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Park, Kwon, Lim, Lee, Kim, Yoon, Cha, Son, Park, Kim, Meng, and Lee: Clustering Characteristics of Risk Variables of Metabolic Syndrome in Korean Rural Populations
Original Article
The Journal of Korean Diabetes Association

The Journal of Korean Diabetes Association 2006; 30(3): 177-189.

Published online: 31 May 2006

DOI: https://doi.org/10.4093/jkda.2006.30.3.177

Clustering Characteristics of Risk Variables of Metabolic Syndrome in Korean Rural Populations

Yong-Moon Park, Hyuk Sang Kwon1, Sun Young Lim, Jin Hee Lee1, Sung Rae Kim1, Kun Ho Yoon1, Bong Yun Cha1, Ho Young Son1, Yong Gyu Park2, Dong Suk Kim3, Kwang-ho Meng, Won Chul Lee

Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Korea.

1Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Korea.

2Department of Biostatistics, College of Medicine, The Catholic University of Korea, Korea.

3Chungju Public Health Center, Korea.

Received 10 January 2006       Accepted 24 May 2006

Copyright © 2006 Korean Diabetes Association

Abstract

Background

The risks of both type 2 diabetes mellitus and cardiovascular disease are mainly associated with the metabolic syndrome which is characterized by clustering of metabolic risk factors, including abdominal obesity, glucose intolerance, hypertension, and dyslipidemia. This study aimed to examine the relations among metabolic risk variables and the underlying structure of the metabolic syndrome that unites related components.

Methods

Subjects were selected by stratified random cluster sampling among persons aged over 40 years from a rural area. Waist circumference, BMI, fasting glucose, fasting insulin, triglycerides, HDL cholesterol, systolic blood pressure, and diastolic blood pressure were used as risk variables of metabolic syndrome. Factor analysis, a multivariate correlation statistical technique, was performed on a dataset from nondiabetic 3,443 men and women without history of coronary heart disease.

Results

Exploratory factor analysis identified three factors in both gender (obesity, hypertension, and dyslipidemia-insulin resistance in men; obesity-insulin resistance, hypertension, and dyslipidemia in women). Fasting insulin was a common contributor to the structure of metabolic syndrome in male subjects, smokers and alcohol drinking group. Confirmatory factor analysis based on the results of exploratory factor analysis revealed that metabolic syndrome was represented primarily by obesity factor in men, obesity-insulin resistance factor in women, and that dyslipidemia factor was highly correlated with obesity factor in men, with insulin resistance factor in women.

Conclusion

Underlying structure of metabolic syndrome was different between men and women, and obesity might be a primary target for prevention of both type 2 diabetes mellitus and cardiovascular disease in Korea.

Keywords: Confirmatory factor analysis, Exploratory factor analysis, Metabolic syndrome, Obesity

Figures and Tables

Fig. 1
Confirmatory factor analysis on three factor model of metabolic syndrome based on the results of exploratory factor analysis in male subjects, with χ2 = 58.449 (df = 12, P = 0.000), CFI = 0.983, NNFI = 0.970, and RMSEA (90%CI) = 0.053 (0.040-0.067).
jkda-30-177-g001
Fig. 2
Confirmatory factor analysis on three factor model of metabolic syndrome based on the results of exploratory factor analysis in female subjects, with χ2 = 123.472 (df = 17, P = 0.000), CFI = 0.972, NNFI = 0.954, and RMSEA (90%CI) = 0.055 (0.046-0.065).
jkda-30-177-g002
Fig. 3
Confirmatory factor analysis of interfactor correlation model of metabolic syndrome based on the results of exploratory factor analysis in male subjects, with χ2 = 58.449 (df = 12, P = 0.000), CFI = 0.983, NNFI = 0.970, and RMSEA (90%CI) = 0.053 (0.040-0.067).
jkda-30-177-g003
Fig. 4
Confirmatory factor analysis of interfactor correlation model of metabolic syndrome based on the results of exploratory factor analysis in female subjects, with χ2 = 123.472 (df = 17, P = 0.000), CFI = 0.972, NNFI = 0.954, and RMSEA (90%CI) = 0.055 (0.046-0.065).
jkda-30-177-g004
Table 1
Characteristics of Subjects by Gender
jkda-30-177-i001

Statistics were analyzed by t-test.

*Statistical analysis was done after log-transformation.

Table 2
Age Adjusted Pearson's Correlation Coefficients Among Risk Variables in Male Subjects
jkda-30-177-i002

*P < 0.0001.

†P < 0.001.

‡P < 0.05.

Table 3
Age Adjusted Pearson's Correlation Coefficients Among Risk Variables in Female Subjects
jkda-30-177-i003

*P < 0.0001.

†P < 0.01.

‡P < 0.05.

Table 4
Factor Loading Patterns after Orthogonal Rotation of Principal Components by Gender
jkda-30-177-i004

Data are factor loadings, the correlation between the individual variable and each factor. Variables with loadings ≥ ± 0.30 are in bold type.

Table 5
Factor Loading Patterns after Orthogonal Rotation of Principal Components by Gender and Age Groups
jkda-30-177-i005

Data are factor loadings, the correlation between the individual variable and each factor. Variables with loadings ≥ ±0.30 are in bold type.

Table 6
Factor Loading Patterns after Orthogonal Rotation of Principal Components by Gender and Age Groups
jkda-30-177-i006

Data are factor loadings, the correlation between the individual variable and each factor. Variables with loadings ≥ ±0.30 are in bold type.

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