|Year : 2021 | Volume
| Issue : 1 | Page : 43-49
|Effects of yoga exercises on diabetic mellitus as validated by magnetic resonance imaging
Arush Arun Honnedevasthana1, S Vatsalya1, Shivaprasad Ashok Chikop2, Sairam Geethanath3
1 Medical Imaging Research Center, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India
2 Medical Imaging Research Center, Dayananda Sagar College of Engineering; Department of Computer Science and Engineering, Dayananda Sagar University, Bengaluru, Karnataka, India
3 Medical Imaging Research Center, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India; Columbia MR Research Center, Magnetic Resonance Research Program, Columbia University in the City of New York, New York, USA
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|Date of Submission||07-Apr-2020|
|Date of Decision||09-Sep-2020|
|Date of Acceptance||20-Oct-2020|
|Date of Web Publication||05-Feb-2021|
| Abstract|| |
Context and Aims: Effects of practicing yoga in diabetic mellitus (DM) patients have been identified to improve in control of blood glucose levels. The purpose of this work is to evaluate changes in blood flow of calf muscles after specific yoga postures for patients with DM using magnetic resonance imaging (MRI) techniques. Time of flight (TOF) magnetic resonance angiography maximum intensity projection (MIP), T1 maps, T2 maps, and diffusion-weighted Imaging are performed on volunteers and DM patients both pre- and post-exercise. Materials and Methods: TOF MIP, T1 maps with variable flip angles, and T2-weighted spin-echo imaging were performed on four volunteers (aged 30 ± 5) and DM patients (aged 32–68) preexercise, on a 1.5 T Siemens scanner. The total acquisition time was 6 min 20 s. Each volunteer and DM patient were then requested to perform yoga postures Supta Padangusthasana, Utkatasana, and Calf raise for 6 min 30 s at maximum effort, outside the scanner, and subsequently rescanned. To calculate significant signal increase, region of interests was drawn on TOF MIP coronal images in arteries of calf muscles. Student t-tests were performed to determine statistical significance. Results: Among volunteers, a significant signal increase in arteries of calf muscles was noticed, signal intensity graphs were plotted. In DM patients, signal increase in TOF MIP, T2-weighted images were seen in specific arteries (posterior, anterior tibial, and posterior tibial) of calf muscles postexercise. Discussion and Conclusions: The study indicates that yoga has a positive short-term effect on multiple DM-related foot complications. This study depicts that MRI provides potential insight into the benefits of yoga for DM patients through deriving biomarkers for preventive medicine relevant to yoga interception.
Calf Muscles, diabetic mellitus, diffusion-weighted imaging, magnetic resonance angiography, yoga
|How to cite this article:|
Honnedevasthana AA, Vatsalya S, Chikop SA, Geethanath S. Effects of yoga exercises on diabetic mellitus as validated by magnetic resonance imaging. Int J Yoga 2021;14:43-9
|How to cite this URL:|
Honnedevasthana AA, Vatsalya S, Chikop SA, Geethanath S. Effects of yoga exercises on diabetic mellitus as validated by magnetic resonance imaging. Int J Yoga [serial online] 2021 [cited 2022 Jun 27];14:43-9. Available from: https://www.ijoy.org.in/text.asp?2021/14/1/43/308737
| Introduction|| |
Diabetic mellitus (DM), widely termed as diabetes, is a diverse group of diseases distinguished by high blood glucose levels over an elongated period. The human body breaks down the consumption of sugar and carbohydrates into glucose. Glucose fuels the cells of the human body. A hormone termed insulin is responsible for carrying glucose into the bloodstreams. DM occurs in cases of the pancreas not producing enough insulin or due to cells of the body not responding to the insulin produced. Symptoms include weight loss, polyuria (frequent urination), (polydipsia) increased thirst, and (polyphagia) increased hunger. Higher blood glucose levels damage blood vessels of the organs, especially the eyes, heart, nervous system, and kidneys. DM is classified into Type 1 diabetes, Type 2 diabetes, and gestational diabetes. Type 1 diabetes is due to the autoimmune destruction of the beta-cells in the pancreas. People diagnosed with Type 1 diabetes require regular insulin dosages to sustain life. Type 2 diabetes also termed as adult-onset diabetes or noninsulin-dependent diabetes accounts for approximately 90–95% of those with DM. In people with Type 2 diabetes, cells are partially or completely incapable to respond to the insulin produced. The possibility of developing Type 2 DM increases with age, obesity, and lack of physical activity. Gestational DM occurs in cases of pregnancy and may increase or disappear after delivery. Management includes changes in the diet, regular blood glucose monitoring, and insulin injection in some cases.
