KR20230037533A - Evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system, and terminal device for mild cognitive impairment - Google Patents

Abstract

An object of the present invention is to provide an evaluation method and the like capable of providing highly reliable information that can be used as a reference in determining the state of mild cognitive impairment. In the present embodiment, the concentration value of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the evaluation target, or an expression including a variable to which the concentration value is substituted, and the concentration value calculated using the above concentration value The state of mild cognitive impairment is evaluated for the evaluation target using the value of the expression.

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

MCI refers to a condition that is considered to be a previous stage or borderline example of various dementias that does not lead to a diagnosis of dementia because there is a problem in cognitive function compared to normal aging according to age, but there is no difficulty in daily life. Regarding the diagnosis of MCI, currently, mainly two diagnostic criteria have been proposed and widely accepted (Non-Patent Documents 1 and 2). In addition to AD (Alzheimer's Disease), background diseases of MCI include Lewy Body Dementia (DLB), Frontotemporal Degeneration (FTLD), Vascular Dementia (VaD), and geriatric depression. There is a possibility that the onset risk of dementia can be reduced by doing. Therefore, by providing a simple screening method capable of discriminating MCI, it is possible to increase the possibility of examination at a specialized medical institution targeting more people at an earlier stage and providing opportunities for prevention and treatment, and It is expected to contribute to the prevention of increase.

However, the neuropsychological test Mini Mental State Examination (hereinafter referred to as “MMSE”), which is currently popular as a screening test for dementia, and the revised Hasegawa-style simplified 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, in addition to the decrease in detection accuracy compared to the determination of dementia, the test time is long. The problem is that the throughput is low and that a skilled skill is required for the inspector. As a screening test for MCI, a new simple test technique to assist conventional neuropsychological tests is required.

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

[Patent Document 1] International Publication No. 2018/008764 [Patent Document 2] International Publication No. 2020/067386

[Non-Patent Document 1] Petersen et al., Arch Neurol (1999) 56(6):760; Mild cognitive impairment: clinical characterization and outcome. [Non-Patent Document 2] Winblad et al., J. Intern. Med. (2004)256(3):240-6.; Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. [Non-Patent Document 3] Nakamura et al., Nature. (2018) 554 (7691): 249-254.; High performance plasma amyloid-β biomarkers for Alzheimer's disease.

However, regarding the high-precision MCI determination technology using the blood concentration of metabolites, including amino acids and amino acid-related metabolites, as an index, further improvement in accuracy to meet the required standards in actual clinical practice, if practical use is put into view. There was a problem with needing this.

The present invention has been made in view of the above, and provides an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, It aims to provide a recording medium, an evaluation system, and a terminal device.

In order to solve the above problems and achieve the object, the evaluation method according to the present invention is a concentration value of at least two amino acids among five types of amino acids (Cit, Lys, Ser, Thr and Trp) in the blood of the subject of evaluation, or an evaluation step of evaluating the state of MCI for the evaluation target using an expression including a variable to which the concentration value is substituted and a value of the expression calculated using the concentration value. .

Here, in this specification, various amino acids are mainly indicated by abbreviations, but their formal names are as follows.

(short name) (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

In addition, in the evaluation method according to the present invention, the concentration value is a 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, 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.

In addition, in the evaluation method according to the present invention, the concentration value is a concentration value of at least six amino acids, and the at least six 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.

Further, the evaluation method according to the present invention is characterized in that the evaluation step is executed by the control unit of an information processing device having a control unit.

In addition, the calculation method according to the present invention is a formula for evaluating the state of MCI, including the concentration values of at least two amino acids among the five amino acids in the blood of the subject to be evaluated, and variables to which the concentration values are substituted. It is characterized in that it comprises a calculation step of calculating the value of the expression using .

Further, the calculation method according to the present invention is characterized in that the calculation step is executed by the control unit of an information processing device having a control unit.

In addition, the evaluation device according to the present invention is an evaluation device having a control unit, wherein the control unit controls the concentration values of at least two amino acids among the five amino acids in the blood of an evaluation target, or a variable to which the concentration values are substituted. and an evaluation means for evaluating the state of MCI for the evaluation target using a value of the formula calculated using an expression including and the concentration value.

In addition, the evaluation device according to the present invention is communicatively connected to a terminal device that provides the value of the concentration value or the expression of the expression through a network, and the control unit controls the concentration value or the expression of the expression transmitted from the terminal device. data receiving means for receiving the value of , and result transmission means for transmitting an evaluation result obtained by the evaluation means to the terminal device, wherein the evaluation means comprises: the concentration value received by the data receiving means; or It is characterized in that the above value of the above formula is used.

In addition, the calculation device according to the present invention is a calculation device having a control unit, wherein the control unit determines the concentration values of at least two amino acids among the five amino acids in the blood of an evaluation target, and a variable to which the concentration values are substituted. It is characterized by having a calculation means for calculating the value of the expression by using an expression for evaluating the state of MCI including

Further, the evaluation program according to the present invention is an evaluation program to be executed in an information processing device having a control unit, and a concentration value of at least two amino acids among the above five amino acids in the blood of a subject to be evaluated, to be executed by the control unit. , or, using an expression including a variable to which the concentration value is substituted and an evaluation step of evaluating the state of MCI for the evaluation target using the value of the expression calculated using the concentration value. do.

In addition, the calculation program according to the present invention is a calculation program to be executed in an information processing device having a control unit, and a concentration value of at least two amino acids among the above five amino acids in the blood of a subject to be evaluated, to be executed by the control unit. , and, using an equation for evaluating the state of MCI including a variable to which the concentration value is substituted, and calculating a value of the equation.

Also, 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, a recording medium according to the present invention is a non-transitory computer-readable recording medium, and is characterized by including programmed instructions for executing the evaluation method or the calculation method in an information processing device.

In addition, an evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit communicatively via a network, wherein the control unit of the terminal device is an evaluation target. Sending the concentration value of at least two amino acids among the five amino acids in the blood of, or the value of the expression calculated using an expression including a variable to which the concentration value is substituted and the value of the expression calculated using the concentration value, to the evaluation device data transmission means and result reception means for receiving an evaluation result transmitted from the evaluation device relating to the state of MCI for the evaluation target, wherein the control unit of the evaluation device determines the concentration transmitted from the terminal device data receiving means for receiving a value or the value of the expression, and evaluation means for evaluating a state of MCI for the evaluation target using the concentration value or the value of the expression received by the data receiving means; , result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

Further, a terminal apparatus according to the present invention is a terminal apparatus having a control unit, wherein the control unit is provided with result acquisition means for obtaining an evaluation result relating to a state of MCI for an evaluation target, and the evaluation result is determined by the evaluation The evaluation target using the concentration value of at least two amino acids among the five amino acids in the subject's blood, or an expression including a variable to which the concentration value is substituted and a value of the expression calculated using the concentration value, For, it is characterized in that the result of evaluating the state of MCI.

In addition, 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 through a network, and the control unit determines the concentration value or the value of the expression as the evaluation unit. It is characterized by comprising data transmission means for transmitting data to an apparatus, wherein the result obtaining means receives the evaluation result transmitted from the evaluation apparatus.

According to the present invention, there is an effect of providing reliable information that can be used as a reference in knowing the state of MCI.

