Protein markers for mild cognitive impairment and Alzheimer's disease

Abstract

This invention relates to protein markers associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD), particularly protein markers detectable in blood samples. Accordingly, methods and compositions for risk assessment and early diagnosis of MCI and AD based on the analysis of these protein markers are provided. Further methods and compositions useful for evaluating the effectiveness of treatments for MCI or AD are also provided.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims priority to U.S. Provisional Patent Application No. 63 / 495,864, filed on April 13, 2023, the entire content of which is incorporated herein by reference for all purposes.

Background Art

[0002] Neurodegenerative diseases are destructive brain conditions that affect a large subset of the population. Many are highly debilitating, currently incurable, and often result in a progressive deterioration of brain structure and cognitive function. For example, Alzheimer's disease (AD) accounts for 60 - 80% of dementia cases and is a major cause of death in the elderly. AD, characterized by progressive cognitive decline, is an age - related progressive neurodegenerative disorder that currently affects 46.8 million people worldwide, approximately 10% of people aged 65 and older, and nearly 10 million new cases occur each year. The pathological features of this chronic disease include the accumulation of amyloid - beta plaques and neurofibrillary tangles in the brain, along with synaptic dysfunction and neuron loss that cause an inflammatory response in the brain. The most common AD symptoms include memory impairment, difficulty in communication, impairment of reasoning and judgment, and a decline in walking ability. As with many neurodegenerative diseases and neuroinflammatory disorders, the current challenge in AD diagnosis is due to the limited understanding of the pathophysiology of this disease. On the other hand, currently available treatments are not effective and only provide a transient effect on symptom relief, and patients still suffer severely from this disease.

[0003] Mild cognitive impairment (MCI), characterized as a transitional state between normal cognition and dementia such as Alzheimer's disease (AD), accounts for approximately 10-20% of people over 65 years of age. Individuals with MCI have cognitive impairment but do not exhibit dementia. Compared to individuals with normal cognition, individuals with MCI have a higher risk of developing AD or other dementias, with a cumulative probability of 33-50% of progressing to AD. MCI is considered a symptomatic pre-dementia stage, but it is believed that a return to normal cognition is possible. Therefore, recognition of MCI is crucial for treating MCI and preventing or delaying its progression to AD. Specifically, early diagnosis and timely intervention of AD and pre-AD MCI are expected to promote the prevention and treatment of AD, thereby effectively delaying the progression of AD and reducing the medical, socioeconomic, and psychological burden on families and society as a whole. This invention satisfies these and related needs. [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] This invention relates to the discovery of novel protein markers associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Accordingly, this invention provides methods and compositions useful for the early diagnosis of MCI and AD in subjects. Methods for evaluating the therapeutic efficacy of agents for the treatment of MCI and AD are also provided. [Means for solving the problem]

[0005] Accordingly, in a first aspect, the present invention provides a method for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method comprises the steps of (a) comparing the level or concentration of at least one protein in a plasma, serum, or whole blood sample of a subject to a standard control level of the same protein, wherein the at least one protein is selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3, and the standard control level of the same protein is the blood of an average healthy subject who does not have MCI or AD or is not at risk of MCI or AD. The method includes (a) detecting the level or concentration of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 protein in the plasma, serum, or whole blood sample of the subject at a level or concentration lower than the standard control level for the same protein, or higher than the standard control level for the same protein, and (c) determining that the subject is at high risk of developing MCI or AD. In some embodiments, the method further includes measuring the level or concentration of at least one protein in the plasma, serum, or whole blood sample of the subject prior to step (a). In some embodiments, the method further includes obtaining a plasma, serum, or whole blood sample from the subject prior to the measurement step. In some embodiments, the step of measuring the level or concentration of the protein involves the use of an antibody-based detection method, an aptamer-based detection method, or a mass spectrometry method.In some embodiments, if in step (c) the subject is determined to be at risk of developing MCI or AD, the subject is then provided with increased follow-up monitoring (e.g., monitoring with increased frequency of examinations compared to the usual monitoring prescribed by a healthcare professional for a person of similar age and medical background who is at no risk or at low risk). In some embodiments, if in step (c) the subject is determined to be at increased risk of developing MCI or AD, the subject is then administered a therapeutic agent to prevent or treat MCI or AD.

[0006] While any one of the 18 proteins identified in Table 1 is suitable for use in this method, in some cases, multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more from the 18 proteins) may be tested simultaneously using this method to achieve a better assessment of the risk of developing MCI or AD in subjects. In some embodiments, the simultaneous use of multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more from the 18 proteins) in this method can improve the accuracy, sensitivity, and specificity in determining the risk of developing MCI or AD. In some cases, the simultaneous use of multiple proteins in this method (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more proteins from the 18 available proteins) allows for the evaluation of multiple biological pathways / systems and thus provides a more comprehensive assessment of the disease state in question.

[0007] In some embodiments, a predictive model is used to predict MCI risk and AD risk by integrating the levels of any two or more proteins from the 18 proteins. In some embodiments, a predictive model that uses multiple protein markers in predicting MCI risk and AD risk achieves better diagnostic performance than a method that uses any one protein from the 18 proteins. In some embodiments, the predictive model uses any two proteins from the 18 proteins. In some embodiments, the predictive model uses any three proteins from the 18 proteins. In some embodiments, the predictive model uses any four or more proteins from the 18 proteins. In some examples, at least two proteins are selected from the group of 18 proteins and measured for risk assessment according to the claimed method. In some examples, at least three or more proteins are selected from the group of 18 proteins and then evaluated according to the claimed method.

[0008] In a second aspect, the present invention provides a kit for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, or for evaluating the therapeutic effectiveness of a treatment regimen for MCI or AD in a subject. The kit comprises at least one reagent capable of determining the level or concentration of one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more types of proteins independently selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 in the subject's plasma, serum, or whole blood. In some embodiments, the kit may further include standard controls for each protein that reflect the levels / concentrations of the same proteins found in the corresponding plasma, serum, or whole blood of an average healthy subject who does not have MCI or AD, or who does not have an increased risk of developing MCI or AD. In some embodiments, the kit is used to determine the levels or concentrations of at least two proteins from 18 proteins in the subject's plasma, serum, or whole blood. In some embodiments, the kit can determine the levels or concentrations of at least three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more proteins from 18 proteins in the subject's plasma, serum, or whole blood.

[0009] In a third aspect, the present invention provides a detection chip for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, or for evaluating the therapeutic effectiveness of a treatment regimen for MCI or AD in a subject. The chip comprises a solid substrate and at least one reagent capable of determining the plasma, serum, or whole blood level of any one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more types of proteins independently selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3, each reagent being immobilized at an addressable position on the substrate. In some embodiments, the chip is used to determine the level or concentration of at least two proteins from 18 proteins in a subject in plasma, serum, or whole blood. In some embodiments, the chip can determine the level or concentration of at least three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more proteins from 18 proteins in a subject in plasma, serum, or whole blood.

[0010] In a fourth aspect, the present invention provides a method for quantifying the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method involves (a) calculating an individual risk score by inputting a set of values ​​into the following formula:

number

[0011] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels for each of the 18 proteins, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 2. Subjects with a risk score higher than 0.356 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0012] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CCL27 and IGFBP-2, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 3. Subjects with a risk score higher than 0.656 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0013] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3, CD27, and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 4. Subjects with a risk score higher than 0.266 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0014] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3 and CD27, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 5, and subjects with a risk score higher than 0.620 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0015] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3 and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 6, and subjects with a risk score higher than 0.833 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0016] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CD27 and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 7, and subjects with a risk score higher than 0.509 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or not have an increased risk of developing MCI or AD.

[0017] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC, KYNU, and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 8. Subjects with a risk score higher than 0.489 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0018] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC and KYNU, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 9, and subjects with a risk score higher than 0.589 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0019] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 10, and subjects with a risk score higher than 0.515 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0020] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of KYNU and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 11, and subjects with a risk score higher than 0.799 are considered to have an increased risk of developing MCI or AD. Otherwise, subjects are considered to have a low risk of MCI or AD, or no increased risk of MCI or AD.

[0021] In some embodiments, the method further includes a step of measuring plasma, serum, or whole blood levels of protein prior to step (a). In some embodiments, the method further includes another step of obtaining a plasma sample, serum sample, or whole blood sample from the subject prior to the step of measurement. In some embodiments, if the subject is determined to have an increased risk of developing MCI or AD in step (b), the subject then receives increased follow-up monitoring (e.g., monitoring tests at an increased frequency compared to the usual monitoring prescribed by a healthcare professional for a person of similar age and medical background who is at no risk or at low risk) and / or treatment for MCI or AD as described in this disclosure. If the subject is determined not to have an increased risk of developing MCI or AD, the subject receives the usual monitoring that is generally prescribed by a physician for a person who is at no risk or at low risk of developing MCI or AD.

