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Soares-Dos-Reis R, Silva P, Ferreira F, Seabra M, Mendonça T, Abreu P, Guimarães J. Multiple Sclerosis After the Age of 50 Years: A Comparative Analysis of Late Onset and Adult Onset. J Clin Neurol 2025; 21:201-212. [PMID: 40308015 DOI: 10.3988/jcn.2024.0302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 02/08/2025] [Accepted: 02/27/2025] [Indexed: 05/02/2025] Open
Abstract
BACKGROUND AND PURPOSE The incidence of multiple sclerosis (MS) among older patients is increasing. Some of these patients develop the disease after the age of 50 years, a condition known as late-onset MS (LOMS). This study aimed to characterize MS in older patients (50-75 years-old) by comparing LOMS with adult-onset MS (AOMS). METHODS We retrospectively analyzed data from 230 patients aged 50-75 years who attended a Portuguese tertiary referral center. RESULTS This study included 189 AOMS patients aged 58 [54-63] years (median [interquartile range]) and 41 LOMS patients aged 67 [61-70] years. Females predominated in both the LOMS (70.7%) and AOMS (75.1%) groups. Primary progressive MS was more common in LOMS than AOMS patients (19.5% vs. 8.0%, p=0.03) and these two groups had equivalent proportions of relapsing-remitting MS (53.7% vs. 59.0%, p=0.55). The Expanded Disability Status Scale (EDSS) score at the diagnosis was higher in the LOMS patients (2 [1-4], p=0.03), but the current EDSS score did not differ significantly between the LOMS and AOMS patients (3.5 [1.75-6] vs. 3 [1.5-6], p=0.86). After adjusting or matching for age and disease duration, the current EDSS scores were not significantly different in the two groups. The proportion of patients currently receiving disease-modifying therapies was higher in LOMS patients (97.6%, p=0.02). A higher proportion of patients with a later onset had infratentorial involvement at a 5-year follow-up (86.7%, p=0.01). The time to an EDSS score of 6.0 was shorter for LOMS patients. CONCLUSIONS The LOMS patients presented with higher EDSS scores at the diagnosis, reaching a level of disability not significantly different from AOMS patients of the same age group despite a shorter disease course.
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Affiliation(s)
- Ricardo Soares-Dos-Reis
- Neurology Department, São João Local Health Unit, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal.
| | - Pedro Silva
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Francisca Ferreira
- Neurology Department, São João Local Health Unit, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Mafalda Seabra
- Neurology Department, São João Local Health Unit, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Teresa Mendonça
- Neurology Department, São João Local Health Unit, Porto, Portugal
| | - Pedro Abreu
- Neurology Department, São João Local Health Unit, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Joana Guimarães
- Neurology Department, São João Local Health Unit, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
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Falet JPR, Nobile S, Szpindel A, Barile B, Kumar A, Durso-Finley J, Arbel T, Arnold DL. The role of AI for MRI-analysis in multiple sclerosis-A brief overview. Front Artif Intell 2025; 8:1478068. [PMID: 40265105 PMCID: PMC12011719 DOI: 10.3389/frai.2025.1478068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Accepted: 03/19/2025] [Indexed: 04/24/2025] Open
Abstract
Magnetic resonance imaging (MRI) has played a crucial role in the diagnosis, monitoring and treatment optimization of multiple sclerosis (MS). It is an essential component of current diagnostic criteria for its ability to non-invasively visualize both lesional and non-lesional pathology. Nevertheless, modern day usage of MRI in the clinic is limited by lengthy protocols, error-prone procedures for identifying disease markers (e.g., lesions), and the limited predictive value of existing imaging biomarkers for key disability outcomes. Recent advances in artificial intelligence (AI) have underscored the potential for AI to not only improve, but also transform how MRI is being used in MS. In this short review, we explore the role of AI in MS applications that span the entire life-cycle of an MRI image, from data collection, to lesion segmentation, detection, and volumetry, and finally to downstream clinical and scientific tasks. We conclude with a discussion on promising future directions.
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Affiliation(s)
- Jean-Pierre R. Falet
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montreal, QC, Canada
| | - Steven Nobile
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Aliya Szpindel
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Berardino Barile
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montreal, QC, Canada
| | - Amar Kumar
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montreal, QC, Canada
| | - Joshua Durso-Finley
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montreal, QC, Canada
| | - Tal Arbel
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montreal, QC, Canada
| | - Douglas L. Arnold
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Brieva L, Calles C, Landete L, Oreja-Guevara C. Current challenges in secondary progressive multiple sclerosis: diagnosis, activity detection and treatment. Front Immunol 2025; 16:1543649. [PMID: 40191208 PMCID: PMC11968352 DOI: 10.3389/fimmu.2025.1543649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Accepted: 02/25/2025] [Indexed: 04/09/2025] Open
Abstract
Approximately 50% diagnosed with relapsing-remitting multiple sclerosis (RRMS) transition to secondary progressive multiple sclerosis (SPMS) within 20 years following disease onset. However, early diagnosis of SPMS and effective treatment remain important clinical challenges. The lack of established diagnostic criteria often leads to delays in identifying SPMS. Also, there are limited disease-modifying therapies (DMTs) available for progressive forms of MS, and these therapies require evidence of disease activity to be initiated. This review examines the challenges in diagnosing SPMS at an early stage and summarizes the current and potential use of biomarkers of disease progression in clinical practice. We also discuss the difficulties in initiating the DMTs indicated for active SPMS (aSPMS), particularly in patients already undergoing treatment with DMTs that suppress disease activity, which may mask the presence of inflammatory activity required for the therapy switch. The article also addresses the DMTs available for both active and non-active SPMS, along with the clinical trials that supported the approval of DMTs indicated for aSPMS or relapsing MS in Europe, which includes aSPMS. We also offer insights on when discontinuing these treatments may be appropriate.
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Affiliation(s)
- Luis Brieva
- Neurology Department, Hospital Universitari Arnau de Vilanova, Lleida, Spain
- Medicine Department, Universitat de Lleida (UdL), Lleida, Spain
- Neuroimmunology Group, Institut de Recerca Biomedica de Lleida (IRBLLEIDA), Lleida, Spain
| | - Carmen Calles
- Neurology Department, Hospital Universitario Son Espases, Palma de Mallorca, Spain
| | - Lamberto Landete
- Neurology Department, Hospital Universitario Doctor Peset, Valencia, Spain
| | - Celia Oreja-Guevara
- Department of Neurology, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
- Departament of Medicine, Medicine Faculty, Universidad Complutense de Madrid (UCM), Madrid, Spain
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Tremlett H, Zhu F, Everett K, Asaf A, Manouchehrinia A, Li P, McKay KA, Hillert J, Zhao Y, Maxwell C, Marrie RA. Healthcare use is elevated two decades before a first demyelinating event and differs by age and sex. Ann Clin Transl Neurol 2025; 12:415-432. [PMID: 39887956 PMCID: PMC11822793 DOI: 10.1002/acn3.52267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 10/29/2024] [Accepted: 11/18/2024] [Indexed: 02/01/2025] Open
Abstract
OBJECTIVE Elevated healthcare use before multiple sclerosis (MS) onset suggests earlier opportunity to identify MS. Yet their timing and sociodemographic effects are unclear. We examined rates of healthcare use (and by age/sex) for >two decades pre-MS onset. METHODS We identified people with MS (PwMS) using administrative data from Canada (Ontario) and Sweden (1991-2020) ("administrative" cohort), and the Swedish MS Registry ("clinical" cohort). The first MS/demyelinating diagnostic code (administrative) or symptom onset (clinical) defined MS onset. We compared annual rates of healthcare use (hospital, physician, and emergency-room [ED]) pre-onset between PwMS and up to five matched population controls using negative binomial regression, and by age/sex. RESULTS The administrative cohort = 35,018/136,007 PwMS/controls (Ontario), and 10,269/51,297 (Sweden). Rates of healthcare use were higher for PwMS than controls up to 28 (of 29) years (Ontario) and up to 15 (of 19) years (Sweden) pre-onset. Annual healthcare use rose steadily as onset approached, particularly escalating 7 years pre-onset in Ontario (e.g., hospital visit rate ratios [RRs] exceeded 1.30), and 6 years in Sweden (physician visit RRs > 1.10). RRs peaked the year pre-onset (ED visits [Ontario] = 3.04; 95% CI: 2.94-3.13, physician visits [Sweden] = 2.51; 95% CI: 2.44-2.59). In the year pre-onset, RRs were disproportionately higher for males (ED RRs [Ontario] = 3.30; 95% CI: 3.13-3.48 vs. females = 2.90; 95% CI: 2.79-3.02), and dropped steadily by age (physician visit RRs [Sweden] = 2.61/2.27/1.97/1.72 for 50/40/30/20-year-olds). The smaller clinical cohort (7604/37,974 PwMS/controls) exhibited similar patterns, albeit more modest, with RRs elevated up to 5 years pre-onset (physician visit RR [year-5] = 1.08; 95% CI: 1.02-1.14; RR [year-1] = 1.39;1.33-1.46). INTERPRETATION Higher healthcare use was evident decades before MS onset, escalating 6-7 years pre-onset, peaking the year before, being disproportionately higher for males and older PwMS.
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Affiliation(s)
- Helen Tremlett
- Faculty of Medicine (Neurology), and the Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Feng Zhu
- Faculty of Medicine (Neurology), and the Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | | | | | - Ali Manouchehrinia
- Department of Clinical Neuroscience, Karolinska Institutet, Department of NeurologyKarolinska University HospitalStockholmSweden
| | | | - Kyla A. McKay
- Department of Clinical Neuroscience, Karolinska Institutet, Department of NeurologyKarolinska University HospitalStockholmSweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Department of NeurologyKarolinska University HospitalStockholmSweden
| | - Yinshan Zhao
- Faculty of Medicine (Neurology), and the Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Colleen Maxwell
- ICESTorontoOntarioCanada
- Schools of Pharmacy and Public Health SciencesUniversity of WaterlooWaterlooOntarioCanada
| | - Ruth Ann Marrie
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health SciencesUniversity of ManitobaWinnipegManitobaCanada
- Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health SciencesUniversity of ManitobaWinnipegManitobaCanada
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Lorenzut S, Negro ID, Pauletto G, Verriello L, Spadea L, Salati C, Musa M, Gagliano C, Zeppieri M. Exploring the Pathophysiology, Diagnosis, and Treatment Options of Multiple Sclerosis. J Integr Neurosci 2025; 24:25081. [PMID: 39862004 DOI: 10.31083/jin25081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 08/09/2024] [Accepted: 08/27/2024] [Indexed: 01/27/2025] Open
Abstract
The complicated neurological syndrome known as multiple sclerosis (MS) is typified by demyelination, inflammation, and neurodegeneration in the central nervous system (CNS). Managing this crippling illness requires an understanding of the complex interactions between neurophysiological systems, diagnostic techniques, and therapeutic methods. A complex series of processes, including immunological dysregulation, inflammation, and neurodegeneration, are involved in the pathogenesis of MS. Gene predisposition, autoreactive T cells, B cells, and cytokines are essential participants in the development of the disease. Demyelination interferes with the ability of the CNS to transmit signals, which can cause a variety of neurological symptoms, including impaired motor function, sensory deficiencies, and cognitive decline. Developing tailored therapeutics requires understanding the underlying processes guiding the course of the disease. Neuroimaging, laboratory testing, and clinical examination are all necessary for an accurate MS diagnosis. Evoked potentials and cerebrospinal fluid studies assist in verifying the diagnosis, but magnetic resonance imaging (MRI) is essential for identifying distinctive lesions in the CNS. Novel biomarkers have the potential to increase diagnostic precision and forecast prognosis. The goals of MS treatment options are to control symptoms, lower disease activity, and enhance quality of life. To stop relapses and reduce the course of the disease, disease-modifying treatments (DMTs) target several components of the immune response. DMTs that are now on the market include interferons, glatiramer acetate, monoclonal antibodies, and oral immunomodulators; each has a unique mode of action and safety profile. Symptomatic treatments improve patients' general well-being by addressing specific symptoms, including pain, sphincter disorders, fatigue, and spasticity. Novel treatment targets, neuroprotective tactics, and personalized medicine techniques will be the main focus of MS research in the future. Improving long-term outcomes for MS patients and optimizing disease treatment may be possible by utilizing immunology, genetics, and neuroimaging developments. This study concludes by highlighting the complexity of multiple MS, including its changing therapeutic landscape, diagnostic problems, and neurophysiological foundations. A thorough grasp of these elements is essential to improving our capacity to identify, manage, and eventually overcome this intricate neurological condition.
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Affiliation(s)
- Simone Lorenzut
- Neurology Unit, "Head, Neck and Neurosciences" Department, University Hospital of Udine, 33100 Udine, Italy
| | - Ilaria Del Negro
- Neurology Unit, S. Tommaso dei Battuti Hospital, 30026 Portrogruaro (Venice), Italy
| | - Giada Pauletto
- Neurology Unit, "Head, Neck and Neurosciences" Department, University Hospital of Udine, 33100 Udine, Italy
| | - Lorenzo Verriello
- Neurology Unit, "Head, Neck and Neurosciences" Department, University Hospital of Udine, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, "Sapienza" University of Rome, 00142 Rome, Italy
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, 300238 Benin, Edo, Nigeria
| | - Caterina Gagliano
- Department of Medicine and Surgery, University of Enna "Kore", 94100 Enna, Italy
- Eye Clinic Catania University San Marco Hospital, 95121 Catania, Italy
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
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Giovannoni G, Hetherington S, Jones E, Dominguez Castro P, Karu H, Ansari S, Karlsson G, de las Heras V, Lines C. MRI versus relapse: optimal activity monitoring for management of progressive multiple sclerosis. Brain Commun 2025; 7:fcaf010. [PMID: 39906569 PMCID: PMC11791681 DOI: 10.1093/braincomms/fcaf010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 12/04/2024] [Accepted: 01/13/2025] [Indexed: 02/06/2025] Open
Abstract
Secondary progressive multiple sclerosis is often categorized as 'active'/'non-active' based on inflammatory activity on MRI, or relapse; however, the value of MRI/relapse as indicators of disease activity in real-world and clinical trial settings merits further investigation. We separately analysed retrospective data from patients with clinically diagnosed secondary progressive multiple sclerosis in the Adelphi Real-World Disease Specific Programme (a cross-sectional survey) in multiple sclerosis (Adelphi: n = 2554) and the placebo group of the Phase III EXploring the efficacy and safety of siponimod in PAtients with secoNDary progressive multiple sclerosis (EXPAND) trial, [EXPAND-PBO (placebo group of the EXPAND): n = 546] to assess: differences between active/non-active disease in the real-world (characteristics; monitoring); the value of MRI and relapse to indicate disease activity; and the number and characteristics of non-active patients with disease activity in the clinical study. In Adelphi, 1889 patients had 'active' disease (≥1 relapse in the year before index date and/or ≥1 new lesion on most recent MRI) versus 665 with 'non-active' disease (no relapses in the previous year and no new lesions on MRI); median age was 48 versus 53 years; 73.5 versus 87.8% had moderate-to-severe disease; 75.7 versus 54.3% were taking disease-modifying treatment; 87.7 versus 58.7% had received an MRI in the past year. Most active cases (n = 1116; 59.1%) were identified by MRI versus 239 (12.7%) by relapse and 534 (28.3%) by MRI plus relapse. In EXPAND-PBO, 263 patients were classified 'active' (≥1 relapse in 2 years before screening and/or ≥1 gadolinium-enhancing lesion) and 270 'non-active' (no relapse in the 2 years before screening and no gadolinium-enhancing lesion[s]) at baseline; similar proportions of these groups had received disease-modifying treatment prior to placebo: 77.2 and 80.7%. Of non-active patients, 53.0% had disease activity on study; in these patients, 74.1% had disease activity identified by MRI, 8.4% by relapse, and 17.5% by MRI plus relapse. In patients classified non-active at baseline: age and percentage with Expanded Disability Status Scale score 6.0-6.5 were similar between patients with disease activity on study versus patients who remained non-active: 48 versus 52 years; 49.7 versus 56.7%, respectively. In real-world and clinical trial settings, MRI could be a better option than relapse for the identification of disease activity. However, in the real-world, fewer non-active patients had received an MRI in the last year than active patients, which is concerning given that most disease activity in EXPAND-PBO was identified via MRI. We highlight difficulties in consistently identifying disease activity and the negative implications of infrequent monitoring of non-active disease.
