Dementia risk assessment method and dementia risk assessment system

Abstract

Provided is a method for evaluating dementia risk, the method allows direct understanding of the physical state of a subject through an analysis of a biological sample of the subject, and objective and highly accurate evaluation of the risk of the subject who might be developing dementia (mild cognitive impairment (MCI) or Alzheimer's disease (AD)).　Concentration data regarding an element group for evaluation in a blood (plasma or serum) sample 2 taken from a subject are acquired (step S1), the concentration data are applied to a discriminant function that determines whether the subject belongs to a control group or a case group, and a correlation between concentrations of the group of elements for evaluation is calculated (step S2), and on the basis of the correlation, an indicator as to whether the subject is suffering from dementia is obtained (step S3). As the group of elements for evaluation, a combination of the 17 elements Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs is used. It is preferable that an estimation that the type of dementia that the subject is suffering from is MCI or AD is included in evaluation results.

Description

Technical Field

[0001] The present invention relates to a method and a system for evaluating the risk of dementia. More specifically, the present invention relates to a method and a system for evaluating the risk of dementia using an index obtained by utilizing the concentration balance (correlation relationship between the concentrations of the evaluation element group) of a group of elements contained in human blood (plasma or serum).

[0002] In the present invention, "dementia" includes not only Alzheimer's disease (hereinafter also referred to as "AD"), but also mild cognitive impairment (hereinafter also referred to as "MCI").

Background Art

[0003] Conventionally, as a method for diagnosing dementia, a method using the diagnostic manual (Diagnostic and Statistical Manual of Mental Disorders - 5) (DSM - 5) by the American Psychiatric Association, the 10th edition of the International Statistical Classification of Diseases (ICD - 10), or the diagnostic criteria defined by the National Institute on Aging and the Alzheimer's Association (NIA - AA) in the United States is known. In these conventional diagnostic methods, concepts such as "decline in cognitive function" provided by the person himself or an informant who knows the person, and evaluations by neuropsychological tests such as the Mini - Mental State Examination (MMSE), the Revised Hasegawa Dementia Scale (HDS - R), GPCOG: General Practitioner Assessment of Cognition), and judgments on whether cognitive deficits in daily activities inhibit independence are used as diagnostic criteria.

[0004] In addition, in the conventional diagnostic methods described above, image examinations such as CT (Computer Tomography), MRI (Magnetic Resonance Imaging), and PET (Positron Emission Tomography) are often used in combination as auxiliary diagnoses.

[0005] Furthermore, in the conventional diagnostic methods described above, blood tests are used for the differential diagnosis of metabolic encephalopathies (for example, hypothyroidism, hepatic encephalopathy, hyponatremia), and urine tests are also used. In that urine test, it has been reported that a specific nucleic acid oxidation metabolite (8-Oxo-GSn) contained in urine is used as an index for MCI diagnosis, that is, it becomes a biomarker specialized for MCI diagnosis. However, no biomarker specialized for dementia diagnosis has been discovered in blood tests yet.

[0006] By the way, the transmission of signals in nerve cells is carried out by using action potentials generated by changes in the balance (distribution) of sodium ions (Na + ) and potassium ions (K + ). The signal that reaches the synapse at the end of a nerve cell by using the action potential is converted into a neurotransmitter (for example, serotonin, dopamine, norepinephrine, acetylcholine) and transmitted to the synapse of the next nerve cell. Here, it is known that the neurotransmitters used for signal transmission between nerve cells are affected by metal elements such as iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) present in the brain. It is also known that these groups of metal elements regulate many functions of the nervous system either as simple metals or as components of enzymes and functional proteins.

[0007] AD and MCI have conventionally been regarded as aging phenomena due to aging. However, for AD, since its pathological characteristics are synaptic loss and neuronal death caused by abnormal accumulation of amyloid-β protein (observed as "senile plaques") or phosphorylated tau protein, AD is considered to be a neurological disease. On the other hand, as described above, it has been found that neurotransmitters used for signal transmission between neurons are affected by metal element groups such as iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) in the brain. Therefore, it is speculated that by detecting the metal element group contained in the blood, it may be possible to diagnose AD, which is a neurological disease. If this is possible, a biomarker that serves as an indicator of the presence or absence of AD can be obtained through a blood test. Thus, in recent years, it has become possible to simply evaluate dementia, which is increasing rapidly among the elderly, and this is extremely useful.

[0008] As described above, until now, at the time of diagnosing AD or MCI, the Mini-Mental State Examination (MMSE) has been used as the core, and imaging examinations such as CT, MRI, and PET have been performed as auxiliary diagnoses. However, in view of the current situation where dementia is increasing rapidly not only among the elderly but also among the middle-aged, it is urgent to develop a method that can simply evaluate the risk of dementia. A method using liquid biopsy using blood, urine, etc. would be more preferable.

[0009] The present inventors have previously developed a cancer risk assessment method that utilizes the correlation between the concentration of the metal element group contained in human serum and the onset of cancer as a method for pre-evaluating the cancer risk, and have filed an international patent application (see Patent Document 1 and Patent Document 2). According to these methods, an indicator for evaluating the cancer risk can be easily obtained through a blood test, and the cancer risk can be simply evaluated using this indicator. Moreover, the cancer risk of general examinees can be estimated with high accuracy. In addition, it can be easily applied to mass screening.

[0010] The method of Patent Document 1 includes a correlation relationship calculation step of applying the concentration data of the evaluation element group in the serum collected from the subject to a discrimination function for discriminating whether the subject belongs to either the control group or the case group, and calculating the correlation relationship between the concentrations of the evaluation element group in the serum, and an index acquisition step of obtaining an index as to whether the subject has developed any cancer based on the correlation relationship calculated in the correlation relationship calculation step. And as the evaluation element group, a combination of 7 elements of S, P, Mg, Zn, Cu, Ti, and Rb, or a combination of 16 elements of Na, Mg, Al, P, K, Ca, Ti, Mn, Fe, Zn, Cu, Se, Rb, Ag, Sn, and S is selected.

[0011] The method of Patent Document 2 includes the same correlation relationship calculation step and index acquisition step as those of the method of Patent Document 1, but is different in that the age data of the subject is used in addition to the concentration data in each of the correlation relationship calculation step and the index acquisition step. In the method of Patent Document 2, as the evaluation element group used in the correlation relationship calculation step, a combination of 17 elements of Na, Mg, P, S, K, Ca, Fe, Cu, Zn, Se, Rb, Sr, As, Mo, Cs, Co, Ag is used, which is different from the evaluation element group in the method of Patent Document 1.

[0012] In addition, in the method of Patent Document 2, based on the correlation relationship calculated in the correlation relationship calculation step, according to the difference between the age data and the concentration data of the element group (these are selected from the evaluation element group) that is significant for discrimination, it is inferred that the type of cancer related to the subject is pancreatic cancer in men, prostate cancer in men, colorectal cancer in men, endometrial cancer in women, breast cancer in women, or colorectal cancer in women, and this inference is included in the index. Thus, in the method of Patent Document 2, it can be said that it is superior to the method of Patent Document 1 because it can infer not only the cancer risk but also the cancer site in one blood test.

[0013] Incidentally, although the function of the brain is essential in human life activities, metal elements are greatly involved in the information activities of the brain. For example, calcium (Ca) increases the enzyme activities such as protein kinase, CaM kinase II, and tyrosine phosphorylation enzyme by raising the concentration inside brain cells, contributing to information transmission. Zinc (Zn) acts as a neuromodulatory factor involved in information transmission in the brain nervous system such as memory formation and sensory transmission. Copper (Cu) acts as a component of catecholamine-producing enzymes involved in norepinephrine production, and depending on its amount, it induces central nervous disorders such as ataxia. It is known that trace elements in the living body all cause deficiency diseases if lacking, and overdose symptoms or poisoning symptoms if ingested excessively, and an appropriate amount of intake is always required. Although major elements are important as components of the body, trace elements are utilized in the enzyme active centers in substance metabolism in the body and only a very small amount is required. When trace elements are lacking or excessive, the balance of substance metabolism in the body is disrupted, and symptoms specific to each element appear. Conversely, it is also said that the composition of trace elements changes due to the onset of diseases such as physical discomfort and immune system disorders, and homeostasis collapses. Such an effect is presumed to exert the same influence in the activities in the brain as in carcinogenesis in each organ of the body.

[0014] Non-Patent Document 1 states that (a) the activity of nerve cells is deeply related to dynamic iron metabolism, (b) in the process of pathological analysis of neurodegenerative diseases such as Alzheimer's disease, physiologically expressed proteins polymerize in the presence of trace metals such as iron, copper, and zinc to form soluble oligomers or protofibrils, which cause synaptic dysfunction and nerve cell death, and (c) the event in (b) is related to the manifestation of symptoms of neurodegenerative diseases.

[0015] Non-Patent Document 2 states that factors affecting the multimerization and conformational changes of amyloid-β protein may play an important role in the onset of Alzheimer's disease, and metals such as Al, Zn, Cu, and Fe are mentioned as factors causing the conformational changes.

[0016] Non-Patent Document 3 states that in the Mini-Mental State Examination (MMSE), the test results of patients with dementia and those of patients with MCI are clearly different.

Prior Art Documents

Patent Documents

[0017]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0018]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0019] It has been found that metal elements are deeply involved in the information activities of the human brain. For example, calcium (Ca) contributes to information transmission by increasing the concentration within brain cells to enhance the enzyme activities of protein kinase, CaM kinase II, tyrosine phosphorylation enzyme, etc. Zinc (Zn) acts as a neuromodulatory factor involved in information transmission in the brain nervous system such as memory formation and sensory transmission. Copper (Cu) acts as a component of the catecholamine-producing enzyme involved in norepinephrine production, and depending on its amount, it induces central nervous disorders such as ataxia.

[0020] It is known that any trace element in the human body will cause deficiency diseases if lacking, and overdose or poisoning symptoms if ingested excessively, and it is necessary to always ingest an appropriate amount. Major elements are important as components of the body, while trace elements are used in the enzyme active centers in substance metabolism in the body and only a very small amount is required. If the trace elements are lacking or in excess, the balance of substance metabolism in the body is disrupted, and symptoms specific to each trace element appear. Conversely, it is also said that the composition of trace elements changes due to the onset of diseases such as poor physical condition and immune system disorders, and homeostasis collapses. These events occurring in the human body are presumed to have a great impact on the onset of dementia, similar to carcinogenesis in each organ of the body.

[0021] In view of the above-mentioned conventional circumstances, the present inventors have conducted intensive research based on the findings obtained in the process of researching and developing the cancer risk assessment methods disclosed in Patent Documents 1 and 2, the subsequent further research findings regarding the methods, and the findings obtained from various reports on dementia described in Non-Patent Documents 1 to 3. As a result, a method for evaluating the risk of developing dementia by using an index obtained by utilizing the correlation relationship of the concentrations of a specific group of trace elements present in blood (plasma or serum), in other words, the present inventors have found the possibility of a new screening method for dementia using blood (plasma or serum), and thus have completed the present invention.

[0022] Therefore, an object of the present invention is to directly grasp the physical condition of a subject through measurement and analysis of a biological sample of the subject, rather than diagnosis by a questionnaire or the like, and objectively and highly accurately evaluate the risk of the subject developing dementia. The present invention provides a dementia risk assessment method and a dementia risk assessment system capable of achieving this.

[0023] Another object of the present invention is to provide a dementia risk assessment method and a dementia risk assessment system that can be effectively used as an auxiliary diagnosis of dementia for patients who visit a medical institution suspected of having a neurological or mental disorder, or as an objective means of dementia risk assessment for the general population other than the above patients.

[0024] Yet another object of the present invention is to provide a dementia risk assessment method and a dementia risk assessment system that can be easily applied to mass screening.

[0025] Other objects of the present invention not specified herein will become apparent from the following description and the accompanying drawings.

Means for Solving the Problems

[0026] (1) The dementia risk assessment method according to the first aspect of the present invention includes: a step of obtaining concentration data of an evaluation element group contained in blood (plasma or serum) collected from a subject; a step of applying the concentration data of the evaluation element group to a discrimination function for discriminating whether the subject belongs to either a control group or a case group, and calculating a correlation relationship between the concentrations of the evaluation element group; a step of generating an index for discriminating whether the subject has dementia based on the correlation relationship; a step of evaluating the dementia risk of the subject based on the index and creating an evaluation result. As the element group for evaluation, a combination of 17 elements, namely Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs, is used, and the 17 elements are selected as the combination of elements with the highest discrimination ability when discriminating between the case group and the control group.

[0027] In the dementia risk assessment method according to the first aspect of the present invention, the concentration data of the element group for evaluation in the blood (plasma or serum) collected from the subject is applied to the discrimination function to calculate the correlation relationship between the concentrations of the element group for evaluation in the blood (plasma or serum). Here, as the element group for evaluation, a combination of 17 elements, namely Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs, is used, and it is selected because the discrimination ability is the highest when using the combination of these 17 elements. Therefore, it becomes possible to estimate the dementia risk of the subject with high accuracy.

[0028] Then, based on the correlation relationship, an index for identifying whether the subject has dementia is generated, and then the dementia risk of the subject is evaluated based on the index to create an evaluation result. As the index, for example, a discrimination score calculated by applying the concentration data to a discriminant formula generated based on the correlation relationship is used.

[0029] Therefore, by directly grasping the physical condition of the subject through the measurement and analysis of the biological sample of the subject, rather than through diagnosis using a questionnaire or the like, the dementia risk of the subject can be objectively and highly accurately evaluated.

[0030] In addition, since the evaluation results are obtained through the measurement and analysis of the biological samples of the subject, objective evaluations are described, which is different from the case of diagnosis using a questionnaire or the like. Therefore, when a hospital control group is used as the control group, by referring to the evaluation results by a medical institution, it can be effectively used as an auxiliary diagnosis of dementia for patients who visited a medical institution with suspicion of neurological or mental diseases. On the other hand, when a resident control group is used as the control group, it can be effectively used as an objective means for evaluating the risk of dementia for the general residents who are not the patients.

[0031] Furthermore, by using a computer to automatically calculate using the concentration data of the evaluation element group in the blood (plasma or serum) collected from the subject, it is possible to determine whether the subject belongs to either the control group or the case group, in other words, whether the subject has dementia. Therefore, even if there are a large number of subjects, it is possible to determine easily and quickly. Thus, it can be easily applied to mass screening.

[0032] Based on the correlation relationship, it has been found which concentration data of any of the 17 elements used as the evaluation element group is significant for discrimination, and moreover, since the elements significant for discrimination vary according to the type of dementia (MCI or AD), when it is presumed that the subject has dementia, it is also possible to presume whether the type of dementia is MCI or AD. Also, since it is only necessary to obtain the concentration data of the evaluation element group contained in the blood (whole blood) collected from the subject, either serum or plasma with similar elemental components may be used.

[0033] (2) In a preferred example of the dementia risk assessment method according to the first aspect of the present invention, as the index, a discrimination score calculated by applying the concentration data to a discriminant formula generated based on the correlation relationship is used. By comparing the discrimination score with a predetermined reference value, it is determined whether the subject belongs to either the control group or the case group. Based on the result of the determination, an inference as to whether the subject has dementia is included in the evaluation result.

[0034] (3) In another preferred example of the dementia risk assessment method according to the first aspect of the present invention, the discrimination score of the subject is compared with the relationship between the discrimination score and the probability that the subject has dementia, thereby inferring the probability that the subject has dementia, and the inferred probability that the subject has dementia is included in the evaluation result.

[0035] (4) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, as the discriminant formula, a discriminant formula generated for mild cognitive impairment (MCI) is used, and the determination result as to whether the subject has mild cognitive impairment (MCI) is included in the evaluation result.

