Diagnostic markers and diagnostic products for systemic lupus erythematosus

Abstract

The present invention discloses diagnostic markers and diagnostic products for systemic lupus erythematosus. The first aspect of the present invention provides the use of a reagent for quantitatively detecting at least one of the following metabolites in the preparation of a diagnostic product for systemic lupus erythematosus: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, 12,13-DiHOME. According to the application of the embodiment of the present application, there are at least the following beneficial effects: the present invention combines metabolomics with bioinformatics analysis methods to screen for statistically significant different serum metabolite components between systemic lupus erythematosus patients and normal people, and uses these metabolites as diagnostic markers for systemic lupus erythematosus, which makes up for the shortcomings of existing systemic lupus erythematosus-related indicators and has a higher diagnostic value.

Description

Technical Field

[0001] The present application relates to the technical field of systemic lupus erythematosus diagnosis, and in particular to diagnostic markers and diagnostic products for systemic lupus erythematosus. Background Art

[0002] Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the production of large amounts of autoantibodies, triggering a systemic inflammatory response, leading to multiple tissue and organ damage and even death. SLE varies in severity, and currently there is no cure, requiring patients to take lifelong medication. Therefore, accurate clinical diagnosis, enabling early detection and standardized treatment, is crucial for alleviating and controlling the disease.

[0003] However, SLE often affects multiple tissues and organs throughout the body, resulting in highly heterogeneous clinical manifestations and difficult clinical diagnosis. The current clinical SLE classification and diagnostic criteria (2019 EULAR / ACR SLE classification and diagnostic criteria) include one entry criterion, three immunological domains, and seven clinical domains. Patients require multiple tests for diagnosis, including laboratory tests (such as blood count, urine count, liver function, complement, antinuclear antibodies, and cerebrospinal fluid examination), imaging studies (such as ultrasound for pericardial effusion and pulmonary hypertension; CT for interstitial lung disease), physical examination (including head, chest, abdomen, skin, neurological awareness, musculoskeletal examination), and special examinations (such as renal biopsy and lumbar puncture). However, these diagnostic criteria have many drawbacks. Their practical application requires a high level of clinical experience, is difficult and time-consuming, and the multiple invasive tests involved can be very painful for patients.

[0004] Metabolomics primarily studies small molecule metabolites that serve as substrates and products of various metabolic pathways, thereby obtaining information beyond genomics and proteomics. It is well known that the occurrence and development of any disease will affect human metabolism, resulting in significant changes in human metabolites. This suggests that researchers can find markers related to disease diagnosis by comparing the body's physiology and disease state, and the differences in metabolites between different types and stages of the same disease. Among various metabolites from different sources, serum metabolites are relatively stable and easy to quantify. Therefore, non-invasive diagnosis and monitoring of diseases through serum metabolites is highly feasible.

[0005] In recent years, major breakthroughs have been made in basic research on SLE, with the discovery of several new potential diagnostic biomarkers. However, these are mostly based on genomic and proteomics studies, and these biomarkers have low specificity and sensitivity, making them of limited clinical significance. Therefore, it is necessary to discover new, more valuable diagnostic biomarkers for SLE from a metabolomics perspective. Summary of the Invention

[0006] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a diagnostic marker for systemic lupus erythematosus.

[0007] In a first aspect of the present application, a reagent for quantitatively detecting at least one of the following metabolites is provided for use in the preparation of a diagnostic product for systemic lupus erythematosus: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, 12,13-DiHOME.

[0008] The application of the embodiments of the present application has at least the following beneficial effects:

[0009] This application uses metabolomics combined with bioinformatics analysis methods to screen out statistically significant differences in serum metabolite components between patients with systemic lupus erythematosus and normal people, and uses these metabolites as diagnostic markers for systemic lupus erythematosus, which makes up for the shortcomings of existing systemic lupus erythematosus-related indicators and has high diagnostic value.

[0010] LTB4 (CAS: 71160-24-2) refers to leukotriene B4, a pro-inflammatory lipid mediator produced from arachidonic acid through the sequential activity of 5-lipoxygenase, 5-lipoxygenase-activating protein, and leukotriene A4 hydrolase. LTB4 has well-characterized biological effects, including promoting leukocyte chemotaxis and regulating pro-inflammatory cytokines by binding to G protein-coupled receptors called Ltb4r1 or Ltb4r2.

[0011] 9-OxoODE (CAS: 54232-59-6), also known as 9-KODE, is 9-oxo-10E, 12Z-octadecadienoic acid, a linoleic acid derivative.

