixed solution of the above solvents to extract the metabolites in the dried blood spots, which may or may not contain the isotope internal standard corresponding to each biomarker. Preferably, a 5-15% (volume ratio) methanol aqueous solution is used to extract the metabolites in the dried blood spots. Preferably, the extraction is carried out at room temperature (20-35°C) with shaking for 20-40 minutes, and the dried blood spot extract is subjected to subsequent determination.

[0052] In the determination, the present invention preferably uses a derivatization method to detect metabolic markers. Derivatization methods include but are not limited to dansyl chloride, phenylisothiocyanate, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, etc. Preferably, dansyl chloride is used for derivatization. Preferably, the supernatant of the dried blood spot extract is taken, and a sodium bicarbonate solution and a derivatization reagent are added. Oscillate for a period of time to allow it to react fully. Preferably, the metabolic markers after dansyl chloride derivatization are extracted with ethyl acetate. The upper organic phase of the extract is transferred to an appropriate container and blown dry with nitrogen. Add methanol aqueous solution for re-dissolution and then detect.

[0053] The present invention preferably uses liquid chromatography-tandem mass spectrometry (LC-MS / MS) to detect and analyze the above diagnostic marker composition.

[0054] Preferably, the liquid chromatography conditions are: a chromatographic column (which can be a C8 or C18 silica gel packing column), a mobile phase (which can be an acetonitrile-water solution, a formic acid aqueous solution, a methanol-water solution, an acetonitrile-isopropanol solution, a methanol-isopropanol solution, an acetic acid aqueous solution, an ammonium acetate aqueous solution), and an elution procedure: a mobile phase A and a mobile phase B are gradient eluted in a certain ratio.

[0055] The liquid chromatography conditions of a specific embodiment of the present invention are as follows: the liquid chromatography column adopts ZORBAX RRExtend-C18, 80A, 4.6×150 mm, 3.5 μm. The liquid chromatography conditions are as follows: injection volume: 5 μL; flow rate: 0.6 mL / min; column temperature: 40°C; automatic sampler temperature: 4°C; mobile phase A: 0.1% (volume ratio) formic acid aqueous solution; mobile phase B: methanol; elution program is 0~1min, linear change from 10% B phase to 40% B; 1~1.5min, linear change from 40% B phase to 50% B phase; 1.5min~2.0min, linear change from 50% B phase to 60% B phase; 2.0min~3.2min, balance at 80% B phase; 3.2min~3.6min, from 80% B phase to 10% B phase; 3.7~4.5min, balance at 10% B phase.

[0056] Preferably, triple quadrupole mass spectrometry is used for detection. Electrospray ionization source (ESI) is used, positive and negative ion transient mode, scanning time is 0min to 4.5min; CUR: 20; CAD: Medium; IS: 5000; TEM: 500; GS1: 30; GS2: 50; Interface heater (ihe): on; DP: ±36; CE: ±26; EP: ±15; CXP: ±10. Dwell Time: 20ms; Resolution Q1: Unit; Resolution Q3: Unit; Pause between mass: 5.007 ms.

[0057] After data collection, each diagnostic marker metabolite is quantified using specific ion pairs, the peak area of ​​the collected signal is compared with the working curve of the corresponding standard, and the internal standard solution is added for correction to obtain the concentration value of the metabolite marker. The content of each metabolite marker in the original blood.

[0058] A specific embodiment of the present invention is a depression diagnosis kit, comprising: 1 mg of a metabolic marker standard lyophilized powder, 1 mg of a marker quality control standard, 13 C or 15 N or 2 H (deuterium) labeled with at least three stable isotope internal standard mixed lyophilized powders, 5 mL of methanol-water-DMSO solution (diluent) for dissolving standard powders and isotope internal standard powders, 1 mL of formic acid solution, 5 mol / L ammonium acetate solution for adjusting the pH of the mobile phase, methanol solution as the metabolite extraction solvent, and 1 analytical chromatographic column.

[0059] Preferably, in the above depression detection kit, the metabolic markers include: proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid. Alternatively, the metabolic markers include: methionine, phenylalanine, guanine, hypoxanthine, γ-aminobutyric acid, allantoin, uric acid, histidine, aspartic acid, threonine, androstenedione, cytosine, xanthine, bilirubin, pyruvic acid, linoleic acid, glutamine, serine, adipic acid, pseudouridine, tyrosine, proline, valine, ornithine, betaine, alanine, tryptophan, leucine, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine, 2-hydroxybutyric acid, glutamic acid, glucose, lysine, arginine.

