Evidence map›Paper›PMID 38605603›Full record

ArticleAnnals of clinical and translational neurology2024

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

Jingzhi Yang, Yinge Zhou, Tianjiao Wang, Na Li, Yufan Chao, Songyan Gao, Qun Zhang, Shuo Wu, Liang Zhao, Xin Dong

Open access · goldAbstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.4field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
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  3. Cellular senescence and aging: molecular mechanisms and convergent pathways.Cellular and molecular life sciences : CMLS · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Jingzhi Yang *Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.ORCID 0000-0001-7812-0916
Yinge Zhou *School of Medicine, Shanghai University, Shanghai, 200444, China.
Tianjiao Wang *School of Medicine, Shanghai University, Shanghai, 200444, China.
Na LiSchool of Medicine, Shanghai University, Shanghai, 200444, China.
Yufan ChaoSchool of Medicine, Shanghai University, Shanghai, 200444, China.
Songyan GaoInstitute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Qun ZhangDepartment of Internal Medicine, Shanghai Baoshan Elderly Nursing Hospital, Shanghai, 200435, China.
Shuo WuNeurology Department, Shanghai Baoshan Luodian Hospital, Shanghai, 201908, China.
Liang ZhaoDepartment of Pharmacy, Shanghai Baoshan Luodian Hospital, Shanghai, 201908, China.
Xin DongInstitute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Shanghai University · CNBaogang Group (China) · CNPunan Hospital · CNShanghai Stomatological Hospital · CN

Funding

National Key Research and Development Program of China 2021YFC2100201
6 · The paper itself

Abstract

objectiveAlzheimer'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.

methodsThis 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.

resultsProteomics 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.

interpretationThe 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.

Indexed as

Alzheimer DiseaseBiomarkersProteomicsAgedAged, 80 and overAgingFemaleHumansMaleMetabolomicsMiddle AgedMultiomicsPhenotypeRetrospective StudiesBiomarkers

Identifiers

PMID38605603
PMCPMC11093245
OpenAlexW4394756675

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.