KR20160107508A - Method for Assessinging Residual Life Using Biological Age - Google Patents

Abstract

The present invention relates to a method for measuring a remaining life of a person by using a biological age and a survival probability and, more specifically, to a method for measuring a survival probability by using a biological age measured through a biological age measurement algorithm to which a health medical examination result is reflected and a method for measuring a remaining life by using the biological age and the survival probability measured by the method. According to the present invention, a remaining life can be predicted more accurately than a remaining life method which does not consider an existing biological age.

Description

[0001] The present invention relates to a method for predicting residual lifetime using biological age,

More particularly, the present invention relates to a method for predicting survival probability using a biological age measured by a biological age measurement algorithm that reflects a result of a health examination, and more particularly, And a method for estimating the remaining service life using the predicted survival probability.

Recently, despite the fact that many researches on the causes of human diseases have been made, most of them are treated as one-time information focused on interest, and it is true that there is no utility for disease prevention. The technique of predicting and treating hepatocellular carcinoma is disclosed in Korean Patent Application No. 2003-0067652, which is related to the liver cancer prediction system for early diagnosis of liver cancer and the control method thereof. The present invention relates to a system for predicting liver cancer for early diagnosis of liver cancer and a control method thereof for classifying the stratification of liver cancer risk group through the relative risk of liver cancer incidence and the risk of occurrence of liver cancer, The technology includes general information of the patient, information of the ultrasound implementation, findings at the time of initial registration of the patient, By storing the clinical information and the risk group information including the information on the time of day observation in the database and calculating the regression coefficient which is the contribution factor corresponding to each risk factor based on the stored clinical information and the risk group information, It is possible to prevent the occurrence of individual hepatocellular carcinoma according to the prediction and to establish the basis of the customized hepatocarcinogenesis prediction model by classifying the risk of hepatocarcinoma risk group by the relative risk of hepatocellular carcinoma incidence and hepatocellular carcinoma incidence And the patient's primary care physician can continuously monitor the risk rate of the patient by receiving the liver cancer prediction result through a mobile communication terminal in the form of a short message or receiving it through the e-mail, and can immediately take measures in case of a dangerous situation There was something about technology.

As described above, the conventional prediction techniques are predominantly technologies for medically predicting a specific disease and treating it based on predicted information, and it has not been possible to measure how the lifespan of a human is changed according to current health conditions.

The present inventors confirmed that the remaining life can be reliably predicted when the remaining life prediction is performed using a large-scale database that examines the age of the living body and the life span of Koreans measured through the data obtained from the examination. It was completed.

It is an object of the present invention to provide a method of measuring a survival probability using a biological age measured based on data obtained from a health examination.

It is still another object of the present invention to provide a method of measuring the remaining life using the biological age and the survival probability measured by the above method.

In order to achieve the above object, the present invention provides a method for predicting the survival probability using the biological age measured using the result of the examination of urination and the following equation (1)

.....(1)

.....(One)

(1) where S ₀ (t) is the survival probability after t years from the presently alive date on the statistical data, D is the age of the organism - birth date, D is the average of D, a is the survival probability (T) represents the probability of survival until t years after calibration using biometric age.

The present invention also relates to a method for estimating the survival probability of a subject on the y-axis from the date of receipt of the examination of the subject's chewing gum by using the x-axis as the survival survival rate (ER) and the y- And the residual service life remaining is set as the remaining service life.

According to the present invention, it is possible to predict the remaining service life much more accurately than the existing service life prediction method that does not consider the age of the living body.

1 is a schematic representation of a residual life predicting method according to the present invention.
Fig. 2 shows the average survival probability after T year based on the 2013 National Statistical Office data.
FIG. 3 is a graph of the survival probability by an average Korean population's life expectancy by the 2013 National Statistical Office.
FIG. 4 is a graph of survival probability of a 50 year-old male (D = living organism age-birth age = 0) measured by the residual life measuring method according to the present invention.
FIG. 5 is a graph of survival probability of a 50 year-old male (D = living organism age-birth age = 5) measured by the residual life measuring method according to the present invention.
FIG. 6 is a graph of survival probability of a 50-year-old male (D = living organism age-birth age = -5) measured by the residual life measurement method according to the present invention.
7 is a graph showing an estimated life expectancy distribution according to the present invention for 10,000 Korean men.
8 is a graph showing an estimated life expectancy distribution according to the present invention for 10,000 Korean women.

