JPWO2020158828A1 - Evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system and terminal device for future occurrence risk of cerebrocardiovascular events - Google Patents

Abstract

In this embodiment, it is calculated using a Cox proportional hazard model including a concentration value of at least one amino acid of Gly, Cit, Trp, Met and Val in the blood to be evaluated and a variable to which the concentration value is substituted. Using the values of the model, the risk of future occurrence of cerebrocardiovascular events is evaluated for the evaluation target.

Description

The present invention relates to an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, a recording medium, an evaluation system, and a terminal device for the future occurrence risk of a cerebrocardiovascular event.

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. All of these diseases, which are collectively called cerebrocardiovascular events, are considered to be one of the main causes of vascular dysfunction represented by arteriosclerosis caused by metabolic syndrome.

In order to prevent cerebrocardiovascular events, early detection of the etiology caused by metabolic syndrome and appropriate intervention according to the etiology are required. It may be useful to utilize biomarkers to detect the etiology.

As a biomarker, amino acids, which are central to metabolic pathways among metabolites in the living body, are attracting attention. Amino acids are abundant in blood among metabolites, and their physical characteristics are relatively similar to each other. Therefore, amino acids have favorable properties as targets for biomarkers, such as the ability to analyze with high accuracy.

Here, it has been reported that the difference in the amino acid concentration distribution in plasma is a risk factor for future cerebrocardiovascular events (Non-Patent Documents 1-4). Further, as a prior patent, Patent Document 1 relating to a method for evaluating a future state of a cardiovascular event using the concentration of amino acids in blood has been published.

Japanese Unexamined Patent Publication No. 2013-178238

Ruiz-Canela M. et al. Clin. Chem. 2016, 62, 582-592 Shah SH. Et al. Circ. Cardiovasc. Genet. 2010, 3, 207-214 Shah SH. Et al. Am. Heart. J. 2012, 163, 844-850 Magnusson M. et al. 2013, Eur. Heart. J. 34, 198201989

However, there is a problem that further accuracy is required for the development of a determination technique for determining the future risk of cerebral cardiovascular events using the amino acid concentration in blood.

The present invention has been made in view of the above, and is an evaluation method, a calculation method, an evaluation device, and a calculation capable of providing highly reliable information that can be used as a reference for knowing the future risk of occurrence of a cerebrocardiovascular event. It is an object of the present invention to provide an apparatus, an evaluation program, a calculation program, a recording medium, an evaluation system, and a terminal apparatus.

In order to solve the above-mentioned problems and achieve the object, the evaluation method according to the present invention comprises at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated. The risk of future occurrence of cerebrocardiovascular events for the evaluation target is determined by using the concentration value of, or the formula including the variable to which the concentration value is substituted, and the value of the formula calculated using the concentration value. It is characterized by including an evaluation step to be evaluated.

Here, in the present specification, cerebrovascular events include aortic dissection, coronary artery disease (myocardial infarction, heart failure, percutaneous coronary angioplasty), etc.), and cerebrovascular accident (stroke (cerebral infarction, intracerebral hemorrhage)). , Subepithelial bleeding), etc.).

In addition, various amino acids are mainly abbreviated in this specification, but their official names are as follows.
(Abbreviation) (Official name)
Ala Alanine
Arg Arginine
Asn Asparagine
Cit Citrulline
Gln Glutamine
Gly Glycine
His Histidine
Ile Isoleucine
Leu Leucine
Lys Lysine
Met Methionine
Orn Ornithine
Phe Phenylalanine
Pro Proline
Ser Serine
Thr Threonine
Trp Tryptophan
Tyr Tyrosine
Val Valine

Further, the evaluation method according to the present invention is characterized in that the at least two amino acids contain Gly.

Further, the evaluation method according to the present invention is characterized in that the at least two amino acids contain Cit and / or Trp.

Further, the evaluation method according to the present invention is characterized in that the at least two amino acids contain Met and / or Val.

Further, the evaluation method according to the present invention is characterized in that the evaluation step is executed in the control unit of the information processing apparatus including the control unit.

Further, in the calculation method according to the present invention, the concentration value of at least two amino acids among Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated and the variable to which the concentration value is substituted. It is characterized by including a calculation step for calculating the value of the formula using a formula for assessing the future risk of occurrence of a cerebrocardiovascular event including.

Further, the calculation method according to the present invention is characterized in that at least one of the at least two amino acids contains Gly.

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

Further, the calculation method according to the present invention is characterized in that the at least two amino acids include Met and / or Val.

Further, the calculation method according to the present invention is characterized in that the calculation step is executed in the control unit of the information processing apparatus including the control unit.

Further, the evaluation device according to the present invention is an evaluation device including a control unit, and the control unit is at least one of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated. Using the concentration values of the two amino acids, or the formula containing the variable to which the concentration value is substituted, and the value of the formula calculated using the concentration value, the future of the cerebrocardiovascular event for the evaluation target It is characterized by having an evaluation means for evaluating the risk of occurrence.

Further, the evaluation device according to the present invention is communicably connected to the terminal device that provides the concentration data related to the concentration value or the value of the above formula via a network, and the control unit is transmitted from the terminal device. The data receiving means for receiving the concentration data or the value of the formula and the result transmitting means for transmitting the evaluation result obtained by the evaluation means to the terminal device are further provided, and the evaluation means receives the data. It is characterized in that the concentration value included in the concentration data received by the means or the value of the formula is used.

Further, the calculation device according to the present invention is a calculation device including a control unit, and the control unit is at least one of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated. It is characterized by comprising a calculation means for calculating the value of the above formula using a formula for evaluating the future occurrence risk of a cerebrocardiovascular event including the concentration value of two amino acids and the variable to which the concentration value is substituted. And.

Further, the evaluation program according to the present invention is an evaluation program to be executed in an information processing apparatus provided with a control unit, and is to be executed in the control unit, such as Gly, Cit, Trp, Met in the blood to be evaluated. , Val, His, Pro and Tyr using the concentration value of at least two amino acids, or the formula including the variable to which the concentration value is substituted and the value of the formula calculated using the concentration value. The evaluation target is characterized by including an evaluation step for evaluating the future risk of occurrence of a cerebrocardiovascular event.

Further, the calculation program according to the present invention is a calculation program to be executed in an information processing apparatus provided with a control unit, and is to be executed in the control unit, such as Gly, Cit, Trp, Met in the blood to be evaluated. , Val, His, Pro and Tyr, using the formula for assessing the risk of future occurrence of cerebrocardiovascular events, including the concentration values of at least two amino acids and the variables to which the concentration values are substituted. It is characterized by including a calculation step for calculating the value of.

Further, the recording medium according to the present invention is a computer-readable recording medium on which the evaluation program or the calculation program is recorded. Specifically, the recording medium according to the present invention is a non-temporary computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method or the calculation method. It is characterized by.

Further, the evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device including a control unit and a terminal device including a control unit so as to be communicable via a network, and the control of the terminal device. The unit includes concentration data regarding the concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or variables to which the concentration values are assigned. Receives a data transmission means for transmitting the formula and the value of the formula calculated using the concentration value to the evaluation device, and an evaluation result regarding the future occurrence risk of a cerebrocardiovascular event transmitted from the evaluation device. A data receiving means for receiving the density data transmitted from the terminal device or the value of the formula by the control unit of the evaluation device, and the density received by the data receiving means. An evaluation means for evaluating the future occurrence risk of a cerebrocardiovascular event for the evaluation target using the concentration value included in the data or the value of the formula, and the evaluation result obtained by the evaluation means. Is provided with a result transmission means for transmitting the data to the terminal device.

