Many studies have shown that elevated biomarkers of inflammation following acute myocardial infarction (AMI) are associated with major adverse cardiovascular events (MACE). However, the optimal way of measuring the complex inflammatory response following AMI has not been determined. In this study we explore the use of principal component analysis (PCA) utilising multiple inflammatory cytokines to generate a combined cytokine score that may be predictive of MACE post-AMI.
Thirteen inflammatory cytokines were measured in plasma of 317 AMI patients, drawn 48-72 h following symptom onset. Patients were followed-up for one year to determine the incidence of MACE. PCA was used to generate a combined score using six cytokines that were detectable in the majority of patients (IL-1β, -6, -8, and -10; MCP-1; and RANTES), and using a subset of cytokines that were associated with MACE on univariate analysis. Multivariate models using baseline characteristics, elevated individual cytokines and PCA-derived scores determined independent predictors of MACE.
IL-6 and IL-8 were significantly associated with MACE on univariate analysis and were combined using PCA into an IL-6-IL-8 score. The combined cytokine score and IL-6-IL-8 PCA-derived score were both significantly associated with MACE on univariate analysis. In multivariate models IL-6-IL-8 scores (OR = 2.77, p = 0.007) and IL-6 levels (OR = 2.18, p = 0.035) were found to be independent predictors of MACE.
An IL-6-IL-8 score derived from PCA was found to independently predict MACE at one year and was a stronger predictor than any individual cytokine, which suggests this may be an appropriate strategy to quantify inflammation post-AMI. Further investigation is required to determine the optimal set of cytokines to measure in this context.