JP6253560B2 - Apparatus, program and recording medium for determining effectiveness of antidiabetic drug - Google Patents

Description

  The present invention relates to a method for determining the effectiveness of a therapeutic drug for diabetes, and more specifically, a method for determining a therapeutic drug effective for a patient from a plurality of types of therapeutic drugs for diabetes based on genetic information for each diabetic patient. About.

  Conventionally, it has been known that, when a drug treatment is performed for a certain disease, a single drug does not act on all patients having the same disease in the same manner. In response to this, in recent years, the momentum of genomic pharmacology and custom-made medical care, which provides the most efficient drug administration according to the genetic characteristics of patients, has been increasing. According to genomic pharmacology, it is considered that whether or not the effect of a drug works effectively is determined based on the genetic background of the patient himself. In response to the growing momentum of genomic pharmacology, in the future, by identifying each patient as a responder and non-responder for the drug, the optimal drug dosing determination and dosing plan for each patient will be determined. Is expected to do.

  Patent Document 1 below discloses a method for selecting a drug. According to the selection method of Patent Document 1, an optimal drug corresponding to a patient can be selected from a plurality of sympathetic nerve blockers based on the genetic background for each patient with benign prostatic hyperplasia.

JP 2008-005785 A

  Thus, the momentum of drug selection based on the genetic background of patients is increasing, and as shown in Patent Document 1, a method for drug selection has been proposed for some diseases. However, among the various diseases, for diabetes whose number of patients has increased due to the recent increase in lifestyle-related diseases, the relationship between the therapeutic effect of drugs and genetic factors has not been sufficiently reported yet. It is a fact.

  If the patient does not improve the diabetic condition even after starting to take antidiabetic drugs, it is necessary to take multiple types of therapeutic drugs in combination. However, in the past, there were only sulfonylurea drugs (SU drugs) as antidiabetic drugs. However, in recent years, the types of antidiabetic drugs have increased, so when a patient starts taking multiple types of therapeutic drugs, It was difficult to understand whether it was an effect of a therapeutic drug. For this reason, many patients with diabetes still take a plurality of types of antidiabetic drugs, which imposes a heavy burden on patients in terms of the timing of taking each therapeutic drug and the cost.

  The present invention has been made to solve the above-mentioned problems, and its object is to appropriately determine a therapeutic drug effective for a patient from a plurality of types of diabetes therapeutic drugs based on the genetic background of each patient. There is to do.

  The inventor has been diligently studying to achieve the above object, and has found a specific genetic polymorphism that is significantly related to the blood HbA1c value, which is an index for determining diabetes, for a specific diabetes therapeutic drug. It was.

  In addition, based on this knowledge, the present inventor can estimate the degree of decrease in blood HbA1c after a predetermined period of time has elapsed after taking a therapeutic drug, depending on the presence or absence of such a specific genetic polymorphism in a diabetic patient. The regression equation used for estimation was found.

  That is, the present invention includes the following aspects.

The first determination method according to the present invention is:
In a device comprising a calculation unit and a recording unit, a method for determining the effectiveness of a therapeutic agent for diabetes,
The recording unit records the obtained genetic polymorphism information and clinical data of the subject;
The operation unit identifies whether or not a specific genetic polymorphism exists in the acquired genetic polymorphism information;
The operation unit determines the effectiveness of a therapeutic agent for diabetes based on the presence / absence information of the specific gene polymorphism and the clinical data;
Including
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
This is a determination method.

The second determination method according to the present invention is the first determination method,
In the determining step, the calculation unit uses a regression equation determined in advance by multiple regression analysis, and an estimated decrease in the blood HbA1c value of the subject and an estimated decrease in the blood HbA1c value of the standard patient, When the estimated decrease in the blood HbA1c value of the subject is greater than the estimated decrease in the blood HbA1c value of the standard patient, it is determined that the antidiabetic agent is effective.
The regression equation was obtained by performing multiple regression analysis on a set of diabetic patient data using the amount of change in blood HbA1c value dHbA1c as an objective variable and information on the presence or absence of the specific gene polymorphism as explanatory variables. Is an expression,
The information on the presence or absence of the specific gene polymorphism in the regression equation is a value of 0 or 1 when the estimated decrease in the blood HbA1c value of the subject is calculated, and the standard patient When calculating the estimated degree of decrease in the blood HbA1c value of the subject, the determination method is a real value between 0 and 1, which means the ratio of the standard patient having the specific gene polymorphism It is.

A third determination method according to the present invention is the second determination method,
The clinical data includes information on the course of administration of the antidiabetic agent of the subject,
If there is no history of taking the therapeutic agent for diabetes in the information of the administration process, and it is determined that the therapeutic agent for diabetes is effective, it is determined to administer the therapeutic agent for diabetes to the subject,
The determination that the diabetes therapeutic agent is not administered to the subject when the diabetes therapeutic agent is taken in the administration progress information and the diabetes therapeutic agent is determined not to be effective. Is the method.

A fourth determination method according to the present invention is the second determination method,
The combination of the therapeutic agent for diabetes and the specific gene polymorphism is the first combination, and the regression equation is
dHbA1c = a ₁ × [MTR_2756A_Allele] + b ₁ × [CCR5_59029AA] + c ₁
Where constants a ₁ , b ₁ , c ₁ are partial regression coefficients determined by the multiple regression analysis, [MTR_2756A_Allele] is information on the presence or absence of the 2756A allele of MTR, [ CCR5_59029AA] is a determination method, which is information regarding the presence or absence of 59029AA in CCR5.

