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Cited by 82Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchBioinformatics and Genomic Networks

Integrated multi-omics analysis of Alzheimer’s disease shows molecular signatures associated with disease progression and potential therapeutic targets

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Kodam, Pradeep, Sai Swaroop, R., Pradhan, Sai Sanwid, Sivaramakrishnan, Venketesh, Vadrevu, Ramakrishna·2023·Scientific Reports
DOI 10.1038/s41598-023-30892-6PMC PMC9986671
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Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid plaques implicated in neuronal death. Genetics, age, and sex are the risk factors attributed to AD. Though omics studies have helped to identify pathways associated with AD, an integrated systems analysis with the available data could help to understand mechanisms, potential biomarkers, and therapeutic targets. Analysis of transcriptomic data sets from the GEO database, and proteomic and metabolomic data sets from literature was performed to identify deregulated pathways and commonality analysis identified overlapping pathways among the data sets. The deregulated pathways included those of neurotransmitter synapses, oxidative stress, inflammation, vitamins, complement, and coagulation pathways. Cell type analysis of GEO data sets showed microglia, endothelial, myeloid, and lymphoid cells are affected. Microglia are associated with inflammation and pruning of synapses with implications for memory and cognition. Analysis of the protein-cofactor network of B 2 , B 6, and pantothenate shows metabolic pathways modulated by these vitamins which overlap with the deregulated pathways from the multi-omics analysis. Overall, the integrated analysis identified the molecular signature associated with AD. Treatment with anti-oxidants, B 2 , B 6 , and pantothenate in genetically susceptible individuals in the pre-symptomatic stage might help in better management of the disease.

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  • Linking genetic variation to downstream protein networks and metabolic processes has led to the identification of novel therapeutic targets.

    “Linking genetic variation to downstream protein networks and metabolic processes has led to the identification of novel therapeutic targets137,160,205.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [137]

    The Introduction (page not provided) describes integrating transcriptomic, proteomic, and metabolomic data to examine protein interactions and metabolic pathways with implications for therapeutic targets. The Methods section on cofactor–protein networks (page not provided) likewise links gene-expression pathways to protein interactions. But the paper does not describe linking genetic variation to those networks, and it presents new therapeutic targets as a possibility rather than an established identification. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI-driven multi-omics analysis has shown substantial promise in identifying novel preclinical biomarkers and constructing risk prediction models that outperform traditional single-omics or clinically based strategies.

    “Recent advances in AI-driven multi-omics analysis have shown substantial promise in identifying novel preclinical biomarkers and constructing risk prediction models that outperform traditional single-omics or clinically based strategies137,367.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [137]

    The Introduction (page unavailable) says integrated multi-omics analysis could help identify markers and support early prognosis. The Results report shared disease-related pathways across omics datasets. The paper does not establish that the analysis was AI-driven, identify preclinical biomarkers, or present risk-prediction models outperforming single-omics or clinical strategies. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Kodam, P., Sai Swaroop, R., Pradhan, S. S., Sivaramakrishnan, V., & Vadrevu, R.
Affiliations
  • Birla Institute of Technology and Science - Hyderabad Campus (IN)
  • Sri Sathya Sai Institute of Higher Learning (IN)
Corresponding author
  • Sivaramakrishnan, Venketesh
  • Vadrevu, Ramakrishna
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2023-03-06
Volume
13
Issue
1
Pages
3695-3695
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-023-30892-6
PMC
PMC9986671
OpenAlex
W4323318599
Cited by
82
References
117
Citations by year
2026: 22 · 2025: 31 · 2024: 23 · 2023: 6
FWCI
14.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University Grants Commission (201920-191620023107)
  • Department of Science and Technology-The Science and Engineering Research Board–Extra Mural Research (EMR/2017/005381)
  • Department of Biotechnology – Bioinformatics facility (BT/BI/25/063/2012)
  • Department of Science and Technology (SR/FST/LSI 616/2014)
  • University Grants Commission-Special Assistance Program (UGC-SAP III: F.3-19 /2018/DRS-III(SAP- II))
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, transcriptomics, proteomics, metabolomics, disease progression, therapeutic target, biomarkers, microglia, oxidative stress, neuroinflammation, riboflavin, vitamin B6, pantothenic acid, complement and coagulation pathways, presymptomatic treatment, antioxidants
MeSH terms
Multiomics, Alzheimer Disease, Humans, Vitamin A, Vitamin K, Vitamins, Disease Progression, Neurodegenerative Diseases, Proteomics

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