[0022] In a fifth aspect, the present invention provides a method for evaluating the effectiveness of a therapeutic agent for treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method comprises: (a) comparing the plasma level, serum level, or whole blood level of any one protein selected from the proteins listed in Table 1 in the subject before and after administration of the therapeutic agent; (b) detecting a decrease in the plasma level, serum level, or whole blood level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3, or an increase in the plasma level, serum level, or whole blood level of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 in the subject after administration of the therapeutic agent; and (c) determining that the therapeutic agent is effective for treating MCI or AD. In some embodiments, the method further comprises, before step (a), measuring the plasma level, serum level, or whole blood level of the one or more proteins before and after administration. In some embodiments, the method may also include obtaining a plasma sample, a serum sample, or a whole blood sample from the subject before and after administration, prior to the measuring step.

[0023] In some embodiments, if in step (c) the therapeutic agent is considered effective for treating MCI or AD, the subject continues treatment by receiving the therapeutic agent, and if in step (c) the therapeutic agent is considered not effective for treating MCI or AD, the subject discontinues treatment by receiving the therapeutic agent and instead initiates a different treatment by receiving a different therapeutic agent. In some embodiments, the subject is of Chinese descent. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] [Figure 1a]Prediction of MCI and AD risk based on a model utilizing 18 blood proteins. Box plots showing risk scores for individuals assigned by a model of 18 proteins (i.e., a combined model of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3; listed in Table 2) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.356) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 1b] Prediction of MCI risk and AD risk based on a model utilizing 18 blood proteins. Receiver operating characteristic (ROC) curves for the 18-protein model (solid line) and the single-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 1c] Prediction of MCI risk and AD risk based on a model utilizing 18 blood proteins. Receiver operating characteristic (ROC) curves for the 18-protein model (solid line) and the single-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 2a] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from 18 blood proteins. Box plots showing risk scores for individuals assigned by the two-protein model (i.e., a combined model of CCL27 and IGFBP-2; listed in Table 3) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.656) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 2b] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from 18 types of blood proteins. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 2c] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from 18 types of blood proteins. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 3a] Prediction of MCI risk and AD risk based on a model utilizing three blood proteins. Box plots showing risk scores for individuals assigned by a model of three proteins (i.e., a combined model of AOC3, CD27, and NCS1; listed in Table 4) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.266) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 3b] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for the three-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 3c] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for the three-protein model (solid line) and the one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4a] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from three blood proteins. Box plots showing risk scores for individuals assigned by the two-protein model (i.e., a combined model of AOC3 and CD27; listed in Table 5) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.620) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 4b] Prediction of MCI risk and AD risk based on models utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4c] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4d] Prediction of MCI risk and AD risk based on a model utilizing three blood proteins. Box plots showing risk scores for individuals assigned to a two-protein model (i.e., a combined model of AOC3 and NCS1; listed in Table 6) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.833) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 4e]Prediction of MCI risk and AD risk based on models utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4f] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4g] Prediction of MCI risk and AD risk based on a model utilizing three blood proteins. Box plots showing risk scores for individuals assigned to a two-protein model (i.e., a combined model of CD27 and NCS1; listed in Table 7) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.509) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 4h] Prediction of MCI risk and AD risk based on models utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 4i] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for two-protein models (solid line) and one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 5a]Prediction of MCI risk and AD risk based on a model utilizing three blood proteins. Box plots showing risk scores for individuals assigned to the three protein models (i.e., a combined model of CTRC, KYNU, and TNNI3; listed in Table 8) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.489) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 5b] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for the three-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 5c] Prediction of MCI risk and AD risk based on a model utilizing three types of blood proteins. Receiver operating characteristic (ROC) curves for the three-protein model (solid line) and the one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6a] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from three blood proteins. Box plots showing risk scores for individuals assigned by the two-protein model (i.e., a combined CTRC and KYNU model; listed in Table 9) in the HK Chinese cohort, stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.589) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 6b]Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6c] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing between CN and AD patients in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6d] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from three blood proteins. Box plots showing risk scores for individuals assigned by the two-protein model (i.e., a combined model of CTRC and TNNI3; listed in Table 10) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.515) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 6e] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6f]Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing between CN and AD patients in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6g] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins from three blood proteins. Box plots showing risk scores for individuals assigned by the two-protein model (i.e., a combined model of KYNU and TNNI3; listed in Table 11) in an HK Chinese cohort stratified by diagnosis (n=9 (CN), n=14 (MCI), and n=16 (AD), respectively). The dashed line (risk score = 0.799) represents the cutoff for the risk of developing MCI and AD. *P<0.05, **P<0.01, ***P<0.001. [Figure 6h] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing MCI patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Figure 6i] Prediction of MCI risk and AD risk based on a model utilizing two blood proteins out of three. Receiver operating characteristic (ROC) curves for the two-protein model (solid line) and the one-protein model (dashed line) when distinguishing AD patients from CN in the HK Chinese cohort. The numbers in parentheses indicate the area under the ROC curve for the corresponding model. [Modes for carrying out the invention]

[0025] definition Unless otherwise specifically indicated, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which this disclosure pertains. In addition, any method or material similar to or equivalent to those described herein may be used when carrying out this disclosure. For the purposes of this disclosure, the following terms are defined:

[0026] The terms “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to polymers of amino acid residues. All three terms apply to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of corresponding natural amino acids, as well as to natural and non-natural amino acid polymers. As used herein, the terms encompass amino acid chains of any length, including full-length proteins, where the amino acid residues are linked by covalent peptide bonds.

[0027] In this disclosure, the terms “biological specimen” or “specimen” include tissue sections, e.g., biopsy specimens and autopsy specimens, as well as frozen sections taken for histological purposes, or any processed form of such specimens. Biological specimens include blood and fractions or products of blood (e.g., whole blood, cell-free fractions of blood (serum, plasma), and blood cells), sputum or saliva, lymphoid tissue and tongue tissue, cultured cells, e.g., primary cultures, explants and transformed cells, feces, urine, gastric biopsy tissue, etc. Biological specimens are typically obtained from eukaryotes, which may be mammals, primates, or human subjects.

[0028] The terms “immunoglobulin” or “antibody” (as used herein without distinction) refer to antigen-binding proteins having a basic four-polypeptide chain structure consisting of two heavy chains and two light chains, which are stabilized, for example, by interchain disulfide bonds that have the ability to specifically bind to an antigen. Both the heavy and light chains are folded into domains.

[0029] The term "antibody" also refers to antigen-binding and epitope-binding fragments of an antibody, such as Fab fragments, which can be used in immunological affinity assays. Several well-characterized antibody fragments exist. For example, pepsin digests the antibody at the C-terminal side of the disulfide bond within the hinge region to produce F(ab)'2, i.e., V H -C H This produces a dimer of Fab, which is a light chain linked to 1 by a disulfide bond. By reducing F(ab)'2 under mild conditions, the disulfide bond in the hinge region can be cleaved, thereby converting the (Fab')2 dimer to the Fab' monomer. The Fab' monomer is essentially Fab with a portion of the hinge region (see, for example, Fundamental Immunology, Paul, ed., Raven Press, NY (1993) for a more detailed description of other antibody fragments). While various antibody fragments are defined in relation to the digestion of intact antibodies, those skilled in the art will understand that fragments can be synthesized de novo, either chemically or by utilizing recombinant DNA methods. Thus, the term antibody also includes antibody fragments produced by modification of the whole antibody or synthesized using recombinant DNA methods.

[0030] As used in this application, “increase” or “decrease” refers to a detectable positive or negative change in a quantity from a comparison control, e.g., an established standard control (such as the mean level / amount of a particular protein found in samples from healthy subjects who have not been diagnosed with MCI or AD and do not have an increased risk of MCI or AD). An increase is typically a positive change of at least 10%, or at least 20%, or 50%, or 100%, and can be at least twice, or at least five times, or even ten times, the control value. Similarly, a decrease is typically a negative change of at least 10%, or at least 20%, or at least 30%, or at least 50%, or even at least 80%, or at least 90%, the control value. Other terms indicating a quantitative change or difference from a comparison standard, e.g., “more,” “less,” “higher,” and “lower,” are used in this application in the same manner as above. In contrast, the terms “substantially the same” or “substantially unchanged” mean that there is little or no change in quantity from the standard control value, typically within ±10% of the standard control, or within ±5%, 2%, or even less than that of the standard control.

[0031] As used in this application, the term "quantity" refers to the amount of the substance of interest, for example, the amount of the protein of interest present in the sample. Such a quantity may be expressed in absolute terms, i.e., the total amount of the substance in the sample, or in relative terms, i.e., the concentration of the substance in the sample.

[0032] The terms “subject,” “individual,” and “patient” are used herein without distinction to refer to mammals, preferably humans, who are seeking medical attention for a risk of MCI or AD (e.g., having a family history) or who have been diagnosed with MCI or AD. Subjects include individuals seeking manipulation of a treatment regimen or individuals currently receiving treatment. Subjects or individuals requiring treatment include subjects or individuals exhibiting symptoms of MCI or AD, or subjects or individuals at risk of developing MCI or AD or its symptoms. For example, subjects include individuals with a genetic predisposition or family history of MCI or AD, individuals who have previously suffered from related symptoms, individuals who have been exposed to triggering substances or events, and individuals suffering from chronic or acute symptoms of the condition. Subjects may be of any sex and of any age.

