Ontology highlight
ABSTRACT: Introduction
Surface electrical impedance myography (sEIM) has the potential for providing information on muscle composition and structure noninvasively. We sought to evaluate its use to predict myofiber size and connective tissue deposition in the D2-mdx model of Duchenne muscular dystrophy (DMD).Methods
We applied a prediction algorithm, the least absolute shrinkage and selection operator, to select specific EIM measurements obtained with surface and ex vivo EIM data from D2-mdx and wild-type (WT) mice (analyzed together or separately). We assessed myofiber cross-sectional area histologically and hydroxyproline (HP), a surrogate measure for connective tissue content, biochemically.Results
Using WT and D2-mdx impedance values together in the algorithm, sEIM gave average root-mean-square errors (RMSEs) of 26.6% for CSA and 45.8% for HP, which translate into mean errors of ±363 μm2 for a mean CSA of 1365 μm2 and of ±1.44 μg HP/mg muscle for a mean HP content of 3.15 μg HP/mg muscle. Stronger predictions were obtained by analyzing sEIM data from D2-mdx animals alone (RMSEs of 15.3% for CSA and 34.1% for HP content). Predictions made using ex vivo EIM data from D2-mdx animals alone were nearly equivalent to those obtained with sEIM data (RMSE of 16.59% for CSA), and slightly more accurate for HP (RMSE of 26.7%).Discussion
Surface EIM combined with a predictive algorithm can provide estimates of muscle pathology comparable to values obtained using ex vivo EIM, and can be used as a surrogate measure of disease severity and progression and response to therapy.
SUBMITTER: Pandeya SR
PROVIDER: S-EPMC8883327 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Pandeya Sarbesh R SR Nagy Janice A JA Riveros Daniela D Semple Carson C Taylor Rebecca S RS Mortreux Marie M Sanchez Benjamin B Kapur Kush K Rutkove Seward B SB
Muscle & nerve 20210407 6
<h4>Introduction</h4>Surface electrical impedance myography (sEIM) has the potential for providing information on muscle composition and structure noninvasively. We sought to evaluate its use to predict myofiber size and connective tissue deposition in the D2-mdx model of Duchenne muscular dystrophy (DMD).<h4>Methods</h4>We applied a prediction algorithm, the least absolute shrinkage and selection operator, to select specific EIM measurements obtained with surface and ex vivo EIM data from D2-md ...[more]