Advertisement

Epidemiological study and genetic characterization of inherited muscle diseases in a northern Spanish region

  • Inmaculada Pagola-Lorz
  • Esther Vicente
  • Berta Ibáñez
  • Laura Torné
  • Itsaso Elizalde-Beiras
  • Virginia Garcia-Solaesa
  • Fermín García
  • Josu Delfrade
  • Ivonne JericóEmail author
Open Access
Research
Part of the following topical collections:
  1. Rare neurological diseases

Abstract

Background

Inherited muscle diseases are a group of rare heterogeneous muscle conditions with great impact on quality of life, for which variable prevalence has previously been reported, probably due to case selection bias. The aim of this study is to estimate the overall and selective prevalence rates of inherited muscle diseases in a northern Spanish region and to describe their demographic and genetic features. Retrospective identification of patients with inherited muscle diseases between 2000 and 2015 from multiple data sources. Demographic and molecular data were registered.

Results

On January 1, 2016, the overall prevalence of inherited muscle diseases was 59.00/ 100,000 inhabitants (CI 95%; 53.35–65.26). Prevalence was significantly greater in men (67.33/100,000) in comparison to women (50.80/100,000) (p = 0.006). The highest value was seen in the age range between 45 and 54 (91.32/100,000) years. Myotonic dystrophy type 1 was the most common condition (35.90/100,000), followed by facioscapulohumeral muscular dystrophy (5.15/100,000) and limb-girdle muscular dystrophy type 2A (2.5/100,000).

Conclusions

Prevalence of inherited muscle diseases in Navarre is high in comparison with the data reported for other geographical regions. Standard procedures and analyses of multiple data sources are needed for epidemiological studies of this heterogeneous group of diseases.

Keywords

Inherited muscle diseases Epidemiology Prevalence Neuromuscular 

Abbreviations

BMD

Becker Muscular Dystrophy

CMD

Congenital Muscular Dystrophy

DM-1

Myotonic Dystrophy type 1

DMD

Duchenne Muscular Dystrophy

FSHD

Facioscapulohumeral Dystrophy

ICD

International Classification of Diseases

IMDs

Inherited Muscle Diseases

LGMD

Limb Girdle Muscular Dystrophy

Background

Inherited muscle diseases (IMDs), defined as rare diseases due to their low prevalence, make up a complex group of clinically and genetically heterogeneous conditions. IMDs can appear at any age and are characterized by a variety of symptoms including progressive muscle weakness, cramps, stiffness, joint deformities, chronic pain, respiratory and/or cardiac involvement, and a broad range of cognitive impairments [1, 2]. These pathologies cause variable degrees of disability in patients and have a major impact on the quality of life and health budgets worldwide [3]. The number of subjects with this type of condition is expected to increase due to better prevention of complications and genetic diagnosis advances, thus, prevalence data are essential for future budget estimates.

Despite the relevance of identifying these data only few epidemiological studies include all types of IMDs, and their methodology and results vary widely [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. There seems to be regional differences concerning the prevalence of these conditions and potential ethnic differences are not fully understood.

For comparison purposes, standardized procedures for conducting epidemiological studies in this field have been proposed [15]. In Spain, there is little published information about IMDs. Most studies have been performed at regional level, focusing on a certain type of IMD and with wide regional variations [16, 17, 18, 19]. The aim of our study is to describe the demographic and genetic features of this group of neuromuscular diseases in Navarre for a 16-year period (2000–2015) and to estimate the prevalence according to IMD subtype, age group and geographical distribution.

Patients and methods

Observational retrospective study based on the identification of adults and children with IMDs using all health databases available within the regional health system (from January 1, 2000 to December 31, 2015). The Navarre Ethics Research Committee approved this study. The procedures followed are in accordance with the Helsinki Declaration of 1975, as revised in 2000.

Study population

The study was conducted on a well-defined population from the Navarre Community in Northern Spain (Fig. 1) with an estimated population of 640,647 inhabitants as per the 2016 census [20]. Most citizens are covered by the Regional Public Health Service of Navarre - Osasunbidea, part of the Spanish National Health Service. Only 3.1% of the population has private o mixed health insurance [21]. Navarre is organized in seven geographic areas (Fig. 1) (Navarre 2000 Zoning) [22].
Fig. 1

Navarre is a region in northern Spain organized in seven geographic areas

Diagnostic criteria

The study considered patients of any age, residents in Navarre during 2000–2015, with a definitive diagnosis or with high suspicion of suffering an IMD even without confirmed genetic diagnosis. We distinguished two groups of patients: 1) The definitive diagnosis of IMD group included subjects with genetically confirmed diagnosis as proposed in the 2017 version of the gene table of monogenic neuromuscular disorders [23] or with typical clinical phenotype consistent with a pathogenic mutation verified within the pedigree or patients with specific and well-correlated histopathological findings even in the absence of genetic confirmation. 2) The unclassified IMD group included patients with suspected but undiagnosed genetic muscle disease according to the phenotypes described by Harris et al. [24] following a thorough analysis of the patient: a) congenital onset and normal or mildly elevated creatine kinase (CK) levels; b) adult onset proximal weakness with significantly elevated CK and possible recessive inheritance; c) myopathy with prominent contractures. Patients from the second group did not meet the criteria of definitive IMD subtype as shown in Table 1. Subjects with muscle channelopathy, mitochondrial myopathies, female carriers of dystrophinopathy or isolated hyperCKemia were excluded from this study.

Genetic analysis

Blood was collected from patient after obtaining informed consent. DNA was extracted using standard procedures from peripheral blood samples taken from all patients. Appropriate genetic studies were performed in each case.

Sequencing techniques after amplification of all coding exons and adjacent areas of different genes associated to inherited muscle disease (CAPN3, DYSF, SGCG, SGCA, FKRP, ANO5, PABPN1, EMD, LMNA, GMPPB, GAA, PYGM, CPT2, MYH-7, ACTA1, LDB3) were performed to determine the DNA variants consistent in base changes; substitutions, and small insertions and deletions. First studies were carried out by Sanger sequencing following diagnostic algorithms gene-to-gene, while the implementation of the next generation sequencing (NGS) techniques in clinical diagnosis was studied by groups or panels in different NGS platforms. Bioinformatic tools were used to the alignment of the sequences to human reference genome; and detection, annotation and prioritization of variants.

DMD gene dosage analysis was determined by multiplex ligation-dependent probe amplification (MLPA). The SALSA® MLPA® P034 DMD-1 and P035 DMD-2 (MRC-Holland, Amsterdam) were used for the detection of exon deletions or duplications in the DMD gene while point mutations were identified by sequencing studies.

Myotonic dystrophy type l (DM-1) is caused by (CTG)n repeat expansion in the 3′-untranslated region of the DMPK gene. The sizing of this expansion was done by conventional PCR, fragment-length analysis, repeat-primed PCR, and fragment-length analysis.

The contraction of the D4Z4 repeat on chromosomes 4 is responsible of the facioscapulohumeral muscular dystrophy type 1 (FSHD1). The size of the D4Z4 repeats was determined by pulsed field gel electrophoresis (PFGE) as previously described [33]. Facioscapulohumeral muscular dystrophy type 2 (FSHD2) was studied by sequencing of SMCHD1 gene, which is involved in the maintenance of D4Z4 methylation.

Variants of interest detected by sequencing were classified according to different databases and the published literature. Population databases: 1000 Genomes Project (http://browser.1000genomes.org), Exome Variant Server (http://evs.gs.washington.edu/EVS) and Exome Aggregation Consortium (http://exac.broadinstitute.org/). Disease databases: Human Gene Mutation Database (http://www.hgmd.org), Leiden Open Variation Database (http://www.lovd.nl) and ClinVar (http://www.ncbi.nlm.nih.gov/clinvar). In silico approach was carried out to assess the pathogenicity of new variants using different tools as Mutation Taster (http://www.mutationtaster.org).

Methodological validation and segregation studies were performed by direct sequencing (ABI 3500 Genetic Analyzer, Applied Biosystems, Warrington, UK) using Big Dye Terminator Cycle Sequencing Kit (Applied Biosystems, Warrington, UK). The subsequent analysis was done with SeqScape software (Thermo Fisher).

Case ascertainment sources

Case ascertainment was achieved using multiple overlapping sources:

(1) Navarre’s Minimum Basic Data Set (MBDS), a regional computer-based database system designed to collect demographic, clinical and administrative data on discharges, including both private and publicly funded hospitals. International Classification of Diseases (Ninth Revision, Clinical Modification, ICD-9-CM), including 271.0, 272.7, 359.0, 359.1, 359.2, 359.21, 359.22, 359.29, 359.89 and 359.9, were used to search patients affected by IMDs [34].

(2) Electronic Clinical Records in Primary Care (ECRPC) of Navarre‘s Public Health System, a regional healthcare information system that allows registering demographic, clinical and administrative data on primary care episodes. International Classification of Primary Care, second edition (ICPC-2) was used to encode healthcare episodes [35]. In Navarre, the ECRPC system proposes several literal descriptors linked to the ICPC-2 codes for general practitioners, including muscular dystrophy and unspecified myopathy for code N99 (Neurological disease, other), which we used for selecting the patients [36].

(3) Temporary Occupational Disability Registry of Navarre, designed to collect data on sick leaves. ICD-9-CM codes, including the aforementioned ones, were used to select patients affected by IMDs [36].

(4) Records from the Medical Genetics Service allowed selecting patients that were being monitored for IMD suspicion. Several keywords enabled us to detect these patients, depending on the reason for the request of the genetic study.

(5) The Congenital Anomalies and Hereditary Diseases Registry of Navarre, a population-based listing affiliated member to EUROCAT [37]. Keyword diagnostic searches were used to select patients suffering IMDs.

(6) Navarre’s Mortality Statistics in which the primary cause of death from the Medical Death Certificate [38, 39] is listed. We used the ICD-10 codes, including G71.0, G71.1, G71.2, G71.3, G71.8, G71.9, G72.8, G72.9 and G73.6 to identify IMD patients.

(7) Electronic Clinical Records from the Neurology Services of Navarre’s public hospitals. Patients with IMDs were detected using keyword diagnostic searches.

The information from the various data sources showed all potential diagnoses of IMDs. This information was cross-checked with the databases for duplication. Next, a neuromuscular neurologist verified the diagnosis of each double-checked case based on the inclusion and exclusion criteria for each condition.

We collected the following information: type of IMD, gender, date of birth, date of death, place of residence on January 1, 2016, and genetic diagnosis.

Data analysis

January 1, 2016 was the date chosen for estimating prevalence. The population at risk is defined as the residents of Navarre as per the Spanish National Statistics Institute and the Statistics Institute of Navarre [20] (Navarre population = 640,647). The 2016 prevalence rates are expressed as cases/100,000 inhabitants. An overall prevalence was estimated globally and by gender, age and geographical area within Navarre. Descriptive results are presented as frequencies and proportions. Poisson distribution was assumed to estimate CI 95% for prevalence and proportions rates. For inter-group proportion comparisons, the Pearson Chi-square test was applied. Statistical analyses were conducted using the OpenEpi program [40].

Results

The search strategy allowed us to retrieve 2729 potential cases after the removal of duplicates, from which 1899 required diagnostic verification (Fig. 2). Five hundred thirteen cases fulfilled the diagnostic standards listed in Table 1, representing 27.01% of the initial potential cases: 281 (54.77%) males and 232 (45.22%) females (1.21:1). Twenty-six different disease entities were detected. During the study period, 23.20% of the subjects (62 males and 57 females) died.
Fig. 2

Flow-chart of potential cases of inherited muscle disease in Navarre, 2000–2015

Genetic and other diagnostic standards

From the 513 identified IMDs, 464 were definitive and 49 unclassified IMDs, respectively. Concerning the definitive IMD cases, 329 (70.90%) were genetically verified, 113 (24.35%) had a clinical phenotype with a pathogenic mutation confirmed with the pedigree, and in 22 (4.74%), characteristic muscle biopsy pathogenic features were found (Table 2).
Table 1

Diagnostic criteria used for each Inherited Muscle Disease in our study

Hereditary muscle disease type

Diagnostic criteria

Muscular Dystrophy

 Myotonic dystrophy types 1 and 2

Genetic confirmation or,

characteristic clinical phenotype + a pathogenic mutation confirmed within the pedigree

 FSHD, LGMD, OPMD, EDMD

Genetic confirmation or,

characteristic clinical phenotype + a pathogenic mutation confirmed within the pedigree

 Dystrophinopathies

Genetic confirmation or,

  DMD

characteristic clinical phenotype + absence of dystrophin in Western blot

 CMD

Genetic confirmation or,

  Dystroglycanopathies

characteristic clinical phenotype + muscle biopsy with loss of α-dystroglycan [25]

  Unclassified

characteristic clinical phenotype with onset < 2 years + muscle biopsy with dystrophic pattern

Metabolic Myopathies

 Glycogen storage disease

Genetic confirmation or,

  GSD-V

characteristic clinical phenotype + increased serum CK + muscle biopsy with vacuoles with glycogen deposition and absence of myophosphorylase activity [26]

