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GSL Journal of Public Health and Epidemiology
Short Communication
Global Scientic Library
Citation: Frumento D. Sarcoglycanopathies: A Novel Predictive Approach. GSL J Public Health Epidemiol. 2019; 2:112.
Sarcoglycanopathies: A Novel Predictive Approach
Davide Frumento
Department of Experimental Medicine, DIMES, University of Genoa, Genoa, Italy
*Corr esponding author: Davide Frumento, PhD, Section of Biochemistry,
Department of Experimental Medicine Viale Benedetto XV 1, 16132
Genova (GE), Italy. Tel: +393334310322; E-mail: davide.frumento@edu.
unige.it
Received: Feb 20, 2019; Accepted: Feb 26, 2019; Published: Feb 28,
2019
Introduction
Progressive Muscular Dystrophies (PMDs) are a heterogeneous
family of neuromuscular diseases. Investigations on these hereditary
disorders started a long time ago, in fact the rst accurate description
of Duchenne Muscular Dystrophy (DMD), was carried out in 1852 [1].
About a century later, some cases of progressive muscular dystrophy
were reported, although they were clinically non-discriminable
from DMD. However, such an obscure form of PMD was later
called “Duchenne-like” autosomal recessive muscular dystrophy [2].
Aerwards, researchers discovered that DMD is ascribable to mutations
in the dystrophin protein gene [3,4]. Both structural and functional
feature of transmembrane dystrophin proteins brought to the nding
of a class of dystrophin-associated proteins, that taken together form
a unique transmembrane Dystrophin-Glycoprotein Complex (DGC).
Within such a molecular complex, one more sub-framework was
discovered, namely a four subunits-one called sarcoglycan [5]. Under a
genetic point of view, the positive correlation between mutations within
the sarcoglycan complex gene and sarcoglycanopathies development
became clear.
Epidemiological aspects
Prevalence data about all types of sarcoglycanopathies have been
calculated only for a restricted number of nations. More specically,
in India 53.8% [6], in USA it is about 15% [7], in Mexico 14% [8], in
Italy 18.1% [9], United Kingdom 11.7% [10]. In Denmark, Germany
and the Czech Republic the muscular dystrophies prevalence is 22.3,
23, and 2.3% [11-13]. e percentages of each of such diseases shows
no statistically signicant variations for about all populations, except
for isolated ones, among which the founder eect is very relevant. e
most common form of LGMD (Limb Girdle Muscular Dystrophy),
regardless of the geographic area, is usually the 2D type. 2E and 2C
forms presence is generally balanced among patients [14]. e rarest
type is, in most nations, is 2F, while in India it comes aer 2C [6]. 2C is
also prevalent, among other forms, in North Africa [15,16].
Abstract
Progressive Muscular Dystrophies (PMDs) are a heterogeneous family of neuromuscular diseases. Although they are considered as a rare
diseases group, their severity and relatively high prevalence make them a suitable target for that kind of scientic research whose target is to give to
the community a better quality of life. With this in mind, it is reasonable to think that a reliable predictive model is needed. Alas, since both PMDs
subtypes prevalence and incidence among general population do not show signicant statistical variations, it is not possible to base a predictive
model on these data. However, the aim of this paper is to elaborate a novel approach in order to crack the code of PMDs unpredictability.
Novel insights
Under an epidemiological point of view, it is legit to put
sarcoglycanopathies among the group of rare diseases. Nevertheless,
they are worldwide acknowledged as serious genetic disorders, as
they deeply aect life quality at all levels of severity. Since it has been
demonstrated that signicant statistical variations between LGMDs
only occur among isolated populations (due to the founder eect), it is
reasonable to infer that predictive models are not feasible only taking
into account general population. e aim of this opinion paper is to
propose a backward-wise methodological approach. e idea consists
in mapping the ethnical background of LGMD patients among big
communities, so that it will be possible to identify the ethnogenetic
basis of these diseases. en, ascending only to antiquely originated
isolated communities; it will be relatively easy to calculate their internal
LGDMs type prevalence. With this in mind, the next step it will be to
ethnogenetically track the origin of patients among general population,
in order to identify the LGMD cases that aect individuals ethnically
linked to the above cited antiquely originated isolated communities and
nally design a reliable predictive model.
References
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Citation: Frumento D. Sarcoglycanopathies: A Novel Predictive Approach. GSL J Public Health Epidemiol. 2019; 2:112.
GSL J Public Health Epidemiol. 2019; 2:112 | Page 2 of 2
Volume 2, Issue 1Frumento D
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