Combination of biomarkers and method for detecting cognitive impairment or risk thereof using said combination

Abstract

The purpose of the present technology is to detect, with high accuracy, cognitive dysfunction or the risk thereof.　The present technology provides a system which is for determining cognitive dysfunction and which includes an information processing apparatus for executing a determination step for determining the presence or risk of cognitive dysfunction on the basis of the amounts of biomarkers (a), (b), (c), (d), and (e) contained in a biological sample. (a) A biomarker including an intact protein of Apolipoprotein A1 including an amino acid sequence represented by SEQ ID NO: 1, or a partial peptide of said protein. (b) A biomarker including an intact protein of Transthyretin including an amino acid sequence represented by SEQ ID NO: 2, or a partial peptide of said protein. (c) A biomarker of an intact protein of Complement C3 including an amino acid sequence represented by SEQ ID NO: 3, or a partial peptide of said protein. (d) A biomarker Aβ1-40 including a peptide having an amino acid sequence represented by SEQ ID NO: 4. (e) A biomarker Aβ1-42 including a peptide having an amino acid sequence represented by SEQ ID NO: 5.

Description

[Technical Field]

[0001] The present technology relates to a combination of biomarkers, particularly a combination of biomarkers suitable for detecting cognitive impairment or the risk thereof, and a method for detecting cognitive impairment or the risk thereof using the combination. [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 involved measuring the abundance of a single specific protein or so-called oligopeptide with a molecular weight of 10,000 or less in blood, or measuring the activity of enzyme proteins, to aid in diagnosis by identifying clear differences between normal (healthy subject) and diseased samples. 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 subjects 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 accurately detect cognitive impairment or the risk of cognitive impairment. [Means for solving the problem]

[0006] The inventors have found that certain combinations of biomarkers are suitable for detecting cognitive impairment or detecting the risk of cognitive impairment.

[0007] That is, the present technology provides the following combinations of biomarkers (a), (b), (c), (d), and (e): (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 having the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4, and (e) Biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO:5. The combination may be used for detecting, diagnosing, or determining cognitive impairment or the risk thereof. The combination may be used for detecting, diagnosing, or determining cognitive decline.

[0008] The present technology also provides the following biomarker combinations (a), (b), (c), (d), (e), and (f): (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 containing the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4; (e) a biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 5, and (f) A biomarker consisting of an intact BACE1 protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 6. The combination may be used for detecting, diagnosing, or determining cognitive impairment or the risk thereof. The combination may be used for detecting, diagnosing, or determining cognitive decline.

[0009] The technology also provides methods for detecting, diagnosing, or determining cognitive impairment or the risk thereof. The technology also provides methods for detecting, diagnosing, or assessing cognitive decline. The present technology also provides a method for determining the progression of cognitive impairment. These methods may include detecting, diagnosing, or determining cognitive impairment or the risk thereof based on the amounts of the biomarkers constituting said combination in a human biological sample. In these methods, preferably, the amounts of biomarkers (a), (b), and (c) and the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, are used. More preferably, the amounts of biomarkers (a), (b), and (c), the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, and the amount of biomarker (f) are used.

[0010] The present technology also provides methods of using the combination of biomarkers to detect cognitive impairment or the risk thereof, to detect, diagnose, or assess cognitive decline, and to assess the progression of cognitive impairment. In these methods, preferably, the amounts of biomarkers (a), (b), and (c) and the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, are used. More preferably, the amounts of biomarkers (a), (b), and (c), the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, and the amount of biomarker (f) are used. In the method, based on these amounts and ratios, cognitive impairment or the risk thereof in a human can be detected, cognitive decline can be detected, or the progression of cognitive impairment can be determined. In the method, the cognitive impairment may be mild cognitive impairment or Alzheimer's disease, i.e., in the method, the combination may be used to detect mild cognitive impairment or Alzheimer's disease in a human, or to detect the risk that a human has of developing mild cognitive impairment or Alzheimer's disease. In the method, the cognitive impairment may be mild cognitive impairment or Alzheimer's disease. That is, in the method, the combination can be used to detect whether a human is at a stage of neither mild cognitive impairment nor Alzheimer's disease, a stage of mild cognitive impairment, or a stage of Alzheimer's disease.

[0011] The method of use and the method of detection, diagnosis, or assessment may include a measurement step of measuring the amount (particularly the concentration) of the biomarkers constituting the combination in a biological sample. In the measurement, the amounts of the biomarkers constituting the combination may be measured simultaneously or separately. Preferably, the amounts of the biomarkers constituting the combination contained in one biological sample (particularly plasma) are measured simultaneously. "Simultaneous measurement" may mean measuring the amounts of all of the biomarkers constituting the combination in one measurement procedure (for example, one ELISA measurement or LC / MS measurement).

[0012] The present technology also provides a kit for measuring biomarkers constituting the combination. The detection kit may include an antibody or an aptamer against the biomarker.

[0013] The present technology also provides a method for obtaining or measuring data on the amounts of biomarkers constituting the combination contained in biological samples from each of a plurality of people; a regression analysis step of performing regression analysis using the presence or absence of cognitive dysfunction or the stage of cognitive dysfunction for each of the plurality of people and the amount measured for each person, and fitting the result to a regression model; Also provided is a method for determining a regression model for detecting cognitive impairment or the risk thereof, comprising: The determination method may include a detection step of detecting cognitive impairment or the risk thereof in a subject using the regression model obtained by the fitting in the regression analysis step. [Effects of the Invention]

[0014] The present technology enables accurate detection or assessment of cognitive impairment or the risk thereof, and furthermore, the present technology can also be used to detect or assess the progression of cognitive impairment in humans. 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]

[0015] [Figure 1] 1 is a graph illustrating the relationship between biomarkers and cognitive impairment. [Figure 2] 1 is a graph illustrating the relationship between biomarkers and cognitive impairment. [Figure 3] 1 is a graph illustrating the clinical efficacy of biomarker combinations of the present technology. [Figure 4] 1 is a graph showing the distribution of VSRAD scores and MMSE scores of 363 samples used in Test Examples 1 and 2. [Figure 5] 1 is a graph showing the distribution of plasma concentrations of ApoA-1, TTR, and C3 in 363 samples used in Test Examples 1 and 2. [Figure 6] FIG. 10 is a diagram showing details of the evaluation results by ROC. [Figure 7] FIG. 10 is a diagram showing details of the evaluation results by ROC. [Figure 8] FIG. 1 shows the Context of Use of biomarkers. [Figure 9] FIG. 1 shows the action of Sequester protein. [Figure 10] FIG. 1 is a block diagram illustrating an example configuration of a determination system according to the present technology. [Figure 11] FIG. 1 is a block diagram illustrating an example configuration of an information processing device according to the present technology. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] 1. Description of Related Art

[0018] As a means for distinguishing between normal and non-normal conditions of a living body using samples, the techniques generally used in in vitro diagnostics are the main prior art. The most common type of in vitro diagnostic reagent is one that performs diagnostic tests by analyzing components in the blood as biomarkers. Conventional techniques in this field have been used to aid in diagnosis by measuring the abundance of a single specific protein in the blood or so-called oligopeptides with a molecular weight of 10,000 or less, or by measuring the activity of enzyme proteins, and by determining the clear difference between normal (healthy individual) samples and disease samples. That is, the amount or activity of a single or multiple specific proteins or specific oligopeptides is measured in advance in biological samples from a certain number of healthy individuals and diseased patients, and the range of abnormal and normal values ​​is determined. Next, the biological samples to be evaluated are measured in the same manner, and the test evaluation is performed based on whether the measurement results fall within this determined range of abnormal or normal values.

[0019] Specific measurement methods include enzyme-linked immunosorbent assays (ELISAs) and chemiluminescent immunoassays (CLIAs), in which a sample is used as is or diluted in advance, and the amount of a single or multiple specific proteins or peptides is measured by the amount of color developed by the sample using a specific primary or secondary antibody labeled with an enzyme that develops color when reacted with a substrate. Other methods include radioimmunoassays (RIAs), which measure the amount of a specific protein or peptide using a radioisotope bound to a primary or secondary antibody, and enzyme activity assays, in which, when the protein is an enzyme, a substrate is directly added and the product is measured by color development or other methods.

[0020] Additionally, the degradation products of enzyme substrates can be analyzed by high-performance liquid chromatography (HPLC), and there are also LC-MS / MS methods that combine HPLC with mass spectrometry, as well as selected reaction monitoring (SRM) and multiple reaction monitoring (MRM) methods. Alternatively, after appropriate pretreatment of the sample, proteins or peptides can be separated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), and the concentration of the target protein or peptide in the sample can be measured by silver staining, Coomassie blue staining, or immunostaining using the corresponding antibody (Western blotting). Another method involves fractionating a biological sample by column chromatography and analyzing the proteins and peptides contained in the fractions by mass spectrometry. Furthermore, there are methods of performing mass spectrometry using a protein chip as a pretreatment, rather than column chromatography, and methods of performing mass spectrometry using magnetic beads as a pretreatment.

[0021] Furthermore, the present inventors have developed an immunoMS method in which an antibody against a target protein or peptide is bound to beads (including magnetic beads), thereby capturing the protein or peptide to be measured, and then eluting it from the beads and measuring it by mass spectrometry. Furthermore, a method has been reported in which intact proteins are analyzed by digesting them with trypsin or the like, followed by mass spectrometry using the above method. However, all of these methods utilize the properties of intact proteins, either by fractionating them as they are or by selecting specifically adsorbed protein molecules and analyzing them by mass spectrometry.

[0022] Cognitive dysfunction diseases, primarily Alzheimer's disease, have been increasing rapidly in Japan in recent years due to the aging of the population. The number of people with Alzheimer's disease was approximately 1.3 million in 1995, but increased to approximately 2.8 million in 2010 and is expected to reach approximately 4.1 million in 2020. Alzheimer's disease is said to account for 60-90% of cognitive dysfunction diseases. This disease not only causes memory loss in patients, but also disrupts their personality, causing them to lose their ability to function in society, and is therefore becoming a social problem. In Japan, the anti-acetylcholinesterase inhibitor Donepezil hydrochloride was approved at the end of 1999, and if administered early, it has a high probability of "delaying" the decline in cognitive function. In Alzheimer's disease, early diagnosis is the most important issue in order to maximize the effectiveness of current treatments and future therapeutic drugs.

[0023] The main diagnostic criteria for Alzheimer's disease according to the American Psychiatric Association (DSM IV) are shown below. A. A manifestation of a variety of cognitive deficits, manifested by both: (1) Memory impairment (impairment of the ability to learn new information or recall previously learned information) (2) One or more of the following cognitive impairments: a) Aphasia (language disorder) b) Apraxia (impairment of the ability to perform movements despite the absence of motor impairment) c) Agnosia (impairment of the ability to recognize or identify objects despite the absence of sensory impairment) d) Impairment of executive skills (planning, organizing, sequencing, abstraction) B. The cognitive deficits in criteria A(1) and A(2) each cause significant impairment in social or occupational functioning and represent a significant decline from premorbid levels of functioning (Nakano Imabari and Mizusawa Hidehiro, eds., Understanding Alzheimer's Disease, 2004, Nagai Shoten).

[0024] There are various disorders related to Alzheimer's disease (AD). Dementia such as AD gradually leads to a decline in cognitive function, so there is a state that can be called a prodromal state of dementia. This state is called mild cognitive impairment (MCI). Data from the United States shows that of those with MCI who visit a memory loss clinic, 10-15% progress to AD within one year, and approximately 50% progress to AD within four years. The majority of prodromal states of AD are included in amnestic MCI. According to the current definition, MCI is a condition in which patients complain of cognitive decline but are able to perform basic daily activities. Frontotemporal dementia (FTD) is characterized by cognitive decline and independent behavior, in contrast to AD, which tends to conform to others. FTD includes Pick's disease, which is characterized by the presence of Pick's globules in the cerebral cortex histologically. Dementia with Lewy bodies (DLB) is characterized by progressive memory loss and visual cognitive impairment, including visual hallucinations. Based on clinical symptoms, DLB accounts for 10-30% of dementia cases, making it the second most common degenerative dementia in the elderly after Alzheimer's disease (AD). Histologically, it is characterized by the presence of Lewy bodies in the cerebrum. Because FTD and DLB are dementia-type conditions, they are also referred to as dementia-type neurological disorders (see "Understanding Alzheimer's Disease" above).

[0025] The tests widely used to diagnose dementia are the Hasegawa Dementia Scale-Revised (HDS-R) and the Mini-Mental State Examination (MMSE), which are based on the results of a medical interview with the subject. The HDS was revised in 1991 and is now called the HDS-R. This consists of nine questions that test orientation, memory, calculation ability, memory and recall, and common sense. A score of 23 or below out of a possible 30 points is considered to be a sign of suspected dementia. The MMSE was developed in the United States to diagnose dementia and covers orientation, memory, calculation, verbal ability, and graphic ability. It consists of 11 questions out of a possible 30 points, and like the HDS-R, a score of 23 or below is considered to be a sign of suspected dementia. The results of both tests are said to be fairly consistent. These interview methods are used solely for screening purposes and do not lead to a definitive diagnosis, and neither the HDS-R nor the MMSE are used to classify the severity of dementia (see "Understanding Alzheimer's Disease" above).

