Coronary microvascular disease-related biomarkers and their applications

Abstract

The present invention relates to the field of molecular diagnostics. In one aspect, it relates to biomarkers associated with coronary microvascular disease (CMVD), including one or both of MW0110047 and MEDN0685. In another aspect, it relates to the use of these biomarkers in the preparation of diagnostic products for detecting CMVD. The metabolites MW0110047 and MEDN0685, proposed in this invention, are biomarkers for the diagnosis of CMVD. Compared with traditional circulating biomarkers, they exhibit higher specificity and can be used in the preparation of diagnostic products for detecting CMVD, simplifying the diagnosis of CMVD and possessing important clinical significance.

Description

Technical Field

[0001] The present invention relates to the field of molecular diagnostic technology, 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 received increasing attention. Currently, there are no large-scale epidemiological data on CMVD in large populations. Previous small-scale clinical studies have shown that the incidence of CMVD in patients with symptoms of myocardial ischemia but non-obstructive lesions on coronary angiography is approximately 45% to 60%. Studies have also speculated that CMVD may be a major contributor to poor prognosis.

[0003] Currently, commonly used methods for assessing coronary microvascular disease include transthoracic ultrasound coronary blood flow imaging, computed tomography, cardiac magnetic resonance imaging, selective coronary angiography, coronary microvascular resistance index, and coronary Doppler flow cytometry. These tests are highly sensitive but require specialized procedures. Therefore, exploring the pathogenesis of coronary microvascular disease and identifying specific biomarkers will enable simpler and faster diagnosis of coronary microvascular disease, leading to 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 disease.

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

[0008] In a first aspect, the present invention provides biomarkers related to coronary microvascular disease, 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 sample of the subject.

[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 in combination, 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 both 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 sample of the subject.

[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 in combination, 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 uses a metabolomics approach to identify and screen differential expression of plasma metabolites between patients with coronary microvascular disease and normal controls. Highly expressed metabolites MW0110047 and MEDN0685 were found in coronary blood samples from patients with coronary microvascular disease. Receiver operating characteristic (ROC) analysis was performed, and the overall predictive accuracy of the two metabolites as biomarkers for coronary microvascular disease was evaluated using area under the curve (AUC) analysis. The metabolites MW0110047 and MEDN0685 proposed in the present invention have higher specificity as biomarkers for the diagnosis of coronary microvascular disease and can be used to prepare diagnostic products for detecting coronary microvascular disease, thereby simplifying the diagnostic process for coronary microvascular disease and possessing important clinical significance. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0037] Figure 2 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 showed significant differences among the diagnostic cohorts; 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] Figure 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] Figure 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 intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as 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 also includes the plural. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example

[0047] 1. Main reagents and manufacturers

[0048] Some of the experimental reagents and 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 Beyotime; 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 at the Heart Center of Beijing Chaoyang Hospital Affiliated to Capital Medical University from January 2024 to July 2024 were selected and divided into a diagnostic cohort (40 cases) and a validation cohort (28 cases). In the diagnostic cohort, there were 30 patients with coronary microvascular disease and 10 normal patients, and in the validation cohort, there were 20 patients with coronary microvascular disease and 8 normal patients. 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 compliance 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 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. At the same time, patients with systemic inflammatory or infectious diseases, coronary artery myocardial bridges, 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 corresponding sample bottle liner 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 to mzXML format using ProteoWizard, and peak extraction, alignment, and retention time correction were performed using the XCMS program. Peaks with a missing rate >50% in each sample group were filtered, and blank values were filled using KNN. Peak areas were corrected using the SVR method. The corrected peaks were then searched for metabolites in the in-house database of Experiment 6, the integrated public library, the prediction library, and the metDNA method. Finally, substances with a comprehensive identification score of 0.5 or above and a QC sample CV value of less than 0.3 were extracted and identified. The positive and negative modes were then merged (retaining the substance 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 (QC) samples are prepared by mixing sample extracts and used to check the repeatability of the analytical samples under the same processing method. During the instrument analysis process, a QC sample is usually inserted into every 10 analytical samples to monitor the repeatability of the analytical process.

[0075] 2.4.2 Total ion current

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

[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 raw data to the mean of the raw data and reflects the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of substances with CV values below the reference value. A higher proportion of substances with low CV values in QC samples indicates more stable experimental data. When the proportion of substances with CV values below 0.3 in QC samples exceeds 75%, the experimental data is very stable.

