Literature DB >> 29125490

Plasma Proteomic Profiles of Cerebrospinal Fluid-Defined Alzheimer's Disease Pathology in Older Adults.

Loïc Dayon1, Jérôme Wojcik2, Antonio Núñez Galindo1, John Corthésy1, Ornella Cominetti1, Aikaterini Oikonomidi3, Hugues Henry4, Eugenia Migliavacca1, Gene L Bowman1, Julius Popp3.   

Abstract

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers of the beta-amyloid and microtubule associated protein tau metabolism have proven the capacity to improve classification of subjects developing Alzheimer's disease (AD). The blood plasma proteome was characterized to further elaborate upon the mechanisms involved and identify proteins that may improve classification of older adults developing an AD dementia.
OBJECTIVE: Identify and describe plasma protein expressions that best classify subjects with CSF-defined presence of AD pathology and cerebral amyloidosis.
METHODS: We performed a cross-sectional analysis of samples collected from community-dwelling elderly with (n = 72) or without (n = 48) cognitive impairment. CSF Aβ1-42, tau, and phosphorylated tau (P-tau181) were measured using ELISA, and mass spectrometry quantified the plasma proteomes. Presence of AD pathology was defined as CSF P-tau181/Aβ1-42 > 0.0779, and presence of amyloidosis was defined as CSF Aβ1-42 < 724 pg/mL.
RESULTS: Two hundred and forty-eight plasma proteins were quantified. Plasma proteins did not improve classification of the AD CSF biomarker profile in the whole sample. When the analysis was separately performed in the cognitively impaired individuals, the diagnosis accuracy of AD CSF profile was 88.9% with 19 plasma proteins included. Within the full cohort, there were 16 plasma proteins that improved diagnostic accuracy of cerebral amyloidosis to 92.4%.
CONCLUSION: Plasma proteins improved classification accuracy of AD pathology in cognitively-impaired older adults and appeared representative of amyloid pathology. If confirmed, those candidates could serve as valuable blood biomarkers of the preclinical stages of AD or risk of developing AD.

Entities:  

Keywords:  Alzheimer’s disease; amyloid-β; amyloidosis; biomarker; dementia; protein; tau

Mesh:

Substances:

Year:  2017        PMID: 29125490     DOI: 10.3233/JAD-170426

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  7 in total

1.  Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV.

Authors:  Lynn Pulliam; Michael Liston; Bing Sun; Jared Narvid
Journal:  J Neurovirol       Date:  2020-07-17       Impact factor: 2.643

2.  An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease.

Authors:  Christopher Clark; Loïc Dayon; Mojgan Masoodi; Gene L Bowman; Julius Popp
Journal:  Alzheimers Res Ther       Date:  2021-04-01       Impact factor: 6.982

3.  Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning.

Authors:  Alexey S Kononikhin; Natalia V Zakharova; Savva D Semenov; Anna E Bugrova; Alexander G Brzhozovskiy; Maria I Indeykina; Yana B Fedorova; Igor V Kolykhalov; Polina A Strelnikova; Anna Yu Ikonnikova; Dmitry A Gryadunov; Svetlana I Gavrilova; Evgeny N Nikolaev
Journal:  Int J Mol Sci       Date:  2022-07-18       Impact factor: 6.208

4.  GPS driving: a digital biomarker for preclinical Alzheimer disease.

Authors:  Sayeh Bayat; Ganesh M Babulal; Suzanne E Schindler; Anne M Fagan; John C Morris; Alex Mihailidis; Catherine M Roe
Journal:  Alzheimers Res Ther       Date:  2021-06-14       Impact factor: 6.982

Review 5.  Proteomic Approaches for the Discovery of Biofluid Biomarkers of Neurodegenerative Dementias.

Authors:  Becky C Carlyle; Bianca A Trombetta; Steven E Arnold
Journal:  Proteomes       Date:  2018-08-31

6.  Plasma sex hormone-binding globulin predicts neurodegeneration and clinical progression in prodromal Alzheimer's disease.

Authors:  Wei Xu; Bing-Jie Su; Xue-Ning Shen; Yan-Lin Bi; Chen-Chen Tan; Jie-Qiong Li; Xi-Peng Cao; Qiang Dong; Lan Tan; Jin-Tai Yu
Journal:  Aging (Albany NY)       Date:  2020-07-22       Impact factor: 5.682

Review 7.  Proteomics Landscape of Alzheimer's Disease.

Authors:  Ankit P Jain; Gajanan Sathe
Journal:  Proteomes       Date:  2021-03-10
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.