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A regional consensus recommendation on brain atrophy as an outcome measure in multiple sclerosis

  • Raed Alroughani1, 2Email authorView ORCID ID profile,
  • Dirk Deleu3,
  • Khalid El Salem4,
  • Jasem Al-Hashel5,
  • K. John Alexander5,
  • Mohamed Assem Abdelrazek6,
  • Adel Aljishi7,
  • Jaber Alkhaboori8,
  • Faisal Al Azri9,
  • Nahida Al Zadjali10,
  • Majed Hbahbih11,
  • Tag Eldin Sokrab12,
  • Mohamed Said13 and
  • Àlex Rovira14
BMC NeurologyBMC series – open, inclusive and trusted201616:240

https://doi.org/10.1186/s12883-016-0762-5

Received: 7 June 2016

Accepted: 15 November 2016

Published: 24 November 2016

Abstract

Background

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammatory and neurodegenerative processes leading to irreversible neurological impairment. Brain atrophy occurs early in the course of the disease at a rate greater than the general population. Brain volume loss (BVL) is associated with disability progression and cognitive impairment in patients with MS; hence its value as a potential target in monitoring and treating MS is discussed.

Methods

A group of MS neurologists and neuro-radiologists reviewed the current literature on brain atrophy and discussed the challenges in assessing and implementing brain atrophy measurements in clinical practice. The panel used a voting system to reach a consensus and the votes were counted for the proposed set of questions for cognitive and brain atrophy assessments.

Results

The panel of experts was able to identify recent studies, which demonstrated the correlation between BVL and future worsening of disability and cognition. The current evidence revealed that reduction of BVL could be achieved with different disease-modifying therapies (DMTs). BVL provided a better treatment and monitoring strategy when it is combined to the composite measures of “no evidence of disease activity” (NEDA). The panel recommended a set of cognitive assessment tools and MRI methods and software applications that may help in capturing and measuring the underlying MS pathology with high degree of specificity.

Conclusion

BVL was considered to be a useful measurement to longitudinally assess disease progression and cognitive function in patients with MS. Brain atrophy measurement was recommended to be incorporated into the concept of NEDA. Consequently, a consensus recommendation was reached in anticipation for implementation of the use of cognitive assessment and brain atrophy measurements on a regional level.

Keywords

Multiple sclerosis Brain atrophy Consensus NEDA Cognitive impairment Disability progression Middle East

Background

MS affects over 2.5 million people worldwide, mainly female adults [1]. In the Middle East and North Africa region, the overall MS prevalence was reported to be 51.52/100,000 [2]. MS is characterized by inflammation and neurodegeneration resulting in irreversible neurological impairment [3, 4]. The acute inflammation component, which occurs during the early phases of the disease, is responsible for the relapses whereas the progressive phase is characterized by the axonal damage leading to accumulation of disability. In addition, brain atrophy occurs in all stages of MS, starting at early stages and progresses throughout the course of the disease at a higher rate than atrophy associated with the normal aging process of healthy individuals [5]. Several disease modifying therapies (DMTs) have shown better efficacy in reducing the clinical and radiological activities which may potentially halt the accumulation of axonal damage and consequently reduce the rate of BVL [68]. Recent studies have prompted debates about the clinical relevance of BVL over time as a measure to quantify neurodegeneration. Measuring brain atrophy through magnetic resonance imaging (MRI) new techniques and software applications development allows better and reliable assessments of brain volume, monitors changes longitudinally, and assesses treatment effects that could be implemented in the routine clinical practice [9].

Methods

A group of thirteen neurologists and neuro-radiologists with expertise in MS from Bahrain, Jordan, Kuwait, Oman, Qatar, and Spain met to address the unmet needs of assessing brain atrophy in patients with MS, discuss the relevant parts of the brain in terms of disease progression with MS. A comprehensive literature search was performed of MEDLINE, EMBASE, and Cochrane databases, systematically reviewing all manuscripts between January 1st, 1995 and May 10th, 2016. The panel identified the relevant literature, using the following MeSH terms: ‘Disease Modifying therapies’ AND ‘multiple sclerosis’ AND ‘brain atrophy’. Additional searches were performed of American Academy of Neurology (AAN) and European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) abstracts for the last two years, using identical search strategies on their respective websites. The recent updates and emerging data in measuring BVL and its clinical relevance were discussed. Different MRI-based methods, protocols, and the challenges in applying them into clinical practice were reviewed with emphasis on the emerging data of immediate and long-term effects of DMTs on brain atrophy. A voting system was followed by the panel to reach a consensus and the votes of the experts were counted for the proposed set of questions for each assessment. The consensus was based on the highest number of votes for each single question of each assessment. Finally, a consensus recommendation on how brain volume and cognition need to be assessed and implemented in clinical practice on a regional level was developed.

