KR20210115882A - Personalized healthcare system based on genetic analysis data and lifestyle data - Google Patents

Abstract

In one aspect, according to a computer-readable recording medium recording a personalized health care system based on the genetic analysis data and lifestyle data and a program for executing thereof on a computer, the personalized healthcare information, based on a clear standard, can be provided. The personalized health care system comprises: an information receiving part; a personalized healthcare computing part; and a personalized health care providing part. Therefore, the present invention is capable of allowing a health status to be predicted.

Description

Personalized healthcare system based on genetic analysis data and lifestyle data

It relates to a personalized healthcare system based on genetic analysis data and lifestyle data.

The current trend of personal health care methods is that individuals obtain their own body information from a hospital, obtain health care information, and make and practice health care plans. This is a state in which individual characteristics are not accurately reflected, and not only undergoes a lot of trial and error, but also has a problem in that it is difficult to maintain a steady management and consequently, the goal of health management cannot be achieved well. In addition, in the conventional health management methods for health management and metabolic disease prevention, only guides for universal management methods such as diet control and exercise exist, and individual-specific sensitivities are not taken into account.

As such, as interest in health increases, the demand for a system that can receive personalized health care is rapidly increasing, but it has been common to provide health care with common sense without objective standards (Patent Publication KR 10-2019- 0073120 A). Recently, with the development of IT devices and technologies, access to information becomes easier, and the demand for effective personal health management according to objective standards is increasing. However, research on the standards for providing health care is lacking, and the standards and rationale for customized health care are not clear.

Therefore, the present inventors set objective criteria for predicting health status risk based on the lifestyle and DNA analysis information of about 40,000 subjects. Accordingly, the present invention solves the above problems by providing a clear standard for providing personalized healthcare and developing a method for providing a customized solution in a form that meets individual needs. These inventions help to improve the integrated quality of life.

One aspect provides a personalized health care system based on lifestyle or human body information.

Another aspect provides a recording medium including the healthcare system.

In one aspect, in a personalized healthcare system based on genetic analysis data and lifestyle data, a device providing healthcare services includes one or more information selected from the group consisting of DNA information, lifelog information, and lifestyle information. Information receiving unit to collect; a personalized healthcare calculator for predicting a user's health risk using the information input through the information receiver; and a personalized healthcare provider that provides a personalized health care method based on the risk calculated through the calculator.

According to another aspect, there is provided a computer-readable recording medium in which a program for executing the health care system or method of providing a health care service in a computer is recorded.

According to a personalized health care system according to an aspect, a method of controlling the health care system, a recording medium and a health care system including the same, the current health state can be predicted based on the user's genetic information, human body information or lifestyle information, , it is possible to provide a personalized health management method based on this.

1 is a schematic diagram of a system for providing personalized healthcare according to an aspect.
2 is a schematic diagram showing the structure of a receiving unit, an analysis unit, a processing unit, a calculating unit, and a providing unit of a healthcare providing system according to an aspect.
3 is a schematic diagram illustrating a specific structure of a receiving unit and a calculating unit of a personalized healthcare system according to an aspect.
4 is an image illustrating an example of driving a personalized healthcare system according to an aspect.

Hereinafter, the present invention will be described in more detail through examples. However, these examples are for illustrative purposes of the present invention, and the scope of the present invention is not limited to these examples.

2 is a schematic diagram illustrating the structure of a receiving unit, an analysis unit, a processing unit, a calculating unit, and a providing unit of a healthcare providing system according to an aspect.

In FIG. 2 , the personalized healthcare providing system includes an information receiving unit 100 , a data collection/management/analysis unit 200 , an input information processing unit 300 , a customized healthcare calculating unit 350 , and a customized healthcare providing unit 400 . ), a feedback information providing unit 500 and a feedback information calculating unit 600 .

The information receiving unit 100 receives genetic analysis data, life log data, or lifestyle data. As shown in FIG. 2 , the information receiving unit 100 includes a first receiving unit 130 , a second receiving unit 160 , and a third receiving unit 190 .

The first receiver 130 receives human body data. The human body data may be genetic analysis data or data obtained by measuring dimensions related to the human body. The genetic analysis data refers to data obtained by analyzing a DNA sample. In addition, the dimension measurement data related to the human body may be one or more items selected from the group consisting of diastolic/systolic blood pressure, HDL-cholesterol, triglyceride, fasting blood sugar, body mass index, and waist circumference. Specifically, the first receiver 130 may receive data from the human body information measuring device 131 or the DNA analyzing device 132 .

The human body information measuring device 131 measures one or more items selected from the group consisting of diastolic/systolic blood pressure, HDL-cholesterol, triglyceride, fasting blood sugar, body mass index, and waist circumference, which are factors related to metabolic diseases.

The DNA analysis device 132 obtains DNA information obtained by analyzing the user's biological sample. Specifically, it may be to detect a gene single nucleotide polymorphism. In addition, it is possible to measure the expression level of the user's metabolic disease-related gene biomarker (1).

The metabolic disease biomarker (1) is a biomarker selected by analyzing the association with each metabolic disease risk factor based on a genetic test result. Specifically, the risk factors are diastolic/systolic blood pressure, HDL-cholesterol, triglycerides, fasting blood sugar, body mass index, or waist circumference.

The DNA analysis device 132 may include a test gene determination unit, a genetic test unit, and a test result transmission unit.

The test gene determining unit may determine a gene to be tested based on the user's DNA. Specifically, it may be to select a risk factor to be tested in order to determine the risk of metabolic disease. In addition, the genetic test unit examines the expression level of the biomarker according to the selected metabolic disease risk factor. In addition, the test result may be transmitted to the genetic test unit.

The biomarker associated with the diastolic blood pressure (SBP) risk factor is at least one selected from the group consisting of rs16998073, rs167479, rs167479, rs4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 and re117747786.

The biomarker associated with the systolic blood pressure risk factor is rs2 from the group consisting of rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047_, rs76776700, rs2681485, rs9508499, rs985740, rs2113918293, rs2 from 167 and rs2 more than one selected.

The biomarkers associated with the risk factors for HDL-cholesterol (HDL) are rs2282231, rs2144300, rs333970, rs6685271, rs333970, rs2144300, rs78598368, rs1873516, rs11762563, rs2253569, rs14564743, rs236673569, rs1456474, rs23667356318, rs76474, rs2366735318, rs76474 , rs2980884, rs10820706, rs10123140, rs3016352, rs921919, rs3117925, rs12910051, rs4983558, rs2925979, rs75318258, rs2292318, rs146606382, one or more selected from the group consisting of, rs6065904, rs20072642480, rs141519113, rs

The biomarkers associated with the risk factors of triglycerides (TG) are rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs180018229588, rs10489326, rs352942926, rs352942926, rs35292926, rs35 at least one selected from the group consisting of rs440446, and rs16990964.

The biomarkers that are associated with the risk factors for fasting blood sugar (FBS) are rs1260326, rs780096, rs12712928, rs12053049, rs6731917, rs80195623, rs704374, rs2590187, rs117338530, rs74590880, rs28684504, rs220966734, rs1741765, 104877467, rs1741765 rs10262191, rs11776687, rs11774700, rs35613378, rs78482117, rs10975941, rs3824420, rs3934283, rs10811660, rs1053878, rs11257655, rs4300299, rs7072873, rs60808706, rs174577, rs10830963, rs671, rs7981781, rs9567063, rs7172432, rs59391831, rs11861659, rs7187556, rs2302783, rs6048209, and at least one selected from the group consisting of rs151353.

