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Prognostic-disability Biomarkers in Multiple Sclerosis: Review of the Literature from the Last Five Years/Multipl Skleroz Olgularinda Prognoz-Ozurluluk Biyoisaretcileri: Son Bes Yila Ait Literaturun Gozden Gecirilmesi.

Introduction

Neuroinflammation and neuroregeneration processes coexist in multiple sclerosis (MS). Although the clinical course differs in each patient, it is not known what the causative factor is in a favorable or unfavorable clinical course. Apart from clinical data, biochemical-serologic, radiologic, electrophysiologic studies and biomarker identification studies have been conducted in order to identify factors and markers related to clinical course. A biomarker is simply an indicator that can be objectively measured and evaluated in response to a normal biologic or pathologic process or response after a treatment. It can be used for diagnosis, staging, and as a prognostic indicator of the disease, predicting treatment response or follow-up of treatment (1). In this short review study, the aim was to compile biomarkers that reflect the progress and development of disability in MS that have been introduced in the past 5 years. The presence of biomarkers at the time of diagnosis will be of value in determining whether high-efficacy/high-risk treatment selection is required or whether monitoring at the time of treatment is effective/ineffective.

Which pathophysiologic processes are observed in MS and why do individual differences arise?

When the progression of MS is examined, it is observed, with the simplest approach, that there is a relapsing-remitting or progressive course. Tissue inflammation consisting predominantly of immune cells creates local areas of demyelination called plaques in brain regions in the course of the episodes, and a 'generalized inflammatory state,' which is a diffuse microglia activation in the entire brain, has been shown recently (2). Immune activation has its own cell destruction effect as well as causing disruption in the mitochondrial genetic structure, thus leading to a decrease in mitochondrial function and energy production (3). The trophic support cycle between neurons and glia is disturbed and the cell life cycle is diverted to apoptosis (4). Besides the reduction in energy production, the contribution of vascular deficiency was identified in recent years in the area of inflammation (5) and the excessive energy consumption of channel structures reorganized during the remyelination process lead to an energy bottleneck. Energy deficiency can lead to increased intracellular calcium accumulation and triggering of apoptotic processes, formation of toxic substances such as reactive oxygen products, and inadequate purification processes (6). Unlike the relapsing-remitting course, immune activation in the progressive course continues with microglia instead of T and B cells. After increased tissue damage, axonal degeneration becomes widespread with anterior-posterior extensions. Mechanisms that generate toxic responses are further activated by releasing iron accumulated in the tissue, the energy bottleneck increases, the regenerative ability decreases with advancing age, and consequently disability develops without episodes (7). Although disability appears to be a result of anatomic destruction and physiologic insufficiency in the neural structure in the relapsing-remitting course, anatomic destruction seems to be associated with physiologic destruction in the progressive course.

The neuroinflammation/neurodegeneration processes in MS pathophysiology could be summarized as follows;

1. Deformation or structural changes occur in normal tissue structure.

2. After the structural change, the energy demand increases and the inefficient-excessive energy consumption develops.

3. The processes developed for adaptation become maladaptive.

4. Cell death rate increases with insufficiency in the regeneration process or the amount of cell damage.

The presence of different prognosis-disability in patients with MS can be explained by the absence of therapeutic agents or differences in the efficacy of these agents, individual variability such as sex and age of onset, presence of toxic agents or other variables not yet defined along with this process.

What are the factors-biomarkers associated with disability?

Clinical, radiologic, and immunologic studies of the biomarkers will be grouped, but it should be remembered that these groups are intertwined (Tables 1, 2, 3). As negative factor and biomarker studies herald disability, they are more noticeable than positive factors and biomarker studies and their number is therefore greater (a summary of the studies is given in Table 4).

Clinical Biomarkers

Disability does not develop in some clinical and radiologic MS cases and these phenomena are known as 'benign MS'. Although benign MS contains different definitions, it is commonly defined as "Expanded Disability Status Scale (EDSS) values being below 2 or 3 within 10-15 years". In a study that questioned the characteristics of patients with benign MS, 307 patients were followed prospectively for 15 years and it was seen that patients with benign MS included those with complete remission of the onset attack, low or moderate initial relapse frequency, and dominating afferent symptoms (8). In another study, benign MS was defined as having EDSS values below 2 and 3 separately for 10-year disease duration and 19.8% to 33.3% among 6258 patients were characterized as having benign MS. Positive prognostic factors at baseline included female sex and younger age at onset, and disease-modifying treatment and longer disease duration were found to be positive predictors at the end of 4th year (9). In another study with long-term follow-up, the benign MS definitions of the previous study were used, female sex and the presence of fully recovering attacks with a low number of attacks indicated favorable outcome at the 20th and 30th years and 30th year, respectively. In this study, the number of cases in the 10th, 20th, and 30th year groups decreased by half every 10 years, and the benign characteristics of the patients disappeared (10). This result shows that patients with benign MS with better outcomes have a long-term risk of developing disability.

A meta-analysis of placebo-controlled, randomized trials of disease-modifying treatments in patients with relapsing MS has shown that disease-modifying treatment reduces the frequency and severity of relapses and slows down disability, independently of the route of administration and their classification as first or second-line therapies (11). It was questioned in a study whether there was any difference in clinical course and disability development before and after the disease-modifying treatment period. The time to reach an EDSS value of 6 during the period between 1980 and 2013 was examined with 5-year intervals, and it was shown that the course had changed after 2000 and the time to EDSS increase had extended. For example, the proportion of patients who reached EDSS 6 at the age of 50 years before 2000 was 27%, whereas it was significantly lower (15%) after 2000. Decreased age of diagnosis due to changing disease definitions, shortening of the delay period in taking the treatment, and the emergence of more effective treatments were emphasized among the reasons for this change, and treatment has been shown to postpone the increase in EDSS (12). The effect of time to initiate treatment on disability was questioned in relapsing MS and it was shown that the initiation of treatment before 3 years from MS onset had a significant decreasing effect on disability (13). The study was conducted with 639 patients for 8 years, and development of early disability and late age at onset were other prognostic factors. In another study in which 512 patients receiving disease-modifying treatment were followed for 17 years, both the duration of disability development and the time of secondary progression were found to be longer than those of natural history studies, which was attributed to the treatment affect (14). During the 17-year follow-up period, 36-50% of the cases were expected to have secondary progressive MS (SPMS), but only 11.3% had progressed to SPMS.

When the role of paroxysmal symptoms (lasting seconds to minutes and occurring many times a day) and unusual cortical findings (aphasia or epileptic seizures) as initial manifestations of MS was examined in a cohort of 512 patients with relapsing onset MS who were followed up for a mean period of 12 years, it was shown to be similar to classic symptoms and findings in terms of conversion rates to MS and causing disability, thus not being benign as it is supposed to be (15).

In a study investigating clinical prognostic factors affecting EDSS elevation, it was found that male sex, being of African descent, non-recovery after the first relapse, two or more relapses during the first year, a short interval between initial relapses, initial polysymptomatic presentation of pyramidal and cerebellar dysfunction and no treatment prior to reaching EDSS 3 were factors associated with EDSS increase (16). The incidence of MS is low among those with African ancestry, but the EDSS increase is early and the clinical course is worse (17).

The age of onset of illness is an important variable in disability worsening. When the relationship between MS disease type, patient age, and disability was examined, it was revealed that disability in progressive MS [SPMS, primary progressive MS (PPMS)] was related to age independently of previous disease activity or disease duration (18). The EDSS increase in patients with an onset age of 50 years or less was shorter than that in those aged less than 50 years, independent of disease course (19). In addition to the age variable, MS duration, baseline brain volume, EDSS score, and T2 disease burden were investigated in another study; higher baseline brain volume and receiving treatment predicted better long-term clinical outcomes, and higher baseline and greater early increase in EDSS score predicted worse outcomes (20). When the importance of the age of onset, cortical lesion volume, and cerebellar cortical volume variables in patients with relapsing-remitting MS (RRMS) were questioned at the time of entry into the study, the model correctly identified 94% of patients who maintained the relapsing-remitting course and 88% patients who became secondary progressive at the end of 5 years (21). The period of transition to a secondary progressive course was observed to be shortened in the presence of male sex, onset after the age of 30 years, and 3 or more relapses in the early period. It was observed that patients reaching EDSS 8 from onset of progression had 3 or more relapses, and had cerebellar and brainstem symptoms during relapses (22). The effect of smoking on the development of secondary progression was investigated with the age of onset of progression, and reaching SP disease was found to be at 48 years in patients who continued to smoke and 56 years of age in patients who quit smoking. Therefore, it was concluded that smoking worsens the course of the disease (23).

