Multi-omics approaches to understand depression associated Alzheimer’s disease
Vivek Kumar Tiwari, Merugu Samyuktha, Shashikala Metri, Alekhya Ayitha
Abstract
Depression and Alzheimer’s disease (AD) are commonly comorbid in the elderly, and an increasing body of evidence indicates that depression in late-life could be a prodromal manifestation and a risk factor on its own in AD. There is some similarity in the pathogenesis between the two disorders, with neuroinflammatory and hypothalamic-pituitary-adrenal axis dysregulation, synaptic damage, mitochondrial damage, and metabolic disruptions. This review will provide a summary of recent developments in the field of multi-omics in order to understand the molecular pathways underlying depression-related AD, as well as to outline their pharmacological consequences. The search and selection have taken place through standard articles published from 2020 to 2025 on the topic of genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics in AD and major depressive disorders. Immune regulation and lipid metabolic pathways are among the common susceptibility loci identified by genomic data. Epigenomic studies reveal that DNA methylation and histone modifications of neurotrophic and inflammatory signals are caused by stress. Convergent attenuation of microglial activity and synaptic dysregulation of genes are found in transcriptomic and single-cell sequencing data. Proteomic and metabolomic studies point to the involvement of complement cascade activation, kynurenine pathway imbalances, oxidative stress, and phospholipid metabolism changes. Multi-omics integration will give a systems-level view of the biological continuum of depression and AD. These methods have a great potential to provide detection of the disease at an early stage, risk identification, and establishment of specific drug-based therapy of depression-related neurodegeneration.
Keywords
References
- Sherif FM, Ahmed SS. Basic aspects of GABA-transaminase in neuropsychiatric disorders. Clinical Biochemistry. 1995; 28(2): 145-154. doi: 10.1016/0009-9120(94)00074-6
- Sherif F, Gottfries CG, Alafuzoff I, Oreland L. Brain gamma-aminobutyrate aminotransferase (GABA-T) and monoamine oxidase (MAO) in patients with Alzheimer's disease. Journal of Neural Transmission. 1992; 4: 227-240. doi: 10.1007/BF02260906
- Adewuyi EO, Auta A, Ossai CI, Anyaegbu CC, Nguyen TTH, Rahman MR, et al. Genome-wide and locus-level analyses reveal modest, heterogeneous genetic sharing between Alzheimer’s disease and myasthenia gravis. International Journal of Molecular Sciences. 2026; 27(11): 4792. doi: 10.3390/ijms27114792
- Braheim ER, AbuAlasad FY, Alshaib WM. Knowledge of metformin-induced vitamin B-12 deficiency and its association with Alzheimer’s disease among medical graduates. Mediterranean Journal of Pharmacy and Pharmaceutical Sciences. 2026; 6(1): 11-20. doi: 10.5281/zenodo.18176820
- Shade LM, Katsumata Y, Abner EL, Aung KZ, Claas SA, Qiao Q, et al. GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia. Nature Genetics. 2024; 56(11): 2407-2421. doi: 10.1038/s41588-024-02046-5
- Saeed NM, Elrayani AS, Sherif RF, Sherif FM. Postpartum depression and associated risk factors in Libya. Mediterranean Journal of Pharmacy and Pharmaceutical Sciences. 2022; 2(2): 77-87. doi: 10.5281/zenodo. 6780513
- Rahimzadeh N, Srinivasan SS, Zhang J, Swarup V. Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches. Alzheimer's and Dementia. 2024; 20(5): 3587-3605. doi: 10.1016/j.arr. 2021.101346
- Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience. 2019; 22(3): 343-352. doi: 10.1038/s41593-018-0326-7
- Müller J, Laroche VT, Imm J, Weymouth L, Harvey J, Reijnders RA, et al. A cell type enrichment analysis tool for brain DNA methylation data (CEAM). Epigenetics. 2026; 21(1): 2604360. doi: 10.1080/15592294.2025. 2604360
- Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019; 570(7761): 332-337. doi: 10.1038/s41586-019-1329-6
- Sforzini L, Cattaneo A, Ferrari C, Turner L, Mariani N, Enache D, et al. Higher immune-related gene expression in major depression is independent of CRP levels: Results from the BIODEP study. Translational Psychiatry. 2023; 13(1): 185. doi: 10.1038/s41398-023-02438-x
