Combined detection kit for assisting in diagnosis of alzheimer's disease and use

Abstract

The present invention relates to the technical field of biological medicines. Disclosed are a combined detection kit for assisting in diagnosis of Alzheimer's disease and the use. Disclosed is the combined use of at least four of amyloid β-proteins, phosphorylated Tau proteins, biomarkers of neurodegeneration or neuron damage, and biomarkers related to neuroimmune disorder, synaptic dysfunction and blood brain barrier alternation in assisting in diagnosis of Alzheimer's disease. Compared with single-biomarker detection existing in the market and the combined detection using a small number of markers disclosed in some published patents, the marker combined detection provided by the present invention achieves a higher specificity or sensitivity. The present invention establishes a brand-new algorithm model, and both AD scale rating and brain function imaging diagnosis result rating are incorporated into the algorithm model for cooperative diagnosis; under the condition of a 95% specificity, the diagnosis sensitivity thereof can reach 95%, which is far higher than that of similar products in the market.

Description

A combined detection kit for auxiliary diagnosis of Alzheimer's disease and its application Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a combined detection kit for auxiliary diagnosis of Alzheimer's disease and its application. Background Art

[0002] Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an insidious onset. Clinically, it is characterized by global dementia, including memory impairment, aphasia, apraxia, agnosia, impaired visual-spatial skills, executive dysfunction, and personality and behavioral changes. The etiology remains unknown. One of the most widely accepted clinical mechanisms of AD is an imbalance in the production and clearance of extracellular amyloid-β (Aβ), leading to abnormal levels of Aβ. These plaques form between neurons in the brain, which are neurotoxic and cause neuronal degeneration. Furthermore, hyperphosphorylation of intracellular tau protein leads to neurofibrillary tangles (NFTs), which cause brain damage and ultimately lead to neurodegeneration.

[0003] Currently, most of the Alzheimer's disease detection products on the market in my country are marketed as single detection products, and have failed to form a joint detection of several detection items. Single detection has the problem of low specificity and sensitivity. The invention patent with domestic application number 202111454517.4 discloses a joint detection kit for detecting Alzheimer's disease and its application. Although the technical solution marker disclosed in this invention patent adopts a joint detection scheme of multiple markers, the application of marker detection is still relatively small and lacks the important project detection of beta-amyloid protein. The joint detection algorithm model used is only a simple calculation of several biomarkers, which lacks important support for the clinical diagnosis results of AD and has the disadvantages of low specificity and sensitivity in predicting and diagnosing AD.

[0004] Summary of the Invention

[0005] The present invention provides a combined detection kit for assisting in the diagnosis of Alzheimer's disease and its application, adding detection items for multiple markers involved in the pathogenesis of Alzheimer's disease. At the same time, combined with the AD scale score and brain function imaging diagnosis results, a predictive algorithm model is introduced to perform joint prediction and diagnosis of Alzheimer's disease, thereby significantly improving the diagnostic specificity and sensitivity.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The use of a combination of at least four of beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune dysregulation, synaptic dysfunction, and blood-brain barrier changes in assisting the diagnosis of Alzheimer's disease.

[0008] In the above-mentioned application, as a preferred embodiment, the β-amyloid protein is at least one of Aβ36, Aβ37, Aβ38, Aβ39, Aβ40, Aβ42, Aβ43, Aβ44, and Aβ53.

[0009] Aβ36: A β-amyloid peptide consisting of 36 amino acid residues;

[0010] Aβ37: A β-amyloid peptide consisting of 37 amino acid residues;

[0011] Aβ38: A β-amyloid peptide consisting of 38 amino acid residues;

[0012] Aβ39: A β-amyloid peptide consisting of 39 amino acid residues;

[0013] Aβ40: A β-amyloid peptide consisting of 40 amino acid residues;

[0014] Aβ42: A β-amyloid peptide consisting of 42 amino acid residues;

[0015] Aβ43: A β-amyloid peptide consisting of 43 amino acid residues;

[0016] Aβ44: A β-amyloid peptide consisting of 44 amino acid residues;

[0017] Aβ53: A β-amyloid peptide consisting of 53 amino acid residues.

[0018] In the above application, as a preferred embodiment, the phosphorylated Tau protein is at least one of PT au181, P-Tau 199, P-Tau 202, P-Tau205, P-Tau217, P-Tau231, P-Tau 40 4, and P-Tau422.

[0019] P-Tau 181: Tau protein is phosphorylated at threonine 181;

[0020] P-Tau 199: Tau protein is phosphorylated at threonine 199;

[0021] P-Tau 202: Tau protein is phosphorylated at threonine 202;

[0022] P-Tau 205: Tau protein is phosphorylated at threonine 205;

[0023] P-Tau 217: Tau protein is phosphorylated at threonine 217;

[0024] P-Tau 231: Tau protein is phosphorylated at threonine 231;

[0025] P-Tau 404: Tau protein is phosphorylated at threonine 404;

[0026] P-Tau 422: Tau protein is phosphorylated at threonine 422.

[0027] The above-mentioned application, as a preferred embodiment, the biomarker of neurodegeneration or neuronal damage is at least one of: neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), cone-like protein-1 (VILIP-1), and total Tau protein (T-Tau).

