KR102471697B1 - Personalized healthcare system based on genetic analysis data and lifestyle data - Google Patents

Abstract

In one aspect, according to a personalized healthcare system based on genetic analysis data and lifestyle data and a computer-readable recording medium recording a program for executing it on a computer, personalized healthcare information can be provided based on clear criteria. can

Description

Personalized healthcare system based on genetic analysis data and lifestyle data}

It is about a personalized healthcare system based on genetic analysis data and lifestyle data.

[0003] In the trend of current personal health management methods, it is common for individuals to acquire their own body information from hospitals, obtain health management information, and make and practice health management plans. This has a problem in that individual characteristics are not accurately reflected, and not only does it go through a lot of trial and error, but it is difficult to manage steadily, and as a result, it does not achieve the goal of health management well. In addition, conventional health management methods related to health management and metabolic disease prevention exist only with guides for universal management methods such as diet control and exercise, and do not take into account individual specific sensitivities.

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

Therefore, the present inventors set objective criteria for prediction of health condition risk based on the lifestyle and DNA analysis information of about 40,000 subjects. Therefore, the present invention has solved the above problems by developing a method for providing a clear standard for providing personalized healthcare and providing a customized solution that meets individual needs. These inventions are conducive to improving the holistic 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 service includes one or more pieces of information selected from the group consisting of DNA information, lifelog information, and lifestyle information. Information receiving unit for collecting; a personalized healthcare calculation unit that predicts a user's health risk using the information input through the information receiving unit; and a personalized healthcare providing unit that provides a personalized health management method based on the degree of risk calculated through the calculating unit.

According to another aspect, a computer-readable recording medium recording a program for executing the method of providing the healthcare system or healthcare service on a computer is provided.

According to a personalized health care system according to one aspect, a method for controlling a health care system, a recording medium and a health care system including the same, the current state of health can be predicted based on the user's genetic information, human body information or lifestyle information, Based on this, personalized health management methods can be provided.

1 is a schematic diagram of a system for providing personalized healthcare according to an aspect.
2 is a schematic diagram showing a structure of a receiving unit, an analyzing unit, a processing unit, a calculating unit, and a providing unit in a healthcare providing system according to an aspect.
3 is a schematic diagram showing specific structures of a receiving unit and a calculating unit of a personalized healthcare system according to an aspect.
4 is an image showing 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 intended to illustrate the present invention by way of example, and the scope of the present invention is not limited to these examples.

2 is a schematic diagram showing a structure of a receiving unit, an analyzing unit, a processing unit, a calculating unit, and a providing unit in a healthcare providing system according to an aspect.

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 calculation unit 350, and a customized healthcare providing unit 400. ), a feedback information providing unit 500 and a feedback information calculating unit 600 are included.

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 measurement device 131 or the DNA analysis device 132 .

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

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

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, triglyceride, fasting blood sugar, body mass index or waist circumference.

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

The test gene determination unit may determine a gene to be tested based on the user's DNA. Specifically, it may be to select risk factors to be examined to determine the risk of metabolic diseases. In addition, the genetic testing unit examines the expression level of biomarkers according to the selected metabolic disease risk factors. In addition, the test result may be transmitted to the genetic testing unit.

상기 이완기 혈압(SBP) 위험인자와 연관성이 있는 바이오 마커는 rs16998073, rs167479, rs167479, rs4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 및 re117747786으로 이루어지는 군으로부터 선택된 하나 이상이다.

상기 수축기 혈압 위험인자와 연관성이 있는 바이오 마커는 rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047_, rs76776700, rs2681485, rs9508499, rs985740, rs12147616, rs139293840, rs167479, rs28618105, 및rs2145814으로 이루어지는 군으로부터 more than one selected

상기 HDL-콜레스테롤(HDL)의 위험인자와 연관성이 있는 바이오 마커는 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, rs2925979, rs75318258, rs2292318, rs146606382, rs138730273, rs72642480, rs519113, rs519113, rs6065904, rs142006554, 및 rs181359으로 이루어지는 군으로부터 선택된 하나 이상이다.

