MSACL 2024 Abstract

Introduction: More than 2.5 million people per year in the United States visit the hospital due to traumatic brain injury (TBI), and it is estimated that 6 million individuals suffer from long-term disabilities as a result of TBI. While severe TBI can be fatal, long-term neurodegenerative prognosis includes decreased motor abilities, decreased cognitive function, and increased risk of neurological disorders. Currently, methods to diagnose TBI include brain imaging and the Glasgow-Coma scale (GCS), but both have limitations. There is a lack of minimally invasive, quantitative diagnostic biomarkers to help diagnose traumatic brain injury employed in clinical use and drug development trials.

Objective: The primary object of this study was to use untargeted lipidomics to identify plasma biomarker candidates that scale with severity in a mouse model of TBI. Secondary objectives were to correlate plasma biomarkers with neuroinflammatory stress markers as well as mouse cognitive and behavioral function.

Methods: Controlled cortical impact (CCI) was performed on mice and severity was attributed to the depth of the impact at mild, moderate, and severe TBI levels. Additionally, a critical muscle defect model was used to determine the brain specificity of lipidomic changes in a non-brain traumatic injury. Mouse behavior studies and analyses related to neuroinflammatory indices were conducted from day 1 up to 28 days after injury. Mouse plasma and cortex were analyzed for their lipidomic profile at days 1, 3, 7, 14, and 28 post-injury. Muscle from the non-brain injury model was analyzed at day 1. Samples were prepared for lipidomic analysis by modified Matyash extraction and analyzed by LC-HDMSE.

Results: Initial lipidomics experiments yielded close to 100 putatively identified lipids that had at least 2-fold change and p≤ 0.05 when comparing Sham to Day 1. Top candidates that scaled with severity were: LPC(18:1), LPC(18:2), PE(36:0), PC(38:4), PE(38:6), PC(38:8), TG(48:1), TG(60:12), TG(60:13), and TG(62:14). Examples of candidates that were excluded based off of lipid composition change in muscle injury were: LPC(20:4), LPC(22:6), PE(P-36:4), and PE(P-40:6). Biomarker candidates correlated with a composite behavioral score and with neuroinflammatory indices (mRNA levels of select proteins). A pilot study of 10 patients from University Maryland Medical Center Shock Trauma was analyzed and showed promise for translational studies.

Conclusion: Lipid species were identified that scaled with severity of TBI and showed specificity to brain related injury as well as correlation with clinical outcome assessments in a generalizable mouse model of TBI. Future studies may include evaluation of larger cohort of human patients to establish biomarker correlation with clinical outcome assessments (GCS score) in human and quantitative LC-MS/MS studies to further characterize lipid changes during TBI.