AAGN disease blood and / or urine metabolism marker, kit and application of AAGN disease blood and / or urine metabolism marker

Abstract

The invention relates to the technical field of nephritis medicine, in particular to an AAGN disease blood and / or urine metabolism marker, a kit and application of the AAGN disease blood and / or urine metabolism marker, and the blood and / or urine metabolism marker is L-citrulline and / or homovanillic acid sodium sulfate. The metabolic marker combination and the kit are used for early auxiliary screening of AAGN and differential diagnosis products of other glomerular diseases, have good repeatability, stability and biological correlation, and have important clinical application potential and transformation value.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technology field of nephritis, in particular to an AAGN disease blood and / or urine metabolic marker, a kit and application thereof. BACKGROUND

[0002] Anti-neutrophil cytoplasmic antibodies (ANCA) associated vasculitis (AAV) is a kind of autoimmune disease characterized by ANCA-mediated necrotizing inflammation of blood vessels. AAV can cause damage and dysfunction of multiple organs and tissues, most commonly involving the kidneys and lungs. The kidney damage caused by AAV is called ANCA-associated glomerulonephritis (AAGN). AAGN is the leading cause of acute kidney injury in elderly patients in China. If not intervened in time, it is extremely easy to progress to end-stage renal disease, and the possibility of spontaneous remission is extremely low.

[0003] Clinically, the diagnosis of AAGN depends on serum ANCA detection, renal function indicators (such as serum creatinine), and invasive renal biopsy. However, the correlation between ANCA titer and disease severity is not stable, and the changes in common serological indicators in different disease stages lack specificity, making early identification difficult. In addition, although renal biopsy is the gold standard, it has the risk of trauma and is not suitable for elderly or coagulopathy patients, and is also not convenient for dynamic monitoring.

[0004] Metabolomics is a high-throughput technology that can simultaneously detect and quantify a large number of small molecule metabolites in biological samples, and has the potential to reflect changes in physiological and pathological states. In recent years, metabolomics has been used to explore AAV-related metabolic changes, and some studies have found differences in amino acid metabolism and carbohydrate metabolism in AAGN patients, but most focus on comparisons between healthy people or active and remission stages of the disease, the research subjects are limited, and the specific diagnostic value for AAGN is insufficient. Currently, there is no literature to systematically evaluate the metabolic characteristics differences between AAGN and other major types of nephritis (such as systemic lupus erythematosus nephritis, IgA nephropathy, and minimal change nephropathy).

[0005] Therefore, there is an urgent need in the clinic for a non-invasive, stable, repeatable, and effective molecular marker that can distinguish AAGN from other kidney diseases, to assist in achieving earlier and specific differential diagnosis. SUMMARY

[0006] The present application aims to solve the problem of lack of specific biomarkers in the current clinical diagnosis of AAGN. The existing evaluation methods mostly rely on serum creatinine, C-reactive protein, ANCA titer and other indicators, which are difficult to make a definite diagnosis at the early stage of the disease or when the disease is atypical, and cannot effectively distinguish AAGN from other types of nephritis, such as systemic lupus erythematosus nephritis (LN), IgA nephropathy (IgAN) and minimal change nephropathy (MCD). The present application aims to screen a group of differential metabolites that show good discrimination ability for AAGN, for the development of non-invasive metabolic markers for AAGN.

[0007] In order to achieve the above purpose, the present application provides a reagent for detecting metabolic markers in blood and / or urine for preparing a product for assisting in the diagnosis of AAGN disease, wherein the metabolic markers in blood and / or urine are L-citrulline and / or high vanillyl acid sulfate sodium salt.

[0008] Preferably, the product comprises a kit and / or a chip.

[0009] Preferably, the product assists in the diagnosis of AAGN disease by detecting the relative content of metabolic markers in blood and / or urine.

[0010] Preferably, the product detects the change in the level of metabolic markers in blood and / or urine alone or jointly when assisting in the diagnosis of AAGN disease.

[0011] Preferably, when the reagent for detecting metabolic markers in blood and / or urine is applied, the product detects a significant increase in L-citrulline and / or high vanillyl acid sulfate sodium salt in blood or urine, thereby assisting in the diagnosis of AAGN disease.