[0012] Arachidonic acid (AA) (CAS: 506-32-1) is a substance that mediates inflammation and multiple organ and system functions. ARA is a substrate for the synthesis of a range of bioactive compounds (eicosanoids), including prostaglandins, thromboxanes, and leukotrienes. These products can act as mediators themselves or as regulators of other processes, such as platelet aggregation, coagulation, smooth muscle contraction, leukocyte chemotaxis, inflammatory cytokine production, and immune function.

[0013] 5-OxoETE (CAS: 106154-18-1), also known as 5-KETE or 5-keto-ete, is 5-Oxo-eicosatetraenoic acid, a long-chain fatty acid obtained by the oxidation of 5-HETE by a specific dehydrogenase.

[0014] 15-OxoEDE (CAS: 105835-44-7), also known as 15-KEDE, is 15-oxo-11Z, 13E-eicosadienoic acid, a long-chain fatty acid.

[0015] 9,10-DiHOME (CAS: 263399-34-4), also known as 9,10-DHOME, is 9,10-dihydroxy-12Z-octadecenoic acid, a derivative of linoleic acid diol. As a naturally occurring proliferator-activated receptor (PPAR) γ2 ligand, it stimulates adipocytes and inhibits osteoblast differentiation. Additionally, it exhibits neutrophil chemotactic activity and can inhibit multiple aspects of neutrophil activation by interacting with respiratory burst inhibitors.

[0016] Adrenic acid (AdA) (CAS: 28874-58-0) is found in blood and human myelin tissue. Adrenic acid participates in the metabolism of α-linolenic acid and linoleic acid. This unsaturated fatty acid is also metabolized by cells into bioactive products. Adrenic acid is a prostacyclin inhibitor and may be a potential prothrombotic factor.

[0017] DHA (CAS: 6217-54-5) is docosahexaenoic acid, an omega-3 essential fatty acid. DHA is most commonly found in fish oil. Dietary DHA can reduce blood triglyceride levels in humans and lower the risk of heart disease.

[0018] 18-HEPE (CAS: 141110-17-0) is (+ / -)-18-hydroxy-5Z,8Z,11Z,14Z,16E-eicosapentaenoic acid, a human isoflavone metabolite and the conjugate acid of 18-HEPE(1-).

[0019] 12,13-DiHOME (CAS: 263399-35-5) is (9Z)-12,13-dihydroxyoctadec-9-enoic acid, which is produced by bacterial epoxide hydrolase (EH).

[0020] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine or all ten of the metabolites LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME.

[0021] In some embodiments of the present application, the metabolites quantitatively detected by the reagent exclude 9,10-DiHOME, 18-HEPE or 12,13-DiHOME alone as a marker.

[0022] In some embodiments of the present application, the reagent quantitatively detects any one of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, DHA.

[0023] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six or all seven of the metabolites LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, and DHA.

[0024] In some embodiments of the present application, the reagent quantitatively detects any one of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, DHA, or quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine or all ten of the metabolites LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, 12,13-DiHOME.

[0025] In some embodiments of the present application, the reagent detects metabolites by liquid chromatography-mass spectrometry.

[0026] In some embodiments of the present application, the metabolites are derived from a blood sample, including but not limited to plasma and serum samples.

[0027] In some embodiments of the present application, when the level of at least one of 18-HEPE and AdA is increased relative to the normal control, or when the level of at least one of LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, and 12,13-DiHOME is decreased relative to the normal control, the patient is judged to have systemic lupus erythematosus.

