Blood biomarker for early coronary heart disease diagnosis and screening method and application thereof

Abstract

The invention relates to the technical field of biomedicine, in particular to a blood biomarker for early coronary heart disease diagnosis and a screening method and application thereof. The blood biomarker comprises blood clinical indexes and plasma metabolome characteristics, the blood clinical indexes comprise direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics comprise pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, pinose and L-valyl-alanine. According to the method, blood clinical indexes are incorporated by using plasma metabolome characteristics and blood routine and blood biochemical detection, and compared with other methods, the physiological and pathological states of an organism are more comprehensively reflected, and the possibility of result deviation is reduced, so that the multi-dimensional blood biomarkers are adopted, and the detection accuracy is improved. And the early coronary heart disease risk can be predicted more accurately. In addition, by adopting the multi-dimensional blood biomarker, the specificity and sensitivity of screening and diagnosing the early coronary heart disease can be improved.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to blood biomarkers for early diagnosis of coronary heart disease and screening methods and applications thereof. Background Art

[0002] Coronary heart disease (CHD), abbreviated as CHD, is the most common cardiovascular disease (CVD). It is mainly caused by atherosclerotic lesions in the coronary arteries (the main blood vessels supplying blood to the heart), which leads to obstruction or complete blockage of blood flow, which may cause serious consequences such as myocardial ischemia and hypoxia, angina pectoris or myocardial infarction, and even heart failure.

[0003] At present, the clinical diagnostic methods of coronary heart disease mainly include electrocardiogram, echocardiogram, coronary CT angiography (CCTA), coronary angiography (CAG) and myocardial biomarkers. Among them, (1) electrocardiogram is the most commonly used method for diagnosing coronary heart disease. It is easy to operate and is particularly suitable for the diagnosis of patients with arrhythmias. Electrocardiogram can reflect the patient's heart state in a short period of time. If the patient is diagnosed when there is no angina attack or abnormal heart rhythm, its specificity is low; (2) echocardiogram can examine the heart morphology, structure, uterine wall motion and left atrium, and is one of the commonly used examination methods. Although it has important diagnostic value for ventricular aneurysm, intracardiac thrombus, heart rupture and papillary muscle function, its accuracy is greatly affected by the subjective influence of the ultrasound examiner and is closely related to his or her experience; (3) CCTA is a non-invasive imaging technology, which is mainly used to diagnose the degree of epicardial coronary artery stenosis. By measuring the amount of calcium deposits around the coronary artery, it is evaluated whether the coronary artery lumen is narrowed, which has important clinical diagnostic value for patients suspected of coronary heart disease. Although CCTA examination has the advantages of being non-invasive, convenient, fast scanning speed and high spatial resolution, not all patients are suitable for CCTA examination, especially those who are allergic to iodine or contrast agents, hyperthyroidism or severe heart, liver and kidney dysfunction; (4) CAG is considered to be the "gold standard" for the diagnosis of coronary heart disease. It can diagnose patients with coronary heart disease and complex coronary artery lesions and provide a precise diagnostic basis for interventional treatment and bypass surgery. Although it has high accuracy and reliable results, and can clearly identify the stenosis of the coronary artery and its location, degree and range, it is an invasive examination that will bring certain examination risks and high costs to patients. Therefore, it is not suitable as a routine means of screening for coronary heart disease; (5) Myocardial biomarkers are important indicators that reflect the biochemical changes of heart disease. For example, C-reactive protein (CRP), troponin (cTn) and myocardial enzyme spectrum examination are the most commonly used and effective examination indicators for diagnosing coronary heart disease, and have important application value for early diagnosis, disease monitoring and prognosis evaluation of coronary heart disease. However, these myocardial biomarkers have relatively low sensitivity, especially in the diagnosis of chronic coronary heart disease, and may be affected by factors such as gender, age, and race.

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

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide blood biomarkers for the diagnosis of early coronary heart disease and their screening methods and applications, aiming to solve the problem of low accuracy of existing methods for diagnosing coronary heart disease using myocardial biomarkers.

[0006] In clinical practice, it is often necessary to combine multiple biomarkers for comprehensive evaluation, and combined index analysis has more advantages than single marker examination. At present, blood biochemical combined index examination is a common disease screening item for patients seeking clinical treatment, and has a certain predictive ability for the diagnosis, screening and prognosis of coronary heart disease. Blood sample sampling is simple, low-cost, has a large amount of information and can be tested repeatedly, which is suitable for primary medical institutions. If blood indicators are combined with metabolomics technology for comprehensive diagnosis and evaluation, it will not only help to determine the best treatment time for patients in a timely manner, but also alleviate their physical and psychological pain. Metabolomics can provide direct evidence for the pathogenesis of metabolic diseases by qualitatively and quantitatively analyzing small molecule metabolites in the disease state and studying the relationship between metabolites and physiological and pathological changes of the disease. Metabolomics can comprehensively qualitatively and quantitatively analyze a variety of small molecule metabolites in blood samples of patients with coronary heart disease, study the changing patterns of endogenous small molecule metabolites under the influence of internal and external environments, establish metabolic fingerprints between the control group and the disease group, and find specific metabolic marker combinations related to the disease for early diagnosis and screening of the disease. Therefore, the present invention aims to provide a group of blood biomarkers based on combined clinical indicators and metabolomics characteristics, and the use of the blood biomarkers to diagnose early coronary heart disease has the advantages of being non-invasive, highly accurate, and non-invasive.

[0007] The technical solution of the present invention is as follows:

[0008] In a first aspect of the present invention, a blood biomarker for the diagnosis of early coronary heart disease is provided, wherein the blood biomarker includes blood clinical indicators and plasma metabolome characteristics, the blood clinical indicators include direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics include pyroglutamate, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

[0009] Optionally, the blood clinical indicators also include total protein, globulin, urine specific gravity and hematocrit, and the plasma metabolome characteristics also include choline.

[0010] Further optionally, the blood clinical indicators also include albumin, red blood cell count and mean hemoglobin concentration, and the plasma metabolome characteristics also include allantoic acid and xanthine.

[0011] Optionally, the blood biomarkers consist of blood clinical indicators and plasma metabolome characteristics, the blood clinical indicators consist of direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics consist of pyroglutamate, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

[0012] The second aspect of the present invention provides a method for screening blood biomarkers for early diagnosis of coronary heart disease according to the present invention, which comprises the following steps:

[0013] The clinical index data of blood routine and blood biochemical tests of the CHD group and the healthy group were obtained respectively;

[0014] The plasma of the coronary heart disease group and the healthy group were extracted to obtain extracts;

[0015] Using a chromatography-mass spectrometer to detect the water phase in the extract to obtain detection data;

[0016] Preprocessing the detection data, and then identifying metabolites to obtain metabolomics data;

[0017] The clinical index data and metabolomics data are sequentially subjected to standardization, t-test, and correlation analysis to obtain the remaining clinical indexes and metabolomics characteristics;

[0018] Univariate ROC analysis was performed on the remaining clinical indicators and metabolome characteristics to screen blood biomarkers for the diagnosis of early coronary heart disease.

[0019] The third aspect of the present invention provides a use of the blood biomarker for early diagnosis of coronary heart disease described in the present invention in the preparation of a product for diagnosing early coronary heart disease.

[0020] Optionally, the product comprises a detection reagent or a kit.

[0021] The fourth aspect of the present invention provides a product for diagnosing early coronary heart disease, which includes the blood biomarker for diagnosing early coronary heart disease described in the present invention.

[0022] Optionally, the product comprises a detection reagent or a kit.

[0023] Beneficial effects: The present invention not only uses plasma metabolome characteristics, but also incorporates blood clinical indicators through routine blood tests and blood biochemical tests. Compared with other methods, it more comprehensively reflects the physiological and pathological state of the body and reduces the possibility of bias in the results. Therefore, the use of multidimensional blood biomarkers can more accurately predict the risk of early coronary heart disease. The present invention uses multidimensional blood biomarkers to improve the specificity and sensitivity of screening and diagnosing early coronary heart disease. In addition, the method of combining clinical indicators and metabolome characteristics in the present invention has the advantages of non-invasive, convenient sample acquisition, and non-invasiveness in diagnosing early coronary heart disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1This is a multivariate ROC curve analysis diagram for distinguishing the healthy group and the coronary heart disease group based on 8 blood biomarkers in the modeling group.

