Abstract
Objective: To assess whether the presence of concomitant diseases at multiple sclerosis (MS) diagnosis is associated with the choice and the treatment persistence in an Italian MS cohort.
Methods: We included newly diagnosed patients (2010–2016) followed in 20 MS centers and collected demographic and clinical data. We evaluated baseline factors related to the presence of comorbidities and the association between comorbidities and the clinical course of MS and the time to the first treatment switch.
Results: The study cohort included 2,076 patients. Data on comorbidities were available for 1,877/2,076 patients (90.4%). A total of 449/1,877 (23.9%) patients had at least 1 comorbidity at MS diagnosis. Age at diagnosis (odds ratio 1.05, 95% confidence interval [CI] 1.04–1.06; p < 0.001) was the only baseline factor independently related to the presence of comorbidities. Comorbidities were not significantly associated with the choice of the first disease-modifying treatment, but were significantly associated with higher risk to switch from the first treatment due to intolerance (hazard ratio 1.42, CI 1.07–1.87; p = 0.014). Association of comorbidities with risk of switching for intolerance was significantly heterogeneous among treatments (interferon β, glatiramer acetate, natalizumab, or fingolimod; interaction test, p = 0.04).
Conclusions: Comorbidities at diagnosis should be taken into account at the first treatment choice because they are associated with lower persistence on treatment.
GLOSSARY
- CI=
- confidence interval;
- DMD=
- disease-modifying drug;
- EDSS=
- Expanded Disability Status Scale;
- GA=
- glatiramer acetate;
- HR=
- hazard ratio;
- IFN-β=
- interferon β;
- MS=
- multiple sclerosis;
- PML=
- progressive multifocal leukoencephalopathy
Despite a therapeutic scenario of increasing variety, personalized medicine for multiple sclerosis (MS) is still an unmet need. Phase II and phase III clinical trials with new drugs for MS are conducted in highly selected patient populations, with ideal characteristics, which include the absence of concomitant diseases or medications.1 However, 40%–66% of patients with MS have at least 1 additional disease,2,3 which may affect the response to disease-modifying drugs (DMDs). For this reason, a more accurate characterization of comorbidities in MS and study on their effect on MS outcomes has been recommended.4 A recent series of review articles suggested that patients with MS may have a specific pattern of comorbidities.5,6 Comorbidities have been associated with older age at MS diagnosis, higher disability, diagnostic delay,7 increased hospitalization,8 and increased mortality.9 However, no studies pointed to the association of comorbidities with the efficacy or safety of DMDs.
On these bases, our aim was to assess (1) the prevalence of comorbidities at MS diagnosis in a large sample of Italian patients with MS; (2) whether patients with MS with comorbidities have different clinical characteristics at diagnosis; (3) whether comorbidities at diagnosis may be associated with DMD choice; (4) whether comorbidities at MS diagnosis may be associated with treatment persistence; and (5) whether comorbidities are correlated with the accumulation of disability.
METHODS
We retrospectively enrolled patients followed up longitudinally in 20 MS centers in Italy. The main inclusion criteria were diagnosis of MS since 2010, according to the 2005 and 2010 revisions of the McDonald criteria,10,11 and starting a DMD within 1 year from diagnosis.
We collected anonymized demographic and clinical data; presence/absence of comorbidities at diagnosis; name and date of first DMD; date and reason for switch, classified as inefficacy or intolerance (the latter including side effects, pregnancy, patient decision); and Expanded Disability Status Scale (EDSS) at last follow-up. Such classification was meant to differentiate switches due to inefficacy from all other reasons for switch. Patients without data on presence of comorbidities were excluded from the analysis. For data collection, neurologists extracted the data from the databases of local MS centers, and statisticians merged them in a unified database, and ran the data quality control. Neurologists entered data regarding patients who were in their care, thus ensuring completeness. As a second step, data regarding all patients identified as having comorbidities were reviewed in order to classify the comorbidities and patients were recalled in case of missing data. Comorbidities were classified according to the series of review articles within the special issue of Multiple Sclerosis Journal “International Workshop on Comorbidities in MS,”5,6,12,–,17 with some changes. To ensure standardization, classification was discussed and agreed upon by the participating neurologists and statisticians in an ad hoc investigators' meeting.
