Predictive model, system and kit for acute myocardial infarction risk assessment

Abstract

The present application relates to the technical field of bioinformatics and in vitro diagnosis, and particularly relates to a prediction model, system and kit for acute myocardial infarction risk assessment. The present application proves that SRGN (serum-derived glycosaminoglycan) is a key regulator of neutrophil inflammatory response in the inflammatory cascade after myocardial infarction, and thus determines a model for predicting the risk of acute myocardial infarction by SRGN and neutrophil count, and designs a corresponding kit and evaluation system according to the model. Compared with the prior art, the model and the related kit and evaluation system can quickly and accurately evaluate the risk of acute myocardial infarction in vitro.

Description

Technical Field

[0001] This invention relates to the fields of bioinformatics and in vitro diagnostics, and in particular to a predictive model, system, and kit for assessing the risk of acute myocardial infarction. Background Technology

[0002] Acute myocardial infarction (AMI) is one of the leading cardiovascular diseases causing death and disability. Current early diagnosis mainly relies on myocardial injury markers (such as cardiac troponin cTnT) and electrocardiographic changes. However, in the early stages of the disease (especially within the first few hours), these indicators may not yet be significantly elevated or may lack diagnostic specificity, thus delaying treatment. In recent years, immune inflammatory responses, neutrophil activation, and their secreted products have been found to play important roles in the early stages of AMI. Serglycin (SRGN), a type of serine proteoglycan, has been found to be mainly secreted by neutrophils, and its plasma / serum concentration is significantly upregulated during the acute phase of AMI. This invention proposes a more sensitive early risk assessment protocol for AMI that can be rapidly implemented in vitro based on SRGN combined with conventional NEUT modeling, and designs corresponding reagent kits and assessment systems for clinical translation. Summary of the Invention

[0003] To achieve the above objectives, the first technical solution of this application discloses a predictive model for acute myocardial infarction risk assessment, the model being the following formula:

[0004] P(AMI) = 1 / [1 + e (–Logit(P(AMI))) ];

[0005] Wherein, P(AMI) represents the probability of acute myocardial infarction, and Logit(P(AMI)) is specifically:

[0006] Logit(P(AMI))= –17.589+0.672×NEUT+0.136×SRGN;

[0007] Wherein, NEUT represents the number of neutrophils in the target patient's blood (×10⁻¹⁰). 9 / L); SRGN represents the concentration of serine proteoglycan in the patient's serum (ng / ml).

[0008] Furthermore, when P(AMI)≥0.7, it is considered high risk; when 0.3≤P(AMI)<0.7, it is considered medium risk; and when P(AMI)<0.3, it is considered low risk.

[0009] The second technical solution of this application discloses a method for constructing the above-mentioned prediction model, which includes collecting SRGN concentration and NUET value of myocardial infarction patients and establishing a prediction model for acute myocardial infarction risk assessment using statistical software.

[0010] And an assessment system for predicting the risk of acute myocardial infarction, including the aforementioned prediction model.

[0011] Furthermore, the system also includes the following modules:

[0012] a) Detection module: used to perform neutrophil counting and serum serine concentration determination on blood samples from target patients;

[0013] b) Calculation module: Calculate the risk probability P (AMI) using one of the above-mentioned predictive models for acute myocardial infarction risk assessment.

[0014] c) Judgment module: used to compare P (AMI) with the threshold to classify it as high / medium / low risk;

[0015] d) Output module: Used to output risk level and recommendations.

[0016] And, a kit for predicting the risk of acute myocardial infarction, comprising:

[0017] Neutrophil counting module;

[0018] SRGN concentration measurement module;

[0019] And an apparatus carrying a formula description or evaluation system for the model.

[0020] Preferably, the SRGN concentration determination module is an ELISA plate coated with anti-human SRGN antibody and matching reagents.

