Relationship between infrared skin radiation and functional tests in patients affected by Emery-Dreifuss muscular dystrophy: Part 2

HYPOTHESIS

This study represents a second part of a recently published study about a new form of evaluation and development of rare genetic neurodegenerative diseases. The objective is to provide a more global vision of thermography with respect to the Emery-Dreifuss pathology, through the analysis of the data collection carried out for one year. The basic hypothesis is that thermography could become a valid tool for the diagnosis and follow-up of this pathology because is a very specific tool for registering temperature changes produced by a constant degenerative evolution of this muscular dystrophy.

Relationship between infrared skin radiation and muscular strength tests in patients affected by Emery-Dreifuss muscular dystrophy

Considering that infrared thermography is presented as a diagnostic technique for non-invasive, non-ionizing, fast and easy to use imaging and Emery-Dreifuss muscular dystrophy is a clinical condition that seems to be related to changes in the emission of infrared radiation at the skin level due to its neurodegenerative character, we have conducted an investigation by infrared thermography and the use of functional strength tests in the lower limbs in a family of 4 affected members of Emery-Dreifuss muscular dystrophy to try to establish a relationship between the evolution of the disease and the emission of infrared radiation in this pathology at the lower limb level and provide a more general view of this disease for a better evaluation and monitoring of the disease.

Emery-Dreifuss muscular dystrophy

Emery-Dreifuss muscular dystrophy (EDMD) is a rare muscular dystrophy, but is particularly important to diagnose due to frequent life-threatening cardiac complications. EDMD classically presents with muscle weakness, early contractures, cardiac conduction abnormalities and cardiomyopathy, although the presence and severity of these manifestations vary by subtype and individual. Associated genes include EMD, LMNA, SYNE1, SYNE2, FHL1, TMEM43, SUN1, SUN2, and TTN, encoding emerin, lamin A/C, nesprin-1, nesprin-2, FHL1, LUMA, SUN1, SUN2, and titin, respectively. The Online Mendelian Inheritance in Man database recognizes subtypes 1 through 7, which captures most but not all of the associated genes. Genetic diagnosis is essential whenever available, but traditional diagnostic tools can help steer the evaluation toward EDMD and assist with interpretation of equivocal genetic test results. Management is primarily supportive, but it is important to monitor patients closely, especially for potential cardiac complications. There is a high potential for progress in the treatment of EDMD in the coming years.

Understanding the molecular consequences of inherited muscular dystrophies: advancements through proteomic experimentation

INTRODUCTION

Proteomic techniques offer insights into the molecular perturbations occurring in muscular-dystrophies (MD). Revisiting published datasets can highlight conserved downstream molecular alterations, which may be worth re-assessing to determine whether their experimental manipulation is capable of modulating disease severity.

AREAS COVERED

Here, we review the MD literature, highlighting conserved molecular insights warranting mechanistic investigation for therapeutic potential. We also describe a workflow currently proving effective for efficient identification of biomarkers & therapeutic targets in other neurodegenerative conditions, upon which future MD proteomic investigations could be modelled. Expert commentary: Studying disease models can be useful for identifying biomarkers and model specific degenerative cascades, but rarely offer translatable mechanistic insights into disease pathology. Conversely, direct analysis of human samples undergoing degeneration presents challenges derived from complex chronic degenerative molecular processes. This requires a carefully planed & reproducible experimental paradigm accounting for patient selection through to grouping by disease severity and ending with proteomic data filtering and processing.