In the search for objective tools to identify children at risk of prolonged concussion symptoms (PCS), Pennsylvania State University researchers found that levels of five microRNAs (miRNAs)—miR-320c-1, miR-133a-5p, miR-769-5p, let-7a-3p, and miR-1307-3p—accurately identified patients with PCS on logistic regression, with area under the receiver operating characteristics curve (AUROC) of 0.856 (JAMA Pediatrics 2017; doi:10.1001/jamapediatrics.2017.3884).
This AUROC compared with the accuracy of symptom reporting by affected children and by their parents with AUROCs of 0.649 and 0.562, respectively. Though the findings need to be validated in an independent, larger cohort, they suggest that salivary miRNAs might be used as part of a toolset to improve concussion management, according to the investigators.
The study involved 52 patients with a mean age of 14 who presented for evaluation of a concussion within 14 days of their initial head injury and who underwent follow-up evaluations at 4 and 8 weeks. The researchers took nonfasting saliva samples from the participants and found 437 microRNAs in at least 22 samples with a mean read count of 2.1 x 105 reads per sample. They used a two-dimensional partial least squares discriminant analysis to identify 15 miRNAs of interest in discerning acute concussion symptoms from PCS. The investigators also explored the functional targets of these miRNAs and found them involved in signaling cascades related to synaptic development, neuronal migration, and repair, and in gene ontology pathways related to neurotrophin tyrosine receptor kinase signaling, axon guidance, and nervous system development. They deployed multivariate regression analysis to evaluate these 15 miRNAs for PCS classification accuracy. The combination of the aforementioned five miRNAs showed the highest classification accuracy.