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Blood Biomarkers and Structural Imaging Correlations Post-Traumatic Brain Injury: A Systematic Review

Whitehouse, Daniel P. MBChB‡,*,**; Vile, Alexander R. MBBS§,*; Adatia, Krishma MBChB‡,*; Herlekar, Rahul MB, BChir; Roy, Akangsha Sur MB, BChir; Mondello, Stefania MD, PhD; Czeiter, Endre MD, PhD#,††,‡‡; Amrein, Krisztina MSc#,††; Büki, András MD, PhD#,††; Maas, Andrew I. R. MD, PhD§§; Menon, David K. PhD, FFICM, FMedSci; Newcombe, Virginia F. J. PhD, FRCEM, FFICM‡,**

Author Information
doi: 10.1227/NEU.0000000000001776


Traumatic brain injury (TBI) is a leading cause of trauma-related mortality and morbidity.1,2 Blood-based biomarkers offer the potential for great insight into the pathophysiology of TBI, as well as aiding in diagnosis, prognosis, and patient stratification. However, it is unclear how biomarker concentrations after injury may relate to structural imaging findings on computed tomography (CT) or magnetic resonance imaging (MRI).

There has been an increasing body of literature assessing blood-based biomarkers in TBI in reference to lesion detection,3,4 with this being the subject of previous systematic reviews.5,6 The release of biomarkers after TBI may also reflect the extent of injury, and if such biomarkers correlate with radiological variables, this could be valuable in the diagnostic and prognostic assessment of patients with TBI. Because pathophysiological mechanisms associated with TBI are dynamic, compared with radiological findings, serial measurements of biomarkers may be more feasible and detect underlying processes and associated microstructural damages in a real-time fashion.

Reflective of the diverse pathophysiology, there are multiple candidate biomarkers, each with distinct anatomical origins and dynamics. Whether this reflects the type and burden of traumatic intracranial lesion present is unclear.7,8 A greater understanding of biomarker–neuroimaging relationships may be integrated into diagnostic algorithms for the utilization and timing of both canonical and advanced neuroimaging techniques to evaluate patients with TBI and prediction of neuroworsening and/or outcomes.

This systematic review aims to summarize the literature surrounding the relationship between acute neuroimaging findings and serum biomarkers in patients after TBI. Specifically, in patients presenting after all severities of TBI, we aim to assess the impact of neuroradiological-derived metrics including intracranial lesion type, lesion volume, and injury classification on serum biomarker concentrations in the acute setting.


This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Figure 1). The protocol was registered on PROSPERO (PROSPERO ID:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses study inclusion/exclusion flow diagram. MEDLINE, EMBASE, and CINAHL database queries returned 3790 articles. After duplicates were removed, 3435 articles were screened according to title, abstract, and key words for full-text read. Two hundred seventy-eight articles were read in full, and 58 were included in this review. An additional article was added from manual reference screening of included texts leaving 59 included publications. CINAHL, Cumulative Index to Nursing and Allied Health Literature; EMBASE, Excerpta Medica dataBASE; MEDLINE, Medical Literature Analysis and Retrieval System Online; TBI, traumatic brain injury.

Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched from inception to May 18, 2021, with references screened for further publications. The search used database-specific iterations of keywords related to TBI, biomarkers, neuroimaging findings, and modalities, alongside free-text searches for keywords in study titles and abstracts (Supplemental Digital Content 1,

Eligibility criteria for inclusion comprised observational studies, enrolling adults, children, or both, after TBI of all severity (mild, moderate, or severe), with acute structural imaging (MRI <1 month, CT <48 hrs, or on hospital admission/attendance), and stating relationships between blood biomarkers and imaging findings as detailed below:

  1. Biomarker concentration and volume or number of intracranial lesions,
  2. biomarker concentration and specific intracranial lesion injury patterns (diffuse or focal injury),
  3. biomarker concentration and imaging classifications, and
  4. biomarker concentration and specific intracranial lesion types.

Abstracts and full-text articles were considered for inclusion. The exclusion criteria were: review articles, interventional studies, studies containing either functional neuroimaging only, neonatal subjects, nonhuman subjects or the imaging-biomarker analysis pertained to secondary or nontraumatic brain injury. The full-text article was included if there was both a conference abstract and full-text publication of the same cohort and biomarker.

References were imported to Rayyan QCRI.9 Duplicates were removed, and the remaining articles screened according to title and abstract by 2 authors independently (D.P.W./K. Adatia). Potentially eligible studies were read in full by 2 independent authors to determine final inclusion (D.P.W./K. Adatia). Differences were reconciled by consensus between 2 reviewers (D.P.W./K. Adatia) or involvement of a third author (A.V.).

Data were extracted by 2 independent authors (2 of D.P.W./K. Adatia/A.V.) using piloted data collection forms, with comparison postextraction. Data extracted included study design, demographics (patient number, number of centers, imaging modality, Glasgow Coma Scale score, biomarker type, and biomarker assay), and the relationship between biomarker and the predefined imaging–biomarker relationships.

Risk of bias was independently assessed by 2 authors (R.H./A.S.R.), with disagreements solved by mutual agreement or involvement of a third author (A.V.). Risk of bias was assessed using a modified version of the Newcastle–Ottawa Scale for observational studies (Supplemental Digital Content 2,


Of 3435 studies, 59 met the inclusion criteria (Figure 1).11-69 Risk of bias assessment is presented in Supplemental Digital Content 2,, with 86% of papers assessed to be fair–good. Heterogeneity in study design, biomarker types, and analytical methods prohibited quantitative analysis. A qualitative synthesis of the literature is therefore presented below. Paper numbers, demographics, and imaging characteristics are presented in Table 1 and Supplemental Digital Content 3, A summary of the seminal findings is presented in Table 2 and Figure 2. Summary tables of the key findings are presented in the Supplemental Digital Content 3 (, Tables 3–5). Unless stated, otherwise all associations/correlations are positive.

TABLE 1. - Characteristics of Study Populations, Imaging Modalities, and Question Answered by the Most Commonly Studied Biomarkers
Biomarker Number of studies Total number of TBI cases per study (median [range]) Number of studies per age grouping Number of studies per patient clinical severity GCS (mild—13-15, moderate—9-12, and severe—3-8) Imaging modality used per study Total percentage positive imaging (median [IQR]) Number of studies in relation to
Scoring system/injury pattern Lesion number or volume Intracranial lesion type
S100B 30 5276 (93 [9-1696]) Adult: 26
Pediatric: 3
Adult/pediatric: 1
Not specified: 1
Mild: 7
Mild and moderate: 2
Mild, moderate, and severe: 13
Severe: 7
CT: 26
MRI: 1
CT and MRI: 3
68.67% (24.5, 99.85)
Not specified: 6
Marshall: 13
Rotterdam: 4
Stockholm: 4
Self-created: 1
Diffuse vs Focal: 1
7 15
GFAP or GFAP-BDP 21 2825 (93 [9-450]) Adult: 19
Pediatric: 2
Not specified: 1
Mild: 4
Mild and moderate: 1
Mild, moderate, and severe: 9
Severe: 6
CT: 16
MRI: 2
CT and MRI: 3
72.8% (33.33, 98)
Not specified: 4
Marshall: 11
Rotterdam: 2
Stockholm: 1
Self-created: 1
4 6
NSE 14 1494 (73 [13-417]) Adult: 12
Pediatric: 1
Adult/pediatric 1
Not specified: 1
Mild, moderate, severe: 10
Severe: 3
CT: 13
CT, MRI: 1
99.8% (65.8,100)
Not specified: 3
Marshall: 8
Rotterdam: 3
Stockholm: 3
Self-created: 1
Diffuse vs Focal: 1
5 4
Neurofilament proteins 9 (NF-H: 5 NF-L: 4) 756 (76 [9-182]) Adult: 8
Pediatric: 1
Mild: 2
Mild, moderate, and severe: 7
CT: 5
MRI: 3
CT and MRI: 1
72.6% (43.98, 96.95)
Not specified: 3
Marshall: 5
Rotterdam: 2
Stockholm: 2
1 5
UCH-L1 8 1113 (114.5 [9-389]) Adult: 6
Pediatric: 1
Adult/pediatric: 1
Mild: 2
Mild, moderate, and severe: 4
Severe: 2
CT: 7
CT and MRI: 1
72.8% (44.06,88)
Not specified: 1
Marshall: 4
Rotterdam: 1
Stockholm: 1
1 3
Tau 7 633 (40 [34-196]) Adult: 6
Pediatric: 1
Mild, moderate, and severe: 5
Moderate and severe: 1
Severe: 1
CT: 6
MRI: 1
57.4% (44.9, 87.50)
Not specified: 2
Marshall: 3
Rotterdam: 1
Stockholm: 1
1 3
CT, computed tomography; GCS, Glasgow Coma Score; GFAP, glial fibrillary acidic protein; GFAP-BDB, GFAP breakdown products; MRI, magnetic resonance imaging; NF-H, neurofilament heavy; NF-L, neurofilament heavy; NSE, neuron-specific enolase; S100B, S100 B calcium-binding protein; TBI, traumatic brain injury; UCH-L1, ubiquitin carboxy-terminal hydrolase L1.

TABLE 2. - Summary of Key Findings of Biomarker Imaging Relationships from This Review
(1) Biomarker concentration and volume or number of intracranial lesions
 • Positive correlations were consistently demonstrated between S100B and volume of intraparenchymal hemorrhage/contusion.15,25,50
 • GFAP,15 tau,15 NFH,15 SAA1,15 CRP,15 PCT,15 YKL-40,15 and CK57 have been shown to correlate in single studies only to correlate with volume of intraparenchymal hemorrhage/contusion.
 • One study demonstrated correlations between S100B, tau, and PCT to EDH volume.15
 • One study demonstrated correlation between SAA1, CRP, PCT, and YKL-40 and SDH volume.15
 • One study demonstrated correlation between S100B, GFAP, tau, NSE, SAA1, CRP, PCT, and YKL-40 and IVH volume.15
 • One study demonstrated correlation between S100B, GFAP, tau, NSE, NFH, SAA1, CRP, PCT, and YKL-40 and tSAH volume.15
 • Increased number of traumatic lesions correlated with increased concentrations of S100B and GFAP.18,19,28,42,65
(2) and (3) Biomarker concentration and specific intracranial lesion injury patterns (diffuse or focal injury) and biomarker concentration and imaging classifications including Marshall, Rotterdam, and Stockholm CT scores
 • Positive correlations have been observed between the Marshall score and S100B,29,41,50,58,64 NSE,41,58,64 NFH,21,51 GFAP,58,64 UCH-L1,58 von Willebrand factor (vWF),16 and an inverse correlation with uric acid levels.23 However, findings were not consistent.
 • Positive correlations have been observed between the Rotterdam score and serum biomarker concentration GFAP-BDP (GFAP breakdown products),35 GFAP,58 UCH-L1,58 NSE,58,59 S100B,58,59 and tau.58
 • The most consistent correlations were observed in reference to the Stockholm CT score S100B,12,58,59,61 NSE,58,59 GFAP,58 UCH-L1,58 tau,58 and NFL.58
 • S100B,25 NSE,25 GFAP,39,49 UCH-L1,39,49 and the glial–neuronal ratio (the ratio between GFAP and UCH-L1)37 have been found to differentiate between focal and diffuse injury.
 • A general trend was observed with increased biomarker concentrations in relation to greater severity of diffuse injury.
(4) Biomarker concentration and specific intracranial lesion type or types
 • No consistent relationship between acute biomarker concentrations of different biomarkers and specific intracranial lesion type.
 • On group-wise comparison, neither S100B40,65 or GFAP66 could differentiate between hemorrhagic lesion types.
 • Although individual studies have found certain patterns of biomarker expression regarding this relationship, the overall contrast between study findings precludes definitive conclusions.
B-FABP, brain fatty acid–binding protein; CK, creatine kinase; CRP, C-reactive protein; EDH, extradural hematoma; GFAP, glial fibrillary acidic protein; GFAP-BDP, glial fibrillary acidic protein breakdown products; IVH, intraventricular hematoma; NFH, neurofilament heavy; NFL, neurofilament light; NSE, neuron-specific enolase; PCT, procalcitonin; S100B, S100 B calcium-binding protein; SAA1, serum amyloid A1; SDH, subdural hemorrhage; tSAH, traumatic subarachnoid hemorrhage; UCH-L1, ubiquitin carboxy-terminal hydrolase L1; vWF, von Willebrand factor; YKL-40, procalcitonin chitinase-3–like protein 1