[0033] As used in this application, the terms “treat” or “treating” refer to any action that results in the elimination, reduction, alleviation, reversal, prevention, and / or delay of the onset or recurrence of any symptom of a given medical condition. In other words, “treating” a condition encompasses both therapeutic and preventive interventions for that condition.

[0034] As used herein, the term “effective dose” refers to the amount of substance administered that produces a therapeutic effect. The effect includes, to a detectable degree, prevention, correction, or inhibition of the progression of any of the symptoms of a disease / condition and associated complications. The exact dose depends on the therapeutic objective and can be determined by those skilled in the art using known techniques (see, for example, Lieberman, Pharmaceutical Dosage Forms (vols. 1-3, 1992); Lloyd, The Art, Science and Technology of Pharmaceutical Compounding (1999); and Pickar, Dosage Calculations (1999)).

[0035] As used herein, the term “standard control” refers to a sample containing a predetermined amount of analyte to indicate the amount or concentration of the analyte present in a sample of this type (e.g., a given DNA / mRNA or protein) taken from an average healthy subject who is not suffering from or at risk of developing a given disease or condition (e.g., MCI or AD). When used in a context describing a value, the term may be used simply to refer to the amount or concentration of the analyte present in the “standard control” sample.

[0036] In the context of describing healthy individuals who do not have and are not at risk of developing the relevant disease or disorder (e.g., MCI or AD), the term “mean” refers to a specific characteristic representing a randomly selected group of healthy individuals who do not have and are not at risk of developing the disease or disorder, such as the level of the relevant protein in a human sample (e.g., serum, plasma, or whole blood). This selected group should include a sufficient number of human subjects so that the mean amount or mean concentration of the analyte of interest in these individuals reflects with reasonable accuracy the corresponding profile in the general population of healthy individuals. If desired, the selected group of subjects may be chosen to have a background similar to that of the individuals being tested for the indication or risk of the relevant disease or disorder, such as matched or equivalent age, sex, ethnicity, and medical history.

[0037] As used herein, the term “Chinese” refers to people of Chinese descent whose ancestors have lived in the historical territories of China, including mainland China and Hong Kong, for, for example, for at least the last three, four, five, six, seven, or eight generations, or for the last 100, 150, 200, 250, or 300 years.

[0038] I. Introduction This disclosure provides novel protein markers for the early diagnosis or risk assessment of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in subjects. In particular, a panel of 18 protein biomarkers representing an "MCI and / or AD signature" has been identified in blood samples, which are differentially expressed between healthy subjects and individuals with MCI or AD. This disclosure provides methods and compositions useful for the early diagnosis of MCI or AD in subjects. This disclosure also provides methods and compositions for evaluating therapeutic treatments for MCI or AD in subjects.

[0039] II. Protein Markers 1. The protein CTRC, also known as chymotrypsin C or caldecrin, is a protease enzyme primarily found in the pancreas. CTRC belongs to the serine protease family and plays a crucial role in regulating digestive enzymes within the pancreas. Specifically, CTRC helps activate other pancreatic enzymes, such as trypsinogen, which is essential for the proper digestion of proteins in the small intestine. Mutations in the CTRC gene are associated with an increased risk of developing chronic pancreatitis, a disease characterized by the gradual onset of pancreatic inflammation and damage.

[0040] 2. Protein NCS1, also known as neuronal calcium sensor protein 1, is a calcium-binding protein primarily expressed in the brain and nervous system. NCS1 is involved in a wide range of physiological processes, including learning and memory, motor coordination, and sensory processing. Mutations in the NCS1 gene are associated with certain neurological disorders, such as schizophrenia, bipolar disorder, and Parkinson's disease.

[0041] 3. The protein PSME1, also known as PA28 alpha or REG alpha, is a regulatory protein involved in immune and cellular stress responses. PSME1 belongs to the family of proteasome activators and plays a crucial role in activating the 20S proteasome, which is responsible for the degradation of damaged or misfolded proteins within cells. PSME1 specifically binds to the 20S proteasome, enhancing its proteolytic activity and thereby promoting the clearance of abnormal proteins. PSME1 has also been shown to be involved in antigen processing and presentation, which are important for the recognition and elimination of foreign pathogens by the immune system. Dysregulation of PSME1 expression is associated with the development and progression of various diseases, including cancer, autoimmune disorders, and neurodegenerative diseases.

[0042] 4. The protein KYNU, also known as kynureninase, is an enzyme involved in the metabolism of the essential amino acid tryptophan. KYNU catalyzes the conversion of kynurenine to anthranilic acid in the kynurenine pathway, a major pathway in human tryptophan metabolism. This pathway plays a crucial role in regulating immune function, inflammation, and neurotransmitter synthesis in the brain. Dysregulation of KYNU activity is implicated in the pathogenesis of various diseases, including autoimmune disorders, neurodegenerative diseases, and cancer. Because KYNU is also involved in regulating immune responses and inflammation, it has been proposed as a potential therapeutic target for these diseases.

[0043] 5. Protein TNNI3, also known as cardiac troponin I, is a regulatory protein primarily expressed in the myocardium. Protein TNNI3 is a component of the troponin complex, which plays a role in regulating muscle contraction in response to calcium signaling. TNNI3 specifically binds to actin filaments within sarcomeres, inhibiting the interaction between actin and myosin, thereby suppressing muscle contraction. Because TNNI3 levels in the blood increase in response to myocardial injury, it is an important biomarker for diagnosing acute myocardial infarction, commonly known as a heart attack. Mutations in the TNNI3 gene are associated with various cardiac disorders, including hypertrophic cardiomyopathy, dilated cardiomyopathy, and restrictive cardiomyopathy.

[0044] 6. The protein IGFBP2, also known as insulin-like growth factor binding protein 2, is a binding protein that interacts with insulin-like growth factor (IGF) and regulates its activity in the body. IGFBP2 is primarily produced in the liver and is found in circulation. IGFBP2 regulates the bioavailability of IGF, a key regulator of cell proliferation, differentiation, and survival. IGFBP2 is involved in a wide range of physiological processes, including embryonic development, tissue repair, and metabolism. Dysregulation of IGFBP2 expression is associated with various diseases, including cancer, metabolic disorders, and neurodegenerative diseases. IGFBP2 is being studied as a potential biomarker for the diagnosis and prognosis of certain diseases, as well as a therapeutic target for the treatment of cancer and other disorders.

[0045] 7. Protein DCBLD2, also known as discoidine, CUB, and LCCL domain-containing protein 2, is a transmembrane protein expressed in a wide range of tissues, including the brain, heart, and lungs. Protein DCBLD2 belongs to the family of adhesion G protein-coupled receptors and is involved in cell adhesion, migration, and angiogenesis. Dysregulation of DCBLD2 expression is associated with a variety of diseases, including cancer, cardiovascular disease, and developmental disorders. Because DCBLD2 is involved in angiogenesis and cell migration, it has been proposed as a potential therapeutic target for cancer and other diseases.

[0046] 8. The protein CCL27, also known as a cutaneous T-cell-attracting chemokine (CTACK), is a chemokine primarily expressed in the skin. Belonging to the CC chemokine family, CCL27 is involved in the recruitment and activation of immune cells, particularly T cells, to the skin. CCL27 is involved in various physiological processes, including inflammatory responses, wound healing, and skin development. Dysregulation of CCL27 expression is associated with various skin disorders, such as psoriasis, atopic dermatitis, and skin cancer. Because CCL27 is involved in regulating immune responses in the skin, it has been proposed as a potential therapeutic target for these diseases.

[0047] 9. Protein CD33, also known as Siglec-3, is a transmembrane protein primarily expressed on the surface of myeloid cells, including monocytes, macrophages, and dendritic cells. CD33 belongs to the family of sialic acid-binding immunoglobulin-like lectins (Siglec) and is involved in regulating immune responses. CD33 binds to sialic acid residues on glycoproteins and glycolipids, thereby regulating cell signaling and adhesion. CD33 is involved in various physiological processes, including phagocytosis, antigen presentation, and cytokine production. Dysregulation of CD33 expression is associated with various diseases, including Alzheimer's disease, acute myeloid leukemia, and autoimmune disorders. Because CD33 is involved in regulating immune function and cell signaling, it has been proposed as a potential therapeutic target for these diseases.

[0048] 10. Protein NEFL, also known as neurofilament light chain, is a cytoskeletal protein primarily expressed in neurons. NEFL is a component of neurofilaments, a network of filamentous proteins that provide structural support to axons and contribute to their electrical properties. NEFL is involved in axonal transport and various physiological processes, including neuronal development, plasticity, and regeneration. Dysregulation of NEFL expression is associated with various neurological disorders, including amyotrophic lateral sclerosis (ALS), Alzheimer's disease, and peripheral neuropathy. Because NEFL is involved in axonal damage and degeneration, it has been proposed as a potential biomarker for these diseases.

