Background:Detection of brain-specific miRNAs in the peripheral blood could serve as a surrogate marker of traumatic brain injury (TBI). Here, we systematically identified brain-enriched miRNAs, and tested their utility for use as TBI biomarkers in the acute phase of care. Methods:Publically-available microarray data generated from 31 postmortem human tissues was used to rank 1,364 miRNAs in terms of their degree of brain-specific expression. Levels of the top five ranked miRNAs were then prospectively measured in serum samples collected from 10 TBI patients at hospital admission, as well as from 10 controls. Results:The top five miRNAs identified in our analysis (miR-137, miR-219a-5p, miR-128-3p, miR-124-3p, and miR-138-5p) exhibited 31 to 74-fold higher expression in brain relative to other tissues. Furthermore, their levels were elevated in serum from TBI patients compared to controls, and were collectively able to discriminate between groups with 90% sensitivity and 80% specificity. Subsequent informatic pathway analysis revealed that their target transcripts were significantly enriched for components of signaling pathways which are active in peripheral organs such as the heart. Conclusions:The five candidate miRNAs identified in this study have promise as blood biomarkers of TBI, and could also be molecular contributors to systemic physiologic changes commonly observed post-injury.

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The identification of precision blood biomarkers which can accurately indicate damage to brain tissue could yield molecular diagnostics with the potential to improve how we detect and treat neurological pathologies. However, a majority of candidate blood biomarkers for neurological damage that are studied today are proteins which were arbitrarily proposed several decades before the advent of high-throughput omic techniques, and it is unclear whether they represent the best possible targets relative to the remainder of the human proteome. Here, we leveraged mRNA expression data generated from nearly 12,000 human specimens to algorithmically evaluate over 17,000 protein-coding genes in terms of their potential to produce blood biomarkers for neurological damage based on their expression profiles both across the body and within the brain. The circulating levels of proteins associated with the top-ranked genes were then measured in blood sampled from a diverse cohort of patients diagnosed with a variety of acute and chronic neurological disorders, including ischemic stroke, hemorrhagic stroke, traumatic brain injury, Alzheimer’s disease, and multiple sclerosis, and evaluated for their diagnostic performance. Our analysis identifies several previously unexplored candidate blood biomarkers of neurological damage with possible clinical utility, many of which whose presence in blood is likely linked to specific cell-level pathologic processes. Furthermore, our findings also suggest that many frequently cited previously proposed blood biomarkers exhibit expression profiles which could limit their diagnostic efficacy.

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Background The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential. Methods Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups. Results Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis. Conclusions Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

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The identification of peripheral blood biomarkers which are associated with traumatic brain injury (TBI) could lead to the development of molecular diagnostics which could be used to aid acute detection in scenarios where neuroimaging in unavailable, or to remotely track injury progression and recovery. It is becoming increasingly evident that brain tissue exhibits patterns of micro‐RNA (miRNA) expression which are distinct from those of other tissues. During TBI, the blood brain barrier becomes disrupted and molecules from damaged neural tissues are released into peripheral circulation. Thus, the detection of brain specific miRNAs in peripheral blood could serve as a surrogate marker of TBI. In this investigation, we aimed to systematically identify brain‐enriched miRNAs, and then test their potential utility for use as TBI biomarkers. First, we obtained publically‐available expression data for over 1,300 miRNAs generated from 31 different human post‐mortem tissues. Tissue specificity index (Tau) was calculated for each miRNA, and they were subsequently ranked in terms of their degree of brain‐specific expression. The abundances of the top five ranked miRNAs were then measured in serum samples collected from 10 TBI patients and 10 healthy controls using qPCR, and evaluated for their ability to discriminate between groups using k‐nearest neighbors (k‐NN). The top five miRNAs identified in our tissue specificity analysis consisted of miR‐137, miR‐219a‐5p, miR‐128‐3p, miR‐124‐3p, and miR‐138‐5p, which exhibited from 31 to 74‐fold higher expression levels in brain relative to other tissues. Three out of the five candidate miRNAs exhibited significantly higher abundance in serum samples from TBI patients relative to control patients in qPCR analysis, and their coordinate expression levels were able to discriminate between groups with 90% sensitivity and 80% specificity. The five candidate miRNAs identified in our analysis have potential utility for use as TBI biomarkers, and such a possibility warrants further investigation.Support or Funding InformationWork was supported by Case Western Reserve University FPB School of Nursing start‐up funds issued to Grant C. O'Connell.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.