Anthropometric indicators as predictors of the risk of metabolic syndrome in adult working men

Authors

  • Janusz Brudecki Faculty of Physical Education and Sport, Department of Anthropology, University of Physical Education in Kraków Al. Jana Pawła II 78, 31-571 Kraków
  • Maria Chrzanowska Faculty of Physical Education and Sport, Department of Anthropology, University of Physical Education in Kraków

DOI:

https://doi.org/10.1515/anre-2015-0005

Keywords:

metabolic syndrome, BMI, WHR, cholesterol, blood pressure, men

Abstract

Measurement of body weight, height, waist and hip circumference is a standard procedure that allows to better define the risk of metabolic syndrome. The aim of the study is to determine the usefulness of anthropometric indicators such as BMI, WC (waist circumference), WHR, WHtR and percentage of body fat to predict the metabolic cardiovascular risk in the adult male population of Krakow, as well as an attempt to determine the metabolic cardiovascular risk with the original anthropometric risk index. The study included 405 men from the population working in the T. Sendzimir Steelworks in Kraków at the age of 30–69 years. Anthropometric measurements: body height measured to the nearest mm, circuits (waist, hips) measured to the nearest centimetre, the percentage of fat (the type of electronic scales Tanita BF 300) measured according to the standard protocol by the same technician and biomedical indicators assessing the functional status of organism, total cholesterol, HDL, LDL, triglycerides, glucose and blood pressure measured with a mercury manometer. As a measure of goodness of fit for the indices of risk (and their components), the AUC method was used for the ROC curves to evaluate the sensitivity and specificity of the diagnostic test. The results show that significant in predicting the risk of metabolic syndrome are not only standard anthropometric measurements specified in the standards of WHO, EGIR, NCEP and IDF. In addition, it is important to take into account the amount of fat and calculate the cumulative risk index based on all relevant measurements and indicators.

Downloads

Download data is not yet available.

References

Alberti KG, Zimmet PZ. 1998. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 15: 539–553.
View in Google Scholar

Balkau B, Charles MA. 1999. Comment on the provisional report from the WHO consultation: European Group for the Study of Insulin Resistance (EGIR). Diabet Med 16: 442–443.
View in Google Scholar

Bauduceau B, Vachey E, Mayaudon H, Burnat P, Dupuy O, Garcia C, Ceppa F, Bordier L. 2007. Should we have more definitions of metabolic syndrome or simply take waist measurement? Diabetes Metab 33: 333–339.
View in Google Scholar

Bisschop ChNS, Peters PHM, Monninkhoff EM, van der Schouw YT, May AM. 2013. Associations of visceral fat, physical activity and muscle strength with the metabolic syndrome. Maturitas 76: 139–145.
View in Google Scholar

Bolanowski J, Bronowicz J, Bolanowska B, Szklarska A, Lipowicz A, Skalik R. 2010. Impact of education and place of residence on the risk of metabolic syndrome in Polish men and women. Int J Cardiol 145: 542–544.
View in Google Scholar

Gozashti MH, Najmeasadat F, Mohadeseh S, Najafipour H. 2014. Determination of most suitable cut off point of waist circumference for diagnosis of metabolic syndrome in Kerman. Diabetes Metab Syndr: Clin Res Rev 8: 8–12.
View in Google Scholar

Huang Z, Willett WC, Manson JE, Rosner B, Stampfer MJ, Speizer FE, Colditz GA. 1998. Body weight, weight change, and risk for hypertension in women. Ann Intern Med 128: 81–88.
View in Google Scholar

Lee CMY, Huxley RR, Wildman RP, Woodward M. 2008. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 61: 646–653.
View in Google Scholar

Li M, McDermott RA. 2010. Using anthropometric indices to predict cardio-metabolic risk factors in Australian indigenous populations. Diabetes Research and Clinical Practice 87: 401–406.
View in Google Scholar

Lorenzo C, Serrano-Ríos M, Martínez-Larrad MT, Gonzalez-Villalpando C, Williams K, Gabriel R, Stern MP, Haffner SM. 2007. Which Obesity Index Best Explains Prevalence Differences in Type 2 Diabetes Mellitus. Obesity. 15: 1294–1301.
View in Google Scholar

Paniagua L, Lohsoonthorn V, Lertmaharit S, Jiamjarasrangsi W, Williams MA. 2008. Comparison of waist circumference, body mass index, percent body fat and other measure of adiposity in identifying cardiovascular disease risks among Thai adults. Obes Res Clin Pract 2: 215–223.
View in Google Scholar

Quan MIS, Peng Y, Nan H, Hong LJ, Rong CX, Bo C, Xia YL, Hua ZW. 2013. BMI, WC, WHtR, VFI and BFI: Which Indictor is the Most Efficient Screening Index on Type 2 Diabetes in Chinese Community Population. Biomed Environ Sci, 26: 485–491.
View in Google Scholar

Rostambeigi N, Shaw JE, Atkins RC, Ghanbarian A, Cameron AJ, Forbes A, Momenan A, Hadaegh F, Mirmiran P, Zimmet PZ, Azizi F, Tonkin AM. 2010. Waist circumference has heterogeneous impact on development of diabetes in different populations: longitudinal comparative study between Australia and Iran. Diabetes Res Clin Pract 88: 117–124.
View in Google Scholar

Salazar MR, Carbajal HA, Espeche WG, Leiva Sisnieguez CE, Balbín E, Dulbecco CA, Aizpurúa M, Marillet AG, Reaven GM. 2012. Relation among the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio, insulin resistance, and associated cardio-metabolic risk factors in men and women. Am J Cardiol 109: 1749–1753.
View in Google Scholar

Schneider HJ, Glaesmer H, Klotsche J, Böhler S, Lehnert H, Zeiher AM, März W, Stalla GK, Wittchen HU, DETECT Study Group. 2007. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 92: 589–594.
View in Google Scholar

Tatoń J, Czech A, Bernas M. 2006. Otyłość–Zespół metaboliczny, Warszawa: Wyd. Lekarskie PZWL.
View in Google Scholar

Taylor RW, Keil D, Gold EJ, Williams SM, Goulding A. 1998. Body mass index, waist girth, and waist-to-hip ratio as indexes of total and regional adiposity in women: evaluation using receiver operating characteristic curves. Am J Clin Nutr 67: 44–49.
View in Google Scholar

Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation 2002, 106, 3143–3421.
View in Google Scholar

Worachartcheewan A, Dansethakul P, Nantasenamat C, Pidetcha P, Prachayasittikul V. 2012. Determining the optimal cut-off points for waist circumference and body mass index for identification of metabolic abnormalities and metabolic syndrome in urban Thai population. Diabetes Res Clin Pract 98: e16–e21.
View in Google Scholar

Zimmet P, Alberti KG, Serrano Rios M. 2005a. A new International Diabetes Federation (IDF) worldwide definition of the metabolic syndrome: the rationale and the results. Rev Esp Cardiol 58: 1371–1376.
View in Google Scholar

Zimmet P, Magliano D, Matsuzawa Y, Alberti G, Shaw J. 2005b. The metabolic syndrome: a global public health problem and a new definition. J Athero Thromb 12: 295–300.
View in Google Scholar

Downloads

Published

2015-03-30

How to Cite

Brudecki, J., & Chrzanowska, M. (2015). Anthropometric indicators as predictors of the risk of metabolic syndrome in adult working men. Anthropological Review, 78(1), 67–77. https://doi.org/10.1515/anre-2015-0005

Issue

Section

Articles

Similar Articles

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

You may also start an advanced similarity search for this article.