Aims: 

There are only few possibilities to identify factors which are alone weak predictors in a disease but have a strong predictive effect together. The Logical Analysis of Data (LAD) an algorithm developed by Eötvös Loránd University - Operations Research Department can be suited to detect such interactions. Endothelial cell dysfunction and chronic inflammation are known to be present in chronic heart failure (CHF). We aimed to investigate how predictive biomarkers for clinical events by LAD and their combinations influence the inflammatory response of endothelial cells.

Methods: 

We used the following predictive biomarkers: C3a as inflammatory marker, urea and creatinine as markers for kidneys function and nutrition and arginine vasopressin as a blood-pressure regulator. Our model was human umbilical vein endothelial cells (HUVEC). We measured IL-8 and MCP-1 production of HUVECs by sandwich ELISA method. The expression of ICAM-1 and ICAM-2 adhesion molecules was investigated by fluorescence microscopic technique.

Results: 

We assessed how the above mentioned four biomarkers, applied in all possible combinations, can influence the pro-inflammatory reactions of HUVECs in the presence or absence of TNF-a. These biomarkers induced no changes in adhesion molecules expression and cytokine production, and they did not alter the pro-inflammatory effect of TNF-a.

Conclusion: 

We failed to exhibit any effect of these biomarkers in our model system. Since endothelial cell dysfunction is known to be present in CHF, using endothelial cells from other origin or measuring other outcomes of endothelial cell activation may reveal endothelial cell stimulatory function of these four biomarkers. Another possibility is that combinations of these biomarkers show no direct correlation with endothelial cell dysfunction.