Multi-omics analysis reveals the key factors involved in the severity of the alzheimer's disease
| dc.contributor.author | Özşimşek, Ahmet | |
| dc.contributor.author | Shoaie, Saeed | |
| dc.contributor.author | Türkez, Hasan | |
| dc.contributor.author | Nielsen, Jens | |
| dc.contributor.author | Hanoğlu, Lütfü | |
| dc.contributor.author | Coşkun, Ebru | |
| dc.contributor.author | Mardinoğlu, Adil | |
| dc.date.accessioned | 2025-08-27T12:12:26Z | |
| dc.date.available | 2025-08-27T12:12:26Z | |
| dc.date.issued | 2024 | |
| dc.department | İstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Nöroloji Ana Bilim Dalı | |
| dc.description.abstract | 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. | |
| dc.description.sponsorship | NAISS through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) | |
| dc.identifier.citation | Özşimşek, A., Shoaie, S., Türkez, H., Nielsen, J., Hanoğlu, L., Coşkun, E. ... Mardinoğlu, A. (2024). Multi-omics analysis reveals the key factors involved in the severity of the alzheimer's disease. Alzheimer's Research and Therapy, 16(1). http://dx.doi.org/10.1186/s13195-024-01578-6 | |
| dc.identifier.doi | 10.1186/s13195-024-01578-6 | |
| dc.identifier.issn | 1758-9193 | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | http://dx.doi.org/10.1186/s13195-024-01578-6 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/13033 | |
| dc.identifier.volume | 16 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Hanoğlu, Lütfü | |
| dc.institutionauthor | Coşkun, Ebru | |
| dc.institutionauthorid | 0000-0003-4292-5717 | |
| dc.institutionauthorid | 0000-0003-1028-6703 | |
| dc.language.iso | en | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/NAISS2023/5-247 | |
| dc.relation.ispartof | Alzheimer's Research and Therapy | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Alzheimer Disease | |
| dc.subject | Metabolomics | |
| dc.subject | Artificial Intelligence | |
| dc.title | Multi-omics analysis reveals the key factors involved in the severity of the alzheimer's disease | |
| dc.type | Article |
