Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

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.

Compared with the monocyte to high-density lipoprotein ratio (MHR) and the neutrophil to lymphocyte ratio (NLR), the neutrophil to high-density lipoprotein ratio (NHR) is more valuable for assessing the inflammatory process in Parkinson’s disease

“…A meta-analysis [ 27 ] illustrated that non-steroidal anti-inflammatory drugs (NSAIDs) are efficient for the treatment as well as prevention of PD and may also lead to a decrease in the neutrophil count. An immunomagnetic reduction method (IMR) was used to detect the dynamic changes in blood biomarkers [ 14 ], and it was found that plasma α-synuclein in PD patients was greater significantly compared with that in the healthy group of control and was the highest in Parkinson’s disease dementia (PDD) patients. Moreover, monocytes were found to be closely related to α-SYN in the course of PD.…”

“…Currently, the diagnosis of PD and its differentiation from other neurodegenerative diseases rely mainly on neurological symptoms and psychological cognitive function assessments, magnetic resonance imaging, positron emission tomography (PET) and other methods [ 13 ]. In addition, other groups have developed a blood-based classifier that includes Aβ40, total Tau, and a-syn levels to monitor the progression of PD, and α-synuclein levels were shown to have high specificity and accuracy in this effort [ 14 ]. However, the relatively high cost of monitoring these levels may limit its large-scale practical application in middle-aged and elderly populations.…”

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

“…Deep learning can also help to identify features that are important for disease progression or serve as markers for clinical trials (Ithapu et al, 2015 ). In addition to harvesting brain imaging data [especially the multimodality imaging data from the public ADNI database ( http://adni.loni.usc.edu/ )], deep learning has been applied to biospecimens (Lee et al, 2019b ; Lin et al, 2020 ), electronic health records (Landi et al, 2020 ; Nori et al, 2020 ), speech (Lopez-de-Ipina et al, 2018 ), neuropsychological data (Choi et al, 2018 ; Kang et al, 2019 ), and a combination of MRI and neuropsychological data (Qiu et al, 2018 ; Duc et al, 2020 ). By contrast, few studies have applied deep learning to cognitive task data, which – by design – is supposed to be more sensitive to detect early and mild neurocognitive impairment (Locascio et al, 1995 ; Perry and Hodges, 1999 ).…”

Systematic Review: Genetic, Neuroimaging, and Fluids Biomarkers for Frontotemporal Dementia Across Latin America Countries

“…This study was supported by two other investigations reporting increased sensitivity (80–86%) and specificity (82%) of the Aβ 42 /pTau181 ratio, suggesting that those proteins are the best biomarker subset to differentiate FTLD from AD ( 37 , 170 ). The plasma levels of p-Tau181 were significantly higher in patients on the AD spectrum groups and FTD patients, with the highest level in the FTD group ( 171 ). In a recent study, plasma p-tau181 distinguished AD of DFT with an AUC of 100% ( 172 ).…”