[Fig. 1] Fig. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
[Fig. 2] Fig. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
[Fig. 3] Fig. 3 is a diagram showing an example of the entire configuration of this system.
[Fig. 4] Fig. 4 is a diagram showing another example of the overall configuration of the present system.
[Fig. 5] Fig. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system.
[Fig. 6] Fig. 6 is a diagram showing an example of information stored in the blood data file 106a.
[Fig. 7] Fig. 7 is a diagram showing an example of information stored in the indicator state information file 106b.
[Fig. 8] Fig. 8 is a diagram showing an example of information stored in the designated indicator state information file 106c.
[Fig. 9] Fig. 9 is a diagram showing an example of information stored in the expression file 106d1.
[Fig. 10] Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106e.
[Fig. 11] Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d.
[Fig. 12] Fig. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system.
[Fig. 13] Fig. 13 is a block diagram showing an example of the configuration of a database device 400 of the present system.

Below, the embodiment of the evaluation method and calculation method according to the present invention (first embodiment) and the evaluation device, calculation device, evaluation method, calculation method, evaluation program, calculation program, recording medium, evaluation system and An embodiment (second embodiment) of the terminal device will be described in detail based on the drawings. In addition, this invention is not limited by these embodiments.

[First Embodiment]

[1-1. Outline of First Embodiment]

Here, an overview of the first embodiment will be described with reference to FIG. 1 . 1 is a principle configuration diagram showing the basic principle of the first embodiment.

First, blood related to concentration values of at least two amino acids among the above five amino acids in blood (eg, including plasma, serum, whole blood, etc.) collected from an evaluation target (eg, an individual such as an animal or human) Data is acquired (step S11). Here, the blood data may, for example, relate to the concentration values 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 also be included. Further, the blood data may, for example, relate to the concentration values of at least six amino acids in the blood collected from the evaluation target, and the at least six 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, Tyr, Lys, and Trp", "Ser, Cit, Tyr, Met, Lys, and Trp", or "Ser, Thr, Cit, Orn, Lys, and Trp" may also be included.

In step S11, blood data measured by, for example, a company or the like that measures concentration values may be acquired. In addition, blood data may be obtained by measuring the concentration value of blood collected from the evaluation target by, for example, the following measuring method (A), (B), or (C). Here, the unit of the concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, and may be obtained by multiplying or subtracting these concentrations by an arbitrary constant.

(A) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80°C until determination of concentration values. In the measurement of the concentration value, acetonitrile is added to deproteinize, and if necessary, contaminants such as phospholipids are removed by high-layer extraction or the like, and a labeling reagent (3-aminopyridyl-N-hydroxy succinimidylcarbamate), pre-column derivatization is performed, and concentration values are analyzed by liquid chromatography mass spectrometry (including tandem mass spectrometry) (International Publication No. 2003/069328, International Publication) see 2005/116629).

(B) Separating plasma from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80°C until determination of concentration values. In measuring the concentration value, after deproteinization by adding sulfosalicylic acid, 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 centrifugal separation to separate plasma or serum from the blood. Plasma or serum samples whose concentration values were not measured immediately after obtaining the plasma or serum are cryopreserved at -80°C until the concentration values are measured. In measuring the concentration value, the concentration value is analyzed by quantifying or the like a substance that increases or decreases according to substrate recognition or a spectroscopic value using a molecule that reacts or binds to a target substance in the blood, such as an enzyme or an aptamer.

Next, the state of MCI is evaluated for the evaluation target using the concentration value included in the blood data obtained in step S11 (step S12). In addition, before executing step S12, data such as missing values or deviation values may be removed from the blood data acquired in step S11. Here, evaluating the state of MCI is, for example, examining the current state of MCI.

As described above, according to the first embodiment, in step S11, the blood data of the evaluation target is acquired, and in step S12, the concentration value included in the blood data of the evaluation target acquired in step S11 is used to determine the MCI for the evaluation target. Evaluate the state (in short, acquire 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). In this way, it is possible to provide reliable information that can serve as a reference for information for evaluating the state of MCI for an evaluation target or for knowing the state of MCI for an evaluation target.

Alternatively, it may be determined that the concentration values of at least two of the above five amino acids reflect the state of MCI for the evaluation target, and further, the concentration values are converted by, for example, the following methods, It may be determined that the subsequent value reflects the state of MCI for 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 for the evaluation target.

To make the range that the concentration value can take fall within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from -10.0 to 10.0, etc.) For example, by subtracting or multiplying an arbitrary value for the concentration value, or converting the concentration value by a predetermined conversion method (eg, exponential conversion, logarithmic conversion, angular conversion, square root conversion, probit conversion, inverse conversion) , Box-Cox transformation, exponential transformation, etc.), or by performing a combination of these calculations for the concentration values, the concentration values may be converted. For example, the value of an exponential function with the concentration value as the exponent and the Napier number as the base (specifically, the state of MCI is a predetermined state (e.g., exceeding the reference value, afflicted with MCI) The value of p/(1-p) in the case where the natural logarithm ln(p/(1-p)) is equal to the concentration value when the probability p is defined ) may be additionally calculated, or a value obtained by dividing the value of the calculated exponential function by the sum of 1 and the corresponding value (specifically, a value of probability p) may be additionally calculated.

Further, the concentration value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the concentration value may be converted so that the value after conversion when the specificity is 60% is 5.0, and the value after conversion when the specificity is 90% is 8.0.

Alternatively, after normalizing the concentration distribution for each amino acid, the deviation values may be set to an average of 50 and a standard deviation of 10.

Incidentally, such conversion may be performed by gender or by age.

In addition, the concentration value in this specification may be the concentration value itself, or may be a value after converting the concentration value.

In addition, the concentration value of at least two amino acids among the above five types of amino acids or the positional information on the position of a predetermined display on a predetermined ruler that is visually displayed on a display device such as a monitor or a physical medium such as paper. In the case where the concentration value is converted, it may be determined that the generated positional information reflects the state of the MCI for the evaluation target by using the value after the conversion. In addition, it is for evaluating the state of a predetermined ruler, MCI, and, for example, as a ruler with a scale, the upper limit value in "the range that the concentration value or the value after conversion can take, or a part of the range" A scale corresponding to the lower limit value is indicated at least. In addition, a predetermined display column corresponds to a concentration value or a value after conversion, and is, for example, a circle mark or an asterisk.

In addition, the concentration value of at least two amino acids among the above five amino acids is lower than a predetermined value (average value ± 1SD, 2SD, 3SD, N quartile, N percentile, or cutoff value for which clinical significance has been confirmed) or a predetermined In the case of less than or equal to a predetermined value or higher than or equal to a predetermined value, the state of MCI may be evaluated for the evaluation target. At this time, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution for each amino acid and for each sex, and then converting the concentration distribution to a mean of 50 and a standard deviation of 10) may be used. For example, if the concentration deviation value is less than the average value -2SD (in the case of the concentration deviation value <30) or if the concentration deviation value is higher than the average value + 2SD (in the case of the concentration deviation value > 70), the state of MCI is evaluated for the evaluation target You can do it.

In addition, the state of MCI may be evaluated for the evaluation target by calculating the value of the expression using an expression including the concentration value of at least two amino acids among the above five amino acids and a variable to which the concentration value is substituted. good night.

