Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension

Rationale: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist. Objective: Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. Methods and Results: In a prospective observational study of group 1 PAH patients evaluated at Stanford University (discovery cohort; n=281) and University of Sheffield (validation cohort; n=104) between 2008 and 2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters...