A Comprehensive Review on the Identification of Blood-Based Biomarkers for Alzheimer’s Disease Detection Through Computational Approaches

Alzheimer’s disease (AD) is a neurodegenerative disorder and most common form of dementia. AD is characterized by decline in learning and memory. Moreover, due to limited treatment efficacy, early and accurate detection of AD is important. However, this disease is very difficult to diagnose clinically based on the sign and symptoms of the patients. Therefore, cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), computerized tomography (CT), and positron emission tomography (PET)-based measures play a critical role in AD detection. These detection methods are highly inaccessible, invasive, and costly, which limits their use as detecting tools. Therefore, identification of biomarkers in blood samples would be a more practical approach. The identification of these biomarkers opens the door to targeted therapeutic and customized medical approaches as well as aids in understanding the aberrant alterations that lead to disease. The cost, time, and resources required for screening and validation utilizing biological studies or clinical trials are also high. As given the ever growing high throughput data from systematic profiling of thousands of patients for various genome scale omics measurements, including mRNA expression, micro-RNA (miRNA) expression, and DNA sequences, hence there is a need to develop computational methods that help in identifying biomarkers for a given phenotype or diseases. As a result of rapid advancement in bioinformatics analysis, AD-related biomarkers can now be identified in blood as recent work showing promising results. This paper is a systematic review of the published research works, which provides an overview of the progress achieved so far in identification of potential blood biomarkers for AD detection through computational approaches. This study suggests that blood-based biomarkers are easy to detect and also shows higher accuracy in the identification of AD. The possible challenges and key area of application along with the future directions are also discussed in this review paper.