Coronary artery microvascular disease related biomarker and application thereof

Abstract

The invention relates to the technical field of molecular diagnosis, on one hand, the invention relates to a coronary artery microvascular disease (CMVD) related biomarker, and the biomarker comprises one or two of MW0110047 and MEDN0685; on the other hand, the invention relates to application of the biomarker in preparation of a diagnostic product for detecting coronary artery microvascular diseases. Compared with a traditional circulating biomarker, the metabolite MW0110047 and MEDN0685 serving as the biomarker for diagnosing the coronary artery microvascular disease has higher specificity, can be used for preparing a diagnostic product for detecting the coronary artery microvascular disease, is beneficial to simplifying the diagnosis of the coronary artery microvascular disease, and has important clinical significance.

Description

Technical Field

[0001] The present invention relates to the technical field of molecular diagnosis, and in particular to biomarkers related to coronary microvascular disease and applications thereof. Background Art

[0002] In recent years, with the rapid development of evidence-based medicine and interventional cardiology, the clinical significance of coronary microvascular disease (CMVD) has been increasingly valued. There is currently no epidemiological data on CMVD in a large sample population. Previous small sample clinical studies have shown that the incidence of CMVD is approximately 45% to 60% in patients with symptoms of myocardial ischemia but non-obstructive lesions on coronary angiography. At the same time, studies have speculated that CMVD may be an important cause of poor prognosis in patients.

[0003] Currently, commonly used methods for evaluating coronary microvascular disease include: transthoracic ultrasound coronary blood flow imaging, computed tomography, cardiac magnetic resonance imaging, selective coronary angiography, coronary microvascular resistance index, coronary Doppler, etc. These examination methods are highly sensitive, but require special examination operations. Therefore, exploring the pathogenesis of coronary microvascular disease and finding specific biomarkers will enable us to diagnose coronary microvascular disease more simply and quickly, thereby formulating more personalized treatment plans.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The first object of the present invention is to provide biomarkers related to coronary microvascular disease, which can be used as biomarkers for detecting coronary microvascular disease.

[0006] The second object of the present invention is to provide the use of biomarkers in the preparation of diagnostic products for detecting coronary microvascular diseases.

[0007] With the development of metabolomics analysis technology, the progress of biomarker discovery and validation will be greatly accelerated. Non-targeted metabolomics shows great potential in comprehensively revealing and validating candidate biomarkers for prediction and prognosis of various diseases.

[0008] In a first aspect of the present invention, biomarkers related to coronary microvascular disease are provided, wherein the biomarkers include: one or two of MW0110047 and MEDN0685.

[0009] MW0110047, English name: Tropate, Chinese name: Scopolamine.

[0010] MEDN0685, English name: Undecanedioc acid, Chinese name: Undecanedioc acid.

[0011] Preferably, the biomarker is MW0110047.

[0012] Preferably, the biomarker is MEDN0685.

[0013] Preferably, the biomarkers are MW0110047 and MEDN0685.

[0014] Preferably, the detection of the biomarkers is the detection of the expression levels of MW0110047 and MEDN0685 in the coronary artery blood samples of the subjects.

[0015] Preferably, the abnormal expression of the biomarker in the subject is detected, and ROC curve statistical analysis is performed simultaneously, and the prediction accuracy of the biomarker is evaluated by AUC analysis.

[0016] Specifically, the biomarkers MW0110047 and MEDN0685 are both highly expressed in coronary blood samples of patients with coronary microvascular disease.

[0017] Preferably, the biomarker is detected in the coronary artery blood sample of the subject, and the AUC value of the biomarker is between 0.693 and 0.733.

[0018] Specifically, when the biomarker is MW0110047, the AUC value is not less than 0.693.

[0019] Specifically, when the biomarker is MEDN0685, the AUC value is not less than 0.733.

[0020] Specifically, when the biomarkers are MW0110047 and MEDN0685 combined, the AUC value is not less than 0.783.

[0021] The second aspect of the present invention provides the use of biomarkers related to coronary microvascular disease in the preparation of diagnostic products for detecting coronary microvascular disease, wherein the biomarkers include: one or two of MW0110047 and MEDN0685.

[0022] Preferably, the biomarker is MW0110047.

[0023] Preferably, the biomarker is MEDN0685.

[0024] Preferably, the biomarkers are MW0110047 and MEDN0685.

[0025] Preferably, the detection of the biomarkers is the detection of the expression levels of MW0110047 and MEDN0685 in the coronary artery blood samples of the subjects.

[0026] Preferably, the abnormal expression of the biomarker in the subject is detected, and ROC curve statistical analysis is performed simultaneously, and the prediction accuracy of the biomarker is evaluated by AUC analysis.

[0027] Specifically, the biomarkers MW0110047 and MEDN0685 are both highly expressed in coronary blood samples of patients with coronary microvascular disease.

[0028] Preferably, the biomarker is detected in the coronary artery blood sample of the subject, and the AUC value of the biomarker is between 0.693 and 0.733.

[0029] Specifically, when the biomarker is MW0110047, the AUC value is not less than 0.693.

[0030] Specifically, when the biomarker is MEDN0685, the AUC value is not less than 0.733.

[0031] Specifically, when the biomarkers are MW0110047 and MEDN0685 combined, the AUC value is not less than 0.783.

[0032] Preferably, the diagnostic product includes: a detection chip, a detection reagent or a detection kit.

[0033] Beneficial effects:

[0034] The present invention identifies and screens the differential expression of plasma metabolites between patients with coronary microvascular disease and normal people through a metabolomics method, finds that the metabolites MW0110047 and MEDN0685 are highly expressed in the coronary blood samples of patients with coronary microvascular disease, performs ROC analysis, and evaluates the overall predictive accuracy of the two metabolites as biomarkers of coronary microvascular disease through AUC analysis; the metabolites MW0110047 and MEDN0685 proposed in the present invention are biomarkers for the diagnosis of coronary microvascular disease, have higher specificity, can be used to prepare diagnostic products for detecting coronary microvascular disease, are conducive to simplifying the diagnostic process of coronary microvascular disease, and have important clinical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a comparison chart of metabolites between left coronary plasma and right coronary plasma;

[0037] Figure 2 is the OPLS-DA graph, showing the differential metabolites between the CMVD group and the normal group;

[0038] Figure 3 The volcano plot of differential metabolites showed that there were differential metabolites between the CMVD group and the normal group;

[0039] Figure 4A metabolites that were significantly different in the diagnostic cohort; Figure 4B Metabolites with significant differences were validated in the validation cohort;

[0040] Figure 5 The expression difference diagram of two metabolites was quantitatively determined by ELISA;

[0041] Fig. 6A and Figure 6B KEGG pathway enrichment bubble diagram and domain enrichment bubble diagram of differential metabolites in CMVD group and normal group, respectively;

[0042] Fig. 7A and Figure 7B They are the ROC curve diagram of the selected metabolite differences and the metabolite diagnostic model diagram respectively. DETAILED DESCRIPTION

[0043] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form also includes the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this description, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example

[0047] 1. Main reagents and manufacturers of the experiment

[0048] Some of the experimental reagents and experimental instruments involved in this experiment are shown in Tables 1 and 2.

[0049] Table 1 Experimental reagents

[0050]

[0051] iRT kit was purchased from Biognosys; Bradford protein quantification kit was purchased from Bio-Time; dithiothreitol DTT was purchased from Sigma (catalog number D9163-25G); iodoacetamide IAM was purchased from Sigma (catalog number I6125-25G).

[0052] Table 2 Experimental instruments

[0053]

[0054] 2. Experimental Methods

[0055] 1. Patient Enrollment

[0056] Inpatients admitted to the Heart Center of Beijing Chaoyang Hospital Affiliated to Capital Medical University from January 2024 to July 2024 were selected and divided into a diagnosis cohort (40 cases) and a validation cohort group (28 cases). Among them, 30 patients with coronary microvascular disease and 10 normal patients were included in the diagnosis cohort, and 20 patients with coronary microvascular disease and 8 normal patients were included in the validation cohort. This study was approved by the Medical Ethics Committee of Beijing Chaoyang Hospital Affiliated to Capital Medical University. All patients with coronary microvascular disease and healthy patients enrolled in the study signed an informed consent. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki. Coronary plasma was collected from all patients, which was quickly transferred to a -80°C refrigerator after centrifugation.