In the present invention, the term " birth age (CA) "refers to the number of years of survival until the day of receiving the health examination based on the day of birth, and" It refers to the age that reflects the state of health of the person who has been calculated.

In the present invention, the term "T year survival probability (SP)" refers to the probability of surviving until the next year based on the day of the health examination, and "Life expectancy (ER) (EL) refers to the age at birth (CA) + life expectancy (ER) at the time of a health checkup.

In the present invention, the graph "Survival probability graph up to life expectancy (SP2ER)" is a graph in which the x axis is the life expectancy (ER) and the y axis is the survival probability (SP) after T year (classified by sex and age, It says.

In the present invention, the "survival probability (TBA) after T year based on the age of the living body" is calculated by using the calculated birth age (CA), the biological age (BA) and the T year survival probability Calculated survival probability refers to the bio-survival probability.

In the present invention, the survival probability graph (SP2ERBA) up to the life expectancy based on the biological age (BA) is calculated by multiplying the x axis by the expected life expectancy (ER), the y axis by the T year survival probability ), Which is classified by sex and age.

In the present invention, the term "remaining life (RL)" is the day when the health checkup calculated using the survival probability graph (SP2ERBA) to the life expectancy based on the survival probability graph (SP2ER) The number of surviving years.

In one aspect, the present invention relates to a method of measuring the survival probability using the bio-age measured using the result of the examination of urination and the following formula (1)

.....(1)

.....(One)

(1) where S ₀ (t) is the survival probability after t years from the presently alive date on the statistical data, D is the age of the organism - birth date, D is the average of D, a is the survival probability (T) represents the probability of survival until t years after calibration using biometric age.

In the above equation (1), S ₀ (t) is the survival probability value provided for each male and female in each year in the National Statistical Office, and can be confirmed through the National Statistical Office's homepage (http://kosis.kr/statHtml/statHtml.do?orgId= 101 & tblId = DT_1B42 & vw_cd = MT_ZTITLE & list_id = A5 & seqNo = & lang_mode = en & language = kor & obj_var_id = & itm_id = & conn_path = E1 #).

In the equation (1), a is the value automatically calculated using the cox proportional hazards model algorithm using the survival data as the input value. It is a statistical analysis such as SPSS, SAS, and R package And can be implemented by running commercial or free analysis software. Here, the term "survival data" refers to data on individuals who have undergone medical examination and have information on whether they have died or survived to a certain point in time.

값은 연령에 따라 표 1과 같은 값을 가질 수 있지만, 이에 한정되는 것은 아니다.
In a preferred embodiment, the value of a in formula (1) according to the present invention and

The value may have a value as shown in Table 1 according to age, but is not limited thereto.

In the present invention, the biological age may be measured by the following formulas (2) to (3):

...(2)

...(2)

.... (3)

는 j번째 검진 항목의 샘플 내 평균값, sd(x_j)는 j번째 검진 항목의 샘플 내 표준 편차, β_ij는 주성분 분석 결과 j번째 검진 항목의 i번째 요인값이 각 샘플의 실제 나이를 설명할 수 있는 정도, m은 주성분 분석의 수행결과 얻어진 요인 개수, P _i는 i번째 요인 값이 preBA 계산에 주는 가중치, y는 각 개체의 출생나이(CA)이며,

는 생체나이(BA) 모델 구축에 사용되는 모든 개체의 출생나이(CA)의 평균, sd(y)는 y의 표준 편차이고,

는 각 샘플에 대하여 실제 나이 및 preBA를 회귀 분석한 결과 생성된 회귀식

를 구성하는 계수이다.Here, preBA is the age of the subject before correction by equation (3), BA is the age of the subject after correction based on the age of the subject (preBA), _xj is the examination value of the subject,

Is the mean value in the sample of the jth item, sd (x _j ) is the standard deviation of the sample in the jth item, and β _ij is the i-th factor of the item in the j th item of the principal component analysis. ( P ₎ is the weight given to the preBA calculation, and y is the birth age (CA) of each individual,

Is the mean of the birth years (CA), sd (y) is the standard deviation of y for all individuals used in constructing the BA model,

Is the regression of the actual age and preBA for each sample,

.

In the present invention, B _ij may be a value calculated by inputting two or more variables (X j) measured through a screening examination into a principal component analysis statistical algorithm using SPSS, SAS and R package programs , m represents the number of principal component factors, which is automatically determined when the eigenvalue is set to 1 when the principal component analysis algorithm is executed.

In one embodiment of the present invention, the method described in Korean Patent Publication No. 2014-0126229 can be used for measurement of living body age.

In the present invention, when the subject is a male, x is expressed by the following equation: WC, FEV 1, G-GTP, urea nitrogen, BN, (S) selected from the group consisting of LDL, triglyceride (TG), fasting blood glucose (FBS), body fat percentage (FBR), muscle rate (BMR), albumin-globulin ratio (AGR) and systolic blood pressure (HDL), triglyceride (TG), fasting blood sugar (FBS), hemoglobin, and low-density lipoprotein (HDL) LDL), body mass index (BMI), and pulse pressure (PP).

In the present invention, when the subject is an exciter, x is expressed by the following equation: WC (waist circumference), FEV1 for 1 second, GTP-GTP), urea nitrogen (BUN) (LDL), triglyceride (TG), fasting blood sugar (FBS), body fat percentage (FBR), muscle rate (BMR), albumin-globulin ratio (AGR), diastolic blood pressure (DBP), erythrocyte sedimentation rate (DBP), liver enzymes (AST), high fat protein (HDL), triglyceride (TG), fasting blood glucose (BMI) And the result of the health screening item selected from the group consisting of FBS, hemoglobin, LDL, body mass index (BMI), and pulse pressure (PP).

In the biological age prediction model used in the present invention, one or more samples that are conventional examination data can be input. In the biological age calculation model, all the samples may be input regardless of whether or not the samples include the examination values of the variables (examination items) included in the biological age calculation model, but the examination values of the variables included in the biological age calculation model It is preferable that only the samples including both of them are inputted. For example, only the samples having both the BMI examination value, the spirometric examination value, and the one-minute examination value are input to the first living body age calculation model, and the second living body age calculation model includes the BMI examination value, the spirometry examination value, Only samples with all numerical values can be entered.

 After one or more samples are input to the biological age computation model, Principal Component Analysis on the input data may be performed. As a result of the principal component analysis, one or more factors that can explain the data input to the biological age calculation model can be obtained. The number of the obtained factors may be smaller than the number of variables of the biological age calculation model. Hereinafter, the number of the factors is m, and the number of variables of the biological age calculation model is described as n.

According to an embodiment of the present invention, through the principal component analysis, the i-th factor values included in the j-th variable values of each sample input to the biological age calculation model indicate the degree to which the actual age can be explained (B _ij ) can be calculated. The value of B _{ij is the} value calculated by the principal component analysis method known from general statistical analysis. That is, B _ij is a factor loading matrix for each variable that is automatically calculated when the principal component analysis process is performed.

According to an embodiment of the present invention, the weight ( P _i ) of the i-th factor value and the bio-age calculation may be calculated through the principal component analysis. Hereinafter, a method of calculating the weight P _i will be described.

The weight ( P _i ) can be calculated using a coefficient of determination (R ² ) between the factor score and the actual age of each sample factor calculated from the principal component analysis. A calculation method of the weight value (P _i ) using the decision coefficient is as follows.

First, we calculate factor scores for each sample used in the analysis by principal component analysis. For example, if you have 100 samples and m factors, then 100 factor scores will be calculated for each of m factors.

) 계산 한다. 이 m개의 결정 계수의 합을 S 라고 하면, i번째 요인이 생체나이 계산에 주는 가중치(P_i )는 아래의 수학식 1을 이용하여 연산될 수 있다.
Next, the factor scores of each factor and the actual age of each sample are regression analyzed to find the coefficient of determination m (

). Assuming that the sum of the m decision coefficients is S, the weight P _i of the i-th factor to the biometric age calculation can be calculated using the following equation (1).

