AAGN disease metabolism marker, kit and application of AAGN disease metabolism marker

Abstract

The invention relates to the technical field of nephritis medicine, in particular to an AAGN disease metabolism marker, a kit and application of the AAGN disease metabolism marker. In the blood or urine metabolism marker, the blood metabolism marker is prepared from one or more of 6-methyl nicotinamide, hypo-sulphosarcosine, guanosine, allopurinol, 8-hydroxy-2 '-deoxyguanosine, triglyceride or lysophosphatidylcholine; the urine metabolism markers comprise one or more of D-malic acid, leucyl-alanyl-valine, N-hydroxymethyl nicotinamide, DL-tribulus terrestris alkali, isoxotrexate, 1, 3-dimethyluric acid, leucyl-leucyl-phenylalanine, alloandrosterone, enterodiphenol and p-coumarin, and the urine metabolism markers comprise one or more of D-malic acid, leucyl-alanyl-valine, N-hydroxymethyl nicotinamide, DL-tribulus terrestris alkali, isoxotrexate, 1, 3-dimethyluric acid, leucyl- The metabolic marker combination and the kit are used for evaluating the disease activity state of a patient suffering from ANCA-related glomerulonephritis (AAGN), and have good repeatability, stability and biological correlation.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technical field of nephritis, and in particular to an AAGN disease 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 timely intervention, it is extremely easy to progress to end-stage renal disease, and the possibility of spontaneous remission is extremely low.

[0003] In clinical practice, accurate assessment of disease activity is crucial for guiding treatment regimens. Currently commonly used evaluation indicators include hematuria, proteinuria, C-reactive protein, serum creatinine, and serum ANCA antibody titer. However, these biomarkers generally have insufficient sensitivity, low specificity, and other issues, making it difficult to accurately reflect disease changes, especially in judging disease activity. Kidney biopsy, as the current "gold standard" for assessing AAGN disease, can directly determine the activity and severity of kidney lesions, but it is an invasive procedure with certain risks, difficult to repeat, and not suitable for dynamic monitoring. Therefore, there is an urgent need to develop a non-invasive, sensitive, specific, and repeatable biomarker for dynamic assessment of AAGN patient disease activity.

[0004] Metabolomics is a high-throughput technology that can simultaneously detect and quantify a large number of small molecule metabolites in biological samples, with the potential to reflect changes in physiological and pathological states. However, currently, there is still a lack of research programs targeting AAGN disease metabolic markers in academia and practice. SUMMARY

[0005] In order to solve the problems that the sensitivity and specificity of traditional markers (such as serum creatinine, C-reactive protein, ANCA antibody titer) are insufficient for AAGN disease activity evaluation, and the disease condition change cannot be dynamically and accurately reflected, and in order to overcome the limitations of invasive, high risk, and non-repeated monitoring of kidney biopsy as a gold standard, based on the above, the paired plasma and urine samples of AAGN patients in the active and remission stages are analyzed by metabolomics method, aiming to screen a group of differential metabolites closely related to disease activity, for developing non-invasive metabolite biomarkers of AAGN.

[0006] In order to achieve the above purpose, the application provides a reagent for detecting metabolic markers in blood or urine for preparing a product for diagnosing the activity of AAGN disease, wherein the blood or urine metabolic markers, the blood metabolic markers include one or more of 6-methylnicotinamide, hypotaurocyamine, guanosine, allopurinol, 8-hydroxy-2'-deoxyguanosine, triglyceride (18:0_18:2_20:2) or lysophosphatidylcholine (22:0 / 0:0); the urine metabolic markers include one or more of D-malic acid, leucyl-alanyl-valine, N-hydroxymethyl nicotinamide, DL-tragacanth, isoalloxanthin, 1,3-dimethyl uric acid, leucyl-leucyl-phenylalanine, allochosterone, enterodiol or p-coumaric alcohol.

[0007] Preferably, the product includes a kit and / or a chip.

[0008] Preferably, the product judges the active and remission stages of AAGN disease by detecting the relative content of blood or urine metabolic markers in blood or urine.

[0009] Preferably, the product judges the active and remission stages of AAGN disease by detecting the relative content of blood or urine metabolic markers in blood or urine.

[0010] Preferably, when the blood metabolic marker reagent is used in the product, the product detects one or more of 6-methylnicotinamide (6-Methylnicotinamide), hypotaurocyamine (Hypotaurocyamine), guanosine (Guanosine), allopurinol (Allopurinol) or 8-hydroxy-2'-deoxyguanosine (8-Hydroxy-2'-Deoxyguanosine) in blood significantly increased, or detects that triglyceride (18:0_18:2_20:2) (TG(18:0_18:2_20:2)) and / or lysophosphatidylcholine (22:0 / 0:0) (LPC(22:0 / 0:0)) in blood significantly decreased, indicating that the AAGN disease is in the active stage; When the urine metabolic marker reagent is used in a product, the product detects significant increase of one or more of D-Malic acid, Leu-Ala-Val, N-(Hydroxymethyl)nicotinamide, DL-Stachydrine or Isoxanthopterin in urine, or detects significant decrease of one or more of 1,3-Dimethyluric Acid, Leu-Leu-Phe, Adrenosterone, Enterodiol or P-Coumaryl Alcohol in urine, indicating that the AAGN disease is in an active stage.

[0011] Preferably, 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-mass spectrometry. The above detection technologies need to have detection sensitivity, repeatability and adaptability equivalent to the ultra-high performance liquid chromatography-mass spectrometry in the application.

[0012] Preferably, the detection adopts ultra-high performance liquid chromatography-mass spectrometry.

[0013] Preferably, the screening method for detecting metabolic markers in blood or urine comprises the following steps: 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 markers in blood or urine.

[0014] Preferably, when the metabolic marker in blood or urine satisfies variable importance projection value ≥ 1, statistical test P value < 0.05 and abundance change multiple ≥ 1.5, it indicates significant increase; When the metabolic marker in blood or urine satisfies variable importance projection value ≥ 1, statistical test P value < 0.05 and abundance change multiple ≤ 0.67, it indicates significant decrease.

[0015] Under the same technical concept, the application further provides a kit containing a metabolic marker in blood or urine.

[0016] The above scheme of the application has the following beneficial effects: (1) The application provides a blood or urine metabolite marker combination based on metabolomics screening, which is used for evaluating the disease activity state of an ANCA-associated glomerulonephritis (AAGN) patient; the marker combination can be detected by a non-invasive method (such as detecting a plasma or urine sample), has good repeatability, stability and biological correlation, can more accurately, dynamically and low-riskly assist in judging the activity degree or recurrence risk of the disease, and thus improves the scientificity and timeliness of clinical diagnosis and treatment decision-making. (2) The blood or urine metabolite marker kit of the application is simple and low in cost, and has high responsiveness, and provides technical basis support for accurately identifying AAGN disease activity.

[0017] Other beneficial effects of the application will be described in detail in the subsequent specific embodiment part. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figures 1-2 is a total ion current chromatogram of a plasma sample in a positive ion and a negative ion mode of a discovery cohort of the application; Figures 3-4 is a total ion current chromatogram of a urine sample in a positive ion and a negative ion mode of a discovery cohort of the application; Figures 5-6 is a total ion current chromatogram of a plasma sample in a positive ion and a negative ion mode of a verification cohort of the application; Figures 7-8 is a total ion current chromatogram of a urine sample in a positive ion and a negative ion mode of a verification cohort of the application; Figure 9 is a volcano plot of a differential metabolite of a plasma sample of a discovery cohort of the application; Figure 10 is a volcano plot of a differential metabolite of a plasma sample of a verification cohort of the application; Figure 11 is a volcano plot of a differential metabolite of a urine sample of a discovery cohort of the application; Figure 12 is a volcano plot of a differential metabolite of a urine sample of a verification cohort of the application. DETAILED DESCRIPTION

[0019] To make the technical problems, technical solutions and advantages to be solved by the application more clear, 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 application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0020] In the description of the present application, it should be pointed out that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply 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 a limitation on 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.