   Unclassified

Characteristic clinical phenotype + increased in serum CK + muscle biopsy with glycogen deposition

Disorders of glycogen degradation

Genetic confirmation

 Lipid storage disease

Genetic confirmation

Congenital myopathies

 Central core

Genetic confirmation or,

clinical phenotype + muscle biopsy with cores with devoid of oxidative enzyme activity and type 1 fibre predominance [27]

 Centronuclear

Genetic confirmation or,

clinical phenotype + muscle biopsy with central nuclei [28]

 Myosin storage myopathy

Genetic confirmation or,

clinical phenotype + muscle biopsy with sarcomeric aggregation of myosin rod filaments [29]

 Nemaline myopathy

Genetic confirmation or,

clinical phenotype + muscle biopsy with rod-like structures in muscle fibres [30]

 Fibre type disproportion

Genetic confirmation or,

clinical phenotype + muscle biopsy with type 1 fibre diameter at least 35–40% smaller than type 2 fibres diameter in the absence of other structural abnormalities [31]

Myofibrillar myopathies

Genetic confirmation

 Distal myopathies

Genetic confirmation or,

clinical phenotype + myopathic findings on muscle biopsy + myopathic findings on electromyography + magnetic resonance imaging patterns [32]

 Unclassified myopathies

Congenital onset and normal or mildly elevated CK levels or,

adult onset proximal weakness + significantly elevated CK and possible recessive inheritance or,

myopathy + prominent contractures

FSHD Facioscapulohumeral muscular dystrophy, LGMD Limg girdle muscular dystrophy, OPMD Oculopharyngeal muscular dystrophy, EDMD Emery-Dreifuss muscular dystrophy, DMD Duchenne muscular dystrophinopathy, CMD Congenital muscular dystrophy

Genetic descriptions of dystrophinopathies are detailed in Table 3, while the rest of IMDs can be seen Table 4.
Table 2

Inherited Muscle Disease subtype and diagnostic standard used

HM Type

Diagnostic standard

N° of cases

DM-1

Genetic confirmation

225

Clinical phenotype + pedigree

97

FSHD

Genetic confirmation

32

Clinical phenotype + pedigree

9

LGMD2

Genetic confirmation

27

Clinical phenotype + pedigree

4

LGMD2C

Clinical phenotype + muscle biopsy

2

OPMD

Genetic confirmation

5

EDMD

Genetic confirmation

5

DMD

Genetic confirmation

13

Clinical phenotype + muscle biopsy

2

BMD

Genetic confirmation

7

Dystroglycanopathies

Genetic confirmation

1

Clinical phenotype + pedigree

1

Clinical phenotype + muscle biopsy

1

Unclassified CMD

Clinical phenotype + muscle biopsy

2

GSD-II

Genetic confirmation

2

GSD-V

Genetic confirmation

4

Clinical phenotype + muscle biopsy

2

Unclassified GSD

Clinical phenotype + muscle biopsy

2

Lipid storage disease

Genetic confirmation

1

Central Core

Clinical phenotype + muscle biopsy

2

Myosin storage myopathy

Genetic confirmation

2

Clinical phenotype + muscle biopsy

1

Nemaline myopathy

Genetic confirmation

1

Clinical phenotype + muscle biopsy

2

Fibre type disproportion

Clinical phenotype + muscle biopsy

3

Zaspopathy

Genetic confirmation

4

Clinical phenotype + pedigree

2

Distal myopathies

Clinical phenotype + magnetic resonance imaging patterns

3

DM-1 Myotonic dystrophy type 1, FSHD Facioscapulohumeral muscular dystrophy, LGMD Limb girdle muscular dystrophy, OPMD Oculopharyngeal muscular dystrophy, EDMD Emery-Dreifuss muscular dystrophy, DMD Duchenne muscular dystrophinopathy, CMD congenital muscular dystrophy, BMD Becker muscular dystrophinopathy, CMD Congenital muscle dystrophy, GSD Glycogen storage disease.

Table 3

Genetically confirmed dystrophinopathies

Case number

Dystrophinopathy type

Gene

Mutation type

Position and/or sequence variation

1

DMD

DMD

Deletion

Exons 44–55

2

DMD

DMD

Deletion

Exons 18–28

3

DMD

DMD

Duplication

Exons 18–48

4

DMD

DMD

Deletion

Exons 49 y 50

5

DMD

DMD

Deletion

Exons 45–53

6

DMD

DMD

Deletion

Exons 45–49

7

DMD

DMD

Duplication

Exon 3

8

DMD

DMD

SNV

c.353G > A, p.Trp118a

9

DMD

DMD

Deletion

Exon 43

10

DMD

DMD

Deletion

Exons 44–50

11a

DMD

   

12a

DMD

   

13b

DMD

   

14

BMD

DMD

Deletion

Exon 52

15

BMD

DMD

Deletion

Intron 49

16

BMD

DMD

Deletion

Exons 3–7

17

BMD

DMD

Deletion

Exons 45–55

18

BMD

DMD

Duplication

Exon 2

19b

BMD

   

20b

BMD

   

aNo mutation identified. Negative deletion/duplication study. Positive familial segregation

bEvidence of genetic confirmation in the clinical record; no access to the identified mutation

Table 4

Pathogenic molecular defect of each genetically confirmed IMD

Muscular disease type

Gene

Mutation type

Sequence variation

Position

Zygosity

Cases, n

Families, n

 Muscular dystrophy

  MD1

DMPK

Expanded CTG (> 40)

 

3′-UTR

 

225

116

  FSHD1

DUX

Deletion D4Z4

   

25

21

  FSHD2

SMCHD1

SNV

c.5602C > T

Exon 45

Het

6

1

SMCHD1

SNV

c.2329A > T

Exon 18

Het

1

1

  LGMD2A

CAPN3

Frameshift variant

c.2362_2363delinsAG/TCATCT

Exon 22

Hom

16

15

CAPN3

CAPN3

SNV

Frameshift variant

c.664G > A

c.2362_2363delinsAG/TCATCT

Exon 5

Exon 22

Het

Het

1

1

  LGMD2B

DYSF

SNV

c.895G > A

Exon 9

Hom

1

1

  LGMD2C

SGCG

SNV

c.848G > A

Exon 8

Hom

2

2

  LGMD2D

SGCA

SNV

c.293G > A

Exon 3

Hom

1

1

  LGMD2I

FKRP

SNV

c.826C > A

Exon 4

Hom

1

1

  LGMD2L

ANO5

Frameshift variant

c.1627dupA

Exon 15

Hom

1

1

ANO5

Frameshift variant

c.191dupA

Exon 5

Hom

2

1

ANO5

ANO5

Frameshift variant

SNV

c.191dupA

c.1664G > T

Exon 5

Exon 16

Het

Het

1

1

ANO5

ANO5

SNV

Splice variant

c.172C > T

c.1119 + 1G > T

Exon 4

Intron 12

Het

Het

1

1

  OPMD

PABPN1

Expanded GCN (> 10)