Further, the terminal device according to the present invention is a terminal device including a control unit, wherein the control unit includes a result acquisition means for acquiring an evaluation result regarding a future occurrence risk of a cerebrocardiovascular event, and the evaluation result is described. However, the concentration value of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or the formula including the variable to which the concentration value is substituted and the concentration thereof. It is characterized in that it is the result of evaluating the future occurrence risk of a cerebrocardiovascular event for the evaluation target using the value of the above formula calculated using the value.

Further, the terminal device according to the present invention is communicably connected to an evaluation device for evaluating the risk of future occurrence of a cerebrocardiovascular event via a network, and the control unit is connected to the concentration data related to the concentration value or the concentration value. The data transmission means for transmitting the value of the expression to the evaluation device is provided, and the result acquisition means receives the evaluation result transmitted from the evaluation device.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide highly reliable information that can be used as a reference for knowing the risk of future occurrence of cerebrocardiovascular events.

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

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

[First Embodiment]
[1-1. Outline of the first embodiment]
Here, the outline of the first embodiment will be described with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.

First, at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in blood (including, for example, plasma or serum) collected from an evaluation target (for example, an individual such as an animal or a human). Acquire the concentration data including the concentration value of (step S11).

Here, in step S11, for example, the concentration data measured by a company or the like that measures the concentration value may be acquired. Further, the concentration data may be obtained from the blood collected from the evaluation target by measuring the concentration value by, for example, the following measuring methods such as (A), (B), or (C). Here, the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, or may be obtained by adding, subtracting, multiplying, or dividing an arbitrary constant to these concentrations.
(A) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80 ° C until the concentration value is measured. At the time of concentration value measurement, acetonitrile is added to perform derivatization treatment, and if necessary, impurities such as phospholipids are removed by solid layer extraction, etc., and a labeling reagent (3-aminopyridyl-N-hydroxysuccinimi) is removed. Pre-column derivatization is performed using zircarbamate), and concentration values are analyzed by liquid chromatography-mass spectrometry (including tandem mass spectrometry) (International Publication No. 2003/06628, International Publication No. 2005/116629). See).
(B) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80 ° C until the concentration value is measured. When measuring the concentration value, sulfosalicylic acid is added to perform deproteinization treatment, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
(C) The collected blood sample is subjected to blood cell separation using a membrane, MEMS (Micro Electro Mechanical Systems) technology or the principle of centrifugation, and plasma or serum is separated from the blood. Plasma or serum samples that are not measured immediately after acquisition of plasma or serum are cryopreserved at -80 ° C until the time of concentration measurement. At the time of measuring the concentration value, a molecule that reacts with or binds to a target blood substance such as an enzyme or an aptamer is used, and the concentration value is analyzed by quantifying the substance that increases or decreases due to substrate recognition or the spectroscopic value.

Next, the risk of future occurrence of cerebrocardiovascular events is evaluated (predicted) for the evaluation target using the concentration value included in the concentration data acquired in step S11 (step S12). Here, to evaluate the risk of future occurrence of cerebrocardiovascular events for the evaluation target, for example, the evaluation target includes cerebrocardiovascular events including stroke and myocardial infarction in the future (for example, within 10 years after blood collection). Evaluating the risks that arise, and so on. Before executing step S12, data such as missing values and outliers may be removed from the concentration data acquired in step S11.

As described above, according to the first embodiment, in step S11, the concentration data of the evaluation target is acquired, and in step S12, the concentration value included in the concentration data acquired in step S11 is used to determine the cerebral cardiovascular event for the evaluation target. Evaluate future risk (in short, obtain information to assess future risk of cerebrocardiovascular events for the evaluation target). This makes it possible to provide highly reliable information that can be used as a reference for understanding the risk of future occurrence of cerebrocardiovascular events for the evaluation target.

In step S12, a cerebral cardiovascular event is performed for the evaluation target by calculating the value of the formula using the formula including the concentration value included in the concentration data acquired in step S11 and the variable to which the concentration value is substituted. You may evaluate the risk of future occurrence of.

Further, it may be determined that the concentration value included in the concentration data reflects the risk of future occurrence of a cerebrocardiovascular event for the evaluation target, and further, the concentration value may be determined by the method listed below, for example. It may be transformed and determined that the transformed values reflect the risk of future occurrence of cerebrocardiovascular events for the evaluation subject (as well as the values in the equation). In other words, the concentration value or the converted value itself may be treated as an evaluation result regarding the future risk of occurrence of a cerebrocardiovascular event for the evaluation target (the same applies to the value of the formula). Here, the conversion method will be described below. In the following description, the concentration value is the conversion target, but the same applies to the value of the equation.
The possible range of concentration values is a predetermined range (eg 0.0 to 1.0, 0.0 to 10.0, 0.0 to 100.0, or -10.0). In order to fit within the range up to 10.0, etc.), for example, any value can be added, subtracted, multiplied, or divided with respect to the cardinality value, or the cardinality value can be converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, etc.). Convert the cardinality value by converting it by (angle conversion, square root conversion, probit conversion, inverse number conversion, Box-Cox conversion, or power conversion), or by combining these calculations with the cardinality value. You may. For example, a value of an exponential function with the concentration value as an index and the base of the Napier number (specifically, the probability p that the future risk of occurrence of a cerebrocardiovascular event is a predetermined state (for example, a high risk state) is defined. The value of p / (1-p) when the natural logarithm ln (p / (1-p)) at the time of May be further calculated by dividing 1 by the sum of the values (specifically, the value of the probability p).
Further, the concentration value may be converted so that the converted value under a specific condition becomes a specific value. For example, the concentration value may be converted so that the converted value when the specificity is 40% is 5.0 and the converted value when the specificity is 95% is 8.0.
Further, after the distribution of the concentration value is normally distributed for each amino acid, the concentration value may be converted into a deviation value so that the average is 50 and the standard deviation is 10. The value of the equation may be converted into a deviation value so that the average is 50 and the standard deviation is 10.
In addition, these conversions may be performed by gender or age.

The concentration value in the present specification may be the concentration value itself or the value after converting the concentration value. Further, the value of the expression in the present specification may be the value of the expression itself or may be the value after converting the value of the expression.

In addition, when the position information regarding the position of a predetermined mark on a predetermined indicator visually shown on a display device such as a monitor or a physical medium such as paper is converted into a density value included in the density data or the density value. May be generated using the converted values and it may be determined that the generated location information reflects the risk of future occurrence of cerebrocardiovascular events for the evaluation target (same for the values in the equation). ). The predetermined ruler is for evaluating the risk of future occurrence of a cerebrocardiovascular event, for example, a ruler with a scale, and "a range in which a concentration value or a converted value can be taken". Or, at least the scale corresponding to the upper limit value and the lower limit value in "a part of the range" is shown (the same applies to the value of the formula). Further, the predetermined mark corresponds to the concentration value or the converted value, and is, for example, a circle mark or a star mark (the same applies to the value of the formula).