Results

Implications of brain volume loss in MS and unmet needs

MS causes focal and diffuse damage to the brain. The focal white matter lesions are the classic hallmark of MS and appear on MRI as T2, gadolinium enhanced T1 lesions, or T1-hypointense lesions (black holes) [10]. The damage also occurs in grey matter as well as diffusely in normal appearing white matter and consequently lead to BVL [11, 12]. Among MRI measures, BVL in MS is considered as one of the prognostic measures which has been directly correlated with cognitive impairment [13, 14], disability progression [1517], and fatigue [18]. A number of longitudinal studies showed that BVL predicts future worsening of disability and cognition. A 10-year follow-up study demonstrated that brain atrophy occurred throughout the course of the disease and was more severe in the group that showed disability progression at 5 years of follow-up. Furthermore, it was demonstrated that the overall grey matter atrophy was a better predictor of disease progression than white matter atrophy during the same follow-up period [19]. In a 13-year follow-up of 75 MS patients, Fillipi et al. showed that grey matter damage was associated with significant worsening of disability and cognitive function in 66% and 34% of patients, respectively [20]. In a study of 261 MS patients, brain atrophy and lesion load were complimentary predictors of long term disability over a 10-year follow-up period [21]. BVL has been observed at the early phases of the disease in patients with clinically isolated syndrome (CIS) [5].

With respect to age, it has been largely known that after adolescence, grey matter volume starts to decline in a linear way (−0.09%/year) while white matter decline starts after midlife, and at a higher rate (−0.2%/year) [22, 23]. Regarding cortical thickness, at midlife a global thinning was apparent, spreading over several cortical regions [24]. The decline in the thickness measured was around 0.016 mm/decade. Both volumetric and cortical thickness age-associated changes had been described, implying that age is a variable that should be considered when assessing global or regional brain volume in MS patients.

There were few reported studies exploring potential gender-associated differences and brain size. Men had larger grey and white matter volumes compared to women although after adjusting by total intracranial volume, these differences disappeared [22]. Regarding cortical thickness, men showed slightly larger cortical thickness compared to females only at midlife, and the rate of progressive thinning was similar for both groups [24]. A recent study reported that cortical thickness was associated with total intracranial volume, but not with gender [25]. Since gender and total intracranial volume are highly correlated, it is a matter of debate whether one or both variables should be considered when assessing global or regional brain volume in MS patients [26, 27].

The participants also identified several unmet needs regarding brain volume measurements and its implementation in the routine clinical practice (Table 1).
Table 1

A list of unmet needs with brain volume as identified by the panel

Unmet Needs

1.

Applicability of MRI volume measurement in clinical practice

2.

Lack of large long-term prospective studies of clinical correlates with brain volume loss

3.

Reliable assessments of cognitive impairment in MS

4.

Validation of disease progression parameters with brain volume loss

5.

Lack of pathological correlation with brain volume loss

6.

Paucity of data on regional brain volume effect in MS

7.

Targeting brain volume loss as one of the main outcome measure in clinical trials

8.

Reproducible effectiveness of DMTs in phase III studies on cognitive impairment

Assessment of brain volume in patients with MS

Conventional MRI techniques are considered the gold standard for diagnosing and monitoring the response to treatment in patients with MS. However, conventional MRI has limited specificity and correlation with disability measures [28, 29]. In addition, conventional MRI does not offer any information about the ongoing degenerative/reparative processes. Therefore, the panel discussed the current MRI methods and software applications that help in capturing and measuring the underlying MS pathology with high degree of specificity. Currently, segmentation- and registration-MRI based methods are used to measure brain volume. The segmentation-based method such as brain parenchymal fraction (BPF), white matter fraction (WMf), grey matter fraction (GMf), and normalized brain volume (NBV) provide assessment of global (BPF, NBV) or regional (WMf, GMf) brain volume at a single time-point in an automated way [2831]. However, the data extracted out of this method is usually heterogeneous and influenced by the quality of T1-weigthed images acquired, and are not recommended for longitudinal analysis. Registration-based methods measure brain volume at two time points, in order to calculate the percentage brain volume change (PBVC). These methods are robust, sensitive to changes over time, less influenced by the disruption of the quality of MR imaging acquisitions, and are most suitable for evaluating global brain volume changes but are not usually designed to analyse regional volume changes over time [32]. The sensitivity and the specificity could vary according to the cut-off value that identify the annualized PBVC. With a cut-off value of 0.4%, the sensitivity and specificity are 65% and 80%, respectively [32]. The panel listed the advantages and disadvantages for brain volume assessment in patients with MS (see Table 2.)
Table 2

Advantages and disadvantages of brain volume assessment in MS

Advantages

 

Whole brain atrophy is easy to measure

Brain atrophy is a “summary measure” of the irreversible/destructive pathological process of MS

Whole brain atrophy is highly reproducible and sensitive to disease-related

changes

Correlates with disability, cognitive impairment and fatigue

Disadvantages:

 

End-stage phenomenon

Pseudoatrophy effect (first 6–12 months)

Fluctuations: steroids, hydration

Co-morbidities: smoking, alcohol, high BMI, etc.

MRI technical confounding factors

Time consuming: reimbursement?

Not enough evidence to use atrophy measures to assess and predict individual treatment response

BMI Body Mass Index

Disease-modifying therapies effect on brain volume

It is important to target the neurodegenerative process in MS patients, in addition to the inflammation component. In general, the results of brain volume data vary from one study to another, which could be attributed to the different mechanism of action of DMTs, the ability of these drugs to cross the blood brain barrier and the heterogeneity of the patient population. In phase III clinical trials, several DMTs demonstrated reduction in the rate of brain volume loss in relapsing-remitting multiple sclerosis (RRMS) patients at variable levels with different response time (Table 3.) [68, 3349]. However, one should be careful in interpreting data across trials with different patient population, using different methods in measuring brain volume. In both placebo-controlled as well as active comparator trials, most of the DMTs provided a delayed effect around the second year of treatment with the exception of daclizumab and fingolimod [68, 3354]. In the FREEDOMS trials, fingolimod demonstrated an effect on brain volume within 6 months of treatment [6, 8]. The first reported brain atrophy study with intramuscular (IM) interferon (IFN)-β-1a demonstrated lower rate of BVL than placebo in the second year of treatment of RRMS patients (−0.23% IFN-β-1a compared to −0.51% in the placebo group; p = 0.03) [33]. However, the subcutaneous (sc) IFN-β-1a produced conflicting results in both CIS and RRMS patients [33, 34, 50, 51]. Data from placebo controlled trials on sc IFN-β-1b and BVL in RRMS patients are not published to date [52, 53]. Glatiramer significantly reduced BVL by 40% at 9–18 months and 25% at 0–18 months compared to placebo [35]. Three trials compared glatiramer and IFN-β and showed a marginal reduction in BVL in one study [3638]. In two trials, natalizumab increased the rate of BVL in the first year and then significantly reduced it versus placebo in the second year [40, 41]. Teriflunomide failed to demonstrate reduction in the rate of BVL compared to placebo [42]. Dimethyl fumarate produced a 21% reduction in BVL compared to placebo in the DEFINE study and failed to show any statistically significant reduction in BVL in the CONFIRM study [43, 44]. In the CARE-MS I and II, alemtuzumab reduced the rate of BVL to 24–42% compared to sc IFN-β-1a [45, 46]. Both the ALLEGRO and the BRAVO studies reported reduction of brain atrophy with laquinimod [47, 48]. Daclizumab had a marginal, but a significant favorable effect on brain atrophy compared to IM IFN-β-1a [49]. In the three trials (FREEDOMS I & II and TRANSFORMS), fingolimod also significantly reduced BVL to 28–45% and the reduction of brain atrophy was evident early during the course of the treatment [68].
Table 3

The effect of DMTs on BVL in RRMS patients in Phase III trials

Drug (REF.)

Changes in Brain Volume Loss

Year 0–1

Year 1–2

Year 0–2

IFN-β-1a IM [33]

x

55% reduction vs. placebo

x

IFN-β-1a SC [34]

-

-

x

IFN-β-1b SC [52, 53]

-

-

-

Glatiramer acetate [35, 3638]

x

x

(Eur/Canadian GA trial)

40% reduction vs. placebo

(Eur/Canadian GA trial)

(Eur/Canadian GA trial)

8% reduction vs. sc IFN-β-1a

(REGARD)+

22% reduction vs. sc IFN-β-1a

(REGARD)+

13% reduction vs. IFN-β-1a

(REGARD)

No sig. difference with GA +/−

sc IFN-β-1b

(BEYOND)

No sig. difference with GA +/−.

sc IFN-β-1b

(BEYOND)

No sig. difference with GA +/−

sc IFN-β-1b

(BEYOND

No sig. difference with GA +/−

sc IFN-β-1a

(COMBIRx)

No sig. difference with GA +/−.

sc IFN-β-1a

(COMBIRx)

No sig. difference with GA +/−

sc IFN-β-1a

(COMBIRx)

Natalizumab [40, 41]

 

x

(AFFIRM)

40% increase vs. placebo

(AFFIRM)

44% reduction vs. placebo

(AFFIRM)

 

19% increase vs. placebo

(SENTINEL)

x

23% reduction with Natalizumab+

IM IFN-β-1a vs. IM IFN-β-1a + placebo

(SENTINEL)

x

(SENTINEL)

Teriflunomide [42]

37% reduction vs. placebo (TEMSO)

31% reduction vs. placebo (TEMSO)-

X

Dimethyl fumarate [43, 44]

-

21% reduction vs. placebo (DEFINE) Significant effect (DEFINE)

ׇ (CONFIRM)

21% reduction vs. placebo

(DEFINE) × ‡ ()

Alemtuzumab [45, 46]

 

-

24–42% reduction vs IFN-β-1a

Laquinimod [47, 48]

-

-

(ALLEGRO)

33% reduction vs placebo

(BRAVO)

28–34% reduction vs placebo

Daclizumab [49]

-

Significant effect

(Week 0–24)

9% reduction vs IM IFN-β-1a

Significant effect

(Week 24–96)

7% reduction vs IM IFN-β-1a

 

Fingolimod [68]

23–40% reduction vs placebo

28–45% reduction vs placebo

33–35% reduction vs placebo

§

45% reduction vs. IM IFN β-1a (TRANSFORMS)

-

-

BID twice daily, TID three times daily, SC subcutaneous, GA Glatiramer acetate, IFN Interferon

– Data not reported/available × No significant effect or not statistically significant Significant effect

* Not all approved therapies have significant effects on BVL and effects can be delayed until the second year of therapy. + No P value reported †Significant effect at 9–18 months

‡Significant effect at 6–24 months in DEFINE (only BID, not TID dose arm), but not in CONFIRM study

§Significant effect also seen at 0–6 months

New emerging strategies in MS

The panel discussed incorporating BVL as a key measure and part of the treatment strategy, which focuses on achieving no evidence of disease activity (NEDA). The measures under NEDA-3 were focused on the composite measures of absence of relapses, disability progression (based on expanded disability status scale (EDSS) scores), and MRI activity (new or enlarging of T2 lesion) [30, 31]. As more evidence is accumulating with respect to the importance of brain atrophy during different stages of MS, absence of BVL may be incorporated to the NEDA measures to be a total of four measures (NEDA-4). Incorporating BVL in NEDA-4, allows a more comprehensive and balanced assessment, capturing both focal and diffuse disease activity [55, 56]. In the pooled analysis of the two FREEDOMS studies with fingolimod, patients on fingolimod were 4 times were more likely to achieve NEDA-4 than those who were on placebo at two years [57]. The advisors discussed the following case as an example of a patient who achieved a NEDA-4 during a period of 3 years. A 35-year old female was diagnosed with MS and had a highly active disease (more than 20 active lesions) at baseline. A disease modifying therapy was initiated in 2012. Over the 3-year longitudinal follow-up, there was no evidence of disease activity (absence of relapses, disease progression and MRI new/enlarging lesions). The change in the annualized brain volume change during the observational period was −0.089, which was below the threshold considered in NEDA 4 of −0.4% (Fig. 1).
Fig. 1

Illustration of a longitudinal effect of treatment on brain parenchymal fraction. (Source: Dr. Rovira; data on file)

Cognitive assessment consensus

The panel acknowledged that cognitive ability is not readily observable in routine neurological examinations, and self-reported cognitive complaints are confounded by mood and other subjective symptoms. One of the core cognitive deficits in MS patients is slowing of information processing speed, which can be subtle and difficult to assess without formal neuropsychological testing. In addition, there is a problem with the testing-retesting reliability as well as confounders at the time of testing. The advisors listed the most common tools that are utilized for cognitive assessment in their clinical practice such as the BICAMS (Brief International Assessment for Cognition for MS), CDT (The Clock Drawing Test), the MSNQ (MS Neuropsychological Screening Questionnaire), the PASAT (The Paced Auditory Serial Addition Task), the SDMT (Symbol Digit Modalities Test), and finally the WLG (Word List Generation) (Table 4.).
Table 4

The tally of cognition assessment

How?

SDMT

BICAMS

PASAT

WLG

CDT

9

3

4

1

4

In Whom?

RIS

CIS

RRMS

SPMS

 

7

8

12

8

 

When?

Initial

3–6 months

12 months

  

2

1

12

  

Where?

Office

Home

Waiting area

  

12

1

0

  

Who?

Physician

Nurse

Assistant

Automated

 

1

10

4

3

 

Abbreviations: BICAMS (Brief International Cognitive Assessment for MS), CDT (The Clock Drawing Test), PASAT (The Paced Auditory Serial Addition Task), Symbol Digit Modalities Test (SDMT), WLG (Word List Generation)

The panel utilized a standard format of questions for assessing cognition, including; 1) the types of tools they would use to test cognitive assessment, 2) the types of MS patients in whom they would apply these tests 3) the timing of the assessment 4) where they would apply these tests 5) and finally who would apply these tools. They used a voting system to rate and highlight the importance and the clinical relevance of each cognition assessment test the in the real world setting. Each voting participant could vote multiple times for different categories.

Of the 12 expert voting panel, 75% voted for the Symbol Digit Modalities Test (SDMT) as the most common test they would utilize for cognition. All voted to use the SDMT in RRMS and equal distribution of votes for the rest of the MS spectrum. All of the advisors recommended to have the assessment test at 12 months during the office visit and 83% of the experts recommended the tools to be conducted by the nurse, since it is time consuming for most neurologists (Table 4).

Brain volume assessment consensus

The advisors utilized the same methodology for assessing brain volume, including; 1) the types of methods they would use to assess BVL, 2) the types of MS patients they would target 3) the timing of the assessment during disease activity 4) and who would conduct these studies. They also used a voting system to rate importance and the clinical applicability of these methods in the routine clinical practice and each voting member was able to vote multiple times for different categories.

As for the brain volume assessment (Table 5.), all (100%) of the advisors recommended the Structural Image Evaluation, using Normalization, of Atrophy (SIENA), a registration-based method, to measure BVL mainly in patients with radiologically isolated syndrome (RIS), CIS, & RRMS at the 6–12 month time frame for most participants. It was recommended to have this measurement taken by the MRI technician.
Table 5

The tally of brain volume assessment

How?

SIENA

SIENAx

BPF

VBM

 

12

4

   

In Whom?

RIS

CIS

RRMS

SPMS

 

7

11

12

4

 

When?

Initial

3–6 months

Anytime with MRI acquisition

6–12 months

Annual

3

 

2

11

1

Who?

Neurologist

Radiologist

MRI Technician

Independent

 
 

1

12

1

 

Abbreviations: BPF (Brain Parenchymal Fraction), SIENA (Structural Image Evaluation, using Normalization, of Atrophy), SIENAx (Structural Image Evaluation, using Normalization, of Atrophy Cross-sectional), VBM (Voxel-Based Morphometry)

Discussion

The expert panel considered BVL a relevant measure of diffuse damage and global marker for neuronal loss and also considered BVL to be a useful measurement to follow up patients with MS. The panel acknowledged that there are several confounders that should be taken into consideration before interpreting the results of these methods. The challenges include issues such non-standardization, variability, high technical expertise and infrastructure requirements, the availability of resources to perform the post-processing comparisons of the images, cost and reimbursements. In addition, there are MRI-related factors, for example variation in imaging protocols, artifacts such as signal heterogeneity, spatial distortions, motion, and the lack of normative data acquired with the same MR protocol. Other confounding factors include pseudoatrophy effect (BVL secondary to reduction in inflammation due to DMTs), and other non-MS related factors such as high body mass index, genetic factors, high alcohol consumption, smoking, dehydration, and cardiovascular risk factors [30, 31]. These challenges should be addressed before considering the implementation of these methods into clinical practice.

Currently, brain atrophy is not measured routinely in clinical practice to longitudinally assess the clinical progression and monitor treatment. Furthermore, brain atrophy measurement requires standardization of MRI acquisition and software techniques to allow proper comparisons at different time points during disease process. As mentioned previously, one should be aware of the DMT-induced pseudoatrophy effect, particularly, relevant in the first few months of treatment and it is more evident with highly anti-inflammatory DMTs. This phenomena is mainly seen in the white matter [30, 31].

The panel also recognized the importance of BVL as a cornerstone of measurement in NEDA-4 in the clinical progression and monitoring of MS, which should lead to improvement in treatment strategies and patient outcomes. As a result, the advisors recommended conducting further validation of NEDA-4 on a regional level to determine whether patients who achieve the four measures in NEDA-4 are at lower risk of future disability than those with disease activity or those achieving NEDA-3.

The panel agreed on the need for a consensus recommendation to share knowledge and opinions among experts in the field in order to unify aspects of clinical and radiological assessments. In addition, the advisors’ aspiration to deliver stronger messages to local neurologists may lead to ultimately improve the overall care of patients. The rationale for the consensus recommendations is not to focus on the inflammatory component but rather, to understand the neurodegenerative component of the disease process and to implement strategies to assess cognitive functions and brain atrophy over time. Studying the evolution and longitudinal changes of cognition and brain volume over time may shed light on different aspects of disease progression and the overall natural history of disease. The current diagnostic criteria lack any relevance to the neurodegenerative aspects and concentrate mainly on the inflammatory process. Indeed, the panel acknowledged that cognition and brain volume assessments might contribute to the overall treatment strategy.

Conclusion

BVL occurs early and continues throughout the course of MS. It is considered to be a valid outcome measure to assess brain tissue loss and one of the best prognostic parameters of disability progression over the long term in patients with MS. BVL is accelerated in MS and correlates with disability and cognitive decline. Incorporating BVL to the composite measures of NEDA-3 is a shift in treatment and monitoring strategy to ultimately improve patient outcome measures. Advanced imaging and processing techniques will enable neurologists to probe diffuse changes in MS along with the clinical endpoints to monitor changes and ultimately improve treatment. Considerations should be made prior to system wide implementation of BVL measurement in clinical practice.

Abbreviations

AAN: 

American Academy of Neurology

AFFIRM: 

Natalizumab Safety and Efficacy in Relapsing-Remitting Multiple Sclerosis

ALLEGRO: 

Assessment of Oral Laquinimod in Preventing Progression in Multiple Sclerosis

BEYOND: 

Betaferon Efficacy Yielding Outcomes of a New Dose

BICAMS: 

Brief International Assessment for Cognition for MS

BID: 

twice daily

BMI: 

Body Mass Index

BPF: 

Brain Parenchymal Fraction

BRAVO: 

Benefit-Risk Assessment of Avonex and Laquinimod

BVL: 

Brain Volume Loss

CDT: 

Clock Drawing Test

CIS: 

Clinically Isolated Syndrome

CombiRx: 

Combination Therapy in Patients With Relapsing-Remitting Multiple Sclerosis

CONFIRM: 

Comparator and an Oral Fumarate in Relapsing–Remitting Multiple Sclerosis

DEFINE: 

Determination of the Efficacy and Safety of Oral Fumarate in Relapsing–Remitting Multiple Sclerosis

DMTs: 

Disease Modifying Therapies

ECTRIMS: 

European Committee for Treatment and Research in Multiple Sclerosis

EDSS: 

Expanded Disability Status Scale

FREEDOMS: 

FTY720 Research Evaluating Effects of Daily Oral therapy in Multiple Sclerosis

GA: 

Glatiramer acetate

GMf: 

Grey Matter fraction

IFN: 

Interferon

IM: 

Intramuscular

MeSH: 

Medical Subject Headings

MRI: 

Magnetic Resonance Imaging

MS: 

Multiple Sclerosis

MSNQ: 

MS Neuropsychological Screening Questionnaire

NBV: 

Normalized Brain Volume

NEDA: 

No Evidence of Disease Activity

PASAT: 

Paced Auditory Serial Addition Task

PBVC: 

Percentage Brain Volume Change

REGARD: 

the REbif vs Glatiramer Acetate in Relapsing MS Disease

RRMS: 

Relapsing-Remitting Multiple Sclerosis

SC: 

Subcutaneous

SDMT: 

Symbol Digit Modalities Test

SENTINEL: 

The Safety and Efficacy of Natalizumab in Combination with Interferon Beta-1a in Patients with Relapsing Remitting Multiple Sclerosis

SIENA: 

Structural Image Evaluation, using Normalization, of Atrophy

SIENAx: 

Structural Image Evaluation, using Normalization, of Atrophy Cross-sectional

TEMSO: 

Teriflunomide Multiple Sclerosis Oral

TID: 

Three times daily

TRANSFORMS: 

Trial Assessing Injectable Interferon versus FTY720 Oral in Relapsing-Remitting Multiple Sclerosis

VBM: 

Voxel-Based Morphometry.

WLG: 

Word List Generation

WMf: 

White Matter fraction

Declarations

Acknowledgement

We would like to acknowledge Dr. Mazen Matalka from Advanced Healthcare Solutions for his help with the manuscript writing.

Funding

The advisory board meeting and the medical writing work were supported and funded by Novartis Pharmaceuticals.

Availability of data and material

Not applicable.

Authors’ contributions

All Authors contributed equally to the contents and writing of this manuscript. All authors read and approved the final manuscript.

Competing interests

All co-authors were paid consultants by Novartis except Dr. Mohamed Said, who is a full-time employee of Novartis.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Neurology, Department of Medicine, Amiri Hospital
(2)
Neurology Clinic, Dasman Diabetes Institute
(3)
Division of Neurology (Neuroscience Institute), Hamad General Hospital
(4)
Department of Neurology, Jordan University of Science and Technology, King Abdullah University Hospital
(5)
Department of Neurology, Ibn Sina Hospital
(6)
Department of Radiology, Ibn Sina Hospital
(7)
Department of Neurology, Salmaniya Hospital & AGU
(8)
Department of Neurology, Royal Hospital
(9)
Department of Radiology, Sultan Qaboos University Hospital
(10)
Department of Radiology, Royal Hospital
(11)
Royal Medical Services
(12)
Division of Neurology (Neuroscience Institute), Hamad General Center
(13)
Medical Manger-Gulf Countries, Novartis pharmaceuticals
(14)
Department of Radiology, Hospital Universitari Vall d’Hebron

References

  1. Prevalence and incidence of multiple sclerosis. https://www.mstrust.org.uk/a-z/prevalence-and-incidence-multiple-sclerosis. Accessed on 30 Apr 2016.
  2. Heydarpour P, Khoshkish S, Abtahi S, Moradi-Lakeh M, Ali SM. Multiple Sclerosis Epidemiology in Middle East and North Africa: A Systematic Review and Meta-Analysis. Neuroepidemiology. 2015;44:232–44.View ArticlePubMedGoogle Scholar
  3. Compston A, Coles A. Multiple sclerosis. Lancet. 2002;359:1221–31.View ArticlePubMedGoogle Scholar
  4. Dutta R, Trapp BD. Mechanisms of neuronal dysfunction and degeneration in multiple sclerosis. Prog Neurobiol. 2011;93:1–12.View ArticlePubMedGoogle Scholar
  5. De Stefano N, Giorgio BM, et al. Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology. 2010;74:1868–76.View ArticlePubMedGoogle Scholar
  6. Kappos L, Radue EW, O’Connor P, et al. A placebo controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med. 2010;362:387–401.View ArticlePubMedGoogle Scholar
  7. Cohen JA, Barkhof F, Comi G, et al. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N Engl J Med. 2010;362:402–15.View ArticlePubMedGoogle Scholar
  8. Calabresi PA, Radue EW, Goodin D, et al. Safety and efficacy of fingolimod in patients with relapsing–remitting multiple sclerosis (FREEDOMS II): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Neurol. 2014;13:545–56.View ArticlePubMedGoogle Scholar
  9. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated, longitudinal and cross-sectional brain change analysis. NeuroImage. 2002;17:479–89.View ArticlePubMedGoogle Scholar
  10. Sahraian MA, Radue E-W, Haller S, Kappos L. Black holes in multiple sclerosis: definition, evolution, and clinical correlations. Acta Neurol Scand. 2010;122:1–8.View ArticlePubMedGoogle Scholar
  11. Filippi M, Rocca MA, Barkhof F, et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 2012;11:349–60.View ArticlePubMedGoogle Scholar
  12. Filippi M, Rocca M. Preventing brain atrophy should be the gold standard of effective therapy in MS (after the first year of treatment). Mult Scler. 2013;19:1005–6.View ArticlePubMedGoogle Scholar
  13. Fisher E, Lee JC, Nakamura K, Rudick RA. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol. 2008;64(3):255–65.View ArticlePubMedGoogle Scholar
  14. Minneboo A, Jasperse B, Barkhof F, et al. Predicting short-term disability progression in early multiple sclerosis: added value of MRI parameters. J Neurol Neurosurg Psychiatry. 2008;79(8):917–233.View ArticlePubMedGoogle Scholar
  15. Benedict RH, Hulst HE, Bergsland N, et al. Clinical significance of atrophy and white matter mean diffusivity within the thalamus of multiple sclerosis patients. Mult Scler. 2013;19(11):1478–84.View ArticlePubMedGoogle Scholar
  16. Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI predictors of cognitive outcome in early multiple sclerosis. Neurology. 2011;76(13):1161–7.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Calabrese M, Rinaldi F, Poretto V, Gallo P. The puzzle of multiple sclerosis: gray matter finds its place. Expert Rev Neurother. 2011;11:1565–8.View ArticlePubMedGoogle Scholar
  18. Yaldizli Ö, Glassl S, Sturm D, Papadopoulou A, Gass A, Tettenborn B. Fatigue and progression of corpus callosum atrophy in multiple sclerosis. J Neurol. 2011;258(12):2199–205.View ArticlePubMedGoogle Scholar
  19. Jacobsen C, Hagemeier J, Myhr KM, et al. Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry. 2014;85(10):1109–15.View ArticlePubMedGoogle Scholar
  20. Filippi M1, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology. 2013;81(20):1759–67.View ArticlePubMedGoogle Scholar
  21. 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;84:1082–91.View ArticlePubMedGoogle Scholar
  22. Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL. Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol. 2002;23(8):1327–33.PubMedGoogle Scholar
  23. Hedman AM, van Haren NEM, Schnack HG, Kahn RS, Hulshoff Pol HE. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp. 2012;33:1987–2002.View ArticlePubMedGoogle Scholar
  24. Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Morris JC, Dale AM, Fischl B. Thinning of the cerebral cortex in aging. Cereb Cortex. 2004;14(7):721–30.View ArticlePubMedGoogle Scholar
  25. Im K, Lee JM, Lyttelton O, Kim SH, Evans AC, Kim SI. Brain size and cortical structure in the adult human brain. Cereb Cortex. 2008;18(9):2181–91.View ArticlePubMedGoogle Scholar
  26. Pell GS1, Briellmann RS, Chan CH, Pardoe H, Abbott DF, Jackson GD. Selection of the control group for VBM analysis: influence of covariates, matching and sample size. NeuroImage. 2008;41(4):1324–35.View ArticlePubMedGoogle Scholar
  27. Barnes J, Ridgway GR. Bartlett, etal. Head size, age and gender adjustment in MRI studies: a necessary nuisance? NeuroImage. 2010;53:1244–55.View ArticlePubMedGoogle Scholar
  28. Rovira A, Auger C, Alonso J. Magnetic resonance monitoring of lesion evolution in multiple sclerosis. Ther AdvNeurol Disord. 2013;6:298–310.View ArticleGoogle Scholar
  29. Zivadinov R, Stosic M, Cox JL, Ramasamy DP, Dwyer MG. The place of conventional MRI and newly emerging MRI techniques in monitoring different aspects of treatment outcome. J Neurol. 2008;255 Suppl 1:61–74.View ArticlePubMedGoogle Scholar
  30. De Stefano N, Airas L, Grigoriadis N, et al. Clinical relevance of brain volume measures in multiple sclerosis. CNS Drugs. 2014;28:147–56.View ArticlePubMedGoogle Scholar
  31. Ziemssen T, Derfuss T, De Stefano N, et al. Optimizing treatment success in multiple sclerosis. J Neurol. 2015;24.Google Scholar
