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

Kraja AT, Province MA, Arnett D, Wagenknecht L, Tang W, Hopkins PN, Djoussé L, Borecki IB - Nutr Metab (Lond) (2007)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA. aldi@wustl.edu.

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.

No MeSH data available.


Related in: MedlinePlus

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2254623&req=5

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.

Mentions: Participants between ages 30 and 93 had a mean BMI of 28.9 ± 5.68 (Table 1). PAI1 levels were relatively high (39.58 ± 44.36) with an interquartile range (IR) of 46 between the 25% quartile (q25) and 75% quartile (q75) (see Table 1 and Figure 1). The CRP mean level was 3.61 ± 5.65. The distribution is shown in Figure 2 which indicates an IR of CRP of 3.4 in a sample of 2,685 participants. The central 50% of the data were more spread around the mean for PAI1 than for CRP. Using the ratio of IR/SD we found that fibrinogen, sICAM1, PAI1, IL6, and MMP3 compared to BMI, had a similar spread of the central 50% of the data. In contrast, the central 50% of the data were less spread around the mean compared to BMI for SAA, CRP, DDIMER, IL2sr, and MCP1.


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

Kraja AT, Province MA, Arnett D, Wagenknecht L, Tang W, Hopkins PN, Djoussé L, Borecki IB - Nutr Metab (Lond) (2007)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2254623&req=5

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.
Mentions: Participants between ages 30 and 93 had a mean BMI of 28.9 ± 5.68 (Table 1). PAI1 levels were relatively high (39.58 ± 44.36) with an interquartile range (IR) of 46 between the 25% quartile (q25) and 75% quartile (q75) (see Table 1 and Figure 1). The CRP mean level was 3.61 ± 5.65. The distribution is shown in Figure 2 which indicates an IR of CRP of 3.4 in a sample of 2,685 participants. The central 50% of the data were more spread around the mean for PAI1 than for CRP. Using the ratio of IR/SD we found that fibrinogen, sICAM1, PAI1, IL6, and MMP3 compared to BMI, had a similar spread of the central 50% of the data. In contrast, the central 50% of the data were less spread around the mean compared to BMI for SAA, CRP, DDIMER, IL2sr, and MCP1.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA. aldi@wustl.edu.

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.

No MeSH data available.


Related in: MedlinePlus