A fifth determination method according to the present invention is the second determination method,
A combination of the antidiabetic agent and the specific gene polymorphism is the second combination, and the regression equation is
dHbA1c = a ₂ × [ALDH2_42421GG] + b ₂ × [ITGB2_1323TT] + c ₂ × [AKR1B10_-106TT] + d ₂
Where constants a ₂ , b ₂ , c ₂ , and d ₂ are partial regression coefficients determined by the multiple regression analysis, and [ALDH2_42421GG] is information on the presence or absence of 42421GG in ALDH2. , [ITGB2_1323TT] is information regarding the presence / absence of 1323TT of ITGB2, and [AKR1B10_-106TT] is information regarding the presence / absence of -106TT of AKR1B10.

A sixth determination method according to the present invention is the second determination method,
A combination of the antidiabetic agent and the specific gene polymorphism is the third combination, and the regression equation is
dHbA1c = a ₃ × [VWF_-1051GG] + b ₃ × [ALDH2_42421GG] + c ₃
Where constants a ₃ , b ₃ , and c ₃ are partial regression coefficients determined by the multiple regression analysis, and [VWF_-1051GG] is information on the presence or absence of -1051GG in VWF. , [ALDH2_42421GG] is a determination method that is information on the presence or absence of 42421GG of ALDH2.

The seventh determination method according to the present invention is the second determination method,
A combination of the antidiabetic drug and the specific gene polymorphism is the fourth combination, and the regression equation is
dHbA1c = a ₄ × [THPO_5713A_Allele] + b ₄ × [CCR5_59029GG] + c ₄ × [IL10_-819TT] + d ₄
Where constants a ₄ , b ₄ , c ₄ , and d ₄ are partial regression coefficients determined by the multiple regression analysis, and [THPO_5713A_Allele] is information on the presence or absence of THPO 5713A allele. Yes, [CCR5_59029GG] is information on the presence / absence of 59029GG in CCR5, and [IL10_-819TT] is information on the presence / absence of IL10-819TT.

An eighth determination method according to the present invention is the second determination method,
The combination of the therapeutic agent for diabetes and the specific gene polymorphism is the fifth combination, and the regression equation is
dHbA1c = a ₅ × [CCL5_-28G Allele] + b ₅
Where constants a ₅ and b ₅ are partial regression coefficients determined by the multiple regression analysis, and [CCL5_-28G Allele] is information on the presence or absence of the -28G allele of CCL5. This is a determination method.

A ninth determination method according to the present invention is the second determination method,
A combination of the antidiabetic agent and the specific gene polymorphism is the sixth combination, and the regression equation is
dHbA1c = a ₆ × [IL18_4383AA] + b ₆ × [MTHFR_677C_Allele] + c ₆
Where constants a ₆ , b ₆ , c ₆ , and d ₆ are partial regression coefficients determined by the multiple regression analysis, and [IL18_4383AA] is information on the presence or absence of IL18 4383AA. , [MTHFR_677C_Allele] is a determination method, which is information on the presence / absence of the MTHFR 677C allele.

A first determination device according to the present invention includes:
An apparatus that includes a calculation unit and a recording unit, and determines the effectiveness of a therapeutic agent for diabetes,
The recording unit records the obtained genetic polymorphism information and clinical data,
The arithmetic unit identifies whether a specific genetic polymorphism exists in the acquired genetic polymorphism information,
The arithmetic unit determines the effectiveness of a therapeutic drug for diabetes based on the presence or absence of the specific gene polymorphism and the clinical data,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
This is a determination device.

The determination program according to the present invention includes:
A program for determining the effectiveness of antidiabetic drugs,
On the computer,
A function to record the acquired genetic polymorphism information and clinical data;
A function for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
A function of determining the effectiveness of an antidiabetic agent based on the presence / absence information of the specific gene polymorphism and the clinical data;
Realized,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
Is a determination program.

A computer-readable recording medium in which a determination program according to the present invention is recorded is as follows.
A computer-readable recording medium storing a program for determining the effectiveness of a therapeutic agent for diabetes,
On the computer,
A function to record the acquired genetic polymorphism information and clinical data;
A function for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
A function of determining the effectiveness of an antidiabetic agent based on the presence / absence information of the specific gene polymorphism and the clinical data;
Realized,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
Or a computer-readable recording medium on which a determination program is recorded.

A tenth determination method according to the present invention includes:
A method for determining the effectiveness of a therapeutic agent for diabetes,
Obtaining genetic polymorphism information and clinical data of the subject;
Identifying whether a specific genetic polymorphism exists in the obtained genetic polymorphism information;
Determining the effectiveness of a therapeutic agent for diabetes based on the presence or absence of the specific gene polymorphism and the clinical data;
Including
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
This is a determination method.

The second determination apparatus according to the present invention is:
A device for determining the effectiveness of a therapeutic agent for diabetes,
Means for recording the obtained genetic polymorphism information and clinical data;
Means for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
Means for determining the effectiveness of a therapeutic agent for diabetes based on the presence / absence information of the specific gene polymorphism and the clinical data;
With
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide, and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination wherein the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes the -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
This is a determination device.

  According to the present invention, by identifying whether or not a specific genetic polymorphism found to be related to the therapeutic effect of a diabetic drug is included in the genetic polymorphism information of a patient, multiple types of diabetes treatment A therapeutic drug effective for the patient can be determined from the drugs, and a more appropriate therapeutic drug can be determined based on the genetic background of each patient.

