Biomarkers for multiple sclerosis.
ABSTRACT The pursuit of personalized medicine requires the development of biomarkers to predict disease course, monitor disease evolution, stratify patient subgroups by disease activity and to predict and monitor response to therapies. Multiple sclerosis (MS) is a common neurological disease in young adults with an unpredictable course that may be associated with significant disability, diminishing the patient's quality of life. Currently, disease prognosis is based on clinical information (relapse rate and disability scales) and diagnostic tests (brain MRI or the presence of oligoclonal bands in the cerebrospinal fluid). However, the ability of neurologists to make an accurate prognosis is very limited based on such information, a situation perceived by patients as one of their biggest concerns. Although many recent studies have identified different molecules and imaging techniques associated with the course of MS, in most cases the diagnostic accuracy of such technologies has not been properly assessed. This shortcoming is partly due to the failure to validate such biomarkers, which impedes their application in clinical practice. However, the recent validation of anti-aquaporin-4 antibodies for Devic's disease and the development of optic coherent tomography for MS, are examples of the benefits that the development of MS biomarkers can offer. Indeed, it may currently be necessary to redress the bias in research towards clinical validation rather than discovery in order to promote translational research and improve patient's quality of life.
SourceAvailable from: Bartolomé Bejarano Herruzo[Show abstract] [Hide abstract]
ABSTRACT: Though multiple sclerosis (MS) is an autoimmune disease of the central nervous system, the etiopathogenesis is not clear, since genetic susceptibility and environmental factors are thought to be involved. Some studies emphasize the role of microRNAs (miRNAs) in its pathogenesis. Their clinical course is extremely heterogeneous and different subtypes with considerable individual variation have been described. However, no biomarkers that reliably correlate with or predict disease activity have been found. The aim of this second part is to review and discuss data mining techniques in the context of MS.
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ABSTRACT: Clinicians treating multiple sclerosis (MS) patients need biomarkers in order to predict an individualized prognosis for every patient, that is, characteristics that can be measured in an objective manner, and that give information over normal or pathological processes, or about the response to a given therapeutic intervention. Pharmacogenetics/Genomics in the fields of MS by now can be considered a promise. In the meanwhile, clinicians should use the information provided by the many clinical epidemiological studies performed by now, telling us that there are some clinical markers of good prognosis (female sex, young age of onset, optic neuritis or isolated sensory symptoms at debut, long interval between initial and second relapse, no accumulation of disability after five years of disease evolution, normal or near normal magnetic resonance imaging (MRI) at onset). Some markers in biological samples are considered as potential prognostic markers like IgM and neurofilaments in CSF or antimyelin and chitinase 3-like 1 in blood (plasma/sera). Baseline MRI lesion number, lesion load and location have been closely associated with a worse evolution, as well as MRI measures related to axonal damage (black holes in T1, brain atrophy, grey matter atrophy (GMA) and white matter atrophy (WMA), magnetization transfer measures and intracortical lesions). Functional measures (OCT, evoked potentials) have a potential role in measuring neurodegeneration in MS and could be very useful tools for prognosis. Several mathematical approaches to estimate the risk of short term use early clinical and paraclinical biomarkers to predict the evolution of the disease.Journal of the neurological sciences 05/2013; DOI:10.1016/j.jns.2013.04.021 · 2.26 Impact Factor
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ABSTRACT: Background Multiple Sclerosis (MS) is a multi-factorial disease, where a single biomarker unlikely can provide comprehensive information. Moreover, due to the non-linearity of biomarkers, traditional statistic is both unsuitable and underpowered to dissect their relationship. Patients affected with primary (PP=14), secondary (SP=33), benign (BB=26), relapsing-remitting (RR=30) MS, and 42 sex and age matched healthy controls were studied. We performed a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs) by flow-cytometry. Semantic connectivity maps (AutoCM) were applied to find the natural associations among immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non linear associations among variables and captures connection schemes among clusters. Results Complex immunological relationships were shown to be related to different disease courses. Low CD4IL25+ cells level was strongly related (link strength, ls=0.81) to SP MS. This phenotype was also associated to high CD4ROR+ cells levels (ls=0.56). BB MS was related to high CD4+IL13 cell levels (ls=0.90), as well as to high CD14+IL6 cells percentage (ls=0.80). RR MS was strongly (ls=0.87) related to CD4+IL25 high cell levels, as well indirectly to high percentages of CD4+IL13 cells. In this latter strong (ls=0.92) association could be confirmed the induction activity of the former cells (CD4+IL25) on the latter (CD4+IL13). Another interesting topographic data was the isolation of Th9 cells (CD4IL9) from the main part of the immunological network related to MS, suggesting a possible secondary role of this new described cell phenotype in MS disease. Conclusions This novel application of non-linear mathematical techniques suggests peculiar immunological signatures for different MS phenotypes. Notably, the immune-network displayed by this new method, rather than a single marker, might be viewed as the right target of immunotherapy. Furthermore, this new statistical technique could be also employed to increase the knowledge of other age-related multifactorial disease in which complex immunological networks play a substantial role.Immunity & Ageing 01/2013; 10(1):1. DOI:10.1186/1742-4933-10-1 · 2.32 Impact Factor