Protein markers for mild cognitive impairment and Alzheimer's disease

Abstract

The present invention relates to protein markers associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD), in particular those 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 provided are methods and compositions for assessing the efficacy of an MCI or AD treatment.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 495,864, filed April 13, 2023, which is hereby incorporated by reference in its entirety for all purposes. BACKGROUND

[0003] Neurodegenerative diseases are devastating brain conditions that affect a large portion of the population. Many people with this condition are highly debilitating, currently incurable, and often lead to progressive deterioration of brain structure and cognitive function. For example, Alzheimer’s disease (AD) accounts for 60-80% of dementia cases and represents a leading cause of death in the elderly. Characterized by progressive cognitive decline, AD is an age-related, progressive neurodegenerative disorder that currently affects 468 million people worldwide, approximately 10% of people 65 years of age or older, with nearly 10 million new cases each year. Pathological markers of this chronic disease include the accumulation of amyloid-β plaques and neurofibrillary tangles in the brain, as well as synaptic dysfunction and neuronal loss, which trigger an inflammatory response in the brain. The most common symptoms of AD include memory problems, difficulty communicating, impaired reasoning and judgment, and decreased motor ability. Similar to many neurodegenerative and neuroinflammatory diseases, the current challenge in AD diagnosis is attributed to limited understanding of the disease pathophysiology. At the same time, currently available treatments are ineffective and can only provide transient effects on symptom relief, while patients still suffer severely from these diseases.

[0004] Characterized as a transitional state between normal cognition and dementia (e.g., AD), mild cognitive impairment (MCI) accounts for approximately 10-20% of people 65 years of age or older. Individuals with MCI are cognitively impaired but not demented. Although these patients are at greater risk of developing AD or other dementia compared to those with normal cognition, they have a cumulative probability of 33-50% of converting to AD. Although MCI is considered a symptomatic pre-dementia stage, recovery to normal cognition is considered possible. Therefore, to treat MCI and prevent or delay conversion of MCI to AD, identification of MCI is critical. Specifically, early diagnosis and timely intervention of AD and pre-AD MCI will facilitate the prevention and treatment of AD, which is expected to effectively delay the progression of AD and reduce the medical, socio-economic, and psychological burden on the family and society as a whole. The present invention satisfies these and related needs. SUMMARY

[0005] The present invention relates to the discovery of novel protein markers associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Accordingly, the present invention provides methods and compositions for the early diagnosis of MCI and AD in a subject. Methods for assessing the therapeutic efficacy of agents for the treatment of MCI and AD are also provided.

[0006] Accordingly, in a first aspect, the present invention provides a method for assessing the risk of a subject developing mild cognitive impairment (MCI) or Alzheimer's disease (AD). The method comprises the steps of: (a) comparing the level or concentration of at least one protein in a plasma or serum or whole blood sample of the subject to a standard control level of the same protein, the at least one protein being 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 being present in the plasma or serum or whole blood of an average healthy subject not suffering from or at risk of MCI or AD, respectively; (b) detecting a lower level or concentration of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1, or a higher level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 in the plasma or serum or whole blood sample of the subject compared to the standard control level of the same protein; and (c) determining the subject as having an increased risk of developing MCI or AD. In some embodiments, the method further comprises the step of measuring the level or concentration of at least one protein in a plasma or serum or whole blood sample of the subject prior to step (a). In some embodiments, prior to the step of measuring, the method further comprises obtaining a plasma or serum 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 mass spectrometry. In some embodiments, when the subject is determined in step (c) to be at risk of developing MCI or AD, then the subject is provided with increased follow-up monitoring (e.g., monitoring tests are performed at an increased frequency compared to routine monitoring prescribed by a medical professional for a person of similar age and medical background who is not at risk or at low risk). In some embodiments, when the subject is determined in step (c) to be at increased risk of developing MCI or AD, then the subject is administered a therapeutic agent for the prevention or treatment of MCI or AD.

[0007] While any of the 18 proteins identified in Table 1 can be used in the 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 of the 18 proteins) are simultaneously examined in the method to achieve a better assessment of the risk of developing MCI or AD in the subject. In some embodiments, the use of multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more of the 18 proteins) in the method can improve the accuracy, sensitivity, and specificity in determining the risk of developing MCI or AD. In some cases, the use of multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more of the 18 proteins) in the method allows for the assessment of multiple biological pathways / systems, thereby providing a more comprehensive assessment of the disease state of the subject.

[0008] In some embodiments, the prediction model is used to integrate the levels from any two or more of the 18 proteins to predict the risk of MCI and the risk of AD. In some embodiments, the use of a prediction model using multiple protein markers in predicting the risk of MCI and the risk of AD achieves better diagnostic performance than the use of a method from any single protein from the 18 proteins. In some embodiments, the prediction model uses any two proteins from the 18 proteins. In some embodiments, the prediction model uses any three proteins from the 18 proteins. In some embodiments, the prediction model uses any four or more proteins from the 18 proteins. In some cases, at least two proteins from the group of 18 proteins are selected and measured for risk assessment according to the claimed method. In some cases, at least three or more proteins from the group of 18 proteins are selected for assessment according to the claimed method.

[0009] In a second aspect, the present application provides a kit for assessing the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject or for assessing the therapeutic efficacy of a treatment regimen for MCI or AD in a subject. The kit comprises at least one reagent capable of determining the plasma or serum or whole blood level or concentration of any one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen or more proteins in a subject, the proteins being 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 some embodiments, the kit can further comprise a standard control for each protein that reflects the level / concentration of the same protein present in the corresponding plasma or serum or whole blood of an average healthy subject who does not have MCI or AD or who is not at increased risk of MCI or AD, respectively. In some embodiments, the kit is for determining the plasma or serum or whole blood level or concentration of at least any two proteins from the group of 18 proteins in a subject. In some embodiments, the kit can determine the plasma or serum or whole blood level or concentration of at least any three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen or more proteins from the group of 18 proteins in a subject.

[0010] In a third aspect, the present application provides a chip for detecting a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), or for assessing the therapeutic efficacy of a treatment regimen for MCI or AD in a subject. The chip comprises a solid substrate and reagents capable of measuring the plasma or 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 proteins of the subject, 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 location on the substrate. In some embodiments, the chip is used to measure the plasma or serum or whole blood level or concentration of at least any two proteins from the group of 18 proteins in the subject. In some embodiments, the chip can measure the plasma or serum or whole blood level or concentration of at least any three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or more proteins from the group of 18 proteins in the subject.

[0011] In a fourth aspect, the present application provides a method for quantifying a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD). The method comprises the steps of: (a) calculating an individual risk score by inputting a set of values into the following formula: , and (b) determining a subject having a score below an optimal cutoff value as having no risk, low risk, or increased risk of developing MCI or AD, and a subject having a score above the optimal cutoff value as having a high risk or increased risk of developing MCI or AD. In this method, the set of values comprises the plasma or serum or whole blood level of a candidate protein from the group of 18 proteins listed in Table 2. In this method, the optimal cutoff value for defining a low risk or high risk of developing MCI or AD is determined as the value having the largest Youden index using the Optimal Cutpoints function from the R optimal.cutpoints() package, β i is a weighting coefficient for the candidate protein, and epsilon is an intercept.

[0012] In some embodiments, the set of values consists of the plasma or serum or whole blood level of each of the 18 proteins, and the weighting coefficient ranges from 0.01 to 0.99.β i ) and the intercept range ( epsilon ) are listed in Table 2, and subjects with a risk score higher than 0.356 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of CCL27 and IGFBP-2, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 3, and subjects with a risk score higher than 0.656 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of AOC3, CD27, and NCS1, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 4, and subjects with a risk score higher than 0.266 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of AOC3 and CD27, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 5, and subjects with a risk score higher than 0.620 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of AOC3 and NCS1, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 6, and subjects with a risk score higher than 0.833 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were considered to have a low risk of MCI or AD or no increased risk of MCI or AD.

[0017] In some embodiments, the panel of values ​​consists of plasma or serum or whole blood levels of CD27 and NCS1, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 7, and subjects with a risk score higher than 0.509 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of CTRC, KYNU and TNNI3, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 8, and subjects with a risk score higher than 0.489 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of CTRC and KYNU, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 9, and subjects with a risk score higher than 0.589 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were 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 or serum or whole blood levels of CTRC and TNNI3, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 10, and subjects with a risk score higher than 0.515 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were considered to have a low risk of MCI or AD or no increased risk of MCI or AD.

[0021] In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of KYNU and TNNI3, and a weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 11, and subjects with a risk score higher than 0.799 were considered to have an increased risk of developing MCI or AD. Otherwise, subjects were considered to have a low risk of MCI or AD or no increased risk of MCI or AD.

