Prediction of Alzheimer disease using plasma RNA sequences from dementia genes. (31st December 2021)

• Record Type: Journal Article
• Authors: Ibanez, Laura; Bergmann, Kristy; Eteleeb, Abdallah; Wang, Fengxian; Norton, Joanne; Gentsch, Jen; Harari, Oscar; Cruchaga, Carlos
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.049885
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Alzheimer's disease (AD) is inexorable, incurable, and in many cases very difficult to diagnose before symptom onset. Thus, there is a clear need to develop tools to detect the pathological changes that occur before clinical symptoms onset. Historically, proteins have been the preferred biomarker. However, biofluids also contains ribonucleic acid in its free form (cfRNA). The source of cfRNA in plasma is not fully understood, but it is thought to be the result of normal cell death throughout the body, thus providing a mirror of the status of a normal functioning body. Therefore, if some organs are functioning incorrectly, we can detect it by changes in cfRNA. To date, several species of cfRNA have been investigated as biomarkers for cancer, fetal development and AD. The aim of this study was to generate predictive models for AD using plasma cell‐free RNA species at different stages of the disease. Method: 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. Result: 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 (Figure 1). Conclusion: 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.