As of 2015, 415 million people have been estimated to be diagnosed with diabetes worldwide, with Type 2 DM comprising about 90% of the cases, representing 8.3% of the adult population, with equal rates in both women and men. According to the World Health Organization (WHO) Statistics in India, 31,705,000 million people were diagnosed with DM in the year 2000. By the year 2030, it is also predicted to rise to 79,441,000 million in India by the WHO. The existence of DM is globally predicted to double from 171 million in the year 2000 to 366 million in the year 2030, with a maximum increase in India.
Complications of the foot due to diabetic mellitus
DM leads to severe complications in the foot such as diabetic peripheral neuropathy, peripheral artery disease, intermittent claudication, and advanced peripheral arterial disease (ischemic rest pain). DM results in narrow and hardening of the blood vessels in the foot and leg. Poor blood circulation forges the foot to be least resistant to fight infections. Nerve damage in arms or legs termed as diabetic peripheral neuropathy (DPN) results from long-term hyperglycemia (increased blood glucose levels), decreasing blood flow to the foot in Type 2 diabetes. Nerve damage decreases the capability to feel pain, heat, and cold, resulting in loss of feeling in cases of injury. Peripheral artery disease occurs due to clogging of the plague (fatty substance) in the arteries of the extremities of the body such as legs and foot. The buildup of plaque calls the arteries in becoming narrow and hard, obstructing blood flow, resulting in atherosclerosis. People with DM feel pain in the calves during running or brisk walk, and the condition is termed as intermittent claudication. Advanced peripheral artery disease outcomes in insufficient blood flow to the tissues, severe pain in the legs, and shrinking of calf muscles. High blood pressure, excess cholesterol levels, smoking, and obesity due to lack of exercise accelerate the effects of DM.
Yoga exercises for diabetic mellitus of the foot
Effects of practicing yoga exercises in DM patients have been identified to improve in control of blood glucose levels. Regular practice of yoga exercises is effectual measures in the prevention of Type 2 diabetes, where the causes are attributed to lifestyle and stress. Due to muscular exercise involved, regular yoga practice helps in reduced high glucose levels in the blood, maintaining nominal blood pressure levels, reduces the symptoms, and decreases the rate of progression of diabetes, as well as lessening the severity of further complications. Studies have emphasized the regular practice of yoga postures as a positive impact in lines with pancreatic secretion through alternate abdominal contractions and relaxation during yoga exercises. Yoga exercises alleviate calf pain by improving blood flow and muscle suppleness. Yoga exercises related to the calf muscles aid in stretching of the soleus (inner calf muscle) and the gastrocnemius (outer thigh muscles). Some of the yoga exercises related to the DM of the foot are Supta Padangushtasana (Reclining Big Toe), Uttanasana (Standing forward Bend), Utkatasana (chair pose), Garudasana (Eagle Pose), and calf raises., These asanas stretch tones and strengthen the calf muscles, thereby easing the blood circulation as depicted in [Figure 1].
|Figure 1: Illustration of the yoga postures that stimulate the blood flow in the calf muscles|
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| Materials and Methods|| |
Experimental setup and participant population
The experiments were carried out on a Siemens 1.5T Magnetom Avanto magnetic resonance imaging (MRI) scanner with a body coil for a duration of 6 months. The participants were requested to lie down in a prone position on the magnetic resonance (MR) scanner table. Two studies were performed, among which one is a study of fat distribution preexercise, and the other is to observe blood flow changes postexercise when compared to preexercise. Fiducial marker for correlation between pre- and post-exercise scan images, Vitamin E capsules visible through MR images were tapped onto the participants' imaging section. The study was performed with an informed consent obtained from the participants as per the guidelines of an ongoing ethical review board approved the study. The study was conducted on a total of nine participants (seven volunteers [aged 25 ± 5] and two patients with DM [aged 35 ± 5]).
Magnetic resonance image acquisitions
T1 maps axial slices with variable flip angles were obtained preexercise with the parameters (Slice thickness = 5 mm, TR/TE = 20/4.4500 ms, averages = 1, flip angles = 9°, 47°, field of view = 128 mm, and pixel bandwidth = 130 Hz/pixel). Pre- and post-exercise imaging was performed with the following parameters: T2 maps axial slices (slice thickness = 5 mm, TR/TE = 2600/22 ms, averages = 1, flip angle = 180°, Field Of View = 128 mm, and pixel bandwidth = 130 Hz/pixel), time of flight (TOF) angiography maximum intensity projection (MIP) coronal slices (TR/TE = 36/8.7000 ms, flip angle = 10°), diffusion-weighted axial images using a Stejskal–tanner diffusion sensitizing gradients and a single-shot echo-planar imaging (b-values 0,500 s/mm2, averages = 6, slice thickness = 5 mm, TR/TE = 4000/84 ms, Pixel bandwidth = 1345 Hz/pixel, matrix size = 128 × 128). Total acquisition time was 13 min (6 min 30 s for each protocol). Each image obtained was further processed and normalized to its maximum intensity. T1 and T2 maps were acquired in axial slices. T1-weighted imaging is performed in typically axial, to assess for fat saturation which appears bright on these images. Through the selection of an axial slice, we are creating images perpendicular to the Z direction. For most applications, the flow direction is along the body, so axial slices were obtained.