[0028] In the examples of the present application, the applicant found that in SLE patients, the level of 18-HEPE was significantly higher than that of a normal control group consisting of healthy people, while the levels of LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, and 12,13-DiHOME were significantly lower than those of a normal control group consisting of healthy people. Therefore, based on the levels of these metabolites, a relatively accurate prediction of whether a subject has SLE can be made. Specifically, a set value for the level of the above-mentioned metabolites can be given, and the set value can be determined based on the level in the normal control (such as a serum sample of a healthy person). For example, the average value of the metabolite level of normal samples with an appropriate number of samples is selected, or a reasonable multiple is set based on the average value as the level of the normal control; for example, for metabolites that are low in expression in SLE patients, the setting values ​​are such as 0.9 times, 0.8 times, 0.7 times, 0.6 times, 0.5 times, etc. When the level of the specified metabolite of the subject is lower than the set value, it indicates that the level of the metabolite of the subject is decreased relative to the normal control, and the subject is judged to have systemic lupus erythematosus; for metabolites that are highly expressed in SLE patients, the setting values ​​are such as 1.1 times, 1.2 times, 1.3 times, 1.5 times, 2 times, etc. When the level of the specified metabolite of the subject is higher than the set value, it indicates that the level of the metabolite of the subject is increased relative to the normal control, and the subject is judged to have systemic lupus erythematosus. It is understood that the set value determined based on the average value or a multiple of the average value needs to have good classification significance. The known samples can be tested using commonly used statistical test methods based on the classification of the set value. When the classification result based on the set value is statistically significant, it indicates that the set value can be used as a judgment standard. Further, when multiple metabolites are used as diagnostic markers, a corresponding diagnostic model can be constructed for the selected metabolites, and the level of the metabolite can be associated with the risk of systemic lupus erythematosus. The levels of each selected lipid metabolite (it is understood that in order to ensure that the data are at the same scale, the lipid level can be mapped in advance for normalization or standardization) are weighted and summed to construct a formula for the lipid risk score. The levels of each metabolite of the subject are substituted into the formula to obtain a risk score, and the diagnosis result of the subject (for example, the risk of having systemic lupus erythematosus) is obtained according to the size of the risk score. It is understandable that in order to ensure the accuracy of the risk score formula and the diagnostic results based on the risk score, some statistical test methods can also be used for testing. When the results are statistically significant, it indicates that the formula or the diagnosis based on the score can be used as a judgment standard.

[0029] In a second aspect of the present application, a diagnostic product for systemic lupus erythematosus is provided, which comprises a reagent for quantitatively detecting at least one of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME.

[0030] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine or all ten of the metabolites LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME.

[0031] In some embodiments of the present application, the metabolites quantitatively detected by the reagent exclude 9,10-DiHOME, 18-HEPE or 12,13-DiHOME alone as a marker.

[0032] In some embodiments of the present application, the reagent quantitatively detects any one of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, DHA.

[0033] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six or all seven of the metabolites LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, and DHA.

[0034] In some embodiments of the present application, the reagent detects metabolites by liquid chromatography-mass spectrometry.

[0035] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1It is the differential expression and metabolic pathway of oxidized lipids in patients with systemic lupus erythematosus and normal controls in Example 1 of the present application, wherein A is the Euclidean distance analysis result of the differential expression of oxidized lipids in the SLE group and the control group, B and C are oxidized lipids that are significantly upregulated or downregulated in the linolenic acid metabolic pathway and the linoleic acid metabolic pathway, respectively. Red (dark color, the relative level value of the SLE group is greater than 1) indicates that the oxidized lipids within the box line are significantly upregulated in the SLE group, and green (light color, the relative level value of the SLE group is less than 1) indicates that the oxidized lipids within the box line are significantly downregulated in the SLE group. * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.

[0037] Figure 2 It is the screening result of the diagnostic markers for patients with systemic lupus erythematosus in Example 1 of the present application, wherein A is the screening flow chart of the markers, B is the ROC curves under different marker combinations obtained by random forest modeling of the discovery set, C is the importance ranking result of the 10 markers, D~F are ROC curves of verifying the combinations of 3, 5, and 10 metabolites among the 10 screened metabolites in the validation set, respectively, and G is the corresponding sensitivity and specificity results in D~F. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the concept and technical effects of this application in conjunction with the embodiments to fully understand the purpose, features and effects of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of this application.

[0039] The embodiments of the present application are described in detail below. The described embodiments are exemplary and are only used to explain the present application, and should not be understood as limiting the present application.

[0040] In the description of this application, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0041] In the description of this application, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0042] Example 1

[0043] 1. Research subjects

[0044] Serum samples were collected from SLE patients and healthy controls. Patients with other autoimmune diseases (e.g., rheumatoid arthritis), systemic metabolic diseases (e.g., hypertension, diabetes), and those who did not meet the 2019 EULAR / ACR diagnostic criteria for SLE were excluded from the SLE group. There were 121 patients in the SLE group and 106 in the control group.