[0060] The preparation method of the above depression detection kit, the specific preparation steps are as follows:

[0061] (1) Sample preparation: Take the sample to be analyzed and ensure that the sample is within the required concentration range. Dilute the sample as needed (usually with mobile phase). Filter the sample to remove possible particulate matter, usually using a 0.45μm or 0.2μm filter membrane.

[0062] (2) Select the mobile phase: Inject the metabolic extract into the Extend C18 column (ZORBAX RR Extend-C18, 80A, 4.6×150 mm, 3.5 um, USA), the metabolites in the blood samples were separated, and the specific liquid chromatography conditions were as follows: injection volume: 5 μL; flow rate: 0.6 mL / min; column temperature: 40 °C; autosampler temperature: 4 °C; mobile phase A: 0.1% (volume ratio) formic acid in water; mobile phase B: methanol; the elution program was 0-1 min, linearly changing from 10% B phase to 40% B; 1-1.5 min, linearly changing from 40% B phase to 50% B phase; 1.5 min-2.0 min, linearly changing from 50% B phase to 60% B phase; 2.0 min-3.2 min, balanced at 80% B phase; 3.2 min-3.6 min, from 80% B phase to 10% B phase; 3.7-4.5 min, balanced at 10% B phase.

[0063] (3) Equipment setup: Check all parts of the HPLC system, including the pump, injector, column, and detector. Make sure they are all in normal working order. Introduce the metabolites separated by chromatography into the triple quadrupole mass spectrometer, and use the multiple reaction detection mode to scan and detect the metabolites. Use the electrospray ionization source (ESI), positive and negative ion instantaneous switching mode, and the scanning time is 0 min to 4.5 min. CUR: 20; CAD: Medium; IS: 5000; TEM: 500; GS1: 30; GS2: 50; Interface heater (ihe): on; DP: ±36; CE: ±26; EP: ±15; CXP: ±10. Dwell Time: 20 ms; Resolution Q1: Unit; Resolution Q3: Unit; Pause between mass: 5.007 ms.

[0064] (4) Sample injection: Use the injector to introduce the prepared sample into the system. The typical injection volume is 10~100μL. Ensure that the liquid flow of the sample is smooth and record the injection time.

[0065] (5) After data collection, the metabolite marker of each diagnostic marker is quantified using a specific ion pair, the peak area of ​​the collected signal is compared with the working curve of the corresponding standard, and the internal standard solution is added for correction to obtain the concentration value of the metabolite marker.

[0066] The quantitative values ​​of the metabolite markers obtained by the above tests are compared with the normal values ​​of each metabolite concentration to conduct the corresponding depression diagnosis risk assessment. The measured metabolite concentrations are compared with the normal values ​​of each metabolite concentration. If the concentrations of proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, and 2-hydroxybutyrate are significantly reduced (less than 0.8 times the normal value), it is judged as depression.

[0067] Example

[0068] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0069] Example 1 Metabolic marker screening and model establishment for depression diagnosis

[0070] 1. Sample inclusion

[0071] In this example, the depression sample group adopted the following inclusion criteria: (1) All included depression patients met the ICD-10 diagnostic criteria; (2) Hamilton Depression Rating Scale (HDRS) 17-item score was greater than or equal to 18 points; (3) patients with first-onset depression did not take any antidepressant drugs; (4) aged greater than 18 years and less than 60 years.

[0072] At the same time, the following exclusion criteria were used for the depression sample group: (1) patients with other mental illnesses or a history of other mental illnesses; (2) patients with organic brain diseases and a history of severe brain trauma, or patients with heart, liver, or kidney diseases, diabetes, or other serious physical diseases; (3) patients with abnormal routine laboratory tests (blood routine, liver function, and urine routine); (4) female research subjects who were pregnant, breastfeeding, or menstruating; and (5) patients with a history of drug and substance abuse.

[0073] The inclusion criteria for the healthy control group were: no history of neuropsychiatric disease, no history of drug abuse or dependence, no systemic physical disease; and no obvious abnormalities in routine laboratory tests.

[0074] 2. Dried Blood Spot Preparation

[0075] Prepare the dried blood spot card: The card should be clean and free of contamination and mold. The card should be sealed before use. Each card should have independent identification information (name, age, gender, code). There is a blood collection area on the card. The dried blood spot card is divided into 2 parts: the first part is the blood collection area, with an inner circle with a radius of 3mm and an outer circle with a radius of 5mm. This area is prohibited from contact; the second part is the holding area. Clean medical gloves must be worn when holding this area. The dried blood spot card contains antioxidants (VC) and enzyme inactivators 1-aminobenzotriazole (ABT).