[0012] Preferably, the detection employs one or more of ultra-high performance liquid chromatography-mass spectrometry, high performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry or liquid chromatography tandem mass spectrometry. The above detection techniques need to have a detection sensitivity, repeatability and adaptability comparable to that of the ultra-high performance liquid chromatography-mass spectrometry in the present application.

[0013] Preferably, the detection employs ultra-high performance liquid chromatography-mass spectrometry.

[0014] Preferably, the screening method for detecting metabolic markers in blood or urine comprises the following steps:

[0015] The integral value of the peak area of the metabolic marker in blood or urine obtained by the detection method is taken as a relative quantitative index, and the variable importance projection value, statistical test P value and abundance change multiple are statistically analyzed to screen the metabolic marker in blood or urine.

[0016] Preferably, when the metabolic marker in blood or urine satisfies the variable importance projection value ≥ 1, the statistical test P value < 0.05, and the abundance change multiple ≥ 1.5, it indicates a significant increase.

[0017] Under the same technical concept, the present application also provides a kit containing a metabolic marker in blood and / or urine, which contains a reagent for detecting the metabolic marker in blood and / or urine.

[0018] The above scheme of the present application has the following beneficial effects:

[0019] (1) The present application provides a metabolic marker combination in blood and / or urine based on metabolomics screening, which can be detected by a non-invasive method (such as detecting plasma or urine samples), has good repeatability, stability and biological correlation, is used for early auxiliary screening of AAGN and differential diagnosis of other glomerular diseases, has good repeatability, stability and biological correlation, has important clinical application potential and transformation value;

[0020] (2) The blood and / or urine metabolic marker kit of the present application is simple to operate, low in cost and high in responsiveness, and provides a technical basis for accurately identifying AAGN disease activity.

[0021] Other beneficial effects of the present application will be described in detail in the subsequent specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figures 1-2 is the total ion current chromatogram of the plasma sample of the discovery cohort in the positive ion and negative ion modes;

[0023] Figures 3-4 is the total ion current chromatogram of the urine sample of the discovery cohort in the positive ion and negative ion modes;

[0024] Figures 5-6 is the total ion current chromatogram of the plasma sample of the verification cohort in the positive ion and negative ion modes;

[0025] Figures 7-8 is the total ion current chromatogram of the urine sample of the verification cohort in the positive ion and negative ion modes;

[0026] Figures 9-12 is the ROC curve analysis of L-citrulline in different samples in the embodiments of the present application;

[0027] Figures 13-16 Figure 1 is a ROC curve analysis of high vanillylmandelic acid sulfate sodium salt in different samples in an embodiment of the present application. DETAILED DESCRIPTION

[0028] To make the technical problems, technical solutions and advantages of the present application clearer, specific embodiments will be described in detail below with reference to the drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0029] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0030] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be a locking connection, or a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0031] Example 1 Screening of metabolic markers in blood or urine for assisting in the diagnosis of AAGN disease

[0032] 1. Study subjects

[0033] 1.1 Inclusion and exclusion criteria for study subjects

[0034] The study subjects were divided into two independent cohorts: a discovery cohort (154 cases) and a validation cohort (118 cases). Both cohorts included patients with ANCA-associated glomerulonephritis (AAGN), lupus nephritis (LN), IgA nephropathy (IgAN), minimal change disease (MCD), and healthy controls (HC). The patients in the discovery cohort were newly diagnosed cases from December 2019 to June 2022, and the patients in the validation cohort were from July 2022 to December 2023.

[0035] This study has been approved by the ethics committee of our hospital, and the informed consent of all subjects has been obtained. The specific number of plasma and urine samples in each group is shown in Table 1.

[0036] Table 1 Number of samples in the discovery cohort and the validation cohort

[0037] The inclusion criteria for AAGN patients were as follows:

[0038] Inclusion criteria:

[0039] (1) Diagnosis consistent with the 2012 Chapel Hill vasculitis consensus;

[0040] (2) AAGN patients confirmed by renal biopsy

[0041] Exclusion criteria:

[0042] (1) Patients with critical illness or concurrent active infection (various types of hepatitis, tuberculosis, AIDS, etc.);

[0043] (2) In the preparation of pregnancy, pregnancy or lactation period;

[0044] (3) History of malignant tumor;

[0045] (4) Existence of any other multiple system autoimmune diseases, such as systemic lupus erythematosus, anti-glomerular basement membrane, etc.;

[0046] (5) Combined with diabetes mellitus, hyperthyroidism;

[0047] (6) Received hemodialysis or plasma replacement therapy within two weeks before sampling;

[0048] (7) Received immunosuppressive agents, hormones and lipid-lowering drugs before entering the study;

[0049] (8) EGPA patients.

