- Open Access
Inflammation in delayed ischemia and functional outcomes after subarachnoid hemorrhage
Journal of Neuroinflammation volume 16, Article number: 213 (2019)
Inflammatory mechanism has been implicated in delayed cerebral ischemia (DCI) and poor functional outcomes after subarachnoid hemorrhage (SAH). Identification of cytokine patterns associated with inflammation in acute SAH will provide insights into underlying biological processes of DCI and poor outcomes that may be amenable to interventions.
Serum samples were collected from a prospective cohort of 60 patients with acute non-traumatic SAH at four time periods (< 24 h, 24–48 h, 3–5 days, and 6–8 days after SAH) and concentration levels of 41 cytokines were measured by multiplex immunoassay. Logistic regression analysis was used to identify cytokines associated with DCI and poor functional outcomes. Correlation networks were constructed to identify cytokine clusters.
Of the 60 patients enrolled in the study, 14 (23.3%) developed DCI and 16 (26.7%) had poor functional outcomes at 3 months. DCI was associated with increased levels of PDGF-ABBB and CCL5 and decreased levels of IP-10 and MIP-1α. Poor functional outcome was associated with increased levels of IL-6 and MCP-1α. Network analysis identified distinct cytokine clusters associated with DCI and functional outcomes.
Serum cytokine patterns in early SAH are associated with poor functional outcomes and DCI. The significant cytokines primarily modulate the inflammatory response. This supports earlier SAH studies linking inflammation and poor outcomes. In particular, this study identifies novel cytokine patterns over time that may indicate impending DCI.
Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease caused by the sudden rupture of an intracranial aneurysm which is associated with a high risk of death or severe disability . In those who survive the initial event, injury expansion after SAH can exacerbate underlying brain injury leading to secondary neurological injuries and poor functional outcomes [2, 3]. About 30% of SAH patients will develop delayed cerebral ischemia (DCI) [4, 5] which is a major complication that occurs at 3–14 days after SAH and can lead to poor outcomes [6, 7].
Recently, inflammation after SAH has received attention, mainly as a mediator of injury expansion and as a potential target for therapies, to improve outcomes [8,9,10]. SAH induces a peripheral immune response and activated peripheral immune cells are recruited to the brain parenchyma  where they release cytokines  that induce the upregulation of intrinsic receptors leading to widespread inflammation . Systematic inflammatory activity peaks at 24–48 h after SAH and correlates with poor clinical status at admission . Also, previous studies investigating biomarkers specific to DCI have identified several pro-inflammatory cytokines including TNF-a, IL-1, and IL-6 and anti-inflammatory cytokine including IL-10 and several growth factors  as well. Systemic inflammation has been proposed as one factor that increases susceptibility to DCI after SAH .
In this study, we aim to identify biomarkers and characterize the immune response at different stages after SAH using a combination of statistical and network informatics techniques. We hypothesize that DCI and poor functional outcomes are associated with an increased systemic inflammatory response. The characterization of the inflammatory response can lead to a better understanding of the pathophysiology after SAH.
Patients with acute SAH admitted to the Neuroscience Intensive Care Unit at the Memorial Herman Hospital-Texas Medical Center, Houston, TX, between July 2013 and March 2015 were enrolled in a prospective observational study. Institutional review board approval was obtained (HSC-MS-12-0637). Inclusion criteria were (a) age > 18 years and (b) spontaneous aneurysmal SAH diagnosed by computed tomography (CT) within 24 h of ictus or by xanthochromia in cerebrospinal fluid if the CT was not diagnostic. Exclusion criteria were (a) non-aneurysmal SAH due to trauma, arteriovenous malformation, or mycotic aneurysms and (b) comorbidities that could significantly affect baseline inflammation including autoimmune diseases, suspicion of infection, history of malignancy, and current pregnancy. Written informed consent was obtained from the patient or surrogate.
Clinical and imaging data and definitions
Clinical data collected included age, sex, medical and social history, neurological status at admission based on the World Federation of Neurosurgical Societies scale (WFNS grade) , and loss of consciousness at ictus. Admission CT images were adjudicated for the presence of intraventricular hemorrhage, hydrocephalus, and cerebral infarction [18, 19]. All CT scans were independently evaluated by a study neurologist for the amount and location of blood as categorized by the modified Fisher score (mFS) [20, 21].
Outcome data definitions
We adhered to the proposed definition of DCI  as “The occurrence of focal neurological impairment (such as hemiparesis, aphasia, apraxia, hemianopia, or neglect), or a decrease of at least 2 points on the Glasgow Coma Scale (either on the total score or on one of its individual components [eye, motor on either side, verbal]). This should last for at least 1 hour, is not apparent immediately after aneurysm occlusion, and cannot be attributed to other causes by means of clinical assessment, CT or MRI scanning of the brain, and appropriate laboratory studies.” DCI was diagnosed only after rigorous exclusion of other possible causes. Delayed cerebral ischemia was adjudicated through consensus of at least two attending neurointensivists in weekly research meetings. In the rare instance when no consensus can be achieved, the principal investigator made the final determination. Characteristics of the CT scan including IVH, modified Fisher scale, hydrocephalus, and cerebral infarction were graded by the PI of the study blinded to other aspects of the clinical course. Specifically, any blood in the ventricles was graded as IVH. Functional outcome was assessed by the 0–6 modified Rankin Scale (mRS). The mRS at discharge was assessed by the attending neurointensivist taking care of the patient. All physicians are certified to grade the mRS. The 3-month mRS was obtained via a follow-up phone call by a trained research personnel using a standardized questionnaire. Unfavorable outcome at 3 months after discharge was defined as a mRS of 3–6. Subjects were treated according to standard guidelines.
Sample collection and processing protocol
Serum samples were collected at four predetermined time points after admission: < 24 h (T1), between 24 and 48 h (T2), 3–5 days (T3), and 6–8 days (T4). Forty-one serum cytokine concentrations in picograms per milliliter were measured following the manufacturer’s protocol using a MAGPIX magnetic bead-based ELISA 41-plex assay (EMD Millipore, Billerica, MA): sCD40L, EGF, Eotaxin (CCL11), FGF-2, Flt-3 ligand (FLT3L), Fractalkine (CX3CL1), G-CSF (CSF3), GM-CSF (CSF2), GRO (CXCL1), IFN-α2, IFN-γ, IL-1α, IL-1β, IL-1ra, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-17A, IP-10, MCP-1 (CCL2), MCP-3 (CCL7), MDC (CCL22), MIP-1α, MIP-1β, PDGF-AA, PDGF-AB/BB, CCL5, TGF-α, TNF-α, TNF-β, and VEGF. They were analyzed based on previously published protocols  (see Additional file 3 under Cytokine analysis).
Study subjects’ characteristics were described using unpaired Student’s t tests for continuous variables and chi-square or Fisher’s exact tests for categorical variables. The non-parametric Mann-Whitney U test (p < 0.10) was used to identify candidate cytokines in each time period that differentiated DCI and functional outcomes (Additional file 3: Table S1). Using these candidate cytokines, multivariate logistic regression analyses were performed to find cytokines independently associated with DCI and unfavorable functional outcome after adjusting for clinically relevant variables including age, sex, mFS (as a radiographic surrogate for injury severity), and WFNS grade (as a clinical surrogate for injury severity), respectively. The results of the multivariate logistic regression analysis were reported as an odds ratio (OR) at a 95% confidence interval (CI). A p-value of ≤ 0.05 was considered statistically significant (two-tailed). All statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL).
Two-dimensional correlation network graphs were constructed to show linked relationships among cytokines and to identify clusters of cytokines associated with DCI or poor outcomes at specific time periods. In these correlation networks, each cytokine was represented as a node (vertex) and the relationship between two cytokines as an edge (undirected line). Cytokine measurements (picograms per milliliter) were normalized via Box-Cox transformations and Pearson’s correlation coefficient (PCC) was calculated (R v3.1.3) for each pair of the 41 cytokines to represent the “strength” of the edge connecting the two cytokines. Only strong edges with a PCC of at least 0.75 (and p < 0.001) were used to construct correlation network graphs for DCI/no-DCI and good/poor functional outcome groups (Cytoscape v3.2.1).
During the study period, 151 patients with an acute aneurysmal SAH were admitted to our unit. One hundred twelve patients met the inclusion criteria and consented to participate in the study. Since the baseline inflammatory status (and cytokine levels) can vary subject-to-subject, to preserve homogeneity, we prioritized the inclusion of consecutive subjects from whom the maximum number of samples were available from T1 to T4. Of the 60 subjects included, 52 subjects had 3 or more samples (a total of 189 samples) and only 8 subjects had less than 3 samples (a total of 14 samples). We analyzed 203 samples in total. The differences in demographics between subjects who were included in the study versus subjects who were not included are shown (Additional file 3: Table S2). The mean age of the subjects was 52.2 ± 13.0 years (range 23–83 years) and 46 (76.7%) were women. Seventeen subjects had their aneurysm clipped, 40 had embolization with coils, and 3 subjects were untreated. Fourteen subjects (23.3%) developed DCI on an average of 6.2 days after admission. Sixteen (26.7%) subjects were reported to have poor functional outcome (mRS ranging from 3 to 6 score at 3 months).
