This preprint is under consideration at Journal of Neuroinflammation. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Eric Peter Thelin
Karolinska Institutet
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2338-4364
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Neuroinflammation following traumatic brain injury (TBI) has been shown to be associated with secondary injury development, however how systemic inflammatory mediators affect this is not fully understood. The aim of this study was to see how systemic inflammation affects markers of neuroinflammation, if this inflammatory response had a temporal correlation between compartments and how different compartments differ in cytokine composition.
Methods: TBI patients recruited to a previous randomized controlled trial studying the effects of the drug anakinra (Kineret®), a human recombinant interleukin-1 receptor antagonist (rhIL1ra), were used (n=10 treatment arm, n=10 control arm). Cytokine concentrations were measured in arterial and jugular venous samples twice a day, as well as in microdialysis-extracted brain extracellular fluid (ECF) following pooling every 6 hours. C-reactive protein level (CRP), white blood cell count (WBC), temperature and confirmed systemic clinical infection were used as systemic markers of inflammation. Principal component analyses, linear mixed-effect models, cross-correlations and multiple factor analyses were used.
Results: Jugular and arterial blood held similar cytokine information content, but brain-ECF was markedly different. No clear arterial to jugular gradient could be seen. No substantial delayed temporal associations between blood and brain compartments were detected. The development of a systemic clinical infection resulted in a significant decrease of IL1-ra, G-CSF, PDGF-ABBB, MIP-1b and RANTES (p<0.05, respectively) in brain-ECF, even if adjusting for injury severity and demographic factors, while an increase in several cytokines could be seen in arterial blood.
Conclusions: Systemic inflammation, and infection in particular, alters cytokine levels with different patterns seen in brain and in blood. Cerebral inflammatory monitoring provides independent information from arterial and jugular samples, which both demonstrate similar information content. These findings could present potential new treatment options in severe TBI patients, and stresses the need of adequate monitoring of inflammatory markers.
Keywords
Traumatic brain injury, inflammation, cytokines, IL1-ra, human, neuroinflammation, infection
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Title of data: Baseline data Description of data: Columns for the first eight rows are for each patient (C, control; I, intervention); moreover, the first four rows have time along the horizontal axis. Columns for the final row are for each cytokine. Rows 1-3 display changes over time of white cell count (WCC), C-reactive protein (CRP) and temperature, with corresponding lines of best fit. Row 4 displays infection status of patients over time. Row 5 shows infection (VAP, ventilator-associated pneumonia; IAS intra-abdominal sepsis; CI, chest infection), severe adverse events (SAE), sex (M, male; F, female) and years of age for each patient. Row 6 shows Stockholm computed tomography (CT) score for each patient, coloured after whether a deterioration was observed during hospital stay. Row 7 shows injury severity score (ISS) for each patient. Row 8 shows fraction of missingness in each compartment and for each patient. Row 9 shows fraction of missing cytokines in each compartment.
- AdditionalFile2.pdf
Title of data: Code Description of data: Code for generating all figures and tables provided. Order of appearance: Figures 1-5, Tables 1-2, Additional file 5-6. Additional file 4 is generated by code for Figures 3-4.
- Additionalfile3.docx
Title of data: Mixed models details Description of data: Statistical details.
- Additionalfile4.xlsx
Title of data: Coefficients and p-values of mixed models Description of data: There are 12 sheets, with coefficients and p-values of mixed models corresponding to each of Figures 3-4 A-B and Additional file 5. In the 6 sheets containing coefficients, maximum and minimum coefficients for every variable are highlighted at the bottom. Theoretical equivalent increments required in other variables than infection to change their respective concentrations of cytokines with maximum or minimum coefficients by the same amount as a change from 0 to 1 in “infection” are shown. Furthermore, theoretical equivalent increments required in other variables than infection to change the concentrations of cytokines with maximum or minimum infection coefficients by the same amount as a change from 0 to 1 in “infection” are shown. The remaining 6 sheets contain p-values of coefficients of linear mixed effect models. Significant values at p < 0.05 were marked with an asterisk in Figures 3-4 A-B.
