Marker for evaluating degree of coronary atherosclerosis and application thereof

Abstract

The invention discloses a marker for evaluating the degree of coronary atherosclerosis and application thereof, and relates to the technical field of biomedicine, and the marker for evaluating the degree of coronary atherosclerosis comprises xylose, glycine, 4-guanidinobutyric acid, acetylcholine, D-galacturonic acid, glucolactone, glycocholic acid and trimethyl lysine. When the marker provided by the invention is used for evaluating the degree of coronary atherosclerosis, the detection is rapid and convenient, the specificity is high, the sensitivity is high, the prediction accuracy is high, the cost is low, and the marker is non-invasive. In addition, when the marker is used for evaluating the coronary atherosclerosis degree, an adopted sample is a plasma sample, and the marker has the advantages of being easy to collect, low in treatment cost and the like.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to markers for evaluating the degree of coronary atherosclerosis and applications thereof. Background Art

[0002] Coronary Heart Disease (CHD), often referred to as CHD, is a type of ischemic heart disease. Coronary artery atherosclerosis is caused by the accumulation of plaques on the walls of the coronary arteries, which are composed of cholesterol and other deposits of substances in the arteries. Plaque accumulation causes the arterial lumen to narrow continuously, which may partially or completely block blood flow. This process is called coronary artery atherosclerosis. Among them, when the degree of vascular stenosis is less than 50%, myocardial blood supply is usually not affected and is not diagnosed as coronary artery atherosclerosis. As the degree of vascular stenosis increases, when the degree of vascular stenosis is greater than 50%, insufficient blood supply leads to chronic stable angina pectoris, which is diagnosed as coronary artery atherosclerosis. Therefore, accurate assessment of the degree of vascular stenosis (i.e., assessment of the degree of coronary artery atherosclerosis) is of great significance for the risk assessment and diagnosis of coronary artery atherosclerosis.

[0003] At present, the detection methods commonly used in clinical practice mainly include blood tests such as testing cholesterol, triglycerides, blood sugar, lipoproteins and various inflammatory markers. Abnormal levels indicate a higher risk of coronary atherosclerotic heart disease, as well as electrocardiograms, echocardiograms, coronary CT angiography and coronary angiography. Among them, coronary angiography can clearly and accurately identify narrowed blood vessels and their locations by injecting contrast agents to image the heart and blood vessels, clarify the diagnosis, guide treatment and evaluate the prognosis. It is a relatively accurate method for assessing the degree of coronary atherosclerosis stenosis and is often referred to as the "gold standard" for the diagnosis of coronary atherosclerotic heart disease. However, coronary angiography is an invasive examination with high costs and high demands on medical resources and professional medical technology. It is not suitable for large-scale screening of people in remote areas or with limited medical resources. Therefore, there is an urgent need to develop a non-invasive, convenient, low-cost and accurate method for assessing the degree of coronary atherosclerosis.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] Based on the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide markers for evaluating the extent of coronary atherosclerosis and their applications, aiming to provide a more accurate, convenient, low-cost and non-invasive method for evaluating the extent of coronary atherosclerosis.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect of the present invention, a marker for assessing the extent of coronary atherosclerosis is provided, wherein the marker for assessing the extent of coronary atherosclerosis comprises xylose, glycine, 4-guanidinobutyric acid, acetylcholine, D-galacturonic acid, gluconolactone, glycocholic acid and trimethyllysine.

[0008] Optionally, the markers for assessing the extent of coronary atherosclerosis also include uric acid, aspartate aminotransferase, hemoglobin and lymphocytes.

[0009] Optionally, the markers for assessing the extent of coronary atherosclerosis also include red blood cells and creatinine.

[0010] Optionally, the markers for assessing the extent of coronary atherosclerosis also include neutrophils.

[0011] Optionally, the markers for assessing the extent of coronary atherosclerosis also include high-density lipoprotein and platelets.