  32. De Stefano N, Stromillo ML, Giorgio A, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87(1):93–9.PubMedGoogle Scholar
  33. Rudick RA, Fisher E, Lee JC, Duda JT, Simon J. Brain atrophy in relapsing multiple sclerosis: relationship to relapses, EDSS, and treatment with interferon beta-1a. Mult Scler. 2000;6:365–72.View ArticlePubMedGoogle Scholar
  34. Kappos L, Traboulsee A, Constantinescu C, et al. Long-term subcutaneous interferon beta-1a therapy in patients with relapsing–remitting MS. Neurology. 2006;67:944–53.View ArticlePubMedGoogle Scholar
  35. Rovaris M, Comi G, Rocca MA et al. Long-term follow-up of patients treated with glatiramer acetate: a multicenter, multinational extension of the European/Canadian double-blind, placebo-controlled, MRI-monitored trial. Mult Scler. 2007;13:502–8.Google Scholar
  36. Comi G, Cohen JA, Arnold DL, et al. Phase III dose comparison study of glatiramer 36. acetate for multiple sclerosis. Ann Neurol 2011;69:75–82.Google Scholar
  37. Mikol DD, Barkhof F, Chang P, et al. Comparison of subcutaneous interferon beta-1a with glatiramer acetate in patients with relapsing multiple sclerosis (the REbif vs Glatiramer Acetate in Relapsing MS Disease [REGARD] study): a multicenter, randomized, parallel, open-label trial. Lancet Neurol. 2008;7:903–14.View ArticlePubMedGoogle Scholar
  38. O’Connor P, Filippi M, Arnason B, et al. 250 microg or 500 microg interferon beta-1b versus 20 mg glatiramer acetate in relapsing-remitting multiple sclerosis: a prospective, randomized, multicenter study. Lancet Neurol. 2009;8:889–97.View ArticlePubMedGoogle Scholar
  39. Lublin FD, Cofield SS, Cutter GR, et al. Randomized study combining interferon and glatiramer acetate in multiple sclerosis: the CombiRx Study. Ann Neurol. 2013;73:327–40.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Miller DH, Soon D, Fernando KT, et al. MRI outcomes in a placebo-controlled trial of natalizumab in relapsing MS. Neurology. 2007;68:1390–401.View ArticlePubMedGoogle Scholar
  41. Radue E-W, Stuart WH, Calabresi PA, et al. Natalizumab plus interferon beta-1a reduces lesion formation in relapsing multiple sclerosis. J Neurol Sci. 2010;292:28–35.View ArticlePubMedGoogle Scholar
  42. O’Connor P, Wolinsky JS, Confavreux C, et al. Randomized trial of oral teriflunomide for relapsing multiple sclerosis. N Engl J Med. 2011;365:1293–303.View ArticlePubMedGoogle Scholar
  43. Arnold DL, Gold R, Kappos L, et al. Effects of delayed release dimethyl fumarate on MRI measures in the Phase 3 DEFINE study. J Neurol. 2014;261:1794–802.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Miller DH, Fox RJ, Phillips JT, et al. Effects of delayed release dimethyl fumarate on MRI measures in the phase 3 CONFIRM study. Neurology. 2015;84:1145–52.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Cohen JA, Coles AJ, Arnold DL, et al. Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing–remitting multiple sclerosis: a randomized controlled phase 3 trial. Lancet. 2012;380:1819–28.View ArticlePubMedGoogle Scholar
  46. Coles AJ, Twyman CL, Arnold DL, et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease modifying therapy: a randomized controlled phase 3 trial. Lancet. 2012;380:1829–39.View ArticlePubMedGoogle Scholar
  47. Comi G, Jeffery D, Kappos L, et al. Placebo-controlled trial of oral laquinimod for multiple sclerosis. N Engl J Med. 2012;366:1000–9.View ArticlePubMedGoogle Scholar
  48. Vollmer TL, Sorensen PS, Selmaj K, et al. A randomized placebo-controlled phase III trial of oral laquinimod for multiple sclerosis. J Neurol. 2014;261:773–83.View ArticlePubMedGoogle Scholar
  49. Kappos L, Wiendl H, Selmaj K, et al. Daclizumab HYP versus Interferon Beta-1a in Relapsing Multiple Sclerosis. N Engl J Med. 2015;373(15):1418–28.View ArticlePubMedGoogle Scholar
  50. Filippi M, Rovaris M, Inglese M, et al. Interferon beta-1a for brain tissue loss in patients at presentation with syndromes suggestive of multiple sclerosis: a randomized, double-blind, placebo-controlled trial. Lancet. 2004;364:1489–96.View ArticlePubMedGoogle Scholar
  51. Kappos L, Freedman MS, Polman CH, et al. Effect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT study. Lancet. 2007;370:389–97.View ArticlePubMedGoogle Scholar
  52. Simon JH. Brain atrophy in multiple sclerosis: what we know and would like to know. Mult Scler. 2006;12(6):679–87.View ArticlePubMedGoogle Scholar
  53. Zivadinov R, Reder AT, Filippi M, et al. Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology. 2008;71(2):136–44.View ArticlePubMedGoogle Scholar
  54. Kappos L, Freedman MS, Polman CH, et al. Long-term effect of early treatment with interferon beta-1b after a first clinical event suggestive of multiple sclerosis: 5-year active treatment extension of the phase 3 BENEFIT trial. Lancet Neurol. 2009;8:987–97.View ArticlePubMedGoogle Scholar
  55. Giovannoni G, Turner B, Gnanapavan S, Offiah C, Schmierer K, Marta M. Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Disord. 2015;4:329–33.View ArticlePubMedGoogle Scholar
  56. Rotstein DL, Healy BC, Malik MT, Chitnis T, Weiner HL. Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA Neurol. 2015;72:152–8.View ArticlePubMedGoogle Scholar
  57. Kappos L, De Stefano N, Freedman MS, et al. Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing-remitting multiple sclerosis. Mult Scler. 2015. [Epub ahead of print].Google Scholar

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