- Additionalfile5.tiff
Description of data: Coefficients of linear mixed effect models, displayed as heatmaps. The colours of the heatmaps are graded such that red represents positive coefficients and blue represents negative coefficients. All coefficients are normalized using the quotient of their standard deviation and that of the dependent variable. Significant coefficients are highlighted with an asterisk. Independent variables are along the x-axis and the dependent variable for each model is the cytokine of the respective row on the y-axis in either (A) the brain extracellular fluid or (B) arterial blood. Differences in cytokines displayed between the two subfigures is due to insufficient data to generate all coefficients from the model.
- Additionalfile6.pdf
Title of data: Principal component analysis of all cytokines, with additional inflammatory variables Description of data: Unlabelled arrows are loadings of cytokines. Labelled and dashed arrows are post-analysis projections of continuous inflammatory markers. Infection and non-infection shaded areas show confidence intervals of scores labelled as infection or non-infection.
- graphicabstract210116revised210317.png
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CURRENT STATUS: Under Review
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Posted 30 Aug, 2021
Review #1 received
Received 21 Aug, 2021
Reviewer #1 agreed
On 19 Aug, 2021
Reviewers invited
Invitations sent on 18 Aug, 2021
Editor invited
On 16 Aug, 2021
Editor assigned
On 15 Aug, 2021
Submission checks complete
On 15 Aug, 2021
First submitted
On 14 Aug, 2021
This version was not preprinted
Version 2
Posted 26 May, 2021
No community comments so far
Editorial decision: Major Revision
On 20 Jul, 2021
Review #3 received
Received 19 Jul, 2021
Review #2 received
Received 21 Jun, 2021
Reviewer #3 agreed
On 20 Jun, 2021
Review #1 received
Received 20 Jun, 2021
Reviewer #2 agreed
On 19 Jun, 2021
Reviewer #1 agreed
On 09 Jun, 2021
Reviews received
Received 09 Jun, 2021
Reviewers invited
Invitations sent on 03 Jun, 2021
Editor assigned
On 14 May, 2021
Submission checks complete
On 13 May, 2021
Editor invited
On 13 May, 2021
First submitted
On 29 Apr, 2021
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Subject Areas
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A new preprint design is coming!
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This preprint is under consideration at Journal of Neuroinflammation. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Eric Peter Thelin
Karolinska Institutet
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2338-4364
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version (no preprint)
Posted 30 Aug, 2021
Review #1 received
Received 21 Aug, 2021
Reviewer #1 agreed
On 19 Aug, 2021
Reviewers invited
Invitations sent on 18 Aug, 2021
Editor invited
On 16 Aug, 2021
Editor assigned
On 15 Aug, 2021
Submission checks complete
On 15 Aug, 2021
First submitted
On 14 Aug, 2021
This version was not preprinted
Version 2
Posted 26 May, 2021
No community comments so far
Editorial decision: Major Revision
On 20 Jul, 2021
Review #3 received
Received 19 Jul, 2021
Review #2 received
Received 21 Jun, 2021
Reviewer #3 agreed
On 20 Jun, 2021
Review #1 received
Received 20 Jun, 2021
Reviewer #2 agreed
On 19 Jun, 2021
Reviewer #1 agreed
On 09 Jun, 2021
Reviews received
Received 09 Jun, 2021
Reviewers invited
Invitations sent on 03 Jun, 2021
Editor assigned
On 14 May, 2021
Submission checks complete
On 13 May, 2021
Editor invited
On 13 May, 2021
First submitted
On 29 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurobiology of Disease
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Neuroinflammation following traumatic brain injury (TBI) has been shown to be associated with secondary injury development, however how systemic inflammatory mediators affect this is not fully understood. The aim of this study was to see how systemic inflammation affects markers of neuroinflammation, if this inflammatory response had a temporal correlation between compartments and how different compartments differ in cytokine composition.
Methods: TBI patients recruited to a previous randomized controlled trial studying the effects of the drug anakinra (Kineret®), a human recombinant interleukin-1 receptor antagonist (rhIL1ra), were used (n=10 treatment arm, n=10 control arm). Cytokine concentrations were measured in arterial and jugular venous samples twice a day, as well as in microdialysis-extracted brain extracellular fluid (ECF) following pooling every 6 hours. C-reactive protein level (CRP), white blood cell count (WBC), temperature and confirmed systemic clinical infection were used as systemic markers of inflammation. Principal component analyses, linear mixed-effect models, cross-correlations and multiple factor analyses were used.