[0012] The second aspect of the present invention provides the use of the marker for assessing the extent of coronary atherosclerosis as described above in constructing an assessment model for the extent of coronary atherosclerosis.

[0013] The third aspect of the present invention provides use of the marker for assessing the extent of coronary atherosclerosis as described above in the preparation of a product for assessing the extent of coronary atherosclerosis.

[0014] Optionally, when the markers for assessing the extent of coronary atherosclerosis contain uric acid, aspartate aminotransferase, hemoglobin, lymphocytes, red blood cells, creatinine, neutrophils, high-density lipoprotein and platelets, the ratio of uric acid concentration to aspartate aminotransferase concentration, the ratio of mean hemoglobin content to uric acid concentration, the ratio of mean hemoglobin concentration to hemoglobin concentration, the ratio of absolute lymphocyte value to aspartate aminotransferase concentration, the ratio of hematocrit to creatinine concentration, the ratio of absolute neutrophil value to neutrophil percentage, and the ratio of high-density lipoprotein concentration to platelet volume are specifically used as markers.

[0015] Optionally, the product is a reagent or a kit.

[0016] Optionally, the sample used in the product for assessing the extent of coronary atherosclerosis is plasma.

[0017] Beneficial effects: The markers provided by the present invention are fast, convenient, highly specific, highly sensitive, highly accurate in prediction, low in cost, and non-invasive and non-invasive when used to assess the extent of coronary atherosclerosis. In addition, the sample used when the marker is used to assess the extent of coronary atherosclerosis is a plasma sample, which has the advantages of easy collection and low processing cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a multivariate ROC curve analysis diagram of 8 metabolic markers for distinguishing NCHD from CHD in the modeling group of Example 2.

[0019] Figure 2 This is a multivariate ROC curve analysis diagram of 8 metabolic markers for distinguishing NCHD from CHD in the validation group of Example 2.

[0020] Figure 3 This is a multivariate ROC curve analysis diagram of 15 important markers for distinguishing NCHD from CHD in the modeling group of Example 3.

[0021] Figure 4 This is a multivariate ROC curve analysis diagram of 15 important markers for distinguishing NCHD from CHD in the validation group of Example 3. DETAILED DESCRIPTION

[0022] The present invention provides markers for assessing the degree of coronary atherosclerosis and their applications. To make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0024] The embodiment of the present invention provides a marker for assessing the degree of coronary atherosclerosis (i.e., for assessing the degree of vascular stenosis, which is caused by coronary atherosclerosis), wherein the marker for assessing the degree of coronary atherosclerosis includes xylose, glycine, 4-guanidinobutyric acid, acetylcholine, D-galacturonic acid, gluconolactone, glycocholic acid and trimethyllysine.

[0025] The markers provided by the present invention (including the above-mentioned 8 metabolites) are used to assess the extent of coronary atherosclerosis, and the detection is fast, convenient, highly specific, highly sensitive, and highly predictive, and is non-invasive and non-invasive. In addition, the sample used when the marker is used to assess the extent of coronary atherosclerosis is a plasma sample, which has the advantages of easy collection and low processing cost. The markers provided by the present invention can accurately assess the extent of coronary atherosclerosis, and as an auxiliary diagnostic method for assessing the risk of coronary atherosclerotic heart disease, they provide important help for the prevention of coronary atherosclerotic heart disease and the reduction of the incidence rate.

[0026] Metabolomics is a qualitative and quantitative study of metabolites in biological systems. The application of metabolomics to identify biomarkers in diseases has become increasingly mature, but it is often difficult to achieve a high accuracy rate by relying solely on metabolomics markers, and analysis based solely on metabolomics characteristics cannot fully reflect the state of the body, and the results may be biased. Therefore, the present invention constructs a coronary atherosclerosis degree assessment model with high sensitivity and specificity based on metabolomics data and clinical indicators, providing an effective auxiliary means for the diagnosis of coronary atherosclerotic heart disease.