[0057] To ensure the homogeneity of patients and exclude the bias caused by cardiac metabolic abnormalities caused by other diseases, the inclusion criteria were as follows: patients with chest pain and chest tightness underwent coronary angiography, and patients without coronary artery stenosis were included, while patients with systemic inflammatory or infectious diseases, coronary artery bridging, and a history of tumors were excluded.

[0058] 2. Sample Preparation

[0059] 2.1 Sample extraction process

[0060] (1) Take out the sample from the -80℃ freezer and thaw it on ice until there is no ice in the sample (all subsequent operations are performed on ice); (2) After the sample is thawed, vortex for 10 seconds to mix, and transfer 50μL of the sample to the corresponding numbered centrifuge tube; (3) Add 20% acetonitrile methanol internal standard to extract 300μL, vortex for 3 minutes, and centrifuge at 12000r / min for 10 minutes at 4℃; (4) After centrifugation, transfer 200μL of the supernatant to another corresponding numbered centrifuge tube and let it stand in a -20℃ refrigerator for 30 minutes; (5) Centrifuge again at 12000r / min for 3 minutes at 4℃, transfer 180μL of the supernatant to the inner liner of the corresponding injection bottle for analysis on the machine.

[0061] 2.2 Chromatographic mass spectrometry acquisition conditions

[0062] 2.2.1T3 Chromatographic Conditions

[0063] (1) Chromatographic column: Waters ACQUITY Premier HSS T3 Column 1.8 μm 2.1 mm*100 mm;

[0064] (2) Mobile phase A: 0.1% formic acid / water; Mobile phase B: 0.1% formic acid / acetonitrile;

[0065] (3) Instrument column temperature: 40°C; flow rate: 0.4 mL / min; injection volume: 4 μL.

[0066] 2.2.2 Mass spectrometry conditions

[0067] Detailed mass spectrometry conditions are shown in Table 3.

[0068] Table 3AB TripleTOF 6600 mass spectrometry conditions

[0069]

[0070] 2.3 Data Preprocessing

[0071] The raw data from the mass spectrometer were converted into mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peaks with a missing rate of >50% in each group of samples were filtered, and the blank values ​​were filled by KNN, and the peak area was corrected by the SVR method. The corrected and screened peaks were identified by searching the self-built database of Experiment 6, integrating the public library, the prediction library, and the metDNA method for metabolite identification. Finally, the substances with a comprehensive identification score of more than 0.5 points and a QC sample CV value of less than 0.3 were extracted and identified, and then the positive and negative modes were merged (retaining the substances with the highest qualitative grade and the smallest CV value) to obtain the ALL_sample_data file.

[0072] 2.4 Data Analysis

[0073] 2.4.1 Sample quality control analysis

[0074] Quality control samples (QC) are prepared by mixing sample extracts and are used to test the repeatability of the analytical samples under the same processing method. During the instrument analysis process, a quality control sample is usually inserted into every 10 test analysis samples to monitor the repeatability of the analysis process.

[0075] 2.4.2 Total ion current

[0076] By overlapping and analyzing the total ion current graphs (TIC graphs) of mass spectrometry analysis of different quality control QC samples, the repeatability of metabolite extraction and detection, i.e. technical repetition, can be determined. The high stability of the instrument provides an important guarantee for the repeatability and reliability of the data.

[0077] 2.4.3 Correlation analysis of QC samples

[0078] Pearson correlation analysis was performed on the QC samples. The higher the correlation of the QC samples (the closer |r| is to 1), the better the stability of the entire detection process and the higher the data quality.

[0079] 2.4.4 CV value distribution diagram of all samples

[0080] The CV value, or coefficient of variation, is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion. The frequency of CV of substances with a value less than the reference value can be analyzed using the empirical cumulative distribution function (ECDF). The higher the proportion of substances with lower CV values ​​in QC samples, the more stable the experimental data. When the proportion of substances with a CV value less than 0.3 in QC samples is higher than 75%, it indicates that the experimental data is very stable.

[0081] 3. Human plasma samples

[0082] A total of 68 patients who were hospitalized in Beijing Chaoyang Hospital affiliated to Capital Medical University and underwent coronary angiography from January 2024 to July 2024 were selected. Clinical data such as gender, age, BMI, smoking history, drinking history, cardiac ultrasound, and laboratory tests of the patients were recorded.