Relapse frequency is an indication of disease activity and also a risk factor for an increase in disability. It is one of the treatment targets. In addition to the frequency of relapses, the domain affected by the relapse is also important in the increase in disability. It has been shown that relapses in pyramidal, cerebellar, and bowel/bladder systems lead to higher EDSS increases compared with relapses with brainstem, visual, sensory, and cerebral involvement (24). In pediatric MS, the use of disease-modifying treatment and age of onset under the age of 15 years were factors that decreased the risk of EDSS increase, and relapses increased the risk. It has been reported that relapses with multifocal or isolated spinal cord or optic nerve involvement during follow-up had a higher incidence of EDSS-worsening compared with relapses with isolated supratentorial or brainstem syndrome (25).

Cognitive impairment is a condition observed during the course of MS. There is a relationship between age and cognitive impairment, as well as a relationship between long duration of illness and high EDSS values and cognitive impairment. This relationship also occurs independently of disease subtypes. Cognitive impairment was observed in 35% of patients with clinical isolated syndrome (CIS), 45% of patients with RRMS, 80% of patients with SPMS, and 91% of patients with PPMS (26). In a meta-analysis study, there were differences in the subfunctions of cognitive impairment observed in patients with RRMS and PPMS. In patients with SPMS, processing speed, and verbal learning and memory were shown to be worse than in those with RRMS. Patients with RRMS had more deterioration in working memory, cognitive fluency, and higher executive functions, and it was emphasized that both groups needed more specialized disease management (27).

When the importance of cognitive status at the onset of disease was investigated in a retrospective series of 78 patients for 8 years, early cognitive impairment was predictive of conversion to definite MS, disability increase, transition to the secondary progressive phase, and cortical thinning (28). Despite a decrease in the frequency of relapses, no difference in progression in the meta-analysis of patients with SPMS treated with interferon-beta (IFN-[beta]) was interpreted as that the anti-inflammatory effect of IFN-[beta] was unable to prevent MS progression (29). In a study with natalizumab, inflammatory activity was defined by the Rio score. According to the Rio score, [less than or equal to]4 new T2 lesions on magnetic resonance imaging (MRI) was scored as 0 points, >4 new T2 lesions on MRI as 1 point, no relapses as 0 points, 1 relapse as 1 point, and [greater than or equal to]2 relapses as 2 points. This scoring was predictive of short-term (1-2 years) EDSS progression, but did not predict longer term (3-7 years) EDSS progression (30). In patients treated with natalizumab, those receiving treatment aged over 50 years were found to be less responsive to treatment patients aged under 50 years (31).

In patients with MS, disease-modifying treatment may be interrupted or discontinued. It can be discontinued due to compulsory reasons such as treatment adverse effects, pregnancy, as well as due to personal preferences such as planning to have a baby, or stable disease course. Patients who discontinued glatiramer acetate and IFN therapy due to similar reasons were retrospectively investigated, and age >45 years at discontinuation, absence of relapses for [greater than or equal to]4 years, and absence of contrast enhancing lesions were found to be independent predictors of absence of relapse after discontinuation. It has also been shown that demographic and clinical data can be predicted well without MRI data. The predictors of EDSS increase after discontinuation of therapy were high EDSS scores, age over 45 years, and long disease duration (32).

The relationship between somatosensory evoked potentials (SEP) and motor evoked potentials (MEP) scores, which are evoked potential examinations, and EDSS values was examined in a cross-sectional study, and found to be related (33). SEP and MEP scores explained 58% of EDSS variability. Consecutive studies have shown that the relationship between EDSS scores and evoked potentials persisted at ten years and up to 15 years after disease onset. This suggests that the EDSS increase may be predicted by the early stage increased evoked potential scores (34). MEP and visual evoked potential data together with age and treatment status were able to predict 58% of EDSS variability at 20 years (35). Neither baseline EDSS nor T2-lesion or gadolinium-enhancing lesion quantities improved the prediction of EDSS.

The relationship between retinal nerve fiber layer thickness measurement by optical coherence tomography and EDSS was evaluated in a 5-year retrospective study, and thickness less than or equal to 87 [micro]m or less than or equal to 88 [micro]m was shown to have a risk of increased EDSS in patients with all other disease variables under control (36).

Radiologic Biomarkers

In a review study examining the effectiveness of first-line treatment, it was investigated whether early-onset MRI parameters could predict clinical response and EDSS increase between 2-5. years. New or enlarging T2-weighted lesions [greater than or equal to]1, new or enlarging T2-weighted lesions [greater than or equal to]2, and Rio score [greater than or equal to]2 were used as criteria. As a result, it was observed that all criteria had a limited predictive value and that more sensitive measures of treatment failure at short term were needed (37).

Upper cervical cord area on MRI has been as reduced in CIS and RRMS, and negatively correlated with muscular weakness and fatigability. The atrophy change rate was higher in the upper cervical cord area compared with white matter and gray matter, and this might have potential value (38). It has been shown that spinal cord lesion number, change in cord lesion number in 5 years, and change in upper cervical cord area in patients with CIS with no spinal involvement were associated with EDSS, and that asymptomatic spinal cord lesions contribute to the development of disability over the first 5 years (39). Regarding the absence of lesions, the presence of at least 1 lesion in the spinal cord was predictive of reaching EDSS 4 (40).

Involvement of cerebellar gray matter is associated with disability scores, independent of cerebral gray matter involvement. Disability scores are EDSS, cerebellar functional system score, and arm and leg functions. In addition, patients with a high burden of cerebellar leukocortical lesions had lower paced auditory serial addition test (PASAT) scores, whereas patients with greater volumes of cerebellar intracortical lesions had worse symbol digit modalities test scores (41). In a study aimed at detecting variables for prediction of disability outcomes using clinical and MRI parameters, T2 lesion number, T1 and T2 lesion volumes, corpus callosum, and thalamic fraction were the best predictors at baseline, and EDSS and its change, corpus callosum volume change and number of new or enlarging T2 lesions at 12 months were the best predictors. Based on 12-year follow-up data, a composite score was generated from a subset of the best predictors and it was shown that scores of [greater than or equal to]4 had greater specificity for predicting worsening compared with the individual predictors (42).

In a retrospective study, the relationship between baseline and first 2-years MRI imaging and EDSS scoring after 10 years was investigated, and it was seen that EDSS at 10 years could be predicted by whole brain and central atrophy, and T2-weighted lesion volume change (43).

In another study, MRI-based brain volume measurements were used to calculate observed and expected normalized brain volume based on age, sex, T2-lesion volume, and baseline EDSS. Low, normal, and high values were calculated, and the difference between observed and expected values was found; there was a relationship between baseline brain volume and 2-4-years disability development (44). The brain volume loss rate in the first 2 years was similarly related to 2-4-years disability development (45). However, more time is needed for using in clinical practice, because brain volume measurements have not yet entered routine practice and the method requires experience. When patients with RRMS in randomized controlled trials were examined by meta-analysis, it was found that the treatment effect on disability progression was independently correlated with brain atrophy and the presence of active MRI lesions (46).

In a group of 42 patients with MRI volume measurement and with a mean follow-up of 30 months, baseline thalamus, caudate, and putamen volumes predicted subsequent 25-ft walk test and MSFC, and as well as loss of volume in these structures (47). A broad review of MRI parameters has also been published recently (48).

Baseline cortical N-acetyl aspartate/creatine (NAA/Cr) and EDSS at month 24 and at the 7-year follow-up were correlated, whereas patients with EDSS [greater than or equal to]4 had a lower baseline cortical NAA/Cr (49).

In a study investigating black holes using 11C-PK11195 positron emission tomography, which shows microglial activity in progressive subjects, black holes were found not to be inactive, but surrounded by activated microglia and associated with disability (50).