[0025] Figure 2 This is the multivariate ROC curve analysis diagram for distinguishing the healthy group and the coronary heart disease group based on 8 blood biomarkers in the validation group.

[0026] Figure 3 This is the multivariate ROC curve analysis diagram for distinguishing the healthy group from the CHD group based on 13 blood biomarkers in the validation group.

[0027] Figure 4 Figure 3 Multivariate ROC curve analysis of 18 blood biomarkers used to distinguish between the healthy group and the CHD group in the validation group. DETAILED DESCRIPTION

[0028] The present invention provides a blood biomarker for early diagnosis of coronary heart disease and a screening method and application thereof. To make the purpose, technical scheme 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 intended to limit the present invention.

[0029] An embodiment of the present invention provides a blood biomarker for the diagnosis of early coronary heart disease, wherein the blood biomarker includes blood clinical indicators and plasma metabolome characteristics, the blood clinical indicators include direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics include pyroglutamate, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

[0030] In order to solve the problems existing in the current diagnosis and screening of coronary heart disease, the embodiments of the present invention provide a group of blood biomarkers, which not only contain plasma metabolome characteristics, but also include blood clinical indicators through routine blood tests and blood biochemical tests, which more comprehensively reflect the physiological and pathological state of the body compared with other methods, and reduce the possibility of bias in the results. Therefore, the use of multidimensional blood biomarkers can more accurately predict the risk of early coronary heart disease. The use of multidimensional blood biomarkers for screening and diagnosing early coronary heart disease also has high specificity and sensitivity. In addition, the method of combining clinical indicators and metabolome characteristics in the embodiments of the present invention has the advantages of being non-invasive, easy to obtain samples, and non-invasive, and provides effective clinical treatment strategies and guidance for early detection, early diagnosis, and early intervention.

[0031] In one embodiment, the blood biomarkers consist of blood clinical indicators and plasma metabolome characteristics, wherein the blood clinical indicators consist of direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics consist of pyroglutamate, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

[0032] Using the above-mentioned blood biomarkers, the risk of early coronary heart disease can be accurately predicted.

[0033] In one embodiment, the blood clinical indicators further include total protein, globulin, urine specific gravity and hematocrit, and the plasma metabolome characteristics further include choline.

[0034] That is to say, the blood clinical indicators specifically include direct bilirubin, aspartate aminotransferase, high-density lipoprotein, total protein, globulin, urine specific gravity and hematocrit, and the plasma metabolome characteristics specifically include pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose, L-valyl-alanine and choline.

[0035] Using the above 13 blood biomarkers, the risk of early coronary heart disease can be accurately predicted.

[0036] In one embodiment, the blood clinical indicators further include albumin, red blood cell count and mean hemoglobin concentration, and the plasma metabolome characteristics further include allantoic acid and xanthine.

[0037] That is to say, the blood clinical indicators specifically include direct bilirubin, aspartate aminotransferase, high-density lipoprotein, total protein, globulin, urine specific gravity, hematocrit, albumin, red blood cell count and mean hemoglobin concentration, and the plasma metabolome characteristics specifically include pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose, L-valyl-alanine, choline, allantoic acid and xanthine.

[0038] Using the above 18 blood biomarkers, the risk of early coronary heart disease can be accurately predicted.

[0039] The embodiment of the present invention provides a method for screening blood biomarkers for early diagnosis of coronary heart disease, which comprises the following steps:

[0040] The clinical index data of blood routine and blood biochemical tests of the CHD group and the healthy group were obtained respectively;

[0041] The plasma of the coronary heart disease group and the healthy group were extracted to obtain extracts;

[0042] Using a chromatography-mass spectrometer to detect the water phase in the extract to obtain detection data;

[0043] Preprocessing the detection data, and then identifying metabolites to obtain metabolomics data;

[0044] The clinical index data and metabolomics data are sequentially subjected to standardization, t-test, and correlation analysis to obtain the remaining clinical indexes and metabolomics characteristics;

[0045] Univariate ROC analysis was performed on the remaining clinical indicators and metabolome characteristics to screen blood biomarkers for the diagnosis of early coronary heart disease.

