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

Abstract

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

Description

[Technical Field]

[0001] The present invention relates to an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a recording medium, an evaluation system, and a terminal device for Mild Cognitive Impairment (hereinafter referred to as "MCI"). [Background technology]

[0002] MCI refers to a condition considered to be a pre- or borderline stage of various dementias, characterized by cognitive impairments relative to normal aging but without interfering with daily life and not requiring a diagnosis of dementia. Currently, two diagnostic criteria for MCI have been proposed and widely accepted (Non-Patent Documents 1, 2). MCI can be caused by a variety of underlying conditions, including Alzheimer's disease (AD), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD), and senile depression. Depending on the underlying condition, appropriate intervention may reduce the risk of developing dementia. Therefore, providing a simple screening method for MCI could potentially increase the likelihood of more individuals seeking specialized medical care and accessing preventive and treatment options at an earlier stage, thereby contributing to preventing the onset of dementia.

[0003] However, when neuropsychological tests currently in widespread use as screening methods for dementia, such as the Mini Mental State Examination (hereinafter referred to as "MMSE"), the Hasegawa Dementia Scale-Revised (hereinafter referred to as "HDS-R"), and the Alzheimer's Disease Assessment Scale-Cognitive (hereinafter referred to as "ADAS-cog"), are applied to screening for MCI, there are issues such as lower detection accuracy compared to determining dementia, low throughput due to the long testing time, and the need for skilled personnel to administer the tests.There is a need for new simple testing technologies that can complement conventional neuropsychological tests as screening methods for MCI.

[0004] Regarding the technology for diagnosing MCI by blood tests, a technology is known that measures the concentration of peptide fragments in the blood and uses this as an indicator to determine MCI (Non-Patent Document 3). Also, a technology for diagnosing AD and MCI by quantitative analysis of amino acids and amino acid-related metabolites in the blood is known (Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2018 / 008764 [Patent Document 2] International Publication No. 2020 / 067386 [Non-patent literature]

[0006] [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. Summary of the Invention [Problem to be solved by the invention]

[0007] However, with regard to highly accurate MCI assessment technology that uses blood concentrations of metabolites, including amino acids and amino acid-related metabolites, as an indicator, there was a problem in that, in order to put it into practical use, further improvement in accuracy was required to meet the required standards in actual clinical practice.

[0008] The present invention has been made in view of the above, and aims to provide an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a recording medium, an evaluation system, and a terminal device that can provide highly reliable information that can be used as a reference for understanding the state of MCI. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems and achieve the object, the evaluation method of the present invention is characterized by including an evaluation step of evaluating the MCI state of the subject using concentration values ​​of at least two of five amino acids (Cit, Lys, Ser, Thr, and Trp) in the blood of the subject, or a value of the formula calculated using a formula including variables into which the concentration values ​​are substituted and the concentration values.

[0010] Herein, various amino acids are mainly represented by abbreviations, but their official names are as follows: (Abbreviation) (Official name) Ala Alanine Arg Arginine Asn Asparagine Cit Citrulline Gln Glutamine Gly Glycine His Histidine Ile Isoleucine Leu Leucine Lys Lysine Methionine Orn Ornithine Phenylalanine Proline Ser Serine Thr Threonine Trp Tryptophan Tyr Tyrosine Val Valine

[0011] The evaluation method of the present invention is also characterized in that the concentration values ​​are concentration values ​​of at least three amino acids, and the at least three amino acids include 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.

[0012] The evaluation method of the present invention is also characterized in that the concentration values ​​are concentration values ​​of at least six amino acids, and the at least six amino acids include 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.

[0013] In addition, the evaluation method according to the present invention is characterized in that the evaluation step is executed by a control unit of an information processing device having a control unit.

[0014] The calculation method of the present invention is also characterized by including a calculation step of calculating the value of a formula for evaluating the state of MCI using a formula including concentration values ​​of at least two of the five amino acids in the blood of the subject to be evaluated and variables into which the concentration values ​​are substituted.

[0015] In addition, the calculation method according to the present invention is characterized in that the calculation step is executed by a control unit of an information processing device having a control unit.

[0016] Furthermore, the evaluation device according to the present invention is characterized in that it is an evaluation device equipped with a control unit, and the control unit is equipped with evaluation means for evaluating the MCI state of the subject using concentration values ​​of at least two of the five amino acids in the blood of the subject, or values ​​of the formula calculated using a formula including variables into which the concentration values ​​are substituted and the concentration values.

[0017] Furthermore, the evaluation device according to the present invention is communicably connected via a network to a terminal device that provides the concentration values ​​or the values ​​of the formula, and the control unit further comprises a data receiving means that receives the concentration values ​​or the values ​​of the formula transmitted from the terminal device, and a result transmitting means that transmits the evaluation results obtained by the evaluation means to the terminal device, and the evaluation means uses the concentration values ​​or the values ​​of the formula received by the data receiving means.

[0018] Furthermore, the calculation device according to the present invention is characterized in that it is a calculation device comprising a control unit, and the control unit comprises calculation means for calculating the value of a formula for evaluating the state of MCI using a formula including concentration values ​​of at least two of the five amino acids in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted.

[0019] Furthermore, 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 is characterized by including an evaluation step, to be executed by the control unit, of evaluating the MCI state of the subject using concentration values ​​of at least two of the five amino acids in the blood of the subject, or values ​​of the formula calculated using an equation including variables into which the concentration values ​​are substituted and the concentration values.

[0020] Furthermore, 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 is characterized by including, to be executed in the control unit, a calculation step of calculating a value of a formula for evaluating the state of MCI using a formula including concentration values ​​of at least two of the five amino acids in the blood of a subject to be evaluated and variables into which the concentration values ​​are substituted.

[0021] Furthermore, a recording medium according to the present invention is a computer-readable recording medium having the evaluation program or the calculation program recorded thereon. Specifically, the recording medium according to the present invention is a non-transitory computer-readable recording medium, characterized in that it contains programmed instructions for causing an information processing device to execute the evaluation method or the calculation method.

[0022] Furthermore, the evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device equipped with a control unit and a terminal device equipped with a control unit so that they can communicate with each other via a network, wherein the control unit of the terminal device comprises: data transmitting means for transmitting to the evaluation device concentration values ​​of at least two of the five amino acids in the blood of the evaluation subject, or values ​​of the formula calculated using a formula including variables into which the concentration values ​​are substituted and the concentration values; and result receiving means for receiving an evaluation result regarding the MCI state of the evaluation subject, transmitted from the evaluation device; and the control unit of the evaluation device comprises: data receiving means for receiving the concentration values ​​or the values ​​of the formula transmitted from the terminal device; evaluation means for evaluating the MCI state of the evaluation subject using the concentration values ​​or the values ​​of the formula received by the data receiving means; and result transmitting means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

[0023] Furthermore, a terminal device according to the present invention is a terminal device equipped with a control unit, wherein the control unit comprises a result acquisition means for acquiring an evaluation result regarding the MCI state of the subject, and the evaluation result is a result of evaluating the MCI state of the subject using concentration values ​​of at least two of the five amino acids in the blood of the subject, or values ​​of the formula calculated using an equation including variables into which the concentration values ​​are substituted and the concentration values.

[0024] Furthermore, the terminal device according to the present invention is communicably connected via a network to an evaluation device that evaluates the MCI state of the evaluation subject, the control unit includes a data transmission means that transmits the concentration value or the value of the formula to the evaluation device, and the result acquisition means receives the evaluation result transmitted from the evaluation device. [Effects of the Invention]

[0025] The present invention has the effect of providing highly reliable information that can be used as a reference for understanding the state of MCI. [Brief explanation of the drawings]

[0026] [Figure 1] FIG. 1 is a diagram showing the basic principle of the first embodiment. [Figure 2] FIG. 2 is a diagram showing the basic principle of the second embodiment. [Figure 3] FIG. 3 is a diagram showing an example of the overall configuration of this system. [Figure 4] FIG. 4 is a diagram showing another example of the overall configuration of the present system. [Figure 5] FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system. [Figure 6] FIG. 6 is a diagram showing an example of information stored in blood data file 106a. [Figure 7] FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. [Figure 8] FIG. 8 is a diagram showing an example of information stored in the designated index state information file 106c. [Figure 9] FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. [Figure 10] FIG. 10 is a diagram showing an example of information stored in the evaluation result file 106e. [Figure 11] FIG. 11 is a block diagram showing the configuration of the evaluation unit 102d. [Figure 12] FIG. 12 is a block diagram showing an example of the configuration of the client device 200 of this system. [Figure 13] FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system. DETAILED DESCRIPTION OF THE INVENTION

[0027] Hereinafter, an embodiment (first embodiment) of the evaluation method and calculation method according to the present invention, and an embodiment (second embodiment) of the evaluation device, calculation device, evaluation method, calculation method, evaluation program, calculation program, recording medium, evaluation system, and terminal device according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[0028] [First embodiment] [1-1. Overview of the First Embodiment] Here, an overview of the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the basic principle of the first embodiment.

[0029] First, blood data on the concentration values ​​of at least two of the five amino acids in blood (including, for example, plasma, serum, whole blood, etc.) collected from a subject to be evaluated (for example, an individual such as an animal or human) is obtained (step S11). Here, the blood data may be on the concentration values ​​of at least three amino acids in the blood collected from the subject to be evaluated, and the at least three amino acids may include, 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." Furthermore, the blood data may relate to, for example, concentration values ​​of at least six amino acids in blood collected from the subject, and the at least six amino acids may include, 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."

[0030] In step S11, blood data measured by a company or the like that measures concentration values ​​may be acquired. Alternatively, blood data may be acquired by measuring concentration values ​​from blood collected from the subject to be evaluated using a measurement method such as (A), (B), or (C) below. Here, the unit of concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, or may be obtained by adding, subtracting, multiplying, or dividing any constant by these concentrations. (A) Plasma is separated from the collected blood samples by centrifugation. All plasma samples are frozen and stored at -80°C until the concentration is measured. At the time of concentration measurement, acetonitrile is added to remove proteins, and then, if necessary, contaminants such as phospholipids are removed by solid phase extraction or other methods. Pre-column derivatization is performed using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and the concentration is then analyzed by liquid chromatography-mass spectrometry (including tandem mass spectrometry) (see International Publication Nos. WO 2003 / 069328 and WO 2005 / 116629). (B) The collected blood samples are centrifuged to separate the plasma from the blood. All plasma samples are frozen and stored at -80°C until the concentration values ​​are measured. At the time of concentration measurement, sulfosalicylic acid is added to remove proteins, and then the concentration values ​​are analyzed using an amino acid analyzer based on the post-column derivatization method using ninhydrin reagent. (C) The collected blood sample is subjected to blood cell separation using membranes, MEMS (Micro Electro Mechanical Systems) technology, or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not measured for concentration immediately after collection are frozen and stored at -80°C until the time of concentration measurement. When measuring concentration, molecules such as enzymes or aptamers that react with or bind to the target blood substance are used to analyze the concentration by quantifying substances that increase or decrease upon substrate recognition or spectroscopic values.

[0031] Next, the concentration values ​​contained in the blood data acquired in step S11 are used to evaluate the MCI state of the evaluation subject (step S12). Note that before executing step S12, data such as missing values ​​and outliers may be removed from the blood data acquired in step S11. Here, evaluating the MCI state means, for example, examining the current state of MCI.

[0032] As described above, according to the first embodiment, in step S11, blood data of the subject is acquired, and in step S12, the concentration values ​​included in the blood data of the subject acquired in step S11 are used to evaluate the MCI state of the subject (in other words, information for evaluating the MCI state of the subject or highly reliable information that can be useful in learning about the MCI state of the subject is acquired). This makes it possible to provide information for evaluating the MCI state of the subject or highly reliable information that can be useful in learning about the MCI state of the subject.

[0033] Alternatively, the concentration values ​​of at least two of the five amino acids may be determined to reflect the MCI state of the subject, and the concentration values ​​may be converted, for example, by the methods listed below, and the converted values ​​may be determined to reflect the MCI state of the subject. In other words, the concentration values ​​or the converted values ​​themselves may be treated as the evaluation results for the MCI state of the subject. In order to make the possible range of concentration values ​​fall within a predetermined range (e.g., a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or a range from -10.0 to 10.0, etc.), the concentration values ​​may be converted by, for example, adding, subtracting, multiplying, or dividing any value, or by converting the concentration values ​​using a predetermined conversion method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by performing a combination of these calculations on the concentration values. For example, the value of an exponential function with the concentration value as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the concentration value when the probability p of the MCI state being a predetermined state (e.g., a state exceeding the reference value, a state with a high possibility of MCI, etc.) is defined) may be further calculated, and the value obtained by dividing the calculated exponential function value by the sum of 1 and the value in question (specifically, the value of the probability p) may be further calculated. Alternatively, the density values ​​may be converted so that the converted values ​​under specific conditions are specific values, for example, the converted density values ​​may be converted so that the converted value is 5.0 when the specificity is 60% and 8.0 when the specificity is 90%. Alternatively, the concentration distribution for each amino acid may be normalized and then converted into a deviation value with an average of 50 and a standard deviation of 10. These conversions may also be performed according to gender or age. It should be noted that the density value in this specification may be the density value itself, or may be a value obtained by converting the density value.

[0034] Alternatively, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper may be generated using the concentration values ​​of at least two of the five amino acids, or the converted values ​​if the concentration values ​​have been converted, and the generated position information may be determined to reflect the MCI state of the subject. The predetermined ruler is used to evaluate the MCI state, and may be, for example, a ruler with a scale that indicates at least the upper and lower limits of the "range of possible concentration values ​​or converted values, or a portion of that range." The predetermined mark corresponds to the concentration value or converted value, and may be, for example, a circle or a star.

[0035] Alternatively, if the concentration values ​​of at least two of the five amino acids are lower than a predetermined value (such as the mean ± 1 SD, 2 SD, 3 SD, N quantile, N percentile, or a cutoff value recognized for clinical significance), or if they are equal to or higher than a predetermined value, the subject may be evaluated for MCI status. In this case, the concentration deviation (a value obtained by normalizing the concentration distribution for each amino acid by gender and then deviating so that the mean is 50 and the standard deviation is 10) may be used instead of the concentration value itself. For example, the subject may be evaluated for MCI status if the concentration deviation is less than the mean -2 SD (if the concentration deviation is <30) or if the concentration deviation is higher than the mean +2 SD (if the concentration deviation is >70).

[0036] In addition, the MCI state of the subject may be evaluated by calculating the value of an equation using a formula including concentration values ​​of at least two of the five amino acids and variables into which the concentration values ​​are substituted.

[0037] Alternatively, the calculated value of the formula may be determined to reflect the MCI state of the subject, or the value of the formula may be converted using, for example, the methods listed below, and the converted value may be determined to reflect the MCI state of the subject. In other words, the value of the formula or the converted value itself may be treated as the evaluation result regarding the MCI state of the subject. In order to make the possible range of the value of the formula fall within a predetermined range (e.g., a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or a range from -10.0 to 10.0, etc.), the value of the formula may be transformed by, for example, adding, subtracting, multiplying, or dividing any value to the value of the formula, or by transforming the value of the formula using a predetermined transformation method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by performing a combination of these calculations on the value of the formula. For example, the value of an exponential function with the value of the formula as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the value of the formula when the probability p of the MCI state being a predetermined state (e.g., a state exceeding the reference value, a state with a high possibility of MCI, etc.) is defined) may be further calculated, and the value obtained by dividing the calculated exponential function value by the sum of 1 and the value in question (specifically, the value of the probability p) may be further calculated. The value of the formula may also be converted so that the converted value under specific conditions becomes a specific value. For example, the value of the formula may be converted so that the converted value becomes 5.0 when the specificity is 60% and 8.0 when the specificity is 90%. Alternatively, the deviation values ​​may be converted to have an average of 50 and a standard deviation of 10. These conversions may also be performed according to gender or age. In this specification, the value of an expression may be the value of the expression itself, or may be a value obtained by converting the value of the expression.

[0038] Alternatively, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper may be generated using the value of the formula or, if the value of the formula has been converted, the converted value, and the generated position information may be determined to reflect the MCI state of the subject. Note that the predetermined ruler is used to evaluate the MCI state, and is, for example, a ruler with a scale that shows at least the upper and lower limit values ​​of "the range in which the value of the formula or the converted value can be taken, or a part of that range." The predetermined mark is, for example, a circle or a star that corresponds to the value of the formula or the converted value.

[0039] Alternatively, the degree of possibility that the subject has MCI may be qualitatively evaluated. Specifically, the subject may be classified into one of a plurality of categories defined by at least considering the degree of possibility that the subject has MCI using "concentration values ​​of at least two of the five amino acids and one or more predetermined thresholds" or "concentration values ​​of at least two of the five amino acids, a formula including variables into which the concentration values ​​are substituted, and one or more predetermined thresholds." The plurality of categories may include a category for subjects who are highly likely to have MCI (e.g., subjects considered to have MCI), a category for subjects who are low likely to have MCI (e.g., subjects considered not to have MCI), and a category for subjects who are moderately likely to have MCI. The multiple categories may include a category for subjects who are highly likely to have MCI, and a category for subjects who are lowly likely to have MCI (for example, a category for subjects who are highly likely to be healthy (e.g., subjects considered to be healthy)). The concentration value or the formula value may be converted by a predetermined method, and the converted value may be used to classify the subject into one of the multiple categories.

[0040] The formula used for evaluation may be in any format, but may be, for example, in the format shown below. Linear models such as multiple regression equations based on the least squares method, linear discriminants, principal component analysis, and canonical discriminant analysis Generalized linear models such as maximum likelihood-based logistic regression and Cox regression Generalized linear mixed model that takes into account random effects such as inter-individual and inter-facility differences in addition to generalized linear models Formulas created by cluster analysis such as K-means and hierarchical cluster analysis Formulas based on Bayesian statistics such as MCMC (Markov Chain Monte Carlo), Bayesian Networks, and Hierarchical Bayesian methods Formulas created by class classification such as support vector machines and decision trees Formulas created using methods that do not belong to the above categories, such as fractional formulas An expression that can be expressed as a sum of different forms of expressions

[0041] Furthermore, the formula used in the evaluation may be prepared, for example, by the method described in International Publication No. WO 2004 / 052191 or International Publication No. WO 2006 / 098192, both of which are international applications filed by the present applicant. Note that formulas obtained by these methods can be suitably used to evaluate the state of MCI, regardless of the units of amino acid concentration values ​​in blood data used as input data.

[0042] In multiple regression equations, multiple logistic regression equations, canonical discriminant functions, and the like, coefficients and constant terms are added to each variable. These coefficients and constant terms are preferably real numbers, more preferably values ​​within the 99% confidence interval of the coefficients and constant terms obtained for performing the various classifications from the data, and even more preferably values ​​within the 95% confidence interval of the coefficients and constant terms obtained for performing the various classifications from the data. Furthermore, the values ​​of each coefficient and its confidence interval may be multiplied by a real number, and the values ​​of the constant terms and their confidence intervals may be obtained by adding, subtracting, multiplying, or dividing any real constant. When using logistic regression equations, linear discriminants, multiple regression equations, and the like for evaluation, linear transformations (addition of a constant, multiplication of a constant) and monotonically increasing (decreasing) transformations (e.g., logit transformation) do not change the evaluation performance and are equivalent to the pre-conversion results, so the results after these transformations may be used.

[0043] A fractional expression is one in which the numerator is the sum of the variables A, B, C, and / or the denominator is the sum of the variables a, b, c, and / or. Fractional expressions also include sums of fractional expressions of this type, such as α, β, γ, and / or α+β. Fractional expressions also include divided fractional expressions. The variables used in the numerator and denominator may each have an appropriate coefficient. The variables used in the numerator and denominator may also be duplicated. Each fractional expression may also have an appropriate coefficient. The coefficient values ​​of each variable and the constant term values ​​may be real numbers. Although the positive or negative sign of the correlation between a given fractional formula and a formula in which the numerator variable and the denominator variable are swapped is generally reversed, the correlation between them is maintained, and therefore the evaluation performance can be considered to be equivalent. Therefore, fractional formulas also include formulas in which the numerator variable and the denominator variable are swapped.

[0044] When assessing the state of MCI, values ​​relating to other biological information (e.g., the values ​​listed below) may be used in addition to the concentration values ​​of at least two of the five amino acids. Furthermore, the formula used for assessment may further include one or more variables into which values ​​relating to other biological information (e.g., the values ​​listed below) are substituted, in addition to the variables into which the concentration values ​​of at least two of the five amino acids are substituted. 1. Concentrations of blood metabolites other than amino acids (amino acid-related metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. 2. Blood test values ​​such as total protein, triglycerides (neutral fats), 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. 3. Values ​​obtained from image information such as ultrasound echoes, X-rays, CT scans, MRI scans, and endoscopic images 4. Values ​​related to biometric indicators such as age, height, weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, dietary information, drinking information, exercise information, stress information, sleep information, family medical history, and disease history (diabetes, etc.) 5. Values ​​obtained from genetic information such as the number of risk genes for Alzheimer's disease (APOE ε4 allele, etc.)

[0045] [Second embodiment] [2-1. Overview of the Second Embodiment] Here, an overview of the second embodiment will be described with reference to FIG. 2. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in the description of this second embodiment, explanations that overlap with those of the first embodiment described above may be omitted. In particular, here, a case in which the value of the formula or a value after conversion thereof is used when evaluating the state of MCI is described as an example, but, for example, concentration values ​​of at least two amino acids out of the five types of amino acids or values ​​after conversion thereof (e.g., concentration deviation values) may also be used.

[0046] The control unit uses a formula stored in advance in the storage unit, which includes concentration values ​​of at least two of the five amino acids contained in blood data of the subject (e.g., an individual such as an animal or human) acquired in advance, and variables into which the concentration values ​​are substituted, to calculate the value of the formula, thereby evaluating the MCI state of the subject (step S21). This makes it possible to provide highly reliable information that can be used as a reference for determining the MCI state.

[0047] The formula used in step S21 may be one created based on the formula creation process (steps 1 to 4) described below. Here, an outline of the formula creation process will be described. Note that the process described here is merely an example, and the formula creation method is not limited to this.

[0048] First, the control unit creates a candidate formula (e.g., y=a1x1+a2x2+···+anxn, where y: index data, xi: blood data, ai: constant, i=1, 2,···, n) based on a predetermined formula creation method from index state information (which may have data with missing values ​​or outliers pre-removed) previously stored in the storage unit, including blood data and index data related to indices that represent the MCI state (step 1).

[0049] In step 1, multiple candidate formulas may be created from the index status information by combining multiple different formula creation methods (including those related to multivariate analysis such as principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, and decision tree). Specifically, multiple candidate formulas for multiple groups may be created simultaneously in parallel using multiple different algorithms for index status information, which is multivariate data composed of blood data and index data obtained by analyzing blood from a large number of healthy groups and MCI groups. For example, two different candidate formulas may be created by simultaneously performing discriminant analysis and logistic regression analysis using different algorithms. Alternatively, the index status information may be converted using a candidate formula created by performing principal component analysis, and then a discriminant analysis may be performed on the converted index status information to create a candidate formula. This ultimately allows the creation of a formula optimal for evaluation.

[0050] Here, the candidate equation created using principal component analysis is a linear equation including each variable that maximizes the variance of all blood data. Furthermore, the candidate equation created using discriminant analysis is a higher-order equation (including exponentials and logarithms) including each variable that minimizes the ratio of the sum of variances within each group to the variance of all blood data. Furthermore, the candidate equation created using support vector machines is a higher-order equation (including kernel functions) including each variable that maximizes the boundary between groups. Furthermore, the candidate equation created using multiple regression analysis is a higher-order equation including each variable that minimizes the sum of distances from all blood data. Furthermore, the candidate equation created using Cox regression analysis is a linear model including a logarithmic hazard ratio, and is a linear equation including each variable and its coefficient that maximizes the likelihood of the model. Furthermore, the candidate equation created using logistic regression analysis is a linear model representing the logarithmic odds of probability, and is a linear equation including each variable that maximizes the likelihood of that probability. The k-means method is a method of searching k neighbors of each blood data, defining the group to which the most neighboring points belong as the group to which that data belongs, and selecting the variable that best matches the defined group to which the input blood data belongs. Cluster analysis is a method of clustering (grouping) points that are closest to each other among all blood data. A decision tree is a method of ranking variables and predicting blood data groups based on the possible patterns of the highly ranked variables.

[0051] Returning to the explanation of the formula creation process, the control unit verifies (cross-validates) the candidate formulas created in step 1 based on a predetermined verification method (step 2). The candidate formulas are verified for each candidate formula created in step 1. In step 2, the candidate formulas may be verified for at least one of the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (area under the receiver characteristic curve), etc. based on at least one of the bootstrap method, hold-out method, N-fold method, leave-one-out method, etc. This makes it possible to create candidate formulas with high predictability or robustness that take into account index state information and evaluation conditions.

[0052] Here, the discrimination rate refers to the proportion of subjects whose true state is negative (e.g., subjects who do not suffer from MCI) correctly evaluated as negative and subjects whose true state is positive (e.g., subjects who suffer from MCI) correctly evaluated as positive, using the evaluation method according to this embodiment. Sensitivity refers to the proportion of subjects whose true state is positive correctly evaluated as positive, using the evaluation method according to this embodiment. Specificity refers to the proportion of subjects whose true state is negative correctly evaluated as negative, using the evaluation method according to this embodiment. Akaike's information criterion (AIC) is a standard used in regression analysis and the like to indicate the degree to which observed data matches a statistical model. The model with the smallest value defined as "-2 × (maximum logarithmic likelihood of the statistical model) + 2 × (number of free parameters of the statistical model)" is determined to be the best. ROC_AUC is defined as the area under the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1 - specificity, sensitivity) on a two-dimensional coordinate system. The ROC_AUC value is 1 for perfect discrimination, and the closer this value is to 1, the higher the discrimination ability. Predictability is the average of the discrimination rate, sensitivity, and specificity obtained by repeatedly verifying the candidate formula. Robustness is the variance of the discrimination rate, sensitivity, and specificity obtained by repeatedly verifying the candidate formula.

[0053] Returning to the explanation of the formula creation process, the control unit selects a combination of blood data included in the index status information to be used when creating the candidate formula by selecting variables for the candidate formula based on a predetermined variable selection method (step 3). In step 3, variable selection may be performed for each candidate formula created in step 1. This allows for appropriate selection of variables for the candidate formula. Then, step 1 is executed again using the index status information including the blood data selected in step 3. In step 3, variables for the candidate formula may be selected based on at least one of the stepwise method, best path method, local search method, and genetic algorithm from the verification results in step 2. In addition, the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index provided by the candidate formula.

[0054] Returning to the explanation of the formula creation process, the control unit repeatedly executes the above-mentioned steps 1, 2, and 3, and creates a formula to be used in evaluation by selecting a candidate formula to be used in evaluation from among multiple candidate formulas based on the verification results accumulated thereby (step 4). Note that the selection of a candidate formula may involve, for example, selecting the most suitable one from candidate formulas created using the same formula creation method, or selecting the most suitable one from all candidate formulas.

[0055] As described above, the formula creation process can create an optimal formula for evaluating MCI by systematizing (systematizing) the processes of creating candidate formulas, validating the candidate formulas, and selecting variables for the candidate formulas in a series of steps based on the indicator state information. In other words, the formula creation process uses at least one of the concentration values ​​of 19 amino acids (Ala, Arg, Asn, Cit, Gln, Gly, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Ser, Thr, Trp, Tyr, and Val) in multivariate statistical analysis, and combines variable selection and cross-validation to select an optimal and robust set of variables to extract a formula with high evaluation performance.

[0056] [2-2. Configuration of the second embodiment] Here, the configuration of an 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. Note that the present system is merely an example, and the present invention is not limited thereto. In particular, here, a case where the value of the formula or a value after conversion thereof is used when evaluating the state of MCI is described as an example, but, for example, concentration values ​​of at least two of the five amino acids or a value after conversion thereof (e.g., concentration deviation value) may also be used.

[0057] First, the overall configuration of the present system will be described with reference to Figures 3 and 4. Figure 3 is a diagram showing one example of the overall configuration of the present system. Also, Figure 4 is a diagram showing another example of the overall configuration of the present system. As shown in Figure 3, the present system is composed of an evaluation device 100 that evaluates the MCI state of an individual to be evaluated, and a client device 200 (corresponding to the terminal device of the present invention) that provides the individual's blood data regarding the concentration values ​​of at least two of the five amino acids in the blood, all of which are communicably connected via a network 300.

[0058] In this system, the client device 200 that provides the data used for evaluation and the client device 200 that receives the evaluation results may be separate devices. As shown in FIG. 4 , this system may be configured by communicatively connecting, via a network 300, in addition to the evaluation device 100 and the client device 200, a database device 400 that stores index state information used to create formulas in the evaluation device 100 and formulas used for evaluation. This allows information useful for determining the state of MCI to be provided from the evaluation device 100 to the client device 200 or the database device 400, or from the client device 200 or the database device 400 to the evaluation device 100, via the network 300. Here, information useful for determining the state of MCI is, for example, information related to measured values ​​of specific items related to the state of MCI in living organisms, including humans. Furthermore, information useful for determining the state of MCI is generated by the evaluation device 100, the client device 200, or other devices (e.g., various measuring devices) and is mainly stored in the database device 400.

[0059] Next, the configuration of the evaluation device 100 of this system will be described with reference to Figures 5 to 11. Figure 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0060] The evaluation device 100 is composed of a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device overall; a communication interface unit 104 that communicatively connects the evaluation device to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line; a memory unit 106 that stores various databases, tables, files, etc.; and an input / output interface unit 108 that connects to an input device 112 and an output device 114, and these units are communicatively connected via any communication path. Here, the evaluation device 100 may be configured in the same housing as various analytical devices (e.g., amino acid analyzers, etc.). For example, a compact analytical device having a configuration (hardware and software) that calculates (measures) the concentrations of at least two of the five amino acids in blood and outputs the calculated values ​​(by printing, displaying on a monitor, etc.) may further include an evaluation unit 102d (described below) and output the results obtained by the evaluation unit 102d using the configuration.

[0061] 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.

[0062] The input / output interface unit 108 is connected to an input device 112 and an output device 114. Here, the output device 114 may be a monitor (including a home television), a speaker, or a printer (hereinafter, the output device 114 may be referred to as the monitor 114). The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function.

[0063] The memory unit 106 is a storage means, and may be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc. The memory unit 106 stores computer programs that work in conjunction with an OS (Operating System) to issue commands to a CPU to perform various processes. As shown in the figure, the memory unit 106 stores a blood data file 106a, an index status information file 106b, a specified index status information file 106c, a formula-related information database 106d, and an evaluation result file 106e.

[0064] Blood data file 106a stores blood data related to the concentration values ​​of at least two of the five amino acids in the blood. FIG. 6 is a diagram showing an example of information stored in blood data file 106a. As shown in FIG. 6, the information stored in blood data file 106a is configured by correlating blood data with an individual number for uniquely identifying the individual (sample) being evaluated. Here, in FIG. 6, the blood data is treated as a numerical value, i.e., a continuous scale, but the blood data may also be a nominal or ordinal scale. In the case of a nominal or ordinal scale, analysis may be performed by assigning an arbitrary numerical value to each state. Furthermore, values ​​related to other biological information (see above) may be combined with the blood data.

[0065] Returning to FIG. 5, the index state information file 106b stores index state information used when creating equations. FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. As shown in FIG. 7, the information stored in the index state information file 106b is configured by interrelating an individual number, index data (T) related to indexes (index T1, index T2, index T3, etc.) that represent the MCI state, and blood data. Here, in FIG. 7, the index data and blood data are treated as numerical values ​​(i.e., continuous scales), but the index data and blood data may also be nominal or ordinal scales. In the case of nominal or ordinal scales, analysis may be performed by assigning arbitrary numerical values ​​to each state. Furthermore, the index data may be known indexes that serve as markers for the MCI state, and numerical data may also be used.

[0066] Returning to Fig. 5, the designated index status information file 106c stores index status information designated by the designation unit 102b, which will be described later. Fig. 8 is a diagram showing an example of information stored in the designated index status information file 106c. As shown in Fig. 8, the information stored in the designated index status information file 106c is configured by associating an individual number, designated index data, and designated blood data with each other.

[0067] Returning to FIG. 5, the formula-related information database 106d is composed of a formula file 106d1 that stores formulas created by the formula creation unit 102c, which will be described later. The formula file 106d1 stores formulas used during evaluation. FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. As shown in FIG. 9, the information stored in the formula file 106d1 is configured by interrelating ranks, formulas (in FIG. 9, Fp(Cit, ), Fp(Cit, Lys, Ser), Fk(Cit, Lys, Ser, ) and the like), thresholds corresponding to each formula creation method, and verification results of each formula (for example, the value of each formula).

[0068] Returning to Fig. 5, the evaluation result file 106e stores the evaluation results obtained by the evaluation unit 102d, which will be described later. Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106e. The information stored in the evaluation result file 106e is configured by interrelating an individual number for uniquely identifying the individual (sample) to be evaluated, blood data of the individual obtained in advance, and evaluation results related to the MCI state (for example, the value of a formula calculated by a calculation unit 102d1, which will be described later, the value obtained by converting the value of the formula by a conversion unit 102d2, which will be described later, position information generated by a generation unit 102d3, which will be described later, or the classification result obtained by a classification unit 102d4, which will be described later).

[0069] Returning to Figure 5, the control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 102 is broadly equipped with an acquisition unit 102a, a designation unit 102b, a formula creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 also performs data processing on the index status information transmitted from the database device 400 and the blood data transmitted from the client device 200, such as removing data with missing values, removing data with many outliers, and removing variables with many missing values.

[0070] The acquiring unit 102a acquires information (specifically, blood data, index status information, formulas, etc.). For example, the acquiring unit 102a may acquire information by receiving information (specifically, blood data, index status information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The acquiring unit 102a may also receive data used for evaluation transmitted from a client device 200 other than the client device 200 to which the evaluation results are transmitted. Furthermore, for example, if the evaluation device 100 includes a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquiring unit 102a may acquire information by reading the information (specifically, blood data, index status information, formulas, etc.) recorded on the recording medium via the mechanism. The designating unit 102b designates the index data and blood data to be used for creating a formula.

[0071] The formula creation unit 102c creates a formula based on the index state information acquired by the acquisition unit 102a and the index state information designated by the designation unit 102b. Note that if the formula is stored in advance in a predetermined storage area of ​​the storage unit 106, the formula creation unit 102c may create the formula by selecting a desired formula from the storage unit 106. Alternatively, the formula creation unit 102c may create the formula by selecting and downloading a desired formula from another computer device (e.g., database device 400) that has formulas stored in advance.

[0072] The evaluation unit 102d evaluates the MCI state of the individual by calculating the value of the formula using a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula acquired by the acquisition unit 102a) and the at least one value included in the individual's blood data acquired by the acquisition unit 102a. Note that the evaluation unit 102d may evaluate the MCI state of the individual using the concentration values ​​of at least two of the five amino acids or values ​​after conversion of the concentration values ​​(for example, concentration deviation values).

[0073] The configuration of the evaluation unit 102d will now be described with reference to Fig. 11. Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d, conceptually illustrating only the parts of the configuration that are relevant to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

[0074] The calculation unit 102d1 calculates the value of the formula using a formula that includes at least the concentration values ​​of at least two of the five amino acids and variables into which the concentration values ​​are substituted. Note that the evaluation unit 102d may store the value of the formula calculated by the calculation unit 102d1 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0075] The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1, for example, using the conversion method described above. The evaluation unit 102d may store the value converted by the conversion unit 102d2 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e. The conversion unit 102d2 may also convert the concentration value included in the blood data, for example, using the conversion method described above.

[0076] The generating unit 102d3 generates position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper, using the value of the formula calculated by the calculating unit 102d1 or the value converted by the converting unit 102d2 (which may be a concentration value or a value converted from the concentration value). Note that the evaluating unit 102d may store the position information generated by the generating unit 102d3 in a predetermined storage area of ​​the evaluation result file 106e as the evaluation result.

[0077] The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value converted by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value) to classify the individual into one of multiple categories defined taking into account at least the degree of possibility that the individual may be suffering from MCI.

[0078] The result output unit 102e outputs the processing results of each processing unit of the control unit 102 (including the evaluation results obtained by the evaluation unit 102d) to the output device 114.

[0079] The sending unit 102f sends the evaluation results to the client device 200 that sent the blood data of the individual, and sends the formula created by the evaluation device 100 and the evaluation results to the database device 400. Note that the sending unit 102f may send the evaluation results to a client device 200 different from the client device 200 that sent the data used for the evaluation.

[0080] Next, the configuration of client device 200 of this system will be described with reference to Fig. 12. Fig. 12 is a block diagram showing an example of the configuration of client device 200 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0081] Client device 200 is composed of control unit 210, ROM 220, HD (Hard Disk) 230, RAM 240, input device 250, output device 260, input / output IF 270, and communication IF 280, and these units are communicably connected via any communication path. Client device 200 may be based on an information processing device (for example, a known information processing terminal such as a personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System) terminal, mobile terminal, mobile communication terminal, PDA (Personal Digital Assistant)) to which peripheral devices such as a printer, monitor, and image scanner are connected as needed.

[0082] The input device 250 is a keyboard, a mouse, a microphone, etc. A monitor 261, which will be described later, also functions as a pointing device in cooperation with a mouse. The output device 260 is an output means for outputting information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker, etc. The input / output IF 270 is connected to the input device 250 and the output device 260.

[0083] The communication IF 280 communicatively connects the client device 200 to the network 300 (or a communication device such as a router). In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line. This allows the client device 200 to access the evaluation device 100 in accordance with a predetermined communication protocol.

[0084] The control unit 210 includes a receiving unit 211 and a transmitting unit 212. The receiving unit 211 receives various information such as the evaluation results transmitted from the evaluation device 100 via the communication IF 280. The transmitting unit 212 transmits various information such as the blood data of the individual to the evaluation device 100 via the communication IF 280.

[0085] The control unit 210 may implement all or any part of the processing performed by the control unit using a CPU and a program that is interpreted and executed by the CPU. ROM 220 or HD 230 stores computer programs that work with the OS to issue instructions to the CPU and perform various processes. The computer programs are loaded into RAM 240 for execution and cooperate with the CPU to form the control unit 210. The computer programs may also be stored in an application program server connected to the client device 200 via a network, and the client device 200 may download all or any part of the computer programs as needed. All or any part of the processing performed by the control unit 210 may also be implemented using hardware such as wired logic.

[0086] Here, the control unit 210 may include an evaluation unit 210a (including a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having functions similar to those of the evaluation unit 102d included in the evaluation device 100. When the control unit 210 includes the evaluation unit 210a, the evaluation unit 210a may convert the value of the formula (which may be a concentration value) using the conversion unit 210a2, generate position information corresponding to the value of the formula or the converted value (which may be a concentration value or a value after conversion of the concentration value) using the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the formula or the converted value (which may be a concentration value or a value after conversion of the concentration value) using the classification unit 210a4.

[0087] Next, the network 300 of this system will be described with reference to Figures 3 and 4. The network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (including both wired and wireless networks). Network 300 may be a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a leased line network (including both analog and digital), a CATV (Community Antenna Television) network, a mobile circuit-switched network or a mobile packet-switched network (including the IMT (International Mobile Telecommunication) 2000 system, the GSM (Registered Trademark) (Global System for Mobile Communications) system, or the PDC (Personal Digital Cellular) / PDC-P system, etc.), a radio paging network, a local wireless network such as Bluetooth (Registered Trademark), a PHS network, or a satellite communication network (including CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcasting), etc.).

[0088] Next, the configuration of the database device 400 of this system will be described with reference to Fig. 13. Fig. 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0089] The database device 400 has a function of storing index state information used when creating an equation in the evaluation device 100 or the database device itself, the equation created in the evaluation device 100, the evaluation results in the evaluation device 100, etc. As shown in Fig. 13, the database device 400 is composed of a control unit 402 such as a CPU that controls the database device in an overall manner, a communication interface unit 404 that communicatively connects the database device to the network 300 via a communication device such as a router and a wired or wireless communication circuit such as a dedicated line, a memory unit 406 that stores various databases, tables, files (for example, files for Web pages), etc., and an input / output interface unit 408 that connects to an input device 412 and an output device 414, and these units are communicatively connected via any communication path.

[0090] The memory unit 406 is a storage means, and may be, for example, a memory device such as RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like. The memory unit 406 stores various programs used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has the function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to an input device 412 and an output device 414. Here, the output device 414 may be a monitor (including a home television), a speaker, or a printer. The input device 412 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in cooperation with a mouse.

[0091] The control unit 402 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b. The transmission unit 402a transmits various information such as index state information and equations to the evaluation device 100. The reception unit 402b receives various information such as equations and evaluation results transmitted from the evaluation device 100.

[0092] In this description, an example has been given in which the evaluation device 100 performs the processes from obtaining blood data, calculating the values ​​of the formulas, classifying the individuals into categories, and transmitting the evaluation results, and the client device 200 receives the evaluation results. However, if the client device 200 is equipped with the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the values ​​of the formulas. For example, the conversion of the values ​​of the formulas, generation of position information, and classification of the individuals into categories may be appropriately shared between the evaluation device 100 and the client device 200. For example, when the client device 200 receives the value of an equation from the evaluation device 100, the evaluation unit 210a may convert the value of the equation using the conversion unit 210a2, generate location information corresponding to the value of the equation or the converted value using the generation unit 210a3, and classify the individual into one of multiple categories using the value of the equation or the converted value using the classification unit 210a4. Furthermore, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a may generate position information corresponding to the converted value using the generation unit 210a3, or may classify the individual into one of multiple categories using the converted value using the classification unit 210a4. Furthermore, when the client device 200 receives the value of the formula or the converted value and the location information from the evaluation device 100, the evaluation unit 210a may classify the individual into one of a plurality of categories using the value of the formula or the converted value in the classification unit 210a4.

[0093] [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 may be implemented in various different embodiments other than the second embodiment described above, within the scope of the technical idea described in the claims.

[0094] Furthermore, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0095] In addition, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0096] Furthermore, with regard to the evaluation device 100, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0097] For example, all or any part of the processing functions of the evaluation device 100, particularly the processing functions performed by the control unit 102, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing an information processing device to execute the evaluation method or calculation method of the present invention, and is mechanically read by the evaluation device 100 as needed. That is, a computer program for providing instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106, such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0098] This computer program may also be stored in an application program server connected to the evaluation device 100 via any network, and all or part of it may be downloaded as needed.

[0099] The evaluation program or calculation program according to the present invention may be stored in a non-transitory computer-readable recording medium or configured as a program product. Here, the term "recording medium" includes any portable physical medium such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM) (registered trademark), a Compact Disc Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) disc.

[0100] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0101] The various databases stored in the memory unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0102] 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 any peripheral device is connected. The evaluation device 100 may also be realized by installing software (including programs or data) that causes the information processing device to implement the evaluation method or calculation method of the present invention.

[0103] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit depending on various additions or functional loads. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Example]

[0104] Blood amino acid concentrations were measured using the amino acid analysis method (A) described above from plasma samples of MCI patients aged 60 years or older who had been definitively diagnosed with MCI (MCI group: 120 people) and healthy elderly people aged 60 years or older who were considered to have normal cognitive function (healthy group: 120 people).

[0105] Using the plasma concentrations (nmol / ml) of Ser and Trp, a two-variable logistic regression equation was calculated to distinguish between the MCI and healthy groups. The discriminatory ability of this logistic regression equation between the MCI and healthy groups was evaluated using ROC_AUC, which showed a value of 0.540. Since the ROC_AUC exceeded 0.5, this logistic regression equation is considered useful for assessing the state of MCI. [Example]

[0106] The sample data used was the same as in Example 1. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including variables into which the amino acid concentration values ​​in plasma were substituted, was determined.

[0107] A logistic regression equation was used as a multivariate discriminant. Combinations of three variables to be included in the logistic regression equation were explored based on the plasma concentrations (nmol / ml) of five amino acids (Cit, Lys, Ser, Thr, and Trp), and a logistic regression equation with good discriminatory ability between the MCI group and the healthy group was explored.

[0108] A list of three-variable logistic regression equations that always include three of the five amino acids as variables is shown in Table 1. These logistic regression equations are considered to be useful in the above evaluation because the lower limits of the 95% confidence intervals (95% CI) of the ROC_AUC values ​​are higher than 0.5.

[0109] [Table 1] [Example]

[0110] The sample data used was the same as in Example 1. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including variables into which plasma amino acid concentration values ​​were substituted, was determined.

[0111] A logistic regression equation was used as a multivariate discriminant. Combinations of four variables to be included in the logistic regression equation (variables for three of the five amino acids mentioned above were required) were searched for from the plasma concentrations of 19 amino acids (Ala, Arg, Asn, Cit, Gln, Glu, Gly, His, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr, and Val), and a logistic regression equation with good discriminatory ability between the MCI group and the healthy group was searched for.

[0112] Table 2 below shows a list of logistic regression equations with four variables whose ROC_AUC values ​​for the MCI and healthy groups were equal to or greater than 0.679, as shown in Table 1. These logistic regression equations are considered useful for the above evaluation because they have high ROC_AUC values ​​and the lower limit of the 95% CI for the ROC_AUC values ​​is higher than 0.5.

[0113] [Table 2] [Example]

[0114] The sample data used was the same as in Example 1. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including variables into which plasma amino acid concentration values ​​were substituted, was determined.

[0115] A logistic regression equation was used as a multivariate discriminant. Combinations of five variables to be included in the logistic regression equation (requiring three of the five amino acids) were searched for from the plasma concentrations of the 19 amino acids, and a logistic regression equation with good discriminatory ability between the MCI group and the healthy group was searched for.

[0116] Of the 981 logistic regression equations obtained above, 270 equations with ROC_AUC values ​​of 0.679 or higher for the MCI and healthy groups are shown in the [5-variable equation] below. These logistic regression equations are considered useful for the above evaluation because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5. The [5-variable equation] below also shows the ROC_AUC value, the lower limit of the 95% CI of the ROC_AUC value, and the upper limit of the 95% CI of the ROC_AUC value for the five amino acids included in the equation.

