Protein Predictors of Worsening Cerebral Edema in Traumatic Brain Injury in Critically Ill Patients
Hannah L. RadabaughiD, Ahmed AbdelhakiD, Kiarra NingiD, Ruchira Menka JhaiD, Susan RowelliD, Jeffrey Michael PollockiD, Esmeralda MendozaiD, Luisa M. Rojas-Valencia, Adam R. FergusoniD, H. E. Hinson
Abstract
Cerebral edema (CE) is a major determinant of poor outcomes after traumatic brain injury (TBI). CE affects approximately 60% of patients with mass lesions on head computed tomography (hCT) and confers a tenfold increase in mortality. Despite this burden, no validated biomarkers exist to identify patients at risk for early CE worsening or to elucidate the biological processes underlying divergent edema trajectories. We conducted high-throughput plasma proteomic profiling in a prospective moderate-severe TBI cohort with evidence of intracranial blood on the initial post-injury hCT scan (n=123 patients) to (1) identify a clinically feasible set of biomarkers plus a predictive model that could be used to assess CE risk at admission, and (2) conduct a mechanistic, hypothesis-generating analysis to identify the molecular networks involved in CE progression in the first hours post-injury. Plasma collected within 3 hours of injury was analyzed using the Olink Explore platform (5394 proteins), and patients underwent head CT imaging upon presentation and at 6 hours. Two complementary analytic pathways, reflecting the studys two goals, were pursued in parallel. First, logistic regression with false discovery rate (FDR) correction identified a conservative 12-protein set associated with baseline CE. When combined with targeted clinical variables (i.e., age, sex, admission GCS, pupil reactivity, admission glucose, admission alcohol), this panel supported high-performing supervised classifiers for predicting CE worsening by 6 hours post-baseline scan (best model: XGBoost, AUC = 0.78; recall = 0.83). Second, a broader 60-protein panel, selected via bootstrapped elastic-net regression, was used to interrogate the mechanistic architecture of CE worsening using random forest SHAP attribution and protein-protein interaction modeling. Proteomic signatures diverged sharply between patients whose CE did and did not worsen. The CE worsening group was characterized by a coherent neuronal-synaptic injury axis dominated by ELAVL4, CEND1, NEFL, NECAB2, GFAP, CHGB, RPH3A, HPCAL4, and HOMER1, proteins involved in neuronal structural integrity, vesicular trafficking, synaptic vesicle cycling, calcium signaling, and axonal degeneration. These reciprocal proteomic states suggest that early edema progression may be driven by coordinated disruption of neuronal and synaptic resilience programs. Together, these findings suggest (1) a hyperacute biomarker panel plus predictive model with potential for prospective validation and (2) mechanistic evidence for a distinct neuronal-synaptic injury network that associates with early edema worsening after TBI.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- High-dimensional proteomic analysis for pathophysiological classification of Traumatic Brain Injury
Lucia M. Li, Eleftheria Kodosaki, … David James Sharp·BrainPeer-reviewed·Sep 24, 2024
- Massive solubility changes of neuronal proteins upon simulated traumatic brain injury reveal the role of shockwave in irreversible damage
Amir Ata Saei, Hassan Gharibi, … Roman A. Zubarev·MoleculesPeer-reviewed·Sep 22, 2023
- Translating from mice to humans: using preclinical blood-based biomarkers for the prognosis and treatment of traumatic brain injury
Ilaria Lisi, Federico Moro, … the InTBIR Fundamental & Translational Working Group·bioRxiv·Dec 1, 2023
- Ceramide-rich extracellular vesicles as pathogenic biomarkers in traumatic brain injury
Zainuddin Quadri, Zhihui Zhu, … Erhard Bieberich·Acta Neuropathologica CommunicationsPeer-reviewed·Jun 25, 2026
- WGCNA and Machine Learning for Screening Potential Biomarkers in traumatic brain injury
Yu Liu, Zongren Zhao, Jinyu Zheng·bioRxiv·Aug 29, 2023
- From: Benjamin LemassonParametric response mapping as a new MRI approach for the characterization of posttraumatic cerebral edema
Jules Greze, Pierre Bouzat, … Benjamin Lemasson·bioRxiv·Dec 10, 2018