Available online at www.sciencedirect.com Neuromuscular Disorders 31 (2021) 265–280 www.elsevier.com/locate/nmd Miyoshi myopathy and limb girdle muscular dystrophy R2 are the same disease Ursula Moore a, Heather Gordish b,c, Jordi Diaz-Manera d,e, Meredith K. James a, Anna G. Mayhew a, Michela Guglieri a, Roberto Fernandez-Torron a, Laura E. Rufibach f, Jia Feng b, Andrew M. Blamire g, Pierre G. Carlier h, Simone Spuler i, John W. Day j, Kristi J. Jones k, Diana X. Bharucha-Goebel l,m, Emmanuelle Salort-Campana n, Alan Pestronk o, Maggie C. Walter p, Carmen Paradas q, Tanya Stojkovic r, Madoka Mori-Yoshimura s, Elena Bravver t,1, Elena Pegoraro u, Linda Pax Lowes v, Jerry R. Mendell v, Kate Bushby a, Volker Straub a,∗ , on behalf of The Jain COS Consortium a The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle upon Tyne, United Kingdom b Center for Translational Science, Division of Biostatistics and Study Methodology, Children’s National Health System, Washington, DC, United States c Pediatrics, Epidemiology and Biostatistics, George Washington University, Washington, DC, United States d Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain e Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau f The Jain Foundation, Seattle, Washington DC, United States g Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, United Kingdom h AIM & CEA NMR Laboratory, Institute of Myology, Pitié-Salpêtrière University Hospital, 47-83 Paris, France i Charite Muscle Research Unit, Experimental and Clinical Research Center, a Joint Cooperation of the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany j Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States k The Children’s Hospital at Westmead, and The University of Sydney, Australia l Department of Neurology Children’s National Health System, Washington, DC, United States m National Institutes of Health (NINDS), Bethesda, MD, United States n Service des maladies neuromusculaire et de la SLA, Hôpital de La Timone, Marseille, France o Department of Neurology Washington University School of Medicine, St. Louis, MO, United States p Friedrich-Baur-Institute, Department of Neurology, Ludwig-Maximilians-University of Munich, Germany q Neuromuscular Unit, Department of Neurology, Hospital U. Virgen del Rocío/Instituto de Biomedicina de Sevilla, Sevilla, Spain r Centre de référence des maladies neuromusculaires, Institut de Myologie, AP-HP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France s Department of Neurology, National Center Hospital, National Center of Neurology and Psychiatry Tokyo, Japan t Neuroscience Institute, Carolinas Neuromuscular/ALS-MDA Center, Carolinas HealthCare System, Charlotte, NC, United States u Department of Neuroscience, University of Padova, Italy v The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH United States Received 12 November 2020; received in revised form 13 January 2021; accepted 18 January 2021 Abstract This study aims to determine clinically relevant phenotypic differences between the two most common phenotypic classifications in dysferlinopathy, limb girdle muscular dystrophy R2 (LGMDR2) and Miyoshi myopathy (MMD1). LGMDR2 and MMD1 are reported to involve different muscles, with LGMDR2 showing predominant limb girdle weakness and MMD1 showing predominant distal lower limb weakness. We used heatmaps, regression analysis and principle component analysis of functional and Magnetic Resonance Imaging data ∗ Corresponding author. E-mail addresses: Volker.straub@newcastle.ac.uk, volker.straub@ncl.ac.uk (V. Straub). 1 Deceased https://doi.org/10.1016/j.nmd.2021.01.009 0960-8966/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 to perform a cross-sectional review of the pattern of muscle involvement in 168 patients from the Jain Foundation’s international Clinical Outcomes Study for Dysferlinopathy. We demonstrated that there is no clinically relevant difference in proximal vs distal involvement between diagnosis. There is a continuum of distal involvement at any given degree of proximal involvement and patients do not fall into discrete distally or proximally affected groups. There appeared to be geographical preference for a particular diagnosis, with MMD1 being more common in Japan and LGMDR2 in Europe and the USA. We conclude that the dysferlinopathies do not form two distinct phenotypic groups and therefore should not be split into separate cohorts of LGMDR2 and MM for the purposes of clinical management, enrolment in clinical trials or access to subsequent treatments. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: [16] Clinical neurology examination; [21] Clinical trials methodology; [54] Cohort study; [176] All neuromuscular disease; [185] Muscle disease. 3-year longitudinal Clinical Outcomes Study for 1. Introduction Dysferlinopathy (COS). This study received ethical approval in all participating countries. Participants attended 15 Dysferlinopathy is an autosomal recessively inherited international sites for six evaluations (screening, baseline, form of muscular dystrophy, which predominantly affects 6 months, 12 months, 24 months, 36 months) between skeletal muscle, resulting in progressive weakness and muscle November 2012 and March 2018. Visits involved medical wasting. review, functional assessments and T1 weighted MRI of the Several clinical phenotypes are associated with mutations lower limb muscles. The study protocol, patient demographics in the DYSF gene, most commonly limb girdle muscular and genetic information, baseline results and a review of dystrophy R2 (LGMDR2) and Miyoshi myopathy type 1 functional progression have been previously published [9,10]. (MMD1). LGMDR2 describes a phenotype with predominant Individual centres identified patients with dysferlinopathy proximal lower and upper limb weakness at presentation [1], from clinic lists to invite for screening. At study enrolment, and was previously called LGMD2B before recent consensus 209 patients were screened and 197 met the inclusion review of LGMD nomenclature [2]. MMD1 describes a criteria. Diagnostic inclusion criteria were the presence of phenotype with predominantly distal lower limb weakness at a) two (predicted) pathogenic mutations in the DYSF gene, presentation [3]. b) one (predicted) pathogenic mutation and absent dysferlin The DYSF gene was identified as the candidate gene on muscle immunoblot or c), one (predicted) pathogenic simultaneously in patients with predominantly proximal mutation and dysferlin protein level ≤20% of normal level [4] and predominantly distal disease [5]. Patients with the determined by blood monocyte testing. Patients also needed same DYSF mutations, including full siblings, have been to be ≥ 10 years of age, able to perform study assessments, described with clinically different patterns of weakness and attend appointments and provide informed consent. Exclusion ascribed different phenotypic diagnoses [4–6]. However, criteria were the presence of significant co-morbidity (in the doubt about the presence of two truly distinct diagnosis was opinion of the