JP5686333B2 - Genetic risk detection method for myocardial infarction - Google Patents

Description

  The present invention relates to a method for detecting a genetic risk of myocardial infarction and the like.

  The success of the Human Genome Project is bringing significant benefits to clinical medicine, including the identification of genes that affect common diseases. One of the methods for identifying such a gene is a method of searching for an appropriately selected gene polymorphism while paying attention to a specific disease. This is because such a gene is known to encode a protein involved in the onset and progression of the disease, or is present in a chromosomal region linked to the disease.

Coronary heart disease (CHD) is the leading cause of death in the United States. The total number of patients with ischemic heart disease and myocardial infarction (MI) was 13 million and 7.2 million, respectively, in 2002. Under these circumstances, despite recent medical advances, approximately 500,000 and approximately 170,000 patients die each year due to ischemic heart disease and myocardial infarction, respectively. In Japan, according to data from the Ministry of Health, Labor and Welfare, the number of patients with ischemic heart disease is about 900,000, and about 45,000 patients die from myocardial infarction every year. Disease prevention is an important strategy to reduce the personal and social burden of ischemic heart disease and myocardial infarction. Identifying risk factors for this disease is important for risk prediction and future prevention.
Risk factors that cause myocardial infarction are hypertension, hypercholesterolemia, diabetes, and smoking. In addition to these risk factors, genome-wide linkage analysis (Non-Patent Document 1 to Non-Patent Document 4) related to family members or relatives and various related analyzes (Non-Patent Document 5 to Non-Patent Documents) related to individuals who are not related. 13), genetic polymorphisms involved in ischemic heart disease or myocardial infarction are being identified. However, these genetic factors have not been fully elucidated.

  This invention is made | formed in view of said situation, The objective is to provide the gene detection method etc. for obtaining one material for judging a genetic risk about myocardial infarction.

  The present inventor conducted a large-scale association analysis on 164 polymorphisms among 137 candidate genes for 3483 Japanese. The purpose of this study is to identify genetic polymorphisms involved in myocardial infarction, and to give useful information for preventing myocardial infarction to a certain person based on this finding.

As a result of the above analysis, the risk detection method for myocardial infarction according to the first invention is FABP2 2445G → A, AKAP10 2073A → G, IPF1 −108 / 3G → 4G, GP1BA 1018C → T, MTHFR 677C → T, ADIPOQ-11377C → G, F7 11496G → A, TNF-863C → A, LPL 1595C → G, TNFSF4 A → G, AGER 268G → A, PAX4 567C → T, TNF-850C → T, CETP −629C → A polymorphism of at least one or more of A, gender difference, presence or absence of hypercholesterolemia, presence or absence of diabetes, presence or absence of hypertension The risk of onset is calculated by multiplying the odds ratio, and this risk is 3 or more depending on the average and variance or percentage category Create multiple groups, and detecting the risk of developing according to each group.
At this time, as a variance used when forming a group, a statistical variance value, a standard deviation value (SD), a percentage division, or the like can be used. In addition, about gene polymorphism, it is not necessarily restricted to said 14 pieces, It can implement by 1-13 pieces, 15 or more.

  In this specification, the description method of a polymorphism is as follows. As a general rule, for each gene, describe in the order of “Polymorphic position, base registered in database (A: adenine, G: guanine, C: cytosine, T: thymine) → polymorphic base”. To do. For example, “MTHFR 677C → T” means a polymorphism in which the C at position 677 is T for the MTHFR gene. However, the insertion polymorphism is described in the order of “position / base number where the polymorphism occurs and bases registered in the database → number of bases and base”. For example, “−108 / 3G → 4G of IPF1” means an insertion polymorphism in which three consecutive Gs at the −108 position become four consecutive Gs for the IPF1 gene. The polymorphism with no location designation (for example, A → G of TNFSF4) can be easily understood from the dbSNP access numbers described in Tables 1 to 5.

  Some of the polymorphisms have been pointed out to be related to diseases (eg, cerebrovascular disorders) different from myocardial infarction in a population other than Japanese. However, the frequency of each polymorphism and the association with the disease differ depending on each group (country, region, ethnic group, etc.). For this reason, conventional reports do not necessarily support the association between polymorphisms and myocardial infarction in Japanese when the country or disease is different.

  In addition, the genetic risk detection method for myocardial infarction according to the second invention includes FABP2 2445G → A, AKAP10 2073A → G, IPF1 −108 / 3G → 4G, GP1BA 1018C → T, MTHFR 677C → T. , ADIPOQ -11377C → G, F7 11496G → A, TNF -863C → A, LPL 1595C → G, TNFSF4 A → G, AGER 268G → A, PAX4 567C → T, TNF-850C → It is preferable to detect at least one or two or more gene polymorphisms of -629C → A of T and CETP.

Furthermore, the present inventors classified the above data into gender differences, presence / absence of hypertension, presence / absence of hypercholesterolemia, presence / absence of diabetes, presence / absence of smoking, and the results of evaluating the relationship between genetic polymorphism and myocardial infarction for each The present invention has been completed.
The risk detection method for myocardial infarction according to the third invention is classified into male and female. In males, LTA 804C → A, GP1BA 1018C → T, LPL C → G (Ser 447 Stop), AGER G → At least one or more gene polymorphisms of A (Gly82Ser), F7 11496G → A, MTHFR 677C → T, GNB3 825C → T, ADIPOQ-11377C → G, F3 −603A → G And the presence or absence of hypercholesterolemia, the presence or absence of diabetes, and the presence or absence of hypertension in women, TNF -863C → A, IPF1 -108 / 3G → 4G, UCP3 -55C → T, PLAT −7351C → T, FABP2 2445G → A, ITGA2 1648A → G, ENG C → G (Asp366H s), at least one or more polymorphisms of G → A (Asp2213Asn) of ROS1 and the presence or absence of hypercholesterolemia, hypertension, diabetes, smoking , Calculate the risk of onset by multiplying the odds ratio of each evaluation factor, create multiple groups of 3 or more according to the average and variance or percentage classification of this risk, and detect the risk of onset according to each group It is characterized by doing.

  The risk detection method for myocardial infarction according to the fourth invention is classified into the presence or absence of hypertension. In hypertensive patients, MTHFR 677C → T, IPF1 −108 / 3G → 4G, UTS2 G → A (Ser89Asn), GP1BA 1018C → T, LPL C → G (Ser447Stop), MMP-2 -1306C → T, F7 11496G → A, PLAT -7351C → T, ADIPOQ -11377C → G The genetic polymorphisms of GAP, gender differences, the presence or absence of hypercholesterolemia, and the presence or absence of diabetes were evaluated as factors. In normal blood pressure, AKAP10 A → G (Ile646Val), TNFSF4 A → G, SAH A → G ( in intron12), at least one or more of CETP's -629C → A Using the child polymorphism, gender difference, diabetes, and hypercholesterolemia as evaluation factors, the risk of onset is calculated by multiplying the odds ratio of each evaluation factor. A plurality of three or more groups are created, and the risk of onset is detected according to each group.

  The risk detection method for myocardial infarction according to the fifth invention is classified into the presence or absence of hypercholesterolemia. In hypercholesterolemic patients, UCP3 −55C → T, EDNRA −231A → G, ENG C → G (Asp366His) and at least one or more gene polymorphisms, gender difference, presence / absence of diabetes, presence / absence of hypertension as evaluation factors. In normal subjects with serum cholesterol, MTHFR 677C → T, AKAP10 A → G (Ile646Val), IPF1 −108 / 3G → 4G, ADIPOQ −11377C → G, F7 11496G → A, FABP2 2445G → A, F12 46C → T, AACT G → A (Ala15Thr), AGER G → A (Gly82Ser), TNF -863C → A, TNFSF4 A → G, L The risk of onset by multiplying at least one or two or more genetic polymorphisms of C → G (Ser447Stop) of L, gender difference, presence / absence of diabetes, presence / absence of hypertension, and odds ratio of each evaluation factor And a plurality of three or more groups are created according to the mean and variance or percentage classification, and the risk of onset is detected according to each group.

  The risk detection method for myocardial infarction according to the sixth invention is classified into the presence or absence of diabetes. In diabetics, MTHFR 677C → T, FABP2 2445G → A, APOE 4070C → T, IPF1 −108 / 3G → At least one or more gene polymorphisms of 4G, gender differences, and the presence or absence of hypercholesterolemia are evaluated factors. In non-diabetics, F7 11496G → A, IPF1 −108 / 3G → 4G , GP1BA 1018C → T, MTHFR 677C → T, FABP2 2445G → A polymorphism, sex difference, presence or absence of hypercholesterolemia, presence or absence of hypertension And calculate the risk of onset by multiplying the odds ratio of each evaluation factor. Create three or more of the plurality of groups, and detecting the risk of developing according to each group.

  The risk detection method for myocardial infarction according to the seventh invention is classified into the presence or absence of smoking, and in smokers, PROZ 79G → A, LTA 804C → A, UCP1 −112A → C, ACE −240A → T Of non-smokers, AKAP10 A → G (Ile646Val) was evaluated using at least one or more of the polymorphisms and the presence or absence of hypercholesterolemia, the presence or absence of diabetes, the presence or absence of hypertension, and gender differences. ), FABP2 2445G → A, PPARG −681C → G, IPF1 −108 / 3G → 4G, MTHFR 677C → T, LPL C → G (Ser447Stop), F12 46C → T, TNF −863C → A, GP1BA 1018C → T, F7 11496G → A, AGER G → A (Gly82Ser), TNFSF4 A G, PCK1 -232C → G of at least one or more genetic polymorphisms, gender difference, presence or absence of hypercholesterolemia, presence or absence of diabetes, presence or absence of hypertension The risk of onset is calculated by multiplying the odds ratio, and three or more groups are created according to the mean and variance or percentage classification of the risk of onset, and the risk of onset is detected according to each group. To do.

  The genetic risk detection method for myocardial infarction according to the eighth invention includes LTA 804C → A, UCP3 −55C → T, UTS2 G → A in addition to the gene polymorphisms described in the second invention. It is characterized by detecting at least one or two or more gene polymorphisms among (Ser89Asn), 79G → A of PROZ, and −681C → G of PPARG.

  ADVANTAGE OF THE INVENTION According to this invention, the detection method etc. for judging a genetic risk and an onset risk are provided about myocardial infarction. By using this invention, it becomes possible to prevent myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness, and reducing medical costs in the future.

  Next, embodiments of the present invention will be described with reference to the table. However, the technical scope of the present invention is not limited by these embodiments, and various forms can be made without changing the gist of the invention. Can be implemented. Further, the technical scope of the present invention extends to an equivalent range.