[0046] In one embodiment, the t-test specifically includes: performing a student t-test on the standardized clinical indicator data and metabolomics data between the healthy group and the coronary heart disease group, and all clinical indicator data and metabolomics data with a P-value <0.05 are included in the subsequent correlation analysis.

[0047] In one embodiment, the correlation analysis specifically includes: performing Pearson correlation analysis on the clinical indicators and metabolome features included in the correlation analysis, and then extracting all indicator pairs and feature pairs whose correlation coefficient R value (Pearson correlation coefficient) is greater than or equal to a threshold value of 0.9, starting with the clinical indicators and metabolome features with the most participation times, until the correlation between any pair of clinical indicators and metabolome features is less than the threshold value of 0.9.

[0048] In one embodiment, the remaining clinical indicators and metabolomics features are subjected to univariate ROC analysis to screen out blood biomarkers for early diagnosis of coronary heart disease, specifically including:

[0049] For the remaining clinical indicators and metabolomic characteristics, univariate ROC analysis was performed between the healthy group and the CHD group to obtain the area under the ROC curve (AUC) values ​​of the clinical indicators and metabolomic characteristics;

[0050] According to AUC>0.73, 10 clinical indicators and 8 metabolomics features were screened out, totaling 18 characteristic variables related to coronary heart disease, which are blood biomarkers for early diagnosis of coronary heart disease. The above 18 blood biomarkers can accurately predict the risk of early coronary heart disease.

[0051] In one embodiment, 7 clinical indicators and 6 metabolome features are further screened out from the screened 10 clinical indicators and 8 metabolome features, totaling 13 characteristic variables that are more relevant to coronary heart disease, namely, blood biomarkers for early diagnosis of coronary heart disease. The above 13 blood biomarkers can accurately predict the risk of early coronary heart disease.

[0052] In one embodiment, the remaining clinical indicators and metabolomics features are subjected to univariate ROC analysis to screen out blood biomarkers for early diagnosis of coronary heart disease, specifically including:

[0053] For the remaining clinical indicators and metabolomic characteristics, univariate ROC analysis was performed between the healthy group and the CHD group to obtain the area under the ROC curve (AUC) values ​​of the clinical indicators and metabolomic characteristics;

[0054] According to AUC>0.73, 10 clinical indicators and 8 metabolomic features were screened;

[0055] After synthesizing the data of the 10 clinical indicators and 8 metabolomics features into a data set, the 8 characteristic variables most relevant to coronary heart disease were screened through the Lasso regression feature screening method, which are the blood biomarkers for early diagnosis of coronary heart disease. The above 8 blood biomarkers can accurately predict the risk of early coronary heart disease.

[0056] An embodiment of the present invention provides a use of the blood biomarker for early diagnosis of coronary heart disease described in the present invention in the preparation of a product for diagnosing early coronary heart disease.

[0057] In one embodiment, the product comprises a detection reagent or a kit.

[0058] An embodiment of the present invention provides a product for diagnosing early coronary heart disease, which includes the blood biomarker for diagnosing early coronary heart disease described in the present invention.

[0059] In one embodiment, the product comprises a detection reagent or a kit.

[0060] The present invention is described in detail below through specific embodiments.

[0061] Step 1: Test object and sample information

[0062] 1. Subjects

[0063] 1) Inclusion criteria for healthy subjects

[0064] (1) Male or female aged ≥18 years; (2) read and fully understood the patient instructions and signed the informed consent form; (3) according to the questionnaire, subjects with common chronic diseases (hypertension, diabetes, coronary heart disease), history of tumor treatment and major surgery were excluded; (4) self-reported no obvious clinical symptoms; (5) physical examination results were within the normal range and no obvious abnormalities were found; (6) physical examination results were beyond the normal range or abnormal, but the doctor judged that there was no clinical significance; (7) subjects who met the above 6 conditions at the same time were included as healthy subjects.