consenting clinician) or anticipated medical raised when pattern of muscle involvement on lower limb intervention which might interfere with ability to attend the MRI was shown not to differ between LGMDR2 and MMD1 assessments. Genetic and protein expression information are [7]. shown in supplementary Tables 1 and 2 and in Fig. 1. Dysferlinopathy is becoming a focus for clinical trials Of these 197, all 10 patients from one site have been [8]. Multiple clinical diagnoses creates difficulties for trial excluded from further analysis due to incomplete longitudinal design and therapy licencing. Pharmaceutical companies want data. One further patient became ineligible, after further to develop therapies for as many patients as possible, which genetic analysis identified a collagen VI related myopathy. is particularly important in a rare disease like dysferlinopathy. Patients’ current clinical diagnosis was reported by the Regulators want to know if results from a trial involving assessing clinician at screening either from reference to LGMDR2 patients are applicable to patients with MMD1. clinical notes (sites seeing local patients) or from patient Here we compare the demographic, MRI, functional self-report of the diagnosis given by their usual clinician and genetic differences between 168 genetically confirmed (out of area patients). Of the 186 patients, 18 did not dysferlinopathy patients with a clinical diagnosis of LGMDR2 report a diagnosis of MMD1 or LGMDR2 – but rather or MMD1 to determine if they are distinct clinical proximodistal dysferlinopathy, asymptomatic hyperCKaemia, phenotypes. pseudometabolic dysferlinopathy or paravertebral muscular dystrophy. They were therefore excluded from this analysis, 2. Methods leaving 168 patients who had a baseline assessment. Of these, five further patients dropped out before year 2 and three We analysed data from 168 patients with genetically missed the visit window, giving 160 (95%) with a 36-month confirmed dysferlinopathy enrolled in the Jain Foundation’s assessment. 266 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 1. Lollipop chart showing the location of pathogenic mutations in dysferlin exons. The chart separates mutations in LGMDR2 patients (top row), MMD1 patients (second row) and then shows those mutations that occur in both groups of patients (bottom row). The height of the bar corresponds to mutation frequency. Black circles denote truncating mutations and green circles denote missense mutations. This figure was generated using the open source ‘mutation mapper’ at https:// www.cbioportal.org/ mutation_mapper. We used Wilcoxon rank sum testing to compare mean age Numbers of patients with complete data for each assessment of onset of muscle weakness (patient reported) and duration are detailed below. of symptoms and Chi-squared testing for comparing gender, previous steroid use, teenage exercise level and specific sports 2.1. NSAD score performed between groups. We categorised teenage exercise levels into high and low, The NSAD score is a dysferlinopathy specific functional based on the metabolic equivalents and frequency of the scale of motor performance, developed using RASCH exercise reported by patients, as previously described [11]. analysis, which demonstrates measurable change over one High levels of exercise describe ‘moderate activity multiple year in Dysferlinopathy [13]. The scale comprises 29 tasks times per week or vigorous activity at least weekly’, with low involving both upper and lower limbs, testing proximal and levels as less than this. distal muscle function. Patients can score 0, 1 or 2 in each To compare the pattern of muscle involvement between domain of the scale, corresponding to ‘unable to perform LGMDR2 and MMD1, we used assessments that graded task’, ‘modified method, but achieves task independent of functional ability or pathology in various muscle groups; physical assistance from another’ and ‘able to perform task’. North Star assessment for limb girdle type muscular Score declines as functional abilities are lost. As the scale was dystrophies (NSAD), manual muscle testing (MMT) scores developed using the first assessments in the natural history and Mercuri scores from T1 MRI. NSAD scores were study, direct measurements using the new scale were not taken from the year 2 study visit. MRI and MMT results performed until the year 2 visit. The NSAD scores used in were from the baseline assessment as this visit had the this analysis are therefore from the year 2 visit. most complete assessments. Semiquantitative assessment of To compare the order in which functional abilities were MRIs were performed by a blinded neurologist (RF-T) impacted, we ordered NSAD items by the cohorts mean score and radiologist (JL), who independently evaluated axial T1- for each item. Items with the lowest score were those that weighted sequences with the Lamminen-Mercuri visual scale, fewest patients could complete. Thus, the order demonstrated as previously reported [12]. Patients with missing data were the average sequence in which the ability to perform an item excluded from the analysis of the scale for which they did not were lost. All item scores of each patient with LGMDR2 were have data. When the scales were combined, only patients with compared to those with MMD1 using a heatmap (Fig. 2a). data from all of the scales were included in the comparison. Mean item scores for LGMDR2 and MMD1 patients were 267 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 2. Functional ability on the NSAD score compared by clinical diagnosis. A: Heatmap of individual patients NSAD scores, split by clinical diagnosis of LGMDR2 or MMD1 (n = 130). B: Scatter plot with linear regression line of the mean score on each component of the NSAD assessment for patients with a clinical diagnosis of LGMDR2 compared to patients with a clinical diagnosis of MMD1 (n = 130). compared using linear regression (Fig. 2b). Points above comparing the mean Mercuri score for each muscle between the regression line represent preserved ability to complete the two diagnoses (Fig. 4b). Mercuri scoring of the MRIs these items, relative to other items, in MMD1 compared was performed manually with visual inspection of the muscle to LGMDR2. The standardised residual for each item was image [12]. calculated using the R Function “rstandard()”. 2.4. Distal to proximal involvement ratio 2.2. MMT score To assess for subgroups of distally or proximally affected The MMT score is a clinical measurement of strength patients irrespective of clinical diagnosis, we compared widely used in general clinical practice. We used an 11 point proximal and distal MMT and Mercuri scores. The mean conversion of the 5 point MRC scale (0, 1, 2, 3−, 3, 3+, MMT score of distal muscles was plotted against the mean 4−, 4, 4+, 5−, and 5) [9]. We ordered each movement by MMT score of proximal muscles for each individual to create the mean MMT scores achieved by the cohort, as done for a distal: proximal scatter plot (Fig. 3c). This method was NSAD scores, and compared scores of each individual patient repeated for Mercuri MRI scores (Fig. 4c). All muscle groups using a heatmap (Fig. 3a). Mean muscle MMT scores for listed in the heat maps were used. ‘Proximal’ muscles were LGMDR2 and MMD1 patients were compared using linear those above, or working across, the knee or elbow and ‘distal’ regression (Fig. 3b). muscles were below the knee or elbow, working across the ankle or wrist. 