 

Exon 1

 

5

4

  Emerin EDMD

EMD

Complete deletion

   

1

1

  Lamin EDMD

LMNA

SNV

c.1130G > A

Exon 6

Het

2

1

LMNA

SNV

c.215G > T

Exon 1

Het

1

1

LMNA

SNV

c.65C > A

Exon 1

Het

1

1

  DMC-Dystroglicanopathy

GMPPB

SNV

c.553C > T

Exon 5

Hom

1

1

 Metabolic myopathy

  GSD-II

GAA

GAA

Intronic variant

SNV

c.-32-13 T > G

c.1933G > T

Intron 1

Exon 14

Het

Het

1

1

GAA

GAA

Intronic variant

SNV

c.-32-13 T > G

c.1724A > G

Intron 1

Exon 12

Het

Het

1

1

  GSD-V

PYGM

Stop gained

c.148C > T

Exon 1

Hom

1

1

PYGM

PYGM

Stop gained

SNV

c.148C > T

c.1468C > T

Exon 1

Exon 12

Het

Het

1

1

PYGMa

    

1

1

PYGMa

    

1

1

  Lipid storage disease

CPT2

SNV

SNV

c.359A > G

c.1547 T > C

Exon 4

Exon 4

Het

Het

1

1

 Congenital myopathy

  Myosin storage

MYH-7

SNV

c.5533C > T

Exon 37

Het

1

1

MYH-7

SNV

c.1314G > A

Exon 14

Het

1

1

  Nemaline

ACTA1

SNV

c.808G > C

Exon 5

Het

1

1

 Myofibrillar myopathy

  Zaspopathy

LDB3

SNV

c.494C > T

Exon 5

Het

4

1

a Evidence of genetic confirmation in the clinical record; no access to the identified mutation

Hom Homozygous, Het Heterozygous, SNV Single Nucleotide Variation

Prevalence

On 1 January 2016, 378 subjects with IMDs (56.61% male and 43.39% female) were residents of Navarre, implying a prevalence of 59.00/100,000 inhabitants (CI 95%; 53.35–65.26) (Table 5). DM-1 represented the most common IMDs, with a prevalence of 35.90/100,000 (CI 95%; 31.55–40.85), followed by facioscapulohumeral muscular dystrophy (FSHD) and limb girdle muscular dystrophy 2A (LGMD2A) affecting 5.15/100,000 (95% CI; 3.67–7.23) and 2.5/100,000 (CI 95%; 1.54–4.05) inhabitants, respectively. There were 11 cases of dystrophinopathies, with Duchenne muscular dystrophy (DMD) present in 0.94/100,000 (CI 95%; 0.43–2.04) and Becker muscular dystrophy (BMD) in 0.78/100,000 (CI 95%; 0.33–1.83) individuals. Congenital myopathy, congenital muscular dystrophy (CMD), myofibrillar myopathy, and metabolic myopathy were observed in 1.25 (CI 95%; 0.63–2.46), 0.62 (CI 95%; 0.24–1.60), 0.78 (CI 95%; 0.33–1.83), and 1.71 (CI 95%; 0.95–3.07) per every 100,000 inhabitants, respectively.
Table 5

Inherited muscle disease prevalence in Navarre by January 1, 2016

Type of myopathy

Cases, n

PRx105 (CI 95%)

PR male

PR female

p

Mean age (SD)

Muscular dystrophy

312

48.70 (43.59–54.41)

53.80

43.67

0.066

46.43 (17.14)

 Myotonic dystrophy 1

230

35.90 (31.55–40.85)

36.18

35.63

0.906

47.06 (15.39)

 FSHD

33

5.15 (3.67–7.23)

6.92

3.41

0.052

55.51 (14.44)

 FSHD1

27

4.21 (2.90–6.13)

5.98

2.48

0.320

58.15 (13.06)

 FSHD2

6

0.94 (0.43–2.04)

0.94

0.93

0.985

43.67 (15.54)

 LGMD2

27

4.21 (0.90–6.13)

5.03

3.41

0.325

45.04 (17.67)

 LGMD2A

16

2.50 (1.54–4.05)

2.52

2.48

0.975

43.81 (14.63)

 LGMD2B

1

0.16 (0.03–0.88)

0.31

0.00

0.496

56 (−)

 LGMD2C

3

0.47 (0.16–1.38)

0.94

0.00

0.122

21.33 (24.45)

 LGMD2D

1

0.16 (0.03–0.88)

0.00

0.31

0.503

46 (−)

 LGMD2I

1

0.16 (0.03–0.88)

0.31

0.00

0.496

53 (−)

 LGMD2L

5

0.78 (0.33–1.83)

0.94

0.62

0.675

59.20 (16.51)

 OPMD

2

0.31 (0.08–1.14)

0.63

0.00

0.246

72 (15,56)

 EDMD

5

0.78 (0.33–1.83)

0.94

0.62

0.675

41.80 (19.32)

 Emerin EDMD

1

0.16 (0.03–0.88)

0.31

0.00

0.496

19 (−)

 Lamin EDMD

4

0.62 (0.24–1.60)

0.63

0.62

0.988

47.5 (16.76)

 Dystrophinopathy

11

1.71 (0.95–3.07)

3.46

0.00

0.000

18.73 (18.26)

 DMD

6

0.94 (0.43–2.04)

1.89

0.00

0.015

8.33 (4.80)

 BMD

5

0.78 (0.33–1.83)

1.57

0.00

0.030

31.2 (21.18)

 CMD

4

0.62 (0.24–1.60)

0.63

0.62

0.988

13.25 (6.34)

 Glycosylation disorder

3

0.47 (0.16–1.38)

0.63

0.31

0.616

10.67 (4.51)

 Unclassified CMD

1

0.16 (0.03–0.88)

0.00

0.31

0.503

21 (−)

Metabolic myopathies

11

1.71 (0.95–3.07)

2.52

0.93

0.139

45.91 (19.13)

 Glycogen storage

10

1.56 (0.85–2.87)

2.52

0.62

0.062

49.10 (16.80)

 GSD-II

2

0.31 (0.08–1.14)

0.63

0.00

0.246

43 (12.73)

 GSD-V

6

0.94 (0.43–2.04)

1.26

0.62

0.442

5.15 (20.83)

 Unclassified

2

0.31 (0.08–1.14)

0.63

0.00

0.246

48 (9.90)

 Lipid storage disease

1

0.16 (0.03–0.88)

0.00

0.31

0.503

14 (−)

Congenital myopathy

8

1.25 (0.63–2.46)

2.20

0.31

0.037

36.12 (14.20)

 Central Core

2

0.31 (0.08–1.14)

0.63

0.00

0.246

22.50 (24.75)

 Myosin storage myopathy

3

0.47 (0.16–1.38)

0.94

0.00

0.122

44.67 (9.07)

 Fibre type disproportion

3

0.47 (0.16–1.38)

0.63

0.31

0.616

36.67 (4.62)

Distal myopathy

3

0.47 (0.16–1.38)

0.63

0.31

0.616

65.67 (17.78)

Zaspopathy

5

0.78 (0.33–1.83)

1.26

0.31

0.212

63.40 (4.88)

Unclassified myopathy

39

6.09 (4.45–8.32)

6.92

5.27

0.402

59.92 (21.86)

Total

378

59.00 (53.35–65.26)

67.33

50.80

0.006

46.93 (17.77)

SD Standard deviation.

OPMD Oculopharyngeal muscular dystrophy, EDMD Emery-Dreifuss muscular dystrophy, DMD Duchenne muscular dystrophinopathy, CMD Congenital muscular dystrophy, BMD Becker muscular dystrophinopathy, CMD congenital muscle dystrophy, GSD glycogen storage disease.

The range of age was 1–89 years, with a mean age of 46.93 years (SD 17.77) (45.70 (SD 19.01) for males and 48.54 (SD 15.93) for females). The highest age-specific prevalence (Table 6) was obtained for the age range between 45 to 54 years, with a prevalence of 91.32/100,000 (CI 95%; 74.31–112.2) subjects. Prevalence was statistically significant higher in males in comparison to females for the following groups: under 15, 25 to 34, and 75 to 84 years of age.
Table 6

Prevalence of Inherited Muscle Disease according to group age and gender

Group age

PR/100,000

(CI 95%)

PR/100,000 (CI 95%)

by gender

p

Women

Men

< 15

21.87 (14.44–33.11)

10.20 (4.36–23.87)

32.97 (20.59–52.80)

0.015

15–24

41.84 (28.56–61.30)

42.84 (25.04–73.29)

40.89 (23.9–69.95)

0.906

25–34

48.99 (35.39–67.82)

21.93 (11.11–43.27)

75,68 (52.37–109.40)

0.000

35–44

72.13 (57.64–90.26)

70.53 (50.95–97.62)

73.63 (54.08–100.24)

0.853

45–54

91.32 (74.31–112.2)

93.36 (69.79–124.92)

89.37 (66.81–119.6)

0.836

55–64

91.50 (72.56–115.40)

82.69 (58.58–116.7)

100.27 (73.37–137.20)

0.421

65–74

59.40 (42.92–82.23)

57.51 (36.38–90.9)

61.43 (38.86–97.09)

0.844

75–84

39.08 (24.06–63.47)

21.42 (9.15–50.14)

62.48 (34.9–111.90)

0.044

≥85

23.38 (9.99–54.73)

13.92 (3.81–50.76)

42.73 (14.53–125.62)

0.245

Total

59.00 (53.35–65.26)

50.80 (43.46–59.36)

67.33 (58.9–76.97)

0.006

The prevalence of IMDs differed notably by geographic areas, with the highest estimate found for the region of Tierra Estella (97.15/100,000 subjects), significantly higher in comparison to all other areas, except for the Eastern Middle area of Navarre. Figure 3 shows the geographical distribution of IMD prevalence.
Fig. 3

Geographical distribution of prevalence (per 100,000) of inherited muscle disease in Navarre

Discussion

In this study, we present data on Inherited Muscle Diseases compiled over a 16-year period from different healthcare information systems. Our results show a prevalence of IMDs in Navarre of 59.00/100,000 subjects, being higher in males than in females, highest for the 45 to 54 years age range, and with remarkable geographical variability. DM-1, FSHD, and LGMD2A are the most common subtypes IMDs.

We believe the non-inclusion in the analyses of the unclassified group of IMDs may underestimate the real prevalence, bearing in mind that each case was thoroughly assessed by a specialized neurologist. To avoid selection biases when comparing with other studies, we also present prevalence with a confirmed genetic diagnosis: 50.10/100,000 (CI 95%; 44.92–55.89), which remains to be high in comparison to the results published elsewhere [5]. Four hundred and forty two cases (86.16%) of IMDs had a confirmed genetic diagnosis.

Epidemiological studies of neuromuscular diseases

Prevalence studies require exhaustiveness. The lack of specific registries and the absence of diagnostic code verifications [9, 41, 42, 43] could lead an IMD selection bias [15]. Moreover, for comparability purposes stringent inclusion criteria is mandatory.

Aiming to avoid these intrinsic limitations in prevalence studies, for this study we used seven case ascertainment sources at different healthcare levels and made an exhaustive verification of the diagnoses with well-defined inclusion and exclusion criteria.

Data sources

This study was affected by the lack of disease codification specificity in the Spanish healthcare information systems for IMDs. Only 27.01% of the cases initially identified with the selected codes and keywords met the inclusion criteria for IMD. Thus, it is essential to review and verify the diagnosis procedures to obtain quality data for this type of epidemiological.

The introduction of population-based registries specific for neuromuscular and/or other rare diseases, including specific codes, would be of great help in future studies.

Overall and disease-specific prevalence data

Our study shows a prevalence of IMDs of 59.00/100,000 (CI 95%; 53.35–65.26) inhabitants for Navarre. Comparisons with prevalence data from other regions is complicated due to the lack of methodological homogeneity and because most studies focus on a specific IMD. Emery [44] reports a global prevalence of hereditary neuromuscular disorders of 1/3500 inhabitants (prevalence 28.57/100,000), including spinal muscular atrophy and hereditary sensitive-motor neuropathy. In another study carried out in the UK, a prevalence of IMDs of 37/100,000 people is described for a Northern region of England [6]. More recently, Theadom et al. [5] describe a prevalence of 22.3/100,000 inhabitants of all genetic muscle disorders in New Zealand, with higher incidence in subjects of European ancestry.

To the best of our knowledge, this is the first time a study includes all types of IMDs in a Spanish region.

