Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Daichi Shigemizu, Shintaro Akiyama, Yuya Asanomi, Keith A. Boroevich, Alok Sharma, Tatsuhiko Tsunoda, Kana Matsukuma, Makiko Ichikawa, Hiroko Sudo, Satoko Takizawa, Takashi Sakurai, Kouichi Ozaki, Takahiro Ochiya, Shumpei Niida

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: 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. More... »

PAGES

77

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s42003-019-0324-7

DOI

http://dx.doi.org/10.1038/s42003-019-0324-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112363477

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30820472


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