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


Main idea: plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181 are predictive markers of Alzheimer’s disease and Parkinson’s disease.


Easily accessible biomarkers for Alzheimer’s disease and related neurodegenerative disorders are urgently needed. In this study, we aimed to develop machine learning algorithms using multiplex blood-based biomarkers. Plasma samples were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI), PD spectrum with variable cognitive severity (PDD), and FTD. 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 with PD with normal cognition. We applied machine learning-based frameworks 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.


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