Judgment support information generation method, judgment support information generation system, and information processing device

Abstract

The present technology aims to provide a technique for accurately determining cognitive impairment, cognitive decline, or the risk of either of these. [Solution] The present technology provides a determination support information generation method including an index value calculation step of calculating (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human, and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human, based on the amounts of two or more biomarkers contained in a biological sample derived from the human, and a support information generation step of generating information to support the determination of cognitive impairment, cognitive decline, or the risk of either of them, based on the first index value and the second index value.

Description

[Technical Field]

[0001] The present technology relates to a method for generating information that supports assessment of cognitive impairment, cognitive decline, or the risk of either of these, an information generation system that generates the information, and an information processing device. [Background technology]

[0002] The primary prior art for distinguishing between normal and non-normal biological samples has been the technology used in in vitro diagnostics. The most common type of in vitro diagnostic involves analyzing blood components as biomarkers. Prior art in this field has used the measurement of the abundance of a single specific protein or so-called oligopeptide with a molecular weight of 10,000 or less in blood, or the activity of an enzyme protein, to identify clear differences between normal (healthy) and diseased samples and aid in diagnosis. Specifically, the amount or activity of a single or multiple specific proteins or oligopeptides is measured in advance in biological samples from a certain number of healthy individuals and diseased patients, and the range between abnormal and normal values ​​is determined. The biological sample to be evaluated is then measured in the same manner, and the test evaluation is performed based on whether the measurement results fall within the determined abnormal or normal range.

[0003] Regarding biomarkers used in the detection of cognitive impairment, for example, Patent Document 1 listed below discloses: (a) a biomarker for detecting cognitive impairment diseases consisting of an intact Apolipoprotein A1 protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 1; (b) a biomarker for detecting cognitive impairment diseases consisting of an intact Transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; and (c) a biomarker for detecting cognitive impairment diseases consisting of an intact Complement C3 protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 3. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2014-207888 Summary of the Invention [Problem to be solved by the invention]

[0005] The purpose of this technology is to provide a technique for accurately determining cognitive impairment, cognitive decline, or the risk of either of these. Cognitive impairment refers to the stage of mild cognitive impairment (hereinafter also referred to as MCI) or the stage of dementia. [Means for solving the problem]

[0006] The present inventors have found that specific judgment support information is suitable for accurately determining cognitive impairment, cognitive decline, or the risk of either of these. The present inventors have also found that a specific combination of biomarkers is suitable for accurately determining cognitive impairment, cognitive decline, or the risk of either of these.

[0007] That is, this technology: Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; The present invention provides a method for generating judgment support information, including: The amounts of the two or more biomarkers used in the index value calculation step may include the amounts of two or more biomarkers selected from the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or its partial peptide (2) alpha-2-antiplasmin or its partial peptide (3) alpha-2-macroglobulin or its partial peptides (4) Albumin or its partial peptide (5) apolipoprotein A1 or a partial peptide thereof (6) apolipoprotein C1 or a partial peptide thereof (7) Complement component 3 or a partial peptide thereof (8) Complement component 4 gamma chain or its partial peptide (9) Hemopexin or its partial peptide (10) Transthyretin or its partial peptide The first index value can be calculated based on the amount of biomarkers including two or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). The second index value can be calculated based on the amount of biomarkers including two or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). The first index value may be an index value indicating whether the human has mild cognitive impairment or dementia. The second index value may be an index value indicating whether the cognitive impairment of the human is at the stage of mild cognitive impairment or dementia. In addition, this technology: Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; Including, In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected. A method for generating decision support information is also provided. The support information may include a result of assessment of the risk of cognitive impairment or a result of assessment of the risk of cognitive decline. The support information includes a result of assessment of a risk of cognitive impairment, The cognitive impairment may be MCI. The range of values ​​of the assessment score may be such that the closer to one end the score is, the more advanced the cognitive impairment in the person is, and the closer to the other end the score is, the more the person does not have cognitive impairment. In the support information generation process, a data table containing the multiple numerical ranges within the range of the judgment score and judgment results associated with each of the multiple numerical ranges may be referenced, and a judgment result corresponding to the judgment score may be selected.

[0008] In addition, this technology: Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; Information processing device for executing A decision support information generation system including the above is also provided. The decision support information generation system may further include a biomarker quantification system that quantifies the amounts of the two or more biomarkers. The biomarker quantification system may include a liquid chromatography mass spectrometer. The system may be configured so that the biological sample is subjected to a proteolytic treatment and then subjected to measurement by the biomarker quantification system. In addition, this technology: Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; an information processing device that executes In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected. A decision support information generation system is also provided.

[0009] In addition, this technology: Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; Also provided is an information processing device that executes the above. In addition, this technology can determine the amount of two or more biomarkers contained in a human-derived biological sample, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; is configured to run In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected. An information processing device is also provided. [Effects of the Invention]

[0010] This technology contributes to the accurate assessment of cognitive impairment, cognitive decline, or the risk of either of these. Furthermore, this technology also makes it possible to assess the progression of a person's cognitive impairment or cognitive decline. Note that the effects of the present technology are not necessarily limited to the effects described here, and may be any of the effects described in this specification. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 10 is a flow diagram illustrating an example of a determination support information generation method according to the present technology. [Figure 2] FIG. 1 is a flow diagram of an example of a data acquisition process. [Figure 3] FIG. 1 is a block diagram of an example of a biomarker quantification system. [Figure 4] FIG. 1 is a diagram showing a specific configuration example of an information processing device included in the biomarker quantification system. [Figure 5] FIG. 10 is a diagram for explaining a first index value. [Figure 6] FIG. 10 is a diagram for explaining a second index value. [Figure 7] FIG. 10 is a diagram for explaining a judgment score. [Figure 8] FIG. 10 is a diagram showing an example of plot data in which determination scores are plotted. [Figure 9] FIG. 10 is a diagram showing an example of plot data including a two-dimensional matrix. [Figure 10] FIG. 10 is a diagram illustrating a specific configuration example of an information processing device that executes an index value calculation step and a support information generation step. [Figure 11] FIG. 1 is a block diagram of an example of a determination support information generation system. [Figure 12] FIG. 10 is a diagram showing evaluation results of discriminants. [Figure 13] FIG. 10 is a diagram showing evaluation results of discriminants. [Figure 14] FIG. 10 is a diagram showing evaluation results of discriminants. [Figure 15] FIG. 10 is a diagram showing evaluation results of discriminants. [Figure 16] FIG. 10 is a diagram illustrating an example of a table explaining a determination result. [Figure 17] FIG. 10 is a diagram illustrating an example of a table explaining a determination result. [Figure 18] FIG. 10 is a diagram showing an example of plot data in which determination scores are plotted. [Figure 19] FIG. 10 is a diagram showing an example of plot data in which determination scores are plotted. [Figure 20] FIG. 1 shows an example of a table containing biomarker abundance data. [Figure 21] FIG. 1 shows an example of plot data relating to the determination results based on the amounts of four types of biomarkers. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments for carrying out the present technology will be described in detail. Note that the embodiments described below are examples of typical embodiments of the present technology, and the present technology is not limited to these embodiments.

[0013] 1. Judgment support information generation method

[0014] The present technology provides a determination support information generation method. The method may generate information to support the determination of cognitive impairment, cognitive decline, or the risk of either of these. As shown in FIG. 1 , the method includes a data acquisition step S1 for acquiring data on the amounts of two or more biomarkers contained in a biological sample derived from a human; an index value calculation step S2 for calculating (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human, and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human, based on the amounts of the two or more biomarkers contained in the biological sample derived from the human; and a support information generation step S3 for generating information to support the determination of cognitive impairment, cognitive decline, or the risk of either of these, based on the first index value and the second index value. The method may further include an output step S4 for outputting the support information generated in the support information generation step S3.

[0015] In this specification, cognitive impairment includes, for example, MCI and dementia. The dementia includes Alzheimer's disease (hereinafter also referred to as AD), dementia with Lewy bodies, and vascular dementia. The dementia is particularly AD. As used herein, cognitive decline refers to a state in which cognitive function is reduced but not cognitive impairment. Generally, cognitively healthy people, for example, with aging, first suffer from cognitive decline, and then suffer from cognitive impairment.Cognitive impairment often progresses gradually, for example, people first suffer from MCI, and then suffer from dementia.This technology can determine which stage of progression people are in.

[0016] These steps are described in more detail below.

[0017] 1-1. Data acquisition process

[0018] In the data acquisition step S1, data to be used in the index value calculation step described below is acquired. The data may include, for example, data on the amounts of two or more biomarkers contained in a biological sample derived from a human.

[0019] The biological sample may be, for example, whole blood, plasma, or serum, preferably plasma or serum, and particularly preferably plasma. That is, the biomarkers may be components present in a biological sample derived from a human, particularly in human whole blood, plasma, or serum, more preferably in human plasma or serum, and even more preferably in human plasma.

[0020] 1-1-1.Biomarkers

[0021] The amounts of the two or more biomarkers preferably include the amounts of two or more biomarkers selected from the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or a partial peptide thereof, (2) alpha-2-antiplasmin or a partial peptide thereof, (3) alpha-2-macroglobulin or a partial peptide thereof, (4) albumin or a partial peptide thereof, (5) apolipoprotein A1 or a partial peptide thereof, (6) apolipoprotein C1 or a partial peptide thereof, (7) complement component 3 or a partial peptide thereof; (8) complement component 4 gamma chain or a partial peptide thereof; (9) hempexin or a partial peptide thereof, and (10) Transthyretin or a partial peptide thereof. In the data acquisition step, the amounts of biomarkers other than those (1) to (10) may be acquired.

[0022] In the data acquisition step, the amounts of the biomarkers (1) to (10) may be quantified based on the amount of an amino acid sequence portion specific to the protein described as each biomarker. The specific amino acid sequence portion may be an amino acid sequence portion that enables each biomarker to be distinguished from other components (more specifically, biological sample components). The amino acid sequence portion may be appropriately selected by those skilled in the art. The amino acid sequence portion may be a protein fragment (also referred to as a quantification fragment) that is to be measured by a mass spectrometer such as a liquid chromatography mass spectrometer.

[0023] The partial peptides described in each of (1) to (10) above may be, for example, peptides containing an amino acid sequence portion specific to the protein described above as each biomarker. The partial peptides described in each of (1) to (10) above may be peptides having, for example, 5 to 30, particularly 6 to 25, and more particularly 7 to 20 amino acid residues. Examples of the partial peptides described in each of (1) to (10) above are the quantitative fragments described in Table 1 below or partial peptides containing the amino acid sequences of the quantitative fragments, but are not limited to these.

[0024] The alpha-1-B-glycoprotein described as the biomarker in (1) above may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 1 below. Alpha-2-antiplasmin, described as the biomarker in (2) above, may be a protein containing an amino acid sequence that has 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 2 below. The alpha-2-macroglobulin described as the biomarker (3) above may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 3 below. Albumin, described as the biomarker in (4) above, may be a protein comprising an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence shown in SEQ ID NO: 4 below. The apolipoprotein A1 described as the biomarker (5) above may be a protein containing an amino acid sequence that has 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 5 below. The apolipoprotein C1 described as the biomarker (6) above may be a protein containing an amino acid sequence that has 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 6 below. Complement component 3, described as the biomarker in (7) above, may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 7 below. The complement component 4 gamma chain described as the biomarker (8) above may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence shown in SEQ ID NO: 8 below. The hempexin described as the biomarker (9) above may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence represented by SEQ ID NO: 9 below. Transthyretin, described as the biomarker (10), may be a protein containing an amino acid sequence having 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more sequence identity with the amino acid sequence shown in SEQ ID NO: 10 below.

[0025] In this specification, the "sequence identity" of amino acid sequences refers to the percentage obtained by aligning two amino acid sequences to be compared so that as many amino acid residues as possible match, dividing the number of matching amino acid residues by the total number of amino acid residues. The alignment and calculation of the percentage may be performed using a well-known algorithm such as BLAST.

[0026] The amino acid sequences (single letter notation) of the above SEQ ID NOs: 1 to 10 are as follows: In the following, the name of the biomarker is written in parentheses after each SEQ ID NO, and the abbreviations used in this specification are also written.

[0027] JPEG0007811389000001.jpg73139

[0028] JPEG0007811389000002.jpg74141

[0029] JPEG0007811389000003.jpg180149

[0030] JPEG0007811389000004.jpg88144

[0031] JPEG0007811389000005.jpg47140

[0032] JPEG0007811389000006.jpg30137

[0033] JPEG0007811389000007.jpg198148

[0034] JPEG0007811389000008.jpg47138

[0035] JPEG0007811389000009.jpg67140

[0036] JPEG0007811389000010.jpg35139

[0037] Examples of amino acid sequence portions (quantitative fragments) specific to the biomarkers (1) to (10) are shown in Table 1 below. The table also lists the sequence numbers corresponding to the quantitative fragments of each biomarker.

[0038] [Table 1]

[0039] The amino acid sequence portion used for quantifying each biomarker is not limited to the amino acid sequence portion shown in Table 1, and may be appropriately selected by those skilled in the art depending on the method of proteolysis treatment, the amino acid sequence of each biomarker, etc.

[0040] As used herein, the "amount" of a biomarker may refer to the absolute amount or relative amount of the biomarker in a biological sample. The relative amount may be, for example, concentration. For example, the concentration of a biomarker may be the mass of the biomarker relative to the amount (volume or mass) of the biological sample, e.g., molar concentration.

[0041] 1-1-2. Obtaining biomarker quantity data by quantification

[0042] In the data acquisition step, the amount of each biomarker contained in the biological sample may be quantified. To perform the quantification, the data acquisition step includes, for example, a pretreatment step S11 in which the biological sample is pretreated, a measurement step S12 in which the amount of quantification fragments contained in the pretreated biological sample is measured, and a biomarker quantification step S13 in which the amount of each biomarker is quantified based on the amount of quantification fragments measured in the measurement step, as shown in Figure 2. These steps are described below.

[0043] 1-1-2-1. Pretreatment process In the pretreatment step S11, for example, the biological sample can be subjected to a proteolytic treatment, which decomposes the biomarker to be quantified to generate protein fragments (quantitative fragments).

[0044] The proteolytic treatment may be, for example, a proteolytic treatment using a proteolytic agent, or a proteolytic treatment using heat and pH.

[0045] The proteolytic agent used in the degradation treatment with the proteolytic agent may be, for example, an enzyme or a chemical degradation agent. The enzyme may be a protease, preferably including an endopeptidase. The endopeptidase may be, for example, one or a combination of two or more selected from trypsin, endoproteinase Lys-C, endoproteinase Glu-C, and endoproteinase Asp-N. Particularly preferably, the enzyme is an enzyme that hydrolyzes peptide bonds on the carboxyl side of basic amino acids (lysine and arginine), more specifically, trypsin. The chemical degradation agent may be, for example, cyanogen bromide. In the degradation treatment, the biological sample is contacted with these degradation agents to generate quantitative fragments.

[0046] In the decomposition treatment using heat and pH, the biological sample can be heated, for example, to a temperature of 60° C. to 140° C., particularly 70° C. to 130° C., and more particularly 80° C. to 120° C. The heating time can be, for example, 0.5 hours to 10 hours, particularly 1 hour to 4 hours, and more particularly 1 hour to 3 hours.

[0047] In the decomposition treatment using heat and pH, the biological sample is subjected to an environment of, for example, pH 5 or less, particularly pH 1 to 4, more particularly pH 1 to 3. The biological sample may be left in this environment for the heating time described above. To adjust the pH, for example, an acid or alkali may be added to the biological sample. In the degradation process, the biological sample is subjected to such heat and pH degradation process to generate quantitative fragments.

[0048] The proteolytic process may be stopped by techniques known in the art. For example, when an enzymatic degradation treatment is performed, the proteolysis may be stopped by an enzyme inactivation treatment, which may be, for example, a pH-lowering treatment by adding formic acid or TFA to the biological sample, or a heat treatment of the biological sample. Furthermore, when thermal decomposition treatment is performed, the decomposition treatment can be stopped by stopping the heating treatment of the biological sample and changing the temperature of the biological sample to a temperature at which decomposition does not proceed. For example, a cooling treatment may be performed to change the temperature. Furthermore, when a pH-dependent degradation treatment is performed, the degradation treatment can be stopped by changing the pH of the biological sample to a pH at which degradation does not proceed, for example, by adding an alkali or an acid.

[0049] 1-1-2-2.Measurement process In the measurement step S12, the amount of quantification fragments contained in the pre-processed biological sample is measured. This measurement may be performed using a liquid chromatography mass spectrometer, more specifically, LC-MS or LC-MS / MS. Among the processes performed by the liquid chromatography mass spectrometer, the liquid chromatography process may be reversed-phase chromatography. Among the processes performed by the liquid chromatography mass spectrometer, the mass analysis process may be performed by MS or MS / MS, preferably by MS / MS. For example, in MS / MS, two or more stages of mass separation are performed using collision-induced dissociation (CID) for specific ion groups. More specifically, the mass analysis process may be performed by MRM (Multiple Reaction Monitoring). That is, the liquid chromatography mass spectrometer may be configured to perform mass analysis by MRM.

[0050] 1-1-2-3.Quantitative process In the quantification step S13, the amount of each biomarker is quantified based on the amount of the quantification fragment measured in the measurement step. For example, the amount of the biomarker is quantified based on the amount of the quantification fragment, the molecular weight of the quantification fragment, and the molecular weight of the biomarker containing the quantification fragment.

[0051] For example, in the quantification step S13, the amounts of the biomarkers (1) to (10) (particularly the proteins described as biomarkers) may be quantified based on the amounts of the quantification fragments described in Table 1 above.

[0052] 1-1-2-4. System for obtaining quantitative biomarker quantity data

[0053] In the present technology, a biomarker quantification system may quantify the amounts of the two or more biomarkers. For example, the biomarker quantification system may be configured to perform the steps described in A to C above, or may be configured to perform one or two of these steps. An example of the biomarker quantification system is shown in Figure 3. Figure 3 is a block diagram of the system.

[0054] As shown in FIG. 3, the biomarker quantification system 10 may include a pre-processing unit 11, a measurement unit 12, and an information processing unit 13.

[0055] The pretreatment unit 11 may be configured to perform the pretreatment step S11. The pretreatment unit may include, for example, a proteolysis treatment unit that performs the proteolysis treatment on the biological sample. The proteolysis treatment unit may include, for example, a storage unit that stores the biological sample or a container that contains the biological sample, a decomposition agent addition device that adds a proteolytic agent to the biological sample, and a termination treatment device that performs a process to terminate the decomposition by the decomposition agent. The pretreatment unit may also include a temperature control device that controls the temperature of the biological sample in the storage unit.

[0056] The decomposing agent adding device may include, for example, a pipette device that adds a proteolytic agent to the biological sample, and a drive unit that drives the pipette device to add the decomposing agent. The stopping processing unit may include, for example, a pipette device that adds an agent to the biological sample to stop the degradation by the proteolytic agent, and a drive unit that drives the pipette device. The temperature control device may be any device known in the art.

[0057] The measurement unit 12 may be configured to perform the measurement step S12. The measurement unit may include, for example, a liquid chromatography mass spectrometer. The liquid chromatography mass spectrometer may measure the amount of the quantification fragment contained in the biological sample that has been preprocessed by the preprocessing unit. The liquid chromatography mass spectrometer may be the device described above in "B. Measurement step."

[0058] The information processing unit 13 may be configured to execute the quantification step S13. The information processing unit calculates the amount of each biomarker based on the amount of the quantification fragment measured in the measurement step S12. The information processing unit may be, for example, an information processing device attached to the liquid chromatography mass spectrometer, or may be an information processing device not attached to the liquid chromatography mass spectrometer. The information processing device receives quantity data of the quantification fragments measured by the liquid chromatography mass spectrometer, and may quantify the amount of the biomarkers (1) to (10) (particularly proteins described as biomarkers) based on the quantity data. For the quantification, for example, the molecular weight of the quantification fragment and the molecular weight of a biomarker containing the quantification fragment may be referenced.

[0059] A specific configuration example of the information processing device will be described with reference to Fig. 4. The information processing device 100 shown in Fig. 4 includes a processing unit 101, a storage unit 102, an input unit 103, an output unit 104, and a communication unit 105. A general-purpose computer or server, for example, may be adopted as the information processing device 100.

[0060] The processing unit 101 may be configured to execute, for example, the quantification step S13, and more specifically, has a circuit configured to execute the quantification step S13. The processing unit 101 may include, for example, a CPU (Central Processing Unit) and RAM (Random Access Memory). The CPU and RAM (Random Access Memory) may be connected to each other, for example, via a bus. An input / output interface may be further connected to the bus. An input unit 103, an output unit 104, and a communication unit 105 may be connected to the bus via the input / output interface.

[0061] The processing unit 101 may further be configured to acquire data from or record data in the storage unit 102. The storage unit 102 stores various types of data. The storage unit 102 may be configured to store, for example, measurement result data acquired in the measurement step 12 and biomarker amount data quantified based on the measurement result data. The storage unit 102 may also store an operating system (e.g., WINDOWS (registered trademark), UNIX (registered trademark), or LINUX (registered trademark)), a program for causing an information processing device to execute the quantification step, and various other programs. These programs may be stored not only in the storage unit 102 but also in a recording medium.

[0062] The processing unit 101 may be configured to be able to control the measurement unit 12 that executes the measurement step 12, and for example, the processing unit 101 may control the liquid chromatography mass spectrometer included in the measurement unit 12. The processing unit 101 may also execute a process of calculating the amount of quantitation fragments based on data related to ions detected in the liquid chromatography mass spectrometer.

