Biomarker combination for Alzheimer's disease and related diseases and application thereof

Abstract

The invention discloses a biomarker combination for early diagnosis, prediction and / or detection of Alzheimer's disease and related diseases. The combination includes an adhesion molecule having an Ig-like domain 2, a lipid transfer inhibitor, a calcium binding protein 1, CD302, a cysteine-rich transmembrane BMP modulator, a C-X3-C motif chemokine ligand, an statin subunit beta B, a MANSC inclusion domain 1, a neurofilament light chain, a protein tyrosine phosphatase receptor type N2, and a conjugate thereof. And one or more of SLIT and NTRK-like family protein 6 and nerve growth factor induction protein. The model constructed by the combination is high in accuracy, has good sensitivity and specificity, and is helpful for early discovery and diagnosis of Alzheimer's disease, AD-derived subjective cognitive decline or mild cognitive impairment.

Description

Technical Field

[0001] The present invention belongs to the fields of medical diagnosis, biochemistry technology and immunology diagnosis technology, and specifically relates to a biomarker combination for early diagnosis, prediction or detection of Alzheimer's disease and related diseases and its application. Background Art

[0002] Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes cognitive decline. It is the most common cause of dementia and seriously endangers the life and health of the elderly. At present, the pathogenesis of AD is still unclear. Its onset is insidious. When the disease is clinically discovered, it has often progressed to the middle and late stages. At this time, the patient's brain tissue has been irreversibly damaged and the best treatment opportunity has been missed. Therefore, moving the prevention and treatment of AD forward, warning patients who have transformed into AD in the early clinical stage, and early effective intervention will help slow down the occurrence and development of AD. Subjective cognitive decline (SCD) refers to the subjective belief that an individual has a decline in memory or cognitive function compared to the previous normal state, but objective neuropsychological tests can be within the normal range. Relevant studies have confirmed that SCD people have physiological changes similar to AD, suggesting that SCD people may be in the preclinical stage of AD. Mild cognitive impairment (MCI) is an intermediate state between normal cognition and AD. Studies have shown that the prevalence of mild cognitive impairment (MCI) in people over 65 years old is 10% to 20%, and the cumulative probability of transformation to AD is 33%. Therefore, early diagnosis and timely intervention of AD and pre-AD MCI and SCD will move the threshold of AD prevention and treatment forward, and is expected to effectively delay the progression of AD and reduce the burden on families, which is meaningful for the entire society and medical development. However, the current diagnosis of MCI and SCD is mainly based on patient complaints and cognitive scale assessments, lacking objective biological indicators. Finding biomarkers for early identification of MCI and SCD and their progression is of great value for early clinical intervention. Based on the above problems, a new method for early diagnosis and prediction of AD is urgently needed.

[0003] Olink targeted proteomics is a targeted, highly sensitive, micro-volume body fluid proteomics based on proximity extension (PEA) technology. It achieves quantitative analysis of target proteins in various body fluid samples such as serum and plasma during disease and physiological processes through antibody-specific recognition of target proteins and amplification of oligonucleotide sequences at the end of antibodies. It has the following advantages for the development of disease biomarkers: 1) High throughput: 3072 protein markers can be detected simultaneously, covering all major signal pathways; 2) High sensitivity: The detection sensitivity can reach the fg / mL level, and low-abundance proteins can be detected; 3) High specificity: PEA patented technology, dual antibody recognition effectively reduces the false positive rate; 4) Wide linear range: The detection range can span 10 orders of magnitude; 5) Small sample requirements: Each test panel only requires 1ul of sample. It is a powerful tool for discovering new biomarkers and helps understand the pathological mechanisms of preclinical MCI and SCD in AD and their progression. Summary of the invention

[0004] The primary purpose of the present invention is to provide a biomarker combination and application thereof for early diagnosis, prediction or detection of Alzheimer's disease and related diseases, so as to achieve early diagnosis, prediction or detection of Alzheimer's disease and related diseases.

[0005] To this end, the present invention provides the following technical solutions.

[0006] The first aspect of the present invention provides a biomarker combination for early diagnosis, prediction and / or detection of Alzheimer's disease and related diseases, the combination comprising one or more of adhesion molecules with Ig-like domain 2, lipid transfer inhibitory protein, calcium binding protein 1, CD302, cysteine-rich transmembrane BMP regulator, C-X3-C motif chemokine ligand, inhibin subunit βB, MANSC containing domain 1, neurofilament light chain, protein tyrosine phosphatase receptor N2 type, SLIT and NTRK-like family protein 6 and nerve growth factor-induced protein.

[0007] In a preferred embodiment of the present invention, the combination consists of adhesion molecules with Ig-like domain 2, lipid transfer inhibitory protein, calcium binding protein 1, CD302, cysteine-rich transmembrane BMP regulator, C-X3-C motif chemokine ligand, inhibin subunit βB, MANSC containing domain 1, neurofilament light chain, protein tyrosine phosphatase receptor N2 type, SLIT and NTRK-like family protein 6 and nerve growth factor-induced protein.

[0008] In a preferred embodiment of the present invention, the biomarker combination is derived from peripheral blood.

[0009] In a preferred embodiment of the present invention, the Alzheimer's disease and related diseases include Alzheimer's disease AD, AD-induced subjective cognitive decline SCD and mild cognitive impairment MCI.

[0010] The second aspect of the present invention provides a reagent for detecting the above-mentioned biomarker combination.

[0011] The third aspect of the present invention provides a kit, which comprises the reagents as described above.

[0012] A fourth aspect of the present invention provides a use of the biomarker combination or reagent as described above in the preparation of a product for early diagnosis, prediction and / or detection of Alzheimer's disease and related diseases.

[0013] In a preferred embodiment of the present invention, the product is a chip, a test kit, a test paper or an analysis platform.

[0014] In a preferred embodiment of the present invention, the Alzheimer's disease and related diseases include Alzheimer's disease AD, AD-induced subjective cognitive decline SCD and mild cognitive impairment MCI.

[0015] By means of the above technical scheme, the present invention has at least the following advantages: the present invention analyzes the peripheral plasma proteome of patients with different clinical stages of AD (SCD, MCI and AD) and healthy subjects (normal control, NC), and performs stratified analysis according to whether the plasma Aβ is positive (simoa / qPCR method), develops a plasma biomarker combination that can diagnose AD and its preclinical stage, and verifies its effectiveness in a multi-center external cohort, which is helpful for early clinical diagnosis of AD and early prediction of the occurrence of AD. The biomarker combination of the present invention has good sensitivity and specificity, which is helpful for the early discovery, diagnosis and detection of SCD, MCI and AD, and improves the effect of active intervention and treatment of SCD, MCI and AD.

[0016] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the Olink PEA technical flow chart;

[0018] Figure 2 is a flow chart of the present invention;

[0019] Figure 3 This is an internal quality control chart;

[0020] Figure 4A pie chart showing the percentage of Pass samples and Warning samples, where green represents the percentage of Pass samples in the total samples, and orange-red represents the percentage of Warning samples in the total samples;

[0021] Figure 5 Comparison of plasma protein changes in AD and its preclinical stage under different Aβ pathological states based on Olink targeted protein group in Dataset 1 (P<0.05, FDR correction);

[0022] Figure 6 The differential protein bioenrichment analysis in dataset 1, where A is the differential protein bioenrichment analysis graph of Aβ+ patients, and B is the differential protein bioenrichment analysis graph of Aβ- patients;

[0023] Figure 7 These are the 12 plasma proteins with consistent changing trends during the progression of Aβ-positive AD spectrum diseases in Datasets 1, 2, and 3;

[0024] Figure 8 For ROC curve analysis, 12 proteins were combined in three datasets to diagnose Aβ+ / SCD, Aβ+ / MCI, and Aβ+ / AD;

[0025] Fig. 9 Figure 3 shows the ability of the 12 proteins to predict the progression of SCD / MCI and the occurrence of AD after follow-up of the UKB cohort. A is the ROC curve analysis of the 12-protein panel for predicting the cognitive progression of SCD / MCI, and B is the ROC curve analysis of the 12-protein panel for predicting the occurrence of AD in the UKB follow-up cohort. DETAILED DESCRIPTION

[0026] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0028] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0029] Example 1: Establishment of research subjects and test methods

[0030] 1. Research subjects

[0031] The research objects of the present invention include four data sets in total, namely the following data sets 1 to 4:

[0032] Dataset 1 includes 400 subjects recruited by Nanjing Drum Tower Hospital in the community, outpatient clinics or inpatient departments of Nanjing City, including 100 healthy controls, 102 SCD groups, 99 MCI groups, and 99 probable AD groups that met the clinical diagnosis. Stratified analysis was performed based on whether peripheral blood Aβ was positive, plasma Aβ42 < 6 pg / ml or the ratio of Aβ42 to Aβ40 (Aβ 42 / 40 ) < 0.06 was defined as Aβ positive (Simoa / qPCR method). It was used to analyze the plasma proteome specific to the progression of AD-induced cognitive impairment.

[0033] Dataset 2 includes: 331 subjects (63 Aβ- / NC, 64 Aβ+ / SCD, 103 Aβ+ / MCI, and 101 Aβ+ / AD) recruited from research centers in various cities in Jiangsu Province. The diagnosis of Aβ pathology is the same as that of Dataset 1. It is used to verify the differences in plasma proteins found in Dataset 1 and to establish a diagnostic model.

[0034] Dataset 3 included: 363 subjects (44 Aβ- / NC, 39 Aβ+ / SCD, 44 Aβ+ / MCI, 66 Aβ+ / AD, 98 Aβ- / SCD, 48 Aβ- / MCI, 23 Aβ- / dementia) recruited from research centers in Nanjing, Shenzhen, and Hangzhou, who were diagnosed with Aβ by PET or CSF. They were used to verify the differences in plasma proteins found in Dataset 1 and to establish a diagnostic model.

[0035] Dataset 4 includes: subjects who completed plasma Olink protein group testing at baseline, downloaded from the UK Biobank (UKB), excluding patients with dementia at baseline, and subjects who converted to AD during follow-up (limited to the first occurrence) as the case group (n = 708), and the control group (n = 46894) is defined as no AD record during follow-up and no previous history of AD. If death occurs, AD is not the cause of death. It is used to verify whether the plasma proteins with consistent trends found in Datasets 1, 2, and 3 can effectively predict the occurrence of AD in the UKB cohort and establish an early warning model.

[0036] The diagnostic criteria for SCD are based on the 2020 revised version / Chinese AD preclinical SCD diagnostic process and standard expert consensus, using the Mini-Mental State Examination (MMSE, Katzman R, Zhang M, Wang Z, et al. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. Journal of clinical epidemiology 1988; 41: 971-978) to identify dementia, that is, illiterate patients with an MMSE score of <18, primary school students with an MMSE score of <21, and junior high school students and above with an MMSE score of <25 are diagnosed with dementia. MCI was diagnosed using the Montreal Cognitive Assessment (MoCA, Lu J, Li D, Li F, Zhou A, Wang F, Zuo X, et al. Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. Journal of geriatric psychiatry and neurology. 2011; 24(4): 184-90), i.e., a MoCA score of ≤13 for illiterate patients, ≤19 for primary school students, and ≤24 for junior high school students and above. For patients with normal cognition according to the MMSE and MoCA scores, MCI was diagnosed if the CDR score was 0.5. Probable AD-induced MCI and dementia were diagnosed according to the 2011 recommendations of the National Institute on Aging and the Alzheimer's Association. All subjects or their legal guardians were fully informed and signed written consent. This study was approved by the Institutional Review Board of Nanjing University Drum Tower Hospital.

[0037] All subjects in datasets 1 and 2 completed demographic data, cognitive function assessment, cranial MRI examination and peripheral blood AD biomarker detection. In addition to the above examinations, subjects in dataset 3 also completed Aβ-PET or cerebrospinal fluid AD biomarker detection, as shown below. Tables 1-4 list the clinical characteristics of the subjects in datasets 1-4, respectively.

[0038] Table 1 Clinical characteristics of subjects in dataset 1

[0039]

[0040]

[0041] Note: Age, years of education, MMSE and MoCA scores, and values ​​of peripheral blood AD biomarkers are expressed as mean values ​​(standard deviation), and gender is expressed as the number of females (percentage). AD, Alzheimer's disease; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; GFAP, glial fibrillary acidic protein; MCI, mild cognitive impairment; NC, normal control; NFL, neurofilament light chain; SCD, subjective cognitive decline; SD, standard deviation. * indicates P < 0.05; a Indicates P < 0.05 compared with the NC group; b Indicates P < 0.05 compared with the SCD group; c Indicates P < 0.05 compared with the MCI group.

[0042] Table 2 Clinical characteristics of participants in dataset 2

[0043]

[0044] Note: Age, years of education, MMSE and MoCA scores, and values ​​of peripheral blood AD biomarkers are expressed as mean (standard deviation), and gender is expressed as the number of females (percentage). AD, Alzheimer's disease; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; GFAP, glial fibrillary acidic protein; MCI, mild cognitive impairment; NC, normal control; NFL, neurofilament light chain; SCD, subjective cognitive decline; SD, standard deviation. a Indicates P < 0.05 compared with the NC group; b Indicates P < 0.05 compared with the SCD group; c Indicates P < 0.05 compared with the MCI group.

[0045] Table 3 Clinical characteristics of participants in dataset 3

[0046]

[0047] Note: Age, years of education, MMSE and MoCA scores, and values ​​of peripheral blood AD biomarkers are expressed as mean values ​​(standard deviation), and gender is expressed as the number of females (percentage). AD, Alzheimer's disease; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; MCI, mild cognitive impairment; NC, normal control; SCD, subjective cognitive decline; SD, standard deviation; SUVR, standardized uptake value ratio. * indicates P < 0.05.

[0048] Table 4 Clinical characteristics of participants in dataset 4

[0049]

[0050] Note: Age, years of education, MMSE and MoCA scores, and values ​​of peripheral blood AD biomarkers are expressed as mean (standard deviation), and gender is expressed as the number of females (percentage). * Indicates P<0.05.

[0051] Example 2: Olink targeted proteomics workflow

[0052] 1. Peripheral Blood Plasma Collection

[0053] (1) Collect 5 ml of peripheral blood using a purple EDTA anticoagulant tube and centrifuge within 2 hours;

[0054] (2) Peripheral blood was centrifuged at 3000 rpm for 10 min at 4°C. The supernatant was dispensed into EP tubes and stored in a -80°C refrigerator.

[0055] 2.Olink targeted proteomics experimental process

[0056] like Figure 1 As shown in the figure, Olink targeted proteomics is based on proximity extension assay (PEA), which can achieve quantitative analysis of proteins in serum, plasma and other samples through the specific recognition of proteins by antibodies and the amplification of oligonucleotide sequences at the ends of antibodies. NPX Exploresoftware (v.1.7.1) was used to perform experimental design, experimental detection, data preprocessing and data analysis on proteins.

[0057] like Figure 2 As shown in the figure, the Olink experimental process includes the following 6 steps:

[0058] 1) Protein sample preparation;

[0059] 2) Antibody incubation: Based on the principle of antigen-antibody specific binding, the target antibody is incubated with the protein to be detected.

[0060] 3) Sequence extension and amplification: The identified protein is terminally extended through two complementary oligonucleotide sequences, and then pre-amplified under the action of DNA polymerase.

[0061] 4) Library preparation and NGS sequencing: Add sample barcodes for library preparation and perform NGS (Next Generation Sequencing) sequencing on the amplified sequences.

[0062] 5) Data preprocessing: The NGS directory (NGS run) generated by Olink sequencing is processed by bcl2counts software into a csv file containing counts and an analysis configuration file: run_metadata.json

[0063] 6) NPX Explore software processing: Import the csv file containing counts and the analysis configuration file: run_metadata.json into the Olink NPX Explore software (v.1.7.1) to perform sample protein NPX (Normalized Protein eXpression) calculation, data quality control, and generate the file required for the bioinformatics analysis process: NPX Data.