Another method of study associated with MRI is to examine the default mode network with functional MRI. In one study, the network connections of the hippocampus region with other cortical-subcortical structures were questioned and reduced hippocampal-resting state functional connectivity was found to be associated with higher T2 lesion volume, longer disease duration, and the severity of depression and disability (51).

Immunologic Biomarkers

Human leukocyte antigen (HLA) is divided into major histocompatibility complex (MHC) class 1 (HLA A, B, C) and MHC class 2 (HLA-DR-DQ). In a study investigating the relationship between HLA genotype and MS prognosis, it was reported that HLA-A2 favored a better prognosis, HLA-B7 and B44 favored a poor prognosis, and HLA-DRB115, HLA-DQB16 and HLA-B8 alleles were inconclusive (52). In another study, HLA-DRB1*01 and DRB1*04 alleles were found to be associated with a worse prognosis when considering the time to reach an EDSS 6 (53). It was investigated whether variations in the myelin basic protein gene altered clinical course (conversion to MS and change in disability) in 127 persons who had had a first demyelinating event and followed up to the 5-years review, and rs12959006 was found to be associated with worse clinical outcomes. It was decisive for relapse and disability progression (54). In a study aimed at identifying loci bearing genetic risk associated with MS, there was a linear relationship between the upper cervical cord area, which is an MRI parameter, and 9 loci and an inverse relationship with 3 loci (55).

Conversion to MS in CIS can be interpreted as a poor prognostic criterion. Cerebrospinal fluid (CSF)-soluble CD27, a T cell activation marker, predicted the conversion to MS in patients with CIS and was also associated with a high relapse rate (56). In a study with a 14-year follow-up period, it was observed that, together with MRI parameters, chitinase-3-like-1 and age predicted conversion to MS. Chitinase-3-like-1 predicted long-term cognitive impairment 1 the PASAT test, and neurofilament light-chain (NfL) predicted long-term disability in the MS severity scale and nine-hole-Peg-test (57). In a study involving 813 patients with CIS, it was shown that CSF chitinase-3-like-1 was an independent risk factor for conversion to MS and reaching EDSS 3 (58). High CSF chitinase-3-like-1 was also associated with early conversion to MS and early development of disability. In a study involving patients with CIS and RRMS with a mean follow-up time of 11 years, glial fibrillary acidic protein and chitinase-3-like-1 were associated with conversion to MS and progression to EDSS 3, and chitinase-3-like-1 was associated with progression to EDSS 6 (59).

Vitamin D and MRI variables were examined during IFN treatment, and a 50 nmol/L (20 ng/mL) increment in average serum vitamin D levels within the first 12 months predicted a lower relapse rate (57%), lower rate of new active lesions on MRI (57%), lower yearly increase in T2 lesion volume (25%), lower yearly loss in brain volume (41%), and less disability (60).

In a cross-sectional study, there was a correlation between higher serum levels of interleukin (IL)-33, a member of the inflammatory IL family, IL-37, a member of the anti-inflammatory IL family, and a soluble form of vascular endothelial growth factor receptor 2 with disease severity according to EDSS (61). However, these parameters, which are high in other diseases with immunopathogenesis, are not specific for MS, thus drawing attention to the necessity of repeating the results and long-term follow-up. The relationship between serum energy metabolism biomarkers and disability, disease course, and MRI was examined, and serum lactate, creatinine, purines (hypoxanthine, xanthine, uric acid, inosine) and pyrimidines (uracil, beta-pseudouridine, uridine) were scored. Although there were different scores among patients with RRMS and progressive MS, there was a relationship with EDSS and MRI parameters (62).

CSF [beta]-amyloid levels were reduced in patients compared with controls, lower CSF [beta]-amyloid levels at baseline were a disability predictor at 3-year follow-up and CSF tau levels correlated with T2- and T1-lesion load on MRI (63). When CSF NfL at baseline in NEDA + (patients who showed no evidence of disease activity) and EDA + (patients who showed evidence of disease activity) patients with CIS and RRMS were compared with normal subjects, NEDA + patients were found to be similar to the normal group, and EDA + patients were different. NfL values predicted disease activity alone with 85% accuracy during 2 years of follow-up (64). CSF neurofilament heavy-chain (NfH) values were found to correlate with EDSS in a 17-year follow-up period, to reflect chronic axonal destruction (65), and also to estimate the amount of brain and spinal cord atrophy on MRI (66). Although it is stated that there is a relationship between CSF-serum values and that they can be used interchangeably, it should be remembered that NfH is not specific to MS (67). The occurrence of CSF oligoclonal bands (OCB) was found associated with an increase in gray matter lesions, high NfH ratio, and increased levels of pro-inflammatory mediators and several inflammatory mediators linked to B lymphocyte activity over a 10-year follow-up. It has also been shown that the occurrence of OCB was associated with physical and cognitive impairment (68), and that OCB- patients had a better prognosis and milder disability than OCB+ patients (69). However, the prognostic value of OCB remains unclear because some other studies have not shown a relationship (70). In a study investigating the value of the immunoglobulin (Ig) M index in patients with RRMS and SPMS, IgM index values, unlike IgG index, were correlated with EDSS and MRI parameters, and were higher in patients with SPMS (71).

Neutralizing antibodies observed during biologic agent treatment can also be a countervailing factor in reducing treatment efficacy. Fourteen percent of patients had neutralizing antibody during IFN-[beta]-1a/1b treatment; neutralizing antibodies lead to an increase of the relapse rate, decreased time to 1st relapse, and a negative trend in the time to reach the EDSS 4 milestone (72).

Conclusion

In addition to current treatments aimed at reducing the frequency and severity of relapses, as well as disability development, it is hoped that treatments providing regeneration and disability-free course will be added to clinical use. The most utopian point is preventive treatment, which can prevent disease formation before it starts. Until these goals are achieved, there is a need to recognize and use disease-related prognosis-disability biomarkers to demonstrate correct behavior at the right time.

In this study, the data obtained in the last 5 years were searched from the PubMed database using the keywords "multiple sclerosis", "disability", "prognosis OR prognostic" and "predictive OR prediction" and 1068 publications were found between 2012-2017. Of these, 295 were reached in full text and 33 different publications were also reviewed. Although the study reflects the recent data, it has limitations due to reasons such as the fact that data from previous years have been partially evaluated and only one database was scanned in the English language.

Ethics

Peer-review: Externally and internally peer-reviewed.

Financial Disclosure: The author declared that this study received no financial support.

References

(1.) Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89-95.

(2.) Gao Z, Tsirka SE. Animal Models of MS Reveal Multiple Roles of Microglia in Disease Pathogenesis. Neurol Res Int 2011;2011:383087.

(3.) Campbell GR, Ziabreva I, Reeve AK, et al. Mitochondrial DNA deletions and neurodegeneration in multiple sclerosis. Ann Neurol. 2011;69:481-492.

(4.) Rice CM, Cottrell D, Wilkins A, Scolding NJ. Primary progressive multiple sclerosis: progress and challenges. J Neurol Neurosurg Psychiatry 2013;84:1100-1106.

(5.) Davies AL, Desai RA, Bloomfield PS, et al. Neurological deficits caused by tissue hypoxia in neuroinflammatory disease. Ann Neurol 2013;74:815-825.

(6.) Rajda C, Pukoli D, Bende Z, Majlath Z, Vecsei L. Excitotoxins, Mitochondrial and Redox Disturbances in Multiple Sclerosis. Int J Mol Sci 2017;18.

(7.) Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol 2015;14:183-193.

(8.) Skoog B, Runmarker B, Winblad S, Ekholm S, Andersen O. A representative cohort of patients with non-progressive multiple sclerosis at the age of normal life expectancy. Brain 2012;135:900-911.

(9.) Zivadinov R, Cookfair DL, Krupp L, et al. Factors associated with benign multiple sclerosis in the New York State MS Consortium (NYSMSC). BMC Neurol 2016;16:102.

(10.) Leray E, Coustans M, Le Page E, Yaouanq J, Oger J, Edan G. "Clinically definite benign multiple sclerosis", an unwarranted conceptual hodgepodge: evidence from a 30-year observational study. Mult Scler 2013;19:458-465.

