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Body Composition Analysis of University Students by Anthropometry and Bioelectrical Impedance Analysis
Authors: Vinti Davar
Abstract:Background: Worldwide, at least 2.8 million people die each year as a result of being overweight or obese, and 35.8 million (2.3%) of global DALYs are caused by overweight or obesity. Obesity is acknowledged as one of the burning public health problems reducing life expectancy and quality of life. The body composition analysis of the university population is essential in assessing the nutritional status, as well as the risk of developing diseases associated with abnormal body fat content so as to make nutritional recommendations. Objectives: The main aim was to determine the prevalence of obesity and overweight in University students using Anthropometric analysis and BIA methods. Material and Methods: In this cross-sectional study, 283 university students participated. The body composition analysis was undertaken by using mainly: i) Anthropometric Measurement: Height, Weight, BMI, waist circumference, hip circumference and skin fold thickness, ii) Bio-electrical impedance was used for analysis of body fat mass, fat percent and visceral fat which was measured by Tanita SC-330P Professional Body Composition Analyzer. The data so collected were compiled in MS Excel and analyzed for males and females using SPSS 16. Results and Discussion: The mean age of the male (n= 153) studied subjects was 25.37 ±2.39 years and females (n=130) was 22.53 ±2.31. The data of BIA revealed very high mean fat per cent of the female subjects i.e. 30.3±6.5 per cent whereas mean fat per cent of the male subjects was 15.60±6.02 per cent indicating a normal body fat range. The findings showed high visceral fat of both males (12.92±3.02) and females (16.86±4.98). BMI, BF% and WHR were higher among females, and BMI was higher among males. The most evident correlation was verified between BF% and WHR for female students (r=0.902; p<0.001). The correlation of BFM and BF% with thickness of triceps, sub scapular and abdominal skin folds and BMI was significant (P<0.001). Conclusion: The studied data made it obvious that there is a need to initiate lifestyle changing strategies especially for adult females and encourage them to improve their dietary intake to prevent incidence of noncommunicable diseases due to obesity and high fat percentage.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1106901Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2391
 Stein CJ, Colditz GA. The epidemic of obesity. J Clin Endocrinol Metab. 2004;89:2522–5. (PubMed)
 Lau DC. The Obesity Canada Clinical Practice Guidelines Steering Committee and Expert Panel. Synopsis of the 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children. Can Med Assoc J. 2007;176: 1103–6. (PMC free article) (PubMed).
 Centers for Disease Control and Prevention, “Overweight and obesity 2012, http://www.cdc.gov/obesity/adult/causes/index.html.
 Kastorini CM, Milionis HJ, Ioannidi A, et al. Adherence to the Mediterranean diet in relation to acute coronary syndrome or stroke nonfatal events: A comparative analysis of a case/case-control study. Am Heart J. 2011;162:717–724. (PubMed)
 WHO. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. WHO Technical Report Series No. 894. Geneva: World Health Organisation, 2000.
 D. J. Hoffman, “Upper limits in developing countries: warning against too much in lands of too little,” Journal of the American College of Nutrition, vol. 23, no. 6, pp. 610S–615S, 2004. View at Google Scholar • View at Scopus
 de Koning L, Merchant AT, Pogue J, Anand SS (2007) Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J 28: 850–856. doi: 10.1093/eurheartj/ehm026
 Krakauer NY, Krakauer JC (2014) Dynamic association of mortality hazard with body shape. PLOS ONE 9: e88793. doi: 10.1371/journal.pone.0088793
 Stevens J, McClain JE, Truesdale KP (2008) Selection of measures in epidemiologic studies of the consequences of obesity. Int J Obes (Lond) 32: Suppl 3, S60–66. doi: 10.1038/ijo.2008.88
 Brown P (2009) Waist circumference in primary care. Prim Care Diabetes 3: 259–261. doi: 10.1016/j.pcd.2009.09.006
 Mason C, Katzmarzyk PT (2009) Variability in waist circumference measurements according to anatomic measurement site. Obesity 17: 1789–1795. doi: 10.1038/oby.2009.87
 Lee SY, Gallagher D (2008) Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care 11: 566–572.
 Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, et al. (2004) Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr 23: 1430–1453. doi: 10.1016/j.clnu.2004.09.012
 Ricciardi R, Talbot LA (2007) Use of bioelectrical impedance analysis in the evaluation, treatment, and prevention of overweight and obesity. J Am Acad Nurse Pract 19: 235–241. doi: 10.1111/j.1745-7599.2007.00220
 Avenell A, Broom J, Brown TJ, Poobalan A, Aucott L, Stearns SC, Smith WC, Jung RT, Campbell MK and Grant AM. Systematic review of the long-term effects and economic consequences of treatments for obesity and implications for health improvement. Health Technol Assess. 2004; 8:iii-iv, 1-182. | PubMed Abstract | PubMed Full Text
 Merten S, MPH and Julia Dratva, et al. Do baby-friendly hospitals influence breastfeeding duration on a national level?. Pediatrics. 2005; 116:702–708. | Article.
 Bosy-Westphal A, Booke CA, Blocker T, Kossel E, Goele K, et al. (2010) Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population. J Nutr 140: 954–961. doi: 10.3945/jn.109.118737
 Siri W.E. Body composition from fluid spaces and density analysis of methods. In: Brozek J, Henschel A.Techniques for measuring body composition. Washington, DC: National Research Council; 1961. p. 223-44.
 Lee S.Y., Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care. 2008;11(5):566– 572. (PMC free article) (PubMed).
 Gibson R.S. Food and Nutrition Guidelines for Healthy Children and Young People (Aged 2–18 years): A background paper. Principle of nutritional assessment (2nd ed.) Oxford: Oxford University Press, 2005: 20-30.
 Flegal KM, Graubard B I, Williamsen D F, Mitchell G H. Excess deaths associated with underweight, overweight, and obesity. JAMA 2005; 293: 1861-67.
 Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: the heritage family study. Int J Obes Relat Metab Disord 2002; 26: 789-96.
 Gallagher D., Heymsfield S. B., Heo M., Jebb S. A., Murgatroyd P. R., and Sakamoto Y.,. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index1–3. Am. J. Clin. Nutr., September 2000; 72 (3): 694-701.
 Zeng Q, Dong SY, Sun XN, Xie J, Cui Y. Percent body fat is a better predictor of cardiovascular risk factors than body mass index. Braz J Med Biol Res 2012; 45: 591-600.
 Gómez-Ambrosi J, Silva C, Galofré JC, Escalada J, Santos S, Millán D, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond) 2012; 36: 286-94.
 Chris Burslem, October. The Changing Face of Malnutrition. IFPRI Forum, International Food Policy Research Institute: Washington, D.C. 2004. | PdfFuzzy Sets and Systems. Vol. 49, No. 1, 1992.
 Amani R. Comparison between bioelectrical impedance analysis and body mass index methods in determination of obesity prevalence in Ahvazi women. Eur J Clin Nutr. 2007;61:478–82. (PubMed).
 Pecoraro P, Guida B, Caroli M, et al. Body mass index and skinfold thickness versus bioimpedance analysis: fat mass prediction in children. Acta Diabetol.2003; 40:S278–81. (PubMed).