[0050] Disease control group (LN, IgAN and MCD patients) inclusion criteria:

[0051] Inclusion criteria:

[0052] (1) LN, IgAN and MCD patients diagnosed by renal biopsy

[0053] (2) In the active stage of the disease

[0054] Exclusion criteria:

[0055] (1) Patients with critical illness or concurrent active infection (various types of hepatitis, tuberculosis, AIDS, etc.);

[0056] (2) In the preparation of pregnancy, pregnancy or lactation;

[0057] (3) A history of malignant tumor;

[0058] (4) There are any other multisystem autoimmune diseases;

[0059] (5) Combined diabetes, hyperthyroidism;

[0060] (6) Within two weeks before sampling, received hemodialysis or plasma replacement therapy;

[0061] (7) Before entering the study, received immunosuppressive agents, hormones and lipid-lowering drugs.

[0062] Inclusion criteria for healthy controls:

[0063] Inclusion criteria:

[0064] (1) Age-matched healthy population;

[0065] (2) No treatment with any drug within 3 months before sampling;

[0066] (3) No abnormalities in related tests of renal function (urine, renal function and urinary imaging);

[0067] (4) No history of diabetes, hyperthyroidism and hyperlipidemia.

[0068] 2 Sample collection Collection and storage method

[0069] Blood and urine samples of all patients were collected before the first diagnosis and the start of immunosuppressive therapy. The samples of all subjects were collected in the morning on an empty stomach. After sample collection, centrifugation (2000g, 10min) was performed immediately, and then the supernatant was stored in a-80℃ refrigerator. Before detecting metabolites in the sample, all samples were kept in a-80℃ refrigerator to avoid repeated freezing and thawing.

[0070] 3 Standard and reagent

[0071] The standard and reagent used in the experiment are shown in Table 2.

[0072] Table 2 Standard and reagent

[0073] 4 Sample extraction

[0074] 4.1 Extraction method of plasma sample

[0075] Extraction of hydrophilic substances in plasma samples:

[0076] ① After the plasma sample is thawed on ice, vortex for 10 seconds, mix well, take 50 μΐ, and put it into a centrifuge tube; ② Add 20% acetonitrile methanol internal standard extraction solution (300 μΐ,) to the centrifuge tube of the previous step; ③ Vortex for 3 minutes, mix the sample well; ④ Centrifuge for 10 minutes (4°C, 13523g / min); ⑤ Take 200 μΐ, of supernatant and put it into a new centrifuge tube; ⑥ Put the centrifuge tube into a -20°C refrigerator for 30 minutes; ⑦ Take out the sample and centrifuge for 3 minutes (4°C, 13523g / min); ⑧ The supernatant obtained after centrifugation is the hydrophilic substance in the plasma sample; ⑨ Take 180 μΐ, for subsequent detection and analysis.

[0077] Extraction of hydrophobic substances in plasma samples:

[0078] ① After the plasma sample is thawed on ice, vortex for 10 seconds, mix well, take 50 μΐ, and put it into a centrifuge tube; ② Add 20% acetonitrile methanol internal standard extraction solution (300 μΐ,) to the centrifuge tube of the previous step; ③ Vortex for 3 minutes, mix the sample well; ④ Centrifuge for 10 minutes (4°C, 13523g / min); ⑤ Take 200 μΐ, of supernatant and put it into a new centrifuge tube; ⑥ Put the centrifuge tube into a -20°C refrigerator for 30 minutes; ⑦ Take out the sample and centrifuge for 3 minutes (4°C, 13523g / min); ⑧ The supernatant obtained after centrifugation is the hydrophilic substance in the plasma sample; ⑨ Take 180 μΐ, for subsequent detection and analysis.

[0079] ⑥ Take 200 μΐ, of supernatant and put it into a new centrifuge tube; ⑦ Concentrate the sample; ⑧ After the sample is completely dry, add a mixture of acetonitrile and isopropanol containing 0.1% formic acid (200 μΐ,), mix well (vortex for 3 minutes); ⑨ Centrifuge for 3 minutes (4°C, 13523g / min); ⑩ The supernatant obtained after centrifugation is the extracted hydrophobic substance.

[0080] 4.2 Urine sample extraction method

[0081] Extraction of hydrophilic substances in urine samples:

[0082] ① After the urine sample is thawed, vortex for 10 seconds, mix well, and then take 200 μΐ, and add it to a clean centrifuge tube; ② Add 20% acetonitrile methanol extraction solution containing internal standard (200 μΐ,) to the centrifuge tube; ③ Mix the sample well (vortex for 3 minutes); ④ Centrifuge for 10 minutes (4°C, 13523g / min); ⑤ Take 350 μΐ, of supernatant and add it to a new centrifuge tube; ⑥ Concentrate the sample; ⑦ After the sample is completely dry, mix well with 70% methanol solution (150 μΐ,) (vortex for 3 minutes); ⑧ Ice water bath ultrasonic (10 minutes); ⑨ Centrifuge (4°C, 13523g / min) for 3 minutes, the supernatant obtained after centrifugation is the hydrophilic substance in the urine; ⑩ Take 120 μΐ, for subsequent detection and analysis.

[0083] Extraction of hydrophobic substances in urine samples:

[0084] ① Thaw the urine sample and vortex for 10 seconds. Take 200 μL of the mixed sample and add it to a clean centrifuge tube; ② Add 1 mL of lipid extraction solution containing internal standard to the centrifuge tube from the previous step. The lipid extraction solution is prepared by mixing methyl tert-butyl ether and methanol at a ratio of 3:1; ③ Vortex the sample for 15 minutes to mix the sample; ④ Add ultrapure water (200 μL) to the sample and mix (vortex for 1 minute); ⑤ Centrifuge for 10 minutes (4 °C, 13523 g / min); ⑥ Take 500 μL of the supernatant and place it in a new centrifuge tube; ⑦ Concentrate the sample; ⑧ After the sample is completely dry, add a mixture of acetonitrile and isopropanol containing 0.1% formic acid (200 μL) and mix (vortex for 3 minutes); ⑨ Centrifuge for 3 minutes (4 °C, 13523 g / min); ⑩ The supernatant obtained after centrifugation is the extracted hydrophobic material.

[0085] 5 Sample detection

[0086] Plasma and urine samples were detected separately. All plasma samples were detected on the same batch of instruments at the same time period, and all urine samples were detected on the same batch of instruments at the same time period. The instrument used for detection was an ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) purchased from SCIEX company. The chromatography mass spectrometry collection conditions of the sample were as follows:

[0087] 5.1 Liquid phase condition of hydrophilic material

[0088] ① Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2.1 mm*100 mm; ② Mobile phase: including A phase and B phase, A phase is ultrapure water (0.1% formic acid), B phase is acetonitrile (0.1% formic acid); ③ Elution gradient: 0 min water / acetonitrile (95:5 V / V), 11.0 min is 10:90 V / V, 12.0 min is 10:90 V / V, 12.1 min is 95:5 V / V, 14.0 min is 95:5 V / V; ④ Flow rate 0.4 ml / min; column temperature 40 °C; sample size 2 μL.

[0089] 5.2 Liquid phase condition of hydrophobic material

[0090] 1. Column: Thermo Accucore™ C30 column, i.d. 2.1 x 100 mm, 2.6 um; 2. Mobile phase: A phase: acetonitrile / water (60 / 40, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate); B phase: acetonitrile / isopropanol (10 / 90, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate); 3. Mobile phase gradient: 0 min A / B (80:20, V / V), 2 min (70:30, V / V), 4 min (40:60, V / V), 9 min (15:85, V / V), 14 min (10:90, V / V), 15.5 min (5:95, V / V), 17.3 min (5:95, V / V), 17.5 min (80:20, V / V), 20 min (80:20, V / V); 4. Flow rate 0.35 ml / min; column temperature 45 °C; injection volume 2 μL.