[0117] [Five-variable formula] 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, Ile, Trp, 0.692 (0.625 , 0.76); Ser, His, Cit, Val, Lys, 0.692 (0.625 , 0.759); Ser, Gly, Thr, Lys, Ile, 0.692 (0.624 , 0.759); Ser, Gln, Cit, Lys, Trp, 0.692 (0.624 , 0.759); Ser, Arg, Met, Lys, Trp, 0.692 (0.624 , 0.759); Ser, Gly, Arg, Lys, Trp, 0.691 (0.624 , 0.758); Ser, Thr, Pro, Lys, Trp, 0.691 (0.624 , 0.759); Ser, Thr, Val, Lys, Trp, 0.691 (0.623 , 0.759); Ser, Asn, Gly, Lys, Trp, 0.691 (0.624 , 0.758); Ser, Arg, Val, Lys, Trp, 0.691 (0.624 , 0.758); Ser, Gly, Thr, Lys, Leu, 0.691 (0.623 , 0.759); Ser, Thr, Arg, Lys, Ile, 0.691 (0.623 , 0.759); Ser, Cit, Orn, Lys, Ile, 0.69 (0.622 , 0.759); Ser, Gly, Thr, Val, Lys, 0.69 (0.623 , 0.758); Ser, Cit, Arg, Lys, Ile, 0.69 (0.622 , 0.758); Ser, Asn, Arg, Lys, Trp, 0.69 (0.623 , 0.757); Ser, Asn, Lys, Leu, Trp, 0.69 (0.623 , 0.757); Ser, His, Cit, Lys, Trp, 0.689 (0.622 , 0.757); Ser, Cit, Lys, Ile, Phe, 0.689 (0.621 , 0.757); Ser, Asn, Thr, Lys, Trp, 0.689 (0.622 , 0.757); Ser, Thr, Lys, Ile, Leu, 0.689 (0.621 , 0.757); Ser, Cit, Arg, Lys, Trp, 0.689 (0.622 , 0.756); Ser, Gly, Thr, Pro, Lys, 0.689 (0.622 , 0.756); Ser, Cit, Lys, Ile, Trp, 0.689 (0.622 , 0.756); Ser, Gly, Pro, Lys, Trp, 0.689 (0.621 , 0.756); Ser, Ala, Cit, Lys, Ile, 0.689 (0.621 , 0.757); Ser, Asn, Gly, Cit, Lys, 0.689 (0.621 , 0.756); Ser, Gly, Lys, Phe, Trp, 0.688 (0.621 , 0.756); Ser, His, Thr, Lys, Trp, 0.688 (0.621 , 0.756); Ser, Cit, Orn, Lys, Trp, 0.688 (0.621 , 0.756); Ser, Cit, Pro, Lys, Ile, 0.688 (0.62 , 0.757); Ser, Cit, Tyr, Lys, Ile, 0.688 (0.62 , 0.756); Ser, Gly, Val, Lys, Trp, 0.688 (0.621 , 0.756); Ser, Arg, Lys, Ile, Trp, 0.688 (0.621 , 0.755); Ser, Gln, Thr, Lys, Trp, 0.688 (0.62 , 0.756); Ser, Lys, Leu, Phe, Trp, 0.688 (0.62 , 0.755); Ser, Gly, Tyr, Lys, Trp, 0.688 (0.62 , 0.756); Ser, His, Cit, Lys, Ile, 0.688 (0.62 , 0.756); Ser, Thr, Tyr, Lys, Trp, 0.688 (0.62 , 0.756); Ser, Thr, Met, Lys, Trp, 0.688 (0.62 , 0.756); Ser, Gly, Met, Lys, Trp, 0.688 (0.62 , 0.756); 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, Ile, Trp, 0.686 (0.619 , 0.754); Ser, Tyr, Lys, Leu, Trp, 0.686 (0.619 , 0.754); Ser, Asn, Pro, Lys, Trp, 0.686 (0.619 , 0.754); His, Cit, Lys, Leu, Trp, 0.686 (0.618 , 0.754); Ser, Asn, Cit, Tyr, Lys, 0.686 (0.618 , 0.754); Ser, Gly, Cit, Orn, Lys, 0.686 (0.618 , 0.754); Ser, Val, Met, Lys, Trp, 0.686 (0.618 , 0.754); Ser, Met, Lys, Ile, Trp, 0.686 (0.618 , 0.754); Ser, Gln, Arg, Lys, Trp, 0.686 (0.618 , 0.753); Ser, Ala, Arg, Lys, Trp, 0.686 (0.618 , 0.753); Ser, Gly, Orn, Lys, Trp, 0.686 (0.618 , 0.753); Ser, Asn, Gln, Cit, Lys, 0.686 (0.617 , 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, Ile, Trp, 0.685 (0.617 , 0.753); Ser, Asn, Thr, Val, Lys, 0.685 (0.616 , 0.753); Ser, His, Arg, Lys, Trp, 0.685 (0.617 , 0.752); Ser, Gly, Thr, Lys, Phe, 0.685 (0.616 , 0.753); Ser, Thr, Val, Lys, Ile, 0.684 (0.616 , 0.753); Ser, His, Thr, Lys, Leu, 0.684 (0.616 , 0.752); Ser, Asn, Ala, Lys, Trp, 0.684 (0.616 , 0.752); Ser, Thr, Cit, Arg, Lys, 0.684 (0.616 , 0.752); Ser, Gln, Cit, Pro, Lys, 0.684 (0.616 , 0.752); Ser, Thr, Pro, Val, Lys, 0.684 (0.616 , 0.752).752); Ser, Thr, Val, Met, Lys, 0.684 (0.616 , 0.752); Cit, Met, Lys, Leu, Trp, 0.684 (0.616 , 0.752); Ser, Asn, Tyr, Lys, Trp, 0.684 (0.616 , 0.752); Ser, Val, Lys, Phe, Trp, 0.684 (0.616 , 0.752); Ser, Gln, Cit, Arg, Lys, 0.684 (0.616 , 0.752); Ser, Thr, Ala, Val, Lys, 0.684 (0.615 , 0.752); Ser, Thr, Met, Lys, Ile, 0.684 (0.615 , 0.752); Ser, Gly, His, Lys, Trp, 0.684 (0.616 , 0.752); Ser, Thr, Cit, Pro, Lys, 0.684 (0.615 , 0.752); Ser, Thr, Lys, Ile, Phe, 0.683 (0.615 , 0.752); Ser, Ala, Lys, Leu, Trp, 0.683 (0.616 , 0.751); Ser, Asn, Lys, Phe, Trp, 0.683 (0.616 , 0.751); Ser, Thr, Ala, Lys, Ile, 0.683 (0.615 , 0.751); Ser, Asn, Met, Lys, Trp, 0.683 (0.615 , 0.751); Ser, Arg, Pro, Lys, Trp, 0.683 (0.616 , 0.751); Ser, Thr, Val, Lys, Phe, 0.683 (0.615 , 0.752); His, Cit, Orn, Lys, Trp, 0.683 (0.615 , 0.751); Ser, Arg, Lys, Phe, Trp, 0.683 (0.616 , 0.751); Ser, Gly, Thr, Met, Lys, 0.683 (0.615 , 0.751); Ser, Thr, Orn, Lys, Leu, 0.683 (0.615 , 0.751); His, Cit, Lys, Phe, Trp, 0.683 (0.615 , 0.751); Ser, Gly, Thr, Tyr, Lys, 0.683 (0.615 , 0.751); His, Ala, Cit, Lys, Trp, 0.683 (0.615 , 0.751); Ser, His, Lys, Leu, Trp, 0.683 (0.615 , 0.751); Ser, Arg, Tyr, Lys, Trp, 0.683 (0.615 , 0.75); Ser, Arg, Orn, Lys, Trp, 0.683 (0.615 , 0.75); Ser, Asn, Cit, Lys, Phe, 0.683 (0.615 , 0.751); Ser, Asn, Thr, Cit, Lys, 0.683 (0.615 , 0.751); Ser, Gln, Val, Lys, Trp, 0.683 (0.615 , 0.751); Ser, Gln, Thr, Cit, Lys, 0.683 (0.614 , 0.751); Ser, His, Thr, Val, Lys, 0.683 (0.614 , 0.751); Ser, Tyr, Lys, Ile, Trp, 0.683 (0.615 , 0.75); Ser, Asn, Cit, Pro, Lys, 0.683 (0.615 , 0.751); Ser, Thr, Cit, Tyr, Lys, 0.683 (0.615 , 0.751); Ser, Thr, Tyr, Val, Lys, 0.683 (0.614 , 0.751); Ser, Lys, Ile, Phe, Trp, 0.683 (0.615 , 0.751); Ser, Gln, Cit, Lys, Phe, 0.683 (0.614 , 0.751); Ser, Asn, Thr, Lys, Ile, 0.682 (0.614 , 0.751); Asn, His, Cit, Lys, Trp, 0.682 (0.615 , 0.75); His, Cit, Val, Lys, Trp, 0.682 (0.614 , 0.75); Cit, Met, Lys, Ile, Trp, 0.682 (0.614 , 0.75); Ser, Val, Lys, Ile, Trp, 0.682 (0.615 , 0.75); Ser, Cit, Tyr, Met, Lys, 0.682 (0.614 , 0.751); Ser, Orn, Lys, Leu, Trp, 0.682 (0.615 , 0.75); Ser, Gln, Cit, Met, Lys, 0.682 (0.614 , 0.751); His, Cit, Lys, Ile, Trp, 0.682 (0.614 , 0.75); Ser, Met, Lys, Phe, Trp, 0.682 (0.614 , 0.75); Ser, Asn, Gly, Thr, Lys, 0.682 (0.614 , 0.75); Ser, Asn, Ala, Cit, Lys, 0.682 (0.614 , 0.75); Ser, Thr, Pro, Lys, Ile, 0.682 (0.614 , 0.75); Ser, Gln, Pro, Lys, Trp, 0.682 (0.614 , 0.75); Ser, His, Val, Lys, Trp, 0.682 (0.614 , 0.75); Ser, Thr, Tyr, Lys, Ile, 0.682 (0.614 , 0.75); His, Thr, Cit, Lys, Trp, 0.682 (0.614 , 0.75); Ser, Asn, Orn, Lys, Trp, 0.682 (0.614 , 0.749); Ser, Gln, Cit, Orn, Lys, 0.682 (0.614 , 0.75); Ser, Thr, Orn, Lys, Ile, 0.682 (0.614 , 0.75); Ser, Asn, His, Lys, Trp, 0.682 (0.614 , 0.75); Ser, Thr, Cit, Lys, Phe, 0.682 (0.613 , 0.75); Gln, His, Cit, Lys, Trp, 0.682 (0.614 , 0.75); Cit, Tyr, Met, Lys, Trp, 0.682 (0.613 , 0.75); Ser, Tyr, Val, Lys, Trp, 0.682 (0.614 , 0.75); Ser, Cit, Pro, Met, Lys, 0.682 (0.613 , 0.75); Ser, Cit, Met, Lys, Phe, 0.682 (0.613 , 0.75); Ser, His, Lys, Ile, Trp, 0.682 (0.614 , 0.75); Ser, Gln, Ala, Cit, Lys, 0.682 (0.613 , 0.75); Ser, His, Thr, Lys, Ile, 0.681 (0.613 , 0.75); Cit, Val, Met, Lys, Trp, 0.681 (0.613 , 0.75); Ser, Pro, Val, Lys, Trp, 0.681 (0. 613 , 0.749); His, Cit, Met, Lys, Trp, 0.681 (0.613 , 0.75); Cit, Arg, Lys, Leu, Trp, 0.681 (0.613 , 0.749); Gln, Cit, Lys, Leu, Trp, 0.681 (0.613 , 0.75); Cit, Lys, Leu, Phe, Trp, 0.681 (0.613 , 0.749); Ser, Asn, Cit, Orn, Lys, 0.681 (0.613 , 0.749); His, Cit, Tyr, Lys, Trp, 0.681 (0.613 , 0.749); Ser, Gln, Thr, Lys, Ile, 0.681 (0.612 , 0.749); Ser, Gln, Met, Lys, Trp, 0.681 (0.613 , 0.749); Ser, Tyr, Met, Lys, Trp, 0.681 (0.613 , 0.749); Thr, Cit, Met, Lys, Trp, 0.681 (0.613 , 0.749); Ser, Pro, Lys, Ile, Trp, 0.681 (0.613 , 0.749); Ser, Thr, Ala, Cit, Lys, 0.681 (0.612 , 0.749); Cit, Pro, Met, Lys, Trp, 0.681 (0.612 , 0.749); Asn, Cit, Met, Lys, Trp, 0.681 (0.612 , 0.749); Ala, Cit, Met, Lys, Trp, 0.681 (0.612 , 0.749); Ser, Asn, Cit, Met, Lys, 0.681 (0.612 , 0.749); Cit, Met, Lys, Phe, Trp, 0.681 (0.612 , 0.749); Ser, Ala, Met, Lys, Trp, 0.681 (0.612 , 0.749); Ser, Asn, Cit, Arg, Lys, 0.681 (0.613 , 0.748); Ser, Gly, His, Thr, Lys, 0.68 (0.612 , 0.749); Ser, Cit, Tyr, Lys, Phe, 0.68 (0.612 , 0.749); Cit, Arg, Met, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Ala, Val, Lys, Trp, 0.68 (0.612 , 0.748); Gly, His, Cit, Lys, Trp, 0.68 (0.612 , 0.749); Ser, Cit, Tyr, Orn, Lys, 0.68 (0.612 , 0.749); Cit, Met, Orn, Lys, Trp, 0.68 (0.612 , 0.749); Ser, Gln, Tyr, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Orn, Lys, Ile, Trp, 0.68 (0.612 , 0.748); Ser, Cit, Pro, Tyr, Lys, 0.68 (0.612 , 0.749); Ser, Gly, Gln, Thr, Lys, 0.68 (0.612 , 0.749); His, Cit, Arg, Lys, Trp, 0.68 (0.612 , 0.748); Ser, His, Met, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Gln, His, Cit, Lys, 0.68 (0.612 , 0.749); Ser, Thr, Val, Orn, Lys, 0.68 (0.612 , 0.748); Thr, Cit, Arg, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Gln, Ala, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Gln, Lys, Phe, Trp, 0.68 (0.612 , 0.748); Ser, Gln, Orn, Lys, Trp, 0.68 (0.612 , 0.748); Ser, Val, Orn, Lys, Trp, 0.68 (0.612 , 0.748); Gln, Cit, Arg, Lys, Trp, 0.68 (0.612 , 0.748); Gln, Cit, Val, Lys, Trp, 0.68 (0.611 , 0.748); Ser, Ala, Lys, Ile, Trp, 0.68 (0.612 , 0.748); 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]

[0118] The sample data (hereinafter referred to as training data) used in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the MCI group and the healthy group, including variables into which the plasma amino acid concentration values ​​were substituted, was determined.

[0119] A logistic regression equation was used as a multivariate discriminant. Combinations of six variables (requiring three of the five amino acids) to be included in the logistic regression equation were searched for from the plasma concentrations of the 19 amino acids, and a logistic regression equation with good discriminatory ability between the MCI group and the healthy group was searched for.

[0120] From the 4,109 logistic regression equations obtained above, 1,490 equations with ROC_AUC values ​​of 0.679 or higher for the MCI group and healthy group were selected, and from the selected 1,490 equations, the top 200 equations with ROC_AUC values ​​were obtained.

[0121] The performance of the logistic regression equation 200 obtained above was verified using blood amino acid concentration data that was completely independent of the training data and was obtained from plasma samples of MCI patients aged 60 years or older who had been definitively diagnosed with MCI (MCI group: 99 people) and healthy elderly people aged 60 years or older who were considered to have normal cognitive function (healthy group: 100 people) (hereinafter referred to as verification data).

[0122] The following [6-variable equation] shows 128 highly robust logistic regression equations with ROC_AUC values ​​of 0.600 or higher for the MCI and healthy groups in the validation data. Because these logistic regression equations have high ROC_AUC values ​​and are highly robust, they are considered to be useful in meeting the required standards for clinical practice in the above-mentioned evaluation. The following [6-variable equation] also shows the ROC_AUC value, the lower limit of the 95% CI of the ROC_AUC value, and the upper limit of the 95% CI of the ROC_AUC value for the six amino acids included in the equation.