[Equation 1]

The weight P _i may be calculated using the eigenvalue of each factor calculated from the principal component analysis. At this time, the weight P _i can be calculated using the following equation (2).

&Quot; (2) "

In Equation (2), e _i is the eigenvalue of the factor i, and m is the number of factors.

When the principal component analysis is completed, a bio-age calculation expression according to the bio-age calculation model can be generated using the factors.

According to one embodiment, the biological age can be calculated according to equation (3) based on equation (2).

... (2)

는 j번째 검진 항목의 샘플 내 평균값, sd(x_j)는 j번째 검진 항목의 샘 플 내 표준 편차, B_ij는 주성분 분석 결과 j번째 검진 항목의 i번째 요인값이 각 샘플의 실제 나이를 설명할 수 있는 정도, m은 주성분 분석의 수행결과 얻어진 요인 개수, P_i는 i번째 요인 값이 BA 계산에 주는 가중치)
(Where preBA is the age of the living organism, x _j is the examination value of the j th examination item of the examinee,

Is the mean value in the sample of the jth item, sd (x _j ) is the sample standard deviation of the jth item, and B _ij is the actual age of each sample The number of factors obtained as a result of the principal component analysis, and P _i is the weight given to BA calculation of the i-th factor value)

Equation 3

는 j번째 검진 항목의 샘플 내 평균값, sd(x_j)는 j번째 검진 항목의 샘플 내 표준 편차, B_ij는 주성분 분석 결과 j번째 검진 항목의 i번째 요인값이 각 샘플의 실제 나이를 설명할 수 있는 정도, m은 주성분 분석의 수행결과 얻어진 요인 개수, P_i는 i번째 요인 값이 preBA 계산에 주는 가중치, y는 각 개체의 출생나이(CA)이며,

는 생체나이(BA) 모델 구축에 사용되는 모든 개체의 출생나이(CA)의 평균, sd(y)는 y의 표준 편차,

및

는 각 샘플에 대하여 실제 나이 및 preBA를 회귀 분석한 결과 생성된 회귀식

를 구성하는 계수)In the above equation (3), BA is the age of the living organism, preBA and preBA 'are the age of the living organism, x _j is the examination value of the j th examination item of the examinee,

Is the average value in the sample of the jth item, sd (x _j ) is the standard deviation of the sample in the jth item, and B _ij is the i-th factor of the item in the j th item of the principal component analysis. (P ₎ is the weight given to the preBA calculation, and y is the birth age (CA) of each individual,

Is the mean of the birth years (CA) of all individuals used in constructing the BA model, sd (y) is the standard deviation of y,

And

Is the regression of the actual age and preBA for each sample,

≪ / RTI >

In Equation (3), according to one embodiment, the y average is an average of the actual age of all samples, and sd (y) may be a standard deviation of the actual age of all samples. (Y) is an average of all of the samples having valid values of all the items of the examination items included in the biological age calculation model, and sd (y) is an average of all the samples included in the biological age calculation model It may also be the standard deviation of the actual age of all samples with valid values for the numerical data of the examination item.

In summary, one or more factors are obtained by inputting the sample data into the biological age calculation model and subjecting the input data to principal component analysis, and the biological age calculating equation can be obtained using the principal component analysis result and the factors. An example of the bio-age calculating equation may refer to equation (2) or equation (3).

It can be understood as a process of finding a set of variables constituting a biological age calculation model for deriving an optimum result while adding the examination items included in the result data of the examinee's examination results to the living body age calculation model one by one as a variable. That is, a so-called step-by-step forward selection method is used in which the best one of the items of the original set of the original set is selected for the above-described living body age calculation model that is empty and the selected items are added as variables.

Hereinafter, the step-by-step forward selection method will be described. It is assumed that the original set includes five items of examination, BMI, lung capacity, daily dose, albumin, and PSA.

