Relationships between in vivo surface and ex vivo electrical impedance myography measurements in three different neuromuscular disorder mouse models.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 20 06 2021
accepted: 11 10 2021
entrez: 29 10 2021
pubmed: 30 10 2021
medline: 15 12 2021
Statut: epublish

Résumé

Electrical impedance myography (EIM) using surface techniques has shown promise as a means of diagnosing and tracking disorders affecting muscle and assessing treatment efficacy. However, the relationship between such surface-obtained impedance values and pure muscle impedance values has not been established. Here we studied three groups of diseased and wild-type (WT) animals, including a Duchenne muscular dystrophy model (the D2-mdx mouse), an amyotrophic lateral sclerosis (ALS) model (the SOD1 G93A mouse), and a model of fat-related atrophy (the db/db diabetic obese mouse), performing hind limb measurements using a standard surface array and ex vivo measurements on freshly excised gastrocnemius muscle. A total of 101 animals (23 D2-mdx, 43 ALS mice, 12 db/db mice, and corresponding 30 WT mice) were studied with EIM across a frequency range of 8 kHz to 1 MHz. For both D2-mdx and ALS models, moderate strength correlations (Spearman rho values generally ranging from 0.3-0.7, depending on the impedance parameter (i.e., resistance, reactance and phase) were obtained. In these groups of animals, there was an offset in frequency with impedance values obtained at higher surface frequencies correlating more strongly to impedance values obtained at lower ex vivo frequencies. For the db/db model, correlations were comparatively weaker and strongest at very high and very low frequencies. When combining impedance data from all three disease models together, moderate correlations persisted (with maximal Spearman rho values of 0.45). These data support that surface EIM data reflect ex vivo muscle tissue EIM values to a moderate degree across several different diseases, with the highest correlations occurring in the 10-200 kHz frequency range. Understanding these relationships will prove useful for future applications of the technique of EIM in the assessment of neuromuscular disorders.

Identifiants

pubmed: 34714853
doi: 10.1371/journal.pone.0259071
pii: PONE-D-21-20232
pmc: PMC8555802
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0259071

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS055099
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS091159
Pays : United States

Déclaration de conflit d'intérêts

Dr. Rutkove has equity in, and serves a consultant and scientific advisor to Myolex, Inc. and Haystack Diagnostics, Inc; he is also a member of Myolex’s Board of Directors. The companies also have options to license patented impedance technology of which Dr. Rutkove is named as an inventor. Dr. Sanchez has equity and serves a consultant and scientific advisor to, HaystackDx Inc., Ioniq Sciences Inc., and B-Secur Ltd. He also serves as a consultant to Myolex Inc., Impedimed Inc., Texas Instruments Inc., and Happy Health Inc., companies that develop impedance related technology for consumer, research and clinical use. We again state that the existing competing interest (with Myolex, Inc) does not alter our adherence to PLOS ONE policies on sharing data and materials.

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Auteurs

Sarbesh R Pandeya (SR)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

Janice A Nagy (JA)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

Daniela Riveros (D)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

Carson Semple (C)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

Rebecca S Taylor (RS)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

Benjamin Sanchez (B)

Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States of America.

Seward B Rutkove (SB)

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.

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Classifications MeSH