[0028] The above-mentioned application, as a preferred embodiment, the biomarkers involved in neuroimmune disorders, synaptic dysfunction and blood-brain barrier changes are: dendritic protein neurogranin (Ng), neurite (GAP43), synaptosome-associated protein 25 (SNAP25), chitinase-3-like protein 1 (YKL-40), glial fibrillary acidic protein (GFAP), α-synuclein (α-SYN), neurogenic differentiation protein 6 (NEUROD6), brain-derived neurotrophic factor (BDNF), Alzheimer's disease-related neurofilament protein (AD7c-NTP), Tar DNA binding protein 43 (TDP-43), aquaporin 4 (AQP4), neuron-specific enolase (NSE), pro-amyloid β-degrading enzyme 1 (BACE1), central nervous system-specific protein S100 (S100), central nervous system-specific protein S100-β protein (S100β), triggering receptor 1 on myeloid cells (TREM1), soluble triggering receptor 1 on myeloid cells (sTREM1), triggering receptor 2 on myeloid cells (TREM2), soluble triggering receptor 2 on myeloid cells (sTREM2), osteopontin (Osteopontin) at least one of tin, monocyte chemoattractant protein-1 (MCP-1), progranulin, interferon-induced protein-10 (IP-10), synaptotagmin, heart-type fatty acid binding protein (H-FAPB4), neuronal pentraxin I (NPTX1), anti-neuronal cytoplasmic antibody 1 (CDR1), neurocan (NCAN), neuromedin S (NMS), neuronalin 1 (NRXN1), neuropeptide S (NPS), and gelsolin (GSN).

[0029] A second aspect of the present application provides a method for assisting in the diagnosis of Alzheimer's disease, comprising the following steps:

[0030] (1) Detect the concentrations of at least four of the following biomarkers: beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes; and score the patients on the AD scale and the diagnostic results of brain functional imaging;

[0031] (2) The concentration of the markers in the measured samples, the AD scale score, and the brain functional imaging diagnosis result score are introduced into at least one algorithm model of logistic regression, support vector machine (SVM), and random forest (RF) to obtain the probability of Alzheimer's disease.

[0032] Furthermore, the logistic regression equation is:

[0033] Wherein, Logit(P) is the result of the logistic regression model for diagnosing Alzheimer's disease, δ is the natural constant obtained by regression, α is the coefficient of each biomarker obtained by regression analysis, β is the coefficient of the AD scale obtained by regression analysis, γ is the coefficient of the brain functional imaging diagnostic method obtained by regression analysis, x is the concentration of each marker, y is the score of each AD scale, z is the diagnostic result score of each brain functional imaging diagnostic method, n is an integer greater than or equal to 2, m is an integer greater than or equal to 1, and j is an integer greater than or equal to 1.

[0034] The biomarker concentration, AD scale score, and diagnostic score of each sample tested are substituted into the regression equation to calculate the probability of each sample suffering from AD. The Youden index of the point closest to the upper left corner of the ROC curve is used to determine the probability cut-off value. When Logit (P) is greater than the above cut-off value, there is a risk of AD, which can remind doctors or patients that further examination, diagnosis, and treatment are needed.

[0035] As a preferred embodiment, the sample in step (1) is at least one of human or animal tissue, cerebrospinal fluid, blood, urine, saliva, tears, sweat or feces, and hair.

[0036] As a preferred embodiment, the method for detecting the concentration of the marker in step (1) is at least one of: a radiometric method, an immunological method, a fluorescence method, a flow cytometry method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method, a single molecule detection method, and a capillary electrophoresis detection method.

[0037] In a third aspect, the present application provides a combined diagnostic kit for Alzheimer's disease, comprising capture and detection antibody reagents corresponding to at least four of the following: beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers of neuroimmune disorders, synaptic dysfunction, and blood-brain barrier alterations. The kit comprises an R1 antibody reagent, an R2 antibody reagent, 2-10 concentration gradients of protein calibrators, and 2-6 concentration gradients of quality control products.

[0038] The beneficial effects of the present invention are:

[0039] 1. The present invention applies at least four biomarkers, including β-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers of neuroimmune disorders, synaptic dysfunction, and blood-brain barrier alterations, to the early screening and auxiliary diagnosis of AD. Compared with single biomarker tests available on the market and the small number of combined biomarker tests disclosed in some published patents, the combined biomarker test of the present invention has higher specificity or sensitivity. The present invention establishes a brand-new algorithm model that creatively incorporates the AD scale score and the diagnostic result score of brain function imaging into the algorithm model for collaborative diagnosis. Under the condition of 95% specificity, its diagnostic sensitivity can reach 95%, far exceeding similar products on the market and filling an international gap. This detection system is the first reported internationally, and its technology and diagnostic performance have reached advanced levels both domestically and internationally.

[0040] 2. This invention adds a new biomarker, UCH-L1, with relatively high specificity and sensitivity. Currently, no domestic patents use UCH-L1 for AD diagnosis; most are markers related to stroke and concussion. This invention is the first to use UCH-L1 for AD diagnosis and discovers its good detection specificity and sensitivity. It also incorporates it into the combined detection kit, greatly improving the overall specificity and sensitivity of the kit.

[0041] 3. The present invention can detect peripheral blood samples instead of invasive cerebrospinal fluid, which greatly reduces the patient's pain and is a more convenient and feasible early diagnosis measure for AD. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1: ROC curves comparing normal healthy subjects and mild cognitive impairment subjects for some plasma samples in Example 2 of the present invention;

[0043] Figure 2: ROC curves comparing normal healthy subjects and Alzheimer's disease subjects for some plasma samples in Example 2 of the present invention.

[0044] In the figure: ADNB01: plasma detection results of C marker; ADNB02: plasma detection results of F marker; ADNB03: plasma detection results of G marker; ADNB04: plasma detection results of I marker; ADNB05: plasma detection results of P marker; ADNB06: plasma detection results of C+F+G+I+P+MMSE+MRI combined detection. DETAILED DESCRIPTION

[0045] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to examples. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0046] The present invention discloses an application of a combination of at least four of beta-amyloid protein, phosphorylated Tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes in the auxiliary diagnosis of Alzheimer's disease.

[0047] The β-amyloid protein is at least one of Aβ36, Aβ37, Aβ38, Aβ39, Aβ40, Aβ42, Aβ43, Aβ44, and Aβ53.

[0048] The phosphorylated Tau protein is at least one of P-Tau181, P-Tau 199, P-Tau 202, P-Tau205, P-Tau217, P-Tau231, P-Tau 404, and P-Tau422.

[0049] The biomarkers of neurodegeneration or neuronal damage are: at least one of neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), cone-like protein-1 (VILIP-1), and total Tau protein (T-Tau).

[0050] The biomarkers involved in neuroimmune disorders, synaptic dysfunction and blood-brain barrier changes are: dendritic protein neurogranin (Ng), neuregulin (GAP43), synaptosome-associated protein 25 (SNAP25), chitinase-3-like protein 1 (YKL-40), glial fibrillary acidic protein (GFAP), α-synuclein (α-SYN), neurogenic differentiation protein 6 (NEUROD6), brain-derived neurotrophic factor (BDNF), Alzheimer's disease-associated neurofilament protein (AD7c-NTP), Tar DNA binding protein 43 (TDP-43), aquaporin 4 (AQP4), neuron-specific enolase (NSE), pro-amyloid β-degrading enzyme 1 (BACE1), central nervous system-specific protein S100 (S100), central nervous system-specific protein S100-β protein (S100β), triggering receptor 1 on myeloid cells (TREM1), soluble triggering receptor 1 on myeloid cells (sTREM1), triggering receptor 2 on myeloid cells (TREM2), soluble triggering receptor 2 on myeloid cells (sTREM2), osteopontin (Osteopontin) at least one of tin, monocyte chemoattractant protein-1 (MCP-1), progranulin, interferon-induced protein-10 (IP-10), synaptotagmin, heart-type fatty acid binding protein (H-FAPB4), neuronal pentraxin I (NPTX1), anti-neuronal cytoplasmic antibody 1 (CDR1), neurocan (NCAN), neuromedin S (NMS), neuronalin 1 (NRXN1), neuropeptide S (NPS), and gelsolin (GSN).

[0051] The present invention also provides a method for assisting in the diagnosis of Alzheimer's disease, comprising the following steps:

[0052] (1) Detect the concentrations of at least four of the following biomarkers: beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes; and score the patients on the AD scale and the diagnostic results of brain functional imaging;

[0053] (2) The concentration of the markers in the measured samples, the AD scale score, and the brain functional imaging diagnosis result score are introduced into at least one algorithm model of logistic regression, support vector machine (SVM), and random forest (RF) to obtain the probability of Alzheimer's disease.

[0054] The AD scale score and brain functional imaging score in step (1) are linearly related to the severity of AD and can be included in the algorithm for comprehensive prediction.

[0055] AD scale scoring is achieved through the AD screening scale, which is currently widely used in hospital clinics and communities and has important reference value for the early screening and clinical diagnosis of AD. The AD scale scoring items include at least one of the following: MMSE, CSI-D, MoCA, HDS and HDS-R, AD8, PAS, GMS, SPM SQ, CDR, ADL, GDS, NPI, HAMD, and QCSS-E. The score of each AD scale is calculated by summing the scores of its various sub-items.

[0056] The specific AD screening scale is described as follows:

[0057] The MMSE is currently recognized as the most commonly used scale for dementia screening both domestically and internationally. Its sub-items include 11 aspects, including orientation, memory, attention and calculation, recall, and language. It is easy to operate and takes a short time (usually 5-10 minutes).

[0058] The CSI-D is a dementia screening scale used in cross-cultural studies. Its sub-items include cognitive tests for illiterate and literate subjects and informant interviews about daily life performance. It has strong adaptability and practicality among people from different socioeconomic backgrounds.

[0059] MoCA is an effective tool for screening MCI, commonly used both domestically and internationally. It covers eight areas: visual-spatial and executive abilities, naming, memory, attention, calculation, orientation, language function, and abstract thinking. It is easy to use and takes a short time.

[0060] HDS and HDS-R focus on the evaluation of memory function and are mainly suitable for screening elderly people who may have dementia;

[0061] AD8 can effectively screen patients with mild dementia in the general population, so it is often used to screen for subjective cognitive impairment in healthy people during physical examinations;

[0062] PAS is a standardized tool that can simultaneously study clinical changes in dementia and depression, but there are few relevant literatures at home and abroad;

[0063] The GMS is currently the most widely used geriatric psychiatric assessment scale and can be used comprehensively and effectively in cross-cultural studies and for the assessment of the mental status of the elderly. However, it takes 20-40 minutes and the operator needs 2-3 days of systematic training.

[0064] The SPMSQ is a widely used cognitive screening tool consisting of 10 sub-items, which is mainly used to determine the presence and severity of intellectual disability in the elderly;

[0065] CDR is often used to assess the severity of social dysfunction in patients with dementia, and can also assess cognitive function in patients with dementia. Its sub-items mainly assess the subjects' performance in six aspects: memory, orientation, judgment and problem-solving ability, social life and communication ability, family life, hobbies and interests, and personal life and self-care ability;

[0066] ADL is commonly used to assess the subject's ability to perform daily activities of daily living, and is mainly obtained through the subject's family members or caregivers to understand the subject's relevant situation. It has a total of 20 sub-items, including the 11-item Basic Activities of Daily Living Scale (BADL) and the 9-item Instrumental Activities of Daily Living Scale (IADL);

[0067] GDS and NPI are most commonly used to screen AD patients for depression and have good reliability and validity;

[0068] HAMD is often used to assess the severity of depression in subjects, and can also be used to assess apathy, anxiety, and depression in the early stages of dementia;

[0069] QCSS-E has good criterion validity and high screening efficiency in screening MCI and mild AD patients in the community population.

