|Year : 2016 | Volume
| Issue : 1 | Page : 5-9
Effect of current glycemic control on qualitative body composition in sedentary ambulatory Type 2 diabetics
Jayesh Dalpatbhai Solanki1, Amit H Makwana2, Hemant B Mehta1, Panna Kamdar1, Pradnya A Gokhale3, Chinmay J Shah1
1 Department of Physiology, Government Medical College, Bhavnagar, Gujarat, India
2 Department of Physiology, GMERS Medical College, Junagadh, Gujarat, India
3 Department of Physiology, Government Medical College, Baroda, Gujarat, India
|Date of Web Publication||15-Apr-2016|
Jayesh Dalpatbhai Solanki
F1, Shivganga Appartments, Plot No. 164, Bhayani Ni Waadi, Opposite Bawaliya Hanuman Temple, Gadhechi Wadlaa Road, Bhavnagar - 364 001, Gujarat
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Obesity and Type 2 diabetes mellitus are on rise with cause–effect relationship. Diabetics monitor blood sugar, neglecting qualitative body composition, leaving residual threat of ectopic fat unattended. We tried to correlate glycemic triad with parameters of body composition derived objectively by bioelectrical impedance analysis (BIA). Materials and Methods: A sample of 78 under treatment sedentary Type 2 diabetics of either sex with known glycemic and lipidemic control from our city. Following baseline assessment measurement was done by instrument Omron Karada Scan (Model HBF-510, China) using the principle of tetra poplar BIA to derive parameters of body composition. We tried to correlate glycemic triad with these parameters, both directly as well as after defining them as per established cutoff norms. Results: We found poor glycemic control in the study group (20% for Hb1AC), high body mass index, subcutaneous fat, visceral fat (VF), total body fat (TBF), and lesser mass of skeletal muscle in Type 2 diabetics. However, there were small, insignificant, and inconsistent difference of these parameters while directly correlating with the fasting blood sugar, postprandial blood sugar, and glycosylated hemoglobin. On qualitative assessment, the impact of glycemic control as per standard norms, the risk of high VF, high TBF, low skeletal muscle mass was though high (between 1 and 2) in Type 2 diabetics with poor glycemic control as compared to good glycemics, but each strength lacks statistical significance. Conclusion: BIA reveals that Type 2 diabetics have more ectopic fat on expense of skeletal muscle that do not correlate with current glycemic status, both quantitatively and qualitatively. Measurement of body composition can be included and subjects can be motivated for lifestyle modification strategies while managing metabolic derangements of Type 2 diabetes.
Keywords: Bioelectrical impedance analysis, body composition, glycemic triad, Type 2 diabetes
|How to cite this article:|
Solanki JD, Makwana AH, Mehta HB, Kamdar P, Gokhale PA, Shah CJ. Effect of current glycemic control on qualitative body composition in sedentary ambulatory Type 2 diabetics. Niger Med J 2016;57:5-9
|How to cite this URL:|
Solanki JD, Makwana AH, Mehta HB, Kamdar P, Gokhale PA, Shah CJ. Effect of current glycemic control on qualitative body composition in sedentary ambulatory Type 2 diabetics. Niger Med J [serial online] 2016 [cited 2020 Feb 27];57:5-9. Available from: http://www.nigeriamedj.com/text.asp?2016/57/1/5/180562
| Introduction|| |
India is facing a shift from undernutrition to overnutrition, later producing obesity and its aftermaths like Type 2 diabetes mellitus (T2DM). There is alarming rise of T2DM in India, a country with an ethnic predisposition for it. Insulin resistance proceeded by obesity is the link between T2DM and cardiovascular deaths  and at least the first event can be monitored and prevented primarily to stop this chain of progression. However, there is a lack of awareness regarding obesity and optimum body composition which are not given importance as due as glycemic control measurement and maintenance in case of known T2DM subjects. Body mass index (BMI), though defining obesity, falls short of qualitative inference of body composition  while the modern imaging techniques that overcomes this deficit are far from reach in our community to the most. Bioelectric impedance analysis (BIA) provides an objective, cost-effective method of qualitative and quantitative body composition analysis with proven efficacy in our population  with inference about visceral fat (VF) that is a risk factor itself and a negative affecter for glycemic control. However, once diagnosed what is the effect of these parameters on body composition especially body fat, remains a question. We conducted this study to find the impact of glycemic control using glucose triad on BIA derived parameters of body composition in terms of both quantity and quality.
