Identification of Factorial Structure of Metabolic Syndrome Variables among People within Age Range of 35-65 in Mashhad, Iran

Document Type : Original article

Authors

1 Professor of Biostatistics, Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

2 Associate Professor of Nutrition, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

3 Assistant Professor of Statistics, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

4 MSc in Biostatistics, Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Introduction:Metabolic syndrome (MS) is a set of simultaneousand related metabolic conditions. Identifying these relationships can affect adopting preventive and even curative measures. Hence, this study was conducted with the aim of evaluating the factorial structure of MS variableson the individuals within the age range of 35-65 years who lived in Mashhad, Iran.
Materials and Methods:This cross-sectional study was performed on9761 cases within the age range of 35-65 years in Mashhad. The mean scores of variables, such as body mass index, waist circumference, systolic and diastolic blood pressure, fast blood sugar, total cholesterol, triglyceride, and high-density lipoprotein (HDL) were compared in two genders, and the correlations among them were calculated. In order to investigate the structure of these variables, exploratory factor analysis was applied.
Results:The results of factor analysis showed a different factorial structure between the two genders. There were a three-factor structure for men and a four-factor structure for women. The factors accounted for approximately 66% variance in men and almost 79% variance in women. It is worth mentioning that obesity with the explained 25%variance played the most important role in the development of MS in men, and due to the factor loading of glucose as an insulin resistance indicator lower than 0.4, it did not have any effects on this structure. In the present study, hypertension was identified as the first factor for women with 22% explained variance, and blood glucose with HDL and triglyceride were placed in one factor with 19% explained variance.
Conclusion:Considering the influence of MS in the development of cardiovascular diseases, preventive policies should be separately designed for each gender. Moreover, it is recommended to highly consider obesity in men and hypertension in women.

Keywords

References

1.     Reaven GM. Role of insulin resistance in human disease. Diabetes. 1988; 37(12):1595-607.
2.     Banerjee D, Misra A. Does using ethnic specific criteria improve the usefulness of the term metabolic syndrome? Controversies andsuggestions. Int J Obes. 2007; 31(9):1340-9.
3.     Misra A, Misra R, Wijesuriya M, Banerjee D. The metabolic syndrome in South Asians: continuing escalation & possible solutions. Indian J Med Res. 2007; 125(3):345-54.
4.     Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care. 2005; 28(7):1769-78.
5.     Sundström J, Risérus U, Byberg L, Zethelius B, Lithell H, Lind L. Clinical value of the metabolic syndrome for long term prediction of total and cardiovascular mortality: prospective, population based cohort study. BMJ. 2006; 332(7546):878-82.
6.     Lorenzo C, Williams K, Hunt KJ, Haffner SM. Trend in the prevalence of the metabolic syndrome and its impact on cardiovascular disease incidence: the San Antonio Heart Study. Diabetes Care. 2006; 29(3):625-30.
7.     Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertension Rep. 2018; 20(2):12.
8.     Herningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health. 2019; 19(1):377.
9.     Devers MC, Campbell S, Simmons D. Influence of age on the prevalence and components of the metabolic syndrome and the association with cardiovascular disease. BMJ Open Diabetes Res Care. 2016;4(1):e000195.
10.  Gurka MJ, Ice CL, Sun SS, Deboer MD. A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences. Cardiovasc Diabetol. 2012; 11:128.
11.  Delavari A, Forouzanfar MH, Alikhani S, Sharifian A, Kelishadi R. First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: the national survey of risk factors for noncommunicable diseases of Iran. Diabetes Care. 2009; 32(6):1092-7.
12.  Zabetian A, Hadaegh F, Sarbakhsh P, Azizi F. Weight change and incident metabolic syndrome in Iranian men and women; a 3 year follow-up study. BMC Public Health. 2009; 9(1):138.
13.  Azizi F, Salehi P, Etemadi A, Zahedi-Asl S. Prevalence of metabolic syndrome in an urban population: Tehran lipid and glucose study. Diabetes Res Clin Pract. 2003; 61(1):29-37.
14.  Fakhrzadeh H, Ebrahimpour P, Pourebrahim R, Heshmat R, Larijani B. Metabolic syndrome and its associated risk factors in healthy adults: a population-based study in Iran. Metab Syndr Relat Disord. 2006; 4(1):28-34.
15.  Esmaillzadeh A, Mirmiran P, Azadbakht L, Etemadi A, Azizi F. High prevalence of the metabolic syndrome in Iranian adolescents. Obesity. 2006; 14(3):377-82.
16.  Mannucci E, Monami M, Rotella CM. How many components for the metabolic syndrome? Results of exploratory factor analysis in the FIBAR study. Nutr Metab Cardiovasc Dis. 2007; 17(10):719-26.
17.  Choi K, Lee J, Kim K, Kim D, Kim S, Shin D, et al. Factor analysis of the metabolic syndrome among elderly Koreans--the South‐west Seoul Study. Diabet Med. 2003; 20(2):99-104.
18.  Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. New Engl J Med. 2004; 350(23):2362-74.
19.  Ghayour-Mobarhan M, Moohebati M, Esmaily H, Ebrahimi M, Parizadeh SM, Heidari-Bakavoli AR, et al. Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. Int J Public Health. 2015; 60(5):561-72.
20.  Sharma S. Applied multivariate techniques. New Jersey: John Wiley & Sons, Inc; 1995.
21.  Ramachandran A, Snehalatha C, Yamuna A, Murugesan N, Narayan KV. Insulin resistance and clustering of cardiometabolic risk factors in urban teenagers in southern India. Diabetes Care. 2007; 30(7):1828-33.
22.  Vikram NK, Pandey RM, Misra A, Goel K, Gupta
N. Factor analysis of the metabolic syndrome components in urban Asian Indian adolescents. Asia Pac J Clin Nutr. 2009; 18(2):293-300.
23.  Oh JY, Hong YS, Sung YA, Barrett-Connor E. Prevalence and factor analysis of metabolic syndrome in an urban Korean population. Diabetes Care. 2004; 27(8):2027-32.
24. Ayoubi E, Khalili D, Del Pisheh A, Hadaegh F, Azizi F. Factor analysis of metabolic syndrome and itsrelationship with type 2 diabetes: Tehran lipid and glucose study. J Diabetes. 2013; 12(3):233-44.        
Navid No
Volume 22, Issue 69
May 2019
Pages 15-24

Files

Share

How to cite

Statistics