[0083] Inclusion criteria: ① patients with angina symptoms; ② signed informed consent.

[0084] Exclusion criteria: ① patients with coronary myocardial bridge; ② patients with coronary artery stenosis; ③ patients with concomitant heart failure; ④ patients with inflammatory or infectious diseases; ⑤ patients with liver and kidney dysfunction.

[0085] 3.1 Blood collection and plasma separation

[0086] Use a 20ml syringe (containing EDTA) to collect 5ml of coronary artery plasma from the patient. Centrifuge at 3000rpm and 4℃ for 10min. The supernatant is plasma, which is divided into 1ml / tube and stored at -80℃.

[0087] 3.2 Sample extraction

[0088] (1) Take out the sample from the -80℃ freezer and thaw it on ice until there is no ice in the sample (all subsequent operations are performed on ice);

[0089] (2) After the sample is thawed, vortex for 10 seconds to mix, and transfer 50 μL of the sample into the corresponding numbered centrifuge tube;

[0090] (3) Add 300 μL of 20% acetonitrile methanol internal standard extract, vortex for 3 min, and centrifuge at 12,000 rpm for 10 min at 4 °C;

[0091] (4) After centrifugation, transfer 200 μL of the supernatant to another corresponding numbered centrifuge tube and place it in a -20°C refrigerator for 30 min;

[0092] (5) Centrifuge again at 12,000 rpm for 3 min at 4°C and transfer 180 μL of the supernatant into the corresponding injection bottle liner for analysis.

[0093] 3.3 Data Preprocessing

[0094] The raw data from the mass spectrometer were converted into mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peaks with a missing rate of >50% in each group of samples were filtered, and the blank values ​​were filled by KNN, and the peak area was corrected by the SVR method. The corrected and screened peaks were identified by searching the self-built database of Experiment 6, integrating the public library, the prediction library, and the metDNA method for metabolite identification. Finally, the substances with a comprehensive identification score of more than 0.5 points and a QC sample CV value of less than 0.3 were extracted and identified, and then the positive and negative modes were merged (retaining the substances with the highest qualitative grade and the smallest CV value) to obtain the ALL_sample_data file.

[0095] 3.4 Statistical analysis

[0096] Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables were expressed as counts and percentages. Intergroup comparisons were performed using independent sample t-tests for continuous variables and chi-square tests or Fisher's exact tests (as appropriate) for categorical variables. Pearson correlation coefficients were calculated to assess the relationship between metabolite levels and clinical parameters. KEGG pathway enrichment analysis was performed for significantly differential metabolites using the hypergeometric test, with the combined table of detected metabolites used as background reference. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic accuracy of the selected metabolites for CMVD, and the area under the ROC curve (AUC) was calculated as a measure of performance. Multiple regression analysis was used to explore the diagnostic accuracy of different diagnostic models for CMVD. All statistical analyses were performed using SPSS software 29.0.1.0 (IBM Corp., Armonk, NY, USA) and R software 4.2.0 (R Foundation for statistical Computing, Vienna, Austria). Two-tailed p values ​​less than 0.05 were considered statistically significant.

[0097] 4. Results

[0098] 4.1 Analysis of metabolites in coronary plasma in CMVD

[0099] A total of 68 patients were included in this experiment. The left coronary artery plasma and right coronary artery plasma of the patients were collected and divided into a diagnostic cohort (40 cases) and a validation cohort (28 cases). The clinical data of the patients, such as gender, age, BMI, smoking history, drinking history, cardiac ultrasound, and laboratory tests, were recorded (Table 4). The raw data of the mass spectrometer were converted into mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peaks with a missing rate of >50% in each group of samples were filtered, and the blank values ​​were filled with KNN, and the peak area was corrected by the SVR method. The peaks after correction and screening were identified by searching the self-built database, integrating the public library, the prediction library, and the metDNA method.

[0100] Table 4 Basic information of the enrolled patients

[0101]

[0102]

[0103] 4.2 Metabolite abundance and pathway analysis

[0104] In this experiment, 80 coronary blood samples from 40 patients in the diagnostic cohort were analyzed. The raw data were converted to mzXML format by ProteoWizard under mass spectrometry, and peak extraction, alignment, and retention time correction were performed using the XCMS program. Peaks with a missing rate of >50% in each group of samples were filtered, and blank values ​​were filled with KNN, and the peak area was corrected using the SVR method. The corrected and screened peaks were identified by searching the self-built database, integrating the public library, the prediction library, and the metDNA method for metabolite identification.