| Materials and Methods|| |
We conducted a cross-sectional observational study from January 2013 to April 2014 in Clinical Research Lab, Physiology Department of our Medical College.
Sample size of 78 for current population of city 6,00,000 and prevalence of T2DM 7.33% in urban area of our state  gave us 90% confidence interval keeping margin for error 5% as calculated by sample size calculator software GraphPad in Stat 3 software (demo version free software of GraphPad Software, Inc., California, USA).
Following approval from the institutional review board and informed consent from participants, the study was carried out in under treatment ambulatory sedentary Type 2 diabetics. Subjects were recruited from medicine outpatient department (OPD) of a tertiary care teaching hospital attached to our medical college and from private OPDs.
About 78 Type 2 diabetics (44 males and 34 females) were undertaken in the age group 30–80 years, living sedentary life, not taking insulin, taking regular medicines, and having a recent investigation for glycemic or lipidemic control. To make the sample heterogeneous, we included patients with and without hypertension, with and without statin therapy, with or without family history of Type 2 diabetes, coming from various socioeconomic statuses so as to make a fairly representative sample of the population.
To evaluate glycemic control of the Type 2 diabetics subjects underwent measurement of (1) fasting blood sugar (FBS) and postprandial blood sugar (PP2BS) done by GOD-POD method (2) glycosylated hemoglobin (HbA1c) done by immunoturbidimetry method. These tests were done as a recent report by fully auto analyzer I LAB-650/MIURA, A-1004 at NAAC certified Biochemistry department of our college using standard SOPs. We defined glycemic control as per criteria laid by American Diabetes Association 2014, and good glycemic control was defined as (1) HbA1c ≤7 g %, (2) FBS ≤126 mg%, and (3) PP2BS ≤180 mg %. Subjects were divided into two groups based on these criteria into those with good or poor glycemic control.
Subjects meeting inclusion and exclusion criteria were undertaken for the study with initial assessment in the form of personal history, medical history, anthropometric measurement, and recent reports of glycemic controls including, FBS, PP2BS, and HbA1c, and lipidemic control.
After entering age, gender, and height taken by stadiometer subject was allowed to stand on the instrument after its calibration. A digital, portable, noninvasive instrument Omron Karada Scan (Model HBF-510, China) working on the principle of tetra polar bioelectrical impedance analysis was used that passes electric current of 500 µA at frequency 5 kHz to scan the whole body to derive regional body composition. We enrolled ambulatory outdoor patients only and took the reading in the morning so as to avoid dehydration  that otherwise would affect the accuracy of this method.
For qualitative analysis, we defined standard norms as-1 (BMI ≤252) (VF ≤103) total body fat (TBF) and skeletal muscle mass as per standard guidelines.
The data were transferred on Excel spreadsheet, and descriptive analysis was expressed as a mean ± standard deviation. All calculations were accomplished by GraphPad InStat 3 software. We evaluated the difference between of these body composition parameters among groups based on glycemic control quantitatively by Student's t-test and qualitative risk calculation by Odds ratio using defined cutoff norms of body composition parameters. Any observed difference was considered statistically significant with P < 0.05.
| Results|| |
[Table 1] shows baseline data of study group reflecting the participation of both sexes, average duration of Type 2 diabetes 7.5 years, high average BMI, good lipidemic control, and poor glycemic control with respect to HbA1c.
[Table 2] shows direct quantitative correlation between values of BIA derived parameters of body composition with means of glycemic control, namely, HbA1c, FBS, and PP2BS reflecting that subjects, regardless the glycemic status, showed the high-fat low muscle mass pattern of body composition which is slightly more so in case of poor glycemics, yet statistically insignificant in most instances.
|Table 2: Quantitative comparison of parameters of body fat distribution among groups based on glycemic control (defined by therapeutic goals ADA guidelines 2014)|
Click here to view
To get a clear picture for correlation, we defined cutoff points for variables, namely, BMI, VF, TBF, skeletal muscle mass, and tried to calculate odds risk ratio for their abnormality owing to exposure to uncontrolled blood sugar for all three measures of glycemic triad. This qualitative comparison showed that there was small, inconsistent, and insignificant odds risk of poor glycemic control (HbA1c, FBS, and PP2BS) on parameters of body composition with none bearing adequate strength of association [Table 3].
|Table 3: Qualitative comparison of parameters of body fat distribution (defined by standard cut off norms) among groups based on glycemic control (defined by therapeutic goals ADA guidelines 2014)|
Click here to view
| Discussion|| |
Obesity has officially been declared a disease by American Medical Association in 2013 and India is no different to other countries when it comes to seriousness of its increasing magnitude that too with unique attributes. India shares one-third of the total burden of T2DM  worldwide that is further compounded by obesity doubling the cost of its management. For given BMI, South Asians have greater adiposity and visceral and ectopic adipose tissue accumulation. Few studies have revealed more adverse fat distribution at BMI >21 kg/m  in South Asians as compared with Caucasians in whom considerable dyslipidemia and dysglycemia are unseen until BMI exceeds 30 kg/m . In previous studies, in Type 2 diabetics of our region, we found poor glycemic control and high prevalence of many preventable risk factor. We also found that Type 2 diabetics have more ectopic fat on expense of skeletal muscle that persists even after matching by weight or BMI, both quantitatively and qualitatively. With this propensity, it seems quite worthful to know body composition and body fat, in particular, both quantitatively and qualitatively in not only high-risk obese subjects but also in Type 2 diabetics with respect to current glycemic control.
T2DM patients of our study had high BMI, VF, subcutaneous fat (SF), TBF, and lesser muscle mass that is attributed to high mean age, average duration of disease 7.5 years, poor glycemic control and sedentary lifestyle, apart from disease itself. We found no effect of glycemic control on parameters of body fat distribution measured indirectly by BIA in ambulatory sedentary T2DM subjects, for almost all three of glycemic triad in terms of both quality and quantity with exception of TF-FBS and SF-HbA1c. This is similar to a recently reported study. Correction of hyperglycemia decreases the risk of microvascular complications but macrovascular complications to a lesser extent that otherwise represent the primary cause of mortality with heart attacks and stroke accounting for around 80% of all deaths.,, Most diabetic patients undergo regular scrutiny of glycemic and lipidemic control, and when it comes to body composition, BMI is perhaps only option offered to the most. However, BMI falls short of many qualitative inferences especially VF, which can be objectively measured by BIA. Obesity is the primary event, and one of the risk factor for T2DM and once T2DM ensues all measures turn to secondary in this regard.
T2DM, a multifaceted metabolic derangement, is more a disease of abnormally altered lipid metabolism than merely that of carbohydrates. It is evident now that it is not the disordered glucose metabolism but rather the chronic elevation of free fatty acid that is the culprit for T2DM. Diabetic patients target blood sugar and blood lipid control at the same time neglecting the deranged pattern of body composition in the form of increased ectopic fat at expense of protein that is associated with higher cardiovascular comorbidities. VF has now proven to bring about Insulin resistance that leads to diabetes and there are evidence based on bariatric surgery  and exercise intervention studies  that reduced VF improves glycemic status as well as insulin resistance. However, the situation is further compounded by the facts that treatment for diabetes itself causes adiposity, preventive pharmacotherapy has the least effect on optimizing body composition, mild to moderate exercise affects body fat little, and Indians are most vulnerable to obesity. There are fallacies while relating glycemic status and body compositions such as effect of glycemic variability making current glycemic status not completely reliable, poor glycemic control in Indian diabetics  especially with regard to HbA1c, use of subjective methods such as waist-hip ratio, no glycemic threshold for micro or macrovascular complications of T2DM  and ethnic vulnerability of Indians for obesity-related complications.
