CN117568458A

CN117568458A - Methylation marker for assisting diagnosis of cardiovascular and cerebrovascular diseases

Info

Publication number: CN117568458A
Application number: CN202210616099.2A
Authority: CN
Inventors: 王俊; 狄飞飞
Original assignee: Tengchen Biotechnology Shanghai Co ltd
Current assignee: Tengchen Biotechnology Shanghai Co ltd
Priority date: 2022-06-01
Filing date: 2022-06-01
Publication date: 2024-02-20

Abstract

The invention discloses a methylation marker for assisting in diagnosing cardiovascular and cerebrovascular diseases. The invention provides an application of a methylated ABCG1 gene as a marker in the preparation of products; the product can be used for auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases (such as coronary heart disease and cerebral apoplexy) before clinical onset. The research of the invention discovers the hypomethylation of the ABCG1 gene in coronary heart disease blood and the hypermethylation phenomenon in cerebral apoplexy blood, and has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of cardiovascular and cerebrovascular diseases and reducing the death rate.

Description

Methylation marker for assisting diagnosis of cardiovascular and cerebrovascular diseases

Technical Field

The invention relates to the field of medicine, in particular to a methylation marker for assisting in diagnosing cardiovascular and cerebrovascular diseases.

Background

Cardiovascular and cerebrovascular diseases are the general terms of cardiovascular and cerebrovascular diseases, and refer broadly to ischemic or hemorrhagic diseases of heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension, etc. Cardiovascular and cerebrovascular diseases are common diseases seriously threatening the health of human beings, especially middle-aged and elderly people over 50 years old, and have the characteristics of high morbidity, high disability rate and high mortality rate. At present, the number of people dying from cardiovascular and cerebrovascular diseases worldwide is up to 1500 ten thousand people each year.

Coronary heart disease refers to heart disease caused by coronary atherosclerosis to narrow, cramp or block lumen, resulting in ischemia, hypoxia or death of the heart muscle, collectively referred to as coronary heart disease or coronary artery disease. Coronary heart disease is classified into 5 types according to clinical characteristics such as lesion sites, ranges, degrees and the like: (1) occult or asymptomatic myocardial ischemia: asymptomatic, but showing myocardial ischemia changes under resting, dynamic or loading electrocardiogram, or radionuclide myocardial imaging suggesting myocardial hypoperfusion, no tissue morphology changes; (2) angina pectoris: posttraumatic sternal pain caused by myocardial ischemia; (3) myocardial infarction: severe ischemic symptoms, acute ischemic necrosis of the myocardium due to coronary occlusion; (4) ischemic cardiomyopathy: chronic myocardial ischemia or death causes myocardial fibrosis, manifested by increased heart, heart failure and cardiac arrhythmias; (5) sudden death: death due to sudden cardiac arrest is often caused by severe arrhythmias resulting from local electrophysiological disturbances in the ischemic myocardium. The main diagnosis method of the coronary heart disease at present comprises the following steps: (1) clinical characteristics: typically, the combination of the medical history and physical examination status of the inspector is used for preliminary diagnosis, but the specificity is very low; (2) imaging method: electrocardiography, echocardiography, and coronary angiography, but are often affected by physician experience and instrumentation; (3) The most commonly used coronary heart disease markers at present are as follows: myocardial injury markers, inflammatory factors, adhesion molecules and cytokine markers, plasma lipoprotein and apolipoprotein markers, coagulation related protein markers, and the like. Because a certain marker reflects only a certain disease mechanism of a disease, these markers are not widely accepted clinically.

Cerebral apoplexy is commonly called as apoplexy, and is an acute cerebrovascular disease, including ischemic cerebral apoplexy and hemorrhagic cerebral apoplexy. Ischemic cerebral apoplexy accounts for 60% -70% of all cerebral strokes, mainly due to cerebral vascular stenosis or occlusion caused by atherosclerosis, thereby causing cerebral ischemia and hypoxia, further causing ischemic necrosis or softening of localized cerebral tissue, patients are more than 40 years old, men are more female, and serious men can cause death. Hemorrhagic stroke is classified into cerebral hemorrhage and subarachnoid hemorrhage, and is mainly caused by long-term hypertension, aneurysm or cerebral vascular congenital weakness, and the like, which cause cerebral vascular rupture hemorrhage, and the blood presses normal brain tissues in the brain, so that the brain cannot perform normal functions, namely 'cerebral hemorrhage', and the death rate is higher. At present, imaging methods are often used for diagnosing cerebral apoplexy, such as CT and nuclear magnetic resonance examination, the sensitivity of CT to the cerebral arterial thrombosis is higher, but the sensitivity to the cerebral arterial thrombosis is only 16%, and the cerebral arterial thrombosis is not suitable for frequent use due to radiation; nuclear magnetic resonance examination has higher sensitivity to ischemic stroke than CT and no radiation effect, but has the disadvantage of lower feasibility, practicality and accessibility (equipment and trained personnel).

Coronary heart disease and cerebral apoplexy both belong to cardiovascular and cerebrovascular diseases. Most cardiovascular diseases can be prevented and treated, and are generally prevented by improving consciousness through popularization knowledge, avoiding exogenous stimulus factors and reasonably dietary moderate exercise, and the treatment effect is greatly dependent on early diagnosis and corresponding intervention measures. Currently, the sensitivity and specificity of diagnostic markers for coronary heart disease and cerebral apoplexy are limited clinically, and especially markers for early diagnosis are lacking, so that more sensitive and specific early molecular markers are urgently needed to be discovered. DNA methylation is a chemical modification important on genes that affects the regulatory process of gene transcription and nuclear structure.

Disclosure of Invention

The invention aims to provide application of ABCG1 gene methylation level in auxiliary diagnosis of cardiovascular and cerebrovascular diseases.

In a first aspect, the invention claims the use of a methylated ABCG1 gene as a marker in the preparation of a product; the application of the product is at least one of the following:

(1) Auxiliary diagnosis of cardiovascular and cerebrovascular diseases or early warning of cardiovascular and cerebrovascular diseases before clinical onset;

(2) Auxiliary diagnosis of coronary heart disease or early warning of coronary heart disease before clinical onset;

(3) Auxiliary diagnosis of cerebral apoplexy or early warning of cerebral apoplexy before clinical onset;

(4) Auxiliary distinguishing coronary heart disease and cerebral apoplexy or early warning and distinguishing coronary heart disease and cerebral apoplexy before clinical onset;

(5) Auxiliary diagnosis of coronary heart disease with different clinical characteristics or early warning of coronary heart disease with different clinical characteristics before clinical onset;

(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset.

Further, the cardiovascular and cerebrovascular diseases described in (1) can be diseases capable of causing the change of the methylation level of the ABCG1 gene in the organism, such as coronary heart disease, cerebral apoplexy and the like.

In the present invention, the auxiliary diagnosis of cardiovascular and cerebrovascular diseases described in (1) may be embodied as at least one of the following: assisting in distinguishing coronary heart disease patients from healthy controls, cerebral apoplexy patients from healthy controls. Wherein, the healthy control can be understood as that no cardiovascular and cerebrovascular diseases or cancers are affected at present and once and no cardiovascular and cerebrovascular diseases are affected in the next 2 years, and the blood routine indexes are all within the reference range. The "healthy control" appearing hereinafter is synonymous.

In the invention, the early warning of cardiovascular and cerebrovascular diseases before clinical onset in (1) is any one of the following: coronary heart disease is pre-warned before clinical onset, cerebral apoplexy is pre-warned before clinical onset, and coronary heart disease and cerebral apoplexy are pre-warned and distinguished before clinical onset.

Further, the testee who early warns of coronary heart disease before clinical onset is a potential patient of coronary heart disease or a healthy control. The potential patients with coronary heart disease are potential patients with coronary heart disease which is happened in the next 2 years or potential patients with coronary heart disease which is happened in the next 1 year (i.e. coronary heart disease can be clinically diagnosed in 2 years or 1 year). The "potential patients for coronary heart disease" appearing hereinafter are synonymous.

Further, the person to be detected who pre-warns of cerebral apoplexy before clinical onset is a potential cerebral apoplexy patient or a healthy control. The potential cerebral apoplexy patient is a potential cerebral apoplexy patient with a future 2 years of onset or a potential cerebral apoplexy patient with a future 1 year of onset (i.e. cerebral apoplexy can be clinically diagnosed within 2 years or within 1 year). The "potential stroke patient" appearing hereinafter is synonymous.

Further, the detected person who early warns and distinguishes coronary heart disease and cerebral apoplexy before clinical onset is a potential patient of coronary heart disease or a potential patient of cerebral apoplexy.

In the present invention, the coronary heart disease of different clinical characteristics described in (5) is: latent or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy or sudden death. The "coronary heart disease of different clinical characteristics" appearing hereinafter is synonymous.

In the present invention, the coronary heart disease of the type described in (5) that assists in diagnosing different clinical characteristics is embodied as at least one of the following: can help to distinguish between latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls. The "diagnosis of coronary heart disease with different clinical features assisted" appearing hereinafter is synonymous.

In the invention, the testees of coronary heart disease with different clinical characteristics, which are early-warned before clinical onset, in the step (5) are coronary heart disease potential patients or healthy controls with different clinical characteristics.

In the present invention, the stroke with different clinical characteristics described in (6) is: ischemic stroke or hemorrhagic stroke. The occurrence of "stroke of different clinical characteristics" hereinafter is synonymous.

In the present invention, the stroke described in (6) that aids in distinguishing different clinical characteristics is embodied as at least one of the following: can help to distinguish ischemic cerebral apoplexy from healthy control, and can help to distinguish ischemic cerebral apoplexy from healthy control. The terms "assisting in differentiating between different clinical features" appear synonymously hereinafter.

In the invention, the subjects with pre-alarm of cerebral apoplexy of different clinical characteristics before clinical onset in (6) are potential cerebral apoplexy patients or healthy controls of different clinical characteristics.

In a second aspect, the invention claims the use of a substance for detecting the methylation level of the ABCG1 gene for the preparation of a product; the use of the product is at least one of the foregoing (1) - (6).

In a third aspect, the invention claims the use of a substance for detecting the methylation level of the ABCG1 gene and a medium storing mathematical modeling methods and/or methods of use for the preparation of a product; the use of the product is at least one of the foregoing (1) - (6).

The mathematical model is obtained according to a method comprising the following steps:

(A1) Detecting the methylation level of the ABCG1 gene of n 1A type samples and n 2B type samples respectively (training set);

(A2) And (3) taking ABCG1 gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type.

Wherein, n1 and n2 can be positive integers more than 50.

The using method of the mathematical model comprises the following steps:

(B1) Detecting the methylation level of the ABCG1 gene of a sample to be detected;

(B2) Substituting the ABCG1 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.

In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.

In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the two classifications are determined according to a specific mathematical model without convention.

The type a sample and the type B sample are any one of the following:

(C1) Potential patients with coronary heart disease and healthy controls for onset within the next 2 years;

(C2) Potential patients with stroke and healthy controls that developed within the next 2 years;

(C3) Coronary heart disease potential patients with onset in the next 2 years and cerebral apoplexy potential patients with onset in the next 2 years;

(C4) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 2 years;

(C5) Stroke potential patients and healthy controls of different clinical characteristics of onset within the next 2 years;

(C6) Potential patients with coronary heart disease and healthy controls for onset within the next 1 year;

(C7) Potential patients with stroke and healthy controls that developed within the next 1 year;

(C8) Coronary heart disease potential patients with onset in the next 1 year and cerebral apoplexy potential patients with onset in the next 1 year;

(C9) Coronary heart disease potential patients and healthy controls with different clinical characteristics of onset within the next 1 year;

(C10) Stroke potential patients and healthy controls with different clinical characteristics of onset within the next 1 year.

In a fourth aspect, the invention claims the use of a medium storing a mathematical model building method and/or a use method for the manufacture of a product; the use of the product is at least one of the foregoing (1) - (6).

Wherein, n1 and n2 can be positive integers more than 50.

The using method of the mathematical model comprises the following steps:

The type a sample and the type B sample are any one of the following:

In a fifth aspect, the invention claims a kit.

The kit claimed in the present invention comprises a substance for detecting the methylation level of the ABCG1 gene; the use of the kit is at least one of the above (1) - (6).

Further, the kit may further comprise a medium storing the mathematical model creation method and/or the use method described in the third or fourth aspect.

In a sixth aspect, the invention claims a system.

The system claimed by the present invention may include:

(D1) Reagents and/or instrumentation for detecting the methylation level of the ABCG1 gene;

(D2) The device comprises a unit X and a unit Y.

The unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;

the data acquisition module is configured to acquire (D1) ABCG1 gene methylation level data of the detected n1 type a samples and n2 type B samples.

The data analysis processing module is configured to receive ABCG1 gene methylation level data from n 1A type samples and n 2B type samples sent by the data acquisition module, and establish a mathematical model through a classification logistic regression method according to classification modes of A type and B type to determine a threshold value of classification judgment.

Wherein, n1 and n2 can be positive integers more than 50.

The model output module is configured to receive the mathematical model transmitted from the data analysis processing module and output the mathematical model.

The unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module.

The data input module is configured to input (D1) the detected ABCG1 gene methylation level data of the to-be-detected person;

the data operation module is configured to receive the ABCG1 gene methylation level data of the testee sent by the data input module, and substitutes the ABCG1 gene methylation level data of the testee into the mathematical model to calculate a detection index.

The data comparison module is configured to receive the detection index transmitted from the data operation module and compare the detection index with the threshold determined in the data analysis processing module in the unit X.

The conclusion output module is configured to receive the comparison result sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result.

The type a sample and the type B sample are any one of the following:

In the foregoing aspects, the period of time prior to clinical onset may be in particular within 2 years of the period of time prior to clinical onset or within 1 year of the period of time prior to clinical onset. The potential coronary heart disease patient with onset within 2 years (or within 1 year) in the future is a potential coronary heart disease patient with onset within 2 years (or within 1 year) earlier than the clinical onset time; the potential cerebral apoplexy patients with the future 2 years (or 1 year) are the potential cerebral apoplexy patients with the clinical onset time 2 years (or 1 year) earlier.

In the foregoing aspects, the methylation level of the ABCG1 gene may be the methylation level of all or part of the CpG sites in the fragments of the ABCG1 gene as shown in (e 1) - (e 5) below. The methylated ABCG1 gene is formed by methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 5) in the ABCG1 gene.

(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;

(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;

(e3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto;

(e4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment having 80% or more identity thereto;

(e5) The DNA fragment shown in SEQ ID No.5 or a DNA fragment having 80% or more identity thereto.

Further, the "all or part of CpG sites" may be any one or more CpG sites of 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ABCG1 gene. The upper limit of the "plurality of CpG sites" described herein is all CpG sites in 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ABCG1 gene. All CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1), all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2), all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3), all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4), and all CpG sites in the DNA fragment shown in SEQ ID No.5 (see Table 5).

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) in the ABCG1 gene.

Or, the "all or part of CpG sites" are all CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites on the DNA fragment shown in SEQ ID No.4 (Table 4) in the ABCG1 gene.

Or, the "all or part of CpG sites" are all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) and all CpG sites on the DNA fragment shown in SEQ ID No.4 (Table 4) in the ABCG1 gene.

Alternatively, the "all or part of the CpG sites" may be all CpG sites on the DNA fragment shown in SEQ ID No.2 (Table 2), all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) and all CpG sites on the DNA fragment shown in SEQ ID No.4 (Table 4) in the ABCG1 gene.

Or, the "all or part of CpG sites" may be all CpG sites (table 2) or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites in the DNA fragment shown in SEQ ID No.2 in ABCG1 gene.

Or, the "all or part of CpG sites" may be all or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 10 CpG sites on the DNA fragment shown in SEQ ID No.2 in the ABCG1 gene:

(f1) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCg1_B_4) from 174 th to 175 th positions of the 5' end;

(f2) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ABCg1_B_5) from 202-203 of the 5' end;

(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCG 1_B_6) from 222 th to 223 rd positions of the 5' end;

(f4) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCg1_B_7) from 341 to 342 positions of the 5' end;

(f5) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCg1_B_8) from 371-372 th site of the 5' end;

(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCG 1_B_9) from 382 rd to 383 rd of the 5' end;

(f7) The CpG site (ABCG 1_B_10) shown in 389-390 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2;

(f8) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ABCG 1_B_11) from 443-444 th position of the 5' end;

(f9) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCg1_B_12) from 456 th to 457 th positions of the 5' end;

(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ABCg1_B_13) from 474 to 475 on the 5' end.

In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when performing methylation level analysis, and when constructing and using a relevant mathematical model, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are described in Table 7).

In the above aspects, the substance for detecting the methylation level of the ABCG1 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ABCG1 gene. The reagent for detecting the methylation level of the ABCG1 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ABCG1 gene; the instrument for detecting the methylation level of the ABCG1 gene may be a time-of-flight mass spectrometry detector. Of course, other conventional reagents for performing time-of-flight mass spectrometry may also be included in the reagents for detecting the methylation level of the ABCG1 gene.

Further, the partial fragment may be at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;

(g5) A DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;

(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g7) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g8) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g9) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;

(g10) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same.

Still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C and/or primer pair D and/or primer pair E.

The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer A2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 7;

the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer B2 is SEQ ID No.9 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 9;

the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is SEQ ID No.10 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 10; the primer C2 is SEQ ID No.11 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 11;

The primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is SEQ ID No.12 or single-stranded DNA shown in 11 th-31 th nucleotides of SEQ ID No. 12; the primer D2 is SEQ ID No.13 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 13;

the primer pair E is a primer pair consisting of a primer E1 and a primer E2; the primer E1 is SEQ ID No.14 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 14; the primer E2 is SEQ ID No.15 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 15.

In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:

(A) The mathematical model may be built as a method comprising the steps of:

(A2) And (3) taking ABCG1 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a threshold value of classification judgment.

Wherein, n1 and n2 in (A1) can be positive integers more than 50.

(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:

(B1) Detecting the ABCG1 gene methylation level of the sample to be detected;

The type a sample and the type B sample are any one of:

In practical applications, any of the above mathematical models may be changed according to the detection method and the fitting method of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.

In the embodiment of the invention, the model is specifically log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into the model as a dependent variable, b0 is a constant, x1-xn is an independent variable which is a methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given by the model to the methylation value of each site.

In the embodiment of the invention, the model can be established by adding known parameters such as age, sex, white blood cell count and the like as appropriate to improve the discrimination efficiency. Two specific models established in the embodiments of the present invention are for assisting in distinguishing patients with cardiovascular and cerebrovascular diseases (coronary heart disease and cerebral apoplexy) from healthy controls. The model one is specifically as follows: log (y/(1-y))=6.287-4.328 abcg1_b_4+0.292 abcg1_b_5+0.320 abcg1_b_6-4.328 abcg1_b_7-7.724 abcg1_b_8+7.726 abcg1_b_9-5.733 abcg1_b_10-3.382 abcg1_b_11-3.094 abcg1_b_12+9.466 abcg1_b_13-0.022 age (integer) +0.063 sex (male assignment 1, female assignment 1)0) +0.381 white blood cell count (unit 10) ⁹ /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold for model one is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were coronary heart disease patients, and patient candidates with less than 0.5 were healthy controls. The second model is specifically as follows: log (y/(1-y))= 12.096-1.733 abcg1_b_4-0.811 abcg1_b_5-2.934 abcg1_b_6-1.733 abcg1_b_7+3.024 abcg1_b_8-3.053 abcg1_b_9-2.136 abcg1_b_10+0.273 abcg1_b_11+3.489 abcg1_b_12-8.621 abcg1_b_13+0.031 for age (integer number) to 0.059 (male number 0) +0.381 for white blood cell number (unit 10) ⁹ /L). Wherein y is a detection index obtained by substituting a dependent variable, namely, methylation values of 10 distinguishable methylation sites of a sample to be detected, and age, sex and white blood cell count into a model and then converting the methylation values. The threshold of the second model is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were stroke patients, and patient candidates with less than 0.5 were healthy controls. In the first model and the second model, the ABCG1_B_4 is the methylation level of CpG sites shown in the 174 th-175 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCG1_B_5 is the methylation level of CpG sites shown in the 202 st-203 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCG1_B_6 is the methylation level of CpG sites shown in 222-223 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCG1_B_7 is the methylation level of CpG sites shown in the 341 th-342 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCG1_B_8 is the methylation level of CpG sites shown at 371-372 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCG1_B_9 is the methylation level of CpG sites shown in 382-383 bits of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ABCg1_B_10 is the methylation level of CpG sites shown in 389-390 th position of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ABCG1_B_11 is the methylation level of CpG sites shown in 443-444 th position of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ABCG1_B_12 is the methylation of CpG sites shown in the 456 th to 457 th positions of the 5' end of the DNA fragment shown in SEQ ID No.2 Level; the ABCG1_B_13 is the methylation level of CpG sites shown in 474-475 bits of the 5' end of the DNA fragment shown in SEQ ID No. 2.

In the above aspects, the detecting the methylation level of the ABCG1 gene is detecting the methylation level of the ABCG1 gene in blood.

Specifically, any of the ABCG1 genes described above may include Genbank accession No.: NM-016818.3 (GI: 1813754697), transcript variant 2; NM-004915.4 (GI: 1890266885), transcript variant 4; NM-207174.1 (GI: 46592955), transcript variant 3; NM-207627.2 (GI: 1890277233), transcript variant 5; NM-207628.1 (GI: 46592970), transcript variant 6; NM-207629.2 (GI: 1890333661), transcript variant 7.

The invention provides hypomethylation of ABCG1 gene in coronary heart disease blood and hypermethylation phenomenon in cerebral apoplexy blood. Experiments prove that the blood can be used as a sample to early warn and distinguish cardiovascular and cerebrovascular diseases (coronary heart disease and cerebral apoplexy) from healthy controls before clinical onset, to early warn and distinguish coronary heart disease patients with different clinical characteristics from healthy controls before clinical onset, and to early warn and distinguish cerebral apoplexy patients with different clinical characteristics from healthy controls before clinical onset. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of cardiovascular and cerebrovascular diseases and reducing the death rate.

Drawings

FIG. 1 is a schematic diagram of a mathematical model.

Fig. 2 is an illustration of a mathematical model of coronary heart disease.

Fig. 3 is an illustration of a mathematical model of cerebral apoplexy.

Detailed Description

The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.

The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.

The adenosine triphosphate binding cassette transporter G1 (adenosine triphophate (ATP) -binding cassette (ABC) transporter G1, ABCG 1) gene quantification assays in the following examples were all performed in triplicate, and the results averaged.

Example 1 primer design for detecting methylation site of ABCG1 Gene

Five fragments (abcg1_a fragment, abcg1_b fragment, abcg1_c fragment, abcg1_d fragment and abcg1_e fragment) of ABCG1 gene were selected for methylation level and cardiovascular and cerebrovascular disease correlation analysis through a number of sequence and functional analyses.

The ABCg1_A fragment (SEQ ID No. 1) is located on the sense strand of the hg19 reference genome chr21: 43619137-43619681.

The ABCg1_B fragment (SEQ ID No. 2) is located on the antisense strand of the hg19 reference genome chr21: 43642037-43642809.

The ABCg1_C fragment (SEQ ID No. 3) is located on the sense strand of the hg19 reference genome chr21: 43652714-43653389.

The ABCg1_D fragment (SEQ ID No. 4) is located on the sense strand of the hg19 reference genome chr21: 43655128-43655708.

The ABCg1_E fragment (SEQ ID No. 5) is located on the hg19 reference genome chr21:43657863-43658473, antisense strand.

CpG site information in the ABCg1_A fragment is shown in Table 1.

CpG site information in the ABCg1_B fragment is shown in Table 2.

CpG site information in the ABCg1_C fragment is shown in Table 3.

CpG site information in the ABCg1_D fragment is shown in Table 4.

CpG site information in the ABCg1_E fragment is shown in Table 5.

Table 1 CpG site information in ABCG1_A fragment

CpG sites	Position of CpG sites in the sequence
		ABCG1_A_1	SEQ ID No.1 from positions 174 to 175 of the 5' end
ABCG1_A_2	SEQ ID No.1 from positions 314-315 of the 5' end
		ABCG1_A_3	SEQ ID No.1 from the 5' end at positions 374-375
ABCG1_A_4	Position 423-424 of SEQ ID No.1 from 5' end
		ABCG1_A_5	SEQ ID No.1 from position 515 to 516 of the 5' end

Table 2, cpG site information in ABCG1_B fragment

CpG sites	Position of CpG sites in the sequence
		ABCG1_B_1	SEQ ID No.2 from positions 51-52 of the 5' end
ABCG1_B_2	SEQ ID No.2 from position 63-64 of the 5' end
		ABCG1_B_3	SEQ ID No.2 from positions 112-113 of the 5' end
ABCG1_B_4	SEQ ID No.2 from positions 174 to 175 of the 5' end
		ABCG1_B_5	SEQ ID No.2 from positions 202-203 of the 5' end
ABCG1_B_6	SEQ ID No.2 from positions 222-223 of the 5' end
		ABCG1_B_7	SEQ ID No.2 from positions 341-342 of the 5' end
ABCG1_B_8	SEQ ID No.2 shows the 371-372 th position from the 5' end
		ABCG1_B_9	SEQ ID No.2 from 382-383 th position at 5' end
ABCG1_B_10	SEQ ID No.2 from position 389 to 390 at the 5' end
		ABCG1_B_11	443 st to 444 nd from the 5' end of SEQ ID No.2
ABCG1_B_12	SEQ ID No.2 from 5' end 456-457 bits
		ABCG1_B_13	SEQ ID No.2 from position 474-475 of 5' end
ABCG1_B_14	SEQ ID No.2 from position 601-602 of the 5' end
		ABCG1_B_15	SEQ ID No.2 from positions 606-607 of the 5' end
ABCG1_B_16	SEQ ID No.2 from position 617-618 of the 5' end
		ABCG1_B_17	SEQ ID No.2 from the 5' end at positions 643-644
ABCG1_B_18	SEQ ID No.2 from position 734-735 of the 5' end

Table 3 CpG site information in ABCG1_C fragment

CpG sites	Position of CpG sites in the sequence
		ABCG1_C_1	SEQ ID No.3 from position 42-43 of the 5' end
ABCG1_C_2	117 th to 118 th positions of SEQ ID No.3 from 5' end
		ABCG1_C_3	SEQ ID No.3 from positions 207-208 of the 5' end
ABCG1_C_4	SEQ ID No.3 from position 354-355 of the 5' end
		ABCG1_C_5	From position 458-459 of the 5' end of SEQ ID No.3
ABCG1_C_6	487-488 of SEQ ID No.3 from the 5' end
		ABCG1_C_7	521-522 th bit of SEQ ID No.3 from 5' end
ABCG1_C_8	SEQ ID No.3 from position 605-606 of the 5' end

Table 4, cpG site information in ABCG1_D fragment

TABLE 5 CpG site information in ABCG1_E fragment

CpG sites	Position of CpG sites in the sequence
		ABCG1_E_1	SEQ ID No.5 from position 42-43 of the 5' end
ABCG1_E_2	SEQ ID No.5 from position 109-110 of the 5' end
		ABCG1_E_3	SEQ ID No.5 from positions 157-158 of the 5' end
ABCG1_E_4	SEQ ID No.5 from the 5' end at positions 268-269
		ABCG1_E_5	SEQ ID No.5 from position 533-534 of the 5' end
ABCG1_E_6	SEQ ID No.5 from position 543 to 544 at the 5' end
		ABCG1_E_7	From the 5' end, SEQ ID No.5 shows positions 575 to 576

Specific PCR primers were designed for five fragments (abcg1_a fragment, abcg1_b fragment, abcg1_c fragment, abcg1_d fragment, and abcg1_e fragment) as shown in table 6. Wherein SEQ ID No.6, SEQ ID No.8, SEQ ID No.10, SEQ ID No.12 and SEQ ID No.14 are forward primers, SEQ ID No.7, SEQ ID No.9, SEQ ID No.11, SEQ ID No.13 and SEQ ID No.15 are reverse primers; positions 1 to 10 of SEQ ID No.6, SEQ ID No.8, SEQ ID No.10 and SEQ ID No.14 from 5' are nonspecific tags, and positions 11 to 35 are specific primer sequences; the 1 st to 10 th positions of the 5' in SEQ ID No.12 are nonspecific labels, and the 11 th to 31 th positions are specific primer sequences; SEQ ID No.7, SEQ ID No.9, SEQ ID No.11, SEQ ID No.13 and SEQ ID No.15 and SEQ ID No.12, the non-specific tags are present at positions 1 to 31 from 5' and the specific primer sequences are present at positions 32 to 56. The primer sequences do not contain SNPs and CpG sites.

TABLE 6 ABCG1 methylation primer sequences

EXAMPLE 2 ABCG1 Gene methylation detection and analysis of results

1. Study sample

The research sample adopts an epidemiological whole group sampling method, and the follow-up investigation is carried out on community groups over 18 years old in a certain city for more than 2 years. The study was reviewed by the ethics committee and all panelists signed informed consent. Cardiovascular and cerebrovascular diseases and cancer morbidity information are recorded annually through local hospitals, disease control center chronic disease management systems, community health service centers and workstation chronic disease routine registration projects and social security center report data. The starting time of the queue is the baseline investigation date, the ending variable is the cardiovascular and cerebrovascular diseases, and the follow-up time of the study subjects without visit is uniformly calculated according to half of the follow-up ending time. The invention selects new cardiovascular and cerebrovascular disease patients in 2 years after the patients are queued as case groups, wherein 342 cases are coronary heart disease patients and 278 cases are cerebral apoplexy patients, and the total cardiovascular and cerebrovascular disease onset is 620 persons after 2018 and 7 months after the follow-up date. After age and sex are matched, the population which does not have cardiovascular and cerebrovascular diseases and cancers in the follow-up period (the follow-up time is more than 2 years) and has blood routine indexes within the reference range is selected as health control, and the total number of the populations is 612.

All patient ex vivo blood samples were collected prior to onset. The disease condition is confirmed by imaging and pathology in the subsequent disease.

342 patients suffering from coronary heart disease within 2 years after the group are classified according to clinical typing: 45 cases of latent or asymptomatic myocardial ischemia, 64 cases of angina pectoris, 83 cases of myocardial infarction, 74 cases of ischemic cardiomyopathy and 76 cases of sudden death. Wherein 137 cases of coronary heart disease occur within 1 year after the administration, including 20 cases of latent or asymptomatic myocardial ischemia, 21 cases of angina pectoris, 33 cases of myocardial infarction, 30 cases of ischemic cardiomyopathy and 33 cases of sudden death.

278 patients suffering from cerebral apoplexy within 2 years after group entry are classified according to clinical typing: 112 cases of cerebral arterial thrombosis and 166 cases of cerebral arterial thrombosis. Of these, 110 cases developed cerebral apoplexy within 1 year after the group, including 49 cases of hemorrhagic cerebral apoplexy and 61 cases of ischemic cerebral apoplexy.

The median of the ages of healthy controls, coronary heart disease and stroke patients were 65, 64 and 65 years, respectively, and the ratio of men and women in each of these 3 populations was about 1:1. The median numbers of the respective ages of coronary heart disease and cerebral apoplexy patients within 1 year after the group is added are 65 and 64 years, and the ratio of men and women in the group is about 1:1.

2. Methylation detection

1. Total DNA of the blood sample is extracted.

2. The total DNA of the blood samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.

3. And (3) carrying out PCR amplification by using the DNA treated by the bisulfite in the step (2) as a template and adopting 5 pairs of specific primer pairs in the table (6) through DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein all primers adopt a conventional standard PCR reaction system and are amplified according to the following procedure.

The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.

4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:

(1) Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [ 0.5U) was added to 5. Mu.l of PCR product]+1.7ml H ₂ O) then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;

(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;

(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;

(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;

(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.

Reagents used for the time-of-flight mass spectrometry detection are all kits (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection isAnalyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.

5. And (5) analyzing the data obtained in the step (4).

Statistical analysis of the data was performed by SPSS Statistics 23.0.

Non-parametric tests were used for comparative analysis between the two groups.

The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.

All statistical tests were double-sided, with p-values <0.05 considered statistically significant.

Through mass spectrometry experiments, 58 distinguishable peak patterns were obtained in total. The methylation level at each CpG site of each sample can be automatically obtained by calculating the peak area according to the "methylation level=peak area of methylated fragments/(peak area of unmethylated fragments+peak area of methylated fragments)" formula using SpectroAcquin v3.3.1.3 software.

3. Analysis of results

1. Healthy control, difference in ABCG1 Gene methylation level in blood of patients suffering from coronary heart disease and cerebral apoplexy (earlier than clinical onset time within 2 years)

The methylation level of all CpG sites in the ABCG1 gene was analyzed by taking blood of 342 coronary heart disease patients, 278 cerebral apoplexy patients and 612 healthy controls as a study material (Table 7), wherein the coronary heart disease and cerebral apoplexy patients are asymptomatic when entering the group, and the patients develop within 2 years after entering the group. The results show that the methylation level median of the healthy control ABCG1 gene is 0.70 (IQR=0.59-0.86), the methylation level median of the cerebral apoplexy ABCG1 gene is 0.74 (IQR=0.62-0.87), and the methylation level median of the coronary heart disease patients is 0.68 (IQR=0.53-0.81). As a result of comparative analysis of methylation levels of the ABCG1 gene among the three, it was found that methylation levels of all CpG sites in the ABCG1 gene of a cerebral apoplexy patient were significantly higher than those of a healthy control (p <0.05, table 7), and methylation levels of all CpG sites in the ABCG1 gene of a coronary heart disease patient were significantly lower than those of the healthy control (p <0.05, table 7). Furthermore, the methylation level of all CpG sites in ABCG1 gene was significantly lower in patients with coronary heart disease than in patients with cerebral stroke (p <0.05, table 7). Therefore, the methylation level of the ABCG1 gene can be used for screening potential patients suffering from cerebral apoplexy and coronary heart disease in 2 years, and is a molecular marker with great clinical value.

2. Healthy control, difference in ABCG1 Gene methylation level in blood of patients suffering from coronary heart disease and cerebral apoplexy (earlier than clinical onset time within 1 year)

Blood of 137 patients with coronary heart disease, 110 patients with cerebral apoplexy and 612 healthy controls is used as a research material to analyze methylation level differences of all CpG sites in the ABCG1 gene among the three (table 8), wherein patients with coronary heart disease and cerebral apoplexy have no symptoms when entering a group, and onset is within 1 year after entering the group. The results show that the methylation level median of the healthy control ABCG1 gene is 0.70 (IQR=0.59-0.86), the methylation level median of the cerebral apoplexy ABCG1 gene is 0.73 (IQR=0.60-0.87), and the methylation level median of the coronary heart disease patients is 0.67 (IQR=0.53-0.81). By comparing and analyzing the methylation levels of the ABCG1 genes of the three, the methylation levels of all CpG sites in the ABCG1 genes of cerebral apoplexy patients are found to be significantly higher than those of healthy controls (p <0.05, table 8), and the methylation levels of all CpG sites in the ABCG1 genes of coronary heart disease patients are found to be significantly lower than those of the healthy controls (p <0.05, table 8). Furthermore, the methylation level of all CpG sites in ABCG1 gene was significantly lower in patients with coronary heart disease than in patients with cerebral stroke (p <0.05, table 8). Therefore, the methylation level of the ABCG1 gene can be used for screening potential patients suffering from cerebral apoplexy and coronary heart disease within 1 year in the population, and is a molecular marker with great clinical value.

3. Methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (within 2 years earlier than clinical onset time)

We compared and analyzed the methylation level difference of ABCG1 genes of 342 patients with coronary heart disease, 278 patients with cerebral apoplexy and 612 healthy controls with different clinical characteristics, wherein the patients with coronary heart disease and cerebral apoplexy have no symptoms when being put into groups, and the patients with coronary heart disease and cerebral apoplexy are ill within 2 years after being put into groups. As a result, it was found that methylation levels of all CpG sites of the ABCG1 gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics were significantly different from those of healthy controls (p <0.05, table 9). Furthermore, we found that methylation levels of all CpG sites in ABCG1 gene were significantly different from healthy controls in stroke patients with different clinical characteristics (p <0.05, table 9).

4. Methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (within 1 year earlier than clinical onset time)

We compared and analyzed the methylation level difference of ABCG1 genes of 137 patients with coronary heart disease, 110 patients with cerebral apoplexy and 612 healthy controls with different clinical characteristics, wherein the patients with coronary heart disease and cerebral apoplexy have no symptoms when being in a group, and the patients with coronary heart disease and cerebral apoplexy are ill within 1 year after being in the group. As a result, it was found that methylation levels of all CpG sites of the ABCG1 gene in patients with coronary heart disease (occult or asymptomatic myocardial ischemia, angina pectoris, myocardial infarction, ischemic cardiomyopathy, sudden death) with different clinical characteristics were significantly different from those of healthy controls (p <0.05, table 10). Furthermore, we found that methylation levels of all CpG sites in ABCG1 gene were significantly different from healthy controls in stroke patients with different clinical characteristics (p <0.05, table 10). Thus, the methylation level of the ABCG1 gene can be used to predict the likelihood of developing coronary heart disease and stroke with different clinical characteristics over a 1 year period.

5. Establishment of mathematical model for assisting cardiovascular and cerebrovascular disease diagnosis

The mathematical model established by the invention can be used for achieving the following purposes:

(1) Before clinical onset, early warning is carried out on individuals with coronary heart disease onset risks within 2 years in the crowd;

(2) Before clinical onset, individuals with coronary heart disease onset risk within 2 years in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;

(3) Before clinical onset, individuals with cerebral apoplexy incidence risks within 2 years of the population are pre-warned.

(4) Before clinical onset, individuals with cerebral apoplexy incidence risks within 2 years of the population are pre-warned, and the method is suitable for cerebral apoplexy of various types;

(5) Before clinical onset, early warning is carried out on individuals with cerebral apoplexy and coronary heart disease onset risks within 2 years of the population, and coronary heart disease patients and cerebral apoplexy patients are distinguished;

(6) Before clinical onset, early warning is carried out on individuals with coronary heart disease onset risks within 1 year in the crowd;

(7) Before clinical onset, individuals with coronary heart disease onset risks within 1 year in the crowd are pre-warned, and the method is suitable for various types of coronary heart diseases;

(8) Before clinical onset, early warning is carried out on individuals with cerebral apoplexy onset risks within 1 year in the crowd;

(9) Before clinical onset, individuals with cerebral apoplexy incidence risks within 1 year in the crowd are pre-warned, and the method is suitable for cerebral apoplexy of various types;

(10) Before clinical onset, individuals with risks of cerebral apoplexy and coronary heart disease onset within 1 year in the crowd are pre-warned, and coronary heart disease patients and cerebral apoplexy patients are distinguished.

The mathematical model is established as follows:

(A) Data sources: the methylation level of the target CpG sites (combination of one or more of tables 1-5) of the isolated blood samples of 342 coronary heart disease patients, 278 cerebral stroke patients and 612 healthy controls listed in step one (detection method is the same as step two).

The data can be added with known parameters such as age, sex, white blood cell count and the like according to actual needs to improve the discrimination efficiency.

(B) Model building

Any two different types of patient data, namely training sets, are selected according to requirements, (for example, coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina pectoris patients and healthy controls, myocardial infarction patients and healthy controls, ischemic myocardial patients and healthy controls, sudden death patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease by 2 years, or, coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic myocardial patients and healthy controls, sudden death patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease by 1 year), are used as statistical models, and statistical models of the statistical data are established by using the statistical methods, and the statistical models are established. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or is directly set to be 0.5 as the threshold value, the detection index obtained by the sample to be tested after the sample is tested and substituted into the model calculation is more than the threshold value and is classified into one type (B type), less than the threshold value and is classified into the other type (A type), and the detection index is equal to the threshold value and is used as an uncertain gray area. When a new sample to be detected is predicted to judge which type belongs to, firstly, detecting methylation levels of one or more CpG sites on the ABCG1 gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model (if known parameters such as age, sex, white cell count and the like are included in the model construction, the step simultaneously substitutes specific numerical values of corresponding parameters of the sample to be detected into a model formula), calculating to obtain a detection index corresponding to the sample to be detected, and then comparing the detection index corresponding to the sample to be detected with a threshold value, and determining which type of sample the sample to be detected belongs to according to a comparison result.

Examples: as shown in fig. 1, the methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites in the ABCG1 gene in the training set is used to construct a mathematical model for distinguishing between class a and class B by using a formula of two classification logistic regression through statistical software such as SAS, R, SPSS. The mathematical model is herein a two-class logistic regression model, specifically: log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained by substituting a dependent variable, i.e., a methylation value of one or more methylation sites of a sample to be tested, into a model, b0 is a constant, x1-xn is an independent variable, i.e., a methylation value (each value is a value between 0 and 1) of one or more methylation sites of the sample to be tested, and b1-bn are weights given to each methylation value of the sites by the model. In specific application, a mathematical model is established according to methylation degrees (x 1-xn) of one or more DNA methylation sites of a sample detected in a training set and known classification conditions (class A or class B, respectively assigning 0 and 1 to y), so that a constant B0 of the mathematical model and weights B1-bn of each methylation site are determined, and a numerical value corresponding to a maximum approximate dengue index calculated by the mathematical model is used as a threshold value or a threshold value divided by 0.5 is directly set. And the detection index, namely the y value, obtained after the sample to be detected is tested and calculated by substituting the sample into the model is classified as B when the y value is larger than the threshold value, and classified as A when the y value is smaller than the threshold value, and the y value is equal to the threshold value and is used as an uncertain gray area. Where class a and class B are the corresponding two classifications (groupings of classifications, which is class a and which is class B, to be determined according to a specific mathematical model, no convention is made herein), such as: coronary heart disease patients and healthy controls, cerebral stroke patients and healthy controls, coronary heart disease patients and cerebral stroke patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral stroke patients and healthy controls, ischemic cerebral stroke patients and healthy controls, and the collection of the above patient samples is earlier than the clinical onset time of the disease for 2 years; alternatively, coronary heart disease patients and healthy controls, stroke patients and healthy controls, coronary heart disease patients and stroke patients, occult or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic stroke patients and healthy controls, ischemic stroke patients and healthy controls, and the collection of the above patient samples is within 1 year earlier than the clinical onset time of the disease. When predicting a sample of a subject to determine which category the sample belongs to, blood of the subject is collected first, and then DNA is extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of single CpG sites or the methylation level of a plurality of CpG sites of the ABCG1 gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the ABCG1 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is larger than a threshold value, the subject judges the class (B class) of which the detection index is larger than the threshold value in the training set; if the methylation level data of one or more CpG sites of the ABCG1 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than a threshold value, the subject belongs to the class (class A) with the detection index smaller than the threshold value in the training set; if the methylation level data of one or more CpG sites of the ABCG1 gene of the subject is substituted into the mathematical model, and the calculated value, namely the detection index, is equal to the threshold value, the subject cannot be judged to be A class or B class.

Examples: the schematic diagram of fig. 2 illustrates methylation of the preferred CpG sites of abcg1_b (abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12 and abcg1_b_13) and mathematical modeling for discrimination of coronary heart disease: the data of methylation level of the 10 distinguishable CpG site combinations of coronary heart disease patients (earlier than clinical onset time less than or equal to 2 years) and health control training sets (342 coronary heart disease patients and 612 health controls here) were tested for ABCg1_B_4, ABCg1_B_5, ABCg1_B_6, ABCg1_B_7, ABCg1_B_8, ABCg1_B_9, ABCg1_B_10, ABCg1_B_11, ABCg1_B_12 and ABCg1_B_13, and the age, sex (male assignment 1, female assignment 0) of the patients, white blood cell count were used to establish a mathematical model for distinguishing coronary heart disease patients and health controls by R software using a formula of a binary logistic regression.

The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: log (y/(1-y))=6.287-4.328 abcg1_b_4+0.292 abcg1_b_5+0.320 abcg1_b_6-4.328 abcg1_b_7-7.724 abcg1_b_8+7.726 abcg1_b_9-5.733 abcg1_b_10-3.382 abcg1_b_11-3.094 abcg1_b_12+9.466 abcg1_b_13-0.022 for age (integer) +0.063 for gender (male for 1, female for 0) +0.381 for white blood cell number (unit 10) ⁹ L), wherein y is the methylation value of the 10 distinguishable methylation sites of the sample to be tested as a function of the variable and the age,Sex, white blood cell count are substituted into the model to obtain the detection index. Under the condition that 0.5 is set as a threshold value, the methylation level of 10 distinguishable CpG sites, namely ABCg1_B_4, ABCg1_B_5, ABCg1_B_6, ABCg1_B_7, ABCg1_B_8, ABCg1_B_9, ABCg1_B_10, ABCg1_B_11, ABCg1_B_12 and ABCg1_B_13, of the sample to be tested is tested, and then is calculated together with information substitution models of age, sex and white blood cell count of the sample to be tested, the obtained detection index, namely y value, is more than 0.5 and is classified as coronary heart disease patients, less than 0.5 and is classified as healthy control, and if the detection index is equal to 0.5, the sample is not determined as coronary heart disease patients or healthy control. The area under the curve (AUC) calculation for this model was 0.76 (table 15).

Blood was collected from two subjects (a, B), DNA was extracted from the blood, and DNA was converted by bisulfite, and methylation levels of 10 distinguishable CpG sites, abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12, and abcg1_b_13, were detected by DNA methylation assay. The methylation level data obtained by detection together with the information of age, sex and white blood cell count of the subject are then substituted into the mathematical model of coronary heart disease. The value calculated by the mathematical model of the coronary heart disease of the first test subject is more than 0.83 and is more than 0.5, and the first test subject is judged to be a potential patient of the coronary heart disease (clinical onset within 2 years in the future); and if the value calculated by the mathematical model of the coronary heart disease of the second subject is less than 0.5, the second subject is judged to be a healthy control (the healthy control standard is still met in the next 2 years). The detection result is consistent with the actual situation.

Examples: the schematic diagram of fig. 3 illustrates methylation of the preferred CpG sites abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12 and abcg1_b_13) of abcg1_c and mathematical modeling for stroke discrimination: the data of methylation levels of the 10 distinguishable preferred CpG site combinations which have been detected in the cerebral apoplexy patients (clinical onset time less than or equal to 2 years) and the healthy control training set (278 cerebral apoplexy patients and 612 healthy controls in this case) are used for establishing a mathematical model for distinguishing cerebral apoplexy patients and healthy controls by using a formula of a two-class logistic regression through R software, wherein the age, the sex (male assignment is 1 and female assignment is 0) and the white blood cell count of the patients. The mathematical model is here a two-class logistic regression model, whereby the constant b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this example in particular: log (y/(1-y))= 12.096-1.733×abcg1_b_4-0.811×abcg1_b_5-2.934×abcg1_b_6-1.733×abcg1_b_7+3.024×abcg1_b_8-3.053×abcg1_b_9-2.136×abcg1_b_10+0.273×abcg1_b_11+3.489×abcg1_b_12-8.621×abcg1_b_13+0.0319×age (integer number) 0.0319×sex (male assignment 1, female assignment 0) +0.381×white blood cell number (unit 10≡9/L), where y is the methylation value of 10 distinguishable methylation sites of the sample to be measured and the age, sex, white blood cell count index obtained after substitution of the variables into the model. Under the condition that 0.5 is set as a threshold value, the methylation level of 10 distinguishable CpG sites, namely ABCg1_B_4, ABCg1_B_5, ABCg1_B_6, ABCg1_B_7, ABCg1_B_8, ABCg1_B_9, ABCg1_B_10, ABCg1_B_11, ABCg1_B_12 and ABCg1_B_13 of the sample to be tested is tested and then calculated together with information substitution models of age, sex and white cell count, and the obtained detection index, namely y value, is more than 0.5 and is classified as a cerebral apoplexy patient, less than 0.5 and is classified as a healthy control, and if the detection index is equal to 0.5, the detection index is not determined as a cerebral apoplexy patient or a healthy control. The area under the curve (AUC) calculation for this model was 0.78 (table 15).

Blood was collected from two subjects (c, d) and DNA was extracted from the blood, and after transformation of the extracted DNA with bisulfite, the methylation level of 10 distinguishable CpG sites, abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12, abcg1_b_13, of the subjects was detected by DNA methylation assay. The methylation level data obtained by detection together with the information of the age, sex and white blood cell count of the subject are then substituted into the cerebral apoplexy model. The value calculated by the brain stroke mathematical model of the first-class subject is more than 0.5, and the first-class subject is judged to be a potential brain stroke patient (clinical onset within 2 years later); and if the value calculated by the cerebral apoplexy mathematical model of the second subject is less than 0.5, the second subject is judged to be a healthy control (the healthy control standard is still met in the next 2 years). The detection result is consistent with the actual situation.

(C) Model Effect evaluation

According to the method, a method for distinguishing coronary heart disease patients from healthy controls, cerebral apoplexy patients from healthy controls, coronary heart disease patients from cerebral apoplexy patients, latent or asymptomatic myocardial ischemia patients from healthy controls, angina patients from healthy controls, myocardial infarction patients from healthy controls, ischemic myocardial patients from healthy controls, sudden death patients from healthy controls, hemorrhagic cerebral apoplexy patients from healthy controls, ischemic cerebral apoplexy patients from healthy controls, and the above patients are all earlier than the clinical onset time of the disease for 2 years; for distinguishing coronary heart disease patients and healthy controls, cerebral stroke patients and healthy controls, coronary heart disease patients and cerebral stroke patients, latent or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral stroke patients and healthy controls, ischemic cerebral stroke patients and healthy controls, and all of which are earlier than a mathematical model within 1 year of the clinical onset time of the disease, and the effectiveness thereof is evaluated by a subject curve (ROC curve). The larger the area under the curve (AUC) from the ROC curve, the better the differentiation of the model, the more efficient the molecular marker. The evaluation results after construction of mathematical models using different CpG sites are shown in tables 11, 12, 13 and 14. In tables 11, 12, 13 and 14, 1 CpG site represents a site of any one CpG site in the amplified fragment of ABCg1_B, 2 CpG sites represent a combination of any 2 CpG sites in ABCg1_B, 3 CpG sites represent a combination of any 3 CpG sites in ABCg1_B, … … and so on. The values in the table are the range of values for the combined evaluation of the different sites (i.e., the results for any combination of CpG sites are within this range).

The above results show that the discrimination ability of the ABCG1 gene for each group (coronary heart disease patient and healthy control, cerebral apoplexy patient and healthy control, coronary heart disease patient and cerebral apoplexy patient, occult or asymptomatic myocardial ischemia patient and healthy control, angina patient and healthy control, myocardial infarction patient and healthy control, ischemic myocardial patient and healthy control, sudden death patient and healthy control, ischemic cerebral apoplexy patient and healthy control, and all of which are earlier than the clinical onset time of the disease by 2 years or less; coronary heart disease patient and healthy control, cerebral apoplexy patient and healthy control, coronary heart disease patient and cerebral apoplexy patient, occult or asymptomatic myocardial ischemia patient and healthy control, angina patient and healthy control, myocardial infarction patient and healthy control, ischemic myocardial patient and healthy control, sudden death patient and healthy control, ischemic cerebral apoplexy patient and healthy control, and all of which are earlier than the clinical onset time of the disease by 1) increases with the increase in the clinical onset time of the disease.

In addition, among the CpG sites shown in tables 1 to 5, there are cases where combinations of a few preferred sites are better in discrimination ability than combinations of a plurality of non-preferred sites. The 10 distinguishable CpG site combinations, such as abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12, abcg1_b_13 shown in tables 15, 16, 17 and 18, are preferred sites for any 10 combinations in abcg1_b.

In summary, the CpG sites on the abcg1 gene and various combinations thereof, the CpG sites on the abcg1_a fragment and various combinations thereof, the CpG sites on the abcg1_b fragment and various combinations thereof, the CpG sites on the abcg1_b_4, abcg1_b_5, abcg1_b_6, abcg1_b_7, abcg1_b_8, abcg1_b_9, abcg1_b_10, abcg1_b_11, abcg1_b_12 and abcg1_b_13CpG sites and various combinations thereof, the CpG sites on the abcg1_d fragment and various combinations thereof, the CpG sites on the abcg1_e fragment and various combinations thereof, and the methylation levels of the CpG sites on abcg1_ A, ABCG1_ B, ABCG1_ C, ABCG1_d and abcg1_e are all effective in controlling patients and healthy patients with coronary heart disease and cerebral infarction, stroke and control and myocardial infarction, and ischemic control and ischemic, and ischemic control and myocardial control and ischemic, and ischemic control and patients, and patients with no ischemic stroke and no symptoms in healthy patients; coronary heart disease patients and healthy controls, cerebral apoplexy patients and healthy controls, coronary heart disease patients and cerebral apoplexy patients, occult or asymptomatic myocardial ischemia patients and healthy controls, angina patients and healthy controls, myocardial infarction patients and healthy controls, ischemic cardiomyopathy patients and healthy controls, sudden death patients and healthy controls, hemorrhagic cerebral apoplexy patients and healthy controls, ischemic cerebral apoplexy patients and healthy controls, and all of which are earlier than the clinical onset time of the disease by less than or equal to 1 year).

