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  • Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data

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    Author(s)
    Shigemizu, Daichi
    Akiyama, Shintaro
    Asanomi, Yuya
    Boroevich, Keith A
    Sharma, Alok
    Tsunoda, Tatsuhiko
    Matsukuma, Kana
    Ichikawa, Makiko
    Sudo, Hiroko
    Takizawa, Satoko
    Sakurai, Takashi
    Ozaki, Kouichi
    Ochiya, Takahiro
    Niida, Shumpei
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2019
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    Abstract
    Alzheimer’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: ...
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    Alzheimer’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.
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    Journal Title
    Communications Biology
    Volume
    2
    Issue
    1
    DOI
    https://doi.org/10.1038/s42003-019-0324-7
    Copyright Statement
    © 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.
    Subject
    Biological sciences
    Science & Technology
    Life Sciences & Biomedicine
    Biology
    Multidisciplinary Sciences
    Life Sciences & Biomedicine - Other Topics
    Publication URI
    http://hdl.handle.net/10072/387851
    Collection
    • Journal articles

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