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dc.contributor.authorShigemizu, Daichi
dc.contributor.authorAkiyama, Shintaro
dc.contributor.authorAsanomi, Yuya
dc.contributor.authorBoroevich, Keith A
dc.contributor.authorSharma, Alok
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorMatsukuma, Kana
dc.contributor.authorIchikawa, Makiko
dc.contributor.authorSudo, Hiroko
dc.contributor.authorTakizawa, Satoko
dc.contributor.authorSakurai, Takashi
dc.contributor.authorOzaki, Kouichi
dc.contributor.authorOchiya, Takahiro
dc.contributor.authorNiida, Shumpei
dc.date.accessioned2019-09-27T03:46:04Z
dc.date.available2019-09-27T03:46:04Z
dc.date.issued2019
dc.identifier.issn2399-3642en_US
dc.identifier.doi10.1038/s42003-019-0324-7en_US
dc.identifier.urihttp://hdl.handle.net/10072/387851
dc.description.abstractAlzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherNature Publishing Groupen_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalCommunications Biologyen_US
dc.relation.ispartofvolume2en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsLife Sciences & Biomedicineen_US
dc.subject.keywordsBiologyen_US
dc.subject.keywordsMultidisciplinary Sciencesen_US
dc.subject.keywordsLife Sciences & Biomedicine - Other Topicsen_US
dc.titleRisk prediction models for dementia constructed by supervised principal component analysis using miRNA expression dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationShigemizu, D; Akiyama, S; Asanomi, Y; Boroevich, KA; Sharma, A; Tsunoda, T; Matsukuma, K; Ichikawa, M; Sudo, H; Takizawa, S; Sakurai, T; Ozaki, K; Ochiya, T; Niida, S, Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data, Communications Biology, 2019, 2 (1)en_US
dcterms.dateAccepted2019-01-24
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2019-09-27T03:40:36Z
dc.description.versionPublisheden_US
gro.rights.copyright© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
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gro.griffith.authorSharma, Alok


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