In addition, it may be determined that the value of the calculated formula reflects the state of the MCI for the evaluation target, and further, the value of the formula is converted, for example, by the method described below, and the value after conversion is the MCI for the evaluation target. It may be determined that it reflects the state of In other words, the value of the expression or the value itself after conversion may be treated as an evaluation result relating to the state of MCI for the evaluation target.

The range that the value of the expression can take falls within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from -10.0 to 10.0, etc.) For example, adding, subtracting, multiplying, or subtracting an arbitrary value for the value of an expression, or converting the value of an expression using a predetermined conversion method (e.g., exponential conversion, logarithmic conversion, angular conversion, square root conversion, probit) transform, reciprocal transform, Box-Cox transform, exponential transform, etc.), or by performing a combination of these calculations on the value of the formula to transform the value of the expression. For example, the value of an exponential function with the value of the expression as the exponent and the Napier number as the base (specifically, the condition of MCI exceeds a predetermined condition (for example, the possibility of being affected by MCI exceeding a reference value) The value of p/(1-p) in the case where the natural logarithm ln(p/(1-p)) is equal to the value of the formula) when defining the probability p of high state, etc.) Alternatively, a value obtained by dividing the value of the calculated exponential function by the sum of 1 and the value (specifically, the value of probability p) may be additionally calculated.

Further, the value of the expression may be converted so that the value after conversion of on a specific condition becomes a specific value. For example, the value of the expression may be converted so that the value after conversion when the specificity is 60% is 5.0 and the value after conversion when the specificity is 90% is 8.0.

Alternatively, the deviation values may be set to an average of 50 and a standard deviation of 10.

Incidentally, such conversion may be performed by gender or by age.

In addition, the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.

Further, when the value of an expression or the value of the expression is converted to positional information about the position of a predetermined display on a predetermined ruler that is visually displayed on a display device such as a monitor or a physical medium such as paper, the conversion is performed. You may determine that it is generated using the value after, and that the generated positional information reflects the state of the MCI for the evaluation target. In addition, it is for evaluating the state of a predetermined ruler, MCI, and, for example, as a ruler with a scale, the upper limit value in "the range that the value of the formula or the value after conversion can take, or a part of the range" and the scale corresponding to the lower limit is indicated at least, and the like. In addition, a predetermined mark corresponds to a value of an expression or a value after conversion, and is, for example, a circle mark or an asterisk.

The degree of possibility that the evaluation target is affected by MCI may be evaluated qualitatively. Specifically, "concentration values of at least two amino acids among the above five amino acids and one or more threshold values set in advance" or "concentration values of at least two amino acids among the above five amino acids and the concentration values are substituted The evaluation target may be classified into any one of a plurality of categories defined by considering at least the degree of possibility of being affected by MCI, by using an expression including a variable that is In addition, in the plurality of categories, a category for assigning a subject with a high degree of possibility of being affected by MCI (for example, a subject considered to be affected by MCI), a subject with a low degree of possibility of being affected by MCI ( For example, a category for including a subject who is considered not to be affected by MCI) and a category for including a subject with an intermediate degree of possibility of being affected by MCI may be included. In addition, among the plurality of categories, a category for assigning subjects with a high degree of possibility of being affected by MCI, and a category for assigning subjects with a low degree of possibility of being affected by MCI (for example, healthy (健常) ) may be included (for example, a classification for belonging to an object considered to be healthy) that is highly likely to be. In addition, the concentration value or the value of the expression 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.

In addition, about the formula used at the time of evaluation, the format is not particularly concerned, but, for example, the format shown below may be used.

Linear models such as multiple regression based on the least squares method, linear discriminant, principal component analysis, and canonical discriminant analysis

· Generalized linear models such as logistic regression and Cox regression based on the maximum likelihood method

In addition to the generalized linear model, a generalized linear mixed model that considers random effects such as differences between individuals and facilities

Equation created by cluster analysis such as K-means method and hierarchical cluster analysis

Formulas created based on Bayesian statistics, such as MCMC (Markov Chain Monte Carlo method), Bayesian networks, and hierarchical Bayes methods

・Formulas created by class classification such as support vector machines and decision trees

・Formulas created by methods not belonging to the above categories, such as fractional expressions

Expressions that appear as sums of expressions of different forms

In addition, even if the expression used at the time of evaluation is created, for example, by the method described in International Publication No. 2004/052191 by the present applicant or by the method described in International Publication No. 2006/098192 by the present applicant. good night. In addition, as long as it is an expression obtained by such a method, the expression can be suitably used for evaluating the state of MCI, regardless of the unit of amino acid concentration value in blood data as input data.

Here, in the multiple regression equation, multiple logistic regression equation, canonical discriminant function, etc., coefficients and constant terms are added to each variable, but these coefficients and constant terms are preferably real numbers, and more preferably, the above various classifications from data Any value that falls within the range of the 99% confidence interval of the coefficients and constant terms obtained for performing It doesn't matter if Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of a constant term and its confidence interval may be multiplied by an arbitrary positive number. When used to evaluate logistic regression, linear discriminant, and multiple regression, linear transformation (constant addition, constant multiplication) and monotonically increasing (decreasing) transformation (e.g., logit transformation) change the evaluation performance. It is not the same as before conversion, so you may use the one after such conversion.

In addition, a fractional expression means that the numerators of the fractional expression are variables A, B, C, . . . is expressed as the sum of and/or the denominator of the fractional expression is the variables a, b, c, ... is expressed as the sum of Further, in the fractional expression, the fractional expression α, β, γ, . . . The sum of (eg α + β) is also included. Also, fractional expressions include divided fractional expressions. In addition, it does not matter if an appropriate coefficient is attached to each variable used for a numerator or a denominator. In addition, it does not matter even if the variable used for a numerator or a denominator overlaps. In addition, an appropriate coefficient may be attached to each fractional expression. Further, as long as the value of the coefficient of each variable or the value of the constant term is a real number, it does not matter. In a certain fractional expression and one in which the numerator variable and the denominator variable are interchanged in the fractional expression, although the positive and negative signs associated with the target variable are generally reversed, since their correlation is maintained, the evaluation performance can be regarded as equivalent. Therefore, fractional expressions include those in which the numerator variable and the denominator variable are interchanged.

In addition, when evaluating the state of MCI, values related to other biometric information (for example, the values listed below) may be additionally used in addition to the concentration values of at least two amino acids among the above five types of amino acids. In addition to the variables to which the concentration values of at least two of the above five amino acids are substituted, in the equations used for evaluation, values related to other biometric information (for example, the values listed below) are substituted into 1 One or more variables may be additionally included.

1. Concentration values of other blood metabolites other than amino acids (amino acid-related metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc.

2. Total protein, triglycerides (triglycerides), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST) , GPT (ALT), GGTP (γ-GTP), glucose (blood sugar level), CRP (C-reactive protein), MCV, MCH, MCHC, etc. blood test values

3. Values obtained from image information such as ultrasound echo, X-ray, CT, MRI, and endoscopic images

4. Age, height, weight, BMI, abdominal circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, meal information, drinking information, exercise information, stress information, sleep information, family history information, medical history information (diabetes, etc.) ) values for biomarkers such as

5. Values obtained from genetic information such as the number of Alzheimer's dementia risk genes (APOEε4 allyl, etc.)

[Second Embodiment]

[2-1. Outline of Second Embodiment]

Here, the outline of the second embodiment will be described with reference to FIG. 2 . Fig. 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in the description of the second embodiment, the description overlapping with that of the first embodiment described above may be omitted. In particular, here, when evaluating the state of MCI, a case in which the value of the formula or the value after conversion thereof is used is described as an example, but, for example, the concentration value of at least two amino acids among the above five amino acids or its Values after conversion (e.g., concentration deviation values, etc.) may be used.