[0021] Beneficial Effects: This application systematically demonstrates that neutrophil-derived serine glucomannan (SRGN) is a key regulator of neutrophil inflammatory response in the post-myocardial infarction inflammatory cascade. Based on this, a model for predicting acute myocardial infarction risk using SRGN and neutrophil count was established, and a corresponding kit and assessment system were designed. Compared with existing technologies, this model and related kits and assessment systems can rapidly and accurately assess acute myocardial infarction risk in vitro. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 To use single-cell sequencing technology to discover the role of highly inflammatory neutrophils in driving cardiac inflammation after myocardial infarction; among them, Figure 1(A) Classification of cardiac infiltrating immune cell subsets and their classification at different times; Figure 1 (B) is a line graph showing the dynamic changes of cell types over time; Figure 1 (C) Histogram of cell statistics for high and low inflammatory responses in each cell type; Figure 1 (D) A violin diagram showing the distribution of neutrophils to indicate the degree of inflammation; Figure 1 (E) shows the results of KEGG enrichment analysis of differentially expressed genes in neutrophils based on high and low inflammatory responses; Figure (F) shows the expression of inflammatory factors in mouse cardiac neutrophils on day 1 after myocardial infarction detected by Real-time PCR.

[0024] Figure 2 hdWGCNA analysis revealed key genes in highly inflammatory neutrophils; among them, Figure 2 (A) is the selection of the optimal soft threshold for hdWGCNA; Figure 2 (B) To construct a co-expression network based on an ideal "8" soft threshold, the genes are divided into multiple modules, resulting in a gene clustering tree; Figure 2 (C) is a heatmap showing the correlation between module genes and inflammatory response levels; Figure 2 (D) Volcano plot showing differential gene expression between the 1d group and the sham-operated group, illustrating the high inflammatory response (IR) of neutrophils; Figure 2 (E) is a Venn diagram showing the overlap of differentially expressed genes (differentially expressed genes) in neutrophils (differentially expressed genes UP) and the High M6 module gene (High_M6).

[0025] Figure 3 To verify the immunofluorescence results that Srgn is a key regulator of neutrophil inflammation from multiple perspectives; among them, Figure 3 (A) Western blot analysis results of Serglycin in mouse myocardial tissue and neutrophil lysates; Figure 3 (B) SRGN of mouse myocardium 1 day after myocardial infarction + and SRGN - Flow cytometry sorting results of neutrophils; Figure 3 (C) Flow cytometry analysis of cardiac neutrophils; Figure 3 (D) is SRGN + and SRGN - The relative mRNA expression levels of pro-inflammatory factors (IL-1β, IL-6, TNF-α) and anti-inflammatory factors (IL-10) in neutrophils.

[0026] Figure 4 To construct and validate a model for applying neutrophil counting and serum SRGN; among which Figure 4(A) Serum SRGN levels in mice at different time points after myocardial infarction (n=5-6 per group); Figure 4 (B) Serum SRGN levels in non-myocardial infarction patients (n=50) and myocardial infarction patients (n=30); Figure 4 (C) Correlation between serum SRGN levels and neutrophil count in patients (n=80); Figure 4 (D) is the ROC curve of serum SRGN level versus acute myocardial infarction event; Figure 4 (E) is the ROC curve of neutrophil level versus acute myocardial infarction event; Figure 4 (F) is the ROC curve of serum SRGN combined with neutrophils on acute myocardial infarction events; Figure 4 (G) is the ROC curve comparing the three indicators.

[0027] Figure 5 This is the patient's basic information. Detailed Implementation

[0028] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0029] Example 1: Confirmation of neutrophils and serine as biomarkers for predicting the risk of acute myocardial infarction

[0030] Experimental methods

[0031] 1. Single-cell technology: Single-cell datasets GSE163129 and GSE163465 related to mouse myocardial infarction were downloaded from the Gene Expression Database (GEO). Data quality control was performed using Seurat software (nFeature_RNA >200 & nFeature_RNA < 6000 & nCount_RNA < 50000 & %), Hb < 3%, Mt < 10%, combined with CCA method, followed by cell annotation using singleR software. Phenotypic gene expression modules were explored using hdWGCNA software.