[0049] 11. Protein FCN2, also known as phycolin-2, is a soluble pattern recognition receptor that is part of the innate immune system. Belonging to the phycolin family, protein FCN2 recognizes and binds to pathogen-associated molecular patterns (PAMPs) on the surface of microorganisms such as bacteria, viruses, and fungi. FCN2 is primarily produced in the liver and is found in circulation. FCN2 is involved in various physiological processes, including host defense, inflammation, and tissue repair. Dysregulation of FCN2 expression is associated with various infectious and inflammatory diseases, including sepsis, pneumonia, and rheumatoid arthritis. FCN2 is being studied as a potential biomarker and therapeutic target for these diseases.

[0050] 12. The protein LGALS7, also known as galectin 7, is a soluble lectin involved in various cellular processes, including cell adhesion, apoptosis, and immunomodulation. LGALS7 belongs to the family of galectins, which are carbohydrate-binding proteins that interact with glycoproteins and glycolipids on the cell surface. LGALS7 is expressed in a wide range of tissues, including the skin, gastrointestinal tract, and immune cells. LGALS7 is involved in various physiological processes such as wound healing, inflammation, and cancer progression. Dysregulation of LGALS7 expression is associated with a variety of diseases, including cancer, inflammatory disorders, and neurodegenerative diseases. Because LGALS7 is involved in regulating cell signaling and immune responses, it has been proposed as a potential biomarker and therapeutic target for these diseases.

[0051] 13. Protein GP1BA, also known as glycoprotein Ib platelet subunit alpha, is a subunit of the glycoprotein Ib-IX-V complex, a receptor complex primarily expressed on the surface of platelets. Protein GP1BA is involved in platelet adhesion and aggregation, which are crucial for normal blood coagulation and wound healing. GP1BA specifically binds to von Willebrand factor (vWF), a protein involved in platelet adhesion and the initial stages of clot formation. Dysregulation of GP1BA expression or activity is associated with various bleeding disorders, such as Bernard-Soulier syndrome and platelet-type von Willebrand disease. GP1BA is also being studied as a potential therapeutic target to prevent thrombosis, the formation of blood clots that can lead to stroke, heart attack, and other serious conditions.

[0052] 14. Protein CES1, also known as carboxylesterase 1, is an enzyme primarily expressed in the liver and involved in drug metabolism and detoxification. Belonging to the serine hydrolase family, CES1 is involved in the hydrolysis of various ester-containing compounds, including drugs, fatty acids, and cholesterol esters. CES1 is also involved in the metabolism of prodrugs, which are inactive compounds that are converted into active drugs when metabolized by the body. Dysregulation of CES1 expression is associated with a variety of diseases, including metabolic disorders, liver disease, and cancer. CES1 is being studied as a potential therapeutic target for these diseases, as well as as a biomarker of drug efficacy and toxicity.

[0053] 15. Protein AOC3, also known as diamine oxidase (DAO), is an enzyme primarily expressed in the small intestine and kidneys. Belonging to the copper-containing amine oxidase family, protein AOC3 is involved in the catabolism of histamine, a biogenic amine involved in various physiological processes, including immune responses and neurotransmission. Dysregulation of AOC3 expression or activity is associated with various inflammatory and allergic diseases, such as asthma, migraines, and irritable bowel syndrome. AOC3 is being studied as a potential therapeutic target for these diseases, as well as a biomarker for their diagnosis and prognosis.

[0054] 16. Protein CD27 is a transmembrane protein primarily expressed on the surface of T cells and involved in regulating the immune response. It belongs to the tumor necrosis factor (TNF) receptor family and interacts with its ligand, CD70, to regulate T cell activation, proliferation, and differentiation. CD27 is involved in various physiological processes, including the development and maintenance of immunological memory and the regulation of autoimmune responses. Dysregulation of CD27 expression or activity is associated with a variety of diseases, including cancer, autoimmune disorders, and infectious diseases. CD27 has been proposed as a potential therapeutic target for these diseases, as well as a biomarker for disease diagnosis and prognosis.

[0055] 17. Protein KIRREL2, also known as IRRE-like protein 2 or nephrin-like protein 2, is a transmembrane protein primarily expressed in the kidneys, brain, and heart. KIRREL2 belongs to a family of immunoglobulin-like domain-containing proteins and is involved in cell signaling, cell adhesion, and histogenesis. In the kidneys, KIRREL2 is involved in the formation and maintenance of the glomerular filtration barrier, which is crucial for proper renal function. KIRREL2 is also involved in the development and function of the central nervous system and heart. Dysregulation of KIRREL2 expression or activity is associated with a variety of disorders, including renal diseases, neurological disorders, and cardiovascular diseases. KIRREL2 is being studied as a potential therapeutic target for these diseases, as well as a biomarker for their diagnosis and prognosis.

[0056] 18. Protein CA5A, also known as carbonic anhydrase 5A, is an enzyme primarily expressed in the salivary glands, pancreas, and liver. Protein CA5A belongs to the family of carbonic anhydrases, which are zinc-containing enzymes that catalyze the reversible hydration of carbon dioxide to bicarbonate ions and protons. CA5A is involved in various physiological processes, including acid-base balance, fluid secretion, and electrolyte transport. Dysregulation of CA5A expression or activity is associated with various diseases, including diabetes, obesity, and liver disease. CA5A is being studied as a potential therapeutic target for these diseases, as well as as a biomarker for their diagnosis and prognosis.

[0057] While any one of the 18 proteins is suitable for use in the methods and compositions disclosed herein, in some cases, multiple proteins (two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or all of the 18 proteins) may be simultaneously tested in a method, kit, or device to achieve a better assessment of the risk of developing MCI or AD in a subject. In some embodiments, the simultaneous use of multiple proteins (two, three, four, five, six, seven, eight, nine, ten, or more, or all of the 18 proteins) in a method, kit, or device can improve the accuracy, sensitivity, and specificity in determining the risk of developing MCI or AD. In some cases, the simultaneous use of multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, or more, or all, from the 18 proteins) in a method, kit, or device enables the evaluation of multiple biological pathways / systems and therefore provides a more comprehensive assessment of the disease state in question.

[0058] In some embodiments, a predictive model is used to predict MCI risk or AD risk by integrating the levels of any two or more proteins from the 18 proteins. In some embodiments, a predictive model that uses multiple protein markers in predicting MCI risk or AD risk achieves better diagnostic performance than a method that uses any one protein from the 18 proteins. In some embodiments, the predictive model uses any two proteins from the 18 proteins. In some embodiments, the predictive model uses any three proteins from the 18 proteins. In some embodiments, the predictive model uses any four or more proteins from the 18 proteins. In some examples, at least two proteins are selected from the group of 18 proteins and measured for risk assessment according to the claimed method. In some examples, at least three or more proteins are selected from the group of 18 proteins and evaluated according to the claimed method.

[0059] In some cases, CCL27 and IGFBP-2 are selected and measured for risk assessment according to the claimed method. In some cases, any two proteins from AOC3, CD27, and NCS1 are selected and measured for risk assessment according to the claimed method. In some examples, AOC3 and CD27 are selected and measured for risk assessment of MCI or AD. In other examples, CD27 and NCS1 are selected and measured for risk assessment of MCI or AD. In yet another example, AOC3 and NCS1 are selected and measured for risk assessment of MCI or AD. In some cases, any two proteins from CTRC, KYNU, and TNNI3 are selected and measured for risk assessment according to the claimed method. In some examples, CTRC and KYNU are selected and measured for risk assessment of MCI or AD. In other examples, KYNU and TNNI3 are selected and measured for risk assessment of MCI or AD. In yet another example, CTRC and TNNI3 are selected and measured for risk assessment of MCI or AD.

[0060] III. Quantification of Marker Proteins 1. Sample acquisition The first step in carrying out the present invention is to obtain blood samples from test subjects to assess the risk of developing MCI or AD, or to monitor the severity or progression of MCI or AD. The same type of sample should be taken from both the control group (healthy individuals who do not have MCI or AD and do not have an increased risk of developing MCI or AD) and the test group (for example, subjects being tested for the possibility of MCI or AD, or for an increased risk of developing MCI or AD). For this purpose, standard procedures commonly used in hospitals or clinics are followed.

[0061] To detect the presence / amount of marker proteins or to assess the risk of developing MCI or AD in a test subject, blood samples from individual patients may be collected and serum, plasma, or whole blood levels of the relevant marker proteins (e.g., one or more proteins selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 identified in Table 1) may be measured and then compared to a standard control. If an increase in the protein levels of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3, or a decrease in the protein levels of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 is observed compared to control levels (depending on the specific β value of the protein markers shown in the table), the subject is considered to have MCI or AD, or to be at high risk of developing MCI or AD.

[0062] To monitor disease progression or to evaluate treatment effectiveness in MCI or AD patients, individual patient blood samples may be taken at various time points to measure the levels of individual marker proteins (e.g., one or more proteins selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 identified in Table 1) to provide information indicating the disease status. For example, if a patient's marker protein levels show a general trend of increasing or decreasing over time, the patient is considered to be improving in the severity of MCI or AD, or the treatment the patient is receiving is considered effective (depending on the specific β values ​​of the protein markers shown in the table). Substantial change in a patient's marker protein levels indicates no change in the MCI or AD status and that the treatment the patient is receiving is ineffective.

[0063] Furthermore, the inventors have devised a novel calculation method for generating a composite risk score based on multiple marker protein levels (e.g., CCL27, IGFBP-2, AOC3, CD27, NCS1, CTRC, KYNU, TNNI3, or one or more proteins identified in Table 1) in order to quantify the risk of developing MCI or AD in an individual, or to compare the relative risk of developing MCI or AD among two or more individuals.

[0064] 2. Sample preparation for protein detection Blood samples derived from the subject are suitable for the present invention and can be obtained by well-known methods as described in standard medical literature. For specific applications of the present invention, serum or plasma may be preferred types of samples. In other cases, whole blood samples may be used.

[0065] Blood samples are obtained from individuals being tested or monitored for MCI or AD using the method of the present invention. Blood samples are collected from individuals according to standard protocols commonly followed by hospitals or clinics. An appropriate amount of blood may be collected and stored according to standard procedures before further preparation.

[0066] The analysis of marker proteins found in a patient's sample according to the present invention can be performed, for example, using serum, plasma, or whole blood. Methods for preparing patient samples for protein extraction / quantitative detection are well known to those skilled in the art.

[0067] 3. Determination of marker protein levels Any protein of a specific identity, such as CCL27, IGFBP-2, AOC3, CD27, NCS1, CTRC, KYNU, TNNI3, or any one of those identified in Table 1, can be detected using a variety of immunological assays. In some embodiments, a sandwich assay can be performed by capturing the protein from a test sample using an antibody with a specific binding affinity to the protein. The protein can then be detected using a labeled antibody with a specific binding affinity to it. Such immunological assays can be performed using microfluidic devices such as microarray protein chips. The target protein (e.g., any one of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 identified in Table 1) can also be detected by gel electrophoresis (such as two-dimensional gel electrophoresis) and Western blot analysis using specific antibodies. Alternatively, standard immunohistochemistry techniques can be used to detect a given protein (e.g., any one of the following identified in Table 1: AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3) using appropriate antibodies. Both monoclonal and polyclonal antibodies (including antibody fragments with desired binding specificity) can be used for the specific detection of polypeptides. Known techniques can be used to generate antibodies and their conjugated fragments that have specific binding affinity to a particular protein (e.g., any one of the following identified in Table 1: AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3).

[0068] Other methods may also be used to measure the level of marker proteins when carrying out the present invention. For example, various methods based on mass spectrometry techniques have been developed to rapidly and accurately quantify target proteins in a large number of samples. These methods involve highly advanced instruments such as triple quadrupole (triple Q) instruments using multiple reaction monitoring (MRM) techniques, matrix-assisted laser desorption / ionization time-of-flight tandem mass spectrometers (MALDI TOF / TOF), ion trap instruments using selective ion monitoring (SIM), and QTOP mass spectrometers based on electrospray ionization (ESI). See, for example, Pan et al., J Proteome Res. 2009 February; 8(2):787-797.

[0069] IV. Establishment of Standard Controls To establish a standard control for carrying out the method of the present invention, a group of healthy individuals is first selected who are free from MCI and AD, or who do not have an increased risk of developing MCI or AD, and who have conventionally normal cognitive function (e.g., MoCA ≥ 26). These individuals are, if applicable, within appropriate parameters for screening for MCI or AD and / or monitoring for MCI or AD using the method of the present invention. Optionally, individuals are of the same sex, similar age, or similar ethnic background as the test subjects.

[0070] The health status of selected individuals will be confirmed by well-established and commonly used methods, including, but not limited to, a general physical examination of the individual and a general review of its medical history.

[0071] Furthermore, the selected group of healthy individuals must be of a reasonable size such that the average amount / concentration of marker proteins in serum, plasma, or whole blood samples obtained from that group can reasonably be considered to represent normal or mean levels within the general population of healthy individuals who do not have MCI and AD, or who do not have an increased risk of developing MCI or AD. Preferably, the selected group includes 10 or more, 20 or more, 30 or more, or 50 or more human subjects.

[0072] Once the mean value of a marker protein is established based on the individual values ​​observed in each subject of the selected healthy control group, this mean, median, representative value, or profile is considered a standard control. The standard deviation is also determined during the same process. In some cases, separate standard controls may be established for separately defined groups with different characteristics, such as age, sex, or ethnic background.

[0073] V. Rating In one embodiment, the present disclosure provides a method for assessing the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method includes (a) comparing the level or concentration of at least one protein in a plasma, serum, or whole blood sample of the subject to a standard control level of the same protein, wherein the at least one protein is selected from the 18 proteins listed in Table 1, and the standard control level of the same protein is found in the plasma, serum, or whole blood of an average healthy subject who does not have MCI or AD or is not at risk of MCI or AD, respectively; and (b) comparing the plasma, serum, or whole blood of the subject. The method includes (a) detecting in a whole blood sample the level or concentration of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 protein below the standard control level of the same protein, or the level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 protein above the standard control level of the same protein, and (c) determining that the subject is at high risk of developing MCI or AD. In some embodiments, the method further includes, prior to step (a), measuring the level or concentration of at least one protein in a plasma sample, serum sample, or whole blood sample of the subject. In some embodiments, prior to the measurement step, the method further includes obtaining a plasma sample, serum sample, or whole blood sample from the subject. In some embodiments, the step of measuring the level or concentration of a protein involves the use of an antibody-based detection method, an aptamer-based detection method, or a mass spectrometry method.

[0074] In some embodiments, if a subject is determined to be at risk of developing MCI or AD in step (c), the subject is then provided with increased follow-up monitoring (e.g., monitoring with increased frequency of examinations compared to the usual monitoring prescribed by a healthcare professional for a person of similar age and medical background who is at no risk or at low risk). In some embodiments, if a subject is determined to be at increased risk of developing MCI or AD in step (c), the subject is then administered a therapeutic agent to prevent or treat MCI or AD. In some embodiments, if a subject is determined not to be at increased risk of developing MCI or AD in step (c), the subject receives the usual monitoring that is commonly prescribed by a physician for a person who is at no risk or at low risk of developing MCI or AD. Patients of any ethnicity, including Chinese, are suitable for evaluation by the claimed method. In some embodiments, the subject is of Chinese descent.

[0075] In another aspect, the present invention provides a method for quantifying the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method is a method of (a) calculating an individual risk score by inputting a set of values ​​into the following formula:

number

[0076] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels for each of the 18 proteins listed in Table 1, with the weighting coefficient range (βi) and intercept range (ε) described in Table 2, and subjects with a risk score higher than 0.356 are considered to have an increased risk of developing MCI and AD.

[0077] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CCL27 and IGFBP-2, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 3, and subjects with a risk score higher than 0.656 are considered to have an increased risk of developing MCI and AD.

[0078] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3, CD27, and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 4, and subjects with a risk score higher than 0.266 are considered to have an increased risk of developing MCI and AD.

[0079] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3 and CD27, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 5, and subjects with a risk score higher than 0.620 are considered to have an increased risk of developing MCI and AD.

[0080] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of AOC3 and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 6, and subjects with a risk score higher than 0.833 are considered to have an increased risk of developing MCI and AD.

[0081] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CD27 and NCS1, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 7, and subjects with a risk score higher than 0.509 are considered to have an increased risk of developing MCI and AD.

[0082] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC, KYNU, and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 8, and subjects with a risk score higher than 0.489 are considered to have an increased risk of developing MCI and AD.

[0083] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC and KYNU, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 9, and subjects with a risk score higher than 0.589 are considered to have an increased risk of developing MCI and AD.

[0084] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of CTRC and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 10, and subjects with a risk score higher than 0.515 are considered to have an increased risk of developing MCI and AD.

[0085] In some embodiments, the set of values ​​consists of plasma, serum, or whole blood levels of KYNU and TNNI3, with the weighting coefficient range (βi) and intercept range (ε) being those listed in Table 11, and subjects with a risk score higher than 0.799 are considered to have an increased risk of developing MCI and AD.

[0086] In some embodiments, the method further includes a step of measuring the plasma, serum, or whole blood level of the protein prior to step (a). In some embodiments, the method further includes another step of obtaining a plasma sample, serum sample, or whole blood sample from the subject prior to the step of measurement.

[0087] In some embodiments, if a subject is determined to have an increased risk of developing MCI or AD in step (b), the subject then receives increased follow-up monitoring (e.g., monitoring with increased frequency of examinations compared to the usual monitoring prescribed by a healthcare professional for a person of similar age and medical background who is at no risk or at low risk) and the treatment described herein. If a subject is determined not to have an increased risk of developing MCI or AD, the subject receives the usual monitoring that is generally prescribed by a physician for a person who is at no risk or at low risk of developing MCI or AD. The subject may be of any ethnicity, including Chinese, suitable for evaluation by the claimed method.

