Acquisition method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium and evaluation system

Abstract

In this embodiment, the future risk of cerebrovascular events is evaluated for the subject using a value calculated using a Cox proportional hazards model including a concentration value of at least one amino acid selected from Gly, Cit, Trp, Met, and Val in the blood of the subject and a variable into which the concentration value is substituted.

Description

[Technical field]

[0001] The present invention relates to a method for assessing a future risk of occurrence of a cerebrovascular event, a calculation method, an evaluation device, an evaluation program, a recording medium, an evaluation system, and a terminal device. [Background technology]

[0002] Heart diseases such as myocardial infarction and brain diseases such as cerebral infarction and cerebral hemorrhage are the second and third leading causes of death in Japan. These diseases, collectively known as cerebrovascular events, are thought to be caused in part by a decrease in vascular function, typically arteriosclerosis, which is a result of metabolic syndrome.

[0003] To prevent cerebrovascular events, early detection of the causes of diseases such as metabolic syndrome and appropriate intervention according to the cause are necessary. It is thought that the use of biomarkers is useful for detecting the causes of diseases.

[0004] As biomarkers, attention has been focused on amino acids, which are central to metabolic pathways among metabolites in the body. Amino acids are present in large amounts in blood among metabolites, and their physical properties are relatively similar to each other. Therefore, amino acids have favorable properties as targets for biomarkers, such as the ability to be analyzed with high precision.

[0005] Here, it has been reported that differences in the distribution of amino acid concentrations in plasma are risk factors for future cerebrovascular events (Non-Patent Documents 1-4). In addition, as a prior patent, Patent Document 1 has been published, which relates to a method for evaluating the future state of cardiovascular events using the concentration of amino acids in blood. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2013-178238 A [Non-patent literature]

[0007] [Non-Patent Document 1] Ruiz-Canela M. et al. Clin. Chem. 2016, 62, 582-592 [Non-Patent Document 2] Shah SH. et al. Circ. Cardiovasc. Genet. 2010, 3, 207-214 [Non-Patent Document 3] Shah SH. et al. Am. Heart. J. 2012, 163, 844-850 [Non-Patent Document 4] Magnusson M. et al. 2013, Eur. Heart. J. 34, 198201989 Summary of the Invention [Problem to be solved by the invention]

[0008] However, there was a problem in that further accuracy was required in the development of technology for determining the future risk of cerebrovascular events using amino acid concentrations in the blood.

[0009] The present invention has been made in consideration of the above, and aims to provide an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a recording medium, an evaluation system, and a terminal device that can provide highly reliable information that can be useful in knowing the future risk of cerebrovascular events. [Means for solving the problem]

[0010] In order to solve the above-mentioned problems and achieve the object, the evaluation method of the present invention is characterized by including an evaluation step of evaluating the future risk of cerebrovascular events for the subject using concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of the subject, or a formula including variables into which the concentration values ​​are substituted and a value of the formula calculated using the concentration values.

[0011] In this specification, cerebrovascular events include aortic dissection, coronary artery disease (myocardial infarction, heart failure, PCI (percutaneous coronary intervention), etc.), cerebrovascular disorders (stroke (cerebral infarction, intracerebral hemorrhage, subarachnoid hemorrhage), etc.).

[0012] In addition, various amino acids are mainly represented by abbreviations in this specification, but their formal names are as follows: (Abbreviation) (Formal name) Ala Alanine Arg Arginine Asparagine Cit Citrulline Gln Glutamine Gly Glycine His Histidine Isoleucine Leu Leucine Lysine Methionine Orn Ornithine Phe Phenylalanine Proline Ser Serine Thr Threonine Trp Tryptophan Tyr Tyrosine Val Valine

[0013] Moreover, the evaluation method according to the present invention is characterized in that the at least two amino acids include Gly.

[0014] Moreover, the evaluation method according to the present invention is characterized in that the at least two amino acids include Cit and / or Trp.

[0015] Moreover, the evaluation method according to the present invention is characterized in that the at least two amino acids include Met and / or Val.

[0016] In the evaluation method according to the present invention, the evaluation step is executed in a control unit of an information processing device having a control unit.

[0017] The calculation method of the present invention is characterized in that it includes a calculation step of calculating the value of a formula for evaluating the future risk of occurrence of a cerebrovascular event, the formula including concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted.

[0018] The calculation method according to the present invention is characterized in that the at least one and the at least two amino acids include Gly.

[0019] Moreover, the calculation method according to the present invention is characterized in that the at least two amino acids include Cit and / or Trp.

[0020] The calculation method according to the present invention is characterized in that the at least two amino acids include Met and / or Val.

[0021] In the calculation method according to the present invention, the calculation step is executed in a control unit of an information processing device having a control unit.

[0022] Furthermore, the evaluation device of the present invention is characterized in that it is an evaluation device equipped with a control unit, and the control unit is equipped with an evaluation means for evaluating the future risk of cerebrovascular events for the evaluation subject using concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood of the evaluation subject, or a formula including variables into which the concentration values ​​are substituted and a value of the formula calculated using the concentration values.

[0023] Moreover, the evaluation device according to the present invention is communicatively connected via a network to a terminal device that provides concentration data relating to the concentration value or the value of the formula, and the control unit further comprises a data receiving means for receiving the concentration data or the value of the formula transmitted from the terminal device, and a result transmitting means for transmitting the evaluation result obtained by the evaluation means to the terminal device, and is characterized in that the evaluation means uses the concentration value or the value of the formula contained in the concentration data received by the data receiving means.

[0024] Further, the calculation device according to the present invention is characterized in that it is a calculation device including a control unit, and the control unit includes a calculation means for calculating a value of a formula for evaluating a future risk of occurrence of a cerebrovascular event, the formula including concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted, using the formula.

[0025] Furthermore, the evaluation program of the present invention is an evaluation program to be executed in an information processing device having a control unit, and is characterized in that it includes an evaluation step for evaluating the future risk of cerebrovascular events for the evaluation subject using concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of the evaluation subject, or a formula including variables into which the concentration values ​​are substituted and the value of the formula calculated using the concentration values, in order to be executed in the control unit.

[0026] Furthermore, the calculation program of the present invention is a calculation program to be executed in an information processing device having a control unit, and is characterized in that it includes a calculation step of calculating a value of a formula for evaluating a future risk of occurrence of a cerebrovascular event, the formula including concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted, using the formula to be executed in the control unit.

[0027] The recording medium according to the present invention is a computer-readable recording medium having the evaluation program or the calculation program recorded therein. Specifically, the recording medium according to the present invention is a non-transitory computer-readable recording medium, and is characterized in that it includes programmed instructions for causing an information processing device to execute the evaluation method or the calculation method.

[0028] The evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit via a network so as to be able to communicate with each other, wherein the control unit of the terminal device comprises: a data transmitting means for transmitting concentration data on concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of the evaluation subject, or a formula including variables into which the concentration values ​​are substituted and a value of the formula calculated using the concentration values; and a result receiving means for receiving an evaluation result on a future risk of cerebrovascular events transmitted from the evaluation device, wherein the control unit of the evaluation device comprises: a data receiving means for receiving the concentration data or the value of the formula transmitted from the terminal device; an evaluation means for evaluating a future risk of cerebrovascular events for the evaluation subject using the concentration values ​​or the value of the formula contained in the concentration data received by the data receiving means; and a result transmitting means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

[0029] Furthermore, the terminal device of the present invention is a terminal device equipped with a control unit, the control unit being equipped with a result acquisition means for acquiring an evaluation result regarding the future risk of cerebrovascular events, and the evaluation result is a result of evaluating the future risk of cerebrovascular events for the evaluation subject using concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood of the evaluation subject, or a formula including variables into which the concentration values ​​are substituted and the value of the formula calculated using the concentration values.

