MScanFit motor unit number estimation and muscle velocity recovery cycle recordings in diabetic polyneuropathy
- PMID: 32927215
- DOI: 10.1016/j.clinph.2020.07.017
MScanFit motor unit number estimation and muscle velocity recovery cycle recordings in diabetic polyneuropathy
Abstract
Objective: Motor Unit Number Estimation (MUNE) methods may be valuable in tracking motor unit loss in diabetic polyneuropathy (DPN). Muscle Velocity Recovery Cycles (MVRCs) provide information about muscle membrane properties. This study aimed to examine the utility of the MScanFit MUNE in detecting motor unit loss and to test whether the MVRCs could improve understanding of DPN pathophysiology.
Methods: Seventy-nine type-2 diabetic patients were compared to 32 control subjects. All participants were examined with MScanFit MUNE and MVRCs in anterior tibial muscle. Lower limb nerve conduction studies (NCS) in peroneal, tibial and sural nerves were applied to diagnose large fiber neuropathy.
Results: NCS confirmed DPN for 47 patients (DPN + ), with 32 not showing DPN (DPN-). MScanFit showed significantly decreased MUNE values and increased motor unit sizes, when comparing DPN + patients with controls (MUNE = 71.3 ± 4.7 vs 122.7 ± 3.8), and also when comparing DPN- patients (MUNE = 103.2 ± 5.1) with controls. MVRCs did not differ between groups.
Conclusions: MScanFit is more sensitive in showing motor unit loss than NCS in type-2 diabetic patients, whereas MVRCs do not provide additional information.
Significance: The MScanFit results suggest that motor changes are seen as early as sensory, and the role of axonal membrane properties in DPN pathophysiology should be revisited.
Keywords: Diabetic polyneuropathy; MScanFit; Motor unit loss; Motor unit number estimation; Muscle velocity recovery cycles.
Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest Professor Hugh Bostock receives royalties from UCL for sales of his Qtrac software used in this study. The other authors have no conflicts of interest to disclose. All authors have approved the final paper.
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