Biomarkers of brain injury in cerebral infections.
Role of Biomarkers in CNS Pathology
Biomarkers are measurable objective indicators of normal function or pathology. They provide information about dynamic processes and pathogen activity that assist diagnosis, prognostication, and evaluation of treatment safety and efficacy. They can also act as surrogate markers for clinical or research end points, such as the effectiveness of novel treatments (9). Their quantification is user independent, and several biological fluids like blood and urine are easily obtained for investigation (10-13).
Biomarkers for CNS pathologies are gaining increasing attention and are being investigated across a spectrum of acute and chronic CNS diseases (14). CNS infection, trauma, hypoxia, inflammation, or degeneration results in cell damage and a collection of breakdown products in the cerebral extracellular fluid as well as increased permeability of the blood-brain barrier (BBB). Diffusing along concentration gradients into the CSF and through a leaky BBB into the bloodstream, these products become accessible measurable indicators of brain injury. The degree to which these biomarker concentrations are increased reflects the severity of injury; the cell specificity hints at the nature and, potentially, the location of injury; and sequential sampling provides information about the evolution of the damage (9, 15, 16).
Ideal biomarkers for brain injury should demonstrate high sensitivity and specificity for the brain. Their release should be associated with irreversible brain injury and reflect the temporal profile of that injury. They should appear rapidly in serum, demonstrate limited variability based on age and sex, and should be easily and speedily quantified by reliable assays (17). However, the task of finding such ideal biomarkers for the brain presents many challenges. The brain is a highly complex and heterogeneous organ with multiple cell types, and disease of the brain varies both in form and severity. CSF is better than serum in reflecting changes in the brain, but is not always accessible. The size and amount of the biomarker infiltrating the blood stream is limited by the BBB, and so serum values may be a function of cell injury as well as the degree of BBB disruption, which commonly occurs in brain injury (10). Even if CSF variability exists, biomarker concentrations may be influenced by the distance between the affected area and the CSF compartment, regional variability of biomarker proteins in the brain, and degradation by proteinases in the parenchyma or CSF (18). In addition, biomarker analysis is purely a quantitative measure which cannot reflect both the qualitative and quantitative functions of the brain (19). One way of overcoming some of these limitations is by using a panel rather than individual biomarkers and by combining these with clinical and radiological tools (10, 20).
Studies in traumatic brain injury (TBI) (19, 21), subarachnoid hemorrhage (16,22), dementia (23), Alzheimer disease (24), stroke (16), cardiac arrest (25), and various other pathologies have found that biomarkers of CNS injury hold promise as diagnostic and prognostic markers. Their role in infections of the CNS has generated less research. From the available studies, however, 2 key biomarkers with potential have emerged, namely S100B and NSE. In this review we focus primarily on these proteins but also briefly summarize studies on other potential biomarkers.
LIMITATIONS OF BIOMARKERS
Biomarker analysis is prone to some methodological pitfalls. The technique with which samples are collected has implications for their suitability; for example, hemolyzed blood samples maybe contaminated by biomarkers released from erythrocytes (26). The collection tube may influence biomarker concentrations, and tubes appropriate to the biomarker of interest should be used (27). Samples should preferably be stored at -70[degrees]C as early as possible to prevent antigen degradation; however, this varies as a function of biomarker stability. Multiple freeze-thaw cycles and prolonged storage can also lead to biomarker breakdown (28). Currently the primary method to test for biomarkers is the immunoassay, which demonstrates good sensitivity and is simple and inexpensive to administer, and for which various kits are commercially available. This technology exploits the high affinity and specificity with which antibodies bind to their antigens, ensuring that only the target antigen in a sample will bind, even when it is present in very low concentrations and in the presence of many other analytes. Immunoassay plates are coated with antibodies for the biomarker of interest; when test samples are added to the plate, the target antigen (biomarker) binds to the antibody and the degree of binding provides a measure of the biomarker present. Because the interaction of antibody and antigen is not associated with a quantifiable physical or chemical change, the binding event is measured by an auxiliary reaction in which one of the immunoreactants is labeled with a substance that can easily be detected by spectrophotometry (29). Platforms differ on the basis of the antibody and label used to detect the antibody-antigen binding; commonly used options include enzymes (ELISAs and RIAs), fluorophores (immunofluorometric assays), and chemiluminescent compounds (chemiluminescence immunoassay). Electrochemical immunoassays, which are label free and rely on changes in charge densities or conductivities to detect antibody-antigen binding, are also becoming increasingly available (29-31). Upper and lower detection limits are variable for different assays, and therefore comparison of reference values and pathology-related measurements across testing platforms is challenging, highlighting the need for the establishment of standardized operating and testing procedures. Large-scale multicenter trials would enable accumulation of sufficient samples for well-powered studies to establish standards, reference values, and appropriate disease-specific cutoff values. Much biomarker work is still confined to dedicated laboratories or projects; however, the true translation of biomarkers from bench to bedside requires the development of rapid, user-friendly, technologically undemanding tests that can be used on demand at the hospital and clinic levels (10).
S100B belongs to the larger S100 family of small acidic proteins approximately 10-12 kDa in size and composed of subunits A and B (32). These proteins regulate intracellular processes, including cell growth, transcription, and differentiation. The extracellular concentration of S100 proteins determines their activity; at nanomolar concentrations they have trophic effects, whereas at micromolar concentrations they may cause cell damage and apoptosis. In the CNS, subunit B is found in the highest concentrations (32) and is synthesized by astrocytes, oligodendrocytes, and Schwann cells (33). It is involved in cell-to-cell communication (34), cell growth, and intracellular signal transduction, as well as the development and maintenance of the CNS. Several S100B assays are available which detect both S100AB and BB, but currently a reference interval for S100B remains uncertain. Laboratories performing S100B assays are therefore required to generate reference intervals for their population, and studies in patients should include a representative control group. Brain injury results in leakage of S100B into the CSF and passage into the bloodstream through transient disruption of the BBB or via CSF circulation (15). S100B is metabolized by the kidneys and excreted in the urine. Concentrations usually normalize within 24 h after an acute injury; therefore, persistent increases in S100B may reflect ongoing or secondary cellular injury (35, 36). At increased concentrations, S100B may have a neurotoxic effect by inducing apoptosis, causing the release of proinflammatory cytokines as well as nitric oxide from astroglial cells, and contributing to oxidative stress (1, 33, 34, 37). Therefore, increased concentrations not only reflect tissue damage but may also exacerbate it (37). This protein is easily quantified in various biological samples, including CSF, blood, and urine. It remains stable over a wide range of temperatures (room temperature to -70[degrees]C) for up to 48 h after collection and does not degrade after freezing, making it a suitable candidate for daily laboratory measurement and batch analysis in studies (38, 39).
S100B is increased in various CNS pathologies including TBI (19, 21), Alzheimer disease (24), dementia (23, 32), stroke (40, 41), and subarachnoid hemorrhage (22). Extracranial sources of S100B include white and brown fat, skin, skeletal muscle, melanocytes, and adipocytes (12, 42-44). Clinically, increased blood concentrations are reported in melanomas or long bone injuries (12, 15).
S100B concentrations differ across various developmental phases from the fetal period through to old age (45). Stages of fetal development are associated with differential S100B concentrations in amniotic fluid and cord blood, suggesting that increased S100B within a physiological range may represent its neurotrophic role in brain maturation (46, 47). Age-related variations in S100B concentrations are, therefore, probably due to the role of S100B in the maturation of glial cells, formation of synapses, and general brain morphogenesis, which occur most frequently during early childhood (48). The pattern of age-related differences in the existing literature is difficult to interpret, however. Portela et al. (48) collected serum samples from 3 age groups of healthy donors: neonates, children (4-16 years), and adults (18-70 years). Theyfound no differences based on sex, but they demonstrated that age had a significant negative correlation with S100B, with the highest values found in neonates. After the age of 20 years, the baseline concentrations seemed to stabilize. Gazzolo et al. (49) also found that S100B concentrations in serum were associated with age in a detailed study of 1004 children between the ages of 0 and 15 years. Highest values were recorded during the first year of life and then again between 7 and 13 years. The authors hypothesize that these epochs correspond to the greatest spurts of growth and maturation; infancy and early adolescence. Different peaks of S100B based on sex were also found, again a possible manifestation of the growth phenomenon, which varies between the sexes. Bouvier et al. (50) found that serum S100B concentrations decreased with age. Their results in an exclusively pediatric cohort showed that the highest concentrations of S100B were recorded before the age of 2 years. The permeability of the BBB, higher protein turnover in neuronal cells, and low renal secretion of S100B, as well as dynamic CNS development in children under 2 years old are possible explanations proposed for this finding. However, Spinella et al. (37) found a positive correlation between age and CSF S100B in their study on 107 pediatric patients ranging in age from 3 days to 17.8 years. Although a significant positive correlation was found between age and S100B, the amount of overall variance attributable to age was only 4%. These results have been mirrored in a study of adults conducted by Nygaard et al. (51), who found that S100B in the CSF increased with age, with 43% of the variability attributed to age in men, and 30% in women. Overall S100B concentrations were also significantly higher in men. S100B could not be detected in serum samples in this study. Contrary to the abovementioned studies, no significant age- or sex-related differences were found in a study by Wiesmann et al. (52), who selected 200 healthy blood donors whom they divided equally in 5 age bands each of approximately 9 years, spanning 18 to 65 years (Table 1).
These seemingly opposing findings between studies may be a function of whether or not children are included in the study cohort, because evidence suggests that the largest influence of age occurs early in life. Furthermore, the heterogeneity of normal cohorts selected to establish reference intervals is challenging because much of the interindividual difference cannot be accounted for, and sample sizes often are not large enough per age group to adequately compensate for this variability. The selection of so-called "normal" samples is also subject to interpretation--in some studies, study participants have been chosen who have symptoms that could indicate neurological disease and warrant invasive procedures such as a lumbar puncture, whereas for other studies participants have been selected who are completely healthy individuals. The characteristics of study populations are thus important considerations when evaluating the role of S100B or any biomarker in conditions of pathology, defining reference values, or matching controls and cases.
Studies in CNS Infections
Bertsch et al. (41) measured serum S100B in a mouse model infected with CNS Candida albicans. They found that concentrations were significantly increased in infected mice relative to control mice and peaked within 1 day postinfection, followed by a subsequent decline. The kinetics of S100B appear to reflect the infective process whereby pathogenic entry into the CNS leads to an immediate inflammatory response, cell destruction, and BBB breakdown. When the infection is contained, tissue damage is halted and S100B concentrations return to baseline. In a sheep model, Garnier et al. found that sheep fetuses exposed to infection in utero developed white matter injury. The degrees of injury and mortality were positively associated with S100B concentrations in both the fetal and maternal circulation, suggesting the value of S100B in the early detection of fetal white matter injury (53). Another experimental model reveals the role of S100B as a marker of hypoxic distress in fetal sheep. Increased plasma S100B was associated with markers of metabolic acidosis, redistribution of blood flow from the peripheries, and currently used indicators of perinatal hypoxia (54).