[0022] In some embodiments, the method further comprises a step of measuring the plasma or serum or whole blood level of the protein prior to step (a). In some embodiments, the method further comprises a step of obtaining a plasma or serum or whole blood sample from the subject prior to the measuring step. In some embodiments, when the subject is determined to have an increased risk of developing MCI or AD in step (b), the subject is then given increased follow-up monitoring (e.g., monitoring tests are performed at increased frequency compared to the routine monitoring prescribed by medical professionals to persons of similar age and medical background who are at no risk or low risk of developing MCI or AD) and / or treatment for MCI or AD as described in the present disclosure. When the subject is determined to have no increased risk of developing MCI or AD, the subject is then given routine monitoring typically performed by a physician to persons of similar age and medical background who are at no risk or low risk of developing MCI or AD.

[0023] In a fifth aspect, the present application provides a method for assessing the efficacy of a therapeutic agent in treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method comprises the following steps: (a) comparing the plasma or serum or whole blood level of any one of the proteins selected from the group consisting of the proteins named in Table 1 before and after administration of the therapeutic agent to the subject; (b) detecting a decrease in the plasma or serum or whole blood level 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 or serum 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 in treating MCI or AD. In some embodiments, the method further comprises a step of measuring the plasma or serum or whole blood level of the one or more proteins before and after administration prior to step (a). In some embodiments, the method can further comprise obtaining a plasma or serum or whole blood sample from the subject before and after administration prior to the measuring step.

[0024] In some embodiments, when the therapeutic agent is deemed effective in treating MCI or AD in step (c), the subject will continue treatment by receiving administration of the therapeutic agent; when the therapeutic agent is deemed ineffective in treating MCI or AD in step (c), the subject will stop treatment of administration of the therapeutic agent; more precisely, the subject will start another different treatment by receiving administration of a different therapeutic agent. In some embodiments, the subject is of Chinese descent. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1. Prediction of MCI risk and AD risk based on a model utilizing 18 blood proteins. (a) Boxplot showing individual risk scores assigned by the 18-protein model (i.e., a model integrating 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 the HK Chinese population, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.356) indicates the cutoff value with risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves of the 18-protein model (solid line) and single proteins (dotted line) to distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese population. Numbers in parentheses indicate the area under the ROC curve for the corresponding model. P <0.05, P <0.01, P < 0.001.

[0026] Figure 2 . Prediction of MCI risk and AD risk based on a model utilizing 2 blood proteins from 18 blood proteins. (a) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., a model integrating CCL27 and IGFBP-2, listed in Table 3) in the HK Chinese population, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.656) indicates the cutoff value with risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves of the 2-protein model (solid line) and single proteins (dotted line) to distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese population. Numbers in parentheses indicate the area under the ROC curve for the corresponding model. P <0.05, P <0.01, P < 0.001.

[0027] Figure 3. Prediction of MCI risk and AD risk based on a model using 3 blood proteins. (a) Box plot showing individual risk scores assigned by a 3-protein model (i.e., a model integrating AOC3, CD27, and NCS1, listed in Table 4) in the HK Chinese population, classified by diagnosis (n is 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.266) represents the cutoff value for the risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves of the 3-protein model (solid line) and a single protein (dashed line) distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese population. The numbers in brackets indicate the area under the ROC curve of the corresponding model. P <0.05, P <0.01, P <0.001.

[0028] Figure 4. (a) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating AOC3 and CD27, listed in Table 5) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.620) represents the cutoff value with risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dotted line) to distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. (d) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating AOC3 and NCS1, listed in Table 6) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.833) represents the cutoff value with risk of developing MCI and AD. (e, f) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dotted line) to distinguish MCI patients from CN (e) and AD patients from CN (f) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. (g) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating CD27 and NCS1, listed in Table 7) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.509) represents the cutoff value with risk of developing MCI and AD. (h, i) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dotted line) to distinguish MCI patients from CN (h) and AD patients from CN (i) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. P <0.05, P <0.01, P < 0.001.

[0029] Figure 5. Predictions of MCI risk and AD risk based on models utilizing 3 blood proteins. (a) Boxplot showing individual risk scores assigned by the 3-protein model (i.e., model integrating CTRC, KYNU, and TNNI3, listed in Table 8) in the HK Chinese cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dotted line (risk score = 0.489) represents the cutoff value with risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves of the 3-protein model (solid line) and single proteins (dashed line) to distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese cohort. Numbers in parentheses indicate the area under the ROC curve for the corresponding model. P <0.05, P <0.01, P <0.001.

[0030] Figure 6. (a) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating CTRC and KYNU, listed in Table 9) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dashed line (risk score = 0.589) indicates the cutoff value with risk of developing MCI and AD. (b, c) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dashed line) to distinguish MCI patients from CN (b) and AD patients from CN (c) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. (d) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating CTRC and TNNI3, listed in Table 10) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dashed line (risk score = 0.515) indicates the cutoff value with risk of developing MCI and AD. (e, f) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dashed line) to distinguish MCI patients from CN (e) and AD patients from CN (f) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. (g) Boxplot showing individual risk scores assigned by the 2-protein model (i.e., the model integrating KYNU and TNNI3; listed in Table 11) in the HK Chinese population cohort, classified by diagnosis (n = 9 CN, 14 MCI, and 16 AD, respectively). The dashed line (risk score = 0.799) indicates the cutoff value with risk of developing MCI and AD. (h, i) Receiver operating characteristic (ROC) curves for the 2-protein model (solid line) and single proteins (dashed line) to distinguish MCI patients from CN (h) and AD patients from CN (i) in the HK Chinese population cohort. The number in the bracket indicates the area under the ROC curve for the corresponding model. P <0.05, P <0.01, P < 0.001.

[0031] Definitions

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Furthermore, any method or material similar or equivalent to those described herein can be used in the practice of the present disclosure. For purposes of the present disclosure, the following terms are defined with the following meanings.

[0033] The terms "polypeptide," "peptide," and "protein" are used interchangeably herein and refer to polymers of amino acids. All three terms apply to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of corresponding naturally occurring amino acids, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers. The terms as used herein encompass amino acid chains of any length, including full-length proteins, in which the amino acid residues are linked by covalent peptide bonds.

[0034] In the present disclosure, the term "biological sample" or "sample" includes tissue sections (e.g., biopsy and autopsy samples), and frozen sections made for histological purposes, or processed forms of any such sample. Biological samples include blood and blood fractions or products (e.g., whole blood, acellular fractions of blood (serum, plasma), and blood cells), sputum or saliva, lymph and tongue tissue, cultured cells (e.g., primary cultures), explants and transformed cells, stool, urine, gastric biopsy tissue, etc. Biological samples are typically obtained from eukaryotic organisms, which can be mammalian, can be primate, and can be a human subject.

[0035] The term "immunoglobulin" or "antibody" (used interchangeably herein) refers to an antigen-binding protein having a basic four polypeptide chain structure composed of two heavy chains and two light chains, e.g., stabilized by interchain disulfide bonds, which has the ability to specifically bind an antigen. Both heavy and light chains are folded into domains.

[0036] The term "antibody" also refers to antigen-binding and epitope-binding fragments of antibodies and uses, e.g., Fab fragments, which can be used in immunoaffinity assays. There are many well-characterized antibody fragments. Thus, for example, pepsin digests an antibody to produce F(ab) '2, which is a dimer of Fab which itself is a light chain joined to VH-C by disulfides. The F(ab) '2 has a valence of two and can bind two antigens simultaneously. H -C H 1Connection. F(ab) '2 can be reduced under mild conditions to break disulfide linkages in the hinge region, thereby converting the F(ab) '2 dimer into an Fab' monomer. The Fab' monomer is essentially an Fab that has part of the hinge region (for a more detailed description of other antibody fragments, see, e.g., Fundamental Immunology, Paul, ed., Raven Press, N.Y. (1993)). Although various antibody fragments are defined in terms of the digestion of an intact antibody, one of skill will appreciate that such fragments can be synthesized de novo either chemically or by utilizing recombinant DNA methodology. Thus, the term antibody also includes antibody fragments produced by the modification of whole antibodies or synthesized using recombinant DNA methods.

[0037] As used in this application, "increase" or "decrease" refers to a detectable positive or negative change in quantity compared to a comparative control, such as a determined standard control (e.g., the average level / amount of a specific protein present in a sample from a healthy subject who has not been diagnosed with MCI or AD and has no increased risk of MCI or AD). An increase is a positive change that is typically at least 10%, or at least 20%, or 50%, or 100% of the control value, and can be as high as at least 2 times, or at least 5 times, or even 10 times the control value. Similarly, a decrease is a negative change that is typically at least 10%, or at least 20%, 30%, or 50% of the control value, or even as high as at least 80% or 90% of the control value. Other terms indicating a quantitative change or difference compared to a comparison basis, such as "more," "less," "higher," and "lower," are used in the same manner as described above in this application. In contrast, the terms "substantially the same" or "substantially unchanged" indicate little or no change in quantity compared to a standard control value, typically within ±10% of the standard control, or within ±5%, ±2%, or even less than a change from a standard control.

[0038] As used herein, the term "amount" refers to the amount of a target substance (e.g., a target protein) present in a sample. Such an amount can 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.

[0039] The terms "subject," "individual," and "patient" are used interchangeably herein and refer to a mammal, preferably a human, who is seeking medical attention due to a risk of MCI or AD (e.g., having a family history) or who has been diagnosed with MCI or AD. Subjects also include individuals who are currently undergoing treatment and are seeking a treatment plan. Subjects or individuals in need of treatment include those who display symptoms of MCI or AD or are at risk of 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 suffered from related symptoms in the past, individuals who have been exposed to a triggering substance or event, and individuals who suffer from symptoms of a chronic or acute condition. Subjects can be of either sex and at any age in life.

[0040] As used herein, the terms "treat" or "treating" describe an action that results in the elimination, alleviation, relief, reversal, prevention, and / or delay of the onset or recurrence of any symptom of a predetermined medical condition. In other words, "treating" a condition includes both therapeutic and prophylactic interventions for that condition.

[0041] The term "effective amount" as used herein refers to an amount that produces the therapeutic effect for which the substance is administered. Effects include preventing, correcting, or inhibiting progression of symptoms and related complications of a disease / condition to any detectable extent. The exact amount will depend on the purpose of the treatment, and will be ascertainable by one skilled in the art using known techniques (see, e.g., Lieberman, Pharmaceutical Dosage Forms (vols. 1-3, 1992); Lloyd, The Art, Science and Technology of Pharmaceutical Compounding (1999); and Pickar, Dosage Calculations (1999)). Pharmaceutical Dosage Forms (vols. 1-3, 1992); Lloyd, The Art, Science and Technology of Pharmaceutical Compounding (1999); and Pickar, Dosage Calculations (1999) )).

[0042] The term "standard control" as used herein refers to a sample comprising a predetermined amount of an analyte to indicate the amount or concentration of that analyte (e.g., a predetermined DNA / mRNA or protein) present in a sample of this type taken from an average healthy subject who does not have a predetermined disease or condition (e.g., MCI or AD) or who is not at risk of developing a predetermined disease or condition (e.g., MCI or AD). When used in the context of describing a value, the term can also be used to simply refer to the amount or concentration of that analyte present in a "standard control" sample.

[0043] The term "average" when used in the context of describing healthy subjects who do not have a relevant disease or disorder (e.g., MCI or AD) and who are not at risk of developing a relevant disease or disorder (e.g., MCI or AD) refers to certain characteristics, e.g., levels of a relevant protein, in a representative sample (e.g., serum or plasma or whole blood) of a randomly selected group of healthy humans who do not have the disease or disorder and who are not at risk of developing the disease or disorder. The selected group should include a sufficient number of human subjects so that the average amount or concentration of the target analyte in these individuals reflects the corresponding profile in the general population of healthy humans with reasonable accuracy. Optionally, the selected group of subjects can be selected to have a background similar to that of the people being tested for signs or risk of their relevant disease or disorder, e.g., matching or comparable age, gender, ethnicity, and medical history, etc.

[0044] As used herein, the term "Chinese" refers to a person of Chinese ethnicity whose ancestors have resided in the historical territory of China for some period of time (e.g., at least the last 3, 4, 5, 6, 7, or 8 generations or the last 100, 150, 200, 250, or 300 years).

[0045] DETAILED DESCRIPTION

[0046] I. INTRODUCTION

[0047] The present disclosure provides novel protein markers for the early diagnosis or risk assessment of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in a subject. In particular, a biomarker of a set of 18 proteins representing "MCI and / or AD signatures" that are differentially expressed between healthy subjects and individuals with MCI or AD has been identified in blood samples. The present disclosure provides methods and compositions that facilitate the early diagnosis of MCI or AD in a subject. The present disclosure also provides methods and compositions for assessing the therapeutic treatment of MCI or AD in a subject.

[0048] II. Protein markers

[0049] 1. The protein CTRC, also known as chymotrypsin C or calcyphosin, is a protease that is primarily found in the pancreas. It belongs to the serine protease family and plays a key role in the regulation of pancreatic digestive enzymes. Specifically, CTRC helps activate other pancreatic enzymes, such as trypsinogen, which is important for the proper digestion of proteins in the small intestine. Mutations in the CTRC gene have been associated with an increased risk of developing chronic pancreatitis, a disease that causes inflammation and damage to the pancreas over time.

[0050] 2. The protein NCS1, also known as neuronal calcium sensor 1, is a calcium-binding protein that is 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 have been associated with certain neurological disorders, such as schizophrenia, bipolar disorder, and Parkinson's disease.

[0051] 3. The protein PSME1, also known as PA28a or REGa, is a regulatory protein involved in immune responses and cellular stress responses. It belongs to the proteasome activator family and plays a key role in the activation of the 20S proteasome, which is responsible for degrading damaged or misfolded proteins in cells. PSME1 specifically binds to the 20S proteasome and enhances its proteolytic activity, thereby facilitating the clearance of abnormal proteins. PSME1 has also been shown to play a role in antigen processing and presentation, which is important for the immune system to recognize and eliminate foreign pathogens. Dysregulation of PSME1 expression has been implicated in the development and progression of various diseases, including cancer, autoimmune diseases, and neurodegenerative diseases.

[0052] 4. The protein KYNU, also known as kynureninease, is an enzyme involved in the metabolism of tryptophan, an essential amino acid. KYNU catalyzes the conversion of kynurenine to anthranilic acid in the kynurenine pathway, which is the major pathway of tryptophan metabolism in humans. This pathway plays a key role in regulating immune function, inflammation, and neurotransmitter synthesis in the brain. Dysregulation of KYNU activity has been implicated in the pathogenesis of various diseases, including autoimmune diseases, neurodegenerative diseases, and cancer. KYNU is considered a potential therapeutic target for these diseases due to its involvement in regulating immune responses and inflammation.

[0053] 5. The protein TNNI3, also known as cardiac troponin I, is a regulatory protein expressed primarily in cardiac muscle. It is a component of the troponin complex, which is responsible for regulating muscle contraction in response to calcium signaling. TNNI3 specifically binds to actin filaments in the sarcomere and inhibits the interaction between actin and myosin, thereby preventing muscle contraction. It is an important biomarker for diagnosing acute myocardial infarction, commonly known as a heart attack, as its levels in the blood increase in response to cardiac injury. Mutations in the TNNI3 gene have been associated with various cardiac conditions, including hypertrophic cardiomyopathy, dilated cardiomyopathy, and restrictive cardiomyopathy.

[0054] 6. The protein IGFBP2, also known as insulin-like growth factor binding protein 2, is a binding protein that interacts with insulin-like growth factors (IGFs) to regulate their activity in the body. It is primarily produced in the liver and found in circulation. IGFBP2 regulates the bioavailability of IGFs, which are important regulators of cell growth, differentiation, and survival. IGFBP2 is involved in a wide range of physiological processes, including embryonic development, tissue repair, and metabolism. Dysregulation of IGFBP2 expression has been associated with various diseases, including cancer, metabolic disorders, and neurodegenerative diseases. IGFBP2 is being investigated as a potential biomarker for diagnosis and prognosis of certain diseases, as well as a therapeutic target for treating cancer and other conditions.

[0055] 7. The protein DCBLD2, also known as discoidin, CUB and LCCL domain-containing protein 2, is a transmembrane protein expressed in a wide range of tissues, including the brain, heart, and lung. It belongs to the adhesion G protein-coupled receptor family and plays a role in cell adhesion, migration, and angiogenesis. Dysregulation of DCBLD2 expression has been associated with various diseases, including cancer, cardiovascular diseases, and developmental disorders. DCBLD2 is considered a potential therapeutic target for cancer and other diseases due to its involvement in angiogenesis and cell migration.

[0056] 8. Protein CCL27, also known as cutaneous T-cell chemoattractant (CTACK), is a chemokine primarily expressed in the skin. It belongs to the CC chemokine family and plays a role 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 has been associated with various skin disorders, such as psoriasis, atopic dermatitis, and skin cancer. CCL27 is considered a potential therapeutic target for these diseases due to its involvement in regulating immune responses in the skin.

[0057] 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. It belongs to the sialic acid-binding immunoglobulin-like lectin (Siglecs) family and plays a role in regulating immune responses. CD33 binds to sialic acid residues on glycoproteins and glycolipids, modulating cell signaling and adhesion. CD33 is involved in various physiological processes, including phagocytosis, antigen presentation, and cytokine production. Dysregulation of CD33 expression has been associated with various diseases, including Alzheimer's disease, acute myeloid leukemia, and autoimmune disorders. CD33 is considered a potential therapeutic target for these diseases due to its involvement in regulating immune function and cell signaling.

[0058] 10. Protein NEFL, also known as neurofilament light chain, is a cytoskeletal protein primarily expressed in neurons. It is a component of neurofilaments, which are filamentous protein networks that provide structural support to axons and contribute to their electrical properties. NEFL plays a role in axonal transport and is involved in various physiological processes, including neuronal development, plasticity, and regeneration. Dysregulation of NEFL expression has been associated with various neurological disorders, including amyotrophic lateral sclerosis (ALS), Alzheimer's disease, and peripheral neuropathy. NEFL has been considered a potential biomarker for these diseases due to its involvement in axonal injury and degeneration.

[0059] 11. Protein FCN2, also known as ficolin-2, is a soluble pattern recognition receptor that is part of the innate immune system. It belongs to the ficolin family and plays a role in recognizing and binding pathogen-associated molecular patterns (PAMPs) on the surface of microorganisms, such as bacteria, viruses, and fungi. FCN2 is primarily produced in the liver and found in circulation. FCN2 is involved in various physiological processes, including host defense, inflammation, and tissue repair. Dysregulation of FCN2 expression has been associated with various infectious and inflammatory diseases, including sepsis, pneumonia, and rheumatoid arthritis. FCN2 is being investigated as a potential biomarker and therapeutic target for these diseases.

[0060] 12. The protein LGALS7, also known as galectin 7, is a soluble lectin involved in various cellular processes, including cell adhesion, apoptosis, and immune regulation. It belongs to the galectin family, which are carbohydrate-binding proteins that interact with glycoproteins and glycolipids on the cell surface. LGALS7 is expressed in a variety of tissues, including the skin, gastrointestinal tract, and immune cells. It is involved in various physiological processes, such as wound healing, inflammation, and cancer progression. Dysregulation of LGALS7 expression has been associated with various diseases, including cancer, inflammatory conditions, and neurodegenerative diseases. LGALS7 has been considered a potential biomarker and therapeutic target for these diseases due to its involvement in regulating cell signaling and immune responses.

[0061] 13. Protein GP1BA, also known as glycoprotein Ib platelet alpha subunit, is a subunit of the glycoprotein Ib-IX-V complex, which is a receptor complex expressed primarily on the surface of platelets. It plays a role in platelet adhesion and aggregation, which is important for normal blood coagulation and wound healing. GP1BA specifically binds to von Willebrand factor (vWF), a protein involved in the initial stages of platelet adhesion and thrombosis. Disorders in GP1BA expression or activity have been associated with various coagulation disorders, such as giant platelet (Bernard-Soulier) syndrome and platelet-type von Willebrand disease. GP1BA has also been studied as a potential therapeutic target for preventing thrombosis, which is the formation of blood clots that can lead to stroke, heart attack, and other serious conditions.

[0062] 14. The protein CES1, also known as carboxylesterase 1, is an enzyme primarily expressed in the liver and plays a role in drug metabolism and detoxification. It belongs to the serine hydrolase family and 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, inactive compounds that are converted to active drugs when metabolized by the body. Dysregulation of CES1 expression has been associated with various diseases, including metabolic disorders, liver disease, and cancer. CES1 is being investigated as a potential therapeutic target for these diseases, as well as a biomarker for drug efficacy and toxicity.

[0063] 15. The protein AOC3, also known as diamine oxidase (DAO), is an enzyme expressed primarily in the small intestine and kidney. It belongs to the copper-containing amine oxidase family and is involved in the catabolism of histamine, a biogenic amine that plays a role in various physiological processes, including immune responses and neurotransmission. Dysregulation of AOC3 expression or activity has been associated with various inflammatory and allergic diseases, such as asthma, migraine, 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.

[0064] 16. Protein CD27 is a transmembrane protein that is primarily expressed on the surface of T cells and plays a role in regulating immune responses. 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 immune memory, and the regulation of autoimmune responses. Dysregulation of CD27 expression or activity has been associated with various diseases, including cancer, autoimmune disorders, and infectious diseases. CD27 has been considered a potential therapeutic target for these diseases, as well as a biomarker for disease diagnosis and prognosis.

[0065] 17. The protein KIRREL2, also known as IRRE protein homologous protein 2 or nephrin-like protein 2, is a transmembrane protein expressed primarily in the kidney, brain, and heart. It belongs to a family of proteins containing immunoglobulin-like domains and plays a role in cell signaling, cell adhesion, and tissue development. KIRREL2 is involved in the formation and maintenance of the glomerular filtration barrier in the kidney, which is important for normal renal function. It also plays a role in the development and function of the central nervous system and heart. Dysregulation of KIRREL2 expression or activity has been associated with a variety of conditions, including kidney disease, neurological disorders, and cardiovascular disease. KIRREL2 is being studied as a potential therapeutic target for these diseases, as well as a biomarker for their diagnosis and prognosis.

[0066] 18. The protein CA5A, also known as carbonic anhydrase 5A, is an enzyme expressed primarily in the salivary glands, pancreas, and liver. It 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 a biomarker for their diagnosis and prognosis.

[0067] While any of the 18 proteins are suitable for use in the methods and compositions disclosed herein, in some cases, multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, or all of the 18 proteins) are simultaneously examined in the method or kit or device to achieve a better assessment of the risk of developing MCI or AD in the subject. In some embodiments, the simultaneous use of multiple proteins (any two, three, four, five, six, seven, eight, nine, ten, or more, or all of the 18 proteins) in the method or kit or device can improve the accuracy, sensitivity, and specificity of 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 of the 18 proteins) in the method or kit or device allows for the assessment of multiple biological pathways / systems, providing a more comprehensive assessment of the disease state of the subject.

[0068] In some embodiments, a prediction model is used to integrate levels from any two or more of the 18 proteins to predict MCI risk or AD risk. In some embodiments, the use of a prediction model for multiple protein markers in predicting MCI risk or AD risk achieves better diagnostic performance than methods using any single protein from the 18 proteins. In some embodiments, the prediction model uses any two proteins from the 18 proteins. In some embodiments, the prediction model uses any three proteins from the 18 proteins. In some embodiments, the prediction model uses any four or more proteins from the 18 proteins. In some cases, at least two proteins from the group of 18 proteins are selected and measured for risk assessment according to the claimed methods. In some cases, at least three or more proteins from the group of 18 proteins are selected and measured according to the claimed methods.

[0069] In some cases, CCL27 and IGFBP-2 are selected and measured for risk assessment according to the claimed methods. In some cases, any two of AOC3, CD27, and NCS1 are selected and measured for risk assessment according to the claimed methods. In some cases, AOC3 and CD27 are selected and measured for risk assessment of MCI or AD. In other cases, CD27 and NCS1 are selected and measured for risk assessment of MCI or AD. In still other cases, AOC3 and NCS1 are selected and measured for risk assessment of MCI or AD. In some cases, any two of CTRC, KYNU, and TNNI3 are selected and measured for risk assessment according to the claimed methods. In some cases, CTRC and KYNU are selected and measured for risk assessment of MCI or AD. In other cases, KYNU and TNNI3 are selected and measured for risk assessment of MCI or AD. In still other cases, CTRC and TNNI3 are selected and measured for risk assessment of MCI or AD.

[0070] III. Quantification of Marker Proteins

[0071] 1. Obtaining a Sample

[0072] A first step in implementing the present application is to obtain a blood sample from a subject being tested for assessing the risk of developing or monitoring the severity or progression of MCI or AD. The same type of sample should be taken from a control group (healthy individuals who do not have MCI or AD and who do not have an increased risk of MCI or AD) and a test group (e.g., subjects who can have MCI or AD, or who have an increased risk of MCI or AD). For this purpose, standard procedures routinely employed in hospitals or clinics are typically employed.

[0073] For the purpose of detecting the presence / amount of marker proteins in a test subject or assessing the risk of developing MCI or AD, a blood sample of an individual patient is taken and the serum or plasma or whole blood levels of the relevant marker proteins (e.g., one or more proteins selected from AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 as identified in Table 1) can be measured and then compared to a standard control. If an increase in protein levels of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3, or a decrease in protein levels of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1 is observed when compared to the control levels (depending on the specific beta values of the protein markers shown in the table), the test subject is considered to have MCI or AD, or to have an elevated risk of developing MCI or AD.

[0074] For the purpose of monitoring disease progression in a patient with MCI or AD or assessing the effectiveness of a treatment, blood samples of an individual patient can be taken at different time points such that the levels of individual marker proteins (e.g., one or more proteins selected from AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 as identified in Table 1) can be measured to provide information indicative of the disease state. For example, when a patient’s marker protein levels show a general trend of increasing or decreasing over time, the patient is considered to have an improvement in the severity of MCI or AD, or the treatment the patient has received is considered to be effective (depending on the specific beta values of the protein markers as shown in the table). A lack of substantial changes in a patient’s marker protein levels would indicate a lack of change in the MCI or AD state and ineffectiveness of the treatment given to the patient.

[0075] Furthermore, the present inventors have devised a new computational method to generate 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) to quantify the risk of an individual developing MCI or AD or to compare the relative risk of developing MCI or AD between two or more individuals.

[0076] 2. Preparing a sample for protein detection

[0077] Blood samples from subjects are suitable for use in the present application and can be obtained by well-known methods and as described in standard medical literature. In certain applications of the present application, serum or plasma can be the preferred sample type. In other cases, whole blood samples can be used.

[0078] Blood samples are obtained from a human to be tested or monitored for MCI or AD using the methods of the present application. Collection of blood samples from individuals is performed according to standard protocols typically followed by hospitals or clinics. An appropriate amount of blood is collected and can be stored according to standard procedures prior to further preparation.

[0079] Analysis for marker proteins present in patient samples according to the present application can be performed using, for example, serum or plasma or whole blood. Methods for preparing patient samples for protein extraction / quantitative detection are well known to those skilled in the art.

[0080] 3. Determining marker protein levels

[0081] A variety of immunoassays can be used to detect a protein of any particular identity, such as CCL27, IGFBP-2, AOC3, CD27, NCS1, CTRC, KYNU, TNNI3, or any of those identified in Table 1. In some embodiments, a sandwich assay can be performed by capturing the protein from a test sample with an antibody having specific binding affinity for the protein. The protein can then be detected with a labeled antibody having specific binding affinity for it. Such immunoassays can be performed using microfluidic devices, such as microarray protein chips. Target proteins (e.g., any of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 as identified in Table 1) can also be detected by gel electrophoresis (e.g., two-dimensional gel electrophoresis) and Western blot analysis using specific antibodies. Alternatively, a given protein (e.g., any of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 as identified in Table 1) can be detected using standard immuno-histochemical techniques with appropriate antibodies. Both monoclonal and polyclonal antibodies, including antibody fragments having the desired binding specificity, can be used for the specific detection of polypeptides. Such antibodies and their binding fragments having specific binding affinity for a particular protein (e.g., any of AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1, and TNNI3 as identified in Table 1) can be produced by known techniques.

[0082] Other methods can also be employed to measure the levels of marker proteins in practicing the present application. For example, a variety of methods based on mass spectrometry techniques have been developed to quantify target proteins more rapidly and accurately in large numbers of samples. These methods involve highly sophisticated equipment, 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) mode, and QTOP mass spectrometers based on electrospray ionization (ESI). See, e.g., Pan et al. , J Proteome Res.2009 February; 8(2):787–797.

[0083] IV. Establishing Standard Controls

[0084] To establish a standard control for practicing the methods of the present invention, a group of healthy individuals who do not suffer from MCI and AD or who are not at increased risk of developing MCI or AD, as conventionally defined by having normal cognition (e.g., MoCA ≥ 26), are first selected. These individuals are within appropriate parameters, if applicable, for the purpose of screening and / or monitoring for MCI or AD using the methods of the present invention. Optionally, the individuals are of the same gender, similar age, or similar ethnic background as the test subject.

[0085] The health status of the selected individual is confirmed by well-established, routinely used methods including, but not limited to, a general physical examination of the individual and a general review of his or her medical history.

[0086] Furthermore, the selected group of healthy individuals must be of a reasonable size such that the average amount / concentration of the marker protein in the serum, plasma, or whole blood samples obtained from the group can be reasonably considered to be representative of the normal or average level in the general population of healthy people who do not suffer from MCI and AD or who do not have an increased risk of MCI or AD. Preferably, the selected group includes at least 10, 20, 30, or 50 human subjects.

[0087] When the mean value of the marker protein is established based on the individual values ​​present in each object of the selected healthy control group, the mean value or median or representative value or spectrum is considered to be a standard control. The standard deviation is also determined during the same process. In some cases, a separate standard control can be established for a group with a separate definition of different characteristics (such as age, sex or ethnic background).

[0088] V. Evaluation

[0089] In a first aspect, the present disclosure provides a method for assessing the risk of a subject developing mild cognitive impairment (MCI) or Alzheimer’s disease (AD). The method comprises the steps of: (a) comparing the level or concentration of at least one protein in a plasma or serum or whole blood sample of the subject to a standard control level of the same protein, the at least one protein being selected from the 18 proteins listed in Table 1, and the standard control level of the same protein being present in the plasma or serum or whole blood of an average healthy subject who does not have MCI or AD, or who is not at risk of developing MCI or AD, respectively; (b) detecting a lower protein level or concentration of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1, or a higher protein level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3 in the plasma or serum or whole blood sample of the subject compared to the standard control level of the same protein; and (c) determining the subject as having an increased risk of developing MCI or AD. In some embodiments, the method further comprises the step of measuring the level or concentration of at least one protein in a plasma or serum or whole blood sample of the subject prior to step (a). In some embodiments, prior to the step of measuring, the method further comprises obtaining a plasma or serum 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 mass spectrometry.

[0090] In some embodiments, when the subject is determined in step (c) to be at risk of developing MCI or AD, then the subject is provided with increased follow-up monitoring (e.g., monitoring tests are performed at an increased frequency compared to routine monitoring prescribed by a medical professional for a person of similar age and medical background who is at no risk or low risk). In some embodiments, when the subject is determined in step (c) to be at increased risk of developing MCI or AD, then the subject is administered a therapeutic agent for the prevention or treatment of MCI or AD. In some embodiments, when the subject is determined in step (c) to be at no increased risk of developing MCI or AD, then the subject is given routine monitoring typically performed by a doctor for a person of similar age and medical background who is at no risk or low risk of developing MCI or AD. Patients of any ethnicity, including Chinese, are suitable for assessment by the claimed method. In some embodiments, the subject is of Chinese descent.

[0091] In another aspect, the present disclosure provides a method for quantifying the risk of a subject developing mild cognitive impairment (MCI) or Alzheimer’s disease (AD). The method comprises the steps of: (a) calculating an individual risk score by inputting a set of values into the following formula: , and (b) determining a subject having a score below an optimal cutoff value as having a low risk of developing MCI or AD and a subject having a score above the optimal cutoff value as having an increased risk of developing MCI or AD. In this method, the values of the panel include plasma or serum or whole blood levels of the candidate proteins of the 18 proteins listed in Table 2. In this method, the optimal cutoff value for defining a low risk or a high risk of developing MCI or AD is determined as the value having the largest Youden index using the function of the ROC Optimal Cutpoints optimal.cutpoints() The function of the ROC β i is a weighting coefficient for the candidate protein, and epsilon is an intercept.

[0092] In some embodiments, the values of the panel consist of plasma or serum or whole blood levels of each of the 18 proteins listed in Table 1, and the weighting coefficient ranges from 0.0001 to 0.0002 β i ) and the intercept ranges from -0.0001 to 0.0001 epsilon , and a subject having a risk score above 0.356 is considered to have an increased risk of developing MCI and AD.

[0093] In some embodiments, the values of the panel consist of plasma or serum or whole blood levels of CCL27 and IGFBP-2, and the weighting coefficient ranges from 0.0001 to 0.0002 β i ) and the intercept ranges from -0.0001 to 0.0001 epsilon , and a subject having a risk score above 0.656 is considered to have an increased risk of developing MCI and AD.

[0094] In some embodiments, the values of the panel consist of plasma or serum or whole blood levels of AOC3, CD27 and NCS1, and the weighting coefficient ranges from 0.0001 to 0.0002 β i ) and the intercept ranges from -0.0001 to 0.0001 epsilon , and a subject having a risk score above 0.266 is considered to have an increased risk of developing MCI and AD.

[0095] In some embodiments, the values of the panel consist of plasma or serum or whole blood levels of AOC3 and CD27, and the weighting coefficient ranges from 0.0001 to 0.0002 β i ) and the intercept ranges from -0.0001 to 0.0001 epsilon , and a subject having a risk score above 0.620 is considered to have an increased risk of developing MCI and AD.

[0096] ​In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of AOC3 and NCS1, and the weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 6, and subjects with a risk score higher than 0.833 were considered to have an increased risk of developing MCI and AD.

[0097] In some embodiments, the panel of values ​​consists of plasma or serum or whole blood levels of CD27 and NCS1, and the weighting coefficient range is ( β i ) and the intercept range ( epsilon ) are listed in Table 7, and subjects with a risk score higher than 0.509 were considered to have an increased risk of developing MCI and AD.

[0098] In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of CTRC, KYNU and TNNI3, and the weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 8, and subjects with a risk score higher than 0.489 were considered to have an increased risk of developing MCI and AD.

[0099] In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of CTRC and KYNU, and the weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 9, and subjects with a risk score higher than 0.589 were considered to have an increased risk of developing MCI and AD.

[0100] In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of CTRC and TNNI3, and the weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 10, and subjects with a risk score higher than 0.515 were considered to have an increased risk of developing MCI and AD.

[0101] In some embodiments, the set of values ​​consists of plasma or serum or whole blood levels of KYNU and TNNI3, and the weighting coefficient range ( β i ) and the intercept range ( epsilon ) are listed in Table 11, and subjects with a risk score higher than 0.799 were considered to have an increased risk of developing MCI and AD.

[0102] In some embodiments, the method further comprises a step of measuring the plasma or serum or whole blood level of the protein prior to step (a). In some embodiments, the method additionally comprises a step of obtaining a plasma or serum or whole blood sample from the subject prior to the measuring step.

[0103] In some embodiments, when the subject is determined to have an increased risk of developing MCI or AD in step (b), the subject is then given increased follow-up monitoring (e.g., monitoring tests are performed at an increased frequency compared to the routine monitoring prescribed by a medical professional to a person of similar age and medical background who is at no risk or low risk of developing MCI or AD) and treatment as described in the present disclosure. When the subject is determined to have no increased risk of developing MCI or AD, the subject is then given routine monitoring typically prescribed by a physician to a person of similar age and medical background who is at no risk or low risk of developing MCI or AD. The subject can be of any ethnicity, including Chinese, suitable for evaluation by the claimed method.

[0104] In yet another aspect, the present application provides a method for evaluating the efficacy of a therapeutic agent in treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject. The method comprises the following steps: (a) comparing the plasma or serum or whole blood level of any one of the proteins selected from the group consisting of the proteins named in Table 1 in the subject before and after administration of the therapeutic agent to the subject; (b) detecting a decrease in the plasma or serum or whole blood level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3, or an increase in the plasma or serum 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 in treating MCI or AD. In some embodiments, the method further comprises a step of measuring the plasma or serum or whole blood level of the one or more proteins before step (a). In some embodiments, the method can further comprise, prior to the measuring step, obtaining a plasma or serum or whole blood sample from the subject before and after administration.

[0105] In some embodiments, when the therapeutic agent is deemed effective in treating MCI or AD in step (c), the subject will continue to be treated by administration of the therapeutic agent; when the therapeutic agent is deemed ineffective in treating MCI or AD in step (c), the subject will cease to be treated by administration of the therapeutic agent; and vice versa, the subject will begin another treatment by administration of a different therapeutic agent. Individuals of any ethnicity, including Chinese, are suitable for evaluation by the claimed method.

[0106] VI. Monitoring and Treatment

[0107] In a related aspect, the present invention also provides methods for treating MCI or AD patients who are at high risk of developing MCI or AD when MCI and AD are detected or subsequently develop MCI or AD. In some embodiments, the method includes administering a treatment to the subject, e.g., administering an acetylcholinesterase inhibitor (e.g., donepezil), galantamine, rivastigmine, or the like, upon determining that the subject is in the early stages of MCI or AD or has an increased risk of MCI or AD. ), 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 extract, huperzine A, omega-3 fatty acids, phosphatidylserine, or any combination thereof.

[0108] In some cases, when the diagnostic method steps described above and herein are completed, additional diagnostic tests are optionally performed to provide further confirmatory information (e.g., by brain imaging via CT scan or other imaging techniques to show excessive loss of brain volume, or by testing cognitive abilities to show accelerated decline), and when the patient has been determined to already have MCI or AD (e.g., in the early stages) or is at a significantly increased risk of subsequently developing MCI or AD, a suitable treatment or prevention program can be prescribed by a physician or other healthcare professional to treat the patient, manage / alleviate ongoing symptoms, or delay future onset of the disease. The U.S. Food and Drug Administration (FDA) has approved a variety of cholinesterase inhibitors, including donepezil (Aricept™, the only cholinesterase inhibitor approved for the treatment of all stages of AD (including moderate to severe)), rivastigmine (Exelon™, approved for the treatment of mild to moderate AD), galantamine (Razadyne™, mild to moderate patients), and memantine (Namenda™). Donepezil is the only cholinesterase inhibitor approved for the treatment of all stages of AD (including moderate to severe). Any one or more of these drugs can be prescribed for the treatment of patients who have been diagnosed with MCI or AD according to the methods of the present invention. Another possibility for 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 the symptoms of MCI or AD.

[0109] 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 displayed any clinical symptoms, ongoing monitoring, particularly at an increased frequency, is also appropriate. For example, patients can undergo more frequent scheduled testing (e.g., every six months, annually, or every two years) to detect any accelerated changes in their cognitive abilities. Suitable methods for such regular monitoring include the General Practitioner Cognitive Assessment Scale (GPCOG), the Mini-Cog, the Eight-Item Informant Interview Questionnaire to Distinguish Aging from Dementia (AD8), and the Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE). In addition, preventive treatment with trazodone may also be recommended.

[0110] VII. Kits and Devices

[0111] The present invention provides compositions and kits for practicing the methods described herein to assess the levels of relevant marker proteins in the serum / plasma or whole blood of a subject, which can be used for various purposes, such as detecting or diagnosing the presence of MCI or AD, determining the risk of developing the condition, and monitoring the progression of the condition in a patient, including assessing the therapeutic efficacy of a therapy administered for the condition in a patient who has received a diagnosis of the disease and has undergone treatment.

[0112] The kit for carrying out the analysis of measuring marker protein level generally includes at least one antibody for specific binding to the marker protein amino acid sequence. Optionally, the antibody is labeled with a detectable portion. The antibody can be a monoclonal antibody or a polyclonal antibody. In some cases, the kit can include at least two different antibodies, one for specific binding to the marker protein (i.e., a primary antibody) and the other usually connected to a detectable portion for detecting a primary antibody (i.e., a secondary antibody).

[0113] Typically, the test kit also includes appropriate standard controls. Standard controls indicate the average values ​​of marker proteins in the serum, plasma, or whole blood of healthy subjects who do not suffer from MCI or AD or are not at increased risk of developing MCI or AD. In some cases, such standard controls can be provided in the form of set values. In addition, the test kit of the present invention can provide an instruction manual to guide the user in analyzing the test sample and assessing the presence or risk of MCI or AD or the disease state / progression in the test subject.

[0114] On the one hand, the present invention provides a test kit for assessing the risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject or for assessing the therapeutic efficacy of a treatment regimen for MCI or AD in a subject. The test kit includes at least one reagent capable of measuring the plasma or serum or whole blood level or concentration of any one, two, three or more proteins of a subject, the protein being independently selected from AOC3, CA5A, CCL27, CD27, CD33, CES1, CTRC, DCBLD2, FCN2, GP1BA, IGFBP-2, KIRREL2, KYNU, LGALS7, NCS1, NEFL, PSME1 and TNNI3. In some embodiments, the test kit may also include a standard control for each protein, which is reflected in the plasma or serum or whole blood of an average healthy subject not suffering from MCI or AD or not at risk of MCI or AD, respectively. In some embodiments, the test kit is used to measure the plasma or serum or whole blood level or concentration of at least two proteins from 18 proteins of a subject. In some embodiments, the kit can determine the plasma or serum or whole blood levels or concentrations of at least any three, four, five, six, seven, eight, nine, ten or more proteins from the 18 proteins in a subject.

[0115] On the other hand, the present invention can also be embodied in a device or a system including one or more such devices, wherein the device or system is capable of performing all or some of the method steps described herein. For example, the device or system performs the following steps after receiving a serum or plasma or whole blood sample: (a) determining the amount or concentration of one or more marker proteins in the sample; (b) comparing the marker protein amount / concentration with a standard control value; and (c) providing an output indicating whether MCI or AD is present in the subject, or whether the subject is at an increased risk of developing MCI or AD, or whether the patient has a higher risk of subsequently developing MCI or AD relative to another tested patient, wherein the serum or plasma or whole blood sample is collected from a subject being tested for detecting MCI or AD, assessing the risk of developing MCI or AD, or assessing disease state / progression. In other cases, the device or system of the present invention performs the tasks of steps (b) and (c) after performing step (a) and inputting the amount or concentration from (a) into the device. Preferably, the device or system is partially or fully automated. In some embodiments, the device includes a detection chip.

[0116] As disclosed herein, the present invention provides a detection chip for assessing the risk of a subject developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), or for assessing the therapeutic efficacy of a treatment regimen for a subject's MCI or AD. The chip comprises a solid substrate and reagents capable of measuring the level of any one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen or all of 18 proteins in the plasma, serum or whole blood of the subject, the proteins being independently selected from 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. In some embodiments, the chip is used to determine the plasma, serum, or whole blood levels or concentrations of at least two of the 18 proteins in a subject. In some embodiments, the chip can determine the plasma, serum, or whole blood levels or concentrations 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 a subject.

[0117] Example

[0118] The following examples are provided by way of illustration only and not by way of limitation. Those skilled in the art will readily recognize that a variety of non-critical parameters can be changed or modified to produce essentially the same or similar results.