The controller controls the concentration values of at least two of the five amino acids contained in blood data of an evaluation target (e.g., an individual such as an animal or human) obtained in advance regarding the concentration values, and the concentration values By calculating the value of the expression using an expression previously stored in the storage unit including the variable to be substituted, the state of MCI is evaluated for the evaluation target (step S21). In this way, it is possible to provide highly reliable information that can serve as a reference in knowing the state of MCI.

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

First, the control unit performs indicator state information stored in advance in a storage unit including blood data and indicator data related to an indicator indicating the state of MCI (data having missing values or deviated values may be removed in advance) ), a candidate expression (eg, y=a1x1+a2x2+…+anxn, y: index data, xi: blood data, ai: constant, i=1,2, …, n ) is written (Step 1).

Further, in step 1, from the index state information, a plurality of different expression creation techniques (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree ) may be used together to create a plurality of candidate equations. Specifically, for indicator status information, which is multivariate data composed of blood data and indicator data obtained by analyzing blood obtained from a number of healthy and MCI groups, a plurality of different algorithms are used to simultaneously evaluate multiple groups of candidate formulas You can also write negatively. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate expressions. Alternatively, a candidate formula may be created by converting the index state information using a candidate expression created by performing principal component analysis, and performing discriminant analysis on the converted index state information. In this way, finally, an expression optimal for evaluation can be created.

Here, the candidate equation created using principal component analysis is a primary equation including each variable that maximizes the variance of all blood data. Further, a candidate expression created using discriminant analysis is a high-order expression (including exponential or logarithmic) that includes each variable that minimizes the ratio of the sum of variances in each group to the variance of all blood data. In addition, the candidate expression created using the support vector machine is a high-order expression (including a kernel function) including each variable that maximizes the boundary between groups. In addition, the candidate expression created using the multiple regression analysis is a high-order expression including each variable that minimizes the sum of distances from all blood data. Further, the candidate expression created using Cox regression analysis is a linear model including a logarithmic hazard ratio, and is a linear expression including each variable that maximizes the likelihood of the model and its coefficient. In addition, the candidate expression created using logistic regression analysis is a linear model expressing log odds of probability, and is a linear expression including each variable that maximizes the likelihood of the probability. In addition, the k-means method searches k neighborhoods of each blood data, defines the largest number of groups among the groups to which the near points belong, as the group to which the data belongs, and the group to which the input blood data belongs and the defined group match the most. It is a method for selecting variables that Further, the cluster analysis is a method of clustering (grouping) points that are closest to each other among all blood data. Further, the decision tree is a method of predicting a group of blood data from a pattern that a variable with a higher sequence can take by assigning a sequence to variables.

Returning to the description of the expression creation process, the control unit verifies (mutually verifies) the candidate expression created in step 1 based on a predetermined verification method (step 2). Verification of the candidate formula is performed for each candidate formula created in Step 1. Further, in Step 2, based on at least one of the bootstrap method, the holdout method, the N-fold method, the leave one-out method, etc., the discrimination rate, sensitivity, specificity, information amount standard, ROC_AUC (receiver characteristic area under the curve), etc. may be verified. In this way, it is possible to create a candidate formula with high predictability or robustness in consideration of index state information and evaluation conditions.

Here, the discriminant rate is the evaluation method according to the present embodiment, in which an evaluation target whose true state is negative (for example, an evaluation target not affected by MCI) is correctly evaluated as negative, and the true state is evaluated as positive This is the ratio of correctly evaluating a target (for example, an evaluation target suffering from MCI) as positive. In addition, the sensitivity is the rate at which the evaluation target whose real state is positive is correctly evaluated as positive by the evaluation method according to the present embodiment. Further, the specificity is the ratio of correctly evaluating an evaluation target whose true state is negative as negative by the evaluation method according to the present embodiment. The Akaike Information Quantity Criterion is a criterion for expressing the extent to which observed data agrees with a statistical model in the case of regression analysis, etc. The model with the minimum value defined as “number)” is judged to be the best. In addition, 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 the value of ROC_AUC is perfect discrimination becomes 1, and the closer this value is to 1, the higher the discrimination. In addition, the predictability is the average of the discriminant rate, sensitivity, and specificity obtained by repeating verification of candidate formulas. Robustness is the discriminant rate, sensitivity, and variance of specificity obtained by repeating verification of candidate formulas.

Returning to the description of the formula creation process, the control unit selects a variable of the candidate formula based on a predetermined variable selection method, thereby selecting a combination of blood data included in the index state information used when creating the candidate formula (step 3). In step 3, selection of variables may be performed for each candidate expression created in step 1. In this way, the variables of the candidate expression can be appropriately selected. Then, process 1 is executed again using the indicator state information including the blood data selected in process 3. Further, in step 3, the variable of the candidate expression may be selected from the verification result in step 2 based on at least one of a stepwise method, a best pass method, a neighborhood search method, and a genetic algorithm. Further, the best pass 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 description of the expression creation process, the control unit repeatedly executes steps 1, 2, and 3 described above, and based on the verification result accumulated thereby, the candidate expression to be used in the evaluation from among the plurality of candidate expressions. By selecting , the expression used at the time of evaluation is created (step 4). In the selection of candidate formulas, for example, there are cases in which an optimal candidate formula is selected from candidate formulas created by the same formula creation method, and an optimal formula is selected from among all candidate formulas.

As described above, in the expression creation process, based on the index state information, processes related to creation of candidate expressions, verification of candidate expressions, and selection of variables of candidate expressions are systematized (systematized) in a series of flows and executed, An expression optimal for MCI evaluation can be created. In other words, in the formula creation process, 19 amino acids (Ala, Arg, Asn, Cit, Gln, Gly, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Ser, Thr, Trp, Tyr, Val ) is used for multivariate statistical analysis, and a variable selection method and cross-validation are combined to select an optimal and robust pair of variables to extract an expression with high evaluation performance.

[2-2. Configuration of the second embodiment]

Here, the configuration of the evaluation system according to the second embodiment (hereinafter sometimes referred to as the present system) will be described with reference to FIGS. 3 to 13 . In addition, this system is only an example, and this invention is not limited to this. In particular, here, when evaluating the state of MCI, a case in which the value of the formula or the value after conversion thereof is used is described as an example, but, for example, the concentration value of at least two amino acids among the above five amino acids or its Values after conversion (e.g., concentration deviation values, etc.) 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. 4 is a diagram showing another example of the overall configuration of this system. As shown in FIG. 3, the present system includes an evaluation apparatus 100 for evaluating the state of MCI for an individual subject to evaluation, and blood data of the individual regarding concentration values of at least two amino acids among the above five amino acids in blood It is configured by connecting a client device 200 (corresponding to the terminal device of the present invention) providing a communicatively via a network 300.