[0070] Brain functional imaging diagnosis helps to observe the main pathological characteristics of AD patients (neuronal loss, neurofibrillary tangle deposition, cholinergic depletion, senile plaques, etc.) and helps to understand its pathophysiological mechanism. Brain functional imaging diagnosis methods include at least one of magnetic resonance imaging (MRI), diffusion-weighted magnetic resonance imaging (DWI), diffusion tensor imaging (DTI), single-photon emission computed tomography (SPECT), amyloid protein PET, p-Tau PET, 18F-FDG PET, and PIB-PET. These methods can be scored when diagnosing AD.

[0071] Specific description of brain functional imaging diagnostic methods:

[0072] Magnetic resonance imaging (MRI): Assess brain structure and volume, including the degree of hippocampal and temporal lobe atrophy, white matter lesions, and ventricular enlargement. Scoring is typically performed using standardized indices that compare brain structure and volume. Commonly used assessment methods include the Medial Temporal Lobe Atrophy (MTA) score and the Global Cortical Atrophy (GCA) score.

[0073] Diffusion-weighted magnetic resonance imaging (DWI) is a technique that is sensitive to the microscopic movement of water molecules in tissues. Amyloid deposition, demyelination, and axonal degeneration in the brains of AD patients can cause cell membrane damage, which increases the diffusion of water molecules. Alternatively, glial proliferation increases the extracellular space, which reduces the restriction on the diffusion of water molecules. This will result in an increase in the apparent diffusion coefficient of the corresponding area. The degree of increase in the apparent diffusion coefficient of the corresponding brain area can be scored. Apparent Diffusion Coefficient (ADC) values, Region of Interest (ROI) analysis, and Visual Rating Scale (VRS) are usually used to calculate the average intensity value or regional volume in DWI images to assess the severity of AD patients.

[0074] Diffusion tensor imaging (DTI): A new MR technology developed based on DWI, it can study the microscopic movement of water in tissues in a directionally or asymmetrical manner, track the direction of nerve fibers, and display the course of white matter fiber bundles. It can also observe the spatial directionality and integrity of white matter fiber bundles. It can also assess the severity of AD patients by comparing standardized indices of white matter fiber bundles, usually using FA (fractional anisotropy) and MD (mean diffusivity) scores.

[0075] Single-photon emission computed tomography (SPECT): Commonly used 99mTc-labeled radiopharmaceuticals, such as 99mTc2 hexamethylpropyleneamine oxime, are injected intravenously as imaging agents. After penetrating the blood-brain barrier, they rapidly enter the brain tissue and emit single photons in the blood-rich brain tissue, proportional to the distribution of regional cerebral blood flow. Using tomography and image reconstruction, multiple cross-sectional and three-dimensional images are constructed. Changes in brain function are objectively reflected through the measurement of regional cerebral blood flow (rCBF). Therefore, scores can be calculated by assessing the degree of reduction in cerebral blood flow and metabolic rate.

[0076] Amyloid PET: This test assesses the severity of AD by detecting the deposition of amyloid in the brain. The assessment method is to score the distribution and density of amyloid in the PET image.

[0077] p-Tau PET: This method assesses the severity of AD by detecting the phosphorylation of tau protein in the brain. The evaluation method is to score the distribution and density of p-Tau in the PET image.

[0078] 18F-FDG PET: Using a glucose metabolism imaging agent (18F-FDG), it can show the distribution of AD lesions and changes in glucose metabolism, directly reflecting the specific location of AD lesions and their metabolic characteristics. Scoring can be performed based on the distribution and density of glucose metabolism in PET images.

[0079] PIB-PET: New PET tracers, such as 11C-PIB (Pittsburgh compound B), are used to observe amyloid proteins in the brains of AD patients. Most of the time, the ratio of the cerebral cortical region of interest (ROI) to the cerebellum, or quantitative analysis of the brain distribution volume ratio (DVR) and brain function analysis software are used to score and reflect the deposition of amyloid proteins in the brain, thereby assessing the severity of AD patients.

[0080] The logistic regression equation in step (2) is:

[0081] Wherein, Logit(P) is the result of the logistic regression model for diagnosing Alzheimer's disease, δ is the natural constant obtained by regression, α is the coefficient of each biomarker obtained by regression analysis, β is the coefficient of the AD scale obtained by regression analysis, γ is the coefficient of the brain functional imaging diagnostic method obtained by regression analysis, x is the concentration of each marker, y is the score of each AD scale, z is the diagnostic result score of each brain functional imaging diagnostic method, n is an integer greater than or equal to 2, m is an integer greater than or equal to 1, and j is an integer greater than or equal to 1.

[0082] The Support Vector Machine (SVM) in step (2) is a binary classification model. Its basic idea is to find an optimal hyperplane in the feature space that can separate samples of different categories and maximize the distance from the sample points closest to the hyperplane. These closest sample points are called support vectors. SVM has good generalization and robustness in classification problems. It can also transform linearly inseparable problems into linearly separable problems through kernel functions.

[0083] The Random Forest (RF) in step (2) is an ensemble learning method that consists of multiple independent decision trees, each trained with data randomly drawn from the original data. In a random forest, the output of each decision tree is a class, and the final classification result is the voting result of all the decision tree outputs. The advantage of random forests is that they can effectively handle high-dimensional data and large amounts of training data, and they have good generalization and robustness.

[0084] As a preferred embodiment, the sample in step (1) is at least one of human or animal tissue, cerebrospinal fluid, blood, urine, saliva, tears, sweat or feces, and hair.

