Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer’s Disease
Open accessAbstract
Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.
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Blood-based transcriptomic analyses have distinguished immune-dominant and metabolic-dominant subgroups, each associated with unique clinical trajectories and therapeutic responses.
“Blood-based transcriptomic analyses have similarly distinguished immune-dominant and metabolic-dominant subgroups, each associated with unique clinical trajectories and therapeutic responses421.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [421]
The Results (§§2.2–2.3; page unavailable) describe clusters of blood-expressed genes, including a metabolic-process cluster that helped predict neurodegenerative disease. They do not identify immune-dominant and metabolic-dominant patient subgroups or associate such subgroups with distinct clinical trajectories or therapeutic responses. The Discussion (§3; page unavailable) instead says further work is needed to determine whether the genetic changes are driven by immune-cell subtypes. (LLM, conf=0.84)
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- Authors
- Shvetcov, A., Thomson, S., Spathos, J., Cho, A.-N., Wilkins, H. M., Andrews, S. J., Delerue, F., Couttas, T. A., Issar, J. K., Isik, F., Kaur, S., Drummond, E., Dobson-Stone, C., Duffy, S. L., Rogers, N. M., Catchpoole, D., Gold, W. A., Swerdlow, R. H., Brown, D. A., & Finney, C. A.
- Affiliations
- UNSW Sydney (AU)
- The University of Sydney (AU)
- Westmead Institute for Medical Research (AU)
- Macquarie University (AU)
- University of Kansas (US)
- University of California, San Francisco (US)
- The University of Texas MD Anderson Cancer Center (US)
- Royal Children's Hospital (AU)
- School of Medical Sciences, Faculty of Medicine Health, The University of Sydney, Sydney, NSW 2050, Australia
- Allied Health, Research and Strategic Partnerships, Nepean Blue Mountains Local Health District, Penrith, NSW 2750, Australia
- Children's Hospital at Westmead (AU)
- Corresponding author
- Finney, Caitlin A.
- ORCID
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2023-10-09
- Volume
- 24
- Issue
- 19
- Pages
- 15011
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC10573468
- OpenAlex
- W4387460002
- Cited by
- 12
- References
- 63
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 2
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Funders
- National Institute on Aging (P01AG060882)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, blood-based biomarkers, whole blood transcriptomics, neurodegeneration, Parkinson's disease, amyotrophic lateral sclerosis, differential diagnosis, spliceosome, RNA binding, mitochondrial ribosome, NADH dehydrogenase, functional network analysis, early detection
- MeSH terms
- Alzheimer Disease, Amyotrophic Lateral Sclerosis, Humans, Parkinson Disease, Biomarkers, Neurodegenerative Diseases, Transcriptome
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