In addition, the concentration value included in the concentration data is lower than the predetermined value (mean value ± 1SD, 2SD, 3SD, N quantile, N percentile, cutoff value with clinical significance, etc.) or less than the predetermined value. The risk of future occurrence of cerebrocardiovascular events may be assessed for the evaluation target if it is above or above the predetermined value (the same applies to the value in the formula). At that time, the deviation value may be used instead of the concentration value itself. For example, if the deviation value is less than the mean value-2SD (deviation value <30) or if the deviation value is higher than the mean value + 2SD (deviation value> 70), the future of the cerebrocardiovascular event for the evaluation target The risk of occurrence may be evaluated (the same applies to the value of the formula).

In addition, the risk of future occurrence of cerebrocardiovascular events for the evaluation target may be qualitatively evaluated. Specifically, "concentration value contained in the concentration data and one or more preset thresholds" or "concentration value contained in the concentration data, an expression including a variable to which the concentration value is assigned, and preset. Using one or more thresholds, the evaluation target may be classified into one of several categories defined with at least the degree of risk of future occurrence of cerebrocardiovascular events. good. In addition, the multiple categories include a category for belonging a target with a high risk of occurrence, a category for belonging a target with a low risk of occurrence, and a category for belonging a target with a medium risk of occurrence. You may. In addition, the plurality of categories may include a category for belonging a target having a high risk of occurrence and a category for belonging a target having a low risk of occurrence. Further, the concentration value or the value of the formula may be converted, for example, by the method described above, and the evaluation target may be classified into any one of a plurality of categories using the converted value.

The format of the formula used for evaluation is not particularly limited, but may be, for example, the formula shown below.
・ Linear models such as multiple regression equations, linear discrimination equations, principal component analysis, canonical discrimination analysis based on the least squares method ・ Generalized linear models such as logistic regression based on the most probable method ・ Semi-parametric by Cox proportional hazard model, etc. In addition to the risk prediction model based on the survival time analysis method and the generalized linear model, a generalized linear mixed model that considers variable effects such as differences between individuals and facilities, K-means method, and cluster analysis such as hierarchical cluster analysis. Formulas created based on Bayesian statistics such as MCMC (Markov Chain Monte Carlo), Baysian Network, Hierarchical Bayes Method, etc. Expressions created by methods that do not belong to ・ Expressions as shown by the sum of expressions of different forms

Further, the formula used for evaluation is described in, for example, the method described in International Publication No. 2004/052191, which is an international application by the applicant, or International Publication No. 2006/098192, which is an international application by the applicant. You may create it by the method. It should be noted that the formulas obtained by these methods are suitable for evaluating the risk of future occurrence of cerebrocardiovascular events regardless of the unit of the concentration value included in the concentration data as input data. Can be used for.

Here, in the multiple regression equation, the multiple logistic regression equation, the canonical discrimination function, etc., a coefficient and a constant term are added to each variable, but the coefficient and the constant term may be preferably a real number, and more preferably. Is any value that belongs to the range of the 99% confidence interval of the coefficient and the constant term obtained for performing the above-mentioned various classifications from the data, and more preferably, the above-mentioned various classifications are obtained from the data. Any value that belongs to the range of the 95% confidence interval of the given coefficient and constant term may be used. Further, 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 obtained by adding, subtracting, multiplying or dividing an arbitrary real constant. When logistic regression equations, linear discrimination equations, multiple regression equations, etc. are used for evaluation, linear transformations (addition of constants, multiplication of constants) and transformations of monotonous increase (decrease) (for example, logit transformation) change the evaluation performance. However, since it is the same as before the conversion, the one after these conversions may be used.

In the fractional expression, the numerator of the fractional expression is represented by the sum of variables A, B, C, ... And / or the denominator of the fractional expression is the sum of variables a, b, c, ... It is represented by. The fractional expression also includes the sum of the fractional expressions α, β, γ, ... (For example, α + β) having such a configuration. The fractional expression also includes a divided fractional expression. The variables used for the numerator and denominator may have appropriate coefficients. Also, the variables used for the numerator and denominator may be duplicated. Further, an appropriate coefficient may be attached to each minute formula. Further, the coefficient value of each variable and the value of the constant term may be real numbers. A certain fractional expression and the one in which the variable of the molecule and the variable of the denominator are exchanged in the fractional expression generally reverse the positive and negative signs of the correlation with the objective variable, but the correlation between them is maintained. Since the evaluation performance can be regarded as equivalent, the fractional expression includes the variable of the molecule and the variable of the denominator interchanged.

Then, in assessing the risk of future occurrence of cerebrocardiovascular events, in addition to the concentration values included in the concentration data, values related to other biological information shown below may be further used. In addition to the variables to which the concentration values included in the concentration data are assigned, the following values related to other biometric information are assigned to the formula used when evaluating the risk of future occurrence of cerebrocardiovascular events. It may further contain one or more variables. Examples of other biometric information include the following.
Body mass index measurement results (height, weight, abdominal 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 ECG test results
MRI, MRA test results Central blood pressure related index measurement results Carotid echo / advanced glycation end product test results Body fat measurement results Medical interview results (hypertension, dyslipidemia, diabetes, stroke, angina / myocardial infarction, gout, Cancer etc.)
Genome information such as SNPs data and exome sequence data

[Second Embodiment]
[2-1. Outline of the second embodiment]
Here, the outline of the second embodiment will be described with reference to FIG. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. In the description of the second embodiment, the description overlapping with the above-mentioned first embodiment may be omitted. In particular, here, a case where the value of the formula or the value after conversion thereof is used when evaluating the future risk of occurrence of a cerebrocardiovascular event is described as an example, but for example, the concentration value included in the concentration data is described. Alternatively, the converted value (for example, a deviation value) may be used.

The control unit has previously acquired the concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood of the evaluation target (for example, an individual such as an animal or a human). Future occurrence of cerebrocardiovascular events for the evaluation target by calculating the value of the formula using the formula stored in advance in the storage unit including the concentration value included in the concentration data and the variable to which the concentration value is assigned. Evaluate the risk (step S21). This makes it possible to provide highly reliable information that can be used as a reference for understanding the risk of future occurrence of cerebrocardiovascular events.

The formula used in step S21 may be one created based on the formula creation process (steps 1 to 4) described below. Here, an outline of the expression creation process will be described. The process described here is just an example, and the method of creating an expression is not limited to this.

First, the control unit has index state information (data with missing values, outliers, etc.) stored in advance in the storage unit, including concentration data and index data related to indicators indicating the future risk of occurrence of cerebrocardiovascular events. Candidate formulas (for example, y = a1x1 + a2x2 + ... + anxn, y: index data, xi: concentration data, ai: constant, i = 1, 2 , ..., n) is created (step 1).

In step 1, a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination) are performed from the index state information. Multiple candidate formulas may be created in combination with those related to multivariate analysis such as trees. Specifically, a group in which a cerebrocardiovascular event occurs (a group consisting of subjects in which a cerebrocardiovascular event occurs after a certain period of time has passed since blood collection) and a group in which a cerebrocardiovascular event does not occur (a group in which a cerebrocardiovascular event occurs after a certain period of time has passed since blood collection). Multiple groups using multiple different algorithms for index state information, which is multivariate data composed of concentration data and index data obtained by analyzing blood obtained from (a group consisting of subjects who did not occur). Candidate expressions for may be created in parallel. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate expressions. Further, the candidate formula may be created by converting the index state information using the candidate formula created by performing the principal component analysis and performing the discriminant analysis on the converted index state information. As a result, the optimum formula for evaluation can be finally created.

