Electrical impedance myography as a biomarker of inclusion body myositis: A cross-sectional study

Clinical Neurophysiology 131 (2020) 368–371

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Electrical impedance myography as a biomarker of inclusion body myositis: A cross-sectional study Bhaskar Roy a,⇑, Seward B. Rutkove b, Richard J. Nowak a a b

Yale School of Medicine, Department of Neurology, 15 York Street, LCI 9, P.O. Box 208108, New Haven, CT 06519, USA Beth Israel Deaconess Medical Center, Department of Neurology, 330 Brookline Ave, Boston, MA 02215, USA

a r t i c l e

i n f o

Article history: Accepted 31 October 2019 Available online 6 December 2019 Keywords: IBM EIM Outcome measures Biomarker

h i g h l i g h t s  Electrical impedance myography (EIM) detects changes in muscle in inclusion body myositis (IBM).  EIM parameters strongly correlate with clinical outcome measures of IBM.  EIM can potentially be used as a biomarker of IBM.

a b s t r a c t Objective: To assess the value of electrical impedance myography (EIM) in inclusion body myositis (IBM). Methods: Patients with clinically defined IBM and healthy controls (HC) of similar age group were recruited. Each participant underwent manual muscle testing (MMT), 6-min walk test (6MWT), handgrip dynamometry, and IBM-functional rating scale assessment (IBM-FRS). EIM measurements were obtained from bilateral deltoid, biceps, forearm-flexors, quadriceps, tibialis anterior, and medial gastrocnemius. Results: Fourteen IBM patients and 12 HCs with mean age 68.6 ± 6 and 67.4 ± 5.4 years were included in the final analysis. Averaged phase value at 50 kHz (EIM50) and ratio of phase value at 50 kHz/200 kHz (EIMPR) from six-muscles were significantly lower in IBM patients when compared to HC (5.23 ± 1.34 vs 7.88 ± 1.9, p-value 0.002, and 0.55 ± 0.09 vs. 0.68 ± 0.09, p-value 0.004, respectively). A strong correlation was noted between IBM-FRS, 6MWT, disease-duration and the averaged value of EIM50 and EIMPR in the IBM patients (Spearman |rho|>0.7, p-values < 0.01). Conclusions: EIM can differentiate between IBM patients and HCs and EIM parameters correlate with clinical outcome measures. Significance: EIM may be a potential objective biomarker for IBM. A longitudinal validation study is warranted. Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

1. Introduction Inclusion body myositis (IBM) studies have traditionally used IBM-functional rating scale (IBM-FRS), 6-min walk test (6MWT) distance, or muscle strength testing as outcome measures (Greenberg, 2019). They reflect disease severity, but they are susceptible to subjective variations (Rose et al., 2001). An objective, easily applicable biomarker to assess muscle health is an unmet need in IBM research.

⇑ Corresponding author at: Yale School of Medicine, Department of Neurology, Division of Neuromuscular Medicine, P.O. Box: 208018, New Haven, CT 06520, USA. E-mail addresses: [email protected] (B. Roy), [email protected] (S.B. Rutkove), [email protected] (R.J. Nowak).

Electrical impedance myography (EIM), an objective and painless measure of muscle health, has gained attention in the past decade. EIM measures the passive electrical characteristics of muscles. In EIM, a low-intensity high-frequency alternating current is applied through a muscle by two outer electrodes, and two inner electrodes measure the resulting voltage. Measured parameters are resistance (which usually increases with muscle disease), reactance (which decreases with muscle disease), and phase (arctan of reactance/resistance, which usually decreases in muscle disease). It is sensitive to both structural and compositional changes in muscles (Sanchez and Rutkove, 2017). It has shown promising result as a potential biomarker in Duchenne muscular dystrophy (DMD), amyotrophic lateral sclerosis (ALS), and spinal muscular atrophy (SMA) in the last decade (Rutkove et al., 2012a, 2012b;

https://doi.org/10.1016/j.clinph.2019.10.030 1388-2457/Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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B. Roy et al. / Clinical Neurophysiology 131 (2020) 368–371