[0063] In addition, the processing unit 101 may be configured to be able to control the pre-processing unit 11 that performs the pre-processing step 11, and for example, the processing unit 101 may control one or more of the decomposition agent addition device, the stop processing device, and the temperature control device included in the pre-processing unit 11.

[0064] The input unit 103 may include an interface configured to be able to accept input of various data, and may include, for example, a mouse, a keyboard, a touch panel, and the like as devices for accepting such operations.

[0065] The output unit 104 may include an interface configured to be able to output various types of data. For example, the output unit 104 may output biomarker amount data quantified in the quantification step S13. The output unit 104 may include, for example, a display device and / or a printing device as a device for performing the output.

[0066] The communication unit 105 may be configured to connect the information processing device 100 to a network via a wired or wireless connection. The communication unit 105 enables the information processing device 100 to acquire various data via the network. The acquired data may be stored in the storage unit 102, for example. The configuration of the communication unit 105 may be appropriately selected by those skilled in the art. The information processing device 100 can transmit the acquired biomarker amount data via the communication unit 105, for example, to an information processing device that executes an index value calculation step S2, which will be described later.

[0067] The information processing device 100 may include, for example, a drive (not shown). The drive can read data (such as the various data listed above) or programs recorded on a recording medium and output them to RAM. The recording medium can be, for example, a microSD memory card, an SD memory card, or a flash memory, but is not limited to these.

[0068] 1-1-3. Acquisition of existing biomarker quantity data

[0069] In the data acquisition step, already acquired biomarker quantity data may be acquired. In this embodiment, the quantification described in 1-1-2 above may not be performed. In this embodiment, for example, an information processing device that executes an index value calculation step described below may receive biomarker quantity data transmitted from another information processing device or biomarker quantity data transmitted from a biomarker quantification system described below. Alternatively, an information processing device that executes an index value calculation step described below may acquire biomarker quantity data from a recording medium on which biomarker quantity data is stored. These receiving processes or acquisition processes may be performed via a network.

[0070] 1-2. Index value calculation process

[0071] In the index value calculation step S2, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human, and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human are calculated based on the amounts of two or more biomarkers contained in a biological sample derived from the human. By using these two index values, highly accurate determination can be made in the support information generation step S3 described below.

[0072] The amounts of the two or more biomarkers may be data acquired in the data acquisition step of 1-1 above. For example, an information processing device may execute the index value calculation step of calculating the first index value and the second index value using the data.

[0073] 1-2-1. First index value

[0074] The first index value may be an index value indicating whether the human has cognitive impairment or cognitive decline. The first index value may be calculated based on the amounts of the two or more biomarkers, preferably based on the amounts of two or more, three or more, or four or more of the biomarkers (1) to (10). The first index value may contribute to achieving better accuracy in the support information generation step S3. Furthermore, the biomarkers used to calculate the first index value may be, for example, 9 or less, 8 or less, or 7 or less of the biomarkers (1) to (10) above.

[0075] Preferably, the first index value may be calculated based on the amounts of two or more, three or more, or four or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). Particularly preferably, the first index value may be calculated using the amounts of four, five, six, seven, or eight biomarkers from the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). Particularly preferably, the amounts of at least the biomarkers (1), (4), (8), and (9) are used to calculate the first index value. For example, all of the biomarkers (1), (4), (8), and (9) and one, two, three, or four of the biomarkers (2), (3), (6), and (7) are used to calculate the first index value. Such a combination of biomarkers is useful as an indicator of the presence or absence of cognitive impairment or decline, and is particularly useful as an indicator of the presence or absence of cognitive impairment.

[0076] Particularly preferably, the first index value is calculated using the amount of each of the biomarkers (1), (4), (8), and (9) and the amount of each of the biomarkers (2) and (3), (2) and (6), (2) and (7), (3) and (6), (3) and (7), or (6) and (7). The first index value calculated using the amount of these biomarkers is particularly suitable for determining cognitive impairment, cognitive decline, or the risk of either of these. In the present invention, the same combination of biomarkers may be used to calculate the first index value for males and the first index value for females, or different combinations of biomarkers may be used.

[0077] Furthermore, in calculating the first index value, the amounts of the biomarkers (5) and / or (10) may or may not be used in addition to the combination of biomarkers.

[0078] The first index value may be calculated using, for example, one or more predetermined discriminants, particularly two or more predetermined discriminants. The discriminant may be, for example, a discriminant created by multivariate analysis, particularly a discriminant obtained by performing multivariate analysis using the amounts of each biomarker constituting the combination of biomarkers for calculating the first index value as explanatory variables and the presence or absence of cognitive impairment or cognitive decline as a response variable. In one embodiment of the present invention, the predetermined discriminant may be, for example, a discriminant prepared for each gender, i.e., a discriminant for calculating a first index value for males and a discriminant for calculating a first index value for females may be prepared. Good determination results can be obtained using discriminants prepared without distinguishing between sexes, or discriminants prepared for each sex.

[0079] The number of discriminants used to calculate the first index value may correspond to the number of stages of progression of cognitive impairment indicated by the second index value described below. For example, when the stages of cognitive impairment indicated by the second index value described below are two stages, MCI and AD, the number of discriminants used to calculate the first index value may also be two. In this case, one of the two discriminants may be a discriminant for discriminating between the healthy stage and the MCI stage, and the other may be a discriminant for discriminating between the healthy stage and the AD stage. The number of discriminants may correspond to the number of stages of progression of cognitive impairment indicated by the second index value, as described above, whether a discriminant is prepared without distinguishing between genders or a discriminant is prepared for each gender. When gender is not distinguished and the stages of cognitive impairment indicated by the second index value described below are MCI and AD, the number of discriminants is two, as described above. In the case where a discriminant is prepared for each gender and the stages of cognitive impairment indicated by the second index value described later are MCI and AD, two discriminant formulas are prepared for use in calculating the first index value for males, and two discriminant formulas are prepared for use in calculating the first index value for females. In this way, the number of discriminant formulas prepared for each gender corresponds to the number of stages of the progression of cognitive impairment indicated by the second index value.

[0080] To calculate the first index value, a value indicating the likelihood of the person being in each stage of the progression (e.g., MCI stage, AD stage, etc.) may be calculated using a value obtained by substituting the amount of biomarker into each of the one or more predetermined discriminants. For example, to calculate the first index value, a probability that the person is in the MCI stage and a probability that the person is in the AD stage may be calculated. The first index value may be calculated using these probabilities. For example, the first index value may be the sum of values ​​indicating the likelihood that the person is in each stage of the progression (for example, the MCI stage and the AD stage). When a discriminant prepared without distinguishing between genders is used, the first index value may be calculated as described above. When using discriminants prepared for each gender, if the subject is male, the amount of biomarker is substituted into the discriminant for men. If the subject is female, the amount of biomarker is substituted into the discriminant for women. This allows the value indicating the likelihood to be calculated as described above.

[0081] To create the discriminant, a population of humans whose presence or absence of cognitive impairment or cognitive decline is known may be used. The number of humans constituting the population may be, for example, 50 or more, 60 or more, or 70 or more. The upper limit of the number of humans constituting the population is not particularly limited, and may be, for example, 500 or less, 400 or less, 300 or less, or 200 or less. The discriminant for calculating the first index value is obtained by performing multivariate analysis using the presence or absence of cognitive impairment or cognitive decline of each of the humans constituting the population and the amount of biomarker contained in a biological sample obtained from each human, and in particular, the coefficient of each term of the discriminant (and the value of the constant term) is obtained. When a gender-independent discriminant is prepared, the population for creating the discriminant may be as described above. When preparing a discriminant for each gender, a population consisting of the above-mentioned number of males is prepared to prepare a discriminant for males. Also, a population consisting of the above-mentioned number of females is prepared to prepare a discriminant for females. By performing the above-mentioned multivariate analysis for each population, the discriminant (especially the coefficients of each term of the discriminant (and the value of the constant term)) can be obtained.

[0082] The multivariate analysis may preferably be logistic regression analysis (particularly multinomial logistic regression analysis). Alternatively, the multivariate analysis may be other linear regression analysis. Alternatively, the multivariate analysis may be multi-class classification, and a machine learning model such as a neural network or a support vector machine may be used. The first index value may be obtained using the machine learning model.

[0083] The first index value may be an index value indicating whether the human has cognitive impairment, and particularly may be an index value indicating whether the human has either MCI or dementia (particularly AD).

[0084] The first index value may be a value within a predetermined range, which may indicate that the closer the first index value is to one end of the range, the more likely the person is not suffering from cognitive impairment or cognitive decline, and the closer the first index value is to the other end of the range, the more likely the person is suffering from cognitive impairment or cognitive decline.

[0085] The predetermined value range may be set appropriately by a person skilled in the art. One endpoint of the predetermined value range may be, for example, -100, -50, -10, -5, -1, 0, 1, 5, 10, 50, or 100. The other endpoint of the predetermined value range may be 100, 50, 10, 5, 1, 0, -1, -5, -10, -50, or -100. The predetermined value range may be a range defined by these endpoints, such as 0 to 1, 0 to 50, 0 to 100, -1 to 1, or -100 to 100, although the values ​​at the endpoints of the range may be values ​​other than these.

[0086] For example, when the specified range is 0 to 1 as shown in Figure 5, the closer the first index value is to 0 (one end point), the more likely it is that the person does not have cognitive impairment (or cognitive decline), and the closer the first index value is to 1 (the other end point), the more likely it is that the person has cognitive impairment (or cognitive decline). Conversely, when the specified range is 0 to 1, the closer the first index value is to 1 (one end point), the more likely the person is not to have cognitive impairment (or cognitive decline), and the closer the first index value is to 0 (the other end point), the more likely the person is to have cognitive impairment (or cognitive decline).

[0087] For example, the range of the first index value may be the same as the range of the second index value described below. In this way, the ranges (numerical ranges) of the first index value and the second index value are the same, which makes it easier to execute the processing in the support information generation step described below. In order to set these two ranges to be the same, a person skilled in the art can appropriately set the discriminant.

[0088] 1-2-2. Second index value

[0089] The second index value is an index value that indicates the degree of progression of cognitive impairment or cognitive decline in the human. The second index value may be calculated based on the amounts of the two or more biomarkers, preferably based on the amounts of two or more, three or more, or four or more biomarkers among the biomarkers (1) to (10). The biomarkers used to calculate the second index value particularly include at least one biomarker different from the biomarkers used to calculate the first index value. The second index value can contribute to achieving better accuracy in the support information generation step S3. Furthermore, the biomarkers used to calculate the second index value may be, for example, 9 or less, 8 or less, or 7 or less of the biomarkers (1) to (10) above. Furthermore, the combination of biomarkers used to calculate the second index value may be different from the combination of biomarkers used to calculate the first index value.

