Identification of Blood-Based Multi-Omics Biomarkers for Alzheimer’s Disease Using Firth’s Logistic Regression

Abstract

Alzheimer’s disease (AD) is a progressive and relentless debilitating neurodegenerative disease. A post-mortem microscopic neuropathological examination of the brain revealed the existence of extracellular β-amyloid plaques and intracellular neurofibrillary tangles. An accurate early diagnosis of AD is difficult because various disorders share the initial symptoms of the disease. Based on system biology, the multi-omics approach captures and integrates information from genomics, transcriptomics, proteomics, cytokinomics, and metabolomics. This study developed an AD prediction model based on the integrated blood-based multi-omics dataset involving 32 AD patients and 15 non-AD subjects. The integrated multi-omics dataset consists of 16 transcript genes, 14 metabolites, and nine cytokines. Due to the complete separation and multicollinearity issues, Firth’s logistic regression model was then developed to predict AD using the principal components. The model revealed 18 potential biomarkers of AD, consisting of seven metabolites, two transcriptomes, and nine cytokines. These potential biomarkers show an upregulated risk in the AD group compared to the non-AD subjects. The possibility of using these biomarkers as early predictors of AD is discussed.