[0032] 2. Flow cytometry: To isolate neutrophils, heart tissue was digested with type II collagenase (500 U / ml) at 37°C for 40 minutes. The cell suspension was stained with single-color antibodies (antid11b, anti-Ly6g, anti-SRGN) on ice for 30 minutes. After washing, the cells were analyzed using a BD FACS Fortessa flow cytometer based on SRGN levels, and the results were then analyzed using FlowJo software.

[0033] (1) Western Blotting: Total protein was extracted from heart tissue using RIPA buffer containing protease and phosphatase inhibitors. Proteins were collected and centrifuged at 12,000g for 30 minutes at 4°C. Protein samples were separated by 10–12% SDS-PAGE and transferred to a PVDF membrane. The PVDF membrane was blocked with 5% bovine serum albumin and incubated overnight at 4°C with the corresponding primary antibody. The membrane was then incubated with enzyme-labeled secondary antibody at room temperature for 1 hour. Protein bands were visualized using ECL reagent and quantified using ImageJ software.

[0034] (2) Real-Time quantitative PCR: Total RNA was extracted from heart tissue using Trizol reagent (Solarbio, Beijing, China). 1 μg of RNA was converted to cDNA by reverse transcription. Gene expression was detected by real-time PCR using PerfectStart Green qPCRSuperMix (2x, Transgen).

[0035] Experimental Results and Analysis

[0036] like Figure 1 The results of single-cell sequencing analysis show high expression of SRGN in neutrophils after myocardial infarction. Single-cell datasets GSE163129 and GSE163465, associated with myocardial infarction in mice, were downloaded from the Gene Expression Database (GEO). After rigorous quality control, batch effect removal, and cell type labeling, nine major cardiac infiltrating immune cell subsets were identified, including macrophages, neutrophils, monocytes, T cells, B cells, NK cells, fibroblasts, endothelial cells, and dendritic cells. Figure 1 A). Notably, neutrophils showed significant accumulation on day 1 after myocardial infarction, followed by a gradual decline over time. Figure 1 B) These results collectively demonstrate the specific infiltration pattern of neutrophils in the acute phase following myocardial infarction.

[0037] To elucidate the key drivers of inflammation, single-cell transcriptomic data were systematically analyzed using an inflammation-related gene set. Results showed that the inflammatory response exhibited significant chemotactic characteristics, with neutrophils showing the highest inflammation score. 87% of neutrophils showed significantly elevated inflammatory activity, particularly at 24 hours after MI (inflammation). Figure 1 CD). KEGG analysis of highly inflammatory neutrophils revealed activation of the NF-κB and TNF signaling pathways (major inflammatory pathways). Figure 1E). Flow cytometry was used to compare neutrophil expression in the hearts of sham-operated mice and mice 24 hours after myocardial infarction. The results showed that the expression of il-1β, il-6, and Tnf-α was significantly increased in the infarcted heart. Figure 1 F). These findings collectively suggest that the infiltration of highly inflammatory neutrophils within 24 hours after myocardial infarction is a key mechanism driving early inflammatory amplification.

[0038] To further investigate key gene modules associated with the high-inflammatory phenotype of neutrophils, the hdWGCNA method was used to identify gene modules co-expressed in neutrophils, such as... Figure 2 As shown in the hdWGCNA identification results, a total of 7 different modules were detected. Figure 2 (AB). Notably, the M6 ​​module showed the highest correlation with neutrophils (especially highly inflammatory neutrophils) on day 1 after myocardial infarction. Figure 2 C). By analyzing the differential expression profiles of highly inflammatory neutrophils between the 1-day myocardial infarction group and the sham-operated group, cross-analysis of M6 module genes was performed; the results showed 6 co-differentially expressed genes ( Figure 2 DE), of which SRGN not only showed the highest expression level in highly inflammatory neutrophils, but was also significantly upregulated in highly inflammatory neutrophils. For example Figure 3 As shown, qPCR and WB assays confirmed that SRGN expression was significantly increased in infarcted tissue and myocardial infiltrating neutrophils one day after infarction. Immunofluorescence assays further confirmed this. Figure 3 AB). Neutrophils were divided into a high-expression group (SRGN) based on their SRGN expression level. + ) and low expression group (SRGN) - ), SRGN in mouse hearts 1 day after myocardial infarction + and SRGN - Neutrophil sorting by flow cytometry Figure 3 C), followed by inflammatory factor detection, the results showed SRGN + The levels of pro-inflammatory cytokines in neutrophils were significantly elevated. Figure 3 D).

[0039] In summary, the above results systematically demonstrate that neutrophil-derived serine glucomannan (SRGN) is a key regulator of neutrophil inflammatory response in the post-myocardial infarction inflammatory cascade.

[0040] Example 2: Establishment of a model for acute myocardial infarction risk assessment using serine combined with neutrophil counting.

[0041] After determining the role of neutrophil-derived serine proteoglycan (SRGN) in mouse experiments in Example 1, the clinical expression of SRGN was further investigated.

[0042] Methods: Eighty subjects who underwent coronary angiography at the Department of Cardiology, Beichen Hospital Affiliated to Nankai University, from January to June 2025 were consecutively recruited. Thirty subjects were divided into an AMI (acute myocardial infarction) group and a control group of 50. To investigate the expression of SRGN in clinical practice, serum SRGN concentrations in the non-myocardial infarction control group (n=50) and the myocardial infarction patient group (n=30) were assessed using ELISA. Ethical Approval: This study was reviewed by the Ethics Committee of Beichen Hospital, Tianjin (Approval No.: 2025011301), and all subjects signed informed consent forms. Basic information of the two groups of patients is as follows: Figure 5 As shown.

[0043] Experimental Results and Analysis

[0044] Experimental results are as follows Figure 4 (Model Construction and Validation Using Neutrophil Count and Serum SRGN) shows that SRGN, as a secreted protein, can be released extracellularly to promote inflammatory responses. Serum SRGN levels were measured at multiple time points in a mouse myocardial infarction model. Results showed that SRGN levels peaked on day 1 after myocardial infarction and then gradually decreased. Figure 4 A). Serum SRGN concentration assessment results showed that significantly elevated SRGN levels were observed in the myocardial infarction patient cohort. Figure 4 B). Furthermore, serum SRGN levels were found to be positively correlated with neutrophil counts in this patient population ( Figure 4 C).

[0045] In summary, we found that highly inflammatory neutrophils significantly drive cardiac inflammation after myocardial infarction, especially since SRGN protein secreted by neutrophils is a key regulator of neutrophilic inflammation. In clinical practice, neutrophil count has a certain predictive value for the severity and prognosis of myocardial infarction, and a positive correlation was found between serum neutrophil count and serum SRGN concentration. Therefore, we hypothesize that neutrophil count combined with serum SRGN level has a good predictive value for the risk of acute myocardial infarction.

[0046] Based on this conclusion, the patient's SRGN and NEUT values ​​were input into statistical software to establish a Logistic regression model, specifically the following formula:

[0047] P(AMI) = 1 / [1 + e (–Logit(P(AMI))) ];

[0048] Wherein, P(AMI) represents the probability of acute myocardial infarction, and Logit(P(AMI)) is specifically:

[0049] Logit(P(AMI))= –17.589+0.672×NEUT+0.136×SRGN;

[0050] Wherein, NEUT represents the number of neutrophils in the target patient's blood (×10⁻¹⁰). 9 / L); SRGN represents the concentration (ng / ml) of serine derived from neutrophils in the patient's blood.

[0051] ROC analysis showed that the combined model had an AUC of 0.951 (95% CI: 0.912–0.990), a sensitivity of 92.3%, and a specificity of 88.0%, which was significantly better than that of the single indicator. Figure 4 DG). Based on the maximum point of the Youden index, the probability cut-off is determined to be ≥0.36, and the risk is further divided into high risk (P≥0.7), medium risk (0.3≤P<0.7), and low risk (P<0.3).

