KR102366096B1 - Diabetes Prediction methods using clinical and genetic Risk score in Korean and method for suggesting diagnosis related to Diabetes - Google Patents

Abstract

The present invention relates to a method for predicting a chance of developing diabetes by integrating clinical information and genetic variation information of Koreans and, more specifically, to a method for predicting a chance of developing diabetes from a Korean-specific viewpoint using a disease predicting model built by integrating SNP marker variation information and clinical information and for suggesting a diagnosis related to diabetes. According to the present invention, the method for predicting the chance of developing diabetes integrally analyzes Korean-specific genetic information and personal clinical information to predict the chance of developing diabetes, so the method is useful for early diagnosis of diabetes and predictive prevention because accuracy is superior to an existing method.

Description

Diabetes Prediction methods using clinical and genetic Risk score in Korean and method for suggesting diagnosis related to Diabetes

The present invention relates to a method for predicting the probability of developing diabetes by integrating clinical information and genetic mutation information of Koreans, and more specifically, using a disease prediction model built by integrating SNP marker mutation information and clinical information It relates to a method of predicting the probability of developing diabetes at a time point and recommending a related test.

The etiology of common diseases and conditions is largely due to both genetic and environmental factors. Recent advances in genotyping techniques have significantly improved our understanding of the genetic contribution to these diseases. Numerous whole-genome association studies have recently been completed, aiming to discover novel associations between common genetic variants and common diseases across the genome. These studies, based on their genetic makeup, have revealed an individual's risk of developing the disease during an individual's lifetime and the mechanism of the disease. Incorporating innate genetic risk information into the clinical decision-making process early in life has important effects in alleviating or further preventing disease symptoms or conditions.

The prevalence of common chronic non-communicable diseases largely overshadows the combined prevalence of both monogenic and communicable diseases. Conventional SNP variants account for some, if not all, significant, if not all, significant, number of germline genetic risks for common diseases and in this regard, when used, allow for better personalization and intensive reduction of exposure, early detection, and early intervention paradigms for the subject. allow

Genetic variations in the genome, such as single nucleotide polymorphisms (SNPs), mutations, deletions, insertions, repeats, microsatellites, etc., are correlated with various phenotypes, such as diseases or conditions. Genetic variations in an individual can be identified and correlated to determine an individual's predisposition or risk for different phenotypes, resulting in an individualized phenotype profile.

Low effect size common SNP variants, rare individual variants, DNA copy number variants, and epigenetic modifications generally account for the majority of congenital risks. Accurately estimating an individual's risk of developing a condition is not an easy task. This risk rate is determined by a number of factors, including genetic risk factor loading, environmental factors, sex, and age. Therefore, for most conditions, the most accurate risk assessment can only provide a probabilistic risk estimate. Factors may include different linked variants, their effect size, their frequency in the population, and their interaction with environmental factors that affect the individual, such as diet, age, family history, and ethnic background.

Therefore, there is a need for methods for generating individual phenotypic profiles using risk rate estimates that take into account the effects of genetic variation but do not require the results of large-scale studies evaluating multiple risk factors simultaneously. There is also a need to generate risk rate estimates that are disease-specific, but also provide additional tools for clinical decision-making that can be combined with environmental data, such as with predictive power as clinical classifiers. am.

Meanwhile, Type 2 Diabetes mellitus is known to account for 90 to 95% of all diabetes patients. Type 2 diabetes occurs in people who produce insulin in the body, but have an abnormal amount of insulin or have low sensitivity to insulin, and show a symptom in which blood glucose levels vary greatly. It is known that it is difficult to obtain energy from food because glucose in the blood cannot be moved into cells due to an abnormality in insulin. It is known that there is a genetic factor in the onset of type 2 diabetes, and other risk factors include age over 45, family history of diabetes, overweight, high blood pressure and cholesterol levels.

Currently, the diagnosis of diabetes is mainly made by a method of measuring a change in the phenotype due to a disease, that is, the blood glucose level, through a fasting blood glucose (FSB) test and an oral glucose tolerance test (OGTT). (National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, http://www.niddk.nih.gov, 2003). In the case of type 2 diabetes, once diagnosed, it can be said that the need for early diagnosis is very high because it can be treated or the progress of diabetes can be slowed through changes in exercise and dietary habits, weight control, and various drug treatments. Millenium Pharmaceuticals announced that it is possible to diagnose and predict type 2 diabetes by detecting genotype mutations in the HNF1 gene (PR Newswire, Sept 1, 1998), Sequonum (Sequenom) reported that FOXA2 (HNF3β gene) is highly associated with the onset of type 2 diabetes (PR Newswire, Oct 28, 2003).

Although several genes have been reported to be associated with the onset of type 2 diabetes as described above, research has been focused on only a small number of specific genes on some chromosomes and experiments were conducted on specific population groups. Accordingly, there is a possibility that different results may appear depending on the target race, not all of the causative genes of type 2 diabetes have been identified, and the current level of diagnosis is that there are not many cases of diagnosing type 2 diabetes using these molecular biological methods. am. Accordingly, there is a need to find new SNPs and related genes highly related to type 2 diabetes from the entire human genome and utilize them for early diagnosis.

Type 2 diabetes mellitus (T2D) is a chronic multi-factorial disease based on polygenic predisposition and environmental risk factors. The first wave of genome-wide association studies (GWAS) was the discovery of the type 2 diabetes mellitus (T2D) susceptibility locus. Most type 2 diabetes loci have so far been identified in European ancestry, along with some East Asian groups. Recently, additional group-specific signals that increase trait change have been identified through transethnic fine-mapping. However, identification of the genetic component of type 2 diabetes is limited due to genetic heterogeneity across ethnically diverse ancestries.

As an integrated analysis method for predicting type 2 diabetes risk, genetic risk scores (GRS) can be an effective and efficient method for estimating risk at the genomic level from GWAS results. Recently, a genetic risk assessment study for type 2 diabetes was reported by evaluating the predictive value of the cumulative genetic score. However, predictive values of genetic scores derived from GWAS have been influenced by genotype frequency, phenotypic effect size, and ethnicity-specific determinants of disease incidence. The development and improvement of additional multi-SNP genetic risk scores could lead to a better understanding of the complex disease prediction and prevention between independent ethnic groups.