Here, the candidate formula created by using the principal component analysis is a linear formula including each variable that maximizes the variance of all the concentration data. In addition, the candidate formula created using discriminant analysis is a high-order formula (including exponent and logarithm) containing each variable that minimizes the ratio of the sum of the variances in each group to the variance of all concentration data. be. In addition, the candidate expression created using the support vector machine is a high-order expression (including the kernel function) including each variable that maximizes the boundary between the groups. Further, the candidate expression created by using the multiple regression analysis is a high-order expression including each variable that minimizes the sum of the distances from all the concentration data. Further, the candidate expression created by using Cox regression analysis is a linear model including a logarithmic hazard ratio, and is a linear expression including each variable and its coefficient that maximizes the likelihood of the model. The candidate expression created by using logistic regression analysis is a linear model representing the logarithmic odds of the probability, and is a linear expression including each variable that maximizes the likelihood of the probability. In the k-means method, k neighborhoods of each density data are searched, the group to which the nearest neighborhood points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. It is a method to select the variable that best matches the group defined as. Further, the cluster analysis is a method of clustering (grouping) the points at the closest distance among all the concentration data. The decision tree is a method of assigning an order to variables and predicting a group of concentration data from possible patterns of variables whose order is higher.

Returning to the explanation of the expression creation process, the control unit verifies (mutually verifies) the candidate expression created in step 1 based on a predetermined verification method (step 2). The verification of the candidate formula is performed for each candidate formula created in step 1. In step 2, the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (ROC_AUC) of the candidate formula are based on at least one of the bootstrap method, the holdout method, the N-fold method, the leave one-out method, and the like. It may be verified with respect to at least one of (the area under the curve of the receiver characteristic curve) and the like. This makes it possible to create a candidate formula with high predictability or robustness in consideration of index state information and evaluation conditions.

Here, the discrimination rate means that the evaluation target whose true state is negative is correctly evaluated as negative by the evaluation method according to this embodiment, and the evaluation target whose true state is positive is evaluated by the evaluation method according to this embodiment. This is the percentage that is correctly evaluated as positive. The sensitivity is the ratio at which the evaluation target whose true state is positive is correctly evaluated as positive by the evaluation method according to the present embodiment. The specificity is the ratio at which the evaluation target whose true state is negative is correctly evaluated as negative by the evaluation method according to the present embodiment. The Akaike Information Criterion is a standard that indicates how well the observed data matches the statistical model in the case of regression analysis, etc., and is "-2 x (maximum log-likelihood of the statistical model) + 2 x (statistics). The model with the smallest value defined in "Number of free parameters of the model)" is judged to be the best. Further, ROC_AUC is defined as the area under the curve of the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1-specificity, sensitivity) on two-dimensional coordinates, and is defined as ROC_AUC. The value of is 1 in the complete discrimination, and the closer this value is to 1, the higher the discrimination. Further, the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of the candidate formula. Robustness is a variance of discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate expressions.

Returning to the explanation of the expression creation process, the control unit selects the combination of the concentration data included in the index state information used when creating the candidate expression by selecting the variable of the candidate expression based on the predetermined variable selection method. (Step 3). In step 3, the variable may be selected for each candidate formula created in step 1. This makes it possible to appropriately select the variables of the candidate expression. Then, step 1 is executed again using the index state information including the concentration data selected in step 3. Further, in step 3, a variable of the candidate expression 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. The best path method is a method of selecting variables by sequentially reducing the variables included in the candidate expression one by one and optimizing the evaluation index given by the candidate expression.

Returning to the explanation of the formula creation process, the control unit repeatedly executes the above-mentioned steps 1, 2 and 3 and, based on the verification results accumulated by this, is a candidate to be used for evaluation from among a plurality of candidate formulas. By selecting the formula, the formula used for the evaluation is created (step 4). In addition, in the selection of the candidate expression, for example, there are a case where the optimum one is selected from the candidate expressions created by the same expression creation method and a case where the optimum one is selected from all the candidate expressions.

As described above, in the expression creation process, the process related to the creation of the candidate expression, the verification of the candidate expression, and the selection of the variable of the candidate expression is systematized (systematized) in a series of flows based on the index state information. By doing so, it is possible to create an optimal formula for assessing the risk of future occurrence of cerebrocardiovascular events. In other words, in the formula creation process, the blood concentration of amino acids is used for multivariate statistical analysis, and variable selection methods and cross-validation are combined to select the optimal and robust set of variables, resulting in a formula with high evaluation performance. To extract.

[2-2. Configuration of the second embodiment]
Here, the configuration of the evaluation system (hereinafter, may be referred to as this system) according to the second embodiment will be described with reference to FIGS. 3 to 14. The present system is merely an example, and the present invention is not limited thereto. In particular, here, a case where the value of the formula or the value after conversion thereof is used when evaluating the future risk of occurrence of a cerebrocardiovascular event is described as an example, but for example, the concentration value included in the concentration data is described. Alternatively, the converted value (for example, a deviation value) may be used.

First, the overall configuration of this system will be described with reference to FIGS. 3 and 4. FIG. 3 is a diagram showing an example of the overall configuration of this system. Further, FIG. 4 is a diagram showing another example of the overall configuration of this system. As shown in FIG. 3, the present system includes an evaluation device 100 for evaluating the future risk of occurrence of a cerebrocardiovascular event for an individual to be evaluated, and a client device 200 (terminal device of the present invention) for providing concentration data of an individual. Corresponds to) and is configured to be communicable via the network 300.

In this system, the client device 200 that is the source of the data used for the evaluation and the client device 200 that is the destination of the evaluation result may be different. As shown in FIG. 4, this system is a database device that stores index state information used when creating an expression in the evaluation device 100, an expression used in the evaluation, and the like, in addition to the evaluation device 100 and the client device 200. The 400 may be connected and configured so as to be communicable via the network 300.

Next, the configuration of the evaluation device 100 of this system will be described with reference to FIGS. 5 to 11. FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of the present system, and conceptually shows only the portion of the configuration related to the present invention.

The evaluation device 100 controls the evaluation device via a control unit 102 such as a CPU (Central Processing Unit) that collectively controls the evaluation device, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. It is composed of a communication interface unit 104 that is communicably connected to the network 300, a storage unit 106 that stores various databases, tables, files, and the like, and an input / output interface unit 108 that is connected to the input device 112 and the output device 114. These parts are connected so as to be communicable via an arbitrary communication path. Here, the evaluation device 100 may be configured in the same housing as various analyzers (for example, amino acid analyzers and the like). For example, in a small analyzer equipped with a configuration (hardware and software) that calculates (measures) the concentration value of amino acids in blood and outputs the calculated value (printing, monitor display, etc.), the evaluation unit 102d described later is used. Further, it may be characterized in that the result obtained by the evaluation unit 102d is output using the above configuration.

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 another terminal via a communication line.