[0021] In the description of the present application, it should be pointed out 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 communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0022] Example 1 Screening of metabolic markers in blood or urine for AAGN activity evaluation 1. Study subjects 1.1 Inclusion criteria for study subjects After the hospital ethics committee agreed, a total of 9 AAGN patients were included in the discovery cohort, and 5 patients were included in the validation cohort. The patients in the discovery cohort were newly diagnosed cases from December 2019 to June 2022, and the patients in the validation cohort came from July 2022 to December 2023. The two cohorts were completely independent and there was no repeated individual. The inclusion criteria for AAGN patients are as follows: Inclusion criteria: (1) Diagnosis in accordance with the 2012 Chapel Hill vasculitis consensus; (2) AAGN patients confirmed by renal biopsy; Exclusion criteria: (1) Patients with critical illness or concurrent active infection (various types of hepatitis, tuberculosis, AIDS, etc.); (2) In the preparation, pregnancy or lactation period; (3) History of malignant tumor; (4) Existence of any other multisystem autoimmune diseases, such as systemic lupus erythematosus, anti-glomerular basement membrane, etc.; (5) Combined with diabetes mellitus, hyperthyroidism; (6) Received hemodialysis or plasma replacement therapy within two weeks before sampling; (7) Patients who had received treatment with immunosuppressants, hormones and lipid-lowering drugs prior to entering the study; (8) Patients with EGPA.

[0023] 2 Sample collection Collection and storage methods Blood and urine samples from all patients were collected prior to the first diagnosis and the start of immunosuppressive treatment. The samples from all study subjects were collected in the morning on an empty stomach. After sample collection, centrifugation was performed immediately, and then the supernatant was stored in a -80 °C refrigerator. Before detecting metabolites in the test samples, all samples were kept in a -80 °C refrigerator to avoid repeated freeze-thaw cycles.

[0024] 3 Standards and reagents The standards and reagents used in the experiment are shown in Table 1.

[0025] Table 1 Standards and reagents

[0026] 4 Sample extraction 4.1 Extraction method of plasma samples Extraction of hydrophilic substances in plasma samples; ① After thawing the plasma sample on ice, vortex for 10 seconds and mix well, take 50 μL into a centrifuge tube; ② Add 20% acetonitrile methanol internal standard extraction solution (300 μL) to the centrifuge tube of the previous step; ③ Vortex for 3 minutes to mix the sample well; ④ Centrifuge for 10 minutes (4°C, 13523g / min); ⑤ Take 200 μL of supernatant into a new centrifuge tube; ⑥ Place the centrifuge tube in 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 μL for subsequent detection and analysis.

[0027] Extraction of hydrophobic substances in plasma samples; ① After thawing the plasma sample on ice, vortex for 10 seconds and mix well, take 50 μL into a centrifuge tube; ② Add 1 mL of internal standard-containing lipid extraction solution to the centrifuge tube of the previous step, and the lipid extraction solution is prepared by mixing methyl tert-butyl ether and methanol at a ratio of 3:1; ③ Vortex for 15 minutes to mix the sample well; ④ Mix well after adding ultrapure water (200 μL) to the sample (vortex for 1 minute); ⑤ Centrifuge for 10 minutes (4°C, 13523g / min); ⑥ Take 200 μL of supernatant into a new centrifuge tube; ⑦ Concentrate the sample; ⑧ After the sample is completely dried, add a mixture of acetonitrile and isopropanol containing 0.1% formic acid (200 μL) and 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.

[0028] 4.2 Urine sample extraction method Extraction of hydrophilic substances in urine samples; ① After the urine sample was thawed, it was vortexed for 10 seconds, and 200 μΐ, was taken into a clean centrifuge tube after mixing; ② 20% acetonitrile methanol extract solution containing an internal standard (200 μΐ,) was added to the centrifuge tube; ③ The sample was thoroughly mixed (vortexed for 3 minutes); ④ Centrifugation for 10 minutes (4 °C, 13523 g / min); ⑤ 350 μΐ, of supernatant was taken into a new centrifuge tube; ⑥ The sample was concentrated; ⑦ After the sample was completely dried, 70% methanol solution (150 μΐ,) was added and mixed (vortexed for 3 minutes); ⑧ Ice water bath ultrasonic (10 minutes); ⑨ Centrifugation (4 °C, 13523 g / min) for 3 minutes, and the supernatant obtained after centrifugation was the hydrophilic substance in the urine; ⑩ 120 μΐ, was taken for subsequent detection and analysis.

