Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer’s disease
Open accessAbstract
Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.
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Plasma inflammatory proteomics, untargeted metabolomics, and gut and salivary metagenomics were integrated to classify cognitive severity in Alzheimer's disease patients.
“In one study, plasma inflammatory proteomics, untargeted metabolomics, and both gut and salivary metagenomics were integrated to classify cognitive severity in AD patients.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [234]
A machine learning model identified SKAP1, NEFL, homovanillate, glutamate, and Paraprevotella clara as key predictors of cognitive severity, demonstrating high accuracy in a validation cohort.
“The machine learning model identified SKAP1, NEFL, homovanillate, glutamate, and the gut microbe Paraprevotella clara as key predictors, demonstrating high accuracy in a validation cohort234.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [234]
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- Authors
- Meng, L., Jin, H., Yulug, B., Altay, O., Li, X., Hanoglu, L., Cankaya, S., Coskun, E., Idil, E., Nogaylar, R., Ozsimsek, A., Shoaie, S., Turkez, H., Nielsen, J., Zhang, C., Borén, J., Uhlén, M., & Mardinoglu, A.
- Affiliations
- Science for Life Laboratory (SE)
- Alanya Alaaddin Keykubat Üniversitesi (TR)
- King's College London (GB)
- Chalmers University of Technology (SE)
- Sahlgrenska University Hospital (SE)
- Corresponding author
- Uhlén, Mathias
- Venue
- Alzheimer's Research & Therapy
- Published
- 2024-10-02
- Volume
- 16
- Issue
- 1
- Pages
- 213-213
- ISSN
- 1758-9193 (ISSN-L)
- PMC
- PMC11448018
- OpenAlex
- W4403066962
- Cited by
- 49
- References
- 76
- Citations by year
- 2026: 25 · 2025: 24
- FWCI
- 14.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Alzheimer s Research & Therapy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion, other-oa)
- Ataturk University - AVESIS (submittedVersion)
- Alanya Alaaddin Keykubat University Institutional Repository (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- Royal Institute of Technology
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- Alzheimer's disease, disease severity prediction, dementia severity, plasma proteomics, plasma metabolomics, gut microbiota, host-microbiome interactions, neurofilament light chain, glutamate, multi-omics integration, artificial intelligence prediction models
- MeSH terms
- Gastrointestinal Microbiome, Multiomics, Aged, Aged, 80 and over, Alzheimer Disease, Artificial Intelligence, Female, Humans, Male, Severity of Illness Index, Biomarkers, Proteomics, Metabolomics, Metagenomics
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