Therefore, in order to determine the prognosis of type 2 diabetes in Koreans, the present inventors selected single nucleotide polymorphisms (SNPs) that show association in association analysis using previously reported diabetes genetic mutations that can be analyzed in Koreans and the Anseong/Ansan cohort. When the genetic risk score was scored and analyzed by integrating with clinical information, it was confirmed that a statistically significant result was shown for the prediction of the onset of type 2 diabetes, and the present invention was completed.

It is an object of the present invention to provide a method for predicting the incidence of diabetes by integrating clinical information and genetic mutation information.

Another object of the present invention is to provide a method of providing information for further examination of diabetic patients.

Another object of the present invention is to provide a method of providing information for the management of diabetic patients.

Another object of the present invention is to provide an insurance design and insurance product matching system using the above method.

In order to achieve the above object, the present invention comprises the steps of: a) extracting DNA from a sample; b) determining the genotypes of rs10811661, rs947474, rs1048886 and rs9268645 SNPs to give a score (Genotype Score, GS); c) deriving a SNP value (SNP point) by assigning a weight to each SNP; and d) calculating the probability of developing diabetes based on the SNP point, wherein GS means a value representing the influence of each SNP genotype on the onset of diabetes by dividing it into 0, 1, or 2, and SNP The point provides a method for predicting the incidence of diabetes, characterized in that it means the total sum of the values multiplied by the weight of the GS.

The present invention also comprises the steps of: a) collecting clinical information of a patient, and extracting DNA; b) deriving clinical information values by assigning weights to clinical information; c) determining the genotypes of rs10811661, rs947474, rs1048886 and rs9268645 SNPs from DNA and assigning a score (Genotype Score, GS); d) deriving an SNP value (SNP point) by assigning a weight to each SNP; and e) calculating the probability of developing diabetes within a specific time point based on the values b) and d), wherein GS represents the influence of each SNP genotype on the onset of diabetes by dividing it into 0, 1, or 2 It means a value, and the SNP point provides a method for predicting the probability of diabetes onset, characterized in that it means the total sum of values obtained by multiplying the GS by a weight.

The present invention also comprises the steps of: a) predicting the probability of developing diabetes within a specific period by the above method; b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; And c) provides a method of providing information for additional examination of a diabetic patient comprising the step of providing additional necessary examination details.

The present invention also comprises the steps of: a) predicting the probability of developing diabetes within a specific period by the above method; b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; And c) provides a method of providing information for the management of diabetic patients, including the step of providing a dietary, exercise and lifestyle guide for lowering the incidence probability.

The present invention also

A device for calculating a user's diabetes incidence probability using the method; a server for receiving and analyzing diabetes incidence probability information from the diabetes incidence probability calculation device and matching insurance products; and outputting the server information on a screen It is configured to include a terminal that does this, calculates the user's diabetes incidence probability through the diabetes incidence probability calculation device, receives diabetes incidence probability information from the server, calculates future diabetes treatment costs, and based on the calculation results In the insurance design and insurance product matching system based on the probability of developing diabetes that matches and recommends insurance products,

The server includes an insurance product storage unit for storing information on all insurance products to be provided to the user, an average treatment cost storage unit for storing average diabetes treatment cost, and customer authentication for authenticating the user as a customer. A diabetes onset probability information receiving unit for receiving the diabetes incidence probability of the diabetes onset probability calculation device based on the customer information authenticated through the customer authentication unit, and the diabetes onset probability information received through the diabetes onset probability information receiving unit An analysis unit that calculates the future treatment cost based on An insurance insurance detail comparison unit that receives from the insurance product storage unit and compares it with the future expected treatment cost of the analysis unit, and the future expected treatment cost analyzed through the analysis unit and future treatment based on the insurance coverage comparison unit Comprising an insurance matching unit that determines whether the cost is covered by the insurance purchased by the customer and matches the insurance based on the determined result, and an insurance information recommendation unit that recommends the insurance matched by the insurance matching unit to the user It provides an insurance design and insurance product matching system based on clinical and genetic variation information.

Since the diabetes incidence probability prediction method according to the present invention predicts the diabetes incidence probability by integrating not only Korean-specific genetic information but also individual clinical information, the accuracy is far superior to that of the existing method, so it is useful for early diagnosis and prevention of diabetes. useful.

1 is a schematic diagram showing a specific process for constructing an integrated clinical/genetic variation information model according to the present invention.
2 is an example of a report reporting the diabetes incidence probability predicted according to the method for predicting and recommending the diagnosis of diabetes according to the present invention.
3 is an example of a report recommending an additional test according to the method for predicting the incidence of diabetes and recommending a test according to the present invention.
4 is a detailed view of the patient's incidence probability report predicted according to the diabetes incidence probability prediction model according to the present invention.
5 is an example of a report providing a management guide according to the information providing method for predicting the incidence of diabetes and patient management according to the present invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In general, the nomenclature used herein and the experimental methods described below are well known and commonly used in the art.

In the present invention, it was attempted to confirm that the probability of developing diabetes can be predicted with higher accuracy when the Korean-specific genetic variation information and clinical information are integrated and analyzed.

That is, in one embodiment of the present invention, study subjects were selected from the Korean Genome and Epidemiology Study (KoGES), which was 8,842 between the ages of 40 and 69 through a population-based prospective cohort study surveyed in the Anseong and Ansan regions of Korea. Genetic data and clinical information of 10,030 study participants were obtained.

First, to analyze clinical information, the obtained clinical information data was converted from continuous to categorical, and then the base cox ph model with age and gender as predictors and the cox ph model with each clinical information factor added were compared. Clinical information factors with a p value of less than 0.05 were selected through the LR test (likelihood ratio test). Then, multiple regression was applied using the selected clinical information factors, age, gender, and family history of diabetes as predictors, and then a primary environmental analysis model was constructed by backward elimination (FIG. 1).