[0029] Extraction of hydrophilic substances in urine samples; ① After the urine sample was thawed, it was vortexed for 10 seconds, and 200 μΐ, was taken into a clean centrifuge tube after mixing; ② 20% acetonitrile methanol extract solution containing an internal standard (200 μΐ,) was added to the centrifuge tube; ③ The sample was thoroughly mixed (vortexed for 3 minutes); ④ Centrifugation for 10 minutes (4 °C, 13523 g / min); ⑤ 350 μΐ, of supernatant was taken into a new centrifuge tube; ⑥ The sample was concentrated; ⑦ After the sample was completely dried, 70% methanol solution (150 μΐ,) was added and mixed (vortexed for 3 minutes); ⑧ Ice water bath ultrasonic (10 minutes); ⑨ Centrifugation (4 °C, 13523 g / min) for 3 minutes, and the supernatant obtained after centrifugation was the hydrophilic substance in the urine; ⑩ 120 μΐ, was taken for subsequent detection and analysis.

[0030] 5 Sample detection 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: ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS), purchased from SCIEX company.

[0031] 5.1 Hydrophilic substance liquid phase condition ① Column: Waters ACQUITY UPLC HSS T3 C18 1.8 μιη, 2.1 mm 100 mm; ② mobile phase: including A and B phases, 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; injection volume 2 μL.

[0032] 5.2 Hydrophobic substance liquid phase condition ① column: Thermo Accucore™ C30 column, i.d. 2.1x100 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.

[0033] 5.3 Hydrophilic substance mass spectrometry condition The temperature of electrospray ionization (ESI) is 500 °C, the mass spectrometry voltage is 5500 V (positive), -4500 V (negative), gas I (GSI) is 55 psi, gas II (GS II) is 60 psi, curtain gas (CUR) is 25 psi, and the collision-activated dissociation (CAD) parameter is set to high. In the triple quadrupole (Qtrap), each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0034] 5.4 Mass spectrometry conditions for hydrophobic substances The electrospray ionization (ESI) temperature was 500 °C, the mass spectrometry voltage was 5500 V in positive ion mode and -4500 V in negative ion mode, the ion source gas 1 (GS1) was 45 psi, the gas 2 (GS2) was 55 psi, the curtain gas (CUR) was 35 psi, and the collision-activated dissociation (CAD) parameter was set to Medium. In the triple quadrupole, each ion pair was scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0035] 6 Qualitative and quantitative analysis of metabolites 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.

[0036] 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.

[0037] The quantitative analysis of metabolites is carried out by using the MRM mode: the first quadrupole selects the parent ion, the parent ion is collision-induced dissociated in the collision chamber to form multiple daughter ions, and the third quadrupole screens the characteristic daughter ions for quantification. This mode can significantly reduce background noise and improve the specificity and sensitivity of detection.

[0038] 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; and the ordinate is the ion flow intensity, in cps (counts per second).

[0039] Figure 1 The total ion chromatogram of the discovery cohort plasma sample in positive ion mode is shown; Figure 2 The total ion chromatogram of the discovery cohort plasma sample in negative ion mode is shown; Figure 3Total ion current chromatograms of urine samples in the discovery cohort in positive ion mode; Figure 4 Total ion current chromatograms of urine samples in the discovery cohort in negative ion mode; Figure 5 Total ion current chromatograms of plasma samples in the validation cohort in positive ion mode; Figure 6 Total ion current chromatograms of plasma samples in the validation cohort in negative ion mode; Figure 7 Total ion current chromatograms of urine samples in the validation cohort in positive ion mode; Figure 8 Total ion current chromatograms of urine samples in the validation cohort in negative ion mode; Under the premise that the quality control of total ion current chromatograms is normal, the original mass spectrum data is collected and processed by using Analyst 1.6.3 software, the extracted ion current chromatograms are generated by extracting characteristic ions, the chromatographic peaks are identified and the peak areas are calculated to reflect the relative abundance of the corresponding metabolites in the samples, which can be used for quantitative comparison analysis between samples.

[0040] 7 Screening of differential metabolites 7.1 Sample grouping mode and research design The AAGN patients involved in the present application all received standardized induction and maintenance period treatment. The treatment regimen adopted is based on the 2024 Kidney disease: improving global outcomes (KDIGO) guidelines, the 2021 Chinese guidelines for the diagnosis and treatment of anti-neutrophil cytoplasmic antibody-associated glomerulonephritis

[75] , and the European league against rheumatism (EULAR) related recommendations, to ensure that the clinical treatment path is widely comparable and consistent in the international range. Remission is defined as no disease activity, with a Birmingham vasculitis activity score (BVAS) score of 0.

[0041] The overall design of the present research is divided into two stages: a discovery stage and a validation stage. All the included subjects are diagnosed as AAGN through kidney biopsy and sign the informed consent, and the research is approved by the ethics committee. The research samples are as follows: Discovery cohort: new AAGN patients diagnosed between December 2019 and June 2022 are included; a total of 9 pairs of plasma samples and 6 pairs of urine samples are collected from the same patient at two time points of active and remission periods.

[0042] Validation cohort: Patients with AAGN diagnosed independently from July 2022 to December 2023 were included; a total of 5 pairs of plasma samples and 4 pairs of urine samples were collected, and the collection method was consistent with the discovery cohort, and there was no overlap between individuals.

[0043] All samples were collected at key time points before or early in treatment, and sample processing, metabolic extraction, mass spectrometry detection, and data analysis were completed on the same platform, ensuring the comparability of data between groups. Using paired sample design with different states of the same patient, we can effectively control the background differences between individuals and improve the reliability of screening results.

[0044] The above cohort setting helps to further verify the stability and consistency of potential differential metabolites after preliminary screening, laying a foundation for subsequent biomarker screening, model construction, and clinical translation.

[0045] 7.2 Screening criteria for differential metabolites between groups After completing the qualitative and quantitative analysis of metabolites, the chromatographic peak area data of all samples were derived for subsequent statistical analysis and differential metabolite screening. To extract effective information from high-dimensional metabolic data, this study used multiple multivariate statistical analysis methods for dimensionality reduction modeling and visual recognition.

[0046] First, unsupervised principal component analysis (PCA) was used to preliminarily evaluate metabolic differences between samples. PCA can effectively reveal metabolic profile differences between groups and variability within groups. The analysis results showed that there was a clear metabolic separation trend between different experimental groups.

[0047] Subsequently, supervised modeling was further performed using orthogonal partial least squares discriminant analysis (OPLS-DA). This method maximizes group differences and removes non-relevant variations, enhancing the ability to identify differential metabolites. After log2 transformation and centering of the original data, the OPLS-DA model was constructed using the MetaboAnalystR package in R software. The model showed good fitting and prediction performance.

[0048] Based on the establishment and verification of the OPLS-DA model, differential metabolites were screened in combination with the following three statistical indicators: (1) Variable importance projection value (VIP) ≥ 1

[0049] VIP values were calculated by OPLS-DA model, reflecting the contribution of each metabolite to the discrimination between groups. VIP ≥ 1 indicates that the metabolite has a significant effect in the model.

[0050] (2) Statistical test P value < 0.05 Hypothesis testing was performed on the relative abundance of each metabolite between the two groups of samples to screen variables with statistically significant differences.

[0051] (3) Fold change (FC) ≥ 1.5 or ≤ 0.67 FC represents the average abundance ratio between the treatment group and the control group, and is commonly used to screen metabolites with significantly up-regulated or down-regulated expression. Generally, the threshold is set as FC ≥ 1.5 or FC ≤ 0.67, wherein: FC ≥ 1.5 indicates that the metabolite is significantly up-regulated in the treatment group relative to the control group; FC ≤ 0.67 indicates that the metabolite is significantly down-regulated in the treatment group relative to the control group.

[0052] 7.3 Screening of differential metabolites in plasma samples and verification analysis

[0053] The present application is based on paired plasma samples collected from the same AAGN patient in the active and remission stages of the disease, to evaluate the metabolic dynamic changes in different disease states. The average follow-up time of the discovery cohort is 25.08 ± 10.25 months, and the verification cohort is 16.82 ± 5.17 months. All remission samples were collected after the patient reached clinical remission (BVAS score of 0), ensuring that the metabolic changes truly reflect the disease activity level.

