WO2022009991A1 - Method for evaluating mild cognitive impairment, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system, and terminal device - Google Patents

Abstract

The present invention addresses the problem of providing an evaluation method, etc., whereby it is possible to provide highly reliable information that can aid in ascertaining a state of mild cognitive impairment. In the present embodiment, a state of mild cognitive impairment in an evaluation subject is evaluated using the concentration value of at least two amino acids from among Cit, Lys, Ser, Thr, and Trp in blood of the evaluation subject, or using a formula including a variable for which the concentration value is substituted and the value of the formula calculated using the concentration value.　

Description

Evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system and terminal device for mild cognitive impairment

The present invention relates to an evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system, and terminal device for Mild Cognitive Impairment (hereinafter referred to as "MCI"). It is a thing.

MCI is a condition that is considered to be a pre-stage or borderline case of various dementias that has problems with cognitive function compared to normal age-appropriate aging but does not interfere with daily life and does not lead to a diagnosis of dementia. Point to. Currently, two main diagnostic criteria have been proposed and widely accepted for the diagnosis of MCI (Non-Patent Documents 1 and 2). In addition to AD (Alzheimer's Disease), there are various background diseases of MCI such as Lewy body dementia (DLB), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD) and senile depression. In some cases, appropriate intervention may reduce the risk of developing dementia. Therefore, by providing a simple screening method that can discriminate MCI, it is possible to increase the possibility of providing medical institution consultations and preventive / treatment opportunities for more people at an earlier stage, and it is recognized. It is expected to contribute to the prevention of an increase in the number of people with dementia.

However, the neuropsychological test Mini Mental State Examination (hereinafter referred to as "MMSE"), which is currently widely used as a screening test method for dementia, and the revised Hasegawa simple intelligence evaluation scale (hereinafter referred to as "HDS-R"). ) And Alzheimer's Disease Assessment Scale-Cognitive (hereinafter referred to as "ADAS-cog"), etc. are applied to the screening of MCI, the detection accuracy is lower than the judgment of dementia, and the examination time is reduced. The problems are that the throughput is low due to the long length, and that the inspector requires skillful skills. As a screening test method for MCI, a new simple test technique that assists the conventional neuropsychological test is required.

As a technique for discriminating MCI by a blood test, a technique for measuring the peptide fragment concentration in blood and discriminating MCI using it as an index is known (Non-Patent Document 3). Further, AD determination and MCI determination techniques by quantitative analysis of amino acids and amino acid-related metabolites in blood are known (Patent Documents 1 and 2).

International Publication No. 2018/008764 International Publication No. 2020/067386

However, regarding the highly accurate MCI determination technology using the blood concentration of amino acids and metabolites including amino acid-related metabolites as an index, in order to meet the requirements in actual clinical practice when the practical application is taken into consideration. There was a problem that it was necessary to improve the accuracy.

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

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 out of five kinds of amino acids (Cit, Lys, Ser, Thr and Trp) in the blood to be evaluated. The evaluation step includes an evaluation step of evaluating the state of MCI for the evaluation target by using the concentration value of the above or the expression including the variable to which the concentration value is substituted and the value of the expression calculated by using the concentration value. It is characterized by that.

Here, various amino acids are mainly abbreviated in the present 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, in the evaluation method according to the present invention, the concentration value is the concentration value of at least three amino acids, and the at least three amino acids are Ser, Lys and Trp, Ser, Cit and Lys, Cit, Lys and Trp. It is characterized by including Ser, Thr and Lys, Thr, Lys and Trp, Thr, Cit and Lys, Ser, Thr and Cit, Ser, Thr and Trp, Thr, Cit and Trp, or Ser, Cit and Trp. do.

Further, in the evaluation method according to the present invention, the concentration value is the concentration value of at least 6 amino acids, and the at least 6 amino acids are Ser, Thr, Ala, Cit, Lys and Trp, Ser, Thr, Cit. Met, Lys and Trp, Ser, Gln, Cit, Val, Met and Lys, Ser, Gln, Cit, Met, Lys and Leu, Ser, Thr, Cit, Tyr, Lys and Trp, Ser, Cit, Tyr, Met, It comprises Lys and Trp, or Ser, Thr, Cit, Orn, Lys and Trp.

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 provided with the control unit.

Further, the calculation method according to the present invention evaluates the concentration value of at least two amino acids among the five kinds of amino acids in the blood to be evaluated, and the state of MCI including the variable to which the concentration value is substituted. It is characterized by including a calculation step for calculating the value of the above formula using the formula for.

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 provided with the control unit.

Further, the evaluation device according to the present invention is an evaluation device including a control unit, wherein the control unit has a concentration value of at least two amino acids among the five types of amino acids in the blood to be evaluated, or the above-mentioned. It is characterized in that an evaluation means for evaluating the state of MCI is provided for the evaluation target by using an expression including a variable to which the concentration value is assigned and the value of the expression calculated by using the concentration value.

Further, the evaluation device according to the present invention is communicably connected to the terminal device that provides the concentration value or the value of the above formula via a network, and the control unit controls the concentration value transmitted from the terminal device. Alternatively, the data receiving means for receiving the value of the above 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 with the data receiving means. It is characterized by using the above-mentioned concentration value or the above-mentioned value of the above formula.

Further, the calculation device according to the present invention is a calculation device including a control unit, wherein the control unit has a concentration value of at least two amino acids among the five types of amino acids in the blood to be evaluated, and the said. It is characterized by comprising a calculation means for calculating the value of the equation by using an equation for evaluating the state of MCI including a variable to which the concentration value is assigned.

Further, the evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus provided with a control unit, and is among the five types of amino acids in the blood to be evaluated for execution in the control unit. The state of MCI is evaluated for the evaluation target using the concentration value of at least two amino acids of the above, or the expression including the variable to which the concentration value is substituted and the value of the equation calculated using the concentration value. It is characterized by including an evaluation step to be performed.

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 among the five types of amino acids in the blood to be evaluated to be executed by the control unit. It is characterized by including a calculation step of calculating the value of the formula using a formula for evaluating the concentration value of at least two amino acids of the above and the state of MCI including the variable to which the concentration value is assigned. do.

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. , 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 is an evaluation system of the terminal device. The control unit was calculated using the concentration value of at least two amino acids out of the five types of amino acids in the blood to be evaluated, or an expression including a variable to which the concentration value is substituted and the concentration value. The evaluation device includes a data transmission means for transmitting the value of the above formula to the evaluation device, and a result receiving means for receiving the evaluation result regarding the state of MCI for the evaluation target transmitted from the evaluation device. The control unit uses the data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, and the concentration value or the value of the formula received by the data receiving means. The evaluation target is provided with an evaluation means for evaluating the state of MCI and a result transmission means for transmitting the evaluation result obtained by the evaluation means 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 the state of MCI with respect to the evaluation target, and the evaluation result is. The concentration value of at least two amino acids out of the five kinds of amino acids in the blood to be evaluated, or the value of 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 a result of evaluating the state of MCI with respect to the evaluation target using the above.

Further, the terminal device according to the present invention is communicably connected to an evaluation device that evaluates the state of MCI for the evaluation target via a network, and the control unit obtains the concentration value or the value of the formula. The data transmission means for transmitting to the evaluation device is provided, and the result acquisition means receives the evaluation result transmitted from the evaluation device.

According to the present invention, it is possible to provide highly reliable information that can be used as a reference for knowing the state of MCI.

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 blood 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.

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, blood data regarding the concentration values of at least two of the five amino acids in blood (including plasma, serum, whole blood, etc.) collected from an evaluation target (for example, an individual such as an animal or a human) is obtained. Acquire (step S11). Here, the blood data may be related to, for example, the concentration value of at least three amino acids in the blood collected from the evaluation target, and the at least three amino acids are, for example, "Ser, Lys and Trp", "Ser, Cit". And Lys ”,“ Cit, Lys and Trp ”,“ Ser, Thr and Lys ”,“ Thr, Lys and Trp ”,“ Thr, Cit and Lys ”,“ Ser, Thr and Cit ”,“ Ser, Thr and Trp ”. , "Thr, Cit and Trp", or "Ser, Cit and Trp" may be included. Further, the blood data may be related to, for example, the concentration value of at least 6 amino acids in the blood collected from the evaluation target, and the at least 6 amino acids are, for example, "Ser, Thr, Ala, Cit, Lys and Trp". , "Ser, Thr, Cit, Met, Lys and Trp", "Ser, Gln, Cit, Val, Met and Lys", "Ser, Gln, Cit, Met, Lys and Leu", "Ser, Thr, Cit, It may include "Tyr, Lys and Trp", "Ser, Cit, Tyr, Met, Lys and Trp", or "Ser, Thr, Cit, Orn, Lys and Trp".

In step S11, for example, blood data measured by a company or the like that measures the concentration value may be acquired. Further, blood data may be acquired 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 the 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 state of MCI is evaluated for the evaluation target using the concentration value contained in the blood data acquired in step S11 (step S12). Before executing step S12, data such as missing values and outliers may be removed from the blood data acquired in step S11. Here, to evaluate the state of MCI is, for example, to inspect the current state of MCI.