<Test method>
Study subjects The study subjects were 3483 Japanese (1913 men, 1570 women) Japanese. They visited the research participating facilities (Gifu Prefectural Gifu Hospital, Gifu Prefectural Tajimi Hospital, Gifu Prefectural Gero Onsen Hospital, Hirosaki University Hospital, Reimeikyo Rehabilitation Hospital, and Yokohama General Hospital) from October 2002 to March 2005. It was a person. All 1192 myocardial infarction patients (926 men, 266 women) underwent coronary angiography and left ventricular angiography. Diagnosis of myocardial infarction was made by typical electrocardiographic changes and increases in serum enzymes (such as creatine kinase, aspartate aminotransferase, and lactate dehydrogenase) and serum troponin T. Diagnosis was made by identifying the presence of abnormal wall motion on the left ventricular angiogram and the stenosis of one of the main coronary arteries or the main trunk of the left coronary artery by coronary angiography.

2291 (987 males, 1304 females) controls were those who visited the study facility for annual health examinations. They may be ischemic heart disease, peripheral arterial obstruction disease, or other atherosclerosis, ischemic or hemorrhagic cerebrovascular disorder or other brain disease, or thrombosis, embolism, other bleeding Did not have a history of disability.
Myocardial infarction patients and controls included those with and without conventional risk factors for ischemic heart disease. Conventional risk factors include obesity (BMI ≧ 25 kg / m ² or more), smoking (10 or more / day), hypertension (systolic blood pressure is 140 mmHg or more, diastolic blood pressure is 90 mmHg or more, or those who satisfy both conditions Or those who are taking antihypertensive drugs), hypercholesterolemia (who has a total blood cholesterol level of 220 mg / dL or higher, or who is taking a lipid-lowering drug), or diabetes (which has a fasting blood glucose level of 126 mg / d dL or more, hemoglobin Alc of 6.5% or more, those who satisfy both conditions, or those who are undergoing treatment for diabetes).
The research protocol was approved by the Mie University School of Medicine, Hirosaki University School of Medicine, Gifu International Institute for Biotechnology, and the ethics committee of the participating research facilities in accordance with the Declaration of Helsinki. Written informed consent was obtained for each participant. Data analysis was performed at the Human Functional Genomics Division, Mie University Life Science Research Support Center.

Selection of polymorphism Through the use of public databases and the inventor's diligent efforts, hypertension, atherosclerosis, arterial spasm, aneurysm, platelet function, leukocytes and monocyte-macrophage biology, coagulation / fibrinolytic cascade, Based on a comprehensive overview of neurological factors, lipid, glucose, and homocysteine metabolism and other metabolic factors, 137 candidate genes suspected of being associated with myocardial infarction were selected. The inventor selected 164 polymorphisms for these 137 genes. Many of these polymorphisms are located in many promoter regions, exons, intron splicing donor or acceptor sites, and are presumed to change the function or expression of the protein encoded by the polymorphism. It is. These 164 polymorphisms are shown in Tables 1 to 5 below. In Tables 1 to 5, the locus (Locus), gene name (Gene), simple notation (Symbol), polymorphism (Polymorphism), and polymorphism database registration number (dbSNP) are shown in order from the left column. . In addition, when there was no polymorphism database registration number, the number registered in NCBI gene bank was shown.

Detection method of gene polymorphism 7 mL of venous blood was collected in a tube containing 50 mmol / L EDTA (disodium salt), and genomic DNA was separated using a kit (Genomics). The 164 polymorphic genotypes were determined by a method using PCR and sequence-specific oligonucleotide probes in combination with Suspension Array Technology (SAT: Luminex 100) (G & G Science Inc.). The primer, probe, and other conditions are shown in Table 6 below. Table 6 shows gene symbol (Gene Symbol), polymorphism (Sensemorph), antisense primer (Antisense primer), probe 1 (Probe 1), probe 2 (Probe 2), annealing from left to right. Temperature (Annealing) and number of cycles (Cycles) are shown. Further, the detailed method was based on the previously reported one (Non-Patent Document 14) and the amplification conditions were appropriately changed. In addition, description was abbreviate | omitted about the conditions for detecting the polymorphism by which the relationship with myocardial infarction was not recognized.

The details of the PCR-SSOP-Luminex method are as described in Non-Patent Document 14. Below, the outline | summary of this method is demonstrated.
FIG. 1 shows the microstructure and characteristics of microbeads detected by Luminex 100 flow cytometry. Microbeads (symbol (A) in the figure) have a diameter of about 5.5 μm and are made of polystyrene. A probe that recognizes a specific base sequence is bound to the bead surface. One type of probe is bound to each bead. By changing the ratio of the red dye and the infrared dye to this microbead, the identification of each bead can be performed in a state where a maximum of 100 kinds are mixed, as indicated by the symbol (B) in the figure. It can be done. Microbeads with multiple types of probes (however, only one type of probe for each microbead) were mixed at an appropriate ratio to prepare a bead mix of 100 beads / μL (reference in the figure). (C)).

In FIG. 2, the outline | summary of the procedure of PCR-SSOP-Luminex method was shown.
<Amplification>
In the PCR reaction for amplifying the target DNA, a primer labeled with biotin at the 5 'end was used. A 1 × PCR solution (50 mM KCl, 10 mM Tris-HCl, pH 8.3) containing 1.5 mM magnesium chloride, 2% DMSO, 0.2 mM dNTPs, and 0.1 μM to 10 μM primer set were mixed, and Taq DNA polymerase (50 U / mL) and 50 ng to 100 ng of genomic DNA were added to make 25 μL. The PCR reaction was treated at 95 ° C. for 10 minutes, followed by denaturation at 94 ° C. for 20 seconds, annealing at 60 ° C. for 30 seconds, and extension at 72 ° C. for 30 seconds, and this was repeated 50 cycles. A GeneAmp9700 thermal cycler (Applied Biosystems) was used as the instrument.

<Hybridization>
The amplified DNA was denatured and then hybridized with the bead mix. In each well of a 96-well plate, 5 μL of amplified PCR solution, 5 μL of bead mix, and 40 μL of hybridization buffer (3.75 M TMAC, 62.5 mM TB (pH 8.0), 0.5 mM) EDTA, 0.125% N-lauroylsarcosine) was added to a total volume of 50 μL. The 96-well plate to which this mixed solution had been added was subjected to denaturation at 95 ° C. for 2 minutes and hybridization at 52 ° C. for 30 minutes (using a GeneAmp 9700 thermal cycler).
FIG. 2 shows a state in which only beads (1) having a probe that recognizes amplified DNA bind to DNA.

<Streptavidin-phycoerythrin reaction (SA-PE Reaction)>
Next, the bead mix-DNA was reacted with SA-PE. After the hybridization reaction, 100 μL of PBS-Tween (1 × PBS (pH 7.5), 0.01% Tween-20) was added to each well, centrifuged at 1000 × g for 5 minutes, and the supernatant was removed. The microbeads were washed. To each microbead remaining in each well, 70 μL of SA-PE solution (commercially available product (G & G Science Co., Ltd.) diluted 100-fold with PBS-Tween) was added and mixed, and then at 52 ° C. for 15 minutes. (A GeneAmp9700 thermal cycler was used.)
FIG. 2 shows that biotinylated DNA is bound only to the probe of the bead (1), and thus SA-PE is bound to the biotin.

<Measurement>
Next, the sample after the reaction used Luminex 100 to identify the bead type and determine whether PE is bound to the bead. The measurement was performed using two types of lasers, the type of beads was identified by a 635 nm laser, and PE fluorescence was quantified using a 532 nm laser. For the DNA bound to the oligo beads, at least 50 beads were measured per measurement, and the quantified median fluorescence intensity (MFI) of PE was used.
In FIG. 2, since each bead (1) to (3) was identified and PE was measured only on the bead (1), the DNA recognized by the probe bound to the bead (1) was amplified. The situation is shown.

Statistical analysis Clinical data were compared between patient and control groups with myocardial infarction by unpaired Student t test. Qualitative data were compared by chi-square test. Allele frequencies were estimated by the gene count method, and the chi-square test was used to determine if the Hardy-Weinberg equilibrium was met. The genotype distribution in the gene polymorphism on each autosome was compared by chi-square test (3 × 2) between the myocardial infarction patient and the control group. For gene polymorphisms on the X chromosome, allele frequencies were compared by chi-square test (2 × 2).

  Polymorphisms associated with myocardial infarction (P <0.06) were analyzed by a multinomial logistic regression analysis method including confounding factors (excluding factors used for stratification). At this time, regarding confounding factors, age (age), gender (gender: female = 0, male = 1), body mass index (BMI), smoking status (smoking: non-smoker = 0, smoker = 1) ・ Metabolic variables (no hypertension, diabetes (diabetes mellitus), or hypercholesterolemia) = 0, their history = 1) and each genotype as an independent variable, and myocardial infarction Dependent variable. Each genotype was evaluated according to dominant, recessive, and two addition (addition 1 and 2) genetic models, and P values, odds ratios, and 95% confidence intervals were calculated.

  Each genetic model consists of two groups. The dominant model is “mutant homozygous and heterozygous binding group” vs. “wild type homozygous”, the recessive model is “mutant homozygous” vs. “wild type homozygous and heterozygous” The “body binding group”, the addition 1 model is “heterozygote” vs. “wild type homozygote”, and the addition 2 model is “mutant homozygote” vs. “wild type homozygote”. To analyze the association between combined genotype and the onset of myocardial infarction, myocardial infarction with age, sex, body mass index, smoking status, hypertension, diabetes, hypercholesterolemia, and combination genotype as independent variables A multinomial logistic regression analysis method was carried out using as the dependent variable. Each genotype used a dominant or recessive model based on statistical significance. Each combined genotype was also compared to a combined genotype that gave the least genetic risk for myocardial infarction.

  To confirm the effect of genotype or other confounding factors on myocardial infarction, we analyzed by stepwise variable increment method. The reference level for inclusion in the model was 0.25, and the reference level for exclusion from the model was 0.1. When obtaining the results of multiple comparisons of myocardial infarction and genotype, the statistical significance (P <0.001 if no stratified analysis is performed, P <0.005 if stratified analysis is performed) Adopted the standard. For other clinical background data, a risk rate of less than 5% (P <0.05) was considered statistically significant. Statistical significance was tested by a two-sided test. Statistical analysis was performed with JMP software version 5.1 (SAS Institute).

I. Analysis results when stratified analysis is not performed <Test results>
The basic data for the 3484 subjects were shown in Table 7 below. In order from the left column, characteristics (Characteristics), myocardial infarction (Myocardial infarction), and controls (Controls) are shown. In addition, in the feature column, the number of subjects (No. of subjects), age (Age), gender (female / male) (Gender (female / male)), body mass index, current or It shows the prevalence of past or current smoker, hypertension, Diabetes mellitus, and hypercholesterolemia.

  The age and body mass index are expressed as mean ± SD. Regarding the smoking rate, smoking was defined as the case where 10 or more cigarettes were smoked per day. Regarding hypertension, the maximum blood pressure was 140 mmHg or higher, the minimum blood pressure was 90 mmHg or higher, or the patient was taking an antihypertensive agent. As for diabetes, the fasting blood glucose level was 126 mg / dL or more, the hemoglobin Alc was 6.5% or more, or a person taking a diabetes drug. For hypercholesterolemia, the total serum cholesterol level was 220 mg / dL or higher, or a person taking a lipid-lowering drug. In the table, (a), (b), and (c) mean P <0.005, P <0.001, and P <0.01 with the control, respectively.

Patients with myocardial infarction are significantly more sensitive than controls in terms of age, percentage of men, and body mass index, including smoking, hypertension, diabetes, and hypercholesterolemia, which have traditionally been considered risk factors for ischemic heart disease. it was high.
Next, in order to extract the factors necessary for risk diagnosis of myocardial infarction, analysis of genetic polymorphism and age, gender, obesity, smoking, hypertension, diabetes, hypercholesterolemia by the stepwise variable increase method (Details will be described later). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction>
Table 8 shows details regarding the risk diagnosis system for myocardial infarction. In the table, the factor (Variable), P value (P value), and odds ratio (95% confidence interval) (OR (95% CI)) are shown in order from the left column. In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

Independent factors (confounding factors) of polymorphism group and age / sex / smoking / obesity / hypertension / diabetes / hyperlipidemia with P <0.05 in the stepwise variable increase method (shown in Table 9 below) A multinomial logistic regression analysis was performed with myocardial infarction as a dependent factor, and P value, odds ratio, and 95% confidence interval were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.
As a result of multinomial logistic regression analysis, gender (male is higher risk), hyperlipidemia, diabetes, and hypertension are related to the onset as conventional risk factors, and genetic factors include FABP2, AKAP10, IPF1, GP1BA, MTHFR, ADIPOQ, F7, TNF (two polymorphisms), LPL, TNFSF4, AGER, PAX4, and CETP gene polymorphisms were associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 97.81, and genetic factors have a minimum odds ratio of 0.10 and a maximum odds ratio of 14.89. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.10, the maximum odds ratio was 1456.45, and a difference of 14565 times was recognized.

Next, the clinical significance of this embodiment will be described. At the hospital, clinic, or medical examination center, the applicant will be examined for the existing risk factors and the genetic factors identified this time to predict the risk of developing myocardial infarction. The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group.
In fact, in this study, the risk value distribution was 30.5% for the disease group and 6.0% for the control group in the high-risk group, and 31.0% for the control group and the control group for the slightly high-risk group. 11.8%, average risk group is disease group 38.3%, control group 73.1%, slightly lower risk group 0.3% disease group 9.0% control group risk In the low group, the disease group was 0% and the control group was 0%. In addition, in the logarithmic distribution of risk values, in the high risk group, the disease group is 0% and the control group is 0%, in the slightly high risk group, the disease group is 41.8% and the control group is 9.3%, In the average risk group, the disease group is 56.3%, the control group is 64.7%, in the slightly low risk group, the disease group is 1.8%, the control group is 20.4%, and in the low risk group, the disease is The group was 0.2% and the control group was 5.7%. As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group. Actually, the distribution of the disease group in this study was 26.6% for the high-risk group (5.0% for the control group), 47.7% for the group with slightly higher risk (20.0% for the control group), Average risk group is 24.1% (control group is 50.2%), moderately low risk group is 1.5% (control group is 20.0%), and low risk group is 0.1% ( The control group was 5.0%).
Although no significant association was found in this study, smoking / obesity is generally considered to be a risk factor for myocardial infarction, and this factor can be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and especially if they belong to the high-risk group or a slightly high-risk group, improvement of lifestyle (smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated (can be calculated from Table 12) Explain to the client. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk judgment system will be described.
By chi-square test, 16 gene polymorphisms showed an association with myocardial infarction (P <0.06). Details are shown in Table 9. In the table, gene notation (Gene symbol), polymorphism (Polymorphism), and risk factor (P) are shown in order from the left column.

  For MTHFR (677C → T (Ala222Val)), wild type homozygote (CC type) in myocardial infarction group was 31.5%, heterozygote (CT type) was 47.8%, mutant homozygote ( TT type) was 20.7%, the control group wild type homozygote was 35.1%, the heterozygote was 49.5%, and the mutant homozygote was 15.4%. For IPF1 (-108 / 3G → 4G), wild-type homozygote (3G / 3G type) in myocardial infarction group was 27.0%, heterozygote (3G / 4G type) was 47.1%, and mutant homozygote ( 4G / 4G type) was 25.9%, wild type homozygotes in the control group were 21.3%, heterozygotes were 51.7%, and mutant homozygotes were 27.0%. For GP1BA (1018C → T (Thr145Met)), wild type homozygote (CC type) in myocardial infarction group was 74.2%, heterozygote (CT type) was 24.6%, mutant homozygote ( TT type) was 1.2%, wild type homozygotes in the control group were 78.4%, heterozygotes were 20.0%, and mutant homozygotes were 1.6%. For FABP2 (2445G → A (Ala54Thr)), the wild-type homozygote (GG type) in the myocardial infarction group was 38.3%, the heterozygote (GA type) was 46.8%, and the mutant homozygote ( AA type) was 14.9%, the control group wild type homozygote was 43.7%, the heterozygote was 43.1%, and the mutant homozygote was 13.2%.

  These polymorphisms were analyzed in more detail for the relationship with myocardial infarction. A multinomial logistic regression analysis that corrected for age, sex, BMI, smoking frequency, and prevalence of hypertension / diabetes / hypercholesterolemia suggested that the following polymorphisms were strongly associated with myocardial infarction. The 677C → T polymorphism of the 5,10-methylenetetrahydrofolate reductase gene (MTHRR) in the recessive model and the addition 2 model is −108 / 3G → 4G polymorphism of the insulin promoting factor 1 gene (IPF1) in the dominant model and the addition 1 model. In type 1 addition, the 1018C → T polymorphism of glycoprotein Ib-platelet-α polypeptide gene (GP1BA) is 2450G → A polymorphism of fatty acid binding protein 2 gene (FABP2) in the dominant model. Significantly (P <0.001) related to morbidity. Details are shown in Table 10 below.

  In the table, the risk rate (P), odds ratio (OR), and 95% confidence interval (95% CI) in gene notation (Gene symbol), polymorphism (Polymorphism), and dominant model (Dominant) are shown in order from the left column. Risk factor (P), odds ratio (OR), 95% confidence interval (95% CI) in the recessive model (Recessive), risk factor (P), odds ratio (OR), 95 in the additive 1 model (Additive 1) The% confidence interval (95% CI) and the risk factor (P), odds ratio (OR), and 95% confidence interval (95% CI) for the additive 2 model (Additive 2) are shown. Multinomial logistic regression analysis was performed by correcting for age, gender, body mass index, and the presence or absence of smoking, hypertension, diabetes, and hypercholesterolemia. In the table, data with a risk factor of less than 0.001 (P <0.001) are shown in bold. The MTHFR 677T allele, the GP1BA 1018T allele, and the FABP2 2445A allele were risk factors for myocardial infarction, whereas the IPF1 -108 / 4G allele was protective against myocardial infarction. It was working.

Next, the effects of genotype, age, sex, BMI, and smoking, hypertension, diabetes, and hypercholesterolemia on myocardial infarction were analyzed by the stepwise variable increase method. The results are shown in Table 11 below. In the table, the factor (Variable), P value (P value), and contribution rate (R ² ) are shown in order from the left column.

In descending order of statistical significance, sex, hypercholesterolemia, diabetes, hypertension, FABP2 genotype (dominant model), IPF1 genotype (dominant model), MTHFR genotype (recessive model), and GP1BA genotype (dominant model) ) Was significant (P <0.001), and each factor was found to affect myocardial infarction independently.
Finally, to assess the genetic risk of myocardial infarction, four polymorphisms (FABP2 2445G → A, IPF1 −108 / 3G → 4G, MTHFR 677C → T, and GP1BA 1018C → T) For the combined genotypes, odds ratios, 95% confidence intervals, and P values were calculated. The results are shown in Table 12. In the table, in order from left to right, FABP2 2445G → A genotype, IPF1 −108 / 3G → 4G genotype, MTHFR 677C → T genotype, GP1BA 1018C → T genotype, each combination The number of patients with myocardial infarction / number of controls, odds ratio (95% confidence interval), and P value in the genotype are shown.

  By analyzing the combined genotypes for the 4 polymorphisms, a maximum odds ratio of 8.02 was found for GA or AA for FABP2, 3G3G for IPF1, TT for MTHFR, CT or TT for GP1BA. It was. On the other hand, a minimum odds ratio of 1.00 was observed for the combined genotypes of GG for FABP2, 3G4G or 4G4G for IPF1, CC or CT for MTHFR, and CC for GP1BA.

<Discussion>
The present inventor examined 164 polymorphisms for 137 candidate genes suspected to be associated with myocardial infarction. A large-scale study of 3483 subjects revealed that the FABP2 2445G → A polymorphism, the IPF1 −108 / 3G → 4G polymorphism, the MTHFR 677C → T polymorphism, and the GP1BA 1018C → T polymorphism Was significantly associated with the prevalence of Japanese myocardial infarction. Analysis of the combined genotype of these 4 polymorphisms revealed a maximum odds ratio of 8.02 as a predisposition to myocardial infarction.

  Fatty acid binding protein 2 (FABP2) is an intracellular protein that is expressed only in columnar absorbing epithelial cells of the small intestine. This protein has a single binding site with high affinity for saturated and unsaturated fatty acids and contributes to the absorption and intracellular transport of long chain fatty acids. In the 2445G → A (Ala54Thr) polymorphism of FABP2, the A allele product showed a stronger affinity for long-chain fatty acids in the in vitro test system than the G allele product (Non-patent Document 15). Furthermore, it has been clarified that individuals having the A allele have stronger insulin resistance and are obese than individuals having the G allele (Non-patent Documents 16 and 17). In addition, the A allele has a higher plasma LDL cholesterol concentration (Non-patent Document 17), and it is recognized that it is associated with metabolic syndrome and hyperlipidemia (Non-patent Document 18).

  In Sweden, the 2445G → A polymorphism has been reported to be associated with a family with cerebrovascular disorders, but not with a family with myocardial infarction (Non-patent Document 19). A Finnish study (Non-Patent Document 20) and a Framingham offspring study (Non-patent Document 17) report that this polymorphism is not associated with ischemic heart disease. However, these studies were small studies with few subjects. Our study found that the FABP2 2445G → A polymorphism is associated with myocardial infarction, and for this condition the A allele is a risk factor. That this polymorphism affects both insulin resistance and lipid metabolism may explain the association with myocardial infarction.