[0105] 4.3 Results of differential metabolite and pathway analysis

[0106] from Figure 1 As can be seen, there were no significant differences in plasma metabolites between the left and right coronary arteries in the diagnostic cohort.

[0107] from Figure 2 From the OPLS-DA diagram, it can be seen that there are obvious differences in metabolites between the CMVD group and the normal group, and the intra-group variability is small.

[0108] Figure 3 The volcano plot of differential metabolites was displayed, showing that there were multiple metabolic differences between the two groups. After that, cluster analysis was performed on the differential metabolites in the samples, and 10 metabolites with significant differences were found in the diagnostic cohort ( Figure 4A ), and then validated in the validation cohort, selecting two metabolites with significant differences ( Figure 4B), and ELISA quantitative determination showed that the expression of two metabolites was different between the two groups ( Figure 5 ), the relative abundance values ​​of the two differential metabolites are shown in Table 5.

[0109] The results are classified according to biological processes, cellular components and molecular functions. Fig. 6A and Figure 6B As shown in the figure, the most significantly enriched KEGG pathway is Ascorbate and aldarate metabolism, which is related to oxidative stress. Oxidative stress can cause endothelial dysfunction by reducing the bioavailability of nitric oxide and promoting inflammation, thereby causing microvascular damage.

[0110] Table 5 Differential metabolites between CMVD group and normal group

[0111]

[0112] 4.4 Application of AUC to evaluate the ability of candidate biomarkers to identify CMVD in tissues

[0113] ROC analysis was performed, and the overall predictive accuracy of the two metabolites found in the study as potential biomarkers was evaluated by AUC analysis. Fig. 7A and Figure 7B As shown in the figure, it can be seen that the AUC values ​​of these metabolites are between 0.693 and 0.733; the metabolite with the highest AUC value is MEDN0685: 0.733, followed by MW0110047: 0.693; when MEDN0685 and MW0110047 are used in combination, the AUC value is 0.783, which is higher than that of any metabolite used alone.

[0114] In summary, the expression profiles of metabolites MW0110047 and MEDN0685 in coronary plasma of the CMVD group were significantly different from those of the normal group and could be used as biomarkers for the diagnosis of CMVD.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biomarker associated with coronary microvascular disease, characterized in that: The biomarkers include: one or two of MW0110047 and MEDN0685.

2. The biomarker associated with coronary microvascular disease according to claim 1, characterized in that: The biomarkers are MW0110047 and MEDN0685.

3. The biomarker associated with coronary microvascular disease according to claim 1, characterized in that: The detection of the biomarkers is the detection of the expression levels of MW0110047 and MEDN0685 in the coronary artery blood samples of the subjects.

4. The biomarker associated with coronary microvascular disease according to claim 3, characterized in that: The abnormal expression of the biomarker in the subject is detected, and ROC curve statistical analysis is performed simultaneously, and the prediction accuracy of the biomarker is evaluated by AUC analysis.

5. The biomarker associated with coronary microvascular disease according to claim 4, characterized in that: The biomarkers were detected in the subjects' coronary artery blood samples, and the AUC values ​​of the biomarkers were between 0.693 and 0.

733.

6. Use of a biomarker related to coronary microvascular disease in the preparation of a diagnostic product for detecting coronary microvascular disease, characterized in that: The biomarkers include: one or two of MW0110047 and MEDN0685.

7. The use according to claim 6, characterized in that: The detection of the biomarkers is the detection of the expression levels of MW0110047 and MEDN0685 in the coronary artery blood samples of the subjects.

8. The use according to claim 7, characterized in that: The abnormal expression of the biomarker in the subject is detected, and ROC curve statistical analysis is performed simultaneously, and the prediction accuracy of the biomarker is evaluated by AUC analysis.

9. The use according to claim 8, characterized in that: The biomarkers were detected in the subjects' coronary artery blood samples, and the AUC values ​​of the biomarkers were between 0.693 and 0.

733.

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

Images