Each participant was requested to perform yoga postures (outside the scanner) at maximum effort and subsequently rescanned (postexercise scan). The protocol involved Supta Padangusthasana, Utkatasana, and Calf Raises. In Supta Padangusthasana, participants were requested to lie on their back with legs stretched, inhale, bend the right knee upward toward the chest, wrap a belt/strap around the sole, and hold the ends of the belt with hands by straightening their leg perpendicular to the floor. Participants stayed in this pose for 1 min 30 s, breathing evenly. The above protocol was repeated by switching on to the left knee. The total exercise duration was 3 min. In Utkatasana, participants were asked to stand erect with both the legs a foot apart, stretch hands to the front with palms facing downward, and bend the knees by gently pushing the pelvis downward (as if sitting in an imaginary chair) and look frontward. The total exercise duration was 1 min 30 s. In calf raises, participants were asked to stand erect and raise heels a few inches above standing on tiptoes. The position was repeated in intervals, by holding it for a few seconds and lowering the heels, thereby feeling a stretch in the calves. The total exercise duration was 2 min.
GraphPad software 2365 Northside Dr. Suite 560 San Deigo, CA 92108, the software was used for statistical analysis of data. Region of interests was drawn three times on the N1 and N2 area, and the respective total number of pixels in the area was obtained. To illustrate fat distribution in calf muscles, the number of pixels in the area of N1 was subtracted from the number of pixels in the area of N2, i.e., N3 = N2–N1 percentage of fat voxels were calculated using the formula (N3/N1) ×100. TOF MR angiography (MRA) MIP images region of interests was drawn six times on popliteal artery, peroneal artery, anterior tibial artery, posterior tibial artery to gather signal intensities both pre- and post-exercise. The mean ± standard deviation was calculated from obtained signal intensities. The same was depicted using a grouped column graph plotting mean ± standard deviation on it to compare significant signal intensity differences between pre- and post-exercise. Diffusion-weighted images apparent diffusion coefficient maps were generated through fitting diffusion into a mono-exponential model as in equation (1). S0 and S are signal intensities without and with diffusion, b is the attenuation factor, and D is the diffusion coefficient. MATLAB's curve fitting tool was used to generate the curve using nonlinear least squares (NLS) based on the trust-region algorithm, data to fit in was specified through a matrix of x rows and y columns, in which the signal intensity S is fit against each b-value ranging from 0 to 500 s/mm2.
| Results|| |
Normal calf muscles are used as a standpoint in delineating signal intensities from musculoskeletal pathologic findings. Normal calf muscles substantiate slightly higher signal intensity when compared to water on T1-weighted MR images. Below the [Figure 2]a,[Figure 2]b illustrates a T1 map axial slice data of volunteer and patient preexercise. The table in [Figure 2]c depicts fat distribution in calf muscles (40.94% ± 2.01% in left calf muscles and 48.59% ± 1.51% in right calf muscles). Below the [Figure 2]c illustrates a T1 map axial slice data of DM patient preexercise. The table in [Figure 2]d depicts fat distribution in calf muscles (53.11% ± 0.77% in left calf muscles and 52.60% ± 1.96% in right calf muscles).
|Figure 2: (a) Representative T1 map axial slice data of a volunteer preexercise, (b) representative T1 map axial slice of a diabetic mellitus patient preexercise, (c) percentage of fat voxels depicting T1 fat distribution in calf muscles of a volunteer, (d) percentage of fat voxels depicting T1 fat distribution in calf muscles of diabetic mellitus patients|
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In T2-weighted MR images, the water signal depicts higher signal intensity than fat. Volunteer's calf muscle evident slightly lesser signal intensities in comparison with water on T2-weighted MR images. Below the [Figure S1] shows a T2 map axial slice of calf muscle labeled with its arteries and veins for reference. [Figure 3]a shows a representative volunteer data depicting the T2 map axial slice. An increase in signal intensity is observed in the Fibula and Plantaris region of the calf muscle postexercise when compared to preexercise depicting its response level to exercise. [Figure 3]b shows a representative DM patient data depicting the T2 map axial slice. An increase in signal intensity is observed in the soleus region of the calf muscle postexercise when compared to preexercise depicting its response to exercise.
