Coronary artery slow blood flow metabolism marker and application thereof

Abstract

The invention provides a coronary artery slow blood flow metabolism marker and application thereof. The coronary artery slow blood flow metabolism marker is composed of at least one of monoglyceride (MG) (16: 0), diglyceride (DG) (16: 018: 0) and short-chain acyl carnitine C6-2OH. The coronary artery slow blood flow metabolism marker can be accurately and early screened and diagnosed by detecting a human plasma sample.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of serum biomarker, in particular to a coronary slow flow metabolic marker and application thereof. BACKGROUND

[0002] Coronary slow flow (CSF) refers to a phenomenon that after excluding primary or secondary myocardial disease, vascular thrombolytic therapy, heart valve disease, percutaneous coronary angioplasty and coronary spasm or dilation and other relevant intervention factors, no coronary dissection or significant stenosis is found by coronary angiography (CAG) examination, but the coronary blood flow velocity is significantly slowed down. At present, there is no effective CSF diagnostic marker. SUMMARY

[0003] Therefore, the present application aims to provide a coronary slow flow metabolic marker and application thereof.

[0004] To achieve the above object, the present application provides a coronary slow flow metabolic marker, which is composed of at least one of monoglyceride_MG (16:0), diglyceride_DG (16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH.

[0005] In some embodiments, the coronary slow flow marker is composed of monoglyceride_MG (16:0), diglyceride_DG (16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH.

[0006] The present application also provides application of the coronary slow flow metabolic marker as described in any one of the preceding embodiments in constructing a model for predicting coronary slow flow.

[0007] The present application also provides application of a reagent for detecting the coronary slow flow metabolic marker as described in any one of the preceding embodiments in preparing a product for detecting coronary slow flow.

[0008] In some embodiments, the product comprises a kit.

[0009] The present application also provides a method for constructing a coronary slow flow detection model, comprising:

[0010] Collecting a plasma sample, wherein the plasma sample comprises plasma samples from coronary slow flow patients, coronary artery disease patients and healthy controls;

[0011] performing group difference analysis on the data set of metabolites in the plasma sample to identify the coronary slow flow metabolite marker in claim 1 based on the criterion of high expression in coronary slow flow patients and coronary artery disease patients and low expression in healthy controls;

[0012] randomly splitting the data set of metabolites in the plasma sample into a training set and a test set, and constructing a prediction model by a machine learning method.

[0013] In some embodiments, the group difference analysis on the data set of metabolites in the plasma sample comprises:

[0014] performing liquid chromatography analysis and tandem mass spectrometry analysis on the plasma sample to detect the content of metabolites in the plasma sample, and obtaining a mass spectrometry data set;

[0015] performing qualitative and quantitative analysis on the mass spectrometry data set;

[0016] performing group difference analysis based on the results of the qualitative and quantitative analysis.

[0017] In some embodiments, the group difference analysis comprises: judging the group difference by chi-square test and Kruskal-Wallis H test.

[0018] In some embodiments, the method further comprises: drawing an ROC curve to evaluate the coronary slow flow detection model.

[0019] The embodiments of the present application provide a coronary slow flow detection system, which is constructed by the method for constructing a coronary slow flow detection model according to any one of the preceding embodiments.

[0020] As can be seen from the above, the coronary slow flow metabolite marker provided by the present application can accurately perform early screening and diagnosis on the coronary slow flow metabolite marker by detecting human plasma samples, by selecting at least one of monoglyceride_MG (16:0) which is an important substance for energy source of the body, and diglyceride_DG (16:0_18:0) and Carnitine C6-2OH which can affect various signal pathways in fat metabolism and cell energy metabolism. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or related art, the drawings needed in the embodiments or related art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 Graphical representation of the 3 metabolite markers of Example 2 in the coronary slow flow, coronary artery disease and normal healthy control groups.

[0023] Figure 2 Graphical representation of the ROC curve of the logistic regression model of the 3 metabolite markers of Example 2 in the newly diagnosed coronary slow flow and coronary artery disease patients and the ROC curve of the logistic regression model of the newly diagnosed coronary slow flow and healthy control groups. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to specific embodiments and with reference to the drawings.

