Research

Do inflammation and procoagulation biomarkers contribute to the metabolic syndrome cluster?

Aldi T Kraja¹ , Michael A Province¹ , Donna Arnett² , Lynne Wagenknecht³ , Weihong Tang⁴ , Paul N Hopkins⁵ , Luc Djoussé⁶ and Ingrid B Borecki¹

¹  Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA

²  Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA

³  Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA

⁴  Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

⁵  Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT, USA

⁶  Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

author email corresponding author email

Nutrition & Metabolism 2007, 4:28doi:10.1186/1743-7075-4-28

Published:	21 December 2007

Abstract

Context

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.

Conclusion

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.