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 (χ2 = 19.1, p = 0.024), days post-injury at the time of saliva collection (χ2 = 22.6; p = 0.007), and number of prior concussions (χ2 = 17.6, p = 0.040). The clusters also differed in symptom reports for fatigue (χ2 = 17.7; p = 0.039), confusion (χ2 = 22.3; p = 0.008), difficulty remembering (χ2 = 22.0; p = 0.009), and trouble falling asleep (χ2 = 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.
Keywords: biomarkers; concussion; personalized medicine; subgroups; traumatic brain injury.