We evaluated baseline factors (age, sex, education, time between onset and diagnosis, EDSS, presence of active lesions at brain or spinal cord MRI) associated with comorbidities and association of comorbidities with first therapy choice by logistic regression adjusted for center and year of diagnosis. The multivariable model was built including all variables that resulted significant at the univariable analysis. The association of comorbidities with time to switch during follow-up was assessed by a multivariate Cox model adjusted for baseline characteristics (i.e., age at diagnosis, year of diagnosis, and center), with time origin being the time at which treatment was initiated. In order to assess whether the association of comorbidities with the probability to switch was different among therapies, an interaction test was used.
The association of specific comorbidities with time to switch was also investigated including the variable into the multivariable Cox model.
The relationship of comorbidities with the EDSS change at the last follow-up was evaluated by a nonparametric analysis of covariance, adjusting for length of follow-up, age at diagnosis, baseline EDSS, first treatment choice, and persistence on treatment. A p value lower than 0.05 was considered statistically significant. Stata (v.13; StataCorp, College Station, TX) was used for the computation.
Standard protocol approvals, registrations, and patient consents.
The study was approved by the ethics committee of the coordinating center (Genova) and the original raw data collections were approved by the local ethics committees at all centers. All Italian centers involved in the study asked for written permission for use of anonymized personal clinical data for research purposes and written informed consent was obtained from all study patients included.
RESULTS
Study cohort.
The study cohort included 2,076 patients. The proportion of patients included in the study among the newly diagnosed patients across the 20 centers who had provided written permission for use of anonymized personal clinical data for research purposes was highly variable (median 40%, range 10%–100%), depending on the number of neurologists from each center involved in the study and the center size. Data on comorbidities were available for 1,877/2,076 patients (90.5%) included in this analysis. Table 1 reports the baseline demographic and clinical characteristics of these patients. Briefly, mean age at diagnosis was 35.3 years (SD 11.3), 1,218 (64.9%) were women, and median time between disease onset and diagnosis was 0.82 years (interquartile range 0.23–3.41). Patients were followed for a median of 2.6 years.
Baseline demographic and clinical characteristics of the enrolled cohort
A total of 449 (23.9%) patients had at least 1 comorbidity at MS diagnosis. At multivariate analysis, age at diagnosis (adjusted odds ratio 1.05, 95% confidence interval [CI] 1.04–1.06; p < 0.001) was the only baseline factor independently related to the presence of comorbidities (table 1).
Ninety-eight (5.2%) patients had at least 2 comorbidities at MS diagnosis. Mean age at diagnosis was 44.8 years (SD 11.4) in those with more than 1 comorbidity compared to 38.7 (SD 10.9) in those with 1 comorbidity (p < 0.001).
Prevalence of specific comorbidities at diagnosis.
Comorbidities categorized by affected system are listed in table 2. In detail, 7.2% of patients had at least another autoimmune disease and 0.3% had at least 2 additional autoimmune diseases. Most common autoimmune diseases were autoimmune thyroid disease (85/136 [62.5%]), type 1 diabetes mellitus (16/136 [11.8%]), and celiac disease (12/136 [8.8%]).
Comorbidities categorized by affected system
A total of 5.3% of patients had a psychiatric comorbidity at MS diagnosis, and 0.3% had at least 2 psychiatric comorbidities. The most common psychiatric diseases were depression (n = 53/99 [53.5%]), anxiety (n = 17 [17.2%]), and bipolar disorder (n = 11 [11.1%]).
A total of 5.1% of patients had at least 1 cardiovascular disease and 1 patient had at least 2 cardiovascular diseases. The most common cardiovascular diseases were arterial hypertension (86/96, 89.6%) and cardiac arrhythmias (3/96 [3.1%]).
A total of 2.8% of patients had at least 1 other metabolic diseases (including type 2 diabetes mellitus) and 0.2% had at least 2 metabolic diseases. The most common metabolic diseases were hyperlipidemia (34/52 [65.4%]) and type 2 diabetes mellitus (12/52 [23.1%]).
A total of 2.3% of patients had at least 1 other neurologic disease and 0.05% had at least 2 other neurologic diseases. The most common other neurologic diseases were migraine (31/44 [70.4%]), other types of headache (4/44 [9.1%]), and epilepsy (7/44 [15.9%]).
Demographics and clinical data among those with specific comorbidities are detailed in table e-1 at Neurology.org.
Association of choice of the first DMD with comorbidities.
First treatment included interferon β (IFN-β) 1a 22–44 μg SC (n = 588 [31.8%]), IFN-β 1a 30 μg IM (n = 323 [17.5%]), IFN-β 1b 250 μg SC (256 [13.8%]), glatiramer acetate (GA) (n = 299 [16.2%]), natalizumab (n = 129 [7%]), dimethyl fumarate (n = 90, 4.9%), fingolimod (n = 75 [4.1%]), teriflunomide (n = 36 [2.0%]), and other/unspecified treatments (n = 81 [4.3%]).
Heterogeneity in proportion of patients with comorbidities according to treatment groups (figure 1) was detected (p = 0.013). In detail, we observed a higher frequency of patients with concomitant diseases among those treated with GA (30.4%), dimethyl fumarate (30%), and teriflunomide (44.4%). However, after adjusting for age at diagnosis, year of diagnosis, and center, no significant differences according to treatment group were observed (p = 0.59).
Association of comorbidities with the switch from the first treatment.
Almost half of treated patients (Kaplan-Meier estimation 47.7%) switched therapy after 3 years. Cumulative incidence of treatment switch due to inefficacy at 3 years was 26.4%, and cumulative incidence of switch due to intolerance was 18.9%. Pregnancy was the reason for 12 (4%) and risk of progressive multifocal leukoencephalopathy (PML) was the reason for 33 (11%) of 297 intolerance-driven switches. Cox regression analysis, adjusted for baseline characteristics (age at diagnosis, year of diagnosis, center) and first DMD, showed that comorbidities did not affect the first treatment switch due to inefficacy (hazard ratio [HR] 1.19, 95% CI 0.92–1.55; p = 0.19), but had a significant effect on the switch due to intolerance (HR 1.42, CI 1.07–1.87; p = 0.014).
Therefore, we focused the analysis on the association of comorbidities with intolerance switch from specific DMDs. We restricted this analysis to IFN-β, GA, natalizumab (which were all available in Italy before 2010), and fingolimod (which became available in Italy at the end of 2011 and could be prescribed since the beginning of the same year in the context of an open-label study; study CFTY720DIT03), since dimethyl fumarate and teriflunomide had a too-short follow-up to draw reliable conclusions. We found a significant interaction between comorbidities and first DMD on the probability to switch due to intolerance (p = 0.04), indicating that comorbidities had a different relationship with the probability to switch according to the first DMD. An increased risk of intolerance switch is confined to patients treated with IFN-β, while comorbidities were not associated with the probability to switch in patients treated with GA, natalizumab, or fingolimod (figure 2). However, in patients with IFN-β as their first DMD, there was no evidence of an association between specific comorbidities and the probability to switch due to intolerance (p = 0.96).
(A) Interferon β (IFN-β), (B) glatiramer acetate (GA), (C) fingolimod (FTY), (D) natalizumab (NTZ). HR = hazard ratio.
Relationship of comorbidities with disability at follow-up.
Mean EDSS change at last follow-up was 0.28 (SE 0.05) for patients with comorbidities and 0.10 (SE 0.03) for those without comorbidities. The difference, after adjusting for follow-up duration, baseline EDSS, center, age at diagnosis, year of diagnosis, and first DMD, and at least 1 switch during follow-up, in a multivariate nonparametric analysis of covariance, was statistically significant (p = 0.018).
DISCUSSION
This retrospective study provides new evidence on the burden of comorbidities at MS diagnosis and reveals that comorbidities increase the risk of switching from the first DMD due to intolerance, particularly when the first DMD is IFN-β.
Previous studies have reported data on incidence and prevalence of comorbidities throughout MS disease course. A study on 8,983 patients enrolled in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry, a self-reported registry for patients with MS, found that almost 40% of patients with MS have a concomitant medical condition, with higher prevalence in male patients, older patients, African American patients, and those with lower income.2 In the same patient cohort, specific comorbidities were associated with longer time between disease onset and diagnosis (diagnostic delay), and partly to self-reported disability at diagnosis.7 There are few data regarding the prevalence of comorbidities at MS diagnosis. A French study on national health care data, including 22,087 patients with MS, reported that about 5% had a registered comorbidity diagnosed before or after MS.18
Compared to these studies, we employed a retrospective approach to analyze the prevalence of neurologist-reported comorbidities at MS diagnosis in a cohort of patients diagnosed from 2010 to 2016. We estimate that patients enrolled in this study are a representative sample of the Italian MS population, including almost 10% of the newly diagnosed patients in Italy in the timeframe of enrollment (given an approximate incidence of 3,400 new cases/year in Italy; aism.it/index.aspx?codpage=2016_05_stampa_sn_barometro_sintesi) and because centers from all geographic zones of Italy were enrolled in this study.