[0036] (5) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, as the discriminant formula, a discriminant formula generated for Alzheimer's disease (AD) is used, and the determination result as to whether the subject has Alzheimer's disease (AD) is included in the evaluation result.

[0037] (6) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, as the discriminant formula, both a discriminant formula generated for mild cognitive impairment (MCI) and a discriminant formula generated for Alzheimer's disease (AD) are used, and the determination result as to which of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is the type of dementia inferred for the subject who is inferred to have dementia is included in the evaluation result.

[0038] (7) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, when the concentration data of four elements, Ca, Zn, Rb, and Mo, selected from the 17 elements used as the group of elements for evaluation are significant for discrimination, the evaluation result includes a presumption that the type of dementia related to the subject presumed to have dementia is mild cognitive impairment (MCI).

[0039] (8) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, when the concentration data of three elements, Mg, Ca, and Rb, selected from the 17 elements used as the group of elements for evaluation are significant for discrimination, the evaluation result includes a presumption that the type of dementia related to the subject determined to have dementia is Alzheimer's disease (AD).

[0040] (9) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, a hospital control group is used as the control group, As the subject, a patient who visited a specific medical institution with suspicion of a neurological or mental disorder is selected, The evaluation result is used as an aid for dementia diagnosis in the medical institution.

[0041] (10) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, a resident control group is used as the control group, As the subject, a general resident who is not a patient who visited a medical institution with suspicion of a neurological or mental disorder is selected, The evaluation result is used as a means for evaluating the dementia risk of the general resident.

[0042] (11) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, when the concentration data of 11 elements of Na, Mg, S, Ca, Fe, Cu, Zn, As, Se, Rb, and Mo selected from among the 17 elements used as the group of elements for evaluation are significant for discrimination, the evaluation result includes a presumption that the type of dementia related to the subject presumed to have dementia is mild cognitive impairment (MCI).

[0043] (12) In still another preferred example of the dementia risk assessment method according to the first aspect of the present invention, when the concentration data of 11 elements of Na, Mg, S, K, Ca, Fe, Cu, Zn, Se, Rb, and Mo selected from among the 17 elements used as the group of elements for evaluation are significant for discrimination, the evaluation result includes a presumption that the type of dementia related to the subject determined to have dementia is Alzheimer's disease (AD).

[0044] (13) The dementia risk assessment system according to the second aspect of the present invention a data storage unit that stores concentration data of a group of elements for evaluation contained in blood (plasma or serum) collected from a subject; an arithmetic unit that applies the concentration data of the subject stored in the data storage unit to a discrimination function for discriminating whether the subject belongs to either a control group or a case group, and calculates a correlation relationship between the concentrations of the group of elements for evaluation; based on the correlation relationship calculated by the arithmetic unit, generates an index for identifying whether the subject has dementia, and evaluates the dementia risk of the subject based on the index and outputs an evaluation result, and as the group of elements for evaluation, a combination of 17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs is used, and the 17 elements are selected as a combination of elements having the highest discrimination ability when discriminating between a case group and a control group.

[0045] In the dementia risk assessment system according to the second aspect of the present invention, in the arithmetic unit, the concentration data of the subject stored in the data storage unit is applied to the discrimination function for discriminating whether the subject belongs to either the control group or the case group, and the correlation between the concentrations of the evaluation element group contained in the blood (plasma or serum) is calculated. Here, as the evaluation element group, a combination of 17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs is used, but it is selected because the discrimination ability is highest when using a combination of these elements. Therefore, it becomes possible to estimate the dementia risk of the subject with high accuracy.

[0046] Then, in the index generation unit, based on the correlation calculated in the arithmetic unit, an index for discriminating whether the subject has dementia is generated, and then the dementia risk of the subject is evaluated based on the index to create an evaluation result. As the index, for example, a discrimination score calculated by applying the concentration data to a discriminant formula generated based on the correlation is used.

[0047] Therefore, by directly grasping the physical condition of the subject through measurement and analysis of the biological sample of the subject, rather than by diagnosis using a questionnaire or the like, the dementia risk of the subject can be objectively and highly accurately evaluated.

[0048] In addition, since the evaluation result is obtained through measurement and analysis of the biological sample of the subject, unlike the case of diagnosis using a questionnaire or the like, an objective evaluation is described. For this reason, when a hospital control group is used as the control group, by referring to the evaluation result by a medical institution, it can be effectively used as an auxiliary diagnosis for dementia for patients who visit a medical institution with suspicion of a neurological or mental disorder. On the other hand, when a resident control group is used as the control group, it can be effectively used as an objective means for evaluating the dementia risk for the general population who are not the patients.

[0049] Furthermore, by automatically calculating with a computer using the concentration data of the evaluation element group in the blood (plasma or serum) collected from the subject, it is possible to determine whether the subject belongs to either the control group or the case group, in other words, whether the subject has dementia. Therefore, even if there are a large number of subjects, it is possible to determine easily and quickly. Thus, it can be easily applied to mass screening.

[0050] In addition, based on the correlation obtained by the calculation unit, it is found which concentration data of any of the 17 elements used as the evaluation element group is significant for discrimination, and moreover, since the elements significant for discrimination vary according to the type of dementia (MCI or AD), when the subject is presumed to have dementia, it is also possible to estimate whether the type of dementia is MCI or AD. Also, since it is only necessary to obtain the concentration data of the evaluation element group contained in the blood (whole blood) collected from the subject, either serum or plasma with similar elemental components may be used.

[0051] (14) In a preferred example of the dementia risk assessment system according to the second aspect of the present invention, as the index, a discrimination score calculated by applying the concentration data to a discriminant formula generated based on the correlation is used. By comparing the discrimination score with a predetermined reference value, it is determined whether the subject belongs to either the control group or the case group. Based on the result of the determination, an estimation of whether the subject has dementia is included in the evaluation result.

[0052] (15) In another preferred example of the dementia risk assessment system according to the second aspect of the present invention, the dementia prevalence probability of the subject is estimated by comparing the discrimination score of the subject with the relationship between the discrimination score and the probability that the subject has dementia. The estimated dementia prevalence probability of the subject is included in the evaluation result.

[0053] (16) In yet another preferred example of the dementia risk assessment system according to the second aspect of the present invention, as the discriminant formula, a discriminant formula generated for mild cognitive impairment (MCI) is used, and a determination result as to whether or not the subject has mild cognitive impairment (MCI) is included in the evaluation result.

[0054] (17) In yet another preferred example of the dementia risk assessment system according to the second aspect of the present invention, as the discriminant formula, a discriminant formula generated for Alzheimer's disease (AD) is used, and a determination result as to whether or not the subject has Alzheimer's disease (AD) is included in the evaluation result.

[0055] (18) In yet another preferred example of the dementia risk assessment system according to the second aspect of the present invention, as the discriminant formula, both a discriminant formula generated for mild cognitive impairment (MCI) and a discriminant formula generated for Alzheimer's disease (AD) are used, and a determination result as to which of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is the type of dementia related to the subject presumed to have dementia is included in the evaluation result.

[0056] (19) In yet another preferred example of the dementia risk assessment system according to the second aspect of the present invention, when the concentration data of the four elements of Ca, Zn, Rb, and Mo selected from among the 17 elements used as the evaluation element group are significant for discrimination, a presumption that the type of dementia related to the subject presumed to have dementia is mild cognitive impairment (MCI) is included in the evaluation result.

[0057] (20) In still another preferred example of the dementia risk assessment system according to the second aspect of the present invention, when the concentration data of three elements, Mg, Ca, and Rb, selected from the 17 elements used as the evaluation element group are significant for discrimination, the type of dementia inferred to be suffered by the subject inferred to have dementia is inferred to be Alzheimer's disease (AD) in the evaluation result.

[0058] (21) In still another preferred example of the dementia risk assessment system according to the second aspect of the present invention, a hospital control group is used as the control group, As the subject, a patient who visited a specific medical institution with suspicion of a neurological or mental disorder is selected, The evaluation result is used as an auxiliary role for dementia diagnosis in the medical institution.

[0059] (22) In still another preferred example of the dementia risk assessment system according to the second aspect of the present invention, a resident control group is used as the control group, As the subject, a general resident who is not a patient who visited a medical institution with suspicion of a neurological or mental disorder is selected, The evaluation result is used as a means for evaluating the dementia risk of the general resident.

[0060] (23) In still another preferred example of the dementia risk assessment system according to the second aspect of the present invention, when the concentration data of 11 elements, Na, Mg, S, Ca, Fe, Cu, Zn, As, Se, Rb, and Mo, selected from the 17 elements used as the evaluation element group are significant for discrimination, the type of dementia inferred to be suffered by the subject inferred to have dementia is inferred to be mild cognitive impairment (MCI) in the evaluation result.

[0061] In still another preferred example of the dementia risk assessment system according to the second aspect of the present invention, when the concentration data of 11 elements of Na, Mg, S, K, Ca, Fe, Cu, Zn, Se, Rb, and Mo selected from among the 17 elements used as the group of elements for evaluation are significant for discrimination, the evaluation result includes a presumption that the type of dementia of the subject determined to have dementia is Alzheimer's disease (AD).

Advantages of the Invention

[0062] In the dementia risk assessment method according to the first aspect of the present invention and the dementia risk assessment system according to the second aspect of the present invention, (a) instead of diagnosis by means of a questionnaire or the like, the physical condition of the subject can be directly grasped through measurement and analysis of the biological sample of the subject, and the risk of dementia of the subject can be objectively evaluated; (b) it can be effectively used as an auxiliary diagnosis of dementia for subjects who visit a medical institution with suspicion of a neurological or mental disorder, or as an objective means of dementia risk assessment for the general population other than the subjects; and (c) it can be easily applied to mass screening, and thus the following effects can be obtained.

Brief Description of the Drawings

[0063]

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Mode for Carrying Out the Invention

[0064] Hereinafter, the details and preferred embodiments of the present invention will be described with reference to the accompanying drawings.

[0065] (Basic Principle of the Dementia Risk Assessment Method of the Present Invention) First, the dementia risk assessment method of the present invention will be described.

[0066] The outline of the dementia risk assessment method of the present invention is as follows.

[0067] First, obtain blood (plasma or serum) belonging to the case group (dementia patient group) and blood (plasma or serum) belonging to the control group, and measure the concentrations of specific element groups contained in each of these bloods (plasma or serum). Then, perform statistical analysis on the obtained concentration data of the element groups to calculate the correlation relationship between the concentration data. Next, obtain a discriminant formula based on the correlation relationship thus calculated. Then, apply the concentration data of each of the bloods (plasma or serum) to the discriminant formula thus obtained to obtain an "index" for identifying whether the person (subject) who provided each of the bloods (plasma or serum) groups belonging to the case group or the control group has dementia. Based on the "index" thus obtained, an evaluation result regarding the dementia risk is obtained. The evaluation result preferably includes inferences regarding whether the type of dementia suffered is MCI or AD and inferences regarding the probability (prevalence rate) of suffering from dementia.

[0068] Specifically, the inventors first performed pretreatment as described below, determined the optimal measurement conditions for measuring the concentrations of element groups contained in blood (plasma or serum), and selected the evaluation element groups to be used for dementia risk assessment.

[0069] (1. Determination of optimal measurement conditions) First, as the optimal conditions for measuring the concentrations of element groups contained in blood (plasma or serum), the optimal measurement conditions used in the "cancer risk assessment method" described in Patent Document 2 mentioned above were adopted. This is because the optimal measurement conditions used in the "cancer risk assessment method" of Patent Document 2 mentioned above, although used for the risk assessment of "cancer", have already been found to be optimal conditions for measuring the concentrations of element groups contained in blood (plasma or serum). That is, the inventors judged that the optimal conditions for measuring the concentrations of element groups contained in blood (plasma or serum) would not change whether for the risk assessment of "cancer" or for the risk assessment of "dementia".

[0070] However, when the optimal conditions for measuring the concentrations of the elemental groups contained in blood (plasma or serum) are unknown, or when new optimal conditions are to be found, for example, they can be found as follows. That is, with the groups of patients with mild cognitive impairment (MCI) and patients with Alzheimer's disease (AD) in mind as the groups of patients with dementia, blood (plasma or serum) belonging to the group of MCI patients, blood (plasma or serum) belonging to the group of AD patients, and blood (plasma or serum) belonging to the hospital control group or the resident control group are obtained. Then, these blood samples (plasma or serum) are randomly divided into two groups by gender, age, and disease, with one group being the "test blood (plasma or serum) group" and the other group being the "evaluation blood (plasma or serum) group". Thereafter, nitric acid is mixed into each of the test blood (plasma or serum) groups (including blood (plasma or serum) belonging to the group of MCI patients, blood (plasma or serum) belonging to the group of AD patients, and blood (plasma or serum) belonging to the hospital control group or the resident control group), and heated to 60°C to 80°C in a sealed container with little metal contamination to decompose proteins and amino acids. In this way, after pretreatment of the test blood (plasma or serum) group so as not to interfere with the measurement of elemental concentrations, it is diluted to a predetermined concentration using ultrapure water free of metal contamination to obtain a treatment solution. Then, the concentrations of the 75 elemental groups contained in each of the treatment solutions thus obtained are measured using inductively-coupled plasma mass spectroscopy (ICP-MS). And using the obtained measurement results, the optimal measurement conditions for measuring the concentrations of the 75 elemental groups contained in the test blood (plasma or serum) group are found. Needless to say, instead of this method, the method disclosed in Patent Document 2 described above may be used to find the optimal conditions.

[0071] For measuring the concentrations of various elements as in the present invention, ICP mass spectrometry is preferred. This is because ICP mass spectrometry is the simplest and is recognized as a method with highly accurate quantification of measurement results. However, it goes without saying that it is not limited to ICP mass spectrometry. For example, Inductively-Coupled Plasma Optical Emission Spectroscopy (ICP-OES), Atomic Absorption Spectrometry (AAS), X-Ray Fluorescence analysis (XRF), etc. can also be used. Also, in the future, if other analytical methods (element concentration measurement methods) more suitable than ICP mass spectrometry are developed, it goes without saying that analytical methods other than ICP mass spectrometry may be used.

[0072] (2. Selection of elements for evaluation) Next, it is necessary to select the group of elements for evaluation used in the dementia risk assessment. Here, the combination of 17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs, which was used in the "cancer risk assessment method" described in Patent Document 2 mentioned above, was used as the group of elements for evaluation in the "dementia risk assessment method" of the present invention. This is because the combination of the 17 elements was selected and used for the discrimination between two groups of cancer patients and control groups, and it has already been found that it has the highest discrimination ability, so it was considered that it can also be preferably used in the present invention. That is, the present inventors judged that the highest discrimination ability can also be obtained in the discrimination between three groups of the MCI patient group, the AD patient group, and the hospital control group or the resident control group, the discrimination between two groups of the MCI patient group and the hospital control group or the resident control group, and the discrimination between two groups of the AD patient group and the hospital control group or the resident control group.

[0073] However, when the group of evaluation elements used for dementia risk assessment is unknown, or when a new group of evaluation elements is to be selected, for example, it can be selected as follows. That is, first, under the optimal measurement conditions determined as described above, using the same test blood (plasma or serum) group as that used in "1. Determination of the measurement conditions for the optimal element concentration", measure the concentrations (contents) of the 75-element group contained in each of these test blood (plasma or serum) groups by ICP mass spectrometry. Then, for the obtained concentration data, statistically analyze the differences in element concentrations among three groups: the MCI patient group (case group), the AD patient group (case group), and the hospital control group or the resident control group (control group). In this analysis, identify the elements involved in the differences in element concentrations among these three groups, and perform discriminant analysis and binary logistic regression analysis to determine the risk of developing MCI or AD.

[0074] At this time, considering the combinations of elements, the combination with the greatest difference between the combined elements, that is, the combination of elements that can best discriminate between the two groups of the MCI patient group (case group) and the hospital control group or the resident control group (control group), and the combination of elements that can best discriminate between the two groups of the AD patient group (case group) and the hospital control group or the resident control group (control group), can be searched by the computer by changing the combinations many times. As a result, in either the discrimination between the two groups of the MCI patient group and the hospital control group or the resident control group, and the discrimination between the two groups of the AD patient group and the hospital control group or the resident control group, when using the combination of 17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs used in the "Cancer Risk Assessment Method" described in Patent Document 2 mentioned above, the result that the discrimination ability is the highest should be obtained, but different results may be obtained for some reason. In the latter case, a combination of elements different from the 17 elements used in the "Cancer Risk Assessment Method" described in Patent Document 2 mentioned above will be newly selected as the group of evaluation elements.

[0075] (3. Measurement and Analysis of Concentrations of Elements for Evaluation) Through the preliminary treatment as described above, the optimal measurement conditions for measuring the concentrations of the element groups contained in blood (plasma or serum) were determined, and the element groups for evaluation used in the dementia risk assessment were selected. Therefore, the concentrations of the evaluation element groups (17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) contained in each of the blood (plasma or serum) belonging to the MCI patient group, the blood (plasma or serum) belonging to the AD patient group, and the blood (plasma or serum) belonging to the hospital control group or the resident control group were measured under their optimal concentration measurement conditions, and the concentration data (concentration values) of these 17 elements were obtained. Then, discriminant analysis was performed on the obtained concentration data to obtain a discriminant formula for use in the dementia risk assessment.

[0076] After that, the concentration data of the evaluation element groups in each of the blood (plasma or serum) belonging to the MCI patient group, the AD patient group, and the hospital control group or the resident control group were applied to the discriminant formula to calculate discriminant scores. The discriminant scores thus obtained can be used as an "index" for estimating the dementia risk of the person (subject). For example, if the discriminant score is equal to or higher than a predetermined reference value (e.g., 0) or lower, the person (subject) who provided the evaluation blood (plasma or serum) group corresponding to the discriminant score can all be determined to belong to the case group, and if the discriminant score is lower than or higher than the reference value, it is possible to determine that they belong to the control group.

[0077] Furthermore, when performing binary logistic regression analysis using the discriminant score, the relationship between the discriminant score and the probability that the person (subject) has dementia (AD or MCI) (discriminant score - morbidity rate relationship) was obtained. Therefore, by comparing the discriminant score with the discriminant score - morbidity rate relationship, it becomes possible to estimate and evaluate the dementia risk of the person (subject) based on the morbidity rate.

[0078] As described above, the evaluation result of the dementia risk of the person (subject) can be obtained. Preferably, the evaluation result includes information indicating whether the dementia the person has is AD or MCI. Further, preferably, the evaluation result includes the probability of having dementia (AD or MCI).

[0079] In addition, in the above “1. Determination of Optimal Measurement Conditions”, when the blood (plasma or serum) belonging to the obtained MCI patient group, the blood (plasma or serum) belonging to the AD patient group, and the blood (plasma or serum) belonging to the hospital control group or the resident control group are divided into the test blood (plasma or serum) group and the evaluation blood (plasma or serum) group, in “3. Measurement and Analysis of Concentrations of Evaluation Element Groups”, using the test blood (plasma or serum) group, under the above-described optimal element concentration measurement conditions, measure the concentration of the evaluation element group contained in the test blood (plasma or serum). Then, by performing discriminant analysis on the obtained concentration data, a discriminant formula for use in dementia risk evaluation can be obtained. That is, in this case, from the determination of optimal measurement conditions to the generation of the discriminant formula, the test blood (plasma or serum) group is used. Thereafter, in the same manner as in the case of the test blood (plasma or serum) group, measure the concentration of the evaluation element group for the evaluation blood (plasma or serum) group to obtain concentration data. Then, apply the concentration data of the evaluation blood (plasma or serum) group thus obtained to the discriminant formula. By doing so, a discriminant score can be calculated. Thereafter, it is the same as the case where the blood (plasma or serum) belonging to the obtained MCI patient group, AD patient group, and hospital control group or resident control group is not divided into the test blood (plasma or serum) group and the evaluation blood (plasma or serum) group.

[0080] (3-1. Details of Discriminant Analysis) Here, the above-described discriminant analysis will be explained in detail.

[0081] First, discriminant analysis was performed on two groups, namely the "hospital control group" or "resident control group" as the control group and the "MCI patient group" as the case group, for the 17 elements (Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) measured as the above-mentioned "evaluation element group". In addition, discriminant analysis was performed on two groups, namely the "hospital control group" or "resident control group" as the same control group and the "AD patient group" as the case group. Since the content of these two discriminant analyses is the same except that the MCI patient group and the AD patient group are different and the hospital control group and the resident control group are different, hereinafter, only the discriminant analysis of the two groups of the hospital control group and the MCI patient group will be described, and the discriminant analysis of the two groups of the hospital control group and the AD patient group, the discriminant analysis of the two groups of the resident control group and the MCI patient group, and the discriminant analysis of the two groups of the resident control group and the AD patient group will be omitted.

[0082] That is, first, a test (t-test) for the difference in the population mean values of the two groups of the hospital control group and the MCI patient group was performed. This is to examine the extent to which the above-mentioned evaluation element group consisting of 17 element groups affects the discrimination between these two groups. In the results of this test, although there are differences between the two groups for each element alone, it was found that the correlation between elements was ignored in this analysis and it contains many problems for use in the risk assessment of cases. Therefore, it was found that it is necessary to use "discriminant analysis", which is a type of multivariate analysis that can consider the correlation between elements, to analyze and solve the above problems.

[0083] Therefore, the discriminant formula was obtained as follows. This is to analyze the concentration balance (correlation) between the above-mentioned evaluation element groups. Since the concentrations of individual elements in the above-mentioned evaluation element group have individual differences and it is difficult to use them as indicators, the correlation between the concentrations of the above-mentioned evaluation element group was obtained.

[0084] The discriminant function can generally be expressed as the following formula (1). Discriminant score (D) = function (F) (explanatory variables 1 to n, discriminant coefficients 1 to n) (1) (However, n is an integer of 2 or more) Considering the weights (influence degrees on discrimination) of each of the explanatory variables 1 to n, the discriminant function of Equation (1) can be written as Equation (2) below. Discriminant score (D) = (discriminant coefficient 1) × (explanatory variable 1) + (discriminant coefficient 2) × (explanatory variable 2) + ···(discriminant coefficient n) × (explanatory variable n) + constant (2) Therefore, taking the concentrations of the evaluation element group (17 kinds: Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) selected from the results of the t-test for the difference in the population mean values between the hospital control group and the MCI patient group as the explanatory variables 1 to n, and using the discriminant coefficients 1 to n as their weights (where n = 17), a discriminant formula was obtained. This discriminant formula is for MCI patients. On a known computer, it can be easily obtained by loading the concentration values (concentration data) of these 17 kinds of element groups into a known discriminant analysis program (for example, SAS, SPSS). The discriminant coefficients 1 to n (where n = 17) of this discriminant formula are determined by the discriminant analysis program so that the correlation ratio between the actual performance value of the presence or absence of dementia and the discriminant score (D) is maximized. In other words, they are determined so that the degree of separation (discrimination ability) between the hospital control group and the MCI patient group is maximized.

[0085] When a discriminant formula for MCI patients is obtained in this way, by applying the concentration values (concentration data) of the evaluation blood (plasma or serum) group to the discriminant formula on the computer, a discriminant score (D) is obtained. If the discriminant score (D) is less than or equal to (or greater than or equal to) a reference value (for example, 0), the subject is determined to belong to the MCI patient group (case group), and if the discriminant score (D) is greater than or equal to (or less than or equal to) the reference value, it is determined to belong to the hospital control group (control group).

[0086] Furthermore, for the two groups of the hospital control group and the AD patient group, and for the two groups of the hospital control group and the MCI patient group, in the same manner as described above, by causing the computer to read the concentration values (concentration data) of the 17 elemental groups into the discriminant analysis program, a discriminant formula for AD patients can be easily obtained.

[0087] When a discriminant formula for AD patients is thus obtained, by applying the concentration values (concentration data) of the evaluation blood (plasma or serum) group to the discriminant formula on the computer, a discriminant score (D) can be obtained. If the discriminant score (D) is less than or equal to (or greater than or equal to) a reference value (for example, 0), the subject is determined to belong to the AD patient group (case group), and if the discriminant score (D) is greater than or equal to (or less than or equal to) the reference value, the subject is determined to belong to the hospital control group (control group).

[0088] The concentration data (concentration values) of the evaluation blood (plasma or serum) group were applied to the discriminant formula for MCI patients obtained by the above-described discriminant analysis (using the hospital control group) to obtain a discriminant score, and when it was determined to which of the MCI patient group and the hospital control group the subject belonged, good prediction results were obtained. Also, the concentration data of the evaluation blood (plasma or serum) group were applied to the discriminant formula for AD patients obtained by the above-described discriminant analysis (using the hospital control group) to obtain a discriminant score, and when it was determined to which of the AD patient group and the hospital control group the subject belonged, good prediction results were obtained.

[0089] The same applies when a resident control group is used instead of the hospital control group.

[0090] That is, for the two groups of the resident control group and the MCI patient group, in the same manner as for the two groups of the hospital control group and the MCI patient group described above, a discriminant formula for MCI patients can be easily obtained.

[0091] Once the discriminant formula for MCI patients is obtained in this way, on the computer, by applying the concentration values (concentration data) of the evaluation blood (plasma or serum) group to the discriminant formula, a discrimination score (D) can be obtained. If the discrimination score (D) is less than or equal to (or greater than or equal to) a reference value (for example, 0), the subject is determined to belong to the MCI patient group (case group), and if the discrimination score (D) is greater than or equal to (or less than or equal to) the reference value, the subject is determined to belong to the resident control group (control group).

[0092] Also, for the two groups of the resident control group and the AD patient group, in the same way as described above for the two groups of the hospital control group and the AD patient group, a discriminant formula for AD patients can be easily obtained.

[0093] Once the discriminant formula for AD patients is obtained in this way, on the computer, by applying the concentration values (concentration data) of the evaluation blood (plasma or serum) group to the discriminant formula, a discrimination score (D) can be obtained. If the discrimination score (D) is less than or equal to (or greater than or equal to) a reference value (for example, 0), the subject is determined to belong to the AD patient group (case group), and if the discrimination score (D) is greater than or equal to (or less than or equal to) the reference value, the subject is determined to belong to the resident control group (control group).

[0094] When the concentration data (concentration values) of the evaluation blood (plasma or serum) group were applied to the discriminant formula for MCI patients obtained by the above-described discriminant analysis (using the resident control group) to obtain a discrimination score and determine to which of the MCI patient group and the resident control group the subject belongs, good prediction results were obtained. Also, when the concentration data of the evaluation blood (plasma or serum) group were applied to the discriminant formula for AD patients obtained by the above-described discriminant analysis (using the resident control group) to obtain a discrimination score and determine to which of the AD patient group and the resident control group the subject belongs, good prediction results were obtained.

[0095] (3-2. Details of Binary Logistic Regression Analysis) Subsequently, the above-described binary logistic regression analysis will be described in detail.

[0096] Not only whether the subject belongs to the MCI patient group or the AD patient group, but also the probability that the subject belongs to the MCI patient group (case group) or the hospital control group (or resident control group) (control group) (the morbidity rate of MCI), and the probability that the subject belongs to the AD patient group (case group) or the hospital control group (or resident control group) (control group) (the morbidity rate of AD) were determined by performing binary logistic regression analysis for each case to obtain the incidence rate.

[0097] Generally, the incidence rate is given by the following formula (3) using the discrimination score (D) obtained by the above-described discriminant analysis. Incidence rate = 1 / [1 + exp(-discrimination score)] (3) Since the incidence rate can be obtained using the discrimination score (D) according to formula (3), the probability that the subject belongs to the case group (MCI patient group or AD patient group) (the morbidity rate of MCI or AD) can be determined. That is, the subject can know their current dementia risk probability.

[0098] If the discrimination score (D) obtained by discriminant analysis of the two groups, namely the hospital control group (or resident control group) (control group) and the MCI patient group (case group), is applied to the above formula (3), the probability that the subject belongs to the MCI patient group (the morbidity rate of MCI) is given. If the discrimination score (D) obtained by discriminant analysis of the two groups, namely the hospital control group (or resident control group) (control group) and the AD patient group (case group), is applied to the above formula (3), the probability that the subject belongs to the AD patient group (the morbidity rate of AD) is given.

[0099] When the discrimination score (D) obtained by applying the concentration data of the evaluation blood (plasma or serum) group to the discriminant formula for MCI patients or the discriminant formula for AD patients is applied to the formula (3) obtained by the above binary logistic regression analysis to obtain the morbidity probability of MCI or AD, good prediction results were obtained.

[0100] The dementia risk assessment method of the present invention uses the 17 elements (Na, Mg, P, S, K, Ca, Fe, Cu, Zn, Se, Rb, Sr, As, Mo, Cs, Co, Ag) selected in the above-described pretreatment as the "evaluation element group", measures the concentrations of these 17 elements contained in the blood (plasma or serum) of an unknown subject under the optimal measurement conditions determined in the above-described pretreatment, applies the obtained concentration data (concentration values) of the 17 elements to a discriminant function, and calculates the correlation relationship between the concentrations of the "evaluation element group". Then, based on the obtained correlation relationship, an index for identifying whether the subject has dementia is generated, and based on that index, the dementia risk of the subject is evaluated to create an evaluation result. When the discriminant coefficient of the discriminant function for discriminating whether the subject belongs to either the control group or the case group is determined based on the correlation relationship, a discriminant formula is generated. Therefore, by applying the concentration data of the "evaluation element group" present in the blood (plasma or serum) of an unknown subject to the discriminant formula, it is possible to obtain an evaluation result as to whether the unknown subject has dementia (MCI or AD).

[0101] By the way, in the above description of "the basic principle of the dementia risk assessment method of the present invention", as the control group used for discriminant analysis, either the subjects belonging to the "hospital control group", that is, outpatients (patients) who visited the neurology department, neuropsychiatry department, psychotherapy department, or general internal medicine department in a specific hospital group, or the subjects belonging to the "resident control group", that is, the general residents who are not the above patients, is selectively used. This is because there is a high possibility that the subjects belonging to the "hospital control group" have developed some disease other than dementia. Therefore, the present inventors had concerns about whether the desired prediction results could be obtained when the dementia risk assessment method of the present invention was directly applied to the dementia risk assessment of general residents who are not patients in the above hospital group.

[0102] Therefore, in the present invention, among the general residents who underwent a health check at a specific medical institution within a predetermined period, general residents who were determined by the Mini-Mental State Examination (MMSE) to have no risk of dementia were selected as the subjects of the "resident control group". Then, instead of the subjects of the "hospital control group" used in the above description, the subjects of the "resident control group" thus selected were used, and discriminant analysis and binary logistic regression analysis were performed in the same manner as described in the above "3. Measurement and analysis of the concentrations of the evaluation element groups". As a result, even when using the subjects belonging to the "resident control group" (that is, those selected from the general residents) as the control group for discriminant analysis, the same results were obtained as when using the subjects belonging to the "hospital control group". Thereby, it became clear that the dementia risk assessment method of the present invention can be applied not only when using the "hospital control group" but also when using the "resident control group".