  That is, when the determination method of the present invention is applied to a patient who has already taken a plurality of types of antidiabetic drugs, a therapeutic drug that effectively lowers the blood HbA1c level among the plurality of types of therapeutic drugs currently taken Can be determined and presented to the doctor, thereby presenting useful information for the doctor to determine which of the multiple types of therapeutic drugs that have already been taken that do not need to be taken Can do.

  In addition, when the determination method of the present invention is applied to a patient who intends to take an antidiabetic drug for the first time, the degree of decrease in blood HbA1c value is estimated for each of a plurality of types of therapeutic drugs before actually prescribing the therapeutic drug. It becomes possible to do. Thereby, when a doctor prescribes a therapeutic drug to a patient, the therapeutic drug contributing to the decrease in the blood HbA1c value can be determined and presented to the doctor.

It is a block diagram which shows the whole system containing the apparatus which determines the effectiveness of the diabetes therapeutic agent which concerns on this invention. It is a flowchart which shows the determination process which a determination apparatus performs. It is a flowchart which shows the determination process of step S4 which a determination apparatus performs.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description and drawings, the same reference numerals indicate the same or similar components, and thus descriptions of the same or similar components are omitted.

  In the following description, “gene polymorphism” refers to the diversity of genes in which two or more alleles (alleles) exist at one locus in the same population. Specifically, it indicates a mutation of a gene that exists at a certain frequency or more in a certain population. The gene mutation referred to here is not limited to the region transcribed as RNA, but includes mutations in all DNAs that can be identified on the human genome including regulatory regions such as promoters and enhancers. 99.9% of human genomic DNA is common among individuals, and the remaining 0.1% is responsible for such diversity, and is involved in individual differences in susceptibility to specific diseases, responsiveness to drugs and environmental factors. obtain. A genetic polymorphism does not always make a difference in phenotype. SNP (single nucleotide polymorphism) is a kind of gene polymorphism, but the gene polymorphism targeted by the present invention is not limited to this.

  A “gene polymorphism set” refers to a combination of a plurality of gene polymorphisms. Here, a plurality of gene polymorphisms means two or more gene polymorphisms having different gene loci. In addition, “gene polymorphism” here refers to and includes a genotype. That is, in the present invention, “gene polymorphism” means a gene polymorphism having a specific genotype.

  FIG. 1 is a block diagram showing the entire system including an apparatus for determining the effectiveness of a therapeutic drug for diabetes according to the present invention (hereinafter referred to as a determination apparatus). As shown in FIG. 1, the system is installed in blood collection means 11 and computer 12 installed in hospital 1, gene polymorphism analysis array 21 and computer 22 installed in analysis institution 2, and service providing institution 3. The determination device 31 is provided. Here, the computers 12 and 22 and the determination device 31 are connected to a communication line 4 such as the Internet.

  The determination device 31 includes a CPU 32, a memory 33, a recording unit 34 such as a hard disk, a communication interface (hereinafter referred to as I / F) unit 35 that performs communication with the outside, an operation unit 36 such as a keyboard, and a liquid crystal display. A display unit 37 such as a display, an input / output I / F unit 38, and an internal bus 39 for exchanging data between the units are provided. In the recording unit 34, information on a specific gene polymorphism found to be related to the therapeutic effect of the diabetes drug is recorded in advance as a reference table.

  Details of the validity determination processing by the determination device 31 will be described later. The operation of the entire system including the determination device 31 will be described as follows.

  First, in the hospital 1, a specimen (hereinafter referred to as a test sample) from which a subject's gene (genome) can be extracted is collected by the blood collection means 11. At this time, clinical data (subject ID, sex, past administration of anti-diabetic drugs, blood collection information (including current and past HbA1c values), past history, physical findings, etc.) are recorded in the recording unit of the computer 12. The The test sample is provided to the analysis organization 2 and analyzed using the gene polymorphism analysis array 21 to detect a gene polymorphism having a genotype. The gene polymorphism analysis array 21 is a probe in which probes for detecting a gene polymorphism are arranged at high density and immobilized on a support such as a silicon wafer or a glass slide. What is necessary is just to specifically recognize and capture a gene polymorphism associated with a disease (in the present invention, diabetes).

  In the present invention, the gene polymorphism detected using the gene polymorphism analysis array 21 only needs to include at least information of a specific gene polymorphism used for determination (recorded in advance as a reference table). Other than these, polymorphisms other than these may be included in relation to conducting tests other than the effectiveness determination of the therapeutic agent for diabetes targeted by the present invention. An example of the gene polymorphism detected using the gene polymorphism analysis array 21 is shown in Table 1. The 100 gene polymorphisms shown in Table 1 are gene polymorphisms known to be associated with diabetes.

  The detected genetic polymorphism information is once recorded in the recording means of the computer 22 and then transmitted to the determination device 31 of the service providing organization 3 via the communication line 4. The determination device 31 receives genetic polymorphism information from the computer 22 of the analysis institution 2 via the communication I / F unit 35 and records it in the recording unit 34. The determination device 31 receives clinical data from the computer 12 of the hospital 1 and records it in the recording unit 34.

  Thereafter, the determination device 31 searches whether or not the received genetic polymorphism information is included in the reference table recorded in the recording unit 34 in advance, and according to the result, the effectiveness of administration of the therapeutic agent Determine. After the determination, the determination device 31 transmits the determination result to the computer 12 of the hospital 1 via the communication line 4. The determination result received by the computer 12 is recorded in the recording unit of the computer 12 in association with clinical data (at least the subject ID), and is appropriately called and used (for example, presented to the subject). Information specifying the computer 12 of the hospital 1 that transmits the determination result may be included in the clinical data and transmitted from the computer 12 of the hospital 1, for example.

