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 Table of Contents  
ORIGINAL ARTICLE
Year : 2015  |  Volume : 2  |  Issue : 2  |  Page : 79-83

Body Mass Index, use of Statins or Current Lipidemic Control: Do they Affect Body Fat Distribution in Sedentary Type 2 Diabetes Mellitus?


Department of Physiology, Government Medical College, Bhavnagar, Gujarat, India

Date of Submission26-Aug-2014
Date of Decision14-Oct-2014
Date of Acceptance11-Nov-2014
Date of Web Publication7-May-2015

Correspondence Address:
Jayesh D Solanki
F1, Shivganga Appartments, Plot No. 164, Bhayani ni Waadi, Opp. Bawaliya Hanuman Temple, Gadhechi Wadlaa Road, Bhavnagar - 364 001, Gujarat
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2347-9906.151755

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  Abstract 

Introduction: Obesity and type 2 diabetes mellitus (T2DM) are a complementary threat around the globe. Deranged body fat distribution in T2DM needs serious attention, starting from its measurement up to guiding appropriate intervention. We tried to associate parameters of body fat distribution T2DM patients with body mass index (BMI), Lipidemic control and preventive pharmacotherapy using bio-electrical impedance analysis (BIA) method. Materials and Methods: We recruited 78 sedentary (42 males, 36 females) T2DM subjects with known glycemic and Lipidemic lipidemic control. Whole body scan was done using BIA principle with Omron Karada Scan (China) to derive total body fat, subcutaneous fat, visceral fat, ratio of subcutaneous fat to visceral fat and BMI. These parameters were compared among group based on BMI, lipidemic control and use of statin/angiotensin-converting enzyme (ACE) inhibitors for difference and statistical significance. Results: Type 2 diabetes mellitus subjects had high mean age, high BMI and fair lipidemic control. All measures of body fat distribution derived by BIA were statistically significantly different among t groups separated by BMI cut-off 25. However, there was small, statistically insignificant difference of body fat parameters amongst groups based on control of high- density lipoproteins, low-density lipoprotein and triglycerides except for subcutaneous fat. Those taking statins or ACE inhibitors did not have significantly better body fat distribution than those not taking it. Conclusion: Deranged body fat distribution in T2DM measured by BIA correlated with BMI. These parameters are improved neither by lipidemic control nor by preventive pharmacotherapy. This suggests the use of other interventions like weight reduction and optimum use of BIA for monitoring utilizing primary health care resources.

Keywords: Body mass index, body fat, lipidemic control, statin, type 2 diabetes mellitus


How to cite this article:
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

How to cite this URL:
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 [serial online] 2015 [cited 2019 Jul 17];2:79-83. Available from: http://www.jomrjournal.org/text.asp?2015/2/2/79/151755


  Introduction Top


India is no exception to other South Asian countries for the alarming increase in prevalence of obesity and its aftermath, type 2 diabetes mellitus (T2DM). [1] Obesity links insulin resistance to T2DM [2] and weight reduction has proven beneficial role against the same. [3] T2DM has been recognized as a disease beyond abnormal glucose homeostasis that too affects protein and lipid metabolism adversely with complication attributed to the same. [4] South Asians are at ethnically predisposed state for obesity and T2DM, suffering from adversity at comparably low body mass index (BMI) as compared to whites. [5] To evaluate body composition tools are available, as simple as BMI and as advanced as computed tomography scan are available, with bio-electrical impedance analysis (BIA) being objective, simple and fairly reliable one. [6] Same is applicable to therapy with intervention being as simple as weight reduction to as advanced as bariatric surgery with pharmacotherapy being considered as intermediate one. [7] The present study aimed to evaluate: (i) Effect of pharmacotherapy on (ii) comparison of BMI norm and means of lipidemic control with-body composition parameters derived by BIA in a sample of T2DM patients.


  Materials and methods Top


Study design

Present cross-sectional observational study was carried out from January 2013 to December 2013 in the clinical research lab, Department of Physiology, Government medical college, Bhavnagar, Gujarat, India.

Study sample

Sample size of seventy-eight for current population and prevalence of disease yields us 95% confidence interval keeping margin for error 5% as calculated by Raosoft sample size calculator software (free online survey software of Raosoft, Inc. 6645 NE Windermere Road Seattle, WA).

Study subjects

After getting approval from Institutional Review Board and informed consent from participants, the study was carried out in type 2 under-treatment ambulatory diabetics and matched healthy controls. Subjects were recruited from medicine outpatient departments (OPDs): Half from a tertiary care teaching hospital attached to our college and half from private OPDs.

