Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data.
作者： Daichi Shigemizu , Shintaro Akiyama , Yuya Asanomi , Keith A. Boroevich , Alok Sharma
DOI: 10.1038/S42003-019-0324-7
关键词:
摘要: Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot attention as novel biomarkers for dementia. Here, using serum miRNA expression 1,601 Japanese individuals, we investigated potential constructed risk prediction models, based on supervised principal component analysis (PCA) logistic regression method, according to The final model achieved high accuracy 0.873 validation cohort in AD, when 78 miRNAs: Accuracy = 0.836 86 miRNAs VaD; Accuracy = 0.825 110 DLB. To our knowledge, this first report applying miRNA-based models dementia prospective cohort. Our study demonstrates be effective prediction, further improvement may contribute practical clinical use
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