[0064] 7) Raw data quality control: Check whether there are outliers based on the original sample NPX distribution box plot and sample principal component analysis PCA plot. Subsequent analysis can decide whether to remove outliers based on this. The overall protein expression pattern of each sample can be preliminarily reviewed through all protein expression cluster heat maps to find abnormally expressed proteins.

[0065] 8) Protein expression quantification: After quality control of the original data, the protein NPX expression matrix (Clean Data) was obtained for subsequent statistical analysis.

[0066] Example 3: AAH-BLG-CUSTOM chip operation process

[0067] 1. Peripheral Blood Plasma Collection

[0068] (1) Collect 5 ml of peripheral blood using a purple EDTA anticoagulant tube and centrifuge within 2 hours;

[0069] (2) Peripheral blood was centrifuged at 3000 rpm for 10 min at 4°C. The supernatant was dispensed into EP tubes and stored in a -80°C refrigerator.

[0070] 2. Experimental Materials and Reagents

[0071] 1) AAH-BLG-CUSTOM kit, the kit contains: glass slide chip --- ( L-Series Human Antibody Array L-1 Glass Slides, Item E), Labeling Reagent (Item B, 2 tubes for 1 set of slides), Stop Solution (Item D, 50μL), Blocking Buffer (Item F, 8mL per bottle, 1 bottle for 1 set of slides), Labeling Buffer (Item K, 8mL per bottle, 1 bottle for 1 set of slides), Fluorescently labeled streptavidin (Cy3-Conjugated Streptavidin, 1 tube for 1 set of slides), Dialysis tube and Floating Rack (Item A,), 20X Wash Buffer I (Item G, 30mL), 20X Wash Buffer II (Item H, 30mL).

[0072] 2) Materials and instruments other than the kit: plastic centrifuge tubes (0.5-4 mL, 50 mL), 1X PBS, pH=8.0, shaker, plastic wrap, double distilled water, InnoScan 300 Microarray Scanner fluorescence scanner, ThermoScientific Wellwash Versa chip washer.

[0073] 3. Experimental Procedure

[0074] 1) Sample dialysis: Unscrew the bottom cap of the spin column and loosen the lid (do not remove it), place the spin column in a 1.5ml conical collection tube, centrifuge at 1500xg for 1 minute to remove the storage buffer; discard the flow-through, clean the column three times with 300ul of the marker buffer, centrifuge at 1500xg for 1 minute to remove the flow-through; dry the bottom of the column to remove excess liquid and transfer to a new collection tube; in the next few minutes, apply the sample to the resin, centrifuge at 1,500xg for 2 minutes, and collect the effluent containing the sample.

[0075] 2) Biotin labeled samples: During the entire biotin labeling process, avoid any reagents being contaminated by amine substances or sodium azide (such as Tris, glycine). Before using the labeling reagent, quickly centrifuge the labeling reagent tube, add 100μL of labeling buffer to the tube to dissolve the powder, and mix the labeling reagent by pipetting up and down to prepare a 1× labeling reagent solution. Add an appropriate amount of labeling reagent to the centrifuge tube containing the sample (for labeled cell supernatant: add 10μl of labeling reagent solution to 600ul of supernatant and inject it into the sample tube), quickly mix, incubate on a shaker at room temperature for 30min, flick the centrifuge tube every 5min to mix the reaction reagents, and add 5μL of stop solution to the resulting reaction solution. Unscrew the bottom cap of the spin column and loosen the lid (do not remove it), place the spin column in a 1.5ml conical collection tube, centrifuge at 1500g for 1 minute to remove the storage buffer, and discard the flow-through; clear the column three times with 300ul of labeling buffer, centrifuge at 1500g for 1 minute to remove the flow-through, aspirate the bottom of the column to remove excess liquid, and transfer to a new collection tube; in the next few minutes, apply the sample to the resin, centrifuge at 1500g for 2 minutes, collect the effluent containing the sample, and then remove the excess unreacted biotin reagent from each sample.

