JP2010142187A - Method for detecting genetic risk of chronic nephropathy - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a genetic detection method or the like for obtaining a material for determining genetic risk of chronic nephropathy. <P>SOLUTION: A gene polymorphism significantly relating to chronic nephropathy is detected by detecting 222 gene polymorphisms on 176 candidate genes of 4,587 Japanese subjects by PCR, sequence specific oligonucleotide probe, and suspension array technology (SAT). The risk detection method is useful for crisis estimation and prevention of chronic nephropathy. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a genetic risk detection method for chronic kidney disease and the like.

In addition to glomerulonephritis, chronic kidney disease (CKD) is one of lifestyle-related diseases because of high risk, diabetes, dyslipidemia (hyperlipidemia), obesity, and smoking. ing. The proportion of elderly people in Japan is said to continue to increase over the next 20 years, and the incidence of chronic kidney disease and end-stage renal disease (ESRD) will continue to increase due to kidney function that declines with age. (Non-Patent Document 1). Chronic kidney disease is not only end-stage renal failure but also a risk factor for cardiovascular disorders (Non-patent Documents 2-3), which causes myocardial infarction, heart failure, cerebral infarction and death. Therefore, identifying risk factors for chronic kidney disease is important not only to prevent the development of chronic kidney disease and end-stage renal failure, but to reduce the number of dialysis patients as well as cardiovascular disorders (non- Patent Document 4).
Linkage analysis (Non-Patent Documents 5-6) and candidate gene association analysis (Non-Patent Documents 7-9) have identified several chromosomal regions and genes that are suggested to be associated with chronic kidney disease. However, the genetic factors of chronic kidney disease have not been fully elucidated. Furthermore, it is important to consider genetic polymorphisms related to chronic kidney disease in each race, taking into account differences in lifestyle, environmental factors, and genetic factors among races.

  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 chronic kidney disease.

The present invention relates to chronic kidney disease and is a large-scale study on 222 polymorphisms in 176 genes for 4,587 Japanese.
We investigated the relationship between chronic kidney disease and genetic polymorphism. The purpose of this study is to identify genetic polymorphisms involved in chronic kidney disease, and to provide useful information to prevent chronic kidney disease for some people based on this finding.

The risk detection method of chronic kidney disease for solving the above-mentioned problems is -35 position C / T of UCP3, 36222 position A / G of PECAM1, 98 position G / C of HMOX1, -203 position T / G of APOE, AGTR1 43651 G / A, PIK3R1 75686 G / A, TNFRSF13B 23375 A / C, ABCA1 −14 C / T, IRS1 3931 G / A, ROS1 124785 G / A, BCHE 63/973 G / A, ANXA5 431 C / T, MMP-1 −1602 2G / 1G, PALLD 259175 A / G, KCNJ11 634 A / G, CXCL16 4660 C / T, FOXC2 -512th C / T, MMP3 722th A / G, COMT 21962th G / A, PDE4D 919475th TAA /-, GCK at −30 position G / A, APOA5 at −1123 position C / T at least one or more gene polymorphisms, age, and sex as evaluation factors, onset from each evaluation factor The risk value is estimated, and the onset risk value is divided into a plurality of groups of three or more according to the average and variance or percentage classification, and the risk of onset is detected 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. The gene polymorphism is not necessarily limited to the above-mentioned 22 polymorphisms, and any 1 to 21 of these 22 polymorphisms, or the other 22 polymorphisms shown in the present specification. It is also possible to carry out with 23 or more including.

  In this specification, the description method of a polymorphism is as follows. In principle, for each gene, “Polymorphic position (position), base registered in the database (A: adenine, G: guanine, C: cytosine, T: thymine) / polymorphic base” did. For example, “-35 position C / T of UCP3” means a polymorphism in which C at position 35 at 5 ′ upstream is T in the UCP3 gene. The information on the position where the polymorphism of each gene occurs was based on the NCBI gene database contig reference. The PDE4D 919475 position TAAA / -polymorphism and the MMP3 position -1610 position 5A / 6A polymorphism are expressed as TTTA / -polymorphism and 5T / 6T polymorphism from the gene direction of the database. Are described as TAAA / -polymorphism and 5A / 6A polymorphism read in the reverse direction in many papers, and are also described in this specification as described above.

