Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice.
LASSO prediction algorithm
electrical impedance myography
muscle hydroxyproline content
muscular dystrophy
myofiber atrophy
myofiber size
Journal
Muscle & nerve
ISSN: 1097-4598
Titre abrégé: Muscle Nerve
Pays: United States
ID NLM: 7803146
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
revised:
19
03
2021
received:
14
11
2020
pubmed:
25
3
2021
medline:
31
7
2021
entrez:
24
3
2021
Statut:
ppublish
Résumé
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). 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. 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 μm 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.
Identifiants
pubmed: 33759456
doi: 10.1002/mus.27240
pmc: PMC8883327
mid: NIHMS1779150
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
941-950Subventions
Organisme : NINDS NIH HHS
ID : R01 NS055099
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS091159
Pays : United States
Informations de copyright
© 2021 Wiley Periodicals LLC.
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