  Any method can be used for analyzing the test sample as long as it is a method for detecting the genotype of the subject. As a general method, a sample containing DNA, such as a subject's blood, sputum, skin, bronchoalveolar lavage fluid, other body fluid, or tissue, is used as a test sample. Many methods are known as analysis methods, for example, sequencing method, PCR method, ASP-PCR method, TaqMan method, invader assay method, MALDI-TOF / MS method, molecular beacon method, ligation method, etc. (Clin. Chem. 43: 1114-1120, 1997). The sequencing method is a method for directly sequencing a DNA region containing a gene polymorphism. In the PCR method, only a certain gene polymorphism is specifically amplified using primers specific to the gene polymorphism. In the PCR method, it is common to place the polymorphic nucleic acid on the 3′-most side, but the genetic polymorphism is second from the 3′-end side as in the Allele Specific Primer (ASP) -PCR method. In the primer design, such as where to place the gene polymorphism in the primer, and what kind of nucleic acid sequence other than the gene to be detected There is no particular limitation as long as it can be identified. In the TaqMan method, an allele-specific probe labeled at both ends with a fluorescent dye and a quenching substance is hybridized to a target site, and a PCR reaction is performed with primers designed to amplify a region containing this site. When the PCR reaction from the primer reaches the region where this allele-specific probe is hybridized, the fluorescent dye present at the 5 ′ end of the hybridized probe is cleaved by the 5-prime nuclease activity of Taq polymerase, leaving the quencher. Causes fluorescence. By this method, it can be seen how much the allele-specific probe has been hybridized. In the invader assay method, an allele probe having a specific sequence 5 ′ from the gene polymorphic site of the template and a flap sequence on the 3 ′ side, and a specific sequence 3 ′ from the gene polymorphic site of the template It is possible to specify which allele probe has hybridized using the same principle as the TaqMan method, using three types of oligonucleotides: an invader probe having a FRET probe and a FRET probe containing a sequence complementary to a flap sequence. In the MALDI-TOF / MS method, a primer adjacent to a gene polymorphic site is prepared and this region is amplified, and then only one base of the gene polymorphic site is amplified using ddNTP. Subsequently, gene polymorphism is identified by identifying the kind of added ddNTP using MALDI-TOF / MS. In a method generically called a DNA chip method such as the Hybridene method, an oligonucleotide probe containing a gene polymorphism is placed on an array, and hybridization with sample DNA amplified by PCR is detected.

  FIG. 2 is a flowchart illustrating the determination process performed by the determination device 31. Hereinafter, the effectiveness determination process by the determination device 31 will be described in detail with reference to the flowchart of FIG. In the following description, the processing performed by the CPU 32 is described unless otherwise specified. Further, the CPU 32 uses the memory 33 as a work area or an area for temporarily storing data during processing, and records data during processing and processing results in the recording unit 34 as necessary.

  In step S1, genetic polymorphism information and clinical data of a subject used for determination are acquired.

First, genetic polymorphism information is acquired from the analysis organization 2 via the communication line 4 and recorded in the recording unit 34. The gene polymorphism information is transmitted and recorded as a gene polymorphism code assigned to each gene polymorphism having a genotype, and the data format is, for example, a hospital code assigned to each requesting hospital and a subject assigned to each subject. This is a format in which a plurality of gene polymorphism codes g _i (i = 1 to n) are associated with each ID group. In addition, the clinical data of the subject is acquired from the hospital 1 via the communication line 4 and is recorded in the recording unit 34 after being given the requesting hospital code.

  As the data format of clinical data, for example, sex is recorded in clinical data as variable SEX = 1 for males and SEX = 2 for females. About the past administration of antidiabetic drugs, the variable name to be used on the system is determined in advance for each therapeutic drug. The value is 1 when there is a history of medication and the value when there is no history of medication. Is set to 0, and variable names and values are set and recorded in clinical data. For example, it is recorded in clinical data as antidiabetic agent [α-glucosidase inhibitor (α-GI)] = 1. If there is a medication history, information such as the number of years of medication history may be recorded together. For past medical history and physical findings, for each disease name or symptom, the variable name to be used on the system is determined in advance, and the value is 1 if there is a medical history or if there is a symptom. The value is set to 0, and the variable name and value are set and recorded in clinical data. For example, the clinical history is recorded as clinical history [prostate tumor] = 0 and physical findings [lower leg edema] = 1.

  In step S2, it is identified whether or not a specific genetic polymorphism exists in the genetic polymorphism information of the subject obtained in step S1.

Specifically, for one {hospital code, subject ID}, a plurality of gene polymorphism codes g _i (i = 1 to n) are read from the recording unit 34, and the read gene polymorphism codes g _i are recorded in advance. Whether it is included in the reference table recorded in the section 34 is identified. In the reference table, information of a specific gene polymorphism that has been found by the present inventor to be related to the therapeutic effect of a diabetic drug (reduction in blood HbA1c value) is recorded in advance. For example, MTR 2756A allele and CCR5 59029AA are recorded in the form of a gene polymorphism code as specific gene polymorphisms related to, for example, an α-glucosidase inhibitor (α-GI). For example, it reads out the polymorphism encoding g ₁ from the recording unit 34, identifies whether this polymorphism code g ₁ is included in the reference table. If it is included, a value “1” (true) is set in the flag corresponding to the genetic polymorphism code g ₁ (assuming that the flag is initialized to “0” (false) in advance). This identification process is executed for all of the plurality of gene polymorphism codes g _i (i = 1 to n).

  Regarding the notation of gene polymorphism, the international name of the gene polymorphism is shown in the third column “GeneName (Intl.)” And the fourth column “Gtype (Intl.)” In Table 2. In the claims described below, gene polymorphisms are described under these international names.