In this system, the client device 200 serving as a source of data used for evaluation and the client device 200 serving as a provider of evaluation results may be different. As shown in FIG. 4 , this system stores, in addition to the evaluation device 100 and the client device 200, index state information used when creating expressions with the evaluation device 100, expressions used during evaluation, and the like. It may be configured by connecting one database device 400 communicatively via the network 300 . As a result, 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 through the network 300, the MCI Information that can be used as a reference for knowing the status is provided. Here, the reference information for knowing the state of MCI is, for example, information about values measured for specific items related to the state of MCI in organisms including humans. In addition, information used as a reference for knowing the state of MCI is generated by the evaluation device 100, the client device 200, or other devices (eg, various measuring devices, etc.), and is mainly stored in the database device 400. accumulate in

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, conceptually showing only the part related to the present invention in the configuration.

The evaluation device 100 is configured via a control unit 102 such as a central processing unit (CPU) 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. is connected to the communication interface unit 104 communicatively connected to the network 300, the storage unit 106 for storing various databases, tables, files, etc., and the input device 112 or output device 114. It is composed of an input/output interface unit 108, and each of these units is communicatively connected via an arbitrary communication path. Here, the evaluation device 100 may be configured with the same housing as various analysis devices (for example, an amino acid analysis device). For example, a mini-analysis equipped with a configuration (hardware and software) for calculating (measuring) the concentration values of at least two of the above five amino acids in blood and outputting (printing or displaying on a monitor) the calculated values. The apparatus may further include an evaluation unit 102d to be described later, and may output results obtained by the evaluation unit 102d using the configuration described above.

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

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

The storage unit 106 is a storage means, and for example, memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, optical disks, etc. can be used. there is. In the storage unit 106, computer programs for giving commands to the CPU in cooperation with an OS (Operating System) to perform various processes are recorded. As shown in the figure, the storage unit 106 includes a blood data file 106a, an indicator state information file 106b, a designated indicator state information file 106c, a food-related information database 106d, and an evaluation result. The file 106e is stored.

The blood data file 106a stores blood data related to concentration values of at least two amino acids among the above five amino acids in 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 associating an individual number for uniquely identifying an individual (sample) to be evaluated with 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 name scale or an ordinal scale. In addition, in the case of the name scale or order scale, you may analyze by assigning an arbitrary numerical value to each state. In addition, blood data may be combined with values related to other biometric information (see above).

Returning to Fig. 5, the index state information file 106b stores index state information used when creating an expression. Fig. 7 is a diagram showing an example of information stored in the indicator state information file 106b. As shown in Fig. 7, the information stored in the indicator state information file 106b includes an individual number, indicator data T related to indicators (index T1, indicator T2, indicator T3...) indicating the state of the MCI, and , It is composed by correlating blood data with each other. Here, in FIG. 7, the index data and blood data are handled as numerical values (ie, continuous scale), but the index data and blood data may be a name scale or an ordinal scale. In addition, in the case of the name scale or order scale, you may analyze by assigning an arbitrary numerical value to each state. In addition, the indicator data is a known indicator used as a marker of the MCI state, etc., and numerical data may be used.

Returning to Fig. 5, the specified indicator state information file 106c stores the indicator state information specified by the designation unit 102b described later. Fig. 8 is a diagram showing an example of information stored in the designated indicator state information file 106c. As shown in Fig. 8, the information stored in the designated indicator state information file 106c is configured by associating an individual number, designated index data, and designated blood data with each other.

Returning to FIG. 5 , the expression-related information database 106d is composed of an expression file 106d1 storing an expression created by an expression creator 102c described later. The expression file 106d1 stores expressions used for to be evaluated. Fig. 9 is a diagram showing an example of information stored in the expression file 106d1. As shown in Fig. 9, the information stored in the expression file 106d1 includes ranks, equations (in Fig. 9, Fp(Cit,...), Fp(Cit, Lys, Ser), Fk(Cit, Lys, Ser) , ...), etc.), the threshold value corresponding to each expression creation method, and the verification result of each expression (for example, the value of each expression) are configured in association with each other.

Returning to FIG. 5, the evaluation result file 106e stores the evaluation result obtained by the evaluation part 102d mentioned later. Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106e. Information stored in the evaluation result file 106e includes an individual number for uniquely identifying an individual (sample) to be evaluated, blood data of the individual obtained in advance, and evaluation results related to the MCI state (e.g., as described below). The value of the expression calculated by the calculation unit 102d1, the value after converting the value of the expression by the conversion unit 102d2 described later, the positional information generated by the generation unit 102d3 described later, or the classification unit described later ( The classification results obtained in 102d4), etc.) are associated with each other.

Returning to Fig. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, etc., required data, etc., and executes various information processes based on these programs. . As shown in the figure, the control unit 102 is roughly classified 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. is provided. The control unit 102 removes data with missing values, removes data with many deviated values, and determines missing values with respect to the indicator state information transmitted from the database device 400 or the blood data transmitted from the client device 200. Data processing such as removal of variables with a lot of data is also performed.

The acquisition unit 102a acquires information (specifically, blood data, indicator state information, formulas, etc.). For example, the acquisition unit 102a receives information transmitted from the client device 200 or the database device 400 (specifically, blood data, indicator state information, equations, etc.) via the network 300. By doing so, you may acquire information. In addition, 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 results are sent. Further, for example, when the evaluation device 100 is equipped with a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquisition unit 102a writes the information to the recording medium. The information may be acquired by reading the information (specifically, blood data, indicator state information, formula, etc.) through the instrument. The designation unit 102b designates target index data and blood data when creating an expression.

The expression creation unit 102c creates an expression based on the index state information acquired by the acquisition unit 102a or the index state information designated by the designation unit 102b. In the case where an expression is previously stored in a predetermined storage area of the storage unit 106, the expression creation unit 102c may create an expression by selecting a desired expression from the storage unit 106. Alternatively, the expression creation unit 102c may create an expression by selecting and downloading a desired expression from another computer device (for example, the database device 400) that stores the expression in advance.

The evaluation unit 102d uses an expression obtained in advance (for example, an expression created by the expression creation unit 102c or an expression acquired by the acquisition unit 102a) and an object acquired by the acquisition unit 102a. By calculating the value of the expression using the at least one value included in the blood data, the state of MCI for the subject is evaluated. In addition, the evaluation unit 102d may evaluate the MCI state of the individual using the concentration values of at least two amino acids among the above five types of amino acids or the values after conversion of the concentration values (for example, concentration deviation values) good night.

Here, the configuration of the evaluation unit 102d will be described with reference to FIG. 11 . Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d, conceptually showing only the part related to the present invention in the configuration. 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 expression using an expression including at least the concentration values of at least two amino acids among the five types of amino acids and a variable to which the concentration values are substituted. Further, the evaluation unit 102d may store the value of the expression 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 expression calculated by the calculation unit 102d1 by, for example, the conversion method described above. Further, 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. In addition, the conversion unit 102d2 may convert the concentration value included in the blood data by, for example, the conversion method described above.

The generating unit 102d3 calculates positional information about the position of a predetermined display on a display device such as a monitor or a predetermined ruler that appears visually on a physical medium such as paper, the value of an expression calculated by the calculation unit 102d1 Alternatively, it is generated using a value after conversion in the conversion unit 102d2 (the concentration value or the value after conversion of the concentration value may be used). Further, the evaluation unit 102d may store the positional information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

The classification unit 102d4 uses the value of the equation calculated by the calculation unit 102d1 or the value converted by the conversion unit 102d2 (the concentration value or the value after conversion of the concentration value may be used) to classify the individual, It is classified into one of a plurality of categories defined by at least considering the degree of possibility of being affected by MCI.