A summary of key biomarkers and findings from this review. B-FABP, brain fatty acid binding protein; CT, computed tomography; GFAP, glial fibrillary acidic protein; GFAP-BDP, glial fibrillary acidic protein breakdown products; MRI, magnetic resonance imaging; NFH, neurofilament heavy; NFL, neurofilament light; NSE, neuron-specific enolase; S100B, S100 B calcium-binding protein; sICAM, soluble intercellular adhesion molecule; UCH-L1, ubiquitin carboxy-terminal hydrolase L1; vWF, von Willebrand factor.

Biomarker Concentration and Volume or Number of Intracranial Lesions

S100B,15,25,50 creatine kinase (CK),57 glial fibrillary acidic protein (GFAP),15 t-tau,15 neurofilament heavy (NFH),15 serum amyloid A1 (SAA1),15 C-reactive protein (CRP),15 procalcitonin (PCT),15 and chitinase-3-like protein 1 (YKL-40)15 correlated with intraparenchymal hemorrhage/contusion volume. S100B, GFAP, neuron-specific enolase (NSE), T-tau, SAA1, CRP, PCT, and YKL-40 correlated with volumes of intraventricular hemorrhage (IVH) and traumatic subarachnoid hemorrhage (tSAH).15 Hermann et al25 demonstrated that S100B and NSE correlated well with contusion volume but did not correlate with subdural hemorrhage (SDH) or extradural hematoma (EDH) volume approximately 30 h after injury. In a single study, NSE and PCT correlated with volume of EDH and NFH, SAA1, CRP, and YKL40 correlated with SDH volume.15 Concentrations of S100B and GFAP related to number of lesions on CT.18,19,28,42,65

Biomarker Concentration and Imaging Classification Systems

Across studies assessing a severe TBI cohort, only positive correlations were found between Marshall score (when used as a continuous variable) and von Willebrand factor (vWF),16 S100B,50,64 GFAP,64 and NSE,64 with an inverse correlation with uric acid.23 When the Marshall score was used as a categorical variable, S100B was higher in abnormal scans46 and seemed to differentiate between Marshall score I-II and VI,29 V and VI,29 II and IV,46 and II and VI.46 GFAP may also differentiate between II and IV,46,47 and II and VI.46,47 GFAP measured 7 h from admission was found to be higher in mass lesions, in contrast to UCH-L1, which was significantly higher in diffuse injury.39

Across studies reporting on the Marshall score for mixed severity cohorts, a positive correlation was found between Marshall score and S100B,41,58 NSE,41,58 NFH,21,51 GFAP,58 and UCH-L1.58 However, these findings were inconsistent with many studies demonstrating no associations.12,13,58,59

Four studies assessed biomarker ability to differentiate between focal and diffuse injuries (Marshall scores I-IV compared with V-VI) for a mixed cohort of TBI severities. S100B,25 NSE,25 GFAP,39,49 UCH-L1,39,49 the glial–neuronal ratio (the ratio between GFAP and UCH-L1),37 and various serum metabolites17 were found to differentiate between focal and diffuse injuries. The degree of midline shift and cisternal compression was correlated with NSE and S100B.32 T-tau, p-tau, and t-tau to p-tau ratio have been shown to differentiate between I and II, and I and ≥III,53 and brain fatty acid binding protein between I and ≥II.63 However, these relationships have not been replicated in other studies.26,68

Four studies reported associations between either the Rotterdam or Stockholm score and biomarkers: all reported results as mixed cohorts of TBI severity. Significant correlations of the Rotterdam score were found with serum GFAP-BDP (GFAP breakdown products),35 GFAP,58 UCH-L1,58 NSE,58,59 S100B,58,59 and tau.58 The Stockholm score was found to correlate with S100B,12,58,59,61 NSE,58,59 GFAP,58 UCH-L1,58 and tau.58

Imaging Associations of the Most Commonly Studied Blood Biomarkers

Astroglial Biomarkers

S100B was found to be significantly higher in cerebral edema as compared with EDH, SDH, tSAH, and contusion.65 S100B was independently increased in the presence of tSAH or contusions compared with patients with normal CT scans,24 with concentrations peaking earlier in contusion (0-24 h for NSE and 25-48 h for S100B) as compared with diffuse axonal injury (DAI) (49-72 h).25 The influence of DAI on serum S100B levels seems uncertain; although 1 study found a significant association,11 the majority did not.33,36,68 A study of moderate to severe TBI found higher mean GFAP levels in those with intracranial injuries compared with extracranial injury alone.45 In a small study of mild TBI, higher levels of GFAP were demonstrated among those with hemorrhagic lesions compared with those without.30 A similar finding was seen for patients with MRI-confirmed DAI and contusions,36 compared with those without. In a CT-negative MRI-positive population, GFAP was higher in those with more than 3 foci of petechial hemorrhages.66

Neuronal Biomarkers

NSE was found to be significantly higher in patients with traumatic cerebral hemorrhages, including tSAH, intraparenchymal hemorrhage, and SDH, in combination with cerebral edema as compared with isolated EDH.22,50 UCH-L1 measured within 6 h of admission was found to be significantly raised in intracranial hemorrhage compared with skull fractures or scalp hematoma.38,44