2.3. Mercuri score As mean values were not normally distributed, the median MMT ratio was calculated for LGMDR2 and for MMD1 and Repeating the methods above, a heatmap was created for compared using a Wilcoxon signed rank test. The Spearman’s Mercuri MRI scores (Fig. 4a) and linear regression performed correlation coefficient between distal and proximal muscle 268 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 3. Pattern of weakness assessed by the MMT score compared by clinical diagnosis. A: Heatmap of individual patients MMT scores, split by clinical diagnosis of LGMDR2 or MMD1, (n = 143). B: Scatter plot with linear regression line of the mean score for each movement tested by MMT for patients with a clinical diagnosis of LGMDR2 compared to patients with a clinical diagnosis of MMD1 (n = 143). C: Scatter plot with linear regression line of mean distal MMT score against mean proximal MMT score for each individual patient (n = 143). groups for LGMDR2 and for MMD1 was compared using with all three assessments combined. PCA is a statistical z-scores for a two tailed test, after a Fisher z transformation. analysis and data visualisation tool for comparing multiple Power calculation demonstrated 87% power to detect a variables [16]. It takes all of the variables for each individual difference in median distal:proximal MMT score ratio of and positions that individual on a 2D chart of principal >0.2. components, in a way that maximises the variation between To assess for site specific differences in muscle individuals. This means that each variable becomes a vector involvement, the mean MMT and MRI Mercuri score distal: along which individuals are positioned. In this way, groups proximal ratios were compared between patients from Tokyo of individuals with distinct characteristics are separated – (high number of MMD1 patients) and Newcastle (high for example if MMD1 patients had a weaker soleus muscle number of LGMDR2 patients) using a scatter plot and relative to the rectus femoris than LGMDR2 patients, then Spearman’s correlation (Fig. 6a and b) in the same way as MMD1 and LGMDR2 would form two distinct groups on for comparing clinical diagnosis. the PCA, without the programmer needing to know which Post-hoc power calculations, to assess the ability to variable would pick out the differences. detect differences between MMD1 and LGMD groups, were performed using an online tool [14]. 2.6. Data availability statement 2.5. Principle component analysis Anonymised aggregate data will be provided on reasonable A principal component analysis (PCA) was carried out request. Requests should be made to the clinical outcomes using the R package ggbiplot [15]. This was completed for study steering committee by contacting the corresponding NSAD scores, MMT scores and MRI both individually and author. 269 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 4. Pattern of weakness assessed by Mercuri scoring of leg T1W MRI compared by clinical diagnosis. A: Heatmap of individual patients Mercuri MRI scores, split by clinical diagnosis of LGMDR2 or MMD1 (n = 59). B: Scatter plot with linear regression line of mean Mercuri score for each leg muscle for patients with a clinical diagnosis of LGMDR2 compared to patients with a clinical diagnosis of MMD1 (n = 59). C: Scatter plot with linear regression line of Mean Mercuri grading for each distal muscle against mean Mercuri grading for each proximal muscle (n = 59). 3. Results 3.2. Genetics 3.1. Demographics LGMDR2 and MMD1 occurred in patients with the same genotype, with 12 patients showing the same pair At baseline, the cohort consisted of 114 patients with a of mutations as a patient with the other clinical diagnosis clinical diagnosis of LGMDR2 and 54 patients with MMD1 (supplementary Table 1 and 2). Discordant phenotypes for (Table 1). Age at symptom onset and at assessment did not the same genotype did not segregate based on gender, differ between clinical diagnoses (p > 0.05). Median symptom ethnicity, location or teenage exercise level. There were duration was higher in MMD1 patients than in LGMDR2 12 pairs of siblings, who all shared their sibling’s patients (p = 0.048). Gender, proportion of non-ambulant diagnosis. patients, previous steroid treatment and teenage exercise Patients with both LGMDR2 and MMD1 displayed a range intensity did not differ between clinical diagnoses (p > 0.05). of missense, non-sense and splice site mutations throughout 270 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Table 1 Demographic information. Clinical diagnosis LGMDR2 MMD1 Number of participants 114 54 Median age symptom onset (range) 19 yrs (0–48) 18 yrs (12–46) Median age at assessment (range) 36.5 yrs (11–86) 37 yrs (19–62) Median symptom duration∗ (range) 14 yrs (1–51) 20 yrs (4–45) Male: female 45:69 30:24 Ambulant 86 (75%) 37(69%) Previous steroid treatment 38 (33%) 26 (48%) active teenage lifestyle (cat 2/3) 74 (65%) 35 (65%) Ethnicity White (%) 79 (69%) 38 (70%) Asian (%) 21 (18%) 11 (20%) Black (%) 3 (3%) 0 (0%) Hispanic (%) 9 (8%) 2 (4%) Mixed race (%) 2 (2%) 0 (0%) Other (%) 0 (0%) 3 (6%) Patient location Europe (%) 65 (57%) 27 (50%) Spain (Barcelona, Seville) 9 (5, 4) 6 (0, 6) Italy (Padova) 7 8 Germany (Berlin, Munich) 11 (4, 7) 2 (0, 2) France (Paris) 4 6 UK (Newcastle) 34 5 USA (Charlotte, Columbus, Stanford, St Louis, Washington DC) (%) 41 (6, 21, 10, 4, 0) (36%) 17 (6, 2, 2, 2, 5) (31%) Australia (Sydney) (%) 4 (4%) 1 (2%) Japan∗∗ (Tokyo) (%) 4 (4%) 9 (17%) No difference in age symptom onset or age at assessment on Wilcoxon rank sum test. ∗ Symptom duration differs between groups (p = 0.048 on Wilcoxon rank sum test).Gender, previous steroid treatment and teenage activity category not significantly different on Chi squared testing. ∗∗ Using Chi squared test patients were more likely to have a MMD1 diagnosis in Japan than in Europe (p = 0.01) or US sites (p = 0.02). the length of the DYSF gene. Neither group displayed a limb movements were involved slightly more prominently in mutational hotspot (Fig. 1). LGMDR2, with hip extension having the largest residual. Upper limb movements were generally less affected in both 3.3. NSAD sequence groups of patients and did not appear to separate by diagnosis. Overall, while the ratio of mean distal: proximal involvement NSAD scores were available for 130 patients at visit 5 (86 did vary between patients (Fig. 3c) it did not vary by LGMDR2 and 44 MMD1). Scores were generally lower for diagnosis, with no significant difference between median the MMD1 patients (Fig. 2b), suggesting a weaker cohort. distal: proximal ratios (LGMDR2 = 1.06, MMD1 = 0.97, The sequence in which functional abilities