[0076] 3) Complete drying of the glass slide chip: Take the glass slide chip out of the box, equilibrate it at room temperature for 1 hour, open the packaging bag, remove the sealing strip, and then place the chip in a vacuum dryer to dry for 1 hour.

[0077] 4) Blocking and incubation

[0078] a. Add 400 μL of 1× blocking solution to each chip well and incubate on a shaker at room temperature for 1 hour to avoid bubbles;

[0079] b. Remove the blocking solution, add 400 μL of sample to each well, one sample per array, and incubate overnight at 4°C with shaking. (The labeled cells are diluted 2000 times with the blocking solution and loaded);

[0080] c. Remove the sample and add about 1 mL of 1× Washing Solution I (20× Washing Solution diluted with deionized water) to each well. Oscillate at room temperature and wash the slides three times, 5 minutes each time.

[0081] d. Remove 1× Wash Solution I, add 1× Wash Solution II, shake at room temperature, and wash the slides twice, 5 min each time;

[0082] e. Remove 1× Wash Solution II, add 1 ml of Cy3 equivalent blocking solution, and dilute 5 times; add 400 μL to each well and incubate at room temperature in the dark for 1 hour;

[0083] f. Wash the slides according to steps c and d.

[0084] 2.5 Fluorescence detection

[0085] The signal was scanned by Innopsys laser scanner InnoScan 300, using Cy3 or green channel (excitation frequency = 532nm)

[0086] a. Scanning instrument: InnoScan 300 Microarray Scanner;

[0087] b. Origin: Parc d'Activités Activestre; 31 390 Carbonne–France;

[0088] c. Scanning parameters: Wave Lengh: 532nm; resoLution: 10μm;

[0089] The chip analysis software was used to extract the data, and the data analysis software (AAH-BLG-CUSTOM) was used to analyze the data.

[0090] Example 4: Statistical analysis

[0091] (1) Comparison of general and clinical data between groups

[0092] SPSS22.0 (IBM Corp., Armonk, NY) was used to analyze the demographic and clinical data of the above four data sets. All measurement data were first tested for normality. One-way analysis of variance was used for comparison between groups that met the normal distribution, and non-parametric tests were used for comparison between groups that did not meet the normal distribution. The chi-square test was used for comparison between groups of count data. P < 0.05 was considered statistically significant. Mean ± standard deviation was used to describe measurement data that met the normal distribution, median (minimum value, maximum value) was used to describe measurement data that did not meet the normal distribution, and absolute value (percentage) was used to describe count data.

[0093] (2) Statistical analysis of differentially expressed proteins and establishment of diagnostic / predictive models

[0094] In Dataset 1, proteins specific to Aβ-positive cognitive dysfunction were screened according to whether plasma Aβ was positive or not. Based on R (R 4.2.0) software, age, gender, years of education and whole genome information were used as covariates. According to cognitive severity, covariance analysis and outcome level variable logistic regression were used to compare the proteomic changes between Aβ-positive and Aβ-negative subjects, and protein pathway and bio-enrichment analysis was performed on the differential proteins. The change trend of the differential proteins in Dataset 1 was verified in Datasets 2 and 3. ROC curves were used to analyze the ability of proteins with consistent change trends during the progression of AD in the three cohorts in Datasets 1, 2 and 3 to diagnose Aβ+ / SCD, Aβ+ / MCI and Aβ+ / AD and construct a diagnostic model, and explore the ability of the above proteins to predict cognitive progression in SCD / MCI patients and the occurrence of AD in the UKB follow-up cohort and construct a predictive model.

