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

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.

Figure 1

Hexagonal binning of MCP1, MMP3, PAI1, SAA, and sICAM1 against NCEP MetS categories (0–5). BMI was added in the figure only for comparison. At each sub-graph the following information is provided: top of the graph: n – the sample size studied for the particular biomarker; q₂₅ – the raw value of biomarker at the 25th percentile, q₅₀ – the 50th percentile, and q₇₅ – the 75th percentile; bottom of the graph: regression equation of the fitted model for a biomarker on MetS NCEP categories; NCEP MetS categories: 0 – none of the 5 categories reached beyond the NCEP thresholds (see Metabolic syndrome paragraph in the Methods section); 1 – at least one of the 5 categories passed the NCEP thresholds; similarly, 5 – all 5 categories passed the MetS NCEP thresholds. Three horizontal dashed lines represented the 25% (gray color), 50% (green color), and the 75% (red color) quartiles of a biomarker distribution. (Details provided in the Results and in the Appendix). For presentation clarity, 1 data point above 2500 pg/ml for MCP1, and 3 data points above 100 mg/l for SAA were removed from the graph.

Figure 2

Hexagonal binning of fibrinogen (FIB), CRP, DDIMER, IL2SR, and IL6 against NCEP MetS categories (0–5). BMI was added in the figure only for comparison. (Details provided in the Results and in the Appendix). For presentation clarity 2 data points above 70 mg/l for CRP, and 1 data point above 6000 pg/ml for IL2SR were removed from the graph.

Figure 3

Factor analysis of "conventional" MetS risk factors with the addition of an individual biomarker at a time. Colors coded are factor domains: blue – Factor 1 – obesity; orange – Factor 2 – blood pressure; yellow – Factor 3 – lipids; and gold – Factor 4 – central obesity. Reported are only factors where a biomarker contributed.

Figure 4

A three-dimensional presentation of relations among PAI1, HDLC, and TG, which accounts for only a small number of relationships of PAI1 with the classical risk factors of MetS. The three variables were natural log transformed and adjusted for a few covariates (for details see Statistical analysis paragraph in the Methods section). Red points represent the upper 25th percentile based on PAI1 values. Green points represent the lower 25th percentile, and in black are the 50% central PAI1 values. The higher TG values in a subject were associated with a lower HDLC value and also with a trend of increased values of PAI1.