Biomarker for predicting early myocardial infarction and application thereof

Abstract

The invention provides a biomarker for predicting early myocardial infarction and application of the biomarker. The biomarker is obtained by analyzing the super-acute stage (lt; a series of novel biomarkers capable of early predicting the occurrence risk of the myocardial infarction super-acute stage are screened from metabolites with significant difference in plasma samples of patients and normal people, are used for constructing a diagnostic model of the early myocardial infarction super-acute stage, can be used for efficient detection of the myocardial infarction super-acute stage, and can be applied to clinical diagnosis of the myocardial infarction super-acute stage. According to the kit, sensitivity and specificity diagnosis and accurate diagnosis and evaluation of acute myocardial infarction patients and potential high-risk groups in a super-acute stage within 6 hours can be realized, so that the early diagnosis rate of acute myocardial infarction can be improved, implementation of accurate medical strategies can be guided, and the treatment level of acute myocardial infarction can be improved.

Description

[0001] This application claims the priority of a prior Chinese application, application number: 202211244210.6, with an application date of October 11, 2022; all of its content is incorporated herein by reference and made a part of the present invention. Technical Field

[0002] The present invention relates to the field of early screening for myocardial infarction. Specifically, it relates to a biomarker for predicting early myocardial infarction and its applications, and in particular to a biomarker for diagnosing and risk stratifying the hyperacute phase (<6 hours) of acute myocardial infarction and its applications. Background Art

[0003] Myocardial infarction, also known as myocardial infarction (AMI), is myocardial necrosis caused by acute and persistent ischemia and hypoxia of the coronary artery. According to the World Health Organization (WHO) criteria: if two of the three indicators of typical chest pain, electrocardiogram changes, and abnormal myocardial enzymes are met, AMI can be diagnosed. With the discovery and understanding of new myocardial injury markers, the current diagnosis of AMI mainly relies on the fourth edition of the "Global Definition of Myocardial Infarction" criteria, that is, the elevation of cardiac troponin (cTn), and at least one time higher than the upper limit of the normal value (99th percentile value of the reference upper limit value), and at the same time there is clinical evidence of acute myocardial ischemia, including: (1) symptoms of acute myocardial ischemia; (2) new ischemic electrocardiogram changes; (3) new pathological Q waves; (4) new evidence of viable myocardial loss or wall motion abnormalities on imaging; (5) coronary angiography or intravascular imaging or autopsy confirmed coronary thrombosis. If the "5 + 1" criteria are met, AMI can be diagnosed.

[0004] AMI has two manifestations clinically: angina pectoris and painless. In China, about 25% of AMI patients are asymptomatic, and about 30% of cases have no typical angina pectoris manifestations. Clinically, electrocardiogram is generally used for diagnosis, and the accuracy of electrocardiogram in diagnosing AMI is about 60% on average. The ST segment elevation with diagnostic significance often shows atypical manifestations in many cases, and pathological Q waves often appear 6 - 8 hours after the onset. At present, electrocardiogram lacks specificity for diagnosing non - ST - segment elevation myocardial infarction (NSTEMI), resulting in a very low early electrocardiogram diagnosis rate for AMI. However, the 1 - 6 hours after its onset is the golden time for thrombolytic therapy and interventional surgery. Therefore, rapid diagnosis of early myocardial infarction within 6 hours of onset is the key link in determining treatment (JACC2021, 78:2218, https: / / www.ncbi.nlm.nih.gov / pubmed / 34756652).

[0005] In recent years, research has shown that the detection of biochemical markers of myocardial injury plays an important role in the diagnosis, monitoring, risk assessment, prognosis, and guiding treatment of acute myocardial infarction. Currently, the most commonly used myocardial injury markers are creatine kinase isoenzyme (CK-MB), cardiac troponin (cTnI or CTnI), and myoglobin. As an early marker of cardiac injury, myoglobin lacks specificity for the myocardium. Although CK-MB is rapid, economical, and effective, it still lacks specificity. Cardiac troponin has the highest accuracy, but for patients in the ultra-early stage of onset (<6 hours), especially those with atypical symptoms and electrocardiograms, the diagnostic efficacy is still significantly insufficient.

[0006] AMI is characterized by high incidence and high mortality. The risk of sudden death is the highest within hours after onset, and it is also the golden window period for medical treatment. Therefore, finding and establishing diagnostic markers for the ultra-early stage has important clinical value and social significance. Summary of the Invention

[0007] Aiming at the problems existing in the prior art, the present invention provides a biomarker for predicting early myocardial infarction and its application. By analyzing the metabolites with significant differences in plasma samples of patients with early myocardial infarction in the ultra-early stage (<6h) and normal populations, a series of new biomarkers that can early predict the risk of occurrence of early myocardial infarction in the ultra-early stage are screened out and used to construct a diagnostic model for early myocardial infarction in the ultra-early stage, which can be used for the efficient detection of early myocardial infarction in the ultra-early stage, and can achieve sensitive and specific diagnosis in the ultra-early stage within 6 hours of onset, accurately diagnose and evaluate acute myocardial infarction patients and potential high-risk populations, so as to improve the early diagnosis rate of acute myocardial infarction, guide the implementation of precision medical strategies, and thus improve the treatment level of acute myocardial infarction.

[0008] The "patients with ultra-early stage myocardial infarction" in the present invention are a group of people with similar characteristics, including: patients with acute myocardial infarction in the ultra-early stage (within 6 hours after onset), acute chest pain, acute coronary syndrome and other diseases. Accordingly, the method provided by the present invention is mainly used for timely diagnosing patients suspected of having acute myocardial infarction (ultra-early stage), and at the same time differentiating the etiologies of cardiac and non-cardiac origin in patients with acute chest pain, so as to initiate precision medical strategies as early as possible.

[0009] On the one hand, the present invention provides the use of a biomarker in the preparation of a reagent for predicting early myocardial infarction, wherein the biomarker is one or more selected from the following: corticosterone, docosatrienoate (22:3n3), N-linoleoyltaurine, linoleate (18:2n6), docosadienoate (22:2n6), pregnenolone sulfate, 17alpha-hydroxypregnenolone 3-sulfate, 21-hydroxypregnenolone monosulfate, androstenediol (3alpha,17alpha) monosulfate (3-α, 17-α-androstenediol monosulfate), etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate (16:1n7), eicosenoate (20:1), dihomo-linoleate (20:2n6), palmitoleoylcarnitine (C16:1)*, octanoylcarnitine (C8), palmitate (16:0), inosine, stearate (18:0), hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine (C2), fumarate, malate, palmitoylcarnitine (C16), valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine (GPC), tryptophan.,

[0010] Through non-targeted metabolomics research, the present invention collects plasma samples from patients at the time of admission. According to the onset of the disease, such as symptoms of chest tightness, chest pain, etc., which persist without relief or recur repeatedly, and whether it progresses to myocardial infarction within a short period (<6 hours), it is divided into a diseased group and a non-diseased group (the diseased group is the population that progresses to myocardial infarction within a short period (<6 hours), and the non-diseased group is the population that does not progress to myocardial infarction within a short period (<6 hours)). Among them, the plasma troponin in the diseased group has not yet increased. The plasma samples of the diseased group and the non-diseased group are analyzed by UPLC-MS / MS ultra-high performance liquid chromatography-tandem mass spectrometry. Statistical analysis (random forest, PLS-DA, LASSO regression analysis) and pathway enrichment analysis are used to identify candidate biomarkers in the discovery set and prioritize them, screen for significantly different metabolites, and finally obtain 13 biomarkers, which can be used to early diagnose the probability of acute myocardial infarction.

[0011] In some ways, the reagent for the biomarker for diagnosing whether early myocardial infarction occurs is a detection reagent prepared with the biomarker as the detection target, such as sample pretreatment reagents, antigens or antibodies, and other biological reagents and kits suitable for the detection of the biomarker; it can also be developed into a standardized reagent or kit suitable for LC-UV or LC-MS detection of the biomarker, etc.

[0012] Further, the early myocardial infarction includes the hyperacute phase: the hyperacute phase refers to the time window range after the patient has an acute myocardial infarction.

[0013] The time window range described in the present invention refers to the most effective golden time for thrombolytic therapy and interventional surgical treatment after an acute myocardial infarction, and is also the key link for effective treatment of acute myocardial infarction.

[0014] Further, the early myocardial infarction includes the hyperacute phase: the hyperacute phase means that the patient will progress to acute myocardial infarction within 6 hours or less than 6 hours.

[0015] Further, the reagent is used to detect the change degree or amplitude of the content or quantity of the biomarker in the blood sample.

[0016] Further, the marker is one or more selected from the following: docosatrienoate(22:3n3) (docosatrienoic acid), docosadienoate(22:2n6) (docosadienoic acid), dihomo-linoleate(20:2n6) (dihomo-linoleic acid), eicosenoate(20:1) (eicosenoate), linoleate(18:2n6) (linoleate), stearate(18:0) (stearate), palmitate(16:0) (palmitate), N-linoleoyltaurine* (linoleoyltaurine), corticosterone (corticosterone), palmitoleate(16:1n7) (palmitoleate), palmitoleoylcarnitine(C16:1)* (palmitoleoylcarnitine), pregnenolone sulfate (pregnenolone sulfate), typtophan (tryptophan), 2-hydroxybutyrate / 2-hydroxyisobutyrate (2-hydroxybutyrate / 2-hydroxyisobutyrate), 17alpha-hydroxypregnenolone 3-sulfate(17-α-hydroxypregnenolone 3-sulfate), acetylcarnitine(C2) (acetylcarnitine), 21-hydroxypregnenolone monosulfate(1) (21-hydroxypregnenolone monosulfate), asparagine (asparagine), xanthine (xanthine), octanoylcarnitine(C8) (octanoylcarnitine), androstenediol(3alpha,17alpha)monosulfate(2) (3-α,17-α-androstenediol monosulfate).

[0017] Further, the biomarker is one or more selected from the following: docosatrienoate (22:3n3) (docosatrienoic acid), docosadienoate (22:2n6) (docosadienoic acid), dihomo-linoleate (20:2n6) (dihomo-linoleic acid), eicosenoate (20:1) (eicosenoate), linoleate (18:2n6) (linoleate), stearate (18:0) (stearate), palmitate (16:0) (palmitate), N-linoleoyltaurine* (linoleoyltaurine), corticosterone (corticosterone), palmitoleate (16:1n7) (palmitoleate), palmitoleoylcarnitine (C16:1)* (palmitoyl carnitine), pregnenolone sulfate (pregnenolone sulfate), typtophan (tryptophan), 2-hydroxybutyrate / 2-hydroxyisobutyrate (2-hydroxybutyrate / 2-hydroxyisobutyrate), 17alpha-hydroxypregnenolone 3-sulfate (17-α-hydroxypregnenolone 3-sulfate), acetylcarnitine (C2) (acetyl carnitine).

[0018] Further, the biomarker is one or more selected from the following: docosatrienoate (22:3n3) (docosatrienoic acid), docosadienoate (22:2n6) (docosadienoic acid), dihomo-linoleate (20:2n6) (dihomo-linoleic acid), eicosenoate (20:1) (eicosenoate), linoleate (18:2n6) (linoleate), stearate (18:0) (stearate), palmitate (16:0) (palmitate), N-linoleoyltaurine* (linoleoyltaurine), corticosterone (corticosterone), palmitoleate (16:1n7) (palmitoleate).

[0019] Further, the reagent is used to detect biomarkers in a body fluid sample, and the body fluid sample includes any one of blood, urine, saliva, and sweat.

[0020] Further, detecting the biomarker in the body fluid sample is to detect the presence, absence, relative abundance, or concentration of the biomarker in the body fluid sample of an individual.

[0021] The presence, absence, or high or low content of the biomarker here is a relative concept. For example, when comparing the diseased group with the non-diseased group, the content of these specific biomarkers is compared relative to the diseased group or the non-diseased group as a reference. There may be some biomarkers whose content in the diseased group is relatively higher than that in the non-diseased group, and this increase is statistically significant, such as a significant or highly significant increase. Therefore, when judging these biomarkers, if it is a single biomarker, if the probability of a certain risk occurrence increases and the content of the biomarker changes, this change may be a relative increase or may also be a relative decrease, and the difference between this relative increase or relative decrease is significantly different, and of course, it can also be highly significantly different. Therefore, no matter what means are used for detection, a pre-specified value can be used as a standard (cut-off value), and if it is higher than this value, it is considered that the content has changed, and having such a result can be used for prediction or diagnostic value.

[0022] Therefore, in some aspects, the biomarker described in the present invention can be obtained by detecting the content of the biomarker in a sample by any known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, GC-MS, CC-MS, LC-MS-MS, NMR, immunochromatographic test strip, immunoreaction chip, capillary electrophoresis, infrared spectroscopy, etc. As long as it can be used to detect the content of the biomarker in the sample, it can be used for the early diagnosis of myocardial infarction. As long as the content in the sample can be detected, it can be used to predict or diagnose the probability of the occurrence of a certain disease. It can be understood that the detection here is for the individual sample, and then compared with the pre-set standard, and the result of the comparison is used to judge or predict the occurrence state of the disease. For example, it can be used to predict the probability of early myocardial infarction. Such prediction or diagnosis is whether it will occur within a certain time. Of course, such detection can be continuous detection, and the progress of the disease can be inferred from the change in the content of certain substances.

[0023] In some ways, the relative abundance is the peak area of the biomarker in the detection spectrum obtained by high performance liquid chromatography-tandem mass spectrometry. For example, if the average peak area of a certain biomarker measured in the control sample is 500, and the average peak area measured in the sample of the non-surviving group of myocardial infarction patients in the short term is 3000, then it is considered that the abundance of the biomarker in the sample is 6 times that of the control sample.

[0024] In some ways, any one or any combination of the 13 markers of the present invention can achieve the diagnosis of early myocardial infarction. Of course, these biomarkers can also be jointly detected with traditional biochemical indicators. For example, traditional biological indicators include creatine kinase isoenzyme (CK-MB), troponin (cTnI or CTNI), and myoglobin.

[0025] On the other hand, a kit for predicting early myocardial infarction includes a detection reagent for the biomarker as described above for use.

[0026] On yet another aspect, the present invention provides a biomarker combination for predicting early myocardial infarction, and the biomarker combination is any combination of 2 or more selected from the following: corticosterone, docosatrienoate, N-linoleoyltaurine, linoleate, docosadienoate, pregnenolone sulfate, 17alpha-hydroxypregnenolone 3-sulfate, 21-hydroxypregnenolone monosulfate, androstenediol(3alpha,17alpha)monosulfate, etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate, eicosenoate, dihomo-linoleate, palmitoleoylcarnitine, octanoylcarnitine, palmitate, inosine, stearate, hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine, fumarate, malate, palmitoylcarnitine, valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine, typtophan.

[0027] In another aspect, the present invention provides a system for predicting early myocardial infarction, the system comprising a data analysis module; the data analysis module is configured to analyze the detection values of biomarkers, the biomarkers being one or more selected from the following: corticosterone, docosatrienoate (22:3n3), N-linoleoyltaurine, linoleate (18:2n6), docosadienoate (22:2n6), pregnenolone sulfate, 17alpha-hydroxypregnenolone 3-sulfate, 21-hydroxypregnenolonemonosulfate, androstenediol (3alpha,17alpha)monosulfate (3-α,17-α-androstenediol monosulfate), etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate(16:1n7), eicosenoate(20:1), dihomo-linoleate(20:2n6), palmitoleoylcarnitine(C16:1)*, octanoylcarnitine(C8), palmitate(16:0), inosine, stearate(18:0), hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine(C2), fumarate, malate, palmitoylcarnitine(C16), valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine(GPC), tryptophan.,

[0028] The biomarker detection described in the present invention can be serum or plasma samples of patients with early myocardial infarction collected under any circumstances, that is, serum or plasma samples of patients with early myocardial infarction collected at different times, in different regions, in different quantities, of different genders, of different ages, etc.

[0029] Furthermore, it further includes a data detection system and a data input / output interface; the data detection system is used to detect biomarkers in the sample to obtain detection values; the input interface in the data input / output interface is used to input the detection values of the biomarkers, and after the detection values are analyzed by the data analysis module, the output interface is used to output the analysis results of early myocardial infarction.

[0030] The method for screening early myocardial infarction patients provided by the present invention has the following beneficial effects:

[0031] 1. 36 brand-new biomarkers that can early predict the risk of the occurrence of the hyperacute phase of myocardial infarction are screened out;

[0032] 2. By using this series of biomarkers to construct Naive Bayes MB, Linear Discriminant Analysis LDA, Random Forest RF, Logistic Regression LR, and Support Vector Machine SVM models respectively, it is found that all can achieve efficient prediction of whether an individual is in the hyperacute phase of acute myocardial infarction;

[0033] 3. It can achieve early sensitivity and specificity diagnosis within 6 hours after the onset;

[0034] 4. It is convenient, fast, and the test results are highly consistent with the clinical gold standard test results;

[0035] 5. It accurately locates the high-risk population of acute myocardial infarction patients, and conducts precise risk assessment on the cardiovascular system of this part of the population, so as to facilitate early detection and early intervention, promote the early discovery and early treatment of myocardial infarction, and meet the urgent needs of the clinic.

[0036] Detailed description

[0037] (1) Diagnosis or detection

[0038] Here, diagnosis or detection, prediction means detecting or assaying the biomarkers in the sample, or the content of the target biomarker, such as the absolute content or relative content, and then indicating whether the individual providing the sample may have or suffer from a certain disease, or the possibility of having a certain disease, based on the presence or quantity of the target biomarker. The meanings of diagnosis and detection here can be interchanged. The result of this detection or the result of this diagnosis cannot be directly used as the direct result of having a disease, but is an intermediate result. If a direct result is to be obtained, other auxiliary means such as pathology or anatomy are required to confirm having a certain disease. For example, the present invention provides a variety of new biomarkers that are related to the occurrence of early myocardial infarction, and the change in the content of these biomarkers is directly related to whether one has early myocardial infarction.

[0039] (2) The relationship between the biomarker or biomarkers and early myocardial infarction

[0040] Markers and biomarkers have the same meaning in the present invention. The connection here refers to the direct relevance between the appearance or change in the content of a certain biomarker in a sample and a specific disease or the disease process. For example, a relative increase or decrease in content indicates that the likelihood of having this disease is relatively higher than that of a healthy person, or the disease process develops into a more severe stage or progresses from one stage to another. For example, a single biomarker or a combination of biomarker substances among multiple new biomarkers of the present invention can be used to predict whether early myocardial infarction will occur.

[0041] If multiple different biomarkers in a sample appear simultaneously or there are relative changes in their contents, it also indicates that the likelihood of having this disease is relatively higher than that of a healthy person. That is to say, among the types of biomarkers, some biomarkers have a strong correlation with the disease, some have a weak correlation with the disease, or some even have no correlation with a specific disease. One or more of those biomarkers with a strong correlation can be used as biomarkers for diagnosing the disease, and those with a weak correlation can be combined with the strong ones to diagnose a certain disease, increasing the accuracy of the test results. Here, the disease can be the process or progression of the disease, such as developing from a relatively good stage of a certain disease to a more malignant or severe stage, or even eventually death.

[0042] Regarding the numerous biomarkers found in the serum in the present invention, these biomarkers can all be used to determine whether a person is a patient with early myocardial infarction; they can also be used to diagnose or predict the probability or likelihood of the occurrence of early myocardial infarction. Here, the biomarker can be directly detected or diagnosed as a single biomarker alone. Selecting such a biomarker indicates that the relative change in the content of this biomarker has a strong correlation with patients with early myocardial infarction. Of course, it can be understood that one or more biomarkers for the early diagnosis of acute myocardial infarction can be detected simultaneously. Normally, in some ways, selecting biomarkers with a strong correlation for detection or diagnosis can achieve a certain standard of accuracy, such as 60%, 65%, 70%, 80%, 85%, 90% or 95% accuracy. This indicates that these biomarkers can obtain an intermediate value for diagnosing a certain disease, but it does not mean that it can directly confirm the presence of a certain disease.

[0043] Of course, it is also possible to select differential biomarkers with a larger AUC value as diagnostic biomarkers. The so-called strong or weak is generally calculated and confirmed through some algorithms, such as the contribution rate or weight analysis of the biomarker to the probability of early myocardial infarction. Such calculation methods can be significance analysis (p-value or FDR value) and fold change. Multivariate statistical analysis mainly includes principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

[0044] (3) Patients with early myocardial infarction

[0045] The patients with early myocardial infarction refer to those who may progress to acute myocardial necrosis due to persistent myocardial ischemia, but have a short onset time (less than 6 hours), atypical clinical phenotypes, and currently available troponin tests are not able to clearly make a diagnosis. These patients may progress to large-area myocardial infarction, or may have a relatively limited infarct area under timely treatment, that is, small-area AMI.

[0046] Therefore, the prediction method provided by the present invention can quickly identify patients with early myocardial infarction from patients with myocardial injury or unstable angina and small-area AMI, so as to perform intervention treatment as early as possible.

[0047] (4) Pathogenesis characteristics of acute myocardial infarction: Patients with symptoms such as acute chest pain, chest tightness or a sense of constriction in the throat, which can radiate to the left upper limb, including those with newly occurring chest pain or acute exacerbation of original chest pain. Such patients may or may not be accompanied by clinical symptoms such as profuse sweating, fever, tachycardia, fatigue, dizziness, syncope, hypotension, shock, etc.

[0048] Electrocardiogram: Changes in ST segment (elevation or depression) and T wave (flattening or inversion), among which dynamic changes in ST segment (elevation or depression ≥ 0.1 mV) are characteristic manifestations of coronary artery lesions. There are also some patients with no electrocardiogram changes or non-specific changes.

[0049] Laboratory tests: Myocardial injury markers such as troponin, creatine kinase, myoglobin, etc. have not shown diagnostically significant elevations; there may be accompanied by elevated white blood cells, elevated C-reactive protein, increased erythrocyte sedimentation rate, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Clinical prediction result diagram of the diagnostic model for early myocardial infarction constructed for 36 biomarkers in Example 2. DETAILED DESCRIPTION OF THE INVENTION

[0051] The present invention will be further described in detail below with reference to the drawings and examples. It should be noted that the following examples are intended to facilitate the understanding of the present invention, but do not limit it in any way. The reagents used in this example are all known products and are obtained by purchasing commercially available products.

[0052] Example 1 Screening of Markers for Predicting Early Myocardial Infarction

[0053] In this embodiment, first, through non-targeted metabolomics research, plasma samples of patients at the time of admission were collected. According to whether they progressed to early myocardial infarction in the short term after onset, they were divided into a diseased group and a non-diseased group. The plasma samples of the diseased group and the non-diseased group were analyzed by UPLC-MS / MS ultra-high performance liquid chromatography-tandem mass spectrometry. Secondly, five statistical methods, namely naive Bayes MB, linear discriminant analysis LDA, random forest RF, logistic regression LR, and support vector machine SVM, were used to screen for metabolites with significant differences between the plasma samples of the diseased group and the non-diseased group respectively. The significantly different metabolites screened by all five statistical analysis methods were selected, and finally 36 metabolites were obtained as biomarkers, and the role of these biomarkers in the diagnosis or differentiation of the ultra-early stage of early myocardial infarction was verified.

[0054] The specific steps are as follows:

[0055] 1. Experimental method

[0056] A total of 200 plasma samples were collected from patients with early acute myocardial infarction and control individuals (non-acute myocardial infarction individuals), including 100 cases that developed into early myocardial infarction and 100 cases without myocardial infarction. Among them, patients in the ultra-early stage of early acute myocardial infarction were individuals confirmed to have acute myocardial infarction through coronary angiography and subsequent myocardial marker detection.

[0057] The baseline plasma samples at the time of admission of the discovery set were analyzed by non-targeted metabolomics using high performance liquid chromatography tandem mass spectrometry for the above specimens. A total of 1062 known-structured metabolites were identified on the non-targeted metabolomics platform, covering a wide range of biochemical pathways, including amino acids, lipids, carbohydrates, nucleotides, xenobiotics, and gut microbiome metabolism. After removing metabolites detected in less than 20% of the samples, the remaining 774 metabolites were further analyzed. We observed that acute myocardial infarction had a profound impact on the serum metabolome, and 206 of the 774 metabolites (75.2%) showed significantly changed levels (FDR < 0.05) between the group that developed into myocardial infarction and the group that did not develop into myocardial infarction.

[0058] m / z ions were extracted from the raw mass spectrometry data obtained by LC-MS / MS, the database was searched to identify metabolites, the peak areas of metabolite chromatographic peaks were obtained by checking the integration of metabolite chromatographic peaks, and data normalization and missing value filling were performed. The obtained data matrix was used for subsequent bioinformatics analysis, including five statistical methods: naive Bayes MB, linear discriminant analysis LDA, random forest RF, logistic regression LR, and support vector machine SVM, to respectively screen the ranking list of the most effective differential metabolites for sample classification between the samples of patients with acute myocardial infarction and control samples. Finally, the metabolites screened by all five methods were selected as biomarkers for predicting myocardial infarction.

[0059] 2. Experimental Results

[0060] By using five statistical methods, namely Naive Bayes MB, Linear Discriminant Analysis LDA, Random Forest RF, Logistic Regression LR, and Support Vector Machine SVM, 56, 63, 59, 72, and 49 differential metabolites were screened respectively. Among them, 36 metabolites were screened in all five data analysis methods, that is, 36 biomarkers, as shown in Table 1.

[0061] Table 1. 36 Biomarkers for Predicting Myocardial Infarction

[0062]

[0063]

[0064]

[0065] The 36 metabolites screened in this example, which have significant differences between the plasma of early acute myocardial infarction patients and normal populations, can efficiently distinguish early acute myocardial infarction patients from normal populations.

[0066] Example 2. Performance Comparative Analysis of Different Biomarkers and Their Combinations

[0067] 1. Single Biomarker

[0068] In this example, the individual diagnostic performances of the 36 biomarkers obtained in Example 1, which can distinguish 100 early acute myocardial infarction patients from 100 normal populations, were analyzed respectively, and their AUC values, Thresholds, specificities, and sensitivities were calculated. The results are shown in Table 1.

[0069] Table 1. Performance of Single Biomarkers for Diagnosing Early Myocardial Infarction

[0070]

[0071]

[0072] As can be seen from Table 1, the AUC values of the 36 biomarkers provided in Example 1 for diagnosing early myocardial infarction are all relatively high, all reaching above 0.727. Among them, 13 have AUC values higher than 0.95, and 16 have AUC values higher than 0.9. In particular, the top three, docosatrienoate (22:3n3), dihomo-linoleate (20:2n6), and docosadienoate (22:2n6), are newly discovered biomarkers that can be used for efficient screening of early myocardial infarction.

[0073] 2. Clinical Verification and Analysis of Combinations of Single and Multiple Biomarkers

[0074] Although a single biomarker can also distinguish early myocardial infarction from normal population blood samples, generally speaking, combining multiple biomarkers can achieve higher accuracy in discrimination or prediction.

[0075] However, for a single biomarker with higher accuracy in predicting early myocardial infarction, its role in a combination with one or more other biomarkers may not necessarily be greater. At the same time, it is not the case that the more biomarkers there are in a combination, the higher the prediction accuracy (AUC value) of the combination. Therefore, a large number of validation experiments are still needed.

[0076] In this embodiment, the 36 biomarkers provided in Embodiment 1 are used to construct a diagnostic model for early myocardial infarction through Naive Bayes MB, Linear Discriminant Analysis LDA, Random Forest RF, Logistic Regression LR, and Support Vector Machine SVM respectively. Then, these five groups of models are used to perform predictive analysis on 57 blood samples (known to include 28 positive myocardial infarction patients and 29 negative normal population), and the prediction results are as Figure 1 shown.

[0077] As Figure 1 can be seen, good prediction results for early myocardial infarction can be obtained using all five groups of models, with an accuracy of 100%. It truly realizes non-invasive, global, and early screening of myocardial infarction with higher sensitivity and specificity, fully meeting clinical requirements.

[0078] In addition, this embodiment also uses 36 single biomarkers respectively to perform predictive analysis on 57 blood samples (known to include 28 positive myocardial infarction patients and 29 negative normal population). The detection accuracies are respectively between 60% and 96%. Among them, the highest detection accuracies are docosadienoate (22:2n6), docosatrienoate (22:3n3), and docosadienoate (22:2n6), which detect 27, 26, and 26 positive myocardial infarction patients respectively.

[0079] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be determined by the scope defined by the claims.

Claims

1. Use of a biomarker in the preparation of a reagent for predicting early myocardial infarction, characterized in that, The biomarker is one or more selected from the following: corticosterone, docosatrienoate(22:3n3), N-linoleoyltaurine, linoleate, docosadienoate(22:2n6), pregnenolone sulfate, 17alpha-hydroxypregnenolone 3-sulfate, 21-hydroxypregnenolone monosulfate, androstenediol(3alpha,17alpha)monosulfate, etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate, eicosenoate, dihomo-linoleate, palmitoleoylcarnitine, octanoylcarnitine, palmitate, inosine, stearate, hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine, fumarate, malate, palmitoylcarnitine, valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine, tryptophan.

2. The use according to claim 1, wherein, The early myocardial infarction includes a hyperacute phase: The hyperacute phase refers to the time window range after a patient has an acute myocardial infarction.

3. The use according to claim 2, wherein, The early myocardial infarction includes a hyperacute phase: The hyperacute phase means that the patient will progress to acute myocardial infarction within 6 hours or less than 6 hours.

4. The use according to claim 3, wherein The biomarker is one or more selected from the following: docosatrienoate, docosadienoate, dihomo-linoleate, eicosenoate, linoleate, stearate, palmitate, N-linoleoyltaurine, corticosterone, palmitoleate.

5. The use according to claim 3, characterized in that, The reagent is used to detect the biomarker in a body fluid sample, and the body fluid sample includes any one of blood, urine, saliva, and sweat.

6. The use according to claim 4, characterized in that Detecting the biomarker in the body fluid sample is to detect the presence, relative abundance or concentration of the biomarker in the body fluid sample of an individual.

7. A kit for predicting early myocardial infarction, characterized in that, A detection reagent comprising a biomarker for the use as described in claim 1.

8. A biomarker combination for predicting early myocardial infarction, characterized in that, The biomarker combination is any combination of two or more selected from the following: corticosterone, docosatrienoate(22:3n3), N-linoleoyltaurine, linoleate, docosadienoate(22:2n6), pregnenolone sulfate, 17alpha-hydroxypregnenolone3-sulfate, 21-hydroxypregnenolone monosulfate, androstenediol(3alpha,17alpha)monosulfate, etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate, eicosenoate, dihomo-linoleate, palmitoleoylcarnitine, octanoylcarnitine, palmitate, inosine, stearate, hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine, fumarate, malate, palmitoylcarnitine, valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine, typtophan.

9. A system for predicting early myocardial infarction, characterized in that, The system includes a data analysis module; the data analysis module is used to analyze the detection values of biomarkers, and the biomarkers are one or more selected from the following: corticosterone, docosatrienoate(22:3n3), N-linoleoyltaurine, linoleate, docosadienoate(22:2n6), pregnenolone sulfate, 17alpha-hydroxypregnenolone 3-sulfate, 21-hydroxypregnenolonemonosulfate, androstenediol(3alpha,17alpha)monosulfate, etiocholanolone glucuronide, stearoyl ethanolamide, palmitoleate, eicosenoate, dihomo-linoleate, palmitoleoylcarnitine, octanoylcarnitine, palmitate, inosine, stearate, hypoxanthine, 2-hydroxybutyrate / 2-hydroxyisobutyrate, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, xanthine, acetylcarnitine, fumarate, malate, palmitoylcarnitine, valine, leucine, phenylalanine, threonine, carnitine, asparagine, glycerophosphorylcholine, typtophan.

10. The system according to claim 9, wherein It further includes a data detection system and a data input / output interface; the data detection system is used to detect the biomarkers in the sample to obtain detection values; the input interface in the data input / output interface is used to input the detection values of the biomarkers, and after the detection values are analyzed by the data analysis module, the output interface is used to output the analysis results of early myocardial infarction.

Images