(11.) Tsivgoulis G, Katsanos AH, Grigoriadis N, et al. The Effect of Disease Modifying Therapies on Disease Progression in Patients with Relapsing-Remitting Multiple Sclerosis: A Systematic Review and Meta-Analysis. PLoS One 2015;10:e0144538.

(12.) Capra R, Cordioli C, Rasia S, Gallo F, Signori A, Sormani MP. Assessing long-term prognosis improvement as a consequence of treatment pattern changes in MS. Mult Scler 2017;23:1757-1761.

(13.) Kavaliunas A, Manouchehrinia A, Stawiarz L, et al. Importance of early treatment initiation in the clinical course of multiple sclerosis. Mult Scler 2017;23:1233-1240.

(14.) University of California, San Francisco MS-EPIC Team: Cree BA, Gourraud PA, Oksenberg JR, et al. Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 2016;80:499-510.

(15.) Bsteh G, Ehling R, Walchhofer LM, et al. Paroxysmal and unusual symptoms as first clinical manifestation of multiple sclerosis do not indicate benign prognosis-The PaSiMS II study. PLoS One 2017;12:e0181458.

(16.) Vasconcelos CC, Aurencao JC, Thuler LC, Camargo S, Alvarenga MP, Alvarenga RM. Prognostic factors associated with long-term disability and secondary progression in patients with Multiple Sclerosis. Mult Scler Relat Disord 2016;8:27-34.

(17.) Aurencao JC, Vasconcelos CC, Thuler LC, Alvarenga RM. Disability and progression in Afro-descendant patients with multiple sclerosis. Arq Neuropsiquiatr 2016;74:836-841.

(18.) Tutuncu M, Tang J, Zeid NA, et al. Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Mult Scler 2013;19:188-198.

(19.) Guillemin F, Baumann C, Epstein J, et al. Older Age at Multiple Sclerosis Onset Is an Independent Factor of Poor Prognosis: A Population-Based Cohort Study. Neuroepidemiology 2017;48:179-187.

(20.) Traboulsee AL, Cornelisse P, Sandberg-Wollheim M, et al. Prognostic factors for long-term outcomes in relapsing-remitting multiple sclerosis. Mult Scler J Exp Transl Clin 2016;2:2055217316666406.

(21.) Calabrese M, Romualdi C, Poretto V, Fet al. The changing clinical course of multiple sclerosis: a matter of gray matter. Ann Neurol 2013;74:76-83.

(22.) Scalfari A, Neuhaus A, Daumer M, Muraro PA, Ebers GC. Onset of secondary progressive phase and long-term evolution of multiple sclerosis. J Neurol Neurosurg Psychiatry 2014;85:67-75.

(23.) Ramanujam R, Hedstrom AK, Manouchehrinia A, et al. Effect of Smoking Cessation on Multiple Sclerosis Prognosis. JAMA Neurol 2015;72:1117-1123.

(24.) Stewart T, Spelman T, Havrdova E, et al. Contribution of different relapse phenotypes to disability in multiple sclerosis. Mult Scler 2017;23:266-276.

(25.) Iaffaldano P, Simone M, Lucisano G, et al. Prognostic indicators in pediatric clinically isolated syndrome. Ann Neurol 2017;81:729-739.

(26.) Ruano L, Portaccio E, Goretti B, et al. Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult Scler 2017;23:1258-1267.

(27.) Johnen A, Landmeyer NC, Burkner PC, Wiendl H, Meuth SG, Holling H. Distinct cognitive impairments in different disease courses of multiple sclerosis-A systematic review and meta-analysis. Neurosci Biobehav Rev 2017;83:568-578.

(28.) Pitteri M, Romualdi C, Magliozzi R, Monaco S, Calabrese M. Cognitive impairment predicts disability progression and cortical thinning in MS: An 8-year study. Mult Scler 2017;23:848-854.

(29.) La Mantia L, Vacchi L, Rovaris M, et al. Interferon for secondary progressive multiple sclerosis: a systematic review. J Neurol Neurosurg Psychiatry 2013;84:420-426.

(30.) Raffel J, Gafson AR, Dahdaleh S, Malik O, Jones B, Nicholas R. Inflammatory Activity on Natalizumab Predicts Short-Term but Not Long-Term Disability in Multiple Sclerosis. PLoS One 2017;12:e0169546.

(31.) Matell H, Lycke J, Svenningsson A, et al. Age-dependent effects on the treatment response of natalizumab in MS patients. Mult Scler 2015;21:48-56.

(32.) Bsteh G, Feige J, Ehling R, et al. Discontinuation of disease-modifying therapies in multiple sclerosis - Clinical outcome and prognostic factors. Mult Scler 2017;23:1241-1248.

(33.) Kiylioglu N, Parlaz AU, Akyildiz UO, Tataroglu C. Evoked potentials and disability in multiple sclerosis: A different perspective to a neglected method. Clin Neurol Neurosurg 2015;133:11-17.

(34.) London F, El Sankari S, van Pesch V Early disturbances in multimodal evoked potentials as a prognostic factor for long-term disability in relapsing-remitting multiple sclerosis patients. Clin Neurophysiol 2017;128:561-569.

(35.) Schlaeger R, Schindler C, Grize L, et al. Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years. Mult Scler 2014;20:1348-1354.

(36.) Martinez-Lapiscina EH, Arnow S, Wilson JA, et al. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study. Lancet Neurol 2016;15:574-584.

(37.) Rio J, Ruiz-Pena JL. Short-term suboptimal response criteria for predicting long-term non-response to first-line disease modifying therapies in multiple sclerosis: A systematic review and meta-analysis. J Neurol Sci 2016;361:158-167.

(38.) Hagstrom IT, Schneider R, Bellenberg B, et al. Relevance of early cervical cord volume loss in the disease evolution of clinically isolated syndrome and early multiple sclerosis: a 2-year follow-up study. J Neurol 2017;264:1402-1412.

(39.) Brownlee WJ, Altmann DR, Alves Da Mota P, et al. Association of asymptomatic spinal cord lesions and atrophy with disability 5 years after a clinically isolated syndrome. Mult Scler 2017;23:665-674.

(40.) D'Amico E, Patti F, Leone C, Lo Fermo S, Zappia M. Negative prognostic impact of MRI spinal lesions in the early stages of relapsing-remitting multiple sclerosis. Mult Scler J Exp Transl Clin 2016;2:2055217316631565.

(41.) Damasceno A, Damasceno BP, Cendes F. The Clinical Impact of Cerebellar Grey Matter Pathology in Multiple Sclerosis. PLoS One 2014;9:e96193.

(42.) Uher T, Vaneckova M, Sobisek L, et al. Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis. Mult Scler 2017;23:51-61.

(43.) Popescu V, Agosta F, Hulst HE, et al. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2013;84:1082-1091.

(44.) Sormani MP, Kappos L, Radue EW, et al. Defining brain volume cutoffs to identify clinically relevant atrophy in RRMS. Mult Scler 2017;23:656-664.

(45.) Jeffery DR, Di Cantogno EV, Ritter S, Meier DP, Radue EW Camu W The relationship between the rate of brain volume loss during first 24 months and disability progression over 24 and 48 months in relapsing MS. J Neurol 2016;263:299-305.

(46.) Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol 2014;75:43-49.

(47.) Nourbakhsh B, Azevedo C, Maghzi AH, Spain R, Pelletier D, Waubant E. Subcortical grey matter volumes predict subsequent walking function in early multiple sclerosis. J Neurol Sci 2016;366:229-233.

(48.) Rocca MA, Comi G, Filippi M. The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis. Front Neurol 2017;8:433.

(49.) Wu X, Hanson LG, Skimminge A, et al. Cortical N -acetyl aspartate is a predictor of long-term clinical disability in multiple sclerosis. Neurol Res 2014;36:701-708.

(50.) Giannetti P, Politis M, Su P, et al. Microglia activation in multiple sclerosis black holes predicts outcome in progressive patients: An in vivo [(11)C](R)-PK11195-PET pilot study. Neurobiol Dis 2014;65:203-210.

(51.) Rocca MA, Pravata E, Valsasina P, et al. Hippocampal-DMN disconnectivity in MS is related to WM lesions and depression. Hum Brain Mapp 2015;36:5051-5063.

(52.) Lysandropoulos AP, Mavroudakis N, Pandolfo M, et al. HLA genotype as a marker of multiple sclerosis prognosis: A pilot study. J Neurol Sci 2017;375:348-354.

(53.) Romero-Pinel L, Pujal JM, Martinez-Yelamos S, et al. HLA-DRB1: genetic susceptibility and disability progression in a Spanish multiple sclerosis population. Eur J Neurol 2011;18:337-342.

(54.) Zhou Y, Simpson S, Charlesworth JC, et al. Variation within MBP gene predicts disease course in multiple sclerosis. Brain Behav 2017;7:e00670.

(55.) Akkad DA, Bellenberg B, Esser S, et al. Multiple sclerosis risk loci correlate with cervical cord atrophy and may explain the course of disability. Neurogenetics 2015;16:161-168.

(56.) van der Vuurst de Vries RM, Mescheriakova JY, Runia TF, Jafari N, Siepman TA, Hintzen RQ. Soluble CD27 Levels in Cerebrospinal Fluid as a Prognostic Biomarker in Clinically Isolated Syndrome. JAMA Neurol 2017;74:286-292.

(57.) Modvig S, Degn M, Roed H, et al. Cerebrospinal fluid levels of chitinase 3-like 1 and neurofilament light chain predict multiple sclerosis development and disability after optic neuritis. Mult Scler 2015;21:1761-1770.

(58.) Canto E, Tintore M, Villar LM, et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain 2015;138:918-931.

(59.) Martinez MA, Olsson B, Bau L, et al. Glial and neuronal markers in cerebrospinal fluid predict progression in multiple sclerosis. Mult Scler 2015;21:550-561.

(60.) Ascherio A, Munger KL, White R, et al. Vitamin D as an Early Predictor of Multiple Sclerosis Activity and Progression. JAMA Neurol 2014;71:306-314.

(61.) Kouchaki E, Tamtaji OR, Dadgostar E, Karami M, Nikoueinejad H, Akbari H. Correlation of Serum Levels of IL-33, IL-37, Soluble Form of Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), and Circulatory Frequency of VEGFR2-expressing Cells with Multiple Sclerosis Severity. Iran J Allergy Asthma Immunol 2017;16:329-337.

(62.) Lazzarino G, Amorini AM, Petzold A, et al. Serum Compounds of Energy Metabolism Impairment Are Related to Disability, Disease Course and Neuroimaging in Multiple Sclerosis. Mol Neurobiol 2017;54:7520-7533.

(63.) Pietroboni AM, Schiano di Cola F, Scarioni M, et al. CSF [beta]-amyloid as a putative biomarker of disease progression in multiple sclerosis. Mult Scler 2017;23:1085-1091.

(64.) Hakansson I, Tisell A, Cassel P, et al. Neurofilament light chain in cerebrospinal fluid and prediction of disease activity in clinically isolated syndrome and relapsing-remitting multiple sclerosis. Eur J Neurol 2017;24:703-712.

(65.) Petzold A. The prognostic value of CSF neurofilaments in multiple sclerosis at 15-year follow-up. J Neurol Neurosurg Psychiatry 2015;86:1388-1390.

(66.) Petzold A, Steenwijk MD, Eikelenboom JM, Wattjes MP, Uitdehaag BM. Elevated CSF neurofilament proteins predict brain atrophy: A 15-year follow-up study. Mult Scler 2016;22:1154-1162.

(67.) Dubuisson N, Puentes F, Giovannoni G, Gnanapavan S. Science is 1% inspiration and 99% biomarkers. Mult Scler 2017;23:1442-1452.

(68.) Farina G, Magliozzi R, Pitteri M, Ret al. Increased cortical lesion load and intrathecal inflammation is associated with oligoclonal bands in multiple sclerosis patients: a combined CSF and MRI study. J Neuroinflammation 2017;14:40.

(69.) Rojas JI, Tizio S, Patrucco L, Cristiano E. Oligoclonal bands in multiple sclerosis patients: worse prognosis? Neurol Res 2012;34:889-892.

(70.) Stangel M, Fredrikson S, Meinl E, Petzold A, Stuve O, Tumani H. The utility of cerebrospinal fluid analysis in patients with multiple sclerosis. Nat Rev Neurol 2013;9:267-276.

(71.) Ozakbas S, Cinar BP, Ozcelik P, Baser H, Kosehasanogullari G. Intrathecal IgM index correlates with a severe disease course in multiple sclerosis: Clinical and MRI results. Clin Neurol Neurosurg 2017;160:27-29.

(72.) Paolicelli D, D'Onghia M, Pellegrini F, et al. The impact of neutralizing antibodies on the risk of disease worsening in interferon [beta]-treated relapsing multiple sclerosis: a 5 year post-marketing study. J Neurol 2013;260:1562-1568.

[iD] Nefati Kiylioglu

Adnan Menderes University Faculty of Medicine, Department of Neurology Division of Clinical Neurophysiology Aydin, Turkey

Address for Correspondence/Yazisma Adresi: Nefati Kiylioglu MD, Adnan Menderes University Faculty of Medicine, Department of Neurology, Division of Clinical Neurophysiology, Aydin, Turkey

Phone: +90 256 444 12 56/3155 E-mail: knefati@gmail.com ORCID ID: orcid.org/0000-0001-5783-1719

Received/Gelis Tarihi: 12.12.2017 Accepted/Kabul Tarihi: 16.02.2018

DOI:10.4274/tnd.79836
Table 1. Clinical prognosis-disability biomarkers

Clinical biomarkers
Good                                   Poor

- Low relapse frequency in             - Male, African descent,
the first 5 years, complete              non-recovery after the
remission, sensory relapses (7)          first relapse, two or more
- Female, younger age at onset,          relapses during the first
treatment (8)                            year, short interval between
- Initiation of treatment within         relapses, relapses with
the first 3 years (12)                   pyramidal and cerebellar
- High baseline brain volume             dysfunction, no treatment
and treatment (19)                       prior to reaching EDSS 3 (16)
- Age of onset under the age of        - Older age at onset (19)
15 years, use of disease- modifying    - High baseline EDSS and EDSS
treatment, isolated supratentorial       increase (20)
and isolated brain stem relapses (24)  - Smoking (23)
- Absence of relapses within the       - Pyramidal, cerebellar,
last 4 years (31)                        bowel/bladder involvement
                                       - Multifocal involvement,
                                         isolated spinal cord and
                                         isolated optic nerve
                                         involvement (24)
                                       - Presence of early cognitive
                                         impairment (28)
                                       - Age over 45 years and presence
                                         of high disease activity (32)
                                       - First-year EDSS and EDSS
                                         change (42)

EDSS: Expanded Disability Status Scale

Table 2. Radiologic prognosis-disability biomarkers

Radiologic biomarkers
Good                             Poor

- Higher observed brain volume   - New or enlarging T2 lesions (37,42)
than the expected brain volume   - Lesion in the spinal cord (39,40)
(44)
- Baseline volumes of thalamus,  - Involvement of cerebellar gray matter
caudate and putamen (47)           (41)
                                 - Baseline T2 lesion number, T1 and T2
                                   lesion volumes, corpus callosum, and
                                   thalamic fractions, corpus callosum
                                   volume change (42)
                                 - Whole brain and central atrophy (43)
                                 - Observed brain volume is lower than
                                   expected brain volume (44), increased
                                   brain volume loss rate (45)
                                 - Lower cortical NAA/Cr (46)

NAA/Cr: N-acetyl aspartate/creatine

Table 3. Immunologic prognosis-disability biomakers

Immunologic biomarkers
Good                       Poor

- Presence of HLA-A2 (52)  - Presence of HLA-B7 and B4 (52)
- Presence of TYK2, RGS1   - Presence of HLA-DRB11 and HLA-DRB14 (53)
CLEC16A gene locus (55)
- High serum vitamin D     - Presence of re12959006 variant in MBP gene
level (60)                   (54)
- Absence of oligoclonal   - Presence of BATF, CYP27BI, ILI2B,
and (69)                     NFKB1, IL7, PLEK, EV15, TAGAP, nrs669607
                             gene locus (55)
                           - High CSF-soluble CD27 level (56)
                           - High CSF chitinase 3-like 1 level
                             (57,58,59)
                           - CSF neurofilament light-chain levels
                             (57,64)
                           - IL-33, IL-37, VEGFR2 levels (61)
                           - Serum lactate, creatinine,
                             purine-pyrimidine levels (62)
                           - Low CSF [beta]-amyloid levels (63)
                           - CSF neurofilament heavy-chain levels (65)
                           - Presence of IgG oligoclonal bands (68,69)
                           - Presence of IgM oligoclonal bands (71)
                           - Presence of neutralizing antibody (72)

HLA: Human leukocyte antigen, MBP: Myelin basic protein, CSF:
Cerebrospinal fluid, IL: Interleukin, VEGFR2: Vascular endothelial
growth factor receptor 2, Ig: Immunoglobulin

Table 4. Features and brief descriptions of the studies

Author-              Year  Number    Follow-up
reference                  of cases  time
number

Skoog et al.         2012    307     15 years
(8)
Zivadinov            2016   6258     4 years-retrospective
et al. (9)
Leray et al.         2013    874     30 years
(10)
Capra et al.         2017   1324     30 years
(12)
Kavaliunas           2017    639     8 years
et al. (13)
Cree et al.          2016    512     17 years
(14)
Bsteh et al.         2017    532     11 years
(15)
Vasconselos          2016    303     10 years
et al. (16)
Aurencao et          2016            Systematic review; 14 articles
al. (17)
Tutuncu et           2012    964     10 years
al. (18)
Guillemin            2017   3597
et al. (19)
Traboulsee           2016    382     8 years
et al. (20)
Calabrese et         2013    334     5 years
al. (21)
Scalfari et          2014    806     28 years
al. (22)
Ramanujam            2015    728     3 years
et al. (23)
Stewart et           2017  19504     Patients with at least one year
al. (24)                             follow-up
Iaffaldano           2017    602     10 years
et al. (25)
Ruano et al.         2017   1040     Cross-sectional-10 months
(26)
Johnen et            2017   4460     Systematic review; 47 articles
al. (27)
Pitteri et al.       2017     78     8 years
(28)
Raffel et al.        2017    161     7 years
(30)
Matell et al.        2015   1872     6 years
(31)
Bsteh et al.         2017    221     4 years
(32)
Kiylioglu et         2015     67     Cross-sectional-2 years
al. (33)
London et            2017    108     15 years
al. (34)
Schlaeger et         2014     28     20 years
al. (35)
Martinez-            2016    879     5 years
Lapiscina et
al. (36)
Rio and              2016            Systematic review; 8 articles
Ruiz-Pena (37)
Hagstrom et          2017    220     2 years
al. (38)
Brownlee et          2015    131     5 years
al. (39)
D'Amico et al.       2016    239     7 years
(40)
Damasceno et         2014     72     Cross-sectional
al. (41)
Uher et al. (42)     2017    177     12 years
Popescu et al.       2013    261     12 years
(43)

Sormani et al.       2017   2342     4 years
(44)
Jeffery et al. (45)  2016   1272     4 years
Sormani et al.       2014  13500     Meta-analysis; 13 articles 30
(46)                                 months
Nourbakhsh et        2016     40
al. (47)
Wu et al. (49)       2014      6     7 years
Giannetti et al.     2014     19     Cross-sectional
(50)
Rocca et al. (51)    2015    111     Cross-sectional
Lysandropoulos       2017    118     2 years
et al. (52)
Romero-Pinel et      2011   1468     Cross-sectional
al. (53)
Zhou et al. (54)     2017    127     5 years
Akkad et al. (55)    2014    141     Cross-sectional

van der Vuurst       2017     77     5 years
de Vries et al.
(56)
Modvig et al.        2015     86     14
(57)
Canto et al. (58)    2015    813     5 years
Martinez et al.      2015    301     11 years
(59)
Ascherio et al.      2014    334     5 years
(60)
Kouchaki et al.      2017    159     Cross-sectional
(61)
Lazzarino et al.     2017    685     Cross-sectional
(62)
Pietroboni et al.    2017     93     3 years
(63)
Hakansson et         2017     63     2 years
al. (64)
Petzold (65)         2015     51     15 years
Petzold et al.       2016     15     15 years
(66)
Farina et al.        2017     50     10 years
(68)
Rojas et al. (69)    2012    196     13 years
Ozakbas et al.       2017     81     Cross-sectional
(71)
Paolicelli et al.    2013    567     5 years
(72)