[0065] 2) Inclusion criteria for subjects with coronary heart disease

[0066] (1) Male or female aged ≥18 years; (2) read and fully understood the patient instructions and signed the informed consent form; (3) after coronary angiography, the degree of coronary artery stenosis was ≥50%; (4) Subjects who met the above three conditions were included as subjects with coronary heart disease.

[0067] 3) Obtaining clinical data of subjects

[0068] When collecting plasma samples from subjects, we collected information about the subjects' basic situation and health status through questionnaires, including basic demographic characteristics, lifestyle, medical history, medication history, etc. At the same time, we collected data on clinical indicators of blood routine and blood biochemical tests (Table 1) for subsequent statistical analysis and model construction.

[0069] Table 1. Clinical indicators of blood routine and blood biochemical tests

[0070]

[0071]

[0072] Step 2: Plasma sample pretreatment and polar metabolite detection

[0073] 1. Plasma sample pretreatment

[0074] After the plasma sample was taken out of the -80℃ refrigerator, it was thawed on ice and then vortexed for 10 seconds. Subsequently, 100μL of the plasma sample was taken and placed in 1000μL of a pre-cooled mixture of methyl tert-butyl ether and methanol, and vortexed to obtain a sample extract. Then, 500μL of a mixture of methanol and water (the volume ratio of methanol to water was 3:1) was added to the sample extract, and centrifuged at 12700rpm for 5min; after centrifugation and stratification, 400μL of the lower layer was transferred to a centrifuge tube, 1100μL of ice methanol was added thereto, and vortexed to precipitate the protein; after protein precipitation, it was incubated at 4℃ for 1h, and then centrifuged at 12700rpm for 10min at 4℃, and 1000μL of the supernatant was transferred to the corresponding centrifuge tube, and then spun dry in a vacuum concentrator (Speed-Vac). After drying, 200 μL of water was added to the centrifuge tube for reconstitution and incubated at room temperature for 15 min. After incubation, the centrifuge tube was vortexed and ultrasonically treated for 5 min. The centrifuge tube was then centrifuged at 12700 rpm for 5 min at room temperature. 180 μL of supernatant was taken from the centrifuge tube and transferred to a 2 mL glass injection bottle, which was the test solution containing polar metabolites and was tested by LC-MS.

[0075] 2. Polar metabolite detection

[0076] (1) Liquid chromatography parameters

[0077] Polar metabolites were analyzed using Waters ACQUTTY HSS T3 chromatographic column (1.8 μm, 2.1*100 mm) was used for small molecule separation. In the column parameters, 1.8 μm represents the particle size of the filler, 2.1*100 mm represents the specifications of the chromatographic column, 2.1 mm is the inner diameter of the chromatographic column, and 100 mm is the length of the chromatographic column. Both the liquid chromatography and mass spectrometer used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific);

[0078] The mobile phase composition is as follows:

[0079] Mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid, and the flow rate is 0.4 mL / min; the separation elution gradient is as follows: 0-1 minute, 1% mobile phase B; 1-11 minutes, 1%-40% mobile phase B; 11-13 minutes, 40%-70% mobile phase B; 13-15 minutes, 70%-99% mobile phase B; 15-19 minutes, 99% mobile phase B; 19-22 minutes, 1% mobile phase B, wherein % are all volume percentages.

[0080] (2) LC-MS mass spectrometry parameters

[0081] Polar metabolites were analyzed by full scan and data-dependent acquisition (DDA) to obtain primary mass spectrometry (MS1) and secondary mass spectrometry (MS2) data. The full scan mass spectrum range was 100-1500Da, and the secondary scan mass spectrum range was 100-310Da, 300-710Da, and 700-1500Da, respectively. The Orbitrap high-resolution mass spectrometer (Thermo Fisher) was equipped with an electrospray ionization source (ESI), and data were collected in positive and negative ionization modes, with an automatic gain control (AGC) target of 3E+6, a Maximum IT of 200 milliseconds, a first-level full scan resolution of 70,000 FWHM (@200 m / z), and in the second-level scan mode (Full MS / dd-MS2), the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion injection time was 50 ms, the relative collision energy (HCD) was 30 eV, the ion spray voltage was 3,500 V in positive mode, 3,000 V in negative mode, the nebulizer was 20 psi, the sheath gas temperature was 400 °C, and the sheath gas flow rate was 10 L / min.