To analyze genetic variation information, 443,816 SNP genotypes from 8,842 individuals were analyzed using Affymetirx Genome-Wide Human SNP array 5.0. First, among SNPs, monomorphic SNPs, SNPs with a minor allele frequency of less than 1%, SNPs with a p value of less than 0.001 as a result of the Hardy-Weinberg equilibrium test, and a missing rate were SNPs exceeding 20% were excluded.

Next, through the GWAS Catalog, SNPs analyzed as being correlated with related diseases in previous studies are selected as candidates, and SNPs with all pairwise correlation constants of SNPs in the same chromosome exceeding 0.9 are defined as tagging SNPs. It was made not to be used repeatedly within the model.

Candidate SNPs were identified by applying an additive genetic model to the cox ph model using age, sex, family history of diabetes, the finally selected clinical information factors, and each SNP as explanatory variables to determine Allele of the genotype with the largest HR among categorical genotypes AA and BB. It was converted to continuous (0,1,2; GS) as a risk allele.

The first selected genetic factors were used as one SNP point by integrating 1 to n SNPs, that is, (nC1+nC2 +.. +nCn) SNP points were applied as candidate factors as one variable. For the cox ph model in which each SNP point is added to the selected environmental factors, a model that satisfies the conditions of (1) to (3) below was constructed.

The final diabetes incidence probability prediction model was constructed by comparing the time-dependent AUC and survival NRI results of all the constructed models.

(1) Whether or not the PH assumption of the SNP point is satisfied, LR test result p value < 0.05 (here, the PH assumption is to confirm the proportional hazard assumption, which is the basic assumption of the cox ph model)

(2) The time-dependent AUC of the model with the SNP point added is larger than that of the model without the SNP point.

(3) The hosmer-lemeshow test of the model with the added SNP point did not deteriorate compared to the model without the SNP point.

As a result, it was confirmed that the constructed model can predict the incidence of diabetes with high accuracy using a small number of genetic factors.

Accordingly, in one aspect, the present invention comprises the steps of: a) extracting DNA from a sample; b) determining the genotypes of rs10811661, rs947474, rs1048886 and rs9268645 SNPs to give a score (Genotype Score, GS); c) deriving a SNP value (SNP point) by assigning a weight to each SNP; and d) calculating the probability of developing diabetes based on the SNP value.

In the present invention, the method comprises the steps of: e) collecting clinical information of a patient; f) deriving clinical information values by assigning weights to each collected clinical information; and g) integrating the SNP value and the clinical information value to calculate the probability of developing diabetes within a specific time point.

The present invention also comprises the steps of: a) collecting clinical information of a patient, and extracting DNA; b) deriving clinical information values by assigning weights to clinical information; c) determining the genotypes of rs10811661, rs947474, rs1048886 and rs9268645 SNPs from DNA and assigning a score (Genotype Score, GS); d) deriving an SNP value (SNP point) by assigning a weight to each SNP; and e) calculating the probability of developing diabetes within a specific time based on the values b) and d).

In the present invention, the step of confirming the genotype can be obtained through various known methods, genotype confirmation through nucleic acid amplification, genotype confirmation through sequence information analysis, genotype confirmation method through a probe, etc. can be used. However, the present invention is not limited thereto.

In the present invention, the term "polymorphism" refers to a case in which two or more alleles exist at one locus, and among polymorphic sites, only a single base differs from one person to another as a single nucleotide polymorphism ( It is called single nucleotide polymorphism (SNP). Preferred polymorphic markers have two or more alleles exhibiting an incidence of at least 1%, more preferably at least 5% or 10% in a selected population.

In the present invention, "allele" refers to several types of one gene present at the same locus on a homologous chromosome. Alleles are also used to indicate polymorphism, for example, SNPs have two types of alleles.

In the present invention, the term “rs_id” or “rs_Number” or “rs” refers to rs-ID, an independent marker, assigned to all initially registered SNPs by NCBI, which has been accumulating SNP information since 1998. In the present invention, it was described in the same form as rs831571.

rs_id described in the table of the present invention means a SNP marker that is a polymorphic marker of the present invention. Those skilled in the art will be able to easily identify the position and sequence of the SNP using the rs_id. The specific sequence corresponding to rs_id, which is NCBI's dbSNP (The Single Nucleotide Polymorphism Database) number, may change slightly over time.

It will be apparent to those skilled in the art that the scope of the present invention also extends to such altered sequences.

As used herein, the term “nucleoside” refers to a glycosylamine compound in which a nucleic acid base (nucleobase) is linked to a sugar moiety. "Nucleotide" means a nucleoside phosphate. Nucleotides can be designated using the alphabetic letters (letter names) corresponding to their nucleosides, as described in Table 3. For example, A refers to adenosine (a nucleoside containing an adenine nucleobase), C refers to cytidine, G refers to guanosine, U refers to uridine, and T refers to thymidine (5- methyl uridine). W refers to A or T/U, and S refers to G or C. N represents a random nucleoside, and dNTP means deoxyribonucleoside triphosphate. N can be any of A, C, G, or T/U.

As used herein, the term “oligonucleotide” refers to an oligomer of nucleotides. As used herein, the term “nucleic acid” refers to a polymer of nucleotides. As used herein, the term “sequence” refers to the nucleotide sequence of an oligonucleotide or nucleic acid. Throughout the specification, whenever an oligonucleotide or nucleic acid is represented by a sequence of letters, the nucleotides are in 5'→ left to right order. The oligonucleotide or nucleic acid may be DNA, RNA, or an analog thereof (eg, a phosphorothioate analog). Oligonucleotides or nucleic acids may also include modified bases and/or backbones (eg, modified phosphate linkages or modified sugar moieties). Non-limiting examples of synthetic backbones that confer stability and/or other advantages to nucleic acids may include phosphorothioate linkages, peptide nucleic acids, locked nucleic acids, xylose nucleic acids, or analogs thereof.

As used herein, the term “nucleic acid” refers to a polymer of nucleotides, and unless otherwise limited, includes known analogs of natural nucleotides that can function in a manner similar to naturally occurring nucleotides (eg, hybridization).

The term nucleic acid includes, for example, genomic DNA; complementary DNA (cDNA), which is usually a DNA representation of an mRNA obtained by reverse transcription or amplification of messenger RNA (mRNA); DNA molecules produced synthetically or by amplification; and any form of DNA or RNA, including mRNA.