[0054] The discovery cohort obtained 9 pairs of paired plasma samples, and the verification cohort obtained 5 pairs of paired samples. By comparing the plasma metabolic profile changes of each patient in the active and remission stages, 504 and 52 differential metabolites were identified in the discovery cohort and the verification cohort, respectively (screening criteria: VIP ≥ 1, P < 0.05, FC ≥ 1.5 or FC ≤ 0.67). The volcano plot of differential metabolites in plasma samples is shown in Figure 1 and 2 .

[0055] Figure 9 Volcano plot of differential metabolites in plasma samples of the discovery cohort; The figure shows the differences in metabolites between remission and active phase plasma samples. The horizontal axis is the log-transformed relative content difference fold (Log2FC), and the vertical axis is the variable importance projection value (VIP). Each point in the figure represents a metabolite. According to the set screening threshold (VIP ≥ 1, FC ≥ 1.5 or FC ≤ 0.67), 119 up-regulated metabolites, 548 down-regulated metabolites, and 2001 non-significant difference metabolites were identified.

[0056] Figure 10 To verify the volcano plot of the differential metabolites in the plasma samples of the cohort; The figure shows the differences in metabolites between remission and active phase plasma samples. The horizontal axis is the log-transformed relative content difference fold (Log2FC), and the vertical axis is the variable importance projection value (VIP). Each point in the figure represents a metabolite. According to the set screening threshold (VIP ≥ 1, FC ≥ 1.5 or FC ≤ 0.67), 119 up-regulated metabolites, 548 down-regulated metabolites, and 2001 non-significant difference metabolites were identified.

[0057] After cross-comparison of the screening results of the two cohorts, 14 overlapping metabolites were found, of which 7 showed consistent trends in both cohorts, with good reproducibility and biological consistency.

[0058] As shown in Table 2, of the 7 metabolites, 5 were significantly increased in the active phase compared to the remission phase, including 6-methylnicotinamide, hypotaurocyamine, guanosine, allopurinol, and 8-hydroxy-2'-deoxyguanosine. These metabolites had high VIP values and significant up-regulation in both cohorts, suggesting that they may play a role in pathological processes such as enhanced inflammation or nucleic acid metabolism.

[0059] In addition, triglyceride (TG(18:0_18:2_20:2)) and lysophosphatidylcholine (LPC(22:0 / 0:0)) were significantly decreased in the active phase, which may be related to lipid metabolism disorders or changes in cell membrane composition.

[0060] Overall, the 7 metabolites showed consistent expression trends in two independent cohorts, with clear change direction and significant differences, supporting their use as stable and reliable plasma metabolite combinations to assist in determining the disease activity status of AAGN, and providing a basis for subsequent individualized management and dynamic monitoring.

[0061] Table 2 Plasma metabolites screened by the present application for AAGN activity assessment and their statistical indicators

[0062] “Significant increase” means that the level of the metabolite in the active stage of the disease is significantly higher than that in the remission stage of the disease; “significant decrease” means that the level of the metabolite in the active stage of the disease is significantly lower than that in the remission stage of the disease.

[0063] The P value and the corrected P value of the sample comparison are both less than 0.05.

[0064] 7.4 Screening of differential metabolites in urine samples and verification analysis

[0065] To further evaluate the metabolic characteristics of AAGN patients in different disease states, paired urine samples of the same patient in the active and remission stages of the disease were collected, and the metabolic profile changes were analyzed to screen differential metabolites with consistency and discriminability. The median follow-up time of the discovery cohort was 22.00 (9.50, 34.00) months, and that of the verification cohort was 16.86 ± 5.13 months. A total of 6 pairs of discovery cohort samples and 4 pairs of verification cohort samples were obtained, all of which were collected from patients diagnosed with AAGN, and the remission stage samples were collected when the BVAS score was 0.

[0066] Based on the above samples, the metabolic profiles in the active and remission stages of the disease were compared in pairs, and 195 and 109 differentially expressed metabolites were screened out in the discovery and verification cohorts, respectively (screening criteria: VIP ≥ 1, P < 0.05, FC ≥ 1.5 or FC ≤ 0.67). The volcano plot of the differential metabolites of the urine samples is shown in Figure 3 and 4 .