[0123] [6-variable formula] Ser, Gly, Thr, Cit, Lys, Trp, 0.707 (0.641 , 0.774); Ser, Thr, Cit, Arg, Lys, Leu, 0.707 (0.639 , 0.774); Ser, Thr, Arg, Lys, Leu, Trp, 0.706 (0.639 , 0.773); Ser, Thr, Arg, Val, Lys, Trp, 0.706 (0.64 , 0.773); Ser, Gly, Thr, Cit, Lys, Leu, 0.706 (0.639 , 0.773); Ser, Cit, Met, Lys, Leu, Trp, 0.706 (0.64 , 0.773); Ser, Thr, Cit, Arg, Lys, Trp, 0.705 (0.638 , 0.772); Ser, Gly, Thr, Arg, Lys, Trp, 0.705 (0.638 , 0.772); Ser, Gly, Thr, Cit, Val, Lys, 0.705 (0.638 , 0.772); Ser, Thr, Cit, Val, Lys, Trp, 0.705 (0.638 , 0.771); Ser, Cit, Arg, Met, Lys, Trp, 0.705 (0.638 , 0.771); Ser, Thr, Cit, Arg, Val, Lys, 0.704 (0.637 , 0.771); Ser, Thr, Cit, Met, Lys, Trp, 0.704 (0.637 , 0.771); Ser, Thr, Cit, Lys, Leu, Trp, 0.703 (0.636 , 0.77); Ser, Gly, Cit, Met, Lys, Trp, 0.703 (0.636 , 0.769); Ser, Thr, Cit, Lys, Phe, Trp, 0.703 (0.636 , 0.77); Ser, Cit, Arg, Val, Met, Lys, 0.703 (0.636 , 0.77); Ser, Thr, Cit, Lys, Ile, Trp, 0.702 (0.636 , 0.769); Ser, Cit, Tyr, Met, Lys, Leu, 0.702 (0.636 , 0.769); Ser, Gln, Thr, Cit, Lys, Trp, 0.702 (0.635 , 0.769); Ser, Cit, Val, Met, Lys, Trp, 0.702 (0.635 , 0.769); Ser, Asn, Thr, Cit, Lys, Trp, 0.702 (0.636 , 0.769); Ser, Thr, Arg, Met, Lys, Trp, 0.702 (0.635 , 0.769); Ser, Gly, Thr, Cit, Lys, Ile, 0.701 (0.634 , 0.769); Ser, Thr, Cit, Orn, Lys, Trp, 0.701 (0.634 , 0.768); Ser, Asn, Thr, Cit, Lys, Leu, 0.701 (0.634 , 0.768); Ser, Thr, Cit, Met, Lys, Leu, 0.701 (0.634 , 0.769); Ser, Gln, Cit, Met, Lys, Leu, 0.701 (0.634 , 0.768); Ser, Thr, Cit, Val, Met, Lys, 0.701 (0.633 , 0.768); Ser, Cit, Met, Lys, Ile, Trp, 0.701 (0.634 , 0.768); Ser, Thr, Arg, Lys, Ile, Trp, 0.701 (0.634 , 0.768); Ser, Asn, Cit, Arg, Val, Lys, 0.701 (0.634 , 0.767); Ser, Gly, Thr, Arg, Lys, Ile, 0.7 (0.634 , 0.767); Ser, Thr, Cit, Lys, Ile, Leu, 0.7 (0.633 , 0.768); Ser, Gln, Thr, Arg, Lys, Leu, 0.7 (0.633 , 0.768); Ser, Ala, Cit, Val, Met, Lys, 0.7 (0.633 , 0.767); Ser, Cit, Met, Orn, Lys, Trp, 0.7 (0.633 , 0.767); Ser, Thr, Cit, Orn, Lys, Leu, 0.7 (0.633 , 0.768); Ser, Thr, Cit, Lys, Leu, Phe, 0.7 (0.633 , 0.768); Ser, Asn, Cit, Lys, Leu, Trp, 0.7 (0.634 , 0.767); Ser, Thr, Cit, Val, Orn, Lys, 0.7 (0.633 , 0.768); Ser, Thr, Arg, Orn, Lys, Leu, 0.7 (0.632 , 0.768); Ser, Thr, Arg, Lys, Phe, Trp, 0.7 (0.633 , 0.767); Ser, Thr, Ala, Cit, Lys, Leu, 0.7 (0.633 , 0.767); Ser, Thr, Cit, Val, Lys, Phe, 0.7 (0.633 , 0.767); Ser, Cit, Val, Met, Lys, Ile, 0.7 (0.633 , 0.767); Ser, Gln, Cit, Arg, Lys, Leu, 0.7 (0.633 , 0.767); Ser, Thr, Cit, Tyr, Lys, Trp, 0.7 (0.633 , 0.767); Ser, Cit, Val, Met, Orn, Lys, 0.7 (0.633 , 0.767); Ser, Cit, Met, Orn, Lys, Ile, 0.7 (0.632 , 0.767); Ser, Asn, Cit, Val, Met, Lys, 0.7 (0.633 , 0.767); Ser, His, Thr, Cit, Lys, Trp, 0.7 (0.633 , 0.766); Ser, Thr, Cit, Pro, Lys, Trp, 0.7 (0.632 , 0.767); Ser, Thr, Ala, Cit, Lys, Trp, 0.7 (0.632 , 0.767); Ser, Cit, Arg, Met, Lys, Ile, 0.7 (0.632 , 0.767); Ser, Thr, Cit, Pro, Val, Lys, 0.7 (0.632 , 0.767); Ser, Thr, Ala, Cit, Val, Lys, 0.7 (0.632 , 0.767); Ser, Asn, Gly, Cit, Lys, Trp, 0.7 (0.633 , 0.766); Ser, Thr, Cit, Pro, Lys, Leu, 0.699 (0.632 , 0.767); Ser, Thr, Arg, Tyr, Lys, Leu, 0.699 (0.632 , 0.767); Ser, Gln, Cit, Lys, Leu, Trp, 0.699 (0.632 , 0.767); Ser, Gly, Thr, Pro, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Thr, Cit, Val, Lys, Leu, 0.699 (0.632 , 0.767); Ser, Thr, Arg, Val, Met, Lys, 0.699 (0.632 , 0.767); Ser, His, Cit, Met, Lys, Leu, 0.699 (0.632 , 0.766); Ser, Asn, Cit, Tyr, Lys, Leu, 0.699 (0.632 , 0.766); Ser, Gln, Cit, Met, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Asn, Cit, Met, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Cit, Tyr, Met, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Asn, Thr, Cit, Val, Lys, 0.699 (0.632 , 0.766); Ser, Thr, Arg, Orn, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Arg, Met, Lys, Ile, Trp, 0.699 (0.632 , 0.766); Ser, Asn, Gln, Cit, Lys, Trp, 0.699 (0.632 , 0.765); Ser, Gly, Thr, Lys, Ile, Trp, 0.699 (0.632 , 0.766); Ser, Gln, Thr, Cit, Lys, Leu, 0.699 (0.631 , 0.766); Ser, Asn, Thr, Arg, Lys, Trp, 0.699 (0.632 , 0.766); Ser, Cit, Pro, Val, Met, Lys, 0.699 (0.632 , 0.766); Ser, Gln, Cit, Orn, Lys, Leu, 0.699 (0.631 , 0.766); Ser, Thr, Cit, Val, Lys, Ile, 0.699 (0.631 , 0.766); Ser, Thr, Arg, Tyr, Lys, Trp, 0.699 (0.631 , 0.766); Ser, Gln, Thr, Cit, Val, Lys, 0.699 (0.631 , 0.766); Ser, Thr, Ala, Arg, Lys, Trp, 0.699 (0.631 , 0.766); Ser, Cit, Tyr, Val, Met, Lys, 0.698 (0.632 , 0.765); Ser, Cit, Val, Met, Lys, Phe, 0.698 (0.631 , 0.766); Ser, Thr, Arg, Val, Orn, Lys, 0.698 (0.631 , 0.766); Ser, Gln, Cit, Lys, Ile, Leu, 0.698 (0.631 , 0.766); Ser, Thr, Cit, Tyr, Lys, Leu, 0.698 (0.631 , 0.766); Ser, Asn, Cit, Val, Orn, Lys, 0.698 (0.631 , 0.765); Ser, Gln, Cit, Pro, Lys, Leu, 0.698 (0.631 , 0.766); Ser, His, Thr, Cit, Lys, Leu, 0.698 (0.631 , 0.765); Ser, Asn, Gln, Cit, Lys, Leu, 0.698 (0.631 , 0.765); Ser, Gly, Cit, Met, Lys, Ile, 0.698 (0.631 , 0.765); Ser, Asn, Thr, Arg, Val, Lys, 0.698 (0.631 , 0.765); Ser, Thr, Cit, Arg, Lys, Ile, 0.698 (0.63 , 0.766); Ser, Gln, Cit, Tyr, Lys, Leu, 0.698 (0.63 , 0.765); Ser, His, Cit, Met, Lys, Trp, 0.698 (0.631 , 0.765); Ser, Ala, Cit, Met, Lys, Trp, 0.698 (0.631 , 0.765); Ser, His, Thr, Arg, Lys, Leu, 0.698 (0.63 , 0.765); Ser, Gly, Gln, Cit, Lys, Trp, 0.698 (0.631 , 0.765); Ser, Asn, Cit, Val, Lys, Ile, 0.698 (0.631 , 0.765); Ser, Gln, Thr, Arg, Val, Lys, 0.698 (0.63 , 0.765); Ser, Asn, Cit, Pro, Val, Lys, 0.698 (0.631 , 0.765); Ser, Gly, Thr, Ala, Lys, Trp, 0.698 (0.63 , 0.765); Ser, Gln, Thr, Arg, Lys, Trp, 0.698 (0.63 , 0.765); Ser, Thr, Arg, Pro, Lys, Trp, 0.698 (0.63 , 0.765); Ser, Gln, Cit, Val, Lys, Trp, 0.698 (0.631 , 0.764); Ser, Asn, Cit, Tyr, Val, Lys, 0.697 (0.63 , 0.764); Ser, Thr, Arg, Val, Lys, Ile, 0.697 (0.63 , 0.765); Ser, Thr, Arg, Pro, Lys, Leu, 0.697 (0.63 , 0.765); Ser, Thr, Cit, Tyr, Val, Lys, 0.697 (0.63 , 0.765); Ser, Asn, Gly, Cit, Lys, Ile, 0.697 (0.63 , 0.764); Ser, Asn, Cit, Val, Lys, Phe, 0.697 (0.63 , 0.764); Ser, His, Thr, Arg, Lys, Trp, 0.697 (0.63 , 0.765); Ser, Thr, Arg, Pro, Val, Lys, 0.697 (0.63 , 0.765); Ser, Thr, Arg, Tyr, Val, Lys, 0.697 (0.63 , 0.764); Ser, Cit, Pro, Met, Lys, Trp, 0.697 (0.63 , 0.764); Ser, Asn, Cit, Lys, Phe, Trp, 0.697 (0.63 , 0.764); Ser, Gln, Cit, Arg, Val, Lys, 0.697 (0.63 , 0.764); Ser, Cit, Val, Lys, Ile, Phe, 0.697 (0.63 , 0.764); Ser, Cit, Met, Lys, Phe, Trp, 0.697 (0.63 , 0.764); Ser, Thr, Cit, Met, Lys, Ile, 0.697 (0.629 , 0.764); Ser, Asn, Cit, Arg, Lys, Trp, 0.697 (0.63 , 0.763); Ser, Thr, Ala, Arg, Val, Lys, 0.697 (0.629 , 0.764); Ser, Gln, Cit, Val, Met, Lys, 0.697 (0.629 , 0.764); Ser, Cit, Pro, Val, Lys, Ile, 0.697 (0.629 , 0.764); Ser, Asn, Cit, Val, Lys, Trp, 0.697 (0.63 , 0.763); Ser, Asn, Arg, Val, Lys, Trp, 0.697 (0.63 , 0.763); Ser, Asn, Cit, Met, Lys, Ile, 0.696 (0.629 , 0.764). [Example]