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

The transmitter 102f transmits evaluation results to the client device 200, which is the source of the blood data of the individual, or transmits expressions and evaluation results created by the evaluation device 100 to the database device 400. In addition, the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is the source of the data used for evaluation.

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

The client device 200 communicates with the control unit 210, the ROM 220, the HD (Hard Disk) 230, the RAM 240, the input device 250, the output device 260, and the input/output IF 270 It is composed of IF 280, and each of these parts is communicatively connected via an arbitrary communication path. The client device 200 is an information processing device (e.g., a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone)) to which peripheral devices such as a printer, monitor, and image scanner are connected as needed. System) terminal, portable terminal, mobile communication terminal, information processing terminal such as PDA (Personal Digital Assistant), etc.).

The input device 250 is a keyboard, mouse, or microphone. In addition, a monitor 261 described later also realizes a pointing device function in cooperation with a mouse. The output device 260 is output means for outputting information received through the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition to this, you may install a speaker etc. to the output device 260. Input/output IF 270 connects to input device 250 or output device 260.

The communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that communication is possible. In other words, the client device 200 is connected to the network 300 through a communication device such as a modem, a terminal adapter (TA), or a router, and a telephone line or a dedicated line. In this way, the client device 200 can access the evaluation device 100 according to a predetermined communication protocol.

The control unit 210 includes a receiving unit 211 and a transmitting unit 212 . The receiver 211 receives various types of information such as evaluation results transmitted from the evaluation device 100 via the communication IF 280 . The transmission unit 212 transmits various types of information such as blood data of the individual to the evaluation device 100 via the communication IF 280 .

The control unit 210 may realize all or an arbitrary part of the processing performed by the control unit with a CPU and a program that is interpreted and executed by the CPU. In the ROM 220 or the HD 230, a computer program for giving commands to the CPU in cooperation with the OS and performing various processes is recorded. The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. In addition, 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 part of it as needed. In addition, all or an arbitrary part of the processing performed by the control unit 210 may be realized by hardware using wired logic or the like.

Here, the controller 210 includes an evaluation unit 210a having the same function as that of the evaluation unit 102d provided in the evaluation device 100 (a calculation unit 210a1, a conversion unit 210a2, and a generation unit). (including 210a3 and classification unit 210a4) may be provided. Then, when the control unit 210 includes the evaluation unit 210a, the evaluation unit 210a converts the conversion unit 210a2 according to the information included in the evaluation result transmitted from the evaluation device 100. The value of the equation (may be a concentration value) is converted, or positional information corresponding to the value of the equation or the value after conversion (the value after conversion of the concentration value or the concentration value may be used) is generated in the generating unit 210a3, or classification is performed. In unit 210a4, the individual may be classified into one of a plurality of categories using the value of the expression or the value after conversion (the concentration value or the value after conversion of the concentration value may be used).

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 able to communicate with each other, and includes, for example, the Internet, an intranet, or a LAN (Local Area Network) (wired/ including both wireless parties), and the like. In addition, the network 300 is a value-added network (VAN), a personal computer communication network, a public switched telephone network (including both analog and digital), a dedicated line network (including both analog and digital), or a CATV ( Community Antenna TeleVision) network, portable circuit switching network or portable packet switching network (IMT (International Mobile Telecommunication) 2000 method, GSM (registered trademark) (Global System for Mobile Communications) method, PDC (Personal Digital Cellular)/PDC-P method, etc. including), wireless paging networks, local wireless networks such as Bluetooth (registered trademark), PHS networks, satellite communication networks (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (Integrated Services Digital Broadcasting), etc. including), etc. may be used.

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

The database device 400 stores the evaluation device 100 or index state information used when creating expressions in the database device, expressions created in the evaluation device 100, evaluation results in the evaluation device 100, and the like. has a function As shown in Fig. 13, the database device 400 connects the database device 400 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 for communicatively connecting devices to the network 300, a storage unit 406 for storing various databases, tables, files (for example, web page files), and the like, and an input device 412 ) and an input/output interface unit 408 connected to the output device 414, and each of these units is communicatively connected 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. In the storage unit 406, various programs and the like used for various processes are stored. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line. The input/output interface unit 408 is connected to an input device 412 or an output device 414 . Here, as the output device 414, a monitor (including a home television), as well as a speaker or a printer can be used. In addition, as the input device 412, a keyboard, a mouse, a microphone, and a monitor that realizes a pointing device function in cooperation with a mouse can be used.

The control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, and the like, required data, and the like, and executes various information processes based on these programs. As shown in the figure, the control unit 402 is roughly divided and includes a transmission unit 402a and a reception unit 402b. The transmitter 402a transmits various types of information such as index state information and formulas to the evaluation device 100 . The receiver 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100 .

In this description, the evaluation device 100 executes everything from acquisition of blood data, calculation of formula values, classification into categories of individuals, and transmission of evaluation results, and the client device 200 executes the evaluation results. Although the case of performing reception of is given as an example, in the case where the evaluation unit 210a is provided in the client device 200, it is sufficient for the evaluation device 100 to calculate the value of the expression, for example, the expression Conversion of the value of , generation of positional information, classification into categories of entities, and the like may be appropriately shared between the evaluation device 100 and the client device 200.

For example, when the client device 200 receives an expression value from the evaluation device 100, the evaluation unit 210a converts the expression value in the conversion unit 210a2 or the generation unit 210a3. ) may generate positional information corresponding to the value of the expression or the value after conversion, or the classification unit 210a4 may use the value of the expression or the value after conversion to classify the object into one of a plurality of categories.

In addition, when the client device 200 receives the value after conversion from the evaluation device 100, the evaluation unit 210a generates positional information corresponding to the value after conversion in the generation unit 210a3 or classifies it. The object may be classified into one of a plurality of categories using the value after conversion in unit 210a4.

In addition, when the client device 200 receives the value of the expression or the value after conversion and position information from the evaluation device 100, the evaluation unit 210a determines the value of the expression or the value after conversion in the classification unit 210a4. A value may be used to classify an object into one of a plurality of divisions.

[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, in addition to the second embodiment described above, are within the scope of the technical idea described in the claims. It may be implemented in various different embodiments within.

In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically may be manually performed, or all or part of the processes described as being performed manually may be automatically performed by a known method. may be

In addition to this, information including parameters such as processing procedures, control procedures, specific names, registration data for each processing and search conditions, examples of screens, and database configurations shown in the above documents and drawings may be arbitrarily modified except as otherwise noted. can

In addition, with respect to the evaluation device 100, each component in the illustration is functionally conceptual, and does not necessarily need to be physically configured as shown.

For example, all or an arbitrary part of the processing functions provided in the evaluation device 100, particularly each processing function executed in the control unit 102, may be realized by a CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic. In addition, the program is recorded in a non-transitory computer readable recording medium containing programmed instructions for executing the evaluation method or calculation method according to the present invention in the information processing device, and the evaluation device 100, if necessary. are read mechanically. That is, a computer program for giving a command to the CPU in cooperation with the OS to perform various processes is recorded in a storage unit 106 or the like such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and constitutes a control unit in cooperation with the CPU.

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

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" means a memory card, a USB (Universal Serial Bus) memory, an SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disk), and Blu-ray (registered trademark) Disc. "Portable physical media" shall be included.

In addition, a "program" is a data processing method described in an arbitrary language or description method, regardless of format such as source code or binary code. In addition, a "program" is not necessarily limited to being configured singly, but also includes those having a distributed configuration as a plurality of modules or libraries, and those that achieve their functions in cooperation with a separate program represented by the OS. Note that, in each device shown in the embodiments, well-known configurations and procedures can be used for specific configurations and reading procedures for reading a recording medium, installation procedures after reading, and the like.

Various databases 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 processing and website provision. It stores various programs, tables, databases, files for web pages, and the like.

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 installing software (including programs, data, etc.) for realizing the evaluation method or calculation method of the present invention in the information processing device.

In addition, the specific form of distribution/integration of the device is not limited to that shown, and all or part thereof can be functionally or physically distributed/integrated in an arbitrary unit according to various additions or functional loads. . That is, the embodiments described above may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Example 1

From plasma samples of MCI patients 60 years of age or older (MCI group: 120 people) with a definitive diagnosis of MCI, and healthy elderly people 60 years of age or older (healthy group: 120 people) whose cognitive functions are considered to be healthy, the amino acid analysis method described above ( A) The concentration of amino acids in blood was measured.

A two-variable logistic regression equation for discriminating between the MCI group and the healthy group was obtained using Ser and Trp plasma concentration values (nmol/ml). The discriminative ability between the MCI group and the healthy group according to this logistic regression equation was evaluated by ROC_AUC, and 0.540 was found. Since ROC_AUC exceeds 0.5, this logistic regression equation is considered to be useful in evaluating the state of MCI.

Example 2

Sample data used in Example 1 were used. A multivariate discriminant (multivariate function) was obtained to discriminate between the 2 groups, the MCI group and the healthy group, including variables to which amino acid concentration values in plasma were substituted.

Logistic regression was used as the multivariate discriminant. Combinations of the three variables included in the logistic regression equation were searched for from plasma concentration values (nmol/ml) of five amino acids (Cit, Lys, Ser, Thr, and Trp), and the discriminating ability between the MCI group and the healthy group was determined. A good logistic regression equation was searched for.

Table 1 below shows a list of logistic regression equations of three variables that necessarily include three amino acids among the five amino acids as variables. These logistic regression equations are considered to be 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.

Example 3

Sample data used in Example 1 were used. A multivariate discriminant (multivariate function) was obtained to discriminate between the 2 groups, the MCI group and the healthy group, including variables to which amino acid concentration values in plasma were substituted.

Logistic regression was used as the multivariate discriminant. Combinations of 4 variables included in the logistic regression equation (requiring 3 amino acid variables among the above 5 kinds of amino acids), 19 kinds of amino acids (Ala, Arg, Asn, Cit, Gln, Glu, Gly, His , Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr, Val) were searched from plasma concentration values, and a logistic regression equation with good discrimination between the MCI group and the healthy group was searched.

A list of logistic regression equations in which the ROC_AUC values of the MCI group and the healthy group were 0.679 or more shown in Table 1 and the number of variables was 4 is shown in Table 2 below. These logistic regression equations are considered to be useful in the above evaluation because the ROC_AUC value is high and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

Example 4

Sample data used in Example 1 were used. A multivariate discriminant (multivariate function) was obtained to discriminate between the 2 groups, the MCI group and the healthy group, including variables to which amino acid concentration values in plasma were substituted.

Logistic regression was used as the multivariate discriminant. Combinations of 5 variables included in the logistic regression equation (essentially 3 amino acids among the 5 amino acids) were searched from plasma concentration values of the 19 amino acids, and the discriminative ability between the MCI group and the healthy group was determined. A good logistic regression equation was searched for.

Among the 981 logistic regression equations obtained above, 270 equations in which the ROC_AUC value of the MCI group and the healthy group were 0.679 or more were shown in the following [Formulas of 5 variables]. This logistic regression equation is considered to be useful in the above evaluation because the ROC_AUC value is high and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5. In addition, in [Equation of 5 variables] below, the ROC_AUC value, the lower limit value of the 95% CI of the ROC_AUC value, and the upper limit value of the 95% CI of the ROC_AUC value corresponding to the five amino acids included in the expression are shown.

[Equation 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); Ser, 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, He, Trp, 0.692 (0.625, 0.76); Ser, His, Cit, Val, Lys, 0.692 (0.625, 0.759); Ser, Gly, Thr, Lys, He, 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, He, 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, He, 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, He, 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, He, 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, 0.756); 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, 0.754); 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, He, 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, He, 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 (0.617, 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, He, 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, He, 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, He, 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, He, 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, He, Phe, Trp, 0.683 (0.615, 0.751); Ser, Gln, Cit, Lys, Phe, 0.683 (0.614, 0.751); Ser, Asn, Thr, Lys, He, 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, He, 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, He, 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, He, 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, 0.682 (0.613, 0.75); Ser, His, Lys, He, Trp, 0.682 (0.614, 0.75); Ser, Gln, Ala, Cit, Lys, 0.682 (0.613, 0.75); Ser, His, Thr, Lys, He, 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, He, 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, He, 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, He, Trp, 0.68 (0.612, 0.748); 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)

Example 5

Sample data used in Example 1 (hereinafter referred to as training data) were used. A multivariate discriminant (multivariate function) was obtained to discriminate between the 2 groups, the MCI group and the healthy group, including variables to which amino acid concentration values in plasma were substituted.

Logistic regression was used as the multivariate discriminant. Combinations of 6 variables included in the logistic regression equation (essentially 3 amino acids among the 5 amino acids) were searched from plasma concentration values of the 19 amino acids, and the discriminative ability between the MCI group and the healthy group was determined. A good logistic regression equation was searched for.

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

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

128 highly robust logistic regression equations in which the ROC_AUC values of the MCI group and the healthy group in the verification data were 0.600 or more are shown in [Equation of 6 variables] below. Since such a logistic regression equation has a high ROC_AUC value and high robustness, it is considered useful in the above evaluation to satisfy the requirements in actual clinical practice. In addition, in the following [Equation of 6 variables], the ROC_AUC value corresponding to the six amino acids included in the equation, the lower limit value of the 95% CI of the ROC_AUC value, and the upper limit value of the 95% CI of the ROC_AUC value It appears.

[Equation 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, He, 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, He, Trp, 0.701 (0.634, 0.768); Ser, Thr, Arg, Lys, He, Trp, 0.701 (0.634, 0.768); Ser, Asn, Cit, Arg, Val, Lys, 0.701 (0.634, 0.767); Ser, Gly, Thr, Arg, Lys, He, 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, He, 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, He, 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, He, 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)

Example 6

Among the top 200 ROC_AUC values of the MCI group and the healthy group in the training data 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. 128 highly robust logistic regression equations used

Blood amino acid concentration data (hereinafter referred to as full data) including training data and verification data were 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 described in Example 1 and the MCI group described in Example 5 Amino acid concentration data in blood obtained from plasma samples of 220 healthy subjects combining 100 healthy subjects were used.