The biomarkers associated with the risk factors of body mass index (BMI) are rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12543514 at least one selected from the group.

The genetic test unit may detect a gene, specifically, single nucleotide polymorphism (SNP). The detection may be by an agent capable of amplifying. In addition, the detection may be by a primer, a probe, an oligonucleotide, or a combination thereof. Specifically, the human body information measuring device 131 or the DNA analysis device 132 may be a SNP detection kit or a device including the same.

The kit may be an RT-PCR kit or a DNA chip kit, and the kit may further include reagents necessary for the polymerization reaction, for example, dNTPs, a polymerase, and a coloring agent.

The kit can diagnose health risk by confirming the SNP polymorphic marker through amplification or by checking the expression level of the SNP polymorphic marker and the mRNA expression level.

The second receiver 160 receives a LIFE LOG result related to the user's personal daily life. Accordingly, the second receiver 160 receives data from the smart device 161 .

The term “LIFE LOG” data refers to the overall daily data of an individual, including the personal movement route, purchase pattern, consumption preference, daily exercise amount, and the like, measured or recorded using the smart device 161 . The smart device 161 may be specifically a wearable smart device 162 .

The third receiving unit 190 receives personal lifestyle information. The third receiver 190 may receive data from the questionnaire/questionnaire 191 .

The questionnaire/questionnaire 191 data may include response data of the consumer to various items for examining the lifestyle of the consumer. Specifically, it includes items for variables that can affect lifestyle factors. For example, include items such as gender, age, body mass index (BMI), smoking, drinking, and exercise frequency.

The big data collection/management/analysis unit 200 includes a DNA/bio-index database 10 , a personal lifelog database 30 , and a lifestyle database 50 .

In the DNA / bio-index database 10, the user's DNA analysis result, diastolic / systolic blood pressure, HDL-cholesterol, triglyceride, fasting blood sugar, body mass index, and analysis or measurement information for one or more items selected from the group consisting of waist circumference is saved In addition, the DNA/bioindex database 10 stores metabolic disease-related data. Specifically, the expression level of the metabolic-related disease biomarker (1) of user DNA is stored.

The personal lifelog database 30 stores personal lifelog data.

In the lifestyle database 50, questionnaire/questionnaire 191 data is stored.

The input information processing unit 300 receives data input through the information receiving unit 100 .

It provides the consumer's current health status and personalized health care method by referring to the DNA / bio-index database 10 , the personal life log database 30 or the personal lifestyle database 50 .

The input information processing unit 300 may be installed in one device together with the information receiving unit 100 or may be installed in a separate device. For example, in the case of the human body information measuring device 131, the body information including both the information receiving unit 100 and the input information processing unit 300 is acquired and based on these values, the current state of the body, health index, Disease risk can be measured. On the other hand, in the case of the smart device 161, the acquired information is transmitted to the input information processing unit 300 installed in the cloud computer, etc. together with the big data collection/management/analysis unit 200, and it is analyzed by the cloud computer. You can also determine your current status, your risk, and your health care methods. Even in the case of DNA information processing, the input information processing unit 300 may be installed in one device together with the information receiving unit 100 or may be installed in a separate device.

The customized healthcare provision calculation unit 350 uses the current state of the body, the health index, and the disease risk information determined by the input information processing unit 300 , and determines the customized health management method by referring to the DNA / biometric index database 10 .

The customized healthcare provision calculation unit 350 includes a DNA analysis data-based calculation unit 60 or a lifestyle data-based calculation unit 70 .

The DNA analysis data-based calculation unit 60 calculates a health risk using the data received from the human body information measuring device 131 or the DNA analysis device 132 using a genetic result prediction formula. Specifically, the health risk may be calculated using the DNA analysis data-based health risk formula (2). The health risk formula (2) may be calculated based on the data of the DNA / bio-index database (10), and additionally calculated based on the data of the personal lifelog database (30) or the personal lifestyle database (50) can

The lifestyle data-based calculation unit 70 may calculate the health risk by using the lifestyle-based formula 3 . The health risk formula 3 may be calculated based on data of the personal lifelog database 30 or the personal lifestyle database 50 .

The human body information calculating unit 60 or lifestyle data-based calculating unit 70 is a diastolic blood pressure (DBP), systolic blood pressure (SBP), cholesterol (HDL), triglyceride (TG), blood sugar (FBS), body mass index (BMI) , or calculate the risk for waist circumference (WC).

Specifically, the risk calculation for the diastolic blood pressure (DBP) is calculated by measuring the expression level of one or more genes selected from the group consisting of rs16998073, rs167479, rs167479, rs4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 and re117747786. It can be done based on data.

Table 1 below is a table showing the relationship between genetic markers related to risk calculation for diastolic blood pressure (DBP) and items according to the phenotype of the gene.

CHR SNP BP Minor allele β SE L95 U95 P DBP 4 rs16998073 81184341 T 0.471 0.061 0.351 0.591 1.29E-14 19 rs167479 11526765 T -0.393 0.058 -0.506 -0.279 1.15E-11 3 rs4684242 14895105 C 0.320 0.064 0.195 0.445 5.08E-07 12 rs12368700 116197943 G 0.285 0.060 0.167 0.403 2.14E-06 9 rs10739694 130526605 A -0.293 0.063 -0.416 -0.170 3.10E-06 6 rs3800469 34481981 T -0.991 0.213 -1.408 -0.574 3.22E-06 10 rs77180047 104666757 A -0.305 0.066 -0.434 -0.175 4.03E-06 17 rs12952581 47448346 A 0.365 0.080 0.209 0.521 4.63E-06 20 rs138744086 2778834 A -1.224 0.270 -1.752 -0.695 5.75E-06 2 rs4674649 223297282 A -0.263 0.058 -0.377 -0.149 6.02E-06 16 rs117747786 59436423 T -1.010 0.225 -1.450 -0.569 7.02E-06

The polymorphic site refers to a site exhibiting single-nucleotide polymorphism in a nucleic acid sequence. The term "single nucleotide polymorphism (SNP)" refers to a genetic change or variation that exhibits a difference of one nucleotide in a nucleic acid sequence. It may be a position in which two or more allele sequences present at a frequency of about 1% or more, or about 5% or more, 2% to 4.5%, 3 to 4%, 2.5% to 3.4% in the population occur.

The SNP may be detected by a detection agent comprising an agent capable of amplifying a single nucleotide polymorphism. In addition, the detection may be a primer, a probe, an oligonucleotide, or a combination thereof.

In addition, the risk calculation for diastolic blood pressure (DBP) was selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER). It may be made based on data for one or more items.

Specifically, the degree of risk for the diastolic blood pressure (DBP) may be derived by Equation 1 below.

[Equation 1]

DBP=56.239+(-3.189)*SEX+0.105*AGE+(-0.637)*SMOKE+0.802*DRINK+0.024*EXER+0.796*BMI+(-0.115)*rs4674649+0.239*rs4684242+0.724*rs16998073+(-0.685) *rs3800469+(-0.120)*rs10739694+(-0.400)*rs77180047+0.272*rs12368700+(-0.302)*rs117747786+0.312*rs12952581+(-0.435)*rs167479+(-0.592)*rs138744086.

The gender (SEX) item may have a value of 1 if the user is a man and 2 if the user is a woman.

The age (AGE) item is to input the user's personal age. For example, if the user is 23 years old, it may have a value of 23.

The body mass index (BMI) item may be an input of the user's personal body mass index item.

The smoking status (SMOKE) item may have a value of 1 when the user is a non-smoker and a value of 2 when the user is a smoker.

The DRINK item may have a value of 1 when the user is a non-drinker and a value of 2 when the user is a drinker.

The exercise frequency (EXER) item may have a value of 2 when the user does not exercise and has a value of 1 when exercising.

In Equation 1, each SNP has a specific value according to the genotype. Specifically, as shown in Table 2 below, it may have a value of 0, 1, or 2 by genotype.

The systolic blood pressure (SBP) rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047_, rs76776700, rs2681485, rs9508499, rs985740, rs12145828618105, rs167479, rs expression selected from the group consisting of one or more genes selected from the group consisting of rs This can be done based on the measured data.

Table 3 below is a table showing the correlation between the types of genetic markers related to the risk calculation for systolic blood pressure (SBP) and the items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

Calculation of the value is the same as described above.

Specifically, the risk for systolic blood pressure (SBP) can be derived by Equation 2 below.

[Equation 2]

SBP=75.963+(-2.800)*SEX+0.388*AGE+1.228*BMI+(-0.841)*SMOKE+0.929*DRINK+0.152*EXER+0.146*rs4618084+0.642*rs1374264+(-0.126)*rs4850337+(-0.184) *rs1175543+1.146*rs16998073+0.473*rs78778431+0.369*rs61391612+(-0.844)*rs77180047+(-0.236)*rs76776700+(-0.773)*rs2681485+(-0.295)*rs9508499+(-0.198)*rs985740+0.255*rs12147616+3.151 *rs139293840+(-0.617)*rs167479+(-0.476)*rs28618105+0.201*rs2145814.

In Equation 2, each SNP has a specific value according to the genotype. Specifically, as shown in Table 4 below, it may have a value of 0, 1, or 2 by genotype.

The risk calculation for the cholesterol (HDL) is rs2282231, rs2144300, rs333970, rs6685271, rs333970, rs2144300, rs78598368, rs1873516, rs11762563, rs2253569, rs1456896, rs62466318, rs2366735, 808, rs7624743 rs3016352, rs921919, rs3117925, rs12910051, rs4983558, rs2925979, rs75318258, rs2292318, rs146606382, rs138730273, rs72642480, rs519113, rs519113, rs6065904, rs142006554, and rs18359 based on data measuring the expression amount of one or more genes selected from the group consisting of: can be done

Table 5 below is a table showing the correlation between the types of genetic markers related to the risk calculation for cholesterol (HDL) and the items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

HDL One rs2144300 230294916 T 0.589 0.116 0.362 0.815 3.65E-07 One rs2282231 39569571 T -0.591 0.117 -0.821 -0.362 4.43E-07 One rs333970 110466338 A -0.437 0.087 -0.608 -0.267 4.79E-07 One rs6685271 93634590 C 0.396 0.089 0.223 0.570 7.66E-06 2 rs78598368 166099391 A 1.703 0.362 0.994 2.412 2.52E-06 4 rs1873516 100465572 A 0.454 0.088 0.282 0.626 2.33E-07 7 rs1456896 50304461 T 0.387 0.085 0.220 0.555 5.72E-06 7 rs2253569 26404071 A 0.389 0.086 0.221 0.557 5.74E-06 7 rs2366735 80156314 G 0.406 0.090 0.230 0.582 6.04E-06 7 rs76624743 144516163 A 0.961 0.213 0.543 1.379 6.63E-06 7 rs62466318 73042085 T 0.598 0.134 0.337 0.860 7.53E-06 7 rs11762563 16097100 A 0.399 0.090 0.224 0.575 8.22E-06 8 rs2980884 126474356 A 0.489 0.093 0.307 0.671 1.43E-07 8 rs76085257 19755127 A 0.607 0.129 0.354 0.859 2.56E-06 8 rs800888 116464988 C -0.502 0.107 -0.711 -0.293 2.57E-06 8 rs1106334 71012811 A 0.381 0.085 0.215 0.547 6.74E-06 9 rs10820706 107446362 T 0.412 0.092 0.233 0.592 6.91E-06 9 rs10123140 133860152 A -0.453 0.102 -0.652 -0.254 8.46E-06 11 rs3016352 116521748 G 0.506 0.102 0.306 0.706 6.97E-07 12 rs921919 125265201 A -0.430 0.088 -0.602 -0.258 9.30E-07 13 rs3117925 22609435 G 0.648 0.139 0.376 0.919 2.99E-06 15 rs12910051 58580525 G 0.691 0.085 0.524 0.857 4.35E-16 14 rs4983558 105277144 T -0.390 0.086 -0.559 -0.222 5.86E-06 16 rs2925979 81534790 T -0.449 0.088 -0.621 -0.277 3.11E-07 16 rs75318258 31374256 C 1.193 0.255 0.693 1.693 2.91E-06 16 rs2292318 67985706 T 0.533 0.116 0.305 0.761 4.55E-06 16 rs146606382 56544656 G 1.714 0.377 0.976 2.453 5.40E-06 17 rs138730273 34377750 A 1.683 0.336 1.025 2.341 5.38E-07 18 rs72642480 47081369 T 0.675 0.120 0.440 0.911 1.87E-08 19 rs519113 45376284 G -0.568 0.121 -0.805 -0.330 2.77E-06 20 rs6065904 44534651 A -0.614 0.088 -0.786 -0.441 3.16E-12 21 rs142006554 47293011 A -1.664 0.368 -2.385 -0.944 5.99E-06 22 rs181359 21928641 A -0.435 0.086 -0.603 -0.267 4.08E-07

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

The values of the variables are the same as described above.

Specifically, the risk for cholesterol (HDL) can be derived by Equation 3 below.

[Equation 3]

HDL=64.604+6.768*SEX+(-0.080)*AGE+(-1.084)*BMI+(-1.010)*SMOKE+3.446*DRINK+1.220*EXER+(-0.590)*rs2282231+0.414*rs6685271+(-0.501)*rs333970+ 0.646*rs2144300+0.202*rs78598368+0.370*rs1873516+0.227*rs11762563+0.210*rs2253569+0.413*rs1456896+0.685*rs62466318+0.493*rs2366735+0.538*rs76624743+0.985*rs76085257+0.249*rs1106334+(-0.582)*rs800888+ 0.386*rs2980884+0.357*rs10820706+(-0.302)*rs10123140+0.553*rs3016352+(-0.576)*rs921919+0.414*rs3117925+(-0.391)*rs4983558+0.681*rs12910051+0.399*rs75318258+2.114*rs146606382+0.703*rs2292318+( -0.607)*rs2925979+0.672*rs138730273+0.739*rs72642480+(-0.959)*rs519113+(-0.562)*rs6065904+(-0.437)*rs142006554+(-0.499)*rs181359.