  Insulin-promoting factor 1 (IPF1) is a protein having a homeodomain, which is an important regulator of the insulin gene in pancreatic β cells, and plays an important role in the development of the pancreas (Non-patent Document 21). IPF1-deficient mice selectively lacked the pancreas from birth. In addition, patients with pancreatic non-formation and insulin-deficient diabetes have a single nucleotide deficiency in codon 63 of IPF1, causing a frame shift in the transcription promoting region (Non-patent Document 21). A 3G → 4G polymorphism of IPF1 was found at 108 bp upstream of the translation initiation site in Japanese, but this polymorphism was not associated with the incidence of type 2 diabetes (Non-Patent Document 22, 23). From the results of this study, it was found that the 3G → 4G polymorphism of IPF1 is involved in myocardial infarction, and that the 4G allele is a protective factor for this condition. As a result of this study, it was found for the first time that the −108 / 3G → 4G polymorphism of IPF1 is associated with myocardial infarction. However, the molecular mechanism has not yet been elucidated.

  Homocysteine is a sulfur-containing amino acid that plays an important role in methionine metabolism. 5,10-methylenetetrahydrofolate reductase (MTHRR) is an enzyme that reduces 5,10 methylenetetrahydrofolate to 5methylenetetrahydrofolate, and this reaction forms a substrate for methionine synthase that synthesizes methionine by methylating homocysteine. It is a reaction to provide. Individuals with MTHFR 677C → T (Ala222Val) substitution have reduced enzyme activity and higher homocysteine concentrations than individuals without such substitution (Non-Patent Documents 24-26). There is a report on the relationship between the MTHFR 677C → T polymorphism and ischemic heart disease or myocardial infarction (Non-Patent Documents 27 to 30). On the other hand, in other studies, such a relationship has not been recognized (Non-Patent Documents 26, 27, 31, 32).

  Such contradictory results are due in part to differences in the intake of folic acid and other vitamin B (Non-patent Document 33). According to a meta-analysis of the relationship between MTHFR 677C → T polymorphism and ischemic heart disease in 11,162 patients and 12,758 controls, based on 40 research publications, the TT gene It was clarified that individuals with the type have an odds ratio of 1.16 for ischemic heart disease compared to individuals with the CC genotype (Non-patent Document 34). These reports suggest that inhibition of folate metabolism and an increase in homocysteine concentration is an important factor in ischemic heart disease. In another meta-analysis, the relationship between MTHFR 677C → T polymorphism and ischemic heart disease was investigated in 26,000 patients and 31,183 controls based on 80 publications.

  As a result, it was found that the overall TT genotype showed an odds ratio of 1.14 compared to the CC genotype. However, for Europe, Australia, and North America, the odds ratio was about 1.0, whereas for the Middle East and Asia, the odds ratio was 2.61 and 1.23, respectively (Non-patent Document 35). ). These results indicate that the MTHFR 677C → T polymorphism is associated with ischemic heart disease in the Middle East and Asia, while in Europe, North America, or Australia it is less associated with ischemic heart disease. Yes. This seems to reflect the high intake of folic acid in Europe, North America and Australia (Non-patent Document 35). These reports support the results of our study, that is, the 677C → T polymorphism is significantly associated with myocardial infarction in Japanese, and in this condition, the TT genotype is a risk factor. .

  The glycoprotein Ib-IX-V complex is the major platelet surface receptor for von Willebrand factor. This complex plays an important role in the adhesion of platelets to the subendothelium of damaged blood vessels and is involved in platelet activation caused by shear stress. This suggests the possibility of contributing to the occurrence of thrombosis. The 1018C → T (Thr145Met) polymorphism of GP1BA is associated with ischemic heart disease, acute myocardial infarction or sudden cardiac death, and it has already been reported that the T allele is a risk factor in these conditions (non-patent literature). 36, 37). These previous reports support our results, that is, the 1018C → T polymorphism is associated with myocardial infarction, and that the T allele is a risk factor under these conditions. It has been reported that the -5T → C polymorphism of GP1BA is related to the onset of complications after acute coronary syndrome and percutaneous coronary interventions (Non-Patent Document) 38).

  The results obtained by the present inventor indicate that FABP2, IPF1, MTHFR, and GP1BA affect Japanese myocardial infarction. Determining the combined genotype of these polymorphisms is beneficial for assessing the genetic risk of myocardial infarction and will help to prevent this disease.

II. Results of analysis by stratified analysis Next, results of stratified analysis for each of the five factors of sex, presence or absence of hypertension, presence or absence of hypercholesterolemia, presence or absence of diabetes, and presence or absence of smoking explain.
1. Results when stratified by sex Table 13 summarizes the characteristics of myocardial infarction patients (MI) and controls when divided into men or women.

About age and BMI, it showed by the average value +/- SD. In men, the prevalence of BMI, hypertension, hypercholesterolemia, and diabetes was significantly higher in patients with myocardial infarction than in controls, and smoking frequency was significantly lower. In women, age, smoking frequency, hypertension, hypercholesterolemia, and diabetes prevalence were significantly higher in patients with myocardial infarction than in controls.
Next, in order to extract factors necessary for risk diagnosis of myocardial infarction, stepwise variable increase analysis was performed for genetic polymorphism and age, smoking, BMI, hypertension, diabetes, and hypercholesterolemia. (Details will be described later). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction in men or women>
Table 14 provides details regarding the risk diagnosis system for myocardial infarction in men or women. The table shows, in order from the left column, parameters (parameters), P values (P values), and odds ratios (95% confidence intervals) (OR (95% CI)). In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

The genetic polymorphism group and age, smoking, BMI, hypertension, diabetes, and hyperlipidemia that were P <0.05 by the stepwise variable increase method (shown in Table 18 below) were independent factors (confounding factors), Multinomial logistic regression analysis was performed for each gender with myocardial infarction as a dependent factor, and P value, odds ratio, and 95% confidence interval were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.
As a result of multinomial logistic regression analysis, in men, hyperlipidemia, diabetes, and hypertension are related to the onset of myocardial infarction as conventional risk factors, and genetic factors include LTA, GP1BA, LPL, AGER, F7. , MTHFR, GNB3, ADIPOQ, and F3 gene polymorphisms were associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 13.39, and genetic factors have a minimum odds ratio of 0.11 and a maximum odds ratio of 4.95. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.11 and the maximum odds ratio was 66.28, indicating a difference of 603 times.

  In women, conventional risk factors include hyperlipidemia, hypertension, diabetes, and smoking, and genetic factors include TNF, IPF1, UCP3, PLAT, FABP2, ITGA2, ENG, and ROS1 gene polymorphisms. Associated with the development of infarctions. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 83.72, and genetic factors have a minimum odds ratio of 0.001 and a maximum odds ratio of 2.53. Therefore, when the conventional risk factors and genetic factors are combined, the minimum odds ratio is 0.001 and the maximum odds ratio is 211.81, and a difference of 211,810 times is recognized.

Next, the clinical significance of this research result will be explained. At the hospital, clinic or health check-up center, the applicant will be examined for the existing risk factors and the genetic factors identified this time, and the risk of developing myocardial infarction will be predicted by gender. The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group. As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group.
Although no significant association was found in this study, smoking / obesity is generally considered to be a risk factor for myocardial infarction in men, and this factor can also be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and improvement of lifestyle habits (especially smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated and explain to clients. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk judgment system will be described.
Chi-square test showed that 14 gene polymorphisms were associated with male or female myocardial infarction (P <0.06). Details are shown in Table 15. In the table, the gene (Gene), polymorphism (Polymorphism), and risk rate (P) in men, and the gene (Gene), polymorphism (Polymorphism), and risk rate (P) in women, in order from the left column. Is shown.

  A multinomial logistic regression analysis that corrected for age, BMI, smoking frequency, hypertension, hypercholesterolemia, and diabetes was strongly suggested for the following polymorphisms with myocardial infarction: . The GP1BA 1018C → T (Thr145Met) polymorphism in the dominant model and the additional 1 model, the LTA 804C → A (Thr26Asn) polymorphism in the dominant model and the additional 1 model, and the AGER G → A (Gly83Ser) polymorphism in the recessive model The type was significantly (P <0.005) related to the prevalence of myocardial infarction. Details are shown in Table 16.

  A similar analysis was performed in women, and the following polymorphisms strongly suggested an association with myocardial infarction. The FABP2 2445G → A (Ala54Thr) polymorphism in the dominant model and the addition 1 model, the ITGA2 1648A → G (Lys505Glu) polymorphism in the dominant model, the addition 1 model and the addition 2 model, and the IPF1 in the dominant model and the addition 1 model, respectively. The -108 / 3G → 4G polymorphism was significantly (P <0.005) associated with the prevalence of myocardial infarction in the dominant model and the additional 1 model. Details are shown in Table 17.

Next, the effects of genotype, age, BMI, smoking, hypertension, hypercholesterolemia, and diabetes on myocardial infarction were analyzed by the stepwise variable increase method. The results are shown in Table 18. In the table, the factor (Parameter), the P value (P value), and the contribution rate (R ² ) are shown in order from the left column.

In the case of men, in order of statistical significance, hypercholesterolemia, diabetes, hypertension, AGER genotype (recessive model), LTA genotype (dominant model), GP1BA genotype (dominant model), and MTHFR gene The type (recessive model) was significant (P ≦ 0.005), and each factor was found to affect myocardial infarction independently.
For women, in descending order of statistical significance, hypertension, hypercholesterolemia, diabetes, TNF genotype (dominant model), FABP2 genotype (dominant model), ROS1 genotype (recessive model), UCP3 genotype (Dominant model), IPF1 genotype (dominant model), and PLAT genotype (dominant model) were significant (P ≦ 0.005), indicating that each factor independently affects myocardial infarction.

2. Results when stratified by presence or absence of hypertension Table 19 summarizes the characteristics of myocardial infarction patients and controls when stratified by the presence or absence of hypertension.

About age and BMI, it showed by the average value +/- SD. In those with hypertension (Hypertension (+)), the proportion of men, smokers, hypercholesterolemia, and diabetes are significantly higher in patients with myocardial infarction than in controls, and age is significantly was young. Among those without hypertension (Hypertension (-)), the frequency of age, smokers, hypercholesterolemia, and diabetes was significantly higher in patients with myocardial infarction than in controls.
Next, in order to extract the factors necessary for risk diagnosis of myocardial infarction, we analyzed stepwise variable increment method for genetic polymorphism and age, gender, BMI, smoking, hypercholesterolemia, and diabetes. Will be described later). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction in hypertensive or normotensive individuals>
Table 20 shows details regarding the risk diagnosis system for myocardial infarction in the presence or absence of hypertension. The table shows, in order from the left column, parameters (parameters), P values (P values), and odds ratios (95% confidence intervals) (OR (95% CI)). In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

  The gene polymorphism group and age / sex / smoking / BMI / diabetes / hyperlipidemia with P <0.05 in the stepwise variable increase method (shown in Table 24 below) are independent factors (confounding factors), Multinomial logistic regression analysis was performed separately for hypertensive and normal blood pressure (non-hypertensive) subjects with myocardial infarction as a dependent factor, and P values, odds ratios, and 95% confidence intervals were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.

