High-dimensional proteomics identifies organ injury patterns associated with outcomes in human trauma.

INTRODUCTION: Severe traumatic injury with shock can lead to direct and indirect organ injury; however, tissue-specific biomarkers are limited in clinical panels. We used proteomic and metabolomic databases to identify organ injury patterns after severe injury in humans. METHODS: Plasma samples (times 0, 24, and 72 hours after arrival to trauma center) from injured patients enrolled in two randomized prehospital trials were subjected to multiplexed proteomics (SomaLogic Inc., Boulder, CO). Patients were categorized by outcome: nonresolvers (died >72 hours or required ≥7 days of critical care), resolvers (survived to 30 days and required <7 days of critical care), and low Injury Severity Score (ISS) controls. Established tissue-specific biomarkers were identified through a literature review and cross-referenced with tissue specificity from the Human Protein Atlas. Untargeted plasma metabolomics (Metabolon Inc., Durham, NC), inflammatory mediators, and endothelial damage markers were correlated with injury biomarkers. Kruskal-Wallis/Mann-Whitney U tests with false discovery rate correction assessed differences in biomarker expression across outcome groups (significance; p < 0.1). RESULTS: Of 142 patients, 78 were nonresolvers (median ISS, 30), 34 were resolvers (median ISS, 22), and 30 were low ISS controls (median ISS, 1). A broad release of tissue-specific damage markers was observed at admission; this was greater in nonresolvers. By 72 hours, nine cardiac, three liver, eight neurologic, and three pulmonary proteins remained significantly elevated in nonresolvers compared with resolvers. Cardiac damage biomarkers showed the greatest elevations at 72 hours in nonresolvers and had significant positive correlations with proinflammatory mediators and endothelial damage markers. Nonresolvers had lower concentrations of fatty acid metabolites compared with resolvers, particularly acyl carnitines and cholines. CONCLUSION: We identified an immediate release of tissue-specific biomarkers with sustained elevation in the liver, pulmonary, neurologic, and especially cardiac injury biomarkers in patients with complex clinical courses after severe injury. The persistent myocardial injury in nonresolvers may be due to a combination of factors including metabolic stress, inflammation, and endotheliopathy.

Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients.

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.