The term nucleic acid includes single-stranded molecules as well as double or triple-stranded nucleic acids. In double or triple stranded nucleic acids, the nucleic acid strands need not be coextensive (ie, double stranded nucleic acids need not be double stranded along the entire length of both strands).

The term nucleic acid also includes any chemical modification thereof, such as by methylation and/or capping. Nucleic acid modifications may include the addition of chemical groups comprising additional charge, polarizability, hydrogen bonding, electrostatic interactions, and functionality to individual nucleic acid bases or to the nucleic acid as a whole. These modifications include sugar modification at the 2' position, pyrimidine modification at position 5, purine modification at position 8, modification at cytosine exocyclic amine, substitution of 5-bromo-uracil, backbone modification, isocytidine and isoguanidine isobases base modifications, such as specific base pair combinations, such as

Nucleic acid(s) can be synthesized from a biological source, such as from a complete chemical synthesis process, such as solid phase-mediated chemical synthesis, through isolation from any species that produces the nucleic acid, or from DNA replication, PCR amplification, reverse transcription. It can be derived from processes related to the handling of nucleic acids by molecular biology tools, such as, or from a combination of these processes.

As used herein, the term “complementary” refers to the ability for correct pairing between two nucleotides. That is, two nucleic acids are considered to be complementary to each other at a given position in a nucleic acid if a nucleotide at a given position is capable of hydrogen bonding with a nucleotide in another nucleic acid. Complementarity between two single-stranded nucleic acid molecules may be “partial” because only a portion of the nucleotides bind, or complementarity may be complete when total complementarity exists between single-stranded molecules. The degree of complementarity between nucleic acid strands significantly affects the efficiency and strength of hybridization between nucleic acid strands.

As used herein, the term “primer” refers to a short linear oligonucleotide that hybridizes to a target nucleic acid sequence (eg, a DNA template to be amplified) for priming a nucleic acid synthesis reaction. A primer may be an RNA oligonucleotide, a DNA oligonucleotide, or a chimeric sequence. Primers may contain natural, synthetic, or modified nucleotides. Both the upper and lower limits of primer length are determined empirically. The lower limit of the primer length is the minimum length required to form a stable duplex after hybridization with a target nucleic acid under nucleic acid amplification reaction conditions. Very short primers (often less than 3 nucleotides in length) do not form thermothermally stable duplexes with the target nucleic acid under these hybridization conditions. The upper limit is usually determined by the likelihood of having duplex formation in a region other than a predetermined nucleic acid sequence in the target nucleic acid. Generally, suitable primer lengths range from about 3 nucleotides in length to about 50 nucleotides in length.

In the present invention, the term “probe” refers to binding to a target nucleic acid of a complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, and usually through hydrogen bond formation and thus forming a duplex structure. It is a nucleic acid capable of forming A probe binds or hybridizes to a “probe binding site”. In particular, the probe may be labeled with a detectable label to facilitate detection of the probe once it hybridizes to its complementary target. Alternatively, however, the probe may not be labeled, but may be detected directly or indirectly by specific binding to a labeled ligand. Probes can vary considerably in size. Generally, probes are at least 7 to 18 nucleotides in length. Other probes are at least 20, 30 or 40 nucleotides in length. Another probe is somewhat longer and is at least 50, 60, 70, 80, or 90 nucleotides in length. Another probe is even longer and is at least 100, 150, 200 or more nucleotides in length. Probes can also be of any length within any range defined by any of the above values (eg, 15-20 nucleotides in length).

In the present invention, the term “hybridization” refers to the formation of double-stranded nucleic acids by hydrogen bonding between single-stranded nucleic acids having complementary nucleotide sequences, and is used in a similar sense to annealing. However, in a slightly broader sense, hybridization includes a case in which two single-stranded sequences are completely complementary (perfect match), as well as a case in which some of the sequences are not complementary (mismatch).

In the present invention, the clinical information can be used without limitation as long as it is clinical information that can be used for the diabetes incidence probability prediction model constructed in the present invention, but preferably age, sex, family history, fasting blood sugar (FBS), liver level (alanine aminotransferase, ALT), gamma GTP, high-density cholesterol (HDL), triglyceride (TG), systolic blood pressure (SBP), and body mass index (BMI) it is not going to be

In the present invention, the SNP point means that several genetic factors are integrated into one score and used as a predictive marker for diabetes onset. , and the SNP point for k among the selected genetic factors was calculated using the semi-partial R ² of each genetic factor calculated from the full model and reduced model as a weight.

In the present invention, the SNP point may be calculated by the following Equation 2:

Equation 2: SNP point=

0.1595Xrs10811661'GS+0.2820Xrs947474'GS+0.2498Xrs1048886'GS+0.3088Xrs9268645'GS

In the present invention, the GS may be characterized as having the values shown in Table 2 below.

rs10811661 rs947474 rs1048886 rs9268645 GS: 0 CC AA AA GG GS: 1 CT AG AG CG GS: 2 TT GG GG CC

Here, the full model means a cox-ph model using clinical information and k genetic factors as predictors, and the reduced model is a cox-ph model using clinical information and (k-1) genetic factors excluding the i-th genetic factor as predictors. It means the ph model.

At this time, semi-partial R ² is characterized in that it is calculated by Equation 3 below.

Formula 3:

은 선별된 SNP 중 i번째 SNP를 제외한 cox ph 모형의 R²값을 의미한다.Here, i means the i-th SNP, R ² is the coefficient of determination, which is a measure of the explanatory power of the model, and means a value calculated from the part that can be explained by the model among the total variability of the data,

is the R ² value of the cox ph model except for the i-th SNP among the selected SNPs.

The weighted GS (WGS) of each i-th SNP was calculated by the following Equation 4:

Formula 4:

는 i번째 SNP의 weighted genotype score를 의미하고,

는 i번째 SNP의 genotype score를 의미하며,

는 i번째 SNP의 weight를 의미한다.here,

is the weighted genotype score of the i-th SNP,

is the genotype score of the i-th SNP,

is the weight of the i-th SNP.