[0103] In addition, as can be easily understood from the reasons described here, in the dementia risk assessment method of the present invention, when using the "hospital control group" as the case group, the method can be preferably used as an "auxiliary diagnosis in dementia diagnosis" in the medical institution for the examinees who have some subjective symptoms or uneasiness that make them suspected of having dementia and who visited the neurology department, neuropsychiatry department, psychotherapy department, general internal medicine department, etc. of a medical institution such as a hospital. On the other hand, in the dementia risk assessment method of the present invention, when using the "resident control group" as the case group, the method can be preferably used as a "screening method for dementia risk" targeting general residents other than the examinees or as an objective means for dementia risk assessment for the general residents.

[0104] (Basic flow of the dementia risk assessment method of the present invention) Subsequently, the basic flow of the dementia risk assessment method of the present invention will be described with reference to FIG. 1.

[0105] First, put the blood (plasma or serum) collected from an unknown subject (the requester of cognitive risk assessment) into a container such as test tube 1 to obtain a blood (plasma or serum) sample 2. Then, place the blood (plasma or serum) sample 2 in an appropriate analyzer (preferably an ICP mass spectrometer, but not limited thereto) for analysis, and measure the concentrations of the 17 elements as the evaluation element group present in the blood (plasma or serum) sample 2. In this way, concentration data (concentration values) of the evaluation element group contained in the blood (plasma or serum) sample 2 are obtained (step S1).

[0106] The evaluation element group to be measured for concentration is a combination of the above-mentioned 17 elements (Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs). These 17 element groups are selected as the combination of elements that can best distinguish between two groups, namely, a case group (a group of dementia patients, specifically, an MCI patient group or an AD patient group) and a control group (a hospital control group or a resident control group) (that is, the discrimination ability is the highest).

[0107] Next, apply the concentration data of the evaluation element group (17 kinds of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) obtained in step S1 to a discrimination function for discriminating whether the subject belongs to a case group (a group of dementia patients, specifically, an MCI patient group or an AD patient group) or a control group (a hospital control group or a resident control group), and calculate the correlation relationship among the 17 concentration data of the evaluation element group (step S2).

[0108] Specifically, when a "hospital control group" is used as the control group, for example, on a computer installed with a known discriminant analysis program (such as SAS, SPSS), the 17 concentration data of the evaluation element group obtained in step S1 are loaded into the discriminant analysis program. At this time, the 17 concentration data (concentration values) of the evaluation element group obtained in step S1 are respectively applied to the explanatory variables 1 to 17 of the discriminant function, and the 17 discriminant coefficient values obtained by the above-mentioned discriminant analysis are respectively applied to the discriminant coefficients 1 to 17 of the discriminant function.

[0109] Then, by applying the 17 discriminant coefficient values of the discriminant formula for MCI patients obtained by the above-mentioned discriminant analysis to the discriminant function, the risk of suffering from MCI can be evaluated. On the other hand, by applying the 17 discriminant coefficient values of the discriminant formula for AD patients obtained by the above-mentioned discriminant analysis to the discriminant function, the risk of suffering from AD can be evaluated.

[0110] When a "resident control group" is used as the control group, the concentration data of the evaluation element group (17 types of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) from the unknown subject obtained in step S1 and the gender data of the unknown subject (male = 1, female = 2) are applied to a discriminant function for determining whether the subject belongs to either the case group (dementia patient group, specifically, MCI patient group or AD patient group) or the control group (resident control group), and the correlation between the 17 concentration data of the evaluation element group and the 1 gender data is calculated (step S2). The discriminant function used here is a discriminant function prepared for the case where a "resident control group" is used as the control group, and is different from the discriminant function prepared for the case where a "hospital control group" is used as the control group.

[0111] Specifically, for example, on a computer installed with a known discriminant analysis program (such as SAS, SPSS), the 17 concentration data of the evaluation element group obtained in step S1 and the gender data of the unknown subject are loaded into the discriminant analysis program. At this time, for explanatory variables 1 to 18 of the discriminant function, the 17 concentration data (concentration values) of the evaluation element group obtained in step S1 and the gender data of the unknown subject (the value is 1 or 2) are respectively applied, and for discriminant coefficients 1 to 18 of the discriminant function, the 18 discriminant coefficient values obtained by the above-described discriminant analysis are respectively applied.

[0112] Then, if the 18 discriminant coefficient values of the discriminant formula for MCI patients obtained by the above-described discriminant analysis are applied to the discriminant function prepared for the case where the "resident control group" is used as the control group, the risk of suffering from MCI can be evaluated. On the other hand, if the 18 discriminant coefficient values of the discriminant formula for AD patients obtained by the above-described discriminant analysis are applied to the discriminant function prepared for the case where the "hospital control group" is used as the control group, the risk of suffering from AD can be evaluated.

[0113] Subsequently, based on the correlation relationship among the 17 concentration data of the evaluation element group calculated in step S2, or among the 17 concentration data of the evaluation element group and the 18 data including 1 gender data, an index for identifying whether the subject suffers from dementia is generated (step S3).

[0114] Specifically, when a "hospital control group" is used as the control group, for example, in step S2, a discrimination score (D) is obtained by the discrimination analysis program to which the 17 concentration data (concentration values) of the evaluation element group obtained in step S1 and the 17 discrimination coefficient values obtained by the above-described discrimination analysis are respectively applied. When a "resident control group" is used as the control group, for example, in step S2, a discrimination score (D) is obtained by the discrimination analysis program to which the 17 concentration data (concentration values) of the evaluation element group obtained in step S1 and the 18 data including 1 gender data and the 18 discrimination coefficient values obtained by the above-described discrimination analysis are respectively applied. The discrimination score (D) thus obtained can be used as an "index" for identifying whether the unknown subject (the requester of the cognitive risk assessment) has dementia (MCI or AD). Based on the discrimination score (D) as the index, the risk of the subject having dementia (MCI or AD) can be estimated.

[0115] The estimation of the dementia risk is performed by comparing the discrimination score (D) with a predetermined reference value (for example, 0). That is, if the discrimination score (D) is greater than or equal to (or less than) the reference value, it is determined that the subject belongs to the case group (MCI patient group or AD patient group). This means that it is presumed that "the subject (the requester) has dementia (MCI or AD)". On the other hand, if the discrimination score (D) is less than or equal to (or greater than) the reference value, it is determined that the subject belongs to the control group (hospital control group or resident control group). This means that it is presumed that "the subject (the requester) does not have dementia (MCI or AD)".

[0116] By the estimation in step S3, the risk of the subject (the requester) having dementia (MCI or AD) is obtained as an evaluation result.

[0117] In the dementia risk assessment method of the present invention, since the discriminant formula used in step S2 differs depending on the type of dementia (MCI or AD), depending on whether the discriminant formula used for dementia risk assessment is for MCI or for AD, there is an advantage that the type of dementia (MCI or AD) that the subject (the applicant) is presumed to have can also be determined. The type of dementia that the subject (the applicant) is presumed to have thus determined is preferably included in the evaluation result.

[0118] Also, in the dementia risk assessment method of the present invention, since specific elements (selected from the 17 elements of the evaluation element group) that are significant for discriminating between the case group and the control group differ depending on the type of dementia (MCI or AD), depending on whether the element significant for discrimination corresponds to MCI or AD, there is an advantage that the type of dementia (MCI or AD) that the subject (the applicant) is presumed to have can also be determined. The type of dementia that the subject (the applicant) is presumed to have thus determined is preferably included in the evaluation result.

[0119] Furthermore, in the dementia risk assessment method of the present invention, based on the results of the binary logistic regression analysis described above, a discrimination score - prevalence relationship can be prepared in advance (see FIGS. 21 and 22). In this case, by comparing the discrimination score (D) obtained in step S3 with the discrimination score - prevalence relationship, it is possible to evaluate the dementia prevalence risk of the subject (the applicant) presumed to have dementia based on the prevalence rate (prevalence probability). The prevalence rate of dementia that the subject (the applicant) is presumed to have thus determined is preferably included in the evaluation result.

[0120] As described above, by using a computer installed with a known discriminant analysis program (such as SAS, SPSS) and applying in advance the 17 or 18 discriminant coefficient values of the discriminant formula for MCI patients or the discriminant formula for AD patients obtained by the above-described discriminant analysis to the discriminant coefficients 1 to 17 or discriminant coefficients 1 to 18 of the discriminant function, only by loading the 17 concentration data of the evaluation element group obtained from the subject (the applicant) obtained in step S1, or a total of 18 data including the 17 concentration data and the gender data of the subject (the applicant) into the discriminant analysis program, a discriminant score (D) as an index can be obtained, and an evaluation result of the dementia risk can be immediately obtained based on the discriminant score (D). According to the request of the applicant for dementia risk assessment, it is possible to easily and quickly assess the dementia risk. That is, an evaluation result regarding whether the applicant is suffering from dementia (MCI or AD) is provided to the applicant. Further, when a determination is made that the applicant is suffering from dementia (MCI or AD), an evaluation result in which the fact and the probability of suffering from the dementia (prevalence rate) are also described together is provided to the subject (the applicant).

[0121] (Basic configuration of the dementia risk assessment system of the present invention) Next, the dementia risk assessment system of the present invention will be described.

[0122] The basic configuration of the dementia risk assessment system 10 of the present invention is shown in FIG. 2. This system 10 is for implementing the dementia risk assessment method of the present invention described above.

[0123] As is apparent from FIG. 2, the dementia risk assessment system 10 of the present invention includes a data storage unit 11, an arithmetic unit 12, and an index generation unit 13.

[0124] Outside the dementia risk assessment system 10, an elemental group concentration measurement unit 5 is provided. Using a blood (plasma or serum) sample 2 collected from a subject (client) placed in a test tube 1, the concentrations of the elemental group for evaluation (17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) in the blood (plasma or serum) sample 2 are measured. The concentration data of the elemental group for evaluation in the blood (plasma or serum) thus obtained by the elemental group concentration measurement unit 5 is supplied to and stored in the data storage unit 11 of the dementia risk assessment system 10. As the elemental group concentration measurement unit 5, for example, a known ICP mass spectrometer is used, but it is not necessarily limited to this. Needless to say, an ICP emission spectroscopic analyzer, an atomic absorption spectrometer, a fluorescent X-ray analyzer, etc. can also be used.

[0125] The data storage unit 11 is a site that stores (saves) the concentration data of the 17 elements of the elemental group for evaluation of the subject (the client) obtained by the elemental group concentration measurement unit 5, or a total of 18 data including the concentration data of the 17 elements of the elemental group for evaluation and the gender data of the subject (the client). It is usually composed of a known storage device (for example, a semiconductor memory or a magnetic memory). Any configuration of a storage device that can store concentration data can be used as the data storage unit 11.

[0126] The calculation unit 12 performs calculations necessary to derive the correlation relationship between the concentration data using the 17 concentration data of the elemental group for evaluation stored in the data storage unit 11, or performs calculations necessary to derive the correlation relationship between the concentration data and the gender data using a total of 18 data including the 17 concentration data of the elemental group for evaluation and the gender data stored in the data storage unit 11. The calculation unit 12 is usually configured using a known discriminant analysis program, but it is not necessarily limited to this. It may be configured with a program prepared to include the function of the discriminant analysis program. The main thing is that it suffices if it can execute the necessary calculations described above, regardless of its configuration.

[0127] Specifically, when a "hospital control group" is used as the control group, the calculation unit 12 reads out the 17 concentration data of the above-mentioned evaluation element group (17 types of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) stored in the data storage unit 11, applies them to a discrimination function for discriminating whether the subject belongs to either the case group (dementia patient group, specifically, the MCI patient group or the AD patient group) or the control group (hospital control group), and calculates the correlation relationship among the 17 concentration data of the evaluation element group.

[0128] When a "resident control group" is used as the control group, the calculation unit 12 reads out the 17 concentration data of the above-mentioned evaluation element group and the gender data (that is, a total of 18 data) stored in the data storage unit 11, applies them to a discrimination function for discriminating whether the subject belongs to either the case group (dementia patient group, specifically, the MCI patient group or the AD patient group) or the control group (resident control group), and calculates the correlation relationship among the 18 data.

[0129] Note that the discrimination function when a "resident control group" is used as the control group is different from the discrimination function when a "hospital control group" is used as the control group.

[0130] For example, when a "hospital control group" is used as the control group, the 17 concentration data of the evaluation element group stored in the data storage unit 11 are read into the discrimination analysis program of the calculation unit 12. At this time, the 17 concentration data (concentration values) of the evaluation element group read from the data storage unit 11 are respectively applied to the explanatory variables 1 to 17 of the discrimination function, and the 17 discrimination coefficient values obtained by the above-mentioned discrimination analysis are respectively applied to the discrimination coefficients 1 to 17 of the discrimination function.

[0131] When the "resident control group" is used as the control group, the 17 concentration data of the evaluation element group and the gender data (that is, a total of 18 data) stored in the data storage unit 11 are read into the discriminant analysis program of the arithmetic unit 12. At this time, the 17 concentration data (concentration values) of the evaluation element group read from the data storage unit 11 are respectively applied to the explanatory variables 1 to 17 of the discriminant function, and the 17 discriminant coefficient values obtained by the above-described discriminant analysis are respectively applied to the discriminant coefficients 1 to 17 of the discriminant function. In addition, the gender data is applied to the explanatory variable 18 of the discriminant function, and the remaining one discriminant coefficient value obtained by the above-described discriminant analysis is applied to the discriminant coefficient 18 of the discriminant function.

[0132] When the "hospital control group" is used as the control group, if the 17 discriminant coefficient values of the discriminant formula for MCI patients obtained by the above-described discriminant analysis are applied to the discriminant function, the risk of developing MCI can be evaluated. On the other hand, if the 17 discriminant coefficient values of the discriminant formula for AD patients obtained by the above-described discriminant analysis are applied to the discriminant function, the risk of developing AD can be evaluated.

[0133] When the "resident control group" is used as the control group, if the 18 discriminant coefficient values of the discriminant formula for MCI patients obtained by the above-described discriminant analysis are applied to the discriminant function, the risk of developing MCI can be evaluated. On the other hand, if the 18 discriminant coefficient values of the discriminant formula for AD patients obtained by the above-described discriminant analysis are applied to the discriminant function, the risk of developing AD can be evaluated.

[0134] The index generation unit 13 is a part that generates an index for identifying whether the subject has dementia based on the calculation result output from the calculation unit 12, that is, the correlation relationship among the 17 concentration data of the evaluation element group, or the correlation relationship among the 17 concentration data of the evaluation element group and the gender data (a total of 18 data), and outputs it outside the dementia risk assessment system 10. The index generation unit 13 is usually composed of a program manufactured to realize its function, but is not limited thereto. Since the function of the index generation unit 13 is closely related to the function of the calculation unit 12, the index generation unit 13 may be configured by a program manufactured to utilize the function of the discriminant analysis program constituting the calculation unit 12. In this case, the functions of the calculation unit 12 and the index generation unit 13 are realized by a single program. In other words, the calculation unit 12 and the index generation unit 13 are integrally configured. In short, as long as the above-described function of the index generation unit 13 can be realized, the configuration thereof does not matter.