[0026] Diagnostic imaging methods include CT and MRI, which look for morphological abnormalities in the brain, such as cerebral atrophy and sulcal ventricle enlargement; cerebral blood flow scintigraphy (SPECT), which looks at cerebral blood flow; and positron emission tomography (PET), which looks at oxygen consumption and glucose consumption. SPECT and PET are nuclear medicine methods that are said to be able to detect abnormalities before morphological abnormalities occur (see "Understanding Alzheimer's Disease," above). However, these imaging diagnostics require specialized equipment, which means they cannot be performed at all medical institutions. Furthermore, judgments may differ depending on the doctor who views the images, resulting in a lack of objectivity.

[0027] As such, the current diagnosis of dementia, including AD, relies on methods that lack objectivity and require the use of expensive equipment, making screening for disease detection impossible. However, if biomarkers that enable objective diagnosis using easily obtainable patient samples such as blood (including serum and plasma) could be found, screening would enable early detection of cognitive dysfunction, which is currently a top priority.

[0028] 2. Combination of biomarkers

[0029] The present technology provides the combination of biomarkers (a) to (e) described above. The present technology also provides the combination of biomarkers (a) to (f) described above. The amino acid sequences of these biomarkers are as follows. The combination of biomarkers according to the present technology can be used for detecting, diagnosing, or determining cognitive impairment or the risk thereof.

[0030] The amino acid sequences of SEQ ID NOs: 1 to 6 described above in (a) to (f) are as follows:

[0031] JPEG0007737723000001.jpg51155

[0032] JPEG0007737723000002.jpg34143

[0033] JPEG0007737723000003.jpg135147 JPEG0007737723000004.jpg75157

[0034] JPEG0007737723000005.jpg19142

[0035] JPEG0007737723000006.jpg22141

[0036] JPEG0007737723000007.jpg122136

[0037] 3. Methods for detecting, diagnosing, or assessing cognitive impairment or the risk thereof

[0038] The present technology also provides a method for detecting, diagnosing, or determining a cognitive impairment or a risk thereof. The method may include a step of detecting, diagnosing, or determining a cognitive impairment or a risk thereof based on the amounts of the biomarkers constituting the combination in a human biological sample (hereinafter also referred to as an "determination step"). In the determination step, the progression of the cognitive impairment may be determined. In the determination step, preferably, cognitive impairment or the risk thereof may be detected, diagnosed, or assessed, or the progression of cognitive impairment may be assessed, based on the amounts of biomarkers (a), (b), and (c) and the ratio of the amounts of biomarkers (d) and (e) (e.g., Aβ40 / Aβ42 or Aβ42 / Aβ40, particularly Aβ40 / Aβ42). More preferably, in the determination step, cognitive impairment or the risk thereof may be detected, diagnosed, or assessed, or the progression of cognitive impairment may be assessed, based on the amounts of biomarkers (a), (b), and (c), the ratio of the amounts of biomarkers (d) and (e) (e.g., Aβ40 / Aβ42 or Aβ42 / Aβ40, particularly Aβ40 / Aβ42), and the amount of biomarker (f).

[0039] In one embodiment, the determining step comprises: an index value calculation step of generating a determination index value based on the amounts of the biomarkers (a), (b), (c), (d), and (e) (preferably the ratio of the amounts of biomarkers (a), (b), and (c) to the amounts of biomarkers (d) and (e), and even more preferably the ratio of the amounts of biomarkers (a), (b), and (c) to the amounts of biomarkers (d) and (e), and the amount of biomarker (f); and a support information generating step of generating support information used to determine the presence or absence of cognitive dysfunction or the risk of cognitive dysfunction based on the index value for determination; may include: The method may include an output step of outputting information indicating the determination result thus generated.

[0040] In the index value calculation step, the determination index value may be calculated by substituting the amounts of the biomarkers (a), (b), (c), (d), and (e) (preferably the ratio of the amounts of biomarkers (a), (b), and (c) to the amounts of biomarkers (d) and (e), and even more preferably the ratio of the amounts of biomarkers (a), (b), and (c) to the amounts of biomarkers (d) and (e), and the amount of biomarker (f)) into a predetermined discriminant (for example, a regression model described below). For example, in the index value calculation step, the information processing device may calculate the determination index value based on the amounts of the biomarkers (a), (b), (c), (d), and (e), more preferably based on the ratio between the amounts of biomarkers (a), (b), and (c) and the amounts of biomarkers (d) and (e), and even more preferably based on the ratio between the amounts of biomarkers (a), (b), and (c), the amounts of biomarkers (d) and (e), and the amount of biomarker (f).

[0041] The discriminant may be, for example, a discriminant created by multivariate analysis, and in particular, may be a discriminant obtained by performing multivariate analysis using the amount of each biomarker constituting the combination of biomarkers (or the amount and the ratio) as an explanatory variable and the presence or absence of cognitive impairment as a response variable.

[0042] The index value for assessment may be any value within a predetermined range. The predetermined range may indicate that the closer the index value is to one end of the range, the more likely the person is to have no cognitive impairment or cognitive decline, and the closer the index value is to the other end of the range, the more likely the person is to have (more advanced) cognitive impairment. A person may initially be healthy (NDC), then progress to a state of MCI, which is a type of cognitive impairment, and, if the cognitive impairment further progresses, to AD. Therefore, the value of the index value within the predetermined range is useful for determining whether a person has cognitive impairment and / or the degree of progression of cognitive impairment.

[0043] To create the discriminant, a population of humans known to have or not have cognitive impairment (particularly humans diagnosed as having AD, MCI, or NDC) 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 index value is obtained by performing multivariate analysis using the presence or absence 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 coefficient of each term of the discriminant (and the value of the constant term) is obtained.

[0044] The multivariate analysis may be preferably logistic regression analysis (particularly multinomial logistic regression analysis), or may be other linear regression analysis. The multivariate analysis may be multi-class classification, for example, using a neural network or a support vector machine.

[0045] 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.

[0046] In the support information generating step, support information for determining the presence or absence of, or risk of, cognitive impairment is generated based on the index value. The index value is used to generate support information that contributes to accurately determining the presence or absence of, or risk of, cognitive impairment.

[0047] The support information may include, for example, one or more of a determination result regarding the presence or absence of cognitive impairment, a determination result regarding the risk of cognitive impairment, and data used for these determinations. For example, the predetermined range may be divided into a plurality of sections, and each section may be assigned a cognitive impairment and a degree of progression if cognitive impairment exists.For example, when the predetermined range is divided into three sections, the three sections may be, for example, an NDC section, an MCI section, and an AD section.The NDC section corresponds to a section without cognitive impairment.The MCI section and the AD section correspond to a section with cognitive impairment.The AD section indicates that cognitive impairment is more advanced than the MCI section.