were lost was p = 0.1) and no difference between regression coefficient variable between patients but similar between LGMDR2 and (p = 0.10) (Fig. 3c). Sample size was adequate to detect a MMD1 (Fig. 2a and 1b). The most common ability to be lost true difference in ratio of greater than 0.18. These ratios did was standing on tiptoes for both clinical diagnoses (Fig. 2b). not highlight discrete groups of distally or proximally affected In general, distal functions appeared below the regression patients, irrespective of diagnosis (Fig. 3c). line, suggesting they were more impaired in MMD1 than LGMDR2 patients for a given overall score, although 3.5. MRI sequence standardised residuals for all items were small (<1.5). A complete set of Mercuri scores for thigh and lower leg 3.4. MMT sequence muscles (whole leg) was available for 59 patients (39 LGMD and 20 MMD1) and for lower leg muscles for 131 patients Complete MMT scores were available for 143 patients (84 LGMDR2 and 47 MMD1) at their baseline visit. Mercuri at baseline (visit 2) (95 LGMDR2 and 48 MMD1. Scores scores across all muscles were generally lower for the MMD1 were generally lower for the MMD1 patients (Fig. 3b). The patients (Fig. 4b). Heatmaps and regression showed a variable sequence in which MMT scores deteriorated was variable pattern of muscle involvement between patients (whole leg – between patients (Fig. 3a) but similar between LGMDR2 and Fig. 4a, lower leg – Fig. 5), but the sequence of severity MMD1 (Fig. 3b). Distal lower limb movements appeared of involvement was similar between LGMDR2 and MMD1 slightly more affected in MMD1, (below the regression (whole leg - Fig. 4b). The largest standardised residuals line in Fig. 3b), particularly ankle plantar flexion (knee were seen for the gastrocnemius medialis muscle, being more flexed), which showed the largest residual. Proximal lower prominently involved in LGMDR2 than MMD1, and for the 271 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 5. Heatmap of the lower leg T1W MRI Mercuri scores (n = 131) by clinical diagnosis. adductor longus muscle, being more prominently involved 3.7. Principal component analysis in MMD1 than in LGMDR2. The ratio of mean distal: proximal involvement did vary between patients (Fig. 4c), but LGMDR2 and MMD1 patients did not separate into not by diagnosis, with no difference between median distal: distinct groups based on PCA analysis of NSAD score, proximal ratio (LGMDR2 = 1.08, MMD1 = 1.04, p = 0.97) MMT score or MRI analysis when each scoring systems was and no difference between regression coefficient (p = 0.59) assessed individually (data not shown) or when combined (Fig. 4c). Sample size was adequate to detect a true difference (Fig. 7a). in ratio of greater than 0.37. These ratios did not highlight discrete groups of distally or proximally affected patients, 4. Discussion irrespective of diagnosis (Fig. 4c). We have demonstrated that MMT scores, the NSAD 3.6. Site to site variation and the pattern of muscle involvement on MRI were similar between the two most common clinical diagnoses in A greater proportion of Japanese patients had a diagnosis dysferlinopathy - MMD1 and LGMDR2. of MMD1 (64%) than Europeans (30%, true difference Distal involvement was common to both diagnoses, even on chi-squared p = 0.01) or Americans (32%, p = 0.02). In in patients with otherwise mild functional impairment, with comparing patients from Tokyo (high proportion of MMD1) difficulty standing on tiptoes being the most commonly and Newcastle (high proportion of LGMDR2), there was affected function in both LGMDR2 and MMD1. Although no difference in regression coefficients for distal: proximal MMD1 patients showed a slightly more distal phenotype, with MMT (p = 0.87) and Mercuri MRI (p = 0.56) scores (Fig. 6a ankle plantar flexion (knee flexed) and some distal NSAD and b). In Newcastle, all patients with an MMD1 diagnosis functions being more prominently involved than in LGMDR2, showed relative distal weakness on MMT distal: proximal these differences were not statistically significant and there ratio, while the pattern was more varied in LGMDR2 patients was a high degree of overlap in pattern of weakness between (Fig. 6a). In Tokyo, all patients with an LGMDR2 diagnosis both diagnoses. Upper limb involvement, as measured by showed relative proximal weakness on MMT distal: proximal MMT scores and functional elements, was less common in ratio, while the pattern was more varied in MMD1 patients both diagnosis and distal upper limb weakness was not more (Fig. 6a). On MRI, patients distal: proximal ratio of Mercuri common in either group. scores did not group by diagnosis at either site (Figure b). In addition to the overlap between groups, some patients PCA analysis of NSAD score, MMT score and MRI did with an MMD1 diagnosis displayed an LMGDR2 phenotype, not split Tokyo and Newcastle patients into distinct groups with striking preservation of distal strength but marked (Fig. 7b) proximal weakness and vice versa for LGMDR2 patients 272 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 6. Comparison of distal vs proximal weakness in Newcastle and Tokyo. A: Mean distal MMT score against mean proximal MMT score for each individual patient in Newcastle (n = 30) and Tokyo (n = 12). There was no significant difference between Spearman’s R coefficient (p = 0.87). B: Mean distal Mercuri score against mean proximal Mercuri score for each individual patient in Newcastle (n = 26) and Tokyo (n = 11). There was no significant difference between Spearman’s R coefficient (p = 0.56). displaying an MMD1 phenotype. This suggests either that West, having been first described in a European cohort [1]. phenotypes were variable and have changed since the time However, we did not assess what the pattern of weakness of diagnosis, or that factors other than pattern of weakness was at initial presentation. It remains possible that a true influenced the diagnosis. difference in pattern of presentation exists, but this study One potential factor influencing diagnosis is a sites’ was not close enough to the time of diagnosis, and that previous experience. A clinical diagnosis of MMD1 was MMD1 and LGMDR2 were both described in the geographic more common in Japan while a diagnosis of LGMDR2 was locations where each presentation was more common. more common in Europe and the United States; however, A potential weakness in this study is the wide range we found no purely ethnic variation in diagnosis prevalence, of age and disease severity of patients when they were no distinct patterns of weakness on PCA and no variation assessed. MMD1 patients had generally had symptoms for in the degree of distal muscle involvement between sites. longer, were slightly weaker than the LGMDR2 patients and Only those Tokyo patients with markedly greater proximal may have presented later initially if they only had mild than distal weakness had an LGMDR2 diagnosis, while calf weakness. In addition, because the NSAD assessment patients in Newcastle only had an MMD1 diagnosis if they was