[0090] Preferably, the second index value may be calculated based on the amounts of two or more, three or more, or four or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). Particularly preferably, the second index value may be calculated using the amounts of four, five, six, seven, or eight biomarkers from the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). For example, all of the biomarkers (1), (4), (8), and (9) and one, two, three, or four of the biomarkers (2), (3), (6), and (7) are used to calculate the first index value. Such a combination of biomarkers is useful as an indicator of the progression of cognitive impairment or decline, and is particularly useful as an indicator of the progression of cognitive impairment.

[0091] Particularly preferably, the second index value is calculated using the amount of each of the biomarkers (1), (4), (8), and (9) and the amount of each of the biomarkers (2) and (3), (2) and (6), (2) and (7), (3) and (6), (3) and (7), or (6) and (7). The second index value calculated using the amount of these biomarkers is particularly suitable for determining cognitive impairment, cognitive decline, or the risk of either of these. The combination of biomarkers used to calculate the second index value may be the same as or different from the combination of biomarkers used to calculate the first index value. When these combinations are the same, the calculation method for the first index value and the calculation method for the second index value may be different. In the present invention, the same combination of biomarkers may be used to calculate the first index value for males and the first index value for females, or different combinations of biomarkers may be used.

[0092] Furthermore, in calculating the second index value, the amounts of the biomarkers (5) and / or (10) may or may not be used in addition to the combination of biomarkers.

[0093] Like the first index value, the second index value may be calculated using one or more predetermined discriminants, particularly two or more predetermined discriminants. The discriminants may be, for example, discriminants created by multivariate analysis, particularly discriminants obtained by performing multivariate analysis using the amount of each biomarker as an explanatory variable and the degree of progression of cognitive impairment or cognitive decline as a response variable. In one embodiment of the present invention, the predetermined discriminant used for calculating the second index value may be, for example, a discriminant prepared for each gender, i.e., a discriminant for calculating the second index value for males and a discriminant for calculating the second index value for females may be prepared. Good determination results can be obtained using discriminants prepared without distinguishing between sexes, or discriminants prepared for each sex.

[0094] The number of discriminants used to calculate the second index value may correspond to the number of stages of progression of cognitive impairment indicated by the second index value. For example, when the stages are MCI and AD, the number of discriminants used to calculate the second index value may also be two. In this case, one of the two discriminants may be a discriminant for discriminating between the healthy stage and the MCI stage, and the other may be a discriminant for discriminating between the healthy stage and the AD stage. The one or more predetermined discriminants used to calculate the second index value may be the same as or different from the one or more predetermined discriminants used to calculate the first index value. If these discriminants are the same, for example, the method of using probability to calculate these index values ​​may be different. The number of discriminants may correspond to the number of stages of progression of cognitive impairment indicated by the second index value, as described above, whether a discriminant is prepared without distinguishing between genders or a discriminant is prepared for each gender. When gender is not distinguished and the stages of cognitive impairment indicated by the second index value are MCI and AD, the number of discriminants is two, as described above. In the case where a discriminant is prepared for each gender and the stages of cognitive impairment indicated by the second index value are MCI and AD, two discriminant formulas are prepared for use in calculating the first index value for males, and two discriminant formulas are prepared for use in calculating the first index value for females. In this way, the number of discriminant formulas prepared for each gender corresponds to the number of stages of the progression of cognitive impairment indicated by the second index value.

[0095] To calculate the second index value, a value indicating the likelihood of the person being in each stage of the progression (e.g., MCI stage, AD stage, etc.) may be calculated using values ​​obtained by substituting the amount of biomarker into each of the one or more predetermined discriminants. For example, to calculate the second index value, a probability that the person is in the MCI stage and a probability that the person is in the AD stage may be calculated. For example, the second index value may be a value indicating the likelihood that the person is in one of the values ​​indicating the likelihood that the person is in each stage of the progression, for example, a value indicating the likelihood that the person is in the AD stage. When a discriminant prepared without distinguishing between genders is used, the second index value may be calculated as described above. When using discriminants prepared for each gender, if the subject is male, the amount of biomarker is substituted into the discriminant for men. If the subject is female, the amount of biomarker is substituted into the discriminant for women. This allows the value indicating the likelihood to be calculated as described above.

[0096] A population of humans whose degree of cognitive impairment or cognitive decline is known may be used to create the discriminant. This population may preferably be the same as the population used to create the discriminant used to calculate the first index value. The number of humans constituting the population may be, for example, 50 or more, 60 or more, or 70 or more. The upper limit of the number of humans constituting the population is not particularly limited, and may be, for example, 500 or less, 400 or less, 300 or less, or 200 or less. A discriminant for calculating the second index value is obtained by performing multivariate analysis using the degree of cognitive impairment or cognitive decline of each human constituting the population and the amount of biomarkers contained in a biological sample obtained from each human, and in particular, the coefficients (and constant terms) of each term of the discriminant are obtained. When a gender-independent discriminant is prepared, the population for creating the discriminant may be as described above. When preparing a discriminant for each gender, a population consisting of the above-mentioned number of males is prepared to prepare a discriminant for males. Also, a population consisting of the above-mentioned number of females is prepared to prepare a discriminant for females. By performing the above-mentioned multivariate analysis for each population, the discriminant (especially the coefficients of each term of the discriminant (and the value of the constant term)) can be obtained.

[0097] The multivariate analysis may preferably be logistic regression analysis (particularly multinomial logistic regression analysis). Alternatively, the multivariate analysis may be other linear regression analysis. Alternatively, the multivariate analysis may be multi-class classification, and a machine learning model such as a neural network or a support vector machine may be used. The second index value may be obtained using the machine learning model.

[0098] The progression of the cognitive impairment may be, for example, the stage of MCI or the stage of dementia. The dementia includes AD, dementia with Lewy bodies, and vascular dementia. The dementia is particularly AD.

[0099] In one embodiment of the present technology, the second index value may be an index value indicating whether the subject is in the stage of MCI or AD. In this embodiment, a discriminant for calculating the index value can be obtained by the discriminant-formula creation method described above.

[0100] In another embodiment of the present technology, the second index value may be an index value indicating whether the subject is at an early stage of MCI, late stage of MCI, early stage of AD, or late stage of AD. In this embodiment, too, a discriminant for calculating the index value can be obtained by the discriminant-formula-creating method described above. For example, a discriminant can be created using a population composed of individuals whose status among these four stages is known. Each stage of cognitive impairment indicated by the second index value may be associated with, for example, an evaluation index according to the Clinical Dementia Rating (CDR). The evaluation index according to the CDR is a value based on clinical information such as a neuropsychological test or an index of activities of daily living. In the CDR, a numerical value of 0 corresponds to normal, 0.5 corresponds to MCI, and 1 to 3 corresponds to AD. Regarding the scores of 1 to 3, the higher the number, the more severe the AD.

[0101] The second index value may be any value within a predetermined range, which may indicate that the closer the second index value is to one end of the range, the more likely the person's cognitive impairment or cognitive decline is not progressing, and the closer the second index value is to the other end of the range, the more likely the person's cognitive impairment or cognitive decline is progressing.

[0102] In one embodiment of the present technology, the predetermined range may be a range indicating that the closer the second index value is to one end value of the range, the more likely the person is at a stage of MCI, and the closer the second index value is to the other end value of the range, the more likely the person is at a stage of dementia (particularly AD).

[0103] In another embodiment of the present technology, the predetermined range may be a range indicating that the closer the second index value is to one of the endpoints of the range, the more likely the person is to be in an earlier stage of MCI, and that the closer the second index value is to the other endpoint, the more likely the person is to be in a later stage of MCI or a later stage of dementia. In this embodiment, the second index value may be a range of values ​​indicating that the closer the second index value is to the other end point of the range, the more likely the person is to be in a later stage of dementia, and the closer the second index value is to one end point, the more likely the person is to be in an earlier stage of dementia or an earlier stage of MCI.

[0104] The predetermined value range may be set appropriately by a person skilled in the art. One end point of the predetermined value range may be, for example, -100, -50, -10, -5, -1, 0, 1, 5, 10, 50, or 100. The other end point of the predetermined value range may be 100, 50, 10, 5, 1, 0, -1, -5, -10, -50, or -100. The predetermined value range may be a range defined by these end points, such as 0 to 1, 0 to 50, 0 to 100, -1 to 1, or -100 to 100.

[0105] For example, as shown in Figure 6A, when the specified value range is 0 to 1, the closer the second index value is to 0 (one end point), the more likely it is that the person's cognitive dysfunction or cognitive decline is not progressing, and the closer the second index value is to 1 (the other end point), the more likely it is that the person's cognitive dysfunction or cognitive decline is progressing. Conversely, when the specified value range is 0 to 1, the closer the second index value is to 1 (one end point), the more likely it is that the person's cognitive dysfunction or cognitive decline is not progressing, and the closer the second index value is to 0 (the other end point), the more likely it is that the person's cognitive dysfunction or cognitive decline is progressing.

[0106] For example, as shown in Figure 6B, when the specified value range is 0 to 1, the closer the second index value is to 0 (one end point), the more likely the person is at a stage of MCI, and the closer the second index value is to 1 (the other end point), the more likely the person is at a stage of dementia (particularly AD). Conversely, when the specified range is 0 to 1, the closer the second index value is to 1 (one end point), the more likely the person is at a stage of MCI, and the closer the second index value is to 0 (the other end point), the more likely the person is at a stage of dementia (particularly AD).

[0107] For example, as shown in FIG. 6C , when the predetermined range is 0 to 1, the second index value may indicate which stage of MCI (e.g., early stage or late stage, etc.) the person is likely to be in, or which stage of dementia (particularly AD) (e.g., early stage or late stage, etc.) the person is likely to be in. More specifically, as shown in FIG. 6C , cognitive impairment progresses from early MCI to late MCI, early AD, and late AD. The closer the second index value is to 0 (one end point), the more likely the person is to be in an early MCI stage, and the closer the second index value is to 1 (the other end point), the more likely the person is to be in a later stage of dementia (particularly AD). Conversely, when the specified range is 0 to 1, the closer the second index value is to 1 (one end point), the more likely the person is to be in an early stage of MCI, and the closer the second index value is to 0 (the other end point), the more likely the person is to be in a later stage of dementia (particularly AD).

[0108] For example, as shown in FIG. 6D , when the predetermined range is 0 to 1, the second index value may indicate a stage corresponding to which score of the above-described CDR rating scale the subject is likely to be. As described above, if the CDR determines that cognitive impairment corresponds to a score of 0.5, which corresponds to MCI, or a score of 1 to 3, which correspond to AD, the CDR progresses from 0.5 to 1, 2, and 3 as cognitive impairment progresses. The closer the second index value is to 0 (one end point), the more likely the CDR of the subject is close to 0.5 (or may be close to 0), and the closer the second index value is to 1 (the other end point), the more likely the CDR of the subject is close to 3. Conversely, when the specified range is 0 to 1, the closer the second index value is to 1 (one end point), the more likely the human CDR is to be closer to 3, and the closer the second index value is to 0 (the other end point), the more likely the human CDR is to be closer to 0.5 (which may also mean the more likely it is closer to 0).