Obesity is a disease and not a choice. Prevention of weight gain is one of the therapeutic goals for T2DM patients. Many rely solely on statins which in the absence of other lifestyle interventions are ineffective to optimize body composition as shown by our another work. We also found current lipidemic control to affect body fat only insignificantly. Weight reduction is good not only for improving glycemic control but also for reduction of cardiovascular risk. Weight regain is very common, and weight loss is difficult to maintain. Subjects can be motivated for optimum body composition by regular BIA scan for body fat and self-monitoring can definitely be reinforced. One can be motivated for lifestyle modifications such as diet plans, exercise, and smoking cessations that can serve as measures of secondary prevention achieved by self-care in T2DM subjects and measures of primary prevention in those at risk by self-awareness, in both the cases, helping to fight against modern epidemic of obesity and its aftermath T2DM.
This study has few limitations such as its cross-sectional nature, small sample size, presence of risk factors which cannot be eliminated and the method which is based on a predictive formula, tending to underestimate body fat. However, it showed that in T2DM patients, abnormality of body composition especially VF has no correlation with glycemic status, hence requiring special attention for knowing, targeting, and achieving optimum body composition using simple methods such as BIA to make sure that prevention turns better than cure.
| Conclusion|| |
We found no correlation of current glycemic status with abnormally elevated ectopic fat and reduced muscle mass in under treatment sedentary Type 2 diabetics, suggesting the need for qualitative body composition by methods like BIA, optimizing it by lifestyle modifications, and maintaining it to reduce adverse outcomes in an attempt to fight against obesity.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Chopra SM, Misra A, Gulati S, Gupta R. Overweight, obesity and related non-communicable diseases in Asian Indian girls and women. Eur J Clin Nutr 2013;67:688-96.
Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab 2008;93 11 Suppl 1:S9-30.
DeFronzo RA. Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: The missing links. The Claude Bernard lecture 2009. Diabetologia 2010;53:1270-87.
Dhurandhar NV. Obesity in India: Opportunities for clinical research. J Obes Metab Res 2014;1:25-9.
Ferrannini E, Natali A, Bell P, Cavallo-Perin P, Lalic N, Mingrone G. Insulin resistance and hypersecretion in obesity. European Group for the Study of Insulin Resistance (EGIR). J Clin Invest 1997;100:1166-73.
Kalra S, Mercuri M, Anand SS. Measures of body fat in South Asian adults. Nutr Diabetes 2013;3:e69.
Gastaldelli A. Visceral fat and metabolic control in diabetes. J Clin Endocrinol Metab 2002;87:5098-103.
Koria B, Kumar R, Nayak A, Kedia G. Prevalence of diabetes mellitus in urban population of Ahmadabad city, Gujarat. Natl J Community Med 2013;4:398-401.
American Diabetes Association. Standards of medical care in diabetes – 2014. Diabetes Care 2014;37 Suppl 1:S14-80.
Brown SP, Miller WC, Eason JM. VO2 max. In: Brown SP, Miller WC, Eason JM, editors. Exercise Physiology: Basis of Human Movement in Health and Disease. 2nd
ed. China: Lippincott Williams and Wilkins; 2006. p. 324.
China: Omron Healthcare; 2008. Omron Instruction Manual. Full Body Sensor Body Composition Monitor and Scale Model HBF-510.
Unnikrishnan AG, Bhattacharyya A, Baruah MP, Sinha B, Dharmalingam M, Rao PV. Importance of achieving the composite endpoints in diabetes. Indian J Endocrinol Metab 2013;17:835-43.
Misra A, Khurana L. Obesity-related non-communicable diseases: South Asians vs white Caucasians. Int J Obes (Lond) 2011;35:167-87.
Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R, et al.
Defining obesity cut points in a multiethnic population. Circulation 2007;115:2111-8.