As described above, according to the first embodiment, in step S11, the blood data to be evaluated is acquired, and in step S12, the concentration value included in the blood data to be evaluated acquired in step S11 is used for the evaluation target. Evaluate the state of MCI (in short, obtain information for evaluating the state of MCI for the evaluation target or highly reliable information that can be used as a reference for knowing the state of MCI for the evaluation target). Thereby, it is possible to provide information for evaluating the state of MCI for the evaluation target or highly reliable information that can be used as a reference for knowing the state of MCI for the evaluation target.

Further, it may be determined that the concentration values of at least two amino acids among the five kinds of amino acids reflect the state of MCI for the evaluation target, and further, the concentration values may be determined by the methods listed below, for example. It may be determined that the converted value reflects the state of MCI with respect to the evaluation target. In other words, the concentration value or the converted value itself may be treated as an evaluation result regarding the state of MCI with respect to the evaluation target.
The possible range of concentration values is a predetermined range (for example, 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, the value of an exponential function whose index is the concentration value and whose base is the number of napiers (specifically, a state in which the state of MCI exceeds a predetermined state (for example, a state in which the state exceeds the reference value and is likely to be affected by MCI). , Etc.) may be further calculated (the value of p / (1-p)) when the natural logarithm ln (p / (1-p)) when the probability p is defined is equal to the concentration value. Further, a value obtained by dividing the calculated value of the exponential function by the sum of 1 and the value (specifically, the value of the probability p) may be further calculated.
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 60% is 5.0 and the converted value when the specificity is 90% is 8.0.
Further, after the concentration distribution is normally distributed for each amino acid, the deviation value may be converted 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.

In addition, the position information regarding the position of the predetermined mark on the predetermined indicator visually shown on the display device such as a monitor or the physical medium such as paper is the concentration value of at least two amino acids among the five kinds of amino acids or the concentration value of the amino acid. When the concentration value is converted, it may be generated using the converted value, and it may be determined that the generated position information reflects the state of MCI for the evaluation target. The predetermined ruler is for evaluating the state of MCI, and is, for example, a ruler with a scale, which is "a range in which a concentration value or a converted value can be taken, or a range thereof. At least the scales corresponding to the upper and lower limits in "part" are shown. Further, the predetermined mark corresponds to the concentration value or the converted value, and is, for example, a circle mark or a star mark.

Further, the concentration value of at least two amino acids among the five kinds of amino acids is a predetermined value (mean value ± 1SD, 2SD, 3SD, N quantile, N percentile, cutoff value having clinical significance, etc. ) May be lower or less than a predetermined value, or more than a predetermined value or higher than a predetermined value, the state of MCI may be evaluated for the evaluation target. At that time, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution for each amino acid for each gender and then converting the concentration distribution into an average of 50 and a standard deviation of 10) may be used. .. For example, when the concentration deviation value is less than the mean value -2SD (when the concentration deviation value <30) or when the concentration deviation value is higher than the mean value + 2SD (when the concentration deviation value> 70), the evaluation target is the MCI. The condition may be evaluated.

Further, by calculating the value of the formula using the concentration value of at least two amino acids among the five types of amino acids and the formula including the variable to which the concentration value is assigned, the state of MCI for the evaluation target. May be evaluated.

Further, it may be determined that the calculated value of the formula reflects the state of MCI for the evaluation target, and further, the value of the formula is converted by, for example, the method described below, and the converted value is obtained. It may be determined that it reflects the state of MCI for the evaluation target. In other words, the value of the expression or the converted value itself may be treated as an evaluation result regarding the state of MCI with respect to the evaluation target.
The possible range of values in the equation is a predetermined range (eg 0.0 to 1.0, 0.0 to 10.0, 0.0 to 100.0, or -10.0). For example, to add, subtract, multiply, or divide an arbitrary value to the value of an expression, or to convert the value of an expression into a predetermined conversion method (for example, exponential conversion, etc.) so that the value falls within the range from 10.0 to 10.0, etc.). By converting by logarithmic conversion, angular conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, or power conversion, etc., or by combining these calculations with respect to the value of the expression. You may convert the value of the expression. For example, the value of an exponential function whose exponent is the value of the formula and whose base is the number of Napiers (specifically, it is highly possible that the state of MCI exceeds a predetermined state (for example, the reference value is exceeded, and the patient is suffering from MCI). The value of p / (1-p) when the natural logarithmic value ln (p / (1-p)) when the probability p is defined is equal to the value of the equation) is further calculated. Alternatively, the value obtained by dividing the calculated value of the exponential function by the sum of 1 and the value (specifically, the value of the probability p) may be further calculated.
Further, the value of the expression may be converted so that the converted value under a specific condition becomes a specific value. For example, the value of the expression may be converted so that the converted value when the specificity is 60% is 5.0 and the converted value when the specificity is 90% is 8.0.
Further, the deviation value may be set so that the average is 50 and the standard deviation is 10.
In addition, these conversions may be performed by gender or age.
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, the position information regarding the position of the predetermined mark on the predetermined ruler visually shown on the display device such as a monitor or the physical medium such as paper is converted into the value of the formula or the value of the formula when converted. It may be generated using the later values, and it may be determined that the generated position information reflects the state of MCI for the evaluation target. The predetermined ruler is for evaluating the state of MCI, for example, a ruler with a scale, which is "a range in which the value of the formula or the value after conversion can be taken, or the range thereof." At least the scales corresponding to the upper and lower limits in "a part of" are shown. Further, the predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.

Further, the degree of possibility that the evaluation target has MCI may be qualitatively evaluated. Specifically, "concentration value of at least two amino acids of the five kinds of amino acids and one or more preset threshold values" or "concentration value of at least two amino acids of the five kinds of amino acids". The evaluation target is defined with at least the degree of likelihood of suffering from MCI, using an expression containing variables to which the concentration value is assigned, as well as one or more preset thresholds. It may be classified into any one of a plurality of categories. In addition, it is possible that a plurality of categories are affected by MCI, which is a category for assigning a subject having a high possibility of suffering from MCI (for example, a subject considered to be suffering from MCI). Classification to belong to subjects with a low degree of sex (eg, subjects considered not to have MCI), and to belong to subjects with a moderate degree of likelihood of having MCI. The division may be included. In addition, the multiple categories include a category for belonging a subject having a high possibility of having MCI and a category for belonging a subject having a low possibility of having MCI (a category for belonging to a subject having a low possibility of having MCI). For example, it may include a category for assigning an object that is likely to be healthy (for example, an object that is considered to be healthy). Further, the concentration value or the value of the formula may be converted by a predetermined method, 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 and Cox regression based on the most probable method ・ In addition to generalized linear models Generalized linear mixed model considering variable effects such as individual differences and facility differences-Formulas created by cluster analysis such as K-means method and hierarchical cluster analysis-MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as the hierarchical Bayesian method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods that do not belong to the above categories such as fractional formulas, and sums of formulas of different formats. Expression as shown by

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. The formulas obtained by these methods can be suitably used for evaluating the state of MCI regardless of the unit of the amino acid concentration value in the blood data as input data.

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, when evaluating the state of MCI, in addition to the concentration values of at least two amino acids among the five kinds of amino acids, values related to other biological information (for example, values listed below) may be further used. No. Further, in the formula used at the time of evaluation, in addition to the variable to which the concentration value of at least two amino acids of the above five kinds of amino acids is substituted, the value related to other biological information (for example, the value listed below). May further include one or more variables to which is assigned.
1. 1. Concentration values of blood metabolites (amino acid-related metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. other than amino acids 2. Total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylases, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST) ), GPT (ALT), GGTP (γ-GTP), glucose (blood glucose level), CRP (C-reactive protein), MCV, MCH, MCHC and other blood test values. 3. Values obtained from image information such as ultrasonic echo, X-ray, CT, MRI, and endoscopic image. Age, height, weight, BMI, abdominal circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, diet information, drinking information, exercise information, stress information, sleep information, family history information, disease history information (diabetes, etc.) ) And other biomarkers 5. Values obtained from genetic information such as the number of possession of risk genes for Alzheimer's disease (APOEε4 allele, etc.)

[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 state of MCI is described as an example, but for example, at least two amino acids among the above five kinds of amino acids are described. A concentration value or a value after conversion thereof (for example, a concentration deviation value) may be used.

The control unit has the concentration value included in the blood data of the evaluation target (for example, an individual such as an animal or a human) acquired in advance regarding the concentration value of at least two amino acids among the five kinds of amino acids, and the concentration. The state of MCI is evaluated for the evaluation target by calculating the value of the formula using the formula stored in the storage unit in advance including the variable to which the value is assigned (step S21). This makes it possible to provide highly reliable information that can be used as a reference for knowing the state of MCI.