[0109] 1-3. Support information generation process

[0110] In the support information generation step S3, support information for determining cognitive impairment, cognitive decline, or the risk of either of them is generated based on the first index value and the second index value. By using these two index values, support information that contributes to accurately determining cognitive impairment, cognitive decline, or the risk of either of them is generated.

[0111] The support information includes, for example, one or more of the following: a judgment result regarding cognitive impairment, a judgment result regarding cognitive decline, a judgment result regarding the risk of cognitive impairment or cognitive decline, and data used for these judgments (e.g., the judgment score and plot data described below). The support information may further include data on the amount of each biomarker, and may further include a determination result based on the amount of each biomarker. These pieces of support information are explained below.

[0112] 1-3-1. Assessment results regarding cognitive impairment

[0113] The support information may include a determination result as to whether the human from whom the biological sample was derived has cognitive impairment, and / or a determination result as to the degree of progression of the cognitive impairment of the human. Furthermore, the support information may include a determination result as to whether a sign of cognitive impairment is observed in the biological sample, and / or a determination result as to a sign indicating which stage of cognitive impairment the biological sample is at among a plurality of stages of progression. By using the first index value and the second index value, a highly accurate determination result can be obtained.

[0114] The cognitive function state in humans transitions, for example, from a healthy state to MCI, and then from MCI to dementia (e.g., AD). Therefore, the support information may include a determination result of whether the human is in a healthy state, an MCI state, or a dementia state (particularly, an AD state). By using the first index value and the second index value, a highly accurate determination result can be obtained.

[0115] Furthermore, MCI can be classified into multiple stages, for example, into early MCI and late MCI, or into early MCI, intermediate MCI, and late MCI. Dementia can also be classified into multiple stages, for example, AD can be classified into early AD and late AD, or into early AD, intermediate AD, and late AD. The support information can include a determination result of which stage the person is in among the progression consisting of healthy, MCI, and dementia (e.g., AD). The method of classifying the stages of MCI and dementia (e.g., AD) can be, for example, as described above, but is not limited thereto.

[0116] 1-3-2. Assessment results regarding cognitive decline

[0117] The support information may include a determination result as to whether the human from whom the biological sample was derived has cognitive decline, and / or a determination result as to the degree of progression of the cognitive decline of the human. The support information may include a determination result as to whether the biological sample contains a sign of cognitive decline, and / or a determination result as to whether the biological sample contains a sign of cognitive decline, among a plurality of stages of cognitive decline. By using the first index value and the second index value, a highly accurate determination result can be obtained.

[0118] 1-3-3. Assessment results regarding risk of cognitive impairment or cognitive decline

[0119] The support information may include a result of assessment of the risk of cognitive impairment or the risk of cognitive decline. As used herein, "risk of cognitive impairment" may mean the possibility that a person will suffer from cognitive impairment or the possibility that a person will be in a state suffering from cognitive impairment. Also, as used herein, "risk of cognitive decline" may mean the possibility that a person will suffer from cognitive decline or the possibility that a person will be in a state suffering from cognitive decline. The assessment result of the risk of cognitive impairment may be, for example, a assessment result indicating the degree of the risk, and more specifically, may include information on whether the risk is low or high. For example, the support information may include a assessment result of the risk that a person has MCI or dementia (particularly AD). The assessment result of the risk of cognitive decline may be, for example, a assessment result indicating the degree of the risk, and more specifically, may include information on whether the risk is low or high.

[0120] 1-3-4.Generating judgment results

[0121] The above-described assessment result of cognitive impairment, cognitive decline, or the risk thereof can be generated based on the first index value and the second index value. Specific examples of generating the assessment result will be described below.

[0122] For example, a determination score calculated using the first index value and the second index value may be used to generate the determination result. The determination score may be calculated using, for example, a combination of "the first index value" and "a value obtained by performing a predetermined process on the second index value," or a combination of "a value obtained by performing a predetermined process on the first index value" and "a value obtained by performing a predetermined process on the second index value," or a combination of "a value obtained by performing a predetermined process on the first index value" and "the second index value." The predetermined process may be a weighting process, such as multiplying or adding a predetermined coefficient. In the calculation, the sum, difference, product, or quotient of the two values ​​constituting the combination may be calculated, and the calculated sum, difference, product, or quotient may be used as the determination score. Particularly preferably, the determination score may be the sum of the two values. The calculation process may be the same whether the person to be determined is male or female, or may be different depending on the gender.

[0123] For example, the predetermined process may be a weighting process according to the first index value. More specifically, the specified processing may be processing such that, when the first index value is within a numerical range indicating the absence of cognitive impairment or cognitive decline, the influence of the "value obtained by performing the specified processing on the first index value" on the judgment score becomes greater and the influence of the "value obtained by performing the specified processing on the second index value" on the judgment score becomes smaller. Furthermore, the specified processing may be processing such that, when the first index value is within a numerical range indicating cognitive impairment or cognitive decline, the influence of the "value obtained by performing the specified processing on the first index value" on the judgment score becomes smaller and the influence of the "value obtained by performing the specified processing on the second index value" on the judgment score becomes greater.

[0124] Alternatively, the predetermined process may be a weighting process according to the second index value. The specified processing may be processing such that, when the second index value is within a specified numerical range indicating that cognitive impairment or cognitive decline is not more advanced, the influence of the "value obtained by performing the specified processing on the first index value" on the judgment score becomes greater and the influence of the "value obtained by performing the specified processing on the second index value" on the judgment score becomes smaller. Furthermore, the specified processing may be processing such that, when the second index value is outside a specified numerical range indicating that cognitive impairment or cognitive decline is more advanced, the influence of the "value obtained by performing the specified processing on the first index value" on the judgment score becomes smaller and the influence of the "value obtained by performing the specified processing on the second index value" on the judgment score becomes greater.

[0125] For example, as shown in FIG. 7, the judgment score may be set to any value in the range of 0 to 2. In this figure, the closer the judgment score is to 2, the more advanced the cognitive dysfunction in the person is (for example, the person has dementia (particularly AD) or is highly likely to have dementia (particularly AD)), and the closer the judgment score is to 0, the less cognitive dysfunction the person has (for example, the person has neither MCI nor dementia or is highly likely to have neither MCI nor dementia). Furthermore, a judgment score around 1 indicates that the person has MCI or is highly likely to have MCI. The judgment score may be used as an index value indicating these.

[0126] Based on the assessment score described above, cognitive impairment, cognitive decline, or the risk thereof may be assessed. To make this assessment, for example, the range of the assessment score may be divided into a plurality of numerical ranges in advance, and each of the plurality of numerical ranges may be associated with a assessment result. The number of the plurality of numerical ranges may be, for example, 2 to 10, preferably 3 to 8, and more preferably 3 to 7.

[0127] For example, when the range of the assessment score is divided into three numerical ranges, one of the three numerical ranges (e.g., the lower numerical range) may be associated with a determination result that the person is healthy (or that the person does not have or is at a low risk of having cognitive impairment). Another of the three numerical ranges (e.g., the middle numerical range) may be associated with a determination result that the person has MCI (or that the person is at a high risk of having MCI). Furthermore, yet another of the three numerical ranges (e.g., the higher numerical range) may be associated with a determination result that the person has dementia (e.g., AD) (or that the person is at a high risk of having dementia).

[0128] For example, when the range of the assessment score is divided into four numerical ranges, one of the four numerical ranges (e.g., the lowest numerical range) may be associated with a determination result that the human is healthy (or that the human does not have or has a low risk of having cognitive impairment). Another of the four numerical ranges (e.g., the second lowest numerical range) may be associated with a determination result that the human has cognitive decline (or that the human has a high risk of having cognitive decline). Another of the four numerical ranges (e.g., the third lowest numerical range) may be associated with a determination result that the human has MCI (or that the human has a high risk of having MCI). Furthermore, yet another of the four numerical ranges (e.g., the highest numerical range) may be associated with a determination result that the human has dementia (e.g., AD) (or that the human has a high risk of having dementia). Alternatively, when the range of the assessment score is divided into four numerical ranges, one numerical range (e.g., the lowest numerical range) of the four numerical ranges may be associated with a determination result that the human is healthy (or that the human does not have or is at low risk of having cognitive impairment). Another numerical range (e.g., the second lowest numerical range) of the four numerical ranges may be associated with a determination result that the human is exhibiting signs characteristic of early cognitive impairment (e.g., characteristic plasma signs). Another numerical range (e.g., the third lowest numerical range) of the four numerical ranges may be associated with a determination result that the human is exhibiting signs characteristic of cognitive impairment (e.g., characteristic plasma signs). Yet another numerical range (e.g., the highest numerical range) of the four numerical ranges may be associated with a determination result that the human is exhibiting signs characteristic of advanced cognitive impairment (e.g., characteristic plasma signs). Alternatively, when the range of the assessment score is divided into four numerical ranges, one of the four numerical ranges (e.g., the lowest numerical range) may be associated with a determination result that the person has no or almost no risk of cognitive dysfunction (e.g., MCI or AD). Another of the four numerical ranges (e.g., the second lowest numerical range) may be associated with a determination result that the person has a low risk of cognitive dysfunction (e.g., MCI or AD). Another of the four numerical ranges (e.g., the third lowest numerical range) may be associated with a determination result that the person has a moderate risk of cognitive dysfunction (e.g., MCI or AD). Another of the four numerical ranges (e.g., the highest numerical range) may be associated with a determination result that the person has a high risk of cognitive dysfunction (e.g., MCI or AD). Generating judgment support information in which the judgment result is associated with the judgment score of the person is useful for supporting the judgment of cognitive dysfunction, cognitive decline, or the risk thereof. Furthermore, data explaining the correspondence between multiple ranges of the assessment score and the assessment results for each range may be included as part of the assessment support information. This data makes the assessment results easier to understand. For example, the person or medical professional can know that a higher assessment score indicates a higher risk of cognitive impairment. Examples of data explaining the correspondence are shown in Figures 16 and 17. A table including multiple ranges of the judgment score and an explanation of the judgment result associated with each range, as shown in Figure 16 or 17, may be included in the judgment support information as data explaining the correspondence.

[0129] The numerical range associated with a determination that the individual has MCI (or that the individual is at high risk of having MCI) may be further divided into multiple numerical ranges, each of which may be associated with a determination that the individual is in early MCI, intermediate MCI, or late MCI.

[0130] In order to perform judgment based on the judgment score, the information processing device 200 may have a data table including the plurality of numerical ranges within the range of the judgment score and judgment results associated with each of the plurality of numerical ranges. The data table may be stored, for example, in the storage unit 202 of the information processing device 200. The information processing device 200 may refer to the data table to select a judgment result corresponding to the judgment score, and select it as a judgment result for the human (or the biological sample).