상기 중성지방(TG)의 위험인자와 연관성이 있는 바이오 마커는 rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs1800588, rs4889326, rs35292636, rs2291029, rs10469182, rs58542926, It is at least one selected from the group consisting of rs440446 and rs16990964.

상기 공복혈당(FBS)의 위험인자와 연관성이 있는 바이오 마커는 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, rs6048209, And at least one selected from the group consisting of rs151353.

상기 체질량지수(BMI)의 위험인자와 연관성이 있는 바이오 마커는 rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12543514, rs12004882, rs17793891, rs35332469, 및 rs571312으로 이루어지는 It is one or more selected from the group.

The gene inspection unit may detect a gene, and specifically, may detect a single nucleotide polymorphism (SNP). The detection may be by an amplifying agent. In addition, the detection may be performed by primers, probes, oligonucleotides, or a combination thereof. Specifically, the SNP detection kit of the human body information measurement device 131 or the DNA analysis device 132 may be used, 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 required for polymerization, such as dNTPs, a polymerase, and a coloring agent.

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

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

The term "LIFE LOG" data is the overall daily data of an individual, including the individual's movement path, purchase pattern, consumption preference, daily exercise amount, etc. measured or recorded using the smart device 161. The smart device 161 may be a wearable smart device 162 in detail.

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

The survey/questionnaire 191 data may include consumer response data for various items for finding out the consumer's lifestyle. Specifically, items for variables that may affect lifestyle factors are included. For example, items on gender, age, body mass index (BMI), smoking, drinking, and exercise frequency are included.

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

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

Personal lifelog data is stored in the personal lifelog database 30 .

The lifestyle database 50 stores questionnaire/paper questionnaire 191 data.

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

Then, by referring to the DNA/biological index database 10, the personal lifelog database 30, or the personal lifestyle database 50, the current health condition of the consumer and a personalized health management method are provided.

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, both the information receiving unit 100 and the input information processing unit 300 are included to obtain body information, and at the same time, based on these values, the current state of the body, health index, The risk of disease 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 together with the big data collection/management/analysis unit 200, and the cloud computer analyzes it to You can also determine your current status, your risk level, and how to manage your health. 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 providing calculation unit 350 uses the current state of the body, health index, and disease risk information determined by the input information processing unit 300 and refers to the DNA/biological index database 10 to determine a customized health management method.

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 operation unit 60 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 genetic result prediction equation. Specifically, the health risk may be calculated using the health risk formula (2) based on DNA analysis data. The health risk formula 2 may be calculated based on the data of the DNA/biological index database 10, and may be additionally calculated based on the data of the personal lifelog database 30 or the personal lifestyle database 50. can

The lifestyle data-based calculator 70 may calculate the health risk 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 calculation unit 60 or lifestyle data-based calculation unit 70 calculates diastolic blood pressure (DBP), systolic blood pressure (SBP), cholesterol (HDL), triglyceride (TG), blood sugar (FBS), and body mass index (BMI). , or calculate the risk for waist circumference (WC).

구체적으로, 상기 이완기 혈압(DBP)에 대한 위험도 산출은 rs16998073, rs167479, rs167479, rs4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 및 re117747786으로 이루어지는 군으로부터 선택된 하나 이상의 유전자의 발현량을 측정 It can be done based on one data.

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

CHR SNPs 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 showing a single nucleotide polymorphism in a nucleic acid sequence. The term “single nucleotide polymorphism (SNP)” refers to a genetic change or variation that results in a difference of one nucleotide in a nucleic acid sequence. It may be a position where two or more allelic sequences occur with a frequency of about 1% or more, about 5% or more, 2% to 4.5%, 3 to 4%, or 2.5% to 3.4% in a population.

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

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

Specifically, the risk for diastolic blood pressure (DBP) can 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)*4rs167479+(-0.592)

The gender (SEX) item may have a value of 1 when the user is male and 2 when the user is female.

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 a 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 if the user is a non-drinker and a value of 2 if the user is a drinking user.

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

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

상기 수축기혈압(SBP) rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047_, rs76776700, rs2681485, rs9508499, rs985740, rs12147616, rs139293840, rs167479, rs28618105, 및rs2145814으로 이루어지는 군으로부터 선택된 하나 이상의 유전자의 발현 It can be based on the data of measuring the quantity.

Table 3 below is a table showing correlations between items according to types of genetic markers related to risk calculation for systolic blood pressure (SBP) and phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (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 depending on the genotype. Specifically, as shown in Table 4 below, it may have a value of 0, 1 or 2 depending on the genotype.

상기 콜레스테롤(HDL)에 대한 위험도 산출은 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, rs2925979, rs75318258, rs2292318, rs146606382, rs138730273, rs72642480, rs519113, rs519113, rs6065904, rs142006554, 및 rs181359으로 이루어지는 군으로부터 선택된 하나 이상의 유전자의 발현량을 측정한 데이터를 기반으로 It can be done.

Table 5 below is a table showing correlations between items according to types of genetic markers related to risk calculation for cholesterol (HDL) and phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (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 depending on the genotype. Specifically, as shown in Table 6 below, it may have a value of 0, 1 or 2 depending on the genotype.

SNPs 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)

상기 중성지방(TG)에 대한 위험도 산출은 rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs1800588, rs4889326, rs35292636, rs2291029, rs10469182, rs58542926, rs440446, 및 rs16990964으로 It can 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 risk calculation for triglyceride (TG) and the items according to the phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (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 neutral fat (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 depending on the genotype. Specifically, as shown in Table 8 below, it may have a value of 0, 1 or 2 depending on the genotype.

SNPs 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)

상기 혈당(FBS)에 대한 위험도 산출은 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, rs6048209, 및 rs151353으로 이루어지는 군으로부터 It can be made based on data measuring the expression level of one or more selected genes.

Table 9 below is a table showing correlations between items according to types of genetic markers related to risk calculation for blood sugar (FBS) and phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). can be made based on

Specifically, the risk for blood sugar (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-rs1205(30-rs1205) )*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*rs3934283+(-1.536 )*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 depending on the genotype. Specifically, as shown in Table 10 below, it may have a value of 0, 1 or 2 depending on the genotype.

SNPs 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)

상기 체질량지수(BMI)에 대한 위험도 산출은 rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12543514, rs12004882, rs17793891, rs35332469, 및 rs571312으로 이루어지는 군으로부터 선택된 하나 이상의 It can be made based on data measuring the expression level of the gene.

Table 11 below is a table showing correlations between items according to types of genetic markers related to risk calculation for body mass index (BMI) and phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). can be made based on

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

Specifically, the 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)*rs13+0.13 *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 depending on the genotype. Specifically, as shown in Table 12 below, it may have a value of 0, 1 or 2 depending on the genotype.

SNPs 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)

The risk calculation for the waist circumference (WC) may be performed based on data obtained by 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 items according to the types of genetic markers related to risk calculation for triglyceride (TG) and the phenotypes of genes. The order of horizontal items in the table is the same 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 status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). can be made based on

Calculation of the constant value of the above item is 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)*rs7+171 (-0.103)*rs7304611+(-0.125)*rs9304393.

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

SNPs 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 calculation 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 by using a lifestyle prediction equation.

More specifically, the lifestyle information calculation unit 70 calculates diastolic blood pressure (DBP), systolic blood pressure (SBP), cholesterol (HDL), triglyceride (TG), blood sugar (FBS), and body mass index (BMI) based on the lifestyle data. Calculate risk for BMI), or waist circumference (WC).