Results: Jugular and arterial blood held similar cytokine information content, but brain-ECF was markedly different. No clear arterial to jugular gradient could be seen. No substantial delayed temporal associations between blood and brain compartments were detected. The development of a systemic clinical infection resulted in a significant decrease of IL1-ra, G-CSF, PDGF-ABBB, MIP-1b and RANTES (p<0.05, respectively) in brain-ECF, even if adjusting for injury severity and demographic factors, while an increase in several cytokines could be seen in arterial blood.
Conclusions: Systemic inflammation, and infection in particular, alters cytokine levels with different patterns seen in brain and in blood. Cerebral inflammatory monitoring provides independent information from arterial and jugular samples, which both demonstrate similar information content. These findings could present potential new treatment options in severe TBI patients, and stresses the need of adequate monitoring of inflammatory markers.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Title of data: Baseline data Description of data: Columns for the first eight rows are for each patient (C, control; I, intervention); moreover, the first four rows have time along the horizontal axis. Columns for the final row are for each cytokine. Rows 1-3 display changes over time of white cell count (WCC), C-reactive protein (CRP) and temperature, with corresponding lines of best fit. Row 4 displays infection status of patients over time. Row 5 shows infection (VAP, ventilator-associated pneumonia; IAS intra-abdominal sepsis; CI, chest infection), severe adverse events (SAE), sex (M, male; F, female) and years of age for each patient. Row 6 shows Stockholm computed tomography (CT) score for each patient, coloured after whether a deterioration was observed during hospital stay. Row 7 shows injury severity score (ISS) for each patient. Row 8 shows fraction of missingness in each compartment and for each patient. Row 9 shows fraction of missing cytokines in each compartment.
- AdditionalFile2.pdf
Title of data: Code Description of data: Code for generating all figures and tables provided. Order of appearance: Figures 1-5, Tables 1-2, Additional file 5-6. Additional file 4 is generated by code for Figures 3-4.
- Additionalfile3.docx
Title of data: Mixed models details Description of data: Statistical details.
- Additionalfile4.xlsx
Title of data: Coefficients and p-values of mixed models Description of data: There are 12 sheets, with coefficients and p-values of mixed models corresponding to each of Figures 3-4 A-B and Additional file 5. In the 6 sheets containing coefficients, maximum and minimum coefficients for every variable are highlighted at the bottom. Theoretical equivalent increments required in other variables than infection to change their respective concentrations of cytokines with maximum or minimum coefficients by the same amount as a change from 0 to 1 in “infection” are shown. Furthermore, theoretical equivalent increments required in other variables than infection to change the concentrations of cytokines with maximum or minimum infection coefficients by the same amount as a change from 0 to 1 in “infection” are shown. The remaining 6 sheets contain p-values of coefficients of linear mixed effect models. Significant values at p < 0.05 were marked with an asterisk in Figures 3-4 A-B.
- Additionalfile5.tiff
Description of data: Coefficients of linear mixed effect models, displayed as heatmaps. The colours of the heatmaps are graded such that red represents positive coefficients and blue represents negative coefficients. All coefficients are normalized using the quotient of their standard deviation and that of the dependent variable. Significant coefficients are highlighted with an asterisk. Independent variables are along the x-axis and the dependent variable for each model is the cytokine of the respective row on the y-axis in either (A) the brain extracellular fluid or (B) arterial blood. Differences in cytokines displayed between the two subfigures is due to insufficient data to generate all coefficients from the model.
- Additionalfile6.pdf
Title of data: Principal component analysis of all cytokines, with additional inflammatory variables Description of data: Unlabelled arrows are loadings of cytokines. Labelled and dashed arrows are post-analysis projections of continuous inflammatory markers. Infection and non-infection shaded areas show confidence intervals of scores labelled as infection or non-infection.
- graphicabstract210116revised210317.png
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