[0025] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood as the common meanings understood by those with ordinary skills in the art to which the present application belongs. The similar words such as "comprise" or "contain" and the like used in the embodiments of the present application mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects.

[0026] The embodiments of the present application provide a coronary slow flow metabolite marker. The coronary slow flow marker can be composed of at least one of monoglyceride_MG(16:0), diglyceride_DG(16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH. For example, it can be composed of any one of monoglyceride_MG(16:0), diglyceride_DG(16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH. Or it can be composed of any two of monoglyceride_MG(16:0), diglyceride_DG(16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH. Or it can be composed of all of monoglyceride_MG(16:0), diglyceride_DG(16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH.

[0027] In some embodiments, the coronary slow flow marker can be composed of monoglyceride_MG(16:0), diglyceride_DG(16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH. In this way, better accuracy of screening for coronary slow flow can be achieved.

[0028] The monoglyceride_MG (16:0) is one of the intermediate products in the hydrolysis process of triglyceride (TG). It is one of the important substances for energy sources of the body, and can affect cell signaling, cell membrane function and metabolic regulation through its metabolites. Its metabolites (such as palmitic acid) have important roles in physiological and pathological processes, but excessive accumulation of palmitic acid can be associated with adverse health effects such as metabolic syndrome and inflammatory response. Its structural formula can be shown as formula (1).

[0029]

[0030] In some embodiments, the diglyceride_DG (16:0_18:0) is an intermediate product of fat metabolism. DG (16:0_18:0) is an activator of protein kinase C (PKC). PKC is an important signal transduction protein involved in the regulation of various cell functions such as cell proliferation, differentiation, apoptosis and inflammatory response. By activating PKC, DG (16:0_18:0) can affect various signaling pathways in cells, thereby regulating the physiological activities of cells. Its structural formula can be shown as formula (2).

[0031]

[0032] In some embodiments, the Carnitine C6-2OH is a short-chain acylcarnitine. Carnitine C6-2OH is an important short-chain acylcarnitine involved in fatty acid metabolism and cellular energy metabolism. It has important significance in the diagnosis and monitoring of metabolic diseases such as fatty acid oxidation disorders. Its structural formula can be shown as formula (3).

[0033]

[0034] The coronary slow flow metabolic marker provided by the present application can accurately screen and diagnose coronary slow flow metabolic markers in the early stage by detecting human plasma samples by selecting at least one of monoglyceride_MG (16:0), which is an important substance for energy sources of the body, and diglyceride_DG (16:0_18:0) and Carnitine C6-2OH, which can affect various signaling pathways in cells in fat metabolism and fatty acid metabolism and cellular energy metabolism.

[0035] Based on the same inventive concept, the embodiments of the present application also provide use of the reagent for detecting the coronary slow flow metabolic marker as in any one of the preceding embodiments in the preparation of a product for detecting coronary slow flow.

[0036] In some of the embodiments, the product comprises a kit.

[0037] In some embodiments, the detection is early screening and / or early diagnosis. It can be understood that the aforementioned metabolic marker for detecting coronary slow flow is a marker for early diagnosis screening of coronary slow flow, etc. The metabolic marker can be specifically used to identify healthy controls, coronary artery disease, and coronary slow flow patients.

[0038] In some embodiments, the reagent comprises various reagents used in liquid chromatography, including methanol, acetonitrile, acetic acid, ammonium formate, chloroform, methyl tert-butyl ether, all of which are chromatographically pure. The MG (16:0), DG (16:0_18:0), and Carnitine C6-2OH standards are chromatographically pure, and the standards can be dissolved in dimethyl sulfoxide (DMSO) or methanol as a solvent. The standards can be diluted to different concentrations before mass spectrometry analysis.

[0039] The kit of the embodiments of the present application can be used to detect healthy controls, coronary artery disease, and coronary slow flow patients. The principle of the kit of the embodiments of the present application for detecting coronary slow flow is that the MG (16:0), DG (16:0_18:0), and Carnitine C6-2OH in individual plasma lipids are calculated by MedCalc software to establish a receiver operating characteristic curve (ROC) for 1) coronary slow flow initial diagnosis without treatment and coronary artery disease patients, as a screening tool for differential diagnosis of coronary slow flow and coronary artery disease; in addition, 2) the ROC curve between screening coronary slow flow and healthy controls. This includes calculating the area under the curve (AUC), sensitivity, and specificity to represent the effectiveness of the three detection indicators in the kit.