[0045] 2. Experimental Methods

[0046] 2.1 Oxidized lipid extraction

[0047] Pipette 200 μL of sample (serum) into an EP tube, add 400 μL of methanol (pre-cooled at -40 degrees, containing 1 μg / mL of isotope internal standard), vortex mix for 30 seconds, and sonicate for 5 minutes (ice water bath); after standing at -20°C for 1 hour, centrifuge the sample at 4°C, 12000 rpm for 15 minutes; take 400 μL of the supernatant, add 267 μL of ultrapure water, and mix well. SPE column purification sample (Solid-phase extraction): use The sample was purified using a PRiME HLB 1cc (30 mg) solid-phase extraction column (Waters Corporation). First, 1 mL of methanol and 1 mL of pure water were added and passed through the column. The sample was then added to the column and passed through. The column was then washed with 1 mL of 5% methanol. Finally, elution was performed with 1 mL of methanol to obtain a sample for mass spectrometry analysis. The column was dried under nitrogen and reconstituted in 80 μL of 30% acetonitrile. The column was centrifuged at 12,000 rpm for 15 minutes at 4°C, and the supernatant was collected for mass spectrometry analysis.

[0048] 2.2 Standard curve

[0049] Each substance has its own corresponding standard. Accurately pipette a predetermined amount of standard into an EP tube and prepare a 1 μg / mL stock standard solution. Add a corresponding amount of the stock standard solution into an EP tube to prepare a mixed standard solution. This mixed standard solution is sequentially diluted to create a series of calibration solutions (containing an isotope-labeled internal standard mixture consistent with the final concentration in the sample). After mass spectrometry analysis, a standard curve is generated for each substance.

[0050] Prior to UHPLC-MS / MS analysis, a standard solution of the target compound was introduced into the mass spectrometer. For each target compound, several precursor ion-daughter ion transitions with the highest signal intensity were selected, and their MRM parameters were optimized. The transition with the best response was selected for quantitative analysis, while the remaining transitions were used for qualitative analysis of the target compound.

[0051] 2.3 Mass spectrometry analysis

[0052] This project used an EXIONLC System (SCIEX) ultra-high performance liquid chromatograph (UPLC) using a Waters ACQUITY UPLC BEH C18 column (150 × 2.1 mm, 1.7 μm, Waters). The HPLC phase A consisted of 0.01% formic acid in water, and the HPLC phase B consisted of 0.01% formic acid in acetonitrile. The column oven temperature was 50°C, the sample tray was set to 4°C, and the injection volume was 10 μL.

[0053] This project used a SCIEX 6500QTRAP+ triple quadrupole mass spectrometer equipped with an IonDrive Turbo V ESI ion source in multiple reaction monitoring (MRM) mode for mass spectrometry analysis. The ion source parameters were as follows: Curtain Gas = 40 psi, Ion Spray Voltage = -4500 V, Temperature = 500°C, Ion Source Gas 1 = 30 psi, and Ion Source Gas 2 = 30 psi.

[0054] 2.4 Data Analysis

[0055] All mass spectrometry data acquisition and target compound quantitative analysis were performed using SCIEX Analyst WorkStation Software (Version 1.6.3) and Multiquant 3.03 software (Version 20.2). The software automatically performed qualitative analysis for each substance based on pre-defined precursor ion-daughter ion pairs. Finally, absolute quantitative analysis was performed for each substance using a standard curve.

[0056] 3. Experimental Results

[0057] 3.1 Targeted metabolomics research results

[0058] Absolute quantitative analysis was performed on 128 oxidized lipids (Oxylipin) (the results obtained were absolute concentrations in serum), of which 92 achieved absolute quantification and were used for subsequent analysis.

[0059] The Euclidean distance analysis was performed on all 92 quantified oxidized lipids. Figure 1 As shown in Figure A, it can be seen that the Euclidean distance between the SLE group (S) and the control group (C) is the largest, indicating that the two groups of samples have great differences in the expression of oxidized lipids.

[0060] By differential analysis (Student's t test), 46 significantly changed differentially oxidized lipids were obtained (p < 0.05). Further analysis of these oxidized lipids revealed that they were mainly metabolites of linoleic acid and linolenic acid. The differentially expressed lipids in these two metabolic pathways were as follows: Figure 1 As shown in Figures B and C. Other down-regulated or up-regulated oxidized lipids such as AdA (FC(S / C)=0.61, p<0.001) and 15-OxoEDE (FC(S / C)=0.36, p<0.001) were independent of the above two metabolic pathways.

