|Year : 2017 | Volume
| Issue : 1 | Page : 82-87
Comparison of the body compositions in obese and nonobese individuals: Can learning body compositions motivate losing weight?
R Kutlu, FG Cihan
Department of Family Medicine, Meram Medical Faculty, University of Necmettin Erbakan, Konya, Turkey
|Date of Acceptance||01-Mar-2016|
|Date of Web Publication||12-Dec-2016|
Dr. R Kutlu
Department of Family Medicine, Meram Medical Faculty, University of Necmettin Erbakan, Konya
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Obesity is one of the leading preventable causes of death worldwide.
Objective: To compare body compositions in obese and nonobese individuals.
Methods: This cross-sectional study constituted of 428 individuals. Body compositions were determined using the Tanita.
Results: Of all the participants, 300 (70.1%) were female. The prevalence of overweight and obesity was 30.8% and 47.7%, respectively. Having low educational level, female gender, not working, being married, and nonsmoker significantly increased more in obese than nonobese (P < 0.001). Total body water, visceral fat accumulation, body mass index, resting metabolic rate, fat-free mass, bone mass, and muscle mass were significantly higher in obese when compared to those with nonobese (P < 0.001). Thirteen percent of the participants were thinking of changing their diet and lifestyle to lose weight at the beginning. After learning their body compositions, the rate increased to 60% who decided to modify their lifestyle and asked for help to lose weight. This behavior change was significant in especially the overweighted participants (P = 0.025).
Conclusion: The changes in body composition are associated with obesity and increased risk for certain cancers, cardiovascular disease, type 2 diabetes mellitus. Learning the health risks can motivate losing weight. Multicentered studies can be illuminating different cultural factors about obesity.
Keywords: Body composition, body fat distribution, body mass index, obesity, weight loss
|How to cite this article:|
Kutlu R, Cihan F G. Comparison of the body compositions in obese and nonobese individuals: Can learning body compositions motivate losing weight?. Niger J Clin Pract 2017;20:82-7
|How to cite this URL:|
Kutlu R, Cihan F G. Comparison of the body compositions in obese and nonobese individuals: Can learning body compositions motivate losing weight?. Niger J Clin Pract [serial online] 2017 [cited 2019 Dec 5];20:82-7. Available from: http://www.njcponline.com/text.asp?2017/20/1/82/180075
| Introduction|| |
Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have a negative effect on health and leading to reduced life expectancy.,, Obesity is a major cause of mortality and morbidity for associated of metabolic disorders, type 2 diabetes mellitus, obstructive sleep apnea, cancer, and cardiovascular diseases. Recent studies indicate that the global prevalence of overweight and obesity has reached epidemic proportions.,
Excess body fat has accumulated to the extent that it may have a negative effect on health. Simple, accurate, and noninvasive methods for assessing body composition are needed in many clinical, community, and research settings. The foot-to-foot bioelectrical impedance analysis (BIA) system may be one method of addressing those needs. BIA is easy and a popular method in research laboratories, hospitals, and wellness centers to assess body composition. Tanita is the world's first lightweight, portable, high capacity body composition monitor.,
In this study, we aimed to compare body compositions in obese and nonobese individuals by using the Tanita. Learning the body composition changes will create awareness and motivate people to weight loss.
| Methods|| |
Study design, setting and population
This cross-sectional study was constituted of 428 individuals between October 2012 and February 2014. The study population was chosen randomly from the patients who applied with any problem to Family Medicine Outpatient Clinic. At first, the female and male subjects were matched according to their ages and weights. After calculating their body mass indexes (BMIs), the participants were categorized as nonobese and obese. The study protocol was approved by the Ethics Committee of Medical Faculty. The participants were duly informed and written, and oral consent was obtained from volunteers according to the Principles of the Helsinki Declaration. A questionnaire investigating demographic characteristics (age, gender, occupation, education, and smoking status) was filled by face-to-face interview technique. None of these subjects had a severe chronic illness, endocrinological disorders, or a malignancy. Of all the patients, systolic and diastolic blood pressures (DBP) were measured in the supine position after at least 5 min of resting.
Before taking the measurements, the participants were asked if they were thinking of changing their diet and lifestyle. Anthropometric parameters including height, weight, and waist circumference (WC) were measured. Weight was measured while subjects wore only light clothing; height was assessed without shoes, back squared against the wall tape, eyes looking straight ahead using a stadiometer. WC (at the smallest point between lower costal and 10th rib border) was determined using a nonelastic fiberglass measuring tape. All of these measurements were done by the same observer with the same device. BMI was calculated as weight (in kilograms) divided by height (in meters) squared based on the World Health Organization (WHO) classification. According to the WHO classification, BMI lower or equal to 18.50 kg/m 2 is defined as underweight, between 18.50 and 24.99 is considered as ideal weight (normal) for individuals. Overweight is defined as BMI between 25.00 and 29.99 kg/m 2, and obesity is defined as BMI above 30 kg/m 2.
Measuring body composition
In this study, we used Tanita InnerScan Body Composition Monitor, Tokyo, Japan. This device is a safe, quick, portable, noninvasive, and monitors with segmental body composition analysis, differentiating fat and muscle. Tanita's accuracy, innovation, and durability are backed by extensive clinical researches and an independent medical advisory board., Body compositions were measured in the morning, between 08:30 and 09:30 h while the subjects did not have breakfast, with only light clothing, after removing shoes, socks, and all metal accessories, before giving blood samples.
None of the participants had known their body composition before this study. The participants were informed about their measurements and body compositions afterward and questioned if they had changed their thoughts about regulating their nutrition and lifestyle after learning their body composition.
In all subjects, a fasting blood sample was collected in the morning after fasting at least 10 h for analysis of the following biochemical parameters using standard techniques: Total cholesterol (TC), triglycerides (TG), high-density-lipoprotein-cholesterol low-density lipoprotein-cholesterol (LDL-c), fasting blood glucose, creatinine, blood urea nitrogen (BUN), alanin aminotransferase (ALT), aspartat aminotransferase (AST), uric asid.
The study protocol was approved by the Ethics Committee of Meram Medical Faculty of Necmettin Erbakan University and written informed consent was obtained from all participants before participation in this study. All of the participants were volunteers.
Statistical analyses were carried out using the SPSS version 20.0 (SPSS Inc, Chicago, IL, USA). Descriptive statistics were used to present subject characteristics. Comparison of the means was performed using Student's t-test and categorical variables were analyzed with the Chi-square test or the Fisher exact test, as appropriate. Analysis of variance testing with Bonferroni correction for multiple comparisons was used to analyze the relationship between continuous and categorical variables. Correlation coefficient (r) was described as a weak correlation between 0.00 and 0.24, moderate between 0.25 and 0.49, strong between 0.50 and 0.74 and very strong between 0.75 and 1.00. Statistical significance was reported at the conventional P < 0.05 level (two-tailed).
| Results|| |
Of all the participants, 300 (70.1%) were female, 128 (29.9%) were male, the mean age of the patients was 36.7 ± 12.3 (min = 18, max = 68), 321 (75.0%) married, 155 (36.2%) primary school and lower educated, 273 (63.8%) secondary school and higher educated, 189 (44.2%) housewives, and 148 (34.6%) smokers. The sociodemographic characteristics of the obese and nonobese individuals were shown in [Table 1]. The prevalence of overweight and obesity was found as 30.8% and 47.7% respectively. Only 92 (21.5%) people were normal weighted. Having low educational level, female gender, not working, being married, and nonsmoker significantly increased more in obese than nonobese (P < 0.001) [Table 1]. The frequency of obesity in primary school and lower educated females is the highest. The comparison of some measurements among males and females is demonstrated in [Table 2]. Thirteen percent of the participants were thinking of changing their diet and lifestyle to loose weight at the beginning. After learning their body compositions, the rate increased to 60.0% who decided to modify their lifestyle and asked for help to lose weight. This behavior change was significant in especially the overweighted participants (P = 0.025).