Clinical variables across groups
There were no significant differences in clinical variables between the DCI and no-DCI group. Subjects with poor functional outcomes were older and had higher WFNS grade and LOC at ictus, higher mFS grade, and hydrocephalus on admission CT compared to those with good functional outcomes (Table 1). In the multivariable model, age and high-grade WFNS were independent predictors of poor functional outcome (Additional file 3: Table S3).
Cytokines associated with DCI and poor functional outcome
At T1, T2, T3, and T4, the levels of 2, 4, 3, and 13 cytokines, respectively, were significantly different across the DCI and no-DCI group. At the same time points, the levels of 8, 7, 5, and 11 cytokines, respectively, were significantly different across the good and poor outcome groups (Additional file 3: Table S1). Multivariable logistic regression analyses reveal that higher PDGF-ABBB at T2, higher CCL5 at T2 and T3, lower IP-10 at T1, and lower MIP-1α at T4 was independently associated with DCI after adjusting for age, sex, mFS, and WFNS. In addition, IL-6 at T2 and MCP-1 at T4 were independently associated with poor mRS at 3 months after adjusting for age, sex, and mFS but not WFNS grade (Table 2).
We plotted the trends of median values of six cytokines (PDGF-ABBB, RANES, IP-10, MIP-1α, IL-6, and MCP-1) that were independently associated with either DCI or poor outcome. Median levels of PDGF-ABBB (particularly at T2) and CCL5 (particularly at T2 and T3) were consistently higher, and median levels of IP-10 (particularly at T1 and T2) and MIP-1α (particularly at T4) were consistently lower in the DCI group compared to the no-DCI group (Fig. 1a–d). Median levels of IL-6 (particularly at T2) and MCP-1 (particularly at T4) were consistently higher in subjects with poor functional outcome than those with good functional outcome (Fig. 1e, f).
Network analysis in DCI and functional outcomes
We identified distinct cytokine associations in the DCI and functional outcome group from T1 to T4. In the DCI network at T1 and T2, we observed a cluster including VEGFA, EGF, and FGF2 and another cluster consisting of IL9, IL15, and IFNA2 in the DCI cohort. Another small cluster including the cytokines PDGF-ABBB and CCL5 was common to both groups and the cytokines were elevated and independently associated with DCI. This cluster was observed from T2 to T4 as well (Fig. 2 and Additional file 1: Figure S1).
For functional outcome networks, IL-6 and MCP-1 (CCL2) were highly correlated (which were independently associated with poor outcomes) and extend to other cytokines. IL-6 was closely associated with a group of cytokines (including CCL2, TNF-α, IL-8, IL-10, IL-1Ra, and IP-10) which formed a unique cluster in poor outcome group from T1 to T4 (Fig. 3 and Additional file 2: Figure S2).
We investigated the peripheral immune response associated with DCI and functional outcomes. We measured the levels of several serum cytokines at different acute time points after SAH and described them using statistical and informatics methods. In subjects who developed DCI, platelet-released cytokines including PDGFs and CCL5 were elevated and IP-10 and MIP-1α were lower. Early elevations of IL-6 and MCP-1 in post-SAH were independently associated with poor functional outcomes at 3 months. Furthermore, network analysis reveals presence of distinct cytokine clusters across the groups.