[0131] 1-3-5.Generating plot data

[0132] 1-3-5-1. Plotting on a single axis

[0133] The information processing device 200 may generate plot data indicating the position of the calculated judgment score within a range of the judgment score. For example, data indicating one axis indicating the range and the position of the judgment score on the axis, as shown in FIG. 8, may be generated. The judgment score shown in FIG. 8 has the same meaning as described above with reference to FIG. 7. In this case, the judgment score shown in FIG. 8 (indicated by a black diamond) is shown to be slightly higher than 1 but significantly lower than 2. Therefore, the plot data shown in FIG. 8 indicates, for example, that a person having the judgment score is likely to have MCI, or that the person has not yet reached the stage of dementia but is likely to be transitioning from MCI to the stage of dementia. Another example of plot data is shown in FIG. 18. In this figure, an axis of the judgment score ranging from 0.0 to 2.0 extends horizontally. The axis is divided into four ranges, A, B, C, and D. Among these, the position of the calculated judgment score of 1.47 is indicated by an arrow and a black line. In addition, a color gradation is applied as the judgment score increases from 0.0 to 2.0, so that it is easier to understand that, for example, the risk of cognitive impairment increases as the judgment score increases. In the present invention, such plot data may be generated. Another example of plot data is shown in FIG. 19. In this figure, the axis of the assessment score ranging from 0.0 to 2.0 extends horizontally. Also in this figure, the vertical axis shows the "number ratio." This number ratio indicates, for example, the ratio of the number of people diagnosed as healthy or MCI (or dementia) in a group of people over a certain age. Such a number ratio may be shown, which is useful for understanding the progression of cognitive dysfunction in humans. Also in this figure, the assessment scores for the "previous time," "previous time," and "current time" are indicated by arrows. By including such previous assessment results in the plot data, the degree of progression of cognitive dysfunction can be known.

[0134] The plot data may be used as support information. Human cognitive function changes gradually, and the plot data is useful for understanding the state or transition of human cognitive function.

[0135] Moreover, the plot data may be generated, and the judgment may be made based on the position of the plotted judgment score.

[0136] 1-3-5-2. Plotting into a two-dimensional matrix

[0137] To make the determination, the first index value and the second index value may be plotted in a two-dimensional matrix. For example, a two-dimensional matrix is ​​prepared in which the first index value is on the X axis and the second index value is on the Y axis. The calculated first index value and second index value are plotted in the two-dimensional matrix. The determination may be made based on the plotted position.

[0138] For example, as shown in FIG. 9A, the closer the plotted location is to the top right in the two-dimensional matrix, the more advanced the cognitive impairment in the person is, and the closer it is to the bottom left, the more likely the person is to have no cognitive impairment or the less advanced the cognitive impairment in the person is. For example, assume that the first index value shown in Figure 9A is the index value as described with reference to Figure 5, and the second index value is the index value as described with reference to Figure 6B. In this case, the positions where these values ​​are plotted (positions of black diamonds) are those where the first index value, which takes a numerical range of 0 to 1, exceeds 0.5 but is smaller than 1, and the second index value, which takes a numerical range of 0 to 1, is 0.5 or less but larger than 0. Therefore, it can be seen that the biological sample or human from which these first index value and second index value were obtained has cognitive dysfunction (or is likely to have cognitive dysfunction) and is at the stage of MCI (or is likely to be at the stage of MCI).

[0139] Alternatively, as shown in Figure 9B, the two-dimensional matrix may be divided into four quadrants in advance, and if the plotted position is in the upper right quadrant of the two-dimensional matrix, it may be determined that the person suffers from (or is at high risk of suffering from) dementia (e.g., AD). Furthermore, if the plotted position is in the lower right quadrant of the two-dimensional matrix, it can be determined that the person suffers from MCI (or is at high risk of suffering from MCI). Furthermore, if the plotted position is in the lower left quadrant in the two-dimensional matrix, the person can be determined to be healthy. If the plotted position is in the upper left quadrant, for example, no determination may be made, or it may be deemed impossible to determine.

[0140] The two-dimensional matrix plot data may be used as support information. Since human cognitive function changes gradually, the plot data is useful for understanding the state or transition of human cognitive function.

[0141] In the support information generating step, as described above, a determination result of cognitive impairment, cognitive decline, or a risk of either of them is generated. Therefore, the support information generating step may be called a determination step.

[0142] 1-3-6. Determination of the amount of each biomarker

[0143] The support information may include data on the amount of each biomarker. The support information may further include a determination result based on the amount of each biomarker. An example of table data including these data will be described below with reference to FIG. 20.

[0144] The figure shows tabular data containing quantitative data for each biomarker. Among the biomarkers shown in the figure, ALB (albumin, (4) above) and TTR (transthyretin, (10) above) are shown as biomarkers indicating nutritional status. These biomarkers are also useful as indicators of nutritional status. In addition, APOA1 (apolipoprotein A1, (5) above) and APOC1 (apolipoprotein C1, (6) above) have been shown to be biomarkers indicating the state of lipid metabolism. These biomarkers are also useful as biomarkers indicating the state of lipid metabolism. In addition, C3 (complement component 3, (7) above), C4G (complement component 4 gamma chain, (8) above), A1BG (alpha-1-B-glycoprotein, (1) above), and HPX (hemopexin, (9) above) have been shown to be biomarkers indicating immune status. These biomarkers are also useful as indicators of immune status. Furthermore, A2M (alpha-2-macroglobulin, (3) above) and A2AP (alpha-2-antiplasmin, (2) above) have been shown to be biomarkers that indicate the coagulation and fibrinolysis state. These biomarkers are also useful as indicators of the coagulation and fibrinolysis state. In this way, the support information may include data on the amount of each biomarker, which makes it possible to understand the nutritional status, lipid metabolism status, immune status, and coagulation / fibrinolysis status.

[0145] As shown in the figure, the table data may include, in addition to the quantity data of each biomarker, a standard numerical range for each biomarker (shown as "standard range" in the figure). Furthermore, as shown in the figure, the table data may include a determination result based on the quantity data of each biomarker. These data allow the subject's condition to be more accurately understood.

[0146] In the present invention, an index value based on the amount of biomarkers indicating the nutritional state and lipid metabolism state, and an index value based on the amount of biomarkers indicating the immune state and coagulation-fibrinolysis state may be calculated. These two index values ​​may be calculated using a predetermined discriminant, similar to the first index value or the second index value described above. These two index values ​​may be plotted, for example, as shown in Figure 21.

[0147] 1-3-7. Other Judgment Results

[0148] The support information may include other determination results in addition to the determination results based on biomarkers, such as, but not limited to, test results regarding the subject's genotype.

[0149] 1-4. Output process

[0150] In the output step S4, the support information generated in the support information generation step S3 is output. For example, the information processing device that executed the support information generation step S3 may output the support information.

[0151] 1-5. Information processing device that executes the index value calculation process, the support information generation process, and the output process

[0152] The information processing device that executes the index value calculation step, the support information generation step, and the output step may be, for example, an information processing device separate from the information processing device included in the biomarker quantification system.

[0153] A specific example of the configuration of an information processing device that executes these steps will be described with reference to Fig. 10. The information processing device 200 shown in Fig. 10 includes a processing unit 201, a storage unit 202, an input unit 203, an output unit 204, and a communication unit 205. A general-purpose computer or server may be used as the information processing device 200, for example.

[0154] The processing unit 201 may be configured to execute, for example, an index value calculation step S2 and a support information generation step S3 (optionally, an output step S4 in addition to these two steps), and more specifically, has a circuit configured to execute these steps. The more specific configuration of the processing unit 201 (for example, a CPU and RAM) is the same as the description of the processing unit 101 given above, so a description of this configuration will be omitted.

[0155] The processing unit 201 calculates the first index value and the second index value based on the amounts of the two or more biomarkers acquired from, for example, the biomarker quantification system described above (particularly, the information processing device 100). The calculation process may be as described in 1-2 above.

[0156] The processing unit 201 generates the support information based on, for example, the first index value and the second index value. The generation process may be as described in 1-3 above.

[0157] The processing unit 201 may further be configured to acquire data from or record data in the storage unit 202. The storage unit 202 stores various data. The storage unit 202 may be configured to store, for example, the two or more biomarker quantity data acquired from the biomarker quantification system, the first index value and the second index value calculated in the index value calculation step S2, and the support information generated in the support information generation step S3. Furthermore, the storage unit 202 may store a discriminant used to calculate the first index value and the second index value. The storage unit 202 may also store the data table used in generating the support information and / or information relating to the association between the discrimination score and the determination result.

[0158] The storage unit 202 may store an operating system (for example, WINDOWS (registered trademark), UNIX (registered trademark), or LINUX (registered trademark)), a program for causing the information processing device to execute the index value calculation step S2 and the support information generation step S3 (optionally the output step S4), and various other programs. Note that these programs may be recorded on a recording medium other than the storage unit 202.

[0159] The input unit 203 may include an interface configured to be able to accept input of various data, and may include, as devices for accepting such operations, a mouse, a keyboard, a touch panel, and the like.

[0160] The output unit 204 may include an interface configured to be able to output various types of data. For example, the output unit 204 may output the first index and second index value calculated in the index value calculation step S2. The output unit 204 may also output the support information generated in the support information generation step S3. The output unit 204 may include, for example, a display device and / or a printing device as a device for performing the output, and the output method may be display on a display device or printing by a printing device.

[0161] The communication unit 205 may be configured to connect the information processing device 200 to a network via a wired or wireless connection. The communication unit 205 enables the information processing device 200 to acquire various data via the network. The acquired data may be stored in the storage unit 202, for example. The configuration of the communication unit 205 may be appropriately selected by one skilled in the art.

[0162] The information processing device 200 may include, for example, a drive (not shown). The drive can read data (such as the various data listed above) or programs recorded on a recording medium and output them to RAM. The recording medium can be, for example, a microSD memory card, an SD memory card, or a flash memory, but is not limited to these.

[0163] The index value calculation step, the support information generation step, and the output step may be performed by one information processing device, or may be performed by two or more information processing devices. For example, the index value calculation step may be performed by one information processing device, and the support information generation step and the output step may be performed by another information processing device. Furthermore, for example, the index value calculation step and / or the support information generation step may be executed by an information processing device included in the biomarker quantification system. The output step may be executed by an information processing device included in the biomarker quantification system.

[0164] The present technology also provides an information processing device that executes the index value calculation step and the support information generation step. The information processing device is as described above.

[0165] 2. Decision support information generation system

[0166] The present technology also provides a determination support information generation system. The system may generate information to support the determination of cognitive impairment, cognitive decline, or the risk of either of them. A block diagram of the determination support information generation system is shown in FIG. 11. As shown in FIG. 11, the determination support information generation system 20 includes a biomarker quantification system 21 and an information processing device 22. The determination support information generation system may be configured to subject the biological sample to a proteolysis treatment and then subject the biological sample to measurement by the biomarker quantification system.

[0167] The biomarker quantification system 21 may be the system described in 1-1-2-4 above, and the same description also applies to the biomarker quantification system included in the decision support information generation system. For example, the biomarker quantification system may include a liquid chromatography mass spectrometer.

