Orthogonal factor analysis on metabolic syndrome.
- Author:
Xu-Hong HOU
1
;
Wei-Ping JIA
;
Yu-Qian BAO
;
Jun-xi LU
;
Yuan-Min WU
;
Hui-Lin GU
;
Yu-Hua ZUO
;
Su-Ying JIANG
;
Kun-San XIANG
Author Information
1. Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, China.
- Publication Type:Journal Article
- MeSH:
Adult;
Aged;
Cross-Sectional Studies;
Female;
Genomics;
Humans;
Metabolic Syndrome;
epidemiology;
genetics;
Middle Aged;
Models, Statistical;
Prevalence;
Proteomics
- From:
Chinese Journal of Epidemiology
2008;29(3):297-301
- CountryChina
- Language:Chinese
-
Abstract:
To elucidate the principal of orthogonal factor analysis, using an example of factor analysis of metabolic syndrome. The basic structures and the fundamental concepts of orthogonal factor analysis were introduced and data involving 1877 women aged of 35-65 years, selected from a cross-sectional study, which was conducted in 1998 - 2001 in Shanghai, were included in this study. Factor analysis was carried out using principle components analysis with Varimax orthogonal rotation of the components of the metabolic syndrome. The different components of the metabolic syndrome were not linked closely with the other components and loaded on the six different factors,which mainly reflected by the variables of obesity, blood pressure, plasma glucose, plasma insulin, triglycerides and HDL-cholesterol respectively. Six major factors of the metabolic syndrome were uncorrelated with each other and explained 86% of the variance in the original data. The factor score and total factor score for the individual could be obtained according to the component score coefficient matrix. Although the components of the metabolic syndrome were related statistically, the finding of six factors suggested that the components of the metabolic syndrome did not show high degrees of intercorrelation. As a linear method of data reduction, the mode reduced a large set of measured intercorrelation variables into a smaller set of uncorrelated factors, which explained the majority of the variance in the original variables. Factor analysis was well suited for revealing underlying patterns or structure among variables showing high degrees of intercorrelation.