The subtype of IMD with the highest number of cases in our series was DM-1 (58.87% of the cases) with a prevalence of 35.90/100,000 (CI 95%; 31.55–40.85). In previous works, the prevalence of this particular condition showed wide geographical variations. The lowest reported for Japan (0.2/100,000) [8] up to 172/100,000 for Quebec [45] due to the founder effect. Regarding data from Spanish regions, Burcet et al. [18] found 10.9/100,000 cases of DM-1 in Majorca, while Munain et al. [16] reported 26.5/100,000 in Guipuzcoa. These regional variations could indicate a possible underestimation of prevalence linked to the used methodology.

However, we believe that despite the used methodology, the high prevalence of DM-1 in Navarre could be explained by a possible founding effect, similar to that reported for Guipuzcoa [16], considering that both regions share cultural and background similarities. Moreover, the management of the patients by multidisciplinary teams in recent years may have a positive effect on survival by lowering the complications.

Prevalence of LGMD also differs between studies. Theadom’s review [15] reports a global prevalence of LGMD of 0.9/100,000 inhabitants. Here, we show a higher prevalence of LGMD (4.21/100,000) (CI of 95% 2.90–6.13), closer to the 4.8/100,000 estimated by Fardeu et al. [46] in a tiny community in Reunion, where high rates of endogamy have been described. In Spain, a study by Urtasun et al. in the Basque Country found a prevalence of 6.9/100,000 [17]. The most common form of LGMD is LGMD2A in the Basque Country and in our study, with over 50% of LGMD cases (59.26 and 61.29%, respectively). Both studies detected a high frequency of the c.2362_2363delinsAG/TCATCT mutation in exon 22 of the CAPN3 gene, which has been observed primarily in chromosomes of Basque natives and more exceptionally in individuals from other parts of the world [17]. In our study, this pathogenic variant is present in 100% of the LGMD2A.

The prevalence of dystrophinopathies in our study is 0.94/100,000 for DMD and 0.78/100,000 for BMD. These values are lower than those reported elsewhere. The meta-analysis conducted by Mah et al. [47] showed an estimated prevalence of 4.78/100,000 (CI 95%; 1.94–11.81) for DMD and 1.53/100,000 (CI 95%; 0.26–8.94) for BMD. The study performed in New Zealand [5] shows a prevalence of DMD of 2.45/100,000 (CI 95%; 2.01–2.98) and 1.67/100,000 (CI 95%; 1.32–2.12) for BMD with ethnic differences. However, some studies show a prevalence of DMD below 2/100,000 [43, 48]. We believe that the poor exploitation of electronic clinical records from Paediatric Services did not cause a biased estimation of dystrophinopathies in our study.

We observed higher prevalence of IMDs in men than in women. This difference could be due to the X-linked inheritance of DMD and BMD. However, we also observed significant differences in the congenital myopathies subgroup, with higher prevalence in men. Furthermore, there was higher prevalence in men in the following age groups: under 15, 25 to 34, and 75 to 84 years of age. In the under 15 group, the X-linked nature of DMD could explain this elevated prevalence [49]. In the other two age groups, the IMD subtypes differ greatly and we have clear explanation for the gender differences. The highest prevalence of IMDs is seen for the working age group (between 35 to 64 years) probably contributing to huge socio-economic burden. Further studies should be designed to analyse the impact these conditions have on the economy.

We also detected prevalence geographical distribution differences within Navarre, which may be useful when planning resources. The highest prevalence of IMDs was determined for Tierra Estella Area (PR 97.15 with IC 95% 70.19–134.50) (Fig. 3).

Unclassified inherited muscle disease

In the course of this study, we identified 49 patients (9.55%) with a potential genetic cause for their muscle disease. During the period of our study (2000 to 2015), most genetic diagnosis followed the gene-by-gene testing strategy based on their phenotype. Current availability of next-generation sequencing is changing the diagnostic approach, increasing confirmed genetic diagnosis, as well as the identification of new IMD-associated mutations.

Thirty-nine patients remained alive by the end of the study (December 2015) and from the end of the study to the present time genetic IMD confirmation was obtained for 18 (46, 15%).

Study limitations

Although the study has been exhaustive, poor exploitation of the electronic clinical records in Paediatric Services could bias childhood IMD data, e.g., DMD. However, we believe that the exploitation of other data sources counteracts this deficiency, consequently with mild underestimation of IMD prevalence in this age group.

Conclusions

The prevalence of IMDs in Navarre is 59.00/100,000 inhabitants CI (53.35–65.26), which is a high number if compared with data reported for other geographical regions. If only patients with confirmed genetic diagnosis are considered, the prevalence is 50.10/100,000 (CI 95%; 44.92–55.89). The high prevalence of DM-1 (35.90/100,000 with CI 31.55–40.85) and of LGMD2A (2.5/100,000 with CI 1.54–4.05) could suggest the existence of a founding effect in Navarre. Genetic confirmation was available in 442 (86.16%) of IMD patients in our region. Our population study has a high sensitivity because all possible sources of information have been used. The lack of specificity of disease coding in our health information system for IMDs has made the study difficult and has forced us to review the clinical data of each case to verify the diagnosis. It is essential to implement specific population based registries for neuromuscular and other rare diseases, taking into account the heterogeneity of these disorders.

Notes

Acknowledgements

The authors would like to thank all those responsible of the case ascertainment sources consulted for their collaboration; to María A. Ramos-Arroyo and Eva Ardanaz for their kind advices; to the Genetic Department of the Sant Pau Hospital (Barcelona) and the Biodonostia Institute for their collaboration in genetic analysis.

Authors’ contributions

Conceptualization and design: IJ, EV and IP; Methodology: IP, EV, BI and IJ; Funding acquisition: IJ and EV; Data curation: IP, EV, LT, FG, IEB and JD; Map design: JD; Supervision: IJ, EV and BI; writing-original draft: IP and IJ. All authors read and approved the final manuscript.

Funding

This study was partially supported by the Health Department of the Navarre Government (project 007–2017), co-funded (50%) by the European Regional Development Fund (ERDF) through the Navarre ERDF 2014–2020 Operative Programme.

Ethics approval and consent to participate

The study protocol was approved by the Navarre Ethical Committee for Medical Research (Pyto 2016/31).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Mercuri E, Muntoni F. Muscular dystrophies. Lancet. 2013;381:845–60.PubMedCrossRefGoogle Scholar
  2. 2.
    D’Angelo MG, Bresolin N. Report of the 95th European neuromuscular Centre (ENMC) sponsored international workshop cognitive impairment in neuromuscular disorders, Naarden, the Netherlands, 13-15 July 2001. Neuromuscul Disord. 2003;13:72–9.PubMedCrossRefGoogle Scholar
  3. 3.
    Boyer F, Drame M, Morrone I, Novella J-L. Factors relating to carer burden for families of persons with muscular dystrophy. J Rehabil Med. 2006;38:309–15.PubMedCrossRefGoogle Scholar
  4. 4.
    Lefter S, Hardiman O, Ryan AM. A population-based epidemiologic study of adult neuromuscular disease in the Republic of Ireland. Neurology. 2017;88:304–13.PubMedCrossRefGoogle Scholar
  5. 5.
    Theadom A, Rodrigues M, Poke G, O’Grady G, Love D, Hammond-Tooke G, et al. A Nationwide, population-based prevalence study of genetic muscle disorders. Neuroepidemiology. 2019;52:128–35.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Norwood FLM, Harling C, Chinnery PF, Eagle M, Bushby K, Straub V. Prevalence of genetic muscle disease in northern England: in-depth analysis of a muscle clinic population. Brain. 2009;132:3175–86.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Hughes MI, Hicks EM, Nevin NCPV. The prevalence of inherited neuromuscular disease in Northern Ireland. Neuromuscul Disord. 1996;6:69–73.PubMedCrossRefGoogle Scholar
  8. 8.
    Araki S, Uchino M, Kumamoto T. Prevalence studies of multiple sclerosis, myasthenia gravis, and myopathies in Kumamoto district. Japan Neuroepidemiology. 1987;6:120–9.PubMedCrossRefGoogle Scholar
  9. 9.
    Darin N, Tulinius M. Neuromuscular disorders in childhood: a descriptive epidemiological study from western Sweden. Neuromuscul Disord. 2000;10:1–9.PubMedCrossRefGoogle Scholar
  10. 10.
    Chung B, Wong V, Ip P. Prevalence of neuromuscular diseases in Chinese children: a study in southern China. J Child Neurol. 2003;18:217–9.PubMedCrossRefGoogle Scholar
  11. 11.
    El-Tallawy HN, Khedr EM, Qayed MH, Helliwell TR, Kamel NF. Epidemiological study of muscular disorders in Assiut. Egypt Neuroepidemiology. 2005;25:205–11.PubMedCrossRefGoogle Scholar
  12. 12.
    Tangsrud SE, Halvorsen S. Child neuromuscular disease in southern Norway. Prevalence, age and distribution of diagnosis with special reference to "non-Duchenne muscular dystrophy". Clin Genet. 1988;34(3):145–52.PubMedCrossRefGoogle Scholar
  13. 13.
    Kurtzke JF. Neuroepidemiology. Ann Neurol. 1984;16:265–77.PubMedCrossRefGoogle Scholar
  14. 14.
    Ahlström G, Gunnarsson LG, Leissner P, Sjödén PO. Epidemiology of neuromuscular diseases, including the postpolio sequelae, in a Swedish county. Neuroepidemiology. 1993;12:262–9.PubMedCrossRefGoogle Scholar
  15. 15.
    Theadom A, Rodrigues M, Roxburgh R, Balalla S, Higgins C, Bhattacharjee R, et al. Prevalence of muscular dystrophies: a systematic literature review. Neuroepidemiology. 2014;43:259–68.PubMedCrossRefGoogle Scholar
  16. 16.
    López de Munain A, Blanco A, Emparanza JI, Poza JJ, Martí Massó JF, Cobo A, et al. Prevalence of myotonic dystrophy in Guipúzcoa (Basque Country, Spain). Neurology. 1993;43:1573–6.PubMedCrossRefGoogle Scholar
  17. 17.
    Urtasun M, Sáenz A, Roudaut C, Poza JJ, Urtizberea JA, Cobo AM, et al. Limb-girdle muscular dystrophy in Guipúzcoa (Basque Country, Spain). Brain. 1998;121:1735–47.PubMedCrossRefGoogle Scholar
  18. 18.
    Burcet J, Cañellas F, Cavaller G, Vich M. Epidemiologic study of myotonic dystrophy on the island of Mallorca. Neurologia. 1992;7:61–4.PubMedGoogle Scholar
  19. 19.
    Arpa J, Cruz-Martínez A, Campos Y, Gutiérrez-Molina M, García-Rio F, Pérez-Conde C, et al. Prevalence and progression of mitochondrial diseases: a study of 50 patients. Muscle Nerve. 2003;28:690–5.PubMedCrossRefGoogle Scholar
  20. 20.
  21. 21.
    Ministerio de Sanidad y Consumo. Encuesta Nacional de Salud 2011–2012. https://www.navarra.es/home_es/Temas/Portal+de+la+Salud/Profesionales/Informacion+tecnica/Sanidad/encuesta+de+salud.htm. Accessed 1 Jun 2018.
  22. 22.
    European data portal. Zonificación Navarra. 2000 https://www.europeandataportal.eu/data/en/dataset/spasitnaestadi_pol_szonnav2000-xml01. Accessed 1 Jun 2018.Google Scholar
  23. 23.
    Bonne G, Rivier F, Hamroun D. The 2018 version of the gene table of monogenic neuromuscular disorders (nuclear genome). Neuromuscul Disord. 2017;27:1152–83.PubMedCrossRefGoogle Scholar
  24. 24.
    Harris E, Laval S, Hudson J, Barresi R, De Waele L, Straub V, et al. Undiagnosed genetic muscle disease in the north of England: an in depth phenotype analysis. PLoS Curr. 2013;5.Google Scholar
  25. 25.
    Bönnemann CG, Wang CH, Quijano-Roy S, Deconinck N, Bertini E, Ferreiro A, et al. Diagnostic approach to the congenital muscular dystrophies. Neuromuscul Disord. 2014;24:289–311.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    McARDLE B. Myopathy due to a defect in muscle glycogen breakdown. Clin Sci. 1951;10:13–35.PubMedGoogle Scholar
  27. 27.
    Jungbluth H, Sewry CA, Muntoni F. Core myopathies. Semin Pediatr Neurol. 2011;18:239–49.PubMedCrossRefGoogle Scholar
  28. 28.
    Bornemann A, Goebel HH. Congenital myopathies. Brain Pathol. 2001;11:206–17.PubMedCrossRefGoogle Scholar
  29. 29.
    Tajsharghi H, Thornell L-E, Lindberg C, Lindvall B, Henriksson K-G, Oldfors A. Myosin storage myopathy associated with a heterozygous missense mutation in MYH7. Ann Neurol. 2003;54:494–500.PubMedCrossRefGoogle Scholar
  30. 30.
    Wallgren-Pettersson C, Sewry CA, Nowak KJ, Laing NG. Nemaline myopathies. Semin Pediatr Neurol. 2011;18:230–8.PubMedCrossRefGoogle Scholar
  31. 31.
    Clarke NF, North KN. Congenital fiber type disproportion--30 years on. J Neuropathol Exp Neurol. 2003;62:977–89.PubMedCrossRefGoogle Scholar
  32. 32.
    Udd B. Distal myopathies. Curr Neurol Neurosci Rep. 2014;14:434.PubMedCrossRefGoogle Scholar
  33. 33.
    Lemmers RJ, van der Vliet PJ, Klooster R, Sacconi S, Camaño P, Dauwerse JG, et al. A unifying genetic model for facioscapulohumeral muscular dystrophy. Science. 2010;329(5999):1650–3.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Fernández-Navarro P, López-Abente G, Salido-Campos C, Sanz-Anquela JM. The minimum basic data set (MBDS) as a tool for cancer epidemiological surveillance. Eur J Intern Med. 2016;34:94–7.PubMedCrossRefGoogle Scholar
  35. 35.
    Esteban-Vasallo MD, Domínguez-Berjón MF, Astray-Mochales J, Gènova-Maleras R, Pérez-Sania A, Sánchez-Perruca L, et al. Epidemiological usefulness of population-based electronic clinical records in primary care: estimation of the prevalence of chronic diseases. Fam Pract. 2009;26:445–54.PubMedCrossRefGoogle Scholar
  36. 36.
    Ancín Ducay J, Erce López S, Extramiana Cameno E, Izcue Argandoña A. Correlación de códigos CEI-9-MC (8a edic) - CIAP-2 para la gestión de Incapacidad Teporal. Inst Salud Pública y Labor Navarra. 2014; https://www.navarra.es/NR/rdonlyres/E520399C-0612-4C75-A912-B16295014FC3/281429/codigosCIE_9_MC.pdf. Accessed 1 Jun 2018.
  37. 37.
    Kinsner-Ovaskainen A, Lanzoni M, Garne E, Loane M, Morris J, Neville A, et al. A sustainable solution for the activities of the European network for surveillance of congenital anomalies: EUROCAT as part of the EU platform on rare diseases registration. Eur J Med Genet. 2018;61:513–7.PubMedCrossRefGoogle Scholar
  38. 38.
    Floristán Floristán Y, Delfrade Osinaga J, Carrillo Prieto J, Aguirre Perez J, Moreno-Iribas C. Coding causes of death with IRIS software. Impact in Navarre mortality statistic. Rev Esp Salud Publica. 2016;90:e1–9.PubMedGoogle Scholar
  39. 39.
    Gómez-Redondo R, Faus-Bertomeu A, Poniakina S, Cámara-Izquierdo N. About Spain data on causes of death. 2017. https://www.causesofdeath.org/Data/ESP/20170126/ESP_bd.pdf. Accessed 1 Jun 2018.Google Scholar
  40. 40.
    Estadísticas epidemiológicas de código abierto para Salud Pública. Versión 3.01. http://www.openepi.com/Menu/OE_Menu.htm. Accessed 1 Jun 2018.
  41. 41.
    Yates JR, Emery AE. A population study of adult onset limb-girdle muscular dystrophy. J Med Genet. 1985;22:250–7.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Merlini L, Stagni SB, Marri E, Granata C. Epidemiology of neuromuscular disorders in the under-20 population in Bologna Province. Italy Neuromuscul Disord. 1992;2:197–200.PubMedCrossRefGoogle Scholar
  43. 43.
    Ballo R, Viljoen D, Beighton P. Duchenne and Becker muscular dystrophy prevalence in South Africa and molecular findings in 128 persons affected. S Afr Med J. 1994;84:494–7.PubMedGoogle Scholar
  44. 44.
    Emery AE. Population frequencies of inherited neuromuscular diseases-a world survey. Neuromuscul Disord. 1991;1:19–29.PubMedCrossRefGoogle Scholar
  45. 45.
    Mathieu J, Prévost C. Epidemiological surveillance of myotonic dystrophy type 1: a 25-year population-based study. Neuromuscul Disord. 2012;22:974–9.PubMedCrossRefGoogle Scholar
  46. 46.
    Fardeau M, Hillaire D, Mignard C, Feingold N, Feingold J, Mignard D, et al. Juvenile limb-girdle muscular dystrophy. Clinical, histopathological and genetic data from a small community living in the Reunion Island. Brain. 1996;119:295–308.PubMedCrossRefGoogle Scholar
  47. 47.
    Mah JK, Korngut L, Dykeman J, Day L, Pringsheim T, Jette N. A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscul Disord. 2014;24:482–91.PubMedCrossRefGoogle Scholar
  48. 48.
    Siciliano G, Tessa A, Renna M, Manca ML, Mancuso M, Murri L. Epidemiology of dystrophinopathies in North-West Tuscany: a molecular genetics-based revisitation. Clin Genet. 1999;56:51–8.PubMedCrossRefGoogle Scholar
  49. 49.
    Birnkrant DJ, Bushby K, Bann CM, Apkon SD, Blackwell A, Brumbaugh D, et al. Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and neuromuscular, rehabilitation, endocrine, and gastrointestinal and nutritional management. Lancet Neurol. 2018;17(3):251–67.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

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

Authors and Affiliations

  • Inmaculada Pagola-Lorz
    • 1
  • Esther Vicente
    • 2
    • 3
  • Berta Ibáñez
    • 4
  • Laura Torné
    • 1
  • Itsaso Elizalde-Beiras
    • 5
    • 6
  • Virginia Garcia-Solaesa
    • 1
    • 7
  • Fermín García
    • 7
  • Josu Delfrade
    • 2
    • 8
  • Ivonne Jericó
    • 1
    • 9
    Email author
  1. 1.Department of NeurologyComplejo Hospitalario de Navarra, IdiSNA (Navarre Institute for Health Research)PamplonaSpain
  2. 2.Community Health Observatory SectionInstituto de Salud Pública y Laboral de Navarra, IdiSNAPamplonaSpain
  3. 3.Department of Health SciencesUniversidad Pública de Navarra (UPNA), IdiSNAPamplonaSpain
  4. 4.Methodology Unit. NavarrabiomedUniversidad Pública de Navarra (UPNA), IdiSNAPamplonaSpain
  5. 5.Primary Care, Servicio Navarro de Salud – Osasunbidea, IdiSNAPamplonaSpain
  6. 6.Miguel Servet Foundation, NavarrabiomedPamplonaSpain
  7. 7.Department of GeneticsComplejo Hospitalario de Navarra, IdiSNAPamplonaSpain
  8. 8.CIBER Epidemiology and Public Health (CIBERESP)MadridSpain
  9. 9.Department of NeurologyComplejo Hospitalario de NavarraPamplonaSpain

Personalised recommendations