Omics-driven strategies for identifying biomarkers in Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with limited treatment options, mainly due to late diagnosis and partial understanding of its molecular aspects. Traditional biomarker discovery approaches have significantly contributed to AD diagnostics but suffer from limitations. The advent of omics technologies (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) has revolutionized the search for novel biomarkers by enabling comprehensive molecular profiling. Genomic studies have identified risk-associated variants such as APOE4, while epigenomic alterations, including DNA methylation alterations, offer insight into gene regulation in AD. Transcriptomic analyses, particularly single-cell and spatial transcriptomics, have uncovered molecular pathways linked to neuroinflammation and synaptic dysfunction. Proteomic advancements, including mass spectrometry and extracellular vesicle profiling, have identified potential blood- and CSF-based biomarkers for early-stage detection. Metabolomic and lipidomic studies indicate that cerebral glucose hypometabolism, insulin resistance, mitochondrial damage, redox imbalance, and disrupted lipid homeostasis are centra contributors to AD pathogenesis rather than secondary considerations of the disease. These metabolic dysfunctions may precede overt neurodegeneration and influence amyloid processing, tau phosphorylation, neuroinflammatory activation, and synaptic loss, thereby generating clinically informative biomarker signatures in blood and cerebrospinal fluid. Within this metabolism-centered paradigm, integrative multi-omics approaches are particularly valuable because they not only enhance biomarker specificity, but also connect molecular signatures with bioenergetic and immune-mediated mechanisms of disease. Accordingly, integrative multi-omics approaches improve biomarker specificity and predictive power, thereby supporting the development of precision medicine and targeted therapeutic interventions. Nevertheless, important challenges remain, including data integration, reproducibility, and clinical translation.