Application of protein marker in preparation of product for predicting future coronary heart disease onset risk of subject

Abstract

The invention relates to the field of biomedicine, in particular to application of a protein marker in preparation of a product for predicting the onset risk of coronary heart disease of a subject in the future. The invention provides application of 11 proteins as markers in preparation of products for effectively distinguishing and predicting future coronary heart disease onset risks of people without traditional risk factors. After the model is popularized to the whole population, the prediction capability of the coronary heart disease can be further improved on the basis of traditional risk factors or genetic risks, and effective identification and layering of the onset risk of the coronary heart disease are realized, so that primary prevention is improved. According to the method, individual coronary heart disease risk assessment can be really realized, so that relatively good risk prediction or assessment can be realized in all people and people who are misjudged as healthy or low risk in the past.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a proteomics-based risk prediction model for patients with coronary atherosclerotic heart disease without traditional risk factors. Background Art

[0002] Coronary atherosclerotic heart disease, commonly referred to as CHD, is a type of ischemic heart disease, which is caused by atherosclerotic lesions in the coronary arteries, leading to stenosis or obstruction of the vascular lumen. It includes subtypes such as myocardial infarction, angina pectoris, and sudden death from coronary heart disease. Coronary heart disease is the leading cause of cardiovascular disease death worldwide and has a serious disease burden in countries around the world. In CHD, targeted strategies for recognized modifiable risk factors (called standard modifiable cardiovascular risk factors, i.e., traditional risk factors) have made great progress in the prevention and treatment of CHD.

[0003] Clinically, a considerable number of patients develop coronary heart disease without any previous symptoms, and no traditional risk factors reach or exceed the diagnostic threshold. In recent years, international clinical practice has found that about 15% to 27% of patients with coronary heart disease do not have the four traditional risk factors of hyperlipidemia, hypertension, diabetes or smoking, and this proportion has increased year by year. Because in the subclinical stage, people without traditional risk factors are often misjudged as "healthy" or low-risk, missing the best window for early prevention and treatment. Compared with patients with at least one traditional risk factor, patients with coronary heart disease without traditional risk factors have a higher risk of in-hospital death, all-cause death after discharge, or death attributed to cardiovascular disease, a poorer prognosis, and a heavier disease burden.

[0004] Currently, the Pooled cohort equation (PCE) score is commonly used in clinical practice to predict the 10-year risk of atherosclerotic cardiovascular disease, which mainly relies on traditional risk factors. However, clinical practice has found that 15% to 27% of patients with coronary heart disease do not have any traditional risk factors that reach or exceed the diagnostic threshold. The PCE model for clinical assessment of the 10-year risk of atherosclerotic cardiovascular disease mainly focuses on the number and intensity of traditional risk factors, so it is impossible to effectively and accurately identify high-risk individuals in the population without traditional risk factors.

[0005] In addition, existing polygenic risk scores for coronary heart disease, such as early risk prediction studies for coronary heart disease based on markers such as N-terminal B-type natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-cTNT) or low-density lipoprotein cholesterol (LDL-c), are mostly conducted in the whole population or based on case-control study designs, and have poor screening effects on coronary heart disease patients without traditional risk factors, and low prediction accuracy. Therefore, for people without traditional risk factors (people who were previously misjudged as "healthy"), exploring key biomarkers for the occurrence of coronary heart disease in the future will help identify high-risk groups, and then carry out targeted primary prevention measures to effectively reduce the burden of coronary heart disease.

[0006] Proteomics can provide valuable clues to potential pathological mechanisms at the molecular level. The blood proteome has been widely characterized in cardiovascular diseases because it is easily accessible and close to the cardiovascular system. Several studies have applied mass spectrometry to quantitatively detect plasma proteins in a small number of samples and identified proteins associated with coronary heart disease. However, the measurement of a single type of protein is one-sided and easily overlooks other key markers that affect the occurrence or progression of the disease. A study used an aptamer-based platform that can analyze more than a thousand proteins in plasma and detected many protein changes in the context of myocardial injury in the Framingham Heart Study cohort, highlighting the potential of proteomics tools for large human cohorts. However, the current studies on early risk prediction of coronary heart disease based on high-throughput proteomics only target the entire population, and do not focus on the special population without traditional risk factors. No study has explored the proteomic characteristics of coronary heart disease in people without traditional risk factors. Moreover, the prediction model developed based on the entire population has poor early risk prediction ability in people without traditional risk factors.

[0007] Therefore, there is an urgent need to invent a protein risk scoring model that can effectively distinguish and predict the future risk of coronary heart disease in people who were previously considered "healthy" and without traditional risk factors. Summary of the invention

[0008] In view of this, the object of the present invention is:

[0009] (1) To construct a protein risk score for CAD without traditional risk factor (PRS-NRCAD) model based on protein markers in people without traditional risk factors (i.e., people who were previously considered "healthy"). This model can effectively distinguish and predict the future risk of CHD in people who were previously considered "healthy";

[0010] (2) The constructed PRS-NRCAD score can effectively predict the future risk of CHD in the entire population;

[0011] (3) The constructed PRS-NRCAD score can further improve the prediction accuracy compared with the Pooled cohort equation (PCE) or polygenic risk score for predicting the 10-year risk of atherosclerotic cardiovascular disease;

[0012] (4) The constructed PRS-NRCAD score can effectively stratify the risk of future CHD in people without traditional risk factors and the entire population.

[0013] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0014] The present invention provides a protein marker, comprising:

[0015] (1) Protein with UniProt ID Q99988; and

[0016] (2) Protein with UniProt ID O14763; and

[0017] (3) Protein with UniProt ID Q9Y286; and

[0018] (4) Protein with UniProt ID P48745; and

[0019] (5) Protein with UniProt ID P35318; and

[0020] (6), protein with UniProt ID P24387; and

[0021] (7), protein with UniProt ID P13236; and

[0022] (8), protein with UniProt ID Q14767; and

[0023] (9), protein with UniProt ID P00750; and

[0024] (10), protein with UniProt ID P02760; and

[0025] (11) Protein with UniProt ID Q9HAV5.

[0026] The present invention also provides the use of the above protein marker in predicting the risk of developing coronary heart disease.

[0027] In some specific embodiments of the present invention, the above-mentioned application of the risk of coronary heart disease is predicted based on PRS-NRCAD, and the judgment rule is: if the PRS-NRCAD ≥ -1.35, the future risk of coronary heart disease is judged to be high risk; if the PRS-NRCAD < -1.35, the future risk of coronary heart disease is judged to be low risk;

[0028] The PRS-NRCAD is derived according to Formula I, which is:

[0029] PRS-NRCAD = 0.36 × c1 + 0.18 × c2 + 0.31 × c3 + 0.31 × c4 + 0.38× c5 + 0.34 × c6 + 0.2 × c7 + 0.28 × c8 + 0.32 × c9 + 0.3 × c 10 + 0.3 ×c 11 ;

[0030] Among them, c1 to c 11 They are the normalized protein expression values ​​(NPX) of proteins with UniProt IDs Q99988, O14763, Q9Y286, P48745, P35318, P24387, P13236, Q14767, P00750, P02760 and Q9HAV5, hereinafter referred to as “NPX values”.

[0031] In some specific embodiments of the present invention, the NPX value described in the above application is obtained based on a plasma sample or a serum sample.

[0032] In some specific embodiments of the present invention, the above-mentioned application of the prediction is based on PRS-NRCAD and PCE prediction.

[0033] In some specific embodiments of the present invention, the prediction described in the above application is based on PRS-NRCAD and any one of the following predictions:

[0034] (i) Polygenic risk scoring model for coronary heart disease;

[0035] (ii) Polygenic risk scoring model for cardiovascular disease.

[0036] In some specific embodiments of the present invention, the risk of developing coronary heart disease described in the above application is the risk of developing coronary heart disease in individuals without traditional risk factors.

[0037] The present invention also provides a prediction model for the risk of developing coronary heart disease, comprising:

[0038] Calculate the individual's PRS-NRCAD. If the PRS-NRCAD ≥ -1.35, the risk of future coronary heart disease is determined to be high risk. If the PRS-NRCAD < -1.35, the risk of future coronary heart disease is determined to be low risk.

[0039] The PRS-NRCAD is derived according to Formula I, which is:

[0040] PRS-NRCAD = 0.36 × c1 + 0.18 × c2 + 0.31 × c3 + 0.31 × c4 + 0.38× c5 + 0.34 × c6 + 0.2 × c7 + 0.28 × c8 + 0.32 × c9 + 0.3 × c 10 + 0.3 ×c 11 ;

[0041] Among them, c1 to c 11 The NPX values ​​of proteins with UniProt IDs Q99988, O14763, Q9Y286, P48745, P35318, P24387, P13236, Q14767, P00750, P02760, and Q9HAV5, respectively.

[0042] The method for predicting the future risk of coronary heart disease includes the following steps:

[0043] S1. Collect individual blood samples and prepare plasma samples or serum samples;

[0044] S2, the obtained plasma or serum samples were subjected to protein quantification by Olink Explore proximity extension analysis (PEA) to obtain the protein level of the samples, and the sample protein level was converted into NPX value;

[0045] S3. Substitute the NPX value into Formula I in the above prediction model to obtain PRS-NRCAD. If the PRS-NRCAD ≥ -1.35, the risk of future coronary heart disease is determined to be high risk; if the PRS-NRCAD < -1.35, the risk of future coronary heart disease is determined to be low risk.

[0046] In some specific embodiments of the present invention, there is no particular limitation on the method for calculating the NPX value described in the above applications and methods, and a method known in the art may be used, such as the method described in https: / / biobank.ndph.ox.ac.uk / ukb / ukb / docs / Olink_1536_B0_to_B7_Normalization.pdf (UKB-Olink Explore 1536-DataNormalization Strategy).

[0047] The present invention also provides the use of the above protein marker in preparing a product for predicting the risk of coronary heart disease;

[0048] The product includes a reagent, a kit, a device or a system.

[0049] In some specific embodiments of the present invention, the reagent, the kit or the device used above comprises a specific antibody for the protein marker.

[0050] The present invention also provides a reagent for predicting the risk of developing coronary heart disease, wherein the detection target is the protein marker in the above application.

[0051] The present invention also provides a kit for predicting the risk of coronary heart disease, wherein the detection target is the protein marker in the above application.

[0052] The present invention also provides a device for predicting the risk of developing coronary heart disease, wherein the detection target is the protein marker in the above application.

[0053] The present invention also provides a system for predicting the risk of developing coronary heart disease, comprising:

[0054] An acquisition module, used for acquiring the concentration of a protein marker in individual plasma or serum and converting the concentration into an NPX value, wherein the protein marker is the protein marker in the above application;

[0055] A processing module is used to predict the risk of coronary heart disease of the individual, and the prediction is based on the PRS-NRCAD prediction in the above application.

[0056] The present invention has the following effects:

[0057] 1. People without traditional risk factors were previously considered "healthy", that is, they would not or only had a very low chance of developing coronary heart disease, but clinical practice has found that a considerable proportion of this group of people will still develop coronary heart disease in the future. The PCE score used in the past to predict the risk of atherosclerotic cardiovascular disease mainly relied on traditional risk factors, so its risk stratification and prediction effects were poor in this group of people. The simple protein score PRS-NRCAD constructed by the present invention, starting from the special population of people without traditional risk factors, can effectively screen for the onset of coronary heart disease and identify people at high risk of developing coronary heart disease in the future, thereby effectively preventing the occurrence of coronary heart disease and reducing the disease burden of this group of people;

[0058] 2. After the simple protein score PRS-NRCAD constructed by the present invention was promoted and applied to the general population, it was found that PRS-NRCAD can further improve the predictive ability of the traditional PCE model and (or) genetic risk score for the risk of coronary heart disease, improve the prediction accuracy, and thus improve the primary prevention effect of coronary heart disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0060] Figure 1 The Shilazo regression penalty model was used to screen protein markers associated with the risk of coronary heart disease in people without traditional risk factors.

[0061] Figure 2 The figure shows the ability of PRS-NRCAD to distinguish the future onset of coronary heart disease in people without traditional risk factors. Lines of different colors represent the distinguishing ability of each individual protein marker, and the red line represents the distinguishing value of PRS-NRCAD when 11 proteins are integrated.

[0062] Figure 3 The figure shows the ability of PRS-NRCAD to predict the future risk of coronary heart disease in people without traditional risk factors. A is the training set and B is the validation set.

[0063] Figure 4 The results showed that PRS-NRCAD can predict the future risk of coronary heart disease in the general population;

[0064] Figure 5 The results showed that PRS-NRCAD has the ability to stratify the risk of coronary heart disease in people without traditional risk factors;

[0065] Figure 6 The results showed that PRS-NRCAD has the ability to stratify the risk of CHD in the general population. DETAILED DESCRIPTION

[0066] The present invention discloses a proteomics-based risk prediction model for patients with coronary atherosclerotic heart disease without traditional risk factors. Those skilled in the art can refer to the content of this article and appropriately improve the process parameters to achieve it. It is particularly important to point out that all similar substitutions and modifications are obvious to those skilled in the art, and they are all considered to be included in the present invention. The methods and applications of the present invention have been described through preferred embodiments, and relevant personnel can obviously modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit and scope of the present invention to implement and apply the technology of the present invention.

[0067] Based on the limitations of existing research progress, the present invention provides a protein risk scoring model that can effectively distinguish and predict the future risk of coronary heart disease for people without traditional risk factors who were previously considered "healthy" or low-risk. After the risk scoring model is extended to the entire population, it can further improve the predictive ability of coronary heart disease on the basis of traditional risk factors or genetic risks, and effectively identify and stratify the risk of coronary heart disease, thereby improving primary prevention. The present invention can truly achieve individual coronary heart disease risk assessment, thereby achieving better risk prediction / assessment in the entire population, as well as in people who were previously misjudged as healthy / low-risk.

[0068] Unless otherwise specified, the raw materials, reagents, consumables and instruments involved in the present invention are all common commercial products and can be purchased from the market.

[0069] The present invention will be further described below in conjunction with the embodiments.

[0070] Example

[0071] (1) Patients with CHD without traditional risk factors and their data collection

[0072] The UK Biobank Pharma Proteomics Project (UKB-PPP) measured plasma proteomics in baseline samples from more than 50,000 UKB participants. The plasma samples were quantified using the Olink platform using the antibody-based Olink Explore 3072 proximity extension assay (PEA) technology, containing 1,463 proteins in the cardiometabolism, inflammation, neurology, and oncology modules, and the protein levels in the samples were then converted to NPX values.

[0073] Clinical risk factor information, such as age, sex, education, and smoking, was collected using standardized questionnaires; physical measurements, such as height, weight, and blood pressure, were assessed by trained nurses; hypertension, diabetes, and high cholesterol were diagnosed based on baseline self-reported medical history, health record diagnosis history, medication history, or baseline measurements. CHD diagnosis was defined based on International Classification of Diseases, Ninth Edition (ICD-9) or Tenth Edition (ICD-10) codes, or hospitalization records or self-reported diagnosis history. Traditional risk factors included smoking (current smoking or more than 100 cigarettes in a lifetime), hypercholesterolemia, diabetes, and hypertension.

[0074] Participants with any form of coronary heart disease at baseline or whose genetic data did not pass quality control requirements were excluded, and finally 49,530 research subjects with genetic, plasma proteome and clinical data were included. Among them, 80% of the population without traditional risk factors at baseline were randomly selected as the test cohort (N=5,772) to screen the proteome features associated with the risk of coronary heart disease, and to build a protein prediction model, and the remaining 20% ​​of the population was used as the validation cohort (N=1,442) to verify the model effect. Then, the protein model was applied to the entire population with PCE scores (N=47,755) to verify the general applicability.

[0075] It was found that patients who would develop CHD in the future were older at baseline, had a higher proportion of males, a higher proportion of obesity, higher average blood pressure, worse liver function, and worse lipid and blood glucose profiles. However, there was no statistical difference in the polygenic risk score levels for predicting the risk of CHD or cardiovascular disease between the two groups, as shown in Table 1.

[0076] Table 1

[0077]

[0078] (2) Protein marker screening

[0079] A two-stage screening method was used to screen proteomic characteristics associated with the risk of CHD in people without traditional risk factors.

[0080] Phase I: The LASSO penalty model was used to screen clinical characteristics (age, gender, and race) and protein markers associated with the risk of CHD in people without traditional risk factors, and a 10-fold cross-validation was performed to select the best penalty parameter. The coefficients (λ) of the three clinical characteristics were all non-zero, so they were selected for the training model. Among the 1,463 proteins, 32 proteins had non-zero coefficients and were selected for further training models, see Figure 1 Next, clinical features and proteins with non-zero coefficients were uniformly incorporated into the Lasso regression model to retain as many proteomic features as possible that may contribute less to disease classification, and 31 protein markers were subsequently screened out.

[0081] Phase II: The Cox risk regression model was used to screen protein markers that can prospectively predict CHD in people without traditional risk factors at baseline. Cox risk regression models were constructed for the 31 protein markers screened above one by one, where the outcome of the Cox risk regression model was a binary variable: whether CHD would occur in the future, and the variables were the NPX values ​​of the 31 proteins. The risk coefficient (coef), hazard ratio (HR), and P value of each single protein were obtained, and the P value was multiple corrected using the Benjamini & Hochberg method, and a total of 11 protein markers were screened (P < 0.05 / 31), as shown in Table 2.

[0082] Table 2

[0083]

[0084] (3) Construction of protein prediction model

[0085] Based on the 11 protein markers discovered through the above multi-stage screening, a simple protein score PRS-NRCAD for the risk of coronary heart disease in people without traditional risk factors was constructed using an additive model:

[0086]

[0087] By detecting the concentration of the above proteins in plasma and calibrating the protein concentration using the NPX method to convert it into protein NPX value, the protein simple score PRS-NRCAD for the future risk of coronary heart disease in people without traditional risk factors can be calculated according to the formula.

[0088] Risk judgment criteria: When the individual PRS-NRCAD score is ≥-1.35, the risk of developing coronary heart disease in the future is high; when the individual PRS-NRCAD score is <-1.35, the risk of developing coronary heart disease in the future is low.

[0089] (4) Analysis of the ability of protein prediction models to distinguish whether coronary heart disease events occur or not

[0090] First, the ability of PRS-NRCAD and 11 individual protein markers to distinguish and identify coronary heart disease events in people without traditional risk factors was compared. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate the classification effect of PRS-NRCAD or 11 individual protein markers (presented by UniProt ID) between patients who will have coronary heart disease events in the future and normal controls who will not have coronary heart disease events. The AUC is between 0 and 1, which can intuitively evaluate the prediction accuracy of the model. The larger the AUC value, the higher the prediction accuracy. The analysis found that the ROC curve of PRS-NRCAD was higher than the ROC curves of the other 11 individual protein markers, and PRS-NRCAD had the highest AUC value, indicating that PRS-NRCAD has better ability to distinguish the onset of coronary heart disease than the use of 11 protein markers alone. In addition, the AUC value of PRS-NRCAD reached 0.767, indicating that PRS-NRCAD has good ability to distinguish the future onset of coronary heart disease, see Figure 2 (Lines of different colors represent the distinguishing power of each individual protein marker, and the red line represents the distinguishing value of the 11 proteins integrated together).

[0091] (5) Analysis of the predictive ability of protein prediction models for future risk of coronary heart disease

[0092] Next, the ability of the protein prediction model PRS-NRCAD to predict the risk of CHD in people without traditional risk factors was prospectively evaluated. First, in the training set, using the Cox regression model, it was found that PRS-NRCAD was positively correlated with the future risk of CHD in people without traditional risk factors. After adjusting for clinical factors (sex, age, and race) and genetic risks (coronary heart disease polygenic risk score and cardiovascular disease polygenic risk score), PRS-NRCAD was still associated with the risk of CHD in people without traditional risk factors (HR and its confidence interval 95% CI did not include 1, indicating P<0.05, as shown in Table 1). Figure 3 ).

[0093] Similarly, in the validation set, the Cox regression model found that the positive association between PRS-NRCAD and the risk of CHD in people without traditional risk factors remained significant after correction for clinical factors or two polygenic risk scores (HR and its 95% CI did not include 1, indicating P < 0.05, Figure 3 B in the figure).

[0094] (6) Extend the ability of protein prediction models to predict the risk of coronary heart disease to the general population

[0095] The protein prediction model PRS-NRCAD was further extended to the whole population, and the ability of PRS-NRCAD to predict the risk of coronary heart disease was evaluated. Currently, the PCE model is commonly used internationally to predict the 10-year risk of atherosclerotic cardiovascular disease in the general population. This model is highly recognized and widely used, including several clinical indicators such as age, gender, race, systolic blood pressure, hypertension treatment, diabetes, total cholesterol and high-density lipoprotein cholesterol.

[0096] According to the data availability of PCE scores and gene scores, a total of 47,755 people were included in the whole population. In the whole population, the Cox regression model showed that after adjusting for the 10-year atherosclerotic cardiovascular disease risk and polygenic risk score, the association between PRS-NRCAD and the risk of coronary heart disease was still significant (HR and its confidence interval 95% CI did not include 1, indicating P < 0.05, such as Figure 4 as shown).

[0097] As shown in Table 3, in the Cox regression model, the predictive ability of PRS-NRCAD, PCE, and genetic risk score for the future risk of CHD was compared. The improvement in predictive ability was evaluated by the consistency index (C-index), and the improvement in disease classification, i.e., risk stratification ability, was evaluated by the comprehensive discrimination improvement index (IDI) and the net reclassification improvement index (NRI). Compared with the prediction using the PCE model alone, the PCE model combined with the CHD polygenic risk score or the cardiovascular disease polygenic risk score can further improve the predictive ability of CHD risk (C-index increased by 0.006 and 0.003, respectively, P<0.001). On the basis of the PCE model, the further addition of PRS-NRCAD can increase the C-index by 0.016 (P<0.001), and the 10-year risk comprehensive discrimination improvement index (IDI) and net reclassification improvement index (NRI) are significantly improved. This shows that adding the detection of 11 protein markers to the existing clinical risk prediction model can further improve the predictive ability of CHD.

[0098] As shown in Table 3, on the basis of PCE and coronary heart disease polygenic risk scoring model, the further addition of PRS-NRCAD can further improve the ability to predict the incidence of coronary heart disease (C-index increased by 0.023, IDI increased by 0.013, NRI increased by 0.180, P<0.001). On the basis of PCE and coronary heart disease polygenic risk scoring model, the further addition of PRS-NRCAD can also improve the ability to predict the incidence of coronary heart disease (C-index increased by 0.023, IDI increased by 0.013, NRI increased by 0.180, P<0.001).

[0099] Table 3

[0100]

[0101] (7) Analysis of the effect of protein prediction model on the risk stratification of coronary heart disease

[0102] Among the population without traditional risk factors, the population was divided into low-risk (PRS-NRCAD <-1.35, n=3,600) and high-risk (PRS-NRCAD ≥ -1.35, n=3,614) groups according to the PRS-NRCAD level. Kaplan-Meier survival analysis was used to compare the cumulative risk of coronary heart disease between the low-risk and high-risk PRS-NRCAD groups during a median follow-up of 13.9 years. The log-rank test confirmed that the high-risk group had a higher risk of coronary heart disease (P<0.001). Figure 5 The two risk curves and their 95% confidence intervals were separated from each other without overlap, and the high-risk group curve was higher than the low-risk group curve at each time point, indicating that the PRS-NRCAD high-risk group had a higher risk of coronary heart disease.

[0103] In the whole population, the population was divided into two groups according to the PRS-NRCAD level: low risk (PRS-NRCAD < -1.35) and high risk (PRS-NRCAD ≥ -1.35). Kaplan-Meier survival analysis was used to compare the cumulative risk of coronary heart disease in the low-risk and high-risk groups of PRS-NRCAD during the median follow-up of 13.9 years. The log-rank test confirmed that even in the whole population, the high-risk group still had a higher risk of coronary heart disease (P<0.001). Figure 6 The two risk curves and their 95% confidence intervals were separated from each other without overlap, and the high-risk group curve was higher than the low-risk group curve at each time point, indicating that the PRS-NRCAD high-risk group had a higher risk of coronary heart disease.

[0104] (8) Clinical application value of the model

[0105] In the population without traditional risk factors (people who were previously misjudged as "healthy"), the ROC curve and AUC value were used to evaluate the screening ability of the protein risk score model PRS-NRCAD or 11 individual protein markers for future coronary heart disease. It was found that PRS-NRCAD had the strongest discrimination ability, with an AUC value of 0.767, and a good discrimination effect. Figure 2Furthermore, the prospective risk prediction ability of PRS-NRCAD was evaluated using a multivariate Cox regression analysis model. After correcting for the confounding interference of demographic characteristics and genetic risks, PRS-NRCAD could still effectively predict the risk of CHD in a population that is often considered "healthy" and has no traditional risk factors (P < 0.05). Figure 3 .

[0106] When PRS-NRCAD was extended to the whole population, it was found that even after correcting the PCE score and genetic risk, which are currently widely used internationally to predict the 10-year risk of atherosclerotic cardiovascular disease in the general population, PRS-NRCAD can still effectively predict the risk of coronary heart disease in the general population (P < 0.05). Figure 4 .