[0048] In the support information generating step, it may be specified which of the multiple intervals the calculated index value falls into. Then, in the support information generating step, information indicating the presence or absence of cognitive dysfunction or a risk assessment result for the human from whom the biological sample is derived may be generated according to the specified interval. In one embodiment, in the determination step (particularly in the support information generation step) of the present disclosure, it may be determined whether the human from whom the biological sample is derived is in a healthy state, a mild cognitive impairment state, or a dementia state. This determination (the generation of the support information) may be performed by an information processing device. The information may include, for example, information indicating a determination result that the human does not have cognitive impairment (or a determination result that the human is highly or low likely to not have cognitive impairment), or information indicating a determination result that the human has cognitive impairment (or a determination result that the human is highly or low likely to have cognitive impairment). Furthermore, the information may include a determination result that the human is in a state of MCI (or a determination result that the human is highly or low likely to have MCI), or a determination result that the human is in a state of AD (or a determination result that the human is highly or low likely to have AD).

[0049] 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. As used herein, a "biological sample" may be a biological sample derived from a human, such as whole blood, plasma, or serum, preferably plasma or serum, and particularly preferably plasma. Plasma is preferred from the viewpoint of the stability of the amount of biomarkers during storage of the biological sample, for example.

[0050] In the present technology, Aβ40 / Aβ42 may be used as the ratio of the amounts of biomarkers (d) and (e) as described above, or alternatively, Aβ42 / Aβ40 may be used.

[0051] The method may include a data acquisition step of acquiring data on the amounts of biomarkers constituting the combination. The data acquisition step may include a step of measuring the amounts of the biomarkers in a human biological sample, or a step of acquiring previously measured biomarker amount data. In the former case, an example of a measurement technique will be described later. In the latter case, biomarker amount data stored in an information processing device or a recording medium may be acquired, for example.

[0052] In the data acquisition step, fluctuations in the amounts of the biomarkers constituting the combination may be measured, or data relating to the fluctuations may be acquired. The data relating to the fluctuations can be used to diagnose cognitive impairment or the risk thereof with even greater accuracy.

[0053] The method can accurately detect, diagnose, or assess cognitive impairment or the risk thereof. For example, the method has both very high accuracy and specificity in detecting, diagnosing, or assessing cognitive impairment or the risk thereof. In particular, the method can accurately detect, diagnose, or assess both MCI and AD. For example, the method can detect, diagnose, or assess cognitive impairment or the risk thereof when the AUC value of ROC for discriminating between MCI and NDC is 0.70 or more, preferably 0.75 or more, particularly preferably 0.80 or more, and when the AUC value of ROC for discriminating between AD and NDC is 0.70 or more, preferably 0.75 or more, particularly preferably 0.80 or more.

[0054] Furthermore, the method is also highly useful for assessing the effectiveness of drugs. That is, the method may include a drug efficacy assessment step of assessing the effectiveness of a drug used to prevent, treat, or cure cognitive impairment or the risk thereof. The assessment step may include a step of assessing the effectiveness of the drug based on changes in the amounts (or ratios) of the biomarkers constituting the combination before and after administration of the drug.

[0055] The method may include a step of comparing the amounts (or ratios) of biomarkers in the combination of biological samples from an NDC with the amounts (or ratios) of biomarkers in the combination of biological samples from a subject, which comparison step is useful for determining cognitive impairment or the risk thereof.

[0056] This technology can determine cognitive impairment in a subject. Furthermore, this technology can also evaluate cognitive impairment in a subject at a mild stage, making it useful for preventive medicine. Furthermore, if psychotherapy or pharmacotherapy is administered to a patient suffering from cognitive impairment, and the progression of the impairment is suppressed, this will be reflected in the amount of protein / partial peptides in biological samples such as serum or plasma. By measuring this, it is possible to evaluate and determine the effectiveness of treatment and to screen target biomolecules for drug discovery.

[0057] As used herein, the term "peptide" in "partial peptide of an intact protein" may include "polypeptides" and "oligopeptides." The "oligopeptide" generally refers to a compound in which amino acids with a molecular weight of 10,000 or less are bonded together, or a compound with a number of amino acid residues (2 or more) to about 50 or less. The "polypeptide" refers to a molecule having a molecular weight of 10,000 or more amino acids bound together, or a molecule having approximately 50 or more amino acid residues. As used herein, a partial peptide of an intact protein refers to a peptide having a partial amino acid sequence of a part of the amino acid sequence of the intact protein. These partial peptides of intact proteins may be generated as partial peptides during the expression synthesis process by transcription and translation, or may be generated as digestive degradation product peptides after being synthesized as intact proteins in vivo. This can be caused by deregulation of protein synthesis and regulatory mechanisms when the body is in an abnormal state, such as a cognitive impairment disorder. This technology makes it possible to evaluate and determine whether a subject is in a normal state or suffering from a cognitive impairment disease using the expression, synthesis, and / or digestion of proteins in the body as indicators, and also to evaluate and determine the degree of progression of cognitive impairment if the subject is suffering from it. "Detection of cognitive impairment" in the present technology refers to detection of whether a subject suffers from cognitive impairment, and may also include evaluation, discrimination, diagnosis, testing, etc. Furthermore, detection of cognitive impairment disease in the present technology may also include assessment of the risk that a subject will suffer from more severe cognitive impairment.

[0058] In this technology, intact proteins that can be used as biomarkers for detecting cognitive dysfunction diseases include Apolipoprotein A1 containing the amino acid sequence shown in SEQ ID NO: 1, Transthyretin containing the amino acid sequence shown in SEQ ID NO: 2, and Complement C3 containing the amino acid sequence shown in SEQ ID NO: 3. In addition, partial peptides of these intact proteins can also be used as biomarkers for detecting cognitive dysfunction diseases. In the present technology, the term "partial peptide of an intact protein" includes peptide fragments of five or more amino acid residues generated from intact proteins and peptides produced during the synthesis or degradation of the intact proteins.

[0059] Furthermore, partial peptides of intact proteins that can be used as biomarkers for detecting cognitive impairment include, for example, a polypeptide consisting of the amino acid sequence shown in SEQ ID NO: 1 (preferably a polypeptide derived from Apolipoprotein A1), a polypeptide consisting of the amino acid sequence shown in SEQ ID NO: 2 (preferably a polypeptide derived from Transthyretin), and a polypeptide consisting of the amino acid sequence shown in SEQ ID NO: 3 (a polypeptide derived from Complement C3). In the present technology, proteins or peptides consisting of amino acid sequences in which one or several amino acids are deleted, substituted, or added in each of the amino acid sequences of the biomarkers described in (a) to (f) above may be used as biomarkers. Here, "one or several" refers to "one to three," "one or two," or "one." In the present technology, the partial peptides used as biomarkers are meant to include proteins or peptides containing the amino acid sequences shown in SEQ ID NOS: 1 to 3, as well as peptide fragments of five or more amino acid residues derived therefrom.

