A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease.

Autor: Yang J; Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China., Zhou Y; School of Medicine, Shanghai University, Shanghai, 200444, China., Wang T; School of Medicine, Shanghai University, Shanghai, 200444, China., Li N; School of Medicine, Shanghai University, Shanghai, 200444, China., Chao Y; School of Medicine, Shanghai University, Shanghai, 200444, China., Gao S; Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China., Zhang Q; Department of Internal Medicine, Shanghai Baoshan Elderly Nursing Hospital, Shanghai, 200435, China., Wu S; Neurology Department, Shanghai Baoshan Luodian Hospital, Shanghai, 201908, China., Zhao L; Department of Pharmacy, Shanghai Baoshan Luodian Hospital, Shanghai, 201908, China., Dong X; Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.; School of Medicine, Shanghai University, Shanghai, 200444, China.; Suzhou Innovation Center of Shanghai University, Suzhou, 215000, Jiangsu, China.
Jazyk: angličtina
Zdroj: Annals of clinical and translational neurology [Ann Clin Transl Neurol] 2024 May; Vol. 11 (5), pp. 1310-1324. Date of Electronic Publication: 2024 Apr 11.
DOI: 10.1002/acn3.52047
Abstrakt: Objective: Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence-associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi-omics approach and to establish diagnostic assays for AD.
Methods: This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high-resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K-nearest neighbors (KNN) algorithm was utilized to construct a multi-dimensional model for distinguishing AD from CN.
Results: Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C-reactive protein (CRP), fibronectin (FN1), and serum amyloid A-1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN-based three-dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD.
Interpretation: The multi-omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging-associated pathophysiology.
(© 2024 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)
Databáze: MEDLINE
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