|Year : 2015 | Volume
| Issue : 4 | Page : 206-209
Gender specificity in detecting obesity with bioelectrical impedance analysis machine
Nilesh Makwana, Aarti Makwana
Muscle n Mind Multispeciality Physiotherapy Centre, 7th Floor, Ozone Biz Commercial Building, Next to Maharashtra College, Mumbai Central East, Mumbai, Maharashtra, India
|Date of Submission||12-Dec-2015|
|Date of Decision||29-Jul-2015|
|Date of Acceptance||08-Oct-2015|
|Date of Web Publication||2-Dec-2015|
Muscle n Mind Multispeciality Physiotherapy Centre, 7th Floor, Ozone Biz Commercial Building, Next to Maharashtra College, Mumbai Central East, Mumbai - 400 008, Maharashtra
Source of Support: None, Conflict of Interest: None
Introduction: The variables such as body mass index (BMI), percentage of body fat (PBF), and waist to hip ratio (WHR) are widely used for the evaluation of overweight and obesity. The aim of this study is to investigate the gender specificity for detecting the degree of obesity using BMI, PBF, and WHR as variables. Materials and Methods: The data on the obesity variables were analyzed in 3224 samples (male - 2091, female - 1133). The data were collected retrospectively from "fab fitness" to "muscle and mind" gymnasiums in Mumbai. The mean age of the subjects was 28.82 years for males and 31.13 years for females. The WHR, BMI, and PBF were recorded using bioelectrical impedance analysis (BIA) in body 230 machine. Results: There was a significant correlation in females between BMI and WHR (r2 = 0.809), BMI and PBF (r2 = 0.78), but no correlation was found between PBF and WHR (r2 = 0.65). However, in the male population, WHR and PBF were found to be correlated (r2 = 0.762), but no correlation was found between BMI and WHR (r2 = 0.69) and BMI and PBF (r2 = 0.51). Conclusion: In females, WHR, BMI, and PBF were correlated. However, in males, WHR and PBF were important in the evaluation of obesity. Hence, it is concluded that evaluation of obesity appears to be gender specific, and therefore obesity measurement should be done with the proper selection of variables and procedures.
Keywords: Bioelectrical impedance analysis, body mass index, genders in obesity, obesity, percentage of body fat, waist to hip ratio
|How to cite this article:|
Makwana N, Makwana A. Gender specificity in detecting obesity with bioelectrical impedance analysis machine. J Obes Metab Res 2015;2:206-9
| Introduction|| |
Bioelectrical impedance analysis (BIA) has been widely used in gymnasiums (gyms) for the evaluation of obesity using body mass index (BMI), percentage of body fat (PBF), and waist to hip ratio (WHR). The prevalence of obesity is significantly rising in both the developed and the developing nations. According to the World Health Organization (WHO), obesity is ranked as a fifth cause of mortality  due to its adverse health effects such as cardiovascular disease and hypercholesterolemia. BMI has been recommended by WHO as a simple, practical, and an epidemiological measure for identifying overweight and obesity. National Nutrition Monitoring Bureau of Indian Council of Medical Research has published the BMI profiles of a representative sample of the Indian rural population. The limitation of this study is that the urban Indian population is not covered. The latter is at higher risk due to the changes in lifestyle. WHR is an easy and a convenient measurement to detect central obesity, implying central adiposity. WHR and BMI have been widely used to measure central obesity and general obesity, respectively. PBF as a measure of the body fat is an effective variable for the degree of obesity. Several studies have shown that the inter-relationships among BMI, PBF, and body fat distribution vary across populations. A Canadian study revealed that, BIA is a good alternative anthropometric measuring device in normal males (r = 0.78) and females (r = 0.85) when correlated with the dual energy X-ray absorptiometry (DEXA). Hence, BIA machine is currently being used by many gyms. However, this device may either overestimate or underestimate the fat percentage in obese individuals. Whereas a study from India with 4-compartment air-displacement plethysmography (4C model) compared with DEXA, BIA, and skin fold technique, showed that 4C and DEXA-measured the fat percentage more accurately.
The recognition of the central obesity is clinically quite important, and it is a better predictor of obesity than BMI. However, WHR per se cannot distinguish abdominal subcutaneous fat, total abdominal fat, and total body fat, and it is strongly correlated with BMI. The adiposity localization over the abdomen is said to be more important to determine cardiovascular risks and lifestyle diseases. Women with WHR of >0.8 and BMI >32 have an increased risk of heart disease, stroke, and diabetes. The BMI is generally a good tool to measure general obesity, except in individuals with a higher muscle mass (body builders) as well as those who have recently lost much muscle mass, for example, cancer or starvation. In these individuals, obesity can be assessed only by a specific anthropometric measurement such as underwater weighing.