[0091] 5.3 Mass spectrometry conditions for hydrophilic substances

[0092] Electrospray ionization (ESI) temperature 500 °C, mass spectrometry voltage 5500 V (positive), -4500 V (negative), gas I (GSI) 55 psi, gas II (GS II) 60 psi, curtain gas (CUR) 25 psi, collision-activated dissociation (CAD) parameters set to high. In the triple quadrupole (Qtrap), each ion pair was scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0093] 5.4 Mass spectrometry conditions for hydrophobic substances

[0094] Electrospray ionization (ESI) temperature 500 ℃, mass spectrometry voltage 5500 V in positive ion mode, mass spectrometry voltage -4500 V in negative ion mode, ion source gas 1 (GS1) 45 psi, gas 2 (GS2) 55 psi, curtain gas (CUR) 35 psi, collision-activated dissociation (CAD) parameter setting Medium. In the triple quadrupole, each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0095] 6 Qualitative and quantitative analysis of metabolites

[0096] The present application provides a metabolite qualitative and quantitative analysis method based on triple quadrupole mass spectrometry, which combines a self-built targeted metabolite standard database (Metware Database, MWDB) and a multiple reaction monitoring mode (Multiple Reaction Monitoring, MRM) to realize high specificity and high sensitivity detection of metabolites.

[0097] The qualitative analysis of metabolites is based on the self-built targeted metabolite database MWDB, and the identification of target metabolites is realized by comparing the retention time, parent / daughter ion pair and secondary spectrum data.

[0098] The quantitative analysis of metabolites is carried out by using the MRM mode: the first quadrupole selects the parent ion, the parent ion is dissociated by collision-induced dissociation in the collision chamber to form multiple daughter ions, and the third quadrupole selects the characteristic daughter ion for quantification. This mode can significantly reduce background noise and improve the specificity and sensitivity of detection.

[0099] Figures 1-8 The total ion chromatograms of the mixed quality control sample in positive ion mode and negative ion mode are shown, which are used to evaluate the overall ion response and chromatographic separation effect. Figures 1-8 In the figure, the abscissa is the retention time of metabolite detection, in minutes; the ordinate is the ion flow intensity, in cps (counts per second).

[0100] Figure 1 The total ion chromatogram of the discovery cohort plasma sample in positive ion mode is shown;

[0101] Figure 2 The total ion chromatogram of the discovery cohort plasma sample in negative ion mode is shown;

[0102] Figure 3 Total ion current chromatograms of urine samples in the discovery cohort in positive ion mode;

[0103] Figure 4 Total ion current chromatograms of urine samples in the discovery cohort in negative ion mode;

[0104] Figure 5 Total ion current chromatograms of plasma samples in the validation cohort in positive ion mode;

[0105] Figure 6 Total ion current chromatograms of plasma samples in the validation cohort in negative ion mode;

[0106] Figure 7 Total ion current chromatograms of urine samples in the validation cohort in positive ion mode;

[0107] Figure 8 Total ion current chromatograms of urine samples in the validation cohort in negative ion mode;

[0108] Under the premise of no abnormality in the total ion current chromatogram quality control, the Analyst 1.6.3 software was used to collect and process the original mass spectrum data, the extracted ion chromatograms were generated by extracting the characteristic ions, the chromatographic peaks were identified and the peak areas were calculated to reflect the relative abundance of the corresponding metabolites in the samples, which could be used for quantitative comparison analysis between samples.

[0109] 7 Multivariate statistical analysis and differential metabolite screening

[0110] 7.1 Screening of differential metabolites between groups

[0111] After the qualitative and quantitative analysis of metabolites was completed, the chromatographic peak area data of all samples were exported for subsequent statistical analysis and differential metabolite screening. In order to extract effective information from high-dimensional metabolic data, various multivariate statistical analysis methods were used to reduce the dimension of the data and visualize the identification.

[0112] Firstly, unsupervised principal component analysis (PCA) was used to preliminarily evaluate the metabolic differences between samples. PCA can effectively reveal the metabolic profile differences between groups and the variability of samples within the group. The analysis results showed that there was a significant metabolic separation trend between different experimental groups.

[0113] Subsequently, supervised modeling was further performed using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). This method maximizes the difference between groups and removes non-relevant variations, enhancing the ability to identify differential metabolites. After log2 transformation and centralization of the raw data, the OPLS-DA model was constructed using the MetaboAnalystR package in R software, and the model showed good fitting and prediction performance.

[0114] Based on the establishment and verification of the OPLS-DA model, the following three statistical indicators were combined to screen for differential metabolites:

[0115] (1) Variable importance projection value (VIP) ≥ 1

[0116] The VIP value is calculated by the OPLS-DA model and reflects the contribution of each metabolite to the discrimination between groups. VIP ≥ 1 indicates that the metabolite has a significant effect in the model.