  In general, it is known that polymorphisms have different types and frequencies for different groups (for example, Japanese group, Western group, etc.). For this reason, even if the polymorphism has been pointed out to be associated with chronic kidney disease in a non-Japanese population, such a relationship is not necessarily observed in the Japanese population. For this reason, conventional reports do not necessarily support the association between polymorphisms and chronic kidney disease in Japanese when countries or diseases differ.

  According to the present invention, a detection method and the like for predicting genetic risk and risk of onset for chronic kidney disease are provided. By using this invention, it becomes possible to prevent chronic kidney disease, decrease the number of dialysis patients, decrease cardiovascular disorders in which chronic kidney disease is a risk factor, and further increase the healthy life expectancy, QOL improvement, bedridden prevention of the elderly In addition, medical and social contributions can be made greatly, such as reduction of medical expenses in the future.

  Next, embodiments of the present invention will be described with reference to the drawings. 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 4,587 people (2,532 men, 2,055 women) consisting of two groups. The first is the participation in research facilities (Gifu Prefectural General Medical Center, Gifu Prefectural Tajimi Hospital, Hirosaki University Hospital, Reimei Rehabilitation Hospital, Hirosaki Stroke Center) from October 2002 to March 2008. Who visited the hospital for a health checkup. Another group was elderly people who participated in epidemiological studies on aging and geriatric diseases in Gunma Prefecture and Tokyo.
The research protocol was approved by the Mie University School of Medicine, Hirosaki University School of Medicine, Gifu Prefectural Institute of Biotechnology, Tokyo Metropolitan Institute for Gerontology, and the Ethics Committee of participating hospitals in accordance with the Declaration of Helsinki. Written informed consent was obtained for each participant.

Chronic kidney disease includes urine abnormalities such as proteinuria including microalbuminuria, abnormal images such as single kidney and multiple cystic kidneys, regardless of the primary diseases such as IgA nephropathy, diabetic nephropathy, nephrosclerosis, blood, There is a finding indicating the presence of renal disorder in the pathology, or a moderate decrease in renal function (eGFR <60 mL / min / 1.73 m ² ) with an estimated glomerular filtration rate (eGFR) value Is diagnosed if it persists for more than 3 months. In addition, NKF-KDOQI ^™ (National Kidney Foundation-Kidney Disease Outcomes Quality Initiative) proposes to diagnose chronic kidney disease when the eGFR value is less than 60 mL / min / 1.73 m ² (Non-patent Document 4). .

The eGFR, which is a diagnostic criterion for chronic kidney disease, was estimated from the serum creatinine value by the following revised MDRD (the Modification of Diet in Renal Disease) formula proposed by the Japanese Society of Nephrology.
eGFR (mL / min / 1.73m ² )
= 194 x [age] ^-0.287 x [serum creatinine level (mg / dL)] ^-1.094
× [0.739 For women].

association between eGFR values and cardiovascular disorders and hospitalization need is increased eGFR value is less than 60mL / min / 1.73m ^2, the risk becomes remarkable in 45mL / min / 1.73m ² or less (Non Patent Document 10). Furthermore, in Japanese, when the eGFR value is less than 50 mL / min / 1.73 m ² at the age of 70 years or younger, it has been clarified that the prognosis is poor because the rate of decrease of the eGFR value thereafter increases. Reference 11).
Therefore, in this study, less than 50 mL / min / 1.73 m ² was used as a diagnostic criterion for chronic kidney disease, and 714 (430 men, 284 women) were analyzed as a chronic kidney disease group. The control group of 3,873 people (2,102 men, 1,771 women) had an eGFR value of 60 mL / min / 1.73 m ² or more. However, in both the chronic kidney disease group and the control group, hypertension (a person with a maximum blood pressure of 140 mmHg or more, a person with a minimum blood pressure of 90 mmHg or more, or a person taking an antihypertensive agent), hyperlipidemia (a serum total cholesterol level of 5 .72 mmol / L or higher, or those taking lipid-lowering drugs), diabetes (fasting blood glucose level of 126 mg / dL or higher, hemoglobin Alc of 6.5% or higher, or taking antidiabetic drugs Who suffer from).