|Table 1: Sociodemographic characteristic features in obese and nonobese individuals|
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As it can be seen in [Table 3] and [Table 4], the measurements were found higher in obese group. The difference was significantly important. The majority of cardiovascular risk factors, especially systolic and DBP, fasting blood sugar, TC, TG, LDL-c, total body water (TBW), visceral fat, BMI, WC, age, basal metabolic rate, fat-free mass (FFM), bone mass, muscle mass were significantly higher in obese when compared to those with nonobese (P < 0.001) [Table 3],[Table 4],[Table 5]. There was not a significant relationship between ALT, AST, TSH, BUN, serum creatinine levels, and obesity (P > 0.05) [Table 6].
|Table 4: Comparison of body composition in obese and nonobese individuals|
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|Table 6: Comparison of some laboratory values in obese and nonobese individuals|
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The frequency of obesity was 2.653 times higher in female gender than male (odds ratio [OR] =2.653, %95 confidence interval [CI]: 1.713–4.109), (P < 0.001). The frequency of obesity was 5.654 times higher in low educational level than high educational level (OR = 5.654, %95 CI: 3.652–8.753), (P < 0.001) [Table 7]. The correlation of some measurements of the patients is seen in [Table 8].
There was an opposite relationship between TBW and BMI. The 49.4 percent of the increase in BMI is attributed to reduction of TBW (r2 = 0.494, P < 0.001) [Figure 1].
There was a direct relationship between visceral fat and BMI. The 58.5 percent of the increase in BMI is attributed to the increase in visceral fat (r2 = 0.585, P < 0.001). There was a direct relationship between fat and BMI. The 49.6% of the increase in BMI is attributed to the increase in fat (r2 = 0.496, P < 0.001) [Figure 2].
| Discussion|| |
Before mentioning the conclusions, the limitations of the study should be considered. Although the overall sample was relatively large, we reached a small group. In addition, although a quite close match, this study group is not entirely representative of the Turkish population. The study includes only the citizens of Konya.
Increased sedentary lifestyle and prevalence of overweight/obesity are common in many countries. In our study, the prevalence of overweight and obesity was 30.8% and 47.7% respectively. Only 92 (21.5%) person was normal weight. The frequency of obesity was higher 2.653 times in female gender than male. The results of the present study showed that having low educational level, female gender, not working, being married and nonsmoker significantly were found higher in obese than nonobese. The frequency of obesity was 5.654 times higher in low educational level than high educational level in our study. According to the research conducted by Visscher et al., between 1993 and 1997, the prevalence of obesity among men aged 20–59 year was 8.5% and increased by 0.54 percentage points per year. The prevalence of obesity among women was 9.6% and increased by 0.35 percentage points per year. The increase in the prevalence of obesity in the period 1993–1997 was strongest in men with a relatively low educational level and in women with a high educational level. On the other hand, Molarius et al. emphasized that lower education was associated with higher BMI in about half of the male and in almost all of the female populations, and the differences in relative body weight between educational levels increased over the study period. Thus, socioeconomic inequality in health consequences of obesity may increase in many countries.
It is obvious that the frequency of obesity in married females is the highest in this study. In accordance with our results, Nyaruhucha et al. state that married adults had higher rate of obesity (27.8%) than the single ones (4.7%). As a result, the majority of cardiovascular risk factors, especially systolic and DBP, fasting blood sugar, TC, TG, LDL-c, TBW, visceral fat, BMI, WC, age, basal metabolic rate, FFM, bone mass, muscle mass were significantly higher in obese when compared to those with nonobese in this study. According to Mirhosseini and friends, the majority of cardiovascular risk factors, especially systolic and DBP, TG concentration, and fasting blood glucose were significantly higher in a group with a high body fat when compared to those with normal and low values. Peppa et al. stressed that indices of total adiposity, peripheral fat distribution and lean body mass were not significantly different between “healthy” and “unhealthy” phenotypes. Bozkirli et al. in their study, had emphasized, there was a positive correlation between body fat percent and BMI.
In this study, we used Tanita device to evaluate the body compositions in adults. BIA of body composition showed the total body fat in obese groups to be greater than nonobese individuals. The results suggest that it could be a convenient and practical approach for body composition analysis. The research of Ritchie et al. revealed that percent body fat estimates from both BIA measures were significantly correlated with WC, BMI, and age. Similarly to our study, Kesavachandran et al. had emphasized that body fat percent was measured using a commercially available digital weight scale incorporating a bioelectric impedance analyzer in their study. The Tanita system was highly correlated to other measures of body composition such as WC and BMI. This device has been used safely in several studies.,,,,,, Mirhosseini et al. stated that BIA provides a direct and precise measure of lean body mass and total fat mass. This method also allows quantification of fat mass in anatomically defined regions of interest, which allows more precise evaluation of the impact of fat distribution. According to the research conducted by LaForgia, BIA measures the impedance associated with the passage of an alternating current through the body which is proportional to TBW and, therefore, can provide expedient estimates of body composition.
Similarly to our findings, Lloret Linares et al. also had emphasized that BIA is a safe, quick, noninvasive and easy method of assessing body composition. As a result, we recommend that Tanita is used to assess body composition. Baccioglu et al. in their study had emphasized that the body decomposition is important in patients with chronic obstructive pulmonary disease (COPD). Assessment of body composition should be a part of nutritional assessment besides BMI in patients with COPD. Similarly to our study, Korkut et al. had stated that as the BMI was increasing, blood glucose, homeostatic model assessment-insulin resistance, insulin levels were increasing also. Inactivity, poor diet, inflammation and hypoxia are suggested as the causes for the deterioration in nutritional status, and it may result in impaired exercise intolerance and poor health status in many patients. If we can measure and explain their body composition changes, this might motivate them to weight loss.
| Conclusion|| |
Many health problems are associated with excess levels of body fatness. Healthy food and physical activity provides protection from the health risks of obesity. We should advise our patients setting a realistic goal and maintain a wellness lifestyle to develop a healthy body composition. The changes in body composition are associated with obesity and increased risk for certain cancers, cardiovascular disease, type 2 diabetes mellitus, COPD, and mortality. In conclusion, Tanita is a cheap, inexpensive and simple method to evaluate the body compositions in adults, and could be a convenient and practical approach for assessment in public health settings and primary health centers. Obese subjects accommodate themselves to their body weight, and most of them do not complain about being obese. When they realized their fat mass, visseral fat ratios were higher, and their TBW was lower, besides their metabolic age was 5–10 years older than their real age, they were convinced to modify their lifestyle and lose weight. It is believed that, in this study, the information about body composition of individuals raised the awareness of this issue, since 85% of overweighted and obese individuals were later directed to the diet clinic voluntarily. The obese people were followed by both departments. Further studies are required to assess motivating factors for losing weight in obese people. Multicentered studies can be illuminating different cultural factors about obesity. The changes in body composition are associated with obesity and increased risk for certain cancers, cardiovascular disease, type 2 diabetes mellitus, and mortality. Learning the health risks can motivate losing weight.
We wish to thank all of the participants in the study. We also thank the team working at our Family Medicine Outpatient Clinic.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Dulloo AG, Montani JP. Body composition, inflammation and thermogenesis in pathways to obesity and the metabolic syndrome: An overview. Obes Rev 2012;13 Suppl 2:1-5.
Bozkirli E, Ertorer ME, Bakiner O, Tutuncu NB, Demirag NG. The validity of the World Health Organisation's obesity body mass index criteria in a Turkish population: A hospital-based study. Asia Pac J Clin Nutr 2007;16:443-7.
Haslam DW, James WP. Obesity. Lancet 2005;366:1197-209.
Florentino RF. The burden of obesity in Asia: Challenges in assessment, prevention and management. Asia Pac J Clin Nutr2002;11; (Suppl 8):676-80.
Mirhosseini NZ, Shahar S, Ghayour-Mobarhan M, Parizadeh MR, Yusoff NA, Shakeri MT. Body fat distribution and its association with cardiovascular risk factors in adolescent Iranian girls. Iran J Pediatr 2012;22:197-204.
Ritchie JD, Miller CK, Smiciklas-Wright H. Tanita foot-to-foot bioelectrical impedance analysis system validated in older adults. J Am Diet Assoc 2005;105:1617-9.
Visscher TL, Kromhout D, Seidell JC. Long-term and recent time trends in the prevalence of obesity among Dutch men and women. Int J Obes Relat Metab Disord 2002;26:1218-24.
Molarius A, Seidell JC, Sans S, Tuomilehto J, Kuulasmaa K. Educational level, relative body weight, and changes in their association over 10 years: An international perspective from the WHO MONICA project. Am J Public Health 2000;90:1260-8.
Nyaruhucha CN, Achen JH, Msuya JM, Shayo NB, Kulwa KB. Prevalence and awareness of obesity among people of different age groups in educational institutions in Morogoro, Tanzania. East Afr Med J 2003;80:68-72.
Peppa M, Koliaki C, Papaefstathiou A, Garoflos E, Katsilambros N, Raptis SA, et al.
Body composition determinants of metabolic phenotypes of obesity in nonobese and obese postmenopausal women. Obesity (Silver Spring) 2013;21:1807-14.
Kesavachandran CN, Bihari V, Mathur N. The normal range of body mass index with high body fat percentage among male residents of Lucknow city in North India. Indian J Med Res 2012;135:72-7.
LaForgia J, Gunn S, Withers RT. Body composition: Validity of segmental bioelectrical impedance analysis. Asia Pac J Clin Nutr 2008;17:586-91.
Johnstone AM, Faber P, Gibney ER, Lobley GE, Stubbs RJ, Siervo M. Measurement of body composition changes during weight loss in obese men using multi-frequency bioelectrical impedance analysis and multi-compartment models. Obes Res Clin Pract 2014;8:e46-54.
Gropper SS, Arsiwalla DD, Lord DC, Huggins KW, Simmons KP, Ulrich PV. Associations among eating regulation and body mass index, weight, and body fat in college students: The moderating role of gender. Eat Behav 2014;15:321-7.
Lazzer S, Boirie Y, Meyer M, Vermorel M. Evaluation of two foot-to-foot bioelectrical impedance analysers to assess body composition in overweight and obese adolescents. Br J Nutr 2003;90:987-92.
Miller CT, Fraser SF, Levinger I, Straznicky NE, Dixon JB, Reynolds J, et al.
The effects of exercise training in addition to energy restriction on functional capacities and body composition in obese adults during weight loss: A systematic review. PLoS One 2013;8:e81692.
Garcia-Lopes MG, Agliussi RG, Avesani CM, Manfredi SR, Bazanelli AP, Kamimura MA, et al.
Nutritional status and body composition after 6 months of patients switching from continuous ambulatorial peritoneal dialysis to automated peritoneal dialysis. Braz J Med Biol Res 2008;41:1116-22.
Iqbal M, Al-Regaiey KA, Ahmad S, Al Dokhi L, Al Naami M, Habib SS. Body composition analysis to determine gender specific physical fitness equations in a cohort of Saudi population. Pak J Med Sci 2014;30:798-903.
Lloret Linares C, Ciangura C, Bouillot JL, Coupaye M, Declèves X, Poitou C, et al.
Validity of leg-to-leg bioelectrical impedance analysis to estimate body fat in obesity. Obes Surg 2011;21:917-23.
Baccioglu A, Gulbay BE, Acican T. Body composition in patients with stable chronic obstructive pulmonary disease: Comparison with malnutrition in healthy smokers. Eurasian J Med 2014;46:169-75.
Korkut Y, Koçak FE, Paşalı-Kilit T. The Evaluation of the “Prevalence of Metabolic Syndrome and Lipid Profile According to Body Mass Index in Obese Women”. Konuralp Med J 2015;7:40-4.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]