Abstract

Understanding and managing the complexity of trauma-induced thrombo-inflammation necessitates an innovative, data-driven approach. This study leveraged a trans-omics analysis of longitudinal samples from trauma patients to illuminate molecular endotypes and trajectories that underpin patient outcomes. We hypothesized that trans-omics profiling reveals underlying clinical differences in severely injured patients that may present with similar clinical characteristics but ultimately have different responses to treatment and outcomes. Here we used proteomics and metabolomics to profile 759 of longitudinal plasma samples from 118 patients at 11 time points and 97 control subjects. Patients were stratified by shock and injury severity, revealing a spectrum of responses to trauma and treatment that are fundamentally tied to their unique underlying biology. Ensemble models were employed, demonstrating the predictive power of these molecular signatures with area under the receiver operating curves of 80 to 94% for key outcomes such as death. Then, transomics-based patient states were defined to create a map of unique pathophysiologic states encountered by trauma patients across time. Last, distinct longitudinal patient trajectories were identified that group patients according to their path through trauma transomics state maps. Unsupervised clustering of longitudinal trans-omics data identified specific clinical phenotypes while omics-based trajectories increased resolution on outcome prediction. The molecularly defined endotypes and trajectories provide an unprecedented lens to understand and potentially guide trauma patient management, opening a path towards precision medicine. This strategy presents a transformative framework that aligns with our understanding that trauma patients, despite similar clinical presentations, might harbor vastly different biological responses and outcomes. One-sentence summaryTransomic analyses of longitudinal plasma samples from severely injured patients identifies endotypes and trajectories that predict clinical outcomes.