An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer’s disease
Open accessAbstract
Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Successful integration requires a multistep preprocessing pipeline that includes batch effect correction, normalization, feature selection, and sample alignment.
“Successful integration requires a multistep preprocessing pipeline that includes batch effect correction, normalization, feature selection, and sample alignment.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [225]
Cites
miRDB: an online database for prediction of functional microRNA targets
Chen, Y., & Wang, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkz757 · Cited by 3,277 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Alzheimer disease in the United States (2010–2050) estimated using the 2010 census
Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013) · Neurology · doi:10.1212/wnl.0b013e31828726f5 · Cited by 2,642 · cited 2× · see citing works
“There is a great need for the development of such therapies given that the incidence of this disease continues to increase (Hebert et al., 2013) and that a diagnosis of AD is concomitant with substantial economic (Hurd et al., 2013) and social (Coon and Edgerly, 1999) costs.”
BackgroundOxidative Stress, Synaptic Dysfunction, and Alzheimer’s Disease
Tönnies, E., & Trushina, E. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161088 · Cited by 1,750 · see citing works
Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline
Bussian, T. J., Aziz, A., Meyer, C. F., Swenson, B. L., van Deursen, J. M., & Baker, D. J. (2018) · Nature · doi:10.1038/s41586-018-0543-y · Cited by 1,289 · see citing works
Zhang, P., Kishimoto, Y., Grammatikakis, I., Gottimukkala, K., Cutler, R. G., Zhang, S., Abdelmohsen, K., Bohr, V. A., Misra Sen, J., Gorospe, M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0372-9 · Cited by 977 · see citing works
Astrocyte Senescence as a Component of Alzheimer’s Disease
Bhat, R., Crowe, E. P., Bitto, A., Moh, M., Katsetos, C. D., Garcia, F. U., Johnson, F. B., Trojanowski, J. Q., Sell, C., & Torres, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045069 · Cited by 586 · see citing works
CD22 blockade restores homeostatic microglial phagocytosis in ageing brains
Pluvinage, J. V., Haney, M. S., Smith, B. A. H., Sun, J., Iram, T., Bonanno, L., Li, L., Lee, D. P., Morgens, D. W., Yang, A. C., et al. (2019) · Nature · doi:10.1038/s41586-019-1088-4 · Cited by 459 · cited 2× · see citing works
“Indeed, creating a deficiency in the cell death-inducer Ppid (Du et al., 2008), clearing senescent oligodendrocyte precursor cells (Zhang et al., 2019), overexpressing the metabolic gene Cyp46a1 (Hudry et al., 2010), pharmacologically inhibiting the histone deacetylase Hdac3 (Janczura et al., 2018), enhancing microgli…”
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
The age factor in Alzheimer’s disease
Guerreiro, R., & Bras, J. (2015) · Genome Medicine · doi:10.1186/s13073-015-0232-5 · Cited by 415 · see citing works
Ogrodnik, M., Evans, S. A., Fielder, E., Victorelli, S., Kruger, P., Salmonowicz, H., Weigand, B. M., Patel, A. D., Pirtskhalava, T., Inman, C. L., et al. (2021) · Aging Cell · doi:10.1111/acel.13296 · Cited by 405 · see citing works
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380 · see citing works
Cellular senescence and Alzheimer disease: the egg and the chicken scenario
Saez-Atienzar, S., & Masliah, E. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0325-z · Cited by 257 · see citing works
Targeting the Synapse in Alzheimer’s Disease
Jackson, J., Jambrina, E., Li, J., Marston, H., Menzies, F., Phillips, K., & Gilmour, G. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00735 · Cited by 215 · see citing works
Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases
Narayanan, M., Huynh, J. L., Wang, K., Yang, X., Yoo, S., McElwee, J., Zhang, B., Zhang, C., Lamb, J. R., Xie, T., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145304 · Cited by 208 · see citing works
Self-citationJohnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166 · see citing works
Self-citationOskar Fischer and the study of dementia
Goedert, M. (2008) · Brain · doi:10.1093/brain/awn256 · Cited by 144 · see citing works
Genome instability in Alzheimer disease
Hou, Y., Song, H., Croteau, D. L., Akbari, M., & Bohr, V. A. (2017) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2016.04.005 · Cited by 120 · see citing works
The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Axonal energy metabolism, and the effects in aging and neurodegenerative diseases
Yang, S., Park, J. H., & Lu, H.-C. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00634-3 · Cited by 94 · see citing works
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
- Authors
- Shokhirev, M. N., & Johnson, A. A.
- Affiliations
- Salk Institute for Biological Studies (US)
- Corresponding author
- Shokhirev, Maxim N.
- Johnson, Adiv A.
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 81
- Pages
- 101721
- ISSN
- 1568-1637 (ISSN-L)
- OpenAlex
- W4293045328
- Cited by
- 42
- References
- 19
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 13 · 2023: 8 · 2022: 2
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ageing Research Reviews (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1RF1AG064049-01, P30AG068635)
- National Institute on Aging
- National Cancer Institute (P30 CA014195-46)
- National Institute of General Medical Sciences (GM102491-07)
- Leona M and Harry B Helmsley Charitable Trust
- Government of Inner Mongolia Autonomous Region
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Alzheimer's disease, multi-omics integration, ensemble learning, Alzheimer's disease diagnosis, microRNA biomarkers, cellular senescence, cell death, oxidative stress, proteostasis, synaptic function, glia, immune system, metal homeostasis, energy metabolism, genome stability, blood-based biomarkers, brain tissue
- MeSH terms
- Machine Learning, Age Factors, Aged, Aged, 80 and over, Alzheimer Disease, Brain, Humans, MicroRNAs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