Peripheral immune response and DCI
PDGF-ABBB (at T2), CCL5 (at T2 and T3), IP-10 (at T1 and T2), and MIP-1α (at T4) were independently associated with DCI. Platelet-released PDGF is a mitogenic growth factor linked to peripheral thrombosis, and increased PDGF levels have been reported in ischemic brain injury and brain puncture wounds . CCL5 is a member of the CC-chemokine family produced by a variety of cells, including T lymphocytes, platelets, endothelial cells, smooth muscle cells, and glial cells. It can interact with chemokine receptors that recruit leukocytes to inflammatory sites, helping migration of leukocytes across the endothelium contributing to the pathogenic process of arterial injury and atherosclerosis [24, 25]. T-cell recruitment and activation in the injured brain parenchyma could be the prelude to secondary ischemic injury [26, 27] and have been shown to migrate across the vessel wall in response to brain injury . T cell-directed chemokines (including CCL5) are particularly important in stroke prediction including DCI . Interestingly, IP-10 and MIP-1α were lower in DCI compared to the no-DCI group. Both cytokines have shown to be involved in both pro-inflammatory and regulatory mechanisms. IP-10 signaling, with the combination of CCL5, is a known promoter of T cell recruitment and activation . Two known pathways involving IP-10, the restraining of IP-10 production by CCL5  and the inhibition of regulatory T cell recruitment by IP-10 , have been previously implicated in controlling inflammatory response and in neuroprotection after ischemic insult [26, 27]. MIP-1α is primarily involved in inflammation and homeostasis  and plays an important role in both acute and chronic inflammation, primarily in the role of recruiting pro-inflammatory cells. It has been implicated in the development of ischemic brain injury ; however, they also promote healing by inducing inflammatory responses against external injuries .
Cytokine clusters in DCI
A small cluster (including platelet-released PDGF-ABBB and CCL5) was observed in the DCI and no-DCI group. The levels of most cytokines in this cluster were significantly different across the DCI/no-DCI group at T2 and T3 suggesting increased coagulatory and inflammatory activity prior to DCI (which typically occurs after T3). Previous studies have shown that early activation of the coagulation cascade and impairment of the fibrinolytic cascade can lead to DCI after SAH  and the levels of platelet-activating factors increase within 4 days after SAH . Elevated concentrations of the von Willebrand factor (the primary initiator of coagulation) have been shown to be associated with DCI as well [35, 36]. Furthermore, other studies have shown that overactive inhibition of fibrinolysis is associated with DCI . Another cluster including IFNg, MIP1A, IL17a, IL12p70, and VEGFa was observed in DCI. VEGF is involved in neurogenesis and in the inhibition of apoptosis , promoting neuroprotection. Also, vascular CD4+ TH1 cells induce the release of IL12 and IFNg  which are mediators of tissue damage.
In addition to cytokine clusters, we observed quantitative differences in the networks as a whole. At T2 (typically a period prior to when DCI occurs), the DCI and no-DCI group had a similar number of cytokines and significant correlations (19 and 13 vs 24 and 32). At T4 (a period where DCI typically occurs), the DCI group had a higher number of cytokines and correlations compared to no-DCI (22 and 14 vs 12 and 10) suggesting an overall increase in systematic inflammatory mechanisms. Additionally, the concentrations of 13 cytokines differed significantly across the DCI and no-DCI groups at T4 (more so than in any other time period) suggesting a peak in systemic inflammation at T4. Overall, there is a simultaneous activation of angiogenesis, inflammation, and thrombotic mechanisms during the course of DCI .
Peripheral immune response and functional outcomes
IL-6 (at T2) and MCP-1 (at T4) were independently associated with functional outcomes confirming the importance of the association between early inflammation and clinical outcomes. Our findings are consistent with previous studies where IL-6 and MCP-1 were shown to be sequentially expressed in CSF after SAH [40, 41]. MCP-1 is a CC chemokine family member and can regulate leukocyte trafficking by affecting their chemotactic activity, activating inflammatory cells, and modulating interactions between leukocytes and endothelial cells . Previous studies have shown that activated mononuclear leukocytes can be found surrounding the major cerebral arteries in the subarachnoid space after SAH , and MCP-1 has been found to be significantly expressed in the major cerebral arteries in experimental vasospasm . Our findings confirm that early rise in serum IL-6 and MCP-1 are associated with poor clinical status and consequently poor clinical outcomes. This finding further emphasizes the importance of “early brain injury” (defined as the injury process occurring < 72 h after aneurysm rupture) and its impact on functional outcomes [44, 45].