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Affiliation(s)
- Gavin Giovannoni
- The Faculty of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London E1 2AT, UK
| | | | | | | | - Himanshu Karu
- Novartis Healthcare Pvt. Ltd, Hyderabad 500081, India
| | | | | | | | - Carol Lines
- Novartis Pharma AG, Basel CH-4056, Switzerland
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Cicero CE, Chisari C, Salafica G, Lo Fermo S, Donzuso G, Maimone D, Marziolo R, Patti F, Nicoletti A. Incidence of late onset multiple sclerosis in Italy: a population-based study. Sci Rep 2024; 14:29649. [PMID: 39609522 PMCID: PMC11604969 DOI: 10.1038/s41598-024-81284-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2024] [Accepted: 11/25/2024] [Indexed: 11/30/2024] Open
Abstract
Late onset multiple sclerosis (LOMS) represents between 0.6 and 12% of all MS patients. However, little is known on the incidence of LOMS in the general population. Therefore, we aimed to study the annual incidence of LOMS in a population-based cohort. The study was conducted in the province of Catania, Italy. Case ascertainment was conducted retrospectively including all patients aged ≥ 50 years at onset and with the onset between 2005 and 2020. Incidence rates (IR) have been calculated for all the study period, according to sex, age classes and for subperiods. Incidence rate ratios (IRR) have been computed to compare incidence rates. During the study period, 183 patients with LOMS were identified (113 women; 61.8%). The mean age at onset was 55.8 ± 5.4 years and the main phenotype was Relapsing Remitting MS (n = 123; 67.2%). The average annual crude IR was 2.87/100,000 person-years (95% Confidence Intervals, CI 2.31-3.13). IR increased from 2.54/100,000 in 2005-2010 to 3.32/100,000 in 2016-2020, especially in in the age group 60-69 (IRR 3.48; 95%CI 1.41-9.76; p-value 0.002). In conclusion, an increased IR over the time was observed in the age-group 60-69, possibly reflecting an increased age at onset of MS.
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Affiliation(s)
- Calogero Edoardo Cicero
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy.
| | - Clara Chisari
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Giuseppe Salafica
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Salvatore Lo Fermo
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Giulia Donzuso
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | | | | | - Francesco Patti
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Alessandra Nicoletti
- Department of Medical, Surgical Sciences and Advanced technologies G.F. Ingrassia, Section of Neurosciences, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy.
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8
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Rocca MA, Valsasina P, Romanò F, Tedone N, Amato MP, Brichetto G, Boccia VD, Chataway J, Chiaravalloti ND, Cutter G, Dalgas U, DeLuca J, Farrell RA, Feys P, Freeman J, Inglese M, Meza C, Motl RW, Salter A, Sandroff BM, Feinstein A, Filippi M. Cognitive rehabilitation effects on grey matter volume and Go-NoGo activity in progressive multiple sclerosis: results from the CogEx trial. J Neurol Neurosurg Psychiatry 2024; 95:1139-1149. [PMID: 38754979 DOI: 10.1136/jnnp-2024-333460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Research on cognitive rehabilitation (CR) and aerobic exercise (EX) to improve cognition in progressive multiple sclerosis (PMS) remains limited. CogEx trial investigated the effectiveness of CR and EX in PMS: here, we present MRI substudy volumetric and task-related functional MRI (fMRI) findings. METHODS Participants were randomised to: 'CR plus EX', 'CR plus sham EX (EX-S)', 'EX plus sham CR (CR-S)' and 'CR-S plus EX-S' and attended 12-week intervention. All subjects performed physical/cognitive assessments at baseline, week 12 and 6 months post intervention (month 9). All MRI substudy participants underwent volumetric MRI and fMRI (Go-NoGo task). RESULTS 104 PMS enrolled at four sites participated in the CogEx MRI substudy; 84 (81%) had valid volumetric MRI and valid fMRI. Week 12/month 9 cognitive performances did not differ among interventions; however, 25-62% of the patients showed Symbol Digit Modalities Test improvements. Normalised cortical grey matter volume (NcGMV) changes at week 12 versus baseline were heterogeneous among interventions (p=0.05); this was mainly driven by increased NcGMV in 'CR plus EX-S' (p=0.02). Groups performing CR (ie, 'CR plus EX' and 'CR plus EX-S') exhibited increased NcGMV over time, especially in the frontal (p=0.01), parietal (p=0.04) and temporal (p=0.04) lobes, while those performing CR-S exhibited NcGMV decrease (p=0.008). In CR groups, increased NcGMV (r=0.36, p=0.01) at week 12 versus baseline correlated with increased California Verbal Learning Test (CVLT)-II scores. 'CR plus EX-S' patients exhibited Go-NoGo activity increase (p<0.05, corrected) at week 12 versus baseline in bilateral insula. CONCLUSIONS In PMS, CR modulated grey matter (GM) volume and insular activity. The association of GM and CVLT-II changes suggests GM plasticity contributes to cognitive improvements. TRIAL REGISTRATION NUMBER NCT03679468.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milan, Italy
- Neurology Unit, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Francesco Romanò
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maria Pia Amato
- Department NEUROFARBA, Section Neurosciences, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Giampaolo Brichetto
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
- AISM Rehabilitation Service, Italian Multiple Sclerosis Society, Genoa, Italy
| | - Vincenzo Daniele Boccia
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, and Center of Excellence for Biomedical Research, University of Genoa, Genoa, Italy
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
| | - Nancy D Chiaravalloti
- Kessler Foundation, West Orange, NJ, USA
- Department of Physical Medicine & Rehabilitation, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Gary Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ulrik Dalgas
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - John DeLuca
- Kessler Foundation, West Orange, NJ, USA
- Department of Physical Medicine & Rehabilitation, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Rachel A Farrell
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Peter Feys
- REVAL, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
- University MS Center, Hasselt University, Pelt, Belgium
| | - Jennifer Freeman
- Faculty of Health, School of Health Professions, University of Plymouth, Plymouth, Devon, UK
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, and Center of Excellence for Biomedical Research, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cecilia Meza
- Department of Psychiatry, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Robert W Motl
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, Illinois, USA
| | - Amber Salter
- Department of Neurology, Section on Statistical Planning and Analysis, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Brian M Sandroff
- Kessler Foundation, West Orange, NJ, USA
- Department of Physical Medicine & Rehabilitation, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Anthony Feinstein
- Department of Psychiatry, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milan, Italy
- Neurology Unit, IRCCS Ospedale San Raffaele, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS Ospedale San Raffaele, Milan, Italy
- Neurophysiology Service, IRCCS Ospedale San Raffaele, Milan, Italy
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9
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Keegan BM, Absinta M, Cohen-Adad J, Flanagan EP, Henry RG, Klawiter EC, Kolind S, Krieger S, Laule C, Lincoln JA, Messina S, Oh J, Papinutto N, Smith SA, Traboulsee A. Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future. Brain Commun 2024; 6:fcae395. [PMID: 39611182 PMCID: PMC11604059 DOI: 10.1093/braincomms/fcae395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 09/30/2024] [Accepted: 11/05/2024] [Indexed: 11/30/2024] Open
Abstract
Spinal cord disease is important in most people with multiple sclerosis, but assessment remains less emphasized in patient care, basic and clinical research and therapeutic trials. The North American Imaging in Multiple Sclerosis Spinal Cord Interest Group was formed to determine and present the contemporary landscape of multiple sclerosis spinal cord evaluation, further existing and advanced spinal cord imaging techniques, and foster collaborative work. Important themes arose: (i) multiple sclerosis spinal cord lesions (differential diagnosis, association with clinical course); (ii) spinal cord radiological-pathological associations; (iii) 'critical' spinal cord lesions; (iv) multiple sclerosis topographical model; (v) spinal cord atrophy; and (vi) automated and special imaging techniques. Distinguishing multiple sclerosis from other myelopathic aetiology is increasingly refined by imaging and serological studies. Post-mortem spinal cord findings and MRI pathological correlative studies demonstrate MRI's high sensitivity in detecting microstructural demyelination and axonal loss. Spinal leptomeninges include immune inflammatory infiltrates, some in B-cell lymphoid-like structures. 'Critical' demyelinating lesions along spinal cord corticospinal tracts are anatomically consistent with and may be disproportionately associated with motor progression. Multiple sclerosis topographical model implicates the spinal cord as an area where threshold impairment associates with multiple sclerosis disability. Progressive spinal cord atrophy and 'silent' multiple sclerosis progression may be emerging as an important multiple sclerosis prognostic biomarker. Manual atrophy assessment is complicated by rater bias, while automation (e.g. Spinal Cord Toolbox), and artificial intelligence may reduce this. Collaborative research by the North American Imaging in Multiple Sclerosis and similar groups with experts combining distinct strengths is key to advancing assessment and treatment of people with multiple sclerosis spinal cord disease.
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Affiliation(s)
- B Mark Keegan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Martina Absinta
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Julien Cohen-Adad
- Institute of Biomedical Imaging, Polytechnique Montreal, Montreal, Canada H3T 1J4
| | - Eoin P Flanagan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Roland G Henry
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Eric C Klawiter
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Shannon Kolind
- Division of Neurology, University of British Columbia, Vancouver, Canada V6T 2B5
| | - Stephen Krieger
- Department of Neurology, Mount Sinai, New York City, NY 10029, USA
| | - Cornelia Laule
- Division of Neurology, University of British Columbia, Vancouver, Canada V6T 2B5
| | - John A Lincoln
- McGovern Medical School, UTHealth, Houston, TX 77030, USA
| | - Steven Messina
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jiwon Oh
- Division of Neurology, University of Toronto, Toronto, Canada M5B 1W8
| | - Nico Papinutto
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Seth Aaron Smith
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Anthony Traboulsee
- Division of Neurology, University of British Columbia, Vancouver, Canada V6T 2B5
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10
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Bayoumi A, Hasan KM, Thomas JA, Yazdani A, Lincoln JA. Glymphatic dysfunction in multiple sclerosis and its association with disease pathology and disability. Mult Scler 2024; 30:1609-1619. [PMID: 39344166 PMCID: PMC11568644 DOI: 10.1177/13524585241280842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/27/2024] [Accepted: 08/18/2024] [Indexed: 10/01/2024]
Abstract
BACKGROUND The role of the glymphatic system in multiple sclerosis (MS)-related disability remains underexplored. Diffusion-tensor image analysis along the perivascular space (DTI-ALPS) offers a non-invasive method to assess glymphatic function. OBJECTIVE To evaluate glymphatic function in MS patients with lower and higher disability. METHODS This study included 118 MS patients who underwent structural, diffusion-weighted imaging, and clinical assessment. The participants were divided into lower (MS-L, n = 57) and higher disability (MS-H, n = 61) subgroups. Brain parenchymal fraction (BPF), lesion load (LL), and DTI-ALPS index were measured. Subgroup differences and correlations between DTI-ALPS index and other measures were explored. Logistic regression was performed to evaluate BPF, LL, and DTI-ALPS index in classifying lower and higher disability patients. RESULTS Significant differences in DTI-ALPS index between MS-H and MS-L (d = -0.71, false discovery rate-corrected p-value (p-FDR) = 0.001) were found. The DTI-ALPS index correlated significantly with disease duration (rp = -0.29, p-FDR = 0.002) and EDSS (rsp = -0.35, p-FDR = 0.0002). It also showed significant correlations with BPF and LL. DTI-ALPS index and LL were significant predictors of disability subgroup (DTI-ALPS: odds ratio (OR) = 1.77, p = 0.04, LL: OR = 0.94, p = 0.02). CONCLUSION Our findings highlight DTI-ALPS index as an imaging biomarker in MS, suggesting the involvement of glymphatic impairment in MS pathology, although further research is needed to elucidate its role in contributing to MS-related disability.
