1933
Skeletal Muscle Adipose Tissue Quantification in Diabetic Peripheral Neuropathy1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States, 2Department of Psychiatry, New York University School of Medicine, New York, NY, United States, 3Department of Orthopaedic Surgery, New York University Langone Hospital for Joint Diseases, New York, NY, United States, 4Department of Radiology, Michigan State University, East Lansing, MI, United States, 5Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States, 6Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States
Synopsis
Diabetic peripheral neuropathy (DPN) is a common and serious complication of diabetes, where persistent hyperglycemia impairs metabolic and microvascular function, ultimately resulting in fatty infiltration. Quantitative adipose measurements using MRI may provide a useful tool to evaluate the effects of therapeutic intervention. We tested a chemical-shift based technique to measure skeletal muscle fatty infiltration in a cohort with DPN. Fatty infiltration was found to be increased in the ankle plantar flexors of DPN patients, with significant elevation detected in the medial gastrocnemius muscle.
Introduction
Diabetes mellitus affects 26 million people in the US (1), in which 30–50% of type-2 diabetes mellitus patients develop diabetic peripheral neuropathy (DPN) (2-4). DPN is characterized by impairments of metabolic and microvascular functions (5), which damage the endoneural capillaries that supply the peripheral nerves (6). Prolonged DPN can lead to local ischemic conditions in the lower extremities, eventually leading to fatty infiltration of skeletal muscle (7,8) and other degenerative effects (9-12). In the past few years, researchers have begun investigating the possibility of reversing muscle degeneration through various interventions including exercise therapy (13). Currently, there is a clear need for quantitative biomarkers to systematically evaluate therapeutic strategies that target improvement in muscle function. Adipose measurements using MRI can help fill this gap by illustrating regional muscle response to therapy or determining an infiltration threshold beyond which patients are unlikely to respond. In this work, we report adipose infiltration in a cohort of DPN patients. To do so, we used a chemical-shift based technique that provides advantages, such as a quantitative output and immunity to coil sensitivity, over prior qualitative measurements in DPN that relied on signal intensity differences to classify adipose/water in a binary manner (14).Methods
Fourteen patients with type-2 diabetes and DPN, along with 4 sex- and age-matched control subjects, were recruited for this IRB-approved study. All subjects provided written informed consent prior to participation. The subjects’ middle-calves were imaged on a 3T whole-body magnet (Prisma, Siemens) using a home-built (15) or commercially available extremity coil (15-channel knee, QED). We quantified adipose with a chemical shift-based technique (16,17) by processing in Matlab 3 GRE datasets with different echo times using an algorithm by Tsao and Jiang (18,19) that is available in the ISMRM fat/water toolbox. Imaging parameters for the GRE acquisitions were: TR/TE/ΔTE/FA = 12ms/2.2ms/0.8ms/3° and resolution = 1.7x1.7x5mm3. To investigate the spatial distribution of adipose tissue in the maps described above, we acquired fat-suppressed PSIF images (0.9mm isotropic) in the mid-calf (defined as the slice with the largest right-left dimension) that were manually segmented into pertinent muscle compartments that included the medial gastrocnemius, lateral gastrocnemius, and soleus. These muscle regions were subsequently eroded by 2.7mm in order to minimize contamination from neighboring interstitial tissue or subcutaneous adipose as described in Karampinos, et al. (20). Secondarily, a global region was created to quantify the adipose in the merged muscle and interstitial regions from which the subcutaneous adipose, tibia, and fibula regions were excluded.Results
Figure 1 shows representative proton density fat fraction maps from a DPN and control highlighting intramuscular fatty infiltration with DPN. Group summaries of fat infiltration are shown in Table 1. Overall, fatty infiltration was significantly elevated in the medial gastrocnemius for DPN.Discussion
This work used chemical-shift based adipose measurements to show increased infiltration in DPN calf muscles, which agrees with the results reported in Refs (14,21) and the general view that long-term fat storage is associated with the release of destructive cytokines that damage Schwann cells and promote peripheral neuropathy (22).
The quantitative technique used in this study provides immunity to several technical complexities such as non-uniform coil sensitivity profile, main magnetic field (B0), and excitation field (B1+), all of which can bias qualitative techniques that use contrast weighted images and statistical methods to classify fat and muscle as binary entities. Given that pixels generally contain a mixture of both fat and muscle, the quantitative technique may also provide improved robustness against partial volume effects.
We found a statistically significant increase in fat infiltration in the medial gastrocnemius region and similar trends, though not statistically significant, in other regions; it is likely that significant differences will emerge with greater enrollment of matched controls. One potential confounding factor in this preliminary study is that DPN patients had higher BMI than controls. However, we point out that BMI is known to be a poor predictor of body composition and phenotype (23). In conclusion, we anticipate that fat infiltration will prove to be an important biomarker that, at baseline, will help identify DPN subjects likely to respond to therapy, and longitudinally, will provide insight on therapy response.
Acknowledgements
This work was partially supported by NIH grants R01DK106292 and 1R01DK114428, and was performed under the rubric of the Center for Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net) at the New York University School of Medicine, which is an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183).References
1. National diabetes statistics. NIH Publication No: 11-3892: National Diabetes Information Clearinghouse; 2011.
2. Reiber GE, Pecoraro RE, Koepsell TD. Risk factors for amputation in patients with diabetes mellitus. A case-control study. Ann Intern Med 1992;117(2):97-105.
3. Lipsky BA WJ, Sun X, Johannes RS, Derby KG, Tabak YP. Developing and validating a risk score for lower-extremity amputation in patients hospitalized for a diabetic foot infection. Diabetes care 2011;34(8):1695-1700.
4. Tesfaye S, Selvarajah D. Advances in the epidemiology, pathogenesis and management of diabetic peripheral neuropathy. Diabetes/metabolism research and reviews 2012;28 Suppl 1:8-14.
5. Tesfaye S, Boulton AJ, Dickenson AH. Mechanisms and management of diabetic painful distal symmetrical polyneuropathy. Diabetes care 2013;36(9):2456-2465.
6. Boulton AJ, Vinik AI, Arezzo JC, Bril V, Feldman EL, Freeman R, Malik RA, Maser RE, Sosenko JM, Ziegler D, American Diabetes A. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes care 2005;28(4):956-962.
7. Allen MD, Major B, Kimpinski K, Doherty TJ, Rice CL. Skeletal muscle morphology and contractile function in relation to muscle denervation in diabetic neuropathy. Journal of applied physiology 2014;116(5):545-552.
8. Hilton TN, Tuttle LJ, Bohnert KL, Mueller MJ, Sinacore DR. Excessive adipose tissue infiltration in skeletal muscle in individuals with obesity, diabetes mellitus, and peripheral neuropathy: association with performance and function. Physical therapy 2008;88(11):1336-1344.
9. Andersen H. Muscular endurance in long-term IDDM patients. Diabetes care 1998;21(4):604-609.
10. Andersen H, Stalberg E, Gjerstad MD, Jakobsen J. Association of muscle strength and electrophysiological measures of reinnervation in diabetic neuropathy. Muscle & nerve 1998;21(12):1647-1654.
11. Andersen H, Gadeberg PC, Brock B, Jakobsen J. Muscular atrophy in diabetic neuropathy: a stereological magnetic resonance imaging study. Diabetologia 1997;40(9):1062-1069.
12. Andreassen CS, Jakobsen J, Andersen H. Muscle weakness: a progressive late complication in diabetic distal symmetric polyneuropathy. Diabetes 2006;55(3):806-812.
13. Kluding PM, Bareiss SK, Hastings M, Marcus RL, Sinacore DR, Mueller MJ. Physical Training and Activity in People With Diabetic Peripheral Neuropathy: Paradigm Shift. Physical therapy 2017;97(1):31-43.
14. Bittel DC, Bittel AJ, Tuttle LJ, Hastings MK, Commean PK, Mueller MJ, Cade WT, Sinacore DR. Adipose tissue content, muscle performance and physical function in obese adults with type 2 diabetes mellitus and peripheral neuropathy. Journal of diabetes and its complications 2015;29(2):250-257.
15. Brown R, Khegai O, Parasoglou P. Magnetic Resonance Imaging of Phosphocreatine and Determination of BOLD Kinetics in Lower Extremity Muscles using a Dual-Frequency Coil Array. Sci Rep 2016;6:30568.
16. Dixon WT. Simple proton spectroscopic imaging. Radiology 1984;153(1):189-194.
17. Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 1991;18(2):371-383.
18. Tsao J, Jiang Y. Hierarchical IDEAL: robust water-fat separation at high field by multiresolution field map estimation. ISMRM. Toronto 2008. p 653.
19. Jiang Y, Tsao J. Fast and robust separation of multiple chemical species from arbitrary echo times with complete immunity to phase wrapping. ISMRM. Melbourne 2012.
20. Karampinos DC, Baum T, Nardo L, Alizai H, Yu H, Carballido-Gamio J, Yap SP, Shimakawa A, Link TM, Majumdar S. Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water/fat separation. J Magn Reson Imaging 2012;35(4):899-907.
21. Tuttle L, Sinacore D, Mueller M. Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res 2012;2012(172957).
22. Obrosova IG. Diabetes and the peripheral nerve. Biochim Biophys Acta 2009;1792(10):931-940.
23. Prado CM, Siervo M, Mire E, Heymsfield SB, Stephan BC, Broyles S, Smith SR, Wells JC, Katzmarzyk PT. A population-based approach to define body-composition phenotypes. The American journal of clinical nutrition 2014;99(6):1369-1377.
Figures
