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LETTER TO EDITOR |
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| Year : 2014 | Volume
: 39
| Issue : 3 | Page : 190 |
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Metabolic syndrome in the rural population of Wardha, Central India: Confounding of factor analysis as result of high correlated variables
Erfan Ayubi
Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences; Department Epidemiology and Biostatistics, School of Public Health and Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| Date of Web Publication | 19-Jul-2014 |
Correspondence Address:
Erfan Ayubi
Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences; Department Epidemiology and Biostatistics, School of Public Health and Health Research Institute, Tehran University of Medical Sciences, Tehran,
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0970-0218.137166
How to cite this article:
Ayubi E. Metabolic syndrome in the rural population of Wardha, Central India: Confounding of factor analysis as result of high correlated variables. Indian J Community Med 2014;39:190 |
How to cite this URL:
Ayubi E. Metabolic syndrome in the rural population of Wardha, Central India: Confounding of factor analysis as result of high correlated variables. Indian J Community Med [serial online] 2014 [cited 2022 May 18];39:190. Available from: https://www.ijcm.org.in/text.asp?2014/39/3/190/137166 |
Sir,
In recent paper published in January-March 2013 issue of Indian J Community Med that Deshmukh and colleagues had done, was about factor analysis of metabolic syndrome (MetS) on rural population of Wardha, central India. [1] The authors aimed to factor analysis MetS components to represent interrelationships among sets of variables.
In factor analysis MetS papers, correlation coefficients and significance level were usually reported because earlier stage from factor analysis is data screening or item analysis. If any correlation coefficients greater than 0.9 are found, then we should be aware that a problem could arise because of singularity in data, as far as it suggested that we need to exclude one of the two variables in analysis. [2] It seems that there is multicollinearity among variables in this study. In their results, systolic blood pressure and diastolic blood pressure in both sexes, total cholesterol and low-density lipoprotein in women have been loaded together on one factor without sharing with other variables because of their high collinearity. One could think that hypertension may not be linked to the MetS to the same extent as other components. Some investigator had used the mean arterial pressure [3] and systolic blood pressure. [4] If we put low density lipoprotein-cholesterol (LDL-C) and total cholesterol (TC) together into model of factor analysis; was seen that Kaiser-Meyer-Olkin value reduced steeply. [3] In addition, investigators usually did factor analysis of MetS without LDL-C and TC in their studies. [5],[6]
 References | |  |
| 1. | Deshmukh PR, Kamble P, Goswami K, Garg N. Metabolic syndrome in the rural population of Wardha, Central India: An exploratory factor analysis. Indian J Community Med 2013;38:33-8. 
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| 2. | Field A. Exploratory factor analysis. Discovering statistics using SPSS. California: SAGE Publication; 2005.641
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| 3. | Tsai CH, Li TC, Lin CC, Tsay HS. Factor analysis of modifiable cardiovascular risk factors and prevalence of metabolic syndrome in adult Taiwanese. Endocrine 2011;40:256-64.
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| 4. | Chen CH, Lin KC, Tsai ST, Chou P. Different association of hypertension and insulin-related metabolic syndrome between men and women in 8437 nondiabetic Chinese. Am J Hypertens 2000;13:846-53.
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| 5. | Hanson RL, Imperatore G, Bennett PH, Knowler WC. Components of the metabolic syndrome and incidence of type 2 diabetes. Diabetes 2002;51:3120-7.
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| 6. | Oh JY, Hong YS, Sung YA, Barrett-Connor E. Prevalence and factor analysis of metabolic syndrome in an urban Korean population. Diabetes Care 2004;27:2027-32.
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