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Affiliation(s)
- Ahmed Bayoumi
- Department of Neurology, McGovern Medical School at UTHealth Houston, Houston, TX, USA
| | - Khader M Hasan
- Department of Diagnostic and Interventional Imaging, McGovern Medical School at UTHealth Houston, Houston, TX, USA
| | - Joseph A Thomas
- Department of Neurology, McGovern Medical School at UTHealth Houston, Houston, TX, USA
| | - Akram Yazdani
- Department of Clinical and Translational Sciences, McGovern Medical School at UTHealth Houston, Houston, TX, USA
| | - John A Lincoln
- Department of Neurology, McGovern Medical School at UTHealth Houston, Houston, TX, USA
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11
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Qian Y, Thorpe CT, Tak C, Iyer S, Seyerle A, Thorpe JM. Impact of discontinuing disease-modifying therapies on health care utilization among midlife patients with multiple sclerosis in the United States. J Manag Care Spec Pharm 2024; 30:1248-1260. [PMID: 39471270 PMCID: PMC11522451 DOI: 10.18553/jmcp.2024.30.11.1248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2024]
Abstract
BACKGROUND Multiple sclerosis (MS) is a lifelong progressive neurological disease treated primarily with disease-modifying therapies (DMTs). Disease activity tends to decline as patients age. Midlife represents a crossroads where the risks of DMT may outweigh the benefits, prompting providers to consider DMT discontinuation to reduce treatment burden. However, real-world evidence on the impact of DMT discontinuation among midlife patients is lacking. OBJECTIVE To evaluate the association between DMT discontinuation and health care utilization among midlife patients with MS. METHODS Midlife patients with MS who received an injectable or oral DMT between 2001 and 2018 were identified from the MarketScan commercial claims database. DMT discontinuation, defined as a treatment gap exceeding 90 days in days supply, was the independent variable. Patients who discontinued DMTs had their index date set as the last gap day, whereas index dates for those who continued DMTs were matched based on the time distribution of index dates of discontinuers. Inpatient hospitalizations (all-cause, MS-related, and non-MS-related), emergency department (ED) visits (all-cause, MS-related, and non-MS-related), and relapse-related hospitalizations and outpatient visits were independently evaluated during the 365-day follow-up. Patients were observed until the occurrence of an event (depending on the model), deviation from the treatment group, disenrollment, death, end of follow-up, or data unavailability. Stabilized inverse probability of treatment weighting (sIPTW) was employed to balance the 2 groups. The associations between DMT discontinuation and each utilization outcome were estimated using Cox proportional hazard regression models with sIPTW. RESULTS Of 149,721 midlife patients with MS, 22.8% discontinued DMTs and 77.2% continued DMTs. Patients who discontinued DMTs had a higher cumulative incidence for all utilization outcomes during the 365-day follow-up than those who continued DMTs. Cox regression showed that DMT discontinuation was associated with a 10.3% and 24.9% higher rate of all-cause and non-MS-related inpatient hospitalizations, respectively, with no significant association found for MS-related hospitalizations. Patients discontinuing DMTs exhibited higher utilization rates for ED visits, with an increase of 21.3% for all-cause, 23.0% for MS-related, and 20.9% for non-MS-related visits compared with those who continued DMTs. We also observed a 15.9% and 52.1% higher rate of relapse-related hospitalizations and outpatient visits associated with DMT discontinuation, respectively. CONCLUSIONS This study revealed that DMT discontinuation was associated with higher health care services utilization among midlife patients with MS, especially relapse-related outpatient visits. DMT discontinuation during midlife may be premature, and DMTs may still be necessary to reduce health care utilization.
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Affiliation(s)
- Yiran Qian
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
| | - Carolyn T. Thorpe
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA
| | - Casey Tak
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City
| | - Stephanie Iyer
- Department of Pharmacy, University of North Carolina Health, Chapel Hill
| | - Amanda Seyerle
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
| | - Joshua M. Thorpe
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA
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12
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Scalfari A, Traboulsee A, Oh J, Airas L, Bittner S, Calabrese M, Garcia Dominguez JM, Granziera C, Greenberg B, Hellwig K, Illes Z, Lycke J, Popescu V, Bagnato F, Giovannoni G. Smouldering-Associated Worsening in Multiple Sclerosis: An International Consensus Statement on Definition, Biology, Clinical Implications, and Future Directions. Ann Neurol 2024; 96:826-845. [PMID: 39051525 DOI: 10.1002/ana.27034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 07/03/2024] [Accepted: 07/05/2024] [Indexed: 07/27/2024]
Abstract
Despite therapeutic suppression of relapses, multiple sclerosis (MS) patients often experience subtle deterioration, which extends beyond the definition of "progression independent of relapsing activity." We propose the concept of smouldering-associated-worsening (SAW), encompassing physical and cognitive symptoms, resulting from smouldering pathological processes, which remain unmet therapeutic targets. We provide a consensus-based framework of possible pathological substrates and manifestations of smouldering MS, and we discuss clinical, radiological, and serum/cerebrospinal fluid biomarkers for potentially monitoring SAW. Finally, we share considerations for optimizing disease surveillance and implications for clinical trials to promote the integration of smouldering MS into routine practice and future research efforts. ANN NEUROL 2024;96:826-845.
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Affiliation(s)
- Antonio Scalfari
- Center of Neuroscience, Department of Medicine, Charing Cross Hospital, Imperial College, London, UK
| | | | - Jiwon Oh
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Canada
| | - Laura Airas
- University of Turku and Turku University Hospital, Turku, Finland
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (Rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | | | | | - Cristina Granziera
- Translational Imaging in Neurology (THiNK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Basel, Switzerland
- Department of Neurology and MS Center, University Hospital Basel Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), Basel, Switzerland
| | | | | | - Zsolt Illes
- Department of Neurology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Jan Lycke
- Department of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Veronica Popescu
- University MS Centre Pelt-Hasselt, Noorderhart Hospital, Belgium Hasselt University, Pelt, Belgium
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, VA Hospital, TN Valley Healthcare System, Nashville, TN, USA
| | - Gavin Giovannoni
- Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
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13
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Simone M, Lucisano G, Guerra T, Paolicelli D, Rocca MA, Brescia Morra V, Patti F, Annovazzi P, Gasperini C, De Luca G, Ferraro D, Margari L, Granella F, Pozzilli C, Romano S, Perini P, Bergamaschi R, Coniglio MG, Lus G, Vianello M, Lugaresi A, Portaccio E, Filippi M, Amato MP, Iaffaldano P. Disability trajectories by progression independent of relapse activity status differ in pediatric, adult and late-onset multiple sclerosis. J Neurol 2024; 271:6782-6790. [PMID: 39179712 PMCID: PMC11447039 DOI: 10.1007/s00415-024-12638-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 08/10/2024] [Accepted: 08/13/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND To compare Expanded Disability Status Scale (EDSS) trajectories over time between Multiple Sclerosis (MS) groups with pediatric (POMS), adult (AOMS) and late (LOMS) onset, and between patients with and without progression independent of relapse activity (PIRA). METHODS Patients with a first visit within 1 year from onset, ≥ 5-year follow-up and ≥ 1 visit every 6 months were selected from the Italian MS Register. Adjusted disability trajectories were assessed by longitudinal models for repeated measures. Comparisons between groups and between patients with and without PIRA in subgroups were performed by evaluating the yearly differences of mean EDSS score changes versus baseline (delta-EDSS). A first CDA event was defined as a 6-months confirmed disability increase from study baseline, measured by EDSS (increase ≥ 1.5 points with baseline EDSS = 0; ≥ 1.0 with baseline EDSS score ≤ 5.0 and ≥ 0.5 point with baseline EDSS > 5.5). PIRA was defined as a CDA event occurring more than 90 days after and more than 30 days before the onset of a relapse. RESULTS 3777 MS patients (268 POMS, 3282 AOMS, 227 LOMS) were included. The slope of disability trajectories significantly diverged in AOMS vs POMS starting from the second year of follow-up (Year 2: delta2-EDSS 0.18 (0.05; 0.31), p = 0.0054) and then mean delta2-EDSS gradually increased up to 0.23 (0.07; 0.39, p = 0.004) at year 5. Patients with PIRA had significant (p < 0.0001) steeper increase in EDSS scores than those without PIRA in all groups, although in POMS, the disability trajectories began to diverge later and at a lesser extent with delta-EDSS score of 0.48 vs 0.83 in AOMS and 1.57 in LOMS, at 3 years after the first PIRA. CONCLUSIONS Age is relevant in determining disability progression in MS. POMS shows a less steep increase in EDSS scores over time than older patients. The effect of PIRA in accelerating EDSS progression is less pronounced in POMS than in AOMS and LOMS.
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Affiliation(s)
- Marta Simone
- Child Neuropsychiatry Unit, Department of Precision and Regenerative Medicine, Jonic Area University of Bari "Aldo Moro", Bari, Italy
| | - Giuseppe Lucisano
- CORESEARCH, Pescara, Italy
- Department of Translational Biomedicine and Neurosciences-DiBraiN, University "Aldo Moro" Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Tommaso Guerra
- Department of Translational Biomedicine and Neurosciences-DiBraiN, University "Aldo Moro" Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Damiano Paolicelli
- Department of Translational Biomedicine and Neurosciences-DiBraiN, University "Aldo Moro" Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Maria A Rocca
- Dipartimento di Neurologia, Neurofisiologia e Neuroriabilitazione, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Vincenzo Brescia Morra
- Department of Neuroscience (NSRO), Multiple Sclerosis Clinical Care and Research Center, Federico II University, Naples, Italy
| | - Francesco Patti
- Dipartimento di Scienze Mediche e Chirurgiche e Tecnologie Avanzate, GF Ingrassia, Sez. Neuroscienze, Centro Sclerosi Multipla, Università di Catania, Catania, Italy
| | - Pietro Annovazzi
- Neuroimmunology Unit - Multiple Sclerosis Centre ASST Valle Olona, Gallarate Hospital, Gallarate, Italy
| | - Claudio Gasperini
- Department of Neurosciences, S.Camillo Forlanini Hospital, Rome, Italy
| | - Giovanna De Luca
- Centro Sclerosi Multipla, Clinica Neurologica, Policlinico SS. Annunziata, Chieti, Italy
| | - Diana Ferraro
- Azienda Ospedaliera Universitaria di Modena/OCB, UO Neurologia, Milano, Italy
| | - Lucia Margari
- Child Neuropsychiatry Unit, Department of Precision and Regenerative Medicine, Jonic Area University of Bari "Aldo Moro", Bari, Italy
| | - Franco Granella
- Unit of Neurosciences, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Multiple Sclerosis Center, S. Andrea Hospital, Rome, Italy
| | - Silvia Romano
- Department of Neurosciences, Mental Health and Sensory Organs, Centre for Experimental Neurological Therapies (CENTERS), Sapienza University of Rome, Rome, Italy
| | - Paola Perini
- Department of Neurosciences, Multiple Sclerosis Centre-Veneto Region (CeSMuV), University Hospital of Padua, Padua, Italy
| | | | | | - Giacomo Lus
- Multiple Sclerosis Center, II Division of Neurology, Department of Clinical and Experimental Medicine, Second University of Naples, Naples, Italy
| | | | - Alessandra Lugaresi
- IRCCS Istituto Scienze Neurologiche di Bologna, Bologna, Italy
- Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy
| | | | - Massimo Filippi
- Dipartimento di Neurologia, Neurofisiologia e Neuroriabilitazione, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Pia Amato
- Department NEUROFARBA, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Pietro Iaffaldano
- Department of Translational Biomedicine and Neurosciences-DiBraiN, University "Aldo Moro" Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy.
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14
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Chisari CG, Amato MP, Di Sapio A, Foschi M, Iaffaldano P, Inglese M, Fermo SL, Lugaresi A, Lus G, Mascoli N, Montepietra S, Pesci I, Quatrale R, Salemi G, Torri Clerici V, Totaro R, Valentino P, Filippi M, Patti F. Active and non-active secondary progressive multiple sclerosis patients exhibit similar disability progression: results of an Italian MS registry study (ASPERA). J Neurol 2024; 271:6801-6810. [PMID: 39190108 PMCID: PMC11446943 DOI: 10.1007/s00415-024-12621-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/29/2024] [Accepted: 08/01/2024] [Indexed: 08/28/2024]
Abstract
'Active' and 'non-active' secondary progressive MS (SPMS) have distinct pathophysiological mechanisms and clinical characteristics, but there is still no consensus regarding the frequency of these MS forms in the real-world setting. We aimed to evaluate the frequency of 'active' and 'non-active' SPMS in a large cohort of Italian MS patients and the differences in terms of clinical and MRI characteristics and disease progression. This multicenter study collected data about MS patients who have transitioned to the SP form in the period between 1st January 2014 and 31st December 2019 and followed by the MS centers contributing to the Italian MS Registry. Patients were divided into 'active SPMS' and 'non-active SPMS', based on both reported MRI data and relapse activity in the year before conversion to SPMS. Out of 68,621, 8,316 (12.1%) patients were diagnosed with SPMS. Out of them, 872 (10.5%) were classified into patients with either 'active' or 'non-active' SPMS. A total of 237 were classified into patients with 'active SPMS' (27.2%) and 635 as 'non-active SPMS' (72.8%). 'Non-active SPMS' patients were older, with a longer disease duration compared to those with 'active SPMS'. The percentages of patients showing progression independent of relapse activity (PIRA) at 24 months were similar between 'active' and 'non-active' SPMS patients (67 [27.4%] vs 188 [29.6%]; p = 0.60). In the 'active' group, 36 (15.2%) patients showed relapse-associated worsening (RAW). Comparison of the survival curves to EDSS 6 and 7 according to disease activity did not show significant differences (p = 0.68 and p = 0.71). 'Active' and 'non-active' SPMS patients had a similar risk of achieving disability milestones, suggesting that progression is primarily attributed to PIRA and only to a small extent to disease activity.
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Affiliation(s)
- Clara Grazia Chisari
- Department of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", Multiple Sclerosis Center, University of Catania, Catania, Italy
- Multiple Sclerosis Unit; Neurology Clinic, Policlinico "G. Rodolico- San Marco", Catania, Italy
| | - Maria Pia Amato
- Department of NEUROFARBA, Section of Neurosciences, University of Florence, Florence, Italy
| | - Alessia Di Sapio
- Department of Neurology, Regional Referral Multiple Sclerosis Center, University Hospital San Luigi Gonzaga, Orbassano, Turin, Italy
| | - Matteo Foschi
- Department of Neuroscience, Multiple Sclerosis Center, S. Maria delle Croci Hospital of Ravenna, Ravenna, Italy
- Department of Biotechnological and Applied Clinical Sciences (DISCAB), University of L'Aquila, L'Aquila, Italy
| | - Pietro Iaffaldano
- Department of Translational Biomedicine and Neurosciences (DiBraiN), University of Bari Aldo Moro, Bari, Italy
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Salvatore Lo Fermo
- Multiple Sclerosis Unit; Neurology Clinic, Policlinico "G. Rodolico- San Marco", Catania, Italy
| | - Alessandra Lugaresi
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto Delle Scienze Neurologiche Di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giacomo Lus
- Multiple Sclerosis Center, Second Division of Neurology, Department of Advanced Medical and Surgical Science, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Nerina Mascoli
- Neurology Unit, Department of Medicine, S. Anna Hospital, Como, Italy
| | - Sara Montepietra
- MS Centre, SMN Hospital, AUSL Reggio Emilia, Reggio Emilia, Italy
| | - Ilaria Pesci
- Centro Sclerosi Multipla Unità Operativa Neurologia, Azienda Unità Sanitaria Locale, Ospedale Di Vaio, Fidenza, Parma, Italy
| | - Rocco Quatrale
- Dipartimento Di Scienze Neurologiche, UOC Di Neurologia, Ospedale Dell'Angelo AULSS 3 Serenissima, Venice Mestre, Italy
| | - Giuseppe Salemi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Valentina Torri Clerici
- Neuroimmunology and Neuromuscular Diseases Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Rocco Totaro
- Demyelinating Disease Center, San Salvatore Hospital, L'Aquila, Italy
| | - Paola Valentino
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Patti
- Department of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", Multiple Sclerosis Center, University of Catania, Catania, Italy.
- Multiple Sclerosis Unit; Neurology Clinic, Policlinico "G. Rodolico- San Marco", Catania, Italy.
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15
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Locatelli G, Stangel M, Rooks D, Boesch J, Pierrel E, Summermatter S. The therapeutic potential of exercise for improving mobility in multiple sclerosis. Front Physiol 2024; 15:1477431. [PMID: 39345788 PMCID: PMC11427913 DOI: 10.3389/fphys.2024.1477431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Accepted: 09/03/2024] [Indexed: 10/01/2024] Open
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammation and demyelination in the central nervous system (CNS) with subsequent axonal and neuronal degeneration. These changes are associated with a broad range of symptoms including skeletal muscle dysfunction. Importantly, musculoskeletal impairments manifest in various ways, compromise the quality of life and often precede the later development of mobility disability. As current standard disease modifying therapies for MS predominantly act on neuroinflammation, practitioners and patients face an unmet medical need for adjunct therapies specifically targeting skeletal muscle function. This review is intended to detail the nature of the skeletal muscle dysfunctions common in people with MS (pwMS), describe underlying intramuscular alterations and outline evidence-based therapeutic approaches. Particularly, we discuss the emerging role of aerobic and resistance exercise for reducing the perception of fatigue and increasing muscle strength in pwMS. By integrating the most recent literature, we conclude that both exercise interventions should ideally be implemented as early as possible as they can address MS-specific muscle impairments. Aerobic exercise is particularly beneficial for pwMS suffering from fatigue and metabolic impairments, while resistance training efficiently counters muscle weakness and improves the perception of fatigue. Thus, these lifestyle interventions or possible pharmacological mimetics have the potential for improving the general well-being and delaying the functional declines that are relevant to mobility.
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Affiliation(s)
- Giuseppe Locatelli
- Immunology Disease Area, Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Martin Stangel
- Translational Medicine, Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Daniel Rooks
- Translational Medicine, Biomedical Research, Novartis Pharma AG, Cambridge, MA, United States
| | - Julian Boesch
- Diseases of Aging and Regenerative Medicine, Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Eliane Pierrel
- Diseases of Aging and Regenerative Medicine, Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Serge Summermatter
- Diseases of Aging and Regenerative Medicine, Biomedical Research, Novartis Pharma AG, Basel, Switzerland
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16
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John N, Li Y, De Angelis F, Stutters J, Prados Carrasco F, Eshaghi A, Doshi A, Calvi A, Williams T, Plantone D, Phan T, Barkhof F, Chataway J, Ourselin S, Braisher M, Beyene T, Bassan V, Zapata A, Chandran S, Connick P, Lyle D, Cameron J, Mollison D, Colville S, Dhillon B, Ross M, Cranswick G, Walker A, Smith L, Giovannoni G, Gnanapavan S, Nicholas R, Rashid W, Aram J, Ford H, Pavitt SH, Overell J, Young C, Arndt H, Duddy M, Guadagno J, Evangelou N, Craner M, Palace J, Hobart J, Sharrack B, Paling D, Hawkins C, Kalra S, McLean B, Stallard N, Bastow R. Brain reserve and physical disability in secondary progressive multiple sclerosis. BMJ Neurol Open 2024; 6:e000670. [PMID: 39262426 PMCID: PMC11387515 DOI: 10.1136/bmjno-2024-000670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 07/27/2024] [Indexed: 09/13/2024] Open
Abstract
Background The brain reserve hypothesis posits that larger maximal lifetime brain growth (MLBG) may confer protection against physical disability in multiple sclerosis (MS). Larger MLBG as a proxy for brain reserve, has been associated with reduced progression of physical disability in patients with early MS; however, it is unknown whether this association remains once in the secondary progressive phase of MS (SPMS). Our aim was to assess whether larger MLBG is associated with decreased physical disability progression in SPMS. Methods We conducted a post hoc analysis of participants in the MS-Secondary Progressive Multi-Arm Randomisation Trial (NCT01910259), a multicentre randomised placebo-controlled trial of the neuroprotective potential of three agents in SPMS. Physical disability was measured by Expanded Disability Status Scale (EDSS), 9-hole peg test (9HPT) and 25-foot timed walk test (T25FW) at baseline, 48 and 96 weeks. MLBG was estimated by baseline intracranial volume (ICV). Multivariable time-varying Cox regression models were used to investigate the association between MLBG and physical disability progression. Results 383 participants (mean age 54.5 years, 298 female) were followed up over 96 weeks. Median baseline EDSS was 6.0 (range 4.0-6.5). Adjusted for covariates, larger MLBG was associated with a reduced risk of EDSS progression (HR 0.84,95% CI:0.72 to 0.99;p=0.04). MLBG was not independently associated with time to progression as measured by 9HPT or T25FW. Conclusion Larger MLBG is independently associated with physical disability progression over 96 weeks as measured by EDSS in SPMS. This suggests that MLBG as a proxy for brain reserve may continue to confer protection against disability when in the secondary progression phase of MS. Trail registration number NCT01910259.
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Affiliation(s)
- Nevin John
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
| | - Yingtong Li
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Floriana De Angelis
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jonathan Stutters
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados Carrasco
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Anisha Doshi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Alberto Calvi
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
| | - Thomas Williams
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Domenico Plantone
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
| | - Thanh Phan
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Sebastien Ourselin
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Marie Braisher
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Tiggy Beyene
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Vanessa Bassan
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Alvin Zapata
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Siddharthan Chandran
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Peter Connick
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Dawn Lyle
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - James Cameron
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Daisy Mollison
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Shuna Colville
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Baljean Dhillon
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Moira Ross
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Gina Cranswick
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Allan Walker
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Lorraine Smith
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Gavin Giovannoni
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Sharmilee Gnanapavan
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Richard Nicholas
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Waqar Rashid
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Julia Aram
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Helen Ford
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Sue H Pavitt
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - James Overell
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Carolyn Young
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Heinke Arndt
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Martin Duddy
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Joe Guadagno
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Nikolaos Evangelou
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Matthew Craner
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Jacqueline Palace
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Jeremy Hobart
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Basil Sharrack
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - David Paling
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Clive Hawkins
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Seema Kalra
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Brendan McLean
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Nigel Stallard
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Roger Bastow
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Monash Health, Clayton, Victoria, Australia
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Advanced Imaging in Neuroimmunological Diseases Lab (ImaginEM), Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
- Department of Medicine, Surgery & Neuroscience, University of Siena, Siena, Italy
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
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17
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Gross RH, Corboy J. De-escalation and Discontinuation of Disease-Modifying Therapies in Multiple Sclerosis. Curr Neurol Neurosci Rep 2024; 24:341-353. [PMID: 38995483 DOI: 10.1007/s11910-024-01355-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2024] [Indexed: 07/13/2024]
Abstract
PURPOSE OF REVIEW Long-term use of multiple sclerosis (MS) disease-modifying therapies (DMTs) is standard practice to prevent accumulation of disability. Immunosenescence and other age-related changes lead to an altered risk-benefit ratio for older patients on DMTs. This article reviews recent research on the topic of de-escalation and discontinuation of MS DMTs. RECENT FINDINGS Observational and interventional studies have shed light on what happens to patients who de-escalate or discontinue DMTs and the factors, such as age, treatment type, and presence of recent disease activity, that influence outcomes. Though many questions remain, recent findings have been valuable for the development of an evidence-based approach to making de-escalation and discontinuation decisions in MS.
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Affiliation(s)
- Robert H Gross
- Department of Neurology, University of Colorado School of Medicine, 12631 East 17thAvenue, Mail Stop F727, Aurora, CO, 80045, USA.
- Department of Neurology, Rocky Mountain Regional Veterans Administration Medical Center, Aurora, CO, USA.
| | - John Corboy
- Department of Neurology, University of Colorado School of Medicine, 12631 East 17thAvenue, Mail Stop F727, Aurora, CO, 80045, USA
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18
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Mazziotti V, Crescenzo F, Turano E, Guandalini M, Bertolazzo M, Ziccardi S, Virla F, Camera V, Marastoni D, Tamanti A, Calabrese M. The contribution of tumor necrosis factor to multiple sclerosis: a possible role in progression independent of relapse? J Neuroinflammation 2024; 21:209. [PMID: 39169320 PMCID: PMC11340196 DOI: 10.1186/s12974-024-03193-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 07/31/2024] [Indexed: 08/23/2024] Open
Abstract
Tumor necrosis factor (TNF) is a pleiotropic cytokine regulating many physiological and pathological immune-mediated processes. Specifically, it has been recognized as an essential pro-inflammatory cytokine implicated in multiple sclerosis (MS) pathogenesis and progression. MS is a chronic immune-mediated disease of the central nervous system, characterized by multifocal acute and chronic inflammatory demyelination in white and grey matter, along with neuroaxonal loss. A recent concept in the field of MS research is disability resulting from Progression Independent of Relapse Activity (PIRA). PIRA recognizes that disability accumulation since the early phase of the disease can occur independently of relapse activity overcoming the traditional dualistic view of MS as either a relapsing-inflammatory or a progressive-neurodegenerative disease. Several studies have demonstrated an upregulation in TNF expression in both acute and chronic active MS brain lesions. Additionally, elevated TNF levels have been observed in the serum and cerebrospinal fluid of MS patients. TNF appears to play a significant role in maintaining chronic intrathecal inflammation, promoting axonal damage neurodegeneration, and consequently contributing to disease progression and disability accumulation. In summary, this review highlights the current understanding of TNF and its receptors in MS progression, specifically focusing on the relatively unexplored PIRA condition. Further research in this area holds promise for potential therapeutic interventions targeting TNF to mitigate disability in MS patients.
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Affiliation(s)
- Valentina Mazziotti
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Francesco Crescenzo
- Neurology Unit - Multiple Sclerosis Center, Scaligera Local Unit of Health and Social Services 9, Mater Salutis Hospital, 37045, Legnago, Verona, Italy
| | - Ermanna Turano
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Maddalena Guandalini
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Maddalena Bertolazzo
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Stefano Ziccardi
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Federica Virla
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Valentina Camera
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Damiano Marastoni
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Agnese Tamanti
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy
| | - Massimiliano Calabrese
- Neurology B Unit - Multiple Sclerosis Center, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134, Verona, Italy.
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19
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Alwahsh M, Nimer RM, Dahabiyeh LA, Hamadneh L, Hasan A, Alejel R, Hergenröder R. NMR-based metabolomics identification of potential serum biomarkers of disease progression in patients with multiple sclerosis. Sci Rep 2024; 14:14806. [PMID: 38926483 PMCID: PMC11208524 DOI: 10.1038/s41598-024-64490-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Multiple sclerosis (MS) is a chronic and progressive neurological disorder, characterized by neuroinflammation and demyelination within the central nervous system (CNS). The etiology and the pathogenesis of MS are still unknown. Till now, no satisfactory treatments, diagnostic and prognostic biomarkers are available for MS. Therefore, we aimed to investigate metabolic alterations in patients with MS compared to controls and across MS subtypes. Metabolic profiles of serum samples from patients with MS (n = 90) and healthy control (n = 30) were determined by Nuclear Magnetic Resonance (1H-NMR) Spectroscopy using cryogenic probe. This approach was also utilized to identify significant differences between the metabolite profiles of the MS groups (primary progressive, secondary progressive, and relapsing-remitting) and the healthy controls. Concentrations of nine serum metabolites (adenosine triphosphate (ATP), tryptophan, formate, succinate, glutathione, inosine, histidine, pantothenate, and nicotinamide adenine dinucleotide (NAD)) were significantly higher in patients with MS compared to control. SPMS serum exhibited increased pantothenate and tryptophan than in PPMS. In addition, lysine, myo-inositol, and glutamate exhibited the highest discriminatory power (0.93, 95% CI 0.869-0.981; 0.92, 95% CI 0.859-0.969; 0.91, 95% CI 0.843-0.968 respectively) between healthy control and MS. Using NMR- based metabolomics, we identified a set of metabolites capable of classifying MS patients and controls. These findings confirmed untargeted metabolomics as a useful approach for the discovery of possible novel biomarkers that could aid in the diagnosis of the disease.
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Affiliation(s)
- Mohammad Alwahsh
- Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, 17138, Jordan.
| | - Refat M Nimer
- Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, 22110, Jordan
| | - Lina A Dahabiyeh
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman, 11942, Jordan
| | - Lama Hamadneh
- Department of Badic Medical Sciences, Faculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan
| | - Aya Hasan
- Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, 17138, Jordan
| | - Rahaf Alejel
- Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, 17138, Jordan
| | - Roland Hergenröder
- Leibniz-Institut Für Analytische Wissenschaften-ISAS-E.V., 44139, Dortmund, Germany
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20
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Wurtz LI, Knyazhanskaya E, Sohaei D, Prassas I, Pittock S, Willrich MAV, Saadeh R, Gupta R, Atkinson HJ, Grill D, Stengelin M, Thebault S, Freedman MS, Diamandis EP, Scarisbrick IA. Identification of brain-enriched proteins in CSF as biomarkers of relapsing remitting multiple sclerosis. Clin Proteomics 2024; 21:42. [PMID: 38880880 PMCID: PMC11181608 DOI: 10.1186/s12014-024-09494-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is a clinically and biologically heterogenous disease with currently unpredictable progression and relapse. After the development and success of neurofilament as a cerebrospinal fluid (CSF) biomarker, there is reinvigorated interest in identifying other markers of or contributors to disease. The objective of this study is to probe the predictive potential of a panel of brain-enriched proteins on MS disease progression and subtype. METHODS This study includes 40 individuals with MS and 14 headache controls. The MS cohort consists of 20 relapsing remitting (RR) and 20 primary progressive (PP) patients. The CSF of all individuals was analyzed for 63 brain enriched proteins using a method of liquid-chromatography tandem mass spectrometry. Wilcoxon rank sum test, Kruskal-Wallis one-way ANOVA, logistic regression, and Pearson correlation were used to refine the list of candidates by comparing relative protein concentrations as well as relation to known imaging and molecular biomarkers. RESULTS We report 30 proteins with some relevance to disease, clinical subtype, or severity. Strikingly, we observed widespread protein depletion in the disease CSF as compared to control. We identified numerous markers of relapsing disease, including KLK6 (kallikrein 6, OR = 0.367, p < 0.05), which may be driven by active disease as defined by MRI enhancing lesions. Other oligodendrocyte-enriched proteins also appeared at reduced levels in relapsing disease, namely CNDP1 (carnosine dipeptidase 1), LINGO1 (leucine rich repeat and Immunoglobin-like domain-containing protein 1), MAG (myelin associated glycoprotein), and MOG (myelin oligodendrocyte glycoprotein). Finally, we identified three proteins-CNDP1, APLP1 (amyloid beta precursor like protein 1), and OLFM1 (olfactomedin 1)-that were statistically different in relapsing vs. progressive disease raising the potential for use as an early biomarker to discriminate clinical subtype. CONCLUSIONS We illustrate the utility of targeted mass spectrometry in generating potential targets for future biomarker studies and highlight reductions in brain-enriched proteins as markers of the relapsing remitting disease stage.