Selection of polymorphisms 176 candidate genes were selected through the use of public databases and through extensive studies by the inventors. The inventor selected 222 polymorphisms for these 176 genes based on conventional knowledge and databases. Many of these polymorphisms are located in many promoter regions, exons, intron splicing donor or acceptor sites, and polymorphisms may alter the function or expression of the encoded protein. There was something. These 222 polymorphisms are shown in Table 1 to Table 6 below. The table shows the locus (Locus), gene name (Gene), simplified description (Symbol), polymorphism (Polymorphism), and polymorphism database registration number (dbSNP rs No.) in order from the left column. When there is no polymorphism database registration number, the number registered in the NCBI gene bank and the position of the polymorphism in the registered sequence are shown. Regarding the notation of polymorphism in this table, for the convenience of detection and analysis of gene polymorphism in this study, alleles were set as allele 1 and allele 2, and “base sequence of allele 1 / allele 2 "Base sequence". This distribution to allele 1 and allele 2 was not necessarily based on the base sequence of the reference sequence of the database as allele 1, but was based on the information source when selecting the candidate gene. Therefore, even if the polymorphism database registration SNP notation is, for example, A / G, it is set to one of A / G, G / A, T / C, and C / T based on the description in the information source .

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 genotypes of 222 polymorphisms 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 and probe sequences are shown in Table 7 below. Table 7 shows gene (Gene Symbol), polymorphism (Polymorphism), detection allele (allele 1, allele 2), sense primer (Sense primer), antisense primer (Anti-sense primer), probe 1 (Probe) 1) Probe 2 is shown. The detailed method for detecting the gene polymorphism was performed by appropriately changing the amplification conditions based on the reported method (Non-patent Document 12). In addition, description about the conditions for detecting a polymorphism that was not associated with chronic kidney disease was omitted.

The details of the PCR-SSOP-Luminex method are as described in Non-Patent Document 12. 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 containing 1.5 mM magnesium chloride (50 mM KCl, 10 mM Tris-HCl, pH 8.3), 2% DMSO, 0.2 mM dNTPs, and 0.1 μM-10 μM primer set were mixed, and Taq DNA polymerase (50 U / mL) and 50 ng-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)-(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 patients and patients with chronic kidney disease by unpaired Student t test. Categorical data were compared by chi-square test. The allele frequency was estimated by the gene count method, and chi-square test was performed to determine whether it fits the Hardy-Weinberg equilibrium (in the case of genes on the X chromosome, it was tested by female genotype distribution) . The genotype distribution was compared by a chi-square test (2 × 2) between a patient group of chronic kidney disease and a control group using a genetic model composed of two groups. In the genetic model, the dominant model is “Allele 2 homozygous and heterozygous binding group” vs. “Allyl 1 homozygote” (22 + 12 vs 11), and the recessive model is “Allele 2 "Homozygote" vs. "Allyl 1 homozygous and heterozygous binding group" (22 vs. 11 + 12). Each genotype was analyzed according to a dominant and recessive genetic model, and the risk rate (p value) was calculated.

  For the polymorphic genetic model associated with chronic kidney disease with a p-value of less than 5% (p <0.05) in the chi-square test, the influence of other factors related to the development of chronic kidney disease (confounding factors) The gene model (dominant model is 1 = “joined group of homozygotes and heterozygotes of allele 2”, 0 = “homozygote of allele 1”, recessive model is 1 = “allele 2 Homozygote ”, 0 =“ Allyl 1 homozygote and heterozygote binding group ”) and confounding factors (age (age: continuous value), sex (sex: 1 = male, 0 = female)), It was analyzed by logistic regression analysis with stepwise variable increasing method. At this time, chronic kidney disease (1 = disease group, 0 = control group) was a dependent variable, and each genotype and confounding factor were independent variables. When the p value of both the dominant model (22 + 12 vs. 11) and the recessive model (22 vs. 11 + 12) was less than 5% with the same polymorphism, a low p value genotype model was adopted. The inclusion criteria (entering significance level) was set to 0.15, and the exclusion criteria (removing significance level) was set to 0.2. Statistical significance was performed by a two-sided test.

<Test results>
Table 8 shows background data on 4,587 subjects. The table shows, in order from the left column, characteristics (Characteristic), disease group (CKD), and control group (Controls). The feature column indicates the number of cases (No. of subjects), age (Age), and sex (male / female) (Sex (male / female)) in order from the top. Age (Age) data are expressed as mean ± SD.