We found that about 1 out of 4 patients with MS has a concomitant medical condition at MS diagnosis, and 5% have 2 concomitant medical conditions. Although univariate analysis showed that patients with MS with comorbidities at diagnosis had higher age, higher baseline disability, higher diagnostic delay, higher relapse rate in the previous year, and lower education compared to those without, multivariate analysis demonstrated that older age at diagnosis was the only factor significantly associated with comorbidities. The association between previous relapses and comorbidities, which was statistically significant at univariable analysis, disappeared when adjusting for age, thus suggesting that younger age is a confounding factor for a higher relapse activity. Association between age and comorbidities may be explained by the fact that many comorbidities, and particularly cardiovascular disease, metabolic diseases, and cancer, increase in incidence over the lifetime.
However, and not unexpectedly, the most prevalent concomitant diseases were other autoimmune diseases, particularly thyroid diseases, type 1 diabetes mellitus, and celiac disease. A common genetic background19 or the effect of similar risk factors are thought to contribute to this association. High prevalence of thyroid diseases in MS has been reported before, although it is debated whether it outnumbers the prevalence in the general population.12 The association between type 1 diabetes and MS has been reported before in the Italian population, particularly from Sardinia and from the Northeast.20,21 Also, celiac disease was reported to be more frequent in MS in a small study.22
In general, the prevalence of specific comorbidities was lower than previously reported; this is explained by the fact that we considered comorbidities that were already present at diagnosis, whereas most studies evaluated comorbidities in the MS population at any time point from diagnosis.5
Very few data, mostly from postmarketing studies, are available on how comorbidities and treatment choice for MS are associated.1,23,24 Neurologists may follow empirical rules while prescribing DMDs in patients with comorbidities, in order to avoid safety issues in patients with more than 1 disease, or, on the contrary, to affect multiple diseases with 1 treatment (i.e., fumarate derivatives in patients with MS and psoriasis,25 or natalizumab in patients with MS and Crohn disease26).
One recently published study, where authors retrospectively analyzed administrative and clinical data on 10,698 patients with MS in Canada, found that specific comorbidities are associated with the likelihood to starting a DMD for MS, with marked difference among provinces, while comorbidities did not influence the choice of treatment.27 Due to the design of our study, which included patients who started the first DMD upon 1 year from diagnosis, we did not assess whether comorbidities were associated with increased diagnosis–treatment lag. While we observed a higher percentage of patients with comorbidities among those prescribed with some DMD (GA, dimethyl fumarate, and teriflunomide), adjusting for age at diagnosis, year of diagnosis, and center showed that such differences were not significant.
Poor tolerance of the first DMD may decrease substantially the quality of life of newly diagnosed patients, as well as expose them to the risk of disease progression due to scarce compliance or to treatment interruption. We found that comorbidities at MS diagnosis are associated with increased rate of switching from the first DMD due to intolerance. Comparing different treatments, IFN-β was associated with high risk of intolerance switch in presence of comorbidities, whereas patients with comorbidities who were treated with GA did not have an increased risk of switching. Results obtained with natalizumab are explained by the fact that most cases of intolerance/safety switch from natalizumab in our cohort were due to risk of PML (33/43 [76%]). Regarding fingolimod, a very low rate of switch due to intolerance was observed in patients overall. However, this analysis has some limitations: the small size of natalizumab and fingolimod treatment groups makes the CIs for the HRs very wide, precluding the possibility to run pairwise comparisons among the groups (with appropriate adjustment for multiple testing). Therefore, the results of this analysis generate a hypothesis that calls for further studies to be validated.
Finally, we found that patients with comorbidities had a slightly higher disability at the last follow-up. The difference in the EDSS gain was little, and likely of low clinical importance, but it was significant despite the fact that the analysis was adjusted for many covariates. Since switches due to inefficacy were not affected by comorbidities, we can speculate that inefficacy switches were mainly driven by acute (inflammatory) events such as relapses or MRI activity, and not by the long-term EDSS increase, because of a lower effect of the DMDs on the underlying neurodegenerative processes that appear to be worsened by comorbidities.
The results of this study show that comorbidities at MS diagnosis are frequent and that they are associated with the response to the first treatment, increasing the switch rate due to intolerance, particularly when the first DMD is IFN-β. Due to introduction of oral drugs in 2014 in MS, fewer data were available on the association of comorbidities on safety and efficacy of these drugs. Further studies are warranted to evaluate how newer drugs will perform in patients with MS with comorbidities.
