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The clustering patterns of metabolic risk factors and its association with sub-clinical atherosclerosis in Korean population.

Authors
 Jin-Ha Yoon ; Jong-Ku Park ; Jang-Young Kim ; Sang-Baek Koh ; Choon-Hee Chung ; Ae-Yong Eom ; Seung-Hwan Lee ; Jun-Won Lee ; Young-Jin Youn ; Hee-Taik Kang ; Jong-Koo Kim ; Sung-Kyung Kim ; Ki-Hyun Lee ; Sung-Soo Oh 
Citation
 Annals of Human Biology, Vol.38(5) : 640~646, 2011 
Journal Title
 Annals of Human Biology 
ISSN
 0301-4460 
Issue Date
2011
Abstract
BACKGROUND AND AIMS: Metabolic syndrome (MetS) is considered to be an insulin-resistance syndrome, but recent evidence suggests that MetS has multiple physiological origins which may be related to atherosclerosis. This study investigated clustering patterns of metabolic risk factors and its association with sub-clinical atherosclerosis. SUBJECTS AND METHODS: This study used factor analysis of 11 metabolic factors in 1374 individuals to define clustering patterns and determine their association with carotid intima-media thickness (CIMT). Eleven metabolic factors were used: body mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), fasting blood insulin (FBI), serum triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), homeostasis model assessment-insulin resistance (HOMA-IR), high-sensitivity C-reactive protein (hsCRP) and adiponectin. Two regression analyses were done, the first using individual metabolic variables and the second using each factor from the factor analysis to evaluate their relationships with CIMT. RESULTS: Four clustering patterns, insulin-resistance factor (FBG, FBI, HOMA-IR), obesity-inflammatory factor (BMI, WC, hsCRP), blood pressure factor (SBP, DBP) and lipid metabolic factor (HDL-C, TG, adiponectin) were categorized. In a multivariate regression model after adjustment for age, sex, low-density lipoprotein cholesterol and smoking history (pack year), insulin resistance factor (B = 11.09, p = 0.026), obesity-inflammatory factor (B = 18.50, p < 0.001), blood pressure factor (B = 12.84, p = 0.010) and lipid metabolic factor (B = - 11.55, p = 0.023) were found to be significantly associated with CIMT. CONCLUSION: In conclusion, metabolic risk factors have four distinct clustering patterns that are independently associated with sub-clinical atherosclerosis.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/93510
DOI
10.3109/03014460.2011.598188
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Family Medicine
Yonsei Authors
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Link
 http://informahealthcare.com/doi/abs/10.3109/03014460.2011.598188
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