  For each genotype examined, the genotype of the control group was estimated by the gene count method and chi-square test was performed, and it was applied to the law of Hardy-Weinberg equilibrium.

  In order to confirm the significance of the genetic polymorphism examined in the target population as a risk predictor of chronic kidney disease, the genetic polymorphism is evaluated by chi-square test as a dominant and recessive model, and further includes age and sex Logistic regression analysis by the stepwise method was performed.

  Table 9 shows gene polymorphism models in which the genetic polymorphisms examined in the target population were evaluated by chi-square test as dominant and recessive models and the risk rate was less than 5% (p <0.05). In the table, from the left column, gene notation (Gene Symbol), polymorphism (Polymorphism), SNP database number (dbSNP rs No.), gene model (dominant / recessive), polymorphism of the gene model (Polymorphism) The number of cases in the CKD group and the control group of the polymorphism, the ratio (CKD (%), control (%)), and p value (p-value) in the group are shown.

  Stepwise variable increase method using genetic polymorphisms with an appearance frequency of less than 5% (p <0.05) as a confounding factor of independent variables along with other background factors (age, gender) Table 10 shows the results of the logistic regression analysis performed on. These confounding factors were independent, and it was found that the product of odds ratios (multiplication) can predict the risk for overall chronic kidney disease. Moreover, the risk value can be estimated by the logistic regression model obtained by the analysis. In the table, from the left column, factor (Variable), p-value (p-value), partial regression coefficient (β) of regression model, odds ratio (Odds-ratio), 95% confidence interval (OR-) of odds ratio 95% CI). The constant of the regression model equation obtained by this analysis was −6.19991.

  As a result of logistic regression analysis by this stepwise variable increasing method, genetic factors include UCP3, PECAM1, HMOX1, APOE, AGTR1, PIK3R1, TNFRSF13B, ABCA1, IRS1, ROS1, BCHE, ANXA5, MMP1, PALLD, KCNJ11, CXCL16, FOXC2, MMP3, COMT, PDE4D, GCK, and APOA5 gene polymorphisms were significantly associated with chronic kidney disease. When these genetic factors were combined, the minimum odds ratio was 0.01 and the maximum odds ratio was 98.46. This could be widely evaluated compared to the odds ratio predicted only by conventional risk factors such as diabetes, hypertension, hypercholesterolemia, age, and sex (Reference 13).

<Risk prediction system for chronic kidney disease>
As an example of a statistical analysis method that has led to the development of a risk prediction system and an actual clinical application, a method and results of determining risk in five stages (AE) will be described.

  By logistic regression analysis of genetic factors and confounding factors significantly related to chronic kidney disease in statistical analysis, a logistic regression model consisting of partial regression coefficients of each factor is obtained, and from the logit value calculated there, disease onset in the subject Probability risk value is estimated. When the risk values predicted for each patient are arranged in ascending order, the risk of onset in the subject increases as the value increases. Therefore, this model makes it possible to estimate the risk of developing chronic kidney disease from the test results of genetic factors and confounding factors that have been shown to be associated with disease.

  In order to obtain a logistic regression model for estimating this risk value, a polymorphic genetic model associated with chronic kidney disease having a p-value of less than 5% (p <0.05) by chi-square test is calculated as age, which is a confounding factor, Logistic regression analysis with stepwise variable increment method was performed along with gender. Because it is easier to operate within 20 gene polymorphisms used for actual risk prediction, increase the stepwise variable with a restriction so that the maximum number of gene polymorphisms included in the logistic regression model is 20 By using this method, gene polymorphisms used as confounding factors were narrowed down.

  The genetic polymorphism selected by logistic regression analysis using the above stepwise variable increase method was adopted as the final factor for risk prediction. In addition, the figures required for the risk prediction system were determined. The results are shown in Table 11. In the table, from the left column, factor (Variable), p-value (p-value), regression model partial regression coefficient (β), odds ratio (Odds-ratio), odds ratio 95% confidence interval (OR -95% CI). Moreover, the constant of the regression model formula obtained by this analysis was -6.002111.