|Figure 3: (a) Representative volunteer data depicting T2 map axial slice, pre- and post-exercise, (b) representative diabetic mellitus patient data depicting T2 map axial slice, pre- and post-exercise showing signal intensity increase in soleus|
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Time of flight magnetic resonance angiography maximum intensity projection
It can be noticed from [Figure 4]a TOF MIP that there is a statistically significant signal increase in anterior tibial, peroneal, and posterior tibial arteries of calf muscles postexercise among volunteers. Labeling of the arteries of calf muscles is shown for reference. [Figure 4]b shows the TOF signal intensity plot of arteries in calf muscles pre- and post-exercise for volunteers. Dotted lines indicate the arteries of the right leg.(*) indicate a statistical signal difference (P < 0.05) between pre- and post-exercise for the corresponding arteries. In DM patients, signal increase in TOF MIP can be seen in specific popliteal, anterior tibial, posterior tibial arteries of calf muscles postexercise as evident in [Figure 4]c. Their corresponding signal intensity graphs [Figure 4]d are depicted. [Figure 4]e depicts a statistical comparison of signal intensity increase/decrease in specific arteries of TOF MIP MRA images between DM patients and volunteers. [Figure S2] depicts a statistical analysis of the same employing an interleaved column graph in terms of mean and standard deviation. The mean and standard deviation of the signal intensities are determined. It is to observe that signal intensity in DM patients postexercise is significantly higher in the popliteal artery, posterior tibial artery, and anterior tibial artery in comparison with volunteer data. In DM patients, the signal intensity has not been observed on the peroneal artery for both pre- and post-exercise and hence not been plotted.
|Figure 4: (a) Data of volunteers depicting time of flight magnetic resonance angiography maximum intensity projection slices at rest and after yoga exercise showing a selective increase in signal intensity, (b) signal intensities across various arteries, their average and standard deviations of volunteers, (c) data of diabetic mellitus patients depicting time of flight magnetic resonance angiography maximum intensity projection coronal slice, pre- and post-exercise, (d) signal intensities across various arteries, their average, and standard deviations of diabetic mellitus patients. Dotted lines indicate the arteries of the right leg. (e) Statistical analysis of signal intensities of images between volunteers and diabetic mellitus patients|
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Apparent diffusion coefficient (ADC) maps generated from diffusion-weighted images by fitting diffusion in the mono-exponential model for both volunteers and DM patients are illustrated and described in the proceeding sections. As per the literature survey, the range of ADC values across the arteries of the calf muscles is from 0 to 1.7 ± 0.25 (×10-3 mm2/s).? [Figure 5]a shows representative volunteer data depicting ADC maps for both pre- and post-exercise. The table in [Figure 5]b depicts a statistical comparison of ADC values (×10-3 mm2/s) increase/decrease in specific arteries of the calf muscles. As per the statistical analysis, a significant increase in ADC values is observed in tibia, gastrocnemius (medial head), soleus regions of the calf muscles postexercise when compared to preexercise. [Figure 5]c shows representative DM patient data depicting ADC maps for both pre- and post-exercise. The table in [Figure 5]d depicts a statistical comparison of ADC values (×10 - 3 mm2/s) increase/decrease in specific arteries of the calf muscles. As per the statistical analysis, a significant increase in ADC values is observed in the left tibia, left gastrocnemius (medial head), left soleus regions of the calf muscles postexercise when compared to preexercise. A decrease in ADC values postexercise is observed in the right tibia, right gastrocnemius (medial head), and right soleus regions of the calf muscles when compared to preexercise.
|Figure 5: (a) Representative data depicting MATLAB generated apparent diffusion coefficient map for slice 1 of DW axial images pre- and post-exercise, (b) statistical analysis of increase/decrease in apparent diffusion coefficient values (×10¯3 mm2/s) in both pre- and post-exercise conditions, (c) representative data depicting, MATLAB generated apparent diffusion coefficient map for slice 1 of DW axial images Pre- and Post-exercise, (d) statistical analysis of increase/decrease in apparent diffusion coefficient values (×10¯3 mm2/s) in both pre-and post-exercise conditions|
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| Discussion|| |
This study demonstrates a signal increase in anterior, peroneal, and posterior arteries for volunteers and an increase in popliteal, anterior, and posterior arteries for DM patients in calf muscles postexercise. No significant signal increase was found in popliteal arteries among volunteers. This in turn can be attributed to deoxygenation of muscles during yoga postures and delayed re-oxygenation after yoga postures. The volunteers and DM patients had no experience of yoga in the past. The study bespeaks that performing yoga postures has a positive interim effect on foot complications concerning DM.