[0081] 3. Human plasma samples

[0082] A total of 68 patients who underwent coronary angiography at Beijing Chaoyang Hospital affiliated to Capital Medical University from January 2024 to July 2024 were selected. Clinical data such as gender, age, BMI, smoking history, drinking history, cardiac ultrasound, and laboratory tests 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] Collect 5 mL of coronary artery plasma from the patient using a 20 mL syringe (containing EDTA). Centrifuge at 3000 rpm and 4°C for 10 minutes. The supernatant is plasma, which is aliquoted into 1 mL tubes and stored at -80°C.

[0087] 3.2 Sample extraction

[0088] (1) Take the sample out of 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 liner tube of the injection bottle for analysis.

[0093] 3.3 Data Preprocessing

[0094] The raw data from the mass spectrometer were converted to mzXML format using ProteoWizard, and peak extraction, alignment, and retention time correction were performed using the XCMS program. Peaks with a missing rate >50% in each sample group were filtered, and blank values were filled using KNN. Peak areas were corrected using the SVR method. The corrected peaks were then searched for metabolites in the in-house database of Experiment 6, the integrated public library, the prediction library, and the metDNA method. Finally, substances with a comprehensive identification score of 0.5 or above and a QC sample CV value of less than 0.3 were extracted and identified. The positive and negative modes were then merged (retaining the substance 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 are expressed as mean ± standard deviation (SD), and categorical variables are expressed as counts and percentages. Intergroup comparisons were performed using the independent-sample t-test for continuous variables and the chi-square test or Fisher's exact test (as appropriate) for categorical variables. Pearson correlation coefficients were calculated to assess the relationship between metabolite levels and clinical parameters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for significantly differentially expressed metabolites using the hypergeometric test, with the combined table of detected metabolites used as a background reference. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic accuracy of 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). A two-tailed p-value of less than 0.05 was considered statistically significant.

[0097] 4. Results

[0098] 4.1 Metabolite Analysis in Coronary Plasma of CMVD

[0099] A total of 68 patients were enrolled in this study. Left and right coronary artery plasma samples were collected from the patients and divided into a diagnostic cohort (40 patients) and a validation cohort (28 patients). Clinical data, including gender, age, BMI, smoking history, alcohol consumption history, echocardiography, and laboratory tests, were recorded (Table 4). The raw data from the mass spectrometer were converted to mzXML format using ProteoWizard, and peak extraction, alignment, and retention time correction were performed using the XCMS program. Peaks with a missing rate >50% in each group of samples were filtered, and blank values were filled using KNN. Peak areas were corrected using the SVR method. The corrected peaks were then searched for metabolites using a self-built database, an integrated public library, a 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] This study analyzed 80 coronary blood samples from 40 patients in the diagnostic cohort. The raw data were converted to mzXML format using ProteoWizard and then peak extracted, aligned, and corrected for retention times using the XCMS program. Peaks with a missing rate >50% in each sample group were filtered, and blank values were filled using KNN. Peak areas were corrected using the SVR method. Metabolite identification was performed using the corrected peaks using a self-built database, an integrated public library, a prediction library, and the metDNA method.

[0105] 4.3 Differential metabolite and pathway analysis results

[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 The OPLS-DA diagram shows that there are significant 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 many metabolic differences between the two groups. After that, cluster analysis of the differential metabolites in the samples was performed, 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 was performed, indicating that the expression of two metabolites was different between the two groups ( Figure 5 ), and the relative abundance values of the two differential metabolites are shown in Table 5.

[0109] Classification by biological process, cellular components and molecular functions, the results are as follows Figure 6A and Figure 6B As shown in the figure, the most significantly enriched KEGG pathway is the Ascorbate and aldarate metabolism pathway, 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. Figure 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 using any metabolite 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 can 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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. Use of a reagent for detecting biomarkers associated with 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; the MW0110047 is scopolamine, and the MEDN0685 is undecanedioic acid.

2. The use 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.

3. The use according to claim 2, characterized in that By detecting the abnormal expression of the biomarkers in the subjects, ROC curve statistical analysis was performed, and the prediction accuracy of the biomarkers was evaluated by AUC analysis.

4. The use according to claim 3, characterized in that The biomarkers were detected in the subjects' coronary artery blood samples, and the AUC values of the biomarkers ranged from 0.693 to 0.

733.

5. The use according to claim 1, characterized in that The diagnostic products include: detection chips, detection reagents or detection kits.

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