Plasma biomarker combination for auxiliary diagnosis of Alzheimer's disease and screening method thereof

Abstract

The invention discloses a plasma biomarker combination for auxiliary diagnosis of Alzheimer's disease and a screening method thereof. The screening method provided by the invention has the characteristics of high detection and analysis speed, high reproducibility and low sample consumption; the method is used for screening out a plasma biomarker combination consisting of A [beta] 1-42 / A [beta] 1-40, NFL, pTau181 and pTau217. The plasma biomarker combination realizes the efficient diagnosis performance of the Alzheimer's disease, the sensitivity is 85.5%, and the specificity is 98.1%. The method has an important application value.

Description

Technical Field

[0001] The present invention belongs to the field of medical care informatics, and specifically relates to a plasma biomarker combination for auxiliary diagnosis of Alzheimer's disease and a screening method thereof. Background Art

[0002] Alzheimer's disease (AD) is the most common type of dementia. The number of AD patients has a continuous growth trend (Ren Rujing, Yin Peng, Wang Zhihui, et al. China Alzheimer's Disease Report 2021 [J]. Diagnostics Theory and Practice, 2021, 20(4): 317-337.). The clinical manifestations of AD mainly include memory impairment, aphasia, agnosia, executive dysfunction, and personality and behavioral changes. It has become the "fourth largest killer" threatening the elderly after cardiovascular, cerebrovascular diseases and cancer, and is also one of the incurable diseases. Therefore, early identification and early intervention of AD are very important.

[0003] The ATN biomarker research diagnostic criteria for AD (referred to as ATN diagnostic criteria) proposed by the National Institute of Aging (NIA) and the Alzheimer's Association (AA) in 2018 have greatly promoted the early diagnosis and treatment of AD (Jack CR, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease [J]. Alzheimers & Dementia, 2018, 14 (4): 535-562.). The detection methods of ATN biomarkers mainly include the detection of cerebrospinal fluid biomarkers and PET imaging.

[0004] Although cerebrospinal fluid biomarkers and PET imaging can detect early AD pathological changes, their widespread application is limited by invasiveness, high cost and radioactive safety issues. Although relevant studies on the detection of new plasma biomarkers for AD diagnosis have been carried out in various parts of the world, there are great differences in the cut-off values ​​reported between different analytical platforms and laboratories (Mattsson N, Andreasson U, Persson S, et al. The Alzheimer's Association external quality control program for cerebrospinal fluid biomarkers [J]. Alzheimers & Dementia, 2011, 7 (4): 386-395. e6. Monge-Argilés JA, C, Sánchez-Payá J, et al. Comparison of two analytical platforms for CSF biomarkers of Alzheimer's disease [J]. BioMed Research International, 2014, 2014: 765130.). Developing more plasma biomarkers for diagnosing Alzheimer's disease has important application value. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention aims to develop plasma biomarkers for diagnosing Alzheimer's disease.

[0006] The present invention firstly protects a plasma biomarker combination for diagnosing Alzheimer's disease, which may include Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217; the Aβ1-42 / Aβ1-40 may specifically be the concentration ratio of Aβ1-42 and Aβ1-40.

[0007] The plasma biomarker combination may specifically consist of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217.

[0008] The detection object of any of the above-mentioned plasma biomarker combinations can be plasma.

[0009] The use of any of the above-mentioned plasma biomarker combinations in the preparation of a product for assessing the risk of Alzheimer's disease or a product for evaluating the possibility of Alzheimer's disease pathological changes in the brain also falls within the scope of protection of the present invention.

[0010] The use of substances for detecting the concentration of Aβ1-42 in plasma, substances for detecting the concentration of Aβ1-40 in plasma, substances for detecting the concentration of NFL in plasma, substances for detecting the concentration of pTau181 in plasma, and substances for detecting the concentration of pTau217 in plasma in the preparation of products for assessing the risk of Alzheimer's disease or preparing products for evaluating the possibility of pathological changes of Alzheimer's disease in the brain also falls within the scope of protection of the present invention.

