Background: To develop therapeutics for Alzheimer's disease, early detection of patients awakes new hope. Circulating small non-coding RNAs are among the prominent candidates for a blood-based diagnosis, requiring however growing cohort sizes. Methods: We determined abundance levels of 21 known circulating microRNAs in 465 individuals encompassing Alzheimer's patients and controls recruited in US and Germany. We computed models to assess the relation between microRNA-expression and phenotypes, gender, age and disease severity (Mini-Mental State Examination MMSE). Results: 20 of 21 miRNAs were consistently dys-regulated in the US and Germany. 18 were significantly correlated to neurodegeneration (adjusted p<0.05) with highest significance for miR-532-5p (adjusted p=4.8x10 -30 ). Ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted p=0.0002). Machine learning models reached an AUC value of 87.6% in differentiating AD patients from controls. Conclusions: Our data provide strong evidence for the relevance of circulating non-coding RNAs to detect Alzheimer's from a blood sample.

Machine learning to detect Alzheimer's disease from circulating non-coding RNAs