[0067] Figure 11 is the volcano plot of the differential metabolites of the urine samples in the discovery cohort; The figure shows the differences in metabolites between the urine samples in the remission and active stages. The horizontal axis is the logarithmic transformed relative content difference fold (Log2FC), and the vertical axis is the variable importance projection value (VIP). Each point in the figure represents a metabolite. According to the set screening threshold (VIP ≥ 1, FC ≥ 1.5 or FC ≤ 0.67), a total of 306 up-regulated metabolites, 254 down-regulated metabolites, and 1490 non-significant differential metabolites were identified.

[0068] Figure 12 is the volcano plot of the differential metabolites of the urine samples in the verification cohort; This figure illustrates the differences in metabolites between urine samples from the remission and active phases. The horizontal axis represents the logarithmic fold change in relative content (Log2FC), and the vertical axis represents the variable importance projection (VIP). Each point in the figure represents a metabolite. Based on the set screening thresholds (VIP ≥ 1, FC ≥ 1.5 or FC ≤ 0.67), a total of 329 upregulated metabolites, 192 downregulated metabolites, and 1204 metabolites with no significant difference were identified.

[0069] After cross-comparison of the results from the two cohorts, a total of 18 overlapping differential metabolites were identified, of which 16 showed consistent trends in both cohorts, demonstrating good reproducibility and data consistency.

[0070] As shown in Table 3, among these 16 metabolites with consistent differences, 10 showed the most stable performance and a clear direction, including 5 metabolites that were significantly elevated during the active phase of the disease: D-malic acid, leu-Ala-Val, N-(Hydroxymethyl)nicotinamide, DL-Stachydrine, and isoxanthopterin; and 5 metabolites that were significantly decreased during the active phase: 1,3-Dimethyluric acid, leu-Leu-Phe, adrenosterone, enterodiol, and p-coumaryl alcohol.

[0071] These metabolites showed consistent expression trends in both phases, with most exhibiting high VIP values ​​and significant abundance changes, suggesting that their metabolic responses under AAGN activity have biological significance. Upregulated metabolites were mostly associated with organic acid metabolism, nicotinamide derivatives, and amino acid metabolism; while downregulated metabolites were mostly involved in steroid metabolism, aromatic metabolites, and polyphenols, possibly reflecting energy regulation and redox stress responses under inflammatory conditions.

[0072] In summary, the above-mentioned urinary metabolites show clear differences in expression and good reproducibility under different disease states, and have the potential to become non-invasive biomarkers for assessing the activity of AAGN. They can be used to assist in disease monitoring and efficacy evaluation, and promote the development and clinical translation of non-invasive detection technologies.

[0073] Table 3. Urinary metabolites and their statistical indicators screened by this invention for AAGN activity assessment

[0074] “Significant increase” means that the level of the metabolite in the active stage of the disease is significantly higher than that in the remission stage of the disease; “significant decrease” means that the level of the metabolite in the active stage of the disease is significantly lower than that in the remission stage of the disease.

[0075] Both the P value and the corrected P value of the sample comparison are less than 0.05.

[0076] 8. Conclusion

[0077] The present application is based on a paired design, and the plasma and urine metabolic characteristics of AAGN patients in the active and remission stages of the disease are systematically evaluated. Through prospective sample collection and multivariate statistical analysis, a combination of differential metabolites with statistical significance, consistent expression trend and good reproducibility is screened out in plasma and urine, respectively.

[0078] In the plasma sample, 7 metabolites showing consistent change trend in the discovery cohort and the validation cohort were finally screened out, including 5 markers significantly increased in the active stage (such as 6-methyl nicotinamide, guanosine, etc.) and 2 markers significantly decreased (such as TG(18:0_18:2_20:2), LPC(22:0 / 0:0)). The above metabolites reflect the characteristics of enhanced inflammation and lipid disorder in the active state of AAGN.

[0079] In the urine sample, a combination of metabolites stably expressed in both cohorts was identified, including organic acids, amino acid derivatives, nicotinamide and steroid metabolites. Among them, D-malic acid, DL-tragacanthine, etc. are significantly increased in the active stage, while 1,3-dimethyl uric acid, allochosterone, etc. are significantly decreased in the active stage, suggesting that they play a key role in inflammation-driven systemic metabolic remodeling.