[0107] Compared with the existing 10-year atherosclerotic cardiovascular disease risk prediction score PCE, adding PRS-NRCAD can further improve the ability to predict the risk of coronary heart disease, as shown in Table 3. Compared with the single PCE score, after adding PRS-NRCAD, the prediction accuracy (reflected by C-index) increased by 1.6%, and the overall prediction effect (represented by IDI) and the prediction success rate (reflected by NRI) were significantly improved, and the improvement effect was better than the genetic risk score. Compared with the combination of PCE score and genetic risk score, further adding PRS-NRCAD variables can still improve the prediction accuracy, overall prediction effect and prediction success rate of coronary heart disease.

[0108] PRS-NRCAD was used to stratify the risk of people without traditional risk factors, and it was found that the high-risk group had a higher risk of developing CHD during the 13.9-year follow-up (P < 0.001, see Figure 5 ), indicating that the use of PRS-NRCAD to stratify the future risk of CHD in people without traditional risk factors can effectively identify high-risk groups. In addition, the ability of PRS-NRCAD to stratify the risk of CHD in the whole population has been further verified, see Figure 6 .

[0109] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Use of a protein marker in the preparation of a product for predicting the future risk of coronary heart disease in a subject, characterized in that: The protein markers are: (1) Protein with UniProt ID Q99988; and (2) Protein with UniProt ID O14763; and (3) Protein with UniProt ID Q9Y286; and (4) Protein with UniProt ID P48745; and (5) Protein with UniProt ID P35318; and (6), protein with UniProt ID P24387; and (7), protein with UniProt ID P13236; and (8), protein with UniProt ID Q14767; and (9), protein with UniProt ID P00750; and (10), protein with UniProt ID P02760; and (11), protein with UniProt ID Q9HAV5; The product includes a reagent, a kit, a device or a system.

2. The use according to claim 1, characterized in that The subject's future risk of developing coronary heart disease is predicted based on PRS-NRCAD, and the judgment rule is: if the PRS-NRCAD ≥ -1.35, the subject's future risk of developing coronary heart disease is judged to be high risk; if the PRS-NRCAD < -1.35, the subject's future risk of developing coronary heart disease is judged to be low risk; The PRS-NRCAD is derived according to Formula I, which is: PRS-NRCAD = 0.36 × c1 + 0.18 × c2 + 0.31 × c3 + 0.31 × c4 + 0.38 × c5+ 0.34 × c6 + 0.2 × c7 + 0.28 × c8 + 0.32 × c9 + 0.3 × c 10 + 0.3 × c 11 ; Among them, c1 to c 11 The NPX values ​​of proteins with UniProt IDs Q99988, O14763, Q9Y286, P48745, P35318, P24387, P13236, Q14767, P00750, P02760, and Q9HAV5, respectively.

3. The use according to claim 1 or 2, characterized in that: The prediction is based on PRS-NRCAD and PCE prediction.

4. The use according to claim 1 or 2, characterized in that: The forecast is based on PRS-NRCAD and any of the following forecasts: (i) Polygenic risk scoring model for coronary heart disease; (ii) Polygenic risk scoring model for cardiovascular disease.

5. The use according to claim 1 or 2, characterized in that: The risk of developing coronary heart disease is the risk of developing coronary heart disease in individuals without traditional risk factors.

6. The use according to claim 1 or 2, characterized in that: The reagent, the kit or the device comprises a specific antibody for the protein marker.

7. A reagent for predicting the future risk of coronary heart disease in a subject, characterized in that: The detection target is the protein marker in the use according to claim 1.

8. A kit for predicting the future risk of coronary heart disease in a subject, characterized in that: The detection target is the protein marker in the use according to claim 1.

9. A device for predicting the future risk of coronary heart disease in a subject, characterized in that: The detection target is the protein marker in the use according to claim 1.

10. A system for predicting the future risk of coronary heart disease in a subject, characterized in that: include: An acquisition module, used for acquiring the concentration of a protein marker in the plasma or serum of a subject and converting the concentration into an NPX value, wherein the protein marker is the protein marker in the application according to claim 1; A processing module for predicting the risk of coronary heart disease in the subject, wherein the prediction is based on the PRS-NRCAD prediction in the application of claim 2.

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