Author-              Objective/monitored
reference            parameters
number

Skoog et al. (8)     Progression monitoring-relapse frequency,
                     clinical, EDSS
Zivadinov            Having EDSS values below 2 and 3 for 10-year
et al. (9)           disease duration 19.8% to 33.3% baseline benign
                     MS.
Leray et al.         Having EDSS values below 2 and 3 for 10-year
(10)                 disease duration.
                     50% decline in benign MS cases every 10 years
Capra et al.         Case rate with EDSS 6
(12)
Kavaliunas           The effect of time to initiate treatment at 1,
et al. (13)          1-3 or 3 years on reaching EDSS 4 Disability Age
Cree et al.          EDSS
(14)                 NEDA
                     Progression to SPMS
Bsteh et al.         Age, sex, disability, MR, initial symptoms, OCD,
(15)                 treatment
Vasconselos          Initial symptoms (mono-polysymptomatic) EDSS, age
et al. (16)          of  onset, sex, race, number of relapses during
                     the first year, interval between two relapses,
                     recovery after the first relapse, treatment status
                     prior to reaching EDSS 3
Aurencao et          Comparison of black race
al. (17)             and white race in terms of MS development and
                     progression
Tutuncu et           Sex, age at onset of MS and age of onset of
al. (18)             progression, age of onset of progression, time to
                     reach EDSS 6 from the onset of progression
Guillemin            Reaching EDSS 4 and 6 Transition to SP
et al. (19)
Traboulsee           Age, disease duration, baseline brain volume, EDSS
et al. (20)          score, T2 lesion burden, early increase in EDSS,
                     receiving treatment
Calabrese et         Age of onset, cortical lesion volume, and
al. (21)             cerebellar cortical volume
Scalfari et          Sex, age of onset, number of attacks in the first
al. (22)             2 years, number of symptoms and type
Ramanujam            Sex, age at onset of disease, smoking prior to
et al. (23)          diagnosis, current smoking
Stewart et           Relationship between disability and frequency of
al. (24)             relapses and relapse types
Iaffaldano           Factors related to disability in pediatric CIS and
et al. (25)          MS cases; Sex, relapse frequency, relapse
                     location, treatment use
Ruano et al. (26)    Cognitive impairment
Johnen et            Cognitive impairment
al. (27)
Pitteri et al.       The importance of the cognitive status at the
(28)                 onset of disease
Raffel et al.        The importance of first year MRI activity and
(30)                 relapses in prediction of long-term disability
                     in patients under natalizumab treatment
Matell et al.        Change in natalizumab efficacy with age Duration
(31)                 of disease, EDSS, age, SMDT test result, CSF
                     CXCL13 level, NfL
Bsteh et al.         Demographic and clinical data, MRI data
(32)
Kiylioglu et         The relationship between SEP + MEP scores
al. (33)             obtained by ordinal scoring and EDSS
London et            The relationship between composite evoked
al. (34)             potential  score and EDSS score at 10th and 15 th
                     years
Schlaeger et         Estimation of disability rate after 20 years with
al. (35)             MEP + VEP score in RRMS and SPMS cases
Martinez-            Predicting the development of disability in MS
Lapiscina et         cases following retinal nerve fiber layer
al. (36)
Rio and              New or enlarging T2-weighted lesions
Ruiz-Pena            [greater than or equal to]1, new or enlarging
(37)                 T2-weighted lesions [greater than or equal to]2
                     and Rio score [greater than or equal to]2
                     Predicting EDSS increase between 2-5 years
Hagstrom et          The relationship between EDSS, upper cervical
al. (38)             spinal cord area, pyramidal and sensory function
                     score, motor fatigue at baseline, 12th and 24th
                     months in CIS and RRMS
Brownlee et          The relationship between brain MRI lesion burden
al. (39)             and brain atrophy, number of spinal cord lesions,
                     upper cervical spinal cord cross-sectional area
                     and EDSS
D'Amico et al.       The relationship between the presence of early
(40)                 spinal cord lesion in RRMS and disability
Damasceno et         Relationship between cerebellar gray matter
al. (41)             involvement and clinical and cognitive impairment
                     in MS cases
Uher et al. (42)     Clinical and MRI parameters were used for
                     prediction of worsening, T2 lesion number, T1 and
                     T2 lesion volumes, corpus callosum, and thalamic
                     fraction at baseline, EDSS and its change,
                     corpus callosum volume change and number of new or
                     enlarging T2 lesions at 1st year
Popescu et al. (43)  MRI imaging and disability status after 10 years
Sormani et al.       MRI-based brain volume measurements were used to
(44)                 calculate observed and expected normalized brain
                     volume based on age, sex, T2-lesion volume, and
                     baseline EDSS
Jeffery et al. (45)  The relationship between brain volume loss rate in
                     the first 2 years and 2-4 year disability
                     development
Sormani et al.       Therapeutic effect of disease-modifying treatment
(46)                 on disability was examined
Nourbakhsh et        The relationship between thalamus, caudate and
al. (47)             putamen volumes, clinical EDSS, subsequent 25-ft
                     walk test and MSFC
Wu et al. (49)       Relation of NAA/Cr ratio to EDSS
Giannetti et al.     Investigating T1 lesions by 11C-PK11195 PET
(50)                 (PK-PET) that shows microglial activity and its
                     relationship with clinical status
Rocca et al. (51)    Relationship between hippocampal-resting state
                     functional connectivity and T2 lesion load,
                     disease duration, disability and depression
Lysandropoulos       The relationship between HLA types and MS
et al. (52)          prognosis
Romero-Pinel et      Investigation of risk characteristics of HLA-DRB1
al. (53)             alleles in MS
Zhou et al. (54)     Relation of MBP re12959006 variant to disability
Akkad et al.         Relationship between genetic loci, clinical
(55)                 status, upper cervical spinal cord parameters
                     in MRI
van der Vuurst       Relationship between CSF-soluble CD27 at the time
de Vries et al.      of initial relapse and MS diagnosis and relapse
(56)                 rate
Modvig et al.        MRI, CSF leukocyte, IgG index, OCB, CSF
(57)                 chitinase-3-like-1, osteopontin, NfL, MBP, CCL2,
                     CXCL10, CXCL13 and MMP-9
Canto et al. (58)    The relationship between CSF chitinase-3-like-1
                     level and transition to MS and development of
                     disability
Martinez et al.      Predicting conversion to MS and disability
(59)                 increase in CIS and RRMS with CSF NfL, t-tau,
                     p-tau, glial fibrillary acidic protein, S-100B,
                     chitinase-3-like-1, monocyte chemoattractant
                     protein, [alpha]-sAPP, [beta]-sAPP, A[beta]38,
                     A[beta]40 and A[beta]42 levels
Ascherio et al.      The relationship between disability and Vitamin D
(60)                 and MRI variables
Kouchaki et al.      Relationship between serum IL-33, IL-37 and
(61)                 sVEGFR2 and EDSS
Lazzarino et al.     The relationship between serum energy metabolism
(62)                 biomarkers, serum lactate, creatinine, purines
                     (hypoxanthine, xanthine, uric acid, inosine) and
                     pyrimidines (uracil, beta-pseudouridine, uridine),
                     and disability, disease course and MRI
Pietroboni et al.    Relationship between CSF amyloid beta and tau
(63)                 levels and MRI and disability
Hakansson et         The relationship between clinical status and MRI
al. (64)             and CSF CXCL1, CXCL8, CXCL10, CXCL13, CCL20,
                     CCL22, NfL, NfH, GFAP, chitinase-3-like-1 MMP-9,
                     osteopontin levels in RIS and RRMS cases
Petzold (65)         Relationship between CSF NfH values and clinical
                     EDSS
Petzold et al.       Relationship between CSF NfH values and MRI
(66)                 atrophy
Farina et al.        The relationship between cognitive, clinical
(68)                 status, MRI and CSF profiles CXCL13, CXCL12,
                     CXCL10, TNFSF13, TNFSF13B, IL-6, IL-10,
                     IFN-[gamma], TNF, MMP2, GM-CSF, osteopontin and
                     sCD163 in OCB + and - cases
Rojas et al. (69)    The relationship between OCB and EDSS
Ozakbas et al.       The relationship between MRI and EDSS, and IgG
(71)                 index, as well as, IgM index in RRMS and SPMS
                     patients
Paolicelli et al.    Neutralizing antibody status and effects on
(72)                 disease course in patients using IFN-beta 1a/b
                     therapy