[0168] The information processing device 22 may be the device as explained in 1-5 above, and the explanation also applies to the information processing device included in the determination support information generation system.

[0169] The present technology also provides an information processing device that executes the index value calculation step and the support information generation step described in 1 above.

[0170] 3. Combination of biomarkers

[0171] The present technology also provides a combination of two or more of the biomarkers (1) to (10). The combination may be used to determine cognitive impairment, cognitive decline, or the risk of either of these.

[0172] The present technology also provides a combination of biomarkers including two or more of the biomarkers (1), (4), (8), and (9). That is, the combination may include two, three, or all four of the biomarkers (1), (4), (8), and (9). These combinations of biomarkers may be used to determine cognitive impairment, cognitive decline, or the risk of either of these, and may particularly be used to determine the presence or absence of cognitive impairment or cognitive decline. Particularly preferably, the combination further comprises one, two, three, or four of the biomarkers (2), (3), (6), and (7) in addition to the biomarkers (1), (4), (8), and (9). These combinations of biomarkers are particularly suitable for determining the presence or absence of cognitive impairment or cognitive decline.

[0173] The combination of biomarkers may also be used to determine cognitive impairment, cognitive decline, or the risk of either of these, and may particularly be used to determine the progression of cognitive impairment or cognitive decline. These combinations of biomarkers are particularly suitable for determining the progression of cognitive impairment or cognitive decline.

[0174] 4. Kit

[0175] The present technology also provides a biological sample collection kit for use in performing the decision support information generation method according to the present technology. The biological sample collection kit may be configured to collect, for example, human blood. For example, the biological sample collection kit may include a blood collection needle and a container for containing the blood collected through the needle. The container may include, for example, an absorbent material (e.g., paper, particularly filter paper, or a sponge-like structure) that is soaked with the blood.

[0176] The biological sample collection kit may be configured to be able to transfer a biological sample to, for example, the above-described biomarker quantification system 10. For example, the biological sample collection kit (particularly the container in the kit) may be configured to be able to be accommodated in the container included in the biomarker quantification system 10.

[0177] 5.Discriminant derivation method

[0178] The present technology also provides a method for determining a discriminant for determining cognitive impairment, cognitive decline, or the risk of either of them. The determination method may include a step of acquiring data on the amounts of the biomarkers constituting the combination contained in the biological sample of each of a plurality of humans, and a discriminant-formula deriving step of deriving a discriminant by performing a regression analysis using the presence or absence of cognitive impairment or cognitive decline and / or the stage of cognitive impairment or cognitive decline for each of the plurality of humans and the amounts measured for each of the biological samples of each human. The discriminant-formula deriving method may include a detection step of detecting cognitive impairment, cognitive decline, or the risk of either of them in a subject using the derived discriminant.

[0179] The regression analysis performed in the discriminant derivation step is, for example, logistic regression analysis, but is not limited thereto and may be any of the other analytical methods described in 1 above. In the discriminant, the objective variable may be, for example, the presence or absence of cognitive dysfunction or the stage of cognitive dysfunction. In the discriminant, the explanatory variable may be, for example, the amount of biomarkers constituting the combination according to the present technology.

[0180] 6. Method for generating diagnostic support information using multiple diagnostic biomarkers

[0181] The present technology also provides a determination support information generation method, which includes carrying out a support information generation step of generating information to support determination of cognitive dysfunction, cognitive decline, or a risk of either of these in a human, based on the amounts of two or more of the following biomarkers (A) to (D) contained in a biological sample derived from the human: (A) at least one biomarker for determining nutritional status; (B) at least one biomarker for determining lipid composition; (C) at least one biomarker for determining immune function, and (D) At least one biomarker for determining the coagulation and fibrinolysis system.

[0182] A combination of two or more of these four biomarkers is useful for determining a person's cognitive impairment, cognitive decline, or the risk of any of these. These biomarkers are also useful for determining nutritional status, lipid composition, immune function, and the coagulation-fibrinolysis system, respectively. Therefore, these biomarkers can be used to determine a person's cognitive impairment or cognitive decline, and to determine two or more of the nutritional status, lipid composition, immune function, and coagulation-fibrinolysis system. That is, this technology can generate information to support the determination of cognitive impairment or cognitive decline in a person, while simultaneously generating information to support the determination of two or more of the nutritional status, lipid composition, immune function, and coagulation-fibrinolysis system. This information can also encourage people to improve their lifestyle to maintain or improve their cognitive function.

[0183] The biomarker (A) may include at least one of the following biomarkers (a1) and (a2): (a1) albumin or a partial peptide thereof, and (a2) transthyretin or a partial peptide thereof.

[0184] The biomarkers (a1) and (a2) are the same as the biomarkers (4) and (10) explained in 1 above, and the explanation also applies to this support information generation method.

[0185] The biomarker (B) may include at least one of the following biomarkers (b1) and (b2): (b1) apolipoprotein A1 or a partial peptide thereof, and (b2) apolipoprotein C1 or a partial peptide thereof.

[0186] The biomarkers (b1) and (b2) are the same as the biomarkers (5) and (6) explained in 1 above, and the explanation also applies to this support information generation method.

[0187] The biomarker (C) may include at least one of the following biomarkers (c1) to (c4): (c1) complement component 3 or a partial peptide thereof (c2) complement component 4 gamma chain or a partial peptide thereof (c3) alpha-1-B-glycoprotein or a partial peptide thereof, and (c4) Hempexin or a partial peptide thereof.

[0188] The biomarkers (c1), (c2), (c3), and (c4) are the same as the biomarkers (7), (8), (1), and (9) described in 1 above, and the same description also applies to this support information generation method.

[0189] The biomarker (D) may include at least one of the following biomarkers (d1) and (d2): (d1) alpha-2-macroglobulin or a partial peptide thereof, and (d2) alpha-2-antiplasmin or a partial peptide thereof.

[0190] The biomarkers (d1) and (d2) are the same as the biomarkers (3) and (2) explained in 1 above, respectively, and the explanation also applies to this support information generation method.

[0191] The determination support information generation method may further include a data acquisition step of acquiring data on the amount of each biomarker. The data acquisition step may be performed in the same manner as the data acquisition step described in 1-1 above.

[0192] The determination support information generating method may also include an output step of outputting the support information generated in the support information generating step. The output step may be performed in the same manner as the output step described in 1-5 above. [Example]

[0193] The present technology will be described in more detail below based on examples. Note that the examples described below are representative examples of the present technology, and the scope of the present technology is not limited to these examples.

[0194] 7. Example 1

[0195] 7-1. Creation of discriminant used to calculate the first index value and the second index value

[0196] We prepared 92 cases who had received a physician's diagnosis of cognitive function. Of these 92 cases, 26 were healthy and 66 had cognitive impairment. Furthermore, the cases with cognitive impairment were further diagnosed with either mild cognitive impairment (MCI) or Alzheimer's disease.

[0197] Biological samples (plasma) were obtained from each of the 92 cases, and the amounts of the following biomarkers (1) to (10) contained in each plasma were quantified. (1) alpha-1-B-glycoprotein (2) alpha-2-antiplasmin (3) alpha-2-macroglobulin (4) albumin (5) apolipoprotein A1 (6) apolipoprotein C1 (7) Complement Component 3 (8)complement component 4 gamma chain (9) hemopexin (10) transthyretin

[0198] The pretreatment of plasma performed for quantifying the amount of biomarkers and the measurement conditions for quantifying the biomarkers after the pretreatment are described below.

[0199] (Pretreatment) 1. 3 μl of plasma was mixed with 22.5 μl of heat denaturation solution (company name: Nacalai Tesque, product name: CHAPS), and the mixture was heat denatured in a thermal cycler at 99.9°C for 5 minutes. 2. 8.5 μl of the heat-denatured plasma-containing solution was mixed with 43.5 μl of trypsin solution (company name: Promega, product name: Sequencing Grade Modified Trypsin, trypsin concentration: approximately 20 ng / μl). The plasma-containing solution to which the trypsin solution had been added was incubated at 37° C. for 16 hours. 3. After the incubation, 140 μl of formic acid solution (company name: FUJIFILM Wako Pure Chemical Industries, product name: formic acid, formic acid concentration: 1%) was added to stop the enzyme reaction. 4. After the enzyme reaction was stopped, a stable isotope-labeled internal standard peptide used for quantification by liquid chromatography mass spectrometry, which will be described later, was added to the plasma-containing solution to prepare an analytical sample.

[0200] (Quantification of biomarkers by liquid chromatography-mass spectrometry) 2 μl of the analytical sample obtained by the pretreatment was subjected to liquid chromatography mass spectrometry. Biomarker quantification was performed using an analyzer. The liquid chromatography mass spectrometer was an LCMS-8060 system manufactured by Shimadzu Corporation. A reversed-phase column (AERIS PEPTIDE XB-C18, Phenomenex) was installed in the system, and separation was performed using reversed-phase chromatography (0.1% formic acid, 2% to 90% acetonitrile), followed by mass spectrometry.

[0201] The mass spectrometry was performed according to the MRM (multiple reaction monitoring) method. The measurement conditions for each biomarker in the MRM method are shown in Table 2 below.

[0202] [Table 2]

[0203] In Table 2 above, the "Retention time (min)" column indicates the retention time of the quantification fragment of each biomarker in the reversed-phase chromatography. In the "Labeled" column, "Unlabeled" indicates an unlabeled quantification fragment (unlabeled peptide), i.e., corresponds to the quantification fragment contained in plasma. In the "Labeled" column, "Labeled" indicates a quantification fragment having a stable isotope-labeled amino acid residue, i.e., a stable isotope-labeled internal standard peptide. In the "Amino acid sequence of quantification fragment" column, the amino acid sequence (single-letter notation) of the quantification fragment is shown, and the amino acid residue in parentheses is the stable isotope-labeled amino acid residue. In other words, [K] and [R] at the ends of the amino acid sequence are the isotope-labeled sites of the stable isotope peptide used as the internal standard.

[0204] The LC-MS signals of the quantitative fragments of each biomarker were identified using the software Skyline (MacCoss Lab, University of Washington).

[0205] The molar concentrations of the biomarker quantification fragments in the analytical samples were measured by the mass spectrometry. For this measurement, a calibration curve was created using a group of samples in which an internal standard peptide was added to a dilution series of unlabeled peptide. The measurement was performed by the internal standard method using this calibration curve.

[0206] For each biomarker, the amount of biomarker (molar concentration) in the analytical sample was quantified using the measured concentration of the quantification fragment, the mass of the quantification fragment, and the mass of the biomarker.

[0207] The biomarker amounts were quantified as described above for the biological samples obtained from each of the 92 cases. The biomarker amounts for the 92 cases were normalized using the mean and standard deviation of the biomarker amounts.