Axonal Biomarkers

Raised NFH concentrations were found in pediatric patients with DAI compared with those without.67 Similarly, NFH was shown to be significantly raised patients with microhemorrhages compared with other traumatic hemorrhage.54 In a small case series, neurofilament light (NFL) was found to discriminate between patients with MRI-proven DAI and controls.33 However, other studies found no significant association between NFL with presence, grade, or location of DAI.12,56 Serum cleaved tau levels within 12 h of admission varied between different groups of CT pathology, including contusion, EDH, SDH, tSAH, skull fractures, and normal CT scans; however, no individual group-wise comparison was offered in the analysis.43 Tau concentrations were elevated in DAI compared with contusions, and SDH, demonstrating a potential to classify different lesion types.20 Furthermore, comparison between patients with and without DAI on MRI showed that patients with DAI had significantly higher tau concentrations.62


vWF measured within the first 48 h of admission was higher in traumatic hemorrhage compared with traumatic vascular injury,54 whereas D-dimer measured within 6 h could not differentiate between patients with isolated skull fracture and mild intracranial injury on CT.31 Although raised in mild head injury compared with controls, copeptin was not significantly different on group-wise comparison between patients with petechial hemorrhages, tSAH, SDH, and EDH.14


We systematically reviewed the relationship between blood biomarkers and intracranial lesion types, intracranial lesion injury patterns, volume/number of intracranial lesions, and imaging classification systems in patients after TBI. This review reveals that higher blood biomarker concentrations are associated with an increased burden of intracranial disease. However, there is a significant gap in knowledge about the relationship between specific biomarkers and specific injury types; this is an area that will require further study to aid interpretation and utility of blood-based biomarkers within clinical practice. The associations between biomarkers and the specified imaging measures are discussed in greater detail in the following sections.

Biomarker Concentration and Volume or Number of Intracranial Lesions

A variety of serum biomarkers (S100B,15,25,50 CK,57 GFAP,15 t-tau,15 NFH,15 SAA1,15 CRP,15 PCT,15 and YKL-4015) were demonstrated to correlate with intraparenchymal hemorrhage/contusion volume, with S100B,15 GFAP,15 NSE,15 T-tau,15 SAA1,15 CRP,15 PCT,15 and YKL-40 correlating with volumes of IVH and tSAH. Less consistent relationships were shown between biomarkers and extra-axial hemorrhage volume with only NSE and PCT correlating with volume of EDH and NFH, SAA1, CRP, and YKL-40 correlated with SDH in a single study.15 Interstudy variation in results may (in part) be accounted by methodological variation of image analysis between the studies, including use of entire lesion annotated volume,15,25 and calculations of lesion size using measurements in different planes.48,50,57

An association between number of traumatic intracranial lesions and biomarker concentration S100B and GFAP was consistently demonstrated.18,19,28,42,65 However, the lack of differentiation between the different traumatic lesion types and sizes can make this an artificial grouping, and not truly reflective of traumatic burden.

Given the potential for increased blood–brain barrier (BBB) disruption with increasing parenchymal damage, it is not surprising that biomarker concentrations correlate with both lesion number and lesion volume. However, the extent to which BBB disruption affects serum biomarker concentrations seems to vary across biomarkers. Disparities between S100B concentration in the cerebrospinal fluid (CSF) and serum, eg, exist in cases where BBB is intact. The same is not true for NSE, the serum level of which better reflects CSF concentration irrespective of BBB integrity assessed using the CSF: blood albumin quotient.70

Biomarker Concentration and Imaging Classification Systems

Radiological scoring systems are used to stratify patients by injury severity particularly in the research setting.7,8,71 The Marshall classification was the first such system to be developed,8 categorizing severity of diffuse lesions (I-IV), by the extent of basal cistern compression and midline shift, and focal lesions (V-VI), depending on surgical evacuation. The Rotterdam score seeks to address limitations of the Marshall system through inclusion of the presence of tSAH.7 The Stockholm CT score differs in its use of midline shift as a continuous variable and has a separate scoring system for tSAH.

Although all radiological scoring systems were found, in general, to positively correlate with a variety of biomarker concentrations, the most consistent associations were demonstrated with the Stockholm CT score.12,58,59,61 This potentially indicates that biomarker concentrations relate to the severity of intracranial injury, as graded on radiological scoring systems.

Differentiation between diffuse and focal brain injury is of direct clinical importance. To determine this, most studies used the Marshall score, comparing Marshall scores I-IV with V-VI. S100B,25 NSE,25 GFAP,39,49 UCH-L1,39,49 the glial–neuronal ratio (the ratio between GFAP and UCH-L1),37 and various serum metabolites17 had different levels for focal and diffuse injury. Specifically, GFAP39 has been demonstrated to be significantly raised in mass lesions, and UCH-L139 and NSE34 higher in diffuse injury.

Given the temporal release of biomarkers after injury and varying kinetics profiles,4,5,72 the timing of both imaging and biomarker sampling is important when assessing relationships. Significant correlations between S100B and NSE and CT scoring metrics have been demonstrated at subsequent scanning but not at admission55 and are perhaps reflective of lesion progression and explanatory of variable associations found in this review.

Imaging Associations of the Most Commonly Studied Blood Biomarkers

Individual studies demonstrated significant relationships between biomarker concentrations and the pathological lesion subtype demonstrated on neuroradiology. However, no clear unifying trend was found in relation to biomarker pathoanatomical origin or hemorrhagic lesion type. Group-wise comparisons were rarely performed between the individual lesion subtypes, with studies focusing on the presence or absence of a specific lesion type, irrespective of other intracranial findings. When group-wise comparison was performed, both S100B40,65 and GFAP66 could not differentiate between hemorrhagic lesion types.