[0082] Step 3: Metabolomics data preprocessing and metabolite identification

[0083] 1. Metabolomics Data Preprocessing

[0084] Metabolomics data preprocessing steps include peak extraction, peak alignment, baseline correction, peak filtering, isotope peak removal, missing value filling, etc. Specifically, first, the mass spectrometry raw data is subjected to peak extraction and peak alignment, then baseline correction is applied to remove noise signals, the original signal peak is retained, the raw data is converted into central discrete data, and the isotope peaks in the mass spectrometry data are removed; then, the peaks in a single sample are aligned with the retention times of the peaks in the chromatograms of other samples, isotope peaks and low-intensity adducts in the same analyte are removed, and characteristic peaks with >50% missing values ​​are eliminated; the random forest chain equation (MICE Forest) is used to fill in missing values ​​to obtain the final mass spectrometry matrix data.

[0085] 2. Identification of Metabolites

[0086] According to the original data, the spectral information of the compound parent ion and secondary fragment ion is obtained after the software is analyzed, such as the mass-to-charge ratio (m / z) of the primary mass spectrum, the fragmentation of the secondary ion, and the retention time. The metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database; commonly used metabolite databases include HMDB (www.hmdb.ca), PubChem (https: / / pubchem.ncbi.nlm.nih.gov), MassBank (http: / / www.massbank.jp), and MassBank of North America (https: / / massbank.us); 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 molecular weight error of the metabolite is less than 10ppm.

[0087] Step 4: CHD diagnosis method based on combined clinical indicators and metabolomics characteristics

[0088] 1. Sample information

[0089] The 179 samples used in the modeling group and the validation group were all actual samples collected from two medical centers, and the samples in the modeling group and the validation group were different samples. The specific distribution is as follows: the modeling group had 134 plasma samples, including 77 plasma samples from the healthy group and 57 plasma samples from the coronary heart disease group; the validation group had 45 plasma samples, including 26 plasma samples from the healthy group and 19 plasma samples from the coronary heart disease group (Table 2). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator.

[0090] Table 2. Sample information

[0091]

[0092]

[0093] 2. Blood biomarker screening method

[0094] 1) Data standardization: Log-transformation was performed on the clinical indicator data and metabolomics data of the modeling group and the validation group, respectively, to scale the original data to a specific range, eliminate the order of magnitude differences between different samples, and obtain a standardized data matrix.

[0095] 2) t-test: A t-test was performed on the standardized clinical index data and metabolomics data of the modeling group between the healthy group and the coronary heart disease group. All clinical index data and metabolomics data with P < 0.05 were included in the subsequent correlation analysis.

[0096] 3) Correlation analysis: Highly correlated variables may cause the model to overfit, and may also cause large fluctuations in actual application. In order to reduce the risk of overfitting and improve model stability, features with correlations higher than the preset threshold will be removed to ensure that the model has good discrimination and stability.

[0097] Specific:

[0098] |R x,y |≥β,for x,y=1,2,…,n;

[0099] Among them, the 1st, 2nd, ..., nth features are K1, K2, ..., Kn respectively, and the correlation coefficient between two features is R, R x,y Represents feature K x , K y The correlation coefficient between them. The threshold β is set according to the correlation coefficient. x and K y The correlation coefficient R x,y If the absolute value of ≥β, the two features are considered to be a pair of features with high correlation. Features that appear in multiple pairs of highly correlated features are preferentially eliminated.

[0100] Therefore, Pearson correlation analysis was performed on the clinical indicators and metabolome characteristics of the modeling group, and then all indicator pairs and feature pairs with the absolute value of the correlation coefficient R greater than or equal to 0.9 were extracted, starting with the clinical indicators and metabolome characteristics with the most participation times, until the correlation between any pair of clinical indicators and metabolome characteristics was less than the threshold value of 0.9.

