Systemic and vascular markers of inflammation in relation to metabolic syndrome and insulin resistance in adults with elevated atherosclerosis risk

Abstract

Objective: In recent years high sensitive C-reactive protein (hsCRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), soluble intercellular cell adhesion molecule-1 (sICAM-1), fibrinogen, and plasminogen activator inhibitor-1 (PAI-1) were recognized as risk factors for cardiovascular disease (CVD). The aim of the present study was to investigate the relationship between these vascular and systemic markers of low-grade inflammation and traditional risk factors, the metabolic syndrome (MetS) or insulin resistance (IR).

Methods and results: In 137 adults (41-78 years) with at least 2 risk factors for atherosclerosis the following parameters were determined: hsCRP, sVCAM-1, sICAM-1, PAI-1, fibrinogen, waist circumference (WC), blood pressure, Body Mass Index (BMI), fasting serum glucose (FSG), insulin, triglycerides (TG), total cholesterol (TC), LDL, and HDL. The presence or absence of MetS according to the AHA/NHLBI Scientific Statement criteria was assessed. IR was defined using the homeostasis model (HOMA-IR). Subjects with MetS had significantly higher values of hsCRP, sICAM-1, sVCAM-1, PAI-1, fibrinogen (each P<0.05) and lower HDL-levels (P<0.05) compared with subjects without MetS. Similar results were found using HOMA-IR-quartiles. Subjects in the bottom quartile (HOMA-IR<or=1.32) had significantly lower levels of hsCRP, sVCAM-1, sICAM-1, and PAI-1 (each P<0.05) than subjects in the top quartile (HOMA-IR>or=5.03). HDL was significantly higher (P<0.05) in subjects in the lowest quartile versus those in the highest quartile. Incidentally we found no significant differences in total and LDL cholesterol among MetS, HOMA, and traditional CVD risk factor groups, respectively.

Conclusion: Systemic and vascular markers of inflammation showed significant associations with IR and the MetS and may be incorporated into traditional CVD risk prediction models. Such models should be established and validated in forthcoming large scale prospective studies on CVD risk.