CLINICAL STUDIES IN ADULTS
Infante et al. (32) examined S100B in the CSF of adult patients with various neurological pathologies, including meningitis, dementia, hydrocephalus, acute cerebral infarction, multiple sclerosis, motor neuron disease, and lymphatic leukemia. S100B was significantly increased in the meningitis, dementia, and acute cerebral infarction groups, with the highest concentrations associated with dementia. The authors suggest that S100B in acute limited injury may peak early and clear the system swiftly, in which case accurate assessment is highly dependent on the timing of sampling relative to the onset of injury. Long-term increases in S100B likely represent ongoing injury or the inflammatory activation of glial cells. Unden et al. (3) found higher serum S100B in adults with cerebral infections compared with extracerebral infections. Viral encephalitis patients demonstrated the highest concentrations, and bacterial meningitis patients recorded higher concentrations than those with viral meningitis, which is congruous with the established cellular injury associated with these diseases. However, some of their encephalitis and meningitis patients did not demonstrate increased values despite radiological and clinical abnormalities, suggesting that S100B may produce some false-negative results when used as a marker of brain injury. Considering that S100B is a brain-derived protein, this finding may reflect the fact that these investigators sampled blood rather than CSF. Further, in one of these encephalitis patients, sampling was done a few days later than when it was performed for the patients with increased concentrations; S100B has been shown to peak earlier in the disease process and so the peak may have been missed (32, 55). The authors also suggest that S100B may demonstrate false-positive results: some cases with extracranial infection and no evidence of cerebral involvement had increased S100B. The exact extracranial sources of these increases in S100B are not known; however, in these instances S100B was mostly only marginally increased and did not reach concentrations as high as in encephalitis. In general, CNS infections were the predominant source of increased S100B.
CLINICAL STUDIES IN CHILDREN
Having examined reference values in infants, Spinella et al. (37) compared CSF S100B between healthy children and those with established meningitis and found the latter had significantly higher concentrations. Hamed et al. (1) examined concentrations of S100B and markers of oxidative stress and antioxidative activity in 40 children with bacterial meningitis. S100B was increased in both serum and CSF samples. These investigators contended that brain injury resulting from bacterial meningitis is evidenced by the increased intrathecal production of S100B and markers of oxidative activity, and that the concentrations of these markers are related to injury severity. A study of infants with bacterial meningitis (56) found increases in CSF S100B, with the highest values recorded in infants who developed encephalitis in addition to their meningitis. An ROC indicated that a CSF S100B concentration above 1.0 [micro]g/L was diagnostic for the early detection of bacterial meningitis-encephalitis, with a sensitivity of 91% and a specificity of 82%, with an area under the curve of 0.92. S100B also surpassed standard monitoring techniques in identifying the development of encephalitis (56) (Table 2).
NSE ([gamma]-enolase) is a stable cell-specific isoenzyme of the glycolytic enzyme enolase, a dimer protein made up of [alpha], [gamma], and [beta] subunits. Although highly localized to neurons and neuroendocrine cells, NSE has also been found in erythrocytes, liver, smooth muscle, and lymphocytes; however, in the latter cases, it is often of the hybrid ([alpha][delta]) enolase form and has low concentrations (<10 ng/mg) (11, 15). In the brain, NSE is concentrated exclusively in the cytoplasm of neurons (11). NSE demonstrates enzymatic activity and is a promising marker of general neuronal function. Reference intervals for serum concentrations range from 5 to 15 ng/mL. When neuronal membranes are injured, NSE easily diffuses into the extracellular space and CSF (26). Consequently, increased concentrations of NSE in the CSF and serum have been identified in several cerebral pathologies, including encephalitis, cerebral infarction, Parkinson disease, amyotrophic lateral sclerosis, spinocerebellar degeneration, cervical spondylosis, polyradiculoneuritis, and TBI (23, 57, 58). NSE is also increased in small cell lung cancer (15) and pediatric neuroblastoma (11).
Van Engelen et al. (4) sought to establish reference values for NSE, S100B, and myelin basic protein (MBP). They collected CSF samples in 79 control patients ranging from 1 to 60 years of age. Their findings suggested a significant increase with age, which was similar across all 3 markers, with a median increase of 1% per year. Possible explanations proposed for this finding relate to an increase in cell and myelin loss with age and/or a parallel reduction in CSF bulk flow. Nygaard et al. (59) studied CSF and serum NSE concentrations in healthy individuals ranging from 20 to 90 years of age. In the serum they found no differences based on sex and age, but in CSF there was a positive significant association with age, and males had higher values. The authors suggest that the increase in NSE with age maybe attributed to increased NSE concentrations in the face of stable cell turnover, increased cell turnover with age, or an increased NSE half-life due to reduced CSF bulk flow. Sex dependency may be explained by similar mechanisms. In addition, this study revealed no correlation between the NSE concentrations in the 2 biological fluids. However, the association with age is not a consistent finding. In a pediatric study, Rodriguez-Nunez et al. (60) examined NSE in the CSF of 37 children aged 1 month to 13 years. Their findings did not suggest an association with age. Casmiro et al. (61) studied both CSF and serum in an exclusively adult population of 108 individuals and also found no significant association with age; however, their sample was skewed to patients over 60 years old. There were no sex-related associations in NSE concentrations and no correlations between serum and CSF NSE values were demonstrated (61) (Table 3).
These results, in conjunction with findings of Nygaard et al. (59), suggest that serum cannot substitute for CSF and that serum NSE within the reference interval does not necessarily exclude the presence of CNS injury, although this requires validation in a sample of patients with CNS disease. Studies on reference intervals in NSE are subject to the same limitations and cautionary interpretation as for S100B.
