Muscle diffusion tensor imaging in glycogen storage disease V (McArdle disease)

  • R. Rehmann
  • L. SchlaffkeEmail author
  • M. Froeling
  • R. A. Kley
  • E. Kühnle
  • M. De Marées
  • J. Forsting
  • M. Rohm
  • M. Tegenthoff
  • T. Schmidt-Wilcke
  • M. Vorgerd



To evaluate differences in diffusion parameters in thigh muscles in patients with glycogen storage disease type V (McArdle disease) using muscle diffusion tensor imaging (mDTI) compared to healthy controls


In this prospective study, we evaluated thigh muscles from hip to knee of 10 McArdle patients (5 female, mean age 33.7 ± 14.4 years) and 10 healthy age- and gender-matched volunteers. MRI scans were performed at 3 T and comprised mDTI, T1-weighted and T2-weighted imaging between May 2015 and May 2017. Needle biopsy of the vastus lateralis muscle was performed in three McArdle patients. The muscle tissue was analyzed by using histochemical and enzyme-histochemical techniques for glycogen content and histopathological changes. Mean values of the eigenvalues (λ1λ3), fractional anisotropy (FA), and mean diffusivity (MD) were obtained for the vastus lateralis, vastus medialis, rectus femoris, biceps femoris, semitendinosus, and semimembranosus and compared between groups using Student’s t tests, as well as ANCOVA; significance level was set at p < 0.05.


Needle biopsy showed intracellular glycogen accumulation in skeletal muscle fibers of three McArdle patients. Extracellular histopathological changes were not found. Muscle DTI analysis did not show statistically significant differences between patients and controls for any of the muscles.


Despite intracellular glycogen accumulation in the three biopsy samples, mDTI parameters were not altered in McArdle patients compared to controls. We conclude that the currently used mDTI acquisition and processing lack the sensitivity to detect intracellular changes due to accumulated glycogen in this cohort of McArdle patients.

Key Points

Despite intracellular glycogen accumulation in three examined biopsy samples, mDTI parameters were not altered in McArdle patients compared to controls.

In its current form, diffusion MR does not provide additional information in quantifying intracellular glycogen accumulations within skeletal muscle fibers in McArdle patients.


Muscle Diffusion tensor imaging Anisotropy Glycogen storage disease 



Adenosine triphosphate


Creatine kinase


Fractional anisotropy


Glycogen storage disease


Hematoxylin and eosin


Mean diffusivity


Muscle diffusion tensor imaging


Periodic acid-Schiff






Trichrome Gomori



We thank Philips Germany, especially Burkhard Maedler, for continuous scientific support.


This study has received funding from FoRUM (research grant by Ruhr-University Bochum), Grant Number: F867R-2016.

Compliance with ethical standards


The scientific guarantor of this publication is Prof. Matthias Vorgerd.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.


• Prospective

• Cross-sectional study

• Performed at one institution

Supplementary material

330_2018_5885_MOESM1_ESM.pptx (47 kb)
Supplementary Table 5 (PPTX 46 kb)


  1. 1.
    De Castro M, Johnston J, Biesecker L (2015) Determining the prevalence of McArdle disease from gene frequency by analysis of next generation sequencing data: McArdle prevalence by NGS data. Genet Med 17:1002–1006CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Quinlivan R, Buckley J, James M et al (2010) McArdle disease: a clinical review. J Neurol Neurosurg Psychiatry 81:1182–1188CrossRefGoogle Scholar
  3. 3.
    Lebo RV, Gorin F, Fletterick RJ et al (1984) High-resolution chromosome sorting and DNA spot-blot analysis assign McArdle’s syndrome to chromosome 11. Science (80- ) 225:57–59CrossRefGoogle Scholar
  4. 4.
    McArdle B (1951) Myopathy due to a defect in muscle glycogen breakdown. Clin Sci 10:13–35PubMedPubMedCentralGoogle Scholar
  5. 5.
    Preisler N, Haller RG, Vissing J (2015) Exercise in muscle glycogen storage diseases. J Inherit Metab Dis 38:551–563CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Godfrey R, Quinlivan R (2016) Skeletal muscle disorders of glycogenolysis and glycolysis. Nat Rev Neurol 12:393–402CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Leite A, Oliveira N, Rocha M (2012) McArdle disease: a case report and review. Int Med Case Rep J 5:1–4PubMedPubMedCentralGoogle Scholar
  8. 8.
    Nogales-Gadea G, Santalla A, Ballester-Lopez A et al (2016) Exercise and preexercise nutrition as treatment for McArdle disease. Med Sci Sports Exerc 48:673–679CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Quinlivan R, Martinuzzi A, Schoser B (2014) Pharmacological and nutritional treatment for McArdle disease (Glycogen Storage Disease type V). Cochrane Database Syst Rev 12:CD003458Google Scholar
  10. 10.
    Vorgerd M, Zange J (2007) Treatment of glycogenosys type V (McArdle disease) with creatine and ketogenic diet with clinical scores and with 31P-MRS on working leg muscle. Acta Myol 26:61–63PubMedPubMedCentralGoogle Scholar
  11. 11.
    De Kerviler E, Leroy-Willig A, Duboc D, Eymard B, Syrota A (1996) MR quantification of muscle fatty replacement in McArdle’s disease. Magn Reson Imaging 14:1137–1141Google Scholar
  12. 12.
    Nadaj-Pakleza AA, Vincitorio CM, Laforêt P et al (2009) Permanent muscle weakness in McArdle disease. Muscle Nerve 40:350–357CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Zange J, Grehl T, Disselhorst-Klug C et al (2003) Breakdown of adenine nucleotide pool in fatiguing skeletal muscle in McArdle’s disease: a noninvasive 31P-MRS and EMG study. Muscle Nerve 27:728–736CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Heinicke K, Dimitrov IE, Romain N et al (2014) Reproducibility and absolute quantification of muscle glycogen in patients with glycogen storage disease by 13C NMR spectroscopy at 7 tesla. PLoS One 9:e108706Google Scholar
  15. 15.
    Lewis SF, Haller RG, Cook JD, Nunnally RL (1985) Muscle fatigue in McArdle’s disease studied by 31P-NMR: effect of glucose infusion. J Appl Physiol (1985) 59:1991–1994Google Scholar
  16. 16.
    Gruetter R, Kaelin P, Boesch C, Martin E, Werner B (1990) Non-invasive31P magnetic resonance spectroscopy revealed McArdle disease in an asymptomatic child. Eur J Pediatr 149:483–486Google Scholar
  17. 17.
    de Kerviler E, Leroy-Willig A, Jehenson P, Duboc D, Eymard B, Syrota A (1991) Exercise-induced muscle modifications: study of healthy subjects and patients with metabolic myopathies with MR imaging and P-31 spectroscopy. Radiology 181:259–264Google Scholar
  18. 18.
    Ai T, Yu K, Gao L et al (2014) Diffusion tensor imaging in evaluation of thigh muscles in patients with polymyositis and dermatomyositis. Br J Radiol 87:20140261CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Budzik JF, Balbi V, Verclytte S, Pansini V, Le Thuc V, Cotten A (2014) Diffusion tensor imaging in musculoskeletal disorders. Radiographics 34:E56–E72Google Scholar
  20. 20.
    Damon BM, Froeling M, Buck AKW et al (2016) Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions. NMR Biomed 30:1–13Google Scholar
  21. 21.
    Froeling M, Oudeman J, Strijkers GJ et al (2015) Muscle changes detected with diffusion-tensor imaging after long-distance running. Radiology 274:548–562CrossRefPubMedGoogle Scholar
  22. 22.