  In step S3, determination is made based on the flag value recorded in step S2, and all the flag values are “0” (false), that is, the specific gene described in the reference table in the genetic polymorphism information of the subject. If the polymorphism does not exist, it is determined that there is no necessity for the validity determination at this point, and the process proceeds to step S5 without performing the process of step S4. If the value of any one of the flags is “1” (true), the process proceeds to step S4 using a predetermined determination formula.

  In step S4, the effectiveness of therapeutic drug administration is determined based on a predetermined determination formula using the flag value recorded in step S2 and the clinical data acquired in step S1.

First, the determination formula used in step S4 will be described. In the judgment formula, the amount of change in blood HbA1c value dHbA1c is the objective variable, information on the presence or absence of a specific genetic polymorphism is an explanatory variable, and regression determined by multiple regression analysis of a set of diabetic patient data Use the formula. For calculating the value of the regression equation, the value of the flag, which is the information on the presence or absence of the specific gene polymorphism, identified in step S2 is used, and using this regression equation, the blood HbA1c value of the subject is estimated A change amount dHbA1c and an estimated change amount dHbA1c _mean of the blood HbA1c value of a standard patient (hereinafter, also referred to as a standard patient) for determination comparison are calculated. In this embodiment, the standard patient is, for example, a type 2 diabetic patient whose morbidity period is a predetermined period (2 to 10 years).
The regression equation for obtaining the change amount dHbA1c in the blood HbA1c value is, for example, in the case of an α-glucosidase inhibitor (α-GI) as follows.

dHbA1c = a × [MTR_2756A_Allele] + b × [CCR5_59029AA] + c (Formula 1)
Here, dHbA1c is the estimated amount of change in blood HbA1c value (unit%, NGSP international standard value), and [MTR_2756A_Allele] and [CCR5_59029AA] are the presence or absence of the 2756A allele of MTR and the presence or absence of 59029AA of CCR5, respectively.

  When calculating the estimated change for the subject, both [MTR_2756A_Allele] and [CCR5_59029AA] take either “1” (having) or “0” (not having), and the estimated change for the standard patient. When calculated, [MTR_2756A_Allele] and [CCR5_59029AA] mean the percentage of standard patients that possess that particular genetic polymorphism, both taking real values between 0 and 1. The constants a, b, and c are partial regression coefficients determined by multiple regression analysis. The unit is the same as the blood HbA1c value and is “%” (NGSP international standard value). Since multiple regression analysis is well known, description thereof is omitted.

  Table 3 shows the partial regression coefficient used in the actual regression equation determined by an experimental example to be described later for each therapeutic agent for diabetes. An example of the result of the multiple regression analysis performed to determine the constants a, b, and c in the regression equation is shown in Table 4 described later. In Table 3, the therapeutic agent AGI is an α-glucosidase inhibitor (α-GI), the therapeutic agent BG_ is a biguanide agent (BG agent), the therapeutic agent GRI is a glinide agent, and the therapeutic agent INS is an insulin preparation The therapeutic agent ISE is a pioglitazone agent, and the therapeutic agent SU is a sulfonylurea agent (SU drug).

  In the case of the above formula 1 for determining the α-glucosidase inhibitor (α-GI), the descent coefficients shown in the row of the therapeutic agent AGI in Table 3 are constants a and b, and Hb0 is the constant c. These constants a, b, and c are partial regression coefficients determined by multiple regression analysis. With reference to Table 4 to be described later, constant a = −1.13611, constant b = −0.18008, constant c = 8. 07017.

Next, an example of the specific determination procedure performed in step S4 will be described below. FIG. 3 is a flowchart showing the determination process of step S4 performed by the determination apparatus 31. In an example of the determination procedure described below, a therapeutic drug is obtained based on the estimated change amount dHbA1c of the blood HbA1c value for the subject and the estimated change amount dHbA1c _mean of the blood HbA1c value of the standard patient obtained from the regression equation. In addition, the clinical data including information on the course of administration of the antidiabetic drug so far is determined to determine whether the therapeutic drug needs to be administered.

  In step S41, the estimated change amount dHbA1c of the blood HbA1c value of the subject is calculated using the flag value recorded in step S2 and the regression equation corresponding to the type of therapeutic agent.

  For example, in the case of an α-glucosidase inhibitor (α-GI), the following regression equation is used.

dHbA1c = −1.13611 × [MTR_2756A_Allele] −0.18008 × [CCR5_59029AA] +8.007017 (Formula 2)
[MTR_2756A_Allele] and [CCR5_59029AA] are both the flag values recorded in step S2, which are “1” or “0”.

In step S42, the estimated change amount dHbA1c _mean of the blood HbA1c value of the standard patient is calculated using the same regression equation used in step S41 used for the subject. The regression equation is as follows.

dHbA1c _mean = −1.13611 × [MTR_2756A_Allele] −0.18008 × [CCR5_59029AA] +8.007017 (Formula 3)
When calculating the estimated change for a standard patient, [MTR_2756A_Allele] and [CCR5_59029AA] use the percentage that the standard patient possesses that particular genetic polymorphism. This is a value corresponding to the column “standard patient holding ratio” shown in Table 3. Referring to Table 3, [MTR_2756A_Allele] = 0.986 and [CCR5_59029AA] = 0.286.

In step S43, based on the estimated change amount (estimated drop) dHbA1c of the obtained subject's blood HbA1c value and the estimated change amount (estimated drop) dHbA1c _{mean of} the obtained standard patient's blood HbA1c value. Determine if the therapeutic is effective.