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Affiliation(s)
- Lincoln I Wurtz
- Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Dorsa Sohaei
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Ioannis Prassas
- Mount Sinai Hospital, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Sean Pittock
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Center for Multiple Sclerosis and Autoimmune Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Ruba Saadeh
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Ruchi Gupta
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Hunter J Atkinson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Diane Grill
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Simon Thebault
- Department of Medicine and The Ottawa Research Institute, Ottawa, Canada
- Division of Multiple Sclerosis, Department of Neurology, The University of Pennsylvania, Philadelphia, USA
| | - Mark S Freedman
- Department of Medicine and The Ottawa Research Institute, Ottawa, Canada
| | | | - Isobel A Scarisbrick
- Center for Multiple Sclerosis and Autoimmune Neurology, Mayo Clinic, Rochester, MN, USA.
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, 55905, USA.
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21
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Baller EB, Sweeney EM, Cieslak M, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis. Biol Psychiatry 2024; 95:1072-1080. [PMID: 37981178 PMCID: PMC11101593 DOI: 10.1016/j.biopsych.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/11/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. METHODS Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. RESULTS MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020). CONCLUSIONS We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
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22
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Jakimovski D, Qureshi F, Ramanathan M, Jalaleddini K, Ghoreyshi A, Dwyer MG, Bergsland N, Weinstock-Guttman B, Zivadinov R. Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation. J Neurol Sci 2024; 461:123055. [PMID: 38761669 DOI: 10.1016/j.jns.2024.123055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/05/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Atrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology. OBJECTIVES To determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS). METHODS Proteomic and MRI data for 202 pwMS (148 clinically isolated syndrome /relapsing-remitting MS and 54 progressive MS (PMS)) were acquired at baseline and at 5.4-year follow-up. The concentrations of 21 proteins related to multiple MS pathophysiology pathways were derived using a custom-developed Proximity Extension Assay on the Olink™ platform. The accrual of aLV was determined as the volume of baseline T2-weighted lesions that were replaced by cerebrospinal fluid over the follow-up. Regression models and age-adjusted analysis of covariance (ANCOVA) were used. RESULTS Older age (standardized beta = 0.176, p = 0.022), higher glial fibrillary acidic protein (standardized beta = 0.312, p = 0.001), and lower myelin oligodendrocyte glycoprotein levels (standardized beta = -0.271, p = 0.002) were associated with accrual of aLV over follow-up. This relationship was driven by the pwPMS population. The presence of LMCE at the follow-up visit was not predicted by any baseline proteomic biomarker nor cross-sectionally associated with any protein concentration. CONCLUSION Proteomic markers of glial activation are associated with chronic lesional WM pathology (measured as aLV) and may be specific to the progressive MS phenotype. LMCE presence in MS does not appear to relate to proteomic measures.
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Affiliation(s)
- Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
| | | | - Murali Ramanathan
- Department of Pharmaceutical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | | | | | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
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23
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Preziosa P, Storelli L, Tedone N, Margoni M, Mistri D, Azzimonti M, Filippi M, Rocca MA. Spatial correspondence among regional gene expressions and gray matter volume loss in multiple sclerosis. Mol Psychiatry 2024; 29:1833-1843. [PMID: 38326561 DOI: 10.1038/s41380-024-02452-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024]
Abstract
In multiple sclerosis (MS), a non-random and clinically relevant pattern of gray matter (GM) volume loss has been described. Whether differences in regional gene expression might underlay distinctive pathological processes contributing to this regional variability has not been explored yet. Two hundred eighty-six MS patients and 172 healthy controls (HC) underwent a brain 3T MRI, a complete neurological evaluation and a neuropsychological assessment. Using Allen Human Brain Atlas, voxel-based morphometry and MENGA platform, we integrated brain transcriptome and neuroimaging data to explore the spatial cross-correlations between regional GM volume loss and expressions of 2710 genes involved in MS (p < 0.05, family-wise error-corrected). Enrichment analyses were performed to evaluate overrepresented molecular functions, biological processes and cellular components involving genes significantly associated with voxel-based morphometry-derived GM maps (p < 0.05, Bonferroni-corrected). A diffuse GM volume loss was found in MS patients compared to HC and it was spatially correlated with 74 genes involved in GABA neurotransmission and mitochondrial oxidoreductase activity mainly expressed in neurons and astrocytes. A more severe GM volume loss was spatially associated, in more disabled MS patients, with 44 genes involved in mitochondrial integrity of all resident cells of the central nervous system (CNS) and, in cognitively impaired MS patients, with 64 genes involved in mitochondrial protein heterodimerization and oxidoreductase activities expressed also in microglia and endothelial cells. Specific differences in the expressions of genes involved in synaptic GABA receptor activities and mitochondrial functions in resident CNS cells may influence regional susceptibility to MS-related excitatory/inhibitory imbalance and oxidative stress, and subsequently, to GM volume loss.
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Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Loredana Storelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Damiano Mistri
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Azzimonti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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Krieger S, Cook K, Hersh CM. Understanding multiple sclerosis as a disease spectrum: above and below the clinical threshold. Curr Opin Neurol 2024; 37:189-201. [PMID: 38535979 PMCID: PMC11064902 DOI: 10.1097/wco.0000000000001262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
PURPOSE OF REVIEW Research in multiple sclerosis (MS) has long been predicated on clinical groupings that do not reflect the underlying biologic heterogeneity apparent within patient populations. This review explicates the various levels of explanation through which the spectrum of disease is described and investigated both above and below the clinical threshold of detection, as framed by the topographical model of MS, to help advance a cogent mechanistic framework. RECENT FINDINGS Contemporary evidence has amended the view of MS as consisting of sequential disease phases in favor of a spectrum of disease with an admixture of interdependent and dynamic pathobiological axes driving tissue injury and progression. Recent studies have shown the presence of acute and compartmentalized inflammation and mechanisms of neurodegeneration beginning early and evolving throughout the disease continuum. Still, the gap between the understanding of immunopathologic processes in MS and the tools used to measure relevant molecular, laboratory, radiologic, and clinical metrics needs attention to enable better prognostication of disease and monitoring for changes along specific pathologic axes and variable treatment outcomes. SUMMARY Aligning on a consistently-applied mechanistic framework at distinct levels of explanation will enable greater precision across bench and clinical research, and inform discourse on drivers of disability progression and delivery of care for individuals with MS.
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Affiliation(s)
- Stephen Krieger
- Corinne Goldsmith Dickinson Center for MS, Icahn School of Medicine at Mount Sinai
| | - Karin Cook
- Medical Education Director, Neurology at Heartbeat/Publicis Health, New York
| | - Carrie M. Hersh
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic Las Vegas, Nevada, USA
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25
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Allogmanny S, Probst Y. Dietary Modification Combined with Nutrition Education and Counseling for Metabolic Comorbidities in Multiple Sclerosis: Implications for Clinical Practice and Research. Curr Nutr Rep 2024; 13:106-112. [PMID: 38676838 PMCID: PMC11133086 DOI: 10.1007/s13668-024-00538-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2024] [Indexed: 04/29/2024]
Abstract
PURPOSE OF REVIEW Metabolic comorbidities such as obesity, diabetes, hypertension, and dyslipidemia are common to multiple sclerosis (MS) and are associated with negative outcomes of the disease. Dietary intervention has the potential to improve MS co-morbidities; thus, it is a high priority for people living with MS to self-manage their disease. The present review aimed to summarize the recent evidence on the impacts of combining dietary modification with nutrition education and counseling on managing metabolic comorbidity markers in MS. RECENT FINDINGS Evidence suggests important roles for tailored dietary change strategies and nutrition education and counseling in managing metabolic comorbidities for MS. There is also indirect evidence suggesting a relationship between dietary fiber, the gut microbiome, and improved metabolic markers in MS, highlighting the need for more research in this area. For people living with MS, addressing both barriers and facilitators to dietary changes through behavior change techniques can help them achieve sustainable and tailored dietary behavior changes. This will support person-centered care, ultimately improving metabolic comorbidity outcomes. Metabolic comorbidities in MS are considered modifiable diseases that can be prevented and managed by changes in dietary behavior. However, the impact of targeted dietary interventions on mitigating MS-related metabolic comorbidities remains inadequately explored. Therefore, this review has provided insights into recommendations to inform future best practices in MS. Further well-designed studies based on tailored dietary strategies applying behavior change theories are needed to address the underlying determinants of dietary practice in this population.
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Affiliation(s)
- Shoroog Allogmanny
- School of Medical, Indigenous and Health Sciences, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, NSW, 2522, Australia.
- Clinical Nutrition Department, College of Applied Medical Sciences, Taibah University, Madinah, 42353, Saudi Arabia.
| | - Yasmine Probst
- School of Medical, Indigenous and Health Sciences, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, NSW, 2522, Australia
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26
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Gill S, Agarwal M. Multiple Sclerosis Part 1: Essentials and the McDonald Criteria. Magn Reson Imaging Clin N Am 2024; 32:207-220. [PMID: 38555137 DOI: 10.1016/j.mric.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) characterized by relapsing-remitting or progressive neurologic symptoms and focal white matter lesions. The hallmark of the disease is the dissemination of CNS lesions in space and time, which is defined by the McDonald criteria. MRI is an essential diagnostic and prognostic biomarker for MS which can evaluate the entire CNS. MS mimics must be excluded before a diagnosis of MS is made.
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Affiliation(s)
- Sonia Gill
- Section of Neuroradiology, Medical College of Wisconsin, Milwaukee, USA
| | - Mohit Agarwal
- Section of Neuroradiology, Medical College of Wisconsin, Milwaukee, USA.
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27
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Desu HL, Sawicka KM, Wuerch E, Kitchin V, Quandt JA. A rapid review of differences in cerebrospinal neurofilament light levels in clinical subtypes of progressive multiple sclerosis. Front Neurol 2024; 15:1382468. [PMID: 38654736 PMCID: PMC11035744 DOI: 10.3389/fneur.2024.1382468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Background Multiple sclerosis (MS) is divided into three clinical phenotypes: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). It is unknown to what extent SPMS and PPMS pathophysiology share inflammatory or neurodegenerative pathological processes. Cerebrospinal (CSF) neurofilament light (NfL) has been broadly studied in different MS phenotypes and is a candidate biomarker for comparing MS subtypes. Research question Are CSF NfL levels different among clinical subtypes of progressive MS? Methods A search strategy identifying original research investigating fluid neurodegenerative biomarkers in progressive forms of MS between 2010 and 2022 was applied to Medline. Identified articles underwent title and abstract screen and full text review against pre-specified criteria. Data abstraction was limited to studies that measured NfL levels in the CSF. Reported statistical comparisons of NfL levels between clinical phenotypes were abstracted qualitatively. Results 18 studies that focused on investigating direct comparisons of CSF NfL from people with MS were included in the final report. We found NfL levels were typically reported to be higher in relapsing and progressive MS compared to healthy controls. Notably, higher NfL levels were not clearly associated with progressive MS subtypes when compared to relapsing MS, and there was no observed difference in NfL levels between PPMS and SPMS in articles that separately assessed these phenotypes. Conclusion CSF NfL levels distinguish individuals with MS from healthy controls but do not differentiate MS subtypes. Broad biological phenotyping is needed to overcome limitations of current clinical phenotyping and improve biomarker translatability to decision-making in the clinic.
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Affiliation(s)
- Haritha L. Desu
- Neuroimmunology Unit, Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montreal, QC, Canada
| | - Katherine M. Sawicka
- Child Health Evaluative Sciences Program, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Emily Wuerch
- Hotchkiss Brain Institute and the Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Vanessa Kitchin
- University of British Columbia Library, Vancouver, BC, Canada
| | - Jacqueline A. Quandt
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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28
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Riley N, Drudge C, Nelson M, Haltner A, Barnett M, Broadley S, Butzkueven H, McCombe P, Van der Walt A, Wong EOY, Merschhemke M, Adlard N, Walker R, Samjoo IA. Comparative efficacy of ofatumumab versus oral therapies for relapsing multiple sclerosis patients using propensity score analyses and simulated treatment comparisons. Ther Adv Neurol Disord 2024; 17:17562864241239453. [PMID: 38525490 PMCID: PMC10960976 DOI: 10.1177/17562864241239453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/14/2024] [Indexed: 03/26/2024] Open
Abstract
Background Evidence from network meta-analyses (NMAs) and real-world propensity score (PS) analyses suggest monoclonal antibodies (mAbs) offer a therapeutic advantage over currently available oral therapies and, therefore, warrant consideration as a distinct group of high-efficacy disease-modifying therapies (DMTs) for patients with relapsing multiple sclerosis (RMS). This is counter to the current perception of these therapies by some stakeholders, including payers. Objectives A multifaceted indirect treatment comparison (ITC) approach was undertaken to clarify the relative efficacy of mAbs and oral therapies. Design Two ITC methods that use individual patient data (IPD) to adjust for between-trial differences, PS analyses and simulated treatment comparisons (STCs), were used to compare the mAb ofatumumab versus the oral therapies cladribine, fingolimod, and ozanimod. Data sources and methods As IPD were available for trials of ofatumumab and fingolimod, PS analyses were conducted. Given summary-level data were available for cladribine, fingolimod, and ozanimod trials, STCs were conducted between ofatumumab and each of these oral therapies. Three efficacy outcomes were compared: annualized relapse rate (ARR), 3-month confirmed disability progression (3mCDP), and 6-month CDP (6mCDP). Results The PS analyses demonstrated ofatumumab was statistically superior to fingolimod for ARR and time to 3mCDP but not time to 6mCDP. In STCs, ofatumumab was statistically superior in reducing ARR and decreasing the proportion of patients with 3mCDP compared with cladribine, fingolimod, and ozanimod and in decreasing the proportion with 6mCP compared with fingolimod and ozanimod. These findings were largely consistent with recently published NMAs that identified mAb therapies as the most efficacious DMTs for RMS. Conclusion Complementary ITC methods showed ofatumumab was superior to cladribine, fingolimod, and ozanimod in lowering relapse rates and delaying disability progression among patients with RMS. Our study supports the therapeutic superiority of mAbs over currently available oral DMTs for RMS and the delineation of mAbs as high-efficacy therapies.