[0030] Furthermore, the terminal device of the present invention is communicatively connected via a network to an evaluation device that evaluates the future risk of a cerebrovascular event, the control unit includes a data transmission means that transmits concentration data regarding the concentration value or the value of the formula to the evaluation device, and the result acquisition means receives the evaluation result transmitted from the evaluation device. Effect of the Invention

[0031] Effects of the Invention According to the present invention, there is an effect of providing highly reliable information that can be used as a reference for knowing the future risk of developing a cerebrovascular event. [Brief description of the drawings]

[0032] [Figure 1] FIG. 1 is a diagram showing the basic principle of the first embodiment. [Diagram 2] FIG. 2 is a diagram showing the basic principle of the second embodiment. [Diagram 3] FIG. 3 is a diagram showing an example of the overall configuration of the present system. [Figure 4] FIG. 4 is a diagram showing another example of the overall configuration of the present system. [Diagram 5] FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system. [Figure 6] FIG. 6 is a diagram showing an example of information stored in the concentration data file 106a. [Figure 7] FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. [Figure 8] FIG. 8 is a diagram showing an example of information stored in the designated index state information file 106c. [Figure 9] FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. [Figure 10] FIG. 10 is a diagram showing an example of information stored in the evaluation result file 106e. [Figure 11] FIG. 11 is a block diagram showing the configuration of the evaluation unit 102d. [Figure 12] FIG. 12 is a block diagram showing an example of the configuration of a client device 200 of this system. [Figure 13] FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system. [Figure 14] FIG. 14 is a diagram showing the values ​​of HR / SD and C-index. [Figure 15] FIG. 15 shows amino acid combinations and HR / SD and C-index values. [Figure 16] FIG. 16 shows amino acid combinations and HR / SD and C-index values. [Figure 17] FIG. 17 shows amino acid combinations and HR / SD and C-index values. [Figure 18] FIG. 18 is a diagram showing the values ​​of HR / SD and C-index. [Figure 19] FIG. 19 shows amino acid combinations and HR / SD and C-index values. [Figure 20] FIG. 20 shows amino acid combinations and HR / SD and C-index values. [Figure 21] FIG. 21 shows amino acid combinations and HR / SD and C-index values. [Figure 22]FIG. 22 shows amino acid combinations and HR / SD and C-index values. [Figure 23] FIG. 23 shows amino acid combinations and HR / SD and C-index values. [Figure 24] FIG. 24 shows amino acid combinations and HR / SD and C-index values. [Diagram 25] FIG. 25 shows amino acid combinations and HR / SD and C-index values. [Figure 26] FIG. 26 shows amino acid combinations and HR / SD and C-index values. [Figure 27] FIG. 27 shows amino acid combinations and HR / SD and C-index values. [Figure 28] FIG. 28 shows amino acid combinations and HR / SD and C-index values. [Figure 29] FIG. 29 shows amino acid combinations and HR / SD and C-index values. [Diagram 30] FIG. 30 shows amino acid combinations and HR / SD and C-index values. [Diagram 31] FIG. 31 shows amino acid combinations and HR / SD and C-index values. [Diagram 32] FIG. 32 shows amino acid combinations and HR / SD and C-index values. [Diagram 33] FIG. 33 shows amino acid combinations and HR / SD and C-index values. [Diagram 34] FIG. 34 shows the relative risk for the occurrence of each event. [Diagram 35] FIG. 35 shows the relative risk for the occurrence of each event. [Diagram 36] FIG. 36 shows the relative risk for the occurrence of each event. [Figure 37]FIG. 37 shows the relative risk for the occurrence of each event. [Figure 38] FIG. 38 shows the relative risk for the occurrence of each event. [Figure 39] FIG. 39 shows the relative risk for the occurrence of each event. [Diagram 40] FIG. 40 shows the relative risk for the occurrence of each event. [Diagram 41] FIG. 41 shows the relative risk for the occurrence of each event. [Diagram 42] FIG. 42 shows the relative risk for the occurrence of each event. [Diagram 43] FIG. 43 shows the relative risk for the occurrence of each event. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] Hereinafter, an embodiment (first embodiment) of the evaluation method and calculation method according to the present invention, and an embodiment (second embodiment) of the evaluation device, calculation device, evaluation method, calculation program, recording medium, evaluation system, and terminal device according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[0034] [First embodiment] [1-1. Overview of the first embodiment] Here, an overview of the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the basic principle of the first embodiment.

[0035] First, concentration data including concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in blood (including, for example, plasma or serum) collected from a subject to be evaluated (for example, an individual such as an animal or human) is obtained (step S11).

[0036] Here, in step S11, for example, concentration data measured by a company or the like that measures concentration values ​​may be obtained. Alternatively, concentration data may be obtained by measuring the concentration value from blood collected from the subject to be evaluated, for example, by the following measurement method (A), (B), or (C). Here, the unit of the concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, or may be obtained by adding, subtracting, multiplying, or dividing any constant by these concentrations. (A) The collected blood sample is centrifuged to separate plasma from the blood. All plasma samples are frozen and stored at -80°C until the concentration value is measured. When measuring the concentration value, acetonitrile is added to perform a deproteinization treatment, and then impurities such as phospholipids are removed by solid phase extraction or the like as necessary, pre-column derivatization is performed using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and the concentration value is analyzed by liquid chromatography mass spectrometry (including tandem mass spectrometry) (see International Publication Nos. WO 2003 / 069328 and WO 2005 / 116629). (B) The collected blood samples are centrifuged to separate plasma from the blood. All plasma samples are frozen and stored at -80°C until the concentration values ​​are measured. When measuring the concentration values, sulfosalicylic acid is added to remove proteins, and then the concentration values ​​are analyzed using an amino acid analyzer based on the post-column derivatization method using ninhydrin reagent. (C) The collected blood sample is subjected to blood cell separation using membranes, MEMS (Micro Electro Mechanical Systems) technology, or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not subjected to concentration measurement immediately after plasma or serum collection are frozen and stored at -80°C until the concentration value is measured. When measuring the concentration value, molecules such as enzymes and aptamers that react with or bind to the target blood substance are used to analyze the concentration value by quantifying substances that increase or decrease due to substrate recognition or spectroscopic values.

[0037] Next, the concentration value included in the concentration data acquired in step S11 is used to evaluate (predict) the future risk of cerebrovascular events for the subject (step S12). Here, evaluating the future risk of cerebrovascular events for the subject means, for example, evaluating the risk of the subject having cerebrovascular events including stroke and myocardial infarction in the future (for example, within 10 years after blood collection). Note that data such as missing values ​​and outliers may be removed from the concentration data acquired in step S11 before executing step S12.

[0038] As described above, according to the first embodiment, in step S11, concentration data of the evaluation subject is acquired, and in step S12, the concentration value included in the concentration data acquired in step S11 is used to evaluate the future risk of cerebrovascular events for the evaluation subject (in other words, information for evaluating the future risk of cerebrovascular events for the evaluation subject is acquired). This makes it possible to provide highly reliable information that can be useful in knowing the future risk of cerebrovascular events for the evaluation subject.

[0039] In addition, in step S12, the future risk of a cerebrovascular event occurring in the subject may be evaluated by calculating the value of an equation using an equation including the concentration value contained in the concentration data acquired in step S11 and a variable into which the concentration value is substituted.

[0040] Also, it may be determined that the concentration value included in the concentration data reflects the future risk of cerebrovascular events for the evaluation subject, and further, the concentration value may be converted, for example, by the methods listed below, and the converted value may be determined to reflect the future risk of cerebrovascular events for the evaluation subject (the same applies to the value of the formula). In other words, the concentration value or the converted value itself may be treated as the evaluation result regarding the future risk of cerebrovascular events for the evaluation subject (the same applies to the value of the formula). Here, the conversion method will be described below. Note that the following description is about the concentration value as the conversion subject, but the same applies to the value of the formula. In order to ensure that the possible range of concentration values ​​falls within a predetermined range (e.g., a range of 0.0 to 1.0, a range of 0.0 to 10.0, a range of 0.0 to 100.0, or a range of -10.0 to 10.0, etc.), the concentration values ​​may be converted, for example, by adding, subtracting, multiplying, or dividing an arbitrary value to the concentration values, by converting the concentration values ​​using a predetermined conversion method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation, etc.), or by performing a combination of these calculations on the concentration values. For example, the value of an exponential function with the concentration value as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the concentration value when the probability p of the future risk of a cerebrovascular event being in a specified state (e.g., a high-risk state) is defined) may be further calculated, and a value obtained by dividing the calculated exponential function value by the sum of 1 and the value itself (specifically, the value of the probability p) may be further calculated. The concentration values ​​may also be converted so that the converted values ​​under specific conditions are specific values, for example, the concentration values ​​may be converted so that the converted value is 5.0 when the specificity is 40% and the converted value is 8.0 when the specificity is 95%. In addition, for each amino acid, the concentration value distribution may be normalized, and then the concentration values ​​may be converted to standard deviation values ​​so that the average is 50 and the standard deviation is 10. In addition, the value of the formula may be converted to standard deviation values ​​so that the average is 50 and the standard deviation is 10. These conversions may also be made according to gender or age.

[0041] In this specification, a concentration value may be the concentration value itself or a value obtained by converting the concentration value, and a value of an equation in this specification may be the value of the equation itself or a value obtained by converting the value of the equation.

[0042] In addition, position information on the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper may be generated using a concentration value included in the concentration data or, if the concentration value is converted, the converted value, and it may be determined that the generated position information reflects the future occurrence risk of a cerebrovascular event for the evaluation subject (the same applies to the value of the formula). Note that the predetermined ruler is for evaluating the future occurrence risk of a cerebrovascular event, and is, for example, a ruler with a scale, which shows at least scales corresponding to the upper and lower limits of the "range in which the concentration value or the converted value can be taken, or a part of the range" (the same applies to the value of the formula). In addition, the predetermined mark is, for example, a circle or a star, which corresponds to the concentration value or the converted value (the same applies to the value of the formula).

[0043] In addition, when the concentration value included in the concentration data is lower than a predetermined value (such as the mean value ± 1SD, 2SD, 3SD, N quantile, N percentile, or a cutoff value recognized as clinically significant), or is equal to or higher than the predetermined value, the future risk of cerebrovascular events may be evaluated for the evaluation subject (similar to the value of the formula). In this case, a standard deviation may be used instead of the concentration value itself. For example, when the standard deviation is less than the mean value - 2SD (standard deviation < 30) or when the standard deviation is higher than the mean value + 2SD (standard deviation > 70), the future risk of cerebrovascular events may be evaluated for the evaluation subject (similar to the value of the formula).

[0044] The future risk of cerebrovascular events may be qualitatively evaluated for the evaluation object. Specifically, the evaluation object may be classified into one of a plurality of categories defined by at least considering the degree of the future risk of cerebrovascular events using "the concentration value included in the concentration data and one or more preset thresholds" or "the concentration value included in the concentration data, an equation including a variable into which the concentration value is substituted, and one or more preset thresholds". The plurality of categories may include a category for subjects with a high risk of occurrence, a category for subjects with a low risk of occurrence, and a category for subjects with a medium risk of occurrence. The plurality of categories may also include a category for subjects with a high risk of occurrence and a category for subjects with a low risk of occurrence. The concentration value or the value of the equation may be converted, for example, by the above-mentioned method, and the evaluation object may be classified into one of a plurality of categories using the converted value.

[0045] The formula used for the evaluation can be in any format, but may be, for example, in the format shown below. Linear models such as multiple regression equations based on the least squares method, linear discriminants, principal component analysis, and canonical discriminant analysis Generalized linear models such as maximum likelihood logistic regression Risk prediction models based on semiparametric survival analysis methods such as the Cox proportional hazards model -Generalized linear mixed model that considers random effects such as individual differences and facility differences in addition to generalized linear models · Equations created by cluster analysis such as K-means and hierarchical cluster analysis Formulas based on Bayesian statistics such as MCMC (Markov Chain Monte Carlo), Bayesian Networks, and Hierarchical Bayesian methods - Formulas created by class classification such as support vector machines and decision trees Formulas created using methods that do not fall into the above categories, such as fractional formulas An equation that can be expressed as a sum of different forms of equations

[0046] Furthermore, the formula used in the evaluation may be prepared, for example, by the method described in International Publication No. WO 2004 / 052191, an international application filed by the present applicant, or the method described in International Publication No. WO 2006 / 098192, an international application filed by the present applicant. Note that, if the formula is obtained by these methods, the formula can be suitably used to evaluate the future risk of occurrence of cerebrovascular events, regardless of the unit of the concentration value contained in the concentration data as input data.

[0047] Here, in the multiple regression equation, multiple logistic regression equation, canonical discriminant function, etc., a coefficient and a constant term are added to each variable, and these coefficients and constant terms may be real numbers, more preferably, values ​​within the range of the 99% confidence interval of the coefficients and constant terms obtained for performing the various classifications from the data, and even more preferably, values ​​within the range of the 95% confidence interval of the coefficients and constant terms obtained for performing the various classifications from the data. In addition, the value of each coefficient and its confidence interval may be multiplied by a real number, and the value of the constant term and its confidence interval may be added, subtracted, multiplied, or divided by any real constant. When a logistic regression equation, a linear discriminant, a multiple regression equation, etc. are used for evaluation, linear transformation (addition of a constant, constant multiplication) and monotonically increasing (decreasing) transformation (for example, logit transformation, etc.) do not change the evaluation performance and are equivalent to the one before the transformation, so the one after these transformations may be used.

[0048] A fractional expression is one in which the numerator is expressed as the sum of the variables A, B, C, ... and / or the denominator is expressed as the sum of the variables a, b, c, .... Fractional expressions also include sums of fractional expressions of this type, α, β, γ, ... (such as α + β). Fractional expressions also include divided fractional expressions. The variables used in the numerator and denominator may each have an appropriate coefficient. The variables used in the numerator and denominator may also be duplicated. Each fractional expression may have an appropriate coefficient. The values ​​of the coefficients of each variable and the values ​​of the constant terms may be real numbers. Although the positive / negative sign of the correlation between a given fractional formula and one in which the numerator variable and denominator variable have been swapped will generally be reversed, the correlation between them is maintained and the evaluation performance can be considered to be equivalent, so fractional formulas also include those in which the numerator variable and denominator variable have been swapped.