 As a first step, a test model containing a single variable is created. Next, samples including the value of the corresponding variable, that is, the examination value of the examination item, are input to the generated test model, and the principal components analysis is performed on the input samples to obtain the factors, Using the results of the analysis, a bioassay equation is generated. Hereinafter, variables newly added to the test model are described as test screening items. Next, each of the input samples is input to the bio-age calculating equation to calculate bio-age of each sample.

)과 실제 나이(BA = y) 와의 결정 계수(R²)을 구할 수 있다. 첫번째 단계의 결과 얻어진 각 검진항목 별 결정 계수 값이 표시되어 있다. 첫번째 단계의 결과, 가장 큰 값을 가지는 폐활량이 선택될 것이다. 이는, 폐활량 검진 수치는 생체나이 연산 모델에 포함되는 것을 의미한다. 폐활량 검진 항목은 상기 생체나이 연산 모델에 추가되었으므로, 상기 원본 집합에서 제거된다. 두번째로 상기 생체나이 연산 모델에 포함될 두번째 검진항목이 선택된다. 두번째 단계의 테스트 모델은 상기 폐활량 검진 항목 및 그 밖의 검진 항목 중 하나를 포함하는, 총 2개의 변수를 가지는 모델이다. 따라서, 두번째 단계에서는 (폐활량, BMI), (폐활량, 일초량), (폐활량, 알부민), (폐활량, PSA) 총 4번의 테스트가 진행될 것이다.Next, a simple regression equation BA = a + by is created through regression analysis of the distribution of the samples. As a result, the regression line of the samples

) And it can be obtained for the coefficient of determination (R ²⁾ between the actual age (BA = y). The result of the first step shows the determination coefficient values for each examination item. As a result of the first step, the lung capacity with the largest value will be selected. This means that the spirometric examination value is included in the biological age calculation model. The spirometry check item is removed from the original set because it has been added to the biological age calculation model. Second, a second examination item to be included in the biological age calculation model is selected. The second-stage test model is a model having a total of two variables, including one of the above-mentioned spirometry examination items and other examination items. Therefore, in the second stage (spirometry, BMI), (lung capacity, one second), (lung capacity, albumin), (lung capacity, PSA)

Next, samples including the value of the corresponding variable, that is, the examination value of the corresponding examination item, are input to the generated biological age calculation model, and the principal components analysis is performed on the input samples to obtain the factors, The result is used to generate a bio-age expression.

를 생성한다. 그 결과, 샘플들의 회귀선 (

)과 실제 나이(BA = y) 와의 결정 계수(R²)를 구할 수 있다.Next, each of the input samples is input to the bio-age calculating equation to calculate bio-age of each sample, and a simple regression equation

. As a result, the regression line of the samples

) And it can be calculated the coefficient of determination (R ²⁾ between the actual age (BA = y).

As a result of the second step, BMI, which is the largest increase in the number of determinants of the model, will be selected. This implies that the BMI value is included in the biological age calculation model together with the spirometric value. The BMI check item is removed from the original set because it has been added to the biological age calculation model. Thirdly, a third examination item to be included in the biological age calculation model is selected. The third-stage test model is a model having a total of three variables, including one of the above-mentioned spirometric examination item, BMI, and other examination items. Therefore, in the second phase, three tests will be conducted (lung capacity, BMI, one second), (lung capacity, BMI, albumin), (lung capacity, BMI, PSA). The third step is also the second step and the benefit process. The one-second amount of determination coefficient is selected as the third examination item included in the biological age calculation model since the determination value of one-minute amount is uniquely increased compared to the conventional determination coefficient value of 0.5. One second is removed from the original set.

Fourth, a third examination item to be included in the biological age calculation model is selected.

Both of the remaining examinations are making biometric calculations with a coefficient of determination lower than 0.6, which is the conventional coefficient of determination. As a result of proceeding to the fourth step, there is no further examination item to be added in the original set. Therefore, in the fourth step, the stepwise advancing step is terminated. As a result, it is preferable that only three of the five examination items, BMI, lung capacity, and one second, are included in the biological age calculation model for the examinee. The biological age calculation model at the end of the stepwise progression step is determined as a biological age calculation model to be applied to the received examination data.