[0027] Specifically, in some embodiments, the markers for assessing the extent of coronary atherosclerosis include not only the above 8 metabolites, but also the following clinical indicators:

[0028] Uric acid, aspartate aminotransferase, hemoglobin, and lymphocytes.

[0029] Specifically, the ratios of these clinical indicators are used as markers. More specifically, the ratios of these clinical indicators are the ratio of uric acid concentration to aspartate aminotransferase concentration (recorded as: uric acid / aspartate aminotransferase), the ratio of average hemoglobin content to uric acid concentration (recorded as: average hemoglobin content / uric acid), the ratio of average hemoglobin concentration to hemoglobin concentration (recorded as: average hemoglobin concentration / hemoglobin), and the ratio of the absolute value of lymphocytes to aspartate aminotransferase concentration (recorded as: absolute value of lymphocytes / aspartate aminotransferase).

[0030] In some embodiments, the markers for assessing the extent of coronary atherosclerosis include not only the above 8 metabolites, but also the following clinical indicators:

[0031] Uric acid, aspartate aminotransferase, hemoglobin, lymphocytes, erythrocytes, and creatinine.

[0032] Specifically, the ratios of these clinical indicators are used as markers. More specifically, the ratios of these clinical indicators are uric acid / aspartate aminotransferase, mean hemoglobin content / uric acid, mean hemoglobin concentration / hemoglobin, absolute lymphocyte value / aspartate aminotransferase, and the ratio of hematocrit to creatinine concentration (recorded as: hematocrit / creatinine).

[0033] In some embodiments, the markers for assessing the extent of coronary atherosclerosis include not only the above 8 metabolites, but also the following clinical indicators:

[0034] Uric acid, aspartate aminotransferase, hemoglobin, lymphocytes, erythrocytes, creatinine, and neutrophils.

[0035] Specifically, the ratios of these clinical indicators are used as markers. More specifically, the ratios of these clinical indicators are uric acid / aspartate aminotransferase, mean hemoglobin content / uric acid, mean hemoglobin concentration / hemoglobin, absolute lymphocyte value / aspartate aminotransferase, hematocrit / creatinine, and the ratio of absolute neutrophil value to neutrophil percentage (recorded as: absolute neutrophil value / neutrophil percentage).

[0036] In some embodiments, the markers for assessing the extent of coronary atherosclerosis include not only the above 8 metabolites, but also the following clinical indicators:

[0037] Uric acid, aspartate aminotransferase, hemoglobin, lymphocytes, erythrocytes, creatinine, neutrophils, high-density lipoprotein, and platelets.

[0038] Specifically, the ratios of these clinical indicators are used as markers. More specifically, the ratios of these clinical indicators are uric acid / aspartate aminotransferase, mean hemoglobin content / uric acid, mean hemoglobin concentration / hemoglobin, absolute lymphocyte value / aspartate aminotransferase, hematocrit / creatinine, absolute neutrophil value / neutrophil percentage, and the ratio of high-density lipoprotein concentration to platelet volume (recorded as: high-density lipoprotein / platelet volume).

[0039] In the embodiment of the present invention, metabolites in plasma samples are detected based on metabolomics, combined with clinical index data, to assess the extent of coronary atherosclerosis, which can better reflect the state of the body, reduce the possibility of bias in the results, and make the diagnosis more accurate. As a method for assessing the extent of coronary atherosclerosis, it provides important help for the prevention and reduction of the incidence of coronary atherosclerotic heart disease.

[0040] The embodiment of the present invention also provides the use of the markers for evaluating the degree of coronary atherosclerosis as described above in constructing an evaluation model for the degree of coronary atherosclerosis.