[0119] introduction

[0120] As life expectancy increases worldwide, the incidence of MCI and AD is projected to increase rapidly in the coming decades. Specifically, the global prevalence of AD is estimated to be 75 million in 2030 and 131 million by 2050, with prevalence rates rising in China, home to the largest elderly population. Indeed, from 1990 to 2010, the number of AD cases in China doubled from 3.7 million to 9.2 million, with a projected 22.5 million cases by 2050. Similarly, Hong Kong's population is rapidly aging, with an estimated 100,000 people currently living with dementia and an estimated 333,000 people, or 11%, expected to have dementia by 2039. The prevalence of MCI with increasing age ranges from 9.74% to 27.8% in Chinese and 2.48% to 35.5% in Europeans. Despite their devastating impact, MCI and AD remain largely underdiagnosed in primary care, with only 19% of patients receiving a diagnosis of dementia as a result of routine medical care, and an estimated even lower proportion seeking diagnosis or medical consultation in Hong Kong. A growing body of research suggests that the disease process of AD begins up to 20 years before the onset of recognizable symptoms, and this underdiagnosis is therefore largely attributed to the challenges and limitations associated with the current diagnosis of dementia, which relies either on subjective assessment of overt presenting symptoms or on expensive brain imaging or invasive sampling from cerebrospinal fluid. This collectively emphasizes the importance of the time interval between disease onset and diagnosis for any potential intervention, necessitating an urgent need for early diagnosis and the importance of novel biomarkers for the detection of MCI, MCI-to-AD progression, and AD.

[0121] To address the current lack of objective diagnostic tools for early detection, the present disclosure provides novel protein markers for the early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in subjects. By conducting a large-scale analysis of blood samples from participants in Hong Kong, China, including individuals with MCI, individuals with AD, and age- and sex-matched healthy and cognitively normal people, a set of 18 protein biomarkers representing "MCI and / or AD characteristics" that are differentially expressed between healthy people and individuals with MCI or AD were identified in the blood samples. The present disclosure provides a simple, ultrasensitive, non-invasive, and inexpensive blood-based technology for the early diagnosis of MCI and AD, as well as for evaluating the therapeutic treatment of MCI or AD in subjects.

[0122] The present disclosure provides novel methods and kits for assessing individual risk of MCI and AD using plasma or serum or whole blood protein markers or a combination thereof. The present invention relates to the discovery of novel blood protein markers associated with MCI and AD. Thus, the present invention provides methods and compositions for risk prediction of MCI and AD and for indicating the therapeutic efficacy of agents for treating MCI and AD. Thus, in a first aspect, the present invention provides a method for assessing the risk of a subject developing MCI or AD in the future. The method comprises the following steps: (1) comparing the plasma or serum or whole blood level or concentration of any one protein selected from the 18-protein group of the subject with a standard control level of the same protein present in the plasma or 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 that the plasma or serum or whole blood level of the protein of the subject 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 comprises, before step (1), a step of measuring the plasma or serum or whole blood level of the protein. In some embodiments, the measuring step is preceded by a step of obtaining a plasma or serum or whole blood sample from the subject. In some embodiments, when the subject is determined to have an increased risk for MCI or AD in step (3), the subject is then provided with increased follow-up monitoring (e.g., monitoring tests performed at an increased frequency compared to routine monitoring prescribed by a healthcare professional for no-risk or low-risk persons of similar age and medical background) or treatment as described in the present disclosure.

[0123] Although any one of the 18 proteins identified is suitable for this method, using multiple biomarkers simultaneously (often referred to as a "composite biomarker panel") to determine disease status is an effective method to fully utilize the predictive value of candidate proteins. Such composite biomarker panels are widely used in predictions of, for example, cardiovascular disease and aging. Compared with using a single protein alone, a biomarker panel that integrates multiple proteins can achieve better performance in classifying diseases, i.e., improved accuracy, sensitivity, and specificity. In addition, such models allow the status and activity of multiple biological pathways / systems to be examined, providing a more comprehensive assessment of the disease state of the subject. To this end, the inventors have also developed a hybrid prediction model that can integrate any two or more proteins from the 18 blood proteins to predict MCI risk and AD risk. The performance of this hybrid prediction model in predicting MCI risk and AD risk is superior to the method using any single protein from the 18 proteins. In addition, the present inventors have also specifically developed and optimized hybrid prediction models that can integrate two or three proteins (i.e., models that integrate two or three proteins from AOC3, CD27, CTRC, KYNU, NCS1, and TNNI3) to predict MCI risk and AD risk. These hybrid prediction models outperform methods that use any single protein from those selected proteins in predicting MCI risk and AD risk.

[0124] Materials and methods

[0125] Recruitment of Hong Kong, China population participants: A total of 39 individuals aged 60 years or older from Hong Kong, China, including 16 individuals with Alzheimer's disease (AD), 14 individuals with mild cognitive impairment (MCI), and 9 cognitively normal controls (CN), who were admitted to the Department of Neurology, Prince of Wales Hospital, The Chinese University of Hong Kong. Participants were clinically diagnosed with AD using the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Arlington (2013, Fifth Edition), which is incorporated by reference. All participants were clinically diagnosed with AD using the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Arlington (2013, Fifth Edition), which is incorporated by reference. et al . TheMontreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society 53.4 (2005):695-699” conducted a medical history assessment, clinical assessment, and cognitive and functional assessment, and through the use of11 Neuroimaging assessments were performed using amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) with C-Pittsburgh compound B (PiB). Participants with a global cortical-cerebellar standardized uptake value ratio ≥ 1.3 were defined as amyloid PET positive. MRI data were analyzed using AccuBrain IV1.2 (BrainNow Medical Technology) for T1-weighted MRI images and brain regional segmentation and gray matter volume quantification. Individuals with neurological or psychiatric disorders other than AD were excluded from the study. Age, sex, body mass index (BMI), years of education, and medical history were recorded for each participant. CN individuals were defined as having normal cognition (MoCA ≥ 26) and amyloid PET negativity; individuals with MCI and AD were defined as having amyloid PET positivity combined with a clinical diagnosis of cognition. This study was approved by the Prince of Wales Hospital, The Chinese University of Hong Kong, and the Hong Kong University of Science and Technology. All participants provided written informed consent for study participation and sample collection.

[0126] Preparation of plasma from blood samples: Whole blood (3 mL) was collected in K3EDTA tubes (VACUETTE) and centrifuged at 2,000 g for 15 minutes to separate the cell pellet and plasma. Plasma was collected, aliquoted, and stored at -80°C until use.

[0127] Measurement of Blood Protein Levels: Blood abundance of 18 proteins was quantified in prepared plasma samples using proximity extension analysis technology from the Olink Proteomics biomarker panel (including cardiometabolic, cardiovascular class II, cardiovascular class III, cell regulation, immune response, neuroexploratory, neurology, oncology class II, oncology class III, and organ damage).

[0128] Correlation analysis between blood protein levels and MCI or AD: The following linear regression model was used ( β i , the weighted coefficient of the corresponding factor; epsilon , intercept of the linear equation) to analyze the association of normalized protein levels with MCI or AD, adjusting for age, sex, and BMI:

[0129] The weighting coefficient of AD (i.e., β 1) Blood proteins with values ​​greater than or less than 0 are considered to be increased or decreased in AD, respectively. β 2) Blood proteins greater than or less than 0 are considered increased or decreased in MCI, respectively.

[0130] Calculation of risk score: For each prediction model, the weighting coefficients of the candidate proteins were calculated by fitting the blood levels of the candidate proteins and the AD diagnosis of the participants to the following logistic regression model ( β i ) and the intercept ( epsilon ):

[0131] Using the blood levels of candidate proteins and the corresponding weighting coefficients ( β i ) and the intercept ( epsilon ), the individual risk score is calculated using the following linear model:

[0132] Using Optimal Cutpoints Package optimal.cutpoints() function, the optimal cutoff value for defining low or high risk of developing MCI or AD was determined as the value with the largest Youden Index.

[0133] The prediction accuracy was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) using the R pROC Package auc() The function evaluates the accuracy of each prediction model or single protein.

[0134] Data visualization: Investigators who performed blood protein measurements were blinded to the participants' diagnoses and phenotypes. All statistical graphs were generated using Prism v8.0 (GraphPad).

[0135] Example 1: Prediction of MCI and AD risk using a model integrating 18 blood proteins

[0136] Compared with cognitively normal (CN) people, 18 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 patients with MCI and / or AD, with a maximum accuracy of 75.40% in distinguishing MCI from CN and a maximum accuracy of 83.33% in distinguishing AD from CN (Table 1). Notably, the present inventors developed a hybrid prediction model that integrates the levels of the 18 blood proteins and assigns a risk score to individuals ( Figure 1a and Table 2). The obtained score can well distinguish MCI from CN with an accuracy of 91.27% ( Figure 1 b), and distinguishing AD from CN with an accuracy of 97.92% ( Figure 1 c) This hybrid prediction model outperformed methods using any single protein from the 18 proteins in predicting MCI risk and AD risk ( Figure 1 b. Figure 1 c). Individuals with a risk score below 0.356 will have a low risk of developing MCI or AD; in contrast, individuals with a risk score greater than 0.356 will have a high risk of developing both MCI and AD ( Figure 1 a).