Cytokine clusters in poor functional outcomes
In the poor functional outcome group, a unique cluster including the cytokines IL1R1, TNFa, IL8, IL6, CCL2, CCL11, IP10, and CSF3 (Fig. 3) was observed at T1 to T4. This suggests the presence of inflammatory mechanisms mediated by IL1a, TNFa, and IL6. IL1 has been shown to upregulate the pro-inflammatory IL6. It is released by macrophages and microglia via an endogenous DAMP-mediated inflammatory response triggered by heme released from lysed blood cells . IL1R1 is a IL1 receptor antagonist which blocks the actions of IL1 and has been identified as a therapeutic candidate in ischemic diseases . In pre-clinical models, administration of IL1RA has been shown to reduce blood-brain barrier disruption and mitigation of cell death, and, clinically, it has also shown to reduce inflammation as well . We observed a high correlation between TNFa and IL8 in MRS ≥ 4 group in comparison to MRS ≤ 3 (0.80 vs 0.33 and T1). This strong correlation persisted through T4 indicating the mediation of inflammation via persistent activation of the NF-kB-dependent pathway. It is likely that inflammation-triggered oxidative stress increases TNFa, which in turn increases IL8 expression . TNFa activates vascular Rac-1 promoting vasoconstriction, and targeting TNFa has shown to mitigate neuronal injury [50, 51]. Network analysis shows strong correlation between TNFa, IL8, and IL1R1 suggesting that IL1- and NF-kB-mediated responses are associated. Additionally, a strong correlation between IL-6 and MCP-1 was observed in the poor outcome group. It is purported that IL-6 may induce MCP-1 expression via signal transducer and activator of transcription 3 after SAH . Overall, network analysis suggests simultaneous activation of pro-inflammatory and anti-inflammatory mechanisms sustained through the acute stages of SAH which consequently leads to poor functional outcomes. This analysis also shows that cytokines that are not significantly elevated across groups can still play an important role in the underlying inflammatory mechanisms.
This study is a single-center observational study and has a relatively small number of enrolled subjects. This can be a possible source of confounders and bias. Second, while several methodological efforts were employed to remove weak correlations, the complete elimination of spurious correlations in the network analysis is not possible. Third, while we excluded subjects with conditions (including suspicion of infection) at admission, a modest number of subjects developed infections (mostly in the T3 and T4 period). Infections are an issue that involves patient cohorts admitted in the ICU; however, we believe that they have a negligible effect on the inflammatory status on subjects in our cohort (see Additional file 3 under Infection). Fourth, as per institutional protocol, all patients are administered steroids on admission. Patients were started on dexamethasone 4 mg q6 on presentation and tapered over a week. There is considerable variation in this across centers; unfortunately, we cannot comment on this as all our patients receive steroids. However, since the subjects across both groups (DCI vs no-DCI and good vs poor outcomes) received steroids, it is likely that the inflammatory findings are consequential of SAH pathophysiology. Fifth, the nature of the study design only permits the observation of associations between the cytokines and does not present any causal mechanisms. To ascertain whether the observations contribute to DCI or poor outcomes, interventional trials are to be undertaken. Fifth, only peripheral inflammation was examined. Examining central inflammation by analyzing cerebrospinal fluid and/or cerebral microdialysis analysis could further our understanding of the underlying pathophysiological mechanisms.
We identified serum cytokines at different time points that were independently associated with DCI and functional outcomes. The disruption in immune activity at the acute phase of SAH persisted through the subacute phase as evidenced by the complex cytokine interactions captured through network analysis. This study is an important step towards an integrated approach to describe global inflammatory reaction after SAH.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Delayed cerebral ischemia
Modified Fisher score
Modified Rankin Scale
World Federation of Neurosurgical Societies scale
Huang J, van Gelder JM. The probability of sudden death from rupture of intracranial aneurysms: a meta-analysis. Neurosurgery. 2002;51:1101–5 discussion 1105-1107.
Rinkel GJ, Algra A. Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol. 2011;10:349–56.
Choi HA, Bajgur SS, Jones WH, Savarraj JP, Ko SB, Edwards NJ, et al. Quantification of cerebral edema after subarachnoid hemorrhage. Neurocrit Care. 2016;25:64–70.
Xu HL, Garcia M, Testai F, Vetri F, Barabanova A, Pelligrino DA, et al. Pharmacologic blockade of vascular adhesion protein-1 lessens neurologic dysfunction in rats subjected to subarachnoid hemorrhage. Brain Res. 2014;1586:83–9.
Miller BA, Turan N, Chau M, Pradilla G. Inflammation, vasospasm, and brain injury after subarachnoid hemorrhage. Biomed Res Int. 2014;2014:384342.
Roos YB, de Haan RJ, Beenen LF, Groen RJ, Albrecht KW, Vermeulen M. Complications and outcome in patients with aneurysmal subarachnoid haemorrhage: a prospective hospital based cohort study in the Netherlands. J Neurol Neurosurg Psychiatry. 2000;68:337–41.
Ahn SH, Savarraj JP, Pervez M, Jones W, Park J, Jeon SB, et al. The subarachnoid hemorrhage early brain edema score predicts delayed cerebral ischemia and clinical outcomes. Neurosurgery. 2018;83:137–45.