[0041] The embodiment of the present invention provides a method for detecting metabolites in plasma samples based on metabolomics, and constructing an assessment model for the degree of coronary atherosclerosis in combination with clinical indicator data (specifically, including uric acid / aspartate aminotransferase, mean hemoglobin content / uric acid, mean hemoglobin concentration / hemoglobin, absolute value of lymphocytes / aspartate aminotransferase, hematocrit / creatinine, absolute value of neutrophils / neutrophil percentage, and high-density lipoprotein / platelet hematocrit), which is used for auxiliary diagnosis of coronary atherosclerotic heart disease and provides important help for the prevention of coronary atherosclerotic heart disease and reduction of the incidence rate.

[0042] The embodiment of the present invention also provides the use of the marker for assessing the degree of coronary atherosclerosis as described above in the preparation of a product for assessing the degree of coronary atherosclerosis. The marker provided by the embodiment of the present invention is fast, highly specific and highly sensitive when used to assess the degree of coronary atherosclerosis, and can be used to prepare a product for assessing the degree of coronary atherosclerosis. Specifically, the embodiment of the present invention provides a metabolite detection in a plasma sample based on metabolomics, combined with clinical indicator data (specifically, including uric acid / aspartate aminotransferase, mean hemoglobin content / uric acid, mean hemoglobin concentration / hemoglobin, absolute value of lymphocytes / aspartate aminotransferase, hematocrit / creatinine, absolute value of neutrophils / neutrophil percentage and high-density lipoprotein / platelet hematocrit) to prepare a product for assessing the degree of coronary atherosclerosis, for auxiliary diagnosis of coronary atherosclerotic heart disease, and provide important help for the prevention and reduction of the incidence of coronary atherosclerotic heart disease.

[0043] In some embodiments, the product is a reagent or a kit.

[0044] In some embodiments, the sample used in the product for assessing the extent of coronary atherosclerosis is plasma.

[0045] The present invention will be further described below by means of specific examples.

[0046] Example 1

[0047] 1. Subjects’ conditions and sample collection

[0048] 1) Sample inclusion criteria

[0049] Written informed consent was obtained from all subjects before the study, and the degree of coronary artery stenosis was confirmed by coronary angiography in all subjects.

[0050] 2) Exclusion criteria

[0051] Under 18 years old; pregnant; underwent trauma surgery in the last 3 months; aortic dissection, pulmonary embolism, pneumonia, pericarditis, myocarditis, stress cardiomyopathy; history of stroke and renal artery stenosis; severe heart failure, liver and kidney failure; blood-borne infectious diseases including AIDS, hepatitis B, hepatitis C, etc.; patients with the following medical histories: malignant tumors, autoimmune diseases, severe traumatic diseases, infectious diseases and abnormal plasma samples.

[0052] 3) Obtaining sample clinical data

[0053] Plasma samples were collected from the subjects (all collected in the early morning on an empty stomach), and specific clinical indicator data of the subjects were also collected for subsequent statistical analysis and model building.

[0054] Among them, a total of 234 subjects were divided into a control group (None-Coronary Heart Disease, NCHD: stenosis <50%) and a coronary heart disease group (Coronary Heart Disease, CHD: stenosis ≥50%) according to the degree of coronary artery stenosis. At the same time, the NCHD group was randomly divided into a modeling group and a validation group, and the CHD group was also randomly divided into a modeling group and a validation group, and the modeling group samples and the validation group samples were different samples.

[0055] Specifically, as shown in Table 1, where:

[0056] Number of modeling groups: 82 in the NCHD group and 114 in the CHD group;

[0057] Number of validation group: 16 in NCHD group and 22 in CHD group.