Rutkove et al., 2017). In this pilot study, we assessed the potential of EIM as a biomarker of muscle health status in IBM. 2. Methods Seventeen patients with clinically defined IBM based on the European Neuromuscular Center (ENMC) (Rose, 2013) criteria and 14 age-matched healthy controls (HC) were enrolled between June 2018 and July 2019 (clinicaltrials.gov || NCT03633318). This study was approved by the institutional review board at Yale University. Potentials participants were identified and recruited from the Yale Neuromuscular Clinic and the Yale IBM Registry (www. publichealth.yale.edu/ibmregistry/). Additionally, some participants reached out to us or were referred directly through our registration on the clinicaltrials.gov website. All participants provided written informed consent. Please refer to Appendix 1 and 2 for details of inclusion/exclusion criteria, and enrollment. 2.1. Clinical outcomes Manual muscle testing (MMT) of neck flexion/extension, and 15 extremity muscles (deltoid, biceps, triceps, wrist extensor, wrist flexors, finger long and short flexors, finger extensors, hip flexors, hip adductors, hip abductors, knee extensors, knee flexors, ankle dorsi-flexors and ankle planti-flexors) on each side using the MRC scale (maximum total-MMT score was 160) was obtained for all participants. Additionally, we collected the 6-MWT distance (meters), IBM-FRS score, and grip strength (JamarÒ dynamometry, kilograms) (Appendix 3). 2.2. EIM measurement EIM measurements were obtained by a commercially available hand-held device, Skulpt ChiselÒ (Skulpt Incorporation), from anatomically predefined locations from six muscles on both sides (deltoid, biceps, forearm-flexors, quadriceps, tibialis anterior, and medial gastrocnemius) by a single rater. All EIM measures were taken with muscle in a relaxed state from the top of muscle bulk at predefined anatomical locations (Rutkove et al., 2017). These muscles were chosen considering their frequent involvement in IBM. EIM phase value (degree) at 50 kHz (EIM50) and the ratio of EIM phase value at 50 kHz/200 kHz (EIMPR) were the primary impedance measures for this study. These raw data values were obtained from the manufacturer upon request, as part of the company’s standard data sharing policy for academic clinical research purposes. Investigators were blinded to these data at the time of collection. For further details of EIM measurements please refer to Appendix 3. 2.3. Data analysis Mann-Whitney U test or Kruskal-Wallis test with Bonferroni correction was performed for independent non-parametric comparison. Chi-square test was used for analysis with nominal variables. Spearman rank-order correlation was used for correlation analysis. Statistical analysis was performed using R version 3.4.3 and SPSS. No outliers were excluded and given the small sample size no further specific analyses for the outlying values were possible. 3. Results Fourteen patients with IBM (mean age 68.6 ± 6, nine men) and 12 age and gender matched HCs (age 67.4 ± 5.4, seven men) were

included in the final analysis. Age and gender were similar between the groups (p-values 0.56 and 0.76 respectively). Please refer to Table 1 and Appendix 4 for other clinical characteristics (Table 1). Two IBM patients with overt lower extremity edema below the knees were excluded since edema can cause spurious reductions in EIM values. One IBM patient with lower extremity muscle atrophy from previous injury prior to the onset of IBM were excluded after initial recruitment. One HC was noted to be on long-term prednisone after consenting for the study and he was excluded, as corticosteroid have the potential of producing a subclinical steroid myopathy, to which EIM may be sensitive. One other patient could not be studied due to device malfunction. Of the total EIM measurements made among the included patients, 7/312 muscles were excluded from analysis due to poor technical quality. Averaged EIM50 and EIMPR values from 6-muscles were significantly lower in the IBM group compared to the HC group (EIM50 5.23 ± 1.34 vs. 7.88 ± 1.9, p-value 0.002; EIMPR 0.55 ± 0.09 vs. 0.68 ± 0.09, p-value 0.004). Similar trend was noted even in individual muscles (Fig. 1A, Table 2). Mean EIM50 value was lowest in quadriceps (3.98 ± 0.96) followed by medial gastrocnemius and forearm-flexors (4.15 ± 2.36 and 5.08 ± 1.56 respectively). Between groups analysis (Kruskal-Wallis test with Bonferroni correction) showed that EIM50 value was significantly lower in quadriceps and medial gastrocnemius when compared to biceps (p-value 0.02 for both comparisons). We examined the relationship between the six-muscle averaged EIM parameters and clinical outcome measures in IBM. Strong correlation was noted with IBM-FRS score (rho 0.84 and pvalues < 0.001, for both EIM50 and EIMPR) (Fig. 1B), 6MWT (rho > 0.7 and p-values 0.002 for both EIM50 and EIMPR), disease duration (rho < 0.75, p-values  0.001). A moderate correlation was noted between the six-muscle averaged EIM parameters and averaged grip strength and total MMT (Table 3, Appendix 5). No such significant correlation was noted among the HCs (Table 3, Appendix 5). We also assessed whether the averaged EIM parameters from particular muscles correlate well with the corresponding clinical outcome. Averaged EIM50 and EIMPR from 3 lower extremity muscles showed a very strong correlation with 6MWT (rho > 0.75 and p-value < 0.001 for both). Rho between the averaged EIM50 of forearm muscles and the averaged handgrip strength was 0.54 but did not reach statistical significance (p-value 0.06). To ensure the reliability of EIM measurements, we repeated EIM measures at 24–48 h from 3 HCs and 2 IBM patients by a single rater. Intra-class correlation coefficient was >0.9 for averaged 6muscles, and averaged upper and lower extremity muscles (Appendix 6).

Table 1 Baseline demographics and outcome measures. IBM

Control

p-value

Baseline demographics Age, years ± SD Men, n (%) Disease Duration, years ± SD

68.6 ± 6 9 (64%) 9.2 ± 6.5

67.4 ± 5.4 7 (58%) NA

0.56 0.76 NA

Clinical outcome measures IBM-FRS 6 MW Grip strength (kg) Total MMT

27.8 ± 5 339 ± 133 14.7 ± 8.4 139.7 ± 10.9

40 ± 0 507 ± 58 37.2 ± 11.5 160 ± 0

<0.001 0.001 0.001 <0.001

IBM-FRS: Inclusion body myositis functional rating scale, 6MWT: 6-min walk test distance in meter, Grip strength was the average between the right and left hand, Total-MMT: Total score from manual muscle strength testing based on MRC scoring (maximum score 160), significant p-values are marked in bold.

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Fig. 1. Key representative findings. (A) Difference in the averaged value of EIMPR (ratio of phase value at 50 kHz and 200 kHz) from six-muscles between the IBM patients and healthy controls (0.55 ± 0.09 vs. 0.68 ± 0.09, p-value 0.004). (B) Correlation between the averaged value of EIMPR from six-muscles and IBM-FRS.

Table 2 EIM parameters in IBM patients and healthy controls. EIM 50

EIMPR

IBM

Control

p-value

IBM

Control

p-value

Averaged Muscle Groups Six-Muscles Upper Extremity Lower Extremity

5.23 ± 1.34 5.72 ± 1.51 4.82 ± 1.92

7.88 ± 1.9 8.78 ± 2.7 7.37 ± 2.52

0.002 0.002 0.003

0.55 ± 0.09 0.55 ± 0.1 0.53 ± 0.11

0.68 ± 0.09 0.71 ± 0.12 0.66 ± 0.11

0.004 0.004 0.006

Individual Muscles Deltoid Biceps Forearm Flexors Quadriceps Tibialis Anterior Medial Gastrocnemius

5.821 ± 1.8 6.26 ± 2.1 5.08 ± 1.56 3.98 ± 0.96 6.01 ± 3.35 4.15 ± 2.36

6.04 8.81 11.5 6.03 8.71 6.39

0.88 0.02 <0.001 0.008 0.040 0.009

0.55 0.55 0.55 0.47 0.58 0.50

0.66 0.70 0.81 0.59 0.71 0.66

0.76 0.02 <0.001 0.045 0.057 0.027

± ± ± ± ± ±

1.69 3.1 4.6 2.8 2.81 2.21

± ± ± ± ± ±

0.11 0.11 0.11 0.09 0.19 0.16

± ± ± ± ± ±

0.11 0.16 0.22 0.16 0.09 0.16

EIM50: Phase value (in degree) at 50 kHz, EIMPR: ratio of phase value at 50 kHz and 200 kHz, Upper extremity muscles were deltoid, biceps, and forearm flexors; Lower extremity muscles were quadriceps, tibialis anterior, and medial gastrocnemius; significant p-values are marked in bold.

Table 3 Correlation analysis between the EIM50 and the clinical outcome measures. EIM50 Spearman Rho

p-value

IBM IBM-FRS 6MWT Grip strength Total MMT Age Disease duration

0.84 0.74 0.58 0.62 0.41 0.84

<0.001 0.002 0.03 0.018 0.148 <0.001

Control 6 MW Grip strength Age

0.49 0.29 0.23

0.1 0.39 0.47

IBM-FRS: Inclusion body myositis functional rating scale, 6MWT: 6-min walk distance in meter, Grip strength was the average between the right and left hand (kg), Total-MMT: total score from manual muscle strength testing based on MRC scoring (maximum score 160), EIM50: Phase value (in degree) at 50 kHz, EIMPR: ratio of phase value at 50 kHz and 200 kHz; significant p-values are marked in bold.