[0085] As a preferred embodiment, the method for detecting the concentration of the marker in step (1) is at least one of: a radiometric method, an immunological method, a fluorescence method, a flow cytometry, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method, a single molecule detection method, and a capillary electrophoresis detection method.

[0086] The third aspect of the present application provides a joint test kit for auxiliary diagnosis of Alzheimer's disease, the joint test kit includes capture antibody reagents and detection antibody reagents corresponding to at least four of the biomarkers of beta-amyloid protein, phosphorylated Tau protein, neurodegeneration or neuronal damage, and neuroimmune disorders, synaptic dysfunction and blood-brain barrier changes. The basic components of the kit include R1 antibody reagent, R2 antibody reagent, 2-10 concentration gradients of protein calibrators, and 2-6 concentration gradients of quality control products. According to different methodologies, the components of the test kit are slightly different and are prepared in accordance with national industry standards or enterprise standards.

[0087] The present invention relates to a novel marker, UCH-L1, a neuron-specific protease that plays an important role in neuronal metabolism and cell death. Recent studies have demonstrated the importance of UCH-L1 in the diagnosis and treatment of Alzheimer's disease (AD). Numerous studies have shown that UCH-L1 gene mutations or abnormal protein expression levels are closely associated with the development and progression of diseases and tumors in these areas of the human body. For example, abnormal UCH-L1 levels are closely associated with neurodegeneration, the infiltration of certain tumors, and prognosis. Furthermore, UCH-L1 can serve as a therapeutic target for AD, playing an important role in neuronal damage and cell death in AD. Therefore, inhibiting UCH-L1 activity may help alleviate AD symptoms and slow disease progression. Drug treatment targeting UCH-L1 can improve cognitive function and neuronal damage in AD mice. In summary, UCH-L1 is of great significance in the diagnosis and prognosis of AD, and changes in its concentration can be used as an indicator to assess disease progression in AD patients.

[0088] Currently, there is no domestic patent that uses UCH-L1 detection for AD diagnosis. The present invention is the first to use UCH-L1 for AD diagnosis and discovers its good detection specificity and sensitivity. At the same time, it incorporates it into the combined detection kit, greatly improving the overall specificity and sensitivity of the kit.

[0089] Example 1

[0090] Based on hospital diagnosis results, a selected community population of comparable age and gender (to minimize bias due to gender and age) was divided into healthy individuals, individuals with mild cognitive impairment (MCI), and individuals with Alzheimer's disease. These individuals were scored using the MMSE scale and MRI. Cerebrospinal fluid samples were collected from these individuals and tested using magnetic microparticle chemiluminescence to measure concentrations of amyloid-β, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and other biomarkers implicated in the pathogenesis of AD, such as neuroimmune dysregulation, synaptic dysfunction, and altered blood-brain barrier function.

[0091] After the marker kits described in this example were prepared as required, the samples were tested. The biomarkers were screened using ROC curve analysis, resulting in 15 biomarkers with an AUC > 0.7: Aβ40, Aβ42, P-Tau181, P-Tau217, P-Tau231, NfL, UCH-L1, T-Tau, GFAP, NEUROD6, BDNF, α-SYN, NSE, YKL-40, and S100β. The selected markers were tested in different combinations. The process involved placing samples from healthy individuals, individuals with mild cognitive impairment (MCI), and Alzheimer's disease patients of comparable gender and age into the sample chamber of a fully automated chemiluminescence instrument. Each sample was then added to the marker reagent components within the instrument. The instrument automatically mixed, incubated, cleaned, and tested, and the concentration of each marker was directly read by the instrument.

[0092] The concentrations of the above-mentioned markers, MMSE scores, and MRI diagnostic scores were transformed into natural logarithms and subjected to logistic regression analysis (using R language programming or Medcalc software). The corresponding AUC values, specificity, and sensitivity results are shown in Table 1. The corresponding logistic regression equation is as follows:

[0093] (1) A+B+C+F+I (Aβ40+Aβ42+P-Tau181+NfL+GFAP) combined regression equation:

[0094] Logit(P)=0.678+0.543*x(Aβ40)+0.667*x(Aβ42)+0.871*x(P-Tau181)+0.662*x(NfL)+0.452*x(GFAP);

[0095] (2) A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP) combined regression equation:

[0096] Logit(P)=0.971+0.611*x(Aβ40)+0.553*x(Aβ42)+0.672*x(P-Tau181)+0.665*x(NfL)+0.779*x(UCH-L1)+0.418*x(GFAP);

[0097] (3) A+B+C+D+E+F+G+H+I+J+K+L+M+N+O(Aβ40+Aβ42+P-Tau181+P-Tau217+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β) combined regression equation:

[0098] Logit(P)=0.834+0.554*x(Aβ40)+0.653*x(Aβ42)+0.612*x(P-Tau181)+0.632*x(P-Tau217)+0.432*x(P-Tau231)+0.575*x(NfL)+0.665*x(UCH- L1)+0.611*x(T-Tau)+0.517*x(GFAP)-0.622*x(NEUROD6)-0.221*x(BDNF)+0.119*x(α-SYN)-0.867*x(NSE)+0.612*x(YKL-40)-0.346*x(S100β);

[0099] (4) C+D+E+F+G+H+I+J+K+L+M+N+O+P(P-Tau181+P-Tau217+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β+Aβ42 / Aβ40) joint test combined regression equation:

[0100] Logit(P)=0.914+0.502*x(P-Tau181)+0.562*x(P-Tau217)+0.611*x(P-Tau231)+0.611*x(NfL)+0.465*x(UCH-L1)-0.511*x(T-Tau)+0.617 *x(GFAP)-0.515*x(NEUROD6)+0.452*x(BDNF)-0.619*x(α-SYN)-0.654*x(NSE)-1.112*x(YKL-40)+0.654*x(S100β)+0.623*x(Aβ42 / Aβ40);