[0060] The reason for the term "5 or more amino acid residues" in the "peptide fragment of 5 or more amino acid residues" in this technology is based on the description in N. Benkirane et al., J. Biol. Chem. Vol. 268, 26279-26285, 1993. N. Benkirane et al. reported that a peptide in which R was substituted with K in the amino acid residue sequence IRGERA at the C-terminus (130-135) of histone H3, and a peptide CGGGERA in which IR was deleted and CGG was instead linked to GERA, were recognized by antibodies obtained using the peptide IRGERA as an immunogen. This indicates that antigenic recognition is achieved by peptides consisting of 4 or more amino acid residues. In this technique, the number of residues was increased by one to five or more to make it more generalizable to sites other than the C-terminus of histone H3. However, the ability to target such small peptides is important when using immunological detection and classification methods such as immunoblotting, ELISA, and immunoMS.

[0061] In some cases, intact proteins or their partial peptides may be glycosylated, and these glycosylated proteins and partial peptides may also be used as biomarkers for detecting cognitive impairment.

[0062] In this technology, the biomarker may be quantified, or its presence or absence may be determined qualitatively. In this case, if the biomarker concentration is equal to or greater than a predetermined measured value or equal to or greater than the standard value for a group of patients with non-cognitive dysfunction, cognitive impairment can be detected, diagnosed, etc. Furthermore, biomarker qualitative analysis can be used to detect, diagnose, etc., as positive or negative. For example, a reaction with a biomarker and color development, etc., is considered positive.

[0063] This technology can be used to separate biomarkers in biological samples such as serum by two-dimensional electrophoresis or two-dimensional chromatography (2D-LC). The chromatography used for two-dimensional chromatography can be selected from known chromatographies such as ion exchange chromatography, reversed-phase chromatography, and gel filtration chromatography. In addition, biomarkers can be separated using this technology by SRM / MRM, which combines chromatography (LC) with triple quadrupole mass spectrometry (LC-MS). The LC used in this case can be one-dimensional LC. Furthermore, as a method for separating biomarkers using this technology, an immunoMS method (see Japanese Patent Application Laid-Open No. 2004-333274) is used, in which an antibody against a target protein or peptide is bound to beads (including magnetic beads), the protein or peptide to be measured is captured, and then the protein or peptide is eluted from the beads and measured by mass spectrometry.This method makes it possible to easily evaluate the presence or amount of a target protein, protein fragment, or peptide without using two-dimensional electrophoresis or chromatography.

[0064] The types and amounts of one or more proteins in a biological sample can be measured simultaneously or separately by various methods. If the target protein (including protein fragments and their partial peptides) has been identified and an antibody (primary antibody) against it has been obtained, the following methods can be used. This technique is preferably carried out by one or more of the following methods: immunoblotting; Western blotting; enzyme, fluorescent, or radioactive labeling; mass spectrometry; immunoMS; and surface plasmon resonance. Furthermore, the biomarkers of this technology can be measured simultaneously or separately even if they are of different types or amounts. This technology is more suitable for measuring a large number of proteins or their partial peptides at once by using 2D-LC-MALDI-TOF-MS, SRM / MRM, or immunoMS, which combine two-dimensional chromatography and mass spectrometry. In this technology, methods using enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay (CLIA), radioimmunoassay (RIA), enzyme activity assay, etc. are referred to as "enzyme-, fluorescent-, or radioactive-substance-labeled methods." These methods using antibodies are referred to as "enzyme-, fluorescent-, or radioactive-substance-labeled antibody methods."

[0065] <Method for measuring biomarker levels>

[0066] Below, examples of methods for measuring the amounts of the biomarkers that make up the combination are described.

[0067] (1) Immunoblotting This is the simplest method. A test biological sample (e.g., serum or plasma) is prepared in several serial dilutions, and a fixed amount (approximately 1 microliter) is dropped onto an appropriate membrane such as a nitrocellulose membrane and air-dried. After treatment with a blocking solution containing a protein such as BSA, the membrane is washed, and then reacted with a primary antibody. After washing, a labeled secondary antibody is reacted to detect the primary antibody. After washing the membrane, the label is visualized and the concentration is measured.

[0068] (2) Western blotting After one- or two-dimensional gel electrophoresis, including isoelectric focusing or SDS-PAGE, the separated proteins are transferred to a suitable membrane such as a PVDF membrane, and the abundance of the target protein is measured using a primary antibody and a labeled secondary antibody in the same manner as in the immunoblotting method described above.

[0069] (3)ELISA method Antibodies against proteins or their partial peptides are bound to a carrier such as a specially chemically modified microtiter plate, and the sample is serially diluted and then added in appropriate amounts to the antibody-bound microtiter plate and incubated. The plate is then washed to remove any uncaptured proteins or partial peptides. A secondary antibody conjugated with a fluorescent or chemiluminescent substance or enzyme is then added and incubated. Detection is performed by adding the respective substrates and then measuring the visible light emitted by a fluorescent or chemiluminescent substance or an enzyme reaction. Instead of antibodies, substances capable of binding to proteins or their partial peptides may be used. For example, aptamers may be used. In this technique, it is preferable to use substances (for example, antibodies or aptamers) against the biomarkers described in (a) to (f) above.

[0070] Furthermore, examples of the method (see JP-A-2006-308533) are given below, but the method is not limited thereto.

[0071] (4) Microarray (microchip) method A microarray is a general term for a device in which substances capable of binding to a substance to be measured are immobilized in an array on a carrier (substrate). In the case of this technology, antibodies or aptamers against proteins or partial peptides can be immobilized in an array. Measurement is performed by adding a biological sample to the immobilized antibody, etc., binding the protein or partial peptide to be measured on the microarray, and then adding a secondary antibody bound to a fluorescent or chemiluminescent substance or enzyme and incubating. Detection is performed by adding the respective substrate and then measuring the visible light produced by the fluorescent or chemiluminescent substance or enzyme reaction.

[0072] (5) Mass spectrometry In mass spectrometry, for example, antibodies against a specific protein or its partial peptides are bound to specially chemically modified microbeads or a substrate (protein chip). The microbeads may be magnetic beads. The material of the substrate is not important. The antibodies used may be (1) antibodies that recognize only the full-length of a specific protein, (2) antibodies that recognize only a partial peptide, or (3) antibodies that recognize both a specific protein and its partial peptide, or a combination of the above (1) and (2), (1) and (3), or (2) and (3). After serially diluting the sample with the original solution or buffer, an appropriate amount is added to antibody-bound microbeads or a substrate and incubated. The mixture is then washed to remove any uncaptured proteins and partial peptides. The proteins and partial peptides captured on the microbeads or substrate are then analyzed by mass spectrometry using MALDI-TOF-MS, SELDI-TOF-MS, or other methods, to measure the mass numbers and peak intensities of the protein, protein fragment, and partial peptide peaks. A fixed amount of an appropriate internal standard substance is added to the original biological sample, and the peak intensity is measured. The ratio of this peak intensity to the peak intensity of the target substance can be calculated to determine the concentration of the target substance in the original biological sample. This method is called immunoMS. Alternatively, samples can be diluted with a buffer solution or after removing some of the proteins, separated by HPLC, and quantified by mass spectrometry using electrospray ionization (ESI). Absolute quantification using SRM / MRM with an isotope-labeled internal standard peptide can then be used to determine the concentration in the sample.

[0073] In addition to the above methods, proteins and partial peptides can also be analyzed by methods using two-dimensional electrophoresis, surface plasmon resonance, and the like.

[0074] The present technology also encompasses a method for detecting cognitive dysfunction diseases by subjecting a biological sample collected from a subject to two-dimensional electrophoresis or surface plasmon resonance, and using the presence or amount of the biomarker as an indicator.

[0075] 4. Methods for using biomarker combinations

[0076] The present technology also provides methods for using the combination of biomarkers to detect cognitive impairment or the risk thereof. The combination may be used, for example, to diagnose cognitive impairment in humans. The combination may also be used to determine the progression of cognitive impairment in humans.

[0077] Particularly preferably, the amounts of biomarkers (a), (b), and (c) and the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, are used in the method. More preferably, the amounts of biomarkers (a), (b), and (c), the ratio of the amounts of biomarkers (d) and (e), Aβ40 / Aβ42, and the amount of biomarker (f) are used in the method. In the method, cognitive impairment or the risk thereof in a human can be detected, cognitive impairment can be diagnosed, or the progression of cognitive impairment can be determined based on these amounts and ratios.

[0078] 5. Cognitive impairment assessment system

[0079] The present technology also provides a system for determining cognitive impairment. The determination system may be configured to execute, for example, the method described in 3. or 4. above. An example configuration of the determination system is shown in FIG. 10. As shown in the figure, a determination system 100 according to the present technology may include, for example, a measurement system 101 that measures the amounts of biomarkers that constitute the combination, and an information processing device 102 that detects, diagnoses, or determines cognitive impairment or the risk thereof based on the amounts of biomarkers acquired by the measurement system (or an information processing device 102 that determines the degree of progression of cognitive impairment). That is, the present technology also provides an information processing device that executes the method according to the present technology.

[0080] The measurement system may be configured to perform the measurements described in 3. above, and particularly includes an apparatus configured to perform any of the measurement methods described in 3. above. The apparatus preferably includes, for example, an antibody or aptamer immobilizing unit (capture unit) and a measurement unit. The antibody or aptamer immobilizing unit preferably includes a solid support, such as a glass slide or a 96-well titer plate, on which the antibody or aptamer is immobilized. The measurement unit preferably includes a light detection means, such as a spectrophotometer or a fluorometer, that corresponds to the target to be detected.

[0081] 11, the information processing device 100 may include a processing unit 103, a storage unit 104, an input unit 105, an output unit 106, and a communication unit 107. The information processing device may be configured as, for example, a general-purpose computer or server, or may be configured as a cloud server.

[0082] The processing unit may be configured to, for example, execute the determination step described in 3. The processing unit may be configured to execute the data acquisition step described in 3. in addition to the determination step.

[0083] The processing unit may include, for example, a CPU (Central Processing Unit) and RAM. The CPU and RAM may be connected to each other, for example, via a bus. An input / output interface may be further connected to the bus. The input unit, the output unit, and the communication unit may be connected to the bus via the input / output interface.

[0084] The processing unit may be further configured to acquire data from the storage unit or record data in the storage unit. The storage unit stores various data. The storage unit may be configured to store, for example, data acquired in the data acquisition process and data related to the determination result in the determination process. The storage unit 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 a method or information processing according to the present technology, and various other programs. These programs may be recorded not only in the storage unit but also on a recording medium. That is, the present technology also provides a program for causing an information processing device to execute a determination process according to the present technology and a recording medium on which the program is stored.

[0085] The input unit may include an interface configured 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.

[0086] The output unit may include an interface configured to output various data. For example, the output unit may output the determination result in the determination step. The output unit may include a display device and / or a printing device as a device for performing the output.

[0087] The communication unit may be configured to connect the information processing device to a network via a wired or wireless connection. The communication unit enables the information processing device to acquire various data (e.g., biomarker amount data acquired in a data acquisition step) via the network. The acquired data may be stored in the storage unit, for example. The configuration of the communication unit may be appropriately selected by those skilled in the art.

[0088] The information processing device 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.

[0089] 6. Biomarker measurement kit for determining cognitive impairment

[0090] The present technology also provides a biomarker measurement kit that comprises a combination of biomarkers according to the present technology. The measurement kit may include, for example, antibodies or aptamers against the biomarkers.

[0091] 7. Regression model determination method

[0092] The present technology also provides a method for determining a regression model for detecting cognitive impairment or the risk thereof. The determination method may include a step of acquiring or measuring data on the amounts of the biomarkers constituting the combination contained in biological samples from each of a plurality of people, and a regression analysis step of performing regression analysis using the presence or absence of cognitive impairment or the stage of cognitive impairment for each of the plurality of people and the amounts measured for each person to fit a regression model. The determination method may also include a detection step of detecting cognitive impairment or the risk thereof in a subject using the regression model obtained by the fitting in the regression analysis step. Furthermore, the determination method may be performed, for example, by the information processing described in 5. above.

[0093] The regression model in the regression analysis step may be, for example, logistic regression analysis, but is not limited thereto. In the regression model, the dependent variable may be, for example, the presence or absence of cognitive impairment or the stage of cognitive impairment. In the regression model, the explanatory variable may be, for example, the amount of biomarkers constituting the combination according to the present technology or the ratio described above. [Example]

[0094] 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.

[0095] (Test Example 1: Clinical efficacy of biomarker combinations)

[0096] (background) Blood biomarkers for MCI and preclinical stages offer important opportunities for the prevention of AD. Plasma Aβ correlates with brain amyloid accumulation, but its clinical utility in detecting early cognitive impairment is unclear.