[0058] Table 1. Subjects

[0059] NCHD group CHD Group Stenosis degree (%) <50％ ≥50％ Modeling team number 82 114 Number of people in the verification group 16 22 total 98 136

[0060] 2. Plasma metabolite detection

[0061] 1) Reagents

[0062] Methanol, acetonitrile, water, acetic acid and isopropanol of mass spectrometry grade purity, and formic acid, ammonium acetate and methyl tert-butyl ether of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0063] 2) Sample preparation

[0064] Take 100 μL of plasma, place it in 1000 μL of pre-cooled mixed solution (composed of methyl tert-butyl ether and methanol, with a volume ratio of methyl tert-butyl ether to methanol of 3:1), vortex mix, and obtain a sample extract;

[0065] Add 500 μL of a mixed solution of methanol and water (the volume ratio of methanol to water is 3:1) to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers;

[0066] After the sample is layered, the upper layer is the organic phase and the lower layer is the aqueous phase. Take 500 μL of the upper organic phase to a centrifuge tube, dry it, add 200 μL of a mixed solution of acetonitrile and isopropanol (the volume ratio of acetonitrile to isopropanol is 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube to mix, ultrasonically treat for 5 minutes, and then centrifuge the centrifuge tube at room temperature (12000rpm) for 5 minutes; take 180 μL of the supernatant from the centrifuge tube to a 2mL glass injection vial as the organic phase test liquid, and detect it on a machine (LC-MS, liquid chromatography-mass spectrometry);

[0067] After the samples were layered, 400 μL of the lower aqueous phase was taken into a centrifuge tube, and 1100 μL of ice methanol was added thereto. After protein precipitation, the centrifuge tube was centrifuged, and 1000 μL of the supernatant was transferred to a new centrifuge tube and dried overnight; 200 μL of water was added to the dried centrifuge tube and incubated at room temperature for 15 minutes; after incubation, the centrifuge tube was vortexed and ultrasonically treated for 5 minutes, and then the centrifuge tube was centrifuged at room temperature (12000 rpm) for 5 minutes; 180 μL of the supernatant was taken from the centrifuge tube into a 2 mL glass injection vial as the aqueous phase test liquid, which was detected by the machine (LC-MS).

[0068] 3) Small molecule metabolite detection

[0069] The organic phase was purified by Waters ACQUTTY BEH C8 1.7 μm 2.1 mm × 100 mm column, the aqueous phase uses Waters ACQUTTY HSS T3 1.8μm2.1mm×100mm column was used for small molecule separation; liquid chromatography and mass spectrometry both used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0070] The mobile phase parameters are as follows:

[0071] Mobile phase parameters of the organic phase test liquid: mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate (the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%); mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%), and the separation elution gradient is as follows: 0-12 minutes is 55%-89% mobile phase B, 12-19.5 minutes is 100% mobile phase B;

[0072] Mobile phase parameters of aqueous test liquid: Mobile phase A is an aqueous solution containing 0.1% formic acid (the mass content of formic acid is 0.1%); Mobile phase B is an acetonitrile solution containing 0.1% formic acid (the mass content of formic acid is 0.1%). The separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B;

[0073] The mass spectrometry parameters are as follows:

[0074] Mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each with positive and negative modes). The parameters used by QExactive were as follows:

[0075] In Full MS mode, the resolution is 70,000, the scan range is 100-1500 m / z (m / z is the ratio of ion mass to charge number), and the AGC (automatic gain control) is 3E+6 (i.e. 3×10 6 ), Maximum IT (maximum injection time) is 200 milliseconds; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, and the AGC is 1E+5 (i.e. 1×10 5 ), the maximum ion injection time is 50ms, and the HCD (high energy collision dissociation) relative collision energy is 30eV.

[0076] 3. Metabolomics data preprocessing and metabolite identification

[0077] 1) Metabolomics data preprocessing

[0078] (1) Extract peaks from the RAW format files of mass spectrometry data into Feature XML format files, reduce the dimension of the original mass spectrometry data, and improve the signal-to-noise ratio;

[0079] (2) using the peak alignment algorithm of OpenMS software, the retention time of the extracted peak format data was corrected and aligned between samples, thereby converting the mass spectrometry data into a data matrix;

[0080] (3) matching and filtering the isotope peaks in the data matrix obtained in step (2), and then replacing abnormal data (0, negative values, background noise, etc.) with vacant values;

[0081] (4) Among all the characteristic peaks obtained in step (3), those with a detection rate of <80% are eliminated, and those with a detection rate of >80% are filled with the median value of the characteristic peak, and 5% random noise (obeying the standard normal distribution) is added.