Studies in CNS Infections
The authors are unaware of any studies of NSE concentrations in experimental models of CNS infection. However, a study of NSE in an ischemic rat model is relevant because ischemia is an important determinant of poor outcome in CNS infections. Hardemark et al. (62) correlated NSE concentrations in CSF with the development and size of infarcts in rats by sampling fluid before and after middle cerebral artery occlusion over several days. NSE peaked between 24 and 72 h after occlusion, with a subsequent return to baseline over 6 days. Infarct size was significantly correlated with NSE concentrations and the area under the NSE concentration curve. These findings suggest that sequential analysis of CSF NSE reflects the development as well as size of infarcts. The temporal variation of NSE reflects the dynamic nature of the ischemic process, which may occur over several days and is subject to inter-individual variation. The timing of sampling as the injury evolves is therefore an important consideration.
CLINICAL STUDIES IN ADULTS
Lima et al. (26) examined whether NSE concentrations correlate with severity of neurological impairment. They sampled serum and CSF in 51 adults: 11 with meningitis, 7 with encephalic injuries, 25 with neurocysticercosis, and 8 controls. They found no age or sex differences. NSE was increased only for the group with encephalic injuries, and only in CSF. It is worth noting that none of the meningitis group was neurologically compromised and so the degree of neurological injury is debatable. Song et al. (63) measured NSE in the CSF and serum of adult patients with tuberculous meningitis (TBM) (n = 15) and aseptic meningitis (n = 28) in comparison with controls. Although serum and CSF NSE concentrations were not significantly different across the 3 groups, the CSF/serum NSE ratio was significantly higher in the TBM group. This ratio was significantly correlated with the diagnosis of TBM in univariate and multivariate analysis, demonstrating its potential as a diagnostic marker. The sample size of this study was small and provides a limited picture of NSE concentrations in meningitis.
CLINICAL STUDIES IN CHILDREN
There is a dearth of literature available regarding biomarkers for brain injury in children, with most of the research in TBI (19, 57, 58, 64). Rodriguez-Nunez et al. (65) examined CSF markers of cell hypoxia and NSE to discern the utility of these markers in the differential diagnosis of meningitis. They examined 160 control children and 100 children with bacterial, viral, or tuberculosis meningitis and found that NSE concentrations in bacterial meningitis and TBM cases were not significantly higher than those in controls, did not differ on the basis of sex, and were not different between meningitis groups. Only the viral meningitis group had significantly increased NSE; however, this was attributed to 9 cases with mumps etiology, in whom NSE concentrations were highest overall. Two limitations of this study were, first, their TBM group was very small, comprising only 0.9% of the meningitis cases, and second, the CSF samples were collected very early in the disease course (1-16 h after the onset of symptoms). Experimental work in animal models suggested that NSE tended to increase beyond 24 h postinjury (62); therefore, the samples may have been taken when hypoxia had not been sufficiently prolonged or intense to induce cell death and cause increases in NSE. In a study of 20 comatose children suffering from acute encephalitis, acute encephalopathy, and Reye syndrome, Nara et al. (66) found that both CSF and serum NSE concentrations were significantly increased in these children relative to concentrations in controls. NSE was associated with neurological compromise and showed higher initial values and subsequent increases in patients with more severe brain damage and poorer outcome. Their findings support the variability of NSE concentrations at different disease stages and between patients. Contrary to other studies, in this study the authors found a good correlation between serum and CSF NSE (Table 4).
Lins et al. (55) performed sequential paired CSF and serum measurements of S100B and NSE in patients with bacterial and viral meningitis. Results from 32 adult patients revealed significantly increased serum and CSF S100B in patients with bacterial meningitis only. Increased S100B was associated with lesions on computed tomography or MRI and higher CSF/serum albumin ratios. In most patients, S100B decreased after the initial samples, except in a patient with TBM in whom biomarker concentrations in CSF and serum continued to climb, peaking by day 27. NSE was not significantly increased in this study. Mokuno et al. (23) examined CSF S100B and NSE in patients with a range of neurological pathologies, including encephalitis, meningitis, cerebral infarction, Parkinson disease, sclerosis, and others. NSE and S100B were increased in several of these conditions, but not always simultaneously, suggesting that their presence may reflect the kind of tissue damage, i.e., glial vs neuronal. Concentrations were highest in encephalitis and cerebral infarction, and S100B concentrations normalized earlier than NSE concentrations.
Other Potential Biomarkers
Although studies on S100B and NSE dominate the literature, work is also being done on other biomarkers. In a case series of 3 patients with cerebral infective disease, Van Engelen et al. (4) examined CSF concentrations of myelin basic protein (MBP), a marker of myelin breakdown, as well as S100B and NSE. They found that concentrations of all 3 biomarkers were increased and that their relative values were in line with the nature of the injury; in patients with demyelinating illnesses, MBP concentrations were higher than S100B and NSE. Tsukahara et al. (67) examined CSF S100B, glial fibrillary acidic protein (GFAP) (a marker of glial injury), and tau protein (a microtubule-associated protein found in neuronal axons) in children with acute encephalitis/encephalopathy. CSF biomarker concentrations were increased only in patients with poor outcome, and S100B had the best predictive accuracy with respect to outcome. GFAP showed poor sensitivity (40%) but was very specific (100%), whereas tau protein had a sensitivity of 70% and specificity of 85%. Combining several markers allowed for the best predictive value for outcome. In a cohort of patients with varicella zoster virus CNS infections, Grahn et al. (68) measured CSF S100B, GFAP, and neurofilament protein light chain (NFL), a potential marker of neuronal injury. NFL showed the highest and most prolonged increases, followed by GFAP, whereas S100B concentrations were within reference intervals. This suggests that damage may have involved astrogliosis while the astrocyte cell membrane remained intact, and that persistent or secondary injury may be responsible for ongoing NFL elevation. A review by Bonneh-Barkay et al. (8) summarizes research into biomarkers for encephalitis in adults; CSF NSE, NFL, GFAP, S100B, a variety of proinflammatory cytokines, and soluble Fas (an apoptotic marker) were identified in herpes encephalitis. The cytokines interleukin-6 and tumor necrosis factor-a together with metabolites showed promise in influenza-associated encephalitis. CSF S100B and NFL were investigated in West Nile virus encephalitis. Work on HIV encephalitis has included the examination of CSF HIV-1 RNA, factors associated with immune activation, like neopterin, and markers of inflammation.
Advances in genomics, transcriptomics, proteomics, and metabolomics offer opportunities for investigating various molecular aspects of the host-pathogen interaction in CNS infections. These include pathogen-specific molecular or metabolic signatures, pathogen-directed alteration to host gene expression patterns, genetic susceptibility to infections, interindividual genetic variation in response to pathogen exposure, and the association between gene expression profiles and disease outcome. These technologies therefore have the potential to identify biomarkers that could accurately diagnose the disease as well as the offending pathogen, allow early selection of appropriate treatment, identify patients at greater risk of severe disease and poor outcome, and allow treatment tailored to both host and pathogen characteristics. This field, however, is still in a fledging state, particularly with regard to CNS infections (5). In the field of proteomics, Goonetilleke et al. (69) used 2-dimensional gel electrophoresis to study the CSF protein profile of patients with pneumococcal meningitis. Increases in various proteins were associated with inflammation, cellular damage, and metabolic derangement in nonsurvivors and could provide insight into novel biomarkers and drug targets. In a proteomics study on the CSF of TBM patients, Kataria et al. (70) found 18 differentially expressed proteins. Upregulated proteins of human origin included arachidonate 5-lipoxygenase, which contributes to the pathophysiology of TB by weakening the host's immune responses; GFAP, a marker of tissue damage; and antithrombin-III, which indicates the involvement of coagulation pathways. Downregulated human proteins included apolipoprotein E and A-1, which are important for their antiinflammatory properties and for neural growth and repair and immune regulation, and transthyretin, which aids in reducing brain edema, neuronal death, and inflammation. Mycobacterial proteins identified in the CSF could present diagnostic markers for swift detection of the causative agent in patients with meningitis. Metabolomics is a high-throughput technology that allows for a broad unbiased analysis of the full complement of metabolites present in biological samples. It is increasingly being used in disease diagnostics, therapeutics, and drug development because it provides insight into the combined effects of metabolites from the infecting organism, inflammatorycells, and cerebral cellular responses to pathogens and therapeutics (71). In a study on patients with meningitis and ventriculitis, Coen et al. (72) demonstrated that metabolomics of the CSF could rapidly diagnose these infections and showed improvements in the performance of routine CSF cell counts and chemistry assays. Subramanian et al. (73) developed an expert system to aid in the differential diagnosis of meningitis using a unique combination of clinical features and metabolic fingerprints.
More in-depth studies looking at broader panels of markers are required. A combination of neurological markers may also be useful in tracking injuries of various tissue types and may mitigate the influence of extracranial sources. Methods combining inflammatory and tissue injury markers have demonstrated improved prognostic power in TBI (20) and may prove beneficial in CNS infections as well. Few of the available studies in CNS infections examined biomarkers in relation to outcomes. Several studies have confirmed significant associations between biomarker concentrations and radiological and clinical outcome in TBI (21, 74, 75); because these are the current tools available it is important to ascertain the strength of the associations with biomarkers in infectious injury.
Despite the limited number of studies of biomarkers in CNS infections, the literature available suggests that in both adults and children biomarkers of neurological injury have the potential to answer several important questions: (a) when presenting symptoms and history are nonspecific, is there CNS involvement?; (b) what is the nature of the pathological process?; (c) what is the likely causative pathogen?; (d) which cells of the CNS are affected?; (e) how severe is the injury?; and (f) is the disease process responding to intervention? Biomarkers of neurological injury carry promise as markers of diagnosis as well as prognosis and may serve as surrogate end points for determining the response to novel interventions. Further studies on biomarkers in this field will add to our understanding of their utility. When conducting biomarker studies it is important to consider limitations presented by heterogeneity in the study cohort, the relative value of CSF vs serum, and the choice of testing platform.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, oranalysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
Consultant or Advisory Role: None declared.
Stock Ownership: None declared.
Honoraria: A.A. Figaji, Speaker's honorarium, Integra Neurosciences and Codman Johnnson and Jonhnson Neurocritical Care Symposium.
Research Funding: Clinical Infectious Diseases Research Initiative, Wellcome Trust grant.
Expert Testimony: None declared.
Patents: None declared.
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Ursula K. Rohlwink  * and Anthony A. Figaji 
 Paediatric Neurosurgery, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa.
* Address correspondence to this author at: 6th Floor ICH Bldg., Red Cross War Memorial Children's Hospital, Klipfontein Rd., Rondebosch, Cape Town, South Africa, 7700. Fax +27-21-685-6632; e-mail email@example.com.
Received July 11, 2013; accepted September 23, 2013.
Previously published online at DOI: 10.1373/clinchem.2013.212472
 Nonstandard abbreviations: CNS, central nervous system; NSE, neuron-specific enlose; CSF, cerebrospinal fluid; CBF, cerebral blood flow; BBB, blood-brain barrier; TBI, traumatic brain injury; TBM, tuberculous meningitis; MBP, myelin basic protein; GFAP, glial fibrillary acidic protein; NFL, neurofilament protein.