[0080] The combination of body fluid metabolites screened by the present application has the advantages of clear change trend, non-invasive source and good data reproducibility, and can be used as an auxiliary evaluation index for the active state of AAGN. The related markers are expected to be applied to the development of in vitro diagnostic products, and promote the precision process of individualized treatment and dynamic monitoring of AAGN.

[0081] Based on metabolomics, the present application screens a combination of differentially expressed plasma or urine metabolites, which has the following technical advantages: 1. Non-invasive detection: the selected metabolites can be detected through plasma or urine samples, avoiding the invasive operation of kidney biopsy, reducing the risk of patient examination, and facilitating multiple sampling and dynamic evaluation at different stages of the disease;

[0082] 2. Significant difference in expression: The screened metabolites showed stable and significant expression differences between the active and remission phases of AAGN. Among them, 6-methylnicotinamide in plasma had Log2FC of 12.300 and 12.540 in the two cohorts, respectively, and D-malic acid in urine had Log2FC of 12.354 and 1.584 in the two cohorts, respectively, with obvious difference in amplitude, indicating that it has high sensitivity in the active state of the disease; 3. Statistical stability and repeatability: The VIP values of all selected metabolites are greater than 1, and the change direction is consistent in the discovery cohort and the validation cohort, indicating that they have good repeatability and model stability in different sample sources; 4. Strong dynamic evaluation ability: The metabolite level and the disease activity state show synchronous increase or decrease. For example, 1,3-dimethyluric acid expression decreased significantly in the active phase, with Log2FC of −11.36 in the discovery cohort and −7.62 in the validation cohort, indicating that it has good responsiveness in the remission process of the disease; 5. Facilitate the development of detection methods and clinical transformation: The screened metabolites are small molecule compounds with clear structure and stable existence in body fluids, which are suitable for the construction of detection methods based on ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) or other platforms, providing potential technical basis for the evaluation of AAGN activity.

[0083] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which should also be considered within the scope of protection of the present application.

Claims

1. Use of a reagent for detecting a metabolic marker in blood or urine in the manufacture of a product for diagnosing AAGN disease activity, characterized in that, The blood metabolic markers include one or more of 6-methylnicotinamide, hypotaurine, guanosine, allopurinol, 8-hydroxy-2'-deoxyguanosine, triglyceride (18:0_18:2_20:2), or lysophosphatidylcholine (22:0 / 0:0); and the urine metabolic markers include one or more of D-malic acid, leucyl-alanyl-valine, N-hydroxymethyl nicotinamide, DL-trigonelline, isoalloxazine, 1,3-dimethyluric acid, leucyl-leucyl-phenylalanine, alloandrosterone, enterodiol, or p-coumaric alcohol.

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

3. The use according to claim 1, wherein The product determines the active phase and the remission phase of the AAGN disease by detecting the relative content of the blood or urine metabolic markers in the blood or urine.

4. The use according to claim 1, wherein The product detects the level change of the blood or urine metabolic markers alone or jointly when determining the active phase and the remission phase of the AAGN disease.

5. The use according to claim 1, wherein the compound is ###0002### When the blood metabolic marker reagent is used in the product, the product detects a significant increase of one or more of 6-methylnicotinamide, hypotaurine, guanosine, allopurinol, or 8-hydroxy-2'-deoxyguanosine in the blood, or detects a significant decrease of triglyceride (18:0_18:2_20:2) and / or lysophosphatidylcholine (22:0 / 0:0) in the blood, indicating that the AAGN disease is in the active phase; When the urine metabolic marker reagent is used in the product, the product detects a significant increase of one or more of D-malic acid, leucyl-alanyl-valine, N-hydroxymethyl nicotinamide, DL-trigonelline, or isoalloxazine in the urine, or detects a significant decrease of one or more of 1,3-dimethyluric acid, leucyl-leucyl-phenylalanine, alloandrosterone, enterodiol, or p-coumaric alcohol in the urine, indicating that the AAGN disease is in the active phase.

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 compound is ###0002### The screening method for detecting the metabolic markers in the blood or urine includes 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, the statistical test P value, and the abundance change multiple 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 multiple ≥ 1.5, it indicates a significant increase; 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 multiple ≤ 0.67, it indicates a significant decrease.

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

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