[0082] (5) In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the data obtained in step (4) was normalized using a normalization autoencoder (NormAE) to remove systematic errors such as batch effects.

[0083] 2) Identification of metabolites

[0084] According to the original data, the spectral information of the primary parent ion (MS1) and secondary fragment ion (MS2) of the compound is obtained after being parsed by the software. The metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the public database. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), the Mass Spectrum Database (www.massbank.jp) and the Lipid Metabolite Database (Lipidmap, www.lipidmaps.org); based on the metabolites identified in the relevant databases, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information when the standard is separated under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.1min difference, and the theoretical value and measured value of the metabolite molecular weight are less than 10ppm.

[0085] 4. Preprocessing of metabolic and clinical data

[0086] (1) After metabolite detection of the above samples, a total of 836 metabolites were annotated, 41 clinical indicator data were collected, and 820 (i.e., C 41 2 ) clinical indicators in the form of ratios, and finally a total of 836 metabolites and 861 clinical indicators were obtained and included in the subsequent data analysis;

[0087] (2) The metabolic data of the modeling group (mass spectrometry detection intensity data of 836 metabolites) were log-transformed to achieve data conversion. Subsequently, a t-test was performed. Based on the P value, metabolic features with P>0.05 were eliminated. Finally, Pearson correlation analysis was performed on the remaining metabolic features. Based on the correlation coefficient R value, metabolic features with R>0.9 were eliminated. In the end, a total of 90 metabolites were retained. The preprocessing steps of the clinical indicator data of the modeling group were the same as above. In the end, a total of 51 clinical indicators were retained.

[0088] (3) Marker screening

[0089] The above-mentioned preprocessed metabolic data and clinical index data were subjected to univariate ROC analysis, and the AUC values ​​were arranged in descending order, and the top 20 metabolic features and clinical indexes were retained. Subsequently, LASSO (Least Absolute Shrinkage and Selection Operator) regression was performed to screen out the metabolic features and clinical indexes with higher correlation from the top 20 retained metabolic features and clinical indexes. Finally, a total of 8 metabolic features and 7 clinical indexes were obtained (Table 2), which were used as important markers for distinguishing NCHD from CHD.

[0090] Table 2. 15 important metabolic and clinical markers for distinguishing NCHD from CHD

[0091]

[0092]

[0093] Example 2

[0094] In this embodiment, 8 metabolic markers are used to construct a coronary atherosclerosis degree assessment model that distinguishes NCHD from CHD (i.e., distinguishing between stenosis degree <50% and stenosis degree ≥50%). For these 8 metabolic markers (xylose, glycine, 4-guanidinobutyric acid, acetylcholine, D-galacturonic acid, gluconolactone, glycocholic acid and trimethyllysine), a multivariate ROC (receiver operating characteristic curve) analysis is performed. Specifically, 3 / 4 of the sample data (obtained from Example 1) of the modeling group (including the NCHD group and the CHD group in the modeling group) are randomly used as a training set (train), and 1 / 4 is used as a test set (test) for learning, wherein the training set is used to construct and train the machine learning classification model, and the test set is used to evaluate the discriminative ability of the trained model, and the support vector machine (SVM) algorithm is used to randomly cycle Diego 1000 times, and the coronary atherosclerosis degree assessment model is constructed by statistically calculating the average value of the final model accuracy. The results show that AUC = 0.838 (such as Figure 1 This shows that the evaluation model is effective.

[0095] The ROC curve is a method to study the relationship between model sensitivity and specificity, with sensitivity as the vertical axis and 1-specificity as the horizontal axis. The evaluation basis is to compare the area under the curve (AUC). When AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance is, indicating a better diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor.

