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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 studi...

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Bibliographic Details
Published in:Neuropathology and applied neurobiology 2023-08, Vol.49 (4), p.e12916-n/a
Main Authors: Magen, Iddo, Yacovzada, Nancy‐Sarah, Warren, Jason D., Heller, Carolin, Swift, Imogen, Bobeva, Yoana, Malaspina, Andrea, Rohrer, Jonathan D., Fratta, Pietro, Hornstein, Eran
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Language:English
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Summary: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. We identified a unique plasma miRNA signature that can differentiate frontotemporal dementia (FTD) patients from healthy controls. A panel of 13 miRNAs was able to predict FTD with an accuracy of ∼90%, taking into account non‐linear relationships between single miRNAs. This work highlights the importance of integrating machine learning into clinical biomarker studies, addressing non‐linearity and exposing cryptic disease‐associated signals.
ISSN:0305-1846
1365-2990
DOI:10.1111/nan.12916