[0135] Specifically, when the "hospital control group" is used as the control group, the index generation unit 13 applies the 17 concentration data of the evaluation element group read from the data storage unit 11 to the discriminant formula (MCI discriminant formula and / or AD discriminant formula) derived by the calculation unit 12 to calculate the discrimination score (D). When the "hospital control group" is used as the control group, the index generation unit 13 applies a total of 18 data, namely, the 17 concentration data of the evaluation element group and the gender data read from the data storage unit 11, to the discriminant formula (MCI discriminant formula and / or AD discriminant formula) derived by the calculation unit 12 to calculate the discrimination score (D). The discrimination score (D) thus obtained becomes an "index" for identifying whether the subject has dementia (MCI or AD). Based on the discrimination score (D) as the index, the risk of the subject having dementia (MCI or AD) can be estimated.

[0136] The index generation unit 13 estimates the risk of dementia by comparing the discrimination score (D) with a predetermined reference value (for example, 0). That is, if the discrimination score (D) is greater than or equal to (or less than) the reference value, the index generation unit 13 determines that it belongs to the case group (MCI patient group or AD patient group). This means that it is inferred that "the subject (the applicant) has dementia (MCI or AD)". On the other hand, if the discrimination score (D) is less than (or greater than) the reference value, it is determined that it belongs to the control group (hospital control group or resident control group). This means that it is inferred that "the subject (the applicant) does not have dementia (MCI or AD)".

[0137] Based on the above inference, the risk of dementia (MCI or AD) of the subject (the applicant) is obtained as an evaluation result.

[0138] In the dementia risk assessment system 10 of the present invention, even if either the "hospital control group" or the "resident control group" is used as the control group, since the discriminant formula used in the calculation unit 12 differs depending on the type of dementia (MCI or AD), depending on whether the discriminant formula used for dementia risk assessment is for MCI or for AD, there is an advantage that the type of dementia (MCI or AD) inferred that the subject (the applicant) has can also be determined. The type of dementia inferred that the subject (the applicant) has thus determined is preferably included in the evaluation result.

[0139] In addition, in the dementia risk assessment system 10 of the present invention, regardless of whether the "hospital control group" or the "resident control group" is used as the control group, depending on the type of dementia (MCI or AD), there are specific elements (selected from 17 elements of the evaluation element group) that are significantly different in discriminating between the case group and the control group. Therefore, depending on whether the element significant for discrimination corresponds to MCI or AD, there is an advantage that the type of dementia (MCI or AD) that the subject (the requester) is presumed to have can also be determined. The type of dementia that the subject (the requester) is presumed to have, thus determined, is preferably included in the evaluation result.

[0140] Furthermore, in the dementia risk assessment system 10 of the present invention, regardless of whether the "hospital control group" or the "resident control group" is used as the control group, based on the results of the above-mentioned binary logistic regression analysis, a discrimination score - morbidity rate relationship can be prepared in advance and stored in the calculation unit 12, the index generation unit 13, or another storage site (not shown) (see FIGS. 21, 22, 40, and 41). In this case, by comparing the discrimination score (D) obtained by the index generation unit 13 with the discrimination score - morbidity rate relationship read from the calculation unit 12, the index generation unit 13, or another storage site, it is possible to evaluate the dementia morbidity risk of the subject (the requester) presumed to have dementia based on the morbidity rate (probability of morbidity). There is an advantage in this. The morbidity rate of dementia that the subject (the requester) is presumed to have, thus determined, is preferably included in the evaluation result.

[0141] The dementia risk assessment system 10 of the present invention having the above-described configuration and functions can be realized using, for example, a known personal computer, but is not limited thereto. Needless to say, any other computer may be used. Further, for example, as the dementia risk assessment system 10, a computer installed with a known discriminant analysis program (e.g., SAS, SPSS) is used, and 17 or 18 discriminant coefficient values of the discriminant formula for MCI patients or the discriminant formula for AD patients described above are applied in advance to the discriminant coefficients 1 to 17 or discriminant coefficients 1 to 18 of the discriminant function. Then, by simply having the discriminant analysis program read the 17 concentration data of the evaluation element group of the subject (the applicant), or a total of 18 data including the 17 concentration data and the gender data, a discriminant score (D) as an index can be obtained, and an evaluation result of the dementia risk can be immediately obtained based on the discriminant score (D).

[0142] When implementing the dementia risk assessment method of the present invention described above with reference to FIG. 1 using the dementia risk assessment system 10 described above with reference to FIG. 2 or other systems, for example, the risk of dementia onset is calculated by pattern analysis of the concentrations of the evaluation element group (17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) in blood (plasma or serum) collected from the subject, and a result probabilistically expressing the possibility of dementia based on the risk is presented. Specifically, a predetermined amount (for example, 0.5 cc) of blood (plasma or serum) is collected from the blood of the subject collected during a health check at a medical institution or a screening institution, and the concentration measurement of the evaluation element group is carried out at the inspection institution. Then, based on the concentration data of the evaluation element group (17 elements of Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) measured at the inspection institution, or the 17 concentration data and the gender data, an institution such as a risk assessment center (tentative name) performs the calculation of the dementia risk. The calculation result of the dementia risk is sent to the blood collection institution, and is passed from the blood collection institution to the subject (patient). When dementia is suspected, for example, the blood collection institution is recommended to undergo the "current dementia screening". Regarding personal information, it is encrypted at the blood collection institution or numbered sequentially so that personal information does not reach the inspection institution or the risk assessment center.

Example

[0143] Hereinafter, the present invention will be described in more detail based on examples.

[0144] In Examples 1 and 2 below, the number of subjects for evaluating the risk of dementia was, as shown in FIG. 3, in the case group (MCI patient group), there were 27 males and 38 females, and a total of 65 MCI patients. The other case group (AD patient group) had 18 males and 32 females, and a total of 50 AD patients. There were many elderly people among both MCI patients and AD patients. On the other hand, the control group (hospital control group) had 18 males and 32 females, and a total of 50 people. The total number of subjects in these three groups was 165, and the gender ratio was 38.2% males and 61.8% females, with no difference among these three groups. As shown in FIG. 3, the subjects in these three groups were divided into five age groups: the 50s, 60s, 70s, 80s, and 90s by age.

[0145] The serum samples used in Examples 1 and 2 below were provided from each of the 165 subjects described above. However, all of them received the "samples from the NCGM biobank collected at the National Center for Global Health and Medicine". Note that the subjects in the control group (hospital control group) described above were patients who visited a predetermined hospital related to the National Center for Global Health and Medicine and were determined not to be MCI patients or AD patients, but patients with other diseases excluding MCI and AD. In Examples 1 and 2 below, serum is used, but since the elemental components of serum and plasma are similar, it is also possible to use plasma instead of serum.

[0146] From the results of Examples 1 and 2 described below, it was shown that the risk of developing MCI (mild cognitive impairment) and AD (Alzheimer's disease) can be calculated using the concentration data of 17 specific elements contained in serum. Using the concentration data of the 17 elements obtained by a single blood draw, it is possible to estimate the risk of developing different types of dementia because, as shown in FIGS. 14 and 17, the elements that have a significant association with discrimination vary depending on the type of dementia (MCI or AD). The only items common to these types of dementia (MCI or AD) are the two elements calcium (Ca) and rubidium (Rb), and the other elements are different. This difference is thought to enable the estimation of the risk of developing different types of dementia.

[0147] By the way, in Examples 1 and 2 below, as the control group used for discriminant analysis, subjects belonging to the "hospital control group", that is, outpatients who visited the neurology department, neuropsychiatry department, psychotherapy department, or general internal medicine department in a specific hospital group were used. Therefore, it was highly likely that the subjects belonging to the "hospital control group" had developed some disease other than dementia. For this reason, the inventors had concerns about whether the desired prediction results could be obtained when the present invention was directly applied to the dementia risk assessment of general residents who were not outpatients of the hospital group.

[0148] Therefore, in Examples 3 and 4 below, among general residents who underwent a health check at a specific medical institution within a predetermined period, residents who were judged by the Mini-Mental State Examination (MMSE) to have no risk of developing dementia were selected to form a "resident control group". Then, instead of the subjects belonging to the "hospital control group" used in Examples 1 and 2, the subjects belonging to the "resident control group" thus selected were used to perform discriminant analysis and binary logistic regression analysis in substantially the same manner as in Examples 1 and 2.

[0149] In Examples 3 and 4 below, as described below, 846 general residents (candidates) (251 males and 595 females) were selected as the subjects belonging to the "resident control group".

[0150] First, with the support of a medical institution related to the inventors, the general public who underwent a health check at the medical institution within a specific period were explained the gist of the present invention and asked for cooperation with the present invention. As a result, consent to cooperate with the present invention in writing was obtained from 910 members of the general public (283 men and 627 women). Therefore, these 910 members of the general public were regarded as candidates for the "resident control group". Subsequently, when the MMSE was conducted on these 910 candidates, the results shown in FIG. 23 were obtained. As is clear from the MMSE score list shown in FIG. 23, the MMSE scores of 64 people (32 men and 32 women) out of the 910 candidates were less than 27 points, where the onset of mild cognitive impairment (MCI) was suspected. Therefore, 846 candidates (251 men and 595 women) excluding these 64 doubtful people were selected as subjects belonging to the "resident control group". As is clear from FIG. 23, the gender of the total 846 members of the general public (251 men and 595 women) selected as subjects belonging to the "resident control group" thus selected was 29.7% male and 70.3% female, and their ages were divided into four age groups: the 50s, 60s, 70s, and 80s.

[0151] In the following Examples 3 and 4, the serum samples provided from each of the above-mentioned 165 subjects (three groups: MCI patient group, AD patient group, and hospital control group) used in Examples 1 and 2, and the serum samples provided from each of the total 846 subjects as the above-mentioned "resident control group" were used.

[0152] It should be noted that in the following Examples 3 and 4 as well, although serum is used, it is the same as in Examples 1 and 2 that it is also possible to use plasma instead of serum.

Example

[0153] Next, Example 1 will be described.

[0154] First, for each of the 165 serum samples provided by each of the 165 subjects described above, the concentrations of the evaluation element group contained in these serum samples were measured. The concentration measurement at that time was performed under the measurement conditions described in "1. Determination of Optimal Measurement Conditions" above. As the evaluation element group, as described in "2. Selection of Evaluation Element Group" above, 17 elements used in the "Cancer Risk Evaluation Method" of Patent Document 2 described above, namely, Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs, were used. These 17 elements selected as the evaluation element group are, as described in "2. Selection of Evaluation Element Group" above, a combination of elements that can best distinguish between two groups, the MCI patient group and the hospital control group (with the highest discriminative ability), and a combination of elements that can best distinguish between two groups, the AD patient group and the hospital control group (with the highest discriminative ability), which were obtained by searching with a computer.

[0155] For the serum samples belonging to the two groups of the MCI patient group and the hospital control group, the concentrations of the evaluation element group contained in these serum samples were measured by ICP mass spectrometry. Also, for the serum samples belonging to the two groups of the AD patient group and the hospital control group, similarly, the concentrations of the "evaluation element group" contained in these serum samples were measured. The results are shown in Figure 4.

[0156] The concentration comparison table in Figure 4 shows the average values of the concentrations of the 17 elements in the evaluation element group in the serum samples belonging to the three groups of the hospital control group, the MCI patient group, and the AD patient group. As shown in Figure 4, among the 17 elements in the evaluation element group, significant differences in the average values of the concentrations were observed between the two groups of the hospital control group and the MCI group for 6 elements, namely, Na, S, Ca, Cu, Mo, and Rb. Among these elements with significant differences in the average values of the concentrations, it was found that Na (P < 0.05), S (P < 0.01), Ca (P < 0.01), Cu (P < 0.05), Mo (0.05) had high significance in discrimination, and Rb (0.01) had low significance.

[0157] Among the other elements, significant differences in the average concentrations were observed between the two groups of the hospital control group and the AD group for eight elements: Na, Mg, S, Ca, Cu, Sr, K, and Rb. Among these elements with significant differences in average concentrations, Na (P<0.01), Mg (0.05), S (0.01), Ca (P<0.01), Cu (P<0.05), and Sr (P<0.05) had high significance for discrimination, while K (P<0.01) and Rb (0.01) had low significance.

[0158] The above elements with significant differences in average concentration compared to the hospital control group suggest the possibility of affecting the onset of MCI or AD, and also suggest that there is some strong correlation between these elements and the factors that caused changes in the homeostasis (steady state) of trace element concentrations in the blood.

[0159] Figure 5 is a radar chart showing the ratio of the average concentrations of the 17 elements in the evaluation element group for the MCI patient group and the AD patient group for each element. In Figure 5, based on the hospital control group, that is, the concentration ratio is shown when the value of the concentration data of the hospital control group is set to 1.

[0160] As shown in Figure 5, in the MCI patient group or the AD patient group, it was found that the eight elements with large concentration ratios compared to the hospital control group were Co, Cu, As, Rb, Sr, Mo, Ag, and Cs. These eight elements with large concentration ratios did not include the five elements Na, Mg, K, S, and Ca with large average concentration differences in Figure 4, and it was also found that there were significant differences in concentration ratios for elements different from Ag and Cs.

[0161] Next, since the 17 elements of the evaluation element group are considered to be biologically and chemically related to each other in terms of relationships such as correlation, dependence, and exclusivity, the correlation relationships among these 17 elements were investigated for each of the three groups: the hospital control group, the MCI patient group, and the AD patient group. The results are shown in FIGS. 6 to 8. These figures show the correlation relationships among the 17 elements of the evaluation element group in these three groups, respectively. The lower left triangular part of each of these figures shows the correlation coefficient, and the upper right triangular part of the figure shows the P value representing the significance of those correlation coefficients. In addition, the correlation coefficients that were significant at a significance level of P < 0.05 are surrounded by a thick frame with the correlation coefficient (numerical value).

[0162] In the hospital control group (alone) of FIG. 6, for the five elements of Na, Fe, Co, Se, and Sr, no significantly correlated elements were found. On the other hand, between Mg and S (+), between Mg and Cu (+), between Mg and Zn (+), between P and Ca (+), between S and Ca (+), between S and Cu (+), between S and Zn (+), between K and Ca (+), between K and Rb (+), between Ca and Zn (+), between Cu and Zn (+), between Zn and As (-), between Rb and Cs (+), and between As and Mo (+), significant correlation relationships were respectively found.

[0163] In the MCI patient group (alone) in Fig. 7, no element was found to be significantly correlated with Fe. On the other hand, there were significant positive correlations between Na and Mg, Mg and Se, P and S, P and Ca, P and Cu, P and Zn, P and As, P and Cs, S and Ca, S and Cu, S and Zn, S and Se, K and As, K and Rb, K and Mo, K and Cs, Ca and Zn, Ca and Rb, Ca and Sr, Ca and Mo (negative), Ca and Cs, Cu and Se, Co and Ag, Zn and Mo (negative), Zn and Cs, As and Mo, Rb and Cs, Mo and Ag (negative).

[0164] In the AD patient group (alone) in Fig. 8, for the four elements Co, As, Sr, and Mo, no element was found to be significantly correlated. On the other hand, there were significant positive correlations between Na and P, Na and Fe, Na and Se, Mg and P, Mg and S, Mg and Se, P and Ca, P and Fe, P and Zn, P and Se, S and Ca, S and Fe, S and Zn, S and Se, S and Ag, K and Rb, K and Cs, Ca and Fe, Ca and Se, Fe and Cu (negative), Fe and Zn, Fe and Se, Cu and Zn (negative), Cu and Se (negative), Zn and Se, Zn and Ag (negative), Rb and Cs.

[0165] As can be seen from the results in FIGS. 6 to 8, significant differences were observed in the average values and ratios of the concentrations of the 17 elements in the evaluation element group among the three groups of the hospital control group, the MCI patient group, and the AD patient group. Further, by comparing and examining the correlation relationships among the 17 elements in each of these three groups, it became clear that the correlation relationships among the 17 elements were clearly different among these three groups. Therefore, it is possible to use the differences in the average values of the concentrations of each of the 17 elements alone and the differences in the concentration ratios as indicators for discriminating and predicting the risk of developing MCI between the two groups of the hospital control group and the MCI patient group, and it was also found that it is possible to use them as indicators for discriminating and predicting the risk of developing AD between the two groups of the hospital control group and the AD patient group. However, in the results of FIGS. 6 to 8, the correlation relationships among the concentrations of the 17 elements and the influence of complex associations were not excluded. Therefore, in order to correct the mutual correlations of the 17 elements and clarify the correlation relationships among the 17 elements, discriminant analysis by multivariate analysis using the 17 elements was performed to analyze the influence of each element alone.

[0166] (Discriminant Analysis of Three Groups: Hospital Control Group, MCI Patient Group, and AD Patient Group) First, discriminant analysis was performed among the three groups of the hospital control group, the MCI patient group, and the AD patient group. In the discriminant analysis among these three groups, the hospital control group, the MCI patient group, and the AD patient group were used as the target variables, and the concentration data of the 17 elements in the evaluation element group obtained from the serum samples of the subjects were used as 17 explanatory variables. This is because, as described above, significant differences were observed in the concentrations of the 17 elements in the evaluation element group and the correlation relationships among these elements among the three groups of the hospital control group, the MCI patient group, and the AD patient group. The results are shown in FIG. 9.

[0167] FIG. 9 shows the distribution of the discriminant scores obtained in the three groups. Since the distributions of the discriminant scores of these three groups overlap with each other at the center, there is a possibility that the discrimination has not been sufficiently performed. Therefore, the results obtained by the discriminant analysis of these three groups were sorted out and summarized in the cross-tabulation table shown in FIG. 10.

[0168] As is clear from the cross-tabulation table of FIG. 10, in the results of the discriminant analysis of the above three groups, the specificity of the hospital control group was 76.0% (38 / 50), the sensitivity of the MCI patient group was 69.2% (45 / 65), and the sensitivity of the AD patient group was 60.0% (30 / 50). Also, the correct diagnosis rate of the entire above three groups was 68.5% (113 / 165).

[0169] FIG. 11 shows the discriminant coefficients of the two discriminant functions (discriminant function 1 and 2) used in the discriminant analysis of the above three groups and their significance. From this figure, it was found that the elements significant for the discrimination of the above three groups are three elements of Ca (P<0.01), Rb (P<0.01), and Mo (P<0.05).

[0170] From the discrimination results shown in FIGS. 9 to 11, it was found that although not sufficient, reliable sensitivity and specificity can be obtained by the discriminant formula (discrimination by discriminant functions 1 and 2 shown in FIG. 11) used for the discrimination of the above three groups. However, considering its use for predicting the risk of suffering from MCI or AD, it was inevitably judged that a sufficient correct diagnosis rate has not yet been obtained. Therefore, in order to estimate the risk of suffering from MCI, discriminant analysis of two groups, the hospital control group and the MCI patient group, was performed.

[0171] (Discriminant analysis of two groups, the hospital control group and the MCI patient group) In the discriminant analysis of two groups, the hospital control group and the MCI patient group, for the subjects for estimating the risk of suffering from MCI, as shown in FIG. 3, the case group (MCI patient group) consisted of 27 males and 38 females, a total of 65 people, and the control group (hospital control group) consisted of 18 males and 32 females, a total of 50 people. The serum samples of these subjects were used as the evaluation targets. The data used for the evaluation were the concentration data of 17 elements (Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) selected as the above evaluation element group. In the discriminant analysis between the two groups as well, similar to the discriminant analysis between the three groups, the hospital control group and the MCI patient group were used as the target variables, and the concentration data of the 17 elements of the above evaluation element group obtained from the serum samples of the subjects were used as 17 explanatory variables.

[0172] The results of the discriminant analysis of the two groups, the hospital control group and the MCI patient group, are shown in FIGS. 12 to 14, 18, and 21.

[0173] FIG. 14 shows the discriminant coefficients of the discriminant formula obtained by discriminant analysis and their significance. From this figure, it was found that four elements, Ca (P < 0.01), Zn (P < 0.05), Rb (P < 0.01), and Mo (P < 0.05), are significant for discrimination. The discriminant coefficients of the discriminant formula used here are as shown in FIG. 14, and the overall composition of the discriminant formula is shown as the formula "F(MCI)" in FIG. 20.

[0174] FIG. 12 is a histogram showing the frequency distribution of the discriminant scores calculated by discriminant analysis. From this figure, it was found that most of the discriminant scores of the hospital control group are distributed in the left region of the figure, and most of the discriminant scores of the MCI patient group are distributed in the right region of the figure. In other words, it was found that they are divided into left and right near the point (position) where the value of the discriminant score is 0.0. Also, it was found that in the region near the point (position) where the value of the discriminant score is 0.0, the discriminant scores of the case group (MCI patient group) and the control group (hospital control group) are mixed.

[0175] FIG. 13 shows the sensitivity and specificity obtained by discriminant analysis. From this figure, in the case group (MCI patient group), 58 out of 65 samples were hit (sensitivity = 58 / 65 = 89.2%), and in the control group (hospital control group), 44 out of 50 samples were hit (specificity = 44 / 50 = 88.0%). Therefore, out of the total 115 samples targeted for discriminant analysis, 102 samples were correctly discriminated, and the correct diagnosis rate was 88.7% (102 / 115). Since this correct diagnosis rate is a sufficiently high value, it can be judged that the discrimination in the discriminant analysis of the two groups in Example 1 was properly performed. Therefore, a highly reliable MCI risk assessment (prediction) was expected.

[0176] Figure 18 shows the ROC (Receiver Operating Characteristic) curve. This ROC curve was obtained by changing the discrimination scores obtained from the discriminant analysis of the two groups in Example 1 in order from the lowest value to the highest value, and calculating the sensitivity and specificity at any time from the number of samples classified into the control group (hospital control group) and the number of samples classified into the case group (MCI patient group). The fitness of the discrimination (prediction) can be evaluated by the area below it.

[0177] Using the ROC curve in Figure 18 to obtain the area under the curve (AUC, Area Under the Curve), as shown in the figure, a high value of 0.9462 was obtained. Since the closer the value of AUC indicating the fitness of the discrimination is to 1, the more accurate the determination is, the value of 0.9462 obtained in Example 1 indicates that the discrimination (prediction) of the two groups in Example 1 was performed very accurately. Therefore, it was found that the method of Example 1 is sufficiently effective as a tool for risk diagnosis of MCI.

[0178] Figure 21 is a graph showing the relationship between the discrimination score obtained from the discriminant analysis of the two groups in Example 1 and the probability that the subject has MCI (discrimination score - MCI prevalence rate relationship). This graph was obtained by performing binary logistic regression analysis using the discrimination score obtained from the discriminant analysis. According to the graph, the higher the discrimination score is a large negative value, the higher the prevalence probability (risk) of MCI. For example, it was found that if the discrimination score is approximately -2.1 or less, it can be evaluated that "the probability of the subject having MCI is inferred to be 90% or more". Conversely, for example, if the discrimination score is approximately +2.1 or more, the prevalence probability is 10% or less, so it was found that it can be evaluated that "the subject is inferred not to have MCI".

[0179] The discriminant formula (F(MCI)) for evaluating the risk of MCI calculated by the discriminant analysis of two groups in Example 1 is shown in FIG. 20. The method for evaluating the risk of MCI using this discriminant formula can be used as an auxiliary when diagnosing dementia for outpatients visiting departments such as the department of neurology, department of neuropsychiatry, department of psychotherapy, and general internal medicine in medical institutions such as hospitals. If this is realized, the MCI onset of the subject can be detected early, and it is expected that the selection of treatment methods for suppressing the progression of MCI will be facilitated.

Example

[0180] Next, Example 2 will be described.

[0181] In the above Example 1, in discriminant functions 1 and 2 (discriminant functions 1 and 2 shown in FIG. 11) used in the discriminant analysis of three groups: the hospital control group, the MCI patient group, and the AD patient group, it was determined that a sufficient correct diagnosis rate could not be obtained. Therefore, discriminant analysis of two groups, the hospital control group and the MCI patient group, was performed. In Example 2, using the serum samples belonging to the AD patient group and the serum samples belonging to the same hospital control group as in Example 1, discriminant analysis of two groups, the hospital control group and the AD patient group, was performed.

[0182] (Discriminant analysis of two groups: the hospital control group and the AD patient group) In the discriminant analysis of two groups, the hospital control group and the AD patient group, in Example 2, for the subjects for whom the risk of AD is inferred, as shown in FIG. 3, in the case group (AD patient group), there are 18 males and 32 females, a total of 50 people, and in the control group (hospital control group), there are 18 males and 32 females, a total of 50 people (the same as in Example 1 above). The serum samples of these subjects were used as the evaluation targets. The data used for the evaluation were the concentration data of 17 elements selected as the above-mentioned evaluation element group, the same as in Example 1. In the discriminant analysis between the two groups, similar to Example 1, the hospital control group and the AD patient group were used as the target variables, and the concentration data of 17 elements of the above-mentioned evaluation element group obtained from the serum samples of the subjects were used as 17 explanatory variables.

[0183] The results of the discriminant analysis for the two groups of the hospital control group and the AD patient group are shown in FIGS. 15 to 17, 19, and 22.

[0184] FIG. 17 shows the discriminant coefficients of the discriminant formula obtained by discriminant analysis and their significance. From this figure, it was found that three elements, Mg (P<0.05), Ca (P<0.01), and Rb (P<0.05), are significant for discrimination. This is clearly different from Example 1 above. The discriminant coefficients of the discriminant formula used here are as shown in FIG. 17, and the overall composition of the discriminant formula is shown as the formula "F(AD)" in FIG. 20.

[0185] FIG. 15 is a histogram showing the frequency distribution of the discriminant scores calculated by discriminant analysis. From this figure, it was found that most of the discriminant scores of the hospital control group are distributed in the right region of the figure, and most of the discriminant scores of the AD patient group are distributed in the left region of the figure. In other words, it was found that they are divided into left and right near the point (position) where the value of the discriminant score is 0.0. Also, in the region close to the point (position) where the value of the discriminant score is 0.0, it was found that the discriminant scores of the case group (AD patient group) and the control group (hospital control group) are mixed. However, in Example 2, the distribution location of the hospital control group is the right region, while in Example 1 above it is the left region. Therefore, the two are different in that the distribution location of the hospital control group in Example 2 is the opposite of that in Example 1.

[0186] Figure 16 shows the sensitivity and specificity obtained from the discriminant analysis. From this figure, among 50 samples in the case group, 42 samples were positive (sensitivity = 42 / 50 = 84.0%), and among 50 samples in the control group, 38 samples were positive (specificity = 38 / 50 = 76.0%). Therefore, out of the total 100 samples targeted for the discriminant analysis, 80 samples were correctly discriminated, and the correct diagnosis rate was 80.0% (102 / 115). Since this correct diagnosis rate is also a high value, it can be judged that the discrimination in the discriminant analysis of the two groups in Example 2 was properly performed. However, compared with Example 1 above, the value was slightly lower. Therefore, although the accuracy decreased slightly compared to the MCI risk assessment in Example 1, a highly reliable AD risk assessment (prediction) was also expected in Example 2.

[0187] Figure 19 shows the ROC curve obtained from the discriminant analysis of two groups: the hospital control group and the AD patient group in Example 2. Using the ROC curve in this figure to calculate the area under the ROC curve (AUC), as shown in the figure, a high value of 0.8824 was obtained. The value of 0.8824 obtained in Example 2 is lower than the value of 0.9462 in Example 1 above, but since it exceeds 0.8, it was found that the determination was also accurate in Example 2. Therefore, it was found that the method of Example 2 is sufficiently effective as a tool for AD risk diagnosis.

[0188] Note that Non-Patent Document 3 reported that the area under the ROC curve (AUC) of the Mini-Mental State Examination (MMSE) was 0.79. Therefore, the value of AUC = 0.8824 in Example 2 is clearly higher than the AUC = 0.79 of the Mini-Mental State Examination. Considering this point, it was found that the method of Example 2 has sufficient effectiveness as a tool for AD risk diagnosis.

[0189] FIG. 22 is a graph showing the relationship (discriminant score - AD morbidity rate relationship) between the discriminant scores obtained from the discriminant analysis of two groups in Example 2 and the probability that the subject has AD. This graph is obtained by performing binary logistic regression analysis using the discriminant scores obtained from the discriminant analysis, similar to Example 1 above. According to this graph, similar to Example 1 above, the greater the negative value of the discriminant score, the higher the probability (risk) of AD morbidity. For example, it was found that if the discriminant score is approximately -2.2 or less, it can be evaluated that "the subject is presumed to have AD with a probability of 90% or more". Conversely, for example, if the discriminant score is approximately +2.2 or more, the morbidity probability is 10% or less, so it was found that it can be evaluated that "the subject is presumed not to have AD".

[0190] The discriminant formula (F(AD)) for AD morbidity risk assessment calculated by the discriminant analysis of two groups in Example 2 is shown in FIG. 20. The AD morbidity risk assessment method using this discriminant formula can be used as an auxiliary when performing a dementia diagnosis for outpatients visiting the neurology department, neuropsychiatry department, psychotherapy department, general internal medicine department, etc. of medical institutions such as hospitals. If this is realized, it is expected that the AD morbidity of the subject can be detected early, making it easier to select a treatment method for suppressing the progression of AD.

Example

[0191] Next, Example 3 will be described.

[0192] First, for the serum samples from a total of 846 subjects belonging to the resident control group, in the same manner as in Example 1 described above, the concentrations of the 17 evaluation element groups (Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cs) contained in those serum samples were measured. Then, the results were compared with the concentrations of the evaluation element groups in the serum samples belonging to the three groups of the hospital control group, MCI patient group, and AD patient group obtained in Example 1 above. The results are shown in FIG. 24.

[0193] In the concentration comparison table of FIG. 24, together with the average concentrations of the 17 elements in the evaluation element group in the serum samples belonging to the resident control group, the average concentrations of the evaluation element group in the serum samples belonging to the three groups of the hospital control group, the MCI patient group, and the AD patient group obtained in Example 1 above are also shown. As shown in FIG. 24, among the 17 elements in the evaluation element group, significant differences (average concentration differences) in the average concentrations were observed between the two groups of the resident control group and the hospital control group for 9 elements: Na, Mg, S, Ca, Fe, Zn, As, Se, and Mo. That is, a significant difference (high discriminant significance) was observed between the two groups of the resident control group and the hospital control group. As a result, it was found that the resident control group is clearly different from the hospital control group. In addition, it was found that there are significant differences (high discriminant significance) for many elements between the resident control group and the MCI patient group, and also between the resident control group and the AD patient group.

[0194] FIG. 25 is a radar chart showing the ratio of the average concentrations of the 17 elements in the evaluation element group in the MCI patient group and the AD patient group for each element. In FIG. 25, based on the resident control group, that is, the concentration ratio when the value of the concentration data of the resident control group is set to 1 is shown.

[0195] As shown in FIG. 25, it was found that in the MCI patient group and the AD patient group, the average concentrations of 5 elements, Mg, Cu, As, Se, and Mo, are higher than those in the resident control group, and the average concentrations of 4 elements, Fe, Zn, Rb, and Cs, are lower than those in the resident control group. From this result, it was found that there are clear differences between the two groups of the resident control group and the MCI patient group, and also between the two groups of the resident control group and the AD patient group. As a result, it was confirmed that when the resident control group is used instead of the hospital control group, it is possible to discriminate between the MCI patient group and the AD patient group using the concentrations of the 17 elements in the evaluation element group.