developed and validated during COS, the most complete were particularly weak distally. We speculate that this may scores were from the year 2 (5th) visit, adding a further 2 be explained by a geographical preference for diagnosis. years to each individual’s disease progression at the point of Japanese sites may be more likely to diagnose MMD1 as assessment. The variation between individuals was greatest the default, unless presentation is strikingly different, due to in less affected patients, so it is possible that a larger site experience, as MMD1 was first described in a Japanese cohort of minimally affected patients would demonstrate true cohort [3]. LGMDR2 may be the default diagnosis in the phenotypic differences. However, in this cohort, even amongst 273 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Fig. 7. Principal component analysis of assessments between a. diagnosis and b. study site. A: Principle component analysis (PCA) of NSAD, MMT scores and lower leg Mercuri MRI score showing no difference in weighting of principle components between diagnoses. B: Principle component analysis (PCA) of NSAD, MMT scores and lower leg Mercuri MRI score showing no difference in weighting of principle components between Newcastle and Tokyo. the mildly affected patients, the pattern of muscle involvement diagnosis should not be the element used to create cohorts for varied and overlapped between diagnostic groups. Even if clinical trials: there were a difference early in the disease course, it seems that the clinical diagnoses given at presentation does not (1) Diagnostic criteria for each diagnosis are not clearly continue to best describe the phenotype of a dysferlinopathy defined allowing individual and regional variation in patient several years into their disease. ascribing diagnosis. Although not forming distinct groups, a wide range (2) There is significant phenotypic overlap between clinical in pattern of muscle involvement is evident and suggests diagnostic groups even in early stages. there may be multiple, rather than a single, genetic or (3) Validated, dysferlinopathy specific, outcome measures (the environmental disease modifying factors [17]. The variation NSAD) are available that incorporate both distal and is not explained by the underlying dysferlin mutations, as proximal muscle functions to allow progression to be patients in this (and previous) studies with the same dysferlin monitored across the spectrum of disease. genotype have different clinical diagnoses and patterns of (4) Outcome measures including the NSAD, 10 m walk and weakness [5,6]. Although siblings in this cohort shared the timed up and go show no difference in rate of progression same clinical diagnosis, previous studies have demonstrated over three years between patients with MMD1 and otherwise [6]. Ethnicity also does not seem to influence LGMDR2 [19]. diagnostic prevalence. We also reviewed exercise intensity and type, which are known to vary culturally, [18] but did not These points suggest that combining the clinical diagnoses identify any association with clinical diagnosis. This suggests of MMD1 or LGMDR2 into a common unified group of that pattern of weakness is not related to demographic factors, ‘dysferlinopathies’, is important for clinical trials in order to such that these findings should be generalizable to the wider prevent potential detrimental artificial distinctions and the risk population of patients with dysferlinopathy. oftreatments being developed with and for one specific group The patient population with dysferlinopathy demonstrate and not the other, when both could benefit. variation in rates of disease progression and the degree Mutations in the DYSF gene are associated with multiple of distal:proximal muscle involvement, particularly early in different clinical diagnoses, including LGMDR2 and MMD1. the disease. Developing interventional therapies for such a We have shown that pattern of weakness did not separate heterogenous cohort is complex and there are incentives patients into distinct clinical entities, with significant to form smaller, more homogeneous cohorts of patients. functional overlap between and within diagnostic groups. However, for several reasons, we argue that a patient’s clinical Initial clinical diagnosis was not a reliable predictor of future 274 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 pattern of weakness, functional ability or rate of deterioration. Ursula Moore reports the grant from the Jain Foundation Therefore, for the purposes of monitoring and the evaluation Heather Gordish reports the grant from the Jain Foundation of therapeutic interventions, we recommend that patients with Jordi Díaz-Manera reports no disclosures dysferlinopathy be considered as a single cohort, rather than Meredith K. James reports the grant from the Jain being separated based on initial clinical diagnosis. Foundation Anna G. Mayhew reports no disclosures Declaration of Competing Interest Michela Guglieri reports the grant from the Jain Foundation None. Roberto Ferenandez Torron reports no disclosures Acknowledgments Laura E. Rufibach works for the Jain Foundation Jia Feng reports no disclosures This study has only been possible thanks to the Andrew M. Blamire reports the grant from the Jain international collaboration of several specialised centres Foundation promoted by the Jain Foundation. The Jain COS consortium Pierre G. Carlier MD reports no disclosures would like to thank the study participants and their families Simone Spuler reports no disclosures for their invaluable contribution and would also like to John W. Day reports the grant from the Jain Foundation, acknowledge the ongoing support the Jain Foundation personal fees from Biogen, Ionis, Avexis, Roche, Sarepta, provides in the development, management, and analysis of Sanofi, Genzyme, Scholar Rock, Pfizer plus patents from this Study. The Jain Foundation, based in Seattle, USA, Athena Diagnostics. is entirely focused on LGMD2B/dysferlinopathy/Miyoshi Kristi J. Jones reports no disclosures Myopathy. The foundation does not solicit funding from Diana X. Bharucha-Goebel reports membership of the patients, but instead funds research and clinical studies Gene Therapy Network (Avexis) worldwide with the goal of finding treatments for Emmanuelle Salort-Campana reports no disclosures dysferlinopathy. Please visit www.jain-foundation.org for Alan Pestronk reports the grant from the Jain Foundation more information about the foundation and if you are a patient Sabine Krause reports no disclosures suffering from dysferlinopathy, please consider enrolling into Olivia Schreiber-Katz reports grants from the German their interactive dysferlinopathy registry that seeks to build Neuromuscular Society (DGM e.V.) and from the Young a strong, engaged, and supportive community (patients@jain- Faculty Program of Hannover Medical School; personal fees foundation.org). and non-financial support from Biogen GmbH, outside the submitted work. Statistical analysis Maggie C. Walter reports advisory board membership for