Solanki JD, Makwana AH, Mehta HB, Gokhale PA, Shah CJ. A study of prevalence and association of risk factors for diabetic vasculopathy in an urban area of Gujarat. J Family Med Prim Care 2013;2:360-4.
Solanki JD, Makwana AH, Mehta HB, Gokhale PA, Shah CJ. Body composition in type 2 diabetes: Change in quality and not just quantity that matters. Int J Prev Med 2015;6:122.
Mavros Y, Kay S, Anderberg KA, Baker MK, Wang Y, Zhao R, et al.
Changes in insulin resistance and HbA1c are related to exercise-mediated changes in body composition in older adults with type 2 diabetes: Interim outcomes from the GREAT2DO trial. Diabetes Care 2013;36:2372-9.
Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al.
Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): Prospective observational study. BMJ 2000;321:405-12.
Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes of death in the WHO multinational study of vascular disease in diabetes. Diabetologia 2001;44 Suppl 2:S14-21.
Ganong WF. Fat metabolism in diabetes. In: Barret KE, Barman SM, Boitano S, Brooks HL, editors. Review of Medical Physiology. 24th
ed. New York: McGraw Hill; 2012. p. 439.
Boden G, Laakso M. Lipids and glucose in type 2 diabetes: What is the cause and effect? Diabetes Care 2004;27:2253-9.
Porter SA, Massaro JM, Hoffmann U, Vasan RS, O'Donnel CJ, Fox CS. Abdominal subcutaneous adipose tissue: A protective fat depot? Diabetes Care 2009;32:1068-75.
Dixon JB, Zimmet P, Alberti KG, Rubino F; International Diabetes Federation Taskforce on Epidemiology and Prevention. Bariatric surgery: An IDF statement for obese type 2 diabetes. Diabet Med 2011;28:628-42.
Bennett WL, Maruthur NM, Singh S, Segal JB, Wilson LM, Chatterjee R, et al.
Comparative effectiveness and safety of medications for type 2 diabetes: An update including new drugs and 2-drug combinations. Ann Intern Med 2011;154:602-13.
Choudhary N, Kalra S, Unnikrishnan AG, Ajish TP. Preventive pharmacotherapy in type 2 diabetes mellitus. Indian J Endocrinol Metab 2012;16:33-43.
Boulé NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: A meta-analysis of controlled clinical trials. JAMA 2001;286:1218-27.
Kramer CK, Choi H, Zinman B, Retnakaran R. Glycemic variability in patients with early type 2 diabetes: The impact of improvement in ß-cell function. Diabetes Care 2014;37:1116-23.
Mohan V, Shah SN, Joshi SR, Seshiah V, Sahay BK, Banerjee S, et al.
Current status of management, control, complications and psychosocial aspects of patients with diabetes in India: Results from the DiabCare India 2011 study. Indian J Endocrinol Metab 2014;18:370-8.
Bosy-Westphal A, Booke CA, Blöcker T, Kossel E, Goele K, Later W, et al.
Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population. J Nutr 2010;140:954-61.
Ceriello A, Colagiuri S, Gerich J, Tuomilehto J; Guideline Development Group. Guideline for management of postmeal glucose. Nutr Metab Cardiovasc Dis 2008;18:S17-33.
Ceriello A. The glucose triad and its role in comprehensive glycaemic control: Current status, future management. Int J Clin Pract 2010;64:1705-11.
Solanki JD, Makwana AH, Mehta HB, Desai CB, Gandhi PH. Body mass index, use of statins or current lipidemic control: Do they affect body fat distribution in sedentary type 2 diabetes mellitus? J Obes Metab Res 2015;2:79-83.
Anderson JW, Kendall CW, Jenkins DJ. Importance of weight management in type 2 diabetes: Review with meta-analysis of clinical studies. J Am Coll Nutr 2003;22:331-9.
Kahn R, Davidson MB. The reality of type 2 diabetes prevention. Diabetes Care 2014;37:943-9.
[Table 1], [Table 2], [Table 3]