[0097] (method) Of the 681 samples collected in this multicenter clinical study, 363 were diagnosed according to the ADNI neuropsychological criteria (AD: 178 cases, MCI: 145 cases, cognitively normal elderly (NDC): 40 cases). Plasma concentrations of Aβ40, Aβ42, BACE1, and triple markers (ApoA-1, C3, and TTR) were measured. The clinical efficacy of this combination of biomarkers for detecting cognitive impairment was evaluated.

[0098] (Results and Discussion) FIG. 1 shows the results of an analysis of the clinical effectiveness of the triple marker score, BACE1 concentration, Aβ40 concentration, Aβ42 concentration, and Aβ40 / 42 ratio in detecting MCI or AD. The triple marker scores were obtained as follows. That is, regression analysis was performed using the triple marker concentrations of each of the 363 samples and information on whether each sample was MCI or NDC, or information on whether each sample was AD or NDC. A discriminant equation was obtained from this analysis. The discriminant equation was set to have the value range shown in Figure 1. The triple marker score for each sample was obtained by substituting the concentration of each sample into these discriminant equations.

[0099] As shown in Figure 1, Aβ40 concentration and the Aβ40 / 42 ratio are excellent for detecting AD, but are difficult to detect MCI. The triple marker is excellent for detecting NDC and MCI. Therefore, by combining these, cognitive impairment can be detected with high clinical efficacy across a wide range of conditions, from MCI to AD. That is, the combination of the three biomarkers ApoA-1, C3, and TTR, and further the biomarker Aβ40 or the biomarker ratio Aβ40 / 42 is useful for determining the presence or absence of cognitive dysfunction (particularly MCI and AD) in humans, or for determining the risk of cognitive dysfunction. Furthermore, the combination of the three biomarkers ApoA-1, C3, and TTR, and further the biomarker Aβ40 or the biomarker ratio Aβ40 / 42 is also useful for determining at which stage a human is in the NDC state to the AD state.

[0100] Figure 2 shows the results of differential analysis using triple marker scores, scores based on triple marker concentrations and the Aβ40 / 42 ratio (also referred to as triple marker + Aβ40 / 42 scores), and scores based on triple marker concentrations, the Aβ40 / 42 ratio, and BACE1 concentrations (also referred to as triple marker + Aβ40 / 42 + BACE1 scores). The Triple marker scores are as described above with respect to FIG. The triple marker + Aβ40 / 42 score was obtained as follows. That is, a discriminant was obtained by performing regression analysis using the triple marker concentration and Aβ40 / 42 ratio of each of the 363 samples and information on whether each sample was MCI or NDC, or information on whether each sample was AD or NDC. The discriminant was set to have the range shown in Figure 2. The concentration of each sample was substituted into the discriminant to obtain the triple marker + Aβ40 / 42 score of each sample. The triple marker + Aβ40 / 42 + BACE1 score was obtained as follows. That is, the triple marker concentration, Aβ40 / 42 ratio, and BACE1 concentration of each of the 363 samples, and information on whether each sample was MCI or NDC, or information on whether each sample was AD or NDC, were used to perform logistic regression analysis to obtain a discriminant. The discriminant was set to have the value range shown in Figure 2. The concentration of each sample was substituted into the discriminant to obtain the triple marker + Aβ40 / 42 + BACE1 score for each sample.

[0101] As shown in Figure 2, the triple marker + Aβ40 / 42 score is superior to the triple marker score in distinguishing between NDC, MCI, and AD. Furthermore, the triple marker + Aβ40 / 42 + BACE1 score is also superior to the triple marker score and the triple marker + Aβ40 / 42 score in distinguishing between NDC, MCI, and AD, with the P value indicating significant differences between the three groups increasing by five orders of magnitude. These results indicate that the combination of triple markers and the Aβ40 / 42 ratio is suitable for distinguishing between NDC, MCI, and AD, and that the combination of triple markers, the Aβ40 / 42 ratio, and BACE1 is even more suitable for distinguishing between NDC, MCI, and AD. That is, the combination of the triple marker and the Aβ40 / 42 ratio is useful for determining the presence or absence of cognitive dysfunction (particularly MCI and AD) in humans or for determining the risk of cognitive dysfunction. The combination of the triple marker, the Aβ40 / 42 ratio, and BACE1 is further useful for determining the presence or absence of cognitive dysfunction (particularly MCI and AD) in humans or for determining the risk of cognitive dysfunction.

[0102] As described above, by using a combination of biomarkers according to the present technology, Cognitive impairment or the risk of cognitive impairment can be detected more accurately.

[0103] (Test Example 2: Evaluation by ROC)

[0104] In order to confirm the effectiveness of the combination of triple marker concentration and Aβ40 / 42 ratio and the combination of triple marker concentration, Aβ40 / 42 ratio and BACE1 concentration in the diagnosis of cognitive dysfunction, ROC (Receiver Operating Characteristic) analysis was performed.In addition, in this evaluation, when only triple marker concentration is used and when only Aβ40 / 42 ratio is used, ROC analysis was also performed.These analyses were performed on the specimens used in the above-mentioned Test Example 1.

[0105] The receiver operating characteristic curves for the discrimination of NDC and MCI, and NDC and AD, using the Aβ40 / 42 ratio, triple marker score, triple marker + Aβ40 / 42 score, or triple marker + Aβ40 / 42 + BACE1 score are shown in Figure 3. The area under the curve (AUC), standard error (SE), and 95% confidence interval (CI) of the ROC curves are also shown in Figure 3. In Figure 3, * indicates significantly higher clinical efficacy compared with the Aβ40 / 42 ratio and triple marker score. † indicates significantly higher clinical efficacy compared with Aβ40 / 42.

[0106] As shown in Figure 3, in both the discrimination between NDC and MCI and the discrimination between NDC and AD, the AUC value increases in the order of Aβ40 / 42 ratio, triple marker score, triple marker + Aβ40 / 42 score, and triple marker + Aβ40 / 42 + BACE1 score. That is, triple marker + Aβ40 / 42 score is more effective in diagnosing MCI and AD than Aβ40 / 42 ratio and triple marker score. Triple marker + Aβ40 / 42 + BACE1 score is more effective in diagnosing MCI and AD than Aβ40 / 42 ratio and triple marker score, and is more effective in diagnosing than triple marker + Aβ40 / 42 score. Therefore, the combination of the three biomarkers ApoA1, C3, and TTR with the biomarker ratio Aβ40 / 42 is effective for determining the presence or absence of cognitive dysfunction (particularly MCI and AD) in humans or for determining the risk of cognitive dysfunction. Furthermore, the combination of the three biomarkers ApoA-1, C3, and TTR with the biomarker ratio Aβ40 / 42 and BACE1 is even more effective for determining the presence or absence of cognitive dysfunction (particularly MCI and AD) in humans or for determining the risk of cognitive dysfunction.