[0124] Of the top 200 equations with ROC_AUC values ​​for the MCI group and the healthy group in the training data obtained in Example 5, 128 highly robust logistic regression equations with ROC_AUC values ​​of 0.600 or higher for the MCI group and the healthy group in the validation data were used.

[0125] Blood amino acid concentration data including training data and validation data (hereinafter referred to as "all data") were used. That is, blood amino acid concentration data obtained from plasma samples of a 219-person MCI group, which was a combination of the 120-person MCI group described in Example 1 and the 99-person MCI group described in Example 5, and a 220-person healthy group, which was a combination of the 120-person healthy group described in Example 1 and the 100-person healthy group described in Example 5, were used.

[0126] The first cutoff value was set to the value of the formula when the specificity was 60%, and the second cutoff value was set to the value of the formula when the specificity was 90%. If the value of the formula was lower than the first cutoff value, it was defined as rank A (a category indicating a low possibility (probability, risk) of MCI), if the value of the formula was higher than the first cutoff value but lower than the second cutoff value, it was defined as rank B (a category indicating a moderate possibility of MCI), and if the value of the formula was higher than the second cutoff value, it was defined as rank C (a category indicating a high possibility of MCI).

[0127] Then, under the above settings and definitions, all data and the 128 logistic regression equations were used to calculate ranks for each person and equation. Furthermore, using the calculation results, the "positive likelihood ratio = sensitivity" was calculated, where ranks A and B are negative and rank C is positive. ※1 / (1-specificity ※2 The positive likelihood ratio, defined as "the probability that the MCI patient's score is positive for the MCI patient's score, is calculated for each formula. The positive likelihood ratio is the probability that the MCI patient's score is positive for the MCI patient's score ... *1: Sensitivity = b / (a+b) a = Number of MCI patients who were calculated as negative (rank A or B) b = Number of MCI patients who were calculated as positive (rank C) *2: Specificity = c / (c+d) c = Number of healthy elderly people who were calculated as negative (rank A or B) d = Number of healthy elderly people who were calculated to be positive (rank C)

[0128] The seven logistic regression equations with a positive likelihood ratio of 3.0 or higher are shown in Table 3. These logistic regression equations have a high positive likelihood ratio and are expected to be more likely to lead to behavioral changes in patients than other equations.

[0129] [Table 3]

[0130] Of the seven logistic regression equations, the logistic regression equation with the variable set "Ser, Thr, Ala, Cit, Lys, Trp," the logistic regression equation with the variable set "Ser, Thr, Cit, Met, Lys, Trp," the logistic regression equation with the variable set "Ser, Thr, Cit, Tyr, Lys, Trp," and the logistic regression equation with the variable set "Ser, Thr, Cit, Orn, Lys, Trp" had high ROC_AUC values ​​of 0.70 or higher for the MCI group and healthy control group in the training data, and also high ROC_AUC values ​​of 0.65 or higher for the MCI group and healthy control group in the validation data, making these equations highly discriminative and particularly robust. [Industrial Applicability]

[0131] As described above, the present invention can be widely implemented in many industrial fields, particularly in the fields of pharmaceuticals, food, and medicine, and is particularly useful in the field of bioinformatics, which performs prediction of the progression of MCI, disease risk prediction, proteome and metabolome analysis, etc. [Explanation of symbols]

[0132] 100 Evaluation device (including calculation device) 102 Control section 102a Acquisition Department 102b Specified part 102c Formula Creation Section 102d Evaluation Section 102d1 Calculation part 102d2 conversion unit 102d3 Generation part 102d4 Classification section 102e Result output section 102f Transmitter 104 Communication interface unit 106 Storage section 106a Concentration data file 106b Index status information file 106c Specified index status information file 106d related information database 106d1 formula file 106e Evaluation result file 108 Input / Output Interface Section 112 Input Device 114 Output Device 200 Client device (terminal device (information communication terminal device)) 300 Network 400 Database Device

Claims

1. an acquisition step of acquiring information for evaluating the state of mild cognitive impairment of the subject, using a value of an equation calculated using concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted; An acquisition method characterized by the above.

2. The acquisition step: (1) The concentration values ​​of Ser, Trp, and Lys in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (2) A value calculated using a formula including the concentration values ​​of Ser, Trp, and Thr in the blood of the subject to be evaluated and variables into which the concentration values ​​are substituted, or (3) The concentration values ​​of Ser, Trp, and Cit in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; The information is acquired about the evaluation target using The acquisition method according to claim 1 ,

3. The acquisition step comprises: (1) The concentration values ​​of Ser, Trp, Lys, and Thr in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (2) The concentration values ​​of Ser, Trp, Lys, and Gly in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (3) The concentration values ​​of Ser, Trp, Lys, and Leu in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (4) The value of the formula calculated using the concentration values ​​of Ser, Trp, Lys, and Asn in the blood of the subject to be evaluated and the formula including variables into which the concentration values ​​are substituted; (5) The concentration values ​​of Ser, Trp, Lys, and Ile in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (6) The concentration values ​​of Ser, Trp, Lys, and Arg in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (7) The concentration values ​​of Ser, Trp, Lys, and Val in the blood of the subject to be evaluated and the value of the formula calculated using the formula including variables into which the concentration values ​​are substituted; (8) The value of the formula calculated using the concentration values ​​of Ser, Trp, Lys, and Met in the blood of the subject to be evaluated and the formula including variables into which the concentration values ​​are substituted; (9) A value calculated using a formula including the concentration values ​​of Ser, Trp, Lys, and Gln in the blood of the subject to be evaluated and variables into which the concentration values ​​are substituted, or (10) The value of the formula calculated using the concentration values ​​of Ser, Trp, Lys, and Pro in the blood of the subject to be evaluated and the formula including variables into which the concentration values ​​are substituted; The information is acquired about the evaluation target using The acquisition method according to claim 2 ,

4. the acquiring step is executed by a control unit of an information processing device including a control unit; The acquisition method according to any one of claims 1 to 3, characterized in that:

5. a calculation step of calculating a value of an equation for evaluating a state of mild cognitive impairment using concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted; A calculation method characterized by:

6. the calculation step is executed by a control unit of an information processing device including a control unit; The calculation method according to claim 5,

7. An evaluation device including a control unit, The control unit An evaluation means for evaluating the state of mild cognitive impairment of a subject using a value calculated using a formula including concentration values ​​of Ser and Trp in the subject's blood and variables into which the concentration values ​​are substituted. To have An evaluation device characterized by:

8. A terminal device that provides the value of the formula is communicably connected via a network, The control unit data receiving means for receiving the value of the formula transmitted from the terminal device; a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device; Furthermore, the evaluation means uses the value of the formula received by the data receiving means; The evaluation device according to claim 7 ,

9. A calculation device including a control unit, The control unit A calculation means for calculating a value of an equation for evaluating the state of mild cognitive impairment using concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted. To have A calculation device comprising:

10. An evaluation program to be executed in an information processing device having a control unit, To be executed in the control unit, an evaluation step of evaluating the state of mild cognitive impairment of the subject using a value calculated using a formula including concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted; containing, An evaluation program featuring:

11. A calculation program to be executed in an information processing device including a control unit, To be executed in the control unit, A calculation step of calculating the value of an equation for evaluating the state of mild cognitive impairment using concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted. containing, A calculation program characterized by:

12. A computer-readable recording medium on which the program according to claim 10 or 11 is recorded.

13. An evaluation system configured by connecting an evaluation device having a control unit and a terminal device having a control unit via a network so that they can communicate with each other, The control unit of the terminal device a data transmission means for transmitting to the evaluation device the value of an equation calculated using the concentration values ​​of Ser and Trp in the blood of the subject of evaluation and a variable into which the concentration values ​​are substituted; a result receiving means for receiving an evaluation result regarding the state of mild cognitive impairment of the evaluation subject transmitted from the evaluation device; Equipped with The control unit of the evaluation device data receiving means for receiving the value of the formula transmitted from the terminal device; evaluation means for evaluating the state of mild cognitive impairment of the subject using the value of the formula received by the data receiving means; a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device; To have A rating system characterized by:

14. A terminal device including a control unit, The control unit A result acquisition means for acquiring an evaluation result regarding the state of mild cognitive impairment of the evaluation subject. Equipped with the evaluation result is a result of evaluating the state of mild cognitive impairment of the subject using a value calculated using a formula including concentration values ​​of Ser and Trp in the blood of the subject and variables into which the concentration values ​​are substituted; A terminal device characterized by:

15. The evaluation subject is communicably connected via a network to an evaluation device that evaluates the state of mild cognitive impairment, the control unit includes a data transmission means for transmitting the value of the formula to the evaluation device; the result acquisition means receives the evaluation result transmitted from the evaluation device; The terminal device according to claim 14,

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