In 2018, the FDA authorized the use of a blood test for glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) in mild traumatic brain injury (mTBI), crowning a long success story of CNS-driven blood biomarker development [1][2][3] . Initial efforts to identify fluid biomarkers for neurological diseases focused on the cerebrospinal fluid (CSF) as, compared with blood, CSF is closer to the brain extracellular space and contains higher concentrations of CNS-derived proteins 4 . The establishment of fourth-generation immune assays in the last decade 3,5 brought the possibility of quickly obtaining rapid and robust protein biomarker measurements from blood samples, opening up new perspectives in the field of CNS-derived markers. For example, levels of classic CSF biomarkers of neuroaxonal damage, such as neurofilament light chain (NfL) 5 , phosphorylated tau 217 (ref. 6), and UCH-L1 (ref. 7 ) can now be readily quantified in blood, indicating that these markers hold potential for use in diagnosis and monitoring of disease activity, and as surrogate end points for treatment trials. The literature on the utility of blood GFAP as a biomarker is also growing, reinforcing the large body of published data on CSF GFAP 3,[8][9][10][11][12][13][14] . The evaluation of blood levels of GFAP has the potential to enable the in vivo longitudinal evaluation of different aspects of the astrocytic response in several neurological disorders. Here, we provide an up-to-date review of the analytical aspects, current evidence, perspectives, and limitations of blood GFAP as a biomarker, with the purpose of outlining how to refine its application in the diagnosis and monitoring of neurological diseases. GFAP biology and analysisAstrocytes represent around 30-40% of the cells in the CNS 15 , form an integral part of the blood-brain barrier (BBB) and establish numerous interactions with other cells in the nervous system, including neurons.

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Post-concussive symptoms occur frequently after mild traumatic brain injury (mTBI) and may be categorized as cognitive, somatic, or emotional. We aimed to: 1) assess whether patient demographics and clinical variables predict development of each of these three symptom categories, and 2) develop a prediction model for six-month post-concussive symptoms. MTBI patients (Glasgow Coma Scale score 13-15) from the prospective multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot study (2010-2012) who completed the Rivermead Post-Concussion Symptoms Questionnaire (RPQ) at six-months post-injury were included. Linear regression was utilized to determine the predictive value of candidate predictors for cognitive, somatic, and emotional subscales individually as well as the overall RPQ. The final prediction model was developed using Lasso shrinkage and bootstrap validation. We included 277 mTBI patients (70% male, median age 42y). No major differences in the predictive value of our set of predictors existed for the cognitive, somatic, and emotional subscales, and therefore one prediction model for the RPQ total scale was developed. Years of education, pre-injury psychiatric disorders and prior TBI were the strongest predictors of six-month post-concussive symptoms. The total set of predictors explained 21% of the variance, which decreased to 14% after bootstrap validation. Demographic and clinical variables at baseline are predictive of six-month post-concussive symptoms following mTBI, however these variables explain less than one-fifth of the total variance in outcome. Model refinement with larger datasets, more granular variables, and objective biomarkers are needed before implementation in clinical practice.