Predicting ME/CFS After Infectious Mononucleosis Using Cytokine Network Correlations

Abstract

We investigated if a predictive modeling strategy based on the interdependence of the cytokine network could accurately predict if a patient would develop Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) after contracting infectious mononucleosis (IM). We analyzed previously collected data from Northwestern University (NU) students in a three-stage experiment, following them from the start of the school year (Stage 1), to development of IM (Stage 2), to six months post development of IM (Stage 3). At all three stages, blood was stored from participants for cytokine measurement and analysis. Additionally, eight psychological and behavioral scales were used to identify participants as healthy controls or as ME/CFS. Using participants’ measured cytokine expression levels, we built a predictive model based on the inherent correlations within the cytokine network. We found that we could predict ME/CFS in patients 6 months after IM with 86.84% accuracy using correlation matrices made from cytokines taken during IM infection. These results suggest that there may be potential in using an approach that is based on the interdependence of the cytokine network to predict ME/CFS post IM. Future work may explore the validity of these findings and if such an approach could have applications in other diseases.