[0040] It should be understood that the application of the above-mentioned embodiments is based on the corresponding coronary slow flow metabolic marker in any one of the preceding embodiments, and has the beneficial effects of the corresponding coronary slow flow metabolic marker embodiments, which are not repeated here.

[0041] Based on the same inventive concept, the embodiments of the present application also provide use of the coronary slow flow metabolic marker as in any one of the preceding embodiments in the construction of a model for predicting coronary slow flow.

[0042] The application further provides a method for constructing a model for detecting coronary slow flow, comprising the following steps:

[0043] Collecting plasma samples, wherein the plasma samples are collected from patients with coronary slow flow, patients with coronary artery disease and healthy controls.

[0044] Performing group difference analysis on the data set of metabolites in the plasma samples, and identifying the coronary slow flow metabolic markers as mentioned above based on the criterion that the metabolites are highly expressed in the patients with coronary slow flow and the patients with coronary artery disease and are lowly expressed in the healthy controls.

[0045] Randomly splitting the data set of metabolites in the plasma samples into a training set and a test set, and constructing a prediction model by means of machine learning.

[0046] In some embodiments, the collecting of the plasma samples can be specifically collecting heparin anticoagulant plasma samples.

[0047] In some embodiments, the performing of the group difference analysis on the data set of metabolites in the plasma samples comprises the following steps:

[0048] Performing liquid chromatography analysis and tandem mass spectrometry analysis on the plasma samples to detect the content of metabolites in the plasma samples, and obtaining a mass spectrometry data set;

[0049] Performing qualitative and quantitative analysis on the mass spectrometry data set;

[0050] Performing group difference analysis based on the results of the qualitative and quantitative analysis.

[0051] In the diagnosis of the patients with coronary slow flow and the healthy controls, the equation of the prediction model is as follows: Model = -1226.95 + 25.50*MG(16:0) + 13.11*DG(16:0_18:0) + 27.37*Carnitine C6-2OH.

[0052] In the diagnosis of the patients with coronary slow flow and the patients with coronary artery disease, the equation of the prediction model is as follows: Model = -940.11 + 29.64*MG(16:0) + 12.72*DG(16:0_18:0) + 7.02*Carnitine C6-2OH.

[0053] In some embodiments, the group difference analysis can comprise: judging the difference among the three groups of the patients with coronary slow flow, the patients with coronary artery disease and the healthy controls by chi-square test and Kruskal-Wallis H test.

[0054] In some of the embodiments, the method can further comprise: plotting a ROC curve to evaluate the coronary slow flow detection model. The expression level of MG (16:0) in the newly diagnosed coronary slow flow and coronary artery disease patients is usually subjected to ROC curve analysis. The expression level of DG (16:0_18:0) in the newly diagnosed coronary slow flow and coronary artery disease patients is subjected to ROC curve analysis. The expression level of Carnitine C6-2OH in the newly diagnosed coronary slow flow and coronary artery disease patients is subjected to ROC curve analysis. The expression level of MG (16:0) in the newly diagnosed coronary slow flow and the healthy control group is subjected to ROC curve analysis. The expression level of DG (16:0_18:0) in the newly diagnosed coronary slow flow and the healthy control group is subjected to ROC curve analysis. The expression level of Carnitine C6-2OH in the newly diagnosed coronary slow flow and the healthy control group is subjected to ROC curve analysis.

[0055] It should be understood that the application of the above-mentioned embodiments is based on the corresponding coronary slow flow metabolic markers in any of the preceding embodiments, and has the beneficial effects of the corresponding coronary slow flow metabolic marker embodiments, which are not repeated here.

[0056] Based on the same inventive concept, the embodiments of the present application also provide a coronary slow flow detection system, which is constructed by the method for constructing a coronary slow flow detection model according to any one of the preceding embodiments.