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

Specifically, the risk for diastolic blood pressure (DBP) can 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 gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). It can be based on data for one or more items.

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

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

[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 gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). It can be made based on the data for the above items.

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

Specifically, the risk for cholesterol (HDL) can be derived by Equation 10 below.

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

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

Specifically, the risk of triglyceride (TG) can be derived by Equation 11 below.

[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 sugar (FBS) is selected from the group consisting of gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), and exercise frequency (EXER) It can be based on data for one or more items.

Calculation of the constant value of the above item is 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 gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), and exercise frequency (EXER). It can be based on data for one or more items.

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

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

[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 gender (SEX), age (AGE), body mass index (BMI), smoking status (SMOKE), drinking status (DRINK), and exercise frequency (EXER) It can be based on data for one or more items.

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

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

[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 a current health condition and/or personalized health care based on the risk level for each item determined based on the lifestyle predictive formula and the gene predictive formula in the customized healthcare providing calculator 350. Provides health management methods. The personalized healthcare provider 400 provides current health status information to the user based on the systolic blood pressure, diastolic blood pressure, neutral fat, body mass index, blood sugar, waist circumference, or cholesterol risk calculated by the calculation unit 350. . In addition, a personalized healthcare or health management method based on a current health condition is provided.

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

In addition, the personalized healthcare provider 400 adjusts dietary intake for health promotion, selects foods, recommends health supplements, recommends exercise plans, controls sleep and wake up, or controls eating habits, based on the health condition provided. method can be provided.

Specifically, a person with a high risk of waist circumference and body mass index is considered to have a high risk of obesity, and aerobic exercise and a low-fat diet may be recommended. If blood sugar is high, the risk of blood sugar can be managed by providing diet management such as the Mediterranean diet. In the long term, based on the daily recorded meal records and exercise records, you can check your lifestyle by week and month, identify areas for improvement, and provide the basis for health management based on this.

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

The feedback information calculation unit 600 evaluates the user's current health condition or health state risk determined based on the feedback information input to the feedback information receiving unit 500 and at least one of the health management methods provided based thereon, and provides customized healthcare. Decides to maintain, improve, or change the system. This information is again interlocked with the calculation unit 350 and the provision unit 400 so that the information by the healthcare system reflecting the feedback information is delivered to the consumer again.

On the other hand, the method, device 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 data used in the method can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes storage media such as magnetic storage media (eg, ROM, floppy disk, hard disk, etc.) and optical reading media (eg, CD-ROM, DVD, etc.).

Hereinafter, the present invention will be described in more detail through examples. However, these examples are intended to illustrate the present invention by way of example, and the scope of the present invention is not limited to these examples.

Example 1. Sample Population

The group used for analysis was data on 72,299 Koreans who were pre-sold 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 data were selected as healthy individuals without underlying diseases and metabolic diseases.

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

Example 2. Selection of metabolic disease markers through genotyping

2.1 Preparation for selection of genetic polymorphic markers

In order to select metabolic disease markers from the sample population, a population without related diseases was constructed to discover markers for each metabolic disease risk factor, and by metabolic disease index in the data collected in Example 1 (diastolic blood pressure, HDL-cholesterol, Fasting blood glucose, systolic blood pressure, triglycerides, body mass, waist circumference) To select polymorphic markers, the present inventors obtained samples from the oral cavity of the sample population with cotton swabs and used ExgeneTM Tissue SV (GeneAll, Seoul, Korea). DNA was detected. All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was subjected to the Theragen Precision Medicine Research Array (Theragen PMRA), a custom assay based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA). assay) were used to sequentially purify, resuspend and hybridize. 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 directed. In order to reduce errors that may occur due to association, etc., a strict quality control method was applied to select SNPs to be used in the invention and 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. As a result, all SNPs have HWE p values > 0.01, and after this filtering, 560,795 polymorphic SNPs within chromosomes 1 to 22 were analyzed.

2.2 Statistical analysis

Association analysis by linear regression between genetic variants was performed on the values 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. Prediction formula calculation method using selectable markers

Regression analysis using SPSS was performed to calculate the formula for each metabolic disease risk factor. Specifically, an analysis was conducted including covariates that could be influencing.

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

Example 4. Confirmation of applicability of predictive formula

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

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

1: Metabolic disease biomarkers 2: Health risk formula
3: Lifestyle-Based Formula 10: DNA/Bioindex Database
30: personal life log 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 provision unit 500: feedback information provision unit
600: feedback information calculating unit 130: first receiving unit
160: second receiving unit 190: third receiving unit
131: human body information measurement 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 receiver for collecting one or more data selected from the group consisting of genetic analysis data, life log data, and lifestyle data;
a personalized healthcare calculation unit that predicts a user's health risk using the information input through the information receiving unit; and
A personalized health care providing system including a personalized health care provider providing a personalized health care method based on the risk calculated through the calculator,
A personalized health care providing system in which the calculation unit calculates the following risks (1) to (7):
(1) rs4618084, rs1374264, rs4850337, rs1175543, rs16998073, rs78778431, rs61391612, rs77180047, rs76776700, rs2681485, rs9508499, rs985740, rs12147616, rs139293840, rs167479, rs28618105, 및 rs2145814의 유전자의 유전자형을 검출한 데이터를 기반으로 수축기혈압 (SBP) risk,
(2) rs16998073, rs167479, rs167479, rs 4684242, rs12368700, rs10739694, rs3800469, rs77180047, rs12952581, rs138744086, rs4674649 및 re117747786의 유전자의 유전자형을 검출한 데이터를 기반으로 이완기 혈압(DBP)의 위험도,
(3) 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, rs2925979, rs75318258, rs2292318, rs146606382, rs138730273, rs72642480, rs519113, rs519113, rs181359 data based on the genotype detection of cholesterol, HDL risk, and rs181359
(4) rs236996, rs7748472, rs61010704, rs117805272, rs325, rs4738141, rs2001846, rs11138651, rs9415699, rs12572629, rs651821, rs1800588, rs4889326, rs35292636, rs2291029, rs10469182, rs58542926, rs440446, 및 rs16990964의 유전자의 유전자형을 검출한 데이터를 Based on the risk of triglycerides (TG),
(5) 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, rs6048209, 및 rs151353의 유전자의 유전자형을 검출한 데이터를 기반으로 The risk of blood sugar (FBS),
(6) rs3127553, rs633715, rs146463458, rs10865546, rs13393304, rs713587, rs2390669, rs13072536, rs4521193, rs13130484, rs7009598, rs12543514, rs12004882, rs17793891, rs35332469, 및 rs571312의 유전자의 유전자형을 검출한 데이터를 기반으로 체질량지수(BMI ) risk, and
(7) Risk of waist circumference (WC) based on data of detecting genotypes of genes of rs7610536, rs28855182, rs1887843, rs17168776, rs7304611, and rs9304393. The method according to claim 1, wherein the feedback information receiving unit for providing feedback information about the health care system based on the health care provided by the user; and
The personalized health care providing system further comprising a feedback information calculator for determining evaluation and improvement of the health care system based on the feedback information. The method according to claim 1, wherein the information receiving unit
a first receiving unit receiving genetic analysis data;
A second receiving unit for receiving life log data; and
A personalized health care providing system comprising 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 personalized health care providing system selected from the group consisting of waist circumference. The method according to claim 1, wherein the calculation 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 calculation 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 user's DNA analysis information A system that calculates the health risk of delete delete delete delete delete delete delete The method according to claim 1, wherein the calculation unit
A system for calculating the user's health risk with 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)*4rs167479+(-0.592)
[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*rs1205 )*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*rs3934283+(-1.536 )*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)*rs13+0.13 *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)*rs7+171 (-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 recording a program for executing the system of claim 1 on a computer.

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