[0095] Example 5: Results and Analysis

[0096] 1. Olink proteome raw data quality control

[0097] like Figure 3 As shown, Olink has a strict quality control (QC) system, including internal quality control and external quality control. Internal quality control is designed to monitor the three steps in the Olink reaction process, namely incubation, extension / amplification, and detection. Each sample well is equipped with three internal quality controls: (1) Incubation Control, 2 types per well, composed of two different non-human antigens, which can monitor potential system fluctuations in the three steps. (2) Extension Control, 1 per well, is a pair of antibodies coupled to DNA tags, used to monitor extension and PCR amplification steps. (3) Amplification Control, 1 per well, is a complete double-stranded DNA amplicon, used to monitor the detection step. Raw data quality control is performed through Incubation Control and Amplification Control. Extension Control is mainly used for standardization steps and NPX generation and does not participate in raw data quality control. For a sample to pass the quality control, the following conditions must be met: (1) the difference between the median value of the Incubation Control 2 on the entire plate and the NPX of each sample must be within 0.3×NPX; (2) the difference between the median value of the Amplification Control on the entire plate and the NPX of each sample must be within 0.3×NPX. If the quality control error exceeds the range allowed by the acceptance criteria, the sample will be marked as a Warning.

[0098] 1.1Pass and Warning sample proportion statistics

[0099] The percentage of Pass samples and Warning samples in this project is counted, and the results are as follows Figure 4 As shown in the figure, green represents the proportion of Pass samples in the total samples, and orange-red represents the proportion of Warning samples in the total samples. Figure 4 As shown in the figure, Pass samples account for 98% of the total samples, while Warning samples account for only 2%.

[0100] In Dataset 1, the proteomic changes of Aβ+ and Aβ- subjects were different according to the cognitive severity and reflected different biological processes. A total of 2886 plasma proteins were detected by the olink targeted proteome in Dataset 1; Figure 5 As shown, when the Aβ pathological status was unknown, there were 36 differential proteins among the clinically diagnosed control group, SCD, MCI and dementia group; there were 37 differential proteins between Aβ+ / dementia, Aβ+ / MCI, Aβ+ / SCD and Aβ- / NC, and there were 20 differential proteins between Aβ- / dementia, Aβ- / MCI, Aβ- / SCD and Aβ- / NC; in addition, using logistic regression analysis of outcome grade variables, it was found that when the Aβ pathological status was unknown, there were 7 proteins that gradually changed with the progression of the disease among the four groups, 16 proteins that gradually changed with the progression of Aβ-positive cognitive impairment, and no proteins related to the progression of Aβ-negative cognitive impairment were found. Figure 6 As shown, protein pathway and bioenrichment analysis found that the differential proteins associated with Aβ cognitive impairment were related to the regulation of chemical synaptic transmission and hormone levels, axonogenesis, regulation of plasma membrane-bounded cell projection organization, positive regulation of transmembrane transport, and cell spectrum of neuronal differentiation; while the differential proteins associated with Aβ-negative cognitive impairment were significantly associated with the adaptive immune system / response, antigen processing and presentation of exogenous peptide antigens through MHC class, leukocyte differentiation and inflammatory response, suggesting that the biological processes involved in the proteins associated with Aβ-positive and negative cognitive impairment are different. In addition, 12 plasma proteins were found in datasets 2 and 3 that had the same changing trend with disease progression as in dataset 1, including adhesion molecule with Ig-like domain 2 (AMIGO2), lipid transfer inhibitory protein (ApoF), calcium binding protein 1 (CALB1), CD302, cysteine-rich transmembrane BMP regulator (CRIM1), C-X3-C motif chemokine ligand (CX3CL1), inhibin subunit βB (INHBB), MANSC domain-containing 1 (MANSC1), neurofilament light chain (NEFL), protein tyrosine phosphatase receptor N2 type (PTPRN2), SLIT and NTRK-like family protein 6 (SLITRK6), nerve growth factor-induced protein (VGF) (Fig. Figure 7The corresponding sequence information is shown in Table 6. Therefore, the present invention uses ROC curves to analyze the ability of the combination of the above 12 plasma proteins to diagnose AD and its preclinical stage in three data sets. The results show that in data sets 1 and 2, the combination of the above 12 proteins can effectively identify Aβ+ / AD, Aβ+ / MCI, and Aβ+ / SCD, and the diagnostic efficacy in data set 3 (PET / CSF clearly diagnoses Aβ pathology) is the highest (the area under the ROC curve is greater than 0.8). After adding age, gender and years of education to the model, the model efficiency is improved (see Tables 5 and 6 for details). Figure 8 ).