[0196] Next, the correlation relationships among the concentrations of the 17 elements in the evaluation element group in the serum samples belonging to the resident control group were investigated. The results are shown in Fig. 26. The lower left triangular part of the figure shows the correlation coefficients, and the upper right triangular part of the figure shows the P values representing the significance of these correlation coefficients. The correlation coefficients (numerical values) enclosed by the thick frame in the figure were determined to be statistically significant (P < 0.05).

[0197] From the correlation coefficients shown in Fig. 26, it can be seen that in the resident control group (alone), the six elements of Na, Mg, P, K, Ca, and Fe have a high correlation with each other. Therefore, it was inferred that these six elements move in synchronization with each other. The correlation relationships among the concentrations of the 17 elements in the evaluation element group in the serum samples belonging to the MCI patient group (alone) and the AD patient group (alone) are the same as those shown in Figs. 7 and 8, respectively.

[0198] From the above description, also in Example 3, similar to Example 1 above, between the three groups of the resident control group, the MCI patient group, and the AD patient group, significant differences were observed in the average values and ratios of the concentrations of the 17 elements in the evaluation element group, and from the correlation relationships among the 17 elements in the evaluation element group in each of these three groups, it became clear that the correlation relationships were clearly different among these three groups. Therefore, it was determined that it is possible to use the differences in the average values of the concentrations of each of the 17 elements alone and the differences in the concentration ratios as indicators for discriminating and predicting the risk of developing MCI between the two groups of the resident control group and the MCI patient group, and it is also possible to use them as indicators for discriminating and predicting the risk of developing AD between the two groups of the resident control group and the AD patient group. However, in the results shown in Figs. 26, 7, and 8, the correlation relationships among the concentrations of the 17 elements and the influence of complex associations, etc. were not excluded. Therefore, in order to correct the mutual correlations of the 17 elements and clarify the correlation relationships among the 17 elements, discriminant analysis by multivariate analysis using the 17 elements was performed to analyze the influence of each element alone.

[0199] (Discriminant analysis of three groups: resident control group, MCI patient group, and AD patient group) First, since significant differences were observed in the concentrations of the 17 elements in the evaluation element group and the correlation relationships among these elements among the three groups of the resident control group, the MCI patient group, and the AD patient group, discriminant analysis was performed among the three groups. In the discriminant analysis among these three groups, the three groups of the resident control group, the MCI patient group, and the AD patient group were used as the target variables, and 18 data consisting of the 17 concentration data of the evaluation element group obtained from the serum samples of the subjects and the gender data of the subjects were used as the explanatory variables.

[0200] Note that the gender data of the subjects was digitized as male = 1 and female = 2. The reason for adding gender as an explanatory variable is that compared with the total number of 846 subjects (251 males and 595 females) belonging to the resident control group, the total number of subjects belonging to the MCI patient group was 65 (27 males and 38 females), and the total number of subjects belonging to the AD patient group was 50 (18 males and 32 females), and the total number of subjects belonging to the disease groups was small.

[0201] As is clear from the cross-tabulation table in Fig. 27, in the results of the discriminant analysis of the three groups, the specificity of the resident control group was 91.3% (772 / 846), the sensitivity of the MCI patient group was 70.8% (46 / 65), and the sensitivity of the AD patient group was 68.0% (34 / 50). Also, the correct diagnosis rate of the three groups as a whole was 68.5% (113 / 165).

[0202] Fig. 28 shows the standardized discriminant coefficients (discriminant coefficients when the mean value is 0.0 and the standard deviation is 1.0) of the two discriminant functions (discriminant function 1 and 2) used in the discriminant analysis of the three groups, and their significance. From the figure, it was found that those significant for the discrimination of the three groups were gender and the 12 elements of Na (P < 0.05), Mg (P < 0.01), S (P < 0.01), K (P < 0.01), Ca (P < 0.01), Fe (P < 0.01), Cu (P < 0.05), Zn (P < 0.01), As (P < 0.05), Se (P < 0.01), Rb (P < 0.01), and Mo (P < 0.05).

[0203] From the discrimination results shown in FIGS. 27 and 28, it was found that among the 17 elements in the evaluation element group, 12 elements have a significant relationship with the discrimination of the three groups. As a result, although not sufficient, reliable sensitivity and specificity were obtained by the discriminant formula (discrimination by discriminant functions 1 and 2 shown in FIG. 28) used for the discrimination of the three groups. However, as described above, since the sensitivity of the MCI patient group and the sensitivity of the AD patient group remain at about 70%, considering using it to predict the risk of developing MCI or AD, it was inevitable to judge that a sufficient positive diagnosis rate has not yet been obtained. Therefore, subsequently, in order to estimate the risk of developing MCI, discriminant analysis was performed on two groups: the resident control group and the MCI patient group.

[0204] (Discriminant analysis 1 of two groups: resident control group and MCI patient group) In the discriminant analysis of two groups, the resident control group and the MCI patient group, described below, as explanatory variables, (i) in the case of only the 17 concentration data of the evaluation element group, (ii) when gender is added to the 17 concentration data of the evaluation element group, and (iii) when gender and age class (age group) are added to the 17 concentration data of the evaluation element group, analysis was performed for each case. The reason for adding age class (age group) as an explanatory variable is that there has been a report that the morbidity rate of the elderly is relatively high as a characteristic of diseases such as MCI and AD.

[0205] (i) Discriminant analysis of two groups using only the 17 concentration data of the evaluation element group The subjects for estimating the risk of developing MCI were 65 cases in the case group (MCI patient group), 27 males and 38 females (see FIG. 3), and 846 cases in the control group (resident control group), 251 males and 595 females. Serum samples of these subjects were used as evaluation targets. The data used for evaluation was only the concentration data of 17 elements selected as the above-mentioned evaluation element group, the same as in Example 1 above.

[0206] The results of the discriminant analysis of the two groups, the resident control group and the MCI patient group, are shown in FIG. 29.

[0207] As shown in the cross-tabulation table of Fig. 29, a high sensitivity value of 92.3% (60 / 65) was obtained, and a high specificity value of 94.3% (798 / 846) was also obtained. In addition, the correct diagnosis rate was 94.2% (858 / 911), which was a high value. Therefore, when using only the concentration data of the 17 elements selected as the evaluation element group as the explanatory variable, it was determined that the discrimination between the resident control group and the MCI patient group was appropriately performed. Thus, it was inferred that highly reliable results could be obtained for the risk assessment of MCI.

[0208] (ii) Discriminant analysis of two groups using the concentration data of 17 elements in the evaluation element group and gender data The subjects for whom the MCI risk was estimated were the same as in the discriminant analysis of (i) above using only the concentration data of the 17 elements in the evaluation element group, and the serum samples of these subjects were used as the evaluation targets. The data used for the evaluation were the concentration data of the 17 elements selected as the evaluation element group, in addition to the gender data (male = 1, female = 2) of the subjects.

[0209] The results of the discriminant analysis of the two groups, the resident control group and the MCI patient group, are shown in Fig. 31.

[0210] As shown in the cross-tabulation table of Fig. 31, a high sensitivity value of 93.9% (61 / 65) was obtained, and a high specificity value of 94.8% (802 / 846) was also obtained. The correct diagnosis rate was 94.7% (863 / 911), which was a high value. Therefore, when using the concentration data of the 17 elements selected as the evaluation element group and gender data as the explanatory variable, it was determined that the discrimination between the resident control group and the MCI patient group was appropriately performed. Thus, it was inferred that highly reliable results could be obtained for the MCI risk assessment.

[0211] (iii) Discriminant analysis of two groups using the concentration data of 17 elements in the evaluation element group, gender data, and age group (decade) data The subjects for estimating the risk of MCI were the same as the discriminant analysis in the above (i) using only the 17 concentration data of the above evaluation element group, and the serum samples of these subjects were used as the evaluation targets. The data used for the evaluation were, in addition to the concentration data of 17 elements selected as the above evaluation element group, the gender data of the subjects (male = 1, female = 2) and the age group (age) data of the subjects (50s = 50, 60s = 60, 70s = 70, 80s = 80).

[0212] The results of the discriminant analysis of the above two groups, the resident control group and the MCI patient group, are shown in FIG. 33.

[0213] As shown in the cross-tabulation table of FIG. 33, in the discriminant analysis of the above two groups in (iii) above, the sensitivity was 95.4% (62 / 65), which was as high as that in the discriminant analysis of the above two groups in (i) and (ii). The specificity was also 95.7% (810 / 846), which was also as high as that in the discriminant analysis of the above two groups in (i) and (ii). The correct diagnosis rate was also as high as 95.7% (872 / 911). Therefore, in the discriminant analysis in (iii) above using the concentration data of 17 elements selected as the above evaluation element group, gender data, and age group (age) data as explanatory variables, it was judged that the discrimination was properly performed. Therefore, it was inferred that a highly reliable result could be obtained for the risk assessment of MCI.

[0214] From the results of the discriminant analysis of the above two groups in (i), (ii), and (iii) using the above three different types of explanatory variables, in Example 3, the discriminant analysis of the above two groups in (iii) using the 17 concentration data of the above evaluation element group, gender data, and age data was found to show the most stable discriminant result in the discrimination between the resident control group and the MCI patient group. However, since the influence of the age data on the discrimination was large, in the following discriminant analysis, the discriminant analysis of the two groups, the resident control group and the MCI patient group, was performed using the 17 concentration data of the above evaluation element group and gender data.

[0215] (Discriminant Analysis 2 of Two Groups: Resident Control Group and MCI Patient Group) In the discriminant analysis of two groups, namely the resident control group and the MCI patient group, which was performed using the 17 concentration data and gender data of the evaluation element group, the standardized discriminant coefficients of the two discriminant functions (discriminant function 1 and 2) used and their significance are shown in Fig. 35.

[0216] From Fig. 35, it was found that for the discrimination between the two groups of the resident control group and the MCI patient group, gender and 11 elements, namely Na (P < 0.01), Mg (P < 0.01), S (P < 0.01), Ca (P < 0.01), Fe (P < 0.01), Cu (P < 0.05), Zn (P < 0.01), As (P < 0.01), Se (P < 0.01), Rb (P < 0.01), Mo (P < 0.01), were significant. Among these elements significant for discrimination, although As was suggested to be involved in MCI, no involvement in AD was shown.

[0217] Fig. 36 is a histogram showing the frequency distribution of the discriminant scores obtained by the discriminant analysis of two groups, namely the resident control group and the MCI patient group, when using the 17 concentration data and gender data of the evaluation element group.

[0218] From Fig. 36, it was found that most of the discriminant scores of the resident control group were distributed in the right region of the figure, and most of the discriminant scores of the MCI patient group were distributed in the central region to the left region of the figure. In other words, it was found that they were divided on the left and right near the point (position) where the value of the discriminant score was -2.0. Also, it was found that in the region near the point (position) where the value of the discriminant score was -2.0, the discriminant scores of the case group (MCI patient group) and the control group (resident control group) were mixed.

[0219] FIG. 38 shows an ROC (Receiver Operating Characteristic) curve. This ROC curve is obtained by changing the discrimination scores obtained from the discriminant analysis of two groups, the resident control group and the MCI patient group, in order from the lowest value to the highest value using the 17 concentration data and gender data of the evaluation element group, and calculating the sensitivity and specificity at any time from the number of samples classified into the control group (resident control group) and the number of samples classified into the case group (MCI patient group).

[0220] Using the ROC curve of FIG. 38 to obtain the area under the curve (AUC) thereof, as shown in the same figure, for MCI, a high value of 0.981 (95% CI: 0.971 - 0.992) was obtained, indicating sufficient effectiveness.

[0221] The closer the value of AUC indicating the goodness of fit of the discrimination is to 1, the more accurate the determination means. Therefore, the value of AUC = 0.981 obtained in Example 3 indicates that the discrimination (prediction) of the two groups in Example 3 is performed very accurately. Therefore, the method of Example 3 was judged to be sufficiently effective as a tool for risk diagnosis of MCI.

[0222] In addition, since the value of AUC = 0.981 in Example 3 is clearly higher than AUC = 0.79 of MMSE, it was recognized that the method of Example 3 has sufficient effectiveness as a tool for risk diagnosis of MCI.

[0223] FIG. 40 is a graph showing the relationship (discriminant score - MCI prevalence rate relationship) between the discriminant scores obtained from the discriminant analysis of two groups in Example 3 and the probability that the subject has MCI. This graph was obtained by performing binary logistic regression analysis using the discriminant scores obtained from the discriminant analysis, similar to Examples 1 and 2 above. According to this graph, similar to Example 1 (see FIG. 21) above, the higher the discriminant score is a large negative value, the higher the probability (risk) of having MCI. For example, if the discriminant score is approximately -4.0 or less, it can be evaluated that "the subject is inferred to have MCI with a probability of 90% or more". Conversely, for example, if the discriminant score is approximately +0.25 or more, the prevalence probability is 10% or less, so it can be evaluated that "the subject is inferred not to have MCI".

[0224] In Example 3, the discriminant formula: F(MCI) for evaluating the risk of MCI calculated by the discriminant analysis in (ii) above, in which the 17 concentration data of the evaluation element group and the gender data were used, is shown in FIG. 39. The method for evaluating the risk of MCI using this discriminant formula: F(MCI) can be suitably used as a "screening method for dementia risk" targeting general residents other than patients visiting departments such as the department of neurology, department of neuropsychiatry, department of psychotherapy, and general internal medicine in medical institutions such as hospitals. If this is realized, it is expected that the MCI prevalence of the subject can be detected early, making it easier to select a treatment method for suppressing the progression of MCI.

[0225] Note that FIG. 39 also shows the discriminant formula: F(MCI) for evaluating the risk of MCI calculated by the discriminant analysis in (i) above, in which only the 17 concentration data of the evaluation element group were used, and the discriminant analysis in (iii) above, in which the 17 concentration data of the evaluation element group, gender data, and age group (age range) data were used.

Example

[0226] Subsequently, Example 4 will be described.

[0227] In Example 3 above, in discriminant functions 1 and 2 (see Fig. 28) used in the discriminant analysis of three groups: the resident control group, the MCI patient group, and the AD patient group, it was determined that a sufficient correct diagnosis rate could not be obtained. Therefore, further discriminant analysis of two groups, the resident control group and the MCI patient group, was performed. In Example 4 described below, using serum samples belonging to the same resident control group as in Example 3 above, together with serum samples belonging to the AD patient group, discriminant analysis of two groups, the resident control group and the AD patient group, was performed.

[0228] (Discriminant analysis 1 of two groups: the resident control group and the AD patient group) In the discriminant analysis of two groups, the resident control group and the AD patient group, described below, similar to Example 3 above, as explanatory variables, (i) when using only the 17 concentration data of the evaluation element group, (ii) when adding gender to the 17 concentration data of the evaluation element group, and (iii) when adding gender and age group (age range) to the 17 concentration data of the evaluation element group, analysis was performed for each case.

[0229] (i) Discriminant analysis of two groups using only the 17 concentration data of the evaluation element group In the discriminant analysis of two groups, the resident control group and the AD patient group, the subjects for whom the AD risk was estimated were 50 in total, 18 males and 32 females in the case group (AD patient group) (see Fig. 3), and 846 in total, 251 males and 595 females in the control group (resident control group). Serum samples of these subjects were used as the evaluation targets. The data used for the evaluation was only the concentration data of 17 elements selected as the above-mentioned evaluation element group, the same as in Example 1 above.

[0230] The results of the discriminant analysis of the two groups, the resident control group and the AD patient group, are shown in Fig. 30.