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Affiliation(s)
- Nicholas Riley
- Novartis Pharmaceuticals Australia, Sydney, NSW, Australia
| | | | - Morag Nelson
- Novartis Pharmaceuticals Australia, Sydney, NSW, Australia
| | | | - Michael Barnett
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
- Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Simon Broadley
- School of Medicine, Griffith University, Southport, QLD, Australia
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Pamela McCombe
- UQ Centre for Clinical Research Faculty of Medicine, University of Queensland, St. Lucia, QLD, Australia
| | - Anneke Van der Walt
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | | | | | | | - Rob Walker
- Novartis Pharmaceuticals Australia, Sydney, NSW, Australia
| | - Imtiaz A. Samjoo
- EVERSANA, Value and Evidence, 113-3228 South Service Road, Burlington, ON, Canada, L7N 3H8
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29
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Cross AH, Gelfand JM, Thebault S, Bennett JL, von Büdingen HC, Cameron B, Carruthers R, Edwards K, Fallis R, Gerstein R, Giacomini PS, Greenberg B, Hafler DA, Ionete C, Kaunzner UW, Kodama L, Lock C, Longbrake EE, Musch B, Pardo G, Piehl F, Weber MS, Yuen S, Ziemssen T, Bose G, Freedman MS, Anania VG, Ramesh A, Winger RC, Jia X, Herman A, Harp C, Bar-Or A. Emerging Cerebrospinal Fluid Biomarkers of Disease Activity and Progression in Multiple Sclerosis. JAMA Neurol 2024:2816158. [PMID: 38466277 DOI: 10.1001/jamaneurol.2024.0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Importance Biomarkers distinguishing nonrelapsing progressive disease biology from relapsing biology in multiple sclerosis (MS) are lacking. Cerebrospinal fluid (CSF) is an accessible fluid that most closely reflects central nervous system biology. Objective To identify CSF biological measures associated with progressive MS pathobiology. Design, Setting, and Participants This cohort study assessed data from 2 prospective MS cohorts: a test cohort provided serial CSF, clinical, and imaging assessments in a multicenter study of patients with relapsing MS (RMS) or primary progressive MS (PPMS) who were initiating anti-CD20 treatment (recruitment: 2016-2018; analysis: 2020-2023). A single-site confirmation cohort was used to assess CSF at baseline and long-term (>10 year) clinical follow-up (analysis: 2022-2023). Exposures Test-cohort participants initiated standard-of-care ocrelizumab treatment. Confirmation-cohort participants were untreated or received standard-of-care disease-modifying MS therapies. Main Outcomes and Measures Twenty-five CSF markers, including neurofilament light chain, neurofilament heavy chain, and glial fibrillary acid protein (GFAP); 24-week confirmed disability progression (CDP24); and brain magnetic resonance imaging measures reflecting focal injury, tissue loss, and progressive biology (slowly expanding lesions [SELs]). Results The test cohort (n = 131) included 100 patients with RMS (mean [SD] age, 36.6 [10.4] years; 68 [68%] female and 32 [32%] male; Expanded Disability Status Scale [EDSS] score, 0-5.5), and 31 patients with PPMS (mean [SD] age, 44.9 [7.4] years; 15 [48%] female and 16 [52%] male; EDSS score, 3.0-6.5). The confirmation cohort (n = 68) included 41 patients with RMS and 27 with PPMS enrolled at diagnosis (age, 40 years [range, 20-61 years]; 47 [69%] female and 21 [31%] male). In the test cohort, GFAP was correlated with SEL count (r = 0.33), greater proportion of T2 lesion volume from SELs (r = 0.24), and lower T1-weighted intensity within SELs (r = -0.33) but not with acute inflammatory measures. Neurofilament heavy chain was correlated with SEL count (r = 0.25) and lower T1-weighted intensity within SELs (r = -0.28). Immune markers correlated with measures of acute inflammation and, unlike GFAP, were impacted by anti-CD20. In the confirmation cohort, higher baseline CSF GFAP levels were associated with long-term CDP24 (hazard ratio, 2.1; 95% CI, 1.3-3.4; P = .002). Conclusions and Relevance In this study, activated glial markers (in particular GFAP) and neurofilament heavy chain were associated specifically with nonrelapsing progressive disease outcomes (independent of acute inflammatory activity). Elevated CSF GFAP was associated with long-term MS disease progression.
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Affiliation(s)
- Anne H Cross
- Washington University School of Medicine, St Louis, Missouri
| | | | - Simon Thebault
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | | | | | | | | | - Robert Fallis
- The Ohio State University Wexner Medical Center, Columbus
| | | | | | | | | | | | | | - Lay Kodama
- Genentech, South San Francisco, California
| | | | | | | | | | | | - Martin S Weber
- Institute of Neuropathology, Department of Neurology, University Medical Center, Göttingen, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology, Göttingen, Germany
| | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Carl Gustav Carus University Clinic, Dresden, Germany
| | - Gauruv Bose
- Department of Medicine in Neurology, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Mark S Freedman
- Department of Medicine in Neurology, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | | | | | | | | | - Ann Herman
- Genentech, South San Francisco, California
| | | | - Amit Bar-Or
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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30
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Jakimovski D, Bittner S, Zivadinov R, Morrow SA, Benedict RH, Zipp F, Weinstock-Guttman B. Multiple sclerosis. Lancet 2024; 403:183-202. [PMID: 37949093 DOI: 10.1016/s0140-6736(23)01473-3] [Citation(s) in RCA: 135] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 06/08/2023] [Accepted: 07/12/2023] [Indexed: 11/12/2023]
Abstract
Multiple sclerosis remains one of the most common causes of neurological disability in the young adult population (aged 18-40 years). Novel pathophysiological findings underline the importance of the interaction between genetics and environment. Improvements in diagnostic criteria, harmonised guidelines for MRI, and globalised treatment recommendations have led to more accurate diagnosis and an earlier start of effective immunomodulatory treatment than previously. Understanding and capturing the long prodromal multiple sclerosis period would further improve diagnostic abilities and thus treatment initiation, eventually improving long-term disease outcomes. The large portfolio of currently available medications paved the way for personalised therapeutic strategies that will balance safety and effectiveness. Incorporation of cognitive interventions, lifestyle recommendations, and management of non-neurological comorbidities could further improve quality of life and outcomes. Future challenges include the development of medications that successfully target the neurodegenerative aspect of the disease and creation of sensitive imaging and fluid biomarkers that can effectively predict and monitor disease changes.
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Affiliation(s)
- Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, State University of New York at Buffalo, Buffalo, NY, USA
| | - Sarah A Morrow
- Department of Clinical Neurological Sciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ralph Hb Benedict
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Bianca Weinstock-Guttman
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA.
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31
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Bellanca CM, Augello E, Mariottini A, Bonaventura G, La Cognata V, Di Benedetto G, Cantone AF, Attaguile G, Di Mauro R, Cantarella G, Massacesi L, Bernardini R. Disease Modifying Strategies in Multiple Sclerosis: New Rays of Hope to Combat Disability? Curr Neuropharmacol 2024; 22:1286-1326. [PMID: 38275058 PMCID: PMC11092922 DOI: 10.2174/1570159x22666240124114126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/21/2023] [Accepted: 09/22/2023] [Indexed: 01/27/2024] Open
Abstract
Multiple sclerosis (MS) is the most prevalent chronic autoimmune inflammatory- demyelinating disorder of the central nervous system (CNS). It usually begins in young adulthood, mainly between the second and fourth decades of life. Usually, the clinical course is characterized by the involvement of multiple CNS functional systems and by different, often overlapping phenotypes. In the last decades, remarkable results have been achieved in the treatment of MS, particularly in the relapsing- remitting (RRMS) form, thus improving the long-term outcome for many patients. As deeper knowledge of MS pathogenesis and respective molecular targets keeps growing, nowadays, several lines of disease-modifying treatments (DMT) are available, an impressive change compared to the relative poverty of options available in the past. Current MS management by DMTs is aimed at reducing relapse frequency, ameliorating symptoms, and preventing clinical disability and progression. Notwithstanding the relevant increase in pharmacological options for the management of RRMS, research is now increasingly pointing to identify new molecules with high efficacy, particularly in progressive forms. Hence, future efforts should be concentrated on achieving a more extensive, if not exhaustive, understanding of the pathogenetic mechanisms underlying this phase of the disease in order to characterize novel molecules for therapeutic intervention. The purpose of this review is to provide a compact overview of the numerous currently approved treatments and future innovative approaches, including neuroprotective treatments as anti-LINGO-1 monoclonal antibody and cell therapies, for effective and safe management of MS, potentially leading to a cure for this disease.
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Affiliation(s)
- Carlo Maria Bellanca
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
- Clinical Toxicology Unit, University Hospital, University of Catania, 95123 Catania, Italy
| | - Egle Augello
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
- Clinical Toxicology Unit, University Hospital, University of Catania, 95123 Catania, Italy
| | - Alice Mariottini
- Department of Neurosciences Drugs and Child Health, University of Florence, Florence, Italy
| | - Gabriele Bonaventura
- Institute for Biomedical Research and Innovation (IRIB), Italian National Research Council, 95126 Catania, Italy
| | - Valentina La Cognata
- Institute for Biomedical Research and Innovation (IRIB), Italian National Research Council, 95126 Catania, Italy
| | - Giulia Di Benedetto
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
- Clinical Toxicology Unit, University Hospital, University of Catania, 95123 Catania, Italy
| | - Anna Flavia Cantone
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
| | - Giuseppe Attaguile
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
| | - Rosaria Di Mauro
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
| | - Giuseppina Cantarella
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
| | - Luca Massacesi
- Department of Neurosciences Drugs and Child Health, University of Florence, Florence, Italy
| | - Renato Bernardini
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), Section of Pharmacology, University of Catania, 95123 Catania, Italy
- Clinical Toxicology Unit, University Hospital, University of Catania, 95123 Catania, Italy
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32
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Menezes FTLD, Lopes AB, Alencar JMD, Bichuetti DB, Souza NAD, Cogo-Moreira H, Oliveira EMLD. A mixture model for differentiating longitudinal courses of multiple sclerosis. Mult Scler Relat Disord 2024; 81:105346. [PMID: 38091806 DOI: 10.1016/j.msard.2023.105346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/07/2023] [Accepted: 11/24/2023] [Indexed: 01/23/2024]
Abstract
BACKGROUND Multiple sclerosis has a broad spectrum of clinical courses. Early identification of patients at greater risk of accumulating disability is essential. OBJECTIVES Identify groups of patients with similar presentation through a mixture model and predict their trajectories over the years. METHODS Retrospective study of patients from 1994 to 2019. We performed a latent profile analysis followed by a latent transition analysis based on eight parameters: age, disease duration, EDSS, number of relapses, multi-topographic symptoms, motor impairment, sphincter impairment, and infratentorial lesions. RESULTS We included 629 patients, regardless of the phenotypical classification. We identified three distinct groups at the beginning and end of the follow-up. The three-classes model disclosed the "No disability regardless disease duration" (NDRDD) class with low EDSS and younger patients, the "Disability within a short disease duration" (DSDD) class with the worse disability besides short illness, and the "Disability within a long disease duration" (DLDD) class that achieved high EDSS over a long disease duration. EDSS, disease duration, and no sphincter impairment had the best entropy to distinguish classes at the initial presentation. Over time, the patients from NDRDD had a 52.1 % probability of changing to DLDD and 7.7 % of changing to DSDD. CONCLUSIONS We identified three groups of clinical presentations and their evolution over time based on considered prognostic factors. The most likely transition is from NDRDD to DLDD.
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Affiliation(s)
- Felipe Toscano Lins de Menezes
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil.
| | - Alexandre Bussinger Lopes
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil
| | - Jéssica Monique Dias Alencar
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil
| | - Denis Bernardi Bichuetti
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil
| | - Nilton Amorim de Souza
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil
| | - Hugo Cogo-Moreira
- Department of Education, ICT and Learning, Østfold University College, Halden, Norway
| | - Enedina Maria Lobato de Oliveira
- Neuroimmunology Clinic, Disciplina de Neurologia, Escola Paulista de Medicina - Universidade Federal de São Paulo, Sao Paulo, Brazil
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Blok KM, van Rosmalen J, Tebayna N, Smolders J, Wokke B, de Beukelaar J. Disease activity in primary progressive multiple sclerosis: a systematic review and meta-analysis. Front Neurol 2023; 14:1277477. [PMID: 38020591 PMCID: PMC10661414 DOI: 10.3389/fneur.2023.1277477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
Background Disease activity in multiple sclerosis (MS) is defined as presence of relapses, gadolinium enhancing lesions and/or new or enlarging lesions on MRI. It is associated with efficacy of immunomodulating therapies (IMTs) in primary progressive MS (PPMS). However, a thorough review on disease activity in PPMS is lacking. In relapsing remitting MS, the prevalence of activity decreases in more contemporary cohorts. For PPMS, this is unknown. Aim To review disease activity in PPMS cohorts and identify its predictors. Methods A systematic search in EMBASE, MEDLINE, Web of science Core Collection, COCHRANE CENTRAL register of trials, and GOOGLE SCHOLAR was performed. Keywords included PPMS, inflammation, and synonyms. We included original studies with predefined available data, extracted cohort characteristics and disease activity outcomes and performed meta-regression analyses. Results We included 34 articles describing 7,109 people with PPMS (pwPPMS). The weighted estimated proportion of pwPPMS with overall disease activity was 26.8% (95% CI 20.6-34.0%). A lower age at inclusion predicted higher disease activity (OR 0.91, p = 0.031). Radiological activity (31.9%) was more frequent than relapses (9.2%), and was predicted by longer follow-up duration (OR 1.27, p = 0.033). Year of publication was not correlated with disease activity. Conclusion Inflammatory disease activity is common in PPMS and has remained stable over the last decades. Age and follow-up duration predict disease activity, advocating prolonged monitoring of young pwPPMS to evaluate potential IMT benefits.