The ability of serum biomarkers to detect DAI is of key importance for clinicians, owing to the relative poor sensitivity of CT for the detection of DAI in comparison with MRI.73 It is hypothesized that the axonal biomarkers will be more sensitive to DAI as compared with other biomarkers. There is some suggestion of NFH,54,67 NFL,33 and tau20 concentrations being higher in DAI rather than hemorrhagic injury, indicating potential utility for DAI detection. However, this was inconsistent in relation to NFL,12,56 and DAI patients in these studies often had greater injury severity than non-DAI patients, acting as a confounder. Of the non–axonal-related biomarkers, S100B would seem to have a poor association with DAI,33,36,68 yet GFAP shows more promise, with significantly greater concentrations of GFAP in DAI on MRI in a CT-negative population.66

Limitations and Areas for Future Research

Strengths of this review include its breadth and clinical relevance. Some weaknesses are acknowledged, but these are mainly related to the underpinning studies and prevented drawing definitive conclusions.

Small sample sizes and methodological inconsistencies were common introducing an element of small-study effect, whereby effect sizes for biomarkers between groups may seem greater than they actually are, and sampling bias. Time to imaging and biomarker sampling, biomarker assays, and neuroimaging reporting techniques were also often different. To address the need for interstudy consistency, there has been the development of common data elements (CDE) for standardization of key elements of TBI research.74,75 Many of the included studies were either written before publication of the CDE or do not acknowledge the utilization of CDE recommendations.

Although variations between studies relate in part to interstudy methodological variation, they also likely reflect the pathological heterogeneity presented in TBI. Numerous studies compared biomarker concentration means and medians across the severity of TBI. Such analysis methods do not account for multiple intracranial and extracranial variables and may falsely attribute elevated biomarker readings to specific lesion types. Heterogeneity arising from other population and injury-related factors should be considered in future studies.

The impacts of age on circulating biomarker concentrations have been described elsewhere, yet only a few studies presented in this review reported the confounding effects of age on biomarker concentrations in their results.58-61 Perhaps reflective of this, a significant difference in S100B concentration was observed in adults with tSAH compared with those without,24,60 a finding not corroborated in a pediatric population.68 The influences of BBB integrity,70 secondary injury sequelae,76,77 and extracranial injury61,78,79 are other injury-related factors that have been shown to potentially influence biomarker concentrations.

It is also important to consider the temporal profile of different biomarkers, which may affect both interpretation of what a level means and when biomarkers sampling should occur. For example, cellular fibronectin concentrations have been found to be different between traumatic hemorrhages and traumatic vascular injury.54 Yet this only arose in biomarker samples taken greater than 48 h after injury.54

With multiple large studies assessing TBI currently being conducted,66,80,81 and the development of new image analysis techniques allowing for increased efficiency in volumetric image analysis, there is the potential for future research to offer more definitive assessment of the questions posed in this review. This includes the BIOmarkers of AXonal injury after TBI (BIO-AX-TBI Study) that aims to clarify key relationships between axonal injury and serum biomarker concentrations.81


Although significant methodological variety exists, it seems that serum concentration of biomarkers is related to lesion burden and severity of intracranial injury. There was a lack of evidence to suggest biomarkers could differentiate between lesion types, perhaps hallmarking the complex and heterogenous nature of TBI. Future multicenter studies, using consistent methodological approaches and large sample sizes, are required to confirm these findings.


This study did not receive any funding or financial support.


The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. Dr Menon reports grants from National Institute for Health Research (UK), during the conduct of the study; grants, personal fees, and nonfinancial support from GlaxoSmithKline; personal fees from Neurotrauma Sciences; personal fees from Lantmaanen AB; personal fees from PresSura; and personal fees from Pfizer, outside the submitted work. Dr Maas reports grants from the Fp7 program of the European Union (602150), the Hannelore Kohl Stiftung, and Integra LifeSciences and declares consulting fees from PresSura Neuro, Integra Life Sciences, and NeuroTrauma Sciences. Dr Czeiter, Krisztina Amrein, and Dr Büki report grants from Higher Education Institutional Excellence Programme— Grant No. 20765-3/2018/FEKUTSTRAT, FIKP II/S, EFOP-3.6.2.-16-2017-00008, GINOP-2.3.2-15-2016-00048, and GINOP-2.3.3-15-2016-00032—and the Hungarian Brain Research Program 2.0 Grant No. 2017-1.2.1-NKP-2017-00002. Dr Newcombe is supported by an Academy of Medical Sciences/The Health Foundation Clinician Scientist Fellowship, holds a grant funded by Roche pharmaceuticals, and received an honorarium for a talk from Neurodiem (money paid to institution).