Avexis, Biogen, Novartis, Roche, Santhera, Sarepta, Statistics conducted by Dr Ursula Moore, academic PTC Therapeutics, Ultragenyx, Wave Sciences, plus personal affiliation 1. fees from Novartis, Biogen, Ultragenyx, Santhera, PTC Statistical approach advised by Dr Heather Gordish Therapeutics, Ask Bio, Audentes Therapeutics, Fulcrum Dressman, academic affiliation 2 and 3. Therapeutics, GIG Consul, Guidepoint Global, Novartis, PTC, Gruenthal Pharma, Study funding Carmen Paradas reports no disclosures The estimated $4 million USD needed to fund this study Tanya Stojkovic reports no disclosures was provided by the Jain Foundation. Madoka Mori-Yoshimura reports no disclosures Volker Straub was supported by an MRC strategic award Elena Bravver deceased to establish an International Centre for Genomic Medicine in Elena Pegoraro reports grants, personal fees and Neuromuscular Diseases (ICGNMD) MR/S005021/1 non financial support from Santhera, personal fees and nonfinancial support from Sarepta, Personal fees and non Study sponsorship and author disclosures financial support from PTC pharmaceuticals all outside this submitted work. The estimated $4 million USD needed to fund this study Linda Pax Lowes reports no disclosures was provided by the Jain Foundation. Jerry R. Mendell reports no disclosures Volker Straub was supported by an MRC strategic award Kate Bushby reports no disclosures to establish an International Centre for Genomic Medicine in Volker Straub reports the Jain Foundation grant and other Neuromuscular Diseases (ICGNMD) MR/S005021/1 grants and personal fees from Sarepta Therapeutics. As detailed below individuals disclosure interests outside the scope of this work and the grant from the Jain Foundation. Supplementary materials As a natural history study, there are no products that are being tested in this study. Therefore in the view of the corresponding Supplementary material associated with this article can be author and consortium lead there are no conflicts of interest found, in the online version, at doi:10.1016/j.nmd.2021.01. relevant to this work. 009. 275 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Appendix 1. Authors Name Location Contribution Ursula Moore The John Walton Muscular Dystrophy Research Centre, Translational and Clinical conception and design of the study, Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation acquisition and analysis of data, Trust, Central Parkway, Newcastle upon Tyne, UK preparation of the manuscript Heather Gordish Centre for translational science, division of biostatistics and study methodology, Review and design of statistical analysis children’s national health system, washington, dc, usa, for the study, drafting Jordi Diaz-Manera Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Conception and design of the study, Sant Pau, acquisition and analysis of data, drafting Meredith K. James The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Conception and design of the study, Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation acquisition and analysis of data, drafting Trust, Central Parkway, Newcastle upon Tyne, UK Anna G. Mayhew The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Conception and design of the study, Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation acquisition and analysis of data, drafting Trust, Central Parkway, Newcastle upon Tyne, UK Michela Gulgieri The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Acquisition and analysis of data, drafting Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle upon Tyne, UK Roberto Fernandez The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Acquisition and analysis of data Torron Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Central Parkway, Newcastle upon Tyne, UK Laura E. Rufibach 6 The Jain Foundation, Seattle, Washington, USA Conception and design of the study, acquisition and analysis of data, drafting Jia Feng Centre for translational science, division of biostatistics and study methodology, Acquisition and analysis of data children’s national health system, washington, dc, usa, Andrew M. Blamire Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle Conception and design of the study, University, UK drafting Pierre G. Carlier AIM & CEA NMR Laboratory, Institute of Myology, Pitié-Salpêtrière University Conception and design of the study, Hospital, 47–83, Paris, France drafting Simone Spuler Charite Muscle Research Unit, Experimental and Clinical Research Centre, a joint Acquisition and analysis of data, drafting cooperation of the Charité Medical Faculty and the Max Delbrück Centre for Molecular Medicine, Berlin, Germany John W. Day Department of Neurology and Neurological Sciences, Stanford University School of Acquisition and analysis of data, drafting Medicine; Stanford, CA, USA Kristi J. Jones The Children’s Hospital at Westmead, and The University of Sydney, Australia. Acquisition and analysis of data, drafting Diana X. Department of Neurology Children’s National Health System, Washington, DC, USA Acquisition and analysis of data, drafting Bharucha-Goebel Emmanuelle Salort- Service des maladies neuromusculaire et de la SLA, Hôpital de La Timone, Marseille, Acquisition and analysis of data, drafting Campana France Alan Pestronk Department of Neurology Washington University School of Medicine, St. Louis, MO, Acquisition and analysis of data, drafting USA Maggie C. Walter Friedrich-Baur-Institute, Dept. of Neurology, Ludwig-Maximilians-University of Acquisition and analysis of data, drafting Munich, Germany Carmen Paradas Neuromuscular Unit, Department of Neurology, Hospital U. Virgen del Rocío/Instituto Acquisition and analysis of data, drafting de Biomedicina de Sevilla, Sevilla, Spain Tanya Stojkovic Centre de référence des maladies neuromusculaires, Institut de Myologie, AP-HP, Acquisition and analysis of data, drafting Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Madoka Department of Neurology, National Centre Hospital, National Centre of Neurology and Acquisition and analysis of data, drafting Mori-Yoshimura Psychiatry Tokyo, Japan Elena Bravver Neuroscience Institute, Carolinas Neuromuscular/ALS-MDA Centre, Carolinas Acquisition and analysis of data, drafting HealthCare System, Charlotte, NC, USA. Elena Pegoraro Department of Neuroscience, University of Padova, Italy Acquisition and analysis of data, drafting Linda Pax Lowes The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Acquisition and analysis of data, drafting Ohio USA Jerry R. Mendell The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Acquisition and analysis of data, drafting Ohio USA Kate Bushby The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Study design, acquisition and analysis of Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation data, drafting Trust, Central Parkway, Newcastle upon Tyne, UK Volker Straub The John Walton Muscular Dystrophy Research Centre, Translational and Clinical Conception and design of the study, Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation acquisition and analysis of data, drafting, Trust, Central Parkway, Newcastle upon Tyne, UK final sign off, corresponding author 276 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Appendix 2 Coinvestigators - The Jain COS Consortium We acknowledge the work of the following members of the Consortium who have contributed to the collection of the data, but do not qualify for authorship on this paper. Name Location Role Contribution Marni Jacobs Children’s National Medical Centre Washington DC, USA Study Statistician Oversight of data management Data Management and Training Adrienne Arrieta MS Children’s National Medical Centre Washington DC, Lead Data Management Data Base development and training Expert Esther Hwang Jain Foundation, Seattle, USA- Patient Advocate Development of study questionnaires Recruitment, Elaine Lee PhD Jain Foundation, Seattle, USA-, Patient Advocate Development of study questionnaires Recruitment Isabel Illa MD Hospital de la Santa Creu i Sant Pau/ CIBERER, Director of the Study Design Barcelona, Spain Neuromuscular Unit Eduard Gallardo MD Hospital de la Santa Creu i Sant Pau/ CIBERER, Consultant Scientist Confirmation of Eligibility using Barcelona, Spain monocyte assay Izaskun Belmonte Servei de Medicina F ísica i Rehabilitació, Hospital de la Physiotherapist Acquisition of physiotherapy data Jimeno PT Santa Creu i Sant Pau Elena Montiel- Morillo Servei de Medicina F ísica i Rehabilitació, Hospital de la Physiotherapist Acquisition of physiotherapy data Santa Creu i Sant Pau Irene Pedrosa- Servei de Medicina F ísica i Rehabilitació, Hospital de la Physiotherapist Acquisition of physiotherapy data Hernandez Santa Creu i Sant Pau Jaume LLauger Rossello Radiology department, Universitat Autònoma de Radiologist Collection and Analysis of MRI Barcelona, Hospital de la Santa Creu i Sant Pau, data Barcelona, Spain, Bruce Harwick Department of Radiology, CMC Mercy Charlotte, NMR Technologist MRI Data Acquistion Carolinas Healthcare System Neurosciences Institute, Charlotte, NC, USA Jackie Sykes RN, BSN Carolinas HealthCare System, Charlotte, NC, Study Coordinator Study Coordination and Data Management Susan Sparks Carolinas HealthCare System, Charlotte, NC, Neurologist Recruiting PI and PI for first three visits. Scott Holsten Carolinas HealthCare System, Charlotte, NC, Physiotherapist Acquisition of physiotherapy data Lindsay Alfano Nationwide Children’s Hospital, Columbus, OH, Physiotherapist Acquisition of physiotherapy data Brent Yetter, MS Nationwide Children’s Hospital, Columbus, OH, Study Coordinator Study Coordination and Data Management Mark Smith MS, Department of Radiology, Nationwide Children’s MRI Technologist MRI Data Acquisition DABMP RT(MR) Hospital, Columbus, OH, USA, Bernard Lapeyssonie PT Neuromuscular and ALS Centre, La Timone Hospital, Physiotherapist Acquisition of physiotherapy data Aix-Marseille Université, Marseille, Bruno Vandevelde Neuromuscular and ALS Centre, La Timone Hospital, Physiotherapist Acquisition of physiotherapy data Aix-Marseille Université, Marseille, David Bendahan PhD Centre de Résonance, MagnétiqueBiologique et Médicale, MRI Lead MRI Technologist UMR CNRS 7339, Marseille, France, Yann Le Fur PhD Aix-Marseille Université, Marseille, France, MRI Technologist MRI Data Acquisition Attarian Shahram, MD, Neuromuscular and ALS Centre, La Timone Hospital, Study Doctor Medical History, Medical and PhD Aix-Marseille Université, Marseille, Physical Examination Testot-Ferry Albane, Neuromuscular and ALS Centre, La Timone Hospital, Study Coordinator Study Coordination and Data CRA Aix-Marseille Université, Marseille, Management Eva M. Coppenrath MD, Department of Clinical Radiology, Radiologist MRI Data Acquisition Ludwig-Maximilians-University Munich, Germany, Simone Thiele Ludwig-Maximilians-University Munich, Germany, Physiotherapist Acquisition of physiotherapy data Sabine Krause Ludwig-Maximilians-University Munich, Germany, Study Doctor Medical History, Medical and Physical Examination Olivia Schreiber- Katz Ludwig-Maximilians-University Munich, Germany, Study Doctor Medical History, Medical and Physical Examination Elizabeth Harris MD The John Walton Muscular Dystrophy Research Centre, Study Doctor Medical History, Medical and Newcastle upon Tyne, UK Physical Examination Michela Guglieri MD The John Walton Muscular Dystrophy Research Centre, Study Doctor Medical History, Medical and Newcastle upon Tyne, Physical Examination Teresinha Evangelista The John Walton Muscular Dystrophy Research Centre, Study Doctor Medical History, Medical and MD Newcastle upon Tyne, Physical Examination (continued on next page) 277 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Name Location Role Contribution Alex Murphy MD The John Walton Muscular Dystrophy Research Centre, Study Doctor Medical History, Medical and Newcastle upon Tyne, Physical Examination Michelle Eagle PhD The John Walton Muscular Dystrophy Research Centre, Physiotherapist Study Design, Acquisition of Newcastle upon Tyne, physiotherapy data Heather Hilsden The John Walton Muscular Dystrophy Research Centre, Project Manager Coordination of whole study Newcastle upon Tyne, Robert Muni Lofra The John Walton Muscular Dystrophy Research Centre, Physiotherapist Acquisition of physiotherapy data Newcastle upon Tyne, Dionne Moat BSc The John Walton Muscular Dystrophy Research Centre, Physiotherapist Acquisition of physiotherapy data Newcastle upon Tyne, Jassi Amritpal Singh The John Walton Muscular Dystrophy Research Centre, Physiotherapist Acquisition of physiotherapy data Sodhi BSc Newcastle upon Tyne, Helen Sutherland The John Walton Muscular Dystrophy Research Centre, Clinical Trial and Data Study Coordination and Data Newcastle upon Tyne, Manager Management Tim Hodgson Magnetic Resonance Centre, Newcastle University, UK, Radiologist MRI Data Acquisition MClinRES, Fiona E. Smith Magnetic Resonance Centre, Newcastle University, UK, NMR Scientist MRI analysis and Training Ian Wilson Magnetic Resonance Centre, Newcastle University, UK, NMR Scientist MRI analysis Dorothy Wallace BSc, Magnetic Resonance Centre, Institute for Cellular Radiologist MRI Data Acquisition Medicine, Newcastle University, UK, Louise Ward DCR, Magnetic Resonance Centre, Institute for Cellular Radiologist MRI Data Acquisition Medicine, Newcastle University, UK Debra Galley Magnetic Resonance Centre, Newcastle University, Radiology Assistant MRI Data Acquisition Chiara Calore University of Padova, Padova, Italy, Study Doctor Medical History, Medical and Physical Examination Claudio Semplicini University of Padova, Sub PI Acquisition of physiotherapy data Medical History, Medical and Physical Examination Luca Bello University of Padova, Padova, Italy, Sub PI Acquisition of physiotherapy data Medical History, Medical and Physical Examination Roberto Stramare MD Radiology Unit, Department of Medicine, University of Radiologist MRI Data Acquisition Padova, Padova, Italy, Alessandro Rampado Radiology Unit, Department of Medicine, University of Radiologist MRI Data Acquisition MRT Padova, Padova, Italy, Aurelie Canal Institut de