AUTHOR AFFILIATIONS
From the Departments of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, and Center of Excellence for Biomedical Research (CEBR), University of Genova, Ospedale Policlinico San Martino, Genova (A.L.), and Department of Health Sciences, Section of Biostatistics (A. Signori, F.G., M.P.S.), University of Genova, Genova; Neurological Clinic and Multiple Sclerosis Centre of “AORN A. Cardarelli” (G.T.M.); Departments of Neurosciences, Reproductive Sciences, and Odontostomatology (R.L., C.V.R., F.S.), Multiple Sclerosis Centre, Federico II University, Naples; Centro SM (E.B.), Dipartimento di Neuroscienze, Ospedale Universitario Città della Salute e della Scienza di Torino; Neurological Clinic AOU Policlinico Vittorio Emanuele (S.L.F.), Catania; 2nd Neurology Unit and CRRSM (Regional Referral Multiple Sclerosis Centre) (A.R.), Careggi University Hospital, University of Florence; Multiple Sclerosis Study Center (P.A., D.B.), ASST Valle Olona, PO di Gallarate (VA); Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences (S.B., L.L., S.E., D.I.), and Multiple Sclerosis Center, II Division of Neurology (E.S.), Second University of Naples; Clinical and Biological Sciences Department (M.C.), Neurology Unit, University of Torino, San Luigi Gonzaga Hospital, Orbassano, Torino; Departments of Neurology and Psychiatry (L.P.), Sapienza University, Rome; Centro Sclerosi Multipla ASST Papa Giovanni XXIII di Bergamo (S.L.G., B.F.); Neuroimmunology and Neuromuscular Diseases Unit (S. Rossi, V.T.C.), IRCCS Foundation Carlo Besta Neurological Institute, Milan; Department of Medical Science and Public Health (E.C., J.F.), University of Cagliari; Neurology Clinic (A. Sartori), Department of Medical, Surgical, and Health Sciences, University of Trieste; Department of Clinical and Experimental Medicine (I.R.Z.), University of Sassari; Multiple Sclerosis Center (S. Rasia, C.C.), ASST Spedali Civili, PO di Montichiari (BS); Neurological Clinic (R.C.), Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona; 2nd Neurology Unit and CReSM (Regional Referral Multiple Sclerosis Centre) (A.D.S.), AOU San Luigi Gonzaga, Orbassano, Torino; Department of Neurology and Psychiatry (S.P.), Sapienza University, Rome; and Department of Neurology (C.B.), Valduce Hospital, Como, Italy.
AUTHOR CONTRIBUTIONS
Alice Laroni proposed the project, acquired the data, participated in data analysis and interpretation, and wrote the manuscript. Alessio Signori managed, analyzed, and interpreted the data and contributed to manuscript draft. Giorgia T. Maniscalco acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Roberta Lanzillo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Cinzia Valeria Russo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Eleonora Binello acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Salvatore Lo Fermo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Annamaria Repice acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Pietro Annovazzi acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Simona Bonavita acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Marinella Clerico acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Damiano Baroncini acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Luca Prosperini acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Sara La Gioia acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Silvia Rossi acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Eleonora Cocco acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Jessica Frau acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Valentina Torri Clerici acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Elisabetta Signoriello acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Arianna Sartori acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Ignazio Roberto Zarbo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Sarah Rasia acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Cinzia Cordioli acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Raffaella Cerqua acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Alessia Di Sapio acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Luigi Lavorgna acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Simona Pontecorvo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Caterina Barrilà acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Francesco Saccà acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Barbara Frigeni acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Sabrina Esposito acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Domenico Ippolito acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Fabio Gallo acquired the data, participated in data analysis and interpretation, and contributed to manuscript draft. Maria Pia Sormani analyzed and interpreted the data, wrote the manuscript, and supervised the study.
STUDY FUNDING
Novartis Pharma supported the meetings of the iMUST group but was not involved in this project nor did it have any access to the data.