Table 7, comparative healthy controls, methylation level differences between patients with coronary heart disease and cerebral apoplexy (earlier than clinical onset time. Ltoreq.2 years)

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Table 8, comparative healthy controls, methylation level differences between patients with coronary heart disease and cerebral apoplexy (earlier than clinical onset time. Ltoreq.1 year)

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Table 9 compares methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time is less than or equal to 2 years)

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Table 10, compares methylation level differences between healthy controls and coronary heart disease and cerebral apoplexy of different clinical characteristics (earlier than clinical onset time less than or equal to 1 year)

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CpG sites of Table 11 and ABCG1_B and combinations thereof are used for distinguishing healthy control and cerebral apoplexy, healthy control and coronary heart disease, cerebral apoplexy and coronary heart disease (the clinical onset time is less than or equal to 2 years)

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CpG sites of Table 12 and ABCG1_B and combinations thereof are used for distinguishing healthy control and cerebral apoplexy, healthy control and coronary heart disease, cerebral apoplexy and coronary heart disease (the clinical onset time is less than or equal to 1 year)

CpG sites of Table 13 and ABCG1_B and combinations thereof are used for distinguishing healthy control and coronary heart disease patients with cerebral apoplexy with different clinical characteristics (earlier than clinical onset time is less than or equal to 2 years)

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Note that: the data in the table are area under the curve (AUC).

CpG sites of Table 14 and ABCG1_B and combinations thereof are used for distinguishing healthy control and coronary heart disease patients with cerebral apoplexy with different clinical characteristics (earlier than clinical onset time is less than or equal to 1 year)

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And (3) injection: the data in the table are area under the curve (AUC).

Table 15, optimal CpG sites of ABCG1_B and combinations thereof for distinguishing healthy controls from cerebral apoplexy, healthy controls from coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 2 years)

Note that: the data in the table are area under the curve (AUC).

Table 16, ABCG1_B optimal CpG sites and combinations thereof are used for distinguishing healthy control and cerebral apoplexy, healthy control and coronary heart disease, cerebral apoplexy and coronary heart disease (earlier than clinical onset time less than or equal to 1 year)

Note that: the data in the table are area under the curve (AUC).

Table 17, ABCG 1-B optimal CpG sites and combinations for distinguishing healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 2 years)

Note that: the data in the table are area under the curve (AUC).

Table 18, ABCG 1-B optimal CpG sites and combinations for distinguishing healthy controls and patients with coronary heart disease and cerebral apoplexy with different clinical characteristics (earlier than clinical onset time less than or equal to 1 year)

Note that: the data in the table are area under the curve (AUC).

The present invention is described in detail above. It will be apparent to those skilled in the art that the present invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with respect to specific embodiments, it will be appreciated that the invention may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The application of some of the basic features may be done in accordance with the scope of the claims that follow.

<110> Nanjing Techno Biotechnology Co., ltd

<120> methylation marker for aiding diagnosis of cardiovascular and cerebrovascular diseases

<130> GNCLN221363

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<170> PatentIn version 3.5

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Claims

1. Application of methylation ABCG1 gene as a marker in preparation of products; the application of the product is at least one of the following:

2. Use of a substance for detecting the methylation level of the ABCG1 gene in the preparation of a product; the application of the product is at least one of the following:

3. Use of a substance for detecting the methylation level of the ABCG1 gene and a medium storing a mathematical model building method and/or a use method for the preparation of a product; the application of the product is at least one of the following:

(6) Auxiliary diagnosis of cerebral apoplexy with different clinical characteristics or early warning of cerebral apoplexy with different clinical characteristics before clinical onset;

(A1) Detecting the methylation level of the ABCG1 gene of n 1A type samples and n 2B type samples respectively;

(A2) Taking ABCG1 gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type;

the using method of the mathematical model comprises the following steps:

(B2) Substituting the ABCG1 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;

the type a sample and the type B sample are any one of the following:

4. Use of a medium storing a mathematical model building method and/or a use method for the preparation of a product; the application of the product is at least one of the following:

the using method of the mathematical model comprises the following steps:

the type a sample and the type B sample are any one of the following:

5. A kit comprising a substance for detecting the methylation level of the ABCG1 gene; the application of the kit is at least one of the following:

6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model establishing method and/or a using method as set forth in claim 3 or 4.

7. A system, comprising:

(D2) A device comprising a unit X and a unit Y;

the data acquisition module is configured to acquire ABCG1 gene methylation level data of n 1A type samples and n 2B type samples obtained by (D1) detection;

the data analysis processing module is configured to receive ABCG1 gene methylation level data of n 1A type samples and n 2B type samples sent by the data acquisition module, establish a mathematical model through a two-classification logistic regression method according to classification modes of the A type and the B type, and determine a threshold value of classification judgment;

the model output module is configured to receive the mathematical model sent by the data analysis processing module and output the mathematical model;

The unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;

the data input module is configured to input ABCG1 gene methylation level data of the tested person detected by the (D1);

the data operation module is configured to receive the ABCG1 gene methylation level data of the person to be tested, which is sent by the data input module, and substitutes the ABCG1 gene methylation level data of the person to be tested into the mathematical model, so as to calculate a detection index;

the data comparison module is configured to receive the detection index sent from the data operation module and compare the detection index with the threshold value determined in the data analysis processing module in the unit X;

the conclusion output module is configured to receive the comparison result sent by the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result;

the type a sample and the type B sample are any one of the following:

8. The use or kit or system according to any one of claims 1-7, wherein: the time of clinical onset is 2 years earlier than the time of clinical onset or 1 year earlier than the time of clinical onset.

9. The use or kit or system according to any one of claims 1-8, wherein: the methylation level of the ABCG1 gene is the methylation level of all or part of CpG sites in fragments shown in the following (e 1) - (e 5) in the ABCG1 gene;

The methylated ABCG1 gene is formed by methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 5) in the ABCG1 gene;

(e5) A DNA fragment shown in SEQ ID No.5 or a DNA fragment having 80% or more identity thereto;

further, the 'all or part of CpG sites' are any one or more CpG sites in 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ABCG1 gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the ABCG1 gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.4 in the ABCG1 gene;

or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.3 and all CpG sites on the DNA fragment shown in SEQ ID No.4 in the ABCG1 gene;

Or, the 'all or part of CpG sites' are all CpG sites on the DNA fragment shown in SEQ ID No.2, all CpG sites on the DNA fragment shown in SEQ ID No.3 and all CpG sites on the DNA fragment shown in SEQ ID No.4 in the ABCG1 gene;

or, the whole or partial CpG sites are all CpG sites in the DNA fragment shown in SEQ ID No.2 in the ABCG1 gene or all 17 or 16 or 15 or 14 or 13 or 12 or 11 or 10 or 9 or 8 or 7 or 6 or 5 or 4 or 3 or 2 or 1 CpG sites;

or, the whole or part of CpG sites are all or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 10 CpG sites on the DNA fragment shown in SEQ ID No.2 in the ABCG1 gene:

(f1) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 174 to 175 positions of the 5' end;

(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 202-203 of the 5' end;

(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 222 th to 223 rd positions of the 5' end;

(f4) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 341 to 342 positions of the 5' end;

(f5) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 371 th to 372 nd of the 5' end;

(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 382 to 383 of the 5' end;

(f7) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 389 to 390 positions of the 5' end;

(f8) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 443 to 444 of the 5' end;

(f9) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 456 th to 457 th positions of the 5' end;

(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 474 to 475 on the 5' end.

10. The use or kit or system according to any one of claims 1-9, wherein: the substance for detecting the methylation level of the ABCG1 gene comprises a primer combination for amplifying the full length or partial fragment of the ABCG1 gene;

the reagent for detecting the methylation level of the ABCG1 gene comprises a primer combination for amplifying the full length or partial fragment of the ABCG1 gene;

further, the partial fragment is at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;

(g5) A DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;

(g10) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;

still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C and/or primer pair D and/or primer pair E;