1. Dewan MC, Rattani A, Gupta S, et al. Estimating the global incidence of traumatic brain injury. J Neurosurg. 2019;130(4):1080-1097.
2. James SL, Bannick MS, Montjoy-Venning WC, et al. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(1):56-87.
3. Unden L, Calcagnile O, Unden J, et al. Validation of the Scandinavian guidelines for initial management of minimal, mild and moderate traumatic brain injury in adults. BMC Med. 2015;13(1):292.
4. Calcagnile O, Undén L, Undén J. Clinical validation of S100B use in management of mild head injury. BMC Emerg Med. 2012;12:13.
5. Dadas A, Washington J, Diaz-Arrastia R, Janigro D. Biomarkers in traumatic brain injury (TBI): a review. Neuropsychiatr Dis Treat. 2018;14:2989-3000.
6. Mondello S, Sorinola A, Czeiter E, et al. Blood-based protein biomarkers for the management of traumatic brain injuries in adults presenting to emergency departments with mild brain injury: a living systematic review and meta-analysis. J Neurotrauma. 2018;38(8):1086-1106.
7. Maas AIR, Hukkelhoven CWPM, Marshall LF, Steyerberg EW. Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery. 2005;57(6):1173-1181.
8. Marshall LF, Marshall SB, Klauber MR, et al. The diagnosis of head injury requires a classification based on computed axial tomography. J Neurotrauma. 1992;9(suppl 1):S287-S292.
9. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.
10. Wells G, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. The Ottawa Hospital Research Institute, 2019. Accessed April 5, 2020.
11. Abbasi M, Sajjadi M, Fathi M, Maghsoudi M. Serum S100B protein as an outcome prediction tool in emergency department patients with traumatic brain injury. Turkiye Acil Tip Derg. 2014;14(4):147-152.
12. Al Nimer F, Thelin E, Nyström H, et al. Comparative assessment of the prognostic value of biomarkers in traumatic brain injury reveals an independent role for serum levels of neurofilament light. PLoS One. 2015;10(7):e0132177.
13. Bogoslovsky T, Wilson D, Chen Y, et al. Increases of plasma levels of glial fibrillary acidic protein, tau, and amyloid β up to 90 days after traumatic brain injury. J Neurotrauma. 2017;34(1):66-73.
14. Castello LM, Salmi L, Zanotti I, et al. The increase in copeptin levels in mild head trauma does not predict the severity and the outcome of brain damage. Biomark Med. 2018;12(6):555-563.
15. Carabias CS, Gomez PA, Panero I, et al. Chitinase-3-like protein 1, serum amyloid A1, C-reactive protein, and procalcitonin are promising biomarkers for intracranial severity assessment of traumatic brain injury: relationship with Glasgow coma scale and computed tomography volumetry. World Neurosurg. 2020;134:e120-e143.
16. De Oliveira CO, Reimer AG, Da Rocha AB, et al. Plasma von Willebrand factor levels correlate with clinical outcome of severe traumatic brain injury. J Neurotrauma. 2007;24(8):1331-1338.
17. Dickens AM, Posti JP, Takala RSK, et al. Serum metabolites associated with computed tomography findings after traumatic brain injury. J Neurotrauma. 2018;35(22):2673-2683.
18. Egea-Guerrero JJ, Revuelto-Rey J, Murillo-Cabezas F, et al. Accuracy of the S100β protein as a marker of brain damage in traumatic brain injury. Brain Inj. 2012;26(1):76-82.
19. Egea-Guerrero JJ, Rodríguez-Rodríguez A, Quintana-Díaz M, et al. Validation of S100B use in a cohort of Spanish patients with mild traumatic brain injury: a multicentre study. Brain Inj. 2018;32(4):459-463.
20. Faulkinberry S, Wang K, Yang Z, Li X, Kerrigan M, Ghosh S. Tau as a potential biomarker for prognosis and diagnosis of pediatric traumatic brain injury. J Neurotrauma. 2019;36(13):A139-A139.
21. Ghonemi MO, Rabah AA, Saber HM, Radwan W. Role of phosphorylated neurofilament H as a diagnostic and prognostic marker in traumatic brain injury. Egypt J Crit Care Med. 2013;1(3):139-144.
22. Guzel A, Er U, Tatli M, et al. Serum neuron-specific enolase as a predictor of short-term outcome and its correlation with Glasgow Coma scale in traumatic brain injury. Neurosurg Rev. 2008;31(4):439-445.
23. Hatefi M, Dastjerdi MM, Ghiasi B, Rahmani A. Association of serum uric acid level with the severity of brain injury and patient’s outcome in severe traumatic brain injury. J Clin Diagn Res. 2016;10(12):OC20-OC24.
24. Heidari K, Asadollahi S, Jamshidian M, Abrishamchi SN, Nouroozi M. Prediction of neuropsychological outcome after mild traumatic brain injury using clinical parameters, serum S100B protein and findings on computed tomography. Brain Inj. 2015;29(1):33-40.
25. Herrmann M, Jost S, Kutz S, et al. Temporal profile of release of neurobiochemical markers of brain damage after traumatic brain injury is associated with intracranial pathology as demonstrated in cranial computerized tomography. J Neurotrauma. 2000;17(2):113-122.
26. Honda M, Tsuruta R, Kaneko T, et al. Serum glial fibrillary acidic protein is a highly specific biomarker for traumatic brain injury in humans compared with S-100B and neuron-specific enolase. J Trauma Inj Infect Crit Care. 2010;69(1):104-109.
27. Ingebrigtsen T, Waterloo K, Jacobsen EA, Langbakk B, Romner B. Traumatic brain damage in minor head injury: relation of serum S-100 protein measurements to magnetic resonance imaging and neurobehavioral outcome. Neurosurgery. 1999;45(3):468-476.
28. Kelmendi FM, Morina AA, Mekaj AY, et al. Serum S100B levels can predict computed tomography findings in paediatric patients with mild head injury. Biomed Res Int. 2018;2018:6954045.
29. Korfias S, Stranjalis G, Boviatsis E, et al. Serum S-100B protein monitoring in patients with severe traumatic brain injury. Intensive Care Med. 2007;33(2):255-260.
30. Kou Z, Gattu R, Kobeissy F, et al. Combining biochemical and imaging markers to improve diagnosis and characterization of mild traumatic brain injury in the acute setting: results from a pilot study. PLoS One. 2013;8(11):e80296.
31. Langness S, Ward E, Halbach J, et al. Plasma D-dimer safely reduces unnecessary CT scans obtained in the evaluation of pediatric head trauma. J Pediatr Surg. 2018;53(4):752-757.
32. Li Q, Zhou Q. Relationship between CT features and serum gfAP, NSE and S100B protein in patients with severe traumatic brain injury. Biomed Res. 2017;28(22):9926-9929.
33. Ljungqvist J, Zetterberg H, Mitsis M, Blennow K, Skoglund T. Serum neurofilament light protein as a marker for diffuse axonal injury: results from a case series study. J Neurotrauma. 2017;34(5):1124-1127.
34. Lo TYM, Jones PA, Minns RA. Pediatric brain trauma outcome prediction using paired serum levels of inflammatory mediators and brain-specific proteins. J Neurotrauma. 2009;26(9):1479-1487.
35. McMahon PJ, Panczykowski DM, Yue JK, et al. Measurement of the glial fibrillary acidic protein and its breakdown products GFAP-BDP biomarker for the detection of traumatic brain injury compared to computed tomography and magnetic resonance imaging. J Neurotrauma. 2015;32(8):527-533.
36. Metting Z, Wilczak N, Rodiger LA, Schaaf JM, Van Der Naalt J. GFAP and S100B in the acute phase of mild traumatic brain injury. Neurology. 2012;78(18):1428-1433.
37. Mondello S, Jeromin A, Buki A, et al. Glial neuronal ratio: a novel index for differentiating injury type in patients with severe traumatic brain injury. J Neurotrauma. 2012;29(6):1096-1104.
38. Mondello S, Kobeissy F, Vestri A, Hayes RL, Kochanek PM, Berger RP. Serum concentrations of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein after pediatric traumatic brain injury. Sci Rep. 2016;6:28203.
39. Mondello S, Papa L, Buki A, et al. Neuronal and glial markers are differently associated with computed tomography findings and outcome in patients with severe traumatic brain injury: a case control study. Crit Care. 2011;15(3):R156.
40. Müller K, Townend W, Biasca N, et al. S100B serum level predicts computed tomography findings after minor head injury. J Trauma Inj Infect Crit Care. 2007;62(6):1452-1456.
