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

 Jin-Ha Yoon  ;  Jong-Ku Park  ;  Sung-Soo Oh  ;  Ki-Hyun Lee  ;  Sung-Kyung Kim  ;  Jong-Koo Kim  ;  Hee-Taik Kang  ;  Young-Jin Youn  ;  Jun-Won Lee  ;  Seung-Hwan Lee  ;  Ae-Yong Eom  ;  Choon-Hee Chung  ;  Jang-Young Kim  ;  Sang-Baek Koh 
 ANNALS OF HUMAN BIOLOGY, Vol.38(5) : 640-646, 2011 
Journal Title
Issue Date
Asian Continental Ancestry Group* ; Atherosclerosis/complications* ; Atherosclerosis/epidemiology* ; Cluster Analysis ; Cohort Studies ; Factor Analysis, Statistical ; Female ; Humans ; Linear Models ; Male ; Metabolic Syndrome/complications* ; Metabolic Syndrome/epidemiology* ; Middle Aged ; Reproducibility of Results ; Republic of Korea/epidemiology ; Risk Factors ; Tunica Intima/pathology ; Tunica Media/pathology
Atherosclerosis ; metabolic risk factors ; factor analysis
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.
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1. College of Medicine (의과대학) > Dept. of Family Medicine (가정의학교실) > 1. Journal Papers
Yonsei Authors
Kang, Hee Taik(강희택)
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