Lucke-Wold BP, Logsdon AF, Manoranjan B, Turner RC, McConnell E, Vates GE, et al. Aneurysmal subarachnoid hemorrhage and neuroinflammation: a comprehensive review. Int J Mol Sci. 2016;17:497.
Savarraj JP, McGuire MF, Parsha K, Hergenroeder G, Bajgur S, Ahn S, et al. Disruption of thrombo-inflammatory response and activation of a distinct cytokine cluster after subarachnoid hemorrhage. Cytokine. 2018;111:334–41.
Bijlenga P, Morita A, Ko NU, Mocco J, Morel S, Murayama Y, et al. Common data elements for subarachnoid hemorrhage and unruptured intracranial aneurysms: recommendations from the working group on subject characteristics. Neurocrit Care. 2019;30:20–7.
Moraes L, Grille S, Morelli P, Mila R, Trias N, Brugnini A, et al. Immune cells subpopulations in cerebrospinal fluid and peripheral blood of patients with aneurysmal subarachnoid hemorrhage. SpringerPlus. 2015;4:195.
Kooijman E, Nijboer CH, van Velthoven CT, Kavelaars A, Kesecioglu J, Heijnen CJ. The rodent endovascular puncture model of subarachnoid hemorrhage: mechanisms of brain damage and therapeutic strategies. J Neuroinflammation. 2014;11:2.
Kwon MS, Woo SK, Kurland DB, Yoon SH, Palmer AF, Banerjee U, et al. Methemoglobin is an endogenous toll-like receptor 4 ligand-relevance to subarachnoid hemorrhage. Int J Mol Sci. 2015;16:5028–46.
Savarraj JPJ, Parsha K, Hergenroeder GW, Zhu L, Bajgur SS, Ahn S, et al. Systematic model of peripheral inflammation after subarachnoid hemorrhage. Neurology. 2017;88:1535–45.
Niwa A, Osuka K, Nakura T, Matsuo N, Watabe T, Takayasu M. Interleukin-6, mcp-1, ip-10, and mig are sequentially expressed in cerebrospinal fluid after subarachnoid hemorrhage. J Neuroinflammation. 2016;13:217.
McMahon CJ, Hopkins S, Vail A, King AT, Smith D, Illingworth KJ, et al. Inflammation as a predictor for delayed cerebral ischemia after aneurysmal subarachnoid haemorrhage. J Neurointervent Surg. 2013;5:512–7.
Rosen DS, Macdonald RL. Grading of subarachnoid hemorrhage: modification of the world federation of neurosurgical societies scale on the basis of data for a large series of patients. Neurosurgery. 2004;54:566–76.
Dorai Z, Hynan LS, Kopitnik TA, Samson D. Factors related to hydrocephalus after aneurysmal subarachnoid hemorrhage. Neurosurgery. 2003;52:763–71.
Claassen J, Bernardini GL, Kreiter K, Bates J, Du YE, Copeland D, et al. Effect of cisternal and ventricular blood on risk of delayed cerebral ischemia after subarachnoid hemorrhage: the Fisher scale revisited. Stroke. 2001;32:2012–20.
Frontera JA, Claassen J, Schmidt JM, Wartenberg KE, Temes R, Connolly ES Jr, et al. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified Fisher scale. Neurosurgery. 2006;59:21–7 discussion 21-27.
Frontera JA, Fernandez A, Schmidt JM, Claassen J, Wartenberg KE, Badjatia N, et al. Defining vasospasm after subarachnoid hemorrhage: what is the most clinically relevant definition? Stroke. 2009;40:1963–8.
Vergouwen MD, Vermeulen M, van Gijn J, Rinkel GJ, Wijdicks EF, Muizelaar JP, et al. Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke. 2010;41:2391–5.
von Hundelshausen P, Weber KS, Huo Y, Proudfoot AE, Nelson PJ, Ley K, et al. Rantes deposition by platelets triggers monocyte arrest on inflamed and atherosclerotic endothelium. Circulation. 2001;103:1772–7.
Qin X, He Z, Zhao D, Li L, Yuan L. The rantes gene promoter polymorphisms are associated with the risk of atherothrombotic cerebral infarction in northern han chinese. Clinica Chimica Acta; Int J Clin Chem. 2011;412:1112–5.
Liesz A, Suri-Payer E, Veltkamp C, Doerr H, Sommer C, Rivest S, et al. Regulatory t cells are key cerebroprotective immunomodulators in acute experimental stroke. Nat Med. 2009;15:192–9.
Shichita T, Sugiyama Y, Ooboshi H, Sugimori H, Nakagawa R, Takada I, et al. Pivotal role of cerebral interleukin-17-producing gammadeltat cells in the delayed phase of ischemic brain injury. Nat Med. 2009;15:946–50.
Bartholomaus I, Kawakami N, Odoardi F, Schlager C, Miljkovic D, Ellwart JW, et al. Effector t cell interactions with meningeal vascular structures in nascent autoimmune cns lesions. Nature. 2009;462:94–8.
Canoui-Poitrine F, Luc G, Mallat Z, Machez E, Bingham A, Ferrieres J, et al. Systemic chemokine levels, coronary heart disease, and ischemic stroke events: the prime study. Neurology. 2011;77:1165–73.
Heller EA, Liu E, Tager AM, Yuan Q, Lin AY, Ahluwalia N, et al. Chemokine cxcl10 promotes atherogenesis by modulating the local balance of effector and regulatory t cells. Circulation. 2006;113:2301–12.
Potteaux S, Combadiere C, Esposito B, Lecureuil C, Ait-Oufella H, Merval R, et al. Role of bone marrow-derived cc-chemokine receptor 5 in the development of atherosclerosis of low-density lipoprotein receptor knockout mice. Arterioscler Thromb Vasc Biol. 2006;26:1858–63.
Menten P, Wuyts A, Van Damme J. Macrophage inflammatory protein-1. Cytokine Growth Factor Rev. 2002;13:455–81.
Kim JS, Gautam SC, Chopp M, Zaloga C, Jones ML, Ward PA, et al. Expression of monocyte chemoattractant protein-1 and macrophage inflammatory protein-1 after focal cerebral ischemia in the rat. J Neuroimmunol. 1995;56:127–34.
Rowland MJ, Hadjipavlou G, Kelly M, Westbrook J, Pattinson KT. Delayed cerebral ischaemia after subarachnoid haemorrhage: looking beyond vasospasm. Br J Anaesth. 2012;109:315–29.
Hirashima Y, Nakamura S, Endo S, Kuwayama N, Naruse Y, Takaku A. Elevation of platelet activating factor, inflammatory cytokines, and coagulation factors in the internal jugular vein of patients with subarachnoid hemorrhage. Neurochem Res. 1997;22:1249–55.
Hirashima Y, Nakamura S, Suzuki M, Kurimoto M, Endo S, Ogawa A, et al. Cerebrospinal fluid tissue factor and thrombin-antithrombin iii complex as indicators of tissue injury after subarachnoid hemorrhage. Stroke. 1997;28:1666–70.
Frijns CJ, Fijnheer R, Algra A, van Mourik JA, van Gijn J, Rinkel GJ. Early circulating levels of endothelial cell activation markers in aneurysmal subarachnoid haemorrhage: associations with cerebral ischaemic events and outcome. J Neurol Neurosurg Psychiatry. 2006;77:77–83.
Ikeda K, Asakura H, Futami K, Yamashita J. Coagulative and fibrinolytic activation in cerebrospinal fluid and plasma after subarachnoid hemorrhage. Neurosurgery. 1997;41:344–9 discussion 349-350.
Cao L, Jiao X, Zuzga DS, Liu Y, Fong DM, Young D, et al. Vegf links hippocampal activity with neurogenesis, learning and memory. Nat Genet. 2004;36:827–35.
Arumugam TV, Granger DN, Mattson MP. Stroke and t-cells. Neuromolecular Med. 2005;7:229–42.
Mathiesen T, Andersson B, Loftenius A, von Holst H. Increased interleukin-6 levels in cerebrospinal fluid following subarachnoid hemorrhage. J Neurosurg. 1993;78:562–7.
Olson TS, Ley K. Chemokines and chemokine receptors in leukocyte trafficking. Am J Physiol Regul Integr Comp Physiol. 2002;283:R7–28.
Kubota T, Handa Y, Tsuchida A, Kaneko M, Kobayashi H, Kubota T. The kinetics of lymphocyte subsets and macrophages in subarachnoid space after subarachnoid hemorrhage in rats. Stroke. 1993;24:1993–2000 discussion 2000-1991.
Lu H, Shi JX, Chen HL, Hang CH, Wang HD, Yin HX. Expression of monocyte chemoattractant protein-1 in the cerebral artery after experimental subarachnoid hemorrhage. Brain Res. 2009;1262:73–80.
Savarraj J, Parsha K, Hergenroeder G, Ahn S, Chang TR, Kim DH, et al. Early brain injury associated with systemic inflammation after subarachnoid hemorrhage. Neurocrit Care. 2018;28:203–11.
Kuhle J, Lindberg RL, Regeniter A, Mehling M, Steck AJ, Kappos L, et al. Increased levels of inflammatory chemokines in amyotrophic lateral sclerosis. Eur J Neurol. 2009;16:771–4.
Greenhalgh AD, Brough D, Robinson EM, Girard S, Rothwell NJ, Allan SM. Interleukin-1 receptor antagonist is beneficial after subarachnoid haemorrhage in rat by blocking haem-driven inflammatory pathology. Dis Model Mech. 2012;5:823–33.
Brough D, Tyrrell PJ, Allan SM. Regulation of interleukin-1 in acute brain injury. Trends Pharmacol Sci. 2011;32:617–22.
Galea J, Ogungbenro K, Hulme S, Patel H, Scarth S, Hoadley M, et al. Reduction of inflammation after administration of interleukin-1 receptor antagonist following aneurysmal subarachnoid hemorrhage: results of the subcutaneous interleukin-1ra in sah (scil-sah) study. J Neurosurg. 2018;128:515–23.
Vlahopoulos S, Boldogh I, Casola A, Brasier AR. Nuclear factor-kappab-dependent induction of interleukin-8 gene expression by tumor necrosis factor alpha: evidence for an antioxidant sensitive activating pathway distinct from nuclear translocation. Blood. 1999;94:1878–89.
Yagi K, Lidington D, Wan H, Fares JC, Meissner A, Sumiyoshi M, et al. Therapeutically targeting tumor necrosis factor-alpha/sphingosine-1-phosphate signaling corrects myogenic reactivity in subarachnoid hemorrhage. Stroke. 2015;46:2260–70.
Vecchione C, Frati A, Di Pardo A, Cifelli G, Carnevale D, Gentile MT, et al. Tumor necrosis factor-alpha mediates hemolysis-induced vasoconstriction and the cerebral vasospasm evoked by subarachnoid hemorrhage. Hypertension (Dallas, Tex : 1979). 2009;54:150–6.
We would like to acknowledge the physicians and nurses at Memorial Hermann Hospital Neuroscience Intensive Care Unit.
H Alex Choi receives funding from the CCTS Scholar Program, University of Texas Health Science at Houston, McGovern Medical School.
Ethics approval and consent to participate
Our study was reviewed and approved by the institutional review board at the Memorial Herman Hospital-Texas Medical Center, Houston, TX (HSC-MS-12-0637), and written informed consent was obtained from the family of all participants. The study was adhered to the tenets of the most recent revision of the Declaration of Helsinki.
Consent for publication
Written informed consent was obtained from all participants.
The authors declares that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figure S1. Dynamics of inflammatory mechanims across DCI beyond 48 hours. A cluster including VEGF, FGF2, IL12p70, IL17A and IFNg, and separate small cluster involving PDGF-ABBB and CCL5 was observed. Quantitative difference in serum level of these cytokines was further increased especially at 6–8 days after stroke onset. ‘↑’ indicates elevated concentration levels of the cytokine in the DCI group compared to the no-DCI group. ‘↓’ indicates decreased concentration levels of the cytokine in the no-DCI group compared to the DCI group. (TIF 98 kb)
Figure S2. Dynamics of inflammatory mechanisms across poor functional outcomes beyond 48 hours. The cluster comprising of IL1R1, TNFa, IL8, IL6, CCL2 (MCP1), CCL11 and GCSF (CSF3), IL10 and IP10 was observed in the poor outcome group at the acute stages after SAH. (TIF 126 kb)
Table S1. Cytokines associated with DCI and functional outcomes in each time point after SAH. Table S2. Differences in baseline variables between subjects who were enrolled and not-enrolled in the study. Table S3. Multivariable logistic regression analysis to identify predictors of poor clinical outcomes. (DOCX 54 kb)
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Ahn, SH., Savarraj, J.P.J., Parsha, K. et al. Inflammation in delayed ischemia and functional outcomes after subarachnoid hemorrhage. J Neuroinflammation 16, 213 (2019). https://doi.org/10.1186/s12974-019-1578-1
- Subarachnoid hemorrhage
- Delayed cerebral ischemia
- Functional outcome
- Cytokine network