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. 2022 Jan:75:103777.
doi: 10.1016/j.ebiom.2021.103777. Epub 2021 Dec 24.

Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study

Daniel P Whitehouse  1 , Miguel Monteiro  2 , Endre Czeiter  3 , Thijs Vande Vyvere  4 , Fernanda Valerio  1 , Zheng Ye  1 , Krisztina Amrein  5 , Konstantinos Kamnitsas  2 , Haiyan Xu  6 , Zhihui Yang  6 , Jan Verheyden  7 , Tilak Das  8 , Evgenios N Kornaropoulos  1 , Ewout Steyerberg  9 , Andrew I R Maas  10 , Kevin K W Wang  11 , András Büki  5 , Ben Glocker  2 , David K Menon  1 , Virginia F J Newcombe  12 ; CENTER-TBI Participants and Investigators
Affiliations

Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study

Daniel P Whitehouse et al. EBioMedicine. 2022 Jan.

Abstract

Background: We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI.

Methods: Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering.

Findings: 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0·48-0·49; p<0·05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0·44-0·44; p<0·01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83·9% of variance, with phenotyping characteristics related to clinical injury severity.

Interpretation: Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury.

Funding: CENTER-TBI is funded by the European Union 7th Framework programme (EC grant 602150).

Keywords: Biomarkers; Brain injury, Traumatic; Computed tomography; Neuroimaging.

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Conflict of interest statement

Declaration of interests DKM reports: grants from the European Union (EU), the National Institute for Health Research UK supporting the submitted work; grants from GlaxoSmithKline Ltd and Lantmannen AB, consulting fees from Calico LLC, GlaxoSmithKline Ltd, Lantmannen AB and NeuroTrauma Sciences LLC, and personal fees from Integra Neurosciences outside the submitted work. BG has received grants from European Commission and UK Research and Innovation Engineering and Physical Sciences Research Council, during the conduct of this study; and is Scientific Advisor for Kheiron Medical Technologies, Advisor and Scientific Lead of the HeartFlow-Imperial Research Team, outside the submitted work. MM reports a ERC grant agreement and consultancy fees from Triradiate Industries, outside the submitted work. ES reports a FP7 grant from the EU supporting the submitted work, and royalties for the book “Clinical Prediction Models” published by Springer. AIRM reports a FP7 grant from the EU supporting the submitted work, and grants from NeuroTrauma Sciences, Hannelore Kohl Foundation, and IntegraLife Sciences, personal fees from PresSura Neuro as DSMB chairman outside of the submitted work. KKWW reports a FP7 grant from the EU supporting the submitted work, and as a shareholder of Gryphon Bio, Inc. VFJN reports an Academy of Medical Sciences/The Health Foundation Clinician Scientist Fellowship, during the conduct of this study; a grant from Roche Pharmaceuticals, and honorarium for talks from Neurodiem, outside the submitted work. All other authors declare no competing interests.

Figures

Fig 1
Figure 1
The Log of serum GFAP (a), NFL (b), NSE (c), S100B (d), total-tau (e) and UCH-L1 (f) concentration by Marshall CT score.Violin plots and boxplots provide median, range and 25-75th percentile of the log10 biomarker concentration per Marshall CT score grouping I (n=1154), II (n=1120), II-IV (n=120) and V-VI (n=475). P values determined by the Dunn Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons was used for group wise comparison across different CT findings. Significance levels are displayed for statistically significant group wise comparisons, * = p< 0·05, ** = p< 0·01, *** = p< 0·001. Red dotted line indicates the median of Marshall Score I group.
Fig 2
Figure 2
The Log of serum GFAP (a), NFL (b), NSE (c), S100B (d), total-tau (e) and UCH-L1 (f) concentration by CT pathology. Violin plots and boxplots provide median, range and 25-75th percentile of the log10 biomarker concentration per pathoanatomical grouping/isolated lesion type: (No acute abnormality (n=1069), Skull fracture (n=86), EDH (n=47), Acute SDH (n=89), SAH (n=184), IPH (n=41), DAI (n=31), Mixed Lesion (n=1313). P values determined by the Dunn Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons was used for group wise comparison across different CT findings. Significance levels are displayed for statistically significant group wise comparisons, * = p< 0·05, ** = p< 0·01, *** = p< 0·001. Patients with mixed lesion on CT (defined as 2 or more lesions types) (n=1362) and those with isolated mixed density SDH (n=9) are not included as a boxplot.
Fig 3
Figure 3
Z-score signatures across different pathoanatomical groups. The Biomarker concentrations presented as means and standard errors of Z scores with the reference group being patients with a normal CT scan following TBI. Panel a shows mixed lesion (n=1313) and isolated pathology groups (Skull fracture (n=86), EDH (n=47), Acute SDH (n=89), SAH (n=184), IPH (n=41), DAI (n=31)) in relation to biomarker expression with patients with normal CT (n=1069) as the reference group. Panel b shows the number of different intracranial lesion types reported on each CT image for patients with mixed lesions, in relation to biomarker expression.
Fig 4
Figure 4
The Log of serum GFAP (a), NFL (b), NSE (c), S100B (d), total-tau (e) and UCH-L1 (f) concentration between patients with DAI and IVH (n = 12), EDH alone (n = 47) or SDH, IPH and tSAH (n = 44). Violin plots and boxplots provide median, range and 25-75th percentile of the log10 biomarker concentration per pathological phenotypes as determined by hierarchical clustering of qualitative CT reports: DAI and IVH (n = 12), EDH alone (n = 47) or SDH, IPH and tSAH (n = 44). P values determined by the Dunn Kruskal-Wallis test with Benjamini-Hochberg correction for multiple comparisons was used for group wise comparison across different CT findings. Significance levels are displayed for statistically significant group wise comparisons, * = p< 0·05, ** = p< 0·01, *** = p< 0·001.
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