Combined marker for predicting acute myocardial infarction, detection kit and application

Abstract

The invention relates to new application of small molecule metabolites such as decanoyl carnitine (CN C10: 0), lauroyl carnitine (CN C12: 0), glutamine, prolyl-leucine, oleic acid and S-adenosine homocysteine in a plasma sample as combined markers in preparation of a kit for predicting acute myocardial infarction patients in subjects. The invention further relates to a kit for detecting acute myocardial infarction patients in subjects, the relative concentrations of the combined markers in blood plasma of the subjects are detected, variables of the combined markers are calculated on the basis of a binary logistic regression equation, and the acute myocardial infarction patients in the subjects are detected on the basis of determined intercept point values. And predicting whether the subject has the risk of acute myocardial infarction or not. The kit can realize high-sensitivity detection of several metabolites involved in the invention, and has the characteristics of low detection cost and good repeatability. The combined use of the small molecule metabolites can be applied to the clinical diagnosis of auxiliary prediction of acute myocardial infarction, and has high development and application values.

Description

Technical Field

[0001] The present invention relates to the application of a new small molecule combined marker in predicting acute myocardial infarction, and to a detection kit and method for detecting acute myocardial infarction in a subject, belonging to the fields of analytical chemistry and clinical medicine. Background Art

[0002] Acute myocardial infarction (AMI) is a clinical syndrome of myocardial ischemia, damage and necrosis caused by a sharp decrease, interruption or relative insufficiency of coronary blood supply. It is the leading cause of death and disability worldwide. For patients with underlying cardiovascular diseases such as atherosclerosis and angina pectoris, early prediction and prevention of AMI are particularly important. However, the current diagnosis of AMI mainly relies on serum markers such as troponin I, myoglobin, creatine kinase isoenzymes, electrocardiogram changes and clinical signs of chest pain. Therefore, the discovery of biomarkers with high sensitivity and good specificity is of great significance for predicting acute myocardial infarction and improving and strengthening treatment for high-risk populations.

[0003] At present, there have been some studies on the prediction of acute myocardial infarction, including prediction models such as imaging and serum. Since the detection of biochemical indicators is less invasive and relatively low in cost, body fluids, especially peripheral blood, have the advantages of convenient sampling, low invasiveness, and fast regeneration, and are one of the ideal sources for biomarker research. Compared with the expression of genes and proteins, which are easily regulated by life processes such as epigenetic and post-translational modifications and have heterogeneous and uncertain existence forms, metabolites, as the final product of upstream gene expression, can reflect the final response of the organism to changes in genes or the external environment, and are therefore easier to associate with biological phenotypes. The present invention detects small molecule metabolites in plasma by using ultra-high performance liquid chromatography-mass spectrometry combined with metabolomics technology, and combines bioinformatics and statistical analysis to screen out target differential metabolites in the hope of predicting AMI. Ultra-high performance liquid chromatography-mass spectrometry provides a rapid, highly sensitive, low-cost, and highly stable detection method for the detection of small molecule metabolites.

[0004] Among the combined markers involved in the present invention, CN C10:0, CN C12:0, Gln, Pro-Leu, FFAC18:1, and SAH are all important metabolites in the human body and are involved in a variety of pathological and physiological processes. Summary of the invention

[0005] The purpose of the present invention is to provide a combination of small molecule metabolites to predict acute myocardial infarction, and to provide a detection method that can be used to detect the above-mentioned combination of small molecule metabolites, in response to the clinical problems of limited accuracy and sensitivity in predicting the risk of acute myocardial infarction and lack of effective biomarkers.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] The metabolomics fingerprint analysis was performed on the plasma of patients with acute myocardial infarction and healthy control group (Ctrl) using ultra-performance liquid chromatography-mass spectrometry-coupled metabolomics technology.

[0008] The separation system was a Waters ACQUITY UPLC system, which used two separation modes. In the positive ion mode, a C8 chromatographic column (ACQUITY UPLC BEH C8 (2.1×50 mm) chromatographic column) was used; mobile phase A was an aqueous solution of 0.1% formic acid by volume, and mobile phase B was an acetonitrile solution of 0.1% formic acid by volume; elution gradient: 5% B phase from 0 to 0.5 min, linearly changed to 40% B phase from 0.5 min to 2 min, linearly changed to 100% B phase from 2 min to 8 min, maintained for 2 min, linearly changed to 5% B phase from 10 min to 10.1 min and balanced for 1.9 min; mobile phase flow rate was 0.40 mL / min; column temperature was 60°C; injection volume was 5 μL. In negative ion mode, a T3 chromatographic column (ACQUITYUPLC HSS T3 (2.1×50 mm) chromatographic column) was used; mobile phase C was a 6.5 mM ammonium bicarbonate aqueous solution, and mobile phase D was a 6.5 mM ammonium bicarbonate methanol aqueous solution (methanol / water=95 / 5, v / v); elution gradient: 2% D phase from 0 to 0.5 min, linearly changed to 40% D phase from 0.5 min to 2 min, linearly changed to 100% B phase from 2 min to 8 min, maintained for 2 min, linearly changed to 2% D phase from 10 min to 10.1 min and balanced for 1.9 min; mobile phase flow rate was 0.40 mL / min; column temperature was 60° C.; injection volume was 5 μL.

[0009] Mass spectrometer is tripleTOF TM 5600plus system. The parameters were set as follows: GS1, 50 psi; GS2, 50 psi; CUR, 35 psi; ion source temperature, 500°C; ion source voltage, 5500 V (in positive ion mode), -4500 V (in negative ion mode).

[0010] Plasma sample pretreatment method:

[0011] Plasma was thawed at 4°C, 50 μL of plasma sample was taken, and 200 μL of extraction solution (i.e., a mixed solution of methanol and acetonitrile, with a volume ratio of 1:1) was added to precipitate protein; after vortexing, centrifuged at 14,000g, 4°C for 15 minutes; the supernatant was freeze-dried and stored in a -80°C refrigerator. Before injection, the sample was reconstituted with 50 μL of methanol / water = 1 / 4 (v / v) solution; after vortexing, centrifuged at 14,000g, 4°C for 15 minutes; 5 μL of supernatant was taken for analysis.

[0012] The six metabolites included in the combined marker were obtained by peak extraction and peak matching software (such as MS-DAIL), among which CN C10:0, CN C12:0, and Pro-Leu peaked in the positive ion mode, and Gln, FFAC18:1, and SAH peaked in the negative ion mode. The peak areas of all ion features were calculated, and the percentages of the above six metabolites in the total of all metabolites in each sample were calculated to determine the relative contents of these six metabolites.

[0013] The obtained data were subjected to binary logistic regression analysis using the data processing software PASW Statistics, regressed into the combined marker variable, and then the receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the combined marker. The regression equation of the constructed model is as follows:

[0014] X=

[0015] -9370.269*A-31513.892*B-47168.874*C+129141.453*D+67.56*E+278413.413*F-2.803

[0016] Pi(AMI)=1 / (1+eX)

[0017] Among them, A, B, C, D, E, and F are the relative concentrations of CN C10:0, CN C12:0, Gln, Pro-Leu, FFAC18:1, and SAH, respectively, and e is the Euler number, that is, the base of the natural logarithm. The variable Pi(AMI) obtained is increased in AMI patients, and the value of this variable can be used to assist in predicting AMI. Based on the clinical samples involved in this experiment, according to the principle of optimal diagnostic sensitivity and specificity, the cutoff value of the variable Pi(AMI) is set to 0.500, that is, when the Pi(AMI) value is greater than 0.500, AMI is likely to occur.

[0018] The established combined marker model has good discriminative ability for distinguishing AMI patients from Ctrl, with an area under the curve (AUC) of 0.992, a sensitivity of 95.4%, and a specificity of 98.4%. The results show that the combined marker model established by the present invention has the potential to be applied to predict AMI. In addition, we also analyzed the combination of markers, and the results showed that the optimal effect can only be achieved when these six metabolites are used in combination.

[0019] The present invention has the effect that the small molecule metabolites CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH in the plasma sample can be used in combination to predict AMI. The detection kit of the above-mentioned several small molecule metabolites involved in the present invention can realize rapid, highly sensitive, low-cost, and highly stable detection. The combined use of the above-mentioned several small molecule metabolites is expected to be applied to the prediction of AMI.

[0020] The present invention detects the relative concentration of each of the above-mentioned combined markers in the plasma from the subject, calculates the combined marker variable based on a binary logistic regression equation, and predicts the risk of the subject suffering from acute myocardial infarction based on a determined cutoff value. The kit can achieve high-sensitivity detection of several metabolites involved in the present invention, and has the characteristics of low detection cost and good repeatability. The combined use of the above-mentioned several small molecule metabolites can be applied to assist in the clinical diagnosis of predicting acute myocardial infarction, and has a high development and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Showing the changes in the levels of CN C10:0 (a), CN C12:0 (b), Gln (c), Pro-Leu (d), FFAC18:1 (e), and SAH (f) in plasma samples of healthy control group (Ctrl) and acute myocardial infarction group (AMI);

[0022] Figure 2 Shows the changes in the levels of homoserine (a), taurine (b), valine (c) and xanthosine (d) in plasma samples of healthy control group (Ctrl) and acute myocardial infarction group (AMI);

[0023] Figure 3 The ROC curve of the combined marker is shown in differentiating patients with acute myocardial infarction from normal subjects. DETAILED DESCRIPTION

[0024] Example 1

[0025] 1. Collection of Plasma Samples

[0026] Plasma samples were collected from 246 healthy people (age: 29-89 years old; people with normal liver function, kidney function, blood sugar, and blood lipids) and 412 patients with acute myocardial infarction (age: 32-91 years old). Before plasma collection, the experiment required approval from the unit's ethics committee and informed consent from all subjects. All subjects had their blood drawn the next morning after fasting for more than 8 hours. The collected blood samples were allowed to stand for 30 minutes, centrifuged at 3000rpm / min for 5 minutes, and then plasma was collected and stored in a -80℃ refrigerator for later use.

[0027] 2. Analytical methods

[0028] 2.1 Pretreatment of plasma samples

[0029] Take out the plasma sample from the -80℃ freezer and thaw at 4℃, vortex to mix thoroughly, and quantitatively take 50μL of plasma sample in a 1.5mL centrifuge tube. Quantitatively add 200μL of extraction solution (a mixed solution of methanol and acetonitrile, volume ratio of 1:1) under ice bath conditions, vortex for 2 minutes to mix thoroughly. Centrifuge at 14,000g for 15 minutes at 4℃, quantitatively take 50μL of supernatant in a 1.5mL centrifuge tube, freeze-dry under vacuum conditions, and store in a -80℃ refrigerator for later use. Before injection, the sample was reconstituted with 50μL of methanol / water = 1 / 4 (v / v) reconstitution reagent, vortexed for 2 minutes to mix, and centrifuged at 14,000g for 10 minutes at 4℃; take 5μL of supernatant for injection analysis.

[0030] 2.2 Ultra-high performance liquid chromatography-mass spectrometry analysis

[0031] (1) Liquid phase conditions: The chromatograph was Waters ACQUITY UPLC (Waters, Ireland).

[0032] In the positive ion mode, a C8 column (ACQUITY UPLC BEH C8 (inner diameter × length = 2.1 × 50 mm) column; Waters Corporation) was used; the eluent A phase was an aqueous solution of 0.1% (v / v) formic acid, and the eluent B phase was an acetonitrile solution of 0.1% (v / v) formic acid. The elution gradient (v / v) was as follows: 0-0.5 min to maintain 5% B phase, 0.5 min to 2 min, B phase linearly increased from 5% to 40%, 2 min to 8 min, B phase linearly changed to 100%, maintained for 2 min, and then linearly changed to 5% B phase within 0.1 min and equilibrated for 1.9 min.

[0033] In the negative ion mode, a T3 column (ACQUITYUPLC HSS T3 (inner diameter × length = 2.1 × 50 mm) column; Waters Corporation) was used; the eluent C phase was a 6.5 mM ammonium bicarbonate aqueous solution, and the D phase was a 6.5 mM ammonium bicarbonate methanol aqueous solution (methanol / water = 95 / 5, v / v). Elution gradient: 0-0.5 min was 2% D phase, 0.5 min-2 min, D phase linearly changed from 2% to 40%, 2 min-8 min and then linearly increased to 100%, maintained for 2 min, and then returned to the initial ratio of 2% within 0.1 min, and balanced for 1.9 min. In the positive and negative ion modes, the mobile phase flow rate was 0.40 mL / min, the column temperature was 60 ° C, and the injection volume was 5 μL.

[0034] (2) Mass spectrometry conditions: using tripleTOF TM 5600plus mass spectrometer. The parameters were set as follows: GS1, 50psi; GS2, 50psi; CUR, 35psi; ion source temperature, 500°C; ion source voltage, 5500V (in positive ion mode), -4500V (in negative ion mode).

[0035] 2.3 Plasma test results and auxiliary diagnostic methods

[0036] The total peak area Sn of each sample was calculated, and the relative contents of CN C10:0, CN C12:0, Gln, Pro-Leu, FFAC18:1, and SAH (the ratio of the peak area of ​​the compound to the total peak area) were calculated. The calculation formula is as follows:

[0037] Relative content CNC10:0 =Peak area CNC10:0 / Total peak area Sn;

[0038] Relative content CNC12:0 =Peak area CNC12:0 / Total peak area Sn;

[0039] Relative content Gln =Peak area Gln / Total peak area Sn;

[0040] Relative content Pro-Leu =Peak area Pro-Leu / Total peak area Sn;

[0041] Relative content FFAC18:1 =Peak area FFAC18:1 / Total peak area Sn;

[0042] Relative content SAH =Peak area SAH / Total peak area Sn. The relative concentration values ​​of the above six metabolites in each clinical sample were imported into PASW Statistics (version 18.0.0) software, and binary logistic regression analysis was performed with sample type (Ctrl for healthy physical examination population as 1, AMI for acute myocardial infarction patients as 2) as the dependent variable and the relative concentration values ​​of the above six metabolites as the covariate, and the regression equation was obtained as follows:

[0043] X=

[0044] -9370.269*A-31513.892*B-47168.874*C+129141.453*D+67.56*E+278413.413*F-2.803

[0045] Pi(AMI)=1 / (1+eX)

[0046] Among them, A, B, C, D, E, and F are the relative concentrations of CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH, respectively; Pi is the predicted value; and e is the Euler number, which is the base of the natural logarithm.

[0047] Compared with the normal control group, the levels of CN C10:0, CN C12:0, and Gln in AMI patients were significantly decreased, while the levels of Pro-Leu, FFA C18:1, and SAH were significantly increased (see Figure 1 ).

[0048] We also analyzed the combination of markers. When a combined marker model was established using 10 metabolites (CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, SAH, homoserine, taurine, valine, and xanthosine), in addition to the changes in the above six metabolites, the expression levels of homoserine, taurine, valine, and xanthosine in AMI patients were significantly reduced compared with the normal control group (see Figure 2 ).

[0049] Data analysis showed that when the 10-metabolite model was used to distinguish AMI patients from the Ctrl group, the AUC was 0.992, and the discrimination effect was not significantly improved;

[0050] When the combined marker model was established using 5 metabolites (CN C10:0, CN C12:0, Gln, Pro-Leu, and SAH) and 4 metabolites (CNC10:0, Gln, Pro-Leu, and SAH), the AUCs of the models for distinguishing AMI patients from the Ctrl group were 0.990 and 0.989, respectively. Figure 3 It can be seen that the differential expression of CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH metabolites compared with the Ctrl group is not the largest combination, but based on the clinical samples involved in this experiment, the fitting values ​​of the contribution and the AUC values ​​after combination are judged. According to the principle of optimal diagnostic sensitivity and specificity, when the combined marker uses a cutoff value of 0.500, the discriminant effect on AMI is shown in Tables 1 and Figure 2 Therefore, only when these six metabolites are used in combination, the combined markers we established can achieve the best discriminative effect for distinguishing normal people from AMI patients.

[0051] The detection kit of the present invention has the characteristics of rapidity, high sensitivity, low cost and high stability. At the same time, the present invention can be applied to the prediction of AMI and has high development and application value.

[0052] Table 1. Sensitivity and specificity of combined markers in distinguishing AMI group from Ctrl group

[0053]

[0054] The present invention also relates to a kit for detecting patients with acute myocardial infarction in a subject, by detecting the relative concentration of each of the above-mentioned combined markers in the plasma from the subject, calculating the combined marker variable based on a binary logistic regression equation, and judging whether the subject has a risk of acute myocardial infarction based on a determined cutoff value. The kit can achieve highly sensitive detection of several metabolites involved in the present invention, and has the characteristics of low detection cost and good repeatability. The combined use of the above-mentioned several small molecule metabolites can be applied to the clinical diagnosis of predicting acute myocardial infarction, and has a high development and application value.

[0055] Example 2

[0056] 1. Random Sampling

[0057] Six samples were randomly selected from the 658 samples collected in Example 1, and the data of CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH metabolites were extracted. The data were substituted into the following regression equation to calculate the Pi value:

[0058] X=

[0059] -9370.269*A-31513.892*B-47168.874*C+129141.453*D+67.56*E+278413.413*F-2.803

[0060] Pi(AMI)=1 / (1+eX)

[0061] 2. Data Analysis

[0062] Six groups of data were randomly selected as shown in Table 2. The Pi (AMI) values ​​of patients 1 and 4 were less than 0.500, and the Pi (AMI) values ​​of patients 2, 3, 5, and 6 were greater than 0.500. The clinical information of the six patients was searched and it was shown that patients 1 and 4 were healthy controls, and patients 2, 3, 5, and 6 were all AMI patients, with an accuracy rate of 100%. This result further verified that the combined marker we established can achieve the best discriminant effect for distinguishing people without myocardial infarction from AMI patients.

[0063] Table 2. Changes of differential metabolites in 6 randomly selected samples

[0064]

Claims

1. A combined marker for predicting acute myocardial infarction, characterized in that: It is composed of small molecule metabolites including decanoylcarnitine (CN C10:0), lauroylcarnitine (CN C12:0), glutamine (Gln), prolyl-leucine (Pro-Leu), oleic acid (FFAC18:1), and S-adenosylhomocysteine ​​(SAH).

2. Use of the combined marker in the preparation of a kit for predicting acute myocardial infarction in a subject, characterized in that: The combined marker is composed of small molecule metabolites including decanoylcarnitine (CN C10:0), lauroylcarnitine (CNC12:0), glutamine (Gln), prolyl-leucine (Pro-Leu), oleic acid (FFAC18:1), and S-adenosylhomocysteine ​​(SAH).

3. A kit for predicting acute myocardial infarction in a subject, the kit comprising: (1) Chemical standards: CN C10:0, CN C12:0, Gln, Pro-Leu, FFAC18:1, SAH; (2) Extraction solution for pretreatment of plasma samples from subjects: a mixed solution of methanol and acetonitrile (volume ratio: 3 / 4-4 / 3); (3) Eluent used to elute the chromatographic column.

4. The kit according to claim 3, characterized in that: The eluent used for eluting the chromatographic column is an eluent used for eluting an ACQUITY UPLC BEH C8 (2.1×50 mm) chromatographic column and an ACQUITY UPLC HSS T3 (2.1×50 mm) chromatographic column; The standards are used for the qualitative analysis of the small molecule metabolites CN C10:0, CN C12:0, Gln, Pro-Leu, FFAC18:1 and SAH in plasma, respectively.

5. A kit according to claim 3 or 4, wherein the eluent comprises: Mobile phase A: 0.05-0.20 v / v formic acid in water, and Mobile phase B: 0.05-0.20% v / v formic acid in acetonitrile; Mobile phase C: 5.5-7.5 mM ammonium bicarbonate in water, and Mobile phase D: 5.5-7.5 mM ammonium bicarbonate in methanol solution, wherein methanol / water = 90-98 / 3-8, v / v; Wherein mobile phases A and B were used to elute the ACQUITY UPLC BEH C8 column in positive ion mode; Mobile phases C and D were used to elute the ACQUITY UPLC HSS T3 column in negative ion mode.

6. The kit according to claim 3, characterized in that: The subjects included patients with acute myocardial infarction and / or healthy people as controls.

7. An application of the kit according to any one of claims 3 to 6, for detecting the relative contents of CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH in plasma samples, and performing binary logistic regression analysis on the obtained data using data processing software. The regression equation is as follows: X＝ -9370.269*A-31513.892*B-47168.874*C+129141.453*D+67.56*E+278413.413*F-2.803 Pi(AMI)=1 / (1+eX) in, A, B, C, D, E, and F are the relative contents of CN C10:0, CN C12:0, Gln, Pro-Leu, FFA C18:1, and SAH, respectively.

8. Use of the kit according to claim 7, characterized in that: The obtained variable Pi(AMI) increases in patients with acute myocardial infarction, and the value of this variable can be used to predict acute myocardial infarction; the cutoff value of the variable Pi(AMI) is set to 0.500, that is, when the Pi(AMI) value is greater than or equal to 0.500, acute myocardial infarction is likely to occur, and when it is less than 0.500, the risk of acute myocardial infarction is low.

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