Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 1; pp. 218 - 226
• Main Authors: Eke, Chima S., Jammeh, Emmanuel, Li, Xinzhong, Carroll, Camille, Pearson, Stephen, Ifeachor, Emmanuel
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer's disease; Apolipoprotein E; Biological system modeling; Biomarkers; Blood; blood biomarker; Blood plasma; Dementia; Diagnostic systems; Disease detection; Identification methods; Information processing; Learning algorithms; Machine learning; Neurodegenerative diseases; Panels; Plasma proteins; Proteins; support vector machine; Support vector machines
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2020.2984355

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.