[0137] Example II: Model integrating two blood proteins from 18 blood proteins to predict MCI risk and AD risk

[0138] It is noteworthy that the present inventors have also developed a hybrid prediction model that can integrate any two or more proteins from 18 blood proteins to predict the risk of MCI and AD. For example, the model integrates the levels of two blood proteins (i.e., CCL27 and IGFBP-2) and can assign a risk score to an individual ( Figure 2 a and Table 3), and the obtained score can well distinguish MCI and CN with an accuracy of 78.57% ( Figure 2 b), and distinguishing AD from CN with an accuracy of 88.89% ( Figure 2 c) This hybrid prediction model outperformed methods using any single protein from the 18 proteins in predicting MCI risk and AD risk ( Figure 2 b. Figure 2 c). Individuals with a risk score below 0.656 will have a low risk of developing MCI or AD; in contrast, individuals with a risk score greater than 0.656 will have a high risk of developing both MCI and AD ( Figure 2 a).

[0139] Example III: Model integrating three blood proteins to predict MCI risk and AD risk

[0140] The present inventors also developed a hybrid prediction model that can integrate three proteins (i.e., AOC3, CD27, and NCS1) to predict the risk of MCI and AD ( Figure 3 a and Table 4). The obtained score can well distinguish MCI from CN with an accuracy of 76.98% ( Figure 3 b), and distinguishing AD from CN with an accuracy of 88.19% ( Figure 3c). The performance of this mixed prediction model in predicting the risk of MCI and AD is better than using any single protein from the 3 proteins Figure 3 b and Figure 3 c). Individuals with a risk score lower than 0.266 will have a low risk of developing MCI or AD; in contrast, individuals with a risk score greater than 0.266 will have a risk of developing MCI and AD Figure 3 a). Notably, the present inventors have also developed a mixed prediction model that can integrate any two of the three proteins to predict the risk of MCI and AD Figure 4 a, Figure 4 d, Figure 4 g and Tables 5 to 7). The resulting scores can well distinguish MCI and CN with an accuracy of 69.84% to 76.19% Figure 4 b, Figure 4 e and Figure 4 h), and AD and CN with an accuracy of 77.78% to 84.03% Figure 4 c, Figure 4 f and Figure 4 i). The performance of these mixed prediction models in predicting the risk of MCI and AD is all better than using any single protein from the 3 proteins Figure 4 b to Figure 4 c, Figure 4 e to Figure 4 f and Figure 4 h to Figure 4 i). Individuals with a risk score lower than 0.620 or 0.833 or 0.509 will have a low risk of developing MCI or AD; in contrast, individuals with a risk score greater than 0.620 or 0.833 or 0.509 will have a risk of developing MCI and AD Figure 4 a, Figure 4 d and Figure 4 g).

[0141] Example IV: Models integrating 3 blood proteins predict the risk of MCI and AD

[0142] In addition, the present inventors have developed further mixed prediction models that can integrate the further 3 proteins (i.e. CTRC, KYNU and TNNI3) to predict the risk of MCI and AD Figure 4 a and Table 8). The resulting scores can well distinguish MCI and CN with an accuracy of 80.95% Figure 4 b), and AD and CN with an accuracy of 92.36% Figure 4c). The performance of this hybrid prediction model in predicting the risk of MCI and AD is more accurate than the model using any single protein from the three proteins Figure 4 b and Figure 4 c). Individuals with a risk score lower than 0.489 will have a low risk of developing MCI or AD, whereas individuals with a risk score greater than 0.489 will have a risk of developing MCI and AD Figure 4 a). Notably, the present inventors have also developed a hybrid prediction model that can integrate any two of the three proteins to predict the risk of MCI and AD Figure 4 a, Figure 5 d, Figure 5 g and Tables 9 to 11). The resulting scores can well distinguish MCI and CN with an accuracy of 71.43% to 76.98% Figure 5 b, Figure 5 e and Figure 5 h), and AD and CN with an accuracy of 80.56% to 85.42% Figure 5 c, Figure 6 f and Figure 6 i). The performance of these hybrid prediction models in predicting the risk of MCI and AD is all superior to the method using any single protein from the three proteins Figure 6 b to Figure 6 c, Figure 6 e to Figure 6 f and Figure 6 h to Figure 6 i). Individuals with a risk score lower than 0.589 or 0.515 or 0.799 will have a low risk of developing MCI or AD; in contrast, individuals with a risk score greater than 0.589 or 0.515 or 0.799 will have a risk of developing MCI and AD Figure 6 a, Figure 6 d and Figure 6 g).

[0143] All patents, patent applications, and other publications cited in this application, including GenBank Accession Numbers, Uniprot IDs, and equivalents, are incorporated by reference in their entireties for all purposes.

[0144] Table 1. List of 18 blood proteins associated with MCI and AD. AUC Area under the receiver operating characteristic curve represents the accuracy of the classification.

[0145]

[0146] Table 2. Range of the weighted coefficients of the models with the 18 blood proteins β i ) and the range of the intercepts (Figure 6

[0147] Table 3. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins from 18 blood proteins

[0148] Table 4. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 3 blood proteins

[0149] Table 5. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins from 3 blood proteins

[0150] Table 6. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins from 3 blood proteins

[0151] Table 7. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins from 3 blood proteins

[0152] Table 8. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 3 blood proteins

[0153] Table 9. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins from 3 blood proteins

[0154] ​​​​​​​​​​​​​​​Table 10. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 ) for models using 2 blood proteins out of 3

[0155] Table 11. Weighting coefficient ranges ( β i ) and intercept ranges ( Figure 6 Figure 6 Figure 6 epsilon epsilon epsilon epsilon epsilon epsilon epsilon epsilon epsilon epsilon ) for models using 2 blood proteins out of 3 ​​​​

Claims

1. A method for assessing a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), comprising the steps of: (a) comparing the level of at least one protein in a plasma, serum, or whole blood sample from the 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; (b) detecting, in a plasma or serum or whole blood sample of the subject, a lower protein level of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU or PSME1, or an elevated protein level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL or TNNI3, compared to the standard control level of the same protein; and (c) determining that the subject has an increased risk of developing MCI or AD.

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

3. The method according to claim 2, wherein before the measuring step, the method further comprises obtaining a plasma, serum or whole blood sample from the subject.

4. The method of any one of claims 1 to 3, further comprising, after step (c), monitoring the level of the at least one protein in the subject at an increased frequency. 5 . The method according to claim 1 , further comprising administering to the subject a therapeutic agent for preventing or treating MCI or AD after step (c).

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

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

8. A kit for assessing a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), comprising reagents capable of determining the level of each of any 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 subject's plasma, serum or whole blood.

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

10. A detection chip for assessing a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), comprising a solid substrate and reagents capable of measuring the levels of any one, two, three or more proteins in the subject's plasma, serum or whole blood, wherein the proteins are independently selected from 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.

11. A method for quantifying a subject's risk of developing mild cognitive impairment (MCI) or Alzheimer's disease (AD), comprising: (a) An individual risk score is calculated by inputting a set of values ​​into the following formula: ， and (b) identifying subjects with a score above the optimal cutoff value as being at risk for developing MCI or AD, The values ​​of the set include the plasma or serum or whole blood levels of candidate proteins of the 18 proteins listed in Table 2, using the values ​​from R Optimal Cutpoints Package optimal.cutpoints() function, determining the optimal cutoff value as the value with the largest Youden index, where β i is the weighting coefficient of the candidate protein; and where ε is the intercept.

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

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

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

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

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

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

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

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

20. A method for evaluating the efficacy of a therapeutic agent for treating mild cognitive impairment (MCI) or Alzheimer's disease (AD) in a subject, comprising: (a) comparing the plasma, serum or whole blood level of any one protein selected from Table 1 in the subject before and after administration of the therapeutic agent to the subject; (b) detecting a decrease in the subject's plasma, serum, or whole blood level of CCL27, CD27, CD33, CTRC, DCBLD2, IGFBP-2, KIRREL2, LGALS7, NCS1, NEFL, or TNNI3, or an increase in the subject's plasma, serum, or whole blood level of AOC3, CA5A, CES1, FCN2, GP1BA, KYNU, or PSME1, after administration of the therapeutic agent; and (c) Identify therapeutic agents that are effective in treating MCI or AD.

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

22. The method of claim 21, further comprising obtaining plasma or serum or whole blood samples from the subject before and after administration, prior to the measuring step.

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

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