[0088] In yet another aspect, the present invention provides a method for evaluating the effectiveness of a therapeutic agent for treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method includes (a) comparing the plasma, serum, or whole blood levels of any one protein selected from the proteins listed in Table 1 in the subject before and after administration of the therapeutic agent to the subject; (b) detecting a decrease in the plasma, serum, or whole blood levels of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 in the subject after administration of the therapeutic agent, or an increase in the plasma, serum, or whole blood levels of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 in the subject after administration of the therapeutic agent; and (c) determining that the therapeutic agent is effective in treating MCI or AD. In some embodiments, the method further includes, prior to step (a), measuring plasma, serum, or whole blood levels of one or more proteins before and after administration. In some embodiments, the method may also include, prior to the measurement step, obtaining plasma, serum, or whole blood samples from the subject before and after administration.

[0089] In some embodiments, if the therapeutic agent is deemed effective in treating MCI or AD in step (c), the subject continues treatment by receiving the therapeutic agent; if the therapeutic agent is deemed not effective in treating MCI or AD in step (c), the subject discontinues treatment with the therapeutic agent and instead initiates a different treatment by receiving a different therapeutic agent. Individuals of any ethnicity, including Chinese, are suitable for evaluation by the claimed method.

[0090] VI. Monitoring and Treatment In a related aspect, the present invention also provides a method for treating a patient with MCI or AD when MCI or AD is detected, or when an increased risk of developing MCI or AD later is detected. In some embodiments, the method includes administering a treatment to a subject who has been determined to be in the early stages of MCI or AD, or who has been determined to be at increased risk of MCI or AD, such as an acetylcholinesterase inhibitor (e.g., donepezil, galantamine, rivastigmine), memantine, glutamate receptor blockers, citalopram, fluoxetine, paroxetine, sertraline, trazodone, lorazepam, oxazepam, aripiprazole, clozapine, haloperidol, olanzapine, quetiapine, risperidone, ziprasidone, nortriptyline, tricyclic antidepressants, benzodiazepines, temazepam, zolpidem, zaleplon, chloral hydrate, coenzyme Q10, ubiquinone, coral calcium, ginkgo biloba, huperzine A, omega-3 fatty acids, phosphatidylserine, or any combination thereof.

[0091] In some cases, additional diagnostic tests may be performed as desired to provide further confirmation (e.g., by brain imaging via CT scan or other imaging techniques to indicate excessive brain volume loss, or by cognitive ability tests to indicate accelerating decline), and if the diagnostic method steps described above and herein are completed and it is determined that the patient already has MCI or AD (e.g., is in the early stages) or is at significantly increased risk of developing MCI or AD later, a suitable treatment regimen or preventive regimen may be prescribed by a physician or other healthcare professional to treat the patient, manage / alleviate ongoing symptoms, or delay the future onset of the disease. Several cholinesterase inhibitors have been approved by the U.S. Food and Drug Administration (FDA), including donepezil (Aricept®, the only cholinesterase inhibitor approved to treat all stages of Alzheimer's disease, including moderate to severe cases), rivastigmine (Exelon®, approved to treat mild to moderate cases of Alzheimer's disease), galantamine (Razadyne®, for patients with mild to moderate cases), and memantine (Namenda®). Donepezil is the only cholinesterase inhibitor approved to treat all stages of Alzheimer's disease, including moderate to severe cases. One or more of these drugs may be prescribed to treat patients diagnosed with MCI or Alzheimer's disease according to the methods of this invention. Another possible treatment is the administration of trazodone, which is currently approved for use as an antidepressant and has been reported as an effective agent for improving symptoms of MCI or Alzheimer's disease.

[0092] For patients who are considered to be at high or increased risk of developing MCI or AD in the future but who have not yet shown clinical symptoms, continuous monitoring, especially at increased frequency, is also appropriate. For example, patients may undergo frequent and scheduled regular examinations (e.g., every six months, once a year, or once every two years) to detect any accelerating changes in cognitive ability. Suitable methods for such regular monitoring include the General Practitioner Assessment of Cognition (GPCOG), Mini-Cog, Eight-item Informant Interview to Differentiate Aging and Dementia (AD8), and Short Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Furthermore, prophylactic treatment with trazodone may be recommended.

[0093] VII. Kits and Equipment The present invention provides compositions and kits for carrying out the methods described herein to evaluate appropriate marker protein levels in the serum, plasma, or whole blood of a subject, and the compositions and kits may be used for a variety of purposes, such as monitoring the progression of a patient's condition, including detecting or diagnosing the presence of MCI or AD, determining the risk of developing a condition, and evaluating the therapeutic effectiveness of treatments administered to a condition in a patient who has been diagnosed with and is being treated for a disease.

[0094] A kit for performing an assay to determine marker protein levels typically includes at least one antibody useful for specific binding to the marker protein amino acid sequence. Optionally, this antibody is labeled with a detectable moiety. The antibody may be either a monoclonal or polyclonal antibody. In some cases, the kit may include at least two different antibodies, one for specific binding to the marker protein (i.e., a primary antibody) and the other for detecting the primary antibody, which is often bound to the detectable moiety (i.e., a secondary antibody).

[0095] Typically, the kit also includes an appropriate standard control. The standard control represents the mean value of marker proteins in serum, plasma, or whole blood of healthy subjects who do not have MCI or AD or who do not have an increased risk of developing MCI or AD. In some cases, such a standard control may be provided in the form of a set value. In addition, the kit of the present invention may provide instructions for use to guide the user when analyzing test samples and evaluating the presence or risk of MCI or AD, or the disease status / progression in the test subject.

[0096] In some embodiments, the present invention provides a kit for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, or for evaluating the therapeutic effectiveness of a treatment regimen for MCI or AD in a subject. The kit comprises at least one reagent capable of determining the level or concentration of one, two, three, or more proteins independently selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 in the subject's plasma, serum, or whole blood. In some embodiments, the kit may further include standard controls for each protein that reflect the levels / concentrations of the same proteins found in the plasma, serum, or whole blood of an average healthy subject who does not have MCI or AD or is not at risk of MCI or AD. In some embodiments, the kit is used to determine the level or concentration of at least two proteins from 18 proteins in the target plasma, serum, or whole blood. In some embodiments, the kit can determine the level or concentration of at least three, four, five, six, seven, eight, nine, ten, or more proteins from 18 proteins in the target plasma, serum, or whole blood.

[0097] In further embodiments, the present invention may also be carried out by an apparatus or system comprising one or more such apparatus capable of performing all or some of the steps of the method described herein. For example, the apparatus or system, upon receiving a serum, plasma, or whole blood sample taken from a subject being tested to detect MCI or AD, to assess the risk of developing MCI or AD, or to assess disease status / progression, performs the steps of (a) determining the amount or concentration of one or more marker proteins in the sample; (b) comparing the amount / concentration of the marker proteins to a standard control value; and (c) providing an output indicating whether MCI or AD is present in the subject, or whether the subject has an increased risk of developing MCI or AD, or whether the patient is at a higher risk of later developing MCI or AD compared to other patients being tested. In other cases, the apparatus or system of the present invention performs the tasks of steps (b) and (c) after step (a) has been performed and the amount or concentration from (a) has been input to the apparatus. Preferably, the apparatus or system is partially or fully automated. In some embodiments, the apparatus comprises a detection chip.

[0098] As disclosed herein, the present invention provides a detection chip for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, or for evaluating the therapeutic effectiveness of a treatment regimen for MCI or AD in a subject. The chip comprises a solid substrate and at least one reagent capable of determining the plasma, serum, or whole blood level of any one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or all of the proteins independently selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 in a subject, each reagent being immobilized at an addressable position on the substrate. In some embodiments, the chip is used to determine the level or concentration of at least two proteins from 18 proteins in the target plasma, serum, or whole blood. In some embodiments, the chip can determine the level or concentration of at least three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or all of the 18 proteins in the target plasma, serum, or whole blood. [Examples]

[0099] The following examples are provided for illustrative purposes only and not for limitation. Those skilled in the art will readily recognize various non-essential parameters that can be changed or modified to produce essentially the same or similar results.

[0100] Introduction With the global increase in life expectancy, the incidence of MCI and AD is projected to surge in the coming decades. In particular, the global prevalence of AD is estimated to reach 75 million by 2030 and 131 million by 2050, but it is surging in China, which has the largest elderly population. In fact, the number of AD cases in China doubled from 3.7 million to 9.2 million between 1990 and 2010, and is projected to reach 22.5 million by 2050. Similarly, Hong Kong's population is also aging rapidly, with an estimated 100,000 people currently living with dementia, and it is projected that approximately 333,000 people, or 11% of the population, will suffer from dementia by 2039. The prevalence of MCI increases with age, ranging from 9.74-27.8% and 2.48-35.5% in the Chinese and European populations, respectively. Despite their devastating impact, MCI and AD remain largely undiagnosed in primary care, with only 19% of patients receiving a confirmed dementia diagnosis as a result of routine medical care, and the proportion of sought diagnoses or medical consultations estimated to be even lower in Hong Kong. Such underdiagnosis is primarily attributable to the challenges and limitations associated with current dementia diagnosis, given the increasing number of studies showing that the disease course of AD begins up to 20 years before the appearance of recognizable symptoms. Current dementia diagnosis relies on either a subjective assessment of apparent symptoms, costly brain imaging, or invasive sampling from cerebrospinal fluid. This highlights the importance of the time lag between disease onset and diagnosis for any potential intervention, and thus the urgent need for early diagnosis, as well as the importance of detecting MCI, the progression of MCI to AD, and novel biomarkers for AD.

[0101] To address the current lack of objective diagnostic tools for early detection, this disclosure provides novel protein markers for the early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in subjects. Through a large-scale analysis of blood samples from Hong Kong Chinese participants, including individuals with MCI, individuals with AD, and age- and sex-matched healthy, cognitively normal individuals, a panel of 18 protein biomarkers representing "MCI and / or AD signatures" was identified in the blood samples, differentially expressed between healthy individuals and individuals with MCI or AD. This disclosure provides a simple, ultra-sensitive, non-invasive, and affordable blood-based technology for the early diagnosis of MCI and AD, and for the evaluation of therapeutic interventions for MCI or AD in subjects.

[0102] This disclosure provides novel methods and kits relating to the use of plasma protein markers, serum protein markers, whole blood protein markers, or combinations thereof for assessing individual risks of MCI and AD. The present invention relates to the discovery of novel blood protein markers associated with MCI and AD. Accordingly, the present invention provides methods and compositions useful for predicting the risk of MCI and AD, and for demonstrating the therapeutic efficacy of agents for treating MCI and AD. Accordingly, in a first aspect, the present invention provides a method for assessing the risk of developing MCI or AD in a subject. The method includes (1) comparing the level or concentration of any one protein selected from a panel of 18 proteins in the subject's plasma, serum, or whole blood to a standard control level of the same protein found in the plasma, serum, or whole blood of an average healthy subject who does not have MCI or AD or is not at increased risk of MCI or AD; (2) detecting whether the level of the protein in the subject's plasma, serum, or whole blood is higher / lower than the standard control level; and (3) determining that the subject is at increased risk of MCI or AD. In some embodiments, the method further includes a step of measuring plasma, serum, or whole blood levels of protein prior to step (1). In some embodiments, a step of obtaining a plasma, serum, or whole blood sample from the subject prior to the measurement step is performed. In some embodiments, if the subject is determined to have an increased risk of MCI or AD in step (3), the subject is then offered increased follow-up monitoring (e.g., monitoring tests at an increased frequency compared to the usual monitoring prescribed by a healthcare professional for a person of similar age and medical background who is at no risk or low risk), or the treatment described herein.

[0103] While any of the 18 identified proteins are suitable for use in this method, the simultaneous use of multiple biomarkers to determine disease status, commonly referred to as a “complex biomarker panel,” is an effective method for fully utilizing the predictive value of protein candidates. Such complex biomarker panels are widely used, for example, to predict cardiovascular disease and aging. Compared to the use of only one protein, biomarker panels integrating multiple proteins can achieve even better performance in classifying diseases, namely improvements in accuracy, sensitivity, and specificity. Furthermore, such models allow for the examination of the state and activity of multiple biological pathways / systems, providing a more comprehensive assessment of the disease status in question. For this reason, the inventors also developed a mixed predictive model that can integrate any two or more types of proteins from the 18 blood proteins to predict MCI risk and AD risk. In predicting MCI risk and AD risk, the performance of this mixed predictive model is superior to the method using any one of the 18 proteins. Furthermore, the inventors have also specifically developed and optimized mixed predictive models that can integrate two or three proteins to predict MCI risk and AD risk (i.e., models integrating two or three proteins from AOC3, CD27, CTRC, KYNU, NCS1, and TNNI3). In predicting MCI risk and AD risk, the performance of these mixed predictive models is superior to methods that use any one of those selected proteins.

[0104] Materials and methods Participant registration for the Hong Kong Chinese cohort: A total of 39 Hong Kong Chinese individuals aged 60 years or older were enrolled, including 16 individuals with Alzheimer's disease (AD), 14 individuals with mild cognitive impairment (MCI), and 9 cognitively normal controls (CN), who visited the Department of Neurology at Prince of Wales Hospital, Chinese University of Hong Kong. Participants were clinically diagnosed with AD according to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Arlington (2013, Fifth Edition), which was incorporated by reference. All participants underwent a medical history assessment, clinical assessment, cognitive and functional assessment according to Nasreddine, Ziad S., et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society 53.4 (2005):695-699, which was incorporated by reference, as well as a medical history assessment, clinical assessment, cognitive and functional assessment, and 11Neuroimaging assessments were performed using C-Pittsburgh Compound B (PiB) and amyloid positron emission tomography (PET) imaging with magnetic resonance imaging (MRI). Participants with a standardized cortical-to-cerebellar uptake ratio ≥1.3 were defined as amyloid PET positive. T1-weighted MRI images were processed using AccuBrain IV1.2 (BrainNow Medical Technology) for brain region segmentation and gray matter volume quantification, and MRI data were analyzed. Individuals with AD or neurological disorders other than psychiatric disorders were excluded from enrollment. Age, sex, body mass index (BMI), years of education, and medical history were recorded for each participant. Individuals with normal cognition (MoCA ≥26) and amyloid PET negativity were defined as CN individuals. Individuals with MCI and AD were defined as amyloid PET positivity, along with a clinical diagnosis of cognition. This study was endorsed by the Prince of Wales Hospital, Chinese University of Hong Kong, and the Hong Kong University of Science and Technology. All participants provided written informed consent for participation in the study and for sample collection.

[0105] Plasma preparation from blood samples: Whole blood (3 mL) was collected in a K3 EDTA tube (VACUETTE) and centrifuged at 2,000 g for 15 minutes to separate the cell pellet from the plasma. The plasma was collected, divided into aliquots, and stored at -80°C until use.

[0106] Blood protein levels were measured: The blood levels of 18 proteins in prepared plasma samples were quantified using the Olink Proteomics biomarker panel with Proximity Extension Assay technology, including Cardiometabolic, Cardiovascular II, Cardiovascular III, Cell Regulation, Immune Response, Neuro Exploratory, Neurology, Oncology II, Oncology III, and Organ Damage.

[0107] Analysis of the association between blood protein levels and MCI or AD The following linear regression model (β i We analyzed the association between age, sex, and BMI-adjusted normalized protein levels and MCI or AD using weighting coefficients (ε, intercept of the linear equation) for the corresponding coefficients. Normalized protein levels are approximately (~)β1AD + β2MCI + β3Age + β4Sex + β5BMI + ε. In AD, we considered blood proteins with a weighting coefficient for AD (i.e., β1) greater than or less than 0 to be increased or decreased, respectively. In MCI, we considered blood proteins with a weighting coefficient for MCI (i.e., β2) greater than or less than 0 to be increased or decreased, respectively.

[0108] Calculating the risk score For each predictive model, the blood levels of candidate proteins and the participants' AD diagnoses are fitted to the following logistic regression model to determine the weighting coefficient (β) of the candidate proteins. i The intercept (ε) was calculated.

number

number

[0109] Evaluation of prediction accuracy The accuracy of each predictive model or a single protein was evaluated by calculating the area under the receiver operating characteristic curve (ROC) (AUC) using the auc() function from the R pROC package.

[0110] Visualization of the process Researchers who performed blood protein measurements were blinded to the participants' diagnoses and phenotypes. All statistical plots were generated using Prism v8.0 (GraphPad).

[0111] Example I: A model integrating 18 types of blood proteins predicts MCI risk and AD risk. Eighteen proteins (i.e., AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3; Table 1) showed increased (effect size >0) or decreased (effect size <0) blood levels in MCI and / or AD patients compared to cognitively normal individuals (CN), with a maximum precision of 75.40% in distinguishing MCI from CN and 83.33% in distinguishing AD from CN (Table 1). In particular, the inventors developed a mixed predictive model that integrates the levels of the 18 blood proteins and assigns a risk score to individuals (Figure 1a and Table 2). The resulting scores can adequately distinguish between MCI and CN with 91.27% accuracy (Figure 1b) and between AD and CN with 97.92% accuracy (Figure 1c). In predicting MCI and AD risk, this mixed prediction model performs better than methods using any one of 18 proteins (Figures 1b, c). Individuals with a risk score of less than 0.356 have a low risk of developing MCI or AD. In contrast, individuals with a risk score greater than 0.356 are at risk of developing both MCI and AD (Figure 1a).

[0112] Example II: A model integrating two blood proteins from 18 different blood proteins predicts MCI risk and AD risk. In particular, the inventors have also developed a mixed predictive model that can integrate two or more proteins from 18 different blood proteins to predict MCI risk and AD risk. For example, the model can integrate the levels of two blood proteins (i.e., CCL27 and IGFBP-2) and assign a risk score to an individual (Figure 2a and Table 3), and the resulting score can sufficiently distinguish between MCI and CN with an accuracy of 78.57% (Figure 2b) and between AD and CN with an accuracy of 88.89% (Figure 2c). In predicting MCI risk and AD risk, the performance of this mixed predictive model is superior to the method that uses only one protein from the 18 different proteins (Figures 2b and 2c). Individuals with a risk score of less than 0.656 have a low risk of developing MCI or AD. In comparison, individuals with a risk score greater than 0.656 have a risk of developing MCI and AD (Figure 2a).

