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Quantitative ultrasound assessment of lower limb muscle morphology in type 2 diabetes with and without peripheral neuropathy

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Diabetic peripheral neuropathy (DPN) is associated with progressive skeletal muscle degeneration, particularly in distal lower limb muscles. Advanced imaging modalities such as CT and MRI can detect these changes; however, their routine clinical use is limited. This study aimed to evaluate the utility of quantitative musculoskeletal ultrasound for assessing lower limb muscle morphology in individuals with type 2 diabetes with and without DPN. Methods: In this cross-sectional study, 215 participants were included and categorized into three groups: T2DM with DPN (n = 129), T2DM without DPN (n = 43), and healthy controls (n = 43). DPN was assessed using the 10-g monofilament test and vibration perception threshold (VPT). Ultrasound measurements of the muscle thickness and cross-sectional area (CSA) were obtained for proximal and distal lower limb muscles using a standardized protocol. Group comparisons were performed using the Kruskal–Wallis test with DSCF post-hoc analysis. Associations were assessed using Spearman’s correlation. Results: Quantitative ultrasound revealed that participants with DPN demonstrated significantly reduced muscle thickness and CSA across most lower limb muscles compared with both T2DM without DPN and controls (p < 0.001), with the largest effect sizes observed in distal muscles, including tibialis anterior, extensor hallucis longus, extensor digitorum brevis, and abductor hallucis brevis. Ultrasound-derived muscle measurements showed significant correlations with neuropathy severity score and age. Conclusions: Quantitative musculoskeletal ultrasound can be used as a feasible and sensitive imaging modality for detecting diabetes-related muscle changes in the lower limb muscles enabling early detection and monitoring of neuromuscular involvement in type 2 diabetes.

Original languageEnglish
Article number488
JournalF1000Research
Volume15
DOIs
Publication statusPublished - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • General Medicine
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Arts and Humanities (miscellaneous)
  • Social Sciences (miscellaneous)
  • General Pharmacology, Toxicology and Pharmaceutics
  • Library and Information Sciences

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