Myologie, Paris, Physiotherapist Acquisition of physiotherapy data Jean Yves Hogrel Institut de Myologie, Paris, Research Scientist Supervision of Physiotherapy and Study Coordination Suna Turk MSc AIM & CEA NMR Laboratory, Institute of Myology, Radiologist MRI Data Acquisition Pitié-Salpêtrière University Hospital, 47–83, Paris, France, Harmen Reyngoudt AIM & CEA NMR Laboratory, Institute of Myology, NMR Scientist MRI Training and analysis Pitié-Salpêtrière University Hospital, 47–83, Paris, France, Ericky Caldas AIM & CEA NMR Laboratory, Institute of Myology, NMR Scientist MRI Training and analysis Pitié-Salpêtrière University Hospital, 47–83, Paris, France Cyrille Theis Institut de Myologie, Paris Radiologist MRI Data Acquisition Oumar Diabaté Institut de Myologie, Paris, Study Coordinator Study Coordination and Data Management Matthew Harms Washington University, St. Louis, MO, USA, Co PI Co PI, Study Doctor Recruitment, Medical History, Medical and Physical Examination Julaine M. Florence Washington University, St Louis, MO, USA, Physiotherapist Acquisition of physiotherapy data Catherine Siener Washington University, St Louis, MO, USA, Physiotherapist Acquisition of physiotherapy data Linda Schimmoeller Washington University, St. Louis, MO, USA, Study Coordinator Study Coordination and Data Management Glenn Foster rtr (mr) Centre for clinical imaging research ccir, washington MRI Technologist MRI Data Acquisition university, st louis, mo, usa, Pilar Carbonell MD Hospital U. Virgen del Rocío/Instituto de Biomedicina de Study Doctor Medical History, Medical and Sevilla Seville, Physical Examination Macarena Cabrera MD Hospital U. Virgen del Rocío/Instituto de Biomedicina de Study Doctor Medical History, Medical and Sevilla, Seville, Physical Examination Juan Bosco Mendez Hospital U. Virgen del Rocío/Instituto de Biomedicina de Rehabilitation Doctor Acquisition of physiotherapy data Sevilla, Seville, (continued on next page) 278 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 Name Location Role Contribution Nieves Sanchez- Hospital U. Virgen del Rocío/Instituto de Biomedicina de Physiotherapist Acquisition of physiotherapy data Aguilera Praxedes Sevilla, Seville, Yolanda Morgado MD Hospital U. Virgen de Valme /Instituto de Biomedicina Study Doctor Medical History, Medical and de Sevilla, Seville, Physical Examination Susana Rico Gala MD Department of Radiology, Hospital U. Virgen de Valme, Radiologist MRI Data Acquisition Sevilla, Spain, Jennifer Perez Stanford University School of Medicine, Stanford, Study Coordinator Study Coordination and Data Management Anne Marie Sawyer Lucas Centre for Imaging, Stanford University School of MRI Lead MRI data acquisition FSMRT, Medicine; Stanford, CA, USA Carolina Tesi-Rocha Stanford University School of Medicine; Stanford, CA, Sub- I Study Doctor Medical History, Medical and USA Physical Examination Tina Duong Stanford University School of Medicine; Stanford, CA, Physiotherapist Training of physiotherapists and USA Acquisition of physiotherapy data Richard Gee Stanford University School of Medicine; Stanford, CA, Physiotherapist Acquisition of physiotherapy data USA Nigel F Clarke MD Institute for Neuroscience and Muscle Research, Sydney r Original PI. Deceased. Recruitment and Study Design Kristy Rose Institute for Neuroscience and Muscle Research, Sydney Physiotherapist Training of physiotherapists and Acquisition of physiotherapy data Sarah Sandaradura MD Institute for Neuroscience and Muscle Research, Sydney Study Doctor Acquisition of Medical History, medical and physical assessments Roula Ghaoui MD Institute for Neuroscience and Muscle Research, Sydney Study Doctor Acquisition of Medical History, medical and physical assessments Kayla Cornett Institute for Neuroscience and Muscle Research, Sydney Physiotherapist Acquisition of physiotherapy data Claire Miller PT Institute for Neuroscience and Muscle Research, Sydney Physiotherapist Acquisition of physiotherapy data Sheryl Foster MHlthSc Department of Radiology, Westmead Hospital; Faculty of Radiologist MRI data acquistion Health Sciences, The University of Sydney, Australia Anthony Peduto MBBS Department of Radiology, Westmead Hospital; Faculty of Radiologist MRI data acquistion Health Sciences, The University of Sydney, Australia, Noriko Sato MD PhD Department of Radiology, National Centre Hospital, Radiologist MRI data acquistion National Centre of Neurology and Psychiatry, Tokyo, Japan Takeshi Tamaru MRT Department of Radiology, National Centre Hospital, Radiologist MRI data acquistion National Centre of Neurology and Psychiatry, Tokyo, Japan, Shin’ich Takeda National Centre of Neurology and Psychiatry, Tokyo, Director Supervision of Site Investigators Ai Ashida PT National Centre of Neurology and Psychiatry, Tokyo, Physiotherapist Acquisition of physiotherapy data Chikako Sakamoto PT National Centre of Neurology and Psychiatry, Tokyo, Physiotherapist Acquisition of physiotherapy data Tatayuki Tateishi National Centre of Neurology and Psychiatry, Tokyo Physiotherapist Acquisition of physiotherapy data Hiroyuki Yajima National Centre of Neurology and Psychiatry, Tokyo, Physiotherapist Acquisition of physiotherapy data Takahiro Nakayama MD Yokohama Rosai Hospital, Yokohama, Study Advisor Study Coordinator PhD Kazuhiko Segawa MD National Centre of Neurology and Psychiatry, Tokyo, Study Doctor Acquisition of Medical History, PhD medical and physical assessments Makiko Endo National Centre of Neurology and Psychiatry, Tokyo, Study Coordinator Study Coordination and Data Management Meganne E Leach, MSN, Children’s National Health System, Washington, Research Nurse Acquisition of Medical History, APRN medical and physical assessments Nora Brody, PT, DPT Children’s National Health System, Washington, Physiotherapist Acquisition of physiotherapy data Brittney DeWolf Children’s National Health System, Washington, Physiotherapist Acquisition of physiotherapy data Allyn Toles Children’s National Health System, Washington, Study Coordinator Study Coordination and Data Management Stanley T. Fricke PhD Department of Diagnostic Imaging and Radiology, Radiologist MRI data acquistion Children’s National Health System, Washington DC, USA, Hansel J. Otero MD Department of Diagnostic Imaging and Radiology, Radiologist MRI data acquistion Children’s National Health System, Washington DC, USA, Ulrike Grieben MD Charite, Berlin, Study Doctor Acquisition of Medical History, medical and physical assessments Elke Marron Physio Plus, Berlin, Physiotherapist Acquisition of physiotherapy data Juliana Prugel Physio Plus, Berlin, Physiotherapist Acquisition of physiotherapy data 279 U. Moore, H. Gordish, J. Diaz-Manera et al. Neuromuscular Disorders 31 (2021) 265–280 References [10] Moore U, Jacobs M, James MK, et al. Assessment of disease progression in dysferlinopathy: a 1-year cohort study. Neurology [1] Bashir R, Strachan T, Keers S, et al. A gene for autosomal recessive 2019;92:e461–74. limb-girdle muscular dystrophy maps to chromosome 2p. 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