[0076] Blood sampling using capillary blood and venous blood: Collect blood samples from the fingertips capillaries, clean the fingertips in advance, and do not use non-volatile disinfectants such as iodine tincture, then drop a drop of blood in each inner circle of the dry blood spot card, ensuring that the entire circle is covered and the filter paper is completely penetrated, while avoiding blood from penetrating the outer circle. Avoid strong light during the collection process to avoid affecting photosensitive substances. After collecting the blood spot, avoid long-term exposure to the air, and use nitrogen blowing or vacuum instruments to dry quickly. Quickly soak the collected dry blood spots in liquid nitrogen to inactivate the enzyme, and then dry them. The cards cannot be stacked, and they should be packaged in separate sealed bags and frozen in a -80°C refrigerator.

[0077] 3. Extraction of biomarkers from dried blood spots

[0078] (1) Use a hole puncher to cut a 10 mm diameter disc along the outer circle line in the dried blood spot sample area, place it in a 1.5 mL centrifuge tube, and then add 500 mL of eluent / precipitant containing internal standard. Vortex for about 5 minutes. Centrifuge at 15000g for 10 minutes, take the supernatant, perform a second centrifugation (or filter with a filter membrane), and take the supernatant for sampling.

[0079] (2) Add 500 μL of the methanol-water solution (metabolite extraction solvent) in the depression detection kit and shake at 20°C and 1450 rpm for 45 min;

[0080] (3) Take 10 μL of the extract and add 90 μL of water. Then add 200 μL of the internal standard working solution, 50 μL of 1M NaHCO3 solution, and 100 μL of 1 mg / mL derivatization reagent in sequence. Oscillate at 30°C and 1450 rpm for 30 minutes.

[0081] 4. Detection of biomarkers in dried blood spots

[0082] The metabolites detected by the targeted metabolomics analysis method based on liquid chromatography-tandem mass spectrometry (LC-MS / MS) include: methionine, phenylalanine, guanine, hypoxanthine, γ-aminobutyric acid, allantoin, uric acid, histidine, aspartic acid, threonine, androstenedione, cytosine, xanthine, bilirubin, pyruvate, linoleic acid, glutamine, serine, adipic acid, pseudouridine, tyrosine, proline, valine, ornithine, betaine, alanine, tryptophan, leucine, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, 2-hydroxybutyrate, glutamate, glucose, lysine, and arginine.

[0083] The specific steps are:

[0084] (1) Sample preparation: Take the sample to be analyzed and ensure that the sample is within the required concentration range. Dilute the sample as needed (usually with mobile phase). Filter the sample to remove possible particulate matter, usually using a 0.45μm or 0.2μm filter membrane.

[0085] (2) Select the mobile phase: Inject the metabolic extract into the Extend C18 column (ZORBAX RR Extend-C18, 80A, 4.6 x 150 mm, 3.5 um, USA), the metabolites in the blood samples were separated, and the specific liquid chromatography conditions were: injection volume: 5μL; flow rate: 0.6mL / min; column temperature: 40℃; autosampler temperature: 4℃; mobile phase A: 0.1% (volume ratio) formic acid in water; mobile phase B: methanol; elution program was 0~1min, linear change from 10% B phase to 40% B; 1~1.5min, linear change from 40% B phase to 50% B phase; 1.5min~2.0min, linear change from 50% B phase to 60% B phase; 2.0min~3.2min, equilibrium at 80% B phase; 3.2min~3.6min, from 80% B phase to 10% B phase; 3.7~4.5min, equilibrium at 10% B phase.

[0086] (3) Equipment setup: Check all parts of the HPLC system, including the pump, injector, column, and detector. Make sure they are all in normal working order. Introduce the metabolites separated by chromatography into the triple quadrupole mass spectrometer, and use the multiple reaction detection mode to scan and detect the metabolites. Use the electrospray ionization source (ESI), positive and negative ion instantaneous switching mode, and the scanning time is 0 min to 4.5 min. CUR: 20; CAD: Medium; IS: 5000; TEM: 500; GS1: 30; GS2: 50; Interface heater (ihe): on; DP: ±36; CE: ±26; EP: ±15; CXP: ±10. Dwell Time: 20 ms; Resolution Q1: Unit; Resolution Q3: Unit; Pause between mass: 5.007 ms.

