Stroke is the second cause of death and one of the main causes of disability worldwide (Roger et al. 2012). Early neurological deterioration (END) is an important concern during acute stroke management and in some series it occurs up to 40% (Arenillas et al. 2002; Lin et al. 2012). Although there is not an international consensus about END, one consistently used definition is the increase of 4 or more points in the National Institutes of Health Stroke Scale (NIHSS) within the first 48–72 h (Alawneh et al. 2009). Several causes of complication such as hemorrhagic transformation, arterial reocclusions, or malignant edema may appear in the first hours after stroke contributing to END. Although nowadays there does not exist a specific therapeutic treatment to solve neurological deterioration, the admission of stroke patients into specialized stroke units have demonstrated to prevent END and thus to reduce the rate of poor outcome after ischemic stroke (Roquer et al. 2008). Therefore, the prediction of END is one of the challenges in stroke, as an accurate identification of patients more prone to worsen might help to optimize the admission to the scarce stroke units.
Some acute neuroimaging factors have been associated with END, such as hypodensity or hyperdense middle cerebral artery sign on computerized tomography or large diffusion weighted image in magnetic resonance (Alawneh et al. 2009). As neuroimaging techniques are not broadly accessible, the use of blood biomarkers could be a more feasible option. For that proposal, the exploration of molecules which could anticipate the development of END is becoming increasingly popular. Some candidates have been explored in that context, such as interleukin-6 (IL-6) (Vila et al. 2000) or b-type natriuretic peptide (Montaner et al. 2012), although their added value to clinical prognostic models is still unclear (Whiteley et al. 2009; Montaner et al. 2012).
The complexity of blood and the interindividual variability make it difficult to validate differential protein expression to distinguish among disease stages. To obtain a common signature of protein changes which are associated with a specific stage, pooled-blood samples are being employed in other diseases (Ernoult et al. 2010; Fragnoud et al. 2012) and are recommended for high-throughput proteomics (Barker et al. 2006). Moreover, multiple subpools, which are generated by random distribution of individual samples, can be performed to estimate variation within population (Karp and Lilley 2009).
To go in depth in the physiopathology of ischemic stroke we performed the first exploratory study of the plasma proteome by screening an antibodies library with a subpooled samples approach. Nowadays there is an assortment of different antibodies libraries in multiplexed arrays available in the market that allow the study of hundreds of proteins involved in different pathways while using few amount of patients' samples. We have used the SearchLight® library, which included 177 antibodies covering the exploration of several cellular processes in a multiplex ELISA-based manner.
We aimed to discover a common signature of protein expression changes for those patients who have an early poor outcome after stroke. Furthermore, after replication of our results in individual stroke samples, we assessed the added value of our biomarker candidates to clinical predictive models by means of comparative statistical metrics such as Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) Indexes.
- Top of page
- Materials and methods
- Acknowledgments and conflict of interest disclosure
- Supporting Information
In this study, we first described the plasma protein profile which is associated with early neurological outcome after ischemic stroke. Furthermore, a methodological improvement was attempted both technically (by introducing the pooling strategy) and statistically (by using comparative metrics). As a result two new outcome biomarkers (BD-2 and IL-4R) were discovered.
We consider the use of pooling strategies highly suitable for the discovery of new candidates to become biomarkers. Pooling reduces the biological variability, as it is assumed that the expression in the pooled sample averages the expression of the individual samples which were contained in the pool (Kendziorski et al. 2005). Another good point of pooling is the reduced costs, both in number of assays and number of biological samples. The use of a subpooling strategy to gain accuracy by including some variability within each group (Zhang et al. 2007) and the replication of the discovery findings in individual samples (Sham et al. 2002; Walker et al. 2010) contribute to a more desirable approach.
Following this design, we found 35 altered proteins which are involved in biological and cellular processes with a known role in ischemic stroke, such as inflammation, apoptosis, or the coagulation cascade (Mehta et al. 2007; Jickling and Sharp 2011). Some of these proteins have been previously associated with stroke outcome, confirming the validity of our strategy: fibrinogen (del Zoppo et al. 2009), D-dimer (Welsh et al. 2009), protein C (Mendioroz et al. 2009), resistin (Efstathiou et al. 2007), metalloproteinase 2 (MMP-2) (Montaner et al. 2001), MCP-1/CCL2 (Worthmann et al. 2010), and MIP-1a/CCL3 (Zaremba et al. 2006).
We have only detected one protein, OPN, which discloses an inversely associated relation in our study and in previous literature; while we found higher levels in those patients who improved, in a previous study from our group OPN was oppositely associated with long-term poor prognosis (Mendioroz et al. 2011). Moreover, some molecules that have been associated with short-term prognosis in our study, such as C-peptide (O'Neill et al. 1991), P-selectin (Bath et al. 1998), and IL-8 (Zeng et al. 2013), have been studied in other cohorts without any association regarding stroke prognosis. Although following different approaches, the possibility of false-positive results could not be overlooked. On the other hand, those proteins typically associated with poor prognosis, such IL-6 (Smith et al. 2004) and C-reactive protein (CRP) (Montaner et al. 2006), were not associated in our pooled cohort. This could be related to the dilution effect which was commented above, a plausible explanation as neither IL-6 nor CRP have shown a great association with outcome in individual studies (Whiteley et al. 2009).