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Affiliation(s)
- Katelijn M. Blok
- Department of Neurology, Albert Schweitzer Hospital, Dordrecht, Netherlands
- Department of Neurology, MS Center ErasMS, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Joost van Rosmalen
- Department of Biostatistics, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Nura Tebayna
- Department of Neurology, Albert Schweitzer Hospital, Dordrecht, Netherlands
| | - Joost Smolders
- Department of Neurology, MS Center ErasMS, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Immunology, MS Center ErasMS, Erasmus University Medical Center, Rotterdam, Netherlands
- Neuroimmunology Researchgroup, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Beatrijs Wokke
- Department of Neurology, MS Center ErasMS, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Janet de Beukelaar
- Department of Neurology, Albert Schweitzer Hospital, Dordrecht, Netherlands
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Maxwell DL, Orian JM. Cerebellar pathology in multiple sclerosis and experimental autoimmune encephalomyelitis: current status and future directions. J Cent Nerv Syst Dis 2023; 15:11795735231211508. [PMID: 37942276 PMCID: PMC10629308 DOI: 10.1177/11795735231211508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 10/15/2023] [Indexed: 11/10/2023] Open
Abstract
Recent decades have witnessed significant progress in understanding mechanisms driving neurodegeneration and disease progression in multiple sclerosis (MS), but with a focus on the cerebrum. In contrast, there have been limited studies of cerebellar disease, despite the common occurrence of cerebellar symptoms in this disorder. These rare studies, however, highlight the early cerebellar involvement in disease development and an association between the early occurrence of cerebellar lesions and risk of worse prognosis. In parallel developments, it has become evident that far from being a region specialized in movement control, the cerebellum plays a crucial role in cognitive function, via circuitry connecting the cerebellum to association areas of the cerebrum. This complexity, coupled with challenges in imaging of the cerebellum have been major obstacles in the appreciation of the spatio-temporal evolution of cerebellar damage in MS and correlation with disability and progression. MS studies based on animal models have relied on an induced neuroinflammatory disease known as experimental autoimmune encephalomyelitis (EAE), in rodents and non-human primates (NHP). EAE has played a critical role in elucidating mechanisms underpinning tissue damage and been validated for the generation of proof-of-concept for cerebellar pathological processes relevant to MS. Additionally, rodent and NHP studies have formed the cornerstone of current knowledge of functional anatomy and cognitive processes. Here, we propose that improved insight into consequences of cerebellar damage in MS at the functional, cellular and molecular levels would be gained by more extensive characterization of EAE cerebellar pathology combined with the power of experimental paradigms in the field of cognition. Such combinatorial approaches would lead to improved potential for the development of MS sensitive markers and evaluation of candidate therapeutics.
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Affiliation(s)
- Dain L. Maxwell
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
- Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, VIC, Australia
| | - Jacqueline M. Orian
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
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Mosconi P, Guerra T, Paletta P, D'Ettorre A, Ponzio M, Battaglia MA, Amato MP, Bergamaschi R, Capobianco M, Comi G, Gasperini C, Patti F, Pugliatti M, Ulivelli M, Trojano M, Lepore V. Data monitoring roadmap. The experience of the Italian Multiple Sclerosis and Related Disorders Register. Neurol Sci 2023; 44:4001-4011. [PMID: 37311951 PMCID: PMC10264214 DOI: 10.1007/s10072-023-06876-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 06/15/2023]
Abstract
INTRODUCTION Over the years, disease registers have been increasingly considered a source of reliable and valuable population studies. However, the validity and reliability of data from registers may be limited by missing data, selection bias or data quality not adequately evaluated or checked. This study reports the analysis of the consistency and completeness of the data in the Italian Multiple Sclerosis and Related Disorders Register. METHODS The Register collects, through a standardized Web-based Application, unique patients. Data are exported bimonthly and evaluated to assess the updating and completeness, and to check the quality and consistency. Eight clinical indicators are evaluated. RESULTS The Register counts 77,628 patients registered by 126 centres. The number of centres has increased over time, as their capacity to collect patients. The percentages of updated patients (with at least one visit in the last 24 months) have increased from 33% (enrolment period 2000-2015) to 60% (enrolment period 2016-2022). In the cohort of patients registered after 2016, there were ≥ 75% updated patients in 30% of the small centres (33), in 9% of the medium centres (11), and in all the large centres (2). Clinical indicators show significant improvement for the active patients, expanded disability status scale every 6 months or once every 12 months, visits every 6 months, first visit within 1 year and MRI every 12 months. CONCLUSIONS Data from disease registers provide guidance for evidence-based health policies and research, so methods and strategies ensuring their quality and reliability are crucial and have several potential applications.
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Affiliation(s)
- Paola Mosconi
- Laboratorio di Ricerca per il Coinvolgimento dei Cittadini in Sanità, Dipartimento di Salute Pubblica, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan, 20156, Italy.
| | - Tommaso Guerra
- Dipartimento Scienze Mediche di Base, Neuroscienze ed Organi di Senso, Università degli Studi Aldo Moro, Bari, Italy
| | - Pasquale Paletta
- Laboratorio di Ricerca per il Coinvolgimento dei Cittadini in Sanità, Dipartimento di Salute Pubblica, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan, 20156, Italy
| | - Antonio D'Ettorre
- Laboratorio di Ricerca per il Coinvolgimento dei Cittadini in Sanità, Dipartimento di Salute Pubblica, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan, 20156, Italy
| | - Michela Ponzio
- Scientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy
| | - Mario Alberto Battaglia
- Scientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy
- Department of Physiopathology, Experimental Medicine and Public Health, University of Siena, Siena, Italy
| | | | - Roberto Bergamaschi
- Centro Interdipartimentale Sclerosi Multipla, Fondazione Istituto Neurologico C. Mondino, Pavia, Italy
| | - Marco Capobianco
- Centro Sclerosi Multipla, SC Neurologia, AO Santa Croce E Carle, Cuneo, Italy
| | - Giancarlo Comi
- Casa di Cura del Policlinico, Università Vita Salute San Raffaele, Milan, Italy
| | - Claudio Gasperini
- UOC di Neurologia e Neurofisiopatologia Azienda Ospedaliera S. Camillo-Forlanini, Rome, Italy
| | - Francesco Patti
- Centro Sclerosi Multipla AOU Policlinico Vittorio Emanuele, Catania, Italy
| | - Maura Pugliatti
- Centro di Servizio e Ricerca sulla Sclerosi Multipla, AOU di Ferrara, Ferrara, Italy
| | - Monica Ulivelli
- Dipartimento di Scienze Mediche Chirurgiche e Neuroscienze, Università degli Studi di Siena, Siena, Italy
| | - Maria Trojano
- Dipartimento Scienze Mediche di Base, Neuroscienze ed Organi di Senso, Università degli Studi Aldo Moro, Bari, Italy
| | - Vito Lepore
- Laboratorio di Ricerca per il Coinvolgimento dei Cittadini in Sanità, Dipartimento di Salute Pubblica, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan, 20156, Italy
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Mariottini A, Muraro PA, Saccardi R. Should autologous hematopoietic stem cell transplantation be offered as a first-line disease modifying therapy to patients with multiple sclerosis? Mult Scler Relat Disord 2023; 78:104932. [PMID: 37572554 DOI: 10.1016/j.msard.2023.104932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/04/2023] [Indexed: 08/14/2023]
Abstract
In multiple sclerosis (MS), progression independent of new focal inflammation may commence shortly after disease onset, and it is increasingly revealed that the risk of disability accrual is reduced by early use of high-efficacy disease-modifying therapies (HE-DMTs). People with aggressive MS may therefore benefit from early treatment with autologous haematopoietic stem cell transplantation (AHSCT), a procedure inducing maximal immunosuppression followed by immune reconstitution, demonstrated to be superior to DMTs in one randomized clinical trial. However, in current practice prior failure to HE-DMTs is typically required to establish the indication for AHSCT. In the present article, the available evidence on the potential role of AHSCT as first-line treatment in aggressive MS and the rationale for its early use will be summarized. Proposed definitions of aggressive MS that could help identifying MS patients eligible for early treatment with AHSCT will also be discussed.
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Affiliation(s)
- Alice Mariottini
- Department of Brain Sciences, Imperial College London, London, United Kingdom; Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Paolo A Muraro
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Riccardo Saccardi
- Cell Therapy and Transfusion Medicine Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
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He A, Manouchehrinia A, Glaser A, Ciccarelli O, Butzkueven H, Hillert J, McKay KA. Premorbid Sociodemographic Status and Multiple Sclerosis Outcomes in a Universal Health Care Context. JAMA Netw Open 2023; 6:e2334675. [PMID: 37751208 PMCID: PMC10523174 DOI: 10.1001/jamanetworkopen.2023.34675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/11/2023] [Indexed: 09/27/2023] Open
Abstract
Importance Multiple sclerosis (MS) severity may be informed by premorbid sociodemographic factors. Objective To determine whether premorbid education, income, and marital status are associated with future MS disability and symptom severity, independent of treatment, in a universal health care context. Design, Setting, and Participants This nationwide observational cohort study examined data from the Swedish MS Registry linked to national population registries from 2000 to 2020. Participants included people with MS onset from 2005 to 2015 and of working age (aged 23 to 59 years) 1 year and 5 years preceding disease onset. Exposures Income quartile, educational attainment, and marital status measured at 1 and 5 years preceding disease onset. Main Outcome and Measures Repeated measures of Expanded Disability Status Scale (EDSS) scores and patient-reported Multiple Sclerosis Impact Scale (MSIS-29) scores. Models were adjusted for age, sex, relapses, disease duration, and treatment exposure. Secondary analyses further adjusted for comorbidity. All analyses were stratified by disease course (relapse onset and progressive onset). Results There were 4557 patients (mean [SD] age, 37.5 [9.3] years; 3136 [68.8%] female, 4195 [92.1%] relapse-onset MS) with sociodemographic data from 1-year preonset of MS. In relapse-onset MS, higher premorbid income and education correlated with lower disability (EDSS, -0.16 [95% CI, -0.12 to -0.20] points) per income quartile; EDSS, -0.47 [95% CI, -0.59 to -0.35] points if tertiary educated), physical symptoms (MSIS-29 physical subscore, -14% [95% CI, -11% to -18%] per income quartile; MSIS-29 physical subscore, -43% [95% CI, -35% to -50%] if tertiary educated), and psychological symptoms (MSIS-29 psychological subscore, -12% [95% CI, -9% to -16%] per income quartile; MSIS-29 psychological subscore, -25% [95% CI, -17% to -33%] if tertiary educated). Marital separation was associated with adverse outcomes (EDSS, 0.34 [95% CI, 0.18 to 0.51]; MSIS-29 physical subscore, 35% [95% CI, 12% to 62%]; MSIS-29 psychological subscore, 25% [95% CI, 8% to 46%]). In progressive-onset MS, higher income correlated with lower EDSS (-0.30 [95% CI, -0.48 to -0.11] points per income quartile) whereas education correlated with lower physical (-34% [95% CI, -53% to -7%]) and psychological symptoms (-33% [95% CI, -54% to -1%]). Estimates for 5-years preonset were comparable with 1-year preonset, as were the comorbidity-adjusted findings. Conclusions and relevance In this cohort study of working-age adults with MS, premorbid income, education, and marital status correlated with disability and symptom severity in relapse-onset and progressive-onset MS, independent of treatment. These findings suggest that socioeconomic status may reflect both structural and individual determinants of health in MS.
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Affiliation(s)
- Anna He
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Ali Manouchehrinia
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
| | - Anna Glaser
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Kyla A. McKay
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
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Li J, Huang Y, Hutton GJ, Aparasu RR. Assessing treatment switch among patients with multiple sclerosis: A machine learning approach. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 11:100307. [PMID: 37554927 PMCID: PMC10405092 DOI: 10.1016/j.rcsop.2023.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/08/2023] [Accepted: 07/09/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients. METHODS This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance. RESULTS In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index. CONCLUSIONS Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.
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Affiliation(s)
- Jieni Li
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
| | - Yinan Huang
- Department of Pharmacy Administration, College of Pharmacy, University of Mississippi, Oxford, MS, USA
| | | | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
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Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review. Cureus 2023; 15:e45412. [PMID: 37854769 PMCID: PMC10581506 DOI: 10.7759/cureus.45412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/20/2023] Open
Abstract
Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.
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Affiliation(s)
- Yahya Naji
- Neurology Department, REGNE Research Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, MAR
- Neurology Department, Agadir University Hospital, Agadir, MAR
| | - Mohamed Mahdaoui
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Raymond Klevor
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Najib Kissani
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
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40
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Jakimovski D, Weinstock-Guttman B, Zivadinov R. Ublituximab-xiiy as a treatment option for relapsing multiple sclerosis. Expert Rev Neurother 2023; 23:1053-1061. [PMID: 37842819 DOI: 10.1080/14737175.2023.2268842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
INTRODUCTION B cell depletion has been established as an efficacious anti-inflammatory therapy in people with relapsing forms of multiple sclerosis (MS). Ublituximab (ublituximab-xiiy) is the latest approved chimeric glycoengineered anti-CD20 monoclonal antibody (mAb) for the treatment of relapsing forms of MS. AREAS COVERED In this narrative review, the authors explore the safety and effectiveness of data derived from the Phase 2 and Phase 3 ublituximab trials and from their respective post-hoc analyses. Moreover, they consider the similarities and differences between the currently available anti-CD20 antibodies for treatment of relapsing MS. Lastly, the authors discuss the role and place of ublituximab in the current disease modifying therapy landscape. EXPERT OPINION Ublituximab is a rapid-acting and effective anti-inflammatory option as a treatment in people with relapsing MS that significantly reduced the annualized relapse rate and MRI-based disease activity. When compared to the Phase III trials of the other two anti-CD20 mAbs (ocrelizumab and ofatumumab), ublituximab did not result with reduction of 3 or 6-month confirmed disability progression. These differences may be attributed to the overall low rate of progression in both the ublituximab and the active comparator teriflunomide arm. Future data from open-label extensions are warranted. There was no significant reduction of ublituximab on whole-brain atrophy compared to teriflunomide.
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Affiliation(s)
- Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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42
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Khaw YM, Anwar S, Zhou J, Kawano T, Lin P, Otero A, Barakat R, Drnevich J, Takahashi T, Ko CJ, Inoue M. Estrogen receptor alpha signaling in dendritic cells modulates autoimmune disease phenotype in mice. EMBO Rep 2023; 24:e54228. [PMID: 36633157 PMCID: PMC9986829 DOI: 10.15252/embr.202154228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/23/2022] [Accepted: 12/16/2022] [Indexed: 01/13/2023] Open
Abstract
Estrogen is a disease-modifying factor in multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE) via estrogen receptor alpha (ERα). However, the mechanisms by which ERα signaling contributes to changes in disease pathogenesis have not been completely elucidated. Here, we demonstrate that ERα deletion in dendritic cells (DCs) of mice induces severe neurodegeneration in the central nervous system in a mouse EAE model and resistance to interferon beta (IFNβ), a first-line MS treatment. Estrogen synthesized by extragonadal sources is crucial for controlling disease phenotypes. Mechanistically, activated ERα directly interacts with TRAF3, a TLR4 downstream signaling molecule, to degrade TRAF3 via ubiquitination, resulting in reduced IRF3 nuclear translocation and transcription of membrane lymphotoxin (mLT) and IFNβ components. Diminished ERα signaling in DCs generates neurotoxic effector CD4+ T cells via mLT-lymphotoxin beta receptor (LTβR) signaling. Lymphotoxin beta receptor antagonist abolished EAE disease symptoms in the DC-specific ERα-deficient mice. These findings indicate that estrogen derived from extragonadal sources, such as lymph nodes, controls TRAF3-mediated cytokine production in DCs to modulate the EAE disease phenotype.
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Affiliation(s)
- Yee Ming Khaw
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Neuroscience ProgramUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Shehata Anwar
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Department of Pathology, Faculty of Veterinary MedicineBeni‐Suef University (BSU)Beni‐SuefEgypt
| | - Jinyan Zhou
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Neuroscience ProgramUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Tasuku Kawano
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Division of Pathophysiology, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical SciencesTohoku Medical and Pharmaceutical UniversitySendaiJapan
| | - Po‐Ching Lin
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Ashley Otero
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Neuroscience ProgramUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Radwa Barakat
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Department of Toxicology and Forensic MedicineCollege of Veterinary Medicine, Benha UniversityQalyubiaEgypt
| | - Jenny Drnevich
- Roy J. Carver Biotechnology CenterUniversity of Illinois Urbana‐ChampaignUrbanaILUSA
| | - Tomoko Takahashi
- Division of Pathophysiology, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical SciencesTohoku Medical and Pharmaceutical UniversitySendaiJapan
| | - CheMyong Jay Ko
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Neuroscience ProgramUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Makoto Inoue
- Department of Comparative BiosciencesUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Neuroscience ProgramUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA
- Beckman Institute for Advanced Science and TechnologyUrbanaILUSA
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43
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Rispoli MG, D'Apolito M, Pozzilli V, Tomassini V. Lessons from immunotherapies in multiple sclerosis. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:293-311. [PMID: 36803817 DOI: 10.1016/b978-0-323-85555-6.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.
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Affiliation(s)
- Marianna G Rispoli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Maria D'Apolito
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valeria Pozzilli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy.
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44
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Forsberg L, Spelman T, Klyve P, Manouchehrinia A, Ramanujam R, Mouresan E, Drahota J, Horakova D, Joensen H, Pontieri L, Magyari M, Ellenberger D, Stahmann A, Rodgers J, Witts J, Middleton R, Nicholas R, Bezlyak V, Adlard N, Hach T, Lines C, Vukusic S, Soilu-Hänninen M, van der Walt A, Butzkueven H, Iaffaldano P, Trojano M, Glaser A, Hillert J. Proportion and characteristics of secondary progressive multiple sclerosis in five European registries using objective classifiers. Mult Scler J Exp Transl Clin 2023; 9:20552173231153557. [PMID: 36816812 PMCID: PMC9936396 DOI: 10.1177/20552173231153557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 01/12/2023] [Indexed: 02/18/2023] Open
Abstract
Background To assign a course of secondary progressive multiple sclerosis (MS) (SPMS) may be difficult and the proportion of persons with SPMS varies between reports. An objective method for disease course classification may give a better estimation of the relative proportions of relapsing-remitting MS (RRMS) and SPMS and may identify situations where SPMS is under reported. Materials and methods Data were obtained for 61,900 MS patients from MS registries in the Czech Republic, Denmark, Germany, Sweden, and the United Kingdom (UK), including date of birth, sex, SP conversion year, visits with an Expanded Disability Status Scale (EDSS) score, MS onset and diagnosis date, relapses, and disease-modifying treatment (DMT) use. We included RRMS or SPMS patients with at least one visit between January 2017 and December 2019 if ≥ 18 years of age. We applied three objective methods: A set of SPMS clinical trial inclusion criteria ("EXPAND criteria") modified for a real-world evidence setting, a modified version of the MSBase algorithm, and a decision tree-based algorithm recently published. Results The clinically assigned proportion of SPMS varied from 8.7% (Czechia) to 34.3% (UK). Objective classifiers estimated the proportion of SPMS from 15.1% (Germany by the EXPAND criteria) to 58.0% (UK by the decision tree method). Due to different requirements of number of EDSS scores, classifiers varied in the proportion they were able to classify; from 18% (UK by the MSBase algorithm) to 100% (the decision tree algorithm for all registries). Objectively classified SPMS patients were older, converted to SPMS later, had higher EDSS at index date and higher EDSS at conversion. More objectively classified SPMS were on DMTs compared to the clinically assigned. Conclusion SPMS appears to be systematically underdiagnosed in MS registries. Reclassified patients were more commonly on DMTs.
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Affiliation(s)
- Lars Forsberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tim Spelman
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Pernilla Klyve
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ali Manouchehrinia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ryan Ramanujam
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Mathematics, Royal Institute of Technology, Stockholm, Sweden
| | - Elena Mouresan
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jiri Drahota
- Czech National Multiple Sclerosis ReMuS, IMPULS Endowment Fund, Prague, Czech Republic
- First Faculty of Medicine and General University Hospital, Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, Prague, Czech Republic
| | - Dana Horakova
- First Faculty of Medicine and General University Hospital, Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, Prague, Czech Republic
| | - Hanna Joensen
- The Danish Multiple Sclerosis Registry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Luigi Pontieri
- The Danish Multiple Sclerosis Registry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Melinda Magyari
- The Danish Multiple Sclerosis Registry, Copenhagen University Hospital, Copenhagen, Denmark
- Danish Multiple Sclerosis Center, Copenhagen University Hospital, Copenhagen, Denmark
| | | | | | | | - James Witts
- Swansea University Medical School, Swansea, UK
| | | | - Richard Nicholas
- Swansea University Medical School, Swansea, UK
- Department of Cellular and Molecular Neuroscience, Imperial College London, London, UK
| | | | | | | | | | - Sandra Vukusic
- Hôpital Neurologique, Service de Neurologie A, the European Database for Multiple Sclerosis (EDMUS), Coordinating Center and INSERM U 433, Lyon, France
| | - Merja Soilu-Hänninen
- Division of Clinical Neurosciences, University Hospital and University of Turku, Turku, Finland
| | - Anneke van der Walt
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Pietro Iaffaldano
- Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
| | - Maria Trojano
- Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
| | - Anna Glaser
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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45
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Freeman L, Longbrake EE, Coyle PK, Hendin B, Vollmer T. High-Efficacy Therapies for Treatment-Naïve Individuals with Relapsing-Remitting Multiple Sclerosis. CNS Drugs 2022; 36:1285-1299. [PMID: 36350491 PMCID: PMC9645316 DOI: 10.1007/s40263-022-00965-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/11/2022]
Abstract
There are > 18 distinct disease-modifying therapy (DMT) options covering 10 mechanisms of action currently approved by the US Food and Drug Administration for the treatment of relapsing-remitting multiple sclerosis (RRMS). Given the multitude of available treatment options, and recent international consensus guidelines offering differing recommendations, there is broad heterogeneity in how the DMTs are used in clinical practice. Choosing a DMT for newly diagnosed patients with MS is currently a topic of significant debate in MS care. Historically, an escalation approach to DMT was used for newly diagnosed patients with RRMS. However, the evidence for clinical benefits of early treatment with high-efficacy therapies (HETs) in this population is emerging. In this review, we provide an overview of the DMT options and MS treatment strategies, and discuss the clinical benefits of HETs (including ofatumumab, ocrelizumab, natalizumab, alemtuzumab, and cladribine) in the early stages of MS, along with safety concerns associated with these DMTs. By minimizing the accumulation of neurological damage early in the disease course, early treatment with HETs may enhance long-term clinical outcomes over the lifetime of the patient.
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Affiliation(s)
- Léorah Freeman
- Department of Neurology, Dell Medical School, The University of Texas at Austin, 1601 Trinity St, Austin, TX, 78701, USA.
| | | | - Patricia K Coyle
- Department of Neurology, Stony Brook University Medical Center, Stony Brook, NY, USA
| | - Barry Hendin
- Banner, University Medicine Neurosciences Clinic, Phoenix, AZ, USA
| | - Timothy Vollmer
- Department of Neurology, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
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46
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Immunopathogenesis, Diagnosis, and Treatment of Multiple Sclerosis. Neurol Clin 2022; 41:87-106. [DOI: 10.1016/j.ncl.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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47
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Filippi M, Amato MP, Centonze D, Gallo P, Gasperini C, Inglese M, Patti F, Pozzilli C, Preziosa P, Trojano M. Early use of high-efficacy disease‑modifying therapies makes the difference in people with multiple sclerosis: an expert opinion. J Neurol 2022; 269:5382-5394. [PMID: 35608658 PMCID: PMC9489547 DOI: 10.1007/s00415-022-11193-w] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 11/05/2022]
Abstract
Multiple sclerosis (MS) is a chronic and progressive neurological disease that is characterized by neuroinflammation, demyelination and neurodegeneration occurring from the earliest phases of the disease and that may be underestimated. MS patients accumulate disability through relapse-associated worsening or progression independent of relapse activity. Early intervention with high-efficacy disease-modifying therapies (HE-DMTs) may represent the best window of opportunity to delay irreversible central nervous system damage and MS-related disability progression by hindering underlying heterogeneous pathophysiological processes contributing to disability progression. In line with this, growing evidence suggests that early use of HE-DMTs is associated with a significant greater reduction not only of inflammatory activity (clinical relapses and new lesion formation at magnetic resonance imaging) but also of disease progression, in terms of accumulation of irreversible clinical disability and neurodegeneration compared to delayed HE-DMT use or escalation strategy. These beneficial effects seem to be associated with acceptable long-term safety risks, thus configuring this treatment approach as that with the most positive benefit/risk profile. Accordingly, it should be mandatory to treat people with MS early with HE-DMTs in case of prognostic factors suggestive of aggressive disease, and it may be advisable to offer an HE-DMT to MS patients early after diagnosis, taking into account drug safety profile, disease severity, clinical and/or radiological activity, and patient-related factors, including possible comorbidities, family planning, and patients' preference in agreement with the EAN/ECTRIMS and AAN guidelines. Barriers for an early use of HE-DMTs include concerns for long-term safety, challenges in the management of treatment initiation and monitoring, negative MS patients' preferences, restricted access to HE-DMTs according to guidelines and regulatory rules, and sustainability. However, these barriers do not apply to each HE-DMT and none of these appear insuperable.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
| | - Maria Pia Amato
- Department NEUROFARBA, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Diego Centonze
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
- Unit of Neurology, IRCCS Neuromed, Pozzilli, IS, Italy
| | - Paolo Gallo
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Claudio Gasperini
- Department of Neurosciences, S Camillo Forlanini Hospital Rome, Rome, Italy
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Francesco Patti
- Department GF Ingrassia, Medical, Surgical Science and Advanced Technologies, University of Catania, Catania, Italy
- Center for Multiple Sclerosis, Policlinico "G Rodolico", University of Catania, Catania, Italy
| | | | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Maria Trojano
- Department of Basic Medical Sciences, Neuroscience, and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
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Bierhansl L, Hartung HP, Aktas O, Ruck T, Roden M, Meuth SG. Thinking outside the box: non-canonical targets in multiple sclerosis. Nat Rev Drug Discov 2022; 21:578-600. [PMID: 35668103 PMCID: PMC9169033 DOI: 10.1038/s41573-022-00477-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2022] [Indexed: 12/11/2022]
Abstract
Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system that causes demyelination, axonal degeneration and astrogliosis, resulting in progressive neurological disability. Fuelled by an evolving understanding of MS immunopathogenesis, the range of available immunotherapies for clinical use has expanded over the past two decades. However, MS remains an incurable disease and even targeted immunotherapies often fail to control insidious disease progression, indicating the need for new and exceptional therapeutic options beyond the established immunological landscape. In this Review, we highlight such non-canonical targets in preclinical MS research with a focus on five highly promising areas: oligodendrocytes; the blood-brain barrier; metabolites and cellular metabolism; the coagulation system; and tolerance induction. Recent findings in these areas may guide the field towards novel targets for future therapeutic approaches in MS.
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Affiliation(s)
- Laura Bierhansl
- Department of Neurology, Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Hans-Peter Hartung
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Orhan Aktas
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tobias Ruck
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- German Center of Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Vollmer BL, Wolf AB, Sillau S, Corboy JR, Alvarez E. Evolution of Disease Modifying Therapy Benefits and Risks: An Argument for De-escalation as a Treatment Paradigm for Patients With Multiple Sclerosis. Front Neurol 2022; 12:799138. [PMID: 35145470 PMCID: PMC8821102 DOI: 10.3389/fneur.2021.799138] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/29/2021] [Indexed: 12/20/2022] Open
Abstract
BackgroundStrategies for sequencing disease modifying therapies (DMTs) in multiple sclerosis (MS) patients include escalation, high efficacy early, induction, and de-escalation.ObjectiveTo provide a perspective on de-escalation, which aims to match the ratio of DMT benefit/risk in aging patients.MethodsWe reanalyzed data from a retrospective, real-world cohort of MS patients to model disease activity for oral (dimethyl fumarate and fingolimod) and higher efficacy infusible (natalizumab and rituximab) DMTs by age. For patients with relapsing MS, we conducted a controlled, stratified analysis examining odds of disease activity for oral vs. infusible DMTs in patients <45 or ≥45 years. We reviewed the literature to identify DMT risks and predictors of safe discontinuation.ResultsYounger patients had lower probability of disease activity on infusible vs. oral DMTs. There was no statistical difference after age 54.2 years. When dichotomized, patients <45 years on oral DMTs had greater odds of disease activity compared to patients on infusible DMTs, while among those ≥45 years, there was no difference. Literature review noted that adverse events increase with aging, notably infections in patients with higher disability and longer DMT duration. Additionally, we identified factors predictive of disease reactivation including age, clinical stability, and MRI activity.ConclusionIn a real-world cohort of relapsing MS patients, high efficacy DMTs had less benefit with aging but were associated with increased risks. This cohort helps overcome some limitations of trials where older patients were excluded. To better balance benefits/risks, we propose a DMT de-escalation approach for aging MS patients.
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50
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The Role of Molecular Imaging as a Marker of Remyelination and Repair in Multiple Sclerosis. Int J Mol Sci 2021; 23:ijms23010474. [PMID: 35008899 PMCID: PMC8745199 DOI: 10.3390/ijms23010474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 12/14/2022] Open
Abstract
The appearance of new disease-modifying therapies in multiple sclerosis (MS) has revolutionized our ability to fight inflammatory relapses and has immensely improved patients’ quality of life. Although remarkable, this achievement has not carried over into reducing long-term disability. In MS, clinical disability progression can continue relentlessly irrespective of acute inflammation. This “silent” disease progression is the main contributor to long-term clinical disability in MS and results from chronic inflammation, neurodegeneration, and repair failure. Investigating silent disease progression and its underlying mechanisms is a challenge. Standard MRI excels in depicting acute inflammation but lacks the pathophysiological lens required for a more targeted exploration of molecular-based processes. Novel modalities that utilize nuclear magnetic resonance’s ability to display in vivo information on imaging look to bridge this gap. Displaying the CNS through a molecular prism is becoming an undeniable reality. This review will focus on “molecular imaging biomarkers” of disease progression, modalities that can harmoniously depict anatomy and pathophysiology, making them attractive candidates to become the first valid biomarkers of neuroprotection and remyelination.
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