Fig. 1

Soluble mediator-derived immune subtypes in discovery cohort. a Unsupervised hierarchical clustering of 14 serum mediators reflecting innate and adaptive immune activation, endothelial dysfunction, and fibrinolysis; dendrogram indicates cluster partition prior to k-means consolidation (N = 201). b Optimal cluster partitions suggested by cluster stability and validation indices as per NbClust package. c First two principal components plotted with the proportion of variance explained by each component; individuals stratified by cluster (subtype) assignment (N = 201). d Heatmap of z-score standardized soluble mediator concentrations, stratified by cluster (subtype) (N = 201). e Squared factor loadings for all serum mediators across the first two principal components in the discovery cohort; higher loading value indicates greater importance for each variable in explaining variance across each principal component (N = 201). f Importance of serum mediator variables in construction of gradient-boosted decision tree algorithm designed to predict cluster (subtype) assignment in discovery cohort (N = 201); 10 most important variables presented. Force-directed correlation networks based on the Fruchterman-Reingold method in discovery cohort subtype 1 g and subtype 2 h; each mediator variable was set as a network node with between-mediator correlations significant at p-value ≤ 0.05 indicated by weighted edges (blue and green edges indicate positive correlation, red edges indicate negative correlation, edge width indicates strength of correlation) (N = 201). Nodes with blue and green shading indicate those mediators considered central in the subtype 1 and 2 networks, respectively, defined as those with ≥ 1 centrality metric (strength, closeness, or betweenness) above the standardized cluster mean (z-score > 0)