[0101] (5) C+F+G+I+P(P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40) combined regression equation:

[0102] Logit(P)=0.978+0.782*x(P-Tau181)+0.813*x(NfL)+1.165*x(UCH-L1)+0.658*x(GFAP)+0.823*x(Aβ42 / Aβ40);

[0103] (6) C+F+G+I+P+MMSE (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE) combined test regression equation:

[0104] Logit(P)=0.972+0.811*x(P-Tau181)+0.624*x(NfL)+0.765*x(UCH-L1)+1.158*x(GFAP)+0.623*x(Aβ42 / Aβ40)+0.512*y(MMSE);

[0105] (7) C+F+G+I+P+MMSE+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE+MRI) combined test regression equation:

[0106] Logit(P)=0.981+0.651*x(P-Tau181)+0.578*x(NfL)+0.815*x(UCH-L1)+ 0.668*x(GFAP)+1.023*x(Aβ42 / Aβ40)+0.713*y(MMSE)+0.558*z(MRI).

[0107] Table 1

[0108] As shown in Table 1, the combined detection of each marker achieved significantly higher AUC, specificity, and sensitivity than individual tests. The six-item combination of A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP) achieved the same diagnostic performance as all 15 tests combined. The combination of C+F+G+I+P (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40) outperformed A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP), indicating that Aβ42 / Aβ40 has a higher diagnostic value than either Aβ42 or Aβ40 alone. The combination of C+F+G+I+P+MMSE+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE+MRI) had an AUC of 0.898, a specificity of 96.8%, and a sensitivity of 94.9% for diagnosing MCI patients; the AUC for diagnosing Alzheimer's disease patients reached 0.993, a specificity of 99.6%, and a sensitivity of 97.2%, which was the best among all combinations. This indicates that the combined testing of six markers plus the scale and imaging diagnostic score has higher diagnostic specificity and sensitivity and better diagnostic performance.

[0109] Example 2

[0110] Based on hospital diagnosis results, a selected community population of comparable age and gender (to minimize bias due to gender and age) was divided into healthy individuals, individuals with mild cognitive impairment (MCI), and individuals with Alzheimer's disease. These individuals were scored using the MMSE scale and MRI. Plasma samples from these individuals were collected and assayed using magnetic microparticle chemiluminescence to measure concentrations of amyloid-β, phosphorylated tau, biomarkers of neurodegeneration or neuronal damage, and other biomarkers implicated in the pathogenesis of AD, such as neuroimmune dysregulation, synaptic dysfunction, and altered blood-brain barrier function.

[0111] After the marker kits described in this example were prepared as required, the samples were tested. The biomarkers were screened using ROC curve analysis, resulting in 15 biomarkers with an AUC > 0.7: Aβ40, Aβ42, P-Tau181, P-Tau217, P-Tau231, NfL, UCH-L1, T-Tau, GFAP, NEUROD6, BDNF, α-SYN, NSE, YKL-40, and S100β. The selected markers were tested in different combinations. The process involved placing samples from healthy individuals, individuals with mild cognitive impairment (MCI), and Alzheimer's disease patients of comparable gender and age into the sample chamber of a fully automated chemiluminescence instrument. Each sample was then added to the marker reagent components within the instrument. The instrument automatically mixed, incubated, cleaned, and tested, and the concentration of each marker was directly read by the instrument.

[0112] The concentrations of the above-mentioned markers, MMSE scores, and MRI diagnostic scores were transformed into natural logarithms and subjected to logistic regression analysis (using R language programming or Medcalc software). The corresponding AUC values, specificity, and sensitivity results are shown in Figures 1 and 2 and Table 2. The corresponding logistic regression equation is as follows:

[0113] (1) A+B+C+F+I (Aβ40+Aβ42+P-Tau181+NfL+GFAP) combined regression equation:

[0114] Logit(P)=0.712+0.446*x(Aβ40)+0.713*x(Aβ42)+0.882*x(P-Tau181)+0.457*x(NfL)+0.618*x(GFAP);

[0115] (2) A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP) combined regression equation:

[0116] Logit(P)=0.923+0.723*x(Aβ40)+0.416*x(Aβ42)+0.768*x(P-Tau181)+0.611*x(NfL)+0.613*x(UCH-L1)+0.902*x(GFAP);

[0117] (3) A+B+C+D+E+F+G+H+I+J+K+L+M+N+O(Aβ40+Aβ42+P-Tau181+P-Tau217+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β) combined regression equation:

[0118] Logit(P)=0.878+0.512*x(Aβ40)+0.679*x(Aβ42)+0.812*x(P-Tau181)+0.763*x(P-Tau217)+0.456*x(P-Tau231)+0.712*x(NfL)+0.567*x(UCH- L1)-0.425*x(T-Tau)+0.477*x(GFAP)-0.712*x(NEUROD6)+1.222*x(BDNF)+0.318*x(α-SYN)-0.567*x(NSE)-0.312*x(YKL-40)-0.562*x(S100β);

[0119] (4) C+D+E+F+G+H+I+J+K+L+M+N+O+P(P-Tau181+P-Tau217+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β+Aβ42 / Aβ40) joint test combined regression equation:

[0120] Logit(P)=0.923+0.607*x(P-Tau181)+0.667*x(P-Tau217)-0.411*x(P-Tau231)+0.811*x(NfL)+1.165*x(UCH-L1 )-0.675*x(T-Tau)+0.417*x(GFAP)+0.575*x(NEUROD6)+0.546*x(BDNF)-0.567*x(α-SYN)-0.752*x(NSE)+0.357*x (YKL-40)-0.612*x(S100β)+0.724*x(Aβ42 / Aβ40);