Inclusion criteria

Seventy-eight type 2 diabetics (42 males and 36 females) were undertaken in age group 30-80 years, not taking insulin, taking regular medicines, and having recent investigation for glycemic control. We took cases with and without hypertension, with and without statin therapy, with or without family history of type 2 diabetes, coming from various socioeconomic statuses, doing work with varying degrees as to make a fairly representative sample of the population.

Research method

Subjects meeting inclusion and exclusion criteria were registered for study with initial assessment in the form of informed consent, personal history, medical history, anthropometric measurements and recent reports of glycemic controls including fasting blood sugar (FBS), PP2BS and HbA1c and lipidemic control were taken.

Defining normative parameters for study: [8]

  1. BMI < 25 kg/m 2 ,
  2. Glycemic control-HbA1c < 7 g %, FBS < 130 mg% and PP2BS < 180 mg %,
  3. Lipidemic control-low density lipoprotein (LDL) <100 mg/dL, high density lipoprotein (HDL) >50 mg/dl and triglycerides (TGA) <150 mg/dL.


Body composition measurement

Having entered age, gender and height taken by standiometer 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 principle of tetra polar BIA was used that passes electric current of 500 μAmp at frequency 5 kHz to scan the whole body to derive parameters of regional body composition. Parameters measured are total body fat, visceral fat, subcutaneous fat, skeletal muscle and subcutaneous adipose tissue to visceral adipose tissue (SAT/VAT) ratio. We measured BIA in the early morning after breakfast in patients who were ambulatory uncomplicated diabetics so that we can avoid variability due to dehydration.

Statistical analysis

The data were transferred on Excel spreadsheet and descriptive analysis was expressed as mean ± standard deviation. All calculations were accomplished by Graph Pad InStat 3 software. We evaluated the difference between various groups for body composition parameters by unpaired Student's t-test. Any observed difference was considered statistically significant with P < 0.05.


  Results Top


[Table 1] shows general characteristics of the study group that has representation of both sexes, mean age 55.39 ± 9.80 years and duration of diabetes 6.83 ± 6.49 years and lipid profile showing low HDL, high LDL and high triglycerides that is copybook picture of diabetic dyslipidemia.
Table 1: Demographic and clinical characteristics of the known under treatment ambulatory type 2 diabetics under study (n=78)

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[Table 2] shows comparison of parameters of body fat distribution in T2DM amongst group based on BMI reflecting a significant positive association between all parameters and BMI with 25 being the demarcation of normality.
Table 2: Comparing parameters of body fat distribution in T2DM amongst group based on BMI

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[Table 3] shows effect of statin and angiotensin-converting enzyme (ACE) inhibitor therapy on body fat composition demonstrating that both affect only subcutaneous fat significantly of all and other don't differ much in groups taking or not taking the drug.
Table 3: Comparing parameters of body fat distribution in T2DM amongst group based on usage of statin therapy or antihypertensive drug

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[Table 4] shows comparison of parameters of body fat distribution in T2DM patients amongst group based on lipidemic control showing that those having HDL, LDL and TGA level within defined norms showed no better body composition than those not having it with few exceptions like BMI and LDL, HDL and SAT/VAT ratio.
Table 4: Comparing parameters of body fat distribution in T2DM amongst group based on lipidemic control (defined by ADA guidelines 2012)

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  Discussion Top


Asian people are more prone to suffer from obesity and T2DM that ensues at comparatively lower BMI and at lower dyslipidemia than whites. [9] Monitoring deranged body fat composition in T2DM is a must tactic in this context [1] to not only motivate the patient, but also to guide the health care provider to seek proper intervention. From all the methods available for measuring body composition, Bio-electrical impedance analysis (BIA) is a simple yet objective as well as a fairly reliable method. [11] As proved previously, BIA is a good method for Indians with BMI > 21 [12] which is more often than not the case for diabetics and this is like a boon for our scenario where T2DM subjects require maintenance of optimum body composition for risk reduction.

As observed in our study, we found BIA derived parameters of body fat distribution correlating well with BMI that is age old standard. [13] However, it is also essential to decipher that body fat derangement of T2DM is to be looked not only quantitatively but also qualitatively [13] and one requires calculation of total body fat, subcutaneous fat, visceral fat and SAT/VAT ratio which cannot be given by means of BMI. [14] BIA revealed excess total body fat, subcutaneous fat, visceral fat, SAT/VAT ratio and declined muscle line presenting classic picture of T2DM [15] and the same is evident when compared in groups based on BMI.