In Equation 3, each SNP has a specific value according to the genotype. Specifically, as shown in Table 6 below, it may have a value of 0, 1, or 2 by genotype.

SNP constant value rs2282231 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs6685271 Genotype: 0 (AA) or 1 (AC) or 2 (CC) rs333970 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs2144300 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs78598368 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs1873516 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs11762563 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs2253569 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs1456896 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs62466318 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs2366735 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs76624743 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs76085257 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs1106334 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs800888 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs2980884 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs10820706 Genotype: 0 (GG) or 1 (GT) or 2 (TT) rs10123140 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs3016352 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs921919 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs3117925 Genotype: 0 (TT) or 1 (TG) or 2 (GG) rs4983558 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs12910051 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs75318258 Genotype: 0 (AA) or 1 (AC) or 2 (CC) rs146606382 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs2292318 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs2925979 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs138730273 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs72642480 Genotype: 0 (GG) or 1 (GT) or 2 (TT) rs519113 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs6065904 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs142006554 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs181359 Genotype: 0 (GG) or 1 (GA) or 2 (AA)

The risk calculation for the triglyceride (TG) is rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs1800588, rs4889326, rs351029, rs1800588, rs4889326, rs355829, 909, rs352926, It may be made based on data measuring the expression level of one or more genes selected from the group consisting of.

Table 7 below is a table showing the correlation between the types of genetic markers related to the risk calculation for triglycerides (TG) and the items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

TG One rs11207992 63041800 G -1.821 0.376 -2.557 -1.085 1.24E-06 2 rs1260326 27730940 C -4.180 0.288 -4.745 -3.615 1.72E-47 4 rs4690098 3447156 T -2.162 0.287 -2.723 -1.600 4.69E-14 4 rs236996 88005203 A -1.568 0.287 -2.130 -1.007 4.45E-08 6 rs7748472 32448763 G 3.155 0.453 2.267 4.043 3.39E-12 7 rs61010704 73019975 G -4.356 0.470 -5.277 -3.435 1.91E-20 7 rs117805272 116427301 C 3.218 0.717 1.813 4.622 7.14E-06 8 rs325 19819328 C -6.562 0.427 -7.399 -5.725 4.11E-53 8 rs2001846 126478450 T 1.689 0.292 1.116 2.262 7.58E-09 8 rs4738141 72469742 A -1.610 0.329 -2.255 -0.965 1.00E-06 9 rs11138651 83021572 T -2.985 0.670 -4.298 -1.672 8.41E-06 10 rs9415699 65143558 C 1.875 0.296 1.295 2.455 2.38E-10 10 rs12572629 113993061 C 1.481 0.309 0.875 2.086 1.65E-06 11 rs651821 116662579 C 8.183 0.318 7.559 8.807 1.72E-144 15 rs1800588 58723675 T 2.495 0.290 1.927 3.063 7.80E-18 16 rs4889326 81533662 T -1.405 0.295 -1.984 -0.827 1.93E-06 17 rs2291029 73221439 C -3.964 0.885 -5.698 -2.229 7.52E-06 17 rs35292636 63466695 G 1.281 0.286 0.720 1.842 7.58E-06 18 rs10469182 68796688 G 3.666 0.752 2.192 5.140 1.09E-06 19 rs440446 45409167 G 3.030 0.297 2.448 3.612 2.09E-24 19 rs58542926 19379549 T -3.000 0.538 -4.054 -1.946 2.46E-08 20 rs16990964 44590732 A 3.348 0.588 2.195 4.500 1.27E-08

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

Calculation of the value of the variable is the same as described above.

Specifically, the risk for triglycerides (TG) can be derived by Equation 4 below.

[Equation 4]

TG=-49.126+(-14.785)*SEX+0.358*AGE+6.676*BMI+21.738*SMOKE+(-0.924)*DRINK+(-6.335)*EXER+(-6.237)*rs11207992+(-10.482)*rs1260326+(-2.762 )*rs4690098+(-2.903)*rs236996+5.747*rs7748472+(-9.745)*rs61010704+0.940*rs117805272+(-13.757)*rs325+(-2.046)*rs4738141+5.806*rs2001846+(-1.201)*rs11138651+2.606*rs9415699+ 1.050*rs12572629+25.906*rs651821+4.043*rs1800588+(-3.467)*rs4889326+0.204*rs35292636+(-2.476)*rs2291029+1.422*rs10469182+(-7.951)*rs58542926+6.902*rs440446+4.767*rs16990964.

In Equation 4, each SNP has a specific value according to the genotype. Specifically, as shown in Table 8 below, it may have a value of 0, 1, or 2 by genotype.

SNP a constant rs11207992 Genotype: 0 (TT) or 1 (TG) or 2 (GG) rs1260326 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs4690098 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs236996 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs7748472 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs61010704 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs117805272 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs325 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs4738141 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs2001846 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs11138651 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs9415699 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs12572629 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs651821 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs1800588 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs4889326 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs35292636 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs2291029 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs10469182 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs58542926 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs440446 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs16990964 Genotype: 0 (GG) or 1 (GA) or 2 (AA)

The risk calculation for the blood glucose (FBS) is rs1260326, rs780096, rs12712928, rs12053049, rs6731917, rs80195623, rs704374, rs2590187, rs117338530, rs74590880, rs28684504, rs2206734, rs3765467, rs17419651, rs3765467, rs1762876554, rs10487766, rs1741655 group consisting of rs35613378, rs78482117, rs10975941, rs3824420, rs3934283, rs10811660, rs1053878, rs11257655, rs4300299, rs7072873, rs60808706, rs174577, rs10830963, rs671, rs79886178161, rs152732, 56 It may be made based on data measuring the expression level of one or more selected genes.

Table 9 below is a table showing the relationship between the types of genetic markers related to the risk calculation for blood sugar (FBS) and items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