[0087] (4) Sample injection: Use the injector to introduce the prepared sample into the system. The typical injection volume is 10~100μL. Ensure that the liquid flow of the sample is smooth and record the injection time.

[0088] (5) After data collection, the metabolite markers of each diagnostic marker are quantified using specific ion pairs, the peak area of ​​the collected signal is compared with the working curve of the corresponding standard, and the internal standard solution is added for correction to obtain the concentration value of the metabolite marker. The measured metabolite concentrations of the depression sample group and the healthy control group are compared. 4) Stability experiment:

[0089] After the dried blood spot paper was dried, it was stored at -80℃, -20℃, and room temperature. Liquid chromatography-mass spectrometry was used to determine the content of substances in the dried blood spots. The samples stored at -80℃ were measured once a month, the samples stored at -20℃ were measured once every half month, and the samples stored at room temperature were measured every other day. The relative standard deviation (RSD) was calculated, and the recovery rate was calculated with the initial determination mean as the initial value; the results showed that the precision was within 10%, and the recovery rate was between 80% and 110%, indicating that the dried blood spot paper was stored at -80℃ for at least one year, at -20℃ for at least 3 months, and at room temperature for 15 days.

[0090] 5) Test results

[0091] 150 normal human dried blood spot samples were collected, and the metabolites in the dried blood spot samples were quantified to determine the normal reference range of the markers. Figure 1 During the analysis of dried blood spot samples, a QC sample (Quality Control samples) was interspersed with every 10 research samples to assess the variability of the derivatization process and instrumental analysis. QC samples are critical to ensure the repeatability, reliability, accuracy, and robustness of metabolite quantification.

[0092] At the same time, samples from 150 healthy people and 60 patients with depression were collected and tested. The sample test results showed that the differences in the levels of the following metabolites can well distinguish normal people from patients with depression: proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, and 2-hydroxybutyrate. That is, the concentrations of these metabolic biomarkers in the depression sample group were significantly lower than those in the healthy control group (Table 1).

[0093] Table 1. Differentially expressed metabolites in the depression group compared with the healthy group

[0094]

[0095] Figure 2 , Figure 3The PCA score graph shows that the QC samples are closely clustered relative to other samples of the dried blood spots, indicating that the method of this embodiment has good repeatability. The QC sample here is BSA (bovine serum albumin solution). Orthogonal partial least squares-discriminant analysis (OPLS-DA) analysis is performed, as shown in FIG. Figure 4 , Figure 5 As shown, normal and depression samples can be well distinguished, indicating that there are significant metabolic differences between depression patients and normal people.

[0096] Different machine learning algorithms are further used to build the model, including generalized linear model, linear discriminant analysis, K-nearest neighbor algorithm, logistic regression, lasso regression, decision tree, artificial neural network, support vector machine, extreme gradient boosting, random forest, principal component analysis, Bayesian network, and linear regression. Among them, the operation method of generalized linear model is: select appropriate link function and distribution, and use maximum likelihood estimation (MLE) to fit the model. It can be implemented through statistical software (such as R's glm function). The operation method of linear discriminant analysis (LDA) is to calculate the mean and covariance matrix of each category, and solve the eigenvalue and eigenvector to obtain the best partition. It is usually implemented in R or Python's sklearn. The operation method of K-nearest neighbor algorithm (KNN) is to select K value, calculate the distance between the point to be classified and all points in the training data, sort by distance, select K nearest neighbors, and decide the category by voting. In Python, sklearn's KNeighborsClassifier can be used. How to operate Logistic Regression: Use the logistic function (sigmoid) to model and use maximum likelihood estimation to optimize parameters. This can be implemented through R's glm or Python's sklearn. How to operate Lasso Regression: Add L1 regularization terms to linear regression and use coordinate descent or least squares to implement it. Python's sklearn has a Lasso class. How to operate a decision tree: Recursively select the best features for node partitioning until the stopping condition is met. This can be implemented using DecisionTreeClassifier or DecisionTreeRegressor in sklearn. How to operate an artificial neural network: Design the network structure (number of layers, number of neurons) and use the backpropagation algorithm for parameter optimization. You can use deep learning frameworks such as TensorFlow or PyTorch. How to operate a support vector machine (SVM): Select a suitable kernel function and optimize the objective function to find the best hyperplane. This can be implemented through SVC in sklearn. How to operate extreme gradient boosting: Construct a gradient boosting tree and iteratively optimize the loss function. This can be implemented using the xgboost library and set hyperparameters for tuning. How to use Random Forest: Build multiple decision trees and use Bootstrap sampling to generate different training sets. Finally, vote or average the prediction results of each tree. You can use RandomForestClassifier or RandomForestRegressor in sklearn. How to use Principal Component Analysis (PCA): Standardize data, calculate covariance matrix, find eigenvalues ​​and eigenvectors, and select the first K eigenvectors as principal components. You can use PCA in sklearn.How to operate Bayesian network: define the network structure, use Bayesian theorem to update the probability, usually use maximum likelihood estimation or Bayesian estimation for parameter learning. This can be implemented using the pgmpy library. How to operate linear regression: establish a linear equation and use the least squares method to fit the parameters. This can be implemented using R's lm or Python's sklearn's LinearRegression.