는 하기 수식 5로 계산하였다:

was calculated with the following Equation 5:

Formula 5:

Therefore, the formula for calculating the sum (SNP point) of the risk scores of the genetic factors for diabetes of the present invention, Equation 2, was calculated by the following Equation 6:

Formula 6:

In the present invention, the probability of onset of diabetes within a specific time point may be calculated by Equation 1 below.

Formula 1: 1-exp[-H ₀ (t)] ^exp(βx)

Hereinafter, the above formula will be described in detail.

In the present invention, a Cox proportional hazard model was constructed using the final selected clinical information and SNP point as predictors.

The structure of the Cox proportional hazard model is as follows.

Equation 7: hazard function at time t

Here, h ₀ (t) means the baseline hazard function, X means the risk factor, and β means the coefficient for each risk factor.

Equation 8: Probability of not developing disease until time t

Therefore, the probability of developing diabetes using the Cox proportional hazard model can be expressed by Equation 9 below:

Equation 9: Probability of developing diabetes within time t

Here, H ₀ (t) means the cumulative baseline hazard function.

β means a coefficient for each risk factor, and in the present invention, the estimation of β is determined as a value that maximizes the partial likelihood function of Equation 10 below:

Equation 10:

, 0<

<…<

:d 개의 distinct failure time을 의미하며,

, 0<

<… <

: means d distinct failure times,

를 의미한다.

means

When there is a tiered failure time, the estimation of β is determined as a value that maximizes the partial likelihood function of Equation 11 below:

Formula 11:

는 failure time 이

인 subject set을 의미하고,here,

is the failure time

means a subject set that is

는 failure time 이

인 k 번째 subject의 위험요인을 의미한다.

is the failure time

It means the risk factor of the k-th subject.

Therefore, the cumulative baseline hazard function can be calculated by Equation 12 below:

Equation 12:

는 failure time 이

인 subject 수를 의미하며,here,

is the failure time

means the number of subjects,

는 추정된 β 값을 의미한다.

is the estimated β value.

In the present invention, βX in Equation 1 may be calculated as a value obtained by multiplying the markers in Table 1 by a weight and adding them all together.

marker range weight age 0.0261 Gender: Male 0 Gender: Female -0.0385 family history has exist 0.4278 Fasting Blood Sugar (FBS) More than 80, less than 85 0.4087 Greater than 85, less than 90 0.6537 over 90 1.3791 Liver level (ALT) More than 30, less than 40 0.1723 over 40 0.2697 Gamma GTP Men: over 18, under 29 0.4149 Women: over 10, under 13 Men: over 29, under 51 0.5690 Women: over 13, under 18 Men: Over 51 0.7651 Women: Over 18 high-density cholesterol concentration
(HDL) Men: over 40 -0.0843 Women: over 50 triglycerides (TG) More than 90, less than 110 0.1530 More than 110, less than 160 0.4344 More than 160, less than 230 0.7288 over 230 0.8647 systolic blood pressure (SBP) More than 130, less than 150 0.1877 over 150 0.2488 body mass index (BMI) greater than 23, less than 25 0.0304 Greater than 25, less than 30 0.1126 over 30 0.3137 SNP point 0.3769

In the present invention, H ₀ (t) of Equation 1 may be 0.00337 for 5 years and 0.00500 for 7 years.

On the other hand, the present inventors, when predicting the probability of onset by using the onset probability prediction model, can distinguish the effects of genetic factors and clinical information on the onset probability prediction, respectively, and using this, additional examination contents and management methods, if necessary expected to be available.

That is, in another embodiment of the present invention, genetic factors and clinical information are distinguished based on the diabetes incidence probability predicted by the above method, and then a report providing additional examination contents and management method is produced, and when provided, the user It was confirmed that the convenience increased ( FIGS. 2 to 5 ).

Therefore, in another aspect, the present invention comprises the steps of: a) predicting the probability of developing diabetes within a specific period by the above method; b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; And c) relates to a method of providing information for additional examination of a diabetic patient comprising the step of providing additional necessary examination details.

In the present invention, the method of providing information for additional examination of a diabetic patient may further include the step of d) providing additional examination details for confirming whether another disease has occurred.

In the present invention, the other diseases can be any disease that can be caused by the genetic factors and environmental factors, and preferably can be selected from the group consisting of hypertension, coronary artery disease and cerebrovascular disease, It is not limited.

The present invention also comprises the steps of: a) predicting the probability of developing diabetes within a specific period by the above method; b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; And c) relates to a method of providing information for the management of diabetic patients, including the step of providing a dietary, exercise and lifestyle guide to lower the incidence probability.

In the present invention, the additional checkup can be used without limitation as long as it is a checkup that can confirm symptoms or other diseases related to diabetes, preferably, it may be a test related to diabetic complications, more preferably arteriosclerosis test, homocysteine, CRP, carotid artery ultrasound , may be characterized as selected from the group consisting of echocardiography and cardiac CT, but is not limited thereto.

In the present invention, the step b) may be characterized in that it is divided by calculating the snpRatio.

The snpRatio of the present invention means the ratio of the linear predictor by genetic factors to the linear predictor value by all risk factors, and it can be characterized by calculating with the following Equation 13:

Equation 13:

는 i번째 subject에서 계산된 SNP point를 의미하고,

는 i번째 subject에서 측정된 j번째 임상정보 값을 의미하며,

는

인 임상정보를 의미하고,

는

에서 측정될 수 있는 최대값을 의미한다.here,

means the SNP point calculated in the i-th subject,

is the j-th clinical information value measured in the i-th subject,

Is

means clinical information,

Is

The maximum value that can be measured in

Based on the above formula, the probability of developing diabetes within time t due to genetic factors is calculated by the following Equation 14:

Equation 14:

The probability of developing diabetes within time t according to clinical information may be characterized by being calculated by the following Equation 15:

Equation 15:

는 t 시점 내에 당뇨가 발병할 확률을 의미한다.here,

is the probability of developing diabetes within time t.

In addition, the report of the present invention may be characterized in that the probability of onset compared to the average of the same year is provided by dividing the ratio according to genetic factors and clinical information.

In another aspect, the present invention

A device for calculating a user's probability of developing diabetes by using the method;

a server that receives and analyzes diabetes onset probability information from the diabetes onset probability calculation device and matches insurance products;

Doedoe configured to include a terminal for outputting the information of the server on the screen,

Diabetes that calculates the user's diabetes incidence probability through the diabetes incidence probability calculation device, receives diabetes incidence probability information from the server, calculates future diabetes treatment costs, and matches insurance products based on the calculation results to recommend diabetes In the insurance design and insurance product matching system based on the probability of occurrence,

The server is

An insurance product storage unit that stores information on all insurance products to be provided to the user;

an average treatment cost storage unit storing the average treatment cost of diabetes;

a customer authentication unit that authenticates the user as a customer;

a diabetes onset probability information receiving unit for receiving the diabetes onset probability of the diabetes onset probability calculation device based on the customer's information authenticated through the customer authentication unit;

an analysis unit that calculates a future expected treatment cost based on the diabetes incidence probability information received through the diabetes incidence probability information receiving unit;

Identifies the insurance purchased by the customer based on the customer's information authenticated through the customer authentication unit, receives the customer's insurance details for the identified insurance from the insurance product storage unit, and compares it with the expected future treatment cost of the analysis unit insurance coverage comparison department;

Based on the expected future treatment cost analyzed through the analysis unit and the insurance coverage detail comparison unit, it is determined whether the expected future treatment cost is covered by the insurance purchased by the customer, and the insurance is matched based on the determined result. insurance matching department,

and an insurance information recommendation unit that recommends the insurance matched by the insurance matching unit to the user.

It relates to an insurance design and insurance product matching system based on clinical and genetic variation information.

In the present invention, the system includes a diabetes incidence probability calculation device for calculating the diabetes incidence probability using the method;

a server that receives and analyzes the probability of developing diabetes from the calculation device and matches insurance products;

It may be configured to include a terminal for outputting the information of the server on the screen.

In the present invention, the server may be configured to include a database, a customer authentication unit, a diabetes onset probability information receiving unit, an analysis unit, an operation interface, an insurance matching unit, and an insurance information recommendation unit.

In the present invention, the database may be configured to include an insurance product storage unit that stores information on all insurance products to be provided to the user, and an average treatment cost storage unit that stores the average diabetes treatment cost.

In the present invention, the customer authentication unit serves to authenticate a user who uses the terminal as a customer, and such authentication is made based on login, and the database may further include a storage unit for storing user information.

At this time, the user information may include an identification number for interworking with the diabetes incidence probability calculation device, through which the user inputs the identification number together when logging in, so that the server can share the incidence probability information of the diabetes incidence probability calculation device there is.

In the present invention, the diabetes onset probability information receiving unit may be characterized in that it receives the diabetes onset probability of the diabetes onset probability calculation device based on customer information authenticated through the customer authentication unit.

In the present invention, the analysis unit performs a function of calculating the expected future treatment cost based on the diabetes onset probability information received through the diabetes onset probability information receiving unit and the average treatment cost of the average treatment cost storage unit.

For example, it can be calculated by multiplying the information on the probability of developing diabetes by the average cost of treatment, or by adding an extra dignified buffer.

In the present invention, the insurance guarantee history comparison unit identifies the insurance purchased by the customer based on the customer's information authenticated through the customer authentication unit, receives the customer's insurance details for the identified insurance from the insurance product storage unit, and receives the insurance information from the analysis unit. It performs the function of comparing with the expected future treatment cost.

In the present invention, the insurance matching unit determines whether the expected future treatment cost is covered by the insurance purchased by the customer based on the future expected treatment cost analyzed through the analysis unit and the insurance coverage detail comparison unit, and the result of the determination It performs the function of matching insurance based on

For example, if the expected future treatment cost is covered by the insurance purchased by the customer, a message indicating that the current insurance is sufficient can be printed on the terminal. You can find and match insurance that covers more than the expected cost of treatment.

In the present invention, the insurance information recommendation unit performs a function of recommending the insurance matched by the insurance matching unit to the user.

Example

Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not to be construed as being limited by these examples.

Example 1: Analysis subject setting

The subjects of the present invention were selected from 10,030 study participants aged 40 to 69 years old, collected through a population-based prospective cohort study conducted in the Anseong and Ansan regions of Korea through the Korean Genome Epidemiological Survey (KoGES).

The investigated clinical epidemiologic data are shown in Table 4 below.

Diabetes mellitus is defined as a case of diabetes diagnosis, current treatment, or drug persistence as a result of the cohort questionnaire. In addition, as a result of blood tests, fasting blood sugar is 126 mg/dL or higher, or blood sugar 200 mg/dL 2 hours after oral glucose tolerance test Abnormalities were defined as diabetes. Among the survey subjects, those who were defined as prediabetic at the time of the baseline survey were excluded from the analysis.

Example 2: Construction of an integrated model for detecting the incidence of type 2 diabetes

2-1. Establishment of clinical information analysis model

In order to analyze the clinical information affecting the onset of diabetes, the risk factors were selected by applying the Cox proportional hazards model (hereinafter the cox ph model) with the items investigated through the basic health checkup as candidate factors. In the cox ph model using age, sex, and each clinical information factor as explanatory variables, the significance was confirmed by categorizing the data type for the clinical information factor with a p value of less than 0.05 as a result of the LR test (likelihood-ratio test) of each clinical information. Therefore, clinical information factors maintaining significance were selected. For multiple regression analysis of the selected clinical information factors, the final clinical information factors were selected by applying the backward elimination method to the cox ph model using age, gender, family history of diabetes and the selected clinical information factors as explanatory variables. At this time, the criterion for removing a variable is to minimize the AIC, but when the p value of the variable is less than 0.05, it was not removed ( FIG. 1 ).

The clinical information factors selected at each stage are shown in Table 5 below: fasting blood sugar (FBS), liver level (alanine aminotransferase, ALT), gamma GTP, high-density cholesterol concentration (HDL), triglyceride (TG), systolic blood pressure (SBP) , and body mass index (BMI).