[0117] (2) Statistical test P value < 0.05

[0118] Hypothesis testing was performed on the relative abundance of each metabolite between the two groups of samples to screen for statistically significant variables.

[0119] (3) Fold change (FC) ≥ 1.5 or ≤ 0.67

[0120] FC represents the average abundance ratio between the treatment group and the control group and is commonly used to screen for significantly up-regulated or down-regulated metabolites. Generally, the threshold is set as FC ≥ 1.5 or FC ≤ 0.67, where FC ≥ 1.5 indicates that the metabolite is significantly up-regulated in the treatment group (such as the AAGN group) relative to the control group, and FC ≤ 0.67 indicates that the metabolite is significantly down-regulated in the treatment group relative to the control group.

[0121] Only metabolites that meet all three criteria are considered to be statistically significant and biologically meaningful differential metabolites under experimental conditions. According to this screening standard, the number of differential metabolites between each control group in the discovery cohort and the validation cohort is shown in Tables 3 and 4:

[0122] Table 3 Number of differential metabolites between AAGN and each control group in the discovery cohort

[0123] Up-regulation means the relative content of metabolites in AAGN group is increased (AAGN > control group), and down-regulation means the relative content of metabolites in AAGN group is decreased (AAGN < control group)

[0124] Table 4 Number of differential metabolites screened between AAGN and each control group in the validation cohort

[0125] Up-regulation means the relative content of metabolites in AAGN group is increased (AAGN > control group), and down-regulation means the relative content of metabolites in AAGN group is decreased (AAGN < control group)

[0126] 7.2 Screening of differential metabolites common to each comparison group

[0127] Through intersection analysis, further screening of differential metabolites common to different comparison groups. In the discovery cohort, plasma and urine sample analysis showed that the relative content of L-citrulline and homovanillic acid sulfate, sodium salt in the AAGN group was significantly higher than that in all control groups (including lupus nephritis, IgA nephropathy, minimal change nephropathy and healthy individuals). In the validation cohort, the same trend was also observed: the expression levels of the above two metabolites in the AAGN group were higher than those in each type of control group, further supporting their stability and reproducibility as potential AAGN-related metabolic markers.

[0128] The chromatographic peak area integral values and inter-group comparison results (including VIP value, P value, Fold Change value) of L-citrulline and homovanillic acid sulfate, sodium salt are shown in Tables 5 to 12. The P values of all inter-group comparisons are less than 0.05, and the differences are statistically significant.

[0129] Table 5 Chromatographic peak area statistics of L-citrulline in samples of each group in the discovery cohort

[0130] Table 6 Inter-group comparison results of L-citrulline chromatographic peak area between each group in the discovery cohort

[0131] P-value represents the significance level of comparison between groups, and FDR (False Discovery Rate) is the corrected P-value after multiple hypothesis testing. The P-value and FDR of all comparisons between groups are much less than 0.05, and the difference is statistically significant.

[0132] Table 7 Statistics of chromatographic peak area of high vanillylmandelic acid sulfate sodium salt in samples of each group in the discovery cohort

[0133] Table 8 Comparison results of chromatographic peak area of high vanillylmandelic acid sulfate sodium salt between groups in the discovery cohort

[0134] P-value represents the significance level of comparison between groups, and FDR (False Discovery Rate) is the corrected P-value after multiple hypothesis testing. The P-value and FDR of all comparisons between groups are much less than 0.05, and the difference is statistically significant.

[0135] Table 9 Statistics of chromatographic peak area of L-citrulline in samples of each group in the verification cohort

[0136] Table 10 Comparison results of chromatographic peak area of L-citrulline between groups in the verification cohort

[0137] P-value represents the significance level of comparison between groups, and FDR (False Discovery Rate) is the corrected P-value after multiple hypothesis testing. The P-value and FDR of all comparisons between groups are much less than 0.05, and the difference is statistically significant.

[0138] Table 11 Statistics of chromatographic peak area of high vanillylmandelic acid sulfate sodium salt in samples of each group in the verification cohort

[0139] Table 12 Comparison results of chromatographic peak area of high vanillylmandelic acid sulfate sodium salt between groups in the verification cohort

[0140] 7.3 ROC curve assesses the diagnostic ability of the marker

[0141] To further verify the potential of the above metabolic markers in the diagnosis of AAGN, we drew the receiver operating characteristic curve (ROC curve) and calculated the area under the curve (AUC) to evaluate its ability to distinguish AAGN from other control groups. The results showed that L-citrulline and high vanillyl sulfate sodium salt showed high AUC values in both plasma and urine samples, indicating good diagnostic performance. The specific curve results are as follows Figures 9-16 .