Author-              Significant parameters/implications
reference
number

Skoog et al.         Benign MS features;
(8)                  Low relapse frequency in the first 5 years,
                     complete remission of the onset attack and
                     dominating afferent symptoms.
Zivadinov            Benign MS features;
et al. (9)           Baseline; female sex and younger age at onset. At
                     the end of 4th year; disease-modifying treatment
                     and longer disease duration.
Leray et al.         Benign MS features;
(10)                 Female sex and presence of fully recovering
                     attacks with low number of attacks at the 30th
                     year.
Capra et al.         Disease-modifying treatment affects disability
(12)                 development; the prevalence of EDSS 6 was
                     significantly lower before 2000 (15-27%).
Kavaliunas           The initiation of treatment within 1 year has an
et al. (13)          impact on disability.
                     In addition, development of early disability and
                     late age at onset were independent variables.
Cree et al.          Progression to SPMS rate is reduced with
(14)                 disease-modifying treatment. Treatment/natural
                     course rates 36/11.3%.
Bsteh et al.         Atypical/cortical symptoms as initial symptoms
(15)                 are not benign.
Vasconselos          Risk factors for rapid progression; male sex,
et al. (16)          being of African descent, non-recovery after the
                     first relapse, two or more relapses during the
                     first year, a short interval between two relapses,
                     initial polysymptomatic presentation of pyramidal
                     and cerebellar dysfunction, no treatment prior to
                     reaching EDSS 3.
Aurencao et          Rarer, but faster development of disability in
al. (17)             black race.
Tutuncu et           Age was related to disability independently of
al. (18)             previous disease activity or disease duration.
Guillemin            Age of onset after age 50 is a risk factor for
et al. (19)          disability increase.
Traboulsee           Good prognosis; higher baseline brain volume and
et al. (20)          receiving treatment. Poor prognosis; higher
                     baseline and greater early increase in EDSS score.
Calabrese et         Age of onset, cortical lesion volume, and
al. (21)             cerebellar cortical volume identified 94% RRMS
                     patients and 88% SPMS patients.
Scalfari et          Risk factors for transition to SPMS; male sex,
al. (22)             onset after the age of 30, and 3 or more relapses
                     in the early period. Three or more relapses, and
                     had cerebellar and brainstem symptoms during
                     relapses were risk factors for reaching to EDSS 8.
Ramanujam            Current smoking accelerates
et al. (23)          the development of secondary progression.
Stewart et           Relapses in pyramidal, cerebellar and
al. (24)             bowel/bladder systems lead to higher EDSS increase
                     compared with relapses with brainstem, visual,
                     sensory, and cerebral involvement.
Iaffaldano           The use of disease-modifying treatment was a
et al. (25)          factor that decreased the risk of disability
                     development, while relapses with multifocal or
                     isolated spinal cord or optic neuritis involvement
                     had higher incidence of disability development
                     compared with relapses with isolated
                     supratentorial or brainstem syndrome.
Ruano et al.         There is a relationship between cognitive
(26)                 impairment and age, long duration of illness and
                     high EDSS values. This relationship also occurs
                     independently of disease subtypes and is higher in
                     progressive cases.
Johnen et            In SPMS cases, processing speed, verbal learning
al. (27)             and verbal memory were worse than RRMS. RRMS
                     patients had more deterioration in working memory,
                     cognitive fluency and higher executive functions.
Pitteri et al.       Early cognitive impairment was predictive of
(28)                 conversion to definite MS, disability increase,
                     transition to secondary progressive phase and
                     cortical thinning.
Raffel et al.        Disability increase was predicted with Rio score
(30)                 for 1-2 years, but not for 3-7 years.
Matell et al.        Discontinuation was higher in patients >50 years
(31)                 (18.7/7.7%), also deterioration in EDSS and SDMT
                     were higher.
Bsteh et al.         Absence of relapses; age >45 years at
(32)                 discontinuation, absence of relapses for
                     [greater than or equal to]4 years and absence of
                     contrast enhancing lesions Relapses; high EDSS
                     score, over 45 years of age and long disease
                     duration.
Kiylioglu et         Ordinal SEP + MEP scores explained 58% of EDSS
al. (33)             variability (r (2)=0.584).
London et            The change in multimodal evoked potential
al. (34)             score predicts future disability score and is
                     related to EDSS.
Schlaeger et         Evoked potential scores together with age and
al. (35)             treatment status were able to predict 58% of
                     EDSS variability at 20 years.
Martinez-            Thickness less than 87 pm-88 pm was has a risk of
Lapiscina et         increased EDSS.
al. (36)
Rio and              Sensitivity values are good, but predictive values
Ruiz-Pena            are not sufficient.
(37)
Hagstrom et          Atrophy change rate was higher in the upper
al. (38)             cervical cord area compared with white matter
                     and gray matter, and this might have a potential
                     value.
Brownlee et          Baseline asymptomatic spinal cord lesion number,
al. (39)             change in cord lesion number and change in upper
                     cervical cord area were associated with EDSS.
D'Amico et al.       The presence of spinal cord lesion is a
(40)                 predictor of poor prognosis in patients with RRMS.
Damasceno et         Cerebellar intracortical lesions predict EDSS
al. (41)             - cerebellar - limb functions.
                     Patients with high burden of intracortical
                     lesions had worse SDMT scores and
                     leukocortical lesions are associated with low
                     PASAT score.
Uher et al. (42)     A composite score generated from clinical and
                     MRI parameters was shown that scores of
                     [greater than or equal to]4 had greater
                     specificity for predicting worsening compared with
                     the individual predictors.
Popescu et al.       EDSS at 10 years was related to whole brain and
(43)                 central atrophy, and T2-weighted lesion volume
                     change at 2 years.
Sormani et al.       There was a relationship between baseline brain
(44)                 volume and 2-4 year disability development.
Jeffery et al. (45)  MS disease activity and severity and baseline
                     brain volume, and brain volume loss rate
                     in the first 2 years were related to 2-4 year
                     disability development.
Sormani et al.       Brain atrophy and active MRI lesions affect
(46)                 75% of these two factors in preventing EDSS
                     increase.
Nourbakhsh et        While atrophy in subcortical structures was
al. (47)             observed as a continuous process, the baseline
                     volumes of these structures predicted the 25-ft
                     walk test and MSFC.
Wu et al. (49)       Baseline cortical NAA/Cr and EDSS were negatively
                     correlated and there was a significant difference
                     between patients with EDSS
                     [greater than or equal to]4 and others.
Giannetti et al.     T1 black holes were found to be not inactive,
(50)                 but surrounded by activated microglia and
                     associated with disability.
Rocca et al. (51)    Reduced hippocampal-resting state functional
                     connectivity was found to be associated
                     with higher T2 lesion volume, longer disease
                     duration, and the severity of depression and
                     disability.
Lysandropoulos       HLA-A2 favor a better prognosis, HLA-B7 and B44
et al. (52)          favor a poor prognosis, and HLA-DRB115, HLA-DQB16
                     and HLA-B8 alleles are inconclusive.
Romero-Pinel et      HLA-DRB1*01 and DRB1*04 alleles were found to be
al. (53)             associated with a worse prognosis.
Zhou et al. (54)     rs12959006 was found to be associated with worse
                     clinical outcomes.
Akkad et al.         The upper cervical spinal cord area was
(55)                 associated with disability, linear relationship
                     between 9 loci (BATF, CYP27B1, IL12B, NFKB1, IL7,
                     PLEK, EVI5, TAGAP, nrs669607), inverse
                     relationship with 3 loci (TYK2, RGS1, CLEC16A).
van der Vuurst       CSF-soluble CD27 predicted the conversion to MS
de Vries et al.      in patients with CIS and was also associated with
(56)                 a high relapse rate.
Modvig et al.        Together with MRI parameters, chitinase 3-like
(57)                 1 and age predicted conversion to MS.
                     Chitinase-3-like-1 predicted cognitive
                     impairment by PASAT test and NfL predicted
                     disability by the MS severity scale and
                     nine-hole-Peg-test.
Canto et al. (58)    CSF chitinase-3-like-1 is an independent risk
                     factor for conversion to MS.
                     High CSF chitinase-3-like-1 was also associated
                     with early conversion to MS and early development
                     of disability.
Martinez et al.      High NfL level was associated with CIS-MS
(59)                 conversion.
                     High GFAP and chitinase-3-like-1 levels were
                     associated with early EDSS 3 increase.
                     Chitinase-3-like-1 was associated with early
                     progression to EDSS 6.
Ascherio et al.      Increment in average serum vitamin D levels
(60)                 within the first year predicted a lower relapse
                     rate (57%), lower rate of new active lesions
                     on MRI (57%), lower yearly increase in T2
                     lesion volume (25%), lower yearly loss in
                     brain volume (41%) and less disability.
Kouchaki et al.      There was a relationship between serum IL-33,
(61)                 IL-37 and sVEGFR2 and EDSS severity.
Lazzarino et al.     Different scores were observed in RRMS and
(62)                 progressive MS cases and they were also
                     related to EDSS and MRI parameters.
                     There was a relationship between MRI and
                     mitochondrial dysfunction findings.
Pietroboni et al.    Lower CSF [beta]-amyloid levels at baseline were a
(63)                 disability predictor at 3-year follow-up.
                     CSF tau levels correlated with T2- and T1-lesion
                     load on MRI.
Hakansson et         The CSF NfL values are not different from the
al. (64)             controls in the NEDA group, but are different
                     in the EDA group. NfL values predicted disease
                     activity alone with 85% accuracy during 2 years of
                     follow-up.
Petzold (65)         NfH values correlate with EDSS and reflect axonal
                     degradation.
Petzold et al.       High CSF NfH levels estimate the amount of brain
(66)                 and spinal cord atrophy on MRI.
Farina et al.        The occurrence of baseline OCB was associated
(68)                 with physical and cognitive impairment over a
                     10-year follow-up OCB + positivity were found
                     associated with an increase in gray matter
                     lesions, high NfH ratio, B lymphocyte activity,
                     lymphoid neogenesis as well as proinflammatory
                     activity.
Rojas et al. (69)    OCB-patients had a better prognosis and milder
                     EDSS values.
Ozakbas et al.       IgM index values, unlike IgG index, were
(71)                 correlated with EDSS and MRI parameters, and were
                     higher in SPMS cases.
Paolicelli et al.    Neutralizing antibody was observed in 14%
(72)                 with treatment. There was an increase in the
                     relapse rate, decrease of the time to 1st relapse
                     and a negative trend on the time to reach EDSS 4.

EDSS: Expanded Disability Status Scale, MS: Multiple sclerosis, NEDA:
No evidence of disease activity, SPMS: Secondary progressive multiple
sclerosis, MRI: Magnetic resonance imaging, OCB: Oligoclonal bands,
CIS: Clinical isolated syndrome, RRMS: Relapsing-remitting multiple
sclerosis, SDMT: Symbol digit modalities test, CSF: Cerebrospinal
fluid, SEP: Somatosensory evoked potentials, VEP: Visual evoked
potential, MSFC: Multiple sclerosis functional composition, NAA/Cr:
N-acetyl aspartate/creatine, PET: Positron emission tomography, HLA:
Human leukocyte antigen, MBP: Myelin basic protein, VEGFR2: Vascular
endothelial growth factor receptor 2, IL: Interleukin, NfL:
Neurofilament light-chain, NfH: Neurofilament heavy-chain, PASAT: Paced
auditory serial addition test, GFAP: Glial fibrillary acidic protein,
Ig: Immunoglobulin, IFN: Interferon, TNF: Tumor necrosis factor,
GM-CSF: Granulocyte-macrophage colony-stimulating factor, NEDA: no
evidence of disease activity.
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Author:Kiylioglu, Nefati
Publication:Turkish Journal of Neurology
Date:Sep 1, 2018
Words:10267
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