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:
1 - Klein S, Wadden T, Sugerman HJ. AGA technical review on obesity. Gastroenterology. 2002; 123: 882-932.
2- Mohamadnejad M, Pourshams A, Malekzadeh R, Mohamadkhani A, Rajabiani A, Asgari AA, et al. Healthy ranges of serum alanine aminotransferase levels in Iranian blood donors. World J Gastroenterol. 2003; 9: 2322-24.
3- Howard BV. Obesity, lipoproteins, and heart disease. Proc Soc Exp Biol Med. 1992; 200 (2): 202-205.
4- Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest. 1995; 96 (1): 88-98.
5- Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994; 73 (7): 460-68.
6- Janssen I, Katzmarzyk PT, Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med. 2002; 162 (18): 2074-79.
7- Wang Y, Rimm EB, Stampfer MJ, Willet WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005; 81 (3): 555-63.
8- Esmaillzadeh A, Mirmiran P, Azizi F. Waist-to-hip ratio is a better screening measure for cardiovascular risk factors than other anthropometric indicators in Tehranian adult men. Int J Obes Metab Disord. 2004; 28 (10): 1325-32.
9- Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser. 1995; 854: 1-452.
10- Okosun IS, Tedders SH, Choi S, Dever GE. Abdominal adiposity values associated with established body mass indexes in white, black and Hispanic Americans. A study from the Third National Health and Nutrition Examination Survey. Int J Obes Relat Metab Disord. 2000; 24 (10): 1279-85.
11- Huang KC, Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, et al.Four anthropometric indices and cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord. 2002; 26 (8): 1060-68.
12- Tanaka K, Kodama H, Sasazuki S, Yoshimasu K, Liu Y, Washio M et al. Obesity, body fat distribution and coronary atherosclerosis among Japanese men and women. Int J Obes Relat Metab Disord. 2001; 25 (2): 191-97.
13- Musaiger AO, Al-Mannai MA. Weight, height, body mass index and prevalence of obesity among the adult
population in Bahrain. Ann Hum Biol. 2001; 28 (3): 346-50.
14- Erem C, Yildiz R, Kavgaci H, Karahan C, Deger O, Can G, et al. Prevalence of diabetes, obesity and hypertension in a Turkish population (Trabzon city). Diabetes Res Clin Pract. 2001; 54 (3): 203-208.
15- WHO. Measuring obesity-classification and description of anthropometric data. Report on a WHO consultation on the epidemiology of Obesity, Warsaw, 21-23 October 1987.Nutrition Unit Document, EUR/ICP/NUT 125.WHO Compenhagen; 1989.
16- Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry. 1972; 18 (6): 499-502.
17- Grinker JA, Tucker KL, Vokonas PS, Rush D. Changes in patterns of fatness in adult men in relation to serum indices of cardiovascular risk: the Normative Aging Study. Int J Obes Relat Metab Disord. 2000; 24 (10): 1369-78.
18- Ho SC, Chen YM, Woo JL, Leung SS, Lam TH, Janus ED. Association between simple anthropometric indices and cardiovascular risk factors. Int J Obes Relat Metab Disord. 2001; 25 (11): 1689-97.
19- Meisinger C, Doring A, Thorand B, Heier M, Lowel H. Body fat distribution and risk of type 2 diabetes in the general population:are there differences between men and women? The MONICA/KORA Augsburg cohort study. Am J Clin Nutr. 2006; 84 (3): 483-89.
20- Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005; 81 (3): 555-63.
21- Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ, et al. Abdominal adiposity and coronary heart disease in women. JAMA. 1998; 280 (21): 1843-48.
22- Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. National Institutes of Health. Obes Res. 1998; 6 Suppl 2:51S-209S.
23- Nicklas BJ, Cesari M, Penninx BW, Kritchevsky SB, Ding J, Newman A, et al. Abdominal obesity is an independent risk factor for chronic heart failure in older people. J Am Geriatr Soc. 2006;54 (3): 413-20.
24- Lemos-Santos MG, Valente JG, Goncalves-Silva RM, Sichieri R. Waist circumference and waist-to-hip ratio as predictors of serum concentration of lipids in Brazilian men. Nutrition. 2004; 20 (10): 857-62.
25- Heitmann, BL. The variation in blood lipid levels described by various measures of overall and abdominal obesity in Danish men and women aged 35-65 years. Eur J Clin Nutr. 1992; 46 (8): 597-605.
26- Esmaillzadeh A, Mirmiran P, Azizi F. Waist-to-hip ratio is a better screening measure for cardiovascular risk factors than other anthropometric indicators in Tehranian adult men. Int J Obes Relat Metab Disord. 2004; 28 (10):
1325-32.
27- Ko GT, Chan JC, Woo J, Lau E, Yeung VT, Chow CC, et al. Simple anthropometric indexes and cardiovascular risk factors in Chinese. Int J Obes Relat Metab Disord. 1997; 21 (11): 995-1001.
28- Dalton M, Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med.
2003; 254 (6): 555-63.
29- Sarraf-Zadegan N, Boshtam M, Rafiei M. Risk factors for coronary artery disease in Isfahan, Iran. European J Public Health. 1999; 9 (1): 20-26.
30- Azadbakht L, Mirmiran P, Azizi F. Predictors of cardiovascular risk factors in Tehranian adults: diet and lifestyle.
East Mediterr Health J. 2006; 12 (1/2): 88-97.
Title:
Determination of the best anthropometric index for predicting of lipid profile in Zahedanian overweight and obese women
Authors: T. Shahraki1
, M. Shahraki
2, M. Roudbari
3 AbstractBackground and Aim: Although several studies have been conducted about the effectiveness of general and
central obesity anthropometric indices on lipid profile, a few surveys are available concerning their relationship. As the determination of the best anthropometric index for the prediction of lipid profile in any population is necessary, the current study was carried out to find out the best anthropometric index in overweight and obese adult women in two nutrition clinics of Zahedan.
Materials and Method: In a clinical cross sectional study, 728 overweight and obese women aged between
20 and 60 years, who had referred to the two nutrition clinics in Zahedan from July 2005 to May 2006, were investigated. Height, weight, waist circumference (WC) and hip circumference (HC) of the subjects were obtained and then BMI (body mass index) -as general obesity index- and WHR (waist hips ratio) and WC (as central obesity indices) were measured according to standard protocols. Individual data was collected by means of a questionnaire for each subject. TC (total cholesterol), TG (triglyceride) and HDL-C were enzymatically measured. The LDL-C was calculated according to Fried Wald Equation. The obtained data was analysed employing Pearson correlation coefficient and Z-test for Fisher’s zeta transformation and P 0.05 was considered as the significant level.
Results:
Mean age of the women was 32±9 years and their mean BMI, WHR, WC, and HC were 32±3.5, 0.89±0.13, 99.8±12 and 111±11, respectively. There was a positive significant correlation between BMI with age (r=0.17, P<0.001), WHR (r=0.11, P<0.003), WC(r=0.49, P<0.001) and HC (r=0.45, P<0.001). Similar results were obtained regarding the correlation between WC with age, WHR, and HC. There was not any significant statistical correlation between WHR and HC with age. Pearson correlation coefficient revealed that, with the exception of HDL-C, BMI and WC indices showed positive significant correlation with TC, TG and LDL-C concentration .Such a correlation was not found for WHR and HC indices. After adjustment to age and BMI, the same results were also held especially for TC (r=0.1, P<0.01) and TG (r=0.1, P<0.001) with WC index. There was not any significant correlation between WHR and HC indices with all of the studied variables after adjustment to age and BMI.
Conclusion: According to the results, WC is a better anthropometric index for the prediction of lipid profile
than WHR in overweight and obese women in Zahedan. However, more studies in this domain are recommended.
Key Words: Anthropometric indices; Lipid profile; Overweight; Obese; Zahedan
1Assistant Professor, Department of Pediatrics, Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran
2 Corresponding Author; Assistant Professor, Department of Nutrition, Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran [email protected]
3Assistant Professor, Department of Public Health, Zahedan University of Medical Sciences, Zahedan, Iran