41. Naeimi ZS, Weinhofer A, Sarahrudi K, Heinz T, Vécsei V. Predictive value of S-100B protein and neuron specific-enolase as markers of traumatic brain damage in clinical use. Brain Inj. 2006;20(5):463-468.
42. Okonkwo DO, Yue JK, Puccio AM, et al. GFAP-BDP as an acute diagnostic marker in traumatic brain injury: results from the prospective transforming research and clinical knowledge in traumatic brain injury study. J Neurotrauma. 2013;30(17):1490-1497.
43. Pandey S, Singh K, Sharma V, et al. A prospective pilot study on serum cleaved tau protein as a neurological marker in severe traumatic brain injury. Br J Neurosurg. 2017;31(3):356-363.
44. Papa L, Mittal MK, Ramirez J, et al. Neuronal biomarker ubiquitin C-terminal hydrolase detects traumatic intracranial lesions on computed tomography in children and youth with mild traumatic brain injury. J Neurotrauma. 2017;34(13):2132-2140.
45. Papa L, Silvestri S, Brophy GM, et al. GFAP out-performs S100β in detecting traumatic intracranial lesions on computed tomography in trauma patients with mild traumatic brain injury and those with extracranial lesions. J Neurotrauma. 2014;31(22):1815-1822.
46. Pelinka LE, Kroepfl A, Leixnering M, Buchinger W, Raabe A, Redl H. GFAP versus S100B in serum after traumatic brain injury: relationship to brain damage and outcome. J Neurotrauma. 2004;21(11):1553-1561.
47. Pelinka LE, Kroepfl A, Schmidhammer R, et al. Glial fibrillary acidic protein in serum after traumatic brain injury and multiple trauma. J Trauma. 2004;57(5):1006-1012.
48. Pleines UE, Morganti-Kossmann MC, Rancan M, Joller H, Trentz O, Kossmann TS. 100β reflects the extent of injury and outcome, whereas neuronal specific enolase is a better indicator of neuroinflammation in patients with severe traumatic brain injury. J Neurotrauma. 2001;18(5):491-498.
49. Posti JP, Takala RSK, Runtti H, et al. The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 during the first week after a traumatic brain injury: correlations with clinical and imaging findings. Neurosurgery. 2016;79(3):456-463.
50. Raabe A, Grolms C, Keller M, Döhnert J, Sorge O, Seifert V. Correlation of computed tomography findings and serum brain damage markers following severe head injury. Acta Neurochir. 1998;140(8):787-792.
51. Radwan W, Rabah A, Saber H. Phosphorylated neurofilament heavy subunit (PNF-H) in blood as a potential diagnostic and prognostic biomarker in traumatic brain injury. ESICM 2013—abstracts of oral presentations and poster. Intensive Care Med. 2013;39:201-539.
52. Romner B, Ingebrigtsen T, Kongstad P, Børgesen SE. Traumatic brain damage: serum S-100 protein measurements related to neuroradiological findings. J Neurotrauma. 2000;17(8):641-647.
53. Rubenstein R, Chang B, Yue JK, et al. Comparing plasma phospho tau, total tau, and phospho tau–total tau ratio as acute and chronic traumatic brain injury biomarkers. JAMA Neurol. 2017;74(9):1063-1072.
54. Sandsmark DK, Bogoslovsky T, Qu B-X, et al. Changes in plasma von Willebrand factor and cellular fibronectin in MRI-defined traumatic microvascular injury. Front Neurol. 2019;10:246.
55. Shakeri M, Dokht YGM, Panahi F, Mahdkhah A, Foladi P. S100B protein value in predicting brain death after head trauma. Neurosurg Q. 2014;24(4):291-296.
56. Skandsen T, Clarke G, Einarsen C, et al. Levels of blood biomarkers in patients with mtbi were related to injury severity, but not to the post-concussive symptoms. J Neurotrauma. 2018;35(16):A209-A209.
57. Skogseid IM, Nordby HK, Urdal P, Paus E, Lilleaas F. Increased serum creatine kinase BB and neuron specific enolase following head injury indicates brain damage. Acta Neurochir. 1992;115(3-4):106-111.
58. Thelin E, Al Nimer F, Frostell A, et al. A Serum protein biomarker panel improves outcome prediction in human traumatic brain injury. J Neurotrauma. 2019;36(20):2850-2862.
59. Thelin EP, Jeppsson E, Frostell A, et al. Utility of neuron-specific enolase in traumatic brain injury; relations to S100B levels, outcome, and extracranial injury severity. Crit Care. 2016;20(1):285.
60. Thelin EP, Johannesson L, Nelson D, Bellander B-M. S100B is an important outcome predictor in traumatic brain injury. J Neurotrauma. 2013;30(7):519-528.
61. Thelin EP, Zibung E, Riddez L, Nordenvall C. Assessing bicycle-related trauma using the biomarker S100B reveals a correlation with total injury severity. Eur J Trauma Emerg Surg. 2016;42(5):617-625.
62. Tomita K, Nakada T-A, Oshima T, Motoshima T, Kawaguchi R, Oda S. Tau protein as a diagnostic marker for diffuse axonal injury. PLoS One. 2019;14(3):e0214381.
63. Vervliet B, Hulscher J, Van Der Naalt J, Ten Duis H, Nijsten M, Wilczak N. The diagnostic value of brain fatty acid binding protein in traumatic brain injury. Brain Inj. 2012;26(4):678-678.
64. Vos PE, Lamers KJB, Hendriks JCM, et al. Glial and neuronal proteins in serum predict outcome after severe traumatic brain injury. Neurology. 2004;62(8):1303-1310.
65. Wolf H, Frantal S, Pajenda G, Leitgeb J, Sarahrudi K, Hajdu S. Analysis of s100 calcium binding protein b serum levels in different types of traumatic intracranial lesions. J Neurotrauma. 2015;32(1):23-27.
66. Yue JK, Yuh EL, Korley FK, et al. Association between plasma GFAP concentrations and MRI abnormalities in patients with CT-negative traumatic brain injury in the TRACK-TBI cohort: a prospective multicentre study. Lancet Neurol. 2019;18(10):953-961.
67. Žurek J, Bartlová L, Fedora M. Hyperphosphorylated neurofilament NF-H as a predictor of mortality after brain injury in children. Brain Inj. 2011;25(2):221-226.
68. Žurek J, Bartlová L, Marek L, Fedora M. Serum S100B protein as a molecular marker of severity in traumatic brain injury in children. Czech Slovak Neurol Neurosurg. 2010;73(1):37-44.
69. Žurek J, Fedora M. Dynamics of glial fibrillary acidic protein during traumatic brain injury in children. J Trauma Inj Infect Crit Care. 2011;71(4):854-859.
70. Lindblad C, Nelson DW, Zeiler FA, et al. Influence of blood-brain barrier integrity on brain protein biomarker clearance in severe traumatic brain injury: a longitudinal prospective study. J Neurotrauma. 2020;37(12):1381-1391.
71. Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165.
72. Ercole A, Thelin EP, Holst A, Bellander BM, Nelson DW. Kinetic modelling of serum S100b after traumatic brain injury. BMC Neurol. 2016;16(1):1-8.
73. Metting Z, Rödiger LA, De Keyser J, van der Naalt J. Structural and functional neuroimaging in mild-to-moderate head injury. Lancet Neurol. 2007;6(8):699-710.
74. Manley GT, Diaz-Arrastia R, Brophy M, et al. Common data elements for traumatic brain injury: recommendations from the biospecimens and biomarkers working group. Arch Phys Med Rehabil. 2010;91(11):1667-1672.
75. Duhaime AC, Gean AD, Haacke EM, et al. Common data elements in radiologic imaging of traumatic brain injury. Arch Phys Med Rehabil. 2010;91(11):1661-1666.
76. Thelin EP, Nelson DW, Bellander BM. Secondary peaks of S100B in serum relate to subsequent radiological pathology in traumatic brain injury. Neurocrit Care. 2014;20(2):217-229.
77. Raabe A, Kopetsch O, Woszczyk A, et al. S100B protein as a serum marker of secondary neurological complications in neurocritical care patients. Neurol Res. 2004;26(4):440-445.
78. Korfias S, Stranjalis G, Psachoulia C, et al. Slight and short-lasting increase of serum S-100B protein in extra-cranial trauma. Brain Inj. 2006;20(8):867-872.
79. Savola O, Pyhtinen J, Leino TK, Siitonen S, Niemelä O, Hillbom M. Effects of head and extracranial injuries on serum protein S100B levels in trauma patients. J Trauma Inj Infect Crit Care. 2004;56(6):1229-1234.
80. Steyerberg EW, Wiegers E, Sewalt C, et al. Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study. Lancet Neurol. 2019;18(10):923-934.
81. Graham NSN, Zimmerman KA, Bertolini G, et al. Multicentre longitudinal study of fluid and neuroimaging BIOmarkers of AXonal injury after traumatic brain injury: the BIO-AX-TBI study protocol. BMJ Open. 2020;10(11):e042093.