[0052] Example 3: Design of an assessment system for predicting the risk of acute myocardial infarction

[0053] Based on the model obtained in Example 2, an assessment system for predicting the risk of acute myocardial infarction was designed. The calculation module of the system is embedded with the calculation formula of the above model, so that it can output the risk probability of acute myocardial infarction of the patient based on the neutrophil count results and the concentration measurement results of serine derived from neutrophils in the blood sample of the target patient.

[0054] In a preferred embodiment, the system includes the following modules:

[0055] a) Detection module: used to perform neutrophil counting and neutrophil-derived serine concentration determination on blood samples from target patients;

[0056] b) Calculation module: Calculates the risk probability P (AMI) using the aforementioned predictive model for acute myocardial infarction risk assessment;

[0057] c) Judgment module: used to compare P (AMI) with the threshold to classify it as high / medium / low risk;

[0058] d) Output module: Used to output risk level and recommendations.

[0059] Example 4: Kit for predicting the risk of acute myocardial infarction

[0060] Based on the model obtained in Example 2, this application also discloses a kit for predicting the risk of acute myocardial infarction, which includes...

[0061] Neutrophil counting module;

[0062] SRGN concentration measurement module;

[0063] And an apparatus carrying a formula description or evaluation system for the model.

[0064] The neutrophil counting module can be a neutrophil detection reagent strip, a neutrophil counter, or other devices or reagents that can be used for neutrophil counting; commonly used equipment includes: disposable blood collection needles, EDTA-K2 vacuum collection tubes, Sysmex XN-1000 blood analyzers (or equivalent precision equipment).

[0065] The SRGN concentration measurement module includes human blood neutrophil detection and human blood SRGN ELISA reagent. In a preferred embodiment of this application, specific counting methods for the SRGN concentration measurement module and the neutrophil counting module are disclosed.

[0066] 1. SRGN Concentration Determination: Includes human blood SRGN ELISA reagent: ELISA plate coated with anti-human SRGN antibody and matching reagents; accessories include: lyophilized standard, standard & sample diluent, concentrated biotinylated antibody (100×), biotinylated antibody diluent, concentrated HRP enzyme conjugate (100×), enzyme conjugate diluent, concentrated washing buffer (25×), chromogenic substrate solution (TMB), and reaction termination solution.

[0067] Operating steps

[0068] (1) Before the experiment, all reagents should be equilibrated to room temperature and all reagents should be prepared in advance. When diluting reagents or samples, they should be thoroughly mixed, and foaming should be avoided as much as possible. Serum samples should be diluted 50 times with standard and sample diluent.

[0069] (2) Add 100 μL of standard or test sample, being careful not to have air bubbles. When adding the sample, place it at the bottom of the well of the microplate, trying not to touch the well wall. Gently mix, cover the plate with a cap or membrane, and incubate at 37°C for 60 minutes.

[0070] (3) Discard the liquid in the wells, shake dry, and wash the plate 1-2 times. Wash each well with 200 μL of washing solution, soak for 1-2 minutes, and shake off the liquid in the plate. After the last wash, pat the plate dry on absorbent paper.

[0071] (4) Add 100 μL of biotin antibody working solution to each well (prepared in 15 minutes), cover the plate with a membrane, and incubate at 37°C for 30 minutes.

[0072] (5) Discard the liquid in the well and wash the plate 1-2 times. Wash each well with 200 μL of washing solution and soak for 1-2 minutes to remove the liquid from the plate.

[0073] (6) Add 100 μL of enzyme conjugate working solution to each well and incubate at 37°C for 30 minutes.

[0074] (7) Discard the liquid in the wells and wash the plate 1-2 times. Wash each well with 200 ml of washing buffer and soak for 1-2 minutes, then shake off the liquid in the microplate.

[0075] (8) Add 90 μL of TMB chromogenic substrate solution to each well and incubate at 37°C in the dark for 20 minutes.

