title: Ideal cardiovascular health behaviors and factors and high sensitivity C-reactive protein: the Kailuan cross-sectional study in Chinese.
abstract: BACKGROUND
  High sensitivity C-reactive protein (hs-CRP) is an inflammatory marker associated with increased cardiovascular disease risk. Health-related behaviors and factors decrease the risk of cardiovascular disease events, but the association between ideal cardiovascular health behaviors and hs-CRP is not clear. This study evaluated the relationship between the number of ideal cardiovascular health metrics and hs-CRP levels.
  
  
  METHODS
  We conducted a cross-sectional study from 2006 to 2007 that included 93,967 healthy subjects and 7869 diabetic patients. hs-CRP was measured by high-sensitivity nephelometry. Multivariate logistic regression was performed to analyze the association between the number of ideal cardiovascular health metrics and hs-CRP levels in the general population and in non-diabetic and diabetic subjects.
  
  
  RESULTS
  We found that hs-CRP levels decreased as the number of ideal cardiovascular health metrics increased in the general population, different genders, non-diabetic and diabetic subgroups. After adjusting for traditional risk factors, logistic regression analyses revealed that the risk of hs-CRP >3 mg/L in the general population with 6-7 ideal cardiovascular health metrics was 0.39 times lower than in those with 0-1 ideal metrics. Similarly, the risks were 0.39 times lower than in those with 0-1 ideal metrics in non-diabetic population. The risk of hs-CRP >3 mg/L in the diabetic population with 4-6 ideal cardiovascular health metrics was 0.45 times lower than those with 0-1 ideal metrics.
  
  
  CONCLUSIONS
  Our findings demonstrate an inverse relationship between the number of ideal health metrics and hs-CRP in the general population, and similar relationships were observed for different gender, non-diabetic and diabetic subgroups.
authors.forename: Hao
authors.initials: H
authors.lastname: Xue
authors.forename: Jianli
authors.initials: J
authors.lastname: Wang
authors.forename: Jinhong
authors.initials: J
authors.lastname: Hou
authors.forename: Jingsheng
authors.initials: J
authors.lastname: Gao
authors.forename: Shuohua
authors.initials: S
authors.lastname: Chen
authors.forename: Hang
authors.initials: H
authors.lastname: Zhu
authors.forename: Yutang
authors.initials: Y
authors.lastname: Wang
authors.forename: Yundai
authors.initials: Y
authors.lastname: Chen
authors.forename: Shouling
authors.initials: S
authors.lastname: Wu
mesh_terms: D000328:Adult; D000368:Aged; D044466:Asian People; D015415:Biomarkers / Q000097:blood; D001774:Blood Chemical Analysis; D002097:C-Reactive Protein / Q000032:analysis / Q000378:metabolism*; D002318:Cardiovascular Diseases / Q000097:blood / Q000517:prevention & control*; D002681:China; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000097:blood; D005260:Female; D015438:Health Behavior; D006801:Humans; D018836:Inflammation Mediators / Q000097:blood; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009391:Nephelometry and Turbidimetry
chemical_list: D015415:Biomarkers; D018836:Inflammation Mediators; D002097:C-Reactive Protein