[0107] Details of the regression analysis used in the above test examples are described below. Note that although logistic regression analysis is described below, the discriminant for calculating the score used for discrimination may be derived by analysis other than logistic regression analysis.

[0108] <Distinguishing MCI and AD using multi-markers with logistic regression analysis> (1) Principles of logistic regression analysis This method obtains the coefficients of each parameter corresponding to a biomarker from a dataset and can give the probability of each patient being classified into two disease categories (normal, disease). A relatively detailed explanation of logistic regression can be found here (Czepiel, SA, http: / / czep.net / stat / mlelr.pdf, 2010, Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation.). This explanation includes the analysis method using the Newton-Raphson method. The principle of this analysis method is as follows: When the probability of an event occurring is P,

[0109]

number

[0110] It is known that can be approximated by a cumulative standard normal distribution (Bowling, SR, et al. JIEM, 2009, 2: 114-127, A logistic approximation to the cumulative normal distribution.). Logistic regression uses this approximation to perform statistical analysis. Z is the multivariate x i ; expressed as a linear combination for i=1,2,...r.

[0111]

number

[0112] Applying a large amount of data to equations (1) and (2), the coefficient β i and its significance (β i is not zero) is determined from the statistical p-value. There are two ways to finalize equation (2): (I) Statistical significance of coefficients If an insignificant coefficient is found, remove it to create equation (2) and repeat the same fitting process. In this way, when all the coefficients in equation (2) become significant, substitute the measured value of xi and find the Z value from (2). Then, the value of P (probability of judgment) can be found from equation (1). Note that the coefficient β i For , the standard error can be calculated.

[0113] (II) How to find the combination of coefficients that gives the maximum correct answer rate The correct answer rate refers to the rate at which a subject is correctly identified as belonging to the group to which it originally belonged, but in this method, the combination of coefficients that gives the maximum correct answer rate is determined by trial and error for all coefficients, including those for which statistical significance is not recognized. The judgment probability is calculated in the same way as in (I). The accuracy rate of logistic regression is defined by the following equation (3). Discrimination is performed by estimating which of two categories (e.g., NDC and MCI) the subject belongs to from the logistic regression equation. The subject's category is defined as i (e.g., MCI), and when the probability of determination obtained from the logistic regression equation is 0.5 or higher, the subject is considered to have been correctly diagnosed as i. Let N be the total number of subjects in category i. i , the number of subjects correctly diagnosed as i is C i As,

[0114]

number

[0115] The percentage of correct answers is shown as follows. In logistic regression, the variable x i The odds ratio for x i The odds of increasing by one unit are divided by the original odds, which is exp(β i ) is equal to the odds ratio of 1. iEven if the odds are increased by one unit, the odds remain the same as before, which means that there is no change in the probability of the event being considered. In other words, in this case, βi is 0, which indicates that it does not contribute to Z. Even if the 95% confidence interval of the odds ratio includes 1, the β i is not significant. Note that exp(0) = 1, of course.

[0116] (2) Logistic regression analysis In the above test example, a logistic regression analysis was performed on the biomarkers measured by the multiplex immunoassay method. In the logistic regression analysis, marker proteins were added or removed based on trial and error, and the combination of marker proteins that gave the highest accuracy rate was determined. The coefficient (β) of the logistic regression equation (2) for this combination was calculated. i ) was calculated. Logistic regression analysis was performed using MedCalc for Windows, version 9, 2007 (MedCalc Software), which uses the Newton-Raphson method.

[0117] Detailed data regarding the above Test Examples 1 and 2 are described below.

[0118] The results of biomarker analysis of plasma samples from 363 subjects in the multicenter clinical study are shown in Table 1. The table also shows the average values ​​for NDC, MCI, and AD. The distributions of VSRAD and MMSE scores for NDC, MCI, and AD, respectively, are shown in FIG. 4, and the plasma concentrations of ApoA-1, TTR, and C3 are shown in FIG. FIG. 6 shows the details of the results of the ROC analysis on the discrimination results between NDC and MCI, and FIG. 7 shows the details of the results of the ROC analysis on the discrimination results between NDC and AD. Figure 8 shows the context of use of biomarkers in the pathophysiology of AD, and Figure 9 shows the role of sequester protein as a biomarker (specifically a blood biomarker) for cognitive decline.

[0119] [Table 1]

Claims

1. A cognitive impairment assessment system including an information processing device that executes a assessment step of determining the presence or absence of cognitive impairment or the risk of cognitive impairment based on the amounts of the following biomarkers (a), (b), (c), (d), and (e) contained in a biological sample. (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 having the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4, and (e) A biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO:

5.

2. The determination system of claim 1, wherein the information processing device determines the presence or risk of cognitive impairment based on the ratio of the amounts of the biomarkers (a), (b), and (c) to the amounts of the biomarkers (d) and (e).

3. The determination system of claim 2, wherein the information processing device is configured to determine the presence or risk of cognitive impairment based on the amount of the biomarker (f) below in addition to the ratio between the amount of the biomarkers (a), (b), and (c) and the amount of the biomarkers (d) and (e). (f) A biomarker consisting of an intact BACE1 protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO:

6.

4. The information processing device determines whether the human from which the biological sample is derived is in a healthy stage, a mild cognitive impairment stage, or a dementia stage in the determination process. The determination system according to any one of claims 1 to 3.

5. An information processing device that executes a determination step of determining the presence or absence of cognitive impairment or the risk of cognitive impairment based on the amounts of the following biomarkers (a), (b), (c), (d), and (e) contained in a biological sample. (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 having the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4, and (e) A biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO:

5.

6. A method for determining cognitive impairment, executed by a cognitive impairment determination system or an information processing device, comprising: The method for determining cognitive impairment includes a determination step of determining the presence or absence of cognitive impairment or the risk of cognitive impairment based on the amounts of the following biomarkers (a), (b), (c), (d), and (e) contained in a biological sample. (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 having the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4, and (e) A biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO:

5.

7. A combination of biomarkers used to determine the presence or absence or risk of cognitive impairment, comprising the following biomarkers (a), (b), (c), (d), and (e). (a) a biomarker consisting of an intact protein of Apolipoprotein A1 containing the amino acid sequence represented by SEQ ID NO: 1 or a partial peptide thereof; (b) a biomarker consisting of an intact transthyretin protein or a partial peptide thereof comprising the amino acid sequence represented by SEQ ID NO: 2; (c) a biomarker consisting of an intact protein of Complement C3 having the amino acid sequence represented by SEQ ID NO: 3 or a partial peptide thereof; (d) a biomarker Aβ1-40 consisting of a peptide having the amino acid sequence represented by SEQ ID NO: 4, and (e) A biomarker Aβ1-42 consisting of a peptide having the amino acid sequence represented by SEQ ID NO:5.

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