  As a result of multinomial logistic regression analysis, in hypertensives, gender (male is high risk), hyperlipidemia, and diabetes are conventional risk factors, and MTHFR, IPF1, UTS2, GP1BA, LPL, MMP2 are genetic factors. F7, PLAT, and ADIPOQ gene polymorphisms were associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 24.11, and genetic factors have a minimum odds ratio of 0.11 and a maximum odds ratio of 21.04. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.11, the maximum odds ratio was 507.27, and a difference of 4612 times was recognized. In normotensive individuals, gender (male is high risk), diabetes and hyperlipidemia are conventional risk factors, and genetic factors AKAP10, TNFSF4, SAH and CETP are polymorphisms of myocardial infarction. Related to the onset. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 175.77, and genetic factors have a minimum odds ratio of 0.44 and a maximum odds ratio of 4.60. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.44 and the maximum odds ratio was 808.54, indicating a difference of 1838 times.

  Next, the clinical significance of this research result is described below. At hospitals, clinics, and health checkup centers, applicants are tested for conventional risk factors and genetic factors identified this time, and the risk of developing myocardial infarction is predicted separately for hypertensive and normal blood pressure individuals. The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group.

As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group.
Although no significant association was found in this study, smoking / obesity is generally considered to be a risk factor for myocardial infarction, and this factor can be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and improvement of lifestyle habits (especially smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated and explain to clients. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk diagnosis system will be described.
By chi-square test, 12 gene polymorphisms in hypertensives and 16 gene polymorphisms in normotensive individuals were associated with myocardial infarction (P <0.06). Details are shown in Table 21. In the table, genes (Gene), polymorphisms (Polymorphism), and risk factors (P) in hypertensive individuals, genes (Gene), polymorphisms (Polymorphism), and risk factors in normal blood pressure individuals, in order from the left column. (P) is shown.

  A multinomial logistic regression analysis that corrected for age, sex, BMI, smoking / hypercholesterolemia / diabetes in hypertensive subjects strongly suggested that the following polymorphisms were associated with myocardial infarction. The GP1BA 1018C → T (Thr145Met) polymorphism in the dominant model and the addition 1 model, the −108 / 3G → 4G polymorphism of the IPF1 in the dominant model and the addition 1 and addition 2 models, and the MTHFR in the recessive model and the addition 2 model, respectively. The 677C → T (Ala222Val) polymorphism was significantly (P <0.005) related to the prevalence of myocardial infarction in the recessive model and the additional 2 model, the G → A (Ser89Asn) polymorphism of UTS2. Details are shown in Table 22.

  On the other hand, when a similar analysis was performed in normal blood pressure subjects, no significant association was found between any polymorphism and myocardial infarction. Details are shown in Table 23.

Next, the effects of genotype, age, sex, hypercholesterolemia, and diabetes on myocardial infarction were analyzed by the stepwise variable increment method for hypertensive and normotensive individuals. The results are shown in Table 24. In the table, the factor (Parameter), the P value (P value), and the contribution rate (R ² ) are shown in order from the left column.

In the case of hypertension, in descending order of statistical significance, sex (male is significantly higher), hypercholesterolemia, diabetes, MTHFR genotype (recessive model), IPF1 genotype (dominant model), UTS2 genotype (Recessive model) and the GP1BA genotype (dominant model) were significant (P ≦ 0.005), and each factor was found to affect myocardial infarction independently.
For normotensive individuals, gender, diabetes, hypercholesterolemia, and age are significant in descending order of statistical significance (P ≦ 0.005), and each factor independently affects myocardial infarction. I found out that

3. Results when stratified by presence or absence of hypercholesterolemia Table 25 summarizes the characteristics of myocardial infarction patients and controls when stratified by the presence or absence of hypercholesterolemia.

About age and BMI, it showed by the average value +/- SD. Among those with hypercholesterolemia (Hypercholesterolemia (+)), the proportion of men, smokers, hypertension and diabetes were significantly higher in patients with myocardial infarction than in controls. In those without hypercholesterolemia (Hypercholesterolemia (-)), the frequency of age, percentage of men, smokers, hypertension and diabetes was significantly higher in patients with myocardial infarction than in controls.
Next, in order to extract factors necessary for risk diagnosis of myocardial infarction, we analyzed stepwise variable increase method for genetic polymorphism and age, gender, BMI, smoking, hypertension, and diabetes (details will be described later) ). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction in hypercholesterolemic or normal serum cholesterol>
Table 26 shows details of a risk diagnosis system for myocardial infarction with or without hypercholesterolemia. The table shows, in order from the left column, parameters (parameters), P values (P values), and odds ratios (95% confidence intervals) (OR (95% CI)). In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

  The genetic polymorphism group and age, sex, smoking, BMI, hypertension, and diabetes that were P <0.05 by the stepwise variable increase method (shown in Table 30 below) were independent factors (confounding factors), and myocardial infarction As a dependent factor, multinomial logistic regression analysis was performed separately for hypercholesterolemic and normal serum cholesterol (non-hypercholesterolemic) individuals, and P values, odds ratios, and 95% confidence intervals were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.

  As a result of multinomial logistic regression analysis, in hypercholesterolemic patients, gender (male is high risk), diabetes, and hypertension are conventional risk factors, and genetic polymorphisms of UCP3, EDNRA, and ENG are genetic factors. Associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 21.90, and genetic factors have a minimum odds ratio of 0.50 and a maximum odds ratio of 1.39. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.50, the maximum odds ratio was 30.44, and a difference of 61 times was recognized. In normal serum cholesterol, gender (male is high risk), diabetes and hypertension as conventional risk factors, and MTHFR, AKAP10, IPF1, ADIPOQ, F7, FABP2, F12, AACT, TNF as genetic factors , TNFSF4, and LPL gene polymorphisms were associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 39.41, and genetic factors have a minimum odds ratio of 0.03 and a maximum odds ratio of 28.45. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.03 and the maximum odds ratio was 1121.21, indicating a difference of 37,374 times.

  Next, the clinical significance of this research result is described below. At the hospital, clinic, and medical checkup center, the applicant is tested for risk factors and genetic factors, and the risk of developing myocardial infarction is predicted separately for hypercholesterolemic and normal serum cholesterol individuals. The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group.

As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group.
Although no significant association was found in this study, smoking / obesity is generally considered to be a risk factor for myocardial infarction, and this factor can be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and improvement of lifestyle habits (especially smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated and explain to clients. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk diagnosis system will be described.
According to the chi-square test, 9 gene polymorphisms were associated with myocardial infarction in hypercholesterolemic individuals and 20 gene polymorphisms in normal serum cholesterol individuals (P <0.06). Details are shown in Table 27. In the table, in order from the left column, genes (Gene), polymorphisms (Polymorphism), and risk factors (P) in hypercholesterolemic patients, genes (Gene), polymorphisms (Polymorphism) in those with normal serum cholesterol , And the risk factor (P).

  In a hypercholesterolemic person, a multinomial logistic regression analysis that corrected for age, sex, BMI, smoking / hypertension / diabetes was found, and no association with myocardial infarction was observed for any of the polymorphisms. Details are shown in Table 28.

  On the other hand, when a similar analysis was performed on normal serum cholesterol, the following polymorphisms strongly suggested an association with myocardial infarction. In addition 1 model, F7 11496G → A (Arg353Gln) polymorphism, in recessive model F12 46C → T polymorphism, dominant model FABP2 2445G → A (Ala54Thr) polymorphism in dominant model and addition 1 model The −108 / 3G → 4G polymorphism of IPF1 is the 677C → T polymorphism of MTHFR in the recessive model and addition 2 model, and the 11377C → G polymorphism of ADIPOQ is the recessive model and addition 2 in the dominant model and addition 1 model. In the model, the A → G (Ile646Val) polymorphism of AKAP10 was significantly (P <0.005) associated with the prevalence of myocardial infarction. Details are shown in Table 29.

Next, the effects of genotype, age, sex, BMI, smoking, hypertension, and diabetes on myocardial infarction were analyzed by the stepwise variable increment method for hypercholesterolemia and normal serum cholesterol. The results are shown in Table 30. In the table, the factor (Parameter), the P value (P value), and the contribution rate (R ² ) are shown in order from the left column.

In the case of hypercholesterolemia, in descending order of statistical significance, gender (male is significantly higher), diabetes, and hypertension are significant (P ≦ 0.005), and each factor is independently myocardial. It was found to affect infarctions.
In the case of normal serum cholesterol, in descending order of statistical significance, sex, diabetes, hypertension, MTHFR genotype (recessive model), FABP2 genotype (dominant model), age, IPF1 genotype (dominant model), AGER Genotype (recessive model), AKAP10 genotype (recessive model), F7 genotype (dominant model), ADIPOQ genotype (dominant model), and F12 gene polymorphism (recessive model) are significant (P ≦ 0.005) ), Each factor independently affected myocardial infarction.

4). Results when stratified by presence / absence of diabetes Table 31 summarizes the characteristics of myocardial infarction patients and controls when stratified by the presence / absence of diabetes.

About age and BMI, it showed by the average value +/- SD. In diabetics (Diabetes mellitus (+)), the proportion of men, BMI, smokers, hypertension and hypercholesterolemia were significantly higher in patients with myocardial infarction than in controls, and age was significantly lower . In normoglycemic (or non-diabetic: Diabetes mellitus (-)) age, percentage of men, smokers, hypertension and hypercholesterolemia are significantly higher in patients with myocardial infarction than in controls It was.
Next, in order to extract factors necessary for risk diagnosis of myocardial infarction, we analyzed stepwise variable increase method for genetic polymorphism and age, gender, BMI, smoking, hypertension, and hypercholesterolemia. Will be described later). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction in diabetics or normal blood sugar>
Table 32 shows details of the risk diagnosis system for myocardial infarction depending on the presence or absence of diabetes. The table shows, in order from the left column, parameters (parameters), P values (P values), and odds ratios (95% confidence intervals) (OR (95% CI)). In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

  In the stepwise variable increase method (shown in Table 36 below), P <0.05 genetic polymorphism group and age / sex / smoking / BMI / hypertension / hyperlipidemia are independent factors (confounding factors) Multinomial logistic regression analysis was performed separately for diabetics and normal glycemic (non-diabetic) patients with myocardial infarction as a dependent factor, and P values, odds ratios, and 95% confidence intervals were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.

