Laura Ibanez, United States of America
Washington University School of Medicine PsychiatryPresenter of 2 Presentations
PREDICTION OF ALZHEIMER DISEASE USING PLASMA RNA SEQUENCES
Abstract
Aims
The aim of this study was to generate predictive models for AD using plasma cell-free RNA species at different stages of the disease.
Methods
We generated cfRNA-Sequence data from AD cases at CDR=1 (N=44) and controls (N=45) and applied standard quality control. Gene expression was quantified with Salmon and corrected by library complexity and log transformed prior to analysis. Genes known to be involved in AD and other neurodegenerative diseases (N=25) were used to create a predictive model using step-wise discriminant analysis in the CDR=1. APOE genotype was included in the model afterwards. The predictive power was tested in early (N=27) and pre-symptomatic (N=21) stages of the disease.
Results
Out of the 25 genes, eight were included in the predictive model after step-wise discriminant analysis. After inclusion of APOE genotype, the area under the ROC curve was 0.96, 0.99 and 0.82 for CDR=1, CDR=0.5 and pre-symptomatic stages respectively (Figure1).
Conclusions
Cell-free RNA is a promising minimally invasive biomarker for AD with an accuracy comparable to the one obtained using CSF biomarkers. This approach can provide a new screening tool for AD that can be used at population level and to evaluate disease-modifying therapies that target amyloid beta and tau.
LIVE DISCUSSION