Next, the received examination data can be inputted into a living body age calculating formula according to the determined living body age calculating model to calculate the living body age of the examinee.

According to one embodiment, by adding the examination data to the sample data, it can be utilized as a sample for the next examinee.

The step-by-step forward selection step according to the present invention is a step of adding at least a part of the examination items to be included in the biological age calculation model among the examination items included in the examination data of the examinee. There may be a case where the decision coefficient value continues to increase as the stepwise advancing selection step is progressed according to the data. Therefore, it may happen that all the examination items included in the examination data of the examinee are included in the biometric age calculation model Of course it is.

On the other hand, using a sample that can show a pattern similar to that of the examinee's test data would be advantageous for generating a more accurate biometric age calculation model. Therefore, in the step-by-step forward selection step, the sample input to the test model may be limited to the sample having the personal information similar to the examinee of the received examination data. For example, the sample input to the test model is the same as the sex of the examinee, and may be limited to having the actual age of the predetermined range based on the actual age of the examinee.

In the step-by-step forward selection step, an additional selection criterion can be used as well as whether the decision coefficient is increased as much as possible compared to that of the existing test model.

According to an embodiment, when the number of samples having the numerical data of the test examination item is less than the predetermined threshold value, it may be excluded from the test examination item. According to another embodiment, if the number of samples having all the examination values of the examination items included in the test model is less than the predetermined threshold value, it can be excluded from the test examination item. When the number of samples is less than the threshold value, it is difficult to give reliability as statistical data.

Also, according to an embodiment, the test screening item for which the correlation coefficient R with at least one of the screening items included in the test model exceeds a predetermined threshold may be excluded from the test screening item. This is to prevent the adverse effect that the influence of the existing test items in the test model is reduced when the test items that are highly correlated with the test items already included in the test model are newly included in the test models. In addition, when the test model includes high correlation items, the problem is that the singularity phenomenon occurs in the matrix operation process performed in the principal component analysis process, and the eigen value is not calculated properly.

For the accuracy of the correlation coefficient calculation, the correlation coefficient may be calculated using sample data having the examination values of all the examination items included in the test model.

In one aspect of the present invention, the survival probability (statistical office data) from the presently-living point to the t-year point later can use, for example, the expected life table of Table 2 and FIG.

Table 3 shows the probability of survival according to one embodiment of the present invention.

In another aspect, the present invention provides a method for estimating the survival probability (T) of a living body by T year measured by a method of measuring survival probability using the bio-age and expression (1) And the value of the x-axis corresponding to the survival probability value of the y-axis in the created graph is set as the remaining remaining life from the date of the examination.

As shown in FIG. 1, the remaining life prediction method of the present invention is a method for estimating the remaining life of an individual using the gender, age data, health examination data, and bio-age calculation algorithm (equations (2) to (3) , And the survival probability after t years by age is calculated using the above-mentioned biological age and equation (1), and the survival probability after t year after the y-axis is measured by the above method is used as the x axis and the y axis, And the remaining life for each age group is calculated using a graph showing the t-year survival probability.

In the remaining life calculation system of the present invention, the age probability of survival based on the data of the National Statistical Office is used (see Table 2 and FIG. 4). However, the present invention is not limited to this, It is obvious to those skilled in the art that the accuracy can be improved.

A method for predicting residual service life according to the present invention will be described with reference to specific examples. The average remaining life of a 50-year-old male (age 50) is 30.57 years (see Table 1). The probability of survival for a 50-year-old man is about 0.5595 (see Table 2).

In the graph showing the t-year survival probability measured by the above method, the x-axis is the survival probability (ER), the y-axis is the survival probability after t year measured by the above method, The x-axis value of the point meeting the 50-year-old male survival probability graph is the remaining life according to the present invention (Fig. 4).

If the survival probability of a 50-year-old man is 5 years older than the age of birth (D = age of the organism - age of birth = 5), the remaining life is significantly lower than 30.5 years (Fig. 5).

In addition, the survival probability of a 50-year-old Korean man whose living age was less than 5 years of age (D = body age - birth age = -5) (Fig. 6).