In India, statin is used as mainstay pharmacotherapy for lipid lowering [16] and the result may be evident in the form of improved dyslipidemia. [17] However, residual risk remains after statin therapy [18] and at times questions are raised on their efficacy and few studies like ours has questioned its usefulness and cost-effectiveness. [18],[19] We found that statin therapy reduces only subcutaneous fat with no significant effect on visceral fat, a fat that has more significant correlation with cardiovascular risk than others. [20],[21] Similarly, those receiving ACE inhibitors as antihypertensive are theoretically to be benefitted in decreasing lipid imbalance. [22] But we do not find that effect on any parameter on comparing ACE inhibitors (ACEI) users with nonusers. This emphasizes that the statin and other adjuvant pharmacotherapy may be correcting blood lipids, but not effective in affecting ectopic fat significantly. So, one has to step back to conventional weight reduction [23],[24] interventions or step up to the option of bariatric surgery [24],[25] or intervention by endocrinologists.

Type 2 diabetes mellitus of our study group reflects the same dyslipidemic pattern as written previously. [26] However, on comparing body fat composition with regard to lipidemic control, there seems to be no association with either of the parameters whether lipidemia was under control or not. Blood lipid level may not correlate with adventurously deposited fat, and it is creating a false sense of security as we saw in our case. Building of visceral fat and TBF inspite of good lipid control post multiple health risks. So for ectopic fat distribution evaluation monitoring by means of an objective, repeatable, fairly reliable tool like BIA is must.

Similarly for therapeutic approach, one should opt for the whole stratum, starting from lifestyle modification, [26],[27] education being part of it and up to the level of bariatric surgery. [25] This requires primary care practitioners, physicians, researchers, dieticians, endocrinologists, bariatric surgeons to work as a team in order to fight against the modern epidemic of metabolic syndrome.


  Conclusion Top


To conclude, we found utility of BIA as well as BMI, no effect of statins or ACEI on body fat distribution, which too does not correlate with any of parameters of lipidemic control. This, along with the conspicuously seen burden of T2DM and obesity warrants the utility of body fat monitoring to stretch it beyond measuring weight or BMI and use of lifestyle interventions effectively at primary care level.

 
  References Top

1.
Boffetta P, McLerran D, Chen Y, Inoue M, Sinha R, He J, et al. Body mass index and diabetes in Asia: A cross-sectional pooled analysis of 900,000 individuals in the Asia cohort consortium. PLoS One 2011;6:e19930.  Back to cited text no. 1
    
2.
Miyazaki Y, DeFronzo RA. Visceral fat dominant distribution in male type 2 diabetic patients is closely related to hepatic insulin resistance, irrespective of body type. Cardiovasc Diabetol 2009;8:44.  Back to cited text no. 2
    
3.
AL-Shahrani AM, Al-Khaldi YM. Obesity among diabetic and hypertensive patients in Aseer region, Saudi Arabia. Saudi J Obes 2013;1:14-7.  Back to cited text no. 3
    
4.
Ganong WF. Fat metabolism in diabetes. In: Barret KE, Barman SM, Boitano S, Brooks HL, editors. Review of Medical Physiology. 24 th ed. New York: McGraw Hill; 2012. p. 439.  Back to cited text no. 4
    
5.
Gujral UP, Pradeepa R, Weber MB, Narayan KM, Mohan V. Type 2 diabetes in South Asians: Similarities and differences with white caucasian and other populations. Ann N Y Acad Sci 2013;1281:51-63.  Back to cited text no. 5
    
6.
Barreira TV, Staiano AE, Harrington DM, Heymsfield SB, Smith SR, Bouchard C, et al. Anthropometric correlates of total body fat, abdominal adiposity, and cardiovascular disease risk factors in a biracial sample of men and women. Mayo Clin Proc 2012;87:452-60.  Back to cited text no. 6
    
7.
John M, George K, Kalra S. New avatars in endocrine practice: The bariatric physician. Indian J Endocrinol Metab 2013;17:953-4.  Back to cited text no. 7
    
8.
American Diabetes Association. Standards of medical care in diabetes-2012. Diabetes Care 2012;35 Suppl 1:S11-63.  Back to cited text no. 8
[PUBMED]    
9.
Wang J, Geiss LS, Cheng YJ, Imperatore G, Saydah SH, James C, et al. Long-term and recent progress in blood pressure levels among U.S. Adults with diagnosed diabetes, 1988-2008. Diabetes Care 2011;34:1579-81.  Back to cited text no. 9
    