[0121] (5) C+F+G+I+P(P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40) combined regression equation:

[0122] Logit(P)=0.982+0.675*x(P-Tau181)+0.913*x(NfL)+0.812*x(UCH-L1)+0.547*x(GFAP)+0.543*x(Aβ42 / Aβ40);

[0123] (6) C+F+G+I+P+MMSE (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE) combined test regression equation:

[0124] Logit(P)=0.988+0.823*x(P-Tau181)+0.456*x(NfL)+0.675*x(UCH-L1)+1.028*x(GFAP)+0.426*x(Aβ42 / Aβ40)+0.715*y(MMSE);

[0125] (7) C+F+G+I+P+MMSE+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE+MRI) combined test regression equation:

[0126] Logit(P)=0.987+0.679*x(P-Tau181)+0.778*x(NfL)+0.614*x(UCH-L1)+1.066*x(GFAP)+1.013*x(Aβ42 / Aβ40)+0.709*y(MMSE)+0.602*z(MRI).

[0127] Table 2

[0128] As shown in Figures 1 and 2, and Table 2, combined detection of each marker achieved significantly higher AUCs, specificity, and sensitivity than individual tests. Results from plasma and cerebrospinal fluid tests were consistent. Similarly, the combined test of six markers (A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP) achieved the same diagnostic performance as all 15 combined tests. Aβ42 / Aβ40 demonstrated higher diagnostic value than either Aβ42 or Aβ40 alone. The combination of MMSE scale and MRI diagnosis, namely C+F+G+I+P+MMSE+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MMSE+MRI), has an AUC of 0.869, a specificity of 96.6%, and a sensitivity of 96.5% for diagnosing MCI people; the AUC for diagnosing Alzheimer's disease people is 0.996, a specificity of 99.8%, and a sensitivity of 98.1%, which is the best among all combinations and has the best diagnostic performance.

[0129] Example 3

[0130] Based on hospital diagnosis results, a selected community population of similar age and gender (to minimize bias due to gender and age) was divided into healthy individuals, individuals with mild cognitive impairment (MCI), and individuals with Alzheimer's disease. These individuals were scored using the MoCA scale and MRI. Plasma samples from these individuals were collected and assayed using magnetic microparticle chemiluminescence to measure concentrations of amyloid-β, phosphorylated tau, biomarkers of neurodegeneration or neuronal damage, and other biomarkers implicated in the pathogenesis of AD, such as neuroimmune dysregulation, synaptic dysfunction, and altered blood-brain barrier function.

[0131] After the marker kits described in this example were prepared as required, the samples were tested. The biomarkers were screened using ROC curve analysis, resulting in 15 biomarkers with an AUC > 0.7: Aβ40, Aβ42, P-Tau181, P-Tau217, P-Tau231, NfL, UCH-L1, T-Tau, GFAP, NEUROD6, BDNF, α-SYN, NSE, YKL-40, and S100β. The selected markers were tested in different combinations. The process involved placing samples from healthy individuals, individuals with mild cognitive impairment (MCI), and Alzheimer's disease patients of comparable gender and age into the sample chamber of a fully automated chemiluminescence instrument. Each sample was then added to the marker reagent components within the instrument. The instrument automatically mixed, incubated, cleaned, and tested, and the concentration of each marker was directly read by the instrument.

[0132] The concentrations of the above-mentioned markers, MoCA scale scores, and MRI diagnostic scores were transformed into natural logarithms and subjected to logistic regression analysis (programmed using R language or Medcalc software). The corresponding AUC values, specificity, and sensitivity results are shown in Table 3. The corresponding logistic regression equation is as follows:

[0133] (1) A+B+C+F+I (Aβ40+Aβ42+P-Tau181+NfL+GFAP) combined regression equation:

[0134] Logit(P)=0.756+0.542*x(Aβ40)+0.513*x(Aβ42)+0.665*x(P-Tau181)+0.658*x(NfL)+0.712*x(GFAP);

[0135] (2) A+B+C+F+G+I (Aβ40+Aβ42+P-Tau181+NfL+UCH-L1+GFAP) combined regression equation:

[0136] Logit(P)=0.919+0.523*x(Aβ40)+0.615*x(Aβ42)+0.557*x(P-Tau181)+0.541*x(NfL)+0.813*x(UCH-L1)+0.778*x(GFAP);

[0137] (3) A+B+C+D+E+F+G+H+I+J+K+L+M+N+O(Aβ40+Aβ42+P-Tau181+P-Tau2 17+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β) combined regression equation:

[0138] Logit(P)=0.882+0.617*x(Aβ40)+0.529*x(Aβ42)+0.789*x(P-Tau181)+0.652*x(P-Tau217)+0.556*x(P-Tau231)+0.546*x(NfL)+0.665*x(UCH- L1)-0.671*x(T-Tau)+0.567*x(GFAP)-0.578*x(NEUROD6)+0.819*x(BDNF)+0.428*x(α-SYN)-0.662*x(NSE)-0.289*x(YKL-40)-0.451*x(S100β);

[0139] (4) C+D+E+F+G+H+I+J+K+L+M+N+O+P(P-Tau181+P-Tau217+P-Tau231+NfL+UCH-L1+T-Tau+GFAP+NEUROD6+BDNF+α-SYN+NSE+YKL-40+S100β+Aβ42 / Aβ40) joint test combined regression equation:

[0140] Logit(P)=0.938+0.678*x(P-Tau181)+0.568*x(P-Tau217)-0.289*x(P-Tau231)+0.456*x(NfL)+1.078*x(UCH-L1)-0.552*x(T-Tau)+0.578 *x(GFAP)+0.615*x(NEUROD6)+0.782*x(BDNF)-0.765*x(α-SYN)-0.489*x(NSE)+0.456*x(YKL-40)-0.661*x(S100β)+0.785*x(Aβ42 / Aβ40);

[0141] (5) C+F+G+I+P(P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40) combined regression equation:

[0142] Logit(P)=0.951+0.578*x(P-Tau181)+0.622*x(NfL)+0.438*x(UCH-L1)+0.719*x(GFAP)+0.672*x(Aβ42 / Aβ40);

[0143] (6) C+F+G+I+P+MoCA (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MoCA) combined test regression equation:

[0144] Logit(P)=0.985+0.887*x(P-Tau181)+0.716*x(NfL)+0.561*x(UCH-L1)+0.882*x(GFAP)+0.567*x(Aβ42 / Aβ40)+0.765*y(MoCA);

[0145] (7) C+F+G+I+P+MoCA+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MoCA+MRI) combined test regression equation:

[0146] Logit(P)=0.992+0.552*x(P-Tau181)+0.635*x(NfL)+0.762*x(UCH-L1)+0.765*x(GFAP)+0.667*x(Aβ42 / Aβ40)+1.112*y(MoCA)+1.109*z(MRI).

[0147] Table 3

[0148] As shown in Table 3, the MoCA scale, when used as a co-diagnostic measure, is relatively consistent with the MMSE scale. The combined detection of six markers (P-Tau181, NfL, UCH-L1, GFAP, Aβ42, and Aβ40) in combination with the MoCA scale and MRI (C+F+G+I+P+MoCA+MRI (P-Tau181+NfL+UCH-L1+GFAP+Aβ42 / Aβ40+MoCA+MRI)) achieved an AUC of 0.875, a specificity of 96.8%, and a sensitivity of 96.4% for diagnosing MCI. The AUC for diagnosing Alzheimer's disease reached 0.991, a specificity of 99.2%, and a sensitivity of 97.3%, achieving the best diagnostic performance of all combinations.

[0149] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. The use of at least four of the following in combination: beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes in the auxiliary diagnosis of Alzheimer's disease.

2. The use according to claim 1, characterized in that: The amyloid β protein is at least one of Aβ36, Aβ37, Aβ38, Aβ39, Aβ40, Aβ42, Aβ43, Aβ44, and Aβ53.

3. The use according to claim 1, characterized in that: The phosphorylated Tau protein is at least one of P-Tau181, P-Tau 199, P-Tau 202, P-Tau205, P-Tau217, P-Tau231, P-Tau404, and P-Tau422.

4. The use according to claim 1, characterized in that: The biomarker of neurodegeneration or neuronal damage is at least one of NfL, UCH-L1, VILIP-1, and T-Tau.

5. The use according to claim 1, characterized in that: The biomarkers involved in the neuroimmune disorders, synaptic dysfunction and blood-brain barrier changes are: at least one of Ng, GAP43, SNAP25, YKL-40, GFAP, α-SYN, NEUROD6, BDNF, AD7c-NTP, TDP-43, AQP4, NSE, BACE1, S100, S100β, TREM1, sTREM1, TREM2, sTREM2, Osteopontin, MCP-1, Progranulin, IP-10, Synaptotagmin, H-FAPB4, NPTX1, CDR1, NCAN, NMS, NRXN1, NPS and GSN.

6. A method for assisting diagnosis of Alzheimer's disease, characterized in that: The following steps are involved: (1) Detect the concentrations of at least four of the following biomarkers in the sample: beta-amyloid protein, phosphorylated tau protein, biomarkers of neurodegeneration or neuronal damage, and biomarkers involved in neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes; and perform AD scale scoring and brain function imaging diagnostic scoring on the patients; (2) The concentration of the markers in the measured samples, the AD scale score, and the brain functional imaging diagnosis result score are introduced into at least one algorithm model of logistic regression, support vector machine, and random forest to obtain the probability of Alzheimer's disease.

7. A method for assisting diagnosis of Alzheimer's disease according to claim 6, characterized in that: The logistic regression equation is: Wherein, Logit(P) is the result of the logistic regression model for diagnosing Alzheimer's disease, δ is the natural constant obtained by regression, α is the coefficient of each biomarker obtained by regression analysis, β is the coefficient of the AD scale obtained by regression analysis, γ is the coefficient of the brain function imaging diagnostic method obtained by regression analysis, x is the concentration of each marker, y is the score of each AD scale, z is the diagnostic result score of each brain function imaging diagnostic method, n is an integer greater than or equal to 2, m is an integer greater than or equal to 1, and j is an integer greater than or equal to 1.

8. The method for assisting diagnosis of Alzheimer's disease according to claim 6, characterized in that: The sample in step (1) is at least one of human or animal tissue, cerebrospinal fluid, blood, urine, saliva, tears, sweat or feces, and hair.

9. The method for assisting diagnosis of Alzheimer's disease according to claim 6, characterized in that: The method for detecting the concentration of the marker in step (1) is at least one of: a radiation method, an immunological method, a fluorescence method, a flow cytometry, a latex turbidimetry, a biochemical method, an enzyme method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method, a single molecule detection method, and a capillary electrophoresis detection method.

10. A combined test kit for assisting in the diagnosis of Alzheimer's disease, characterized in that: The joint test kit includes capture antibody reagents and detection antibody reagents corresponding to at least four of the biomarkers of beta-amyloid protein, phosphorylated Tau protein, neurodegeneration or neuronal damage, and neuroimmune disorders, synaptic dysfunction, and blood-brain barrier changes, 2-10 calibrators with different concentration gradients, and 2-6 quality control products with different concentration gradients.

Images