Specifically, the subject's estimated change dHbA1c and the standard patient's estimated change dHbA1c _mean are compared in magnitude, and when the subject's estimated change dHbA1c is greater than the standard patient's estimated change dHbA1c _mean , It is determined that the therapeutic agent is effective, and the information is recorded as data in the recording unit. Thereby, the effectiveness of the therapeutic agent when the subject takes the antidiabetic agent to be determined is determined.

  In step S44, as an optional step, it is determined whether or not it is necessary to administer the therapeutic agent to be determined based on the past administration of the therapeutic agent. The course of administration of the therapeutic agent of the subject is included in the clinical data acquired in step S1.

  For example, even if the subject has no history of taking the diabetes therapeutic agent to be determined, if it is determined in step S43 that the diabetes therapeutic agent to be determined is genetically effective, in such a case, the subject However, when taking the therapeutic agent, it is expected to effectively reduce the blood HbA1c level, and the improvement of symptoms can be expected. Therefore, it is determined that the determination target therapeutic agent for diabetes is administered to the subject.

  On the other hand, in the case where it is determined in step S43 that the determination target diabetes therapeutic agent is not genetically effective in spite of the subject having a history of taking the determination target diabetes therapeutic agent, in such a case Even if the subject continues to take the therapeutic agent, it is not expected to effectively reduce the blood HbA1c level, and improvement in symptoms cannot be expected, so it is determined that the target diabetes therapeutic agent will not be administered to the subject To do.

  In step S45, it is determined whether or not determination has been made for all antidiabetic agents, and the processing in steps S41 to S44 is repeated until it is determined that determination has been completed. If it is determined that the determination has been completed for all antidiabetic agents, the determination process in step S4 is terminated.

  In step S5, it is determined whether or not the process has been completed for all {hospital code, subject ID}, and the processes in steps S2 to S4 are repeated until it is determined that the process has been completed.

  If it is determined that the processing has been completed for all {hospital code, subject ID}, in step S6, the information on the effectiveness of therapeutic agent administration determined in step S3 or S4 is associated with the subject ID. The data is transmitted to the computer 12 corresponding to the requesting hospital code via the communication line 4.

  With the above processing, a series of validity determination processing by the determination device 31 is completed. Note that the determination process described above uses a general-purpose computer to read a computer program recorded on a computer-readable recording medium such as a hard disk or a CD-ROM, or obtain a computer program via a communication line. It can also be performed by the CPU executing it.

  As mentioned above, although this invention was demonstrated by specific embodiment, this invention is not limited to above-described embodiment.

  In the above embodiment, among various antidiabetic drugs, the effectiveness is determined for the α-glucosidase inhibitor (α-GI), and the necessity of administration of the therapeutic agent to the patient is determined. Similarly, with respect to other antidiabetic drugs, the effectiveness of the therapeutic drug can be determined to determine whether the therapeutic drug needs to be administered. Regarding other anti-diabetic drugs other than the α-glucosidase inhibitor (α-GI) described in the above embodiment, in the experimental examples described later, gene polymorphisms related to the therapeutic agents and partial regression of specific regression equations The coefficient will be described. In addition, although not shown in the experimental examples to be described later, dipeptidyl peptidase (DPP) IV inhibitor, which is a therapeutic drug for diabetes, also has specific gene polymorphisms related to the therapeutic drug and specific examples, as well as other therapeutic drugs for diabetes. And a partial regression coefficient of the regression equation can be determined.

  Further, in the above embodiment, when determining whether or not a therapeutic drug needs to be administered in step S44, the determination is based only on the past administration of the therapeutic drug. Not limited. For example, in addition to the course of administration of the therapeutic drug so far by the subject, the necessity of administration of the therapeutic drug may be determined by further considering the subject's past history and physical presence / absence. . Furthermore, when a new report is made regarding a past history or physical findings that should be taken into consideration in the determination, the determination criteria may be appropriately added or changed accordingly.

  In addition, as long as the above-described determination process can be realized, the apparatus of the present invention can be an apparatus in which other configurations are appropriately added or replaced with other configurations as necessary. For example, instead of the display unit 37 and the operation unit 36, a touch panel type configuration in which these units are integrated may be realized.

  In the above embodiment, the genetic polymorphism information and clinical data of the subject used for the determination are acquired via the communication line 4, but the data acquisition route is not limited to this, and the data used for the determination, etc. May be recorded in some recording means (for example, portable recording means such as an IC card or a memory card provided for each individual), and data or the like may be read out and used accordingly.

  In addition, the individual's genetic polymorphism information and clinical data acquired from the analysis organization are recorded in the database of the service provider in correspondence with the individual ID, and if the individual ID is notified to each individual, the individual ID Just by receiving the communication, the effectiveness determination process can be performed again using the corresponding gene polymorphism information and clinical data recorded in the database.

  Moreover, in the said embodiment, although the determination process shown in FIG. 2 and FIG. 3 is described as a process which CPU32 performs, the process which CPU32 performs is classified into each function, and a dedicated electronic circuit for every function These electronic circuits may share and execute the determination processing of FIG. 2 and FIG.

  Moreover, in the said embodiment, although the determination process shown in FIG.2 and FIG.3 is described as a process which CPU32 performs, as long as the data used for a process are visualized by paper data etc., it performs by a determination process. The calculation process and the identification process may be appropriately performed by a human.

  Hereinafter, the present invention will be described more specifically through description of experimental examples. However, the present invention is not limited to the following experimental examples.