[0049] When evaluating the future risk of cerebrovascular events, values ​​related to other biological information shown below may be used in addition to the concentration values ​​included in the concentration data. Furthermore, the formula used when evaluating the future risk of cerebrovascular events may further include one or more variables into which values ​​related to other biological information shown below are substituted, in addition to variables into which concentration values ​​included in the concentration data are substituted. Examples of other biological information include the following: Body mass index measurement results (height, weight, waist circumference, BMI) Glucose metabolism index measurement results (blood glucose level, HbA1c, HOMA-IR, insulin) Liver function index measurement results (AST, ALT, γGTP) Lipid metabolism index measurement results (T-cho, LDL, HDL, TG) Nutrition-related index measurement results (total protein, albumin) Blood count index measurement results (red blood cells, white blood cells, hematocrit, platelets, lymphocytes, monocytes, neutrophils, eosinophils, basophils) Inflammation-related index measurement results (CRP) Visceral fat-related index measurement results (visceral fat area) Aortic velocity-related index measurement results Electrocardiogram results MRI and MRA test results Central blood pressure related index measurement results Carotid Artery Echo / Advanced Glycation End Products Test Results Body fat measurement results Results of medical history interview (hypertension, dyslipidemia, diabetes, stroke, angina pectoris, myocardial infarction, gout, cancer, etc.) Genomic information such as SNPs data and exome sequence data

[0050] [Second embodiment] [2-1. Overview of the second embodiment] Here, an overview of the second embodiment will be described with reference to FIG. 2. FIG. 2 is a diagram showing the basic principle of the second embodiment. In the description of the second embodiment, explanations that overlap with those of the first embodiment may be omitted. In particular, a case in which the value of the formula or its converted value is used when evaluating the future risk of a cerebrovascular event is described here as an example, but for example, a concentration value included in the concentration data or its converted value (such as a standard deviation) may also be used.

[0051] The control unit calculates the value of a formula using a formula stored in advance in the storage unit, which includes concentration values ​​of at least two amino acids selected from Gly, Cit, Trp, Met, Val, His, Pro, and Tyr in the blood of the evaluation subject (e.g., an individual such as an animal or human), and variables into which the concentration values ​​are substituted, thereby evaluating the future risk of cerebrovascular events for the evaluation subject (step S21). This makes it possible to provide highly reliable information that can be used as a reference for knowing the future risk of cerebrovascular events.

[0052] The formula used in step S21 may be one created based on the formula creation process (steps 1 to 4) described below. Here, an overview of the formula creation process will be described. Note that the process described here is merely an example, and the formula creation method is not limited to this.

[0053] First, the control unit creates a candidate equation (e.g., y=a1x1+a2x2+···+anxn, y: index data, xi: concentration data, ai: constant, i=1,2,···,n) based on a predetermined equation creation method from index state information (which may be data from which missing values, outliers, etc. have been removed in advance) that includes concentration data and index data related to indicators that represent the future risk of cerebrovascular events and that is previously stored in the storage unit (step 1).

[0054] In step 1, multiple candidate formulas may be created from the index state information by using multiple different formula creation methods (including those related to multivariate analysis such as principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, and decision tree). Specifically, multiple candidate formulas for multiple groups may be created in parallel using multiple different algorithms for index state information, which is multivariate data consisting of concentration data and index data obtained by analyzing blood obtained from a cerebrovascular event occurrence group (a group consisting of subjects who experienced a cerebrovascular event after a certain period of time has passed since blood collection) and a cerebrovascular event non-occurrence group (a group consisting of subjects who did not experience a cerebrovascular event after a certain period of time has passed since blood collection). For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas. In addition, the index state information may be converted using a candidate formula created by performing principal component analysis, and a discriminant analysis may be performed on the converted index state information to create a candidate formula. This allows the creation of a formula optimal for evaluation.

[0055] Here, the candidate formula created using principal component analysis is a linear formula including each variable that maximizes the variance of all concentration data. Also, the candidate formula created using discriminant analysis is a high-order formula (including exponential and logarithmic) including each variable that minimizes the ratio of the sum of variances in each group to the variance of all concentration data. Also, the candidate formula created using support vector machine is a high-order formula (including kernel function) including each variable that maximizes the boundary between groups. Also, the candidate formula created using multiple regression analysis is a high-order formula including each variable that minimizes the sum of distances from all concentration data. Also, the candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear formula including each variable and its coefficient that maximizes the likelihood of the model. Also, the candidate formula created using logistic regression analysis is a linear model that represents the log odds of probability, and is a linear formula including each variable that maximizes the likelihood of the probability. The k-means method is a method that searches k neighbors of each concentration data, defines the group to which the most neighboring points belong as the group to which the data belongs, and selects the variable that best matches the defined group to which the input concentration data belongs. Cluster analysis is a method of clustering (grouping) points that are closest to each other among all concentration data. A decision tree is a method of ranking variables and predicting groups of concentration data from the possible patterns of variables with higher rankings.

[0056] Returning to the explanation of the formula creation process, the control unit verifies (cross-validates) the candidate formulas created in step 1 based on a predetermined verification method (step 2). The candidate formulas are verified for each candidate formula created in step 1. In step 2, at least one of the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (area under the receiver characteristic curve), etc. of the candidate formula may be verified based on at least one of the bootstrap method, hold-out method, N-fold method, leave-one-out method, etc. This makes it possible to create a candidate formula with high predictability or robustness that takes into account index state information and evaluation conditions.

[0057] Here, the discrimination rate is the rate at which evaluation objects whose true state is negative are correctly evaluated as negative by the evaluation method according to this embodiment, and evaluation objects whose true state is positive are correctly evaluated as positive by the evaluation method according to this embodiment. Moreover, the sensitivity is the rate at which evaluation objects whose true state is positive are correctly evaluated as positive by the evaluation method according to this embodiment. Moreover, the specificity is the rate at which evaluation objects whose true state is negative are correctly evaluated as negative by the evaluation method according to this embodiment. Moreover, the Akaike information criterion is a standard that indicates the degree to which observed data matches a statistical model in the case of regression analysis, etc., and the model that has the smallest value defined by "-2 x (maximum logarithmic likelihood of statistical model) + 2 x (number of free parameters of statistical model)" is judged to be the best. Moreover, the ROC_AUC is defined as the area under the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1 - specificity, sensitivity) on a two-dimensional coordinate system, and the value of ROC_AUC is 1 for perfect discrimination, and the closer this value is to 1, the higher the discrimination ability is. Predictability is the average of the discrimination rate, sensitivity, and specificity obtained by repeatedly testing the candidate formula, and robustness is the variance of the discrimination rate, sensitivity, and specificity obtained by repeatedly testing the candidate formula.

[0058] Returning to the explanation of the formula creation process, the control unit selects a combination of concentration data included in the index state information used when creating the candidate formula by selecting variables of the candidate formula based on a predetermined variable selection method (step 3). In step 3, variable selection may be performed for each candidate formula created in step 1. This allows the variables of the candidate formula to be appropriately selected. Then, step 1 is executed again using the index state information including the concentration data selected in step 3. In step 3, the variables of the candidate formula may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2. In addition, the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.

[0059] Returning to the explanation of the formula creation process, the control unit repeatedly executes the above-mentioned steps 1, 2, and 3, and creates a formula to be used in evaluation by selecting a candidate formula to be used in evaluation from among multiple candidate formulas based on the verification results accumulated thereby (step 4). Note that the selection of the candidate formula may involve, for example, selecting the optimal one from among candidate formulas created using the same formula creation method, or selecting the optimal one from among all candidate formulas.

[0060] As described above, in the formula creation process, the processes related to the creation of candidate formulas, the verification of the candidate formulas, and the selection of variables for the candidate formulas are systematized (systemized) in a series of flows and executed based on the index state information, thereby making it possible to create a formula optimal for evaluating the future occurrence risk of cerebrovascular events. In other words, in the formula creation process, the blood concentrations of amino acids are used in multivariate statistical analysis, and a formula with high evaluation performance is extracted by combining a variable selection method and cross-validation to select an optimal and robust set of variables.

[0061] [2-2. Configuration of the second embodiment] Here, the configuration of the evaluation system according to the second embodiment (hereinafter, sometimes referred to as this system) will be described with reference to Figs. 3 to 14. Note that this system is merely an example, and the present invention is not limited thereto. In particular, here, a case is described as an example in which the value of a formula or its converted value is used when evaluating the future risk of a cerebrovascular event, but, for example, a concentration value included in the concentration data or its converted value (such as a standard deviation) may also be used.

[0062] First, the overall configuration of the present system will be described with reference to Fig. 3 and Fig. 4. Fig. 3 is a diagram showing an example of the overall configuration of the present system. Fig. 4 is a diagram showing another example of the overall configuration of the present system. As shown in Fig. 3, the present system is configured by connecting an evaluation device 100 that evaluates the future risk of a cerebrovascular event for an individual to be evaluated, and a client device 200 (corresponding to a terminal device of the present invention) that provides concentration data of the individual via a network 300 so that they can communicate with each other.

[0063] In this system, the client device 200 that is the source of data used in the evaluation and the client device 200 that is the destination of the evaluation results may be separate devices. As shown in Fig. 4, in addition to the evaluation device 100 and the client device 200, the system may be configured by connecting a database device 400 that stores index state information used when creating a formula in the evaluation device 100 and formulas used in the evaluation, etc., via a network 300 so as to be able to communicate with each other.

[0064] Next, the configuration of the evaluation device 100 of this system will be described with reference to Fig. 5 to Fig. 11. Fig. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system, and conceptually shows only the parts of the configuration that are related to the present invention.

[0065] The evaluation device 100 is composed of a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication interface unit 104 that communicatively connects the evaluation device to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line, a storage unit 106 that stores various databases, tables, files, etc., and an input / output interface unit 108 that connects to an input device 112 and an output device 114, and these units are communicatively connected via any communication path. Here, the evaluation device 100 may be composed in the same housing as various analyzers (e.g., amino acid analyzers, etc.). For example, a small analyzer having a configuration (hardware and software) that calculates (measures) the concentration value of amino acids in blood and outputs the calculated value (printing, displaying on a monitor, etc.) may further include an evaluation unit 102d described later, and the result obtained by the evaluation unit 102d may be output using the configuration.

[0066] The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.

[0067] The input / output interface unit 108 is connected to an input device 112 and an output device 114. Here, the output device 114 may be a monitor (including a home television), a speaker, or a printer (hereinafter, the output device 114 may be referred to as a monitor 114). The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function.

[0068] The memory unit 106 is a storage means, and may be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc. The memory unit 106 records computer programs for cooperating with an OS (Operating System) to give instructions to a CPU to perform various processes. As shown in the figure, the memory unit 106 stores a concentration data file 106a, an index state information file 106b, a specified index state information file 106c, a formula related information database 106d, and an evaluation result file 106e.

[0069] The concentration data file 106a stores concentration data. FIG. 6 is a diagram showing an example of information stored in the concentration data file 106a. As shown in FIG. 6, the information stored in the concentration data file 106a is configured by associating individual numbers for uniquely identifying individuals (samples) to be evaluated with concentration data. Here, in FIG. 6, the concentration data is treated as a numerical value, i.e., a continuous scale, but the concentration data may be a nominal scale or an ordinal scale. In the case of a nominal scale or an ordinal scale, analysis may be performed by assigning an arbitrary numerical value to each state. In addition, the concentration data may be combined with values ​​related to other biological information (see above).

[0070] Returning to FIG. 5, the index state information file 106b stores the index state information used when creating the formula. FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. As shown in FIG. 7, the information stored in the index state information file 106b is configured by mutually associating an individual number, index data (T) related to indexes (index T1, index T2, index T3, . . . ) representing the future occurrence risk of cerebrovascular events, and concentration data. Here, in FIG. 7, the index data and concentration data are treated as numerical values ​​(i.e., continuous scale), but the index data and concentration data may be nominal scale or ordinal scale. In the case of nominal scale or ordinal scale, analysis may be performed by giving an arbitrary numerical value to each state. In addition, the index data is a known index that is a marker of the future occurrence risk of cerebrovascular events, and numerical data may be used.

[0071] Returning to Fig. 5, the specified index status information file 106c stores index status information specified by the specification unit 102b, which will be described later. Fig. 8 is a diagram showing an example of information stored in the specified index status information file 106c. As shown in Fig. 8, the information stored in the specified index status information file 106c is configured by associating individual numbers, specified index data, and specified concentration data with each other.

[0072] Returning to Fig. 5, the formula-related information database 106d is composed of a formula file 106d1 that stores formulas created by a formula creation unit 102c, which will be described later. The formula file 106d1 stores formulas used in evaluation. Fig. 9 is a diagram showing an example of information stored in the formula file 106d1. As shown in Fig. 9, the information stored in the formula file 106d1 is composed of ranks, formulas, thresholds corresponding to each formula creation method, and verification results of each formula (e.g., the value of each formula), which are mutually associated with each other.

[0073] Returning to Fig. 5, the evaluation result file 106e stores the evaluation results obtained by the evaluation unit 102d described later. Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106e. The information stored in the evaluation result file 106e is configured by mutually associating individual numbers, concentration data of individuals, and evaluation results regarding the future occurrence risk of cerebrovascular events (for example, values ​​of a formula calculated by a calculation unit 102d1 described later, values ​​converted by a conversion unit 102d2 described later, position information generated by a generation unit 102d3 described later, or classification results obtained by a classification unit 102d4 described later, etc.).

[0074] Returning to Fig. 5, the control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, and required data, and executes various information processing based on these programs. As shown in the figure, the control unit 102 is roughly divided into an acquisition unit 102a, a designation unit 102b, a formula creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 also performs data processing such as removal of data with missing values, removal of data with many outliers, and removal of variables with many data with missing values ​​for the index state information transmitted from the database device 400 and the concentration data transmitted from the client device 200.

[0075] The acquiring unit 102a acquires information (specifically, concentration data, index state information, formula, etc.). For example, the acquiring unit 102a may acquire information by receiving information (specifically, concentration data, index state information, formula, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The acquiring unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 to which the evaluation result is transmitted. For example, when the evaluation device 100 includes a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquiring unit 102a may acquire information by reading information (specifically, concentration data, index state information, formula, etc.) recorded on the recording medium via the mechanism. The designating unit 102b designates the index data and concentration data to be used in creating the formula.

[0076] The equation creation unit 102c creates an equation based on the index state information acquired by the acquisition unit 102a and the index state information designated by the designation unit 102b. When the equation is stored in advance in a predetermined storage area of ​​the storage unit 106, the equation creation unit 102c may create an equation by selecting a desired equation from the storage unit 106. The equation creation unit 102c may also create an equation by selecting a desired equation from another computer device (e.g., database device 400) that has equations stored in advance and downloading the equation.

[0077] The evaluation unit 102d evaluates the future risk of a cerebrovascular event for the individual by calculating the value of the formula using a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula acquired by the acquisition unit 102a) and a concentration value included in the concentration data acquired by the acquisition unit 102a. The evaluation unit 102d may evaluate the future risk of a cerebrovascular event for the individual by using the concentration value included in the concentration data or a value after conversion of the concentration value (for example, a standard deviation).

[0078] Here, the configuration of the evaluation unit 102d will be described with reference to Fig. 11. Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d, and conceptually shows only the parts of the configuration related to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

[0079] The calculation unit 102d1 calculates the value of the equation using an equation that includes at least a concentration value included in the concentration data and a variable into which the concentration value is substituted. The evaluation unit 102d may store the value of the equation calculated by the calculation unit 102d1 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0080] The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1, for example, using the conversion method described above. The evaluation unit 102d may store the value converted by the conversion unit 102d2 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e. The conversion unit 102d2 may also convert the concentration value included in the concentration data, for example, using the conversion method described above.

[0081] The generating unit 102d3 generates position information on the position of a predetermined mark on a predetermined ruler that is visibly shown on a display device such as a monitor or a physical medium such as paper, using the value of the formula calculated by the calculating unit 102d1 or the value converted by the converting unit 102d2 (which may be a concentration value or a value converted from the concentration value). The evaluating unit 102d may store the position information generated by the generating unit 102d3 in a predetermined storage area of ​​the evaluation result file 106e as an evaluation result.

[0082] The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value converted by the conversion unit 102d2 (which may be a concentration value or a value converted from the concentration value) to classify the individual into one of a plurality of categories defined taking into consideration at least the degree of future risk of occurrence of a cerebrovascular event.

[0083] The result output unit 102e outputs to the output device 114 the processing results of each processing unit in the control unit 102 (including the evaluation result obtained by the evaluation unit 102d) and the like.

[0084] The transmission unit 102f transmits the evaluation result to the client device 200 that transmitted the concentration data of the individual, and transmits the formula created by the evaluation device 100 and the evaluation result to the database device 400. The transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that transmitted the data used for the evaluation.

[0085] Next, the configuration of client device 200 of this system will be described with reference to Fig. 12. Fig. 12 is a block diagram showing an example of the configuration of client device 200 of this system, and conceptually shows only the parts of the configuration that are related to the present invention.

[0086] The client device 200 is composed of a control unit 210, a ROM 220, a HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280, and these units are communicably connected via any communication path. The client device 200 may be based on an information processing device (for example, a known information processing terminal such as a personal computer, a workstation, a home game device, an Internet TV, a PHS (Personal Handyphone System) terminal, a mobile terminal, a mobile communication terminal, a PDA (Personal Digital Assistant), etc.) to which peripheral devices such as a printer, a monitor, an image scanner, etc. are connected as necessary.

[0087] The input device 250 is a keyboard, a mouse, a microphone, etc. A monitor 261, which will be described later, also realizes a pointing device function in cooperation with a mouse. The output device 260 is an output means for outputting information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker, etc. The input / output IF 270 is connected to the input device 250 and the output device 260.

[0088] The communication IF 280 communicatively connects the client device 200 and the network 300 (or a communication device such as a router). In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router and a telephone line, or via a dedicated line. This allows the client device 200 to access the evaluation device 100 in accordance with a predetermined communication protocol.

[0089] The control unit 210 includes a receiving unit 211 and a transmitting unit 212. The receiving unit 211 receives various information such as the evaluation result transmitted from the evaluation device 100 via the communication IF 280. The transmitting unit 212 transmits various information such as the concentration data of the individual to the evaluation device 100 via the communication IF 280.

[0090] The control unit 210 may realize all or any part of the processing performed by the control unit with a CPU and a program that is interpreted and executed by the CPU. A computer program for giving instructions to the CPU in cooperation with the OS and performing various processing is recorded in the ROM 220 or the HD 230. The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. The computer program may also be recorded in an application program server connected to the client device 200 via any network, and the client device 200 may download all or any part of the computer program as necessary. Also, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.

[0091] Here, the control unit 210 may include an evaluation unit 210a (including a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d included in the evaluation device 100. When the control unit 210 includes the evaluation unit 210a, the evaluation unit 210a may convert the value of the formula (which may be a concentration value) in the conversion unit 210a2, generate position information corresponding to the value of the formula or the converted value (which may be a concentration value or a value after the conversion of the concentration value) in the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the formula or the converted value (which may be a concentration value or a value after the conversion of the concentration value) in the classification unit 210a4, depending on information included in the evaluation result transmitted from the evaluation device 100.

[0092] Next, the network 300 of this system will be described with reference to Figures 3 and 4. The network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (including both wired and wireless networks). In addition, network 300 may be a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a leased line network (including both analog and digital), a CATV (Community Antenna TeleVision) network, a mobile circuit-switched network or a mobile packet-switched network (including the IMT (International Mobile Telecommunication) 2000 system, the GSM (registered trademark) (Global System for Mobile Communications) system, or the PDC (Personal Digital Cellular) / PDC-P system, etc.), a radio paging network, a local wireless network such as Bluetooth (registered trademark), a PHS network, or a satellite communication network (including CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcasting), etc.).

[0093] Next, the configuration of the database device 400 of this system will be described with reference to Fig. 13. Fig. 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only the parts of the configuration that are related to the present invention.

[0094] The database device 400 has a function of storing index state information used when creating an equation in the evaluation device 100 or the database device itself, an equation created in the evaluation device 100, an evaluation result obtained in the evaluation device 100, etc. As shown in Fig. 13, the database device 400 is composed of a control unit 402 such as a CPU that controls the database device in an overall manner, a communication interface unit 404 that communicatively connects the database device to the network 300 via a communication device such as a router and a wired or wireless communication circuit such as a dedicated line, a storage unit 406 that stores various databases, tables, files (e.g., files for Web pages), etc., and an input / output interface unit 408 that connects to an input device 412 and an output device 414, and these units are communicatively connected via any communication path.

[0095] The memory unit 406 is a storage means, and may be, for example, a memory device such as a RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like. The memory unit 406 stores various programs and the like used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to an input device 412 and an output device 414. Here, the output device 414 may be a monitor (including a home television), a speaker, or a printer. The input device 412 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function.

[0096] The control unit 402 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly equipped with a transmission unit 402a and a reception unit 402b. The transmission unit 402a transmits various information such as index state information and equations to the evaluation device 100. The reception unit 402b receives various information such as equations and evaluation results transmitted from the evaluation device 100.

[0097] In this description, an example has been given in which the evaluation device 100 performs the processes from obtaining concentration data, calculating the values ​​of the formulas, classifying the individuals into categories, and transmitting the evaluation results, while the client device 200 receives the evaluation results. However, if the client device 200 is equipped with the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the values ​​of the formulas. For example, processes such as converting the values ​​of the formulas, generating position information, and classifying the individuals into categories may be appropriately shared between the evaluation device 100 and the client device 200. For example, when the client device 200 receives a value of an equation from the evaluation device 100, the evaluation unit 210a may convert the value of the equation in the conversion unit 210a2, generate location information corresponding to the value of the equation or the converted value in the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the equation or the converted value in the classification unit 210a4. Furthermore, when the client device 200 receives a converted value from the evaluation device 100, the evaluation unit 210a may generate position information corresponding to the converted value in the generation unit 210a3, or classify the individual into one of a plurality of categories using the converted value in the classification unit 210a4. In addition, when the client device 200 receives the value of the formula or the converted value and location information from the evaluation device 100, the evaluation unit 210a may classify the individual into one of a plurality of categories using the value of the formula or the converted value in the classification unit 210a4.

[0098] [2-3. Other embodiments] The evaluation device, calculation device, evaluation method, calculation program, recording medium, evaluation system, and terminal device according to the present invention may be embodied in various different embodiments within the scope of the technical idea described in the claims, in addition to the second embodiment described above.

[0099] Furthermore, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0100] In addition, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0101] Moreover, with regard to the evaluation device 100, the components shown in the figures are merely functional concepts, and the evaluation device 100 does not necessarily have to be physically configured as shown in the figures.

[0102] For example, the processing functions of the evaluation device 100, particularly the processing functions performed by the control unit 102, may be realized in whole or in part by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware using wired logic. The program is recorded in a non-transient computer-readable recording medium including programmed instructions for causing an information processing device to execute the evaluation method or calculation method according to the present invention, and is mechanically read by the evaluation device 100 as necessary. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into a RAM, and constitutes a control unit in cooperation with the CPU.