Supplemental Digital Content

Supplemental Digital Content 1. Database search strategy.

Supplemental Digital Content 2. Risk of bias. 3 Figures, 4 Tables. Figure 1. AHRQ assessment of observational papers (n = 59). Figure 2. Summary of Newcastle–Ottawa score for cohort studies. Figure 3. Summary of Newcastle–Ottawa score for case–control/case series studies. Table 1. Summary of Newcastle–Ottawa score for cohort studies. Table 2. Summary of Newcastle–Ottawa score for case–control/case series studies. Table 3. Newcastle–Ottawa score breakdown for each cohort study. Table 4. Newcastle–Ottawa score breakdown for each case–control/case series study.

Supplemental Digital Content 3. Results. 5 Tables. Table 1. Basic demographics, imaging modality, and review question for all included studies. Table 2. Biomarkers of TBI highlighted in this review. Table 3. Biomarker, assay, time to biomarker, and key findings of each included study assessing biomarker concentration and volume or number of intracranial lesions. Table 4. Biomarker, assay, time to biomarker, analysis technique, and key findings of each included study assessing biomarker concentration and imaging classification systems. Table 5. Biomarker, assay, time to biomarker, and key findings of each included study assessing biomarker concentration and intracranial lesion type as demonstrated on neuroimaging.


Traumatic brain injury; Biomarkers; Imaging; Neuroimaging

Supplemental Digital Content

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