Dataset Information

Plasma contact factors as novel biomarkers for diagnosing Alzheimer's disease.

ABSTRACT:

Background

Alzheimer's disease (AD) is the most common cause of dementia and most of AD patients suffer from vascular abnormalities and neuroinflammation. There is an urgent need to develop novel blood biomarkers capable of diagnosing Alzheimer's disease (AD) at very early stage. This study was performed to find out new accurate plasma diagnostic biomarkers for AD by investigating a direct relationship between plasma contact system and AD.

Methods

A total 101 of human CSF and plasma samples from normal and AD patients were analyzed. The contact factor activities in plasma were measured with the corresponding specific peptide substrates.

Results

The activities of contact factors (FXIIa, FXIa, plasma kallikrein) and FXa clearly increased and statistically correlated as AD progresses. We present here, for the first time, the FXIIa cut-off scores to as: >?26.3?U/ml for prodromal AD [area under the curve (AUC) = 0.783, p ?27.2?U/ml for AD dementia (AUC = 0.906, p 1-42 versus the contact factors. Of these, the representative ratio cut-off scores of A?_1-42/FXIIa were to be: ConclusionThe activation of plasma contact system is closely associated with clinical stage of AD, and FXIIa activity as well as the cut-off scores of CSF A?_1-42/FXIIa can be used as novel accurate diagnostic AD biomarkers.

SUBMITTER: Park JE

PROVIDER: S-EPMC7796542 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Plasma contact factors as novel biomarkers for diagnosing Alzheimer's disease.

Park Jung Eun JE Lim Do Sung DS Cho Yeong Hee YH Choi Kyu Yeong KY Lee Jang Jae JJ Kim Byeong C BC Lee Kun Ho KH Lee Jung Sup JS

Biomarker research 20210109 1

<h4>Background</h4>Alzheimer's disease (AD) is the most common cause of dementia and most of AD patients suffer from vascular abnormalities and neuroinflammation. There is an urgent need to develop novel blood biomarkers capable of diagnosing Alzheimer's disease (AD) at very early stage. This study was performed to find out new accurate plasma diagnostic biomarkers for AD by investigating a direct relationship between plasma contact system and AD.<h4>Methods</h4>A total 101 of human CSF and plas ...[more]

PMID: 33422144

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Project description:Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Dataset Information

Plasma contact factors as novel biomarkers for diagnosing Alzheimer's disease.

Background

Methods

Results

Publications

Plasma contact factors as novel biomarkers for diagnosing Alzheimer's disease.

Similar Datasets

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