FBS 2 rs12053049 169767148 C 0.962 0.069 0.826 1.098 7.33E-44 2 rs12712928 45192080 C 0.735 0.068 0.602 0.869 4.58E-27 2 rs1260326 27730940 C 0.685 0.067 0.554 0.816 1.30E-24 2 rs780096 27741072 G 0.616 0.067 0.486 0.747 2.30E-20 2 rs6731917 173593678 T -0.590 0.067 -0.721 -0.460 9.71E-19 3 rs80195623 23584637 A 0.419 0.091 0.241 0.596 3.73E-06 3 rs117338530 170669759 A 0.958 0.209 0.549 1.366 4.46E-06 3 rs704374 63865947 C -0.314 0.069 -0.448 -0.179 4.83E-06 3 rs2590187 106327107 G 0.410 0.092 0.229 0.590 9.08E-06 4 rs74590880 140852344 T -0.665 0.145 -0.950 -0.380 4.85E-06 5 rs28684504 176727438 C 0.311 0.066 0.182 0.441 2.52E-06 6 rs2206734 20694884 T 0.543 0.066 0.413 0.673 2.73E-16 6 rs3765467 39033595 A -0.537 0.081 -0.697 -0.377 4.42E-11 6 rs1741655 117263833 G -0.316 0.068 -0.449 -0.184 2.90E-06 7 rs6975024 44231886 C 1.218 0.084 1.053 1.383 2.15E-47 7 rs17168486 14898282 T 0.658 0.067 0.527 0.789 9.65E-23 7 rs10487796 15063430 A -0.596 0.071 -0.734 -0.458 2.98E-17 7 rs10262191 156804510 A 0.479 0.094 0.294 0.664 3.73E-07 8 rs11774700 118220270 C -0.687 0.067 -0.819 -0.556 1.43E-24 8 rs11776687 95836307 C 0.842 0.162 0.524 1.160 2.14E-07 9 rs10811660 22134068 A -0.738 0.067 -0.869 -0.608 1.75E-28 9 rs3934283 4285119 G 0.554 0.067 0.423 0.686 1.35E-16 9 rs10975941 705168 T 0.567 0.086 0.397 0.736 5.48E-11 9 rs78482117 633500 T 0.515 0.087 0.345 0.685 2.99E-09 9 rs1053878 136131651 A 0.434 0.076 0.285 0.584 1.32E-08 9 rs3824420 712766 A 0.526 0.096 0.338 0.713 4.09E-08 9 rs35613378 555140 A 0.498 0.092 0.317 0.679 6.89E-08 10 rs11257655 12307894 T 0.384 0.067 0.254 0.515 7.78E-09 10 rs7072873 77210191 C 0.314 0.067 0.184 0.445 2.36E-06 10 rs4300299 68587424 C 1.288 0.290 0.719 1.857 9.27E-06 11 rs10830963 92708710 G 1.127 0.067 0.997 1.257 3.71E-64 11 rs60808706 2857233 A -0.579 0.068 -0.712 -0.446 1.34E-17 11 rs174577 61604814 A -0.392 0.072 -0.533 -0.251 5.54E-08 12 rs671 112241766 A -0.578 0.097 -0.768 -0.388 2.47E-09 13 rs9567063 43471403 G -0.379 0.074 -0.523 -0.234 2.75E-07 13 rs7981781 28499962 A 0.340 0.066 0.209 0.470 3.31E-07 15 rs7172432 62396389 G -0.507 0.066 -0.636 -0.377 1.71E-14 15 rs59391831 77884186 G -0.306 0.066 -0.436 -0.176 3.79E-06 16 rs11861659 48767773 T -0.353 0.078 -0.506 -0.201 5.66E-06 16 rs7187556 84038703 G 0.384 0.086 0.215 0.554 8.69E-06 17 rs2302783 66447073 T 0.387 0.067 0.257 0.517 5.99E-09 20 rs6048209 22571083 G -0.702 0.097 -0.893 -0.511 5.98E-13 20 rs151353 57604343 A 0.390 0.083 0.228 0.553 2.48E-06

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

Specifically, the risk for blood glucose (FBS) can be derived by Equation 5 below.

[Equation 5]

FBS=57.426-3.415*SEX+0.292*AGE+0.967*BMI+1.913*SMOKE+0.337*DRINK+(-0.312)*EXER+1.548*rs1260326+(-0.445)*rs780096+1.045*rs12712928+0.880*rs12053049+(-0.628) )*rs6731917+0.574*rs80195623+(-0.637)*rs704374+0.176*rs2590187+0.356*rs117338530+(-0.299)*rs74590880+0.101*rs28684504+1.582*rs2206734+(-0.701)*rs3765467+(-0.317)*rs1741655+0.911 rs17168486+(-1.054)*rs10487796+0.934*rs6975024+0.441*rs10262191+0.649*rs11776687+(-1.153)*rs11774700+0.287*rs35613378+0.219*rs78482117+0.382*rs10975941+(-0.131)*rs3824420+0.806*rs3824420+0.806*rs )*rs10811660+0.569*rs1053878+0.648*rs11257655+0.989*rs4300299+0.398*rs7072873+(-1.399)*rs60808706+(-0.547)*rs174577+1.247*rs10830963+(-1.388)*rs671+0.412*rs7981781+(-0.181)* rs9567063+(-0.697)*rs7172432+(-0.482)*rs59391831+(-0.345)*rs11861659+0.137*rs7187556+0.461*rs2302783+(-1.046)*rs6048209+0.408*rs151353.

In Equation 5, each SNP has a specific value according to the genotype. Specifically, as shown in Table 10 below, it may have a value of 0, 1, or 2 by genotype.

SNP a constant rs1260326 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs780096 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs12712928 Genotype: 0 (GG) or 1 (GC) or 2 (CC) rs12053049 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs6731917 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs80195623 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs704374 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs2590187 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs117338530 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs74590880 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs28684504 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs2206734 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs3765467 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs1741655 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs17168486 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs10487796 Genotype: 0 (TT) or 1 (TA) or 2 (AA) rs6975024 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs10262191 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs11776687 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs11774700 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs35613378 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs78482117 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs10975941 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs3824420 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs3934283 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs10811660 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs1053878 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs11257655 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs4300299 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs7072873 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs60808706 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs174577 Genotype: 0 (CC) or 1 (CA) or 2 (AA) rs10830963 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs671 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs7981781 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs9567063 Genotype: 0 (TT) or 1 (TG) or 2 (GG) rs7172432 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs59391831 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs11861659 Genotype: 0 (GG) or 1 (GT) or 2 (TT) rs7187556 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs2302783 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs6048209 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs151353 Genotype: 0 (CC) or 1 (CA) or 2 (AA)

The risk calculation for the body mass index (BMI) is rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12545732469, and one or more selected from the group consisting of rs37792, rs1, It can be made based on the data measured the expression level of the gene.

Table 11 below is a table showing the correlation between the types of genetic markers related to the risk calculation for body mass index (BMI) and items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

Calculation of the constant value of the above item is the same as described above.

Specifically, the degree of risk for body mass index (BMI) can be derived by Equation 6 below.

[Equation 6]

BMI=21.854+(-0.352)*SEX+0.025*AGE+0.088*SMOKE+0.058*DRINK+(-0.25)*EXER+0.202*rs633715+(-0.102)*rs146463458+0.033*rs10865546+(-0.173)*rs13393304+0.123 *rs713587+0.094*rs2390669+0.157*rs13072536+(-0.019)*rs4521193+0.104*rs13130484+0.042*rs7009598+0.083*rs12543514+0.081*rs12004882+0.032*rs17793891+(-0.059)*rs35332469+0.222*rs571312.

In Equation 6, each SNP has a specific value according to the genotype. Specifically, as shown in Table 12 below, it may have a value of 0, 1, or 2 by genotype.

SNP a constant rs633715 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs146463458 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs10865546 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs13393304 Genotype: 0 (GG) or 1 (GA) or 2 (AA) rs713587 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs2390669 Genotype: 0 (AA) or 1 (AC) or 2 (CC) rs13072536 Genotype: 0 (AA) or 1 (AT) or 2 (TT) rs4521193 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs13130484 Genotype: 0 (CC) or 1 (CT) or 2 (TT) rs7009598 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs12543514 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs12004882 Genotype: 0 (CC) or 1 (CG) or 2 (GG) rs17793891 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs35332469 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs571312 Genotype: 0 (CC) or 1 (CA) or 2 (AA)

Calculation of the risk for the waist circumference (WC) may be made based on data measuring the expression level of one or more genes selected from the group consisting of rs7610536, rs28855182, rs1887843, rs17168776, rs7304611, and rs9304393.

