microRNA-based predictor for diagnosis of frontotemporal dementia

Aims: This study aimed to explore the non-linear relationships between cell-free microRNAs (miRNAs) and their contribution to prediction of Frontotemporal dementia (FTD), an early onset dementia that is clinically heterogeneous, and too often suffers from delayed diagnosis. Methods: We initially studied a training cohort of 219 subjects (135 FTD and 84 non-neurodegenerative controls) and then validated the results in a cohort of 74 subjects (33 FTD and 41 controls). Results: On the basis of cell-free plasma miRNA profiling by next generation sequencing and machine learning approaches, we develop a non-linear prediction model that accurately distinguishes FTD from non-neurodegenerative controls in ~90% of cases. Conclusions: The fascinating potential of diagnostic miRNA biomarkers might enable early-stage detection and a cost-effective screening approach for clinical trials that can facilitate drug development.