    Hooijmans MT, Damon BM, Froeling M et al (2015) Evaluation of skeletal muscle DTI in patients with Duchenne muscular dystrophy. NMR Biomed:1589–1597.
  23. 23.
    Ponrartana S, Ramos-Platt L, Wren TA et al (2015) Effectiveness of diffusion tensor imaging in assessing disease severity in Duchenne muscular dystrophy: preliminary study. Pediatr Radiol 45:582–589CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bergström J (1975) Percutaneous needle biopsy of skeletal muscle in physiological and clinical research. Scand J Clin Lab Invest 35:609–616CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ekblom B (2017) The muscle biopsy technique. Historical and methodological considerations. Scand J Med Sci Sports 27:458–461CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Shanely RA, Zwetsloot KA, Triplett NT, Meaney MP, Farris GE, Nieman DC (2014) Human skeletal muscle biopsy procedures using the modified Bergström technique. J Vis Exp 10:51812Google Scholar
  27. 27.
    Schlaffke L, Rehmann R, Froeling M et al (2017) Diffusion tensor imaging of the human calf: variation of inter- and intramuscle-specific diffusion parameters. J Magn Reson Imaging 4:1137–1148
  28. 28.
    Damon BM (2008) Effects of image noise in muscle diffusion tensor (DT)-MRI assessed using numerical simulations. Magn Reson Med 60:934–944CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Froeling M, Nederveen AJ, Nicolay K, Strijkers GJ (2013) DTI of human skeletal muscle: the effects of diffusion encoding parameters, signal-to-noise ratio and T2 on tensor indices and fiber tracts. NMR Biomed 26:1339–1352Google Scholar
  30. 30.
    Froeling M, Nederveen AJ, Heijtel DF et al (2012) Diffusion-tensor MRI reveals the complex muscle architecture of the human forearm. J Magn Reson Imaging 36:237–248CrossRefPubMedGoogle Scholar
  31. 31.
    Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E (2016) Denoising of diffusion MRI using random matrix theory. Neuroimage 142:394–406Google Scholar
  32. 32.
    Leemans A, Jones DK (2009) The B-matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med 61:1336–1349CrossRefPubMedGoogle Scholar
  33. 33.
    Veraart J, Sijbers J, Sunaert S, Leemans A, Jeurissen B (2013) Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. Neuroimage 81:335–346Google Scholar
  34. 34.
    Froeling M, Tax CMW, Vos SB, Luijten PR, Leemans A (2017) “MASSIVE” brain dataset: multiple acquisitions for standardization of structural imaging validation and evaluation. Magn Reson Med 77:1797–1809Google Scholar
  35. 35.
    Li GD, Liang YY, Xu P, Ling J, Chen YM (2016) Diffusion-tensor imaging of thigh muscles in Duchenne muscular dystrophy: correlation of apparent diffusion coefficient and fractional anisotropy values with fatty infiltration. AJR Am J Roentgenol 206:867–870Google Scholar
  36. 36.
    Scheel M, Winkler T, Scheel M et al (2013) Fiber type characterization in skeletal muscle by diffusion tensor imaging. NMR Biomed 26:1220–1224Google Scholar
  37. 37.
    Berry DB, Regner B, Galinsky V, Ward SR, Frank LR (2018) Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle. Magn Reson Med 80:317–329Google Scholar
  38. 38.
    Jehenson P, Leroy-Willig A, de Kerviler E, Duboc D, Syrota A (1993) MR imaging as a potential diagnostic test for metabolic myopathies: importance of variations in the T2 of muscle with exercise. Am J Roentgenol 161:347–351Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • R. Rehmann
    • 1
  • L. Schlaffke
    • 1
    • 2
    Email author return OK on get
  • M. Froeling
    • 2
  • R. A. Kley
    • 1
  • E. Kühnle
    • 1
  • M. De Marées
    • 3
  • J. Forsting
    • 1
  • M. Rohm
    • 1
  • M. Tegenthoff
    • 1
  • T. Schmidt-Wilcke
    • 4
    • 5
  • M. Vorgerd
    • 1
  1. 1.Department of Neurology, BG-University Hospital BergmannsheilRuhr-University BochumBochumGermany
  2. 2.Department of RadiologyUniversity Medical Centre UtrechtUtrechtThe Netherlands
  3. 3.Department of Sports Medicine and Sports Nutrition, Faculty of Sport ScienceRuhr-University BochumBochumGermany
  4. 4.St. Mauritius TherapieklinikMeerbuschGermany
  5. 5.Institute of Clinical Neuroscience and Medical PsychologyHeinrich Heine University of DüsseldorfDüsseldorfGermany

Personalised recommendations