[0101] 4) Univariate ROC analysis: After eliminating some clinical indicators and metabolome characteristics through t-test and correlation analysis, univariate ROC analysis was performed between the healthy group and the CHD group in the modeling group for the remaining clinical indicators and metabolome characteristics to obtain the area under the ROC curve (AUC) values ​​of clinical indicators and metabolome characteristics.

[0102] According to AUC>0.73, 10 clinical indicators (direct bilirubin, aspartate aminotransferase, high-density lipoprotein, total protein, globulin, urine specific gravity, hematocrit, albumin, red blood cell count and mean hemoglobin concentration) and 8 metabolomic features (pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose, L-valyl-alanine, choline, allantoic acid and xanthine) were screened out, totaling 18 characteristic variables, namely blood biomarkers. The above 18 blood biomarkers can effectively predict the risk of early coronary heart disease.

[0103] Then, 7 clinical indicators (direct bilirubin, aspartate aminotransferase, high-density lipoprotein, total protein, globulin, urine specific gravity and hematocrit) and 6 metabolomic characteristics (pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose, L-valyl-alanine and choline) were further selected from the 10 clinical indicators and 8 metabolomic characteristics, totaling 13 characteristic variables more related to coronary heart disease, namely blood biomarkers. The above 13 blood biomarkers can also effectively predict the risk of early coronary heart disease.

[0104] 5) Lasso regression feature screening: After synthesizing the data of the 10 clinical indicators and 8 metabolomic features into a data set, the Lasso regression feature screening method was used to finally screen out 8 characteristic variables most relevant to coronary heart disease, namely blood biomarkers, for the construction of early coronary heart disease risk prediction model. Among them, there are 3 clinical indicators (direct bilirubin, aspartate aminotransferase and high-density lipoprotein) and 5 metabolomic features (pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine) (as shown in Table 3).

[0105] Table 3. 8 blood biomarkers that distinguish healthy and coronary heart disease groups

[0106]

[0107] 3. Construction of early coronary heart disease risk prediction model

[0108] In order to evaluate the reliability of the 8 screened blood biomarkers in distinguishing the healthy group from the CHD group, a multivariate ROC curve analysis was performed on the 8 blood biomarkers. Three-quarters of the samples were randomly selected from the modeling group data as the training set (training), and the remaining 1 / 4 samples were selected as the test set (test). The support vector machine (SVM) was randomly iterated 1000 times. By calculating the average value of the final model accuracy, an early CHD risk prediction model was constructed. Figure 1The results showed that AUC = 0.977 (sensitivity: 92.4%, specificity: 94.7%), indicating that the constructed model has a high predictive efficiency. Generally speaking, the evaluation of the quality of the model is based on the area under the ROC curve AUC, which is usually between 0.5 and 1. The closer the AUC value is to 1, the better the model performance and the better the diagnostic effect; if the AUC value is less than 0.5, it means that the accuracy of the model is poor; Sensitivity and specificity reflect the model's ability to identify positive samples and negative samples, respectively. Sensitivity, also known as the true positive rate (TPR), indicates the probability that the model correctly diagnoses as positive in the case group; specificity, also known as the true negative rate (TNR), indicates the probability that the model correctly diagnoses as negative in the control group; the higher the sensitivity, the stronger the model's ability to identify positive samples. The higher the specificity, the stronger the model's ability to distinguish negative samples.

[0109] 4. Validation of early coronary heart disease risk prediction model

[0110] In order to verify the effectiveness of the model, only the data set of the 8 blood biomarkers screened above was retained from the standardized data set of the validation group. Then, this data set was used as an unknown sample and put into the early coronary heart disease risk prediction model constructed by the modeling group. Figure 2 The results showed that AUC = 0.984 (sensitivity: 89.5%, specificity: 96.2%), indicating that the early coronary heart disease risk prediction model established based on the combination of 8 clinical indicators and metabolic group characteristics also has good predictive ability in the validation group data set.

