Advancements in multi-omics research to address challenges in Alzheimer's disease: a systems biology approach utilizing molecular biomarkers and innovative strategies

Abstract

Alzheimer's disease (AD) is a growing global challenge, representing the most common neurodegenerative disorder and affecting millions of lives. As life expectancy continues to rise and populations expand, the number of individuals coping with the cognitive declines caused by AD is projected to double in the coming years. By 2050, we may see over 115 million people diagnosed with this devastating condition. Unfortunately, while we currently lack effective cures, there are preventative measures that can slow disease progression in symptomatic patients. Thus, research has shifted toward early detection and intervention for AD in recent years. With technological advances, we are now harnessing large datasets and more efficient, minimally invasive methods for diagnosis and treatment. This review highlights critical demographic insights, health conditions that increase the risk of developing AD, and lifestyle factors in midlife that can potentially trigger its onset. Additionally, we delve into the promising role of plant-based metabolites and their sources, which may help delay the disease's progression. The innovative multi-omics research is transforming our understanding of AD. This approach enables comprehensive data analysis from diverse cell types and biological processes, offering possible biomarkers of this disease's mechanisms. We present the latest advancements in genomics, transcriptomics, Epigenomics, proteomics, and metabolomics, including significant progress in gene editing technologies. When combined with machine learning and artificial intelligence, multi-omics analysis becomes a powerful tool for uncovering the complexities of AD pathogenesis. We also explore current trends in the application of radiomics and machine learning, emphasizing how integrating multi-omics data can transform our approach to AD research and treatment. Together, these pioneering advancements promise to develop more effective preventive and therapeutic strategies soon.

Keywords: Alzheimer’s biomarkers; CRISPR; biomarkers; genomics; machine learning and radiomics; metabolomics; multi-omics; proteomics; radiomics.

FIGURE 1

The analysis of Alzheimer’s disease (AD) biomarkers begins with tissue sampling, where brain tissue is collected post-mortem through biopsy. The samples are then prepared, processed, and molecular extraction is performed to isolate proteins, RNA, or metabolites. Researchers utilize techniques such as mass spectrometry and RNA sequencing, combined with computational models, to detect key biomarkers that aid in diagnosis, disease tracking, and the development of potential treatments.

FIGURE 2

Systems network of AD-associated genes, with key genes highlighted in larger circles. Physical interactions and co-expression patterns are analyzed using a network prediction database. Red lines indicate physical interactions, purple lines represent co-expression, green lines denote genetic interactions, and blue lines signify pathway associations or colocalization within the same organelle. This network visualization offers insights into the complex molecular interplay underlying the pathology of Alzheimer’s disease.

FIGURE 3

The integration of multi-omics data, facilitated by artificial intelligence (AI), provides a more comprehensive understanding of AD pathology. By analyzing this data, models can identify dysregulated pathways and various biomarkers, improving patients’ lives through early diagnosis, risk assessments, and targeted therapeutic interventions.