[0208] For the above 92 cases, logistic regression analysis was performed using the standardized biomarker amounts of the biomarker group including (1), (4), (8), and (9) among the biomarkers (1) to (10) as explanatory variables and information on whether each case was healthy, MCI, or AD as the objective variable. From the regression analysis, a discriminant for discriminating between healthy and MCI and a discriminant for discriminating between healthy and AD were obtained. These discriminants were used to calculate a first index value and a second index value. The first index value is the sum of the probability of MCI and the probability of AD calculated using the values ​​obtained using these discriminant formulas. The second index value is the probability of AD.

[0209] 7-2. Evaluation of the created discriminant (discrimination by the first index value)

[0210] The amount of biomarker in each of the 92 cases was substituted into the discriminant to obtain the first index value for each case. Figure 12 shows a boxplot plotting the first index value for each case, a confusion matrix relating to the determination results based on the discriminant and the doctor's diagnosis, and a receiver operating characteristic curve (ROC) relating to the determination results based on the discriminant.

[0211] The results shown in Figure 12 indicate that the first index value is useful for distinguishing between cognitively normal individuals and cognitively impaired individuals. For example, the accuracy calculated from the confusion matrix was 80%. The AUC (area under the curve) value of the ROC curve was 0.86.

[0212] 7-3. Evaluation of the created discriminant (discrimination by the second index value)

[0213] The amount of biomarker in each of the 59 cases was substituted into the discriminant to obtain the second index value for each case. Figure 13 shows a boxplot plotting the second index value for each case, a confusion matrix relating to the determination results based on the discriminant and the doctor's diagnosis, and an ROC curve relating to the determination results based on the discriminant.

[0214] The results shown in Figure 13 indicate that the second index value is useful for distinguishing between MCI and AD. For example, the accuracy rate calculated from the confusion matrix was 68%. The AUC value of the ROC curve was 0.77.

[0215] 7-4. Usefulness of the first and second index values

[0216] As explained above in 7-1 to 7-3, it is understood that cognitive impairment or the risk thereof can be accurately determined by using the first index value and the second index value according to the present technology. It is also understood that the use of the first index value and the second index value makes it possible to determine the progression of a person's cognitive impairment (for example, whether the person is at the stage of MCI or AD). Furthermore, human cognitive function gradually transitions from a normal state to a cognitive decline state and then to a cognitive impairment state. As described above, the first index value and the second index value can be used to determine, for example, whether a person's cognitive function is in a normal state but is close to a cognitive impairment state, or whether the person is at a high risk of developing a cognitive impairment state. Therefore, it is believed that the first index value and the second index value according to the present technology can accurately determine cognitive decline or its risk.

[0217] 8. Example 2

[0218] 8-1. Creation of discriminant used to calculate the first index value and the second index value

[0219] We prepared a large number of cases whose cognitive function had been diagnosed by a physician, and each case was diagnosed as healthy, with early MCI, late MCI, early AD, or late AD.

[0220] Biological samples (plasma) were obtained from each of the cases. The amounts of the following biomarkers (1) to (10) contained in each plasma were quantified as described in 7-1 above to obtain standardized biomarker amounts.

[0221] For the above cases, logistic regression analysis was performed using the standardized biomarker amounts of the biomarker group including (1), (4), (8), and (9) among the biomarkers (1) to (10) as explanatory variables and information on whether each case was healthy, MCI, or AD as the objective variable. From the regression analysis, a discriminant for discriminating between healthy and MCI and a discriminant for discriminating between healthy and AD were obtained. These discriminants were used to calculate a first index value and a second index value. The first index value is the sum of the probability of MCI and the probability of AD calculated using the values ​​obtained using these discriminants. The second index value is the probability of AD.

[0222] 8-2. Evaluation of the created discriminant (discrimination by the first index value)

[0223] The amount of biomarker in each of the cases was substituted into the discriminant to obtain the first index value for each case. Figure 14 shows a boxplot plotting the first index value for each case and an ROC curve for the determination results based on the discriminant.

[0224] 14, it can be seen that the first index value is useful for distinguishing between cognitively healthy individuals and cognitively impaired individuals. For example, the AUC value of the ROC curve was 0.865.

[0225] 8-3. Evaluation of the created discriminant (discrimination by the second index value)

[0226] The amount of biomarker in each of the cases was substituted into the discriminant to obtain the second index value for each case. Figure 15 shows a box plot in which the second index values ​​for each case are plotted.

[0227] 15, it can be seen that the second index value tends to increase with the progression of cognitive impairment. Therefore, it can be seen that the second index value is useful for determining the progression of cognitive impairment. For example, it can be seen that the second index value is useful as an indicator of which stage a person is in among early MCI, late MCI, early AD, and late AD.

[0228] 8-4. Usefulness of the first and second index values

[0229] As explained above in 8-1 to 8-3, it is understood that cognitive impairment or the risk thereof can be accurately determined by using the first index value and the second index value according to the present technology. It is also understood that the use of the first index value and the second index value makes it possible to determine the progression of a person's cognitive impairment (for example, whether the person is in the early MCI stage, the late MCI stage, the early AD stage, or the late AD stage).

[0230] The technology also provides: [1] Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; A method for generating judgment support information, comprising: [2] The determination support information generating method according to [1], wherein the amounts of the two or more biomarkers used in the index value calculation step include the amounts of two or more biomarkers selected from the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or its partial peptide (2) alpha-2-antiplasmin or its partial peptide (3) alpha-2-macroglobulin or its partial peptides (4) Albumin or its partial peptide (5) apolipoprotein A1 or a partial peptide thereof (6) apolipoprotein C1 or a partial peptide thereof (7) Complement component 3 or a partial peptide thereof (8) Complement component 4 gamma chain or its partial peptide (9) Hemopexin or its partial peptide (10) Transthyretin or its partial peptide [3] The method for generating judgment support information described in [2], wherein the first index value is calculated based on the amount of biomarkers including two or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). [4] The method for generating judgment support information described in [2] or [3], wherein the second index value is calculated based on the amount of biomarkers including two or more of the biomarkers (1), (2), (3), (4), (6), (7), (8), and (9). [5] The determination support information generating method according to any one of [1] to [4], wherein the first index value is an index value indicating whether the person has mild cognitive impairment or dementia. [6] The method for generating judgment support information described in any one of [1] to [5], wherein the second index value is an index value indicating whether the person's cognitive dysfunction is at the stage of mild cognitive impairment or dementia. [7] Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; Information processing device for executing A decision support information generation system including: [8] The system described in [7] further includes a biomarker quantification system that quantifies the amount of the two or more biomarkers. [9] The system described in [8], wherein the biomarker quantification system includes a liquid chromatography mass spectrometer.

[10] The system according to [8] or [9], wherein the system is configured to subject the biological sample to a proteolytic treatment and then subject it to measurement by the biomarker quantification system.

[11] Based on the amount of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; An information processing device that executes the above. [Explanation of symbols]

[0231] 10 Biomarker Quantitation System 20. Decision Support Information Generation System

Claims

1. Based on the amounts of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; Including, The amount of the biomarkers is quantified based on the amount of an amino acid sequence portion specific to each protein serving as a biomarker; In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected, The two or more biomarkers include at least two or more of the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or its partial peptide (2) alpha-2-antiplasmin or its partial peptide (3) alpha-2-macroglobulin or its partial peptide (4) Albumin or its partial peptide (5) Apolipoprotein A1 or a partial peptide thereof (6) apolipoprotein C1 or a partial peptide thereof (7) Complement component 3 or a partial peptide thereof (8) Complement component 4 gamma chain or a partial peptide thereof (9) Hempexin or a partial peptide thereof (10) transthyretin or a partial peptide thereof Judgment support information generation method.

2. The method for generating determination support information according to claim 1 , wherein the support information includes a result of assessment of a risk of cognitive impairment or a result of assessment of a risk of cognitive decline.

3. The support information includes a result of assessment of a risk of cognitive impairment, The cognitive impairment is mild cognitive impairment (MCI). The judgment support information generating method according to claim 1 or 2.

4. The method for generating judgment support information according to any one of claims 1 to 3, wherein the range of the judgment score indicates that the cognitive impairment in the person is more advanced the closer it is to one end, and indicates that the person does not have cognitive impairment the closer it is to the other end.

5. A judgment support information generation method described in any one of claims 1 to 4, wherein in the support information generation process, a data table containing the multiple numerical ranges within the value range of the judgment score and judgment results associated with each of the multiple numerical ranges is referenced, and a judgment result corresponding to the judgment score is selected.

6. The determination support information generating method according to any one of claims 1 to 5, wherein the biological sample is subjected to a proteolytic treatment, and the proteolytic treatment decomposes the biomarker to be quantified to generate protein fragments.

7. Based on the amounts of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; an information processing device that executes The amount of the biomarkers is quantified based on the amount of an amino acid sequence portion specific to each protein serving as a biomarker; In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected, The two or more biomarkers include at least two or more of the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or its partial peptide (2) alpha-2-antiplasmin or its partial peptide (3) alpha-2-macroglobulin or its partial peptide (4) Albumin or its partial peptide (5) Apolipoprotein A1 or a partial peptide thereof (6) apolipoprotein C1 or a partial peptide thereof (7) Complement component 3 or a partial peptide thereof (8) Complement component 4 gamma chain or a partial peptide thereof (9) Hempexin or a partial peptide thereof (10) transthyretin or a partial peptide thereof Decision support information generation system.

8. Based on the amounts of two or more biomarkers in a biological sample derived from a human, (I) a first index value indicating the presence or absence of cognitive impairment or cognitive decline in the human; and (II) a second index value indicating the degree of progression of cognitive impairment or cognitive decline in the human; An index value calculation step of calculating a support information generation step of generating information to support the assessment of cognitive impairment, cognitive decline, or a risk of any of these, based on the first index value and the second index value; is configured to run The amount of the biomarkers is quantified based on the amount of an amino acid sequence portion specific to each protein serving as a biomarker; In the support information generating step, a determination score is calculated using the first index value and the second index value, wherein the range of the judgment score is divided in advance into a plurality of numerical ranges associated with the judgment results, a determination result that the human has no or almost no risk of cognitive impairment is associated with one of the plurality of numerical ranges; another numerical range among the plurality of numerical ranges is associated with a determination result that the human has a low risk of cognitive impairment; a determination result that the human is at a moderate risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; a determination result that the person has a high risk of cognitive impairment is associated with yet another numerical range among the plurality of numerical ranges; In the support information generation step, a judgment result corresponding to the judgment score is selected, The two or more biomarkers include at least two or more of the following biomarkers (1) to (10): (1) alpha-1-B-glycoprotein or its partial peptide (2) alpha-2-antiplasmin or its partial peptide (3) alpha-2-macroglobulin or its partial peptide (4) Albumin or its partial peptide (5) Apolipoprotein A1 or a partial peptide thereof (6) apolipoprotein C1 or a partial peptide thereof (7) Complement component 3 or a partial peptide thereof (8) Complement component 4 gamma chain or a partial peptide thereof (9) Hempexin or a partial peptide thereof (10) transthyretin or a partial peptide thereof Information processing device.

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