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

Commun Biol. 2019 Feb 25:2:77. doi: 10.1038/s42003-019-0324-7. eCollection 2019.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Alzheimer Disease / diagnosis
  • Alzheimer Disease / genetics
  • Dementia / classification
  • Dementia / diagnosis
  • Dementia / genetics*
  • Dementia, Vascular / diagnosis
  • Dementia, Vascular / genetics
  • Diagnosis, Differential
  • Female
  • Gene Expression Profiling*
  • Gene Regulatory Networks
  • Humans
  • Lewy Body Disease / diagnosis
  • Lewy Body Disease / genetics
  • Male
  • MicroRNAs / blood
  • MicroRNAs / genetics*
  • Middle Aged
  • Principal Component Analysis*
  • Prospective Studies
  • ROC Curve
  • Reproducibility of Results
  • Risk Factors

Substances

  • MicroRNAs