Table 1. S100B reference intervals ([micro]g/L). Cohort and reference Age group n Specimen Pediatric cohort Bouvier et al. (50) (b) 0-2 years 139 Serum >2 years 97 Gazzolo et al. (49) (b,c) 0-1 year 85 Serum 2-7 years 461 9-11 years 11-12 years 32 13-14 years 35 14-15 years 18 Spinella et al. (37) <1 month 16 CSF 1-2 months 29 2-3 months 11 3-12 months 20 1-5 years 10 5-10 years 8 10-15 years 7 15-18 years 6 Mixed cohort Portela et al. (48) Neonates 19 Serum 4-9 years 16 10-15 years 8 16-20 years 10 21-25 years 13 26-30 years 9 30-70 years 54 Van Engelen et al. 1 year CSF (4) (d) 20 years 40 years 60 years Adult cohort Wiesmann et al. (52) 18-65 years 200 Plasma Nygaard et al. (51) 20-89 years 110 Serum CSF Cohort and reference Age group Mean (SD) Pediatric cohort Bouvier et al. (50) (b) 0-2 years 0.21 (0.12) >2 years 0.11 (0.03) Gazzolo et al. (49) (b,c) 0-1 year 2-7 years 9-11 years 11-12 years 13-14 years 14-15 years Spinella et al. (37) <1 month 1-2 months 2-3 months 3-12 months 1-5 years 5-10 years 10-15 years 15-18 years Mixed cohort Portela et al. (48) Neonates 4-9 years 10-15 years 16-20 years 21-25 years 26-30 years 30-70 years Van Engelen et al. 1 year (4) (d) 20 years 40 years 60 years Adult cohort Wiesmann et al. (52) 18-65 years Nygaard et al. (51) 20-89 years Undetectable 1.9 (0.7) (males) 1.5 (0.5) (females) Cohort and reference Age group Median Range (a) Pediatric cohort Bouvier et al. (50) (b) 0-2 years 0.07-0.83 >2 years 0.07-0.2 Gazzolo et al. (49) (b,c) 0-1 year 0.95 0.44-2.55 2-7 years 0.73 0.44-1.06 9-11 years 1.65 0.91-1.74 11-12 years 0.45 0.39-0.45 13-14 years 1.23 1.12-2.01 14-15 years 0.78 0.5-0.87 Spinella et al. (37) <1 month 0.79 0.52-1.32 1-2 months 0.6 0.44-0.84 2-3 months 0.61 0.4-0.81 3-12 months 0.58 0.46-0.73 1-5 years 0.96 0.77-1.33 5-10 years 0.87 0.65-1.07 10-15 years 1.3 0.87-1.8 15-18 years 1.2 0.66-1.8 Mixed cohort Portela et al. (48) Neonates 1.79 1.57-2.44 4-9 years 0.37 0.27-0.41 10-15 years 0.31 0.26-0.37 16-20 years 0.11 0.02-0.2 21-25 years 0.1 0.05-0.15 26-30 years 0.1 0.06-0.15 30-70 years 0.06 0.03-0.1 Van Engelen et al. 1 year 0.9-2.6 (e) (4) (d) 20 years 1.1-3.3 40 years 1.3-4 60 years 1.6-5 Adult cohort Wiesmann et al. (52) 18-65 years 0.05 0.02-0.1 (f) Nygaard et al. (51) 20-89 years (a) Range is the 25th to 75th percentile unless otherwise stated. (b) Additional age-related data available in original articles. (c) Sample sizes derived from article where possible. (d) Sample sizes not stated in article. (e) Range reflects 5th to 95th percentiles. (f) 10th to 90th percentile. Table 2. S100B data from clinical studies ([micro]g/L). Cohort and Age Pathology n Specimen reference Pediatric cohort Hamed et al. (1) < 15 years Controls 20 Serum <15 years Bacterial 40 Serum meningitis CSF Spinella et al. 0.08-1.5 Controls 107 CSF (37) years Meningitis 34 CSF Gazzolo et al. 38 weeks Controls 44 CSF (56) 38 weeks Bacterial 44 CSF meningitis Mixed cohort Infante et al. 1-79 years Controls 22 CSF (32) (b) Lymphocytic 19 meningitis Bacterial-fungal 10 meningitis Dementia 6 Acute cerebral 10 infarction Adult cohort Unden et al. 15-84 Viral 5 Serum (3) (c) years encephalitis Lins et al. (55) 41 (13) Controls 13 Serum years CSF Bacterial 11 Serum meningitis CSF Viral meningitis 13 Serum CSF Mokuno et al. Controls 18 CSF (23) (b) Encephalitis 6 Meningitis 24 Cerebral 10 infarction Parkinson 18 disease Cervical 9 spondylosis Acute 5 poly(radiculo) neuritis Chronic 6 poly(radiculo) neuritis Cohort and Age Pathology Mean (SD) reference Pediatric cohort Hamed et al. (1) < 15 years Controls 0.13(0.01) <15 years Bacterial 0.22 (0.07) meningitis 0.61 (0.03) Spinella et al. 0.08-1.5 Controls (37) years Meningitis Gazzolo et al. 38 weeks Controls (56) 38 weeks Bacterial meningitis Mixed cohort Infante et al. 1-79 years Controls 0.88 (0.08) (32) (b) Lymphocytic 2.51 (0.71) meningitis Bacterial-fungal 5.06 (2.43) meningitis Dementia 2.09 (0.62) Acute cerebral 1.69 (0.26) infarction Adult cohort Unden et al. 15-84 Viral 0.58 (3) (c) years encephalitis Lins et al. (55) 41 (13) Controls 0.05 (0.03) years 1.22 (0.49) Bacterial > Controls meningitis > Controls Viral meningitis < Controls < Controls Mokuno et al. Controls 0.33 (0.09) (23) (b) Encephalitis 1.9 (1.8) Meningitis 0.6 (0.48) Cerebral 1.4 (1.4) infarction Parkinson 0.58 (0.23) disease Cervical 0.51 (0.2) spondylosis Acute 0.83 (0.62) poly(radiculo) neuritis Chronic 1.5 (2.3) poly(radiculo) neuritis Cohort and Age Pathology Median