Classifications of neurodegenerative disorders using a multiplex blood biomarkers-based machine learning model
Journal
International Journal of Molecular Sciences
Journal Volume
21
Journal Issue
18
Pages
1-15
Date Issued
2020
Author(s)
Abstract
Easily accessible biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders. ? 2020, MDPI AG. All rights reserved.
Other Subjects
alpha synuclein; amyloid beta protein[1-42]; biological marker; tau protein; alpha synuclein; amyloid beta protein; amyloid beta protein[1-40]; amyloid beta-protein (1-42); biological marker; MAPT protein, human; peptide fragment; SNCA protein, human; tau protein; aged; Article; blood sampling; classifier; cognition; controlled study; degenerative disease; disease classification; disease severity; feature extraction; female; human; immunoassay; machine learning; major clinical study; male; mild cognitive impairment; Parkinson disease; protein blood level; blood; classification; clinical trial; cognitive defect; middle aged; very elderly; Aged; Aged, 80 and over; alpha-Synuclein; Amyloid beta-Peptides; Biomarkers; Cognitive Dysfunction; Female; Humans; Machine Learning; Male; Middle Aged; Neurodegenerative Diseases; Peptide Fragments; tau Proteins
Publisher
MDPI AG
Type
journal article