Table 13 below is a table showing the correlation between the types of genetic markers related to the risk calculation for triglycerides (TG) and the items according to the phenotype of the gene. The horizontal items in the table are in the same order as in Table 1.

In addition, the risk calculation is data on one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) can be made based on

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for waist circumference (WC) can be derived by Equation 7 below.

[Equation 7]

WC=80.353+(-5.779)*SEX+0.193*AGE+0.632*SMOKE+0.289*DRINK+(-0.731)*EXER+(-0.175)*rs7610536+(-0.076)*rs28855182+0.101*rs1887843+(-0.069)*rs17168776+ (-0.103)*rs7304611+(-0.125)*rs9304393.

In Equation 67, each SNP has a specific value according to the genotype. Specifically, as shown in Table 14 below, it may have a value of 0, 1, or 2 by genotype.

SNP a constant rs7610536 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs28855182 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs1887843 Genotype: 0 (TT) or 1 (TC) or 2 (CC) rs17168776 Genotype: 0 (GG) or 1 (GT) or 2 (TT) rs7304611 Genotype: 0 (AA) or 1 (AG) or 2 (GG) rs9304393 Genotype: 0 (TT) or 1 (TC) or 2 (CC)

The lifestyle information calculating unit 70 calculates the health risk by using the data received from the human body information measurement device 131 or the DNA analysis device 132 using a lifestyle prediction equation.

More specifically, the lifestyle information calculator 70 is based on the lifestyle data, diastolic blood pressure (DBP), systolic blood pressure (SBP), cholesterol (HDL), triglycerides (TG), blood sugar (FBS), body mass index ( BMI), or waist circumference (WC), is calculated.

The risk calculation for diastolic blood pressure (DBP) is one selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), alcohol consumption (DRINK), and exercise frequency (EXER). It may be made based on data for the above items.

Specifically, the degree of risk for the diastolic blood pressure (DBP) may be derived by Equation 8 below.

[Equation 8]

DBP=-0.812+(SEX)*(-0.052)+(AGE)*0.014+(BMI)*(-0.015)+(SMOKE)*0.012+(DRINK)*0.059+(EXER)*0.101.

The risk calculation for the systolic blood pressure (SBP) is selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) It may be made based on data for one or more items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for the systolic blood pressure (SBP) can be derived by the following Equation 9

[Equation 9]

SBP=-1.24+(SEX)*(-0.012)+(AGE)*0.018+(BMI)*(-0.016)+(SMOKE)*0.01+(DRINK)*0.057+(EXER)*0.102.

The risk calculation for cholesterol (HDL) is one selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) It may be made based on data for the above items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for cholesterol (HDL) can be derived by the following formula 10

[Equation 10]

HDL=1.221+(SEX)*(-0.078)+(AGE)*0.007+(BMI)*(0.011)+(SMOKE)*0.016+(DRINK)*(-0.028)+(EXER)*(-0.001) .

The risk calculation for the triglyceride (TG) is selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) It may be made based on data for one or more items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for the triglyceride (TG) may be derived by the following Equation 11.

[Equation 11]

TG=-42.216+(-14.596)*SEX+0.351*AGE+6.658*BMI+22.020*SMOKE+(-1.124)*DRINK+(-6.182)*EXER.

The risk calculation for the fasting blood glucose (FBS) is selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) It may be made based on data for one or more items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for the fasting blood sugar (FBS) may be derived by Equation 12 below.

[Equation 12]

FBS=58.367+(-3.191)*SEX+0.293*AGE+0.955*BMI+1.795*SMOKE+0.838*DRINK+(-0.314)*EXER.

The risk calculation for the body mass index (BMI) is selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER) It may be made based on data for one or more items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for the body mass index (BMI) may be derived by the following Equation 13.

[Equation 13]

BMI=23.203+(-0.488)*SEX+0.027*AGE+0.071*SMOKE+0.088*DRINK+(-0.047)*EXER.

The risk calculation for the waist circumference (WC) is selected from the group consisting of sex (SEX), age (AGE), body mass index (BMI), smoking (SMOKE), drinking (DRINK), and exercise frequency (EXER). It may be made based on data for one or more items.

Calculation of the constant value of the above item is the same as described above.

Specifically, the risk for waist circumference (WC) can be derived by the following Equation 14

[Equation 14]

WC=80.197+-5.775*SEX+0.193*AGE+0.637*SMOKE+0.288*DRINK+-0.731*EXER.

The personalized healthcare provider 400 provides the user with the current health status and/or personalized information based on the risk level for each item determined based on the lifestyle prediction formula and the genetic prediction formula in the customized healthcare provision calculator 350 Provides health care methods. The personalized healthcare provider 400 provides the user with current health status information based on the systolic blood pressure, diastolic blood pressure, triglyceride, body mass index, blood sugar, waist circumference, or cholesterol risk calculated by the calculator 350 . . In addition, it provides personalized health care or health care methods based on current health conditions.

In one embodiment, the personalized health care provider 400 may be a smart device that receives current health status information and provides personalized health care or health management method to the user according to the received information. The smart device may be a device installed in a store, and in some cases may be a home device individually installed in an individual consumer's home.

In addition, the personalized healthcare provider 400 is based on the received health status, food amount control for health promotion, food selection, health supplement recommendation, exercise plan recommendation, bedtime and wake up control, or eating habit control method can be provided.

Specifically, a person with a high risk of waist circumference and body mass index may be recommended aerobic exercise and a low-fat diet, as the risk of obesity is high. When blood sugar is high, it is possible to manage the risk of blood sugar by providing diet management similar to the Mediterranean diet. In the long term, based on daily meal records and exercise records, it is possible to check one's lifestyle by week and month, identify improvements, and provide a basis for health management based on this.

The feedback information receiving unit 500 may receive the customer's satisfaction with the customized health care or health care method provided above. The customer satisfaction provision may be a user's feedback immediately after providing a customized health care or health management method or after continuously being provided for a certain period of time.

The feedback information calculating unit 600 evaluates at least one or more of the user's current health state or health state risk level determined based on the feedback information input to the feedback information receiving unit 500, and a health management method provided based thereon, and provides customized health care Decide to maintain, improve, or change the system. This information is again linked with the calculating unit 350 and the providing unit 400 so that the information by the healthcare system in which the feedback information is reflected is transmitted to the consumer again.

Meanwhile, the method, apparatus, or the following embodiments can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. In addition, the structure of the data used in the method may be recorded in a computer-readable recording medium through various means. The computer-readable recording medium includes a storage medium such as a magnetic storage medium (eg, ROM, floppy disk, hard disk, etc.) and optically readable medium (eg, CD-ROM, DVD, etc.).

Hereinafter, the present invention will be described in more detail through examples. However, these examples are for illustrative purposes of the present invention, and the scope of the present invention is not limited to these examples.

Example 1. Sample Population

The group used for the analysis is data from 72,299 Koreans who received pre-sale from the Korea Centers for Disease Control and Prevention (AS cohort: 5,493 people, City cohort: 58,701 people, non-City cohort: 8,105 people). Individuals participating in the above data were selected as healthy individuals without underlying disease or metabolic disease.