The value of the formula at a specificity of 60% was set as the first cutoff value, and the value of the formula at a specificity of 90% was set as the second cutoff value. In addition, when the value of the equation is lower than the first cutoff value, it is rank A (classification meaning that the possibility (probability, risk) of MCI is low), and the value of the equation is higher than the first cutoff value and lower than the second cutoff value The case was defined as rank B (classification indicating a medium probability of MCI), and the case where the value of the equation was higher than the second cutoff value was defined as rank C (classification indicating a high probability of MCI).

And, under the above settings and above definitions, the ranks were calculated by person and by identification using the total data and the 128 logistic regression equations. In addition, using this calculation result, the positive likelihood ratio defined as "positive likelihood ratio = sensitivity _{* 1} / (1 - specificity _{* 2} )" when ranks A and B are negative and rank C is positive , was calculated as identification. That is, the ratio of the ratio of the ratio of those counting positive among MCI patients to the ratio of those counting positive among healthy elderly persons was calculated by identification.

※1: Sensitivity = b/(a+b)

a = number of MCI patients who were calculated as negative (rank A or rank B)

b = number of MCI patients who are counted as positive (rank C)

※2: Specificity = c/(c+d)

c=Number of people who tested negative (rank A or rank B) among healthy elderly people

d=Number of persons tested positive (rank C) among healthy elderly persons

Seven logistic regression equations for which the positive likelihood ratio was 3.0 or more were shown in Table 3 below. It can be expected that such a logistic regression equation has a high positive likelihood ratio and is more likely to lead to behavioral changes of examinees than other equations.

Among the above seven logistic regression equations, a logistic regression equation with a pair of variables “Ser, Thr, Ala, Cit, Lys, Trp” and a logistic equation with a pair of variables “Ser, Thr, Cit, Met, Lys, Trp” A regression equation, a logistic regression equation having a pair of variables "Ser, Thr, Cit, Tyr, Lys, Trp" and a logistic regression equation having a pair of variables "Ser, Thr, Cit, Orn, Lys, Trp" are in the training data The ROC_AUC value of the MCI group and the healthy group is high at 0.70 or more, and the ROC_AUC value of the MCI group and the healthy group in the verification data is also high at 0.65 or more, so the discrimination performance is high, and the robustness is particularly high.

As described above, the present invention can be widely applied in many industrial fields, particularly in fields such as pharmaceuticals, foods, and medical care, and in particular, predicting the progress of MCI, predicting disease risk, proteome and metabolom analysis, etc. It is very useful in the field of bioinformatics.

100 evaluation unit (including calculation unit)
102 Control
102a acquisition department
102b designation
102c Expression Builder
102d evaluation department
102d1 calculation unit
102d2 conversion unit
102d3 generator
102d4 classification unit
102e result output unit
102f transmitter
104 communication interface
106 storage unit
106a concentration data file
106b Indicator Status Information File
106c Designated Indicator Status Information File
106d Expression Related Information Database
106d1 expression file
106e Evaluation Results File
108 input/output interface
112 input devices
114 output devices
200 client device (terminal device (information communication terminal device))
300 network
400 database device

Claims (15)

Concentration values of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the subject of evaluation, or an expression including a variable to which the concentration values are substituted, and a value of the above formula calculated using the concentration values are used. To include an evaluation step of evaluating the state of mild cognitive impairment for the evaluation target,
Evaluation method characterized by. The method of claim 1, wherein 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 , containing Thr and Trp, Thr, Cit and Trp, or Ser, Cit and Trp;
Evaluation method characterized by. The method of claim 2, wherein the concentration value is a concentration value of at least six amino acids,
The at least six 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 , containing 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;
Evaluation method characterized by. The method according to any one of claims 1 to 3, wherein the evaluation step is executed by the control unit of an information processing device having a control unit;
Evaluation method characterized by. Using an equation for evaluating the state of mild cognitive impairment including concentration values of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood of the evaluation target, and variables to which the concentration values are substituted, including a calculation step for calculating the value of the expression;
Calculation method characterized by. The method according to claim 5, wherein the calculation step is executed by the control unit of an information processing device having a control unit;
Calculation method characterized by. An evaluation device having a control unit,
The control unit,
Concentration values of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the subject of evaluation, or an expression including a variable to which the concentration values are substituted, and a value of the above formula calculated using the concentration values are used. and evaluation means for evaluating the state of mild cognitive impairment for the evaluation target
to provide,
Evaluation device characterized by. The method of claim 7, communicatively connected via a network with a terminal device that provides the concentration value or the value of the expression,
The control unit,
data receiving means for receiving the concentration value or the value of the expression transmitted from the terminal device;
result transmission means for transmitting evaluation results obtained by the evaluation means to the terminal device;
additionally provided,
the evaluation means uses the concentration value received by the data receiving means or the value of the expression;
Evaluation device characterized by. A calculation device having a control unit,
The control unit,
Using an equation for evaluating the state of mild cognitive impairment including concentration values of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood of the evaluation target, and variables to which the concentration values are substituted, Calculation means for calculating the value of the expression
to provide,
Characterized by a calculation device. As an evaluation program to be executed in an information processing device having a control unit,
For the control unit to execute,
Concentration values of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the subject of evaluation, or an expression including a variable to which the concentration values are substituted, and a value of the above formula calculated using the concentration values are used. and an evaluation step of evaluating the state of mild cognitive impairment for the evaluation target
to include,
Characterized by an evaluation program. As a calculation program to be executed in an information processing device having a control unit,
For the control unit to execute,
Using an equation for evaluating the state of mild cognitive impairment including concentration values of at least two amino acids of Cit, Lys, Ser, Thr and Trp in the blood of the evaluation target, and variables to which the concentration values are substituted, Calculation step for calculating the value of an expression
to include,
Characterized by a calculation program. A computer-readable recording medium on which the program according to claim 10 or 11 is recorded. An evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit communicatively via a network, the evaluation system comprising:
The control unit of the terminal device,
Concentration values of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the subject to be evaluated, or an expression including a variable to which the concentration value is substituted, and the value of the expression calculated using the concentration value, data transmitting means for transmitting to the evaluation device;
Result receiving means for receiving an evaluation result regarding the state of mild cognitive impairment of the evaluation target, transmitted from the evaluation device;
to provide,
The control unit of the evaluation device,
data receiving means for receiving the concentration value or the value of the expression transmitted from the terminal device;
evaluation means for evaluating the state of mild cognitive impairment of the evaluation target using the concentration value or the value of the expression received by the data receiving means;
result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device;
to provide,
Rating system characterized by. As a terminal device having a control unit,
The control unit,
Result acquisition means for acquiring evaluation results regarding the state of mild cognitive impairment for the evaluation target
to provide,
The evaluation result is calculated using a concentration value of at least two amino acids among Cit, Lys, Ser, Thr, and Trp in the blood of the evaluation subject, or an expression including a variable to which the concentration value is substituted and the concentration value That it is the result of evaluating the state of mild cognitive impairment for the evaluation target using the value of the above formula;
A terminal device characterized by The method of claim 14, which is communicably connected to an evaluation device for evaluating the state of mild cognitive impairment of the evaluation target through a network,
The controller includes data transmission means for transmitting the concentration value or the value of the expression to the evaluation device;
the result acquiring unit receives the evaluation result transmitted from the evaluation device;
A terminal device characterized by

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