Metabolic syndrome and inflammatory biomarkers: a community-based cross-sectional study at the Framingham Heart Study

Abstract

Background: Prior studies reported conflicting findings on the association between metabolic syndrome and inflammatory biomarkers. We tested the cross-sectional associations between metabolic syndrome and nine inflammatory markers.

Methods: We measured C-reactive protein, CD40 ligand, interleukin-6, intercellular adhesion molecule-1, monocyte chemoattractant protein-1, osteoprotegerin, P-selectin, tumor necrosis factor-alpha, and tumor necrosis factor receptor-2 in 2570 Framingham Offspring Study participants free of diabetes and cardiovascular disease at examination 7. Metabolic syndrome was defined by National Cholesterol Education Program criteria. We performed multivariable linear regressions for each biomarker with metabolic syndrome as the exposure adjusting for age, sex, smoking, aspirin use, and hormone replacement. We subsequently added to the models components of the metabolic syndrome as continuous traits plus lipid lowering and hypertension treatments. We considered P < 0.05 as statistically significant.

Results: Metabolic syndrome was present in 984 (38%) participants and was statistically significantly associated with each biomarker (all P < 0.02) except osteoprotegerin. After adjusting for its component variables, the metabolic syndrome was associated only with P-selectin (1.06 fold higher in metabolic syndrome, 95% CI 1.02, 1.10, p = 0.005).

Conclusions: Metabolic syndrome was associated with multiple inflammatory biomarkers. However, adjusting for each of its components eliminated the association with most inflammatory markers, except P-selectin. Our results suggest that the relation between metabolic syndrome and inflammation is largely accounted for by its components.

Figure 1

Geometric mean concentrations of C-reactive protein (CRP) by BMI category with/without metabolic syndrome (MetS) obtained from the multivariable-adjusted regression model with natural log(CRP) as dependent variable adjusting for age, sex, smoking, aspirin use and hormone replacement therapy. Whiskers extend to upper limits of two-sided 95% confidence intervals.