    In order of increasing statistical significance, UCP3 -35 position C / T (recessive model), PIK3R1 75686 position G / A (recessive model), PECAM1 position 36222 A / G (dominant) Model), HMOX1 98th position G / C (dominant model), APOE -203 position T / G (dominant model), AGTR1 position 43651 G / A (dominant model), IRS1 position 3931 G / A (dominant model) ), TNFRSF13B at 23375 A / C (recessive model), ABCA1 at −14 C / T (recessive model), ROS1 at 124785 G / A (dominant model), ANXA5 at 431 C / T (recessive model) , BCHE 63973 G / A (dominant model), KCNJ11 634 A / G (dominant model), MMP1 −1602 2G / 1G (dominant model) ), PALLD 259175 A / G (dominant model), CXCL16 4660 C / T (dominant model), FOXC2 -512 C / T (recessive model), MMP3 722 A / G (dominant model) ), COMT 21962 position G / A (dominant model), PDE4D 919475 position TAAA /-(recessive model) were significant, and it was found that each factor was independently associated with chronic kidney disease.

  The clinical significance of this research result is described. At hospitals, clinics, or health checkup centers, applicants are tested for conventional risk factors and current genetic factors to predict the risk of developing chronic kidney disease. This is based on genetic factors and confounding factors (age, gender) that are related to the onset of the disease in this study. It can be carried out by dividing it into stages.

  An example in which the risk of chronic kidney disease is judged in five levels will be described. The risk value is estimated by the logistic regression equation obtained by the logistic regression analysis. The risk value of the control group is divided into 5%, 20%, 50%, 20%, and 5% in descending order, and the risk group is higher, slightly higher, average, slightly lower, and lower. Can be separated from groups. However, it is possible that the control group in this study is far from the genotype distribution of the entire Japanese population. Therefore, in order to verify it, the group is divided into 1 to 9 arbitrarily, the distribution of the control group and the disease group is confirmed, and it is confirmed that each group is not overfitted with the risk value distribution category 10 times. Repeatedly. By this cross-validation, the boundary value (threshold value) of the risk value distribution category was corrected. By the threshold correction of the risk value distribution classification by cross validation, the distribution of the chronic kidney disease group in this study is actually 17.4% in the high-risk group (4.4% in the control group), and the group with a slightly high risk 34.0% (18.4% in the control group), 40.6% in the average risk group (50.5% in the control group), 7.3% in the slightly lower risk group (21.3% in the control group). 2%), the low-risk group was 0.7% (the control group was 5.5%).

  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 moderately high-risk group, improvement of lifestyle habits (smoking cessation, alcohol consumption reduction, diet therapy, ie, salt intake) Proactively promote primary and secondary prevention of chronic kidney disease by giving restrictions or low protein diet) or early drug treatment. It is especially effective for people with a family history of chronic kidney disease. This system enables custom-made prevention of chronic kidney disease and leads to prevention of various diseases caused by chronic kidney disease, that is, end stage renal failure, myocardial infarction, cerebral infarction and the like. As a result, the number of dialysis patients can be reduced, the healthy life expectancy of the elderly can be increased, QOL can be improved, bedridden prevention, and future medical expenses can be reduced.

<Discussion>
The present inventor examined the association between 222 polymorphisms in 176 candidate genes and chronic kidney disease in a population of 4,587 Japanese. By this large-scale study, UCP3 -35 position C / T polymorphism, PECAM1 36222 position A / G polymorphism, HMOX1 98 position G / C polymorphism, APOE -203 position T / G polymorphism, AGTR1 43651 G / A polymorphism of PIK3R1, 75686 G / A polymorphism of PIK3R1, 23375 A / C polymorphism of TNFRSF13B, −14 C / T polymorphism of ABCA1, 3931 G / A polymorphism of IRS1 ROS1 124785 G / A polymorphism, BCHE 63973 G / A polymorphism, ANXA5 431 C / T polymorphism, MMP-1 −1602 2G / 1G polymorphism, PALLD 259175 A / G polymorphism , KCNJ11 position 634 A / G polymorphism, CXCL16 position 4660 C / T polymorphism, FOXC2 position −512 C / T polymorphism, MMP3 position 722 A / G polymorphism, COMT 219 2nd position G / A polymorphism, PDE4D 9191475 position TAAA / -polymorphism, GCK −30 position G / A polymorphism, and APOA5 −1123 position C / T polymorphism may be significantly associated with chronic kidney disease It was revealed.