Nonetheless, our discovery experiment provides a list of interesting candidates which have not been previously explored in the context of stroke prognosis. From them, we chose 10 candidates (BCA-1/CXCL13, BD-2, Exodus-2/CCL21, IL-12p40, IL-4R, LIF, MIP-1b, MIP-3b, PAI-1 active form, and TWEAK) to be tested in our replication cohort. BD-2 and IL-4R were found as independent predictors of neurological worsening in the acute phase of ischemic stroke, within 24 and 48 h after symptoms onset, mainly when the infarct has not been fully established.
Human beta-defensins play a role in immune-inflammatory responses, mainly acting as antimicrobial peptides and also as chemoattractants. BD-2 is mainly expressed in the respiratory tract epithelia, but it can also be expressed by monocytes and macrophages and, at brain level, by capillary endothelial cells and astrocytes. BD-2 expression is inducible by cytokines, such as tumor necrosis factor alpha (TNF-a) or IL-1b, and bacteria (Schröder and Harder 1999). Moreover, in vitro and in vivo models have shown an increase in expression and the release of BD-2 after hypoxic/ischemic stimuli (Liu et al. 2009; Nickel et al. 2012). In stroke patients, only the copy number variant of BD-2 gene (DEFB4) has been studied and reflects a higher protein plasma concentration in those patients with more gene copies (Tiszlavicz et al. 2012). Thus, the higher levels of BD-2 which were associated with worsening might be reflecting the inflammatory state that is produced after stroke.
IL-4 is a multifunctional cytokine which is required for the development of Th2 cells and thus with anti-inflammatory properties. To exert this effect, IL-4 binds to membrane-bound IL-4R; however, there exists a soluble form of IL-4R (mainly produced by MMPs-mediated proteolysis (Jung et al. 1999a)) which can block or prolong IL-4 effects depending on its concentration (Jung et al. 1999b). Together with the higher expression of MMPs detected in patients who worsened, the association of soluble IL-4R with poor outcome is also in accordance with the pro-inflammatory state that is suggested by BD-2.
We also wanted to prove that, aside from being independent predictors, both biomarkers add value to clinical information. Clinical variables typically associated with neurological deterioration are stroke severity at admission and age, as non-modifiable factors, together with risk factors such as diabetes mellitus or arterial hypertension. The clinical variables that were independently associated with early worsening in our cohort comprise NIHSS at admission and diabetes mellitus, after being adjusted by age and gender. The deleterious effect of diabetes mellitus on stroke outcome has been previously observed by several groups, as recently reviewed (Desilles et al. 2013). When this clinical model was considered, comparison of AUCs showed how BD-2 and IL-4R, alone or in combination, improved the measure of discrimination of clinical variables, which changes from acceptable to excellent discrimination between patients who worsened and those who did not. However, some authors suggested that the comparison of AUCs is not the best way to know the additional value of biomarkers (Pepe et al. 2004). Therefore, we have employed statistical tools which are being applied to know the capacities of biomarkers regarding discrimination (IDI) and patients' reclassification into risk categories (NRI). These two tests are based on the risk prediction models (i.e. logistical regression models) and the probability of an event for each patient (Pickering and Endre 2012). Both IDI and NRI can be calculated separately for events (i.e., worsening) and non-events (i.e., non-worsening) to better interpret how the biomarker is adding value, if it is by better recognizing the events or if it is by reducing the rate of false positives for non-events. Regarding our results for IDI test, we found an improvement in discrimination by the identification of real events for BD-2 and particularly for the combination of both BD-2 and IL-4R. This may be explained because BD-2 cut-off point had great sensitivity and IL-4R increased specificity, thus, the combination of both biomarkers gained statistical power. Regarding reclassification of the patients into extreme risk categories, BD-2 alone showed the best performance at 24 h, reclassifying correctly a 28.2% of patients, mainly by reducing false positives. At 48 h, the combination of both BD-2 and IL-4R achieved this non-negligible reclassification rate. If confirmed in prospective studies as prognostic biomarkers, the measurement of plasma levels of BD-2 and IL-4R in the acute phase of stroke might help in decision-making processes, such as in giving information to patients and relatives, optimization of inclusion in specialized stroke units, evaluation of treatment benefits, or inclusion into clinical trials.
Our study stands with several limitations. The results of our discovery phase have not been corrected in accordance to multiple testing; we consider this phase merely exploratory and the limited sample size does not have enough statistical power to sustain this kind of correction. Moreover, sample size of our replication cohort is relatively small and limits the assessment of biomarkers association with specific worsening causes that could influence outcome, as has been suggested in larger cohorts, although non-modifiable factors are the main force (Grube et al. 2013); thus, an independently larger study might conduct subanalysis by specific causes and to assess the value of these biomarkers in all types of stroke patients as the results of this study can only be generalized to stroke patients who underwent thrombolysis. In the future, BD-2 and IL-4R might be further explored in a prospective cohort that is being recruited in our hospital and that will collect a more complete information regarding outcome. Furthermore, different detection methods (simple ELISA, Luminex arrays, or others) should be explored to confirm our results. Finally, molecules which have been found altered in our discovery phase, but have not been included in the replication because of the impossibility of performing multiplex ELISA as well as molecules that are not included in the discovery array might be of interest, such as the recently described stroke outcome biomarker copeptin (De Marchis et al. 2013) or others.