[0103] In addition, this computer program may be stored in an application program server connected to the evaluation device 100 via any network, and it is also possible to download all or part of it as necessary.

[0104] The evaluation program or calculation program according to the present invention may be stored in a non-transitory computer-readable recording medium, or may be configured as a program product. Here, the term "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM) (registered trademark), a Compact Disc Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) Disc.

[0105] Moreover, a "program" is a data processing method written in any language or description method, and may be in any form, such as source code or binary code. Note that a "program" is not necessarily limited to a single configuration, but also includes a distributed configuration as multiple modules or libraries, and a program that works with a separate program, such as an OS, to achieve its function. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, may use well-known configurations and procedures.

[0106] The various databases etc. stored in the memory unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files etc. used for various processes and providing websites.

[0107] The evaluation device 100 may be configured as an information processing device such as a known personal computer or a workstation, or may be configured as the information processing device to which any peripheral device is connected. The evaluation device 100 may be realized by installing software (including a program or data) that realizes the evaluation method or calculation method of the present invention in the information processing device.

[0108] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed and integrated in any unit according to various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the embodiments can be implemented selectively. EXAMPLES

[0109] Health checkups were conducted on local residents between 2008 and 2010, and blood free amino acid concentrations were measured using the amino acid analysis method (A) described above from blood samples taken during the checkups. A follow-up study of the occurrence of cerebrovascular events (stroke (cerebral infarction, intracerebral hemorrhage, subarachnoid hemorrhage), myocardial infarction, or PCI) was conducted on this group of residents for up to 10 years from the time of the checkups until 2018. Of this group, 4039 residents who had their blood drawn in the morning and had their checkups more than 8 hours after eating were included in the study. Of these, 64 out of 4039 had events, 32 had strokes, 22 had myocardial infarction, and 10 had PCI.

[0110] The measured blood free amino acid concentration was substituted into the formulas (the three formulas shown below) for discriminating the occurrence of cardiovascular events described in Patent Document 1 (JP Patent Publication 2013-178238 A) to obtain linear predictor values. The hazard ratio per standard deviation (HR / SD) and C-index for the occurrence of cerebrovascular events of the obtained linear predictor values ​​were calculated. The calculation results are shown in FIG. 14. Here, the C-index is an index that indicates the accuracy of prognosis prediction proposed by Harrell et al., and is a nonparametric index that indicates the degree to which the probability of event occurrence predicted by the model coincides with the magnitude relationship between the actual probability of event occurrence. Logistic regression equation: 0.3452-0.009171*Ser-0.01119*Thr+0.006555*Lys+0.01067*Arg-0.03345*Trp Linear discriminant:-0.5164+0.0018*Pro-0.004*Thr+0.0038*Orn+0.0026*Arg-0.0074*Trp Cox proportional hazards model: 0.0042*Pro-0.0133*Thr+0.0075*Lys+0.0137*Cit-0.0279*Trp

[0111] In addition, all combinations of two amino acids selected from 19 kinds of amino acids (Thr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Tyr, Phe, His, Trp, Orn, Lys and Arg) were extracted, and the measured blood free amino acid concentration was substituted into the Cox proportional hazard model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the occurrence or nonoccurrence of cerebrovascular events during the follow-up period of the obtained linear predictor value were calculated. The combinations of two amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in FIG. 14 are shown in FIG. 15 to FIG. 17. The combinations of two amino acids in the Cox proportional hazard model consisting of two amino acids and the HR / SD or C-index values ​​higher than the values ​​shown in FIG. 14 are shown, as well as the HR / SD and C-index values. EXAMPLES

[0112] Among the population described in Example 1, 5,479 residents who underwent health checkups in 2008 or 2009 after blood was drawn in the morning or afternoon and more than 8 hours had passed since eating were included in the study. Of these, 104 out of 5,479 had events, 50 had strokes, 35 had myocardial infarctions, and 19 had undergone PCI.

[0113] The measured blood free amino acid concentrations were substituted into the three formulas described in Patent Document 1 described in Example 1 to obtain linear predictor values. The HR / SD and C-index for the occurrence of cerebrovascular events of the obtained linear predictor values ​​were calculated. The calculation results are shown in FIG.

[0114] In addition, all combinations of two amino acids selected from the 19 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. Combinations of two amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in FIG. 18 are shown in FIG. 19 and FIG. 20. In FIG. 19 and FIG. 20, combinations of two amino acids in a Cox proportional hazards model consisting of two amino acids and having HR / SD or C-index values ​​higher than the values ​​shown in FIG. 18, and the values ​​of HR / SD and C-index are shown. EXAMPLES

[0115] The study included 4,039 residents as described in Example 1.

[0116] All combinations of two amino acids selected from 13 kinds of amino acids (Gly, Gln, His, Thr, Cit, Pro, Tyr, Val, Met, Lys, Ile, Phe and Trp) were extracted, and the measured blood free amino acid concentration was substituted into the Cox proportional hazard model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the occurrence or nonoccurrence of cerebrovascular events during the follow-up period of the obtained linear predictor value were calculated. The combinations of two amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in FIG. 14 are shown in FIG. 21 and FIG. 22. In FIG. 21 and FIG. 22, the combinations of two amino acids in the Cox proportional hazard model consisting of two amino acids and having HR / SD or C-index higher than the values ​​shown in FIG. 14, and the values ​​of HR / SD and C-index are shown. EXAMPLES

[0117] The study included 5,479 residents as described in Example 2.

[0118] All combinations of two amino acids selected from the 13 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. The combinations of two amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in FIG. 18 are shown in FIG. 23. FIG. 23 shows the combinations of two amino acids in the Cox proportional hazards model consisting of two amino acids, in which the HR / SD or C-index is higher than the values ​​shown in FIG. 18, and the values ​​of HR / SD and C-index. EXAMPLES

[0119] The study included 4,039 residents as described in Example 1.

[0120] All combinations of three amino acids selected from the 19 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. The combinations of three amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in Figures 21 and 22 are shown in Figure 24. Figure 24 shows the combinations of three amino acids in the Cox proportional hazards model consisting of three amino acids, in which the HR / SD or C-index is higher than the values ​​shown in Figures 21 and 22, and the values ​​of HR / SD and C-index. EXAMPLES

[0121] The study included 5,479 residents as described in Example 2.

[0122] All combinations of three amino acids selected from the 19 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. The combinations of three amino acids showing HR / SD or C-index higher than the values ​​shown in FIG. 23 are shown in FIG. 25. FIG. 25 shows the combinations of three amino acids in the Cox proportional hazards model consisting of three amino acids, in which the HR / SD or C-index is higher than the values ​​shown in FIG. 23, and the HR / SD and C-index. EXAMPLES

[0123] The study included 4,039 residents as described in Example 1.

[0124] All combinations of three amino acids selected from the 13 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. The combinations of three amino acids showing HR / SD or C-index values ​​higher than the values ​​shown in FIG. 24 are shown in FIG. 26. FIG. 26 shows the combinations of three amino acids in the Cox proportional hazards model consisting of three amino acids, in which the HR / SD or C-index is higher than the values ​​shown in FIG. 24, and the values ​​of HR / SD and C-index. EXAMPLES

[0125] The study included 5,479 residents as described in Example 2.

[0126] All combinations of three amino acids selected from the 13 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the presence or absence of cerebrovascular events during the follow-up period of the obtained linear predictor value were calculated. The combinations of three amino acids showing HR / SD or C-index higher than the values ​​shown in FIG. 25 are shown in FIG. 27. FIG. 27 shows the combinations of three amino acids in the Cox proportional hazards model consisting of three amino acids, in which the HR / SD or C-index is higher than the values ​​shown in FIG. 25, and the HR / SD and C-index. EXAMPLES

[0127] The study included 4,039 residents as described in Example 1.

[0128] All combinations of 4 to 6 amino acids selected from the 13 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. Combinations of 4 to 6 amino acids showing HR / SD or C-index higher than the values ​​shown in FIG. 26 are shown in FIG. 28. FIG. 28 shows combinations of 4 to 6 amino acids in a Cox proportional hazards model consisting of 4 to 6 amino acids and HR / SD and C-index values ​​higher than the values ​​shown in FIG. 26. EXAMPLES

[0129] The study included 5,479 residents as described in Example 2.

[0130] All combinations of 4 to 6 amino acids selected from the 13 types of amino acids were extracted, and the measured blood free amino acid concentrations were substituted into the Cox proportional hazards model corresponding to each extracted combination to obtain the linear predictor value of the model. The HR / SD and C-index for the obtained linear predictor value with respect to the occurrence or absence of cerebrovascular events during the follow-up period were calculated. Combinations of 4 to 6 amino acids showing HR / SD or C-index higher than the values ​​shown in FIG. 27 are shown in FIG. 29 to FIG. 33. Combinations of 4 to 6 amino acids in a Cox proportional hazards model consisting of 4 to 6 amino acids and having HR / SD or C-index higher than the values ​​shown in FIG. 27, and HR / SD and C-index are shown in FIG. 29 to FIG. 33. EXAMPLES

[0131] The study included 4,039 residents as described in Example 1.

[0132] The 40th and 95th percentiles of the linear predictor value of the model described in Example 9 were set as cutoff values, and the subjects were ranked. Specifically, subjects with linear predictor values ​​below 40% were ranked A, subjects with values ​​between 40% and 95% were ranked B, and subjects with values ​​above 95% were ranked C.

[0133] For each combination of four to six amino acids, the relative risk of cerebrovascular events occurring during the follow-up period for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A, were calculated.

[0134] For each combination of four to six amino acids, the relative risk of occurrence of cerebrovascular events during the follow-up period was calculated for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A.

[0135] For each combination of four to six amino acids, the relative risk of cardiovascular events occurring during the follow-up period was calculated for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A.

[0136] The calculated relative risks are shown in Figure 34. Figure 34 shows the relative risks of cerebrovascular events, cerebrovascular events, and cardiovascular events for subjects in rank B relative to subjects in rank A, and for subjects in rank C relative to subjects in rank A, when subjects are ranked based on the linear predictor value of the Cox proportional hazards model described in Example 9. EXAMPLES

[0137] The study included 5,479 residents as described in Example 2.

[0138] The 40th and 95th percentiles of the linear predictor value of the model described in Example 10 were set as cutoff values, and the subjects were ranked. Specifically, subjects with linear predictor values ​​below 40% were ranked A, subjects with values ​​between 40% and 95% were ranked B, and subjects with values ​​above 95% were ranked C.

[0139] For each combination of four to six amino acids, the relative risk of cerebrovascular events occurring during the follow-up period for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A, were calculated.

[0140] For each combination of four to six amino acids, the relative risk of occurrence of cerebrovascular events during the follow-up period was calculated for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A.

[0141] For each combination of four to six amino acids, the relative risk of cardiovascular events occurring during the follow-up period was calculated for subjects in rank B compared to subjects in rank A, and the relative risk for subjects in rank C compared to subjects in rank A.

[0142] The calculated relative risks are shown in Figures 35 to 43. Figures 35 to 43 show the relative risks of cerebrovascular events, cerebrovascular events, and cardiovascular events for subjects in rank B relative to subjects in rank A, and for subjects in rank C relative to subjects in rank A, when subjects are ranked based on the linear predictor value of the Cox proportional hazards model described in Example 10. [Industrial Applicability]

[0143] As described above, the present invention can be widely implemented in many industrial fields, particularly in the fields of pharmaceuticals, food, and medicine, and is particularly useful in the field of bioinformatics for predicting the future risk of cerebrovascular events. [Explanation of symbols]

[0144] 100 Evaluation device (including calculation device) 102 Control section 102a Acquisition Department 102b Specified part 102c Expression Creation Section 102d Evaluation Section 102d1 Calculation part 102d2 Converter 102d3 Generation part 102d4 Classification section 102e Result output section 102f Transmitter 104 Communication interface section 106 Storage section 106a Concentration data file 106b Index status information file 106c Specified index status information file 106d type related information database 106d1 Formula File 106e Evaluation result file 108 Input / Output Interface Section 112 Input Devices 114 Output Device 200 Client device (terminal device (information communication terminal device)) 300 Network 400 Database device

Claims

1. and acquiring information for evaluating a future risk of cerebrovascular events for the subject, using concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of the subject, and values ​​of the formula calculated using the formula consisting of variables into which the concentration values ​​are substituted. An acquisition method characterized by the above.

2. The acquiring step is executed by a control unit of an information processing device having a control unit. The acquisition method according to claim 1 ,

3. and a calculation step of calculating a value of an equation for evaluating a future risk of occurrence of a cerebrovascular event, the equation comprising concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted. A calculation method characterized by:

4. The calculation step is executed by a control unit of an information processing device having a control unit. The calculation method according to claim 3 ,

5. An evaluation device including a control unit, The control unit is An evaluation means for evaluating a future risk of cerebrovascular events in a subject by using a value calculated using a formula consisting of concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of the subject and variables into which the concentration values ​​are substituted. To be prepared An evaluation device comprising:

6. a terminal device that provides the value of the formula, the terminal device being communicatively connected via a network; The control unit is a data receiving means for receiving the value of the formula transmitted from the terminal device; a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device; Further equipped with The evaluation means uses the value of the formula received by the data receiving means. The evaluation device according to claim 5 .

7. A calculation device including a control unit, The control unit is a calculation means for calculating a value of an equation for evaluating a future risk of occurrence of a cerebrovascular event, the equation comprising concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of a subject to be evaluated, and variables into which the concentration values ​​are substituted; To be prepared A calculation device comprising:

8. An evaluation program to be executed in an information processing device having a control unit, To be executed in the control unit, an evaluation step of evaluating a future risk of cerebrovascular events for the subject using concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of the subject and values ​​of the formula calculated using a formula consisting of variables into which the concentration values ​​are substituted; Contains An evaluation program that features:

9. A calculation program to be executed in an information processing device having a control unit, To be executed in the control unit, A calculation step of calculating a value of an equation for evaluating a future risk of occurrence of a cerebrovascular event, the equation being composed of concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of the subject to be evaluated, and variables into which the concentration values ​​are substituted. Contains A calculation program comprising:

10. 10. A computer-readable recording medium having the program according to claim 8 or 9 recorded thereon.

11. An evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit via a network so as to be able to communicate with each other, The control unit of the terminal device a data transmitting means for transmitting to the evaluation device concentration values ​​of (1) Met, Gly, and Cit, (2) Met, Gly, and Val, (3) Met, Cit, and Trp, (4) Met, Cit, and Val, (5) Met, Gly, Cit, and Trp, or (6) Met, Gly, Cit, Trp, and Val in the blood of the subject to be evaluated, and a value of the formula calculated using a formula consisting of variables into which the concentration values ​​are substituted; a result receiving means for receiving an evaluation result regarding a future risk of occurrence of a cerebrovascular event transmitted from the evaluation device; Equipped with The control unit of the evaluation device a data receiving means for receiving the value of the formula transmitted from the terminal device; an evaluation means for evaluating a future risk of a cerebrovascular event for the subject by using the value of the formula received by the data receiving means; a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device; To be prepared A rating system characterized by:

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