Table 15 below shows the average age, gender distribution, and distribution by cohort of the experimental group.

Example 2. Selection of metabolic disease markers through genotyping

2.1 Preparation for screening of genetic polymorphic markers

In order to select metabolic disease markers from the sample population, a population group without related diseases was constructed in order to discover markers for each metabolic disease risk factor, and by metabolic disease indicators (diastolic blood pressure, HDL-cholesterol, Fasting blood sugar, systolic blood pressure, triglyceride, body mass number, waist circumference) To select genetic polymorphism markers, the present inventors obtained a sample from the oral cavity of the sample population with a cotton swab and used ExgeneTM Tissue SV (GeneAll, Seoul, Korea). and DNA was detected. All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was collected from the Theragen Precision Medicine Research Array (Theragen PMRA), a customized analysis based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA). assay), followed by purification, resuspension and hybridization. After hybridization, the DMA was washed under stringent conditions to remove background to minimize noise. Next, 820,000 SNPs were analyzed using Thaeragen PMRA analysis as instructed. In order to reduce errors that may occur due to association, a strict quality control method was applied to select SNPs to be used in the invention and to control the data set. In addition, quality control procedures were performed for 820,000 SNPs. SNP sets were filtered based on the genotype call rates (≥ 0.95) and MAF (≥ 0.10), and Hardy-Weinberg equilibrium (HWE) was calculated for each SNP. Consequently, all SNPs have HWE p values > 0.01, and after the filtering, 560,795 polymorphic SNPs in chromosomes 1-22 were analyzed.

2.2 Statistical analysis

Association analysis by linear regression between genetic mutations was performed on the level of risk factors for each metabolic disease, and the results of this analysis were adjusted according to age and gender. Most statistical analyzes were performed using PLINK version 1.9 and the SPSS program. P-values were not adjusted for multiple tests. Statistical significance was determined based on P-value;1.0 x 10 ^-5.

2.3 GWAS analysis results

Based on the statistical analysis, the analysis results for each metabolic disease risk factor are shown in Table 16 below.

Example 3. Method of calculating a predictive formula using a selection marker

Regression analysis using SPSS was performed to calculate the formula for each metabolic disease risk factor. Specifically, analysis was performed including covariates that may have an effect.

As a result, as shown in Tables 1 to 14, risk factors having significance for each phenotype were calculated. In addition, prediction formulas using selection markers could be calculated as shown in Equations 1 to 14.

Example 4. Confirmation of Applicability of Predictive Formula

Using the calculated formula and clinical data for the applicability of the marker, the predicted value of metabolic disease risk factors through the actual blood test and the status of the actual clinical test were compared, and based on this, the predicted value for application to the application was calculated.

4 shows an example of a screen displayed on the application.

1: Metabolic disease biomarker 2: Health risk formula
3: Lifestyle-based formula 10: DNA/Bioindex database
30: Personal Lifelog Database 50: Lifestyle Database
100: information receiving unit 200 data collection/management/analysis unit
300: input information processing unit 350: customized healthcare calculation unit
400: customized healthcare provider 500: feedback information provider
600: feedback information calculating unit 130: first receiving unit
160: second receiver 190: third receiver
131: human body information measuring device 132: DNA analysis device
161: smart device 162: wearable smart device
191: Questionnaire/Questionnaire

Claims (15)