[0142] Figure 9 ROC curve analysis of L-citrulline in the discovery cohort plasma samples

[0143] The ROC curve was used to evaluate the diagnostic performance of L-citrulline in distinguishing AAGN patients from healthy and disease controls in the discovery cohort plasma samples. AUC = 0.863, P < 0.001.

[0144] Figure 10 ROC curve analysis of L-citrulline in the validation cohort plasma samples

[0145] The ROC curve was used to evaluate the diagnostic performance of L-citrulline in distinguishing AAGN patients from healthy and disease controls in the validation cohort plasma samples. AUC = 0.843, P < 0.001.

[0146] Figure 11 ROC curve analysis of L-citrulline in the discovery cohort urine samples

[0147] The ROC curve was used to evaluate the diagnostic performance of L-citrulline in distinguishing AAGN patients from healthy and disease controls in the discovery cohort urine samples. AUC = 0.902, P < 0.001.

[0148] Figure 12 ROC curve analysis of L-citrulline in the validation cohort urine samples

[0149] The ROC curve was used to evaluate the diagnostic performance of L-citrulline in distinguishing AAGN patients from healthy and disease controls in the validation cohort urine samples. AUC = 0.906, P < 0.001.

[0150] Figure 13 ROC curve analysis of high vanillyl sulfate sodium salt in the discovery cohort plasma samples

[0151] The ROC curve was used to evaluate the diagnostic performance of high vanillyl sulfate sodium salt in distinguishing AAGN patients from healthy and disease controls in the discovery cohort plasma samples. AUC = 1.000, P < 0.001.

[0152] Figure 14 ROC curve analysis of high vanillylmandelic acid sodium sulfate salt in the validation cohort plasma samples;

[0153] ROC curve was used to evaluate the diagnostic performance of high vanillylmandelic acid sodium sulfate salt in distinguishing AAGN patients from healthy and disease controls in the validation cohort plasma samples. AUC = 0.909, P < 0.001.

[0154] Figure 15 ROC curve analysis of high vanillylmandelic acid sodium sulfate salt in the discovery cohort urine samples;

[0155] ROC curve was used to evaluate the diagnostic performance of high vanillylmandelic acid sodium sulfate salt in distinguishing AAGN patients from healthy and disease controls in the discovery cohort urine samples. AUC = 0.824, P < 0.001.

[0156] Figure 16 ROC curve analysis of high vanillylmandelic acid sodium sulfate salt in the validation cohort urine samples;

[0157] ROC curve was used to evaluate the diagnostic performance of high vanillylmandelic acid sodium sulfate salt in distinguishing AAGN patients from healthy and disease controls in the validation cohort urine samples. AUC = 0.839, P < 0.001.

[0158] 8. Conclusion

[0159] The present application successfully screened out metabolites significantly related to AAGN through systematic metabolomics analysis combined with multivariate statistical modeling and diagnostic performance evaluation, and verified the clinical value as potential biomarkers.

[0160] Firstly, based on the PCA and OPLS-DA modeling results, the metabolic profile separation trend between the experimental group and the control group was determined, and the different metabolites with statistical significance and biological significance were identified through the triple screening standard of VIP value, P value and Fold Change. In the discovery cohort and the validation cohort, the expression levels of L-citrulline and high vanillylmandelic acid sodium sulfate salt in AAGN patients were significantly higher than those in other control groups, showing good stability and repeatability.

[0161] ROC curve further verified its diagnostic performance. The AUC value of L-citrulline in plasma and urine samples was more than 0.84, and the highest was 0.906. The AUC of high vanillylmandelic acid sodium sulfate salt in the discovery cohort plasma samples reached 1.000, and in the validation cohort, it also maintained at a high level (the highest AUC was 0.909), indicating that it had excellent sensitivity and specificity in identifying AAGN patients.

[0162] In summary, L-citrulline and sodium salt of homovanillate sulfate have good biological interpretation and diagnostic performance, and are expected to be used as an auxiliary diagnostic marker of AAGN.