[0231] As shown in the cross-tabulation table of FIG. 30, a high value of 90.0% (45 / 50) was obtained for sensitivity, and a high value of 91.8% (777 / 846) was also obtained for specificity. Furthermore, the positive diagnosis rate was a high value of 91.7% (822 / 896). Therefore, when using only the concentration data of the 17 elements selected as the evaluation element group as explanatory variables, it was determined that the discrimination between the resident control group and the AD patient group was appropriately performed. Thus, it was presumed that highly reliable results could also be obtained for the AD risk assessment.

[0232] (ii) Discriminant analysis of two groups using the concentration data of 17 elements in the evaluation element group and gender data The subjects for estimating the AD risk were the same as those in the discriminant analysis in (i) above using only the concentration data of 17 elements in the evaluation element group, and the serum samples of these subjects were used as the evaluation targets. The data used for the evaluation were the concentration data of 17 elements selected as the evaluation element group, in addition to the gender data (male = 1, female = 2) of the subjects.

[0233] The results of the discriminant analysis of the two groups, the resident control group and the AD patient group, are shown in FIG. 32.

[0234] As shown in the cross-tabulation table of FIG. 32, a high value of 88.0% (44 / 50) was obtained for sensitivity, and a high value of 91.8% (777 / 846) was also obtained for specificity. The positive diagnosis rate was a high value of 91.7% (822 / 896). Therefore, when using the concentration data of 17 elements selected as the evaluation element group and gender data as explanatory variables, it was determined that the discrimination between the resident control group and the AD patient group was appropriately performed. Thus, it was presumed that highly reliable results could also be obtained for the AD risk assessment.

[0235] (iii) Discriminant analysis of two groups using the concentration data of 17 elements in the evaluation element group, gender data, and age group (decade) data The subjects for estimating the risk of AD were the same as in the discriminant analysis of (i) using only the 17 concentration data of the above-mentioned evaluation element group, and the serum samples of these subjects were used as the evaluation targets. The data used for the evaluation included, in addition to the concentration data of 17 elements selected as the above-mentioned evaluation element group, the gender data of the subjects (male = 1, female = 2) and the age group (decade) data of the subjects (50s = 50, 60s = 60, 70s = 70, 80s = 80).

[0236] The results of the discriminant analysis of the above two groups, the resident control group and the AD patient group, are shown in FIG. 34.

[0237] As shown in the cross-tabulation table of FIG. 34, the sensitivity was as high as 92.0% (46 / 50), and the specificity was also as high as 95.2% (805 / 846). The correct diagnosis rate was as high as 95.0% (851 / 896). Therefore, when using the concentration data of 17 elements selected as the above-mentioned evaluation element group, gender data, and age group (decade) data as explanatory variables, it was judged that the discrimination between the resident control group and the AD patient group was properly performed. Thus, it was inferred that highly reliable results could be obtained for the risk assessment of AD.

[0238] From the results of the discriminant analysis of (i), (ii), and (iii) of the above two groups using the above three different types of explanatory variables, in Example 4, similar to Example 3 above, the discriminant analysis of the above two groups of (iii) using the 17 concentration data of the above-mentioned evaluation element group, gender data, and decade data was found to show the most stable discriminant results in the discrimination between the resident control group and the AD patient group. However, since the influence of the decade data on the discrimination is large, in the following discriminant analysis, the discriminant analysis of the two groups, the resident control group and the AD patient group, was performed using the 17 concentration data of the above-mentioned evaluation element group and gender data.

[0239] (Discriminant Analysis 2 of the Resident Control Group and the AD Patient Group) In the discriminant analysis of two groups, namely the resident control group and the AD patient group, which was performed using the 17 concentration data and gender data of the evaluation element group, the standardized discriminant coefficients of the two discriminant functions (discriminant function 1 and 2) used and their significance are shown in Fig. 35.

[0240] From Fig. 35, it was found that for the discrimination between the two groups of the resident control group and the AD patient group, gender and 11 elements, namely Na (P<0.01), Mg (P<0.01), S (P<0.05), K (P<0.01), Ca (P<0.01), Fe (P<0.01), Cu (P<0.01), Zn (P<0.01), Se (P<0.01), Rb (P<0.05), Mo (P<0.01), were significant. Among these elements significant for discrimination, K was suggested to be involved in AD but not in MCI.

[0241] Fig. 37 is a histogram showing the frequency distribution of the discrimination scores obtained by discriminant analysis of two groups, namely the resident control group and the AD patient group, when using the 17 concentration data and gender data of the evaluation element group.

[0242] From Fig. 37, it was found that most of the discrimination scores of the resident control group are distributed in the right region of the figure, and most of the discrimination scores of the AD patient group are distributed in the left region of the figure. In other words, it was found that they are divided on the left and right near the point (position) where the value of the discrimination score is -1.5. Also, in the region near the point (position) where the value of the discrimination score is -1.5, it was also found that the discrimination scores of the case group (AD patient group) and the control group (resident control group) are mixed.

[0243] The ROC curve obtained in Example 4 is shown in Fig. 38 together with the ROC curve obtained in Example 3.

[0244] Using the ROC curve in Fig. 38, when calculating the area under the curve (AUC), as shown in the figure, for AD, a high value of 0.972 (95% CI: 0.956 - 0.987) was obtained. Although this value was slightly lower compared to the AUC value for MCI, since it was above 0.95, it was inferred that the discriminative effectiveness for AD was also sufficient.

[0245] The value of 0.972 obtained in Example 4 indicated that the discrimination (prediction) between the two groups in Example 4 was carried out very accurately. Therefore, it was determined that the method of Example 4 was sufficiently effective as a tool for AD risk diagnosis.

[0246] Note that since the value of AUC = 0.972 in Example 4 was clearly higher than the AUC = 0.79 of MMSE, it was recognized that the method of Example 4 had sufficient effectiveness as a tool for AD risk diagnosis.

[0247] Fig. 41 is a graph showing the relationship (discriminant score - AD morbidity rate relationship) between the discriminant score obtained from the discriminant analysis of the two groups in Example 4 and the probability that the subject has AD. Similar to Examples 1 and 2 above, this graph was obtained by performing binary logistic regression analysis using the discriminant score obtained from the discriminant analysis. According to this graph, similar to Example 2 (refer to Fig. 22) above, the greater the negative value of the discriminant score, the higher the morbidity probability (risk) of AD. For example, if the discriminant score was approximately -3.9 or lower, it could be evaluated that "the probability of the subject having AD is inferred to be 90% or more." Conversely, for example, if the discriminant score was approximately +0.25 or higher, the morbidity probability would be 10% or less, so it could be evaluated that "it is inferred that the subject does not have MCI."

[0248] In Example 4, the discriminant formula for MCI risk assessment: F(AD) calculated by the discriminant analysis in (ii) using the 17 concentration data of the evaluation element group and the gender data is shown in FIG. 39. The AD risk assessment method using this discriminant formula: F(AD) can be suitably used as a "dementia risk screening method" for the general public other than the patients who visit the departments of neurology, neuropsychiatry, psychotherapy, general internal medicine, etc. in medical institutions such as hospitals. If this is realized, the AD onset of the subject can be detected at an early stage, and it is expected that the selection of treatment methods for suppressing the progression of AD will be facilitated.

[0249] In addition, FIG. 39 also shows the discriminant formula for MCI risk assessment: F(AD) calculated by the discriminant analysis in (i) using only the 17 concentration data of the evaluation element group, and the discriminant analysis in (iii) using the 17 concentration data of the evaluation element group, the gender data, and the age group (age range) data.

Industrial Applicability

[0250] The present invention is widely applicable to the fields where it is desired to quickly and simply estimate the presence or absence of dementia (MCI or AD) in humans (or animals) and the risk of onset.

Explanation of Signs

[0251] 1 Test tube 2 Blood (plasma or serum) sample 5 Element group concentration measurement unit 10 Dementia risk assessment system 11 Data storage unit 12 Calculation unit 13 Index generation unit

Claims

1. A method for assessing a subject's risk of developing dementia, the method comprising: a correlation calculation step of calculating a correlation between the concentrations of the evaluation elements contained in the blood sampled from the subject by applying a discriminant function for determining whether the subject belongs to a control group or a case group; and and generating an index for identifying whether or not the subject is suffering from dementia based on the correlation calculated in the correlation calculation step. The evaluation element group includes a combination of 17 elements, namely, Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs. The correlation calculation step is executed by a calculation unit of the computer, A dementia risk assessment method, characterized in that the index generation step is executed by an index generation unit of the computer.

2. Applying the concentration data of the evaluation element group to a discriminant equation generated based on the correlation to calculate a discriminant score; By comparing the discriminant score with a predetermined reference value, the subject is determined to belong to either the control group or the case group; The method for assessing a risk of dementia according to claim 1 , further comprising the step of predicting whether or not the subject is suffering from dementia based on the result of the discrimination.

3. By comparing the discriminant score with a predetermined discriminant score-incidence rate relationship, a probability of dementia of the subject is estimated; The method for assessing dementia risk according to claim 2 , wherein the estimated probability of developing dementia is attached to the index.

4. As the discriminant, a discriminant generated for mild cognitive impairment (MCI) is used, The method for assessing dementia risk according to claim 2 or 3, wherein the index is accompanied by a determination result of whether the subject suffers from mild cognitive impairment (MCI).

5. As the discriminant, a discriminant generated for Alzheimer's disease (AD) is used, The method for assessing a risk of dementia according to claim 2 or 3, wherein the index is accompanied by a determination result as to whether or not the subject is suffering from Alzheimer's disease (AD).

6. As the discriminant, both a discriminant generated for mild cognitive impairment (MCI) and a discriminant generated for Alzheimer's disease (AD) are used, The method for assessing dementia risk according to claim 2 or 3, wherein the index is accompanied by a determination result as to whether the type of dementia of the subject suspected of having dementia is mild cognitive impairment (MCI) or Alzheimer's disease (AD).

7. A dementia risk assessment method as described in any one of claims 1 to 3, wherein when the concentration data of four elements, Ca, Zn, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements is determined to be significant for discrimination based on the correlation, a presumption that the type of dementia related to the subject presumed to be suffering from dementia is mild cognitive impairment (MCI) is attached to the index.

8. A dementia risk assessment method as described in any one of claims 1 to 3, wherein when the concentration data of three elements, Mg, Ca, and Rb, selected from the 17 elements used as the group of evaluation elements is determined to be significant for discrimination based on the correlation, a prediction that the type of dementia related to the subject presumed to be suffering from dementia is Alzheimer's disease (AD) is attached to the index.

9. A hospital control group was used as the control group, The subjects are selected from patients who visit a specific medical institution with a suspected neurological or psychiatric disorder, The method for assessing a risk of dementia according to any one of claims 1 to 3, wherein the index is used as an auxiliary role in dementia diagnosis at the medical institution.

10. A resident control group was used as the control group, The subjects were selected from the general population, who were not patients visiting medical institutions for suspected neurological or psychiatric disorders. The method for assessing a risk of dementia according to any one of claims 1 to 3, wherein the index is used as a means of assessing the risk of dementia in the general population.

11. A dementia risk assessment method as described in any one of claims 1 to 3, wherein when the concentration data of 11 elements, namely Na, Mg, S, Ca, Fe, Cu, Zn, As, Se, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements, is determined to be significant for discrimination based on the correlation, a presumption that the type of dementia related to the subject presumed to be suffering from dementia is mild cognitive impairment (MCI) is attached to the index.

12. A dementia risk assessment method as described in any one of claims 1 to 3, wherein when the concentration data of 11 elements, namely Na, Mg, S, K, Ca, Fe, Cu, Zn, Se, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements, is determined to be significant for discrimination based on the correlation, a presumption that the type of dementia related to the subject determined to be suffering from dementia is Alzheimer's disease (AD) is attached to the index.

13. a data storage unit that stores concentration data of the evaluation elements contained in blood collected from the subject; a calculation unit that calculates a correlation between concentrations of the evaluation elements by applying the concentration data of the subject stored in the data storage unit to a discriminant function for discriminating whether the subject belongs to a control group or a case group; and an index generating unit that generates an index for identifying whether or not the subject suffers from dementia based on the correlation calculated by the calculation unit; A dementia risk assessment system characterized in that a combination of 17 elements, namely Na, Mg, P, S, K, Ca, Fe, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, and Cs, is used as the group of evaluation elements.

14. Applying the concentration data of the evaluation element group to a discriminant equation generated based on the correlation to calculate a discriminant score; By comparing the discriminant score with a predetermined reference value, the subject is determined to belong to either the control group or the case group; The dementia risk assessment system of claim 13, which infers whether or not the subject is suffering from dementia based on the result of the discrimination.

15. By comparing the discriminant score with a predetermined discriminant score-incidence rate relationship, a probability of dementia of the subject is estimated, The dementia risk assessment system of claim 14, wherein the estimated dementia incidence probability is attached to the index.

16. As the discriminant, a discriminant generated for mild cognitive impairment (MCI) is used, The dementia risk assessment system according to claim 14 or 15, wherein the index is accompanied by a determination result as to whether the subject suffers from mild cognitive impairment (MCI).

17. As the discriminant, a discriminant generated for Alzheimer's disease (AD) is used, The dementia risk assessment system according to claim 14 or 15, wherein the index is accompanied by a determination result as to whether or not the subject is suffering from Alzheimer's disease (AD).

18. As the discriminant, both a discriminant generated for mild cognitive impairment (MCI) and a discriminant generated for Alzheimer's disease (AD) are used, The dementia risk assessment system of claim 14 or 15, wherein the index is accompanied by a determination result as to whether the type of dementia of the subject suspected of suffering from dementia is mild cognitive impairment (MCI) or Alzheimer's disease (AD).

19. A dementia risk assessment system as described in any one of claims 13 to 15, wherein when the concentration data of four elements, Ca, Zn, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements is determined to be significant for discrimination based on the correlation, a prediction that the type of dementia related to the subject suspected of suffering from dementia is mild cognitive impairment (MCI) is attached to the index.

20. A dementia risk assessment system as described in any one of claims 13 to 15, wherein when the concentration data of three elements, Mg, Ca, and Rb, selected from the 17 elements used as the group of evaluation elements is determined to be significant for discrimination based on the correlation, a prediction that the type of dementia related to the subject suspected to be suffering from dementia is Alzheimer's disease (AD) is attached to the index.

21. A hospital control group was used as the control group, The subjects are selected from patients who visit a specific medical institution with a suspected neurological or psychiatric disorder, The dementia risk assessment system according to any one of claims 13 to 15, wherein the index is used as an auxiliary role in dementia diagnosis at the medical institution.

22. A resident control group was used as the control group, The subjects were selected from the general population, who were not patients visiting medical institutions for suspected neurological or psychiatric disorders. The dementia risk assessment system according to any one of claims 13 to 15, wherein the index is used as a means of assessing the dementia risk of the general population.

23. A dementia risk assessment system as described in any one of claims 13 to 15, wherein when the concentration data of 11 elements, namely Na, Mg, S, Ca, Fe, Cu, Zn, As, Se, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements, is determined to be significant for discrimination based on the correlation, a prediction that the type of dementia related to the subject presumed to be suffering from dementia is mild cognitive impairment (MCI) is attached to the index.

24. A dementia risk assessment system as described in any one of claims 13 to 15, wherein when the concentration data of 11 elements, namely Na, Mg, S, K, Ca, Fe, Cu, Zn, Se, Rb, and Mo, selected from the 17 elements used as the group of evaluation elements, is determined to be significant for discrimination based on the correlation, a presumption that the type of dementia related to the subject determined to be suffering from dementia is Alzheimer's disease (AD) is attached to the index.

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