[0097] The results showed that, as shown in Table 2, the best model was the generalized linear model (GLM), and the receiver operating characteristic curve (ROC) of the significantly changed metabolites was 1, with an area under the curve (AUC) value of 1, an accuracy of 99.30%, a sensitivity of 100%, and a specificity of 98.96%. Figure 6 The model showed good diagnostic value. The combination of metabolites proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, and 2-hydroxybutyrate showed excellent specificity and sensitivity.

[0098] Table 2. Comparative analysis of the results of different models for the diagnosis of depression

[0099]

[0100] Example 2 Evaluation of the diagnostic effect of the metabolic biomarker combination and diagnostic model established in Example 1 on depression

[0101] Dried blood spot samples from outpatients or inpatients of a hospital and dried blood spot samples from recruited healthy controls were randomly collected (a total of 300 samples, all samples were collected by professional doctors with clear diagnosis data according to the ICD-10 standard, and informed consent was signed), and the ion abundance ratios of biomarkers (proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine, 2-hydroxybutyrate) and internal standards were directly detected according to the method of Example 1. The data were input into the diagnostic model established in Example 1 to obtain diagnostic results. The diagnostic results were compared with the results of depression diagnosis of the 300 samples by professional doctors using ICD-10. The results are shown in Table 3.

[0102] Table 3. Evaluation of the effectiveness of metabolic marker kits in diagnosing depression

[0103]

[0104] As can be seen from Table 3, the accuracy, sensitivity and specificity of the marker combination of Example 1 and the diagnostic model established therefrom for diagnosing depression are 100%, 100% and 100%.

[0105] The embodiments of the present invention are described in detail above, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments are made without departing from the principles and spirit of the present invention, and still fall within the protection scope of the present invention.

Claims

1. Use of a metabolic biomarker combination in the preparation of a kit for diagnosing depression, wherein the metabolic biomarker combination consists of proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinic acid, taurine and 2-hydroxybutyric acid, and wherein the depression is selected from mild depression, moderate to severe depression or bipolar depression.

2. The use according to claim 1, wherein the detection sample for the metabolic biomarker is human serum, plasma, or dried blood spot.

3. The use according to claim 2, wherein the kit comprises: a standard substance of the metabolic biomarker combination, a quality control substance, an isotope internal standard, a diluent, formic acid and an ammonium acetate solution.

4. The use according to claim 3, wherein the kit comprises: 1 mg of lyophilized powder of a standard substance of a metabolic marker; 1 mg of a quality control standard substance of the marker; 13 C or 15 N or 2 H-labeled stable isotope internal standard mixed lyophilized powder with at least three ions; 5 mL of methanol-water-DMSO solution, used to dissolve the standard powder and isotope internal standard powder; 1 mL of formic acid solution and 5 mol / L ammonium acetate solution, used to adjust the pH of the mobile phase; Methanol solution was used as the metabolite extraction solvent; 1 analytical column.

5. The use according to claim 4, wherein the method of using the kit is: (1) Collecting a blood sample from a subject, preparing a dried blood spot paper, and using the dried blood spot sample as a test sample; (2) extracting the dried blood spot sample using a metabolite extraction solvent and an isotope internal standard solution to obtain an extract; (3) Using a targeted metabolomics analysis method coupled with liquid chromatography-tandem mass spectrometry, the concentration of metabolites in the dried blood spot extract was measured; (4) The measured metabolite concentrations are compared with the normal values ​​of each metabolite concentration. If the concentrations of proline, betaine, alanine, tryptophan, kynurenine, 5-hydroxytryptamine, creatine, succinate, taurine and 2-hydroxybutyrate are less than 0.8 times the normal value, it is diagnosed as depression.

Images