Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer's disease

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dc.authorid0000-0001-5834-4533
dc.authorid0000-0002-4254-6090
dc.authorid0000-0001-9986-9205
dc.authorid0000-0002-3721-8586
dc.contributor.authorMeng, Lingqi
dc.contributor.authorJin, Han
dc.contributor.authorYulug, Burak
dc.contributor.authorAltay, Ozlem
dc.contributor.authorLi, Xiangyu
dc.contributor.authorHanoglu, Lutfu
dc.contributor.authorCankaya, Seyda
dc.date.accessioned2026-01-24T12:29:10Z
dc.date.available2026-01-24T12:29:10Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractAlzheimer'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.sponsorshipRoyal Institute of Technology [NAISS 2023/5-247]; NAISS through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)
dc.description.sponsorshipThe computations were performed on resources provided by NAISS through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project NAISS 2023/5-247.
dc.identifier.doi10.1186/s13195-024-01578-6
dc.identifier.issn1758-9193
dc.identifier.issue1
dc.identifier.pmid39358810
dc.identifier.scopus2-s2.0-85205527457
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1186/s13195-024-01578-6
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5174
dc.identifier.volume16
dc.identifier.wosWOS:001327038600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofAlzheimers Research & Therapy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectAlpha-Iib-Beta-3 Activation
dc.subjectAutophagy
dc.subjectGlutamate
dc.subjectReceptors
dc.subjectDementia
dc.subjectMarker
dc.subjectBrain
dc.subjectAcid
dc.subjectAdap
dc.titleMulti-omics analysis reveals the key factors involved in the severity of the Alzheimer's disease
dc.typeArticle

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