[0011] The present invention also protects a method for screening plasma biomarkers for assessing the risk of Alzheimer's disease or evaluating the possibility of pathological changes of Alzheimer's disease in the brain, comprising the following steps:

[0012] (1) using amyloid positron emission tomography to collect amyloid PET images of each subject in the test group;

[0013] The purpose of performing step (1) is to evaluate whether each subject in the test group suffers from Alzheimer's disease; Alzheimer's disease can be diagnosed by amyloid protein PET images;

[0014] (2) detecting the concentration and / or ratio of small molecule proteins in the plasma of each subject in the test group, and then comparing the types and ratios of small molecule proteins in the plasma and the types and ratios of small molecule proteins in the plasma with the results of amyloid protein PET image diagnosis of Alzheimer's disease in step (1) through receiver operating characteristic curve analysis, so as to screen out plasma biomarkers for assessing the risk of Alzheimer's disease or evaluating the possibility of Alzheimer's disease pathological changes in the brain.

[0015] In the above method, the test group can specifically be the 182 people mentioned in the embodiment from the Southern China Aging Brain Cohort (SCABI cohort).

[0016] In the above method, in step (1), after obtaining the amyloid PET image, all PET scan images are visually evaluated according to FDA recommendations: when one or more areas show an increase in cortical gray matter signal, resulting in a decrease or disappearance of the contrast between gray matter and white matter, the scan is classified as "positive"; when the contrast between gray matter and white matter is clear, the scan is classified as "negative"; the subjects in the test group are divided into an amyloid negative group and an amyloid positive group. The amyloid negative group includes cognitive intact (CN) and mild cognitive impairment (MCI). The amyloid positive group includes CN, MCI or dementia.

[0017] In the above method, in step (2), the receiver operating characteristic curve analysis may include calculating the area under the curve, 95% confidence interval, sensitivity, specificity, false negative rate and / or false positive rate.

[0018] In the above method, in step (2), the small molecule protein may be Aβ1-42, Aβ1-40, NFL, pTau181 and / or pTau217. The ratio of the small molecule protein may be Aβ1-42 / Aβ1-40.

[0019] In the above method, in step (2), the screening criteria are as follows: the sensitivity and specificity are both above 85%.

[0020] In an embodiment of the present invention, a process for acquiring amyloid positron emission tomography (PET) images and a method for screening plasma biomarkers are described in detail.

[0021] The present invention also protects a data processing device for evaluating the risk of a subject suffering from Alzheimer's disease or evaluating the possibility of pathological changes of Alzheimer's disease occurring in the brain of a subject, which may include the following modules:

[0022] (1) a data receiving module; the data receiving module is configured to receive the content of Aβ1-40, the content of Aβ1-42, the content of NFL, the content of pTau181 and the content of pTau217 in the plasma of the subject;

[0023] (2) a data processing module; the data processing module is configured as (2-1) and (2-2);

[0024] (2-1) receiving the Aβ1-40 content and the Aβ1-42 content in the plasma of the subject from the data receiving module, and calculating the ratio of the Aβ1-42 content to the Aβ1-40 content, which is recorded as Aβ1-42 / Aβ1-40;

[0025] (2-2) establishing a multifactor regression model for the levels of Aβ1-42 / Aβ1-40, NFL, pTau181, and pTau217 to obtain model values;

[0026] (3) a data storage module; the data storage module is configured to store the judgment threshold;

[0027] (4) a data comparison module; the data comparison module is configured to receive the model value sent by the data processing module, and call the judgment threshold from the data storage module for comparison with the model value;

[0028] (5) a judgment module; the judgment module is configured to receive the comparison result sent by the data comparison module, judge the comparison result according to a predetermined judgment condition, and output the judgment result;

[0029] The predetermined judgment condition is: if the model value is greater than the judgment threshold, the subject has a high risk of Alzheimer's disease or a high possibility of pathological changes of Alzheimer's disease in the brain; otherwise, the subject has a low risk of Alzheimer's disease or a low possibility of pathological changes of Alzheimer's disease in the brain.

[0030] In the data processing device, the judgment threshold stored in the data storage module may specifically be 0.66.

