Background and Purpose: Alzheimer's Disease (AD) is a complex neurodegenerative disease that has been becoming increasingly prevalent in recent decades. Efforts to identify predictive biomarkers of the disease have proven difficult. Advances in the collection of multi-omic data and deep learning algorithms have opened the possibility of integrating these various data together to identify robust biomarkers for predicting the onset of the disease prior to the onset of symptoms. This study performs a systematic review of recent methods used to predict AD using multi-omic and multi-modal data. Methods: We systematically reviewed studies from Google Scholar, Pubmed, and Semantic Scholar published after 2018 in relation to predicting AD using multi-omic data. Three reviewers independently identified eligible articles and came to a consensus of papers to review. The Quality in Prognosis Studies (QUIP) tool was used for the risk of bias assessment. Results: 22 studies which use multi-omic data to either predict AD or develop AD biomarkers were identified. Those studies which aimed to directly classify AD or predict the progression of AD achieved area under the receiver operating characteristic curve (AUC) between .70 - .98 using varying types of patient data, most commonly extracted from blood. Hundreds of new genes, single nucleotide polymorphisms (SNPs), RNA molecules, DNA methylation sites, proteins, metabolites, lipids, imaging features, and clinical data have been identified as successful biomarkers of AD. The most successful techniques to predict AD have integrated multi-omic data together in a single analysis. Conclusion: This review has identified many successful biomarkers and biosignatures that are less invasive than cerebral spinal fluid. Together with the appropriate prediction models, highly accurate classifications and prognostications can be made for those who are at risk of developing AD. These early detection of risk factors may help prevent the further development of cognitive impairment and improve patient outcomes.

The authors have declared no competing interest.

This study did not receive any funding

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