[0061] 3.2 Random forest model identification marker results

[0062] To identify possible biomarkers for SLE diagnosis, a random forest machine learning model was constructed based on 92 differentially expressed serum metabolites to screen markers. Figure 2 As shown in Figure A: The SLE and control groups were divided into a discovery set (84 SLE subjects, 63 controls) and a validation set (37 SLE subjects, 43 controls). A random forest model was constructed using the discovery set based on 92 quantitative oxidized lipids to screen candidate markers. These candidate markers were then inserted into the validation set for validation, resulting in the final markers or marker combinations. The 10 metabolic markers ultimately screened were as follows: LTB4, 9-OxoODE, ARA (AA), 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME.

[0063] Among them, the ROC curve obtained by random forest modeling of the above 92 differentially expressed oxidized lipids in the discovery set is as follows Figure 2 As shown in Figure B, it can be seen from the figure that the AUC of the combination of 3 metabolites can reach 0.884, the AUC of the combination of 5 metabolites can reach 0.923, and the AUC of the combination of 10 metabolites can reach 0.942.

[0064] refer to Figure 2 In the metabolite diagnostic model finally trained, 10 differential metabolites were identified as potential markers, which were ranked from most to least important in the model as LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME.

[0065] refer to Figure 2 D~F, respectively in the validation set Figure 2 The top 3 (LTB4, 9-OxoODE, AA), top 5 (LTB4, 9-OxoODE, AA, 5-OxoETE and 15-OxoEDE) and all 10 markers in C were validated. The diagnostic models constructed based on 3, 5 and all 10 metabolites all achieved good results, with AUCs of 0.866, 0.868 and 0.871, respectively. Comparing the corresponding sensitivity and specificity in D to F, the results are as follows: Figure 2 As shown in G, the specificity of diagnostic models constructed with 3, 5, or 10 metabolites was not significantly different, ranging from 76% to 79%, but the sensitivity was highest with 10 metabolites, reaching 81%. These results indicate that constructing diagnostic models using multiple or all of the above 10 metabolites can achieve high diagnostic efficiency.

[0066] In all samples (including all samples in the discovery set and validation set), separate diagnostic models were established for each of the 10 metabolites according to the above method, and univariate ROC curve analysis was performed to evaluate the application prospects of individual metabolites in the diagnosis of SLE. The results are shown in Table 1.

[0067] Table 1. Individual diagnostic value of the 10 metabolites

[0068]

[0069] From the results in the table, it can be seen that the AUC values ​​of LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, and DHA can reach above 0.7, and can achieve good diagnostic utility even when used as individual markers.

[0070] Based on the above results, the group of metabolites of LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, and 12,13-DiHOME provided in the embodiments of the present application can be used to select one or more metabolites as markers to construct a corresponding diagnostic model for application to the diagnosis of systemic lupus erythematosus. In order to achieve better diagnostic results, a combination of 3, 5 or more metabolites can be selected from the above-mentioned metabolite groups as markers. Of course, in order to reduce the complexity of the test while ensuring the accuracy of the test, LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, and DHA can also be selected as single markers for diagnosis.

[0071] The present application has been described in detail above with reference to the embodiments. However, the present application is not limited to the above embodiments. Various modifications can be made within the scope of knowledge possessed by a person of ordinary skill in the art without departing from the purpose of the present application. In addition, the embodiments of the present application and the features of the embodiments can be combined with each other unless there is a conflict.

Claims

1. Use of reagents for the quantitative detection of at least three of the following metabolites in the preparation of diagnostic products for systemic lupus erythematosus: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, 12,13-DiHOME.

2. The use according to claim 1, characterized in that The reagent quantitatively detects at least five of the metabolites.

3. The use according to claim 2, characterized in that The reagent quantitatively detects all ten of the metabolites.

4. The use according to claim 1, characterized in that The reagent quantitatively detects any three of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, AdA, and DHA.

5. The use according to any one of claims 1 to 4, characterized in that The reagent detects the metabolites by liquid chromatography-mass spectrometry.

6. The use according to any one of claims 1 to 4, characterized in that When the level of 18-HEPE is increased compared with the normal control, or when the level of at least one of LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, and 12,13-DiHOME is decreased compared with the normal control, the patient is diagnosed as having systemic lupus erythematosus.

7. A diagnostic product for systemic lupus erythematosus, characterized in that: Includes reagents for the quantitative detection of at least three of the following metabolites: LTB4, 9-OxoODE, ARA, 5-OxoETE, 15-OxoEDE, 9,10-DiHOME, AdA, DHA, 18-HEPE, 12,13-DiHOME.

8. The diagnostic product according to claim 7, characterized in that Includes reagents for the quantitative detection of all ten of these metabolites.

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