ArticleScientific reports2021
Correlation between Alzheimer's disease and type 2 diabetes using non-negative matrix factorization.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 31 citations in OpenAlex.
- New Genetic Associations Between Alzheimer's Disease and Its Key Risk Factors.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2026Article
- The Variant T Allele ofBiomolecules · 2026Article
- Antioxidant, Anti-Cholinesterase, and Neuroprotective Properties ofAntioxidants (Basel, Switzerland) · 2026Article
- Genomics link obesity and type 2 diabetes to Alzheimer's disease to unveil novel biological insights.medRxiv : the preprint server for health sciences · 2026Article
- Glycation in Alzheimer's Disease and Type 2 Diabetes: The Prospect of Dual Drug Approaches for Therapeutic Interventions.Molecular neurobiology · 2025Review
- Metabolites associated with type 2 diabetes and Alzheimer's disease trigger differential intracellular signaling responses in mouse primary neurons.Brain research · 2025Article
- The expression of insulin signaling and N-methyl-D-aspartate receptor genes in areas of gray matter atrophy is associated with cognitive function in type 2 diabetes.Journal of Alzheimer's disease : JAD · 2025Article
- Antidiabetic GLP-1 Receptor Agonists Have Neuroprotective Properties in Experimental Animal Models of Alzheimer's Disease.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Alzheimer's Disease as Type 3 Diabetes: Understanding the Link and Implications.International journal of molecular sciences · 2024Review
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- Targeting redox imbalance in neurodegeneration: characterizing the role of GLP-1 receptor agonists.Theranostics · 2023Review
- Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease.Heliyon · 2023Article
- Bioinformatic prediction of the molecular links between Alzheimer's disease and diabetes mellitus.PeerJ · 2023Article
- Effects of copy number variations on longevity in late-onset Alzheimer's disease patients: insights from a causality network analysis.Frontiers in aging neuroscience · 2023Article
- Dysfunctional Glucose Metabolism in Alzheimer's Disease Onset and Potential Pharmacological Interventions.International journal of molecular sciences · 2022Review
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them.Frontiers in aging neuroscience · 2022Article
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Authors and funding
3 authors at 20 institutions in 5 countries.
Funding
Abstract
Alzheimer's disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal-Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.
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