The input / output interface unit 108 is connected to the input device 112 and the output device 114. Here, as the output device 114, a speaker or a printer can be used in addition to a monitor (including a home television) (in the following, the output device 114 may be described as the monitor 114). In addition to the keyboard, mouse, and microphone, the input device 112 can use a monitor that realizes a pointing device function in cooperation with the mouse.

The storage unit 106 is a storage means, and 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, or the like can be used. A computer program for giving instructions to the CPU and performing various processes in cooperation with the OS (Operating System) is recorded in the storage unit 106. As shown in the figure, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression-related information database 106d, and an evaluation result file 106e.

The density data file 106a stores the density data. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. As shown in FIG. 6, the information stored in the concentration data file 106a is configured by correlating the individual number for uniquely identifying the individual (sample) to be evaluated and the concentration data. Here, in FIG. 6, the concentration data is treated as a numerical value, that is, 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 giving an arbitrary numerical value to each state. Further, the concentration data may be combined with values related to other biological information (see above).

Returning to FIG. 5, the index state information file 106b stores the index state information used when creating the expression. 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 status information file 106b is an index related to an individual number and an index (index T1, index T2, index T3 ...) Representing the future risk of occurrence of a cerebrocardiovascular event. The data (T) and the concentration data are configured in association with each other. Here, in FIG. 7, the index data and the concentration data are treated as numerical values (that is, continuous scales), but the index data and 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 giving an arbitrary numerical value to each state. In addition, the index data is a known index or the like that serves as a marker for the future risk of occurrence of a cerebrocardiovascular event, and numerical data may be used.

Returning to FIG. 5, the designated index state information file 106c stores the index state information designated by the designated unit 102b, which will be described later. FIG. 8 is a diagram showing an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by correlating the individual number, the designated index data, and the designated concentration data with each other.

Returning to FIG. 5, the formula-related information database 106d is composed of a formula file 106d1 that stores the formula created by the formula creation unit 102c described later. The expression file 106d1 stores the expression used at the time of 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 expression file 106d1 mutually includes the rank, the expression, the threshold value corresponding to each expression creation method, and the verification result of each expression (for example, the value of each expression). It is associated and configured.

Returning to FIG. 5, the evaluation result file 106e stores the evaluation results obtained by the evaluation unit 102d, which will be 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 includes the individual number, the concentration data of the individual, and the evaluation result regarding the future risk of occurrence of a cerebrocardiovascular event (for example, the value of the formula calculated by the calculation unit 102d1 described later, which will be described later. The value after conversion by the conversion unit 102d2, the position information generated by the generation unit 102d3 described later, the classification result obtained by the classification unit 102d4 described later, etc.) are associated with each other.

Returning to FIG. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and various information processing is performed based on these programs. To execute. As shown in the figure, the control unit 102 is roughly divided into an acquisition unit 102a, a designation unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 removes data having missing values, removes data having many outliers, and data having missing values with respect to the index state information transmitted from the database device 400 and the density data transmitted from the client device 200. It also performs data processing such as removal of variables with many.

The acquisition unit 102a acquires information (specifically, concentration data, index state information, formula, etc.). For example, the acquisition unit 102a acquires 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. May be done. The acquisition 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. Further, for example, when the evaluation device 100 includes a mechanism (including hardware and software) for reading out the information recorded on the recording medium, the acquisition unit 102a may use the information recorded on the recording medium (specifically, the information on the recording medium). Specifically, the information may be acquired by reading out the concentration data, the index state information, the formula, etc.) via the mechanism. The designation unit 102b designates index data and density data to be targeted when creating an expression.

The formula creation unit 102c creates a formula 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 expression is stored in a predetermined storage area of the storage unit 106 in advance, the expression creating unit 102c may create the expression by selecting a desired expression from the storage unit 106. Further, the formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, a database device 400) in which the formula is stored in advance.

The evaluation unit 102d uses 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. By calculating the value of, the risk of future occurrence of cerebrocardiovascular events is evaluated for the individual. The evaluation unit 102d may evaluate the risk of future occurrence of cerebrocardiovascular events for an individual by using the concentration value included in the concentration data or the converted value (for example, deviation value) of the concentration value.

Here, the configuration of the evaluation unit 102d will be described with reference to FIG. FIG. 11 is a block diagram showing the configuration of the evaluation unit 102d, and conceptually shows only the portion 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.

The calculation unit 102d1 calculates the value of the formula using a formula including at least the concentration value included in the concentration data and the variable to which the density value is assigned. The evaluation unit 102d may store the value of the formula calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 by, for example, the above-mentioned conversion method. The evaluation unit 102d may store the value after conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e. Further, the conversion unit 102d2 may convert the density value included in the density data by, for example, the above-mentioned conversion method.

The generation unit 102d3 uses the value of the formula calculated by the calculation unit 102d1 or the conversion unit 102d2 to obtain position information regarding the position of a predetermined mark on a predetermined ruler visually shown on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion by (the concentration value or the value after conversion of the concentration value may be used). The evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value) to obtain an individual for a cerebrocardiovascular event. Classify into one of multiple categories defined with at least the degree of risk of future occurrence.

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

The transmission unit 102f transmits the evaluation result to the client device 200 that is the transmission source of the density data of the individual, and transmits the formula and the evaluation result created by the evaluation device 100 to the database device 400. The transmission unit 102f may transmit the evaluation result to the client device 200 different from the client device 200 from which the data used for the evaluation is transmitted.

Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 12 is a block diagram showing an example of the configuration of the client device 200 of the present system, and conceptually shows only the portion of the configuration related to the present invention.

The client device 200 is composed of a control unit 210, a ROM 220, an 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 each of these units is via an arbitrary communication path. Is connected so that communication is possible. The client device 200 is an information processing device (for example, a known personal computer, a workstation, a home-use game device, an Internet TV, a PDS (Personal Handyphone System)) to which peripheral devices such as a printer, a monitor, and an image scanner are connected as needed. It may be based on a terminal, a mobile terminal, a mobile communication terminal, an information processing terminal such as a PDA (Personal Digital Assistant), or the like).

The input device 250 is a keyboard, a mouse, a microphone, or the like. The monitor 261 described later also realizes the pointing device function in cooperation with the 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, a speaker or the like may be provided in the output device 260. The input / output IF 270 is connected to the input device 250 and the output device 260.

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), a router, and a telephone line, or via a dedicated line. As a result, the client device 200 can access the evaluation device 100 according to a predetermined communication standard.

The control unit 210 includes a reception unit 211 and a transmission 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 transmission unit 212 transmits various information such as individual concentration data to the evaluation device 100 via the communication IF 280.

The control unit 210 may be realized by a CPU and a program that interprets and executes all or any part of the processing performed by the control unit by the CPU and the CPU. A computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the ROM 220 or 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. Further, the computer program may be recorded in an application program server connected to the client device 200 via an arbitrary network, and the client device 200 may download all or a part thereof as needed. .. Further, all or any part of the processing performed by the control unit 210 may be realized by hardware using wired logic or the like.