[0101] Table 5 The ability of the combination of 12 plasma proteins to diagnose AD and its preclinical stage

[0102]

[0103] Note: AUC, area under the curve; MCI, mild cognitive impairment; SCD, subjective cognitive decline; D, dementia.

[0104] Table 6 Sequence information

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Note: The sequence information of each substance described in the table are all known sequences. According to common knowledge in the art, one or several substances may involve two or more sequences, which can be easily retrieved from the Uniprot database by those skilled in the art.

[0115] Finally, a study of 57 SCD / MCI patients who completed 0.5-3 years of follow-up found that 14 patients had cognitive progression, and the 12 proteins discovered in the present invention can effectively predict the cognitive progression of SCD / MCI patients (MoCA score decreased by 3 points or more after follow-up compared with that before follow-up, and the baseline information of SCD / MCI patients who completed follow-up in Dataset 1 is shown in Table 7), with an area under the ROC curve of 0.852 (see Fig. 9 A). In addition, in Dataset 4, it was found that the above 12 proteins as predictive variables can effectively predict the occurrence of AD during the follow-up of the UKB cohort, among which the prediction efficiency within 5 years was higher, with an area under the ROC curve of 0.865 (see Fig. 9 B). The above results suggest that the 12 protein combinations of the present invention have the potential to diagnose, predict and / or detect Alzheimer's disease and related diseases at an early stage, and can be used as biomarkers for Alzheimer's disease and related diseases.

[0116] Table 7 Clinical characteristics of SCD / MCI patients with cognitive progression and non-progression

[0117]

[0118] Note: Age, years of education, MMSE and MoCA scores, and values ​​of peripheral blood AD biomarkers are expressed as mean values ​​(standard deviation), and gender is expressed as the number of females (percentage). MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; GFAP, glial fibrillary acidic protein; MCI, mild cognitive impairment; NFL, neurofilament light chain; SCD, subjective cognitive decline; SD, standard deviation. * Indicates P<0.05.

[0119] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes by using the methods and technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A biomarker combination for early diagnosis, prediction and / or detection of Alzheimer's disease and related diseases, characterized in that: The combination includes one or more of adhesion molecules with Ig-like domain 2, lipid transfer inhibitory protein, calcium binding protein 1, CD302, cysteine-rich transmembrane BMP regulator, C-X3-C motif chemokine ligand, inhibin subunit βB, MANSC containing domain 1, neurofilament light chain, protein tyrosine phosphatase receptor N2 type, SLIT and NTRK-like family protein 6 and nerve growth factor-induced protein.

2. The biomarker combination according to claim 1, characterized in that: The combination consists of adhesion molecule with Ig-like domain 2, lipid transfer inhibitory protein, calcium binding protein 1, CD302, cysteine-rich transmembrane BMP regulator, C-X3-C motif chemokine ligand, inhibin subunit βB, MANSC domain-containing 1, neurofilament light chain, protein tyrosine phosphatase receptor N2 type, SLIT and NTRK-like family protein 6, and nerve growth factor-inducible protein.

3. The biomarker combination according to claim 1, characterized in that: The biomarker combination is derived from peripheral blood.

4. The biomarker combination according to claim 1, characterized in that: The Alzheimer's disease and related diseases include Alzheimer's disease AD, AD-induced subjective cognitive decline SCD and mild cognitive impairment MCI.

5. A reagent for detecting the biomarker combination according to any one of claims 1 to 4.

6. A kit, characterized in that: The kit comprises the reagent according to claim 5.

7. Use of the biomarker combination according to any one of claims 1 to 4 or the reagent according to claim 5 in the preparation of products for early diagnosis, prediction and / or detection of Alzheimer's disease and related diseases.

8. The use according to claim 7, characterized in that: The product is a chip, a test kit, a test paper or an analysis platform.

9. The use according to claim 7, characterized in that: The Alzheimer's disease and related diseases include Alzheimer's disease AD, AD-induced subjective cognitive decline SCD and mild cognitive impairment MCI.

Images