Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice

Sarbesh R. Pandeya, Janice A. Nagy, Daniela Riveros, Carson Semple, Rebecca S. Taylor, Marie Mortreux, Benjamin Sanchez, Kush Kapur, Seward B. Rutkove

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: Electrical impedance myography (EIM) provides insight into muscle composition and structure. We sought to evaluate its use in a mouse obesity model characterized by myofiber atrophy. Methods: We applied a prediction algorithm, ie, the least absolute shrinkage and selection operator (LASSO), to surface, needle array, and ex vivo EIM data from db/db and wild-type mice and assessed myofiber cross-sectional area (CSA) histologically and triglyceride (TG) content biochemically. Results: EIM data from all three modalities provided acceptable predictions of myofiber CSA with average root mean square error (RMSE) of 15% in CSA (ie, ±209 μm2 for a mean CSA of 1439 μm2) and TG content with RMSE of 30% in TG content (ie, ±7.3 nmol TG/mg muscle for a mean TG content of 25.4 nmol TG/mg muscle). Conclusions: EIM combined with a predictive algorithm provides reasonable estimates of myofiber CSA and TG content without the need for biopsy.

Original languageEnglish
Pages (from-to)127-140
Number of pages14
JournalMuscle and Nerve
Volume63
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Keywords

  • LASSO prediction algorithm
  • electrical impedance myography
  • muscle triglyceride content
  • myofiber atrophy
  • myofiber size
  • obesity-induced sarcopenia

Fingerprint

Dive into the research topics of 'Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice'. Together they form a unique fingerprint.

Cite this