title: Impact of multiple cardiovascular risk factors on carotid intima-media thickness and elasticity.
abstract: BACKGROUND
  Carotid intima-media thickness (IMT) and elasticity have been shown to be independent predictors of cardiovascular disease (CVD). Cardiovascular risk factors (CVRFs) includes hypertension, dyslipidemia, diabetes, overweight and smoking. The objective was to investigate whether the clustering of three or more components of CVRFs has a greater impact on carotid IMT and elasticity than individual components of CVRFs.
  
  
  METHODS
  One hundred and seventy-three participants without clinical CVD were classified as the multiple CVRFs patients with three or more CVRFs (n = 55) and control group with two or less CVRFs (n = 118). Carotid IMT and elastic modulus were measured by B-mode ultrasound and vessel texture matching method (VTMM), respectively.
  
  
  RESULTS
  The multiple CVRFs conferred a disproportionate increase in carotid IMT (43%, p<0.0001) and elastic modulus (60%, p<0.0001), compared with control group. Multiple regression models, which included age, gender, as well as each individual component of CVRFs as continuous variables, showed that multiple CVRFs was an independent determinant of both IMT (p = 0.042) and elasticity (p = 0.008). In the analysis of variance adjusted with age, subjects with single, double, and multiple CVRFs, increased by 8.1%, 42.2%, and 66% for IMT, 54.6%, 94.3%, and 125.2% for elastic modulus, respectively, compared to subjects without CVRFs.
  
  
  CONCLUSIONS
  The clustering of multiple CVRFs has a greater impact on carotid IMT and elasticity than individual components of CVRFs. This suggests that the components of CVRFs interact to synergistically impact carotid IMT and elasticity.
authors.affiliation: Paul C Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Lili
authors.initials: L
authors.lastname: Niu
authors.forename: Yanling
authors.initials: Y
authors.lastname: Zhang
authors.forename: Ming
authors.initials: M
authors.lastname: Qian
authors.forename: Long
authors.initials: L
authors.lastname: Meng
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Rongqin
authors.initials: R
authors.lastname: Zheng
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging / Q000209:etiology / Q000503:physiopathology*; D002339:Carotid Arteries / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D002340:Carotid Artery Diseases / Q000150:complications / Q000000981:diagnostic imaging / Q000503:physiopathology; D059168:Carotid Intima-Media Thickness; D004548:Elasticity / Q000502:physiology*; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment / Q000706:statistics & numerical data; D012307:Risk Factors; D055815:Young Adult