Literature DB >> 31605717

Prognostic plasma protein panel for Aβ deposition in the brain in Alzheimer's disease.

Jong-Chan Park1, Sun-Ho Han2, Hangyeore Lee3, Hyobin Jeong4, Min Soo Byun5, Jingi Bae6, Hokeun Kim7, Dong Young Lee8, Dahyun Yi9, Seong A Shin10, Yu Kyeong Kim11, Daehee Hwang12, Sang-Won Lee13, Inhee Mook-Jung14.   

Abstract

Alzheimer's disease (AD) is the most common age-associated dementia. Many studies have sought to predict cerebral amyloid deposition, the major pathological hallmark of AD, using body fluids such as blood or cerebral spinal fluid (CSF). The use of blood in diagnostic procedures is widespread in medicine; however, existing blood biomarkers for AD remain unreliable. We sought to discover blood biomarkers that discriminate Aβ deposition status in the brain. This study used 107 individuals who were cognitively normal (CN), 107 patients with mild cognitive impairment (MCI), and 40 AD patients with Pittsburg compound B positron emission tomography (PiB-PET) amyloid imaging data available. We found five plasma biomarker candidates via mass spectrometry (MS) based-proteomic analysis and validated these proteins using enzyme-linked immunosorbent assay (ELISA). Our integrated models were highly predictive of brain amyloid deposition, exhibiting 0.871 accuracy with 79% sensitivity and 84% specificity overall, and 0.836 accuracy with 68% sensitivity and 90% specificity in patients with MCI. These results indicated that a combination of proteomic-based blood proteins might be a possible biomarker set for predicting cerebral amyloid deposition.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Alzheimer’s disease; Cerebral amyloid deposition; PiB-PET; Plasma biomarker; Proteomics; TMT

Year:  2019        PMID: 31605717     DOI: 10.1016/j.pneurobio.2019.101690

Source DB:  PubMed          Journal:  Prog Neurobiol        ISSN: 0301-0082            Impact factor:   11.685


  8 in total

1.  Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry.

Authors:  Miriam T Ashford; John Neuhaus; Chengshi Jin; Monica R Camacho; Juliet Fockler; Diana Truran; R Scott Mackin; Gil D Rabinovici; Michael W Weiner; Rachel L Nosheny
Journal:  Alzheimers Dement (Amst)       Date:  2020-09-24

2.  Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers.

Authors:  Duygu Tosun; Dallas Veitch; Paul Aisen; Clifford R Jack; William J Jagust; Ronald C Petersen; Andrew J Saykin; James Bollinger; Vitaliy Ovod; Kwasi G Mawuenyega; Randall J Bateman; Leslie M Shaw; John Q Trojanowski; Kaj Blennow; Henrik Zetterberg; Michael W Weiner
Journal:  Brain Commun       Date:  2021-02-02

3.  Performance of the QPLEX™ Alz plus assay, a novel multiplex kit for screening cerebral amyloid deposition.

Authors:  Jong-Chan Park; Keum Sim Jung; Jiyeong Kim; Ji Sung Jang; Sunghoon Kwon; Min Soo Byun; Dahyun Yi; Gihwan Byeon; Gijung Jung; Yu Kyeong Kim; Dong Young Lee; Sun-Ho Han; Inhee Mook-Jung
Journal:  Alzheimers Res Ther       Date:  2021-01-06       Impact factor: 6.982

4.  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

5.  Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.

Authors:  Jack Albright; Miriam T Ashford; Chengshi Jin; John Neuhaus; Gil D Rabinovici; Diana Truran; Paul Maruff; R Scott Mackin; Rachel L Nosheny; Michael W Weiner
Journal:  Alzheimers Dement (Amst)       Date:  2021-06-09

6.  The clinical use of blood-test factors for Alzheimer's disease: improving the prediction of cerebral amyloid deposition by the QPLEXTM Alz plus assay kit.

Authors:  Haeng Jun Kim; Jong-Chan Park; Keum Sim Jung; Jiyeong Kim; Ji Sung Jang; Sunghoon Kwon; Min Soo Byun; Dahyun Yi; Gihwan Byeon; Gijung Jung; Yu Kyeong Kim; Dong Young Lee; Sun-Ho Han; Inhee Mook-Jung
Journal:  Exp Mol Med       Date:  2021-06-09       Impact factor: 8.718

7.  Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

Authors:  Afaf El-Ansary; Wail M Hassan; Maha Daghestani; Laila Al-Ayadhi; Abir Ben Bacha
Journal:  PLoS One       Date:  2020-01-16       Impact factor: 3.240

8.  Application of QPLEXTM biomarkers in cognitively normal individuals across a broad age range and diverse regions with cerebral amyloid deposition.

Authors:  Dongjoon Lee; Jong-Chan Park; Keum Sim Jung; Jiyeong Kim; Ji Sung Jang; Sunghoon Kwon; Min Soo Byun; Dahyun Yi; Gihwan Byeon; Gijung Jung; Yu Kyeong Kim; Dong Young Lee; Sun-Ho Han; Inhee Mook-Jung
Journal:  Exp Mol Med       Date:  2022-01-20       Impact factor: 12.153

  8 in total

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