Key result
Four distinct metabolic risk factor clusters (insulin-resistance, obesity-inflammatory, blood pressure, and lipid) were independently associated with sub-clinical atherosclerosis measured by CIMT.
Why the study?
Are clustering patterns of metabolic risk factors associated with sub-clinical atherosclerosis (CIMT) in a Korean population?
Observational (n=1,374)
Are clustering patterns of metabolic risk factors associated with sub-clinical atherosclerosis (CIMT) in a Korean population?
Effect estimate: B = 18.50 (for obesity-inflammatory factor)
p-value: p=<0.001
Metabolic risk factors cluster into four distinct patterns (insulin resistance, obesity-inflammatory, blood pressure, and lipid metabolism) that are independently associated with sub-clinical atherosclerosis.
Metabolic clustering may aid CIMT risk stratification; hypothesis-generating and should not yet change practice.
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.
No takes yet. Share an insight, caveat, or question.
Yoon et al. (2011) conducted an observational in Metabolic syndrome and sub-clinical atherosclerosis (n=1,374). Metabolic risk factor clustering patterns was evaluated on Carotid intima-media thickness (CIMT) (B = 18.50 (for obesity-inflammatory factor), p=<0.001). Four distinct metabolic risk factor clusters (insulin-resistance, obesity-inflammatory, blood pressure, and lipid) were independently associated with sub-clinical atherosclerosis measured by CIMT.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: