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.
Keywords: LASSO prediction algorithm; electrical impedance myography; muscle triglyceride content; myofiber atrophy; myofiber size; obesity-induced sarcopenia.
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