[0163] The present application first proposes the combination of the two metabolites as AAGN-related non-invasive biomarkers existing in plasma and urine at the same time, which shows consistent expression characteristics in different sample types, has good detection stability and cross-sample applicability. The combination can be used for early auxiliary screening of AAGN and differential diagnosis of other glomerular diseases, and has important clinical application potential and transformation value.

[0164] Based on metabolomics, the present application screens a group of differentially expressed plasma or urine metabolites, which has the following technical advantages:

[0165] 1. Improved specificity: The L-citrulline and sodium salt of homovanillate sulfate screened by the present application can significantly distinguish AAGN from systemic lupus erythematosus nephritis (LN), IgA nephropathy (IgAN), minimal change disease (MCD) and healthy controls, and shows higher disease specificity.

[0166] 2. Suitable for non-invasive sample types, easy to promote: The metabolite combination can be stably detected in plasma and urine, avoiding the risk of invasive methods such as kidney biopsy, and is suitable for early screening and routine clinical detection, and has good sample applicability and promotion foundation.

[0167] 3. Stable expression, strong cross-cohort reproducibility: The two metabolites in the present application show consistent expression trends in the discovery cohort and the validation cohort, and the consistency of repeated detection results in different sample types is high, meeting the requirements of clinical detection index stability and repeatability, and providing data basis for subsequent standardized product development.

[0168] 4. Significant detection signal, clear expression difference: According to the metabolomics screening standard (VIP ≥ 1, P < 0.05, FC ≥ 1.5 or FC ≤ 0.67), the expression levels of L-citrulline and sodium salt of homovanillate sulfate in the AAGN group are significantly increased, and the difference is clear, which is convenient for subsequent establishment of detection model with good sensitivity and specificity.

[0169] 5. Possessing clinical transformation feasibility: both metabolites are small molecule metabolites with clear structure and stable properties, suitable for development by ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) or high-throughput immune detection platform, and convenient for constructing standardized detection reagent kit or diagnosis system, and possessing higher industrial transformation feasibility.

[0170] In summary, the combination of the metabolite markers provided by the present application can provide a higher specificity, more stable detection and more simple operation auxiliary diagnosis scheme without increasing the risk of patient trauma, and has a significant technical advantage in early identification of AAGN and identification from other glomerular diseases.

[0171] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. Use of a reagent for detecting a metabolic marker in blood and / or urine in the manufacture of a product for aiding in the diagnosis of AAGN disease, characterized in that, The metabolic markers in the blood and / or urine are L-citrulline and / or sodium salt of homovanillic acid sulfate.

2. Use according to claim 1, wherein The product comprises a kit and / or a chip.

3. The use according to claim 1, wherein The product is used for assisting in diagnosing AAGN disease by detecting the relative content of metabolic markers in the blood and / or urine.

4. The use according to claim 1, wherein The product detects the change of the level of metabolic markers in the blood and / or urine alone or jointly when assisting in diagnosing AAGN disease.

5. The use according to claim 1, wherein the compound is ###0002### When the reagent for detecting metabolic markers in the blood and / or urine is applied, the product detects that the content of L-citrulline and / or sodium salt of homovanillic acid sulfate in the blood or urine is significantly increased, thereby assisting in diagnosing the disease as AAGN disease.

6. Use according to any one of claims 1 to 5, wherein The detection method adopts one or more of ultra-high performance liquid chromatography-mass spectrometry, high performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry or liquid chromatography tandem mass spectrometry.

7. Use according to claim 6, wherein The detection adopts ultra-high performance liquid chromatography-mass spectrometry.

8. The use according to claim 6, wherein The screening method for detecting metabolic markers in the blood or urine comprises the following steps: The integral value of the peak area of the metabolic markers in the blood or urine obtained by the detection method is taken as a relative quantitative index, and the variable importance projection value, statistical test P value and abundance change fold are statistically analyzed to screen the metabolic markers in the blood or urine.

9. Use according to claim 8, wherein the compound is ###0002### When the metabolic markers in the blood or urine satisfy the variable importance projection value ≥ 1, the statistical test P value < 0.05 and the abundance change fold ≥ 1.5, it indicates a significant increase.

10. A kit comprising reagents for detecting a metabolic marker in blood and / or urine, characterized in that, The kit contains reagents for detecting metabolic markers in the blood and / or urine as claimed in any one of claims 1-9.

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