The current work integrates yoga and variations in calf muscles for both DM patients/volunteers validated by MRI. In contrast, previous work carried out included impact of yoga on blood glucose level. Prediction of diabetes from the perspective of public health and MR utilized as a tool for osteomyelitis in diabetic foot infections. The work does not focus on regular practice yoga. That might have given more insights on the anatomy considered with respect to regular yoga practice. The quantification of fat distribution and predictive models in the calf muscle is not focused in the current work.
Future work includes perfusion imaging and MRI monitoring of patients performing yoga on a periodical basis for validation of benefits that can be accrued through interceptive medicine. This is expected to aid in the development of predictor models based on yoga for diabetes with a focus on understanding the fat distribution and blood flow in calves. The data presented here forms part of an ongoing study. In the present work, data for volunteers and DM patients are low in number. The data are expected to provide further insight over the next 6 months through an increased sample size for this ongoing study. It can be inferred through this study that MRI provides potential insight into the benefits of yoga for DM patients through deriving biomarkers for preventive medicine relevant to yoga interception.
| Conclusions|| |
The study demonstrates increase in blood flow at anterior, peroneal, and posterior arteries for volunteers and an increase in popliteal, anterior, and posterior arteries for DM patients. Regular practice of yoga postures might regulate the blood flow in the calf muscles.
The work was carried out with the approval of the Institutional Ethical Review Board. Assessment procedure was explained and consent was obtained from participants.
Financial support and sponsorship
The authors would like to thank Ministry of Electronics and Information Technology, “National Mission on Indigenous MRI” 1 (15)/2014-ME&HI, Department of Science and Technology (DST), grant no. DST/TSG/NTS/2013/100-G and DST/VGST/KFIST/LII/GRD333 for support.
Conflicts of interest
There are no conflicts of interest.
| References|| |
The United States Renal Data System. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. Bethesda, MD: The United States Renal Data System; 2014. p. 188210.
D'Adamo E, Caprio S. Type 2 diabetes in youth: Epidemiology and pathophysiology. Diabetes Care 2011;34 Suppl 2:S161-5.
Kaveeshwar SA, Cornwall J. The current state of diabetes mellitus in India. Australas Med J 2014;7:45-8.
Boulton AJ. Management of diabetic peripheral neuropathy. Clin Diabetes 2005;23:9-15
American Diabetes Association. Peripheral arterial disease in people with diabetes. Diabetes Care 2003;26:3333-41.
Aljasir B, Bryson M, Al-Shehri B. Yoga practice for the management of type II diabetes mellitus in adults: A systematic review. Evid Based Complement Alternat Med 2010;7:399-408.
Alexander GK, Taylor AG, Innes KE, Kulbok P, Selfe TK. Contextualizing the effects of yoga therapy on diabetes management: A review of the social determinants of physical activity. Fam Community Health 2008;31:228-39.
May DA, Disler DG, Jones EA, Balkissoon AA, Manaster BJ. Abnormal signal intensity in skeletal muscle at MR imaging: Patterns, pearls, and pitfalls. Radiographics 2000;20 suppl_1: S295-315.
Balaji PV. Effects of 10 weeks yoga training on blood glucose and lipid profile in type II diabetic patients. Sch J App Med Sci 2015;3:1876-9.
Bhambhani YN, et al.
Muscle oxygenation trends during dynamic exercise measured by near-infrared spectroscopy. Physiol Meas 2010;31:1257. Available from: http://iopscience.iop.org?/0967-3334/31/9/014
. [Last Accessed on 09 2019].
Vijayakumar V, Balakundi M, Metri KG. Challenges faced in diabetes risk prediction among an indigenous South Asian population in India using the Indian diabetes risk score. Public Health 2019;176:114-7.
Croll SD, Nicholas GG, Osborne MA, Wasser TE, Jones S. Role of magnetic resonance imaging in the diagnosis of osteomyelitis in diabetic foot infections. J Vascu Surg 1996;24:266-70.
Singh A, Tekur P, Nagaratna R, Nagendra HR. Impact of yoga on blood glucose level among patients with type 2 diabetes mellitus: A multicentre controlled trial. J Stem Cells 2018;13:49-55.
Shivaprasad Ashok Chikop
Department of Computer Science and Engineering, Dayananda Sagar University, Hosur Main Road, Kudlu Gate. Hongasandra, Bengaluru - 560 068, Karnataka
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
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