[0031] In the data processing device, the Aβ1-40 content, Aβ1-42 content, NFL content, pTau181 content and pTau217 content in the plasma of the subject to be tested received by the data receiving module can be obtained according to a method including the following steps: collecting peripheral blood of the subject to be tested, separating the plasma, and then detecting the Aβ1-40 content, Aβ1-42 content, NFL content, pTau181 content and pTau217 content in the plasma.

[0032] The data processing device may specifically be composed of any of the above-mentioned data receiving modules, any of the above-mentioned data processing modules, any of the above-mentioned data storage modules, any of the above-mentioned data comparison modules and any of the above-mentioned judgment modules.

[0033] Any of the above-mentioned data processing devices may also include a control module or a controller; the control module or the controller is used to control each module.

[0034] The data processing device may specifically be composed of any of the above-mentioned data receiving modules, any of the above-mentioned data processing modules, any of the above-mentioned data storage modules, any of the above-mentioned data comparison modules, any of the above-mentioned judgment modules and a control module (or controller).

[0035] The present invention also protects a system for assessing the risk of developing Alzheimer's disease or the possibility of Alzheimer's disease pathological changes in the brain, which may include a control device; the control device is configured or programmed to perform the following steps:

[0036] receiving the content of Aβ1-40, the content of Aβ1-42, the content of NFL, the content of pTau181 and the content of pTau217 in the plasma of the subject to be tested;

[0037] According to the content of Aβ1-40 and the content of Aβ1-42 in the plasma of the test subject, the ratio of the content of Aβ1-42 to the content of Aβ1-40 is calculated, and recorded as Aβ1-42 / Aβ1-40;

[0038] A multi-factor regression model was established for the above-mentioned Aβ1-42 / Aβ1-40, NFL content, pTau181 content and pTau217 content to obtain the model value of the subject;

[0039] Comparing the model value of the subject to be tested with the judgment threshold to obtain a comparison result; judging the comparison result according to a predetermined judgment condition, and outputting the judgment result;

[0040] The predetermined judgment condition is: if the model value of the subject is greater than the judgment threshold, the subject has a high risk of Alzheimer's disease or a high possibility of pathological changes of Alzheimer's disease in the brain; otherwise, the subject has a low risk of Alzheimer's disease or a low possibility of pathological changes of Alzheimer's disease in the brain.

[0041] Furthermore, the system may also include an input device and an output device;

[0042] The input device is configured to input the content of Aβ1-40, the content of Aβ1-42, the content of NFL, the content of pTau181 and the content of pTau217 in the plasma of the subject to be tested;

[0043] The output device is configured to output the determination result.

[0044] The present invention also protects a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the following steps:

[0045] receiving the content of Aβ1-40, the content of Aβ1-42, the content of NFL, the content of pTau181 and the content of pTau217 in the plasma of the subject to be tested;

[0046] According to the content of Aβ1-40 and the content of Aβ1-42 in the plasma of the test subject, the ratio of the content of Aβ1-42 to the content of Aβ1-40 is calculated, and recorded as Aβ1-42 / Aβ1-40;

[0047] A multi-factor regression model was established for the above-mentioned Aβ1-42 / Aβ1-40, NFL content, pTau181 content and pTau217 content to obtain the model value of the subject;

[0048] Comparing the model value of the subject to be tested with the judgment threshold to obtain a comparison result; judging the comparison result according to a predetermined judgment condition, and outputting the judgment result;

[0049] The predetermined judgment condition is: if the model value of the subject is greater than the judgment threshold, the subject has a high risk of Alzheimer's disease or a high possibility of pathological changes of Alzheimer's disease in the brain; otherwise, the subject has a low risk of Alzheimer's disease or a low possibility of pathological changes of Alzheimer's disease in the brain.

[0050] The plasma biomarker combination screening method provided by the present invention has the characteristics of fast detection and analysis speed, high reproducibility and low sample consumption; the plasma biomarker combination consisting of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217 was screened by this method. The plasma biomarker combination achieved efficient diagnostic performance for Alzheimer's disease, with a sensitivity of 85.5% and a specificity of 98.1%; the plasma biomarker combination was used for risk assessment of Alzheimer's disease, and there was a significant difference between the low-risk group and the high-risk group (p<0.05). The present invention has important application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is the cut-off value discrimination diagram of Aβ1-42 / Aβ1-40 in the plasma of the SCABI cohort.

[0052] Figure 2 This is the cut-off value discriminant diagram of NFL in plasma of the SCABI cohort.

[0053] Figure 3 This is the cut-off value discriminant diagram of pTau217 in plasma of the SCABI cohort.

[0054] Figure 4 This is the cut-off value discrimination diagram of pTau181 in plasma of the SCABI cohort.

[0055] Figure 5 This is the ROC curve of Aβ1-42 / Aβ1-40 in the plasma of the SCABI cohort.

[0056] Figure 6 Figure 2 is the ROC curve of NFL in plasma of the SCABI cohort.

[0057] Figure 7 Figure 2 is the ROC curve of pTau217 in plasma of the SCABI cohort.

[0058] Figure 8 Figure 2 is the ROC curve of pTau181 in plasma of the SCABI cohort.

[0059] Fig. 9This is the cut-off value discrimination diagram of the plasma combined index combination (multiple index combination of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217 in plasma) in the SCABI cohort plasma.

[0060] Fig.10 This is the ROC curve diagram of the plasma combined index combination in the SCABI cohort plasma (multiple index combination of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217 in plasma). DETAILED DESCRIPTION

[0061] The present invention is further described in detail below in conjunction with specific embodiments, and the examples provided are only for illustrating the present invention, rather than for limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art, and do not constitute a limitation of the present invention in any way.

[0062] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0063] The quantitative tests in the following examples were all repeated three times, and the results were averaged.

[0064] Cut-off value refers to the critical value in medical testing, which is used to determine whether the test result is higher or lower than the clinical or analytical decision point. If the positive and negative values ​​are determined when a marker is tested, the critical value with the highest sensitivity and specificity is usually used as the decision value, and this value is called the Cut-off value. The Cut-off value is mainly a critical point set based on statistical analysis.

[0065] The following examples were approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (ID: AF / SC-08 / 02.3) and complied with the 1964 Declaration of Helsinki and its subsequent amendments or similar ethical standards.

[0066] Example 1. Application of plasma index combination (multiple index combination of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217 in plasma) in the diagnosis of Alzheimer's disease

[0067] This example aims to explore the feasibility of using Aβ1-42, Aβ1-40, NFL, pTau181, pTau217 in plasma and Aβ1-42 / Aβ1-40, NFL, pTau181, pTau217 in plasma for the combined diagnosis of Alzheimer's disease and its clinical value analysis.

[0068] 1. Experimental Design

[0069] The present embodiment aims to determine the optimal cut-off value for diagnosing Alzheimer's disease by using the automatic immunoassay analyzer Lumipulse G1200, among single or multiple indicators of Aβ1-42, Aβ1-40, NFL, pTau181, and pTau217 in plasma, and to evaluate the consistency of single or multiple indicators of Aβ1-42 / Aβ1-40, NFL, pTau181, and pTau217 in plasma with the amyloid protein PET results.

[0070] 2. Experimental Methods

[0071] 1. Source of subjects and their inclusion and exclusion criteria

[0072] All subjects were from the Southern China Aging Brain Cohort (SCABI cohort) and the clinical cohort of the Affiliated Brain Hospital of Guangzhou Medical University. From March 2021 to June 2024, 182 SCABI cohort subjects underwent blood sample and amyloid PET image collection. Among them, the inclusion and exclusion criteria are as follows:

[0073] (1) Inclusion criteria

[0074] ①Age ≥50 years old;

[0075] ② Normal cognition, or MCI (according to Peterson criteria: Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256: 183–94. https: / / doi.org / 10.1111 / j.1365-2796.2004.01388.x), or dementia (according to DSM-IV criteria for any dementia: Association AP. Diagnostic and Statistical Manual of Mental Disorders. Text Revision 2000.).