  As a result of multinomial logistic regression analysis, in diabetics, gender (male is high risk) and hyperlipidemia are conventional risk factors, and genetic factors such as MTHFR, FABP2, APOE, and IPF1 are polymorphisms. Associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 10.19, and genetic factors have a minimum odds ratio of 0.70 and a maximum odds ratio of 3.78. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.70 and the maximum odds ratio was 38.52, indicating a difference of 55 times. Moreover, in normal blood sugar, gender (male is high risk), hyperlipidemia, and hypertension are conventional risk factors, and genetic polymorphisms of F7, IPF1, GP1BA, MTHFR, and FABP2 are genetic factors. Associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 59.04, and genetic factors have a minimum odds ratio of 0.45 and a maximum odds ratio of 2.24. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.45 and the maximum odds ratio was 132.25, indicating a difference of 294 times.

  Next, the clinical significance of this research result is described below. At hospitals, clinics, and health checkup centers, applicants are tested for risk factors and genetic factors, and the risk of developing myocardial infarction is predicted separately for diabetics and normal blood sugar (non-diabetics). The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group.

As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group.
Although no significant association was found in this study, smoking / obesity / hypertension is generally considered to be a risk factor for diabetics and smoking / obesity is considered to be a risk factor for myocardial infarction in patients with normal blood sugar. Factors can also be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and improvement of lifestyle habits (especially smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated and explain to clients. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk judgment system will be described.
Chi-square test showed that 11 genetic polymorphisms in diabetics and 12 genetic polymorphisms in non-diabetics were associated with myocardial infarction (P <0.06). Details are shown in Table 33. In the table, the gene (Gene), polymorphism (Polymorphism), and risk factor (P) in diabetics, and the gene (Gene), polymorphism (Polymorphism), and risk factors in non-diabetics are listed in order from the left column. (P) is shown.

  In diabetics, we performed multinomial logistic regression analysis that corrected for age, sex, BMI, smoking / hypertension / hypercholesterolemia and found that the FABP2 2445G → A (Ala54Thr) polymorphism is associated with myocardial infarction in a dominant model. Significantly (P <0.005) related to rate. Details are shown in Table 34.

  A similar analysis was performed on non-diabetic individuals, and the −108 / 3G → 4G polymorphism of IPF1 was significantly (P <0.005) related to myocardial infarction in the dominant model and the additional 1 model. Details are shown in Table 35.

Next, for diabetics and non-diabetics, the effects of genotype, age, sex, BMI, smoking, hypertension, and hypercholesterolemia on myocardial infarction were analyzed by the stepwise variable increment method. The results are shown in Table 36. In the table, the factor (Parameter), the P value (P value), and the contribution rate (R ² ) are shown in order from the left column.

In the case of diabetics, sex, hypercholesterolemia, FABP2 genotype (dominant model), and MTHFR genotype (recessive model) are significant (P ≦ 0.005) in descending order of statistical significance. It was found that the factors independently affected myocardial infarction.
In the case of non-diabetics, sex, hypercholesterolemia, hypertension, IPF1 genotype (dominant model) are significant (P ≦ 0.005) in descending order of statistical significance, and each factor is independent. It was found to affect myocardial infarction.
5. Results when stratified by presence or absence of smoking Table 37 summarizes the characteristics of myocardial infarction patients and controls when stratified by the presence or absence of smoking.

About age and BMI, it showed by the average value +/- SD. Among smokers, the proportion of men, BMI, hypertension / hypercholesterolemia, and diabetes were significantly higher in patients with myocardial infarction than in controls. Among nonsmokers, age, the proportion of men, and the frequency of hypertension / hypercholesterolemia and diabetes were significantly higher in patients with myocardial infarction than in controls.
Next, in order to extract factors necessary for risk diagnosis of myocardial infarction, we analyzed stepwise variable increase method for genetic polymorphism and age, gender, BMI, hypertension, hypercholesterolemia, and diabetes. Will be described later). As a result, it was found that risk diagnosis regarding myocardial infarction can be performed as described below.

<Risk diagnosis system for myocardial infarction in smokers and non-smokers>
Table 38 shows details of the risk diagnosis system for myocardial infarction by the presence or absence of smoking. The table shows, in order from the left column, parameters (parameters), P values (P values), and odds ratios (95% confidence intervals) (OR (95% CI)). In the bottom row, the total risk (total risk) multiplied by the odds ratio of conventional risk factors and genetic factors is shown.

  The genetic polymorphism group and age, sex, BMI, hypertension, hyperlipidemia, and diabetes that were P <0.05 by the stepwise variable increase method (shown in Table 42 below) were independent factors (confounding factors), Multinomial logistic regression analysis was performed separately for smokers and non-smokers with myocardial infarction as a dependent factor, and P values, odds ratios, and 95% confidence intervals were calculated. Therefore, these factors are independent, and the overall disease risk can be predicted by the product (multiplication) of the odds ratio.

  As a result of multinomial logistic regression analysis, in smokers, conventional risk factors are hyperlipidemia, diabetes, hypertension, and sex (male is high risk), and genetic factors are PROZ, LTA, UCP1, and ACE. Genetic polymorphism was associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 38.86, and genetic factors have a minimum odds ratio of 0.54 and a maximum odds ratio of 5.53. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.54, the maximum odds ratio was 214.90, and a difference of 398 times was recognized. In addition, in non-smokers, gender (male is high risk), hyperlipidemia, diabetes, and hypertension are conventional risk factors, and genetic factors are AKAP10, FABP2, PPARG, IPF1, MTHFR, LPL, F12, TNF, GP1BA, F7, AGER, TNFSF4, and PCK1 gene polymorphisms were associated with the development of myocardial infarction. Conventional risk factors have a minimum odds ratio of 1.00 and a maximum odds ratio of 111.09, and genetic factors have a minimum odds ratio of 0.10 and a maximum odds ratio of 32.67. Therefore, when the conventional risk factors and genetic factors were combined, the minimum odds ratio was 0.10, the maximum odds ratio was 362.31, and a difference of 36,293 times was recognized.

  Next, the clinical significance of this research result is described below. At hospitals, clinics, and health checkup centers, applicants are tested for risk factors and genetic factors, and the risk of developing myocardial infarction is predicted separately for smokers and nonsmokers. The degree of risk is divided into five levels based on the distribution of the product of the conventional risk factor and odds ratio of the whole genetic factor. That is, the average ± 1SD range is set as the average risk group, the average + 1SD to the average + 2SD is set as a slightly high risk group, and the average + 2SD or more is set as a high risk group. Further, the average −1SD to the average −2SD are set as a low risk group, and the average −2SD or lower is set as a low risk group.

As another method, the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order of risk value, and the group with the highest risk value of 5% 20% group is a slightly higher risk group, the next 50% group is an average risk group, the next 20% group is a slightly lower risk group, and the 5% group with the lowest risk value is a lower risk group A group.
Although no significant association was found in this study, obesity is generally considered as a risk factor for myocardial infarction, and this factor can be included.

  The results will be counseled, including the judgment of qualified personnel such as doctors, and improvement of lifestyle habits (especially smoking cessation, diet therapy, exercise therapy, obesity reduction, Actively promote primary and secondary prevention of myocardial infarction by providing stress relief and sleep insufficiency) and early treatment of risk factors (treatment of hypertension, diabetes, hyperlipidemia, etc.). Although genetic factors cannot be changed, conventional risk factors can be reduced or treated, so predict how much the risk of onset will be reduced when these factors are treated and explain to clients. In particular, application to people with a family history of myocardial infarction or cerebral infarction is effective. This system makes it possible to make a custom-made prevention of myocardial infarction, and can greatly contribute medically and socially, such as prolonging the healthy life of the elderly, improving QOL, preventing stickiness and reducing medical costs in the future.

<Statistical analysis>
Next, the results of statistical analysis that led to the development of the risk judgment system will be described.
Chi-square test showed that 8 genetic polymorphisms in smokers and 21 genetic polymorphisms in non-smokers were associated with myocardial infarction (P <0.06). Details are shown in Table 39. In the table, in order from the left column, the gene (Gene), polymorphism (Polymorphism), and risk factor (P) in smokers, and the gene (Gene), polymorphism (Polymorphism), and risk factors in non-smokers (P) is shown.

  A multinomial logistic regression analysis that corrected for age, sex, BMI, hypertension / hypercholesterolemia / diabetes in smokers, and the following gene polymorphism strongly suggested an association with myocardial infarction. The LTA 804C → A (Thr264Asn) polymorphism was significantly (P <0.005) related to the onset of myocardial infarction in the dominant model and the additional one model, and the 79G → A polymorphism in PROZ in the recessive model. Details are shown in Table 40.

  When the same analysis was performed on non-smokers, the following gene polymorphisms strongly suggested an association with myocardial infarction. The FABP2 2445G → A (Ala54Thr) polymorphism in the dominant model and the additional 1 model, the −108 / 3G → 4G polymorphism of the IPF1 in the dominant model and the additional 1 model, and the MTHFR 677C → T in the recessive model and the additional 2 model. The (Ala222Val) polymorphism is significantly affected by the PPARG −681C → G polymorphism in the dominant model and the addition 1 model, and the AKAP10 A → G (Ile646Val) polymorphism in the depression model and the addition 2 model. (P <0.005) related. Details are shown in Table 41.

Next, for smokers and non-smokers, the effects of genotype, age, sex, BMI, hypertension, hypercholesterolemia, and diabetes on myocardial infarction were analyzed by the stepwise variable increment method. The results are shown in Table 42. In the table, the factor (Parameter), the P value (P value), and the contribution rate (R ² ) are shown in order from the left column.

  In the case of smokers, hypercholesterolemia, diabetes, hypertension, and PROZ genotype (recessive model) are significant in descending order of statistical significance (P ≦ 0.005). It was found to affect infarctions.

  For non-smokers, in descending order of statistical significance, sex, hypercholesterolemia, diabetes, hypertension, MTHFR genotype (recessive model), FABP2 genotype (dominant model), AKAP10 genotype (recessive model) , PPARG genotype (dominant model) and IPF1 genotype (dominant model) were significant (P ≦ 0.005), and it was found that each factor independently affects myocardial infarction.

<Discussion>
The present inventor determined 164 gene polymorphisms for 137 candidate genes suspected to be associated with myocardial infarction, male or female, hypertensive and normal blood pressure, hypercholesterolemia and serum cholesterol normal, diabetes The analysis was divided into those who were non-diabetic and those who were smokers and non-smokers. As a result of this analysis, it was shown that gene polymorphisms involved in myocardial infarction differ depending on gender and the presence or absence of conventional risk factors (hypertension, hypercholesterolemia, diabetes, smoking). However, some genetic polymorphisms were found to be related in more than one factor group. That is, the GP18BA 1018C → T (Thr145Met) polymorphism is associated with myocardial infarction in men and hypertensives, and the FABP2 2445G → A (Ala54Thr) polymorphism is associated with females, normal serum cholesterol, diabetics, and non-smokers. IPF1 −108 / 3G → 4G is associated with females, hypertensives, normal serum cholesterol, non-diabetics, non-smokers, and MTHFR 677C → T (Ala222Val) polymorphism is associated with hypertension and normal serum cholesterol. -It was associated with non-smokers, and the A → G (Ile646Val) polymorphism of AKAP10 was associated with normal serum cholesterol and non-smokers.

The main cause of myocardial infarction is coronary atherosclerosis. When the coronary arteries harden, the hemodynamics of the arterial lumen deteriorates, making it difficult to control vasoconstriction and expansion, and tendencies of platelet adhesion / aggregation and thrombus formation appear.
The inventor has worked diligently on the basis of comprehensive knowledge about biology related to blood vessels, biology related to platelets and leukocytes, coagulation / fibrinolytic cascade, lipid, glucose, homocysteine metabolism, and other metabolic factors, 137 candidate genes suspected to be involved in myocardial infarction were selected. In this study, genes that have been associated with myocardial infarction actually have various roles. For example, intracellular signaling (AKAP10), vascular inflammation (LTA, AGER), vasoconstriction (UTS2), platelet function (GP1BA), blood coagulation (PROZ), lipid / sugar metabolism (FABP2, ADIPOQ, PPARG), insulin production ( IPF1), homocysteine metabolism (MTHFR), and energy regulation (UCP3).

  Regarding 5 of 12 polymorphisms (LTA, GP1BA, FABP2, IPF1, and MTHFR), there have been reports on association with myocardial infarction or ischemic heart disease (Non-Patent Documents 11, 27, 29, 36). 37). The two genetic polymorphisms (ADIPOQ, PPARG) investigated this time have not been associated with myocardial infarction or ischemic heart disease, but other polymorphisms in these two genes There is a previous report showing the relationship (Non-Patent Documents 39 and 40). Regarding the remaining 5 gene polymorphisms (AKAP10, AGER, UTS2, PROZ, UCP3), there has been no previous report describing the association with myocardial infarction or ischemic heart disease.

Regarding polymorphisms involved in myocardial infarction when stratified into males or females In the case of males, GP1BA 1018C → T (Thr145Met) polymorphism, LTA 804C → A (Thr26Asn) polymorphism, and AGER G polymorphism → A (Gly83Ser) polymorphism was associated with the development of myocardial infarction. In the case of females, the FABP2 2445G → A (Ala54Thr) polymorphism, IPF1 −108 / 3G → 4G polymorphism, and UCP3 −55C → T polymorphism were associated with the onset of myocardial infarction. From these results, it was shown that genetic polymorphisms related to myocardial infarction are different between men and women. In this study, the mechanism of sex differences in genetic polymorphisms associated with myocardial infarction remains unclear.

In general, considering that the age at which women suffer from atherosclerotic diseases such as ischemic heart disease and myocardial infarction is about 10 years behind that of men, men and women of the same generation have coronary artery disease. The mechanisms involved may be different. Estrogen, a sex hormone, regulates blood vessel walls and vasoconstriction by promoting the production of vasodilators such as NO and PGI ₂ from vascular endothelial cells and suppressing the production of vasoconstrictors such as endothelin 1. It has a favorable effect on non-patent document 41. Therefore, genetic polymorphisms associated with myocardial infarction may differ due to differences in sex hormone levels such as estrogen between men and women. Furthermore, the gene polymorphism investigated this time is a part of the gene polymorphism related to myocardial infarction, and there is a possibility that a gene polymorphism related to myocardial infarction common to men and women may be found further.

Genetic polymorphisms associated with myocardial infarction when stratified by the presence or absence of hypertension, the presence or absence of hypercholesterolemia, the presence or absence of diabetes, or the presence or absence of smoking The interaction between genetic factors and environmental factors is important as the cause of myocardial infarction Therefore, we analyzed the effects of polymorphism on the onset of myocardial infarction when divided into the presence or absence of hypertension, the presence or absence of hypercholesterolemia, the presence or absence of diabetes, and the presence or absence of smoking habits. As a result, in hypertensive individuals, GP1BA 1018C → T (Thr145Met) polymorphism, IPF1 −108 / 3G → 4G polymorphism, MTHFR 677C → T (Ala222Val) polymorphism, and UTS2 G → A (Ser89Asn) The polymorphism was significantly associated with myocardial infarction. On the other hand, no such polymorphism was observed in normotensive individuals.

In normal serum cholesterol, the FABP2 2445G → A (Ala54Thr) polymorphism, the IPF1 −108 / 3G → 4G polymorphism, the MTHFR 677C → T (Ala222Val) polymorphism, the ADIPOQ −11377C → G polymorphism, and The A → G (Ile646Vla) polymorphism of AKAP10 was significantly associated with myocardial infarction. On the other hand, such polymorphism was not observed in hypercholesterolemic patients.
In diabetics and non-diabetics, the FABP2 2445G → A (Ala54Thr) polymorphism and the IPF1 −108 / 3G → 4G polymorphism were significantly associated with myocardial infarction, respectively.

In smokers, the 79G → A polymorphism of PROZ was significantly associated with the development of myocardial infarction. In non-smokers, FABP2 2445G → A (Ala54Thr) polymorphism, IPF1 −108 / 3G → 4G polymorphism, MTHFR 677C → T (Ala222Val) polymorphism, PPARG −681C → G polymorphism, The A → G (Ile646Val) polymorphism of AKAP10 was significantly associated with the development of myocardial infarction.
From these results, it was found that gene polymorphisms related to myocardial infarction differ depending on the presence or absence of risk factors for conventional coronary artery disease. However, the reason remains unknown for now. Since the degree to which one gene polymorphism contributes to the onset of myocardial infarction is small, the association between the polymorphism and the onset of myocardial infarction may be influenced by the presence or absence of conventional risk factors for coronary artery disease. Furthermore, since conventional risk factors such as hypertension, hypercholesterolemia, and diabetes have genetic factors, genetic polymorphisms related to myocardial infarction and genetic polymorphisms related to conventional risk factors for coronary artery disease There may be some interaction between them.

  Thus, according to the present embodiment, it is possible to provide a detection method for determining a genetic risk and an onset risk for myocardial infarction. Use of this embodiment makes it possible to prevent myocardial infarction, and can greatly contribute medically and socially, such as extending the life expectancy of elderly people, improving QOL, preventing stickiness, and reducing medical costs in the future.

Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, ComuzzieAG, Blangero J, Nurnberg P, Reis A, Riegger GA, Jacob HJ, Schunkert H. A comprehensive linkage analysis for myocardial infarction and its related risk factors.Nat Genet 2002; 30: 210-214. Harrap SB, Zammit KS, Wong ZY, Williams FM, Bahlo M, Tonkin AM, Anderson ST.Genome-wide linkage analysis of the acute coronary syndrome suggests a locus on chromosome 2. Arterioscler Thromb Vasc Biol. 2002; 22: 874-878 . Wang Q, Rao S, Shen GQ, Li L, Moliterno DJ, Newby LK, Rogers WJ, Cannata R, Zirzow E, Elston RC, Topol EJ. Premature myocardial infarction novel susceptibility locus on chromosome 1p34-36 identified by genomewide linkage analysis. Am J Hum Genet. 2004; 74: 262-271. Hauser ER, Crossman DC, Granger CB, Haines JL, Jones CJ, Mooser V, McAdam B, Winkelmann BR, Wiseman AH, Muhlestein JB, Bartel AG, Dennis CA, Dowdy E, Estabrooks S, Eggleston K, Francis S, Roche K , Clevenger PW, Huang L, Pedersen B, Shah S, Schmidt S, Haynes C, West S, Asper D, Booze M, Sharma S, Sundseth S, Middleton L, Roses AD, Hauser MA, Vance JM, Pericak-Vance MA , Kraus WE. A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet. 2004; 75: 436-447. Cambien F, Poirier O, Lecerf L, Evans A, Cambou JP, Arveiler D, Luc G, Bard JM, Bara L, Ricard S, Tiret L, Amouyel P, Alhenc-Gelas F, Soubrier F. Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction.Nature. 1992; 359: 641-644. Weiss EJ, Bray PF, Tayback M, Schulman SP, Kickler TS, Becker LC, Weiss JL, Gerstenblith G, Goldschmidt-Clermont PJ.A polymorphism of a platelet glycoprotein receptor as an inherited risk factor for coronary thrombosis.N Engl J Med. 1996; 334: 1090-1094. Iacoviello L, Di Castelnuovo A, De Knijff P, D'Orazio A, Amore C, Arboretti R, Kluft C, Benedetta Donati M. Polymorphisms in the coagulation factor VII gene and the risk of myocardial infarction.N Engl J Med. 1998; 338: 79-85. Kuivenhoven JA, Jukema JW, Zwinderman AH, de Knijff P, McPherson R, Bruschke AV, Lie KI, Kastelein JJ.The role of a common variant of the cholesterol ester transfer protein gene in the progression of coronary atherosclerosis.N Engl J Med. 1998; 338: 86-93. Topol EJ, McCarthy J, Gabriel S, Moliterno DJ, Rogers WJ, Newby LK, Freedman M, Metivier J, Cannata R, O'Donnell CJ, Kottke-Marchant K, Murugesan G, Plow EF, Stenina O, Daley GQ. Single nucleotide polymorphisms in multiple novel thrombospondin genes may be associated with familial premature myocardial infarction. Circulation. 2001; 104: 2641-2644. Yamada Y, Izawa H, Ichihara S, Takatsu F, Ishihara H, Hirayama H, Sone T, Tanaka M, Yokota M. Prediction of the risk of myocardial infarction from polymorphisms in candidate genes.N Engl J Med. 2002; 347: 1916 -1923. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Hori M, Nakamura Y, Tanaka T. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002; 32: 650-654. Ozaki K, Inoue K, Sato H, Iida A, Ohnishi Y, Sekine A, Sato H, Odashiro K, Nobuyoshi M, Hori M, Nakamura Y, Tanaka T. Functional variation in LGALS2 confers risk of myocardial infarction and regulates lymphotoxin-α secretion in vitro. Nature. 2004; 429: 72-75. Shiffman D, Ellis SG, Rowland CM, Malloy MJ, Luke MM, Iakoubova OA, Pullinger CR, Cassano J, Aouizerat BE, Fenwick RG, Reitz RE, Catanese JJ, Leong DU, Zellner C, Sninsky JJ, Topol EJ, Devlin JJ , Kane JP.Identification of four gene variants associated with myocardial infarction. Am J Hum Genet. 2005; 77: 596-605. Itoh Y, Mizuki N, Shimada T, Azuma F, Itakura M, Kashiwase K, Kikkawa E, Kulski JK, Satake M, Inoko H. High-throughput DNA typing of HLA-A, -B, -C, and -DRB1 loci by a PCR-SSOP-Luminex method in the Japanese population.Immunogenetics. 2005; 57: 717-729. Baier LJ, Sacchettini JC, Knowler WC, Eads J, Palisso G, Tataranni PA, Mochizuki H, Bennett PH, Bogardus C, Prochazka M. An amino acid substitution in the human intestinal fatty acid binding protein is associated with increased fatty acid binding, increased fat oxidation, and insulin resistance. J Clin Invest. 1995; 95: 1281-1287. Yamada K, Yuan X, Ishiyama S, Koyama K, Ichikawa F, Koyanagi A, Koyama W, Nonaka K. Association between Ala54Thr substitution of the fatty acid-binding protein 2 gene with insulin resistance and intra-abdominal fat thickness in Japanese men. Diabetologia. 1997; 40: 706-710. Galluzzi JR, Cupples LA, Otvos JD, Wilson PW, Schaefer EJ, Ordovas JM. Association of the A / T54 polymorphism in the intestinal fatty acid binding protein with variations in plasma lipids in the Framingham Offspring Study. Atherosclerosis. 2001; 159: 417 -424. Guettier JM, Georgopoulos A, Tsai MY, Radha V, Shanthirani S, Deepa R, Gross M, Rao G, Mohan V. Polymorphisms in the fatty acid-binding protein 2 and apolipoprotein C-III genes are associated with the metabolic syndrome and dyslipidemia in a South Indian population. J Clin Endocrinol Metab. 2005; 90: 1705-1711. Carlsson M, Orho-Melander O, Hedenbro J, Almgren P, Groop LC.The T54 allele of the intestinal fatty acid-binding protein 2 is associated with a parental history of stroke.J Clin Endocrinol Metab. 2000; 85: 2801-2804 . Saarinen L, Pulkkinen A, Kareinen A, Heikkinen S, Lehto S, Laakso M. Variants of the fatty acid-binding protein 2 gene are not associated with coronary heart disease in nondiabetic subjects and in patients with NIDDM. Diabetes Care. 1998; 21 : 849-850. Stoffers DA, Zinkin NT, Stanojevic V, Clarke WL, Habener JF. Pancreatic agenesis potentially to a single nucleotide deletion in the human IPF1 gene coding sequence. Nat Genet. 1997; 15: 106-110. Yamada K, Yuan X, Ishiyama S, Ichikawa F, Kohno S, Shoji S, Hayashi H, Nonaka K. Identification of a single nucleotide insertion polymorphism in the upstream region of the insulin promoter factor-1 gene: an association study with diabetes mellitus Diabetologia. 1998; 41: 603-605. Hansen L, Urioste S, Petersen HV, Jensen JN, Eiberg H, Barbetti F, Serup P, Hansen T, Pedersen O. Missense mutations in the human insulin promoter factor-1 gene and their relation to maturity-onset diabetes of the young and late-onset type 2 diabetes mellitus in Caucasians. J Clin Endocrinol Metab. 2000; 85: 1323-1326. Deloughery TG, Evans A, Sadeghi A, McWilliams J, Henner WD, Taylor LM Jr, Press RD.Common mutation in methylenetetrahydrofolate reductase. Correlation with homocysteine metabolism and late-onset vascular disease. Circulation. 1996; 94: 3074-3078. Ma J, Stampfer MJ, Hennekens CH, Frosst P, Selhub J, Horsford J, Malinow MR, Willett WC, Rozen R. Methylenetetrahydrofolate reductase polymorphism, plasma folate, homocysteine, and risk of myocardial infarction in US physicians. Circulation. 1996; 94 : 2410-2416. Schwartz SM, Siscovick DS, Malinow MR, Rosendaal FR, Beverly RK, Hess DL, Psaty BM, Longstreth WT Jr, Koepsell TD, Raghunathan TE, Reitsma PH.Myocardial infarction in young women in relation to plasma total homocysteine, folate, and a common variant in the methylenetetrahydrofolate reductase gene. Circulation. 1997; 96: 412-417. Kluijtmans LA, van den Heuvel LP, Boers GH, Frosst P, Stevens EM, van Oost BA, den Heijer M, Trijbels FJ, Rozen R, Blom HJ. Molecular genetic analysis in mild hyperhomocysteinemia: a common mutation in the methylenetetrahydrofolate reductase gene is a genetic risk factor for cardiovascular disease. Am J Hum Genet. 1996; 58: 35-41. Gallagher PM, Meleady R, Shields DC, Tan KS, McMaster D, Rozen R, Evans A, Graham IM, Whitehead AS.Homocysteine and risk of premature coronary heart disease. Evidence for a common gene mutation. Circulation. 1996; 94: 2154 -2158. Morita H, Taguchi J, Kurihara H, Kitaoka M, Kaneda H, Kurihara Y, Maemura K, Minamino T, Ohno M, Yamaoki K, Ogasawara K, Aizawa T, Suzuki S, Yazaki Y. Genetic polymorphism of 5,10-methylenetetrahydrofolate reductase (MTHFR) as a risk factor for coronary artery disease. Circulation. 1997; 95: 2032-2036. Mager A, Lalezari S, Shohat T, Birnbaum Y, Adler Y, Magal N, Shohat M. Methylenetetrahydrofolate reductase genotypes and early-onset coronary artery disease. Circulation. 1999; 100: 2406-2410. Schmitz C, Lindpaintner K, Verhoef P, Gaziano JM, Buring J. Genetic polymorphism of methylenetetrahydrofolate reductase and myocardial infarction: a case-control study. Circulation. 1996; 94: 1812-1814. Folsom AR, Nieto FJ, McGovern PG, Tsai MY, Malinow MR, Eckfeldt JH, Hess DL, Davis CE.Prospective study of coronary heart disease incidence in relation to fasting total homocysteine, related genetic polymorphisms, and B vitamins: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 1998; 98: 204-210. Verhoef P, Rimm EB, Hunter DJ, Chen J, Willett WC, Kelsey K, Stampfer MJ.A common mutation in the methylenetetrahydrofolate reductase gene and risk of coronary heart disease: results among US men.J Am Coll Cardiol. 1998; 32: 353-359. Klerk M, Verhoef P, Clarke R, Blom HJ, Kok FJ, Schouten EG; MTHFR Studies Collaboration Group.MTHFR 677C → T polymorphism and risk of coronary heart disease: a meta-analysis. JAMA. 2002; 288: 2023-2031. Lewis SJ, Ebrahim S, Smith GD.Meta-analysis of MTHFR 677C → T polymorphism and coronary heart disease: Does totality of evidence support causal role for homocysteine and preventive potential of folate? Br Med J. 2005; 331: 1053-1056. Murata M, Matsubara Y, Kawano K, Zama T, Aoki N, Yoshino H, Watanabe G, Ishikawa K, Ikeda Y. Coronary artery disease and polymorphisms in a receptor mediating shear stress-dependent platelet activation. Circulation. 1997; 96: 3281 -3286. Mikkelsson J, Perola M, Penttila A, Karhunen PJ. Platelet glycoprotein Ibα HPA-2 Met / VNTR B haplotype as a genetic predictor of myocardial infarction and sudden cardiac death. Circulation. 2001; 104: 876-880. Meisel C, Afshar-Kharghan V, Cascorbi I, Laule M, Stangl V, Felix SB, Baumann G, Lopez JA, Roots I, Stangl K. Role of Kozak sequence polymorphism of platelet glycoprotein Ib as a risk factor for coronary artery disease and catheter interventions. J Am Coll Cardiol. 2001; 38: 1023-1027. Ohashi K, Ouchi N, Kihara S, Funahashi T, Nakamura T, Sumitsuji S, Kawamoto T, Matsumoto S, Nagaretani H, Kumada M, Okamoto Y, Nishizawa H, Kishida K, Maeda N, Hiraoka H, Iwashima Y, Ishikawa K , Ohishi M, Katsuya T, Rakugi H, Ogihara T, Matsuzawa Y. Adiponectin I164T mutation is associated with the metabolic syndrome and coronary artery disease.J Am Coll Cardiol 2004; 43: 1195-200. Doney AS, Fischer B, Leese G, Morris AD, Palmer CN.Cardiovascula r risk in type 2 diabetes is associated with variation at the PPARG locus: a Go-DARTS study.Arterioscler Thromb Vasc Biol 2004; 24: 2403-7. Koh KK. Effects of estrogen on the vascular wall: vasomotor function and inflammation.Cardiovasc Res 2002; 55: 714-26.

It is a figure which shows the fine structure and characteristic of a microbead detected with Luminex100. It is a figure which shows the outline | summary of the procedure of PCR-SSOP-Luminex method.

Claims (2)

AKAP10 2073A → G, IPF1 −108 / 3G → 4G, ADIPOQ-11377C → G, AGER 268G → A, and PAX4 567C → T gene polymorphisms were detected, and AKAP10 2073A → G, ADIPOQ Japanese who have -11377C → G have a high risk of myocardial infarction, Japanese who have −108 / 3G → 4G of IPF1, 268G → A of AGER, and 567C → T of PAX4 have a high risk of myocardial infarction. A method for detecting a genetic risk of myocardial infarction in Japanese, characterized in that low is one of the judgment materials for evaluation. The method for detecting the genetic polymorphism is a PCR-SSOP-Luminex method, and among the nucleotide sequences for use in carrying out this method,
For detection of 2073A → G of AKAP10, the base sequence of the PCR primer is SEQ ID NO: 47: ggcccaggaa gagctagctt g and SEQ ID NO: 48: gtagatttct ctaacggttg atcat, and the base sequence for polymorphism check is SEQ ID NO: 49: gatagtcagt gacattatgc ag and SEQ ID NO: 50: cctgctgcat aacgtcactg,
For detection of −108 / 3G → 4G of IPF1, the base sequence of the PCR primer is SEQ ID NO: 9: tggctgtggg ttccctctgag and SEQ ID NO: 10: gatttggcac tgtgtggcgt tc, and the base sequence for polymorphism check is the sequence No. 11: cgagcagggg tggcgcc and SEQ ID No. 12: ggcgccaccc tgctcgct
For the detection of -11377C → G of ADIPOQ, the base sequences of PCR primers are SEQ ID NO: 51: taattcatca gaatgtgtgg cttg and SEQ ID NO: 52: ttaggcttga agtggcaaca ttc, and the base sequence for polymorphism check is SEQ ID NO: 53 : Gctcagatcc tgcccttcaa a and SEQ ID NO: 54: gttttgtttt tgaagcgcag gat,
For detection of AGER 268G → A, the base sequence of the primer for PCR is SEQ ID NO: 39: acaggccgga cagaagcttgg and SEQ ID NO: 40: cattcctgtt cattgcctgg cac, and the base sequence for polymorphism check is SEQ ID NO: 41: The genetic risk detection method for myocardial infarction according to claim 1, which is gaagagggag ccgttgggaa and SEQ ID NO: 42: gtccttccca acagctccct.