  Finally, in chronic kidney disease, among the above 22 polymorphisms, 20 polymorphisms that are particularly related to the disease by statistical analysis and information on age and gender are input into the logistic regression equation, and these genotypes are The genetic risk was evaluated in 5 grades of AE according to the combination. The results are shown in Table 12. In the table, in order from the left, each polymorphism is expressed as a variable (Gene Symbol), genotype (polymorphism), age (Age), gender (Sex), logit value (logit), risk value ( Pseudo Probability), odds ratio (Odds-ratio), and 5-level evaluation (estimate A, B, C, D, E) are shown. In the 5-level evaluation, the risk value of the onset probability of the examinee is estimated, and the risk value distribution of the control group is divided by the threshold value corrected by the cross-validation of the study group. High risk group, the next 18.4% is a slightly higher risk group, the next 50.5% is an average risk group, the next 21.2% is a slightly higher risk group, the lowest risk value5 .5% was rated in the low risk group. In the present invention, the onset risk can be easily estimated by presenting the genetic risk in a five-step evaluation.

MMP1 (Matrix metallopeptidase 1)
Matrix metal-bound proteolytic enzyme 1 (MMP1) degrades fibrillar collagen (particularly types I and III), which is a protein resistant to most proteolytic enzymes (Non-patent Document 14). It is expressed in macrophages of atherosclerotic plaques, and is present together with fibrillar collagen to be decomposed in a flank of plaques that are likely to be ruptured (Non-patent Documents 15 and 16). Increased expression of MMP1 promotes rupture of atherosclerotic plaque (Non-patent Document 17). The −1602 2G / 1G polymorphism of MMP1 is located in the promoter region and is involved in the risk of myocardial infarction including other polymorphisms of MMP1 (Non-patent Document 18). It is speculated that the effect of this polymorphism on atherosclerosis is also related to chronic kidney disease.

APOE (Apolipoprotein E)
Apolipoprotein E (APOE) is a component of chylomicron and ultra-low density lipoprotein residues and is involved in the binding and uptake of these particles via low density lipoprotein (LDL) receptors and LDL receptor-like proteins. (Non-Patent Documents 19 and 20). The -203 T / G polymorphism of APOE was reported to be associated with male myocardial infarction in France and Northern Ireland (Non-patent Document 21). This polymorphic T allyl was reported to be a risk factor for coronary heart disease in Japanese low-risk men (Non-patent Document 22). In this study, the -203 T / G polymorphism of APOE has been shown to be associated with chronic kidney disease in T allele. This polymorphism is in the promoter region of APOE and has been shown to be associated with the plasma concentration of APOE (APOE concentration decreases with T allele) (Non-patent Document 21). Abnormalities in lipoprotein metabolism play an important role in the development of atherosclerosis and glomerulosclerosis (Non-patent Documents 23 and 24), but the AP / -203 T / G multiple in chronic kidney disease. The role of the type is unknown.

MMP3 (Matrix metallopeptidase 3)
Matrix metal-binding proteolytic enzyme 3 is expressed in atheroma (fatting of arterial intima), has a wide substrate specificity, and activates other MMPs (matrix metal-binding proteolytic enzymes) such as collagenolytic enzymes and gelatinolytic enzymes (Non-patent Document 25). Inactivation of MMP3 in apolipoprotein E-deficient mice increases both the amount of damaged matrix protein and the size of atherosclerotic plaques (Non-Patent Document 26). In plaques of coronary atherosclerosis, the presence of MMP3 mRNA has been confirmed by in situ hybridization most prominently in a place that seems to be easily ruptured (Non-patent Document 27). The expression of MMP3 is mainly regulated at the level of transcription, and the promoter of this gene responds to various stimuli (Non-patent Document 28). We have previously shown that there is an association between the prevalence of myocardial infarction in Japanese women and the -1610 5A / 6A polymorphism of MMP3 (Non-patent Document 29). It has also been reported that atherosclerotic plaques that cause severe stenosis are associated with this MMP gene polymorphism (Non-patent Documents 30 and 31). These findings suggest that MMP3 is a candidate gene involved in atherosclerosis. This time, we showed the relationship between the onset of chronic kidney disease and the 722 position A / G (Lys45Glu) polymorphism of MMP3, but the mechanism is unknown.

