Posts Tagged Biomarkers

[REVIEW ARTICLE] Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy – Full Text

Blood biomarkers have been explored for their potential to provide objective measures in the assessment of traumatic brain injury (TBI). However, it is not clear which biomarkers are best for diagnosis and prognosis in different severities of TBI. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mild TBI (mTBI), predicting delayed recovery after mTBI, and predicting adverse outcome after severe TBI (sTBI). We conducted a literature search of publications on biomarkers in TBI published up until July 2018. Operating characteristics were pooled for each biomarker for comparison. For detecting concussion, 4 biomarker panels and creatine kinase B type had excellent discriminative ability. For detecting intracranial injury and the need for a head CT scan after mTBI, 2 biomarker panels, and hyperphosphorylated tau had excellent operating characteristics. For predicting delayed recovery after mTBI, top candidates included calpain-derived αII-spectrin N-terminal fragment, tau A, neurofilament light, and ghrelin. For predicting adverse outcome following sTBI, no biomarker had excellent performance, but several had good performance, including markers of coagulation and inflammation, structural proteins in the brain, and proteins involved in homeostasis. The highest-performing biomarkers in each of these categories may provide insight into the pathophysiologies underlying mild and severe TBI. With further study, these biomarkers have the potential to be used alongside clinical and radiological data to improve TBI diagnostics, prognostics, and evidence-based medical management.

Introduction

Traumatic brain injury (TBI) is a common cause of disability and mortality in the US (1) and worldwide (2). Pathological responses to TBI in the CNS include structural and metabolic changes, as well as excitotoxicity, neuroinflammation, and cell death (34). Fluid biomarkers that may track these injury and inflammatory processes have been explored for their potential to provide objective measures in TBI assessment. However, at present there are limited clinical guidelines available regarding the use of biomarkers in both the diagnosis of TBI and outcome prediction following TBI. To inform future guideline formulation, it is critical to distinguish between different clinical situations for biomarker use in TBI, such as detection of concussion, prediction of positive and negative head computed tomography (CT) findings, and prediction of outcome for different TBI severities. This allows for comparisons to determine which biomarkers may be used most appropriately to characterize different aspects of TBI.

The identification of TBI severity has become a contentious issue. Currently, inclusion in TBI clinical trials is primarily based on the Glasgow Coma Scale (GCS), which stratifies patients into categories of mild, moderate, and severe TBI. The GCS assesses consciousness and provides prognostic information, but it does not inform the underlying pathologies that may be targeted for therapy (56). Furthermore, brain damage and persistent neurological symptoms can occur across the spectrum of TBI severity, limiting the use of GCS-determined injury severity to inform clinical management. Biomarkers in TBI have the potential to provide objective and quantitative information regarding the pathophysiologic mechanisms underlying observed neurological deficits. Such information may be more appropriate for guiding management than initial assessments of severity alone. Since the existing literature primarily focuses on applications of biomarkers in either suspected concussion, mild TBI (mTBI), or severe TBI (sTBI), we will discuss biomarker usage in these contexts.

Concussion is a clinical syndrome involving alteration in mental function induced by head rotational acceleration. This may be due to direct impact or unrestrained rapid head movements, such as in automotive crashes. Although there are over 30 official definitions of concussion, none include the underlying pathology. Missing from the literature have been objective measures to not only identify the underlying pathology associated with the given clinical symptoms, but also to indicate prognosis in long-term survival. Indeed, current practices in forming an opinion of concussion involve symptom reports, neurocognitive testing, and balance testing, all of which have elements of subjectivity and questionable reliability (7). While such information generally reflects functional status, it does not identify any underlying processes that may have prognostic or therapeutic consequences. Furthermore, because patients with concussion typically present with negative head CT findings, there is a potential role for blood-based biomarkers to provide objective information regarding the presence of concussion, based on an underlying pathology. This information could inform management decisions regarding resumption of activities for both athletes and non-athletes alike.

Blood-based biomarkers have utility far beyond a simple detection of concussion by elucidating specific aspects of the injury that could drive individual patient management. For example, biomarkers may aid in determining whether a mTBI patient presenting to the emergency department requires a CT scan to identify intracranial pathology. The clinical outcome for a missed epidural hematoma in which the patient is either discharged or admitted for routine observation is catastrophic; 25% are left severely impaired or dead (8). The Canadian CT Head Rule (9) and related clinical decision instruments achieve high sensitivities in predicting the need for CT scans in mild TBI cases. However, they do this at specificities of only 30–50% (10). Adding a blood biomarker to clinical evaluation may be useful to improve specificity without sacrificing sensitivity, as recently suggested (11). In addition, given concern about radiation exposure from head CT scans in concussion cases, particularly in pediatric populations, identification of patients who would be best assessed with neuroimaging is crucial. Thus, the use of both sensitive and specific biomarkers may serve as cost-effective tools to aid in acute assessment, especially in the absence of risk factors for intracranial injury (12). S-100B, an astroglial protein, has been the most extensively studied biomarker for TBI thus far and has been incorporated into some clinical guidelines for CT scans (1314). However, S-100B is not CNS-specific (1516) and has shown inconsistent predictive capacity in the outcome of mild TBI (1718). Given that several other promising biomarkers have also been investigated in this context, it is important to evaluate and compare the discriminative abilities of S-100B with other candidate blood-based biomarkers for future use.

Blood biomarkers also have the potential to help predict unfavorable outcomes across the spectrum of TBI severity. Outcome predication is difficult; in mTBI, existing prognostic models performed poorly in an external validation study (19). Identifying biomarkers that best predict delayed recovery or persistent neurological symptoms following mTBI would help with the direction of resources toward patients who may benefit most from additional rehabilitation or prolonged observation. In sTBI, poorer outcome has often been associated with a low GCS score (20). However, factors such as intoxication or endotracheal intubation may make it difficult to assess GCS reliably in the acute setting (2122). The addition of laboratory parameters to head CT and admission characteristics have improved prognostic models (23). Thus, prognostic biomarkers in sTBI could help determine whether patients are likely to benefit from intensive treatment. Several candidate biomarkers that correlate with various pathologies of mild and severe TBI have been studied (24), but their relative prognostic abilities remain unclear.

Existing reviews on biomarkers in TBI have provided valuable insight into the pathologic correlates of biomarkers, as well as how biomarkers may be used for diagnosis and prognosis (2531). However, there has been no previous quantitative comparison of the literature regarding biomarkers’ discriminative abilities in specific clinical situations. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mTBI, predicting delayed recovery after mTBI, and predicting adverse outcome after sTBI.[…]

 

Continue —-> Frontiers | Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy | Neurology

Figure 2. Anatomical locations of potential TBI biomarkers. The biomarkers included in this schematic all rated as “good” (AUC=0.800.89) or better for any of the four clinical situations studied (detecting concussion, predicting intracranial damage after concussion, predicting delayed recovery after concussion, and predicting adverse outcome after severe TBI). Biomarkers with a pooled AUC <0.8 are not shown. 1Also found in adipose tissue; 2synthesized in cells of stomach and pancreas; may regulate HPA axis; 3found mostly in pons; 4also found extracellularly; 5lectin pathway of the complement system; 6also found in endothelial cells. BBB, blood brain barrier. ECM, Extracellular matrix. Image licensed under Creative Commons Attribution-ShareAlike 4.0 International license. https://creativecommons.org/licenses/by-sa/4.0/deed.en. See Supplementary Material for image credits and licensing.

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[Thesis] Recovery of arm-hand function after stroke: developing neuromechanical biomarkers to optimize rehabilitation strategies. – Leiden University

Abstract

The aim of this thesis was to explore the neuromechanics of recovery of arm-hand function after stroke. A literature review revealed six articles that measured biomechanical and electromyographical outcome measures simultaneously, while applying active and passive tasks and multiple movement velocities to separate neural and non-neural contributors to movement disorders after stroke. Therefore, a neuromechanic assessment protocol was developed. Parameters were responsive to clinical status and had good to excellent test-retest reliability. Selective muscle activation was assessed with high measurement reliability and was significantly lower in chronic stroke patients compared to healthy participants. Longitudinally, neuromechanical parameters were combined with data on arm-hand function at six months after stroke. Paresis and diminished modulation of reflexes were associated with poor functional outcome. Changes in tissue properties were represented by a shift in wrist rest angle towards flexion and decline in passive range of motion. Increase in active range of motion and steady rest angle contributed most to prediction of functional outcome. The precision diagnostics provided by a neuromechanical assessment protocol could support clinical decision making and should be used in prediction models and as biomarkers in recovery of arm-hand function after stroke, for example by improving the selection of time-window and patients.

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[Abstract] Big data sharing and analysis to advance research in post-traumatic epilepsy – Review

Highlights

  • We have created the infrastructure for a centralized data repository for multi-modal data.
  • Innovative image and electrophysiology processing methods have been applied.
  • Novel analytic tools are described to study epileptogenesis after traumatic brain injury.

Abstract

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

 

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[European Commission] Predicting and treating epilepsy – CORDIS

Predicting and treating epilepsy

Epilepsy is a devastating condition affecting over 50 million people worldwide. An EU consortium is using a combinatorial approach to identify biomarkers and develop antiepileptogenic therapeutics.
Predicting and treating epilepsy

A five-year EU-funded project, EPITARGET (Targets and biomarkers for antiepileptogenesis), is studying the processes leading to epilepsy in adults. The consortium, consisting of 18 partners from 9 countries, aims to identify novel biomarkers and their combinations in clinically relevant animal models. These biomarkers, defining the different stages of epilepsy, will be used to predict/diagnose early and late stages of the evolution of the disease.

After the first year the project advanced significantly towards its objectives. Sample collection from various animal models of epileptogenesis is ongoing. The project developed a biocompatible electrode for fast and sensitive glucose measurement in the brain.

EPITARGET characterised expression patterns of a number of molecules at various time points after epileptogenic insults. They include lipid mediators and their receptors, microRNAs, immunoproteasomes, free radicals and antioxidants, amyloidogenic factors, inflammatory molecules, extracellular matrix proteins and synaptic proteins.

The consortium members are investigating complex mechanisms of epileptogenesis with the goal to design disease-modifying combinatorial treatment strategies, targeted to the different stages. EPITARGET optimised the two-stage approach for drug screening and explored pharmacokinetics and tolerability of combinations of clinically available drugs in naive and epileptic rodents. For targeting compounds to the brain, EPITARGET developed a lipid encapsulation approach. The first formulation of amiloride is undergoing in vitro and in vivo testing. The first generation of long-term transgene expression viral vectors was already verified in vivo.

EPITARGET findings using animal models will be translated into clinical use by validating biomarkers in human blood and brain tissue samples. Ongoing sample collection includes human brain tissue and biofluids from status epilepticus, traumatic brain injury and pharmacoresistant epilepsy patients. The creation of animal and human databases is under way. The first year of the project resulted in 20 peer-reviewed publications.

Upon completion, EPITARGET is expected to improve diagnosis of epilepsy, and to develop new antiepileptogenic treatments and means to predict their efficacy. It will improve the quality of life for millions of people and reduce treatment costs.

Source: European Commission : CORDIS : Projects and Results : Predicting and treating epilepsy

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[ARTICLE] Hemispheric asymmetry in myelin after stroke is related to motor impairment and function – Full Text

Fig. 1

Abstract

The relationships between impairment, function, arm use and underlying brain structure following stroke remain unclear. Although diffusion weighted imaging is useful in broadly assessing white matter structure, it has limited utility in identifying specific underlying neurobiological components, such as myelin. The purpose of the present study was to explore relationships between myelination and impairment, function and activity in individuals with chronic stroke. Assessments of paretic upper-extremity impairment and function were administered, and 72-hour accelerometer based activity monitoring was conducted on 19 individuals with chronic stroke. Participants completed a magnetic resonance imaging protocol that included a high resolution T1 anatomical scan and a multi-component T2 relaxation imaging scan to quantify myelin water fraction (MWF). MWF was automatically parcellated from pre- and post-central subcortical regions of interest and quantified as an asymmetry ratio (contralesional/ipsilesional). Cluster analysis was used to group more and less impaired individuals based on Fugl-Meyer upper extremity scores. A significantly higher precentral MWF asymmetry ratio was found in the more impaired group compared to the less impaired group (p < 0.001). There were no relationships between MWF asymmetry ratio and upper-limb use. Stepwise multiple linear regression identified precentral MWF asymmetry as the only variable to significantly predict impairment and motor function in the upper extremity (UE). These results suggest that asymmetric myelination in a motor specific brain area is a significant predictor of upper-extremity impairment and function in individuals with chronic stroke. As such, myelination may be utilized as a more specific marker of the neurobiological changes that predict long term impairment and recovery from stroke.

1. Introduction

Improved medical management of stroke has resulted in decreasing mortality rates (Grefkes and Ward, 2014). As a result, the number of individuals living with long-term disability as a result of stroke is rising (Krueger et al., 2015). Due to the heterogeneity of clinical presentation following stroke, it is imperative to identify biomarkers that may predict long-term impairment and function in order to appropriately individualize clinical rehabilitation goals and objectives (Bernhardt et al., 2016). With advances in diagnostic and prognostic tools, it is necessary to isolate modalities that can predict long-term outcomes for individuals with stroke, and to understand the underlying neurobiology that contributes to the predictive value of those measures.

Neuroimaging can be utilized to aid in the identification of biomarkers that may predict recovery status in individuals with stroke. White matter imaging is often used as a predictor of stroke recovery (Feng et al., 2015 and Stinear et al., 2012). Diffusion tensor imaging (DTI) can be performed within 10 days post stroke to quantify initial post stroke structural degeneration (Werring, 2000). Such indices have been found to strongly predict upper-extremity motor function at both 3- and 6-months post stroke (Puig et al., 2010 and Stinear et al., 2012). The combination of acute corticomotor function, derived from DTI and motor evoked potentials, using transcranial magnetic stimulation, has also been demonstrated to strongly predict recovery from upper-extremity impairment after stroke (Byblow et al., 2015). Although these modalities are predictive of long-term upper-extremity impairment, the underlying neurobiological bases driving the relationship between white matter microstructure and motor capacity remains unclear. Although relationships between white matter integrity, quantified with DTI, and motor impairment have been established after stroke, it is important to note that DTI measures are not a specific marker for myelination (Arshad et al., 2016). While DTI can grossly identify water movement, it is unable to differentiate between individual white matter substrates, which may produce the observed signal. Multiple structural features can be individually or collectively responsible for the observed changes in DTI measures, including: 1) axonal membrane status, 2) myelin sheath thickness, 3) number of intracellular neurofilaments and microtubules, and 4) axonal packing density (Alexander et al., 2007 and Beaulieu, 2002). To understand the neurobiological components contributing to the change in motor outcome observed there is a need to adopt neuroimaging techniques that can quantify these structural features.

Myelin formation has been identified as a specific target for therapeutic intervention following stroke, as recovery of axonal fibres is not complete without adequate myelination (Mifsud et al., 2014). Oligodendrocytes are responsible for initiating a cascade of events that result in the formation of myelin. Acute cerebral ischemia, such as that caused by a stroke, causes a rapid breakdown of oligodendrocytes and demyelination (Tekkök and Goldberg, 2001), which greatly limits overall axonal integrity in the lesioned area (Saab and Nave, 2016). Although animal work has underlined the importance of active myelination on motor recovery after stroke (Chida et al., 2011 and McKenzie et al., 2014), it is unclear how these findings transfer to humans.

Until recently, technical limitations prevented the imaging of myelin in vivo. Myelin water fraction (MWF) can be derived in humans non-invasively in vivo from multi-component T2-relaxation imaging (Alonso-Ortiz et al., 2014 and Prasloski et al., 2012b). Formalin-fixed human brains yield T2 distributions similar to those found in vivo, and histopathological studies show strong correlations between MWF and staining for myelin (Laule et al., 2004 and Moore et al., 2000). With the development of non-invasive imaging techniques, myelin can be quantified in the human brain (Prasloski et al., 2012b), both cross-sectionally and longitudinally (Lakhani et al., 2016) Work form the Human Connectome Project and others have identified that the primary motor and sensory regions are among the most densely myelinated and most easily delineated in the human brain, allowing for more reliable automatic identification and parcellation of myelinated regions (Glasser et al., 2016, Glasser and Van Essen, 2011 and Nieuwenhuys and Broere, 2016). In addition, myelination of corticospinal projections from these regions may vary based on the length of the tract and the size the axon. As such, quantification of corticospinal tract (CST) myelin using in vivo neuroimaging has not been validated to date (Glasser and Van Essen, 2011). Previous work from our group did not reveal a relationship between ipsi- and contralesional CST MWF, measured from the posterior limb of the internal capsule, and motor function or impairment (Borich et al., 2013). In order to limit variability arising from CST tract heterogeneity between individuals with stroke, the current study focused on the most well defined, myelinated regions of interest, located in precentral and postcentral areas.

Recent work has demonstrated that oligodendrocyte precursor cell proliferation and myelin structure are associated with motor learning in rodent models (Gibson et al., 2014 and Xiao et al., 2016). In particular, this work emphasized the possibility that functional motor activity may influence myelination of redundant neural pathways and improve conduction velocity via more efficient neural synchrony (Fields, 2015). The current study will extend previous lines of inquiry by exploring the relationship between real-world activity in the upper-extremity to myelination in humans. The ability to use the stroke-affected upper-limb in ‘everyday tasks’ is cited as a primary goal for individuals living with stroke (Barker and Brauer, 2005 and Barker et al., 2007). Monitoring upper-extremity usage after stroke using accelerometers is a low-cost, non-invasive way to measure functional activity and to quantify overall real-world activity (Hayward et al., 2015). Use of the stroke affected upper-limb correlates with long-term motor impairment as greater activity generally results in reduced impairment (Gebruers et al., 2014, Lang et al., 2007 and Shim et al., 2014). Identifying relationships between accelerometer based measures of activity and myelination will inform future investigations about the potential specificity of myelin as predictive biomarker for understanding what people can do, via measurement of impairment and function, versus what people actually do in the real-world.

Given the important relationships between white matter, activity and post-stroke impairment as well as the recent advances in imagining techniques, it is imperative to consider the contribution of myelination to post-stroke impairment, function and activity in humans. In order to identify potential differences in myelination based on the level of impairment after stroke, the current study identified ‘more impaired (M)’ and ‘less impaired (L)’ groups of participants. Therefore, the primary objective of the current investigation is to understand whether MWF in sensorimotor regions of interest is a biomarker of long term impairment, function or arm use in a population of individuals living with chronic stroke. Furthermore, we sought to identify if there were differences in MWF in sensorimotor regions of interest between individuals classified as ‘more impaired’ versus those who were ‘less impaired’.

Continue —> Hemispheric asymmetry in myelin after stroke is related to motor impairment and function

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