Iranian Journal of Diabetes and Obesity
Shahid Sadoughi University Of Medical Sciences
Thu, Dec 26, 2024
[Archive]
Volume 11, Issue 1 (3-2019)
IJDO 2019, 11(1): 14-21 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Gholipour M. Comparing the Metabolic Syndrome Frequency and Components among Inactive College Students Using Standard and Asian Definitions of Body Mass Index. IJDO 2019; 11 (1) :14-21
URL: http://ijdo.ssu.ac.ir/article-1-462-en.html
URL: http://ijdo.ssu.ac.ir/article-1-462-en.html
1. Department of physical Education, Sharif University of Technology, Tehran, Iran.
Abstract: (2589 Views)
Objective: Metabolic syndrome (MetS) is a combination of some risk factors including obesity which can be assessed by body mass index (BMI). The purpose of the present study was to determine the accuracy of Standard BMI Cut-off points (SBC) and Asian BMI Cut-off points (ABC), to categorize the young people with MetS.
Materials and Methods: In this cross-sectional study, 198 inactive college students (66 female (33.33%) and 132 male (66.66%)) participated. The prevalence of MetS was diagnosed according to the modified NCEP-ATPIII guidelines10, with exception of ≥2 risk factors. All required data were collected through blood sampling, blood pressure and anthropometric measurements.
Results: The prevalence rate of MetS and its components within the normal category of MetS was divided into two categories of normal and overweight according ABC with no significant differences between those categories. The high frequency of MetS and its components were observed in both genders even among underweight students. Among MetS risk factors, low level of HDL-C (female; 45.45%, male; 43.18%) which included underweight students was most prevalent. The lowest incidence belonged to the impaired fasting plasma glucose (FPG).
Conclusion: The ABC more accurately categorize the inactive students. Despite the high frequency of MetS among the young inactive students, the low incidence of elevated FPG indicates that some MetS definitions may not precisely diagnose the susceptible students. Therefore, redefining the MetS criteria for more precise identification the young people at risk seems to be essential.
Materials and Methods: In this cross-sectional study, 198 inactive college students (66 female (33.33%) and 132 male (66.66%)) participated. The prevalence of MetS was diagnosed according to the modified NCEP-ATPIII guidelines10, with exception of ≥2 risk factors. All required data were collected through blood sampling, blood pressure and anthropometric measurements.
Results: The prevalence rate of MetS and its components within the normal category of MetS was divided into two categories of normal and overweight according ABC with no significant differences between those categories. The high frequency of MetS and its components were observed in both genders even among underweight students. Among MetS risk factors, low level of HDL-C (female; 45.45%, male; 43.18%) which included underweight students was most prevalent. The lowest incidence belonged to the impaired fasting plasma glucose (FPG).
Conclusion: The ABC more accurately categorize the inactive students. Despite the high frequency of MetS among the young inactive students, the low incidence of elevated FPG indicates that some MetS definitions may not precisely diagnose the susceptible students. Therefore, redefining the MetS criteria for more precise identification the young people at risk seems to be essential.
Type of Study: Research |
Subject:
Special
Received: 2019/08/28 | Accepted: 2019/08/28 | Published: 2019/08/28
Received: 2019/08/28 | Accepted: 2019/08/28 | Published: 2019/08/28
References
1. Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes Metab Syndr Obes. 2014;7:587-91.
2. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome a new world‐wide definition. A consensus statement from the international diabetes federation. Diabetic medicine. 2006;23(5):469-80.
3. Grundy SM. Metabolic syndrome pandemic. Arteriosclerosis, thrombosis, and vascular biology. 2008;28(4):629-36.
4. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112(17):2735-52.
5. World Health Organization. Obesity and overweight. Fact sheet 311. Available at: http://www.who.int/mediacentre/factsheets/fs311/en/.
6. World Health Organization. The world health report. Chapter 4. Other diet-related risk factors and physical inactivity. Available at:
https://www.who.int/whr/2002/chapter4/en/index4.html
7. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, Xiang AH, Watanabe RM. A better index of body adiposity. Obesity. 2011;19(5):1083-9.
8. Eckel RH, Kahn SE, Ferrannini E, Goldfine AB, Nathan DM, Schwartz MW, et al. Obesity and type 2 diabetes: what can be unified and what needs to be individualized?. The Journal of Clinical Endocrinology & Metabolism. 2011;96(6):1654-63.
9. World Health Organization (WHO). Obesity: preventing and managing the global epidemic Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000; 894 (i–xii):1-253.
10. Seidell JC, Kahn HS, Williamson DF, Lissner L, Valdez R. Report from a Centers for Disease Control and Prevention Workshop on use of adult anthropometry for public health and primary health care. 2001: 123-6.
11. WHO/IASO/IOTF. The Asia-Pacific perspective: redefining obesity and its treatment. Health Communications Australia: Melbourne. ISBN 0-9577082-1-1.
12. Yahia N, Brown CA, Snyder E, Cumper S, Langolf A, Trayer C, et al. Prevalence of metabolic syndrome and its individual components among midwestern university students. Journal of community health. 2017;42(4):674-87.
13. Kanitkar SA, Kalyan M, Diggikar P, More U, Kakrani AL, Gaikwad A, et al. Metabolic syndrome in medical students. Journal International Medical Sciences Academy. 2015;28(1):14-5.
14. Huang TT, Shimel A, Lee RE, Delancey W, Strother ML. Metabolic risks among college students: prevalence and gender differences. Metabolic syndrome and related disorders. 2007;5(4):365-72.
15. Mackenzie B. Performance evaluation tests. London: Electric World plc. 2005;24(25):57-158.
Kline GM, Porcari JP, Hintermeister R, Freedson PS, Ward A, Mccarron RF, et al. Estimation of VO2 from a one-mile track walk, gender, age and body weight. Med Sci Sports Exerc. 1987;3:253-9.
16. Heyward VH. The physical fitness specialist certification manual. Dallas, TX: The Cooper Institute for Aerobics Research. 1998:48.
17. Jackson AS, Pollock ML, Ward AN. Generalized equations for predicting body density of women. Medicine and science in sports and exercise. 1980;12(3):175-81.
18. Jackson AS, Pollock ML. Generalized equations for predicting body density of men. British journal of nutrition. 1978;40(3):497-504
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
