Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization

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...

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Autores principales:	Yeonwoo Chung, Hyunju Lee, the Alzheimer’s Disease Neuroimaging Initiative
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7fcee6a29474ee0a79c7802b598e366
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spelling	oai:doaj.org-article:b7fcee6a29474ee0a79c7802b598e3662021-12-02T16:24:22ZCorrelation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization10.1038/s41598-021-94048-02045-2322https://doaj.org/article/b7fcee6a29474ee0a79c7802b598e3662021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94048-0https://doaj.org/toc/2045-2322Abstract 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.Yeonwoo ChungHyunju Leethe Alzheimer’s Disease Neuroimaging InitiativeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yeonwoo Chung Hyunju Lee the Alzheimer’s Disease Neuroimaging Initiative Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
description	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.
format	article
author	Yeonwoo Chung Hyunju Lee the Alzheimer’s Disease Neuroimaging Initiative
author_facet	Yeonwoo Chung Hyunju Lee the Alzheimer’s Disease Neuroimaging Initiative
author_sort	Yeonwoo Chung
title	Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
title_short	Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
title_full	Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
title_fullStr	Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
title_full_unstemmed	Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
title_sort	correlation between alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b7fcee6a29474ee0a79c7802b598e366
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Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization

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