DISCLOSURE
A. Laroni has received personal compensation from Novartis, Genzyme, Biogen, Merck, and Teva for public speaking and advisory boards. A. Signori received teaching honoraria from Novartis. G. Maniscalco received personal compensation from Serono, Biogen, and Teva for public speaking and advisory boards. R. Lanzillo received personal compensation from Merck Serono, Novartis, Almirall, Genzyme, and Teva for public speaking, editorial work, and advisory boards. C. Russo and E. Binello report no disclosures relevant to the manuscript. S. Lo Fermo has received funding for travel and for advisory board from Genzyme, Biogen Idec, Teva, and Merck-Serono. A. Repice has received personal compensation from Biogen Idec, Genzyme, Novartis, and Merck Serono for public speaking and advisory boards. P. Annovazzi served as advisor and received speaking honoraria from Novartis, Merck Serono, Genzyme, Biogen, and Teva Italia. S. Bonavita received speaker honoraria from Merck Serono, Novartis, Teva, and Genzyme; and advisory board honoraria from Teva, Novartis, and Biogen. M. Clerico received personal compensation for participating in advisory boards by Merck Serono and Biogen; and travel expenses for congresses paid by Merck, Biogen, Novartis, and Genzyme. D. Baroncini received honoraria from Almirall for the creation of editorial publications and travel grants for participation in international congresses from Genzyme and Teva. L. Prosperini received consulting fees from Biogen and Novartis; speaker honoraria from Biogen, Genzyme, Novartis, and Teva; travel grants from Biogen, Genzyme, Novartis, and Teva; and research grants from the Italian MS Society (Associazione Italiana Sclerosi Multipla) and Genzyme. S. La Gioia reports no disclosures relevant to the manuscript. S. Rossi acted as an advisory board member of Biogen Idec, Bayer Schering, Merck Serono, Teva, Novartis, and Genzyme, and received funding for traveling and honoraria for speaking or writing from Biogen Idec, Merck Serono, Teva, Novartis, Bayer Schering, Genzyme, and Almirall; received support for research projects by Teva, Merck Serono, and Bayer Schering; and is involved as principal investigator in clinical trials for Teva, Novartis, and Roche. E. Cocco received personal compensation from Almirall, Bayer, Biogen, Genzyme, Novartis, Serono, and Teva for public speaking, editorial work, and advisory boards. J. Frau serves on scientific advisory boards for Biogen; received honoraria for speaking from Merck Serono, Biogen, and Teva; and received research grant from Serono. V. Torri Clerici received personal compensation from Novartis, Almirall, Genzyme, and Teva for public speaking, editorial work, and advisory boards. E. Signoriello received personal compensation from Almirall, Biogen, Genzyme, Novartis, and Teva for traveling and advisory boards. A. Sartori has received funding for travel and/or speaker honoraria from Novartis, Teva, Merck-Serono, and Genzyme. I. Zarbo has served on scientific advisory boards for Biogen Idec and received funding for travel and/or speaker honoraria from Genzyme, Biogen Idec, Teva, Merck, and Novartis. S. Rasia reports no disclosures relevant to the manuscript. C. Cordioli received personal compensation for consultant from Merck Serono and Novartis. R. Cerqua has received funding for travel and/or speaker honoraria from Genzyme, Biogen Idec, Teva, Merck-Serono, and Novartis. A. Di Sapio received personal compensation from Novartis, Biogen, Merck Serono, Teva, and Bayer Schering for public speaking and advisory boards; and received funding to participate in congresses and meetings from Merck Serono, Biogen, Novartis, Genzyme, Allergan, and Medtronic. L. Lavorgna has received funding for travel and/or speaker honoraria from Novartis, Genzyme, Teva, Merck, Almirall, and Bayer. S. Pontecorvo received personal compensation from Almirall, Biogen, Genzyme, and Teva for public speaking and advisory boards. C. Barrilà reports no disclosures relevant to the manuscript. F. Saccà received personal compensation from Novartis, Almirall, Genzyme, Forward Pharma, and Teva for public speaking, editorial work, and advisory boards. B. Frigeni, S. Esposito, and D. Ippolito report no disclosures relevant to the manuscript. F. Gallo received teaching fees from Novartis. M. Pia Sormani has received personal compensation for consulting services and for speaking activities from Merck Serono, Teva, Novartis, Roche, Genzyme, and Biogen. Go to Neurology.org for full disclosures.
ACKNOWLEDGMENT
The authors thank Novartis Pharma for supporting the meetings of the iMUST group.
Footnotes
Author affiliations are provided at the end of the article.
Coinvestigators are listed at Neurology.org.
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Supplemental data at Neurology.org
Editorial, page 2218
- Received January 28, 2017.
- Accepted in final form August 24, 2017.
- © 2017 American Academy of Neurology
REFERENCES
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