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

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.

An observation study of urinary biomarker exploratory in Alzheimer's disease using high-resolution mass spectrometry.

Alzheimer's disease (AD) is regarded as a progressive neurodegenerative dementia, characterized by degeneration of distinct neuronal populations. A case-control study was carried out using high-resolution mass spectrometry to explore AD-associated urinary metabolic biomarkers from 30 AD patients and 30 cognitively normal (CN) individuals. In total, 49 metabolites were determined and validated as known compounds using LC/MS analysis. Using the two-sample t-test statistical analysis (P < 0.05), 19 metabolites were shown to be significantly different from AD to CN. A diagnostic model of the receiver operating characteristic curve was constructed with a combination of nine molecules out of 19 metabolites, it yielded a separation with an area under the curve value of 0.976 between the two groups. This study indicated that urinary metabolites showed a significant expression between AD and CN. AD-related metabolites enable to satisfy the diagnostic power of disease discrimination. In addition, as a noninvasive approach, urine collection is done easily in clinical diagnosis of AD.