[0057] It should be understood that the system of the above-mentioned embodiments is based on the corresponding coronary slow flow metabolic markers in any of the preceding embodiments, and has the beneficial effects of the corresponding coronary slow flow metabolic marker embodiments, which are not repeated here.

[0058] The technical solutions of the present application will be further described below in combination with specific embodiments.

[0059] The experimental methods in the following embodiments are all conventional methods unless otherwise specified.

[0060] The test materials used in the following embodiments are all purchased from conventional biochemical reagent stores unless otherwise specified.

[0061] Example 1: Early diagnosis kit for coronary slow flow based on plasma lipid metabolites

[0062] The pulmonary tuberculosis efficacy evaluation kit based on plasma lipid metabolome composition includes various reagents used in liquid chromatography, including methanol, acetonitrile, acetic acid, ammonium formate, chloroform, methyl tert-butyl ether, all of which are chromatographically pure, and the brand can be Merck.

[0063] MG (16:0), DG (16:0_18:0) and Carnitine C6-2OH standards were chromatographically pure brand BioBioPha / Sigma-Aldrich, and the standards were dissolved with dimethyl sulfoxide (DMSO) or methanol as solvent, stored at -20°C, and diluted with 70% methanol to different gradient concentrations before mass spectrometric analysis.

[0064] Example 2 ROC curve analysis of coronary slow flow early screening kit

[0065] The expression levels of MG (16:0), DG (16:0_18:0) and Carnitine C6-2OH in the plasma of healthy controls, coronary artery disease, and coronary slow flow patients were detected using the coronary slow flow early screening kit. Among them, the plasma samples of healthy controls, coronary artery disease, and coronary slow flow patients came from Youbei People's Hospital.

[0066] 1. Sample collection

[0067] Heparin anticoagulated plasma samples were collected from 25 cases of coronary slow flow (initial diagnosis and no treatment), 37 cases of coronary artery disease (initial diagnosis and no cure), and 30 cases of healthy controls. Coronary slow flow and coronary artery disease patients were diagnosed according to the Chinese Expert Consensus on Diagnosis and Treatment of Coronary Microvascular Disease (2023 version), and the healthy controls met the following criteria: gender and age matched with coronary slow flow patients, excluded immune system diseases, excluded tumors, cardiovascular or other known infectious diseases.

[0068] 2. Sample processing

[0069] The plasma sample was taken out from the freezer and thawed at room temperature. After complete thawing, vortex for 10 seconds in the centrifuge, then set the centrifuge to 4°C, 3000 rpm, and centrifuge at this temperature for 5 minutes. After centrifugation, completely remove the sample and transfer it to a clean EP tube, then add 1 mL of lipid extraction solution to the EP tube and mix thoroughly by vortexing for 2 minutes. Then put the mixed sample into the ultrasonic machine for ultrasonic emulsification, mix with 500 uL of water after 5 minutes, vortex for 1 minute, then centrifuge at 4°C, 12000g for 10 minutes. Collect 500 uL of supernatant and dry with nitrogen, and re-dissolve with 100 uL of mobile phase B. After vortexing the sample for 1 minute, centrifuge at 4°C 14000g for 15 minutes, and perform UPLC-MS / MS analysis on the machine.

[0070] 3. Liquid chromatography and tandem mass spectrometry

[0071] The Ultra Performance Liquid Chromatography (UPLC) collection system is Shim-pack UFLC SHIMADZU CBM30A (https: / / www.shimadzu.com / ), and the Tandem mass spectrometry (MS / MS) collection system is Sciex API 6500 system (https: / / sciex.com / ).

[0072] (1) The liquid phase conditions mainly include:

[0073] 1) Chromatographic column: Thermo C30 column, i.d. 2.1 x 100 mm, 2.6 um;

[0074] 2) Mobile phase: A phase acetonitrile / water (60 / 40, containing 0.04% acetic acid, 5 mmol / L ammonium formate);

[0075] B phase acetonitrile / isopropanol (10 / 90, containing 0.04% acetic acid, 5 mmol / L ammonium formate);

[0076] 3) 0 min A / B (80:20 V / V), 3 min 50:50 V / V, 5 min 35:65 V / V, 9 min 25:75 V / V, 15.5 min 10:90 V / V;

[0077] 4) Flow rate 0.35 ml / min; column temperature 45°C; injection volume 2 μl.

