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Updated: Jan 7, 2026

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022
Blood-Based Biomarkers for Traumatic Brain Injury: A New Era in Diagnosis and Prognosis
Giulia Pignataro1, Marta Sacco Fernandez2, Marcello Candelli1,2
1Department of Emergency, Anesthesiological and Reanimation Sciences, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy.
Abstract:
Traumatic brain injury (TBI) is a major global health concern and a leading cause of mortality and disability. Head computed tomography (CT) remains indispensable for the detection of intracranial hemorrhage; however, its indiscriminate use in mild trauma increases radiation exposure, cumulative oncogenic risk, and healthcare costs. Consequently, there is growing interest in tools capable of improving sensitivity in mild or early-stage TBI. Protein-based biomarkers are promising complements to conventional assessment. Molecules such as glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein B (S100B), and neurofilament light chain (NfL) reflect astroglial activation, neuronal injury, and axonal damage, enabling objective evaluation of neurotrauma. Beyond protein biomarkers, metabolomic and lipidomic approaches capture alterations associated with early metabolic distress, oxidative stress, mitochondrial dysfunction, and membrane disruption following TBI. High-resolution mass spectrometry studies have identified reproducible metabolite and lipid signatures correlating with injury severity and functional outcomes. Longitudinal profiling further reveals dynamic metabolic trajectories that distinguish secondary injury progression from stabilization, supporting predictive modeling and risk stratification. Together, these advances pave the way toward precision medicine in neurotrauma. Nevertheless, variability in assay performance and sampling timing continues to limit widespread clinical adoption. Future research should prioritize methodological standardization, analytical validation, and the integration of multi-omic data with machine learning-based predictive models.

