Background: Early, accurate diagnosis of mild traumatic brain injury (mTBI) can improve clinical outcomes for patients, but mTBI remains difficult to This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Objective The goals of this study were to assess the ability of salivary non-coding RNA (ncRNA) levels to predict post-concussion symptoms lasting ≥ 21 days, and to examine the ability of ncRNAs to identify recovery compared to cognition and balance. Methods RNA sequencing was performed on 505 saliva samples obtained longitudinally from 112 individuals (8–24-years-old) with mild traumatic brain injury (mTBI). Initial samples were obtained ≤ 14 days post-injury, and follow-up samples were obtained ≥ 21 days post-injury. Computerized balance and cognitive test performance were assessed at initial and follow-up time-points. Machine learning was used to define: (1) a model employing initial ncRNA levels to predict persistent post-concussion symptoms (PPCS) ≥ 21 days post-injury; and (2) a model employing follow-up ncRNA levels to identify symptom recovery. Performance of the models was compared against a validated clinical prediction rule, and balance/cognitive test performance, respectively. Results An algorithm using age and 16 ncRNAs predicted PPCS with greater accuracy than the validated clinical tool and demonstrated additive combined utility (area under the curve (AUC) 0.86; 95% CI 0.84–0.88). Initial balance and cognitive test performance did not differ between PPCS and non-PPCS groups (p > 0.05). Follow-up balance and cognitive test performance identified symptom recovery with similar accuracy to a model using 11 ncRNAs and age. A combined model (ncRNAs, balance, cognition) most accurately identified recovery (AUC 0.86; 95% CI 0.83–0.89). Conclusions ncRNA biomarkers show promise for tracking recovery from mTBI, and for predicting who will have prolonged symptoms. They could provide accurate expectations for recovery, stratify need for intervention, and guide safe return-to-activities.

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Concussion is a heterogeneous injury that relies predominantly on subjective symptom reports for patient assessment and treatment. Developing an objective, biological test could aid phenotypic categorization of concussion patients, leading to advances in personalized treatment. This prospective multi-center study employed saliva micro-ribonucleic acid (miRNA) levels to stratify 251 individuals with concussion into biological subgroups. Using miRNA biological clusters, our objective was to assess for differences in medical/demographic characteristics, symptoms, and functional measures of balance and cognition. The miRNAs that best defined each cluster were used to identify physiological pathways that characterized each cluster. The 251 participants (mean age: 18 ± 7 years; 57% male) were optimally grouped into 10 clusters based on 22 miRNA levels. The clusters differed in age (χ 2 = 19.1, p = 0.024), days post-injury at the time of saliva collection (χ 2 = 22.6; p = 0.007), and number of prior concussions (χ 2 = 17.6, p = 0.040). The clusters also differed in symptom reports for fatigue (χ 2 = 17.7; p = 0.039), confusion (χ 2 = 22.3; p = 0.008), difficulty remembering (χ 2 = 22.0; p = 0.009), and trouble falling asleep (χ 2 = 17.2; p = 0.046), but not objective balance or cognitive performance ( p > 0.05). The miRNAs that defined concussion clusters regulate 16 physiological pathways, including adrenergic signaling, estrogen signaling, fatty acid metabolism, GABAergic signaling, synaptic vesicle cycling, and transforming growth factor (TGF)-β signaling. These results show that saliva miRNA levels may stratify individuals with concussion based on underlying biological perturbations that are relevant to both symptomology and pharmacological targets. If validated in a larger cohort, miRNA assessment could aid individualized, biology-driven concussion treatment.