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 may have previously removed index status information (data having missing values, outliers, etc.) stored in the storage unit in advance, including blood data and index data relating to an index representing the MCI status. From (good) to a candidate formula (for example, y = a1x1 + a2x2 + ... + anxn, y: index data, xi: blood data, ai: constant, i = 1, 2, ..., N) based on a predetermined formula creation method. ) 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 plurality of different algorithms are used for index state information, which is multivariate data composed of blood data and index data obtained by analyzing blood obtained from a large number of healthy groups and MCI groups. You may create multiple groups of candidate expressions 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 using the principal component analysis is a linear formula including each variable that maximizes the variance of all blood 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 within each group to the variance of all blood 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 formula created by using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all the blood 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 blood data are searched, the group to which the nearest points belong is defined as the group to which the data belongs, and the group to which the input blood data belongs. It is a method to select the variable that best matches the group defined as. In addition, cluster analysis is a method of clustering (grouping) points that are closest to each other in all blood data. The decision tree is a method of ordering variables and predicting a group of blood data from possible patterns of variables whose rank is higher.

Returning to the explanation of the formula creation process, the control unit verifies (mutually verifies) the candidate formula 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 is an evaluation method according to the present embodiment, in which an evaluation target whose true state is negative (for example, an evaluation target not suffering from MCI) is correctly evaluated as negative, and the true state is determined. It is the ratio that a positive evaluation target (for example, an evaluation target suffering from MCI) is correctly evaluated as positive. Further, the sensitivity is the ratio at which the evaluation target whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment. Further, the specificity is the ratio in which the evaluation target whose true state is negative is correctly evaluated as negative in 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 blood 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 blood 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 formula, for example, there are a case where the optimum one is selected from the candidate formulas created by the same formula creation method and a case where the optimum one is selected from all the candidate formulas.

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 executing this, the optimum formula for evaluating MCI can be created. In other words, in the formula creation process, 19 kinds of amino acids (Ala, Arg, Asn, Cit, Gln, Gly, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Ser, Thr, Trp, Tyr, Val ), At least one of the concentration values is used for multivariate statistical analysis, and variable selection methods and cross-validation are combined to select the optimal and robust set of variables to extract highly evaluated equations. ..

[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 13. 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 state of MCI is described as an example, but for example, at least two amino acids among the above five kinds of amino acids are described. A concentration value or a value after conversion thereof (for example, a concentration 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, this system has an evaluation device 100 for evaluating the state of MCI for an individual to be evaluated, and the blood of an individual having a concentration value of at least two of the five kinds of amino acids in the blood. It is configured by connecting a client device 200 (corresponding to the terminal device of the present invention) that provides data so as to be communicable via a 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. As a result, information that can be used as a reference for knowing the state of MCI is provided from the evaluation device 100 to the client device 200 or the database device 400, or from the client device 200 or the database device 400 to the evaluation device 100 via the network 300. Will be done. Here, the information that can be used as a reference for knowing the state of MCI is, for example, information about values measured for a specific item regarding the state of MCI of an organism including humans. In addition, information that can be used as a reference for knowing the state of the MCI is generated by the evaluation device 100, the client device 200, and other devices (for example, various measuring devices), and is mainly stored in the database device 400.

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, it is provided with a configuration (hardware and software) that calculates (measures) the concentration values of at least two of the five types of amino acids in blood and outputs the calculated values (printing, monitor display, etc.). The small-sized analyzer may further include an evaluation unit 102d, which will be described later, and output the results obtained by the evaluation unit 102d 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 a 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) / 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 blood 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 blood data file 106a stores blood data relating to the concentration values of at least two of the five types of amino acids in the blood. FIG. 6 is a diagram showing an example of information stored in the blood data file 106a. As shown in FIG. 6, the information stored in the blood data file 106a is configured by correlating the individual number for uniquely identifying the individual (sample) to be evaluated and the blood data. Here, in FIG. 6, the blood data is treated as a numerical value, that is, a continuous scale, but the blood 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 blood 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 state information file 106b includes an individual number, index data (T) relating to an index (index T1, index T2, index T3 ...) Representing the state of MCI, and index data (T). It is configured to correlate blood data with each other. Here, in FIG. 7, the index data and the blood data are treated as numerical values (that is, continuous scales), but the index data and the blood 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 index data is a known index or the like that serves as a marker of the state of MCI, and numerical data may be used.

Returning to FIG. 5, the designated index status information file 106c stores the index status 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 blood 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 includes a rank, an expression (in FIG. 9, Fp (Cit, ...), Fp (Cit, Lys, Ser), Fk (Cit, Lys,). (Ser, ...), Etc.), the threshold value corresponding to each formula creation method, and the verification result of each formula (for example, the value of each formula) are configured in association with each other.

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 an individual number for uniquely identifying an individual (sample) to be evaluated, blood data of an individual acquired in advance, and an evaluation result regarding the state of MCI (for example, which will be described later). The value of the formula calculated by the calculation unit 102d1, the value after converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification result obtained by the classification unit 102d4 described later. , Etc.) and are configured in association 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 blood 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, blood data, index status information, formula, etc.). For example, the acquisition unit 102a acquires information by receiving information (specifically, blood 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, information may be acquired by reading out blood data, index state information, formulas, etc.) via the mechanism. The designation unit 102b designates index data and blood data to be targeted in creating the formula.

The formula creation unit 102c creates a formula based on the index status information acquired by the acquisition unit 102a and the index status 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 is included in the formula obtained in advance (for example, the formula created by the formula creation unit 102c or the formula acquired by the acquisition unit 102a) and the blood data of the individual acquired by the acquisition unit 102a. The state of MCI is evaluated for an individual by calculating the value of the equation using at least one of the above values. In addition, the evaluation unit 102d evaluates the state of MCI for an individual using the concentration value of at least two amino acids among the five kinds of amino acids or the value after conversion of the concentration value (for example, the concentration deviation value). May be good.

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 by using the concentration value of at least two amino acids among the five kinds of amino acids and the formula including at least the variable to which the concentration 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 converted 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 concentration value contained in the blood 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 in (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 infects an individual with MCI using the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (may be a concentration value or a value after conversion of the concentration value). Classify into one of a plurality of categories defined with at least the degree of possibility of being present.

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 source of the blood 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, mouse, 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 IF280 connects the client device 200 and the network 300 (or a communication device such as a router) so as to be communicable. 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 blood data of an individual 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 includes an evaluation unit 210a (calculation unit 210a1, conversion unit 210a2, generation unit 210a3, and 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 connecting the evaluation device 100, the client device 200, and the database device 400 so as to be communicable with each other. 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 PDC. (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 in the evaluation device 100, an evaluation result in 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 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 that defines 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 executes from acquisition of blood data, calculation of the value of the formula, classification into individual categories, and transmission of 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. The generation of the data and the classification of the individual into categories 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.
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 technically described in the scope of claims in addition to the above-mentioned second embodiment. It may be implemented in a variety of 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 a functional concept 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 the application program server connected to the evaluation device 100 via an arbitrary network, and it is also possible to download all or part of the computer program 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 Programle Read Only Memory), and an EEPROM (Epil). Erasable and Program Read Only Memory (registered trademark), CD-ROM (Compact Disk Ready Memory), MO (Magnet-Optical disk), MO (Magnet-Optical disk), DVD (Digital Disk), DVD (Digital Disk) It shall include any "portable physical medium".

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 a plurality of modules or libraries, or cooperates with a separate program represented by an 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.

From plasma samples of MCI patients aged 60 years or older (MCI group: 120) who had a definitive diagnosis of MCI, and healthy elderly people aged 60 years or older (healthy group: 120) who are considered to have healthy cognitive function. The blood amino acid concentration was measured by the above-mentioned amino acid analysis method (A).

Using the plasma concentration values of Ser and Trp (nmol / ml), a two-variable logistic regression equation for discriminating between the MCI group and the healthy group was obtained. When the ability to discriminate between the MCI group and the healthy group by this logistic regression equation was evaluated by ROC_AUC, it showed 0.540. Since ROC_AUC exceeds 0.5, this logistic regression equation is considered to be useful in assessing the state of MCI.

The sample data used in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including the variable to which the amino acid concentration value in plasma is substituted, was obtained.

A logistic regression equation was used as a multivariate discriminant. The combination of 3 variables to be included in the logistic regression equation is searched from the plasma concentration values (nmol / ml) of 5 kinds of amino acids (Cit, Lys, Ser, Thr and Trp), and the ability to discriminate between the MCI group and the healthy group. Performed a search for a good logistic regression equation.

Table 1 below shows a list of logistic regression equations for three variables that always include three of the five amino acids as variables. These logistic regression equations are considered useful in the above evaluation because the lower limit of the 95% confidence interval (95% CI) of the ROC_AUC value is higher than 0.5.

The sample data used in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, which includes a variable to which the amino acid concentration value in plasma is substituted, was obtained.

A logistic regression equation was used as a multivariate discriminant. The combination of 4 variables included in the logistic regression equation (3 amino acid variables out of the 5 amino acids are required) is combined with 19 amino acids (Ala, Arg, Asn, Cit, Gln, Glu, Gly). , His, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr, Val), and search for a logistic regression equation with good discrimination between the MCI group and the healthy group. Was carried out.

Table 2 below shows a list of logistic regression equations in which the ROC_AUC values of the MCI group and the healthy group are 0.679 or more and the number of variables is 4 as shown in Table 1. Since these logistic regression equations have a high ROC_AUC value and the lower limit of 95% CI of the ROC_AUC value is higher than 0.5, they are considered to be useful in the above evaluation.

The sample data used in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, which includes a variable to which the amino acid concentration value in plasma is substituted, was obtained.

A logistic regression equation was used as a multivariate discriminant. The combination of 5 variables to be included in the logistic regression equation (three amino acids out of the five amino acids are essential) was searched from the plasma concentration values of the 19 amino acids, and the MCI group and the healthy group were searched. We searched for a logistic regression equation with good discriminant ability.

Of the 981 logistic regression equations obtained above, 270 equations with ROC_AUC values of 0.679 or more in the MCI group and the healthy group are shown in the following [5-variable equations]. Since these logistic regression equations have a high ROC_AUC value and the lower limit of 95% CI of the ROC_AUC value is higher than 0.5, they are considered to be useful in the above evaluation. In the following [5-variable expression], the ROC_AUC value corresponding to the five amino acids included in the expression, the lower limit value of 95% CI of the ROC_AUC value, and the upper limit value of 95% CI of the ROC_AUC value are shown. Is shown.

[Expression of 5 variables]
Ser, Asn, Cit, Lys, Leu, 0.701 (0.635, 0.768); Ser, Thr, Cit, Lys, Trp, 0.7 (0.633, 0.767); Ser, Thr, Cit, Val, Lys, 0.7 (0.633, 0.767) Ser, Cit, Met, Lys, Leu, 0.7 (0.633, 0.767); Ser, Thr, Cit, Lys, Leu, 0.699 (0.632, 0.767); Ser, Cit, Val, Met, Lys, 0.699 (0.632, 0.766) ); Ser, Thr, Arg, Lys, Trp, 0.699 (0.632, 0.766); Ser, Thr, Arg, Lys, Leu, 0.699 (0.631, 0.767); Ser, Gln, Cit, Lys, Leu, 0.698 (0.631, 0.766); Ser, Cit, Val, Lys, Phe, 0.698 (0.631, 0.766); Ser, Gly, Cit, Lys, Leu, 0.698 (0.631, 0.766); Ser, Asn, Cit, Val, Lys, 0.698 (0.631) , 0.765); Ser, Cit, Pro, Val, Lys, 0.698 (0.631, 0.765); Ser, Cit, Val, Orn, Lys, 0.698 (0.631, 0.765); Ser, Gly, Cit, Val, Lys, 0.698 ( 0.63, 0.765); Ser, Thr, Arg, Val, Lys, 0.698 (0.63, 0.765); Ser, Cit, Lys, Leu, Phe, 0.697 (0.63, 0.764); Ser, Cit, Met, Lys, Trp, 0.697 (0.629, 0.764); Ser, Ala, Cit, Val, Lys, 0.696 (0.629, 0.763); Ser, Cit, Val, Lys, Ile, 0.696 (0.628, 0.763); Ser, Cit, Met, Lys, Ile, 0.696 (0.628, 0.763); S er, Gly, Thr, Lys, Trp, 0.696 (0.628, 0.763); Ser, Cit, Arg, Lys, Leu, 0.696 (0.628, 0.763); Ser, Cit, Val, Lys, Leu, 0.695 (0.628, 0.763) Ser, Cit, Orn, Lys, Leu, 0.695 (0.628, 0.763); Ser, Cit, Lys, Ile, Leu, 0.695 (0.628, 0.763); Ser, Cit, Arg, Val, Lys, 0.695 (0.628, 0.763) ); Ser, Gly, Thr, Cit, Lys, 0.695 (0.628, 0.762); Ser, Cit, Pro, Lys, Leu, 0.695 (0.628, 0.762); Ser, Cit, Tyr, Val, Lys, 0.695 (0.627, 0.762); Ser, Asn, Cit, Lys, Trp, 0.695 (0.628, 0.761); Ser, Gln, Cit, Val, Lys, 0.695 (0.627, 0.762); Ser, Asn, Cit, Lys, Ile, 0.694 (0.627) , 0.762); Ser, Cit, Tyr, Lys, Leu, 0.694 (0.627, 0.762); Ser, Gly, Cit, Lys, Trp, 0.694 (0.627, 0.762); Ser, Ala, Cit, Lys, Leu, 0.694 ( 0.627, 0.762); Ser, Arg, Lys, Leu, Trp, 0.694 (0.627, 0.761); Ser, Gly, Cit, Lys, Ile, 0.694 (0.626, 0.761); Ser, Cit, Lys, Leu, Trp, 0.694 (0.626, 0.761); Ser, Thr, Lys, Leu, Trp, 0.693 (0.626, 0.761); Ser, Thr, Cit, Lys, Ile, 0.693 (0.625, 0.76); Ser, His, Cit, Lys, Leu, 0.693 (0.625, 0. 76); Ser, Cit, Val, Lys, Trp, 0.692 (0.625, 0.76); Ser, Thr, Lys, Ile, Trp, 0.692 (0.625, 0.76); Ser, His, Cit, Val, Lys, 0.692 (0.625) , 0.759); Ser, Gly, Thr, Lys, Ile, 0.692 (0.624, 0.759); Ser, Gln, Cit, Lys, Trp, 0.692 (0.624, 0.759); Ser, Arg, Met, Lys, Trp, 0.692 ( 0.624, 0.759); Ser, Gly, Arg, Lys, Trp, 0.691 (0.624, 0.758); Ser, Thr, Pro, Lys, Trp, 0.691 (0.624, 0.759); Ser, Thr, Val, Lys, Trp, 0.691 (0.623, 0.759); Ser, Asn, Gly, Lys, Trp, 0.691 (0.624, 0.758); Ser, Arg, Val, Lys, Trp, 0.691 (0.624, 0.758); Ser, Gly, Thr, Lys, Leu, 0.691 (0.623, 0.759); Ser, Thr, Arg, Lys, Ile, 0.691 (0.623, 0.759); Ser, Cit, Orn, Lys, Ile, 0.69 (0.622, 0.759); Ser, Gly, Thr, Val, Lys , 0.69 (0.623, 0.758); Ser, Cit, Arg, Lys, Ile, 0.69 (0.622, 0.758); Ser, Asn, Arg, Lys, Trp, 0.69 (0.623, 0.757); Ser, Asn, Lys, Leu, Trp, 0.69 (0.623, 0.757); Ser, His, Cit, Lys, Trp, 0.689 (0.622, 0.757); Ser, Cit, Lys, Ile, Phe, 0.689 (0.621, 0.757); Ser, Asn, Thr, Lys , Trp, 0.689 (0.622, 0. 757); Ser, Thr, Lys, Ile, Leu, 0.689 (0.621, 0.757); Ser, Cit, Arg, Lys, Trp, 0.689 (0.622, 0.756); Ser, Gly, Thr, Pro, Lys, 0.689 (0.622) , 0.756); Ser, Cit, Lys, Ile, Trp, 0.689 (0.622, 0.756); Ser, Gly, Pro, Lys, Trp, 0.689 (0.621, 0.756); Ser, Ala, Cit, Lys, Ile, 0.689 ( 0.621, 0.757); Ser, Asn, Gly, Cit, Lys, 0.689 (0.621, 0.756); Ser, Gly, Lys, Phe, Trp, 0.688 (0.621, 0.756); Ser, His, Thr, Lys, Trp, 0.688 (0.621, 0.756); Ser, Cit, Orn, Lys, Trp, 0.688 (0.621, 0.756); Ser, Cit, Pro, Lys, Ile, 0.688 (0.62, 0.757); Ser, Cit, Tyr, Lys, Ile, 0.688 (0.62, 0.756); Ser, Gly, Val, Lys, Trp, 0.688 (0.621, 0.756); Ser, Arg, Lys, Ile, Trp, 0.688 (0.621, 0.755); Ser, Gln, Thr, Lys, Trp , 0.688 (0.62, 0.756); Ser, Lys, Leu, Phe, Trp, 0.688 (0.62, 0.755); Ser, Gly, Tyr, Lys, Trp, 0.688 (0.62, 0.756); Ser, His, Cit, Lys, Ile, 0.688 (0.62, 0.756); Ser, Thr, Tyr, Lys, Trp, 0.688 (0.62, 0.756); Ser, Thr, Met, Lys, Trp, 0.688 (0.62, 0.756); Ser, Gly, Met, Lys , Trp, 0.688 (0.62, 0.7 56); Ser, Thr, Pro, Lys, Leu, 0.688 (0.62, 0.756); Ser, Gly, Gln, Lys, Trp, 0.688 (0.62, 0.755); Ser, Gly, Cit, Tyr, Lys, 0.688 (0.62) , 0.755); Ser, Gln, Cit, Lys, Ile, 0.688 (0.62, 0.756); Ser, Met, Lys, Leu, Trp, 0.687 (0.62, 0.755); Ser, Gly, Cit, Pro, Lys, 0.687 ( 0.619, 0.755); Ser, Ala, Cit, Lys, Trp, 0.687 (0.62, 0.755); Ser, Gly, Gln, Cit, Lys, 0.687 (0.619, 0.755); Ser, Thr, Lys, Leu, Phe, 0.687 (0.619, 0.755); Ser, Gly, Lys, Ile, Trp, 0.687 (0.619, 0.755); Ser, Gln, Lys, Leu, Trp, 0.687 (0.62, 0.755); Ser, Cit, Pro, Lys, Trp, 0.687 (0.62, 0.754); Ser, Gly, Ala, Lys, Trp, 0.687 (0.619, 0.755); Ser, Gly, Cit, Lys, Phe, 0.687 (0.619, 0.755); Ser, Gly, Ala, Cit, Lys , 0.687 (0.619, 0.755); Ser, Thr, Ala, Lys, Trp, 0.687 (0.619, 0.755); Ser, Gly, Lys, Leu, Trp, 0.687 (0.619, 0.754); Ser, Thr, Lys, Phe, Trp, 0.687 (0.619, 0.755); Ser, Thr, Ala, Lys, Leu, 0.687 (0.618, 0.755); Ser, Thr, Val, Lys, Leu, 0.687 (0.618, 0.755); Ser, Gly, Cit, Met , Lys, 0.687 (0.619, 0.7 54); Ser, Val, Lys, Leu, Trp, 0.687 (0.619, 0.754); Ser, Cit, Tyr, Lys, Trp, 0.686 (0.619, 0.754); Ser, Thr, Tyr, Lys, Leu, 0.686 (0.618) , 0.754); Ser, Asn, Thr, Lys, Leu, 0.686 (0.618, 0.754); Ser, Asn, Val, Lys, Trp, 0.686 (0.619, 0.754); Ser, Thr, Orn, Lys, Trp, 0.686 ( 0.618, 0.754); Ser, Gly, Thr, Arg, Lys, 0.686 (0.619, 0.754); Ser, Pro, Lys, Leu, Trp, 0.686 (0.619, 0.754); Ser, Thr, Met, Lys, Leu, 0.686 (0.618, 0.754); Ser, Asn, Lys, Ile, Trp, 0.686 (0.619, 0.754); Ser, Tyr, Lys, Leu, Trp, 0.686 (0.619, 0.754); Ser, Asn, Pro, Lys, Trp, 0.686 (0.619, 0.754); His, Cit, Lys, Leu, Trp, 0.686 (0.618, 0.754); Ser, Asn, Cit, Tyr, Lys, 0.686 (0.618, 0.754); Ser, Gly, Cit, Orn, Lys , 0.686 (0.618, 0.754); Ser, Val, Met, Lys, Trp, 0.686 (0.618, 0.754); Ser, Met, Lys, Ile, Trp, 0.686 (0.618, 0.754); Ser, Gln, Arg, Lys, Trp, 0.686 (0.618, 0.753); Ser, Ala, Arg, Lys, Trp, 0.686 (0.618, 0.753); Ser, Gly, Orn, Lys, Trp, 0.686 (0.618, 0.753); Ser, Asn, Gln, Cit , Lys, 0.686 (0.6) 17, 0.754); Ser, Gly, Thr, Ala, Lys, 0.685 (0.617, 0.754); Ser, Cit, Lys, Phe, Trp, 0.685 (0.618, 0.753); Ser, Asn, Gln, Lys, Trp, 0.685 (0.618, 0.753); Ser, Lys, Ile, Leu, Trp, 0.685 (0.618, 0.753); Ser, Pro, Met, Lys, Trp, 0.685 (0.617, 0.753); Ser, Gly, His, Cit, Lys, 0.685 (0.618, 0.753); Ser, Gly, Cit, Arg, Lys, 0.685 (0.618, 0.753); Ser, Gln, Thr, Val, Lys, 0.685 (0.617, 0.753); Ser, Gln, Cit, Tyr, Lys , 0.685 (0.617, 0.753); Ser, Gln, Thr, Lys, Leu, 0.685 (0.617, 0.753); Ser, Gln, Lys, Ile, Trp, 0.685 (0.617, 0.753); Ser, Asn, Thr, Val, Lys, 0.685 (0.616, 0.753); Ser, His, Arg, Lys, Trp, 0.685 (0.617, 0.752); Ser, Gly, Thr, Lys, Phe, 0.685 (0.616, 0.753); Ser, Thr, Val, Lys , Ile, 0.684 (0.616, 0.753); Ser, His, Thr, Lys, Leu, 0.684 (0.616, 0.752); Ser, Asn, Ala, Lys, Trp, 0.684 (0.616, 0.752); Ser, Thr, Cit, Arg, Lys, 0.684 (0.616, 0.752); Ser, Gln, Cit, Pro, Lys, 0.684 (0.616, 0.752); Ser, Thr, Pro, Val, Lys, 0.684 (0.616, 0.752); Ser, Thr, Val , Met, Lys, 0. 684 (0.616, 0.752); Cit, Met, Lys, Leu, Trp, 0.684 (0.616, 0.752); Ser, Asn, Tyr, Lys, Trp, 0.684 (0.616, 0.752); Ser, Val, Lys, Phe, Trp , 0.684 (0.616, 0.752); Ser, Gln, Cit, Arg, Lys, 0.684 (0.616, 0.752); Ser, Thr, Ala, Val, Lys, 0.684 (0.615, 0.752); Ser, Thr, Met, Lys, Ile, 0.684 (0.615, 0.752); Ser, Gly, His, Lys, Trp, 0.684 (0.616, 0.752); Ser, Thr, Cit, Pro, Lys, 0.684 (0.615, 0.752); Ser, Thr, Lys, Ile , Phe, 0.683 (0.615, 0.752); Ser, Ala, Lys, Leu, Trp, 0.683 (0.616, 0.751); Ser, Asn, Lys, Phe, Trp, 0.683 (0.616, 0.751); Ser, Thr, Ala, Lys, Ile, 0.683 (0.615, 0.751); Ser, Asn, Met, Lys, Trp, 0.683 (0.615, 0.751); Ser, Arg, Pro, Lys, Trp, 0.683 (0.616, 0.751); Ser, Thr, Val , Lys, Phe, 0.683 (0.615, 0.752); His, Cit, Orn, Lys, Trp, 0.683 (0.615, 0.751); Ser, Arg, Lys, Phe, Trp, 0.683 (0.616, 0.751); Ser, Gly, Thr, Met, Lys, 0.683 (0.615, 0.751); Ser, Thr, Orn, Lys, Leu, 0.683 (0.615, 0.751); His, Cit, Lys, Phe, Trp, 0.683 (0.615, 0.751); Ser, Gly , Thr, Tyr, Lys, 0.683 (0.615, 0.751); His, Ala, Cit, Lys, Trp, 0.683 (0.615, 0.751); Ser, His, Lys, Leu, Trp, 0.683 (0.615, 0.751); Ser, Arg, Tyr, Lys , Trp, 0.683 (0.615, 0.75); Ser, Arg, Orn, Lys, Trp, 0.683 (0.615, 0.75); Ser, Asn, Cit, Lys, Phe, 0.683 (0.615, 0.751); Ser, Asn, Thr, Cit, Lys, 0.683 (0.615, 0.751); Ser, Gln, Val, Lys, Trp, 0.683 (0.615, 0.751); Ser, Gln, Thr, Cit, Lys, 0.683 (0.614, 0.751); Ser, His, Thr , Val, Lys, 0.683 (0.614, 0.751); Ser, Tyr, Lys, Ile, Trp, 0.683 (0.615, 0.75); Ser, Asn, Cit, Pro, Lys, 0.683 (0.615, 0.751); Ser, Thr, Cit, Tyr, Lys, 0.683 (0.615, 0.751); Ser, Thr, Tyr, Val, Lys, 0.683 (0.614, 0.751); Ser, Lys, Ile, Phe, Trp, 0.683 (0.615, 0.751); Ser, Gln , Cit, Lys, Phe, 0.683 (0.614, 0.751); Ser, Asn, Thr, Lys, Ile, 0.682 (0.614, 0.751); Asn, His, Cit, Lys, Trp, 0.682 (0.615, 0.75); His, Cit, Val, Lys, Trp, 0.682 (0.614, 0.75); Cit, Met, Lys, Ile, Trp, 0.682 (0.614, 0.75); Ser, Val, Lys, Ile, Trp, 0.682 (0.615, 0.75); Ser , Cit, Tyr, Met , Lys, 0.682 (0.614, 0.751); Ser, Orn, Lys, Leu, Trp, 0.682 (0.615, 0.75); Ser, Gln, Cit, Met, Lys, 0.682 (0.614, 0.751); His, Cit, Lys, Ile, Trp, 0.682 (0.614, 0.75); Ser, Met, Lys, Phe, Trp, 0.682 (0.614, 0.75); Ser, Asn, Gly, Thr, Lys, 0.682 (0.614, 0.75); Ser, Asn, Ala , Cit, Lys, 0.682 (0.614, 0.75); Ser, Thr, Pro, Lys, Ile, 0.682 (0.614, 0.75); Ser, Gln, Pro, Lys, Trp, 0.682 (0.614, 0.75); Ser, His, Val, Lys, Trp, 0.682 (0.614, 0.75); Ser, Thr, Tyr, Lys, Ile, 0.682 (0.614, 0.75); His, Thr, Cit, Lys, Trp, 0.682 (0.614, 0.75); Ser, Asn , Orn, Lys, Trp, 0.682 (0.614, 0.749); Ser, Gln, Cit, Orn, Lys, 0.682 (0.614, 0.75); Ser, Thr, Orn, Lys, Ile, 0.682 (0.614, 0.75); Ser, Asn, His, Lys, Trp, 0.682 (0.614, 0.75); Ser, Thr, Cit, Lys, Phe, 0.682 (0.613, 0.75); Gln, His, Cit, Lys, Trp, 0.682 (0.614, 0.75); Cit , Tyr, Met, Lys, Trp, 0.682 (0.613, 0.75); Ser, Tyr, Val, Lys, Trp, 0.682 (0.614, 0.75); Ser, Cit, Pro, Met, Lys, 0.682 (0.613, 0.75); Ser, Cit, Met, Lys, Phe, 0.6 82 (0.613, 0.75); Ser, His, Lys, Ile, Trp, 0.682 (0.614, 0.75); Ser, Gln, Ala, Cit, Lys, 0.682 (0.613, 0.75); Ser, His, Thr, Lys, Ile , 0.681 (0.613, 0.75); Cit, Val, Met, Lys, Trp, 0.681 (0.613, 0.75); Ser, Pro, Val, Lys, Trp, 0.681 (0.

613, 0.749); His, Cit, Met, Lys, Trp, 0.681 (0.613, 0.75); Cit, Arg, Lys, Leu, Trp, 0.681 (0.613, 0.749); Gln, Cit, Lys, Leu, Trp, 0.681 (0.613, 0.75); Cit, Lys, Leu, Phe, Trp, 0.681 (0.613, 0.749); Ser, Asn, Cit, Orn, Lys, 0.681 (0.613, 0.749); His, Cit, Tyr, Lys, Trp, 0.681 (0.613, 0.749); Ser, Gln, Thr, Lys, Ile, 0.681 (0.612, 0.749); Ser, Gln, Met, Lys, Trp, 0.681 (0.613, 0.749); Ser, Tyr, Met, Lys, Trp , 0.681 (0.613, 0.749); Thr, Cit, Met, Lys, Trp, 0.681 (0.613, 0.749); Ser, Pro, Lys, Ile, Trp, 0.681 (0.613, 0.749); Ser, Thr, Ala, Cit, Lys, 0.681 (0.612, 0.749); Cit, Pro, Met, Lys, Trp, 0.681 (0.612, 0.749); Asn, Cit, Met, Lys, Trp, 0.681 (0.612, 0.749); Ala, Cit, Met, Lys , Trp, 0.681 (0.612, 0.749); Ser, Asn, Cit, Met, Lys, 0.681 (0.612, 0.749); Cit, Met, Lys, Phe, Trp, 0.681 (0.612, 0.749); Ser, Ala, Met, Lys, Trp, 0.681 (0.612, 0.749); Ser, Asn, Cit, Arg, Lys, 0.681 (0.613, 0.748); Ser, Gly, His, Thr, Lys, 0.68 (0.612, 0.749); Ser, Cit, Tyr , Lys, Phe, 0.68 (0.612, 0.749); Cit, Arg, Met, Lys, Trp, 0.68 (0.612, 0.748); Ser, Ala, Val, Lys, Trp, 0.68 (0.612, 0.748); Gly, His, Cit, Lys, Trp, 0.68 (0.612, 0.749); Ser, Cit, Tyr, Orn, Lys, 0.68 (0.612, 0.749); Cit, Met, Orn, Lys, Trp, 0.68 (0.612, 0.749); Ser, Gln, Tyr, Lys, Trp , 0.68 (0.612, 0.748); Ser, Orn, Lys, Ile, Trp, 0.68 (0.612, 0.748); Ser, Cit, Pro, Tyr, Lys, 0.68 (0.612, 0.749); Ser, Gly, Gln, Thr, Lys, 0.68 (0.612, 0.749); His, Cit, Arg, Lys, Trp, 0.68 (0.612, 0.748); Ser, His, Met, Lys, Trp, 0.68 (0.612, 0.748); Ser, Gln, His, Cit , Lys, 0.68 (0.612, 0.749); Ser, Thr, Val, Orn, Lys, 0.68 (0.612, 0.748); Thr, Cit, Arg, Lys, Trp, 0.68 (0.612, 0.748); Ser, Gln, Ala, Lys, Trp, 0.68 (0.612, 0.748); Ser, Gln, Lys, Phe, Trp, 0.68 (0.612, 0.748); Ser, Gln, Orn, Lys, Trp, 0.68 (0.612, 0.748); Ser, Val, Orn , Lys, Trp, 0.68 (0.612, 0.748); Gln, Cit, Arg, Lys, Trp, 0.68 (0.612, 0.748); Gln, Cit, Val, Lys, Trp, 0.68 (0.611, 0.748); Ser, Ala, Lys, Ile, Trp, 0.68 (0.612, 0.7 48); Thr, Arg, Met, Lys, Trp, 0.68 (0.611, 0.748); Ser, Cit, Pro, Orn, Lys, 0.68 (0.611, 0.748); Ser, His, Thr, Cit, Lys, 0.679 (0.611) , 0.748); Gln, Cit, Lys, Ile, Trp, 0.679 (0.611, 0.748); Gln, Cit, Lys, Phe, Trp, 0.679 (0.611, 0.748); Gln, Ala, Cit, Lys, Trp, 0.679 ( 0.611, 0.748); Gln, Cit, Pro, Lys, Trp, 0.679 (0.611, 0.748); Gln, Cit, Met, Lys, Trp, 0.679 (0.611, 0.748); Ser, Pro, Lys, Phe, Trp, 0.679 (0.611, 0.748); Ser, Met, Orn, Lys, Trp, 0.679 (0.611, 0.747); Ser, Ala, Cit, Tyr, Lys, 0.679 (0.611, 0.748); Gly, Cit, Met, Lys, Trp, 0.679 (0.611, 0.748); Ser, Thr, Cit, Met, Lys, 0.679 (0.611, 0.748); Ser, Cit, Arg, Met, Lys, 0.679 (0.611, 0.748)

The sample data used in Example 1 (hereinafter referred to as training data) was used. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including the variable to which the amino acid concentration value in plasma is substituted, was obtained.

A logistic regression equation was used as a multivariate discriminant. The combination of 6 variables to be included in the logistic regression equation (three amino acids out of the five amino acids are essential) was searched from the plasma concentration values of the 19 amino acids, and the MCI group and the healthy group were searched. We searched for a logistic regression equation with good discriminant ability.

From the 4,109 logistic regression equations obtained above, 1,490 equations with ROC_AUC values of 0.679 or more in the MCI group and the healthy group were selected, and further, from the selected 1,490 equations. , ROC_AUC value top 200 equations were obtained.

Plasma amino acid concentration data completely independent of the training data, MCI patients aged 60 years or older (MCI group: 99) who had a definitive diagnosis of MCI, and 60 years old who are considered to have healthy cognitive function. The performance of the logistic regression equation 200 obtained above was verified using the plasma samples obtained from the above healthy elderly people (healthy group: 100 persons) (hereinafter referred to as verification data).

128 highly robust logistic regression equations in which the ROC_AUC value of the MCI group and the healthy group in the verification data is 0.600 or more are shown in the following [6-variable equation]. Since these logistic regression equations have a high ROC_AUC value and high robustness, they are considered to be useful in the above evaluation to meet the requirements in clinical practice. In the following [6-variable formula], the ROC_AUC value corresponding to the 6 amino acids contained in the formula, the lower limit value of 95% CI of ROC_AUC value, and the upper limit value of 95% CI of ROC_AUC value are shown. Is shown.

[Expression of 6 variables]
Ser, Gly, Thr, Cit, Lys, Trp, 0.707 (0.641, 0.774); Ser, Thr, Cit, Arg, Lys, Leu, 0.707 (0.639, 0.774); Ser, Thr, Arg, Lys, Leu, Trp, 0.706 (0.639, 0.773); Ser, Thr, Arg, Val, Lys, Trp, 0.706 (0.64, 0.773); Ser, Gly, Thr, Cit, Lys, Leu, 0.706 (0.639, 0.773); Ser, Cit, Met , Lys, Leu, Trp, 0.706 (0.64, 0.773); Ser, Thr, Cit, Arg, Lys, Trp, 0.705 (0.638, 0.772); Ser, Gly, Thr, Arg, Lys, Trp, 0.705 (0.638, 0.772) ); Ser, Gly, Thr, Cit, Val, Lys, 0.705 (0.638, 0.772); Ser, Thr, Cit, Val, Lys, Trp, 0.705 (0.638, 0.771); Ser, Cit, Arg, Met, Lys, Trp, 0.705 (0.638, 0.771); Ser, Thr, Cit, Arg, Val, Lys, 0.704 (0.637, 0.771); Ser, Thr, Cit, Met, Lys, Trp, 0.704 (0.637, 0.771); Ser, Thr , Cit, Lys, Leu, Trp, 0.703 (0.636, 0.77); Ser, Gly, Cit, Met, Lys, Trp, 0.703 (0.636, 0.769); Ser, Thr, Cit, Lys, Phe, Trp, 0.703 (0.636) , 0.77); Ser, Cit, Arg, Val, Met, Lys, 0.703 (0.636, 0.77); Ser, Thr, Cit, Lys, Ile, Trp, 0.702 (0.636, 0.769); Ser, Cit, Tyr, Met, Lys, Leu, 0.702 (0.636, 0.769) Ser, Gln, Thr, Cit, Lys, Trp, 0.702 (0.635, 0.769); Ser, Cit, Val, Met, Lys, Trp, 0.702 (0.635, 0.769); Ser, Asn, Thr, Cit, Lys, Trp , 0.702 (0.636, 0.769); Ser, Thr, Arg, Met, Lys, Trp, 0.702 (0.635, 0.769); Ser, Gly, Thr, Cit, Lys, Ile, 0.701 (0.634, 0.769); Ser, Thr, Cit, Orn, Lys, Trp, 0.701 (0.634, 0.768); Ser, Asn, Thr, Cit, Lys, Leu, 0.701 (0.634, 0.768); Ser, Thr, Cit, Met, Lys, Leu, 0.701 (0.634, 0.769); Ser, Gln, Cit, Met, Lys, Leu, 0.701 (0.634, 0.768); Ser, Thr, Cit, Val, Met, Lys, 0.701 (0.633, 0.768); Ser, Cit, Met, Lys, Ile , Trp, 0.701 (0.634, 0.768); Ser, Thr, Arg, Lys, Ile, Trp, 0.701 (0.634, 0.768); Ser, Asn, Cit, Arg, Val, Lys, 0.701 (0.634, 0.767); Ser, Gly, Thr, Arg, Lys, Ile, 0.7 (0.634, 0.767); Ser, Thr, Cit, Lys, Ile, Leu, 0.7 (0.633, 0.768); Ser, Gln, Thr, Arg, Lys, Leu, 0.7 ( 0.633, 0.768); Ser, Ala, Cit, Val, Met, Lys, 0.7 (0.633, 0.767); Ser, Cit, Met, Orn, Lys, Trp, 0.7 (0.633, 0.767); Ser, Thr, Cit, Orn , Lys, Leu, 0.7 (0.633, 0.768); Ser , Thr, Cit, Lys, Leu, Phe, 0.7 (0.633, 0.768); Ser, Asn, Cit, Lys, Leu, Trp, 0.7 (0.634, 0.767); Ser, Thr, Cit, Val, Orn, Lys, 0.7 (0.633, 0.768); Ser, Thr, Arg, Orn, Lys, Leu, 0.7 (0.632, 0.768); Ser, Thr, Arg, Lys, Phe, Trp, 0.7 (0.633, 0.767); Ser, Thr, Ala, Cit, Lys, Leu, 0.7 (0.633, 0.767); Ser, Thr, Cit, Val, Lys, Phe, 0.7 (0.633, 0.767); Ser, Cit, Val, Met, Lys, Ile, 0.7 (0.633, 0.767) Ser, Gln, Cit, Arg, Lys, Leu, 0.7 (0.633, 0.767); Ser, Thr, Cit, Tyr, Lys, Trp, 0.7 (0.633, 0.767); Ser, Cit, Val, Met, Orn, Lys , 0.7 (0.633, 0.767); Ser, Cit, Met, Orn, Lys, Ile, 0.7 (0.632, 0.767); Ser, Asn, Cit, Val, Met, Lys, 0.7 (0.633, 0.767); Ser, His, Thr, Cit, Lys, Trp, 0.7 (0.633, 0.766); Ser, Thr, Cit, Pro, Lys, Trp, 0.7 (0.632, 0.767); Ser, Thr, Ala, Cit, Lys, Trp, 0.7 (0.632, 0.767); Ser, Cit, Arg, Met, Lys, Ile, 0.7 (0.632, 0.767); Ser, Thr, Cit, Pro, Val, Lys, 0.7 (0.632, 0.767); Ser, Thr, Ala, Cit, Val , Lys, 0.7 (0.632, 0.767); Ser, Asn, Gly, Cit, Lys, Trp, 0.7 (0.633, 0.766); Ser, Thr, Cit, Pro, Lys, Leu, 0.699 (0.632, 0.767); Ser, Thr, Arg, Tyr, Lys, Leu, 0.699 (0.632, 0.767); Ser, Gln, Cit, Lys, Leu, Trp, 0.699 (0.632, 0.767); Ser, Gly, Thr, Pro, Lys, Trp, 0.699 (0.632, 0.766); Ser, Thr, Cit, Val, Lys, Leu, 0.699 (0.632, 0.767) Ser, Thr, Arg, Val, Met, Lys, 0.699 (0.632, 0.767); Ser, His, Cit, Met, Lys, Leu, 0.699 (0.632, 0.766); Ser, Asn, Cit, Tyr, Lys, Leu , 0.699 (0.632, 0.766); Ser, Gln, Cit, Met, Lys, Trp, 0.699 (0.632, 0.766); Ser, Asn, Cit, Met, Lys, Trp, 0.699 (0.632, 0.766); Ser, Cit, Tyr, Met, Lys, Trp, 0.699 (0.632, 0.766); Ser, Asn, Thr, Cit, Val, Lys, 0.699 (0.632, 0.766); Ser, Thr, Arg, Orn, Lys, Trp, 0.699 (0.632, 0.766); Ser, Arg, Met, Lys, Ile, Trp, 0.699 (0.632, 0.766); Ser, Asn, Gln, Cit, Lys, Trp, 0.699 (0.632, 0.765); Ser, Gly, Thr, Lys, Ile , Trp, 0.699 (0.632, 0.766); Ser, Gln, Thr, Cit, Lys, Leu, 0.699 (0.631, 0.766); Ser, Asn, Thr, Arg, Lys, Trp, 0.699 (0.632, 0.766); Ser, Cit, Pro, Val, Met, Lys, 0.699 (0.632, 0.766); Ser, Gln, Cit, Orn, Lys, Leu, 0.699 (0.631, 0.766); Ser, Thr, Cit, Val, Lys, Ile, 0.699 (0.631, 0.766); Ser, Thr, Arg , Tyr, Lys, Trp, 0.699 (0.631, 0.766); Ser, Gln, Thr, Cit, Val, Lys, 0.699 (0.631, 0.766); Ser, Thr, Ala, Arg, Lys, Trp, 0.699 (0.631, 0.766) ); Ser, Cit, Tyr, Val, Met, Lys, 0.698 (0.632, 0.765); Ser, Cit, Val, Met, Lys, Phe, 0.698 (0.631, 0.766); Ser, Thr, Arg, Val, Orn, Lys, 0.698 (0.631, 0.766); Ser, Gln, Cit, Lys, Ile, Leu, 0.698 (0.631, 0.766); Ser, Thr, Cit, Tyr, Lys, Leu, 0.698 (0.631, 0.766); Ser, Asn , Cit, Val, Orn, Lys, 0.698 (0.631, 0.765); Ser, Gln, Cit, Pro, Lys, Leu, 0.698 (0.631, 0.766); Ser, His, Thr, Cit, Lys, Leu, 0.698 (0.631) , 0.765); Ser, Asn, Gln, Cit, Lys, Leu, 0.698 (0.631, 0.765); Ser, Gly, Cit, Met, Lys, Ile, 0.698 (0.631, 0.765); Ser, Asn, Thr, Arg, Val, Lys, 0.698 (0.631, 0.765); Ser, Thr, Cit, Arg, Lys, Ile, 0.698 (0.63, 0.766); Ser, Gln, Cit, Tyr, Lys, Leu, 0.698 (0.63, 0.765); Ser , His, Cit, Met, Lys, Trp, 0.698 (0.631, 0.765); Ser, Ala, Cit, Met, Lys, Trp, 0.698 (0.631, 0.765); Ser, His, Thr, Arg, Lys, Leu, 0.698 (0.63, 0.765); Ser, Gly , Gln, Cit, Lys, Trp, 0.698 (0.631, 0.765); Ser, Asn, Cit, Val, Lys, Ile, 0.698 (0.631, 0.765); Ser, Gln, Thr, Arg, Val, Lys, 0.698 (0.63) , 0.765); Ser, Asn, Cit, Pro, Val, Lys, 0.698 (0.631, 0.765); Ser, Gly, Thr, Ala, Lys, Trp, 0.698 (0.63, 0.765); Ser, Gln, Thr, Arg, Lys, Trp, 0.698 (0.63, 0.765); Ser, Thr, Arg, Pro, Lys, Trp, 0.698 (0.63, 0.765); Ser, Gln, Cit, Val, Lys, Trp, 0.698 (0.631, 0.764); Ser , Asn, Cit, Tyr, Val, Lys, 0.697 (0.63, 0.764); Ser, Thr, Arg, Val, Lys, Ile, 0.697 (0.63, 0.765); Ser, Thr, Arg, Pro, Lys, Leu, 0.697 (0.63, 0.765); Ser, Thr, Cit, Tyr, Val, Lys, 0.697 (0.63, 0.765); Ser, Asn, Gly, Cit, Lys, Ile, 0.697 (0.63, 0.764); Ser, Asn, Cit, Val, Lys, Phe, 0.697 (0.63, 0.764); Ser, His, Thr, Arg, Lys, Trp, 0.697 (0.63, 0.765); Ser, Thr, Arg, Pro, Val, Lys, 0.697 (0.63, 0.765) Ser, Thr, Arg, Tyr, Val, Lys, 0.697 (0.63, 0.764); Ser, Cit, Pro, Met, Lys, Trp, 0.697 (0.63, 0.764); Ser, Asn, Cit, Lys, Phe, Trp, 0.697 (0.63, 0.764); Ser, Gln, Cit , Arg, Val, Lys, 0.697 (0.63, 0.764); Ser, Cit, Val, Lys, Ile, Phe, 0.697 (0.63, 0.764); Ser, Cit, Met, Lys, Phe, Trp, 0.697 (0.63, 0.764) ); Ser, Thr, Cit, Met, Lys, Ile, 0.697 (0.629, 0.764); Ser, Asn, Cit, Arg, Lys, Trp, 0.697 (0.63, 0.763); Ser, Thr, Ala, Arg, Val, Lys, 0.697 (0.629, 0.764); Ser, Gln, Cit, Val, Met, Lys, 0.697 (0.629, 0.764); Ser, Cit, Pro, Val, Lys, Ile, 0.697 (0.629, 0.764); Ser, Asn , Cit, Val, Lys, Trp, 0.697 (0.63, 0.763); Ser, Asn, Arg, Val, Lys, Trp, 0.697 (0.63, 0.763); Ser, Asn, Cit, Met, Lys, Ile, 0.696 (0.629) , 0.764)

Of the top 200 formulas of ROC_AUC values of the MCI group and the healthy group obtained in Example 5, the ROC_AUC values of the MCI group and the healthy group in the verification data are 0.600 or more, which is highly robust 128. The street logistic regression equation was used.

Blood amino acid concentration data (hereinafter referred to as all data) including training data and verification data was used. That is, the 219 MCI group, which is a combination of the 120 MCI group described in Example 1 and the 99 MCI group described in Example 5, and the 120 healthy group and Example described in Example 1. Blood amino acid concentration data obtained from plasma samples of 220 healthy groups in which the 100 healthy groups described in 5 were combined was used.

The value of the formula when the specificity was 60% was set as the first cutoff value, and the value of the formula when the specificity was 90% was set as the second cutoff value. If the value of the formula is lower than the first cutoff value, it is ranked A (classification meaning that the possibility of MCI (probability, risk) is low), and the value of the formula is lower than the first cutoff value. If it is high and lower than the second cutoff value, it is rank B (a division that means that the probability of MCI is medium), and if the value of the expression is higher than the second cutoff value, it is rank C (MCI). It is defined as a category that means that there is a high possibility that it is.

Then, under the above-mentioned setting and the above-mentioned definition, the rank was calculated for each person and each formula by using all the data and the 128 kinds of logistic regression equations. Furthermore, using this calculation result, when ranks A and B are negative and rank C is positive, the positive probability defined by _{"positive probability ratio = sensitivity * 1} / (1-specificity _{* 2)"} The degree ratio was calculated for each formula. That is, the ratio of the ratio of those who are calculated to be positive among MCI patients to the ratio of those who are calculated to be positive among healthy elderly people was calculated by formula.
* 1: Sensitivity = b / (a + b)
a = Number of MCI patients calculated to be negative (Rank A or Rank B) b = Number of MCI patients calculated to be positive (Rank C) * 2: Specificity = c / ( c + d)
c = Number of healthy elderly people calculated as negative (Rank A or Rank B) d = Number of healthy elderly people calculated as positive (Rank C)

Table 3 below shows seven logistic regression equations with a positive likelihood ratio of 3.0 or higher. These logistic regression equations have a high positive likelihood ratio, and it can be expected that they are more likely to lead to behavior change of the examinee than other equations.

Of the above seven logistic regression equations, it has a logistic regression equation having a variable set "Ser, Thr, Ala, Cit, Lys, Trp" and a variable set "Ser, Thr, Cit, Met, Lys, Trp". Logistic regression equation, logistic regression equation with variable set "Ser, Thr, Cit, Tyr, Lys, Trp" and logistic regression equation with variable set "Ser, Thr, Cit, Orn, Lys, Trp" are trained. The ROC_AUC value of the MCI group and the healthy group in the data is as high as 0.70 or more, and the ROC_AUC value of the MCI group and the healthy group in the verification data is also as high as 0.65 or more. Is a high expression.

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, MCI status progression prediction, disease risk prediction, proteome, and metabolome analysis. It is extremely useful in the field of bioinformatics.

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 (15)

1. The concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated, or the above calculated using the formula including the variable to which the concentration value is substituted and the concentration value. Including an evaluation step for evaluating the state of mild cognitive impairment for the evaluation target using the value of the formula.
   An evaluation method characterized by.
2. The concentration value is a concentration value of at least three amino acids.
   The at least three amino acids are Ser, Lys and Trp, Ser, Cit and Lys, Cit, Lys and Trp, Ser, Thr and Lys, Thr, Lys and Trp, Thr, Cit and Lys, Ser, Thr and Cit, Ser. , Thr and Trp, Thr, Cit and Trp, or Ser, Cit and Trp,
   The evaluation method according to claim 1.
3. The concentration value is a concentration value of at least 6 amino acids.
   The at least 6 amino acids are Ser, Thr, Ala, Cit, Lys and Trp, Ser, Thr, Cit, Met, Lys and Trp, Ser, Gln, Cit, Val, Met and Lys, Ser, Gln, Cit, Met. , Lys and Leu, Ser, Thr, Cit, Tyr, Lys and Trp, Ser, Cit, Tyr, Met, Lys and Trp, or Ser, Thr, Cit, Orn, Lys and Trp.
   2. The evaluation method according to claim 2.
4. The evaluation step is executed in the control unit of the information processing apparatus including the control unit.
   The evaluation method according to any one of claims 1 to 3.
5. An expression for evaluating the state of mild cognitive impairment including the concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated and the variable to which the concentration value is substituted Including a calculation step to calculate the value of the equation using
   A calculation method characterized by.
6. The calculation step is executed in the control unit of the information processing apparatus including the control unit.
   5. The calculation method according to claim 5.
7. An evaluation device equipped with a control unit
   The control unit
   The concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated, or the above calculated using the formula including the variable to which the concentration value is substituted and the concentration value. To provide an evaluation means for evaluating the state of mild cognitive impairment for the evaluation target using the value of the formula.
   An evaluation device characterized by.
8. Communicatably connected via a network to the terminal device providing the concentration value or the value of the equation.
   The control unit
   A data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, and
   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 means uses the concentration value received by the data receiving means or the value of the formula.
   7. The evaluation device according to claim 7.
9. It is a calculation device equipped with a control unit.
   The control unit
   An expression for evaluating the state of mild cognitive impairment including the concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated and the variable to which the concentration value is substituted To provide a calculation means for calculating the value of the above formula by using.
   A calculation device characterized by.
10. 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 Cit, Lys, Ser, Thr and Trp in the blood to be evaluated, or the above calculated using the formula including the variable to which the concentration value is substituted and the concentration value. Including an evaluation step for evaluating the state of mild cognitive impairment for the evaluation target using the value of the formula.
    An evaluation program featuring.
11. 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
    An expression for evaluating the state of mild cognitive impairment including the concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated and the variable to which the concentration value is substituted Including a calculation step to calculate the value of the equation using
    A calculation program featuring.
12. A computer-readable recording medium on which the program according to claim 10 or 11 is recorded.
13. 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
    The concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated, or the above calculated using the formula including the variable to which the concentration value is substituted and the concentration value. A data transmission means for transmitting the value of the expression to the evaluation device,
    A result receiving means for receiving the evaluation result regarding the state of mild cognitive impairment for the evaluation target transmitted from the evaluation device.
    Equipped with
    The control unit of the evaluation device
    A data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, and
    An evaluation means for evaluating the state of mild cognitive impairment with respect to the evaluation target using the concentration value 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.
    To prepare for
    An evaluation system featuring.
14. It is a terminal device equipped with a control unit.
    The control unit
    Equipped with a result acquisition means to acquire evaluation results regarding the state of mild cognitive impairment for the evaluation target
    The evaluation result is a concentration value of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood to be evaluated, or an expression including a variable to which the concentration value is substituted and the concentration value. It is the result of evaluating the state of mild cognitive impairment for the evaluation target using the value of the above formula calculated using the above.
    A terminal device characterized by.
15. The evaluation target is connected to an evaluation device that evaluates the state of mild cognitive impairment via a network so as to be communicable.
    The control unit includes data transmission means for transmitting the concentration value or the value of the formula to the evaluation device.
    The result acquisition means receives the evaluation result transmitted from the evaluation device.
    14. The terminal device according to claim 14.

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