10.
Joob B, Wiwanitkit V. Waist circumference cutoff and metabolic syndrome. Indian J Endocrinol Metab 2012;16:475-6.  Back to cited text no. 10
    
11.
Bray GA, Jablonski KA, Fujimoto WY, Barrett-Connor E, Haffner S, Hanson RL, et al. Relation of central adiposity and body mass index to the development of diabetes in the Diabetes Prevention Program. Am J Clin Nutr 2008;87:1212-8.  Back to cited text no. 11
    
12.
Kalra S, Mercuri M, Anand SS. Measures of body fat in South Asian adults. Nutr Diabetes 2013;3:e69.  Back to cited text no. 12
    
13.
Lee JM, Gebremariam A, Vijan S, Gurney JG. Excess body mass index-years, a measure of degree and duration of excess weight, and risk for incident diabetes. Arch Pediatr Adolesc Med 2012;166:42-8.  Back to cited text no. 13
    
14.
Feller S, Boeing H, Pischon T. Body mass index, waist circumference, and the risk of type 2 diabetes mellitus: Implications for routine clinical practice. Dtsch Arztebl Int 2010;107:470-6.  Back to cited text no. 14
    
15.
Kaess BM, Pedley A, Massaro JM, Murabito J, Hoffmann U, Fox CS. The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia 2012;55:2622-30.  Back to cited text no. 15
    
16.
Maji D, Shaikh S, Solanki D, Gaurav K. Safety of statins. Indian J Endocrinol Metab 2013;17:636-46.  Back to cited text no. 16
    
17.
Choudhary N, Kalra S, Unnikrishnan AG, Ajish TP. Preventive pharmacotherapy in type 2 diabetes mellitus. Indian J Endocrinol Metab 2012;16:33-43.  Back to cited text no. 17
    
18.
Judge EP, Phelan D, O′Shea D. Beyond statin therapy: A review of the management of residual risk in diabetes mellitus. J R Soc Med 2010;103:357-62.  Back to cited text no. 18
    
19.
Sampson UK, Fazio S, Linton MF. Residual cardiovascular risk despite optimal LDL cholesterol reduction with statins: The evidence, etiology, and therapeutic challenges. Curr Atheroscler Rep 2012;14:1-10.  Back to cited text no. 19
    
20.
Neeland IJ, Turer AT, Ayers CR, Powell-Wiley TM, Vega GL, Farzaneh-Far R, et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA 2012;308:1150-9.  Back to cited text no. 20
    
21.
Ichikawa R, Daimon M, Miyazaki T, Kawata T, Miyazaki S, Maruyama M, et al. Influencing factors on cardiac structure and function beyond glycemic control in patients with type 2 diabetes mellitus. Cardiovasc Diabetol 2013;12:38.  Back to cited text no. 21
    
22.
Putnam K, Shoemaker R, Yiannikouris F, Cassis LA. The renin-angiotensin system: A target of and contributor to dyslipidemias, altered glucose homeostasis, and hypertension of the metabolic syndrome. Am J Physiol Heart Circ Physiol 2012;302:H1219-30.  Back to cited text no. 22
    
23.
Fujimoto WY, Boyko EJ, Hayashi T, Kahn SE, Leonetti DL, McNeely MJ, et al. Risk factors for type 2 diabetes: Lessons learned from Japanese Americans in Seattle. J Diabetes Investig 2012;3:212-224.  Back to cited text no. 23
    
24.
Westerink J, Visseren FL. Pharmacological and non-pharmacological interventions to influence adipose tissue function. Cardiovasc Diabetol 2011;10:13.  Back to cited text no. 24
    
25.
Lebovitz HE. Science, clinical outcomes and the popularization of diabetes surgery. Curr Opin Endocrinol Diabetes Obes 2012;19:359-66.  Back to cited text no. 25
    
26.
Pandit K, Goswami S, Ghosh S, Mukhopadhyay P, Chowdhury S. Metabolic syndrome in South Asians. Indian J Endocrinol Metab 2012;16:44-55.  Back to cited text no. 26
    
27.
InterAct Consortium, Langenberg C, Sharp SJ, Schulze MB, Rolandsson O, Overvad K, et al. Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: The EPIC-InterAct case-cohort study. PLoS Med 2012;9:e1001230.  Back to cited text no. 27
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]


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