Selection of clinical information factors single analysis Multiple analysis continuous categorical Final Selection Factors Fasting Blood Sugar (FBS) <0.001 <0.001 <0.001 creatinine 0.239 not done except Liver value (AST) <0.001 <0.001 except Liver level (ALT) <0.001 <0.001 <0.001 Gamma GTP <0.001 <0.001 <0.001 total cholesterol concentration <0.001 <0.001 except High-density cholesterol (HDL) <0.001 <0.001 0.081 triglycerides (TG) <0.001 <0.001 <0.001 hemoglobin <0.001 <0.001 except systolic blood pressure (SBP) <0.001 <0.001 0.006 diastolic blood pressure (DBP) <0.001 <0.001 except Waist circumference <0.001 <0.001 except body mass index (BMI) <0.001 <0.001 0.119

* Single analysis was corrected for age and gender, and multiple analysis was adjusted for age, gender and family history.

2-2 Genetic information analysis model construction

To analyze genetic variation information, 443,816 SNP genotypes from 8,842 individuals were analyzed using Affymetirx Genome-Wide Human SNP array 5.0. First, among SNPs, monomorphic SNPs, SNPs with a minor allele frequency of less than 1%, SNPs with a p value of less than 0.001 as a result of the Hardy-Weinberg equilibrium test, and a missing rate were SNPs exceeding 20% were excluded.

Next, through the GWAS Catalog, SNPs analyzed as being correlated with related diseases in previous studies are selected as candidates, and SNPs with all pairwise correlation constants of SNPs in the same chromosome exceeding 0.9 are defined as tagging SNPs. It was made not to be used repeatedly within the model.

Candidate SNPs (;rs10842994, rs10811661, rs947474, rs3024505, rs4402960, rs1048886, rs9268645) were an additive genetic model to the cox ph model using age, sex, family history of diabetes, the final selected clinical information factors, and each SNP as explanatory variables. Among the categorical genotypes AA and BB, Allele of the genotype with the highest HR was used as a risk allele, and it was converted to continuous (0,1,2; GS).

+... +

)개의 SNP point를 후보 요인으로 하나의 변수로 적용하는 것이다. 2-1 에서 최종 선정된 환경요인에 각 SNP point를 추가한 cox ph 모형에 대해서 아래 (1)~(3)의 조건을 만족하는 SNP point로 후보군을 좁혔다. Several primary selected genetic factors were integrated into one score to be used as a predictive marker for diabetes onset, and the genotype scores (GS) of 1 to 7 SNPs from 1 of the 7 primary selected factors were integrated into one SNP point. was used, i.e. (7+

+... +

) SNP points are applied as a single variable as a candidate factor. For the cox ph model in which each SNP point is added to the final selected environmental factors in 2-1, the candidate group was narrowed down to SNP points that satisfy the conditions of (1) to (3) below.

(1) Whether or not the PH assumption of the SNP point is satisfied, LR test result p value < 0.05

(2) The time-dependent AUC of the model with the SNP point added is larger than that of the model without the SNP point.

(3) The hosmer-lemeshow test result of the model with the added SNP point did not deteriorate compared to the model without the SNP point.

59 out of 127 SNP points satisfying the conditions (1) to (3) corresponded, and the results of the top 7 models with time-dependent AUC and survival NRI values are presented in Table 6. Significance of the risk of diabetes mellitus as the SNP point increased was confirmed in all 7 models. Regardless of the time of onset, the time-dependent AUC value was the highest in the model using SNP point 117, but the difference between the model using SNP point 89 and the model using SNP point 89 was small and statistically significant. It was confirmed that there was no difference. Also, the survival NRI value of the model using SNP point 117 had the largest value, but there was no significant difference in the survival NRI value of the model using SNP point 89, and it was confirmed that the event NRI was larger. . Therefore, it was confirmed that it is most effective to predict the onset of diabetes using SNP point 89 using a smaller number of SNPs.

Results of different SNP points SNP point ^*1 HR of SNP point (p value) Onset within 5 years Onset within 7 years Onset within 5 years Onset within 7 years AUC (95% CI) AUC (95% CI) NRI ^{*2 (} Event, non Event NRI) NRI ^{*2 (} Event, non Event NRI) number 47
1.3049 (0.0009) 0.7570
(0.7365-0.7774) 0.7494
(0.7312-0.7676) 0.1305
(0.1090,0.0215) 0.1238
(0.0992,0.0244) number 53 1.2880 (0.0013) 0.7571
(0.7366-0.7776) 0.7492
(0.7310-0.7674) 0.1353
(0.0759,0.0594) 0.1033
(0.0473,0.0558) number 86 1.3570 (0.0007) 0.7568
(0.7363-0.7772) 0.7490
(0.7308-0.7672) 0.1423
(0.0976,0.0446) 0.1273
(0.0807,0.0464) 89 1.4578 (0.0001) 0.7573
(0.7369-0.7778) 0.7495
(0.7312-0.7677) 0.1505
(0.1018,0.0487) 0.1316
(0.0807,0.0507) 92 1.3451 (0.0010) 0.7568
(0.7363-0.7773) 0.7488
(0.7306-0.7671) 0.1454
(0.0643,0.0811) 0.1130
(0.0337,0.0791) number 116 1.5142 (0.0001) 0.7571
(0.7366-0.7776) 0.7491
(0.7309-0.7674) 0.1455
(0.0972,0.0483) 0.1231
(0.0750,0.0479) number 117
1.4976 (0.0001) 0.7575
(0.7370-0.7780) 0.7496
(0.7313-0.7678) 0.1666
(0.0877,0.0789) 0.1362
(0.0558,0.0802)

*One. SNP point 47 (rs10842994, rs10811661, rs1048886), SNP point 53 (rs10842994, rs4402960, rs1048886), SNP point 86 (rs10842994, rs10811661, rs947474, rs1048886), SNP point 89 (rs10842994, rs108116) ), SNP point 92 (rs10842994, rs947474, rs4402960, rs1048886), SNP point 116 (rs10842994, rs10811661, rs947474, rs4402960, rs1048886), SNP point 117 (rs10842994, rs10811661, rs3024505)

*2. Event NRI: Among the cases in which diabetes occurred within a specific period, the probability of the model adding the SNP point increased compared to the model without the SNP point - decreased probability Reduced Probability - Increased Probability over Models That Don't

NRI = Event NRI + non Event NRI

2-3. Build a unified model

A cox ph model was constructed using the clinical information and genetic factors selected in Examples 2-1 and 2-2 as explanatory variables.