In the personalized healthcare system provided based on genetic analysis data and lifestyle data,
an information receiving unit for collecting one or more data selected from the group consisting of genetic analysis data, life log data, and lifestyle data;
a personalized healthcare calculator for predicting a user's health risk by using the information input through the information receiver; and
A system for providing personalized health care including a personalized health care provider that provides a personalized health care method based on the degree of risk calculated through the calculator. The method according to claim 1, wherein the user is based on the provided health care feedback information receiving unit for providing feedback information on the health care system; and
The personalized health care providing system further comprising a feedback information calculating unit for evaluating and improving the health care system based on the feedback information. The method according to claim 1, wherein the information receiving unit
a first receiver for receiving genetic analysis data;
a second receiver for receiving life log data; and
A system for providing personalized health care that includes a third receiver for receiving lifestyle data. The method according to claim 1, wherein the data
DNA analysis information, gender, age, diastolic blood pressure, systolic blood pressure, cholesterol level, triglyceride level, fasting blood sugar, body mass index, and a system for providing personalized health care that is selected from the group consisting of waist circumference. The method according to claim 1, wherein the operation unit
A system that calculates the risk of one or more items selected from the group consisting of diastolic blood pressure, systolic blood pressure, cholesterol level, triglyceride level, fasting blood sugar, body mass index, and waist circumference based on lifestyle information or human body information. The method according to claim 1, wherein the operation unit
User with one or more items selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), exercise frequency (EXER), and the user's DNA analysis information A system that calculates the health risk of The method according to claim 1, wherein the operation unit
rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047, rs76776700, rs2681485, rs9508499, rs985740, rs12147616, rs139293840, rs167479, rs28618105, and rs218618105 of one or more genes selected from the group consisting of measuring the expression level of one or more genes A system that calculates the risk of systolic blood pressure (SBP) based on The method according to claim 1, wherein the operation unit
rs16998073, rs167479, rs167479, rs 4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 and re117747786 risk of diastolic blood pressure (DBP) based on data measuring the expression level of one or more genes selected from the group consisting of A system that computes. The method according to claim 1, wherein the operation unit
rs2282231, rs2144300, rs333970, rs6685271, rs333970, rs2144300, rs78598368, rs1873516, rs11762563, rs2253569, rs1456896, rs62466318, rs2366735, rs76624743, rs76085257, rs1106334, rs800888, rs2980884, rs10820706, rs10123140, rs3016352, rs921919, rs3117925, rs12910051, rs4983558, Calculating the risk of cholesterol (HDL) based on data measuring the expression level of one or more genes selected from the group consisting of rs2925979, rs75318258, rs2292318, rs146606382, rs138730273, rs72642480, rs519113, rs519113, rs6065904, rs142006554, and rs181359 in system. The method according to claim 1, wherein the operation unit
rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs1800588, rs4889326, rs35292636, rs2291029, rs1046990964, the expression of one or more genes selected from the group consisting of: A system that calculates the risk of triglycerides (TG) based on the measured data. The method according to claim 1, wherein the operation unit
rs1260326, rs780096, rs12712928, rs12053049, rs6731917, rs80195623, rs704374, rs2590187, rs117338530, rs74590880, rs28684504, rs2206734, rs3765467, rs1741655, rs17168486, rs10487796, rs6975024, rs10262191, rs11776687, rs11774700, rs35613378, rs78482117, rs10975941, rs3824420, rs3934283, rs10811660, rs1053878, rs11257655, rs4300299, rs7072873, rs60808706, rs174577, rs10830963, rs671, rs7981781, rs9567063, rs7172432, rs59391831, rs11861659, rs7187556, rs2302783, one or more genes selected from the group consisting of the expression amount of one or more rs6048209, A system that calculates the risk of blood sugar (FBS) based on data. The method according to claim 1, wherein the operation unit
rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12543514, rs12004882, rs17793891, rs35332469, based on the expression amount of one or more genes selected from the group consisting of rs12004882, rs17793891, rs35332469, and A system that calculates the risk of body mass index (BMI). The method according to claim 1, wherein the operation unit
rs7610536, rs28855182, rs1887843, rs17168776, rs7304611, and rs9304393 based on the data measuring the expression level of one or more genes selected from the group consisting of a system that calculates the risk of waist circumference (WC). The method according to claim 1, wherein the operation unit
A system for calculating a user's health risk by one or more equations selected from the group consisting of the following equations 1 to 14:
[Equation 1]
DBP=56.239+(-3.189)*SEX+0.105*AGE+(-0.637)*SMOKE+0.802*DRINK+0.024*EXER+0.796*BMI+(-0.115)*rs4674649+0.239*rs4684242+0.724*rs16998073+(-0.685) *rs3800469+(-0.120)*rs10739694+(-0.400)*rs77180047+0.272*rs12368700+(-0.302)*rs117747786+0.312*rs12952581+(-0.435)*rs167479+(-0.592)*rs138744086;
[Equation 2]
SBP=75.963+(-2.800)*SEX+0.388*AGE+1.228*BMI+(-0.841)*SMOKE+0.929*DRINK+0.152*EXER+0.146*rs4618084+0.642*rs1374264+(-0.126)*rs4850337+(-0.184) *rs1175543+1.146*rs16998073+0.473*rs78778431+0.369*rs61391612+(-0.844)*rs77180047+(-0.236)*rs76776700+(-0.773)*rs2681485+(-0.295)*rs9508499+(-0.198)*rs985740+0.255*rs12147616+3.151 *rs139293840+(-0.617)*rs167479+(-0.476)*rs28618105+0.201*rs2145814;
[Equation 3]
HDL=64.604+6.768*SEX+(-0.080)*AGE+(-1.084)*BMI+(-1.010)*SMOKE+3.446*DRINK+1.220*EXER+(-0.590)*rs2282231+0.414*rs6685271+(-0.501)*rs333970+ 0.646*rs2144300+0.202*rs78598368+0.370*rs1873516+0.227*rs11762563+0.210*rs2253569+0.413*rs1456896+0.685*rs62466318+0.493*rs2366735+0.538*rs76624743+0.985*rs76085257+0.249*rs1106334+(-0.582)*rs800888+ 0.386*rs2980884+0.357*rs10820706+(-0.302)*rs10123140+0.553*rs3016352+(-0.576)*rs921919+0.414*rs3117925+(-0.391)*rs4983558+0.681*rs12910051+0.399*rs75318258+2.114*rs146606382+0.703*rs2292318+( -0.607)*rs2925979+0.672*rs138730273+0.739*rs72642480+(-0.959)*rs519113+(-0.562)*rs6065904+(-0.437)*rs142006554+(-0.499)*rs181359;
[Equation 4]
TG=-49.126+(-14.785)*SEX+0.358*AGE+6.676*BMI+21.738*SMOKE+(-0.924)*DRINK+(-6.335)*EXER+(-6.237)*rs11207992+(-10.482)*rs1260326+(-2.762 )*rs4690098+(-2.903)*rs236996+5.747*rs7748472+(-9.745)*rs61010704+0.940*rs117805272+(-13.757)*rs325+(-2.046)*rs4738141+5.806*rs2001846+(-1.201)*rs11138651+2.606*rs9415699+ 1.050*rs12572629+25.906*rs651821+4.043*rs1800588+(-3.467)*rs4889326+0.204*rs35292636+(-2.476)*rs2291029+1.422*rs10469182+(-7.951)*rs58542926+6.902*rs440446+4.767*rs16990964;
[Equation 5]
FBS=57.426-3.415*SEX+0.292*AGE+0.967*BMI+1.913*SMOKE+0.337*DRINK+(-0.312)*EXER+1.548*rs1260326+(-0.445)*rs780096+1.045*rs12712928+0.880*rs12053049+(-0.628) )*rs6731917+0.574*rs80195623+(-0.637)*rs704374+0.176*rs2590187+0.356*rs117338530+(-0.299)*rs74590880+0.101*rs28684504+1.582*rs2206734+(-0.701)*rs3765467+(-0.317)*rs1741655+0.911 rs17168486+(-1.054)*rs10487796+0.934*rs6975024+0.441*rs10262191+0.649*rs11776687+(-1.153)*rs11774700+0.287*rs35613378+0.219*rs78482117+0.382*rs10975941+(-0.131)*rs3824420+0.806*rs3824420+0.806*rs )*rs10811660+0.569*rs1053878+0.648*rs11257655+0.989*rs4300299+0.398*rs7072873+(-1.399)*rs60808706+(-0.547)*rs174577+1.247*rs10830963+(-1.388)*rs671+0.412*rs7981781+(-0.181)* rs9567063+(-0.697)*rs7172432+(-0.482)*rs59391831+(-0.345)*rs11861659+0.137*rs7187556+0.461*rs2302783+(-1.046)*rs6048209+0.408*rs151353;
[Equation 6]
BMI=21.854+(-0.352)*SEX+0.025*AGE+0.088*SMOKE+0.058*DRINK+(-0.25)*EXER+0.202*rs633715+(-0.102)*rs146463458+0.033*rs10865546+(-0.173)*rs13393304+0.123 *rs713587+0.094*rs2390669+0.157*rs13072536+(-0.019)*rs4521193+0.104*rs13130484+0.042*rs7009598+0.083*rs12543514+0.081*rs12004882+0.032*rs17793891+(-0.059)*rs35332469+0.222*rs571312;
[Equation 7]
WC=80.353+(-5.779)*SEX+0.193*AGE+0.632*SMOKE+0.289*DRINK+(-0.731)*EXER+(-0.175)*rs7610536+(-0.076)*rs28855182+0.101*rs1887843+(-0.069)*rs17168776+ (-0.103)*rs7304611+(-0.125)*rs9304393;
[Equation 8]
DBP=-0.812+(SEX)*(-0.052)+(AGE)*0.014+(BMI)*(-0.015)+(SMOKE)*0.012+(DRINK)*0.059+(EXER)*0.101;
[Equation 9]
SBP=-1.24+(SEX)*(-0.012)+(AGE)*0.018+(BMI)*(-0.016)+(SMOKE)*0.01+(DRINK)*0.057+(EXER)*0.102;
[Equation 10]
HDL=1.221+(SEX)*(-0.078)+(AGE)*0.007+(BMI)*(0.011)+(SMOKE)*0.016+(DRINK)*(-0.028)+(EXER)*(-0.001) ;
[Equation 11]
TG=-42.216+(-14.596)*SEX+0.351*AGE+6.658*BMI+22.020*SMOKE+(-1.124)*DRINK+(-6.182)*EXER;
[Equation 12]
FBS=58.367+(-3.191)*SEX+0.293*AGE+0.955*BMI+1.795*SMOKE+0.838*DRINK+(-0.314)*EXER;
[Equation 13]
BMI=23.203+(-0.488)*SEX+0.027*AGE+0.071*SMOKE+0.088*DRINK+(-0.047)*EXER;
[Equation 14]
WC=80.197+-5.775*SEX+0.193*AGE+0.637*SMOKE+0.288*DRINK+-0.731*EXER. A computer-readable recording medium in which a program for executing the system of claim 1 in a computer is recorded.

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