In one embodiment, the life expectancy of a man with a birth age of 50 years is calculated using the residual life prediction method according to the present invention. As a result, when D (age of the human body - birth age) is -4.5 years, Was 91.9 years old and was 80.8 years old (average Korean) when D = 0 years old and 69.3 years old when D = 4.5 years old.

In another aspect, the life expectancy of a woman with a birth age of 50 years was 94.3 years at D = -4.3 years, 86.3 years at Korean D = 0 years, 79.1 years at D = 4.2 years, It was three.

In another embodiment of the present invention, the remaining life is measured by dividing 10,000 Korean male / female by age group using the remaining life measuring method of the present invention, and the results are shown in FIG. 7 and FIG. 8, respectively.

While the present invention has been particularly shown and described with reference to specific embodiments thereof, those skilled in the art will appreciate that such specific embodiments are merely preferred embodiments and that the scope of the present invention is not limited thereto will be. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

Claims (8)

A method of measuring the survival probability by using the biological age and the following equation (1) measured using the result of the examining of the urine;

.....(One)

(1) where S ₀ (t) is the survival probability after t years from the presently alive date on the statistical data, D is the age of the organism - birth date, D is the average of D, a is the survival probability (T) represents the probability of survival until t years after calibration using the age of the living body.
2. The method according to claim 1, wherein the biological age is measured by the following formulas (2) to (3):

...(2)

... (3)

Here, preBA is the age of the subject before correction to equation (3), BA is the age of the subject after adjusting the age of the subject (preBA) to equation (3), and x _j is the jth item of examination Examination figures,

Is the mean value in the sample of the jth item, sd (x _j ) is the sample standard deviation of the jth item, and B _ij is the actual age of each sample about, m is the weight, y is the number of factors obtained performance results of principal component analysis, P _i is the i-th factor to the value that can be calculated preBA is birth age (CA) of each object,

Is the mean of the birth years (CA), sd (y) is the standard deviation of y for all individuals used in constructing the BA model,

Is the regression of the actual age and preBA for each sample,

.
3. The method according to claim 2, wherein B _ij is a value calculated through a principal component analysis statistical algorithm with two or more variables (X j) measured through a screening examination as an input value.
3. The method according to claim 2, wherein when the subject is a male, x is at least one of a waist circumference (WC), an FEV1 for one second, G-GTP, urea nitrogen, A physical examination selected from the group consisting of low fat protein (LDL), triglyceride (TG), fasting blood sugar (FBS), body fat percentage (FBR), muscle rate (BMR), albumin-globulin ratio (AGR), and systolic blood pressure Item is a result value of the item.
The method according to claim 2, wherein when the subject is a male, x is at least one selected from the group consisting of WC, SBP, G-GTP, HDL, TG, ), Hemoglobin, low-density lipoprotein (LDL), body mass index (BMI), and pulse pressure (PP).
3. The method according to claim 2, wherein when the subject is an exciter, x is expressed as a function of the waist circumference (WC), the FEV1 for 1 second, gamma-GTP) G-GTP, urea nitrogen, (LDL), triglyceride (TG), fasting blood sugar (FBS), body fat percentage (FBR), muscle rate (BMR), albumin-globulin ratio (A GR), diastolic blood pressure (DBP), erythrocyte sedimentation rate And body mass index (BMI). ≪ RTI ID = 0.0 > 21. < / RTI >
The method according to claim 2, wherein when the subject is an female, x is at least one selected from the group consisting of waist circumference (WC), diastolic blood pressure (DBP), liver enzyme (AST), high fat protein (HDL), triglyceride (TG) Wherein the value is a result of a medical examination item selected from the group consisting of hemoglobin, low density lipoprotein (LDL), body mass index (BMI) and pulse pressure (PP).
In the graph prepared by taking the x axis as the survival probability (ER) and the y axis as survival probabilities until t years measured by the method of any one of claims 1 to 7, the value of the x axis corresponding to the survival probability value of the subject on the y axis Is set as the remaining life remaining after the date of receipt of the examination by the examinee.

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