[0096] In order to further verify the effectiveness of the constructed evaluation model, the validation group data was used to verify the above model and perform multivariate ROC curve analysis. The results showed that AUC = 0.741 ( Figure 2 This indicates that the evaluation model has certain clinical diagnostic significance.

[0097] Example 3

[0098] In this embodiment, 8 metabolic markers and 7 clinical indicators (i.e., 15 markers in Table 2) are used to construct a coronary atherosclerosis degree assessment model that distinguishes NCHD from CHD (i.e., distinguishes between stenosis <50% and stenosis ≥50%). In order to verify the diagnostic effect of the 15 selected markers in distinguishing NCHD from CHD, a multivariate ROC curve analysis was performed on the 15 markers. Specifically, 3 / 4 of the sample data of the modeling group (including the NCHD group and the CHD group) was randomly used as a training set, and 1 / 4 was used as a test set for learning, and SVM was used for random loop iteration 1000 times. By statistically calculating the average value of the final model accuracy, an assessment model for the degree of coronary atherosclerosis was constructed. The results showed that AUC = 0.926 (such as Figure 3 As shown), sensitivity = 0.706, specificity = 0.917, the results show that the constructed model has high diagnostic efficacy.

[0099] In order to further verify the effectiveness of the coronary atherosclerosis assessment model constructed based on the modeling group data, the validation group data was used to verify the above model and perform multivariate ROC curve analysis. The results showed that AUC = 0.929 ( Figure 4 As shown), sensitivity = 0.864, specificity = 0.875. The results showed that the established coronary atherosclerosis degree assessment model also had a good diagnostic effect in the validation group.

[0100] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A marker for assessing the extent of coronary atherosclerosis, characterized in that: The markers for evaluating the extent of coronary atherosclerosis include xylose, glycine, 4-guanidinobutyric acid, acetylcholine, D-galacturonic acid, gluconolactone, glycocholic acid and trimethyllysine.

2. The marker for assessing the extent of coronary atherosclerosis according to claim 1, characterized in that: The markers for evaluating the extent of coronary atherosclerosis also include uric acid, aspartate aminotransferase, hemoglobin and lymphocytes.

3. The marker for assessing the extent of coronary atherosclerosis according to claim 2, characterized in that: The markers for assessing the extent of coronary atherosclerosis also include red blood cells and creatinine.

4. The marker for assessing the extent of coronary atherosclerosis according to claim 3, characterized in that: The markers for assessing the extent of coronary atherosclerosis also include neutrophils.

5. The marker for assessing the extent of coronary atherosclerosis according to claim 4, characterized in that: The markers for assessing the extent of coronary atherosclerosis also include high-density lipoprotein and platelets.

6. Use of the marker for assessing the extent of coronary atherosclerosis according to any one of claims 1 to 5 in constructing an assessment model for the extent of coronary atherosclerosis.

7. Use of the marker for assessing the extent of coronary atherosclerosis according to any one of claims 1 to 5 in the preparation of a product for assessing the extent of coronary atherosclerosis.

8. The use according to claim 6 or 7, characterized in that: When the markers for assessing the degree of coronary atherosclerosis contain uric acid, aspartate aminotransferase, hemoglobin, lymphocytes, red blood cells, creatinine, neutrophils, high-density lipoprotein and platelets, the ratio of uric acid concentration to aspartate aminotransferase concentration, the ratio of mean hemoglobin content to uric acid concentration, the ratio of mean hemoglobin concentration to hemoglobin concentration, the ratio of absolute lymphocyte value to aspartate aminotransferase concentration, the ratio of hematocrit to creatinine concentration, the ratio of absolute neutrophil value to neutrophil percentage, and the ratio of high-density lipoprotein concentration to platelet volume are specifically used as markers.

9. The use according to claim 7, characterized in that: The product is a reagent or a kit.

10. The use according to claim 7, characterized in that: The sample used by the product for evaluating the extent of coronary atherosclerosis is plasma.

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