[0111] Based on the 13 characteristic variables that are more relevant to coronary heart disease, which are further screened out from the 10 clinical indicators and 8 metabolomics features, blood biomarkers are used. For these 13 blood biomarkers, the SVM method is used to build an early coronary heart disease risk prediction model in the modeling group samples, and then the validation group samples, as unknown samples, are put into the above prediction model to verify the effectiveness of different blood biomarkers in predicting the risk of early coronary heart disease. Figure 3 The results showed that AUC = 0.989 (sensitivity: 85.4%, specificity: 99.8%), indicating that the early coronary heart disease risk prediction model established based on the combination of 13 blood biomarkers also has good predictive ability.

[0112] According to the metabolomics and clinical index data, 10 clinical indicators and 8 metabolomics features were initially screened based on AUC univariate. For these 18 blood biomarkers, the SVM method was used to build an early coronary heart disease risk prediction model in the modeling group samples, and then the validation group samples, as unknown samples, were put into the above prediction model to verify the effectiveness of different blood biomarkers in predicting early coronary heart disease risk. Figure 4 The results showed that AUC = 0.996 (sensitivity: 89.5%, specificity: 96.2%). It shows that the early coronary heart disease risk prediction model established based on the combination of 18 blood biomarkers also has good predictive ability. In summary, the blood biomarkers and screening methods and applications for the diagnosis of early coronary heart disease provided by the present invention not only use plasma metabolomics characteristics, but also incorporate blood clinical indicators through blood routine and blood biochemical tests. Compared with other methods, it more comprehensively reflects the physiological and pathological state of the body and reduces the possibility of deviation in the results. Therefore, the use of multidimensional blood biomarkers can more accurately predict the risk of early coronary heart disease. The present invention uses multidimensional blood biomarkers to improve the specificity and sensitivity of screening and diagnosing early coronary heart disease. In addition, the method of combining clinical indicators and metabolomics characteristics for diagnosing early coronary heart disease has the advantages of non-invasiveness, convenient sample acquisition, and non-invasiveness.

[0113] 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 blood biomarker for early diagnosis of coronary heart disease, characterized in that: The blood biomarkers include blood clinical indicators and plasma metabolome characteristics. The blood clinical indicators include direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics include pyroglutamate, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

2. The blood biomarker for early diagnosis of coronary heart disease according to claim 1, characterized in that: The blood clinical indicators also include total protein, globulin, urine specific gravity and hematocrit, and the plasma metabolome characteristics also include choline.

3. The blood biomarker for early diagnosis of coronary heart disease according to claim 2, characterized in that: The blood clinical indicators also include albumin, red blood cell count and mean hemoglobin concentration, and the plasma metabolome characteristics also include allantoic acid and xanthine.

4. The blood biomarker for early diagnosis of coronary heart disease according to claim 1, characterized in that: The blood biomarkers are composed of blood clinical indicators and plasma metabolome characteristics The blood clinical indicators consist of direct bilirubin, aspartate aminotransferase and high-density lipoprotein, and the plasma metabolome characteristics consist of pyroglutamic acid, N-formyl-L-methionine, hypoxanthine, turanose and L-valyl-alanine.

5. A method for screening blood biomarkers for early diagnosis of coronary heart disease according to any one of claims 1 to 4, characterized in that: The steps include: The clinical index data of blood routine and blood biochemical tests of the CHD group and the healthy group were obtained respectively; The plasma of the coronary heart disease group and the healthy group were extracted to obtain extracts; Using a chromatography-mass spectrometer to detect the water phase in the extract to obtain detection data; Preprocessing the detection data, and then identifying metabolites to obtain metabolomics data; The clinical index data and metabolomics data are sequentially subjected to standardization, t-test, and correlation analysis to obtain the remaining clinical indexes and metabolomics characteristics; Univariate ROC analysis was performed on the remaining clinical indicators and metabolome characteristics to screen blood biomarkers for the diagnosis of early coronary heart disease.

6. Use of the blood biomarker for early diagnosis of coronary heart disease according to any one of claims 1 to 4 in the preparation of a product for diagnosing early coronary heart disease.

7. The use according to claim 6, characterized in that: The products include detection reagents or kits.

8. A product for diagnosing early coronary heart disease, characterized in that: A blood biomarker for early diagnosis of coronary heart disease comprising any one of claims 1-4.

9. The product for diagnosing early coronary heart disease according to claim 8, characterized in that: The products include detection reagents or kits.

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