reference Pediatric cohort Hamed et al. (1) < 15 years Controls <15 years Bacterial meningitis Spinella et al. 0.08-1.5 Controls 0.71 (37) years Meningitis 1.1 Gazzolo et al. 38 weeks Controls 0.16 (56) 38 weeks Bacterial 1.34 meningitis Mixed cohort Infante et al. 1-79 years Controls (32) (b) Lymphocytic meningitis Bacterial-fungal meningitis Dementia Acute cerebral infarction Adult cohort Unden et al. 15-84 Viral (3) (c) years encephalitis Lins et al. (55) 41 (13) Controls years Bacterial meningitis Viral meningitis Mokuno et al. Controls (23) (b) Encephalitis Meningitis Cerebral infarction Parkinson disease Cervical spondylosis Acute poly(radiculo) neuritis Chronic poly(radiculo) neuritis Cohort and Age Pathology Range reference Pediatric cohort Hamed et al. (1) < 15 years Controls 0.11-0.16 <15 years Bacterial 0.13-0.57 meningitis 0.55-0.7 Spinella et al. 0.08-1.5 Controls 0.48-1.07 (37) years Meningitis 0.91-1.4 Gazzolo et al. 38 weeks Controls 0.11-0.33 (56) 38 weeks Bacterial 0.84-1.78 meningitis Mixed cohort Infante et al. 1-79 years Controls (32) (b) Lymphocytic meningitis Bacterial-fungal meningitis Dementia Acute cerebral infarction Adult cohort Unden et al. 15-84 Viral (3) (c) years encephalitis Lins et al. (55) 41 (13) Controls years Bacterial meningitis Viral meningitis Mokuno et al. Controls 0.16-0.52 (23) (b) Encephalitis 0.34-5.1 Meningitis 0.06-2.4 Cerebral 0.12-4.1 infarction Parkinson 0.21-0.96 disease Cervical 0.24-0.84 spondylosis Acute 0.2-1.8 poly(radiculo) neuritis Chronic 0.34-6.6 poly(radiculo) neuritis Cohort and Age Pathology Cases > reference controls (a) Pediatric cohort Hamed et al. (1) < 15 years Controls <15 years Bacterial [check] meningitis Spinella et al. 0.08-1.5 Controls (37) years Meningitis [check] Gazzolo et al. 38 weeks Controls (56) 38 weeks Bacterial [check] meningitis Mixed cohort Infante et al. 1-79 years Controls (32) (b) Lymphocytic [check] meningitis Bacterial-fungal [check] meningitis Dementia [check] Acute cerebral [check] infarction Adult cohort Unden et al. 15-84 Viral [check] (3) (c) years encephalitis Lins et al. (55) 41 (13) Controls years Bacterial [check] meningitis [check] Viral meningitis X X Mokuno et al. Controls (23) (b) Encephalitis [check] Meningitis [check] Cerebral [check] infarction Parkinson [check] disease Cervical [check] spondylosis Acute [check] poly(radiculo) neuritis Chronic [check] poly(radiculo) neuritis (a) [check], Cases > controls at P > 0.05; X, case > controls at P < 0.05. (b) Only pathologies with S100B concentrations significantly greater than those of controls included, further detail in the original reports. (c) Several other pathologies included in the study had elevated S100B concentrations; however, no descriptive statistics are reported in the reference. Table 3. NSE reference intervals ([micro]g/L). Cohort and reference Age group n Specimen Pediatric cohort Rodriguez-Nunez et al. (60) 1 month-13 years 37 CSF Rodriguez-Nunez et al. (65) 1 month-13 years 160 CSF Mixed cohort Van Engelen et al. (4) (b) 1 year CSF 20 years 40 years 60 years Adult cohort Casmiro et al. (61) 62.7 (16.7) years 108 Serum CSF Nygaard et al. (59) 20-89 years 87 Serum CSF Cohort and reference Age group Mean (SD) Pediatric cohort Rodriguez-Nunez et al. (60) 1 month-13 years 1.5 (1.01) Rodriguez-Nunez et al. (65) 1 month-13 years 1.5 (1.01) Mixed cohort Van Engelen et al. (4) (b) 1 year 20 years 40 years 60 years Adult cohort Casmiro et al. (61) 62.7 (16.7) years 8.7 (3.9) (d) 17.3(4.6) Nygaard et al. (59) 20-89 years 7.1 (3.6) 5.1 (1.6) (males) 4.1 (1.4) (females) Cohort and reference Age group Median Pediatric cohort Rodriguez-Nunez et al. (60) 1 month-13 years 1.5 Rodriguez-Nunez et al. (65) 1 month-13 years Mixed cohort Van Engelen et al. (4) (b) 1 year 20 years 40 years 60 years Adult cohort Casmiro et al. (61) 62.7 (16.7) years Nygaard et al. (59) 20-89 years Cohort and reference Age group Range (a) Pediatric cohort Rodriguez-Nunez et al. (60) 1 month-13 years 0-4.8 Rodriguez-Nunez et al. (65) 1 month-13 years Mixed cohort Van Engelen et al. (4) (b) 1 year 2.2-10.2 (c) 20 years 2.7-12 40 years 3.1-13.8 60 years 3.8-16 Adult cohort Casmiro et al. (61) 62.7 (16.7) years Nygaard et al. (59) 20-89 years 6.2-8.1 (e) (a) Range is the 25th-to-75th percentile unless otherwise stated. (b) Sample sizes not stated in article. (c) Range is the 5th-to-95th percentile. (d) Based on data from 98 patients. (e) 95% CI. Table 4. NSE data from clinical studies ([micro]g/L). Cohort and Age Pathology n Specimen reference Pediatric cohort Rodriguez-Nunez 1-168 months Controls 160 CSF et al. (65) Viral meningitis 46 Bacterial 45 meningitis Tuberculous 9 meningitis Mixed cohort Lima et al. (26) 13-82 years Controls 8 Serum CSF Meningitis 11 Serum CSF Neurocysticerosis 25 Serum CSF Encephalitis 7 Serum CSF Adult cohort Lins et al. (55) 41 (13) years Controls 13 Serum CSF Bacterial 11 Serum meningitis CSF Viral meningitis 13 Serum CSF Mokuno et al. Controls 18 CSF (23) (b) Encephalitis 6 Amyotrophic 10 lateral sclerosis Cerebral 10 infarction Parkinson 18 disease Cervical 9 spondylosis Acute 5 poly(radiculo) neuritis Chronic 6 poly(radiculo) neuritis Spinocerebellar 10 degeneration Song et al. (63) Adults Control 37 Serum CSF Tuberculous 15 Serum meningitis CSF Aseptic 28 Serum meningitis CSF Cohort and Age Pathology Mean (SD) reference Pediatric cohort Rodriguez-Nunez 1-168 months Controls 1.52 (1.01) et al. (65) Viral meningitis 2.87 (1.21) Bacterial 2.47 (1.59) meningitis Tuberculous 2.25 (0.49) meningitis Mixed cohort Lima et al. (26) 13-82 years Controls 4.7 (2.3) 6.3 (3.6) Meningitis 5.8 (4.6) 4.9 (4.6) Neurocysticerosis 6.6 (3.4) 3.9 (3.2) Encephalitis 20.9 (19.2) 35.8 (39.2) Adult cohort Lins et al. (55) 41 (13) years Controls 5.82 (1.75) 6.78(2.1) Bacterial 5.1 (1.3) meningitis 8.38 (3.7) Viral meningitis 5.73 (3.7) 8.23 (3.4) Mokuno et al. Controls 4.5 (1.2) (23) (b) Encephalitis 11.3 (12.9) Amyotrophic 6(2.1) lateral sclerosis Cerebral 9.9 (6.2) infarction Parkinson 7 (3.6) disease Cervical 8.7 (3.4) spondylosis Acute 6.2 (2.3) poly(radiculo) neuritis Chronic 2.5 (0.9) poly(radiculo) neuritis Spinocerebellar 7.2 (2.5) degeneration Song et al. (63) Adults Control 10.42 (6.06) 9.46 (5.76) Tuberculous 8(2.17) meningitis 12.35 (3.52) Aseptic 9.57 (7.16) meningitis 9.79(6.1) Cohort and Age Pathology Median reference Pediatric cohort Rodriguez-Nunez 1-168 months Controls et al. (65) Viral meningitis Bacterial meningitis Tuberculous meningitis Mixed cohort Lima et al. (26) 13-82 years Controls 4.9 6.1 Meningitis 5.4 4.4 Neurocysticerosis 7.1 3 Encephalitis 9.2 29.7 Adult cohort Lins et al. (55) 41 (13) years Controls Bacterial meningitis Viral meningitis Mokuno et al. Controls (23) (b) Encephalitis Amyotrophic lateral sclerosis Cerebral infarction Parkinson disease Cervical spondylosis Acute poly(radiculo) neuritis Chronic poly(radiculo) neuritis Spinocerebellar degeneration Song et al. (63) Adults Control Tuberculous meningitis Aseptic meningitis Cohort and Age Pathology Range reference Pediatric cohort Rodriguez-Nunez 1-168 months Controls et al. (65) Viral meningitis Bacterial meningitis Tuberculous meningitis Mixed cohort Lima et al. (26) 13-82 years Controls Meningitis Neurocysticerosis Encephalitis Adult cohort Lins et al. (55) 41 (13) years Controls Bacterial meningitis Viral meningitis Mokuno et al. Controls 1.3-7.2 (23) (b) Encephalitis 3.4-40 Amyotrophic 2.5-10 lateral sclerosis Cerebral 3.3-25 infarction Parkinson 0.8-16 disease Cervical 4.1-14.4 spondylosis Acute 4-10 poly(radiculo) neuritis Chronic 1.2-4.2 poly(radiculo) neuritis Spinocerebellar 3.2-10.8 degeneration Song et al. (63) Adults Control Tuberculous meningitis Aseptic meningitis Cohort and Age Pathology Cases > reference controls (a) Pediatric cohort Rodriguez-Nunez 1-168 months Controls et al. (65) Viral meningitis [check] Bacterial X meningitis Tuberculous X meningitis Mixed cohort Lima et al. (26) 13-82 years Controls Meningitis X X Neurocysticerosis X X Encephalitis [check] [check] Adult cohort Lins et al. (55) 41 (13) years Controls Bacterial X meningitis X Viral meningitis X X Mokuno et al. Controls (23) (b) Encephalitis [check] Amyotrophic [check] lateral sclerosis Cerebral [check] infarction Parkinson [check] disease Cervical [check] spondylosis Acute [check] poly(radiculo) neuritis Chronic [check] poly(radiculo) neuritis Spinocerebellar [check] degeneration Song et al. (63) Adults Control Tuberculous X meningitis X Aseptic X meningitis X (a) Range is the 25th-to-75th percentile unless otherwise stated. [check], cases > controls at P > 0.05; X, case>controls P < 0.05. b Only pathologies with S100B concentrations significantly greater than those of controls included in table, further detail in original article.
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|Author:||Rohlwink, Ursula K.; Figaji, Anthony A.|
|Date:||Jun 1, 2014|
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