[0076] ③Dividing amyloid-negative and amyloid-positive groups according to visual interpretation of PET amyloid: Image interpretation was completed by imaging experts who were unaware of the subject's diagnosis and biomarker levels, and all PET scan images were visually evaluated according to FDA recommendations: When one or more areas showed increased cortical gray matter signals, resulting in reduced or absent contrast between gray matter and white matter, it was classified as "positive". When the contrast between gray matter and white matter was clear, it was classified as "negative" (Approval for Amyvid (Florbetapir F 18 Injection) 2012. https: / / www.accessdata.fda.gov / drugsatfda_docs / nda / 2012 / 202008Orig1s000Approv.pdf (accessed October 18, 2024).)

[0077] ④The subjects and their families agreed to participate in this study and signed the informed consent form.

[0078] (2) Exclusion criteria

[0079] ① Malignant tumors and severe cerebrovascular diseases (accompanied by neurological deficits, cerebral infarction and cerebral hemorrhage)

[0080] ②Severe neurological diseases (such as metabolic encephalopathy, encephalitis, multiple sclerosis, epilepsy, traumatic brain injury, normal pressure hydrocephalus, etc.)

[0081] ③Severe mental illness (schizophrenia, bipolar disorder, schizoaffective disorder, paranoid psychosis, intellectual disability, etc.)

[0082] ④ Other physical diseases that cause cognitive impairment (liver dysfunction, renal insufficiency, thyroid dysfunction, severe anemia, folic acid or vitamin B12 deficiency, syphilis, HIV infection, alcohol and drug abuse, etc.)

[0083] 2. Data Collection

[0084] (1) Clinical data collection

[0085] The age, gender, height, weight, waist circumference, medical history (including hypertension, diabetes, hypercholesterolemia, coronary heart disease, etc.), personal history (such as smoking, drinking, etc.), family history, medication history, education level, overall cognitive function scale score (MMSE), dementia grade scale score, etc. of each subject were recorded.

[0086] (2) Obtaining and testing plasma samples

[0087] ① Use 5 ml polypropylene tubes to collect blood samples. The blood samples should be collected within three months after the amyloid protein PET image is collected.

[0088] ② Take the collected blood sample, centrifuge it at 4°C and 2000rpm for 10min, collect 0.5ml of supernatant and store it at -80°C; the supernatant is the plasma sample (the plasma sample must be separated within 4h after the blood sample is collected, for specific reference: Giulia, Musso, Chiara, Cosma, Martina, Zaninotto et al. Pre-analytical variability of the Lumipulse immunoassay for plasma biomarkers of Alzheimer's disease. [J]. Clin Chem Lab Med, 2022, 61: 0.).

[0089] ③ The automatic immunoassay analyzer Lumipulse G1200 (Fujirebio) was used to detect the levels of biomarkers in 182 plasma samples, including Aβ1-42, Aβ1-40, NFL, pTau181 and pTau217. The measurement process uses special kits, including Lumipulse Gβ-Amyloid 1-40Plasma, Lumipulse Gβ-Amyloid1-42Plasma, Lumipulse G pTau 181Plasma, Lumipulse G pTau 217Plasma and Lumipulse GNFL kit (Francisco, Martínez-Dubarbie, Armando, Guerra-Ruiz, Sara, López-García et al. Accuracy of plasma Aβ40, Aβ42, and p-tau181 to detect CSF Alzheimer'spathological changes in cognitively unimpaired subjects using the Lumipulseautomated platform. [J]. Alzheimers Res Ther, 2023, 15: 0.).

[0090] (3) Amyloid PET Image Acquisition

[0091] 18F-AV45 was prepared by the PET Center of Guangdong Provincial Hospital of Traditional Chinese Medicine, University Town Hospital. The preparation method was referred to the following literature: Wei, Yin, Xue, Zhou, Jinping, Qiao et al. Study the pharmacokinetics of AV-45 in rat plasma and metabolism in liver microsomes by ultra-performance liquid chromatography with mass spectrometry. [J]. Biomed Chromatogr, 2011, 26: 0.

[0092] A total of 182 subjects underwent amyloid positron emission tomography (PET) imaging using 18F-AV45. PET data were captured 50 minutes after intravenous injection of 18F-AV45 10 ± 1 mCi using a SIGNA PET magnetic resonance (MR) device or a Siemens PET computed tomography (CT / MR) device.

[0093] PET subjects were given 10±1mCi of AV45 intravenously and the scan was started after 50 minutes of rest. The acquisition time was 15min. The ordered subset expectation maximization (OSEM) reconstruction algorithm was used, combined with the joint time of flight (TOF) and point spread function (PSF) algorithm to reconstruct the PET images, using 28 subsets and 6 iterations. The zero echo time (ZTE) sequence was used to correct the attenuation of the PET images. The PET image matrix size was 256×256; the DFOV (display field of view) was 46.2cm×30cm; the layer thickness was 2.78mm; and the pixel size was: 2.8mm×2.8mm.

[0094] The PET CT / MR scans were acquired in 15 min using a 3D iterative algorithm (4 iterations and 21 subsets) with a matrix size of 336 × 336, a zoom factor of 2.0 mm, a slice thickness of 2.0 mm, and an all-pass filter. PET image reconstruction used a time-of-flight and point spread function algorithm involving 5 iterations and 16 subsets. The matrix size was 336 × 336, the zoom factor was 2.0, the slice thickness was 2.0 mm, and a Gaussian filter with a full width at half maximum of 5.0 was used.

[0095] PET amyloid visual interpretation: The image interpretation is performed by 2 professionally trained nuclear medicine physicians. When the 2 physicians disagree, a senior physician will interpret the image. The image results are classified as positive or negative. According to the recommendations of the U.S. Food and Drug Administration (FDA), the scan is classified as "positive" when one or more areas show increased cortical gray matter signal, resulting in reduced or absent contrast between gray and white matter. When the contrast between gray and white matter is clear, the scan is classified as "negative."

[0096] 3. Statistical analysis

[0097] To establish and internally validate the cutoff values ​​for plasma biomarkers, optimal cutoff values ​​for plasma biomarkers were determined in the SCABI cohort. This step is intended to identify biomarker levels that can best distinguish between different states (e.g., disease and non-disease states).

[0098] In order to determine the ability of plasma markers and their ratios in distinguishing between positive and negative amyloid states, receiver operating characteristic (ROC) curve analysis was performed for Aβ1-42, Aβ1-40, NFL, pTau181, pTau217 and multiple indicators in plasma, and the area under the curve (AUCs) and 95% confidence intervals were calculated; the sensitivity, specificity, false negative rate and false positive rate were further determined. The optimal threshold (Cut-off value) was determined based on the corresponding maximum Youden J index of the marker. All statistical analyses were performed in R software version 4.3.2. Statistical significance was defined as α = 0.05, and all tests were two-sided.

[0099] 3. Experimental Results

[0100] 1. Demographic data statistics:

[0101] The SCABI cohort eventually included 182 subjects, including 36 normal elderly people, 120 patients with mild cognitive impairment, and 26 patients with dementia. See Table 1 for specific clinical data collection.

[0102] Table 1. SCABI cohort demographic data (mean ± standard deviation)

[0103]

[0104] 2. Single indicator analysis

[0105] The cut-off value discrimination diagrams of Aβ1-42 / Aβ1-40, NFL, pTau217, and pTau181 in plasma of the SCABI cohort are shown in Table 1. Figure 1-Figure 4 .

[0106] The ROC curves of Aβ1-42 / Aβ1-40, NFL, pTau217, and pTau181 in plasma of the SCABI cohort are shown in order. Figure 5-Figure 8 .

[0107] The results of single-indicator analysis of Aβ1-42 / Aβ1-40, Aβ1-40, Aβ1-42, NFL, pTau217, and pTau181 in the SCABI cohort are shown in Table 2 .

[0108] Table 2. Single index analysis results of Aβ1-42 / Aβ1-40, Aβ1-40, Aβ1-42, NFL, pTau217 and pTau181 (mean ± standard deviation)

[0109]

[0110]

[0111] The results are as follows:

[0112] In the SCABI cohort, the diagnostic performance of pTau217 in plasma was excellent, and the diagnostic performance of Aβ1-42 / Aβ1-40, NFL, and pTau181 was good. Specifically, the sensitivity and specificity of Aβ1-42 / Aβ1-40 were 0.855 and 0.774, respectively; the sensitivity and specificity of NFL were 0.789 and 0.594, respectively; the sensitivity and specificity of pTau217 were 0.868 and 0.934, respectively; and the sensitivity and specificity of pTau181 were 0.711 and 0.783, respectively.

[0113] Receiver operating curve (ROC) analysis showed that pTau217, Aβ1-42 / Aβ1-40, NFL, and pTau181 in plasma had excellent or good discriminant effects in distinguishing pathological changes of Alzheimer's disease. The area under the curve (AUC) was: Aβ1-42 / Aβ1-40 (0.846), NFL (0.693), pTau217 (0.95), pTau181 (0.814), respectively.

[0114] In terms of critical values, the cut off of Aβ1-42 / Aβ1-40 was 0.089, the cut off of NFL was 18.6 pg / mL, the cut off of pTau217 was 0.242 pg / mL, and the cut off of pTau181 was 3.135 pg / mL. According to these critical values, if the pTau217 in the subject's plasma is greater than 0.242pg / mL, Aβ1-42 / Aβ1-40 is less than 0.089, NFL is greater than 18.6pg / mL, or pTau181 is greater than 3.135pg / mL, the subject is more likely to have pathological changes of Alzheimer's disease (i.e., the risk of Alzheimer's disease is higher); conversely, if pTau217 is 0.242pg / mL and below, Aβ1-42 / Aβ1-40 is 0.089 and above, NFL is 18.6pg / mL and below, or pTau181 is 3.135pg / mL and below, the subject is less likely to have pathological changes of Alzheimer's disease (i.e., the risk of Alzheimer's disease is lower).

[0115] 3. Joint indicator analysis

[0116] The diagnostic performance diagrams of the combined plasma index combination of the SCABI cohort (multivariate regression model of Aβ1-42 / Aβ1-40, NFL, pTau181 and pTau217 in plasma) are shown in the figure below. Figure 9-10 .

[0117] The results of the regression model showed that in the SCABI cohort, the model sensitivity was 85.5%, the specificity was 98.1%, the area under the curve (AUC) was 0.97, and the model threshold (model predicted probability value, Model predicted probabilities) was 0.66. That is, if the model-predicted probability value in the subject's plasma is greater than 0.66, then the subject is likely to have Alzheimer's disease pathological changes in the brain (high risk of Alzheimer's disease); if the model-predicted probability value in the subject's plasma is 0.66 or less, then the subject is unlikely to have Alzheimer's disease pathological changes in the brain (low risk of Alzheimer's disease).

[0118] 4. Advantages of the joint model

[0119] The advantage of the joint model is that by integrating information from multiple biomarkers (Aβ1-42 / Aβ1-40, NFL, pTau217, and pTau181), it can significantly improve the accuracy of predicting pathological changes in Alzheimer's disease. Compared with a single biomarker, the joint model can fully consider the interactions between various indicators and improve diagnostic performance, especially in terms of the balance between high sensitivity and specificity. For example, the model performed better in the SCABI cohort with a sensitivity of 85.5%, a specificity of 98.1%, and an AUC of 0.97, showing its superiority in distinguishing Alzheimer's patients from non-patients. In addition, the model's threshold and predicted probability values ​​provide an intuitive and actionable tool that can accurately assess individual risk of disease, thereby providing more personalized decision support for clinicians.

[0120] The present invention has been described in detail above. For those skilled in the art, without departing from the purpose and scope of the present invention, and without the need to carry out unnecessary experimental conditions, the present invention can be implemented in a wide range under equivalent parameters, concentrations and conditions. Although the present invention provides specific embodiments, it should be understood that the present invention can be further improved. In a word, according to the principles of the present invention, the application is intended to include any changes, uses or improvements to the present invention, including departure from the disclosed scope in the application, and changes made with conventional techniques known in the art.

Claims

1. A plasma biomarker combination, including Aβ1-42 / Aβ1-40, NFL, pTau181, and pTau217; wherein Aβ1-42 / Aβ1-40 is the concentration ratio of Aβ1-42 to Aβ1-40.

2. The plasma biomarker combination according to claim 1, characterized in that: The plasma biomarker panel consisted of Aβ1-42 / Aβ1-40, NFL-BL, pTau181, and pTau217.

3. The plasma biomarker combination according to claim 1 or 2, characterized in that: The detection object of the plasma biomarker combination is plasma.

4. Use of the plasma biomarker combination according to any one of claims 1 to 3 in the preparation of a product for assessing the risk of Alzheimer's disease or a product for evaluating the likelihood of Alzheimer's pathological changes occurring in the brain.

5. Use of substances for detecting the concentration of Aβ1-42 in plasma, substances for detecting the concentration of Aβ1-40 in plasma, substances for detecting the concentration of NFL in plasma, substances for detecting the concentration of pTau181 in plasma, and substances for detecting the concentration of pTau217 in plasma in the preparation of products for assessing the risk of Alzheimer's disease or the preparation of products for evaluating the possibility of Alzheimer's pathological changes in the brain.

6. A method for screening plasma biomarkers for assessing the risk of Alzheimer's disease or evaluating the likelihood of Alzheimer's disease pathological changes in the brain, comprising the following steps: (1) using amyloid positron emission tomography to collect amyloid protein PET images of each subject in the test group; The purpose of performing step (1) is to evaluate whether each subject in the test group suffers from Alzheimer's disease; Alzheimer's disease can be diagnosed by amyloid protein PET imaging; (2) detecting the concentration and / or ratio of small molecule proteins in the plasma of each subject in the test group, and then comparing the types, ratios, and combinations of types and ratios of small molecule proteins in the plasma with the consistency of the results of the amyloid protein PET image diagnosis of Alzheimer's disease in step (1) through receiver operating characteristic curve analysis, thereby screening plasma biomarkers for assessing the risk of Alzheimer's disease or evaluating the possibility of Alzheimer's disease pathological changes in the brain.

7. The method according to claim 6, characterized in that: In the step (2), the small molecule proteins are Aβ1-42, Aβ1-40, NFL, pTau181 and / or pTau217; and the ratio of the small molecule proteins is Aβ1-42 / Aβ1-40.

8. The method according to claim 6, wherein: In the step (2), the screening criteria are as follows: the sensitivity and specificity are both above 90%.

9. A data processing device for assessing a subject's risk of developing Alzheimer's disease or evaluating the likelihood of Alzheimer's pathological changes occurring in the subject's brain, comprising the following modules: (1) a data receiving module; the data receiving module is configured to receive the content of Aβ1-40, Aβ1-42, NFL, pTau181, and pTau217 in the plasma of the subject; (2) a data processing module; the data processing module is configured as (2-1) and (2-2); (2-1) receiving the Aβ1-40 content and the Aβ1-42 content in the plasma of the subject from the data receiving module, and calculating the ratio of the Aβ1-42 content to the Aβ1-40 content, which is recorded as Aβ1-42 / Aβ1-40; (2-2) A multivariate regression model was established for the levels of Aβ1-42 / Aβ1-40, NFL, pTau181, and pTau217 to obtain the model value; (3) a data storage module; the data storage module is configured to store the judgment threshold; (4) a data comparison module; the data comparison module is configured to receive the model value sent by the data processing module and call the judgment threshold from the data storage module for comparison with the model value; (5) a judgment module; the judgment module is configured to receive the comparison result sent by the data comparison module, judge the comparison result according to a predetermined judgment condition, and output the judgment result; The predetermined judgment condition is: if the model value is greater than the judgment threshold, the person being tested has a high risk of developing Alzheimer's disease or is likely to have pathological changes of Alzheimer's disease in the brain; otherwise, the person being tested has a low risk of developing Alzheimer's disease or is unlikely to have pathological changes of Alzheimer's disease in the brain.

10. The data processing device according to claim 9, characterized in that: The Aβ1-40 content, Aβ1-42 content, NFL content, pTau181 content and pTau217 content in the plasma of the subject received by the data receiving module are obtained according to a method including the following steps: collecting peripheral blood of the subject, separating the plasma, and then detecting the Aβ1-40 content, Aβ1-42 content, NFL content, pTau181 content and pTau217 content in the plasma.

Images