- Huang YN, Liu S, Park T, Chaudhuri S, Kuchenbecker LA, Carrasquillo MM, et al. Plasma proteomic analysis identifies proteins and pathways related to Alzheimer's risk. Alzheimer's and Dementia. 2025; 21(8): e70579. doi: 10.1002/alz.70579
- Qiu Y, Hou Y, Wetzel L, Caldwell JZ, Zhu X, Pieper AA, Liu T, Cheng F. Systematic characterization of cell type-specific master metabolic regulators in Alzheimer’s disease. Research Square. 2025; rs.3.rs-7207381. doi: 10.21203/rs.3.rs-7207381/v1
- Liu Y, Yieh L, Yang T, Drinkenburg W, Peeters P, Steckler T, et al. Metabolomic biosignature differentiates melancholic depressive patients from healthy controls. BMC Genomics. 2016; 17(1): 669. doi: 10.21203/rs.3.rs-7207381/v1
- Li Z, Lai J, Zhang P, Ding J, Jiang J, Liu C, et al. Multi-omics analyses of serum metabolome, gut microbiome, and brain function reveal dysregulated microbiota-gut-brain axis in bipolar depression. Molecular Psychiatry. 2022; 27(10): 4123-4135. doi: 10.1038/s41380-022-01569-9
- Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, et al. Gut microbiome alterations in Alzheimer’s disease. Scientific Reports. 2017; 7(1): 13537. doi: 10.1038/s41598-017-13601-y
- Gouveia C, Gibbons E, Dehghani N, Eapen J, Guerreiro R, Bras J. Genome-wide association of polygenic risk extremes for Alzheimer's disease in the UK Biobank. Scientific Reports. 2022; 12(1): 8404. doi: 10.1038/s 41598-022-12391-2
- Müller J, Laroche VT, Imm J, Weymouth L, Harvey J, Reijnders RA, et al. A cell type enrichment analysis tool for brain DNA methylation data (CEAM). Epigenetics. 2026; 21(1): 2604360. doi: 10.1080/15592294.2025. 2604360
- Gao F, Kellis M, Mathys H, Davila-Velderrain J, Peng Z, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019; 571: 7763. doi: 10.1038/s41586-019-1329-6
- Le Bars S, Glaab E. Single-cell cortical transcriptomics reveals common and distinct changes in Cell-Cell communication in alzheimer’s and parkinson’s disease. Molecular Neurobiology. 2025; 62(3): 2655-2673. doi: 10.1007/s12035-024-04419-7
- Mews MA, Naj AC, Griswold AJ; Alzheimer's Disease Genetics Consortium; Below JE, Bush WS. Brain and blood transcriptome‑wide association studies identify five novel genes associated with Alzheimer’s disease. Journal of Alzheimer's Disease. 2025; 105(1): 228-244. doi: 10.1177/ 13872877251326288
- Johnson EC, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nature Medicine. 2020; 26(5): 769-780. doi: 10.1038/s41591-020-0815-6
- Torrealba-Acosta G, Yang S, Calvo-Marín J, Moghekar AR, Kamalian A, Lutz M, Alzheimer’s disease neuroimaging initiative. Identifying a proteomics signature of cognitive impairment and dementia in blood and cerebrospinal fluid through a mediation analysis framework. Neurobiology of aging. 2025; 157: 79-88. doi: 10.1016/j.neurobiolaging.2025.10.004
- Tiwari P, Gupta A, Kaushik M, Yadav A, Anjali A, Dwivedi R, et al. Comprehensive metabolomics profiling reveals novel biomarkers and pathways for early detection of Alzheimer’s disease. Brain Communications. 2025; 7(6): fcaf410. doi: 10.1093/braincomms/fcaf410
- He S, Xu Z, Han X. Lipidome disruption in Alzheimer’s disease brain: detection, pathological mechanisms, and therapeutic implications. Molecular Neurodegeneration. 2025; 20(1): 11. doi: 10.1186/s13024-025-00803-6
- Cao Y, Zhao LW, Chen ZX, Li SH. New insights into lipid metabolism: Potential therapeutic targets for the treatment of Alzheimer’s disease. Frontiers in Neuroscience. 2024; 18: 1430465. doi: 10.3389/fnins.2024. 1430465
- Ji X, Wang J, Lan T, Zhao D, Xu P. Gut microbial metabolites and the brain–gut axis in Alzheimer’s disease: A review. Biomolecules and Biomedicine. 2025; 26(2): 240. doi: 10.17305/bb.2025.12921
- Oso TA, Ahmed MM, Okesanya OJ, Adebayo UO, Obadeyi KB, Othman ZK, et al. Exploring the gut-brain-microbiome axis in Alzheimer's disease: Integrating metagenomics, metabolomics, and artificial intelligence for next-generation biomarker discovery. Journal of Alzheimer’s Disease. 2025: 109(4):1542-1557. doi: 10.1177/13872877251407700
- Böckels L, Alexa D, Antal DC, Gațcan C, Alecu C, Kacani K, et al. The microbiome–neurodegeneration interface: Mechanisms, evidence, and future directions. Cells. 2026; 15(2): 135. doi: 10.3390/cells15020135
Submitted date:
04/03/2026
Reviewed date:
06/04/2026
Accepted date:
06/11/2026
Publication date:
06/13/2026