The present study was carried out to investigate gender specificity of variables in detecting the degree of obesity with BMI, WHR, and PBF, as indicators taken from the BIA machine.
| Materials and Methods|| |
A total of 3224 subjects were included with both genders (males = 2091, females = 1133) retrospective sample. The data collected were for the period of 2009 to 2012 from "fab fitness" to "muscle and mind" gyms in Mumbai, India. The exclusion criteria are individuals who have implanted pacemakers, internal fixators following bone fractures, edematous foot, to rule out this, prior medical history was taken by the Physiotherapist present in the gym. The subjects, who had undergone proper techniques of measurements, were included. The measurements were taken by trained, specially qualified fitness professionals. These professionals had undergone mock drills and viva voce examination on BIA machine and the techniques. For a proper calibration of the machine, inter-professional variability in BIA readings of the same individual was checked. Such a calibration was carried every week for standardization. The BIA in body 230 machine is with 8 tactile electrodes channels, which passes very low amplitude currents through the body and measures the water content of specific tissues, and depending on the degree of impedance, algorithms measure fat, muscle, bone, and water mass. The standard instructions for the use of the machine were followed by all the professionals.
Statistical analysis was done using Microsoft Excel 2007. The correlation of the means and standard deviations was carried out by the statistical analysis of correlation coefficient. Correlations among BMI, PBF, and WHR were analyzed using r value. An r value of more than 0.7 was considered significant for this study.
| Results|| |
Physical characteristics of and the variables measured in the study are shown in [Table 1]. The age ranged from 20 years to 80 years. As a group, the men were 2.3 years younger than women, and in the height, men was 15 cm taller than women. The men were 7.99 kg heavier in the body weight than women. The values of BMI, WHR, and PBF were higher in the females as compared to males.
The correlation coefficient (r) between PBF and BMI in the total subjects (n = 3224) was 0.51, in males (n = 2091) it was 0.63 and in females (n = 1133) it was 0.78. There was no correlation between PBF and BMI in detecting obesity in the male population, but there was a significant correlation in the female population that is 0.78 [Figure 1].
|Figure 1: Correlations between the percentage of body fat and body mass index in male and the female populations. The r= 0.78 in the female population is significant|
Click here to view
The correlation coefficient (r) between BMI and WHR in all the subjects was 0.67. In the females, there was a significant correlation between BMI and WHR that is 0.80, but not in the males [Figure 2].
|Figure 2: Correlations between body mass index and waist to hip ratio in the male and the female populations. The r value in the males was significant (0.80)|
Click here to view
The correlation coefficient (r) between WHR and PBF in all the subjects was 0.48. In males, the population correlation value was higher (0.76) compared to females (0.65). There was a significant correlation between WHR and PBF for measuring obesity in the male population [Figure 3].
|Figure 3: Correlation between waist to hip ratio and percentage of body fat in the male and the female population. The correlation value was significant (0.76) in the males|
Click here to view
| Discussion|| |
BMI is usually considered a surrogate marker of excess adiposity in terms of overweight and obesity,, but BMI has its limitation in measuring its value between a range of 24–27.9 kg/m 2. BIA is a widely used technique available in clinic and gyms for body composition measurement due to its merits of ease, safety, accuracy, reliability, and low cost as compared to other methods. According to cutoffs given by the WHO, the BMI >30 kg/m 2 is considered obese, but the individual who has higher muscle mass and bone density this measurement is limited. PBF is a good criterion to measure body fat mass and hence easier to calculate obesity and it has high correlation with DEXA. WHR is very easy to measure and also detects central obesity, which is a major risk factor for cardiovascular diseases. BMI, WHR, and PBF measures obesity, this study was done to correlate all the three variables for measuring obesity with the sample size of 3224. It is the largest study in India as per our knowledge.
When these variables are compared as per the age groups, it was found that BMI, PBF, and WHR increased with age. These values are highest in the age group of 70–80, primarily due to the loss in muscle mass, sedentary lifestyle, and more fat mass. In the females, PBF, BMI, and WHR were correlated to each other; however, in males, there was no such correlation, except for the WHR and PBF values.
The results suggest that obesity measurement is gender-specific. Hence, all the three variables should be used. There is a recent trend to use BIA machine in gyms to evaluate obesity, but gender specificity is not being evaluated. This study shows that the measurement of BMI, WHR, and PBF is good to measure obesity in female population. However, in males, more than BMI, WHR and PBF would help to detect obesity.
BIA may sometimes overestimate or underestimate the fat percentage. BIA could have been compared with a more sophisticated method such as DEXA for PBF.
| Conclusion|| |
BMI, WHR, and PBF are variables used in practices for measuring obesity. There is gender specificity. In women, all the three parameters are useful for evaluating obesity as shown by correlations. However, in the male population, WHR and PBF are more useful for obesity evaluation.
We would like to thank all the individuals who have participated directly or indirectly in this study. We would like to give special thanks to Dr. Waqaar Qureshi, Mr. Huzefa Lokhandwala from Fab Fitness and Muscle and Mind gym, most importantly Dr. Avinash N. Bhisey and his wife Dr. (Mrs.) Bhisey, for proper guidance for this research.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Sultan N, Nawaz M, Sultan A, Fayaz M. Waist hip ratio as an index for identifying women with raised TC/HDL ratios. J Ayub Med Coll Abbottabad 2004;16:38-41.
WHO. Global Burden of Disease Obesity and Overweight. Fact Sheet No. 311; May, 2008.
Goodman-Gruen D, Barrett-Connor E. Sex differences in measures of body fat and body fat distribution in the elderly. Am J Epidemiol 1996;143:898-906.
Naidu AN, Rao NP. Body mass index: A measure of the nutritional status in Indian populations. Eur J Clin Nutr 1994;48 Suppl 3:S131-40.
Nakagami T, Qiao Q, Carstensen B, Nhr-Hansen C, Hu G, Tuomilehto J, et al.
Age, body mass index and type 2 diabetes-associations modified by ethnicity. Diabetologia 2003;46:1063-70.
Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, et al.
Associations of general and abdominal obesity with multiple health outcomes in older women: The Iowa Women's Health Study. Arch Intern Med 2000;160:2117-28.
Razak F, Anand S, Vuksan V, Davis B, Jacobs R, Teo KK, et al.
Ethnic differences in the relationships between obesity and glucose-metabolic abnormalities: A cross-sectional population-based study. Int J Obes (Lond) 2005;29:656-67.
Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG, et al.
Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr 2005;81:74-8.
Kuriyan R, Thomas T, Ashok S, Jayakumar J, Kurpad AV. A 4-compartment model based validation of air displacement plethysmography, dual energy X-ray absorptiometry, skinfold technique and bio-electrical impedance for measuring body fat in Indian adults. Indian J Med Res 2014;139:700-7.
Hartemink N, Boshuizen HC, Nagelkerke NJ, Jacobs MA, van Houwelingen HC. Combining risk estimates from observational studies with different exposure cutpoints: A meta-analysis on body mass index and diabetes type 2. Am J Epidemiol 2006;163:1042-52.
Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al.
Meta-analysis of observational studies in epidemiology: A proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group. JAMA 2000;283:2008-12.
Duval S, Vazquez G, Baker WL, Jacobs DR Jr; CODA study group. The Collaborative Study of Obesity and Diabetes in Adults (CODA) project: Meta-analysis design and description of participating studies. Obes Rev 2007;8:263-76.
WHO Expert Committee on Diabetes Mellitus: Second report. World Health Organ Tech Rep Ser 1980;646:1-80.
Kuczmarski RJ, Flegal KM. Criteria for definition of overweight in transition: Background and recommendations for the United States. Am J Clin Nutr 2000;72:1074-81.
Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D. Limits of body mass index to detect obesity and predict body composition. Nutrition 2001;17:26-30.
Wang C, Hou XH, Zhang ML, Bao YQ, Zou YH, Zhong WH, et al.
Comparison of body mass index with body fat percentage in the evaluation of obesity in Chinese. Biomed Environ Sci 2010;23:173-9.
Stewart SP, Bramley PN, Heighton R, Green JH, Horsman A, Losowsky MS, et al.
Estimation of body composition from bioelectrical impedance of body segments: Comparison with dual-energy X-ray absorptiometry. Br J Nutr 1993;69:645-55.
Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV, et al.
The loss of skeletal muscle strength, mass, and quality in older adults: The health, aging and body composition study. J Gerontol A Biol Sci Med Sci 2006;61:1059-64.
[Figure 1], [Figure 2], [Figure 3]