In the integrated analysis model, the variables disclosed in Table 1 are multiplied by weights and summed, and then calculated using Equation 1.

Formula 1: 1-exp[-H ₀ (t)] ^exp(β

As a result, it was confirmed that the built model can predict the probability of diabetes with high accuracy (FIG. 2).

Example 3: Performance verification of the built model

As a result of comparing and analyzing the result of predicting the onset of diabetes using the model constructed in Example 2-3 with the result of predicting the onset of diabetes using only clinical information, the value of time-dependent AUC when predicting the onset using the integrated model and event NRI and non-event NRI values all increased regardless of the time of onset, confirming that onset prediction was possible with higher accuracy. (Table 7)

Performance evaluation of diabetes onset prediction model Onset within 5 years Onset within 7 years Onset within 5 years Onset within 7 years AUC (95% CI) AUC (95% CI) NRI ⁽ Event, non Event NRI) NRI ⁽ Event, non Event NRI) ENV model 0.7552
(0.7348-0.7757) 0.7483
(0.7301-0.7665) - - integrated model 0.7573
(0.7369-0.7778) 0.7495
(0.7312-0.7677) 0.1505
(0.1018,0.0487) 0.1316
(0.0807,0.0507)

As a specific part of the present invention has been described in detail above, for those of ordinary skill in the art, it is clear that this specific description is only a preferred embodiment, and the scope of the present invention is not limited thereby. will be. Accordingly, it is intended that the substantial scope of the present invention be defined by the appended claims and their equivalents.

Claims (13)

delete delete In a method for predicting the incidence of diabetes performed by a computing device including a processor,
a) collecting clinical information of a patient and receiving DNA information;
b) deriving clinical information values by assigning weights to clinical information;
c) determining the genotypes of rs10811661, rs947474, rs1048886, and rs9268645 SNPs from DNA information to give a score (Genotype Score, GS);
d) deriving an SNP value (SNP point) by assigning a weight to each SNP; and
e) calculating the probability of developing diabetes within a specific time point based on the values b) and d),
Here, GS means a value indicating the influence of each SNP genotype on the onset of diabetes by dividing it into 0, 1, or 2,
The SNP point means the total sum of the values multiplied by the weight of the GS,
The probability of developing diabetes within a specific time point is calculated by Equation 1 below:
Equation 1: 1-exp[-H0(t)]exp(βX)
The βX is calculated as the sum of all markers in Table 1 by multiplying them by a weight.
According to claim 3, wherein the clinical information is age, sex, family history, fasting blood glucose (FBS), liver level (alanine aminotransferase, ALT), gamma GTP, high-density cholesterol concentration (HDL), triglycerides ( TG), systolic blood pressure (SBP), and body mass index (BMI).
delete The method of claim 3, wherein the SNP point is calculated by the following Equation 3:
Equation 3: SNP point=
0.1595Xrs10811661'GS+0.2820Xrs947474'GS+0.2498Xrs1048886'GS+0.3088Xrs9268645'GS
The method of claim 6, wherein the GS has the same value as in Table 2.
The method of claim 3, wherein H ₀ (t) is 0.00337 at 5 years, and 0.00500 at 7 years.
A method of providing information for further screening of diabetic patients, comprising the steps of:
a) estimating the probability of developing diabetes within a specific period by the method of any one of claims 3, 4, 6 to 8;
b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; and
c) providing additional necessary examination contents.
10. The method of claim 9,
d) A method of providing information for an additional check-up of a diabetic patient, characterized in that it further comprises the step of providing additional check-up details for confirming the onset of other diseases.
The method of claim 10, wherein the other disease is one or more selected from the group consisting of hypertension, coronary artery disease, and cerebrovascular disease.
A method of providing information for the management of a diabetic patient comprising the steps of:
a) estimating the probability of developing diabetes within a specific period by the method of any one of claims 3, 4, 6 to 8;
b) distinguishing the influence of genetic factors and environmental factors on the incidence probability; and
c) Providing dietary, exercise, and lifestyle guides to lower the incidence rate.
A device for calculating a user's probability of developing diabetes using the method of any one of claims 3, 4, and 6 to 8;
a server that receives and analyzes diabetes onset probability information from the diabetes onset probability calculation device and matches insurance products;
Doedoe configured to include a terminal for outputting the information of the server on the screen,
Diabetes that calculates the user's diabetes incidence probability through the diabetes incidence probability calculation device, receives diabetes incidence probability information from the server, calculates future diabetes treatment costs, and matches insurance products based on the calculation results to recommend diabetes In the insurance design and insurance product matching system based on the probability of occurrence,
The server is
An insurance product storage unit that stores information on all insurance products to be provided to the user;
an average treatment cost storage unit storing the average treatment cost of diabetes;
A diabetes onset probability information storage unit for storing the diabetes onset probability information calculated by the diabetes onset probability calculation device;
A customer authentication unit that authenticates the user as a customer;
a diabetes onset probability information receiving unit for receiving the diabetes onset probability of the diabetes onset probability calculation device based on the customer's information authenticated through the customer authentication unit;
an analysis unit that calculates a future expected treatment cost based on the diabetes incidence probability information received through the diabetes incidence probability information receiving unit;
Identifies the insurance purchased by the customer based on the customer's information authenticated through the customer authentication unit, receives the customer's insurance details for the identified insurance from the insurance product storage unit, and compares it with the expected future treatment cost of the analysis unit insurance coverage comparison department;
Based on the expected future treatment cost analyzed through the analysis unit and the insurance coverage details comparison unit, it is determined whether the expected future treatment cost is covered by the insurance purchased by the customer, and the insurance is matched based on the determined result. insurance matching department,
and an insurance information recommendation unit that recommends the insurance matched by the insurance matching unit to the user.
Insurance design and insurance product matching system based on clinical and genetic mutation information.

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