[0078] (2) The mass spectrometry conditions mainly include: electrospray ionization (ESI) temperature 550°C, mass spectrometry voltage 5500V, curtain gas (CUR) 35 psi, collision-activated dissociation (CAD) parameter setting is medium. In the triple quadrupole (Qtrap), each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0079] (3) Detection method:

[0080] The lipid metabolites of the plasma sample are preliminarily separated on an ultra performance liquid chromatograph, and the chromatographic separation is performed on a Thermo Accucore TM ​C30 column (2.6 μm, 2.1 mm x 100 mm i.d.) with column temperature set at 45 °C. Mobile phase (A) was applied with 60% acetonitrile in water, mobile phase (B) was applied with 10% acetonitrile in isopropanol. Elution gradient was set at 20% B at 0 min, 50% B at 3 min, 75% B at 9 min, 90% B at 15 min, and equilibration at 50% B. A total of 2 uL of sample was injected, and the flow rate was set at 350 uL / min.

[0081] The preliminarily separated plasma sample was introduced into LC-MS / MS system with a triple quadrupole with ion trap scanning, equipped with an ESI Turbo ion spray interface, which can be operated in positive and negative ion mode, and controlled by Analyst 1.6.3 software. The ESI source operating parameters are as follows: the ion source temperature is set to 500 °C; the spray voltage is set to 5500 V in positive ion mode; the mass spectrometry voltage is -4500 V in negative ion mode, the ion source gas I, gas II, and curtain gas are set to 45, 55, and 35 psi respectively, the collision gas is set to medium, and 10 and 100 μmol / L polypropylene glycol solutions are applied for instrument tuning and mass PPG calibration respectively.

[0082] 3. Qualitative and quantitative analysis of lipid metabolites

[0083] The mass spectrometry data is processed by the software Analyst 1.6.3. Qualitative analysis is performed by comparing the retention time RT, daughter ion pair information, and secondary spectrum data of the detected substances with the spectrum of the standard. The signal intensity (CPS) of the characteristic ions obtained in the detector is opened by the MultiQuant software, and the chromatographic peak is integrated and corrected. The linear equation and calculation formula are substituted, and finally the qualitative and quantitative analysis results of all samples are obtained.

[0084] 4. Statistical data analysis

[0085] The data of the baseline characteristics of the study population is applied to chi-square test and Kruskal-Wallis H test to determine the differences between groups. The R (4.1.1) software is used to draw the receiver operating characteristic (ROC) curve to analyze the AUC, sensitivity, and specificity of the three lipid substances between groups, and the binary Logist regression is fitted to evaluate the diagnostic value of the combined model.

[0086] 5. Results

[0087] See Figure 1 and Figure 2 . Among them, Figure 2Schematic diagram of the ROC curve of the logistic regression model of three metabolite markers in patients with newly diagnosed coronary slow blood flow and coronary artery disease in part A. Figure 2 Part B is a schematic diagram of the ROC curve of the logistic regression model of three lipid metabolite markers in newly diagnosed coronary slow blood flow and healthy control groups.

[0088] 6. Results Analysis

[0089] from Figure 1 The results showed that compared with the healthy control group (NCA) and the coronary artery disease group (SAP), MG (16:0), DG (16:0_18:0) and Carnitine C6-2OH were significantly upregulated in coronary slow flow (SCF).

[0090] Figure 2 The results of Part A in the middle section showed that ROC curve analysis of MG (16:0) expression in patients with newly diagnosed slow coronary flow and coronary artery disease showed an AUC of 0.924, a sensitivity of 0.920, and a specificity of 0.865 for the differential diagnosis of slow coronary flow. ROC curve analysis of DG (16:0_18:0) expression in patients with newly diagnosed slow coronary flow and coronary artery disease showed an AUC of 0.859, a sensitivity of 0.760, and a specificity of 0.838 for the differential diagnosis of slow coronary flow. ROC curve analysis of carnitine C6-2OH expression in patients with newly diagnosed slow coronary flow and coronary artery disease showed an AUC of 0.929, a sensitivity of 0.840, and a specificity of 0.892 for the differential diagnosis of slow coronary flow.

