Characterizing spinal muscular atrophy with electrical impedance myography

Rutkove, Seward B.; Shefner, Jeremy M.; Gregas, Matt; Butler, Hailly; Caracciolo, Jayson; Lin, Cheng‐Kuan; Fogerson, P. Michelle; Mongiovi, Phillip; Darras, Basil T.

doi:10.1002/mus.21784

Cited by 93 publications

(105 citation statements)

References 18 publications

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“…Of all the predetermined EIM measures studied, only the high‐frequency reactance slope distinguished SMA from healthy children; many of the standard measures that have shown differences in older children27, 35 did not do so in this group of infants. Moreover, EIM measures only correlated with motor function in the healthy children.…”

Section: Discussionmentioning

confidence: 86%

Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study

Kolb

Coffey

Yankey

et al. 2016

Ann Clin Transl Neurol

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113

128

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ObjectiveThis study prospectively assessed putative promising biomarkers for use in assessing infants with spinal muscular atrophy (SMA).MethodsThis prospective, multi‐center natural history study targeted the enrollment of SMA infants and healthy control infants less than 6 months of age. Recruitment occurred at 14 centers within the NINDS National Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT) Network. Infant motor function scales and putative electrophysiological, protein and molecular biomarkers were assessed at baseline and subsequent visits.ResultsEnrollment began November, 2012 and ended September, 2014 with 26 SMA infants and 27 healthy infants enrolled. Baseline demographic characteristics of the SMA and control infant cohorts aligned well. Motor function as assessed by the Test for Infant Motor Performance Items (TIMPSI) and the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP‐INTEND) revealed significant differences between the SMA and control infants at baseline. Ulnar compound muscle action potential amplitude (CMAP) in SMA infants (1.4 ± 2.2 mV) was significantly reduced compared to controls (5.5 ± 2.0 mV). Electrical impedance myography (EIM) high‐frequency reactance slope (Ohms/MHz) was significantly higher in SMA infants than controls SMA infants had lower survival motor neuron (SMN) mRNA levels in blood than controls, and several serum protein analytes were altered between cohorts.InterpretationBy the time infants were recruited and presented for the baseline visit, SMA infants had reduced motor function compared to controls. Ulnar CMAP, EIM, blood SMN mRNA levels, and serum protein analytes were able to distinguish between cohorts at the enrollment visit.

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Section: Discussionmentioning

confidence: 86%

Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study

Kolb

Coffey

Yankey

et al. 2016

Ann Clin Transl Neurol

Self Cite

113

128

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“…Another American research group 13 showed that 21 patients with types II and III SMA have higher levels of adiposity than subjects without the disease, considering either the thickness of subcutaneous fat tissue or the use of multifrequency bioelectric impedance. In agreement with this study, subcutaneous fat evaluated by a skinfold thickness caliper in 25 SMA patients (type II: 15; type III: 10 patients) was higher (type II: 12.5 mm; type III: 8.82 mm) than that of individuals without the disease (average of 5.37 mm) in the United States 26 .…”

Section: Discussionmentioning

confidence: 99%

“…Among the methods used to evaluate body composition in children, bioelectrical impedance stands out as a noninvasive and painless method, which has already been adopted by other authors while evaluating SMA patients 13 , or children and adolescents without the disease [14][15][16] . Body girth measurements are frequently used in clinical practice 17,18 due to their practical character and low price.…”

Section: Introductionmentioning

confidence: 99%

Body Composition and Chest Expansion of Type II and III Spinal Muscular Atrophy Patients

Mainenti

Sousa

Dias

et al. 2013

J. Hum. Growth Dev.

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Introduction: spinal muscular atrophy patients present muscle weakness, orthopedic problems, nutritional complications and respiratory impairment. Lean mass and fat mass modifications are also expected in this population. Objective: to verify the body composition and chest expansion of type II and III spinal muscular atrophy patients. Methods: fourteen individuals were evaluated: seven patients in Group I of 9 (7-12) years of age, weighing 29.7 (23.5-60.0) kg; and seven children without the disease in Group II of 9 (9-12) years, weighing 31.0 (27.8-54.1) kg. Patients' monofrequency bioelectrical impedance was used for analyze body composition. Chest, hip and abdominal girths were measured by a flexible steel tape. The SPSS program was used to statistical analysis (p < 0.05). Results: patients presented higher impedance: 1416.9 (850.5-1559.1) vs 788.0 (683.6-853.8), P < 0.05; and fat percentage: 31.2 (23.9-46.6) vs 19.1 (14.9-27.0)%, P < 0.05. The difference between forced inspiration and forced expiration thorax girth was smaller for patients when comparing to Group II: 3.0 (0.8-4.4) vs. 5.0 (3.9-6.5) cm, P<0.05. Conclusions: patients with spinal muscular atrophy presented higher adiposity and lower chest expansion.

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“…In this study, we take this approach for the first time in neuromuscular disease, by combining features from 2 unrelated technologies, quantitative muscle ultrasound (QMU) 10 -16 and electrical impedance myography (EIM) 17,18 to determine whether SVM algorithms can more accurately classify normal muscles and those affected by type 2 and type 3 spinal muscular atrophy (SMA) than either method alone.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms to classify spinal muscular atrophy subtypes

Srivastava¹,

Darras²,

Wu³

et al. 2012

Neurology

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Objectives: The development of better biomarkers for disease assessment remains an ongoing effort across the spectrum of neurologic illnesses. One approach for refining biomarkers is based on the concept of machine learning, in which individual, unrelated biomarkers are simultaneously evaluated. In this cross-sectional study, we assess the possibility of using machine learning, incorporating both quantitative muscle ultrasound (QMU) and electrical impedance myography (EIM) data, for classification of muscles affected by spinal muscular atrophy (SMA).Methods: Twenty-one normal subjects, 15 subjects with SMA type 2, and 10 subjects with SMA type 3 underwent EIM and QMU measurements of unilateral biceps, wrist extensors, quadriceps, and tibialis anterior. EIM and QMU parameters were then applied in combination using a support vector machine (SVM), a type of machine learning, in an attempt to accurately categorize 165 individual muscles.Results: For all 3 classification problems, normal vs SMA, normal vs SMA 3, and SMA 2 vs SMA 3, use of SVM provided the greatest accuracy in discrimination, surpassing both EIM and QMU individually. For example, the accuracy, as measured by the receiver operating characteristic area under the curve (ROC-AUC) for the SVM discriminating SMA 2 muscles from SMA 3 muscles was 0.928; in comparison, the ROC-AUCs for EIM and QMU parameters alone were only 0.877 (p Ͻ 0.05) and 0.627 (p Ͻ 0.05), respectively. Conclusions:Combining EIM and QMU data categorizes individual SMA-affected muscles with very high accuracy. Further investigation of this approach for classifying and for following the progression of neuromuscular illness is warranted. Neurology ® 2012;79:358-364 GLOSSARY AUC ϭ area under the curve; EIM ϭ electrical impedance myography; PCA ϭ principal component analysis; QMU ϭ quantitative muscle ultrasound; ROC ϭ receiver operating characteristic; SMA ϭ spinal muscular atrophy; SVM ϭ support vector machine.

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Characterizing spinal muscular atrophy with electrical impedance myography

Cited by 93 publications

References 18 publications

Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study

Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study

Body Composition and Chest Expansion of Type II and III Spinal Muscular Atrophy Patients

Machine learning algorithms to classify spinal muscular atrophy subtypes

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