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Biomarker and kit for predicting AD (Alzheimer's disease) risk of old people and application of biomarker and kit

A technology for the elderly and kits, applied in the field of risky biomarkers and kits, can solve the problems of time-consuming and labor-consuming, expensive AD diagnostic methods, etc., and achieve the effect of fast detection speed, low cost and high accuracy

Active Publication Date: 2021-07-16
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a group of biomarkers and kits for predicting the risk of AD in the elderly and their applications in order to overcome the problems of expensive, time-consuming and labor-intensive AD diagnostic methods in the prior art

Method used

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  • Biomarker and kit for predicting AD (Alzheimer's disease) risk of old people and application of biomarker and kit
  • Biomarker and kit for predicting AD (Alzheimer's disease) risk of old people and application of biomarker and kit
  • Biomarker and kit for predicting AD (Alzheimer's disease) risk of old people and application of biomarker and kit

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046]Populations with mild cognitive impairment (MCI, MMSE=18-23), severe cognitive impairment (AD, MMSE=2-17) and age / gender matched normal cognition (MMSE=29-30) ( Ctrl Group) conducted a comprehensive and in-depth analysis of platelet proteomics using TMT-LC-MS / MS technology. The specific process includes: firstly, use TMT tag to mark each protein sample, then conduct mass spectrometry identification, then conduct differential protein analysis and Pearson correlation analysis to determine candidate proteins, and finally use machine learning methods to establish the best combination of markers based on candidate proteins.

[0047] The results of proteomic analysis were as figure 1 As shown in A, a total of 2994 platelet proteins were captured by proteomics. Compared with the normal cognitive population, a total of 360 differential proteins were identified in MCI and AD patients (Pfigure 1 Shown in B); AD patients compared with normal cognitive population, 121 differential p...

Embodiment 2

[0049] According to the results obtained in Example 1, through MMSE correlation and pathway analysis, it was determined that 26 core platelet proteins were defined as candidate proteins, and the correlation coefficients between the 26 platelet candidate proteins and MMSE scores were further ranked, and the candidates were ranked according to the Pearson correlation coefficient. A complex association matrix (such as figure 2 A), where the ratio of shading to circles indicates the degree of correlation, and the relative expression abundance of candidate biomarkers as figure 2 Shown in B. It can be seen from the figure that the increase or decrease of the candidate proteins is very uniform, and all 26 candidate proteins show a moderate degree of MMSE correlation (|r|=0.371-0.552). Among them, the reduction of platelet CD63 showed the strongest correlation with MMSE score (r=0.552, P=0.002). The correlation coefficients (r) of antiproliferative protein, cytochrome b-c1 complex...

Embodiment 3

[0052] To explore the dynamics of the platelet proteome during cognitive decline, we performed clustering and protein-protein interaction (PPI) network analysis in MCI, AD, and cognitively normal control populations. Compared with the normal cognitive control group, the differential proteins and related bioinformatics analysis results in MCI and AD patients were as follows: image 3 shown. image 3 In A, according to the change trend of differential proteins from Ctrl, MCI to AD group, the differential proteins are divided into three clusters (each row represents a protein); image 3 In B, the signal pathway enrichment analysis of the differential proteins of the three clusters was carried out through Metascape online analysis (overlapping proteins ≥ 3, P image 3 C represents the protein-protein interaction modules detected in the three clusters; image 3 D represents the biological function process with significant enrichment of differential proteins. Among them, the circle...

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Abstract

The invention relates to the field of medicines and particularly relates to a biomarker and a kit for predicting AD (Alzheimer's disease) risk of old people and an application of the biomarker and the kit. The biomarkers comprise an anti-proliferative protein, a cytochrome b-c1 complex subunit 6, a platelet glycoprotein Ib chain and a fibronectin. The biomarker can be used for evaluating old people, is high in accuracy, high in detection speed, low in cost, small in trauma and easy to accept by subjects, and provides a scientific and effective diagnosis scheme for AD. The kit can be used for screening AD in a large range of old people, and detection can be completed only by collecting peripheral blood, so screening of the large range of old people becomes possible.

Description

technical field [0001] The invention relates to the field of medicine, in particular to biomarkers and kits for predicting the risk of developing AD in the elderly and applications thereof. Background technique [0002] Alzheimer's disease (AD) is a primary degenerative disease of the central nervous system and the most common type of dementia. The main clinical symptom of AD is slow cognitive decline, including progressive loss of memory, logical reasoning ability and language function, and finally develops into severe dementia. [0003] At present, there is still a lack of early diagnostic measures for AD, and irreversible brain damage often occurs when patients see a doctor, resulting in poor treatment effects. Therefore, the discovery of early risk factors is of great significance for early diagnosis and intervention. [0004] Platelets and neurons have many biological similarities, therefore, platelets are ideal materials for finding peripheral biomarkers of central n...

Claims

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Application Information

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IPC IPC(8): G01N33/68
CPCG01N33/6896
Inventor 王建枝余海涛刘彦超柯丹
Owner HUAZHONG UNIV OF SCI & TECH
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