[0091] Figure 2 The results of Part B show that ROC curve analysis of MG (16:0) expression in newly diagnosed coronary artery slow blood flow and healthy controls showed an AUC of 0.885, a sensitivity of 0.920, and a specificity of 0.833 for differential diagnosis of coronary artery slow blood flow. ROC curve analysis of DG (16:0_18:0) expression in newly diagnosed coronary artery slow blood flow and healthy controls showed an AUC of 0.847, a sensitivity of 0.800, and a specificity of 0.800 for differential diagnosis of coronary artery slow blood flow. ROC curve analysis of carnitine C6-2OH expression in newly diagnosed coronary artery slow blood flow and healthy controls showed an AUC of 0.960, a sensitivity of 0.867, and a specificity of 0.909 for differential diagnosis of coronary artery slow blood flow.

[0092] It can be seen that the kit has high accuracy in early screening and diagnosis of coronary slow flow, and provides a new objective method for early screening and diagnosis of coronary slow flow.

[0093] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary and is not intended to imply that the scope (including claims) of the present disclosure is limited to these examples; the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments of the present disclosure as described above, which are not provided in details for the sake of brevity.

[0094] Although the present disclosure has been described in conjunction with the specific embodiments thereof, it is to be understood that many alternatives, modifications and variations will be apparent to those skilled in the art in the light of the foregoing description.

[0095] The embodiments of the present disclosure are intended to cover all such alternatives, modifications and variations as falling within the broad scope of the appended claims. Accordingly, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A coronary slow blood flow metabolic marker, characterized in that: The coronary slow blood flow marker consists of at least one of monoglyceride_MG (16:0), diglyceride_DG (16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH.

2. The coronary slow blood flow metabolic marker according to claim 1, characterized in that: The coronary slow blood flow marker consists of monoglyceride_MG (16:0), diglyceride_DG (16:0_18:0) and short-chain acylcarnitine Carnitine C6-2OH.

3. Use of the coronary slow blood flow metabolic marker according to any one of claims 1 to 2 in constructing a model for predicting coronary slow blood flow.

4. Use of a reagent for detecting the metabolic marker of coronary slow blood flow according to claim 1 or 2 in the preparation of a product for detecting coronary slow blood flow.

5. The use according to claim 4, characterized in that The product includes a kit.

6. A method for constructing a coronary slow blood flow detection model, characterized in that: include: Collecting plasma samples; the plasma samples include plasma samples from patients with slow coronary blood flow, patients with coronary artery disease, and healthy controls; performing inter-group difference analysis on the data set of metabolites in the plasma samples, and confirming the coronary slow blood flow metabolic markers of claim 1 based on the criterion of high expression in patients with coronary slow blood flow and patients with coronary artery disease and low expression in healthy controls; The data set of metabolites in the plasma sample is randomly divided into a training set and a test set, and a prediction model is constructed using a machine learning method.

7. The method for constructing a coronary slow blood flow detection model according to claim 6, characterized in that: The performing inter-group difference analysis on the data set of metabolites in the plasma samples comprises: performing liquid chromatography analysis and tandem mass spectrometry analysis on the plasma sample to detect the content of metabolites in the plasma sample and obtain a mass spectrometry data set; performing qualitative and quantitative analysis on the mass spectrometry data set; The inter-group difference analysis was performed based on the qualitative and quantitative analysis results.

8. The method for constructing a coronary slow blood flow detection model according to claim 6, characterized in that: The inter-group difference analysis included: determining the inter-group differences through chi-square test and Kruskal-Wallis H test.

9. The method for constructing a coronary slow blood flow detection model according to claim 6, characterized in that: The method further includes: drawing a ROC curve to evaluate the coronary slow blood flow detection model.

10. A coronary slow blood flow detection system, characterized in that: The coronary slow blood flow detection system is constructed by the method for constructing a coronary slow blood flow detection model according to any one of claims 6 to 9.

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