[0076] (9) Add 50 μL of stop solution to each well to terminate the reaction. The order of adding the stop solution should be as similar as possible to the order of adding the colorimetric reagent.

[0077] (10) Immediately use an enzyme-linked immunosorbent assay (ELISA) reader to measure the optical density (OD value) of each well in the plate at a wavelength of 450 nm to obtain the SRGN concentration.

[0078] 2. Human blood neutrophil detection:

[0079] Sample type: Peripheral venous blood (2 mL, EDTA-K2 tube anticoagulation, anticoagulant concentration 1.8 mg / mL)

[0080] Processing timeframe: Centrifuge within 30 minutes of collection (3000 rpm × 10 minutes) to separate the supernatant serum (avoid hemolysis; hemolyzed samples must be collected again).

[0081] Storage conditions: Serum samples can be stored at 2-8℃ for 48 hours, or frozen at -20℃ for 3 months (with no more than 2 freeze-thaw cycles).

[0082] Testing equipment: Sysmex XN-1000 blood analyzer (or equivalent precision equipment)

[0083] Key parameters: Detection channel is a DIFF channel, sheath fluid flow rate is 10 μL / min, laser wavelength is 638 nm, and counting range is 1-30 × 10⁻⁶. 9 / L, intraday CV≤5%, interday CV≤8% (calibration is required every time the machine is turned on with CS-600 calibrator).

[0084] Specifically, the device containing the formula description or evaluation system of the model can be a kit that directly contains the formula and detailed description of the model, allowing relevant personnel to directly calculate the risk probability P (AMI) given the neutrophil count and SRGN concentration of the patient to be tested; or the kit can contain a device equipped with the evaluation system shown in Example 3, such as a computer-readable medium, which can directly derive the risk probability P (AMI) and provide risk assessment and recommendations based on the neutrophil count and SRGN concentration of the patient to be tested.

[0085] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A predictive model for acute myocardial infarction risk assessment, characterized in that, The model is defined by the following formula: P(AMI)=1 / [1+e (–Logit(P(AMI))) ]; Where P(AMI) represents the probability of acute myocardial infarction, and Logit(P(AMI)) is specifically: Logit(P(AMI))= –17.589+0.672×NEUT+0.136×SRGN; Wherein, NEUT represents the number of neutrophils in the target patient's blood, expressed as ×10⁻¹⁰. 9 / L; SRGN represents the SRGN concentration in the patient's serum, in ng / ml; When P(AMI) ≥ 0.7, it is considered high risk; when 0.3 ≤ P(AMI) < 0.7, it is considered medium risk; and when P(AMI) < 0.3, it is considered low risk.

2. A method for constructing a predictive model for acute myocardial infarction risk assessment as described in claim 1, characterized in that, This includes collecting serum SRGN concentration and NEUT values ​​from patients with myocardial infarction, and using statistical software to establish a predictive model for acute myocardial infarction risk assessment.

3. An assessment system for predicting the risk of acute myocardial infarction, characterized in that, This includes a predictive model for acute myocardial infarction risk assessment as described in claim 1.

4. The assessment system for predicting the risk of acute myocardial infarction according to claim 3, characterized in that, The system also includes the following modules: a) Detection module: used to perform neutrophil counting and serum serine concentration determination on blood samples from target patients; b) Calculation module: Calculates the risk probability P (AMI) using the prediction model for acute myocardial infarction risk assessment as described in claim 1; c) Judgment module: used to compare P (AMI) with the threshold to classify it as high / medium / low risk; d) Output module: Used to output risk level and recommendations.

5. A kit for predicting the risk of acute myocardial infarction, characterized in that, include: Neutrophil counting module; SRGN concentration measurement module; And an apparatus containing a formula description of a predictive model for assessing the risk of acute myocardial infarction as described in claim 1, or an assessment system for predicting the risk of acute myocardial infarction as described in claim 3.

6. The reagent kit according to claim 5, characterized in that, The SRGN concentration measurement module consists of an ELISA plate coated with anti-human SRGN antibody and matching reagents.

Images