[0113] Example III: A model integrating three types of blood proteins predicts MCI risk and AD risk. The inventors also developed a mixed predictive model that can integrate three proteins (i.e., AOC3, CD27, and NCS1) to predict MCI risk and AD risk (Figure 3a and Table 4). The resulting scores can sufficiently distinguish between MCI and CN with an accuracy of 76.98% (Figure 3b) and between AD and CN with an accuracy of 88.19% (Figure 3c). In predicting MCI risk and AD risk, the performance of this mixed predictive model is superior to methods using any one of the three proteins (Figures 3b and 3c). Individuals with a risk score of less than 0.266 have a low risk of developing MCI or AD. In contrast, individuals with a risk score greater than 0.266 have a risk of developing MCI and AD (Figure 3a). In particular, the inventors also developed a mixed predictive model that can integrate any two of the three proteins to predict MCI risk and AD risk (Figures 4a, 4d, 4g, and Tables 5-7). The obtained scores could adequately distinguish between MCI and CN with an accuracy of 69.84% to 76.19% (Figures 4b, 4e, and 4h), and between AD and CN with an accuracy of 77.78% to 84.03% (Figures 4c, 4f, and 4i). In terms of predicting MCI and AD risk, the performance of these mixed prediction models was superior to methods using any one of the three proteins (Figures 4b-4c, 4e-4f, and 4h-4i). Individuals with a risk score less than 0.620, 0.833, or 0.509 had a low risk of developing MCI or AD. In contrast, individuals with a risk score greater than 0.620, 0.833, or 0.509 had a risk of developing MCI and AD (Figures 4a, 4d, and 4g).

[0114] Example IV: A model integrating three types of blood proteins predicts MCI risk and AD risk. Furthermore, the inventors developed an additional mixed predictive model that can integrate three other proteins (i.e., CTRC, KYNU, and TNNI3) to predict MCI risk and AD risk (Figure 5a and Table 8). The resulting scores can adequately distinguish between MCI and CN with an accuracy of 80.95% (Figure 5b) and between AD and CN with an accuracy of 92.36% (Figure 5c). In predicting MCI risk and AD risk, this mixed predictive model performs better than models using any one of the three proteins (Figures 5b and 5c). Individuals with a risk score of less than 0.489 have a low risk of developing MCI or AD, while individuals with a risk score greater than 0.489 have a risk of developing MCI and AD (Figure 5a). In particular, the inventors also developed a mixed predictive model that can integrate any two of the three proteins to predict MCI risk and AD risk (Figures 6a, 6d, 6g, and Tables 9-11). The obtained scores could adequately distinguish between MCI and CN with an accuracy of 71.43% to 76.98% (Figures 6b, 6e, and 6h), and between AD and CN with an accuracy of 80.56% to 85.42% (Figures 6c, 6f, and 6i). In terms of predicting MCI and AD risk, the performance of these mixed prediction models was superior to methods using any one of the three proteins (Figures 6b-6c, 6e-6f, and 6h-6i). Individuals with a risk score less than 0.589, 0.515, or 0.799 had a low risk of developing MCI or AD. In contrast, individuals with a risk score greater than 0.589, 0.515, or 0.799 had a higher risk of developing MCI and AD (Figures 6a, 6d, and 6g).

[0115] All patents, patent applications, and other publications, including GenBank accession numbers, Uniprot IDs, and equivalents, cited in this application are incorporated by reference in their entirety for all purposes.

[0116] [Table 1]

[0117] Table 2

[0118] Table 3

[0119] Table 4

[0120] Table 5

[0121] Table 6

[0122] Table 7

[0123] Table 8

[0124] Table 9

[0125] Table 10

[0126] Table 11

Claims

1. (a) A step of comparing the level of at least one protein in a target plasma sample, serum sample, or whole blood sample with a standard control level of the same protein, wherein the at least one protein is selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3. (b) The steps of detecting in the target plasma sample, serum sample, or whole blood sample the level of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 protein that is lower than the standard control level of the same protein, or the level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 protein that is higher than the standard control level of the same protein, and (c) A step in which the subject is determined to be at high risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD). including, A method for evaluating the risk of developing MCI or AD in a given group of subjects.

2. The method according to claim 1, further comprising measuring the level of at least one protein in the target plasma sample, serum sample, or whole blood sample before step (a).

3. The method according to claim 2, further comprising obtaining the plasma sample, serum sample, or whole blood sample from the subject before the measurement step.

4. The method according to any one of claims 1 to 3, further comprising monitoring the level of the at least one protein of the subject at an increased frequency after step (c).

5. The method according to any one of claims 1 to 3, further comprising administering a therapeutic agent for preventing or treating MCI or AD to the subject after step (c).

6. The method according to any one of claims 1 to 5, wherein the levels of at least two types of proteins are measured.

7. The method according to any one of claims 1 to 6, wherein the levels of at least three types of proteins are measured.

8. The reagent includes a method for determining the level of one, two, three, or more proteins selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 in the target plasma, serum, or whole blood. A kit for evaluating the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in the target population.

9. The kit according to claim 8, further comprising a standard control for each of the aforementioned proteins.

10. The invention comprises a solid substrate and reagents capable of determining the plasma, serum, or whole blood levels of one, two, three, or more types of proteins independently selected from the group consisting of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3, wherein each reagent is immobilized at an addressable position on the substrate. A detection chip for assessing the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject.

11. (a) Calculate individual risk scores by inputting a set of values ​​into the following formula, and [Math 1] (b) Determining that individuals with a score higher than the optimal cutoff are at risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), The set of values ​​includes plasma, serum, or whole blood levels of candidate proteins from the 18 proteins listed in Table 2, and the optimal cutoff is determined as the value with the maximum Youden index using the optim.cutpoints() function of the R OptimalCutpoints package, β ｉ is the weighting coefficient for candidate proteins, and ε is the intercept. A method for quantifying the risk of developing MCI or AD in a given group.

12. The set of values ​​consists of plasma, serum, or whole blood levels for each of the 18 types of proteins, and the weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as shown in Table 2, and subjects having a risk score higher than 0.356 are at risk of developing MCI and AD.

13. The set of values ​​consists of plasma, serum, or whole blood levels of CCL27 and IGFBP-2, with a weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as shown in Table 3, and subjects having a risk score higher than 0.656 are at risk of developing MCI and AD.

14. The set of values ​​consists of plasma, serum, or whole blood levels of AOC3, CD27, and NCS1, with a weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as shown in Table 4, and subjects having a risk score higher than 0.266 are at risk of developing MCI and AD.

15. The set of values ​​consists of plasma, serum, or whole blood levels of two proteins from AOC3, CD27, and NCS1, with a weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as described in Tables 5 to 7, and subjects having a risk score higher than 0.620 for AOC3 and CD27, or a risk score higher than 0.833 for AOC3 and NCS1, or a risk score higher than 0.509 for CD27 and NCS1 are at risk of developing MCI and AD.

16. The set of values ​​consists of plasma, serum, or whole blood levels of CTRC, KYNU, and TNNI3, with a weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as shown in Table 8, and subjects having a risk score higher than 0.489 are at risk of developing MCI and AD.

17. The set of values ​​consists of plasma, serum, or whole blood levels of two proteins from CTRC, KYNU, and TNNI3, with a weighting coefficient range (β ｉ The method according to claim 11, wherein the intercept range (ε) is as described in Tables 9 to 11, and subjects having a risk score higher than 0.589 for CTRC and KYNU, or a risk score higher than 0.515 for CTRC and TNNI3, or a risk score higher than 0.799 for KYNU and TNNI3 are at risk of developing MCI and AD.

18. The method according to any one of claims 11 to 17, further comprising measuring the plasma, serum, or whole blood level of the protein prior to step (a).

19. The method according to claim 18, further comprising obtaining a plasma sample, serum sample, or whole blood sample from the subject before the measurement step.

20. A method for evaluating the effectiveness of therapeutic agents for treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, (a) Compare the plasma, serum, or whole blood levels of any one protein selected from Table 1 in the subject before and after administering the therapeutic agent to the subject. (b) Detecting a decrease in plasma, serum, or whole blood levels of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 in the subject after administration of the therapeutic agent, or an increase in plasma, serum, or whole blood levels of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 in the subject after administration of the therapeutic agent, and (c) Determining that the therapeutic agent is effective in treating MCI or AD. Methods that include...

21. The method according to claim 20, further comprising measuring the plasma, serum, or whole blood levels of the protein before and after administration, prior to step (a).

22. The method according to claim 21, further comprising obtaining a plasma sample, serum sample, or whole blood sample from the subject before and after administration, prior to the measurement step.

23. The method according to any one of claims 1 to 7 and 11 to 22, wherein the subject is of Chinese descent.

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