4. Discussion These results demonstrate that EIM can detect changes in muscle health of IBM patients, and correlates to standard clinical outcome scales. There was a strong correlation between averaged EIM50 and EIMPR with IBM-FRS, 6MWT, and disease duration, among the IBM patients, further validating the clinical importance of these EIM parameters as a disease biomarker of IBM. We found significantly lower EIM50 and EIMPR values in IBM patients. Given the fatty atrophy of muscles in IBM, the phase value was expected to be lower, consistent with previous reports

(Sanchez and Rutkove, 2017; Rutkove et al., 2012a, 2012b, Rutkove et al., 2017). EIM50 value was particularly lower in quadriceps, forearm-flexors, and medial gastrocnemius muscles in IBM. This is not surprising as quadriceps and forearm-flexors are often the most affected muscles in IBM (Greenberg, 2019; Rose and Griggs, 2007). Plantar flexion weakness is not a common clinical feature of IBM, however MRI studies have shown significant fatty infiltration of medial gastrocnemius in IBM patients (Greenberg, 2019; Rose and Griggs, 2007; Morrow et al., 2016; Guimaraes et al., 2017). Moreover, the difference in the EIM parameters between IBM and HC groups was robust. We observed a strong correlation between the EIM parameters and IBM-FRS and 6MWT, whereas only a moderate correlation was noted with total-MMT and grip strength. As there is a certain pattern of muscle involvement in IBM, the total-MMT score may have diluted the overall impact of weak muscles. For grip strength it was multifactorial. Both dynamometer and the EIM measurements are probably subjected to floor effect after significant muscle atrophy. Moreover, MRI findings have suggested that flexor digitorum profundus is the most affected forearm muscles whereas the EIM measurements were probably more influenced by superficial muscles (Guimaraes et al., 2017). Of note, among the IBM patients, IBMFRS also showed strongest correlation with 6MWT, but moderate correlation with handgrip strength (Appendix 7). Not surprisingly, we noted a strong negative correlation with disease duration and EIM parameters implicating a progression of disease with time (Table 3). This study has several limitations, including a small sample size and cross-sectional design. Additionally, the instrument used provided limited number of EIM parameters at fixed frequencies, whereas, it is known that multifrequency EIM parameter, such as

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EIM-slope, the least-square fit of EIM data at several different frequencies, may provide a more robust index of muscle health. We have not corrected for subcutaneous fat which can influence the EIM parameters. Similarly, specific adjustments were not made for BMI. Generally, a multifrequency phase ratio (EIMPR) is more resistant to the impact of subcutaneous fat (Schwartz et al., 2015; Li et al., 2016). Moreover, given the similar age and gender of IBM patients and HCs, significant influence of the subcutaneous fat is unlikely (Kortman et al., 2013). Nevertheless, it would be important to incorporate BMI and subcutaneous fat in future longitudinal studies. While EIM can provide information on muscle health, it cannot provide any information on patient’s functionality, including dysphagia or self-caring ability, which is better captured by some traditional measures such as IBM-FRS (Jackson et al., 2008). Moreover, how much change in specific EIM parameters is clinically meaningful has yet to be defined. Statistical significance does not necessarily translate into clinical significance. Despite promising results from previous works with EIM in DMD, ALS, FSHD, and SMA, it has not been used as a sole primary outcome measure in a clinical trial yet (Sanchez and Rutkove, 2017; Statland et al., 2016). It has been used as an exploratory outcome measure in a natural history study of SMA (Kolb et al., 2016). However, further work on optimization of EIM technology is ongoing. In summary, our findings are promising and support the hypothesis that EIM may be a potentially useful and sensitive clinical status biomarker for IBM. However, a cross sectional study with patients at different stages of disease severity is not adequate to establish EIM as a biomarker of IBM. Further validation study assessing disease progression is warranted. The ultimate proof of EIM utility will depend on its ability to measure an impact of a therapy in future clinical trials. Disclosures Dr. Roy reports serving as an advisor/consultation for Alexion pharmaceuticals. Dr. Rutkove is a founder of the Myolex, Inc, the company which have designed the instrument used in this study. However, apart from providing us the EIM measures in a completely blinded manner, he was not directly involved in patient recruitment, data acquisition or analysis. He also reports equity in, and serves as a consultant and scientific advisor to, Myolex Inc; he is also a member of the company’s Board of Directors. The company also has an option to license patented impedance technology of which he is named as an inventor. Dr. Rutkove has also received consulting income from Biogen and Roche Pharmaceuticals. Dr. Nowak has received research support from the National Institutes of Health (NIH), Genentech, Alexion Pharmaceuticals, Ra Pharmaceuticals, Myasthenia Gravis Foundation of America, Momenta, and Grifols. He has served as consultant/advisor for Alexion Pharmaceuticals, CSL Behring, Grifols, Ra Pharmaceuticals, Roivant, and Momenta. Funding sources Dr. Roy received partial salary support through the NeuroNEXT INFINITY Fellowship, supported by NINDS (grant number 5U24NS107213-02).

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Acknowledgments We are deeply grateful to the patients and healthy controls who participated in this study, without whom this study would not have been possible. Many of them have traveled from far away to be a part of this study. We cannot thank them enough for their support of this study. We also would like to acknowledge the help of Kristin Qi. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.clinph.2019.10.030. References Greenberg SA. Inclusion body myositis: clinical features and pathogenesis. Nat Rev Rheumatol 2019;15:257–71. https://doi.org/10.1038/s41584-019-0186-x. Guimaraes JB, Zanoteli E, Link TM, de Camargo LV, Facchetti L, Nardo L, Fernandes ADRC. Sporadic inclusion body myositis: MRI findings and correlation with clinical and functional parameters. AJR Am J Roentgenol 2017;209(6):1340–7. Jackson CE, Barohn RJ, Gronseth G, Pandya S, Herbelin L, Tawil R, et al. Inclusion body myositis functional rating scale: a reliable and valid measure of disease severity. Muscle Nerve 2008;37(4):473–6. Kolb SJ, Coffey CS, Yankey JW, Krosschell K, David Arnold W, Rutkove SB, et al. Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study. Ann Clin Transl Neurol 2016;3:132–45. Kortman HG, Wilder SC, Geisbush TR, Narayanaswami P, Rutkove SB. Age- and gender-associated differences in electrical impedance values of skeletal muscle. Physiol Meas 2013;34(12):1611–22. https://doi.org/10.1088/0967-3334/34/12/ 1611. Li L, Li X, Hu H, Shin H, Zhou P. The effect of subcutaneous fat on electrical impedance myography: electrode configuration and multi-frequency analyses. PLoS ONE 2016;11(5):e0156154. Morrow JM, Sinclair CD, Fischmann A, Machado PM, Reilly MM, Yousry TA, Thornton JS, Hanna MG. MRI biomarker assessment of neuromuscular disease progression: a prospective observational cohort study. Lancet Neurol 2016;15 (1):65–77. https://doi.org/10.1016/S1474-4422(15)00242-2. Rose MR, McDermott MP, Thornton CA, Palenski C, Martens WB, Griggs RC. A prospective natural history study of inclusion body myositis: implications for clinical trials. Neurology 2001;57(3):548–50. Rose MR, Griggs RC. Inclusion body myositis. Handb Clin Neurol 2007;86:255–72. Rose MR. 188th ENMC international workshop: inclusion body myositis, 2–4 December 2011, Naarden, The Netherlands. Neuromuscul Disord 2013;23:1044–55. Rutkove SB, Caress JB, Cartwright MS, Burns TM, Warder J, David WS, et al. Electrical impedance myography as a biomarker to assess ALS progression. Amyotroph Lateral Scler 2012(a);;13(5):439–45. https://doi.org/10.3109/17482968.2012. 688837. Rutkove SB, Gregas MC, Darras BT. Electrical impedance myography in spinal muscular atrophy: a longitudinal study. Muscle Nerve 2012(b);;45(5):642–7. Rutkove SB, Kapur K, Zaidman CM, Wu JS, Pasternak A, Madabusi L, et al. Electrical impedance myography for assessment of Duchenne muscular dystrophy. Ann Neurol 2017;81(5):622–32. Sanchez B, Rutkove SB. Electrical impedance myography and its applications in neuromuscular disorders. Neurotherapeutics 2017;14(1):107–18. https://doi. org/10.1007/s13311-016-0491-x. Schwartz S, Geisbush TR, Mijailovic A, Pasternak A, Darras BT, Rutkove SB. Optimizing electrical impedance myography measurements by using a multifrequency ratio: a study in Duchenne muscular dystrophy. Clin Neurophysiol 2015;126(1):202–8. Statland JM, Heatwole C, Eichinger K, Dilek N, Martens WB, Tawil R. Electrical impedance myography in facioscapulohumeral muscular dystrophy. Muscle Nerve 2016;54(4):696–701.