UCP3 (Uncoupling protein 3)
Mitochondrial uncoupling protein 3 (UCP3) is a mitochondrial membrane transport protein, which is expressed mainly in skeletal muscle and plays a role in regulating energy metabolism (Non-patent Document 32). UCP3 -35 position C / T is located in the promoter region of this gene, and its association with obesity (Non-patent document 33) and myocardial infarction (Non-patent document 34) has been shown. This polymorphic T allyl increased the risk of progression of type 2 diabetes (Non-patent Document 35). In this study, we showed that T-allyl of the -35 C / T polymorphism of UCP3 is involved in the disease risk of chronic kidney disease. The impact of this polymorphism on obesity and type 2 diabetes progression may be associated with chronic kidney disease.

PECAM1 (Platelet-endothelial cell adhesion molecule 1)
PECAM1 is a 130 kD immunoglobulin superfamily, and is a protein expressed on the surface of endothelial cells, platelets, leukocytes and blood precursor cells (Non-patent Document 36). PECAM1 plays an important role in both leukocyte adhesion by regulating integrin affinity (Non-patent Document 37) and endothelial permeation transport in response to stimulating molecules (Non-patent Document 38). The PECAM1 position 36222 A / G (Gln668Arg) polymorphism is located in exon 12, which encodes the cytoplasmic domain of this protein that plays a major role in signal transduction (36). A polymorphism of A allele (Gln) has been shown to be involved as a factor that increases the risk of myocardial infarction in Japanese (Non-patent Document 39). Here we show that PECAM1 36222 A / G polymorphism A allele (Gln) is involved as a suppressor in the risk of chronic kidney disease. The mechanism by which this polymorphic A allele (Gln) is involved in both risk factors for myocardial infarction and inhibitors of chronic kidney disease is not clear.

<Conclusion>
According to our study, UCP3, PECAM1, HMOX1, APOE, AGTR1, PIK3R1, TNFRSF13B, ABCA1, IRS1, ROS1, BCHE, ANXA5, MMP1, PALLD, KCNJ11, CXCL16, FOXCMT, MMP3 PDE4D, GCK, APOA5 polymorphisms were associated with chronic kidney disease. Based on this result, it is possible to know the risk of developing chronic kidney disease by examining genetic polymorphisms.
Thus, according to the present embodiment, a detection method for predicting a genetic risk can be provided for chronic kidney disease. By using this embodiment, it becomes possible to prevent chronic kidney disease, decrease the number of dialysis patients, decrease cardiovascular disorders, prolong the healthy life of the elderly, improve QOL, prevent stickiness, and reduce medical expenses in the future. , Can contribute greatly medically and socially.

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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)

UCP3 -35 position C / T, PECAM1 36222 position A / G, HMOX1 98 position G / C, APOE -203 position T / G, AGTR1 43651 position G / A, PIK3R1 75686 position G / A, TNFRSF13B at 23375 A / C, ABCA1 at −14 C / T, IRS1 at 3931 G / A, ROS1 at 124785 G / A, BCHE at 63973 G / A, ANXA5 at 431 C / T, MMP1 -1602 2G / 1G, PALLD 259175 A / G, KCNJ11 634 A / G, CXCL16 4660 C / T, FOXC2 -512 C / T, MMP3 722 A / G, COMT 21962 position G / A, PDE4D 919475 position TAAA /-, GCK -30 position G / A, APOA5 -112 The risk of onset is calculated by multiplying at least one or more genetic polymorphisms of position C / T, gender, and age as evaluation factors, and the odds ratio of each evaluation factor, and this risk is calculated as the average A method for detecting a risk of chronic kidney disease, comprising preparing a plurality of groups of three or more according to variance or percentage classification, and detecting a risk of onset according to each group. UCP3 -35 position C / T, PECAM1 36222 position A / G, HMOX1 98 position G / C, APOE -203 position T / G, AGTR1 43651 position G / A, PIK3R1 75686 position G / A, TNFRSF13B at 23375 A / C, ABCA1 at −14 C / T, IRS1 at 3931 G / A, ROS1 at 124785 G / A, BCHE at 63973 G / A, ANXA5 at 431 C / T, MMP1 -1602 2G / 1G, PALLD 259175 A / G, KCNJ11 634 A / G, CXCL16 4660 C / T, FOXC2 -512 C / T, MMP3 722 A / G, COMT 21962 position G / A, PDE4D 919475 position TAAA /-, GCK -30 position G / A, APOA5 -112 Position risk detection for chronic kidney disease, which comprises detecting at least one or more polymorphisms of the C / T.