title: Do inflammation and procoagulation biomarkers contribute to the metabolic syndrome cluster?
abstract: BACKGROUND
  The metabolic syndrome (MetS), in addition to its lipid, metabolic, and anthropomorphic characteristics, is associated with a prothrombotic and the proinflammatory state. However, the relationship of inflammatory biomarkers to MetS is not clear.
  
  
  OBJECTIVE
  To study the association between a group of thrombotic and inflammatory biomarkers and the MetS.
  
  
  METHODS
  Ten conventional MetS risk variables and ten biomarkers were analyzed. Correlations, factor analysis, hexagonal binning, and regression of each biomarker with the National Cholesterol Education Program (NCEP) MetS categories were performed in the Family Heart Study (n = 2,762).
  
  
  RESULTS
  Subjects in the top 75% quartile for plasminogen activator inhibitor-1 (PAI1) had a 6.9 CI95 [4.2-11.2] greater odds (p < 0.0001) of being classified with the NCEP MetS. Significant associations of the corresponding top 75% quartile to MetS were identified for monocyte chemotactic protein 1 (MCP1, OR = 2.19), C-reactive protein (CRP, OR = 1.89), interleukin-6 (IL6, OR = 2.11), sICAM1 (OR = 1.61), and fibrinogen (OR = 1.86). PAI1 correlated significantly with all obesity and dyslipidemia variables. CRP had a high correlation with serum amyloid A (0.6) and IL6 (0.51), and a significant correlation with fibrinogen (0.46). Ten conventional quantitative risk factors were utilized to perform multivariate factor analysis. Individual inclusion, in this analysis of each biomarker, showed that, PAI1, CRP, IL6, and fibrinogen were the most important biomarkers that clustered with the MetS latent factors.
  
  
  CONCLUSIONS
  PAI1 is an important risk factor for MetS. It correlates significantly with most of the variables studied, clusters in two latent factors related to obesity and lipids, and demonstrates the greatest relative odds of the 10 biomarkers studied with respect to the MetS. Three other biomarkers, CRP, IL6, and fibrinogen associate also importantly with the MetS cluster. These 4 biomarkers can contribute in the MetS risk assessment.
authors.affiliation: Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA. aldi@wustl.edu.
authors.forename: Aldi T
authors.initials: AT
authors.lastname: Kraja
authors.forename: Michael A
authors.initials: MA
authors.lastname: Province
authors.forename: Donna
authors.initials: D
authors.lastname: Arnett
authors.forename: Lynne
authors.initials: L
authors.lastname: Wagenknecht
authors.forename: Weihong
authors.initials: W
authors.lastname: Tang
authors.forename: Paul N
authors.initials: PN
authors.lastname: Hopkins
authors.forename: Luc
authors.initials: L
authors.lastname: Djoussé
authors.forename: Ingrid B
authors.initials: IB
authors.lastname: Borecki