・ Investigation contents In the following experimental examples, the relationship between antidiabetic drugs and various gene polymorphisms was investigated. Specifically, after taking a prescribed therapeutic drug, the change in the blood HbA1c level of the subject after the lapse of a prescribed period (in this experimental example, 6 months) and the genetic polymorphism associated with diabetes We investigated and identified genetic factors that affect the blood glucose control effect of a given therapeutic agent. The following six types of anti-diabetic drugs were investigated for relevance.

Α-glucosidase inhibitor (α-GI)
・ Biguanide (BG drug)
・ Glinide ・ Insulin preparation (administered by injection to subjects)
・ Pioglitazone ・ Sulphonylurea (SU drug)

・ Subjects were patients with type 2 diabetes who had a disease duration of 2 to 10 years. The number of people surveyed for each anti-diabetic drug is as follows.

・ Α-glucosidase inhibitor (α-GI): 967 ・ Biguanide (BG): 846 ・ Glinide: 151 ・ Insulin preparation: 558 ・ Pioglitazone: 252 ・ Sulphonylurea (SU): 763 In selecting human subjects, type 2 diabetes patients were determined based on the criteria of the World Health Organization (WHO). All subjects were given written informed consent after fully explaining the purpose and content of this study.

-The genetic polymorphism whose relationship was investigated The genetic polymorphism whose relationship was investigated is as having shown in Table 1 in description of embodiment. The 100 gene polymorphisms shown in Table 1 are gene polymorphisms known to be associated with diabetes.

・ Survey results will be shown for each of the 6 types of anti-diabetic drugs investigated for their relevance.

(1) α-glucosidase inhibitor (α-GI)
Among the various gene polymorphisms shown in Table 1, a significant association was found between the 2756A allele of MTR and 59029AA of CCR5 and changes in the blood HbA1c level of the subjects. Specifically, single correlation coefficients were calculated for various gene polymorphisms shown in Table 1, and significant gene polymorphisms were extracted under the condition of p <0.05 using the p value of the chi-square test as an index. . The extracted significant genetic polymorphism was further subjected to multiple regression analysis, and the genetic polymorphism judged to be significantly related independently was identified.

  Next, in order to estimate the change value of the blood HbA1c value of the subject after a predetermined period (six months), a regression equation for determining the change amount dHbA1c of the blood HbA1c value was determined by multiple regression analysis. Multiple regression analysis was performed on the data set of 967 subjects who took an α-glucosidase inhibitor (α-GI). The amount of change in HbA1c value dHbA1c was used as the objective variable. The obtained regression equation was as shown in the following (Equation 4), and the results of the multiple regression analysis were as shown in Table 4 below.

dHbA1c = −1.13611 × [MTR_2756A_Allele] −0.18008 × [CCR5_59029AA] +8.007017 (Formula 4)

From the regression equation of (Equation 4), by using the value of “1” or “0” as information on the presence or absence of the 2756A allele of the gene polymorphism MTR and the presence or absence of 59029AA of CCR5, the estimated change in the blood HbA1c value of the subject The quantity dHbA1c could be determined. In addition, from the regression equation of (Equation 4), the percentage of standard patients who possess the specific gene polymorphism shown in Table 3 as information on the presence or absence of the 2756A allele of the gene polymorphism MTR and the presence or absence of 59029AA of CCR5 By using, the estimated change dHbA1c _mean of the blood HbA1c value of the standard patient could be obtained.

(2) Biguanide (BG drug)
Among various gene polymorphisms shown in Table 1, a significant association was found between ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, and changes in blood HbA1c levels in subjects. Similarly to the example shown for the α-glucosidase inhibitor of (1), a regression equation for determining the amount of change dHbA1c in the blood HbA1c value of the subject after the lapse of a predetermined period (6 months) was determined by multiple regression analysis. Multiple regression analysis is an explanatory variable for the collection of data for 846 subjects who took biguanide (BG drug) with or without genetic polymorphism ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT. The amount of change in blood HbA1c value dHbA1c was used as the objective variable. The obtained regression equation was as shown in the following (Equation 5), and the results of the multiple regression analysis were as shown in Table 5 below.

dHbA1c = −0.44041 × [ALDH2_42421GG] −0.35498 × [ITGB2_1323TT] −0.40731 × [AKR1B10_-106TT] +7.30465 (Formula 5)

(3) Glinide Agent Among the various gene polymorphisms shown in Table 1, a significant relationship was found between the change in the blood HbA1c value of the subject for VWF-1051GG and ALDH2 42421GG. Similarly to the example shown for the α-glucosidase inhibitor of (1), a regression equation for determining the amount of change dHbA1c in the blood HbA1c value of the subject after the lapse of a predetermined period (6 months) was determined by multiple regression analysis. Multiple regression analysis was performed on the set of data of 151 subjects who took glinide, with the presence or absence of -1051GG gene polymorphism and the presence or absence of 42421GG of ALDH2 as explanatory variables, and the change in blood HbA1c value dHbA1c. This was done as an objective variable. The obtained regression equation was as shown in the following (Equation 6), and the results of the multiple regression analysis were as shown in Table 6 below.

dHbA1c = −0.36332 × [VWF_-1051GG] −0.62017 × [ALDH2 — 42421GG] +6.994473 (Formula 6)

(4) Insulin preparations Among the various gene polymorphisms shown in Table 1, THPO 5713A allele, CCR5 59029GG and IL10 -819TT have a significant relationship with changes in blood HbA1c levels in subjects. I found it. Similarly to the example shown for the α-glucosidase inhibitor of (1), a regression equation for determining the amount of change dHbA1c in the blood HbA1c value of the subject after the lapse of a predetermined period (6 months) was determined by multiple regression analysis. Multiple regression analysis explains the presence or absence of the 5713A allele of the polymorphic THPO, the presence or absence of 59029GG of CCR5, and the presence or absence of -819TT of IL10, for a data set of 558 subjects who received insulin injections The amount of change in blood HbA1c value dHbA1c was used as a variable. The obtained regression equation was as shown in the following (Equation 7), and the results of the multiple regression analysis were as shown in the following Table 7.

dHbA1c = −0.257551 × [THPO_5713A_Allele] −0.25929 × [CCR5_59029GG] −0.21041 × [IL10_-819TT] +7.880514 (Formula 7)

(5) Pioglitazone Agent Among various gene polymorphisms shown in Table 1, the CCL5 -28G allele was found to have a significant relationship with the change in the blood HbA1c value of the subject. Similarly to the example shown for the α-glucosidase inhibitor of (1), a regression equation for determining the amount of change dHbA1c in the blood HbA1c value of the subject after the lapse of a predetermined period (6 months) was determined by multiple regression analysis. Multiple regression analysis was performed on a set of data for 252 subjects who took pioglitazone, with the presence or absence of the -28G allele of the gene polymorphism CCL5 as the explanatory variable and the change in blood HbA1c value dHbA1c as the objective variable. . The obtained regression equation was as shown in the following (Equation 8), and the results of the multiple regression analysis were as shown in Table 8 below.

dHbA1c = −0.48248 × [CCL5_−28G Allele] +7.05964 (Formula 8)

(6) Sulfonylurea agent (SU drug)
Among various gene polymorphisms shown in Table 1, a significant association was found between the IL18 4383AA and MTHFR 677C alleles with changes in the blood HbA1c level of the subjects. Similarly to the example shown for the α-glucosidase inhibitor of (1), a regression equation for determining the amount of change dHbA1c in the blood HbA1c value of the subject after the lapse of a predetermined period (6 months) was determined by multiple regression analysis. Multiple regression analysis was performed on 763 subjects who took sulfonylurea (SU drug), with the presence or absence of gene polymorphism IL18 4383AA and the presence or absence of MTHFR 677C allele as explanatory variables, and the blood HbA1c level The amount of change dHbA1c was used as the objective variable. The obtained regression equation was as shown in the following (Equation 9), and the result of the multiple regression analysis was as shown in the following Table 9.

dHbA1c = −0.25487 × [IL18_4383AA] −0.18756 × [MTHFR — 677C_Allele] +7.59879 (Formula 9)

DESCRIPTION OF SYMBOLS 1 Hospital 2 Analysis organization 3 Service provision organization 4 Communication line 11 Blood collection means 12, 22 Computer 21 Array for gene polymorphism analysis 31 Judgment device 32 CPU
33 Memory 34 Recording unit 35 Communication interface unit 36 Operation unit 37 Display unit 38 I / O interface unit 39 Internal bus

Claims (4)

An apparatus that includes a calculation unit and a recording unit, and determines the effectiveness of a therapeutic agent for diabetes,
The recording unit records the obtained genetic polymorphism information and clinical data,
The arithmetic unit identifies whether a specific genetic polymorphism exists in the acquired genetic polymorphism information,
The arithmetic unit determines the effectiveness of a therapeutic drug for diabetes based on the presence or absence of the specific gene polymorphism and the clinical data,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination in which the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes a -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
The determination apparatus which is either. A program for determining the effectiveness of antidiabetic drugs,
On the computer,
A function to record the acquired genetic polymorphism information and clinical data;
A function for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
A function of determining the effectiveness of an antidiabetic agent based on the presence / absence information of the specific gene polymorphism and the clinical data;
Realized,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination in which the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes a -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
A judgment program that is either A computer-readable recording medium storing a program for determining the effectiveness of a therapeutic agent for diabetes,
On the computer,
A function to record the acquired genetic polymorphism information and clinical data;
A function for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
A function of determining the effectiveness of an antidiabetic agent based on the presence / absence information of the specific gene polymorphism and the clinical data;
Realized,
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination in which the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes a -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
A computer-readable recording medium on which a determination program is recorded. A device for determining the effectiveness of a therapeutic agent for diabetes,
Means for recording the obtained genetic polymorphism information and clinical data;
Means for identifying whether or not a specific genetic polymorphism exists in the obtained genetic polymorphism information;
Means for determining the effectiveness of a therapeutic agent for diabetes based on the presence / absence information of the specific gene polymorphism and the clinical data;
With
A combination of the antidiabetic agent and the specific gene polymorphism is
The diabetes therapeutic agent is an α-glucosidase inhibitor (α-GI), and the specific genetic polymorphism includes a 2756A allele of MTR and a 59029AA of CCR5,
The diabetes therapeutic agent is a biguanide agent (BG drug), and the specific genetic polymorphism includes ALDH2 42421GG, ITGB2 1323TT, and AKR1B10 -106TT, a second combination,
A third combination in which the antidiabetic agent is a glinide and the specific polymorphism includes -1051GG of VWF and 42421GG of ALDH2;
A fourth combination in which the antidiabetic agent is an insulin preparation, and the specific polymorphism includes THPO 5713A allele, CCR5 59029GG and IL10-819TT;
The diabetes therapeutic agent is a pioglitazone agent, and the specific genetic polymorphism includes a -28G allele of CCL5, or
A sixth combination wherein the antidiabetic agent is a sulfonylurea agent (SU drug), and the specific gene polymorphism includes IL83 4383AA and MTHFR 677C allele;
The determination apparatus which is either.