Next Article in Journal
Evaluation of the VISAGE Basic Tool for Appearance and Ancestry Prediction Using PowerSeq Chemistry on the MiSeq FGx System
Previous Article in Journal
A Private 16q24.2q24.3 Microduplication in a Boy with Intellectual Disability, Speech Delay and Mild Dysmorphic Features
Previous Article in Special Issue
Cognitive Decline in Alzheimer’s Disease: Limited Clinical Utility for GWAS or Polygenic Risk Scores in a Clinical Trial Setting
Open AccessFeature PaperArticle

Predicting Clinical Dementia Rating Using Blood RNA Levels

Department of Biology, Brigham Young University, Provo, UT 84602, USA
*
Author to whom correspondence should be addressed.
Genes 2020, 11(6), 706; https://doi.org/10.3390/genes11060706
Received: 29 May 2020 / Revised: 11 June 2020 / Accepted: 24 June 2020 / Published: 26 June 2020
(This article belongs to the Special Issue Genetics and Genomics of Alzheimer’s Disease)
The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients and is included in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer’s disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer’s disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning. View Full-Text
Keywords: diagnosis; machine learning; chloride intracellular channel 1 (CLIC1); clinical dementia rating; Alzheimer’s disease diagnosis; machine learning; chloride intracellular channel 1 (CLIC1); clinical dementia rating; Alzheimer’s disease
Show Figures

Figure 1

MDPI and ACS Style

Miller, J.B.; Kauwe, J.S.K. Predicting Clinical Dementia Rating Using Blood RNA Levels. Genes 2020, 11, 706.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop