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Eric Peter Thelin
Karolinska Institutet
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2338-4364
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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 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: The development of a systemic clinical infection results in an altered brain-ECF cytokine response (e.g. increase in G-CSF and decrease in PDGF-ABBB, p<0.05 respectively), even if adjusting for injury severity and demographic factors. rhIL1ra administration had a strong effect on the inflammatory response, independently altering different blood (n=6) and brain cytokine (n=3) levels. No substantial delayed temporal association between blood and brain compartments could be detected. Jugular and arterial blood held similar cytokine information content, but brain-ECF was markedly different. No clear arterial to jugular gradient could be seen.
Conclusions: Systemic inflammation, and infection in particular, alters cerebral cytokine levels, and rhIL1ra administration potently affects both systemic and cerebral cytokine levels. 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
Additional file 1 File format: portable document format (.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
Additional file 2 File format: Portable Document Format (.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 6. Additional file 5 is generated by code for Figures 1-3.
- Additionalfile3.docx
Additional file 3 File format: Office Open XML Document (.docx) Title of data: Mixed models details Description of data: An in-depth explanation of the choices made when applying the linear-mixed models.
- Additionalfile4.docx
Additional file 4 File format: Office Open XML Document (.docx) Title of data: Cross-correlation Description of data: An in-depth explanation of the exclusion algorithm for cross-correlations.
- Additionalfile5.xlsx
Additional file 5 File format: Office Open XML Workbook (.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 1-3 A-B. 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 1-3 A-B.
- Additionalfile6.pdf
Additional file 6 File format: Portable Document Format (.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.
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Eric Peter Thelin
Karolinska Institutet
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2338-4364
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The most recent version of this article is available here.
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 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: The development of a systemic clinical infection results in an altered brain-ECF cytokine response (e.g. increase in G-CSF and decrease in PDGF-ABBB, p<0.05 respectively), even if adjusting for injury severity and demographic factors. rhIL1ra administration had a strong effect on the inflammatory response, independently altering different blood (n=6) and brain cytokine (n=3) levels. No substantial delayed temporal association between blood and brain compartments could be detected. Jugular and arterial blood held similar cytokine information content, but brain-ECF was markedly different. No clear arterial to jugular gradient could be seen.
Conclusions: Systemic inflammation, and infection in particular, alters cerebral cytokine levels, and rhIL1ra administration potently affects both systemic and cerebral cytokine levels. 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
Additional file 1 File format: portable document format (.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
Additional file 2 File format: Portable Document Format (.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 6. Additional file 5 is generated by code for Figures 1-3.
- Additionalfile3.docx
Additional file 3 File format: Office Open XML Document (.docx) Title of data: Mixed models details Description of data: An in-depth explanation of the choices made when applying the linear-mixed models.
- Additionalfile4.docx
Additional file 4 File format: Office Open XML Document (.docx) Title of data: Cross-correlation Description of data: An in-depth explanation of the exclusion algorithm for cross-correlations.
- Additionalfile5.xlsx
Additional file 5 File format: Office Open XML Workbook (.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 1-3 A-B. 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 1-3 A-B.
- Additionalfile6.pdf
Additional file 6 File format: Portable Document Format (.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.
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