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Background <p>Amyotrophic lateral sclerosis (ALS) presents with marked clinical heterogeneity, complicating diagnosis and management. Neuromuscular ultrasound (NMUS) provides a non-invasive means to visualize peripheral nerve and muscle integrity, but its potential to delineate ALS subtypes has not been systematically explored.</p> Objective <p>To identify clinically meaningful ALS subgroups through unsupervised clustering of NMUS features integrated with clinical and electrophysiological data.</p> Methods <p>A total of 454 ALS patients (August 2024–December 2025) underwent standardized NMUS assessment, including muscle thickness, echogenicity, and nerve cross-sectional area, alongside ALSFRS-R, manual muscle testing (MMT), and compound muscle action potentials (CMAPs). K-means clustering was applied to standardized NMUS variables, with cluster stability assessed using silhouette coefficients, sensitivity analyses (<i>k</i> = 2–5), and resampling-based adjusted Rand indices. Multivariable regression examined associations between cluster membership and ALSFRS-R.</p> Results <p>Two reproducible NMUS-based subgroups were identified: a Mild cluster (<i>n</i> = 288, 63.4%) and a Severe cluster (<i>n</i> = 166, 36.6%). The Severe cluster showed reduced muscle thickness and higher echogenicity across multiple sites, together with lower ALSFRS-R scores (adjusted <i>β</i> = − 3.84, 95% CI − 5.41 to − 2.27, <i>P</i> &lt; 0.001). Cluster membership correlated negatively with MMT and CMAP amplitudes, supporting functional and electrophysiologic validity. Stability metrics confirmed robustness of the two-cluster solution.</p> Conclusion <p>Integrating NMUS with clinical data enables objective, imaging-derived stratification of ALS patients into biologically and functionally distinct subgroups. This approach offers a pragmatic framework for phenotypic characterization and may inform personalized monitoring and trial design in ALS.</p>

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Imaging-derived neuromuscular ultrasound phenotypes are associated with functional status in amyotrophic lateral sclerosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Ying Wang,
  • Hao Zhang,
  • Tianhua Yang,
  • Jialei Luo,
  • Ting Lin,
  • Xinyi Yan,
  • Junlin Ding,
  • Yuxuan Qiu,
  • Min Zhao,
  • Gaoyi Yang

摘要

Background

Amyotrophic lateral sclerosis (ALS) presents with marked clinical heterogeneity, complicating diagnosis and management. Neuromuscular ultrasound (NMUS) provides a non-invasive means to visualize peripheral nerve and muscle integrity, but its potential to delineate ALS subtypes has not been systematically explored.

Objective

To identify clinically meaningful ALS subgroups through unsupervised clustering of NMUS features integrated with clinical and electrophysiological data.

Methods

A total of 454 ALS patients (August 2024–December 2025) underwent standardized NMUS assessment, including muscle thickness, echogenicity, and nerve cross-sectional area, alongside ALSFRS-R, manual muscle testing (MMT), and compound muscle action potentials (CMAPs). K-means clustering was applied to standardized NMUS variables, with cluster stability assessed using silhouette coefficients, sensitivity analyses (k = 2–5), and resampling-based adjusted Rand indices. Multivariable regression examined associations between cluster membership and ALSFRS-R.

Results

Two reproducible NMUS-based subgroups were identified: a Mild cluster (n = 288, 63.4%) and a Severe cluster (n = 166, 36.6%). The Severe cluster showed reduced muscle thickness and higher echogenicity across multiple sites, together with lower ALSFRS-R scores (adjusted β = − 3.84, 95% CI − 5.41 to − 2.27, P < 0.001). Cluster membership correlated negatively with MMT and CMAP amplitudes, supporting functional and electrophysiologic validity. Stability metrics confirmed robustness of the two-cluster solution.

Conclusion

Integrating NMUS with clinical data enables objective, imaging-derived stratification of ALS patients into biologically and functionally distinct subgroups. This approach offers a pragmatic framework for phenotypic characterization and may inform personalized monitoring and trial design in ALS.