Here, the control unit 210 has 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 function as that of the evaluation unit 102d provided in the evaluation device 100. May be provided. When the control unit 210 is provided with the evaluation unit 210a, the evaluation unit 210a uses the conversion unit 210a2 to obtain the value of the equation according to the information included in the evaluation result transmitted from the evaluation device 100. The concentration value may be converted), the generation unit 210a3 may generate the position information corresponding to the value of the formula or the converted value (the concentration value or the converted value of the concentration value), or the classification unit 210a4. Individuals may be classified into any one of a plurality of categories using the value of the formula or the converted value (which may be the concentration value or the converted value of the concentration value).

Next, the network 300 of this system will be described with reference to FIGS. 3 and 4. The network 300 has a function of communicably connecting the evaluation device 100, the client device 200, and the database device 400, for example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like. Is. The network 300 includes VAN (Value-Added Network), personal computer communication network, public telephone network (including both analog / digital), dedicated line network (including both analog / digital), and CATV (). Community Antenna TeleVision) network, mobile line exchange network or mobile packet exchange network (IMT (International Mobile Telecommunication) 2000 system, GSM (registered trademark) (Global System for Mobile Communications) system or PDCell (PD) (Including methods, etc.), wireless calling networks, local wireless networks such as Bluetooth (registered trademark), PHS networks, satellite communication networks (CS (Communication Satellite), BS (Roadcasting Satellite), or ISDB (Integrated Services Digital Broadcast). ) Etc.) may be used.

Next, the configuration of the database device 400 of this system will be described with reference to FIG. FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of the present system, and conceptually shows only the portion of the configuration related to the present invention.

The database device 400 has a function of storing index state information used when creating a formula in the evaluation device 100 or the database device, a formula created by the evaluation device 100, an evaluation result obtained by the evaluation device 100, and the like. As shown in FIG. 13, the database device 400 is connected to the database via a control unit 402 such as a CPU that collectively controls the database device, a communication device such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that connects the device to the network 300 so that it can communicate, a storage unit 406 that stores various databases, tables, files (for example, files for Web pages), and an input / output device 412 or an output device 414 that is connected to the input device 412 or the output device 414. It is composed of an output interface unit 408, and each of these units is connected so as to be communicable via an arbitrary communication path.

The storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage 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 another terminal via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, as the output device 414, a speaker or a printer can be used in addition to a monitor (including a home television). Further, as the input device 412, a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.

The control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into 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 receiving unit 402b receives various information such as an expression and an evaluation result transmitted from the evaluation device 100.

In this description, the evaluation device 100 performs the process of acquiring the concentration data, calculating the value of the formula, classifying the individual into categories, and transmitting the evaluation result, and the client device 200 receives the evaluation result. However, when the client device 200 is provided with the evaluation unit 210a, it is sufficient for the evaluation device 100 to execute the calculation of the value of the expression, for example, the conversion of the value of the expression and the position information. Processing such as generation and classification of individuals may be appropriately shared between the evaluation device 100 and the client device 200.
For example, when the client device 200 receives the value of the expression from the evaluation device 100, the evaluation unit 210a converts the value of the expression by the conversion unit 210a2, or the value of the expression or the value after conversion by the generation unit 210a3. The position information corresponding to the above may be generated, or the individual may be classified into any one of a plurality of categories by using the value of the formula or the value after conversion in the classification unit 210a4.
When the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value by the generation unit 210a3, or the classification unit 210a4 converts the value. Individuals may be classified into any one of a plurality of categories using the later values.
Further, when the client device 200 receives the value of the formula or the converted value and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the formula or the converted value in the classification unit 210a4. Individuals may be classified into any one of a plurality of categories.

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

Further, among the processes described in the second embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed. It is also possible to automatically perform all or part of the above by a known method.

In addition, processing procedures, control procedures, specific names, information including parameters such as registration data and search conditions for each processing, screen examples, and database configurations shown in the above documents and drawings are not specified unless otherwise specified. Can be changed arbitrarily.

Further, with respect to the evaluation device 100, each component shown in the figure is functional and conceptual, and does not necessarily have to be physically configured as shown in the figure.

For example, with respect to the processing functions included in the evaluation device 100, particularly each processing function performed by the control unit 102, even if all or any part thereof is realized by the CPU and a program interpreted and executed by the CPU. Well, it may be realized as hardware by wired logic. The program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing the information processing apparatus to execute the evaluation method or calculation method according to the present invention, and is evaluated as necessary. It is read mechanically by the device 100. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in a storage unit 106 such as a ROM or an HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and cooperates with the CPU to form a control unit.

Further, this computer program may be stored in an application program server connected to the evaluation device 100 via an arbitrary network, and all or part of the computer program may be downloaded as needed.

Further, the evaluation program or calculation program according to the present invention may be stored in a non-temporary computer-readable recording medium, or may be configured as a program product. Here, the "recording medium" includes a memory card, a USB (Universal Serial Bus) memory, an SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), and an EPROM (Emply). Erasable and Program Read Only Memory (registered trademark), CD-ROM (Compact Disk Read Only Memory), MO (Magnet-Optical disk), MO (Magnet-Optical disc), DVD (Digital Digital), etc. It shall include any "portable physical medium".

Further, the "program" is a data processing method described in any language or description method, regardless of the format such as source code or binary code. The "program" is not necessarily limited to a single program, but is distributed as multiple modules or libraries, or cooperates with a separate program represented by the OS to achieve its function. Including things. It should be noted that well-known configurations and procedures can be used for the specific configuration and reading procedure for reading the recording medium and the installation procedure after reading in each device shown in the embodiment.

Various databases and the like stored in the storage unit 106 are memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and storage means such as optical disks, and are used for various processes and website provision. Stores programs, tables, databases, files for web pages, etc.

Further, the evaluation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which an arbitrary peripheral device is connected. Further, the evaluation device 100 may be realized by mounting software (including a program or data) that realizes the evaluation method or calculation method of the present invention on the information processing device.

Furthermore, the specific form of distribution / integration of the device is not limited to the one shown in the figure, and all or part of the device may be functionally or physically in any unit according to various additions or functional loads. It can be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

A medical examination was conducted on local residents from 2008 to 2010, and the blood free amino acid concentration was measured from the blood collected at that time by the above-mentioned amino acid analysis method (A). The longest follow-up survey of this population of residents for the occurrence of cerebrovascular events (stroke (cerebral infarction, intracerebral hemorrhage, subarachnoid hemorrhage), myocardial infarction, or PCI) from the time of medical examination to 2018 I went there for 10 years. Of this group, 4039 residents who had blood drawn in the morning and had a medical examination at least 8 hours after eating a meal were included. Of these, 64 out of 4039 had events, 32 had stroke, 22 had myocardial infarction, and 10 had PCI.

The measured blood free amino acid concentration is substituted into the formula (three formulas shown below) for determining the presence or absence of a cardiovascular event described in Patent Document 1 (Japanese Unexamined Patent Publication No. 2013-178238), and is linear. Obtained the value of the predictor. Hazard ratios (HR / SD) and C-index per standard deviation of the obtained linear predictor values for the occurrence of cerebrocardiovascular events were calculated. The calculation result is shown in FIG. Here, C-index is an index showing the accuracy of prognosis prediction proposed by Harrell et al., And is a non-type indicating how much the magnitude relationship between the event occurrence probability predicted from the model and the actual event occurrence probability matches. It is a parametric index.
Logistic regression equation: 0.3452-0.009171 * Ser-0.01119 * Thr + 0.0065555 * Lys + 0.0167 * 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 Hazard Model: 0.0042 * Pro-0.0133 * Thr + 0.0075 * Lys + 0.0137 * Cit-0.0279 * Trp

Further, it is 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) 2 All combinations of one amino acid 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 value of the linear predictor of the model. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. The combinations of the two amino acids showing a higher value of HR / SD or C-index than the value shown in FIG. 14 are shown in FIGS. 15 to 17. 15 to 17 show the combination of two amino acids and HR / SD in a Cox proportional hazard model consisting of two amino acids in which the value of HR / SD or C-index is higher than the value shown in FIG. And the values of C-index are shown.

Among the population described in Example 1, 5479 inhabitants who collected blood in the morning or afternoon and performed a medical examination in 2008 or 2009 after 8 hours or more after eating a meal were included. Of these, 104 out of 5479 had events, 50 had stroke, 35 had myocardial infarction, and 19 had PCI.

The measured blood free amino acid concentration was substituted into the three equations described in Patent Document 1 described in Example 1 to obtain the value of the linear predictor. The values of the obtained linear predictors were calculated for HR / SD and C-index for the occurrence of cerebrocardiovascular events. The calculation result is shown in FIG.

Further, all combinations of the two amino acids selected from the 19 kinds of amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each combination, and the linear predictor of the model is used. I got the value of. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. 19 and 20 show combinations of two amino acids that have higher HR / SD or C-index values than those shown in FIG. 19 and 20 show the combination of two amino acids and the combination of two amino acids in a Cox proportional hazard model consisting of two amino acids with a higher HR / SD or C-index value than shown in FIG. And the values of C-index are shown.

The subjects were the 4039 inhabitants described in Example 1.

All combinations of 2 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 measured in blood. The free amino acid concentration was substituted into the Cox proportional hazard model corresponding to each extracted combination to obtain the value of the linear predictor of the model. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. 21 and 22 show combinations of two amino acids that have higher HR / SD or C-index values than those shown in FIG. 21 and 22 show the combination of two amino acids in a Cox proportional hazard model consisting of two amino acids with a higher HR / SD or C-index than the value shown in FIG. 14, and HR / SD and C. The value of -index is shown.

The 5479 inhabitants described in Example 2 were targeted.

All combinations of the two amino acids selected from the above 13 amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination, and the linear predictor of the model is used. I got the value of. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. A combination of two amino acids showing a higher HR / SD or C-index value than the value shown in FIG. 18 is shown in FIG. 23. FIG. 23 shows the combination of two amino acids in a Cox proportional hazard model consisting of two amino acids with HR / SD or C-index higher than the value shown in FIG. 18, and HR / SD and C-index. The value is shown.

The subjects were the 4039 inhabitants described in Example 1.

All combinations of the three amino acids selected from the 19 amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination, and the linear predictor of the model is used. I got the value of. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. FIG. 24 shows a combination of three amino acids showing a higher HR / SD or C-index value than the values shown in FIGS. 21 and 22. FIG. 24 shows a combination of three amino acids and HR / SD and C in a Cox proportional hazard model consisting of three amino acids in which HR / SD or C-index is higher than the values shown in FIGS. 21 and 22. The value of -index is shown.

The 5479 inhabitants described in Example 2 were targeted.

All combinations of the three amino acids selected from the 19 amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination, and the linear predictor of the model is used. I got the value of. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. FIG. 25 shows a combination of three amino acids showing a higher HR / SD or C-index value than the value shown in FIG. 23. FIG. 25 shows a combination of three amino acids in a Cox proportional hazard model consisting of three amino acids in which HR / SD or C-index is higher than the value shown in FIG. 23, and HR / SD and C-index. It is shown.

The subjects were the 4039 inhabitants described in Example 1.

All combinations of the three amino acids selected from the above 13 kinds of amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination to obtain the linear predictor of the model. Got a value. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. FIG. 26 shows a combination of three amino acids showing a higher HR / SD or C-index value than the value shown in FIG. 24. FIG. 26 shows a combination of three amino acids in a Cox proportional hazard model consisting of three amino acids in which HR / SD or C-index is higher than the value shown in FIG. 24, and HR / SD and C-index. The value is shown.

The 5479 inhabitants described in Example 2 were targeted.

All combinations of the three amino acids selected from the above 13 kinds of amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination, and the linear predictor of the model is used. I got the value of. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. FIG. 27 shows a combination of three amino acids showing a higher HR / SD or C-index value than the value shown in FIG. 25. FIG. 27 shows a combination of three amino acids in a Cox proportional hazard model consisting of three amino acids with HR / SD or C-index higher than the value shown in FIG. 25, as well as HR / SD and C-index. It is shown.

The subjects were the 4039 inhabitants described in Example 1.

All combinations of 4 or more and 6 or less amino acids selected from the above 13 kinds of amino acids are extracted, and the measured blood free amino acid concentration is substituted into the Cox proportional hazard model corresponding to each extracted combination as a model. The value of the linear predictor of is obtained. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. FIG. 28 shows a combination of 4 or more and 6 or less amino acids showing a value higher in HR / SD or C-index than the value shown in FIG. 26. FIG. 28 shows a combination of 4 or more and 6 or less amino acids in a Cox proportional hazard model consisting of 4 or more and 6 or less amino acids in which HR / SD or C-index is higher than the value shown in FIG. 26. And the values of HR / SD and C-index are shown.

The 5479 inhabitants described in Example 2 were targeted.

All combinations of 4 or more and 6 or less amino acids selected from the above 13 kinds of amino acids were extracted, and the measured blood free amino acid concentration was substituted into the Cox proportional hazard model corresponding to each extracted combination. The values of the linear predictors of the model were obtained. HR / SD and C-index were calculated for the values of the obtained linear predictors for the presence or absence of cerebrocardiovascular events during the follow-up period. The combinations of 4 or more and 6 or less amino acids showing a value higher in HR / SD or C-index than the value shown in FIG. 27 are shown in FIGS. 29 to 33. 29 to 33 show 4 or more and 6 or less in a Cox proportional hazard model consisting of 4 or more and 6 or less amino acids in which HR / SD or C-index is higher than the value shown in FIG. 27. Amino acid combinations and HR / SD and C-index are shown.

The subjects were the 4039 inhabitants described in Example 1.

The 40% and 95% points of the linear predictor values of the model described in Example 9 were set as cutoff values, respectively, and the subjects were ranked. Specifically, subjects having a linear predictor value of less than 40% were classified into rank A, subjects having a linear predictor value of 40% or more and less than 95% were classified into rank B, and subjects having a linear predictor value of 95% or more were classified into rank C.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subject of rank B to the subject of rank A and the subject of rank A regarding the occurrence of cerebrocardiovascular events during the follow-up period. The relative risk of the subject corresponding to rank C was calculated.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subject of rank B to the subject of rank A and the relative risk of the subject of rank A with respect to the occurrence of cerebrovascular events during the follow-up period. The relative risk of subjects corresponding to rank C was calculated.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subjects corresponding to rank B to the subjects corresponding to rank A and the subjects corresponding to rank A regarding the occurrence of cardiovascular events during the follow-up period. The relative risk of subjects corresponding to rank C was calculated.

The calculated relative risk is shown in FIG. FIG. 34 shows the subjects corresponding to rank B and rank A with respect to the subjects corresponding to rank A when the subjects were ranked based on the values of the linear predictors of the Cox proportional hazard model described in Example 9. The relative risks associated with the occurrence of cerebrocardiovascular events, the relative risks associated with the occurrence of cerebrovascular events, and the relative risks associated with the occurrence of cardiovascular events are shown for subjects of rank C with respect to the relevant subjects.

The 5479 inhabitants described in Example 2 were targeted.

The 40% and 95% points of the linear predictor values of the model described in Example 10 were set as cutoff values, respectively, and the subjects were ranked. Specifically, subjects having a linear predictor value of less than 40% were classified into rank A, subjects having a linear predictor value of 40% or more and less than 95% were classified into rank B, and subjects having a linear predictor value of 95% or more were classified into rank C.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subject of rank B to the subject of rank A and the subject of rank A regarding the occurrence of cerebrocardiovascular events during the follow-up period. The relative risk of the subject corresponding to rank C was calculated.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subject of rank B to the subject of rank A and the relative risk of the subject of rank A with respect to the occurrence of cerebrovascular events during the follow-up period. The relative risk of subjects corresponding to rank C was calculated.

For each combination of 4 or more and 6 or less amino acids, the relative risk of the subjects corresponding to rank B to the subjects corresponding to rank A and the subjects corresponding to rank A regarding the occurrence of cardiovascular events during the follow-up period. The relative risk of subjects corresponding to rank C was calculated.

The calculated relative risks are shown in FIGS. 35 to 43. 35 to 43 show the subjects corresponding to rank B with respect to the subjects corresponding to rank A when the subjects were ranked based on the values of the linear predictors of the Cox proportional hazard model described in Example 10. The relative risk for the occurrence of a cerebrocardiovascular event, the relative risk for the occurrence of a cerebrovascular event, and the relative risk for the occurrence of a cardiovascular event are shown for the subject corresponding to the rank A with respect to the subject corresponding to the rank A.

As described above, the present invention can be widely implemented in many industrial fields, especially in fields such as pharmaceuticals, foods, and medical treatments, and in particular, bioinformatics for predicting the risk of future occurrence of cerebrocardiovascular events. Extremely useful in the field.

100 Evaluation device (including calculation device)
102 Control unit 102a Acquisition unit 102b Designation unit 102c Expression creation unit 102d Evaluation unit 102d1 Calculation unit 102d2 Conversion unit 102d3 Generation unit 102d4 Classification unit 102e Result output unit 102f Transmission unit 104 Communication interface unit 106 Storage unit 106a Concentration data file 106b Index status information File 106c Designated index status information file 106d Expression-related information database 106d1 Expression file 106e Evaluation result file 108 Input / output interface unit 112 Input device 114 Output device 200 Client device (terminal device (information and communication terminal device))
300 network 400 database device

Claims (19)

The concentration value of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or the formula including the variable to which the concentration value is substituted and the concentration value. An evaluation method comprising an evaluation step for evaluating the future occurrence risk of a cerebrocardiovascular event for the evaluation target by using the value of the above formula calculated using the above method. The evaluation method according to claim 1, wherein the at least two amino acids contain Gly. The evaluation method according to claim 1 or 2, wherein the at least two amino acids contain Cit and / or Trp. The evaluation method according to any one of claims 1 to 3, wherein the at least two amino acids contain Met and / or Val. The evaluation method according to any one of claims 1 to 4, wherein the evaluation step is executed in the control unit of the information processing apparatus including the control unit. Future occurrences of cerebrocardiovascular events containing concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated and variables to which the concentration values are substituted. A calculation method comprising a calculation step for calculating the value of the formula using a formula for evaluating risk. The calculation method according to claim 6, wherein the at least two amino acids contain Gly. The calculation method according to claim 6 or 7, wherein the at least two amino acids contain Cit and / or Trp. The calculation method according to any one of claims 6 to 8, wherein the at least two amino acids contain Met and / or Val. The calculation method according to any one of claims 6 to 9, wherein the calculation step is executed in the control unit of the information processing apparatus including the control unit. An evaluation device equipped with a control unit
The control unit
The concentration value of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or the formula including the variable to which the concentration value is substituted and the concentration value. An evaluation device comprising an evaluation means for evaluating the future occurrence risk of a cerebrocardiovascular event for the evaluation target by using the value of the above formula calculated using the above. Communicatably connected via a network to a terminal device that provides concentration data for the concentration value or the value of the formula.
The control unit
A data receiving means for receiving the density data transmitted from the terminal device or the value of the formula.
A result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device, and a result transmission means.
Further prepare
The evaluation device according to claim 11, wherein the evaluation means uses the concentration value included in the concentration data received by the data receiving means or the value of the formula. It is a calculation device equipped with a control unit.
The control unit
Future occurrences of cerebrocardiovascular events containing concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated and variables to which the concentration values are substituted. A calculation device comprising a calculation means for calculating the value of the formula using a formula for evaluating risk. It is an evaluation program to be executed in an information processing device equipped with a control unit.
To be executed in the control unit
The concentration value of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or the formula including the variable to which the concentration value is substituted and the concentration value. An evaluation program comprising an evaluation step for evaluating the risk of future occurrence of a cerebrocardiovascular event for the evaluation target using the value of the above formula calculated using the above. It is a calculation program to be executed in an information processing device equipped with a control unit.
To be executed in the control unit
Future occurrences of cerebrocardiovascular events containing concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated and variables to which the concentration values are substituted. A calculation program comprising a calculation step of calculating the value of the formula using a formula for assessing risk. A computer-readable recording medium on which the program according to claim 14 or 15 is recorded. It is an evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit so as to be able to communicate with each other via a network.
The control unit of the terminal device
Concentration data on the concentration values of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or an expression including a variable to which the concentration value is assigned and the above. A data transmission means for transmitting the value of the formula calculated using the concentration value to the evaluation device, and
A result receiving means for receiving an evaluation result regarding the future risk of occurrence of a cerebrocardiovascular event transmitted from the evaluation device, and a result receiving means.
Equipped with
The control unit of the evaluation device
A data receiving means for receiving the density data transmitted from the terminal device or the value of the formula.
An evaluation means for evaluating the future risk of a cerebrocardiovascular event for the evaluation target by using the concentration value included in the concentration data received by the data receiving means or the value of the formula.
A result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device, and a result transmission means.
An evaluation system characterized by being equipped with. It is a terminal device equipped with a control unit.
The control unit
Equipped with a result acquisition method to acquire evaluation results regarding the risk of future occurrence of cerebrocardiovascular events.
The evaluation result is an expression including a concentration value of at least two amino acids of Gly, Cit, Trp, Met, Val, His, Pro and Tyr in the blood to be evaluated, or a variable to which the concentration value is substituted. And a terminal device characterized in that it is the result of evaluating the future occurrence risk of a cerebrocardiovascular event for the evaluation target by using the value of the formula calculated by using the concentration value. It is communicably connected via a network to an evaluator that assesses the risk of future occurrence of cerebrocardiovascular events.
The control unit includes data transmission means for transmitting concentration data related to the concentration value or the value of the formula to the evaluation device.
The terminal device according to claim 18, wherein the result acquisition means receives the evaluation result transmitted from the evaluation device.

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