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

Shigemizu, Daichi and Akiyama, Shintaro and Asanomi, Yuya and Boroevich, Keith and Sharma, Alokanand and Tsunoda, Tatsuhiko and Matsukuma, Kana and Ichikawa, Makiko and Sudo, Hiroko and Takizawa, Satoko and Sakurai, Takashi and Ozaki, Kouichi and Ochiya, Takahiro and Niida, Shumpei (2019) Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data. Communications Biology, 2 (77). pp. 1-8. ISSN NA

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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: 